esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Water Circulation Structure in the Paldang Reservoir, South Korea
## 5 Conclusions  
In this study, measurement data on the flow velocity, water temperature, and meteorological conditions were collected at key sites in the Paldang Reservoir, and analyses were conducted to determine the spatiotemporal characteristics of water circulation in the reservoir. The main results are as follows.  
In the lower reaches of the North Han River, distinct diurnal flow variations were observed due to the diurnal discharge characteristics of the CPD. These were accompanied by backwater flowing backward in the upstream direction in the surface layer as well as two-layer flow during the period of zero discharge from the CPD.  
In the lower reaches of the South Han River, two-layer flow occurred with upstream flow in the lower layer. Additionally, the thickness of upper and lower layers varied depending on the discharge rate of the PDD. Strong flow variations were observed in the vicinity of the PDD depending on its instantaneous discharge rate.  
Flow variations, associated with depth-specific variations in water temperatures, were examined for different periods. During the vertical mixing period, the results showed that downstream flow prevailed in all layers of the North Han River and the South Han River. With regard to the North Han River, during the stratification period, the discharge water temperature of the IPW was higher than that of the CPD. This allowed for relatively high temperature water to flow in the surface layer in the upstream direction while a relatively low-temperature downstream flow occurred in the lower layer.  
It can be concluded that the intermittent discharges from the CPD and the periodic discharges from the PDD and the IPW affect the formation of a convergence zone near the middle reaches of the North Han River. With regard to the South Han River, a relatively high-temperature downstream flow was observed in the surface layer. The low-temperature water residing in the lower layer allowed the water body to remain stable, thus leading to a water circulation structure that was resistant to vertical mixing between the lower layer and surface layer.  
**Author Contributions:** [PERSON] and [PERSON] designed the experiments; [PERSON], [PERSON], and [PERSON] collected and analyzed the field data. [PERSON] and [PERSON] wrote the manuscript. [PERSON], [PERSON], and [PERSON] provided critical comments in planning this paper and edited the manuscript. All the authors discussed the results and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the Post-Doctoral Fellowships Program of the National Institute of Environmental Research (NIER), which is funded by the Ministry of Environment (MOE) of the Republic of Korea (NIER-RP2018-01-01-073).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Proposed Method
### Task-Specific Prompt Learning  
#### 3.3.1 Task-Specific Prompt Inference  
During the pretraining phase, CLIP's image encoder accumulates extensive visual knowledge through image-text contrastive learning on large-scale datasets, forming a robust foundation for guiding the learning of task-specific keys. We freeze the parameters of the image encoder and construct the task classifier based on this foundation. The classifier is fine-tuned to accurately predict keys, enabling the selection of a task-specific prompt for each test sample.  
As shown in Figure 3, given an input image \(\mathbf{x}\), we feed it into image feature extractor \(f(\mathbf{x};\theta_{f})\) of CLIP with frozen parameters \(\theta_{f}\). Then, the extracted features are passed into a classifier \(g(\mathbf{z};\theta_{g})\), where \(\mathbf{z}\) represents the extracted features and \(\theta_{g}\) denotes the parameters of the classifier. Finally, we obtain a logits vector \(\mathbf{y}_{\text{logits}}=g(f(\mathbf{x};\theta_{f});\theta_{g})\), which represents the predicted probabilities for different classes.  
Assuming the true labels are \(\mathbf{y}_{\text{true}}\), we can quantify the difference between the predicted values and the true values using the cross-entropy loss function. The expression for the cross-entropy loss function is given by  
\[L_{K}=-\sum_{i}y_{\text{true},i}\log(\sigma(\mathbf{y}_{\text{logits}})_{i}) \tag{1}\]  
Figure 2: Illustration of the proposed IT-Prompt framework. Frozen components are indicated by a green background and lock symbol, whereas the trainable parts are denoted by the unlock symbol. A task-specific prompt is concatenated with the current-task image and inputted into an image encoder, generating prompt-guided image features. Simultaneously, a text prompt is constructed by using the class names of all learned categories and fed into the text encoder, generating a text prompt.  
Here, \(y_{\text{true},i}\) represents the probability of the \(i\)-th class in the true labels (usually 0 or 1 in one-hot encoding), and \(\sigma(y_{\text{logits}})_{i}\) represents the predicted probability of the \(i\)-th class after applying the Softmax function to the logits vector.  
\[\text{key}=\begin{cases}1,&\text{if }\hat{y}\in Y_{t1}\\ 2,&\text{if }\hat{y}\in Y_{t2}\\ \ldots&\ldots\\ n,&\text{if }\hat{y}\in Y_{tn}\end{cases} \tag{2}\]  
After obtaining the logits vector \(\mathbf{y}_{\text{logits}}\), we select the category with the highest probability as the predicted category, denoted as \(\hat{y}\). Subsequently, utilizing Equation (2), we can determine the corresponding key by leveraging the predicted category.
## 3 Analysis and Discussion
### Study on the \(M_{3}\) Model  
The results of \(M_{3}\) model training are shown in Table 8, where the use of complex networks in AFEM improved the model validation accuracy by up to 8.6% compared to the hand-crafted features. Furthermore, it is significantly better to use the power function as the activation function than the log function. This score-level fusion is more valuable than concatenating on the feature dimensions, which can result in training with 5.5% fewer parameters and 3.1% less calculation time.  
Figure 14: Learned weight matrix. (**a**) Amplitude feature extraction: log activation (**up**) and power activation (**down**); (**b**) phase feature extraction: Log activation (**up**), power activation (**down**).
# FPGA-Based Methodology for Detecting Positional Accuracy Degradation in Industrial Robots
## 2 Materials and Methods
### Statistical Indicators  
Statistical indicators are used to find characteristics that help to know the behavior of a dynamic system. For this, they are applied to the signals of interest, and through these, it is possible to determine if there are changes or variations in the system at different moments in time. In this sense, the statistical indicators proposed in this work are the root mean square (RMS) value and the variance.  
The RMS and variance metrics are useful for identifying patterns or anomalies within a signal. On one hand, RMS provides information about the signal's amplitude or energy, while variance focuses on the signal's dispersion around the average amplitude.  
Figure 2: Block diagram of (**a**) DWT decomposition process of a signal \(x[n]\). (**b**) Inverse DWT reconstruction diagram of a signal \(x^{\prime}[n]\).  
Additionally, the calculation of both metrics is similar and can be easily implemented in an FPGA-based system. This allows for the optimization of the structure to perform the calculations of both metrics without the need for separate structures.  
These indicators have been widely used in applications such as fault diagnosis in transformers [45] and bearings [46], among others.  
The Root Mean Square (RMS) defined by Equation (5) value of a dataset or a discrete-time waveform is the square root of the arithmetic mean of the squares of the values  
\[RMS=\sqrt{\frac{1}{N}\sum_{i=1}^{N}x_{i}^{2}}. \tag{5}\]  
The variance measures the dispersion of a data set with respect to the mean and is defined by the Equation (6)  
\[\sigma^{2}=\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\mu)^{2}. \tag{6}\]
## 2 Method
### Acoustic Field Sub-Region Look-Up Table Method  
To the best of our knowledge, this is the first time that the look-up table method has been applied to the calculation of the acoustic field of an ultrasonic transducer. Moreover, we further simplify the calculation according to the specificity of the ultrasonic transducer. An arbitrarily shaped ultrasonic transducer is expressed by a discretized point source distribution. The ALUT method records the look-up tables of individual point sources at different depths, so the calculation of the sound field at each depth requires a superposition equal to the number of point sources of the sound source. The points on the ultrasonic transducer vibrate synchronously. Thus, we propose the acoustic field sub-region lookup table (ASLUT) method to further simplify the calculation process.  
As shown in Figure 3, the amplitude of each point source on the ultrasonic transducer is the same, and thus the adjacent point sources can be composed as a sub-region. As an example, in Figure 3, a 3 \(\times\) 3 point source square is integrated into a sub-region. The entire diamond-shaped transducer component is divided into a single point source and a 3 \(\times\) 3 point source group. The shape of the transducer can also be decomposed into other combinations of different point sets, which will not be discussed in detail here. The table is also adjusted accordingly to save the spatial propagation distances of single point sources and 3 \(\times\) 3 point source groups respectively as shown in Figure 4.  
Because the point source array superimposes the spatial sound field propagation information of all the different location points, the size of the recorded table is larger than the size of the individual point source recording table. Taking 3 \(\times\) 3 point source array as an example, if the size of a single point source recorded table is \(L\times L\), where \(L=(2N-1)\times\Delta p\), then the record table length and width of the 3 \(\times\) 3 point source array are \(L+2\Delta p\). After calculating and storing the acoustic field distribution information for each depth, the  
Figure 4: Comparison of lookup tables for point source and point source arrays.  
Figure 3: Area division of point and point arrays for arbitrarily shaped electrodes.  
acoustic field distribution in space can be quickly calculated by the look-up table method. The whole process is represented in Figure 5 for a clearer representation.
# UV-Induced Aging in Thermochromic Pigment-Integrated Food-Grade Polymers: A Performance Assessment
## 2 Materials and Methods
### UV Exposure Protocol  
UV exposure refers to the exposure of materials or biological systems to UV radiation, a portion of the electromagnetic spectrum with wavelengths shorter than visible light but longer than X-rays [60]. To evaluate the UV resistance of the thermochromic polymer blends, each blend was exposed to simulated indoor and outdoor conditions representative of typical food contact conditions (Table 2).  
Indoor light exposure, mimicking retail display lighting, was simulated using an RS 559-934 UV exposure unit (RS Components Ltd., Northemptonshire, UK), which emits UVA radiation centered around \(\sim\)360 nm. The spectral output aligns with the emission profile of standard fluorescent lighting, commonly used in commercial settings, and is known to emit low-level UVA. Although the irradiance is lower than full-spectrum sunlight, this UVA range can induce photofading and thermochromic degradation in color-sensitive materials. This setup allowed for assessing the stability of pigments and polymers under extended indoor lighting conditions for 500 and 1000 h.  
Outdoor exposure was simulated using a QUV/Solar Eye (SE) accelerated weathering tester (Q-Lab Corporation, Bolton, UK), which operated at 0.72 W/m\({}^{2}\) at 340 nm. The system used UVA-340 lamps with Solar Eye(r) irradiance control to maintain consistent UV intensity throughout the exposure.  
UV-accelerated aging of polymer specimens was conducted according to ISO 4892-3 [61] using fluorescent UVA lamps UVA-340 and UVA-360 in QUV/Solar Eye and RS 559-934 devices, respectively. Following UV exposure, the samples were conditioned at 23 \({}^{\circ}\)C for 24 h under controlled laboratory conditions prior to subsequent analysis. Figure 1 shows the spectral distribution of the UVA-340 lamps used in the QUV/Solar Eye device to validate the fidelity of the UVA exposure setup. The plot confirms that the UVA-340 lamp spectrum closely matches the UV portion of natural sunlight (295-365 nm), which is essential for simulating realistic outdoor aging conditions in accelerated testing.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Device** & **Wavelength** & **UV Type** & **Use Case** & **Exposure Duration (h)** \\ \hline RS 559-934 UV Exposure Unit & 360 nm & UVA & Simulated indoor lighting & 500 h and 1000 h \\ QUV/Solar Eye (SE) & 340 nm & UVA & Simulated outdoor lighting & 500 h and 1000 h \\ \hline \end{tabular}
\end{table}
Table 2: UV exposure devices, wavelengths, and test conditions.
# Integration of LTE 230 and LTE 1800 in Power Wireless Private Networks
## 2 High-Level Design of the PWPN  
### Architecture of the PWPN  
Figure 1 shows the architecture of a power wireless private network. This is similar to the public LTE system, except that the services are all related to the power industry. The PWPN is designed for the power industry, so it is a private network. The base station can be located on the roof of a power building to reduce the cost. Renting the base station tower from the operator is another possible choice at the first phase of construction.  
The main usage of the PWPN is to collect information about power usage and the real-time status of power devices. There is a large number of different services utilizing the current power public network, depending on the operator. According to latency requirements, the power services can be divided into two categories: real-time services, which have a high requirement for transmission latency, such as distribution automation and emergency repairing, and non-real-time services, such as power usage information collection, which may send data once per day [3].
## Appendix A  
The load conductance parameter \(G\) is determined by the ratio of the active power transferred to it and the squared _rms_ value of the three-phase voltage applied to its terminals, Equation (9). Both terms must be estimated.  
The average (active) power consumed by the load is only due to the current fundamental component for the generic conditions of sinusoidal balanced/unbalanced voltage. That is, the instantaneous power transferred by one of the phase currents \(i\) is:  
\[p_{i}(t)=v_{i}(t)\cdot i_{i}(t)=\left(A_{Vi}\cos\omega\,t+B_{Vi}\sin\omega\,t \right)\cdot\left(A_{II}\cos\omega\,t+B_{II}\sin\omega\,t\right) \tag{11}\]  
with \(i=a\), \(b\), \(c\). The application of some trigonometric identities in (11) allows us to obtain (12):  
\[p_{i}(t)=A_{Vi}A_{II}+\frac{A_{Vi}B_{II}+B_{Vi}A_{II}}{2}\sin 2\omega\,t+ \left(B_{Vi}B_{II}-A_{Vi}A_{II}\right)\sin^{2}\omega\,t \tag{12}\]  
The active power for the phase \(i\) is the average value of \(p_{i}(t)\) over a period. This is:  
\[P_{i}=\frac{1}{T}\int_{0}^{T}p_{i}(t)\,dt= \frac{A_{Vi}A_{II}+B_{Vi}B_{II}}{2} \tag{13}\]In a three-phase system, the calculation of the three active powers of each phase (A3) determines the total active power:  
\[P=\frac{1}{2}(A_{Va}A_{Ia}+B_{Va}B_{Ia}+A_{Vb}A_{IB}+B_{Vb}B_{IB}+A_{Vc}A_{Ic}+B_ {Vc}B_{Ic})\] (A4)  
An analogous approach allows obtaining the _rms_ value of the three-phase voltage as a function of the rectangular coefficients, therefore:  
\[V^{2}=V_{a}^{2}+V_{b}^{2}+V_{c}^{2}\] (A5)  
where for each of the phases it is verified (A6):  
\[\sqrt{2}\,V_{i}=\sqrt{A_{Vi}^{2}+B_{Vi}^{2}}\] (A6)  
and therefore:  
\[V^{2}=\frac{1}{2}\Big{(}A_{Va}^{2}+B_{Va}^{2}+A_{Vb}^{2}+B_{Vb}^{2}+A_{Vc}^{2} +B_{Vc}^{2}\Big{)}\] (A7)  
From (A4) and (A7), \(G\) is obtained:  
\[G=\frac{A_{Va}A_{Ia}+B_{Va}B_{Ia}+A_{Vb}A_{IB}+B_{Vb}B_{IB}+A_{Vc}A_{Ic}+B_{Vc} B_{Ic}}{A_{Va}^{2}+B_{Va}^{2}+A_{Vb}^{2}+B_{Vb}^{2}+A_{Vc}^{2}+B_{Vc}^{2}+B_{Vc} ^{2}}\] (A8)  
The computation of (A8) requires the knowledge of twelve rectangular coefficients. If the voltages are balanced, (A8) is reduced to (12), and only four rectangular coefficients corresponding to only one of the phases would be necessary.
## 4 Naturally Occurring Microbiota in Plant Samples  
Plant materials, including grass and legumes, are intended for use as silages that have different types of aerobic and anaerobic microbes on their surfaces. They are called epiphytic microflora. The microflora levels are based on types of raw materials (grass and legumes) at different stages and environmental factors (temperature, soil, harvesting method, and agro-technical). Native bacteria greatly determine the fermentation of ensiled materials via different types and amounts of fermentative metabolite productions. At the same time, they affect the stability of obtained silages [16]. The abundance of microflora in plant parts consist of LAB and other undesirable microbes that influence the fermentation process and the quality of silages. Firmicutes and Proteobacteria are the most abundant phyla in ensiled silages [17,18]. Bacteria of the Enterobacteriaceaeze family, acetic acid bacteria, spore-forming bacteria (_Bacillus_ and _Clostridium_), and _Listeria_ are considered undesirable [5]. Several species of molds and yeasts are also considered undesirable microbes [19]. Epiphytic LAB plays a major role in spontaneous silage fermentation under anaerobic conditions. They are a relatively small group of microorganisms, not exceeding 1% of total microflora in plants. Dominant LAB involved in the fermentation of silages includes bacteria belonging to the genera _Lactobacillus_, _Pediococcus_, _Leuconostoc_, _Weissela_, and _Lactococcus_[20]. Common species belonging to the genus _Lactobacillus_, such as _Lactiplantiobacillus plantarum_, _Levilactobacillus brevis_, _Lactiaseobacillus casei_, _Lactiaseobacillus rhamnosus_, _Latilactobacillus curvatus_, _Lactobacillus gasseri_, and _Lactiplantiobacillus pentosus_, as well as species belonging to the genus of _Pediococcus_, such as _P. pentosaceus_, _P. acidlacti_, and _P. damosus_ have received great attention for preserve silage quality with high nutrients. Other microflora such as _Bacillus megaterium_, _Weissella kimchi_, _Enterococcus flavescens_, _Lactobacillus taivanensis_, _P. Iolii_, _Leuconostoc lactis_, _E. mundtii_, and _W. cibaria_ are also involved in the fermentation of silages [11]. However, the effects of these inoculants on silage production can vary based on the crops used [1].
## 2 Materials and Methods
### Data Processing and Comparison Method  
The data process chain, implemented to perform the preliminary comparisons, can be summarized in three macro-phases:  
* GS14 differential single constellations trajectory computation for all the possible combinations of four, five, and six satellites;
* Reference trajectory computation from MMS data;
* Solutions filtering and comparisons.  
Figure 3: Real-time kinematic (RTK) PLOT-computed satellite GDOP (geometric dilution of precision) and Skyplot of all the available satellites during the survey.  
As stated in the Introduction section of this paper, all the comparisons were performed under the hypothesis that the MMS solution was more precise than the single differenced constellation solutions. Each single constellation differential solution was achieved by means of the Free and Open-Source Software (FOSS) RTKLIB [46]. RTKLIB was chosen for its easily configurable CUI (command user interface [41]) by means of the python subprocess module [47]. Apart from the MMS output result computation, performed with the MMS-associated proprietary software, all the other computations, including the ones with RTKLIB, were launched or executed by using the Python 2.7 programming language and by exploiting the parallel computing Python multiprocessing package [48], whose time-saving capability has already been reported in the scientific literature [49].
## 4 Discussion
### Considerations for Implementing CMSS Framework in Real-World Applications  
The successful implementation of a CMSS framework requires meticulous planning and execution in data management, ensuring system interoperability, and a strong emphasis on human-centric design principles. These considerations are crucial to ensuring that the CMSS not only functions effectively but also aligns closely with the strategic goals and operational needs of the entire organization. Firstly, the implementation of the proposed CMSS framework is heavily dependent on the format and quality (value) of input data. Poor data quality can result in inaccurate analyses and flawed decision making. This step is critical, as the quality and format of the input data can significantly impact the performance of the system. Additionally, data preprocessing is an essential step in converting raw data into a format that is easily analyzable and compatible with the system. Moreover, the data structures and workflows must be designed with the end goals of the CMSS in mind (see Appendices C and D). This purpose-driven design ensures a clear understanding of the system's end goals and is essential for ensuring that the CMSS effectively serves its intended purposes (see teleology architecture in Section 2.5.5). Furthermore, it is advisable to organize data and functionalities based on thematic requirements rather than solely on geographical or spatial data, enabling more targeted and relevant analyses. Thematic decomposition, i.e., breaking down system functionalities and data into distinct themes or modules, can aid in better system interoperability (see Section 2.5.2). This allows different components of the CMSS to communicate and function seamlessly together. Similarly, facilitating cross-system communication ensures that the CMSS can integrate and communicate effectively with other existing systems within the organization or with external systems. Lastly, the CMSS should be designed with the end-user in mind, adopting a human-centric approach. This involves ensuring ease of use, relevance, and accessibility. The system should be flexible enough to be customized according to user needs and adaptable to changing requirements or contexts.
# Impacts of Drought and Heatwave on the Vegetation and Ecosystem in the Yangtze River Basin in 2022
## 4 Discussion
### Differences in Response of Different Vegetation Types to Drought and Heatwave  
The response of vegetation growth in different ecosystems to drought and heatwave varies. Overall, our study results indicate that forests are less sensitive to drought compared to agricultural ecosystems. [PERSON] utilized three different proxies for vegetation conditions, also demonstrating that regions with a higher proportion of forests exhibit stronger ecosystem resilience to extreme drought than agricultural fields [57]. This discrepancy may be attributed to several mechanisms. Forests typically possess deeper and wider root systems [58], enabling them to better access soil moisture and maintain relatively stable water levels during drought periods [59]. In contrast, many crops in agricultural ecosystems may have shallower root systems, relying primarily on shallow soil water, which evaporates rapidly during drought and heatwave without timely replenishment from precipitation. Additionally, trees in forests generally have larger leaf areas, implying higher transpiration rates. While this may lead to water loss, it also aids in reducing ambient temperatures through the transpiration process and maintaining a certain level of humidity, thus alleviating the impacts of drought and heatwaves. Furthermore, forests possess climate-regulating capabilities, as they can decrease surface temperatures and create relatively cooler microclimates in the surrounding environment. In contrast, agricultural ecosystems may be more susceptible to high temperatures due to their typically sparse tree canopy and lack of microclimate regulation.