esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
For the purposes of this study, two separate experiments were conducted. A laboratory petri dish experiment and a pot experiment, both designed to assess the effects of different concentrations of LDPE MPs on plant development.  
Both experiments utilized white mustard (_Sinapis alba_ L.) seeds and LDPE MPs of the same origin. The growth conditions remained consistent across both experiments, with all experimental parts conducted in a controlled growth chamber at the Department of Agronomy, Biotechnical Faculty, University of Ljubljana, Slovenia (46\({}^{\circ}\)02\({}^{\circ}\)59.1\({}^{\prime\prime}\) N; 14\({}^{\circ}\)28\({}^{\prime}\)21.9\({}^{\prime\prime}\) E). The chamber was maintained at 20-22 \({}^{\circ}\)C, with a 12 h light/dark cycle (LED illumination: 150 umol m\({}^{-2}\) s\({}^{-1}\)) and 40% relative humidity.  
The LDPE MPs were produced by milling commercially available agricultural mulch film. Smaller pieces of film were ground using a cryogenic milling process to prevent melting and aggregation. The morphology, size distribution, and shape of the resulting particles were examined using scanning electron microscopy (SEM). The milling process generated a broad size distribution of particles up to 250 um. For the experiment, particles with sizes \(\leq\)250 um were used. The density of the LDPE particles was 0.93 g/cm\({}^{3}\). The treatment concentrations selected for both the Petri dish (0.01% and 1% _w/w_) and pot experiments (1% and 5% _w/w_) are consistent with concentrations frequently applied in previous studies investigating microplastic effects in soil-plant systems [16; 34; 48; 49].  
While these concentrations exceed typical levels currently observed in most agricultural soils--where MP contamination generally remains below 0.1% \(w/w\)[50]--they enable the evaluation of potential effects of future pollution scenarios. Furthermore, they facilitate comparability with the existing body of literature and contribute to a broader understanding of concentration-dependent responses. Concentrations approaching 1% \(w/w\) have been reported in soils subjected to intensive plastic use or near urbanized areas [51], highlighting the relevance of including such levels in experimental designs.  
Commercial white mustard (_Sinapis alba_ L.) seeds were obtained from Autrosaat, distributed by RWA Slovenia, and stored at room temperature. As the seeds were clean and calibrated, no further sterilization was performed before use.
# Energy Efficient Transceiver Design for NOMA VLC Downlinks with Finite-Alphabet Inputs
## 3 Transceiver Design for NOMA VLC
### Joint Detection and Decoding Receiver Design  
Intended for the optimal power allocation scheme we have proposed in the last subsection, in this subsection, we are committed to developing an efficient demodulation and decoding algorithm.  
Figure 3: Illustration of our proposed NOMA principle.  
Let \(\mathcal{G}_{m}=\{h_{m}\sum_{i=1}^{M}\hat{p}_{i}s_{i}:s_{i}\in\mathcal{S}_{i}\}\). Then, according to Theorem 1, we can obtain that \(\frac{h_{M}\mathcal{G}_{m}}{h_{m}D}=\{\frac{h_{M}}{D}\sum_{i=1}^{M}\hat{p}_{i}s_ {i}:s_{i}\in\mathcal{S}_{i}\}=\{m\}_{m=0}^{m=2\sum_{j=1}^{M}\hat{s}_{j}-1}\) is a \(2^{\sum_{j=1}^{M}\hat{s}_{j}}\)-ary equally-spaced PAM constellation. In this context, the following low-complexity JDD algorithm can be utilized for signal detection.  
**Algorithm 1**.: _(joint detection and decoding (JDD)) Given the received signals \(y_{m}\) denoted by (2), the optimal ML estimate of the sum signal \(g_{m}\in\mathcal{G}_{m}\) can be attained by:_  
\[\hat{g}_{m}=\frac{h_{m}\bar{D}}{h_{M}}\left\{\begin{array}{ll}0,& \frac{h_{M}y_{m}}{h_{m}D}\leq 0\\ \lfloor\frac{h_{M}y_{m}}{h_{m}D}+\frac{1}{2}\rfloor,&0<\frac{h_{M}y_{m}}{h_{m} D}\leq 2^{\sum_{j=1}^{M}\hat{K}_{j}}-1\\ 2^{\sum_{j=1}^{M}\hat{K}_{j}}-1,&\frac{h_{M}y_{m}}{h_{m}D}>2^{\sum_{j=1}^{M} \hat{K}_{j}}-1.\end{array}\right.\]  
_Then, the estimate signal \(\hat{s}_{mi}\) of \(s_{i}\) at user \(m\) is given as follows._  
\[\left\{\begin{array}{ll}\hat{s}_{m1}=\frac{h_{M}\bar{D}_{m}}{h_{m}D}mod^{ \mathcal{K}_{1}},\\ \hat{s}_{mi}=\frac{\frac{h_{M}\bar{D}_{m}}{h_{m}D}\frac{h_{M}y_{m}}{h_{m}D}mod^{ \mathcal{K}_{j}^{1}-1}\hat{K}_{j}}{2^{\sum_{j=1}^{M}\hat{K}_{j}}}mod^{ \mathcal{K}_{i}},i\geq 2.\end{array}\right.\]  
From Algorithm 1, we can observe that our proposed JDD algorithm treats all the signals as useful information, which is different from SIC, which treats other signals as noise when detecting. In addition, we can get that the overall complexity of our proposed JDD algorithm is \(O(\sum_{j=1}^{i-1}K_{j})\), and it is lower than the traditional ML-based SIC algorithm with complexity \(O(2^{\sum_{j=1}^{i}\hat{K}_{j}})\)[22].
# FORCE--Landsat + Sentinel-2 Analysis Ready Data and Beyond
## 3 Processing Capability
### Level 2: Analysis Ready Data  
The FORCE Level 2 Processing System (FORCE L2 PS) generates harmonized, standardized, and radiometrically consistent Level 2 products with per-pixel quality information, i.e., analysis ready data. L2 PS pulls each enqueued Level 1 image and processes it to ARD specification. Each image (box in Figure 4) is processed independently using multiprocessing [19]. The pipeline is memory resident to minimize input/output (I/O), i.e., input data are read once, and only the final, gridded data products are written to disc.  
Figure 3: FORCE Level 1 Archiving Suite (L1 AS) workflow.
# A Multi-Level Fuzzy Evaluation Method for the Reliability of Integrated Energy Systems
## 2 Problem Formulation
### Factors of IES Reliability  
1. Technical Factors  
An IES can realize coordinated control of electricity, heat and cold and comprehensive energy efficiency management, apply clean energy on a large scale and integrate heat pump, heat storage and cold storage technologies [32]. Therefore, we need to evaluate its energy efficiency level, utilization degree of clean energy and contribution of peak cutting and valley filling. The technical factors include reliability, network loss, integrated energy efficiency, demand-side response and clean energy ratio. Reliability is used to evaluate the normal operating conditions of an integrated energy system and its emergency treatment ability after failure. The network loss index is used to evaluate the energy loss degree of each energy network in the process of energy transmission in an integrated energy system. Energy efficiency reflects the discretion of the energy utilization efficiency in an integrated energy system. Demand-side response is used to evaluate the construction of the demand side of an integrated energy system and its contribution to peak cutting and valley filling.  
2. Economic Factors  
In an actual project evaluation, economic factors are also indispensable [33]. In the process of an IES's system evaluation, not only the overall economic benefit of the target system but also the economic benefit of the equipment should be considered. Therefore, the economic factors include the payback period of the system investment and the economy of the equipment. The system investment payback period is used to evaluate the overall investment recovery of an integrated energy system. The equipment economy is used to evaluate the economic benefits of the equipment in the target system, including equipment utilization and equipment operation cost savings.  
3. Environmental Factors  
The environmental factors mainly include gas pollutant emission reduction and carbon dioxide emission reduction [34], which are used to evaluate the contribution of a comprehensive energy system to pollutant emission reduction and carbon emission reduction. The evaluation results are closely related to our lives.
## 3 Anomaly Detection and Quantification of Seismicity Parameters
### KSU Seismographic Network (KSUSN)  
Since 1985, King Saud University (KSU) has operated a seismological network (KSUSN), which includes a central station in Riyadh and has expanded to thirty stations, with focused subnetworks in the Gulf of Aqaba and southwestern Saudi Arabia [39]. The Riyadh station is equipped with both short-period and long-period seismometers, and has both analog and digital recording. Data processing and analysis within the KSUSN network are utilized to handle real-time event detection, automated analysis, and more detailed waveform analysis [39], including event location and magnitude determination. The network's comprehensive setup allows for a thorough analysis of seismic data across the network [39; 40]. The spatial distribution of seismic events reported by the King Saud University Seismic Network (KSUSN) is depicted in Figure 2b.  
Table 1 presents a comprehensive statistical analysis of seismic activity as reported by the KSUSN. The statistical analysis of seismic activity in the Red Sea area, spanning the years 1985-2003. The data, encompassing a count of 22,304 events. The mean year of occurrence is centered around the year 2000, with a standard deviation suggesting a concentration of activity within a relatively short timeframe. The interquartile range and quartile deviation further indicate that the majority of seismic events occurred in a narrow time span. Monthly and daily distributions show a more uniform spread throughout the year and month, respectively, with negligible skewness and kurtosis, implying a random distribution of seismic events over these periods. The spatial analysis of latitude and longitude data, as illustrated in Figure 2b, reveals a notable concentration of seismic activity  
Figure 2: (**a**) Geographic Distribution of Utilized Seismographic Stations in Saudi Arabian Networks [6]. (**b**) Compilation of Seismic Data Recorded by the King Saud University Seismic Network (KSUSN). (**c**) Aggregated Seismic Activity Data Captured by the Saudi Arabian National Digital Seismographic Network (SANDSN). (**d**) Comprehensive Collection of Seismic Events Recorded by the Saudi National Seismographic Network (SNSN).  
within distinct geographic areas, primarily in the northern Red Sea region and the Gulfs of Aqaba and Suez. This concentration is characterized by lower standard deviations and higher kurtosis values, indicating a focused clustering of seismic events within these regions. The significant standard deviation and notably high kurtosis observed in the depth data suggest a broad spectrum of seismic event depths, including several exceptionally deep occurrences. This pattern may indicate the presence of data contaminations or outliers within the dataset. The magnitude data, characterized by an average of 1.92 and a comparatively low standard deviation, indicates that most seismic events recorded were of low to moderate intensity. An exception to this trend is the period of the catastrophic 7.2 magnitude Aqaba earthquakes in 1995, which stands out as a significant deviation from the general pattern.
## 4 Experimental Verification
Furthermore, according to measurement records, the unit-length amplitude and phase angle of integrated impedance were 0.3446 \(\Omega\)/km and 73.54\({}^{\circ}\) in the first short-circuit experiment; and the unit-length amplitude and phase angle of integrated impedance were 0.3614 \(\Omega\)/km and 71.63\({}^{\circ}\) in the second short-circuit experiment. Relatively, the derived unit-length amplitude and phase angle of integrated impedance were 0.3576 \(\Omega\)/km and 69.95\({}^{\circ}\), considering the iterative solution of approaching the constraint condition. Figure 9 depicts the comparison of the above results by using histograms. It can be demonstrated that both the measurement results of the two short-circuit experiments are almost identical to the derived results. \begin{table}
\begin{tabular}{c c c} \hline
**Cable** & **Related Loop Circuits** & **Deduced Coefficient** \\ \hline JW & 1, 2... 10 & 0.3296 \\ CW & 10, 11... 20 & 0.2939 \\ R(R1 + R2 + R3 + R4) & 1, 2, 5, 6, 11, 12, 15, 16 & 0.1315 \\ PW(PW1 + PW2) & 3, 7, 13, 17 & 0.0518 \\ EW(EW1 + EW2) & 4, 8, 14, 18 & 0.0305 \\ FW(FW1 + FW2) & 10, 20 & 0.3765 \\ E & 9, 19 & 0.0332 \\ \hline \end{tabular}
\end{table} 
Table 4: Derived current distribution coefficients aimed at the short-circuit experiments. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{3}{c}{**First Short-Circuit Experiment**} & \multicolumn{6}{c}{**Second Short-Circuit Experiment**} \\ \hline \multirow{2}{*}{**Current**} & **Effective** & \multirow{2}{*}{**Current**} & **Effective** & \multirow{2}{*}{**Current**} & **Effective** & \multirow{2}{*}{**Current**} & **Effective** \\  & **value (A)** & & & & & & **Value (A)** & **Current** \\ \hline \(I_{\rm T}\) & 2755 & \(I_{\rm G}\) & 867.48 & \(I_{\rm T}\) & 2224.1 & \(I_{\rm G}\) & 777.77 \\ \(I_{\rm F}\) & 1605 & \(I_{\rm T1}\) & 2755.43 & \(I_{\rm F}\) & 1447.0 & \(I_{\rm T1}\) & 2223.7 \\ \(I_{\rm F1}\) & 1605.27 & \(I_{\rm T1}\)\({}^{\prime}\) & 1395.07 & \(I_{\rm F1}\) & 1446.68 & \(I_{\rm T1}\)\({}^{\prime}\) & 1148.04 \\ \(I_{\rm F1}\)\({}^{\prime}\) & 1395.48 & \(I_{\rm T}\)\({}^{\prime}\) & 1395.73 & \(I_{\rm F1}\)\({}^{\prime}\) & 1148.02 & \(I_{\rm T}\)\({}^{\prime}\) & 1148.1 \\ \(I_{\rm G}\)\({}^{\prime}\) & 2790.37 & \(I_{\rm F}\)\({}^{\prime}\) & 1395.89 & \(I_{\rm G}\)\({}^{\prime}\) & 2296.05 & \(I_{\rm F}\)\({}^{\prime}\) & 1148.06 \\ \(I_{\rm T1}\)\({}^{\prime\prime}\) & 219.69 & \(I_{\rm F1}\)\({}^{\prime\prime}\) & 219.40 & \(I_{\rm T1}\)\({}^{\prime\prime}\) & 196.29 & \(I_{\rm F1}\)\({}^{\prime\prime}\) & 196.24 \\ \(I_{\rm T}\)\({}^{\prime\prime}\) & 219.89 & \(I_{\rm G}\)\({}^{\prime\prime}\) & 439.41 & \(I_{\rm T}\)\({}^{\prime\prime}\) & 196.34 & \(I_{\rm G}\)\({}^{\prime\prime}\) & 392.56 \\ \(I_{\rm F}\)\({}^{\prime\prime}\) & 219.71 & \(I\) & 4370.19 & \(I_{\rm F}\)\({}^{\prime\prime}\) & 196.32 & \(I\) & 3568 \\ \hline \end{tabular}
\end{table} 
Table 3: Measurement record of currents of the short-circuit experiments. Figure 9: Comparison between measurement results and results of deduction for the integrated impedance.
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 3 Methods  
### Satellite Data Pre-Processing  
In order to reduce errors resulting from instrumental variations in data acquisition, image noise, and misregistration, the optical images were corrected from radiometric and atmospheric effects [44]. The atmospheric correction was done by the ENVI FLAASH model. In this paper, the normalized difference vegetation index (NDVI, Equation (2)) [45] and the simple ratio index (SR, Equation (3)) [46] vegetation index were selected and calculated, and the latter reduced the effect of variable illumination due to topography [47].  
\[NDVI=\frac{NIR-RED}{NIR+RED} \tag{2}\]  
\[SR=\frac{NIR}{RED} \tag{3}\]  
where NIR and RED correspond to the surface reflectance of band 4 and band 5 of Landat-8, respectively.  
The SAR data were pre-processed by SARscape 5.2 software (Sarmap, Purasca, Switzerland), which included multi-look, registration, speckle filtering, geocoding, and radiometric calibration. A Lee filter with a 3 \(\times\) 3 window was applied to reduce speckle noise [48]. The SAR data was resampled to a pixel size of 20 m spatial resolution, and the digital number values (DN) were converted to a decibel (dB) scale backscatter coefficient (\(\sigma^{\circ}\)). Then, the images were geocoded using the shuttle radar topography mission (SRTM) DEM. All data were also geometrically rectified using 30 ground control points, with the root mean square error (RMSE) less than 0.5 pixels. Ground coordinates of these points were provided from the 1:10,000 LULC map developed by Nanjing Institute of Surveying and Geotechnical Investigation.
# A Deep Evolution Policy-Based Approach for RIS-Enhanced Communication System
## 5 Numerical Results
Figure 11 illustrates the relationship between the sum rate and time steps across varying population sizes. The results indicate that the DEP algorithm with a population size of 500 exhibits an optimal performance for the parameters \(M=4,N=32,K=4\). While smaller population sizes demonstrate a more rapid convergence rate, they are prone to becoming trapped in local optima due to insufficient population diversity. Conversely, excessively large population sizes result in slower convergence rates and may also lead to local optima, as they tend to contain a high number of individuals with similar characteristics. Figure 12 illustrates the relationship between sum rate and time steps across various subspace proportions, thereby highlighting the impact of the parent agent selection process as outlined in Algorithm 1. In this context, it is necessary to select two agents from a population consisting of \(P\) agents to serve as parents, followed by the execution of crossover and mutation processes to generate an offspring population. Consequently, the strategy employed for selecting parent agents is of paramount importance. It can be seen that when the population size is \(P\), the subspace proportion of \(2/P\) corresponds to a random selection of parent agents. This random selection implies that the offspring may not effectively inherit the dominant genes, resulting in no significant improvement in system performance. Moreover, when the subspace proportion is set to 1, the selection of parent agents necessitates a comprehensive traversal of the population to identify the two individuals with the highest fitness scores. This exhaustive search can lead to a lack of randomness in the hereditary process, thereby increasing the likelihood of the population becoming trapped in local optima during the iterative process.
## 2 Materials and Methods  
The research is based on the significant advancements in machine vision and defect detection technologies that have occurred over the past few decades. The authors of this paper conducted a thorough literature review to comprehend the historical development and advancements in machine vision and defect detection technologies. This provided a fundamental understanding of the current state-of-the-art and identified gaps in existing research. Through the review, the authors identified key techniques and algorithms that have been successfully applied in similar contexts, with a particular focus on the YOLO family of algorithms due to their efficiency and accuracy in real-time object detection.  
A comprehensive dataset of strawberry images, comprising both defective and non-defective samples, was compiled. Based on the literature review and preliminary experiments, the authors selected the YOLOv8 and YOLOv9 algorithms for their proven effectiveness in object detection tasks. These models were trained on a Lenovo ThinkPad P16 (Lenovo, Quarry Bay, Hong Kong) with an Intel Core iHX processor (Intel, Santa Clara, CA, USA) and NVIDIA RTX GPU (NVIDIA, Santa Clara, CA, USA). This setup ensured efficient and powerful computation capabilities for model training. A Dahua 2 MP network camera (Dahua Technology, Hangzhou, China) with full-color technology was used for real-time image acquisition, strategically positioned above a conveyor belt to capture images of strawberries as they moved along the production line. A local server (Lenovo) was configured to process the images in real-time, hosting the trained YOLOv8 models and managing the data received from the camera. A Unitronics PLC (Unitronics, Tel Aviv, Israel) control unit was integrated to manage the operational aspects of the system, facilitating the activation of a jet mouth mechanism to remove defective strawberries based on the model's detections.  
Processed data were archived in the cloud, and data visualization tools and statistical modules were employed to analyze defect patterns and system performance. A web interface was developed to provide real-time insights and reports on defect detection, allowing stakeholders to monitor the system's performance and make informed decisions. The integrated system was tested in a real-world environment to validate its accuracy and reliability. Performance metrics such as detection accuracy, processing speed, and false positive rates were measured and analyzed. Based on the validation results, the models and system configurations were continuously refined to enhance overall performance and address any identified issues.
## 3 Methodology
### Data Investigation & Pre-Processing  
At this level of the suggested approach, the data set is thoroughly inspected if the data are in an eligible format for use. In order to achieve an impactful work, the data sets were pre-processed in several steps.  
Proper handling of missing values is important because they could result in errors during model building. In this study, no additional data cleaning technique was needed, as the data set was pre-processed during its generation to discard the incomplete packets that contained null feature values. Additionally, the timestamp feature was eliminated, as it adds no significance to network behavior and, rather, acts as ordering elements. Therefore, the remaining features were ready to be utilized to assess the efficacy of several machine learning models [11]. In our study, we opted for no balancing technique implemented, as we wanted to sustain the real-nature of network traffic. We wanted to observe the performance of the proposed framework in a real-life-like scenario. Apart from that, several researchers have pointed out different perspectives on using balancing techniques. For example, in the paper [39], the authors clarified that the significance of the balancing technique is hardly impactful in the resultant of the framework, specifically in binary class detection. Figure 2 is the step-by-step illustration of the techniques that were employed  
Figure 1: Pictorial representation of the experimented data set with the attack categories.  
in this study. The details of the pre-processing is visually represented in Figure 2 and described below.
# Knowledge Distillation-Based GPS Spoofing Detection for Small UAV
## 5 Methodology
### Long-Short Term Memory (LSTM)-Based Detection
The output activation vector \(m_{t}\) is  
Figure 2: False rate under different \(Th_{p}\). \[P_{t^{\prime}}=P_{t}+\int_{t}^{t^{\prime}}A\times\delta \tag{2}\]0  
Here, \(h()\) is cell output activation function, like \(tanh\). The predicted position we have is  
\[P_{t^{\prime}}=P_{t}+\int_{t}^{t^{\prime}}A\times\delta \tag{2}\]1  
Here, \(W_{ym}\) is the weight matrices for the final result to \(m_{t}\). \(b_{y}\) is the bias for the final result. Based on the above, the pseudo Algorithm 1 for LSTM-based detection is shown as follows:  
```
\(P_{t_{0}}\leftarrow(x,y,z)\gets GPS\); \(A_{t_{0}}\leftarrow(A_{x},A_{y},A_{z})\gets IMU\);  Initial \(PA_{t_{0}}=\{P_{t_{0}},A_{t_{0}}\}\);  Load-trained weights of LSTM; whilePower is ondo \(P_{t}\gets GPS\); \(A_{t}\gets IMU\); \(PA_{t}\leftarrow\{P_{t},\ A_{t}\}\); \(P_{t}\gets LSTM(PA_{t})\); \(P_{t+1}\gets GPS\); if\(\parallel P_{t}-P_{t+1}\parallel\geq Th_{p}\)then  Spoofing attack is determined; else  continue;
```  
**Algorithm 1**GPS spoofing detection  
If GPS spoofing is detected, alerts are sent to the UAV's operating system, which then utilizes its IMUs to navigate to the launch location while disregarding data from the GPS module. The algorithm's complexity and latency are constrained by those of the LSTM. To implement the algorithm on a UAV platform with limited resources, further steps are necessary to optimize its compactness and efficiency.