esa-sceva/satcom-chunk-collection huggingface dataset:
# Semi-CNN Architecture for Effective Spatio-Temporal Learning in Action Recognition
## 4 Experiments
### Experiment on Discontinued Motion  
So far, the evaluation for semi-CNNs and 3D CNNs are based on continuous motion input with 16 consecutive frames. This section further investigated the network capability in learning spatio-temporal features when there exists motion discontinuity between frames. For experiment, we divided a full video into four segments and sample four consecutive frames from each segment to form the same input size of 16 frames. These \(4\times\ 4\) frames with motion discontinuity were then fed to CNN architectures for training. We utilized the same architecture and training parameters as described in Section 4.1 and reported the comparison results in Table 4.  
Semi-CNNs with frames sampled from different segments of a full video provides better action prediction than using a stack of consecutive frames. The average performance boost for semi-CNNs is 3.2% when compared to the results in Table 3. For 3D CNNs, the performance varies across models. VGG model shows an improvement of 6.08%, while the accuracy of ResNet-34 and ResNet-50 decreases. Do note that 3D-VGG-16 has more than double the number of training parameters as compared to Semi-VGG-16, but its accuracy is still much lower than our network's performance, with a difference of 15.3%. For ResNet models, Semi-ResNet consistently shows improvement of 3% even for deeper models, while the performances of 3D-ResNets deteriorate (from 0.8% to \(-\)3.6%) when the network goes deeper. For DenseNet-121, 3D-CNN does not show obvious improvement, with 0.5% increment, while semi-CNN shows an increment of 1.8% in accuracy.  
This experiment has further validated the high learning capability of semi-CNN on spatio-temporal features, even when motion discontinuity occurs. This provides an advantage for video-level action recognition where motion changes in a full action can be captured for better discrimination.
## 4 Wireless Body Area Networks (WBANs)
### Requirements for Wireless Medical Sensors in WBANs  
Wireless medical sensors should satisfy the following main requirements such as wearability, reliability, security, and interoperability [46]:
# A Heuristic Algorithm for Locating Line-to-Line Faults in Photovoltaic Systems
## 2 System Modeling
### PV Model  
In common simulation environments, the equivalent circuit of a photovoltaic (PV) module can be categorized as a single-diode model [1; 24] or a dual-diode model [25]. This study primarily uses the single-diode model, with its equivalent circuit shown in Figure 1. Figure 1 shows the equivalent circuit of the single-diode model, which includes a diode with current \(I_{d}\), series resistance \(R_{s}\), and shunt resistance \(R_{sh}\). The photocurrent \(I_{ph}\) is generated by the photovoltaic effect, while \(I_{d}\) represents the diode current. The model can be divided into ideal and practical models, distinguished in that the practical model takes into account the internal resistance of the module, which allows it to better reflect real-world conditions. As a result, most simulation systems adopt the practical model.  
The output voltage-current relationship of a photovoltaic (PV) module using the single-diode model is expressed by Equation (1):  
\[I=I_{L}-I_{0}\bigg{[}exp\bigg{(}\frac{q(V+IR_{s})}{\alpha kT}\bigg{)}-1\bigg{]} -\frac{V+IR_{s}}{R_{sh}} \tag{1}\]  
where \(I\) is the output current of the PV module, \(V\) is the output voltage of the PV module, \(I_{L}\) is the photocurrent generated by solar energy, \(I_{0}\) is the diode reverse saturation current, \(R_{s}\) is the equivalent series resistance, \(R_{sh}\) is the equivalent parallel resistance, \(\alpha\) is the diode ideality factor, \(k\) is the Boltzmann constant, \(q\) is the charge of a carrier, and \(T\) is the temperature of the module in Kelvin.  
The PV module model used in this study is the GTEC-305G6S6A, a monocrystalline silicon photovoltaic module. The detailed specifications are shown in Table 1, with a power output of approximately 305 W per module.  
To validate the accuracy of the PV model implementation, this study compared the simulated I-V curves with the manufacturer's datasheet values under various operating conditions. The maximum power point deviation was less than 2% across all test conditions, confirming the model's reliability for fault analysis studies. This validation approach aligns with established practices in PV modeling research [24].
# Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI
## 2 Artificial Intelligence
### Unsupervised Learning: Dimensionality Reduction
#### 2.3.1 Principal Component Analysis  
PCA [22] is a widely employed technique for linear dimensionality reduction that transforms the original data into a new coordinate system. PCA is effective in reducing dimensionality while preserving the most significant variance in the dataset. In this new system, the axes, known as principal components, are orthogonal directions that capture the maximum variance present in the data. PCA first computes the eigenvectors and eigenvalues of the data's covariance matrix. The eigenvectors determine the directions of maximum variance, while the eigenvalues represent the magnitude of this variance. By projecting the original data onto these principal components, PCA creates a reduced-dimensional representation that maintains most of the data's variability. The process can be summarized as follows:  
Figure 3: Linear and non-linear techniques for dimensionality reduction, including PCA, t-SNE, and autoencoders graphically represented on the right.  
* **Step 1**: center the dataset by subtracting the mean from each feature.
* **Step 2**: calculate the covariance matrix \(\Sigma\) of the centered data \(X_{c}\): \[\Sigma=\frac{1}{n-1}{X_{c}}^{T}X_{c}\]
* **Step 3**: compute the eigenvalues \(\lambda\) and eigenvectors \(v\) the covariance matrix: \[\Sigma v=\lambda v\]
* **Step 4**: select the top \(k\) eigenvectors corresponding to the largest eigenvalues and project the data onto these eigenvectors to obtain the reduced-dimensional representation \(X_{r}\): \[X_{r}=X_{c}V_{k}\] where \(V_{k}\) is the matrix containing the top \(k\) eigenvectors.  
Moreover, this method simplifies the data, improves computational efficiency, and tackles multicollinearity. However, it assumes linear relationships, which may not capture non-linear structures, limiting its effectiveness in such cases.
# High-Precision Combined Tidal Forecasting Model
## 3 Combined Tidal Forecasting Model Based on Harmonic Analysis and ARIMA-SVR
### Model Checking  
The combined tide-forecasting model presented in this paper was checked against the observed tide level data at the Bay Waveland Yacht Club port in the USA, obtained from the website of the National Oceanic and Atmospheric Administration. The harmonic constants of four tidal constituents of the Bay Waveland Yacht Club tidal station are shown in Table 1. The tidal coefficient of the port is 10.767, indicating a diurnal tide-only one high and low tide each day. The tidal level of this port was predicted by the proposed model. The astronomical part of the tidal height was calculated via harmonic analysis, obtaining 720 tidal calculations at 1 h intervals over 30 consecutive days in November 2018. Meanwhile, the non-astronomical part of the water level variation was determined by the ARIMA-SVR model, wherein the training set was compiled from 744 prediction residual data of the astronomical tidal levels from GMT0000 on 1 October 1 2018 to GMT2300 on 31 October 2018, and 720 prediction  
Figure 2: Schematic of the combined tidal prediction model.  
residual data from GMT0000 on 1 November 2018 to GMT 2300 on 30 November 2018, which were used as the test set for verifying the prediction results. Finally, both parts of the tidal calculation results were superimposed to obtain the predicted tidal levels throughout November 2018.
# Displacement Monitoring of Subway Tracks and Tunnels According to Adjacent Construction
## 2 Displacement Monitoring of Tunnel Structures
### Gaussian Analysis Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Section**} & \multirow{2}{*}{**Sensor**} & \multirow{2}{*}{**Average (mm)**} & \multicolumn{4}{c}{**Standard Deviation (mm)**} & \multirow{2}{*}{**Field Maintenance Criteria (mm)**} \\ \cline{3-4} \cline{6-9}  & & & **10 Months** & & **18 Months** & & **1 Month** & **10 Months** & **18 Months** \\ \hline \multirow{2}{*}{A} & TL-1 & 0.002 & 0.001 & \((-)0.020\) & 0.044 & 0.010 & 0.014 \\ \cline{2-9}  & TL-2 & 0.009 & \((-)0.246\) & \((-)0.783\) & 0.191 & 0.127 & 0.154 \\ \hline \multirow{2}{*}{B} & TL-1 & \((-)0.049\) & \((-)0.137\) & \((-)0.286\) & 0.114 & 0.041 & 0.068 \\ \cline{2-9}  & TL-2 & 0.073 & 0.018 & \((-)0.036\) & 0.067 & 0.051 & 0.044 \\ \hline \multirow{2}{*}{C} & TL-1 & 0.015 & \((-)0.033\) & \((-)0.159\) & 0.107 & 0.119 & 0.089 \\ \cline{2-9}  & TL-2 & \((-)0.143\) & \((-)1.672\) & \((-)1.777\) & 0.123 & 0.029 & 0.078 \\ \hline \end{tabular}
\end{table} 
Table 3: Safety evaluation results of the tunnel structure (mm).
# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
## 2 Related Work
### Broader Applications of CVNNs  
Beyond imaging and signal classification, CVNNs have been effectively employed in diverse prediction tasks. These include protein secondary structure prediction, financial time series forecasting, soil moisture estimation, and audio signal prediction to enhance MP3 compression. For instance, the multilayer multivalued neuron model (MLMVN) is used for soil moisture prediction [43], the fully complex-valued recurrent network (FCRN) is applied in protein structure analysis [44], and the complex-valued functional neural tree (CVFNT) is adopted in financial forecasting [45], with optimization via genetic and artificial bee colony algorithms. The integration of CVNN modules into MP3 encoders enables better compression ratios while preserving audio quality [46].  
CVNNs have also found utility in specialized tasks such as organic compound classification using Boolean logic-based complex neurons [47], forgery detection in medical imaging [48], and solving complex-valued linear equations [49]. On the hardware side, [PERSON] et al. [50] demonstrated an optical neural chip that executes complex arithmetic via optical interference. Their optical CVNN implementation effectively handled handwriting recognition and classification tasks, suggesting its future relevance in optical and quantum-inspired computing systems.  
In summary, CVNNs have demonstrated substantial advantages in preserving complex signal characteristics and improving task performance across multiple domains, including image reconstruction, signal classification, biomedical diagnosis, and time-series prediction. These findings motivate further exploration of CVNN-based architectures for complex-domain tasks. Unlike many existing CVNN architectures adapted for MRI or speech processing tasks, the CV-CNN model proposed in this study is specifically designed for eddy current signal analysis, where the magnitude and phase components are closely related to defect characteristics. In particular, a magnitude-based max pooling strategy is introduced to enhance local defect features and suppress background noise, differing from conventional CVNNs that often treat magnitude and phase symmetrically or adopt generic pooling approaches.
## 2 State of Knowledge
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors, Date of Publication** & **Area of Interest** & **Subject of Study** & **Methods** \\ \hline [PERSON], 2016 [28] & Poland, Walbrzych & \begin{tabular}{c} analysis and modelling of \\ mining induced land subsidence \\ \end{tabular} & \begin{tabular}{c} Weighted Spatial Regression method in \\ geographic information systems (GIS) \\ leveling and global positioning system \\ (GPS) measurements, implementation \\ of results into GIS for \\ public administration \\ \end{tabular} \\ [PERSON], 2015 [29] & Slovakia, Kosice & \begin{tabular}{c} three-dimensional (3D) \\ deformation vectors \\ \end{tabular} & \begin{tabular}{c} Weighted Spatial Regression method in \\ geographic information systems (GIS) \\ leveling and global positioning system \\ (GPS) measurements, implementation \\ of results into GIS for \\ public administration \\ \end{tabular} \\ [PERSON], 2014 [30] & Poland, Upper Silesia & \
\begin{tabular}{c} class in mining areas in \\ changeable exploitation \\ conditions \\ \end{tabular} & GIS and a classification and regression \\ [PERSON] et al., 2014 [31] & Korea, Samcheok &
\begin{tabular}{c} ground-subsibence hazard \\ indexes and maps \\ \end{tabular} & fuzzy ensemble techniques and a GIS \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies of terrain deformations caused by seismicity induced by underground extraction of resources using satellite radar interferometry. The publications presented above concern mainly modelling and prediction of long-term surface displacements resulting from past and active mining. Geographic information systems analytical functions have been used for: assessing the impact of mining on the surface and surface infrastructure [30; 31; 32; 33], time-spatial analysis of risks related to deformations [34; 35; 37], and to support public administration [29]. In addition, attempts were also made to identify and quantify the relationship between the location of terrain deformations and factors related to mining and geological conditions [28; 36]. The hybrid (GIS and other) methods proposed in some cases were aimed at preparing preliminary data, which were then processed using GIS analytical functions, e.g., spatial regression methods to obtain the final result. In addition to using GIS to study displacements, this environment has been widely applied to assess the environmental impact of mining [38; 39] and mineral resource management [40; 41] as a leading analytical support system facilitating studies of mining influence on its surroundings. However, apart from mapping terrain deformations [34; 35], GIS has not so far been extensively used to analyze surface displacements resulting from mining induced seismicity. Thus, in our paper we investigate the characteristics of such terrain deformations based on a large number of datasets (8 seismic events and 37 maps of vertical displacements) and map algebra spatial statistics functions.
## 4 Proposed CPCROK Scheme  
As previously discussed, in high vehicle density scenarios, there is no need to use CPCROK, as the abundance of neighboring vehicles is sufficient for a standard mix-zone strategy to meet the privacy protection needs of vehicles. However, in low-vehicle-density situations, applying CPCROK to create fake traces would be an appropriate method to enhance privacy protection.  
Beacons inside the mix zone are encrypted, and therefore the vehicle behavior inside the mix zone is unknown and invisible to the adversary. In contrast, mRNAs can interpret trajectory preferences from internal beacons, including the pattern of acceleration and deceleration, the steering arc, and the duration of staying in a junction. Thus, the fake trace by trajectory prediction is more likely to attract the attention of attackers.  
As less realistic fake trajectories have the potential to expose vehicle trajectories to attackers via the beacon semantic analysis mentioned above, most current fake beacon schemes address privacy vulnerabilities in low-vehicle-density scenarios by incorporating a large number of fake beacons. However, the generation of these fake beacons requires significant resource consumption to ensure desirable performance [24]. Hence, we propose the CPCROK approach to effectively protect vehicle privacy in VANET scenarios with minimal additional network overhead, even in situations of low vehicle density and limited real-time vehicle information.
## 2 Materials and Methods  
### Zeolites and Silica Material  
The studied composites were made with unmodified mesoporous silica materials and zeolites synthesized from fly ash by the classical hydrothermal method, as well as zeolite and silica materials modified with hexadecyltrimethylammonium (HDTMA) bromide supplied by the Lublin University of Technology. Data of the literature show the positive effect of surface modification of materials using surfactants from the group of quaternary ammonium salts (HDTMA type) on properties against volatile organic compounds (VOC) [17; 18].  
Na-A, Na-P1 zeolites were obtained by the hydrothermal conversion of fly ash with aqueous sodium hydroxide according to the following reaction:  
\[\begin{array}{l}\mathit{flyash}+x\mathit{mol}\cdot\mathit{dm}^{-3}NaOH\quad \rightarrow\quad\quad\mathit{zeolite}+\mathit{residue}\\ \mathit{temperature}\end{array} \tag{1}\]Zeolites were obtained on the prototype line of a technical quarter located at the Lublin University of Technology at the Faculty of Civil Engineering and Architecture. Using appropriate reaction parameters, such as the temperature, time, and substrate concentration, a zeolite containing zeolite phases was obtained: Na-A, Na-P1 [19]. The MCM-41 material was formed from the waste filtrate resulting from the synthesis of zeolites. The surfactant (hexadecyltrimethylammonium bromide (CTAB)) was the template for obtaining the structure of MCM-41 [20]. For the modifications obtained by synthesizing the materials, a HDTMA water solution in the amount of 24.4 mmol HDTMA/g material (NaP1, Na-A, MCM-41) was used. 200 cm\({}^{3}\) of distilled water was added to a portion of 200 g of material. The dissolved surfactant was poured into a beaker with modified material and stirred for 24 h at room temperature. The solution was then decanted, the material transferred to a dish, and dried at 60 \({}^{\circ}\)C [21].  
The basic research method confirming the structure of materials obtained by synthesis was X-ray diffraction (XRD). The presence of individual crystalline phases for zeolites and mesoporous silica material was recognized on the basis of characteristic inter-plane distances [22; 23]. Surface modification with HDTMA does not change the crystalline structure of materials [21].  
The textural parameters of the studied zeolite and silica materials are shown in Table 1.
# Performance Prediction and Optimization of Multi-Mirror Combined Solar Dish Collector
## 2 Method: The Optical Efficiency Calculation Model and Optimization Method for Combined Disk System
### Optical Efficiency Calculation Model for Combined Dish System
If point O is outside of area S, as shown in Figure 3 (right), then  
\[\eta_{P}=f_{at}*f_{rec}*\int_{\theta_{T}}^{\theta_{p}}2\pi g(\theta)B(\theta) \sin\theta d\theta, \tag{8}\]  
\[\gamma_{P}=\int_{\theta_{T}}^{\theta_{p}}2\pi g(\theta)B(\theta)\sin\theta d\theta. \tag{9}\]  
If \(\theta\) is less than or equal to \(\theta_{\text{T}}\), it means that no reflected light is intercepted; thus, \(g\left(\theta\right)=0\)  
Figure 3: The intersection of reflected rays on the receiver reference plane: the area of circle S represents the receiver plane; point O is cross-point of the central solar ray on the receiver; right: O is in the area of the receiver; left: O is out of the receiver. The convolution of the actual solar intensity distribution and the optical error Gaussian distribution is used to obtain the distribution of reflected light intensity [30]. Here, a polynomial fitting method is used to approximate the actual solar intensity distribution data [31], and the Buie exponential decreasing model [32] is applied to simulate the halo distribution [30]. Therefore, the calculation formulas of the average optical efficiency \(\eta\) and the interception rate \(\gamma\) of a single mirror are  
\[\eta =\iint_{S}\eta_{p}dS/S, \tag{10}\] \[\gamma =\iint_{S}\gamma_{p}dS/S, \tag{11}\]  
where \(S\) represents the projected area of the mirror under direct sunlight. The Gauss-Legendre integration method is used to solve them. Here, the space between adjacent mirrors is big enough in the combined dish system; thus, the shadowing and blocking of heliostats is small enough to be neglected. Therefore, in the case of a fixed system focal length, the interception rate of a single mirror at different locations can be calculated. Finally, the interception rate of all mirrors is averaged to be the interception rate of the entire dish system. Compared to other models, the proposed model is more precise because of the complex model, as it only approximates the elliptic Gaussian distribution to a circular Gaussian distribution which is a general assumption. The model considers the optical error, the actual solar intensity distribution including the halo distribution and circumsolar ratio, and system design parameters such as mirror size, specular focal length, and receiver size. This model can be used to calculate mirrors with various shapes although only the spherical mirror is analyzed as an example.
# Fine-Tuning a Personalized OpenBioLLM Using Offline Reinforcement Learning
## 4 Experiment
### Similarity Comparison  
This experiment aims to evaluate the consistency between the treatment model and the treatment decisions of clinicians in the patient dataset, and furthermore, to analyze the performance of this consistency in patient groups with different risk levels to test the personalized level of the treatment case. Among them, the patient data are divided into a set of survival patients and a set of dead patients. The study found that in the set of surviving patients, the similarity between the treatment model and the clinician's treatment decision was significantly higher than that in the set of dying patients.  
In this experiment, the patient dataset is randomly divided into a train set and a test set in an 8:2 ratio, where the train set includes 15,824 patients and the test set includes 3956 patients. The train set is used to train the treatment model, and the test set is used to verify the treatment model. Among them, the patients in the test set are further grouped according to whether they died or not, where the survival patient group contains 2572 cases and the dead patient group contains 1384 cases.  
In terms of PEEP, FiO2, and tidal volume treatment actions, this experiment defines the absolute difference threshold with the clinician's action value as a criterion for evaluating similarity. Specifically, the similarity threshold for the PEEP maneuver was set to 2 cmH2O, the contraction threshold for the FiO2 maneuver was set to 5%, and the contraction threshold for the tidal volume maneuver was set to 2.5 mL/kg.  
The experimental results show that in the set of surviving patients, the similarity between the treatment model and the clinician's actions reached 90.4%, indicating that the model's decision making in this patient group is highly consistent with clinical practice. In the set of deceased patients, the similarity dropped to 72.3%, which reflects that the treatment model adopted a treatment strategy different from conventional clinical practice when facing more complex and severe cases.  
The original intention of the design of the treatment model is to provide more personalized and accurate treatment recommendations by optimizing the treatment strategies of clinicians. The decline in similarity in the set of deceased patients indicates that the model is trying to avoid the adverse consequences that may be caused by conventional treatment strategies and seek effective treatment methods instead. Therefore, it can be inferred that this strategy shift, although it may differ from the conventional practices of clinicians in the short term, may improve the success rate of treatment and improve patient prognosis in the long run.