esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals
## 2 Related Work
proposed PhaseFi, a fingerprinting system, using phase information of CSIs and incorporated a greedy algorithm to train the weights of a deep network [32]. [PERSON] et al. proposed an indoor localization system based on CSI and SVM [33]. Density-based Spatial Clustering Of Applications With Noise (DBSCAN) was utilized in the system to reduce the noise in CSIs. CSI based human Activity Recognition and Monitoring (CARM) was proposed based on CSIs of wireless channel that quantified the relationship between the movement speeds of different body parts and activities, and it had the ability to recognize human activities [34]. Activity recognition has a wide range of applications, such as somatosensory games. Wi-Play extracted CSI waveforms from commercial WiFi devices to model some specified activity and achieved an activity recognition system [35]. Wifi-based GEsture Recognition (WiGeR) utilized the fluctuation scheme of CSIs generated by the moving of human hands to recognize gestures [36]. Smokey leveraged WiFi signals and had the ability to recognize smoking activity even in the non-line-of-sight (NLOS) and through-wall environments [37]. Wi-Chase utilized the CSIs from all subcarriers to achieve a higher activity recognition accuracy [38]. It is confirmed that human's gait is unique among different people, thus it can be used to identify the human's identity. WifiU was presented to construct the gait profiles of different people utilizing the unique variations in the CSIs [39]. WiWho was presented as a framework of human identification utilizing human's gait extracted from CSIs [40]. FreeSense combined Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT), and Dynamic Time Warping (DTW) to achieve a nine-user human identification [41]. Wii extracted time and frequency-domain features and used time\(-\)frequency analysis to achieve an accurate human identification system [7]. Although there have been quantities of work on human detection, they only perform well when the people move in regular patterns. When an intruder appears, he is more likely to move in an irregular way. As a result, a more robust human detection system is proposed in this paper to meet the challenges of intruder detection.
# Relationship between Unilateral Leg Extension Strength and Dynamic Balance in Healthy Young Men
## 4 Discussion
In another EMG study, normalized peak EMG activity of the rectus femoris (RF) and vastus lateralis (VL) was greater than 74% in subjects with stable ankle groups in the posteromedial and posterolateral directions. In the same study, the activation values of the RF and VL muscles were found to be greater than 55% in the anterior direction [34]. In another study, high levels of activation of the RF (32-42.4%), VL (65.9-88.5%), and VM (63.2-97.1%) muscles were found with respect to three directions [35]. Examination of these results suggests that dynamic balance testing may be a training strategy for strength development. Furthermore, the quadriceps muscle is significantly active during the test and fires a large number of motor units. The relationship between knee extensor strength and dynamic balance found in the study can therefore be explained by the quadriceps muscle group having a high level of activity in all three directions identified during the Y Balance Test. It is well known that the dynamic balance tests, especially the Y Balance Test, are used for performance monitoring of active athletes as well as for injury prevention and injury prediction strategy. A logistic regression study in Division I athletes found that players with a difference of 4 cm or more in the posteromedial direction between the lower extremities had a 3.86 higher risk of lower extremity injury (\(p=0.001\)). The results show that players with below-average scores in each direction independently have almost twice the risk of injury [36]. In another study, anterior asymmetry was found to be associated with non-contact injuries (odds ratio: 2.33; 95% CI: 1.15-4.76) [37]. It was mentioned that quadriceps muscle strength is extremely active during the Y Balance Test, especially in the anterior direction, and the asymmetries found can be an important feedback for the injury [38]. For this reason, it can be stated that quadriceps muscle strength should be taken into account in tests such as the Y Balance Test or the SEBT, which measure high-level neuromuscular balance abilities, and that exercises for the quadriceps should be included in training programs. The study has some limitations. Although an open kinetic chain exercise was preferred in the measurement of strength, balance performance was studied as closed kinetics. However, the reason for this situation was to determine lower limb strength with a more isolated exercise, especially in the quadriceps group. Another limitation is the determination of the 1 RM for leg extension as the only lower limb strength. However, it can be said that the core muscle group is also very effective in terms of balance, especially with regard to the axis-appendicular connection. In future research, a deeper understanding can be gained through regression analyses by determining the strength of the muscle groups thought to contribute during the Y Balance Test.
# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 3 Problem Definition
### The Problem Definition
It can be expressed as follows:_  
\[\chi_{j,i}=euclidian(SN_{i,j},pos,BS_{i},pos) \tag{3}\]  
_where \(<Node>.pos\) computes the position (in terms of latitude and longitude) of the sensor node._  
**Definition 4**. : _Used Networks Ratio (\(\eta\)). This ratio takes into account the number of WSN used in a solution over the total number of WSN in the sensor cloud environment. The Used Network Ratio tries to avoid the overusing of some WSN regarding the others and can be defined as:_  
\[\eta=\frac{total\_of\_networks}{selected\_networks} \tag{4}\]  
**Definition 5**. : _Selected Sensors Nodes (\(\Omega\)). It is the set of sensor nodes chosen to compose the virtual sensors to respond the users' queries._  
\[\Omega=\{SN_{i,j}^{1},SN_{i,j}^{2},...,SN_{i,j}^{N_{\Omega}}\} \tag{5}\]  
Thus, we formulate the provisioning task as an optimization problem, given by:  
\[\min\sum_{id=1}^{N_{Q}}\sum_{n=1}^{N_{Q}}(\kappa_{j,i,id}+\lambda_{j,i}+ \chi_{j,i})+\eta \tag{6}\]  
Subject to:  
1. _Sensors constraints_, which requires the selected sensors to satisfy all queries submitted by the middleware. This constraint can be stated as: \[\Phi(\Omega)\geq\Phi(QUERY)\] (7)
2. _Energy constraint_, which requires all sensors to have a minimum energy level (usually defined by the user). The Energy constraint can be stated as: \[\lambda_{j,i}>minimum\_energy\] (8) where \(j=1,...,|Query|;i=1,...,|\Omega|\); and \(minimum\_energy>0\). 3. _Coverage constraint_, which requires all selected sensors to be in the region of interest. This constraint is formulated as: \[Cv(\Omega,id)=True\] (9) where \(Cv(...)\) returns _True_ if the sensor nodes in \(\Omega\) fully cover \(R_{id}\), and \(False\) otherwise. and \[\kappa_{j,i,id}=\frac{\Phi(SN_{i,j})}{\Phi(Query_{id})} \tag{1}\]0
## 3 LPR Cell Design
### Circuit Simulation of the Readout Circuits  
Figure 17 shows simulated waveforms of LPR cell source follower output, readout amplifier output and pulsed (digital) output using a circuit simulator SPECTRE. Results for two photo current levels of 100 pA and 500 pA are shown.  
The DC offset levels at the charge sensing node for the two signals with different photo current levels are purposely changed and the resulting source follower outputs of the two input levels have different DC offset levels. At the output of the bandpass amplifier, however, the two signals have the same offset level of V\({}_{\textrm{COM}}\) (= 1.5 V) because the input capacitor C\({}_{1}\) does not pass the DC offset of the source follower output. This high-pass filtering function of the amplifier is helpful for receiving signals with small amplitude despite the charge sensing node and the source follower have a large offset deviations. Circuit  
\begin{table}
\begin{tabular}{|l|l|} \hline Light Aperture Size & 3 \(\upmu\)m \(\times\)3 \(\upmu\)m \\ \hline Light Power Per Unit Area & 0.49 W/cm\({}^{2}\) \\ \hline Photocurrent & 15.1 nA \\ \hline Voltage Amplitude & 82.3 mV \\ \hline Time Constant & ON & 14.0 ns \\ \cline{2-3}  & OFF & 14.2 ns \\ \hline Node capacitance & ON & 6.8 fF \\ \cline{2-3}  & OFF & 6.9 fF \\ \hline \end{tabular}
\end{table}
Table 1: Summary of the simulation results.  
Figure 17: Simulated waveforms of the LPR cell, bandpass amplifier and comparator outputs.  
parameters in Figure 6 are C\({}_{2}\) = 3.8 pF, C\({}_{1}\) = 22.8 pF, R = 20 M\(\Omega\) and g\({}_{\mathrm{ma}}\) = 1.37E - 3 [1/\(\Omega\)] and the resulting high-pass and low-pass cutoff frequencies are 2 kHz and 9.6 MHz, respectively. The pass-band gain is 15.5 dB which is identical to the capacitance ratio of C\({}_{1}\)/C\({}_{2}\) = 6.
# A Software-Defined Radio for Future Wireless Communication Systems at 60 GHz
## 2 5G Testbed Overview  
The proposed system was inspired on the previous Figure 1 and is composed of a baseband/low-Intermediate Frequency (IF) processing unit and a RF front-end at both TX/RX ends, as illustrated in Figure 2. In fact, it was the result of SDR OFDM integration with a RF front-end designed for mmWave. In Figure 2, it is evident that the low-IF waveform generation/demodulation is completely independent from the RF up-conversion/down-conversion. This modular approach was adopted to enhance the flexibility of the prototype radio system to enable signal transmissions at distinct frequency bands. That is, the mmWave carrier frequency can be tuned (to match other RF communication systems standards) by only changing the RF front-end.  
This SDR system (Figure 2) is composed of an embedded platform engine called Field Programmable Gate Array (FPGA) [8], and at least by one pair DAC/ADC responsible for signal translation between the digital and analog domains. A FPGA is an integrated circuit intended for general purpose usage, which contains programmable logic blocks that form higher-level functions (multiplexers, adders, multipliers and RAM memories) and a reconfigurable interconnection between them [9].  
FPGA platforms bring three main benefits for the designer: faster time-to-market (no layout for manufacturing needed), simpler design (the software handles much of the routing, placement, and timing) and re-programmability. Traditionally, all the above features would become available at the expense of chip performance, e.g., clock speed. However, in recent _Xilinx's_ FPGA technology digital clock values can now go over the 500 MHz performance barrier [9].  
Consequently, FPGA technology was used for the 5G mmWave wireless communication that we propose especially due to their faster time-to-market and reduced cost characteristics. Due to its primal importance in this work, a brief overview of FPGA considerations is given in Section 2.1.
## 4 Results
### Optimal Configurations for Different Models Across Different Performance Metrics
#### 4.2.2 Optimal Configurations for Precision  
Table 4 outlines the settings that optimize precision, a metric critical for minimizing false positives. Again, ResNet50 leads with a precision of 0.958333, employing AdamW (learning rate 0.0001), CosineAnnealingLR, and FocalLoss. DenseNet121 follows closely at 0.957295 with SGD (learning rate 0.01), StepLR, and CrossEntropy, leveraging its dense connectivity to maintain high precision through efficient feature propagation and momentum-based optimization.  
MobileNetV3 achieves 0.947552 using SGD (learning rate 0.01), CosineAnnealing-WarmRestarts, and CrossEntropy, where cyclical learning rate adjustments may enhance stability in positive predictions. ResNet101, with 0.945392, reflects DenseNet121's SGD and StepLR setup with CrossEntropy, benefiting from its depth and periodic learning rate drops. ConvNeXt records 0.939502 with Adam (learning rate 0.0001), ReduceLROnPlateau, and CrossEntropy, where adaptive learning rate reduction stabilizes precision for this modern convolutional architecture.  
EfficientNet-B2 and ViT-B-16 yield precisions of 0.938983 and 0.936842, respectively. EfficientNet-B2 uses SGD (learning rate 0.01), CosineAnnealingWarmRestarts, and FocalLoss, suggesting that while cyclical schedules are beneficial, they may introduce variability in precision. ViT-B-16 employs SGD (learning rate 0.001), CosineAnnealingWarmRestarts, and FocalLoss, indicating that transformer models may require further tuning to match convolutional counterparts in precision.
## 4 Discussion
Hence, further research is required in this regard. Although questionnaires are not particularly reliable regarding clinical peri-implant parameters, including PI, PD, and BOP [48], they are valid and reliable tools for evaluation of SPP and other oral symptoms [48]. Moreover, validated and well-designed questionnaires could too yield valuable data in survey-based epidemiological large sample-size reports [26]. In the current study, NPRS was utilized to assess the SPP following the recommendations by [PERSON] and co-workers [5] almost forty years ago, who reported that the NPRS (0-10) was a better method to assess SPP than other scales including the four-point descriptive and the visual analogue scales. Because NPRS was the only utilized scale for assessing the SPP, it is difficult to either contradict or support the [PERSON] study [5]. Based on the present study's findings, the NPRS appears to be an efficient scale for pain evaluation in periodonontology and implant dentistry. However, future studies utilizing several pain evaluation scales are required to identify the most efficient and suitable pain scale that can be employed on individuals with peri-implant diseases. It is important to recognize the limitations of this study. First, the cross-sectional study and self-reported results that depend on the subject's recall capacities might have influenced the outcomes of our findings. The assessment of pain biomarker(s) would have helped to evaluate the pain parameter on the molecular level. Second, strict criteria were adopted regarding patient selection. For example, patients with endocrine disorders and tobacco-product consumers were excluded. As habitually consuming tobacco products is a predisposing factor of peri-implant diseases [49], it was speculated that the levels of PICF proinflammatory cytokines and the severity of SPP are less in never smokers than smokers with peri-implantitis. The primary strength of this study is the assessment of local proinflammatory cytokines. In terms of accuracy, the use of bitewing radiographs in this study provided better-standardized measurements. Future research studies using several pain evaluation scales and biomarkers related to pain and dental implant therapy are required to identify the most efficient and suitable diagnostic criterion that could be employed on individuals with peri-implant diseases. In addition, longitudinal reports might help to evaluate the PICF biomarkers' evolution over time for better understanding in terms of peri-implant infections among smokers and non-smokers.
## 3 Results  
### Spatial Frequency Analysis  
In the steady state, the analytical solution of the cable equation shows that the transmembrane potential depends on the spatial frequency \(k\) (Equation (5)). In traditional magnetic stimulation, the stimulating electric field varies over centimeters, so \(k\) is about \(1\) cm\({}^{-1}\). The length constant is on the order of a millimeter, \(\lambda=0.1\) cm. Therefore, \(k\lambda<<1\), and the transmembrane potential is equal to \(S_{0}\). During microcoil stimulation, the electric field varies over tenths of a millimeter, so \(k\) is about \(100\) cm\({}^{-1}\) and \(k\lambda>>1\). The transmembrane potential is smaller than that of \(S_{0}\); therefore, a stronger stimulus is needed to excite the nerve. As a result, we cannot use \(S_{0}\) from traditional magnetic stimulation to estimate the threshold for microcoil stimulation. Figure 4 shows the relationship between the induced transmembrane potential and spatial frequency \(k\).
# Design and Verification of Large-Scaled Flapping Wings for High Altitude Environment
## 3 Results
### Periodic-Averaged Aerodynamic Performance Trim of Flapping Wings  
The parameters of the trim state are so critical to the vehicle that it is necessary to use the available data for trim predictions. For the trim of flexible flapping wings, it is more accurate to use the data from wind tunnel experiments to establish the model. However, since the experimental state of the wind tunnel is discrete and not conducive to the solution of the leveling state, the neural network toolbox in Matlab needs to be used for interpolation first. The BP neural network is a multi-layer feed-forward neural network trained according to the error backpropagation algorithm, and it is one of the most widely used neural network models.  
The wind tunnel experimental data of full state points were imported into Matlab for collation, and the correspondence between the independent variables (\(V\), \(AOA,f\)) and the dependent variables (\(L\), \(T\), \(My\)) was established by using the Neural Network Fitting app. The percentages of training, validation, and testing samples are 70%, 15%, and 15%, respectively. The topology of the BP is further defined, where the number of hidden neuron layers is 10 and the number of output layers is 2. Levenberg-Marquardt was chosen as a training algorithm. As shown in Figure 15, the regression coefficients R between the output values and the target values are all close to 1, indicating that the predictions of the BP neural network model are relatively accurate.  
Figure 15: Neural network training regression (plot regression): (**a**) 6 wing ribs + wing film material of polyester, (**b**) 6 wing ribs + wing film material of liner, (**c**) 7 wing ribs + wing film material of polyester, and (**d**) 7 wing ribs + wing film material of liner.  
The input values of the above neural network prediction model are then interpolated to traverse as many state points as possible. The condition that the lift force is equal to the gravity, i.e., 1300 g, the thrust force is balanced with the drag force, and the pitch moment value of 0 is used to end the traversal of the state points. The output value of the neural network model is constrained to obtain the value of the independent variables that satisfy the trim states in Table 5.  
The prediction of the trim state can provide guidance for the flight of the vehicle in the cruise status. By comparing the speed, angle of attack, and flapping frequency of the four wings in cruise mode, it can be found that the wing film material has almost no effect on the flapping frequency in cruise mode, while the linen material reduces the angle of attack in this condition. The reduction in the number of wing ribs effectively weakens the cruising speed and flapping frequency. After comparing the performance of the four wings, a combination of six wing ribs and linen film material was selected for the prototype assembly.
# Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
## 3 Current XAI State of the Art
### Explanator Formats
#### 3.1.1 Visual Explanations  
In the XAI field, visual explanations are a type of detail generated by AI algorithms that use visual cues to interpret the reasoning behind the machine learning models, particularly in image processing. This type of explanation is considered particularly useful when interpreting image-based models or in computer-vision tasks. Visual explanations aim to highlight the parts of the input (such as an image or video) that are most relevant to a model's decision-making process [42]. These explanations are typically in the form of heatmaps, saliency maps, or attention maps that visually guide the user's understanding of the AI model's focus areas, as illustrated in Figure 2.  
The key methods used to generate visual explanations include the following:  
**Grad-CAM** (Gradient-weighted Class Activation Mapping) [44]: This is one of the most popular methods found in the literature for generating visual explanations. Grad-CAM generates heatmaps by computing the gradient of the output category with respect to the feature maps of the last convolutional layer. This heatmap helps visualize which regions of an image contributed most to the model's prediction.  
**Saliency Maps**[45]: This method computes the gradient of the predicted class with respect to the input image and then highlights the areas where small changes in the input would most influence the prediction. It is particularly useful for CNNs in image classification. Practically, saliency maps are the images that highlight the region of interest in a given computer-vision model, where a heatmap is generally superimposed over the input image to highlight the pixels of the image that provided the most important contributions in the prediction task. Figure 3 is an example of such images.  
**Activation Maximization**[46]: This technique seeks to visualize the features that maximally activate specific neurons in the convolutional network. By generating inputs that lead to high activation in specific neurons, it provides insight into the types of patterns that a deep learning model is sensitive to.  
**DeepLIFT** (Deep Learning Important FeaTures) [47]: Unlike Grad-CAM, DeepLIFT computes the contribution of each input feature by comparing the activations to a reference activation, offering a clearer breakdown of the model's reasoning.  
Other XAI methods used to generate visual explanations include, but are not limited to, Layer-wise Relevance Propagation (LRP) [48], Integrated Gradients (IG) [49], Guided Back propagation [50], RISE [51], Class-Enhanced Attentive Response (CLEAR) [52], and Vision Transformer (ViT) [53].  
Figure 2: Chest X-ray examples labeled as **(a)** COVID-19, **(b)** Pneumonia, and **(c)** Normal. Each row (from left to right) shows the input image, AI prediction, and Grad-CAM heatmap. Highlights reflect regions influencing the model’s decision. Source: [43].