esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Topography and Link Budget of the 8-Channel T/R Module with 7-Bit TTD  
As shown in Figures 1 and 2, a 4-bit (0.25/0.5/1/2 \(\lambda\)) TTD MMIC (\(f_{0}\) = 16.7 GHz and \(t_{\text{delay}}\) = 15 ps) is used for every four channels and a 3-bit (1/2/4 \(\lambda\)) TTD MMIC (\(f_{0}\) = 16.7 GHz and \(t_{\text{delay}}\) = 60 ps) is used for every eight channels. Not only TTD function but also receive gain compensation, transmit power drive and switches are all integrated in TTD MMICs.  
Each channel mainly consists of a GaAs beamformer MMIC, a GaAs low noise amplifier (LNA), a GaAs limiter, a GaN power amplifier (PA), a circulator, a pulse modulation circuit and a power supply circuit. Amplifiers, phase shifters, attenuators and serial-to-parallel converters are integrated in the beamformer MMIC. A 2-way power divider and two 4-way power dividers are realized through LTCC-based striplines. The circulators used in this module are three-port ferrite circulators. Under transmit mode, a wave incident on port 1 is coupled into port 2; under receive mode, a wave incident on port 2 is coupled into port 3. The typical isolation S32 is more than 27 dB.  
Figure 1: Topography of the 8-channel T/R module with 7-bit TTD.  
Figure 2: Link budget of receive (red) and transmit (blue).  
Under receive mode, the theoretical net gain is 41 dB and the noise figure (NF) is less than 3.6 dB. Under transmit mode, the maximum output power is 42.1 dBm (16 W).  
Increased integration density and decreased weight are achieved with 15 layers of stacked LTCC substrates and a 50 AlSi shell. High power GaN PAs are welded to molybdenum copper substrates and then sintered on 50 AlSi shells to improve heat dissipation. The fabricated 8-channel T/R module is shown in Figure 3. The size is 110 mm \(\times\) 65 mm \(\times\) 10 mm and the weight is 85 g.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 5 Solving a Pseudo-Static Scenario
### Example Solution  
This section offers a first example solution and validates the models used in the previous section with the network-simulator ns-3. We define a scenario where 40 ground users are randomly placed in a square area of (100 m \(\times\) 100 m). The parameters used in the scenario are listed in Table 3. Unless otherwise specified, such settings are common to all experiments in this paper.  
The solution to the optimization problem has been found by implementing Algorithm 1 in Matlab(r). A graphical representation of the solution is provided in Figure 4. Notice that different colors represent different WiFi associations between users and UAVs.  
The scenario (including the UAVs) has been simulated with the ns-3 network simulator. In the simulation, no interferences between APs have been considered and transmission buffer sizes have been set to one packet to match the small queue assumption made in Section 5.2.2.  
The results obtained are summarized in Table 4, which shows the most relevant performance measurements (location, flight time, quality level and radio power). We have performed 30 runs of simulations and the results presented represent average values.  
The results in Table 4 indicate that the solution meets both coverage and speech quality constraints, and the QoS and energy models used in Algorithm 1 are aligned with simulation results, which validates the models proposed in Section 5.2.2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Drone**} & \multirow{2}{*}{\(\mathbf{x}_{i}\)} & \multirow{2}{*}{\(\left|\mathbf{C(i)}\right|\)} & \multicolumn{2}{c}{\(\mathbf{T_{\text{Flight}}}\)} & \multicolumn{2}{c}{\(\mathbf{R}\)} & \multicolumn{2}{c}{\(\mathbf{P_{\text{Radio}}}\)} \\  & & & (min) & & & **(W)** \\ \hline
1 & (69, 77, 14) & 12 & 25.61 & 89 / 88 & 20.56 / 20.27 \\ \hline
2 & (83, 22, 30) & 11 & 25.64 & 90 / 89 & 20.42 / 20.16 \\ \hline
3 & (24, 36, 26) & 14 & 25.56 & 87 / 85 & 20.86 / 20.53 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Algorithmic and simulation results.  
Figure 4: Example solution.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**IEEE Standard**} & \multicolumn{2}{c}{**Scenario**} & \multicolumn{2}{c}{**Traffic**} & \multicolumn{2}{c}{**Constraints**} & \multicolumn{2}{c}{**Energy**} \\ \hline Revision & 802.11 n & Users & 40 & Calls/hour/user & 5 & RSS\({}_{\text{min}}\) & \(-\)82 dBm & \(E_{\text{history}}\) & 60 Wh \\ GI & 800 ns & Size & 100 m \(\times\) 100 m & Call length & 180 s & SN\({}_{\text{min}}\) & 20 dB & \(P_{\text{Hover}}\) & 120 W \\ Preamble & Greenfield & X-Y-Z step & 1 m & VoIP codec & G.711 & \(R_{\text{min}}\) & 65 & \(\rho_{\text{tr}}\) & 16 W \\ Bandwidth & 20 MHz & Altitude layers & \{10,..., 40 m\} & On/Off times & CBR & \(C_{\text{min}}\) & 0.9 & \(\rho_{\text{tr}}\) & 9.7 W \\ Retries & 7 & Prop. Exponent & 3.3 & Packet interval & 20 ms & & \(\rho_{\text{diff}}\) & 9.7 W \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example solution input parameters.
# Learning-Assisted Rain Attenuation Prediction Models
## 6 Open Research Issues and Challenges
### Integrating Learning-Based Rainfall Prediction  
In [10], a radar-based rainfall map was used to predict rain attenuation using kNN and ANN learning techniques. In [25], it was shown that the radar-originated rain map can preserve the side diversity (distance dependence of rainfall correlation), and transforming this rain map-originated rain rate can help to improve the rain attenuation prediction capability. Inspired by the works [10; 25], a question arises whether it is possible to use rainfall prediction data to estimate rain attenuation with a good accuracy level? In fact, among many rainfall prediction techniques recently emerged, the commercial microwave links (CMLs)-based technique, and the learning-based rainfall prediction  
Figure 9: The bar chart shows the root mean square error (RMSE) of few LARA models. For simplicity, the accuracy value of AANN+ILA [12], and RA [13] was considered as the 0.05 and 0.03 RMSE value, respectively. Besides, we could not plot SML [6], and BPNN [8] due to data unavailability, and FFMLP+SL [9] due to data mismatch. Further, if there exists a range of RMSE values (Table 5), we considered the maximum RMSE for such cases.  
technique can be a good candidate to use for rain attenuation estimation. The CML-based machine learning or prediction of rain data through a learning algorithm or radar or satellite-based machine learning model can be used as the aided tool for better rain attenuation prediction. [PERSON], [PERSON] et al. (2016) suggested an echo state network (ESN) and deep ESN model where the training dataset was the hourly meteorological data at the Tainan Observatory in southern Taiwan from 2002 to 2014. The model is a good candidate to predict almost a similar region's rainfall rate. However, training with a similar type of dataset, it may be possible to predict the future rainfall rate. The key drawback of the tactic is that the recorded weather training and recorded data on the implementation area should be almost correlated. In fact, many techniques exist in the literature that can predict the rainfall rate. Since 2000, the numerical weather prediction (NWP) technique has gained in popularity to predict rainfall and thus get the attention of weather prediction industries, researchers, as well as other stakeholders. However, due to limited portability, the NWP-based technique may not be a good candidate for use in remote locations. Consequently, learning-assisted rain attenuation prediction will get popularity as it can solve the problems with NWP techniques. In (2016; 2017; 2018; 2019; 2020; 2021; 2022; 2023; 2024; 20
## 3 Experiment Results and Analysis
### Result and Analysis
#### 3.2.1 Comparison of Different Feature Extraction Networks  
Figure 10 showed the test results of the visibility evaluation of these four networks. As shown in Table 2, the accuracies (goodness of fit) of the four network models were shown, respectively. The PSO method can reduce the dimension of the feature vectors and the accuracy can be kept at about 90%.  
From Figure 10, we could see that the test results of each network were quite good, and the accuracy of their respective visibility numerical estimat  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Network** & **Overall Accuracy (\%)** \\ \hline VGG-16 (\%) & 90.26 \(\rightarrow\) (* 90.78) \\ VGG-19 (\%) & 90.32 \(\rightarrow\) (* 90.85) \\ DenseNet (\%) & 91.46 \(\rightarrow\) (* 91.86) \\ ResNet\_50 (\%) & 92.72 \(\rightarrow\) (* 92.23) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of the proposed method by using different networks. (* reference data without PSO selection).  
Figure 10: True and Predicted visibilities values by various ANN networks.  
Even though the test results of the four networks were above 90%, it could be seen from the figure that the numerical points of DenseNet network and ResNet_50 network were more concentrated near to the region around the datum line, which showed that the use of DenseNet network and ResNet_50 network to extract image features was more robust and stable.
# FedSeq: Personalized Federated Learning via Sequential Layer Expansion in Representation Learning
## 4 Experiments
To ensure a fair comparison, the accuracy figures for FedBABU and our scheduling algorithms at \(T\) represent the performance after fine-tuning. Additionally, it is noteworthy that our scheduling algorithms have achieved high accuracy in environments with significant data and class heterogeneity, demonstrating their effectiveness in processing complex and diverse datasets. Figure 3: Results of sampling the CIFAR-10 dataset among 10 clients from a Dirichlet distribution. The Dirichlet parameter \(\alpha\) is 0.1, generating highly heterogeneous data. However, the nature of the CIFAR-10 dataset does not offer much class heterogeneity. Figure 4 shows the average accuracy of clients on the CIFAR-100 dataset, visualizing the performance of our scheduling algorithms compared to baselines. As can be seen, the earlier round accuracy of our scheduling algorithm is lower than FedAvg and FedBABU, which is attributed to the fact that, in the earlier rounds, not only all base but also head is used, and the training is conducted using only a portion of the unfrozen base layers. Similarly, Figure 5 represents the average accuracy of clients on the Tiny-ImageNet dataset. Our scheduling algorithm's earlier round accuracy is lower than FedAvg and FedBABU, which is consistent with its characteristics. This visualization helps underscore how our scheduling algorithms gradually catch up and potentially surpass other algorithms as the training progresses. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**MNIST**} & \multicolumn{2}{c}{**CIFAR-10**} & \multicolumn{2}{c}{**CIFAR-100**} & \multicolumn{2}{c}{**Tiny-ImageNet**} \\ \cline{2-9}  & **Acc. (\%)** & **T** & **Acc. (\%)** & **T** & **Acc. (\%)** & **T** & **Acc. (\%)** & **T** \\ \hline FedAvg [3] & 91.48 & 100 & 36.87 & 100 & 29.04 & 500 & 16.97 & 300 \\  & 96.32 & 200 & 42.00 & 200 & 30.95 & 1000 & 14.06 & 500 \\  & 97.48 & 300 & 49.36 & 300 & 30.34 & 1500 & 14.27 & 1500 \\ \hline FedPer [16] & 98.30 & 100 & 86.16 & 100 & 40.22 & 500 & 29.04 & 300 \\  & 98.63 & 200 & 87.34 & 200 & 41.03 & 1000 & 29.24 & 500 \\  & 98.69 & 300 & 86.66 & 300 & 40.05 & 1500 & 29.11 & 1000 \\ \hline LG-FedAvg [17] & 97.95 & 100 & 85.75 & 100 & 41.06 & 500 & 26.46 & 300 \\  & 98.17 & 200 & 86.10 & 200 & 41.01 & 1000 & 26.35 & 500 \\  & 98.13 & 300 & 85.91 & 300 & 40.80 & 1500 & 26.36 &
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 4 Experimental Evaluation
### Datasets and Experimental Setup
#### 4.1.2 Implementation Details  
We implemented our framework using PyTorch 1.9.0. For the object detection backbone, we employed Faster R-CNN [52] with a ResNet-101 [53] backbone pre-trained on ImageNet [54]. The feature dimensions for region features were set to 2048.  
For semantic representations, we used BERT [42] (bert-base-uncased) to generate word vectors for each hazard class. We constructed detailed textual descriptions incorporating visual attributes, affected food types, and safety implications. For example, \"Mold Growth on Bread\" was described as \"visible fungal mold contamination growing on bread surface showing fuzzy texture and discoloration\". These descriptions were tokenized using BERT's WordPiece tokenizer with a maximum sequence length of 128 tokens. We extracted the [CLS] token embeddings by averaging the last four hidden layers, resulting in 768-dimensional semantic vectors that capture rich contextual information about each hazard class.  
For the Knowledge-Enhanced Feature Synthesizer (KEFS), we set the dimension of knowledge representation to 512 and the hidden dimension of the graph convolutional networks to 256. The semantic vectors were transformed from 768 to 512 dimensions through a learned linear projection layer. The threshold \(\tau\) for binarizing adjacency matrices was set to 0.4. In the RFDM, we used 100 diffusion steps with a linear noise schedule starting from \(\gamma_{1}=8.5\times 10^{-4}\) to \(\gamma_{100}=1.2\times 10^{-2}\).  
During training, we used the Adam optimizer [55] with an initial learning rate of \(1\times 10^{-4}\) for the detector and \(1\times 10^{-5}\) for the KEFS. We set the loss weights \(\lambda_{1}=0.1\) and \(\lambda_{2}=0.1\) to balance the different loss terms. The batch size was set to 8, and the models were trained for 100 epochs on 4 NVIDIA Tesla V100 GPUs.  
For data augmentation, we applied random horizontal flipping, random scaling (0.8-1.2), and random cropping during training. At the inference time, we synthesized 500 features for each unseen hazard class and set the calibration factor \(\gamma\) to 0.7 for GZSD.
# Smart Inverters for Microgrid Applications: A Review
## 2 Smart Inverters
### Autonomy  
The different DGs and loads of a microgrid may be installed several kilometers apart, if not more. This means that the implementation of fast communication links to control every aspect of their operation is neither feasible nor cost-effective. On the other hand, the requirement of communication in microgrids cannot be completely omitted, but the inverters need to present some autonomous characteristics to be able to function properly. In this section, the most important autonomous features for smart inverters has been addressed. Control of the power flow using local measurements based on droop methods is explained briefly, as it is the foundation of the decentralized control idea. In addition, the mode flexibility feature is described. The importance of this feature emerges from the fact that it is necessary for a smart inverter to be able to autonomously switch operating modes due to different microgrid configurations.  
Figure 4: Islanding detection methods.
# Herbal Support for the Nervous System: The Impact of Adaptogens in Humans and Dogs
## 1 Introduction  
For centuries, plants have been a fundamental element of natural therapies, and their impact on the nervous system, as well as their ability to alleviate symptoms of neurological disorders, has been attracting increasing interest from scientists and therapists [1, 2, 3, 4, 5]. Modern phytotherapy and research on biologically active compounds provide evidence that many plant-derived substances can influence the nervous system through mechanisms such as neurotransmitter modulation, oxidative stress reduction, and cognitive function enhancement. Similarly to humans, dogs also respond to plant-derived compounds, opening new perspectives for veterinary medicine and animal behavior studies [6, 7, 8, 9].  
Medicinal plants are well known for their calming and anxiolytic properties, and their extracts are frequently used in formulations supporting mental health in both humans and companion animals. Active compounds in these plants, such as linalool and rosmarinic acid, interact with the GABAergic system, leading to relaxation effects [10, 11, 12].  
On the other hand, not all plant-derived substances are safe. Certain alkaloids present in some plants can cause severe neurotoxic reactions, disrupting cognitive and motor functions and even leading to poisoning. In dogs, whose metabolism differs significantly from that of humans, reactions to specific compounds can be more intense, necessitating particular caution when using phytotherapy [13, 14].  
Understanding the mechanisms through which plant-derived compounds affect the nervous systems of both humans and dogs is crucial for developing effective and safe therapies. An increasing number of studies confirm their potential in treating neurological,anxiety, and depressive disorders, paving the way for new advancements in science and medical practice.  
The most widely recognized adaptogenic plant is ginseng (_Panax ginseng_ [PERSON], family: Araliaceae) [12; 15]. Other widely studied adaptogens include Siberian ginseng (_Eleutherococcus sentiosus_ ([PERSON] & [PERSON].) [PERSON], family: Araliaceae) [16; 17], Chinese magnolia vine (_Schisandra chinensis_ ([PERSON].) [PERSON], family: Schisandracae) [18; 19; 20], maral root (_Leuzea carthamoides_ (Wild.) [PERSON], family: Asteraceae) [21; 22; 23], and golden root (_Rhodida rose_ L., family: Crassulaceae) [24; 25; 26]. This literature review will discuss the most significant adaptogens influencing the nervous systems of humans and dogs, focusing on those that are studied in humans but lack sufficient research in dogs, despite the potential for similar effects. The choice was based on their well-documented adaptogenic properties in human medicine and their emerging but understudied potential in veterinary applications, particularly for dogs.
## 4 Results and Discussion
### Feature Classification  
The decision tree classifier model was trained with the selected number of statistical features (mean, sample variance, standard error, and kurtosis) that were selected  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mean**} & **Standard** & \multirow{2}{*}{**Median**} & **Std. De-viation** & **Sample Variance** & \multirow{2}{*}{**Kurtosis**} & **Skewness** & **Range** & **Minimum** & **Maximum** & **Condition** \\ \hline
0.04 & 0.42 & \(-\)0.05 & 13.56 & 183.87 & 2.72 & 0.20 & 135.7 & \(-\)68.17 & 67.58 & LM \\
0.00 & 0.32 & \(-\)0.05 & 10.10 & 101.99 & 3.18 & 0.43 & 94.76 & \(-\)40.38 & 54.38 & LM \\
0.03 & 0.38 & \(-\)0.05 & 12.26 & 150.21 & 1.70 & 0.09 & 113.3 & \(-\)56.39 & 56.94 & LM \\ \(-\)0.05 & 0.05 & \(-\)0.05 & 1.49 & 2.23 & 20.74 & \(-\)1.45 & 24.74 & \(-\)15.66 & 9.08 & DLM \\ \(-\)0.04 & 0.11 & 0.00 & 3.42 & 11.69 & 7.86 & \(-\)0.70 & 42.82 & \(-\)24.79 & 18.04 & DLM \\ \(-\)0.04 & 0.12 & \(-\)0.04 & 3.93 & 15.47 & 14.56 & \(-\)1.04 & 59.90 & \(-\)38.31 & 21.59 & DLM \\ \hline \hline \end{tabular}
\end{table}
Table 4: Extracted features from the experimental data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No. of Features**} & \multicolumn{6}{c}{**Classification Accuracy (\%)**} \\ \cline{2-10}  & **C4.5 Decision Tree** & **Random Forest** & **Naive Bayes** & **Bayes Net** \\ \hline
1 & 56.00 & 54.25 & 57 & 47.5 \\
2 & 80.90 & 80 & 86.25 & 84.36 \\
3 & 90.72 & 96.25 & 88.75 & 87.45 \\
4 & 100 & 98 & 100 & 96.25 \\
5 & 100 & 100 & 99.0 & 100 \\
6 & 100 & 99.12 & 98.2 & 99.4  
by the attribute evaluator. The trained model was tested and validated using a 10-fold cross-validation. The obtained results are disclosed in the form of a matrix known as the \"confusion matrix\" (Table 6).  
Referring to Table 6, the first row denotes the data related to the laminated composite plates (LAM) and the second row represents the data relevant to delaminated composite plates. The first element in the first column represents the correctly classified elements. Among the 40 laminated composite plate data, all were correctly classified. Hence, there was no misclassification. The second element in the second column represents the data points corresponding to the delaminated plate (DLAM). In this case, all 40 data points were also correctly classified and none were misclassified. Among all 80 simulated data points, all 80 were correctly categorized. Hence, the classification accuracy with the DT algorithm is 100%. The time taken to build the model was 0.01 s.  
Summary of the classifier results:  
\begin{tabular}{l c c} Correctly Classified Instances & 100 (100\%) \\ Incorrectly Classified Instances & 0 (0\%) \\ Kappa statistic & 1 \\ Mean absolute error & 0 \\ Root mean squared error & 0 \\ Relative absolute error & 0 \\ Coverage of cases (0.95 level) & 100\% \\ Mean rel. region size (0.95 level) & 50\% \\ Total Number of Instances & 100 \\ \end{tabular}
## 4 Results and Discussion
### Factors Influencing the Productivity of Wells
#### 4.2.2 Wells Depth Impact  
The drilled wells exhibit a total depth range of 12 to 103 m, with the highest flow rates observed at shallow depths (<43 m) and the lowest rates (<2 L/s) recorded across all depths (Figure 6c). A discernible trend in declining well productivity with increasing depth is evident. These findings regarding the influence of well depth on productivity align with previous studies conducted by [PERSON] and [PERSON] [68] and [PERSON] et al. [69], particularly for deeper wells. While they identified a decreasing tendency in well productivity with depth, the correlation coefficient between variables was relatively low. [PERSON] and [PERSON] [70] and [PERSON] [71] attributed the decrease in well productivity to the closure of discontinuities by lithostatic pressure, which leads to lower fracture density and connectivity with increasing depths. This explanation aligns with our study if we focus only on wells in discontinuous aquifers such as limestones, schists, and schistose limestones.
# Diversity of Potential (Bio)Technological Applications of Amino Acid-Based Ionic Liquids
## 2 Bioapplications of AA ILs: An Overview
### Antimicrobial Activity, Antibacterial Coatings, and Antibiotic Formation
The antimicrobial activity of the investigated AA ILs increases in the order of [EmPyr][Ser] \(<\) [EmPyr][Pro] \(<\) [EmPyr][Ala] \(<\) [EmPyr][Gly]. Additionally, the activity can be enhanced by extending the chain length of the alkyl substituents on the cation.
# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 5 Experimental Validation
### Results and Analysis
#### 5.4.3 Comparison with Other Machine Learning Methods  
The proposed method is compared with other machine learning methods based on three evaluation metrics. As shown in Table 7, the fused CNN-DS and CNN-SVM algorithms outperform other algorithms in terms of accuracy. Moreover, the CNN-DS algorithm demonstrates superior accuracy, speed, and stability compared to the CNN-SVM algorithm. Although the CNN, DT, and RF algorithms exhibit relatively fast speed, their accuracy and stability are far inferior to those of the CNN-SVM and CNN-DS algorithms. In summary, compared to other learning methods and CNN, the CNN-DS method proposed in this study offers significant advantages.