esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Accuracy Analysis of Real-Time PPP PWV  
In order to reflect the accuracy of real-time PPP PWV, this paper calculated the PWV of real-time PPP inversion (PPP PWV) according to the PWV formula calculated by ZTD in Section 2.2. Taking the PWV of the radiosonde station as a reference, the PPP PWV during June 2021 (DOY 173-DOY 179, during rainfall) and August 2021 (DOY 229-DOY 235, during nonrainfall) was evaluated [34]. Three stations near the radiosonde station were selected for comparative testing. Table 7 displays the statistical values of the average deviation, STDEV, and RMS of the HKSC station. Because the HKSC station was closest to the radiosonde station and the height difference was small, it was considered to be consistent. Therefore, taking the HKSC station as an example, the accuracy comparison of PPP PWV inversion between the HKSC station during rainfall and nonrainfall periods was drawn, as shown in Figures 5 and 6. During rainfall periods, except for DOY 176, there was rainfall on all days. As can be seen from Figures 5 and 6, the overall PWV during rainfall periods was higher than the PWV during nonrainfall periods. During rainfall periods, the PPP PWV of the HKSC station had a large deviation from the radiosonde station PWV (RS PWV), and the bias, STDEV, and RMS between the two sets of data were 3.45 mm, 1.79 mm, and 3.85 mm, respectively. During the nonrainfall period, the PPP PWV of the HKSC station and the PWV of the radiosonde station had good consistency between the two groups of data. The bias, STDEV, and RMS between the two groups of data were 0.93 mm, 1.21 mm, and 1.18 mm, respectively. The experimental results show that the accuracy of PPP PWV meets the requirements of the weather forecast and other related meteorological applications [35]. The large deviation in PPP PWV during rainfall may be due to the severe change in tropospheric delay over the station caused by weather changes [36].  
The PWV retrieved through NWP (NWP PWV) is still used as the PWV reference value in other stations. It can be seen from Table 8 that during nonrainfall periods, the bias, STDEV, and RMS were generally lower than those during rainfall periods. In addition to individual sites, the RMS of real-time PPP PWV was below 2 mm when NWP PWV was used as a reference. The RMS of real-time PPP PWV was more than 2 mm during rainfall, and even more than 3 mm at some sites.
## 2 Materials and Methods
### Building Energy Model (BEM)  
BEM was initially constructed using AutoDesk Revit, leveraging features for the automatic generation of analytical spaces and surfaces. These objects encapsulate information inputted during modeling and are subsequently compiled in the gbXML file. The energy model underwent scrutiny to ensure proper boundary conditions for spaces and surfaces. Subsequently, another energy model was established for the building by importing the gbXML file into DesignBuilder software, as illustrated in Figure 14. The BEM model was then primed for modifications to the building envelope, replacing conventional insulation material (extruded polystyrene) with suitable phase change materials in the exterior wall layers. This modification aims to scrutinize and compare the effects on the thermal performance of the studied building.  
Furthermore, the HVAC system was schematically defined as energy object included in the BEM model by means of the HVAC plant templates in DesignBuilder software. The HVAC system used in the building is VRF-air cooled-heat recovery-DOAS system, with all conditioned spaces in the building grouped in one thermal HVAC zone.  
Moreover, a dedicated outdoor-air system (DOAS) was employed with a variable refrigerant flow (VRF) system. In this configuration, the DOAS manages air ventilation, while the VRF system handles cooling and heating tasks. This integrated approach enhances ventilation efficiency and temperature control, ultimately optimizing the overall performance of the HVAC system. Figure 15 provides an overview of the HVAC system utilized in the building, with all energy-related data incorporated into the pre-defined template within DesignBuilder software. These data are instrumental in the subsequent energy analysis and simulation, encompassing various energy consumption sources.
## 10 Future Directions
### Energy-Efficient Federated Learning  
Given the diverse range of devices involved in FL, energy efficiency is a critical concern. Incorporating energy-efficient techniques into defense mechanisms can enhance their practicality and adoption. For instance, lightweight cryptographic protocols can be designed to provide robust security without imposing heavy computational loads on client devices. Additionally, optimizing secure aggregation techniques to minimize communication overhead can conserve energy while maintaining the integrity and confidentiality of model updates. Future research should focus on creating a balance between energy efficiency and robust defense strategies. By doing so, FL systems can achieve high levels of security and privacy without compromising on the sustainability and performance of the participating devices. This holistic approach ensures that FL remains an effective and viable solution for decentralized learning across diverse and resource-constrained environments.
# Analysis and Characterization of Micro-Nano Pores in Coal Reservoirs of Different Coal Ranks
## 1 Introduction
Presently, CT scanning technology is predominantly employed in characterizing reservoirs' pore structures, encompassing parameters such as pore throat size, spatial distribution, and connectivity [18]. [PERSON] et al. [19] utilized X-ray micro-CT 3D reconstruction technology and the Avizo visualization software 2020.1 to obtain a microscopic pore model of coal. The study establishes a 3D distribution model of coal pores and a pore ball rod model, analyzing statistical parameters such as the porosity, pore radius, throat radius, pore volume, and coordination number for long-flame coal. [PERSON] [20] employed Avizo 3D visualization reconstruction technology to identify the internal cracks within coal samples. Through the statistical analysis of the 3D geometric parameters of the cracks, the spatial distribution characteristics were accurately described. [PERSON] et al. [21] investigated the development, distribution, morphology, and structural differences of fractures with openings exceeding 15 \(\upmu\)m in two tectonic coal types through SEM combined with digital image processing technology. Furthermore, they established a permeability prediction model based on Poiseuille's law, a triaxial permeability experiment, and CT scanning fracture structure parameters, discussing the impact of the control mechanism of the fracture structure on permeability. [PERSON] et al. [22] utilized CT scanning technology to quantitatively characterize the pore-fracture structure of medium-high-rank coal. Through the statistical analysis of pore throat parameters such as pore volume, pore radius, throat radius, throat length,and coordination number, the pore structure differences in the pore structures between medium-rank coal and high-rank coal were identified. Compared to studying only one or two coal ranks, in this study, the primary coal reservoir in a key area of coalbed methane exploration and development in China was the research focus, and the coal samples in the experiment covered three coal ranks. The same high-precision CT scanner was employed to scan the coal body by micro-CT scanning and nano-CT scanning, and then the representative elementary volume (REV) model of each coal sample was determined based on the box-counting method, filtering the interference noise in the original image using median filtering. The Avizo visualization software facilitated the reconstruction of a 3D pore network model of the coal body, enabling the accurate and quantitative characterization of different coal ranks' micro-nano pore structures from different angles. This study is conducive to the optimization of coalbed methane surface development blocks and the formulation of reasonable development plans.
# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
## 3 Results
### Comparison of Forest Area
However, despite the two datasets for 2017 being essentially the same product, there are cases where there is a large difference in the proportion of forests between states, such as Magway and Kayah. The difference in the proportion of forests between the two datasets is 16.05% for Magway and 28.69% for Kayah. Nevertheless, the overall situation is significantly better than different source datasets, and such large differences are only found in a few states. Through the comparison of forest areas, it can be seen that the forest cover in Kachin, Sagaing, Mandalay, Yangon is more similar in different datasets. Kachin and the northern areas of Sagaing are higher in altitude, the landscape pattern is mostly plateau, the mountainous forest features can be better identified [38]. Mandalay, Yangon and southern Sagaing are located on plains. These regions are low and flat and dominated by croplands. Forests are better identified when the surrounding land type is mostly cropland [38]. Figure 4: Eight global land cover datasets on forest area in Myanmar (Purple indicates the dataset of year 2010, Orange indicates the dataset of year 2015, Green indicates the dataset of year 2017, Blue indicates the dataset of year 2020).
# Laser Absorption Sensing Systems: Challenges, Modeling, and Design Optimization
## 4 Strategies for Noise Reduction and Design Optimization
### Sources of Noise
#### 4.1.1 Laser and Detector Noise  
The laser and detector excess noise are both frequency-dependent and known to have a pink noise spectrum. They are known as \(1\)/\(f\) noise and are usually the dominant noise component at low frequencies from 1 to 10 kHz. The detector shot noise and thermal noise, on the other hand, are independent of frequency and known to have a white noise spectrum. Table 2 gives a brief summary and comparison of the different laser and detector noises.
## 1 Introduction
The results show that the industrial control protocol feature extraction method proposed in this paper can successfully extract the protocol keyword features, and the decision tree classifier trained with the feature dataset has a great performance in classification accuracy, precision, recall rate, training time, and classification time, which proves the superiority of our method. The remainder of this paper is organized as follows. Section 2 describes the related work about network protocol traffic classification. Section 3 describes the proposed feature extraction method of industrial control protocol. Section 4 introduces the characteristics of decision tree briefly, and training process of decision tree classifier for ICS protocol traffic. Section 5 describes the experiments and results. Section 6 compares and discusses the classification effects of different classifiers based on feature datasets, and the latest traffic classification methods. Finally, the conclusion is in Section 7.
###### Abstract  
The effect of the extraction conditions on the DPPH radical scavenging activity and isolation of bioactive compounds from the maca (_Lepidium meyenii_) root was investigated. Different extraction techniques (maceration, maceration with shaking, ultrasound-assisted extraction, and reflux extraction) were compared. Moreover, the effect of the extraction time and two various solvents (water and ethanol) was studied. The antioxidant activity of extracts was determined by the spectrophotometric method with the DPPH radical, while total phenolic content (TPC) was analyzed by the Folin-Ciocalteu method. Using gas chromatography with a mass selective detector (GC-MS), some characteristics of maca bioactive compounds were identified in the extracts: benzylalkamides (macamides), sterols, nitriles, fatty acids, and their derivatives. The influence of various factors on the extraction process of health-promoting antioxidant compounds from maca root was discussed. It was found that water was a more effective solvent than ethanol for obtaining extracts characterized by high radical scavenging activity and phenolics content. Nevertheless, some ethanol-extractable valuable compounds specific for maca, e.g., macamides or fatty acids derivatives, were not present in water extracts. In developing nutritional and therapeutic formulations based on maca extracts, it is important to take into account that the bioactivity of maca extracts varies depending on the solvent used.  
antioxidant activity; bioactive compounds; DPPH scavenging activity; extraction techniques; _Lepidium meyenii_; maca; maca root extracts; macamides; total phenolic content
## 3 Improved Parallel Architecture  
The architecture of the serial Gardner algorithm can be found in [14; 15]. The parallel architecture improvements will be carried out in this section. A parallel First In First Out (FIFO) structure based on an index associated rearranging method and a dual feedback loop based on the [PERSON]'s algorithm are adopted in the proposed parallel architecture. The overall architecture is depicted in Figure 1.  
Figure 1: 2m-Parallel Architecture of Proposed Algorithm.  
To generate parallel digital signals, I Q analog signals are firstly sampled by two high-speed ADCs. The sample frequency relation of ADC and parallel structure is shown in Equation (1).  
\[f_{adc}=2m\cdot f_{s}, \tag{1}\]  
where, \(f_{adc}\) is the sampling frequency of high speed ADC, \(m\) is the number of parallel channels, and \(f_{s}\) is the sampling frequency of parallel structure. The digital signals are stored by parallel FIFO and then rearranged to ensure the stability of data flow. Afterwards, interpolated signals from interpolators will be imported to (timing error detector) TEDs to obtain timing errors, and then filtered by loop filter. Ultimately, timing errors are compensated in interpolators by fractional interval and basepoint index provided by NCOs.  
FPGA implementation block diagram with delays and bit width of the parallel architecture is depicted in Figure 2.  
The stability of feedback loop is quite sensitive to timing error, so the error processing related modules require bit width more than 8 bits, even though the source and output are all 8-bit signals. The FPGA sources consumed and delay caused by each module will be discussed in detail in 3.1 and 3.2.  
If not specified, the following descriptions are based on \(m\) channel parallel module, and the delay adder and multiplier brought into FPGA are 1 and 3 respectively.
# Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
## 2 Beamforming and Deconvolution  
### Conventional Beamforming  
In this section, we consider a horizontal line array (HLA) with N sensors, uniformly spaced with a separation d. The signal from a narrow band source located at the far field of the array, with a look direction \(\theta_{i}\) (see Figure 1), arrives at the array as a plane wave and is denoted by \(\mathbf{r}=S_{i}\mathbf{a}_{i}+\mathbf{n}\). \(S_{i}\) is the signal strength; \(\mathbf{a}=\left[a_{0}^{i},a_{1}^{i},\dots,a_{N-1}^{i}\right]^{T}\) is the steering vector related to \(\theta_{i}\), defined by \(a_{n}^{i}=e^{jn\frac{\pi\pi}{\lambda}d\sin\theta}\) and \(n=0,1,\dots,N-1\); the superscript \(T\) denotes vector transpose; j denotes the imaginary number; and \(\mathbf{n}\) denotes the noise vector.  
The signal amplitude \(S_{i}\) is a random under the environment where the multibeam sonar works which obeys the K distribution [10; 11; 12]. Consequently, the beam power averaged over several snapshots of data, can be given by  
\[B(\sin\theta)=\left\langle\left|\mathbf{W}^{H}\mathbf{r}\right|^{2}\right\rangle=\mathbf{ W}^{H}\left\langle\mathbf{r}\mathbf{r}^{H}\right\rangle W=\sum_{i=1}^{M}\left\langle \left|S_{i}^{2}\right|\right\rangle\left|\frac{\sin\cbr{\pi N(d/\lambda)x_{i}} }{\sin\cbr{\pi(d/\lambda)x_{i}}}\right|^{2} \tag{1}\]  
Figure 1: The geometric shape of the linear array.  
where \(\mathbf{W}=[w_{0},w_{1},\ldots,w_{N-1}]^{T}\), \(w_{n}=\frac{1}{N}e^{jn^{\frac{n}{2}}d\sin\theta}\) and \(\langle\mathbf{r}^{H}\rangle\) is the signal sample covariance matrix (\(\langle\ \rangle\) denotes the average over the samples). Because the signals that from different sources are uncorrelated or incoherent against each other, thus \(\big{\langle}S_{i}^{H}S_{j}\big{\rangle}=\Big{\langle}|S_{i}|^{2}\Big{\rangle} \delta_{ij}\).  
\[\mathbf{W}^{H}\Big{\langle}\mathbf{r}^{H}\Big{\rangle}\mathbf{W}=\mathbf{W}^{H}[\sum_{i=1}^{M} \sum_{j=1}^{M}\mathbf{a}_{i}\Big{\langle}S_{i}^{H}S_{j}\Big{\rangle}\mathbf{a}_{j}^{H }]\mathbf{W}=\sum_{i=1}^{M}\Big{\langle}|S_{i}|^{2}\Big{\rangle}\Big{|}\mathbf{W}^{H} \mathbf{a}_{i}\Big{|}^{2} \tag{2}\]
# KACFormer: A Novel Domain Generalization Model for Cross-Individual Bearing Fault Diagnosis
## 2 Proposed Methods  
### Problem Definition  
This study focuses on addressing the challenge of cross-individual bearing fault diagnosis by developing a deep learning model capable of recognizing variable fault classes. The primary objective is to train the model using labelled vibration signal data from a diverse set of bearing instances. To be specific, the bearing set used for training \(S_{training}\) comprises bearing instances \(n_{1},n_{2},\cdots n_{t1}\), whereas the test bearing set \(S_{test}\) consists of bearing instances \(m_{1},m_{2},\cdots m_{t2}\). It is ensured that the intersection of these two sets is an empty set, i.e.,\(S_{training}\ \cap\ S_{test}=\varnothing\). Simultaneously, the sets of fault classes in both the training and test bearing datasets fully overlap and are drawn from the same fault set \(C_{f}=\left\{1,2,\cdots,n_{fault}\right\}\), where \(n_{fault}\) represents the total number of fault categories. From the two sets of bearing instances described above, the training set and the test set are constructed as follows:  
\[\mathbf{D}_{train}=\left\{\left(\mathbf{x}_{i}^{(train)},\mathbf{y}_{i}^{(train)}\right) \left|\mathbf{i}=\mathbf{1},\mathbf{2},\cdots,\mathbf{N}_{train}\right\}, \tag{1}\]  
\[\mathbf{D}_{test}=\left\{\left(\mathbf{x}_{i}^{(test)},\mathbf{y}_{i}^{(test)}\right) \left|\mathbf{i}=\mathbf{1},\mathbf{2},\cdots,\mathbf{N}_{test}\right.\right\}, \tag{2}\]  
where \(\forall\mathbf{y}_{i}^{(train)},\mathbf{y}_{i}^{(test)}\in C_{f}\). Each \(\mathbf{x}_{i}^{(train)}\) and \(\mathbf{x}_{i}^{(test)}\) is an \(I_{seq}\)-dimensional vibration signal vector, i.e.,\(\mathbf{x}_{i}^{(train)},\mathbf{x}_{i}^{(test)}\in\mathbb{R}^{I_{seq}}\). Let the model be a function \(F(\mathbf{x};\mathbf{\Theta}):\mathbb{R}^{I_{seq}}\to C_{f}\), where \(\mathbf{\Theta}\) denotes the set of trainable parameters. The key objective is to determine the parameter set \(\mathbf{\Theta}\) that maximizes the classification accuracy on \(\mathbf{D}_{test}\). The accuracy on the test set can be defined as  
\[A(\mathbf{D}_{test};\mathbf{\Theta})=\frac{1}{\mathbf{N}_{test}}\sum_{j=1}^{N_{test}} \mathbb{I}(\mathbf{F}(\mathbf{x};\mathbf{\Theta})=\mathbf{y}_{i}^{(test)}), \tag{3}\]  
where \(\mathbb{I}(\cdot)\) is the indicator function, which is defined as follows:  
\[\mathbb{I}\Big{(}\mathbf{F}(\mathbf{x};\mathbf{\Theta})=\mathbf{y}_{i}^{(test)}\Big{)}=\begin{cases} 1,\ \ if\ F(\mathbf{x};\mathbf{\Theta})=\mathbf{y}_{i}^{(test)}\\ 0,\ \ if\ F(\mathbf{x};\mathbf{\Theta})\
eq\mathbf{y}_{i}^{(test)}\end{cases} \tag{4}\]  
The optimization problem could be formulated as follows:  
\[\mathbf{\Theta}^{*}=\underset{\mathbf{\Theta}}{\text{argmax}}A(\mathbf{D}_{test};\mathbf{ \Theta}). \tag{5}\]  
Figure 2: (**a**) t-SNE visualizations on cross-individual task with ball fault; (**b**) t-SNE visualizations on cross-individual task with OR fault; (**c**) t-SNE visualizations on cross-individual task with IR fault.  
This formulation ensures the model achieves reliable fault detection and diagnosis for the bearing individuals in the target set.
## 4 Discussion
### Future Research Prospects  
(1) To comprehensively explore the impact of different CAs on the PAPE effect, more rigorous experimental designs are warranted. Randomized parallel-control or crossover trials can provide deeper insights into the acute enhancement effect of various CAs on athletic performance and the duration of this enhanced state. (2) Regarding the selection of outcome measures, in addition to kinetic parameters expressed through actions such as running and jumping, the selection of endpoints could also be considered from the perspective of physiological changes within the body, including alterations in lower-limb muscle-tendon stiffness, pennation angle, muscle activation, and changes in muscle cross-sectional area. (3) Investigating the role and differences of PAPE effects induced by various CAs across different sports, ages, and genders holds the potential to provide valuable evidence-based guidelines for the practical application of PAPE effects.
## 5 Discussion  
Existing agricultural machinery faces challenges in performing intricate and precise tasks. This has led to a growing need for agricultural robots that possess advanced autonomous capabilities, including perceptual decision-making and eye-hand coordination. The crop growth conditions necessitate sensing technology that is both very precise and immediate, as well as cost-effective and straightforward in terms of algorithmic design. The template matching technique is better suitable for agricultural machinery and equipment compared with machine learning-based recognition algorithms due to its simplicity and ability to handle various items without requiring extensive training costs. This study employs this approach, in conjunction with other target recognition methods, to accurately detect cucumber targets. Recognition investigations are conducted on target photographs collected in greenhouse settings with different lighting conditions, and the results of the recognition are presented in Table 6.  
Figure 14: Cucumber target field identification: (**a**) gray; (**b**) RGB; (**c**) exposure.  
The aforementioned experimental findings demonstrate that the algorithm suggested in this research surpasses other conventional algorithms in terms of recognition rate and matching efficiency. The color segmentation algorithm faces difficulties in effectively identifying the cucumber target in its natural context due to the presence of a background that closely matches its color. The SIFT and SURF features are considered local features, meaning they only capture information from a small region of an image. As a result, they are not suitable for accurately representing the overall characteristics of a target in a specific scene and, hence, cannot be effectively used for target matching. The conventional form matching algorithm has a commendable identification rate, albeit with an accompanying increase in matching time as the number of templates grows. The technique described in this study aims to decrease the time required for matching by first dividing the color information, which, in turn, greatly reduces the amount of edge information in the image that needs to be matched.