esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result
### Prediction Results of MOOC Learners' Dropout Behavior
#### 3.2.2 Prediction Results  
The comparison data of different models prediction effects is shown in Table 7. The best performance of the experimental results is shown in bold. It can be seen from the table that although the recall rate of MOOC learner dropout behavior prediction model based on random forest is slightly lower than that of LR model, it has the highest accuracy, precision, F1 value and AUC value among the three models, namely accuracy = 0.917260, accuracy = 0.930923, F1 = 0.954145, AUC = 0.925341. Therefore, considering the five performance evaluation metrics: accuracy, precision, recall, F1 and AUC comprehensively, we can conclude that the MOOC learner dropout behavior prediction model based on random forest has better prediction performance than the model based on logical regression and the model based on K-nearest neighbor. That is, random forest has the best performance in predicting the dropout rate of MOOC learners.  
The ROC curve of model prediction effect comparison is shown in Figure 11. It can be seen from Figure 11 that the ROC curve of random forest surrounds the ROC curve of k nearest neighbor and the ROC curve of logistic regression, and the ROC curve of KNN includes the ROC curve of logistic regression. Therefore, the area under the ROC curve, that is, the AUC score of random forest is greater than that of k nearest neighbor and greater than that of logistic regression, which is consistent with the AUC result calculated above. The higher the AUC score, the better the prediction effect of the model, that is to say, the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **F1** & **AUC** \\ \hline LR & 0.902697 & 0.905907 & **0.992476** & 0.947217 & 0.808006 \\ KNN & 0.913950 & 0.928674 & 0.977239 & 0.952337 & 0.912316 \\ RF & **0.917260** & **0.930923** & 0.978555 & **0.954145** & **0.925341** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of different models prediction effect.  
prediction effect of the random forest model is better than that of the k-nearest neighbor model, and better than that of the logistic regression model.
## 2 Description of the THz Wireless Communication Setup  
### THz Transmission Setup  
A schematic overview of the wireless THz link architecture is given in Figure 1a. To modulate the baseband In-phase and Quadrature (I/Q) signal in 16-quadrature amplitude modulation (QAM) format, it is first digitally up-converted to an IF using the sample rate (Rs/2 + 1.5 GHz). The analog signal at the output of the Arbitrary Waveform Generator is used as data in the Mach-Zehnder modulator (MZM), which is responsible for the modulation of the optical carrier. The system's structure involves the utilization of two lasers operating at 193 and 193.3 THz, respectively. Laser two is intensity-modulated using the MZM, and the resulting modulated signal is combined with the optical THz output from laser one through a 50:50 combiner. To ensure appropriate power, an Erbium Doped Fiber Amplifier (EDFA) is placed after the combiner, amplifying the signal before driving it to the high-power modified uni-traveling-carrier photodiode, MUTC-PD [32], which converts from the optical to the electrical domain generating the 300 GHz signal. To transmit the output of the MUTC-PD across 1.5 m of free space, this is connected to a WR3.4 probe and subsequently radiated by a WR3.4 horn antenna with 24 dBi gain. To minimize the impact of free space losses between the transmitter and receiver, two Teflon lenses are placed and carefully aligned.
## 4 Discussion
It is important to note that accuracy does not necessarily equate with realism if attained by altering the geometry employed in modeling. Therefore, it is imperative to strike a balance between computational efficiency and maintaining fidelity to real-world anatomical structures for meaningful medical research applications [48]. Likewise, the use of an FSI technique is essential to accurately study CSF behavior in scenarios where the brain experiences multiple acceleration and deceleration events. Although FSI algorithms are more computationally expensive, they allow for fluid domains to move back and forth around the brain several times, which is necessary to demonstrate how CSF cushioning diminishes under such conditions. Using only solid elements would render it impossible to analyze this effect with any degree of accuracy or reliability.
# Locating Multiple Sources of Contagion in Complex Networks under the SIR Model
## 3 Simulation and Analysis
### Optimal Iteration Frequency Choice  
We choose the nodes with the maximal value of the potential concentration label as the sources and the potential concentration label is related to the number of iterations. Therefore, we next test the performance of our multiple sources localization method under six iteration frequencies in synthetic networks and real networks, which can help us find the appropriate iteration frequency.  
Figure 3 shows that the source's localization accuracy changes sharply when \(t1\) is different. It is an interesting phenomenon that the accuracy reaches its highest when \(t1=2\). The hub node plays a decisive role in the change of accuracy. On the first iteration, the hub node is an unnecessary node which brings error to the potential concentration label of each node. However, on the next iterations, the hub node transmits all node labels to each node as a bridge, which increases the accuracy of sources localization. Moreover, there is a turning point when \(\beta=0\). The accuracy is higher for \(\beta<0\) than it is for \(\beta>0\). The main reason lies in the incomplete infection information where a recovered node has actually been infectee, especially the sources, but it is considered to be uninfected when calculating. To get a better multiple sources localization performance, we choose \(t1=2,\beta=-1\) in the following experiments.  
\begin{table}
\begin{tabular}{c c c c l} \hline \hline
**Network** & \(N\) & \(|E|\) & \(<k>\) & **Description** \\ \hline Karate & 34 & 78 & 4.59 & The friendships between 34 members of a karate club at a US university in the 1970s \\ [PERSON] & 77 & 258 & 6.70 & The characters relationships in the novel Les Miserables \\ Adjnoun & 112 & 425 & 7.59 & The common adjectives and nouns in the novel David \\  & & & & Copperfield by [PERSON] \\ Football & 115 & 613 & 10.66 & The games among 115 American college football teams \\ Jazz [14] & 198 & 2742 & 27.70 & The jazz bands attending performance from 1912 to 1940 \\ USAir [15] & 332 & 2126 & 12.81 & The flights in the USA \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameter of real network. The quantities \(N\), \(|E|\), \(<k>\) represent the number of nodes, the number of edges and the average degree, respectively.
## 2 Materials and Methods
### Research Framework
#### 2.4.2 Evaluation Procedure  
The classification performance was evaluated by the classification accuracy, which denotes the percentage of correctly classified samples. The evaluation procedure was explicitly designed to maintain the robustness of the comparative analysis and compatibility between applied classifiers. To avoid overfitting, the following strategy was used:  
* A maximum of 6 selected features was allowed as inputs for the classifiers.
* Deep feature extraction was performed in various CAE configurations consisting of 4 or 6 neurons in the 3 rd fully connected layer (FCL 3), thus providing 4 or 6 deep features.
* For the DT classifier, the number of splits was limited to 15. Without this limitation, even higher accuracy (such as 95%) can be obtained, but the results are subject to overfitting.
## 4 Experiments
### Results & Analysis
#### 4.2.3 Comparison with Other Methods  
In this part, we evaluate the recognition accuracy and the computational complexity of four algorithms: Diff-SwinT, Swin-Transformer, VGG-Net (Visual Geometry Group Network) [22], and FFB (Feature Fusion Based) [46] under different JNR levels from \(-16\) dB to \(6\) dB.  
From Figure 7, we can see that our proposed Diff-SwinT achieves superior performance over VGG and FFB at all JNR levels. Specifically, at extremely low JNRs between \(-16\) dB and \(-10\) dB, Diff-SwinT obtains \(66.7\)-\(83.5\%\) accuracy, outperforming VGG and FFB by \(56.7\)-\(27\%\), with the advantage expanding as JNR decreases. As JNR increases from \(-10\) dB to \(6\) dB, the performance gap gradually reduces but remains significant, with the overall recognition rate of Diff-SwinT reaching \(90\%\) at JNR \(=-8\) dB, and gradually increasing to \(98.6\%\). VGG and FFB reach above \(80\%\) at JNR \(=-6\) dB and \(0\) dB respectively, and continue to increase to around \(94\%\) and \(88\%\). Table 2 shows the number of learnable parameters, floating point operations (FLOPs), and per-sample inference time on the hardware platform (Intel i7-12700H CPU and NVIDIA GeForce RTX 3060 with 6 GB) for the four methods. It can be seen that although Diff-SwinT has more parameters and FLOPs than SwinT, it still has fewer parameters and FLOPs than VGG. Moreover, Diff-SwinT achieves slightly faster inference time compared to VGG.  
While SwinT alone outperforms prior methods, its recognition accuracy still degraded significantly under low JNRs. Owing to the diffusion-based denoising module, compared to using Swin Transformer alone, Diff-SwinT achieves around \(10\%\) identification rate improvement at JNR between \(-16\) dB and \(-8\) dB. This ablation experiment clearly verifies the efficacy of the denoising module in enhancing model robustness under strong noise environments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Parameters (M)** & **FLOPs (G)** & **Time (ms)** \\ \hline VGG & 138.36 & 15.50 & 15.42 \\ FFB & - & - & 7.01 \\ SwinT & 28.29 & 4.36 & 8.22 \\ DIF-SwinT & 59.41 & 10.52 & 12.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Computational Cost of Four Methods.  
Figure 6: The Confusion Matrix of Diff-SwinT Recognition at JNR \(=-12\) dB.
# Water Segmentation for Unmanned Ship Navigation Based on Multi-Scale Feature Fusion
## 3 Experimental Data and Processing
### Experimental Environment and Parameter Settings  
In this study, the proposed model was implemented based on the MMSegmentation framework on the Ubuntu 20.04.3 operating system. MMSegmentation is an open-source image segmentation framework that supports a variety of segmentation tasks and model architectures. The experimental environment included Pytorch 1.11.0, mmcv-full 1.7.1, andPython 3.8. Pytorch provides flexible deep learning model building and training interfaces. The hardware configuration for the experiment included an Intel(R) Xeon(R) Platinum 8255C CPU and an NVIDIA GeForce RTX 3090 GPU (NVIDIA, Santa Clara, CA, USA) with 24 GB of VRAM. Additionally, CUDA 11.0 and cuDNN were installed to support GPU-accelerated deep learning and parallel computing tasks.  
During the training process, the model optimization used Stochastic Gradient Descent (SGD). SGD is a classic optimization algorithm that updates model parameters by computing the gradient of the loss function. The momentum factor was set to 0.9, which helped to accelerate the gradient descent process and reduce oscillation, making the optimization process more stable. The principle behind this was to adjust the current gradient direction by incorporating a weighted average of the previous gradients, thus maintaining inertia during parameter updates. The weight decay coefficient was set to 0.0005, which is a regularization technique that applies L2 regularization to the model parameters to prevent overfitting. A smaller weight decay value helped to control the model's complexity without significantly affecting its performance. The learning rate scheduling strategy of Polynomial Decay was used, such that  
\[Lr=Lr_{base}\times(1-\frac{N_{i}}{N_{max}})^{p} \tag{4}\]  
where \(Lr_{base}\) represents the initial learning rate, set to 0.01, which is the step size for updating the model parameters at the start of training. A larger learning rate can speed up initial convergence, but may cause instability during training. \(N_{i}\) represents the current iteration of training, and \(N_{max}\) is the maximum number of iterations, which takes the value of 20,000 in this experiment, ensuring that the model can fully learn the data features. \(p\) is the exponent for the polynomial decay, set to 0.9, controlling the rate of learning rate decay--the larger the value, the faster the learning rate decays.
# Performance Analysis of a Zero-Energy Building Using Photovoltaics and Hydrogen Storage
## 1 Introduction
The investigated unit included photovoltaics, battery, electrolyzer, hydrogen storage, fuel cell, hot water tank, absorption chiller, and evacuated tube solar thermal collectors. They gave emphasis to the satisfaction of the building's energy needs and an interesting result is that the fuel cell can provide 90% of the hot water demand. The concept of flexible buildings with photovoltaics and buildings has been studied by [PERSON] and [PERSON] [18]. They examined a configuration that includes also charging electric vehicles and the examined location was in China. They found that hydrogen storage can enhance off-peak renewable energy shifting. Another work by [PERSON] et al. [19] investigated the use of biomass and grid electricity for feeding a polygeneration system for the building sector that includes hydrogen storage. They found 12% primary energy reduction and 87% CO\({}_{2}\) emissions reduction with the followed strategy. The combination of wind energy and solar thermal energy in a multi-generation system has been examined by [PERSON] et al. [20]. They used hydrogen and water storage in their configuration, while there are several other devices such as electolyzer, absorption chiller, and reverse osmosis unit. They found that the payback period for this system ranges from 8 to 21 years depending on the examined location. In addition, the use of hydrogen as the storage technology in polygeneration systems for the building sector has been studied by other researchers in the literature [21]. More specifically, in this work [21], there are parabolic trough solar concentrating collectors in combination with an internal combustion engine for producing hydrogen, power, fresh water, and hot water. They found that the system energy efficiency is around 23.9%, while the system exergy efficiency is at 28.2%. The previous analysis clearly indicates a high interest in energy storage based on hydrogen production. Hydrogen as a storage solution is a promising solution for the building sector, except for the national grid. The present work comes to investigate an off-grid building of 400 m\({}^{2}\) floor area in Greece which uses photovoltaics and hydrogen storage for covering all its energy needs which are for heating, cooling, domestic hot water (DHW), and covering the appliances/lighting. Moreover, the system includes an electrolyzer for converting electricity into hydrogen, a storage tank for the hydrogen, and a fuel cell for converting the hydrogen into electricity when there is demand. The analysis is a dynamic one using results from simulation with TRNSYS software. The analysis is conducted on a yearly basis aiming to determine the PV area and the hydrogen storage capacity that is required for ensuring a safe operation during the year. The result can be used in the future for designing zero-energy buildings and the final conclusions can be used as guidelines in this direction.
## 3 Proposed Framework
### Hierarchical Federated Learning  
In our hierarchical federated-learning approach, the aggregation process occurs at two levels: edge-level aggregation and global aggregation. Each IoT device \(i\) trains its local model using its data \(D_{i}\), resulting in a model update \(w_{i}\). The edge node \(e\) performs aggregation over the subset \(S_{e}\) of IoT devices using the following formula:  
\[w_{e}^{(t)}=\frac{1}{|A_{t}|}\sum_{i\in A_{t}}w_{i}, \tag{1}\]  
where \(w_{e}^{(t)}\) represents the aggregated model at the edge node \(e\) at time \(t\), and \(A_{t}\) is the set of IoT devices that have successfully uploaded their updates by time \(t\). The asynchronous nature of this aggregation allows the system to maintain responsiveness even if some devices are slower to complete their local training.  
After edge-level aggregation, the aggregated model \(w_{e}^{(t)}\) is sent to the central server, which performs a global aggregation over all edge nodes. The central server uses an adaptive weighting mechanism to aggregate the models received from the edge nodes, where each edge node is assigned a weight \(v_{e}\) based on its participation frequency \(p_{e}\) and model quality metric \(q_{e}\):  
\[v_{e}=\beta p_{e}+(1-\beta)q_{e}, \tag{2}\]  
where \(\beta\) is a tunable parameter controlling the influence of participation frequency versus model quality. The global aggregation is then given by:  
\[w_{global}=\frac{1}{\sum_{e=1}^{E}v_{e}}\sum_{e=1}^{E}v_{e}w_{e}^{(t)}. \tag{3}\]  
This weighted aggregation ensures that edge nodes with higher reliability and better model quality contribute more significantly to the global model, leading to improved convergence and overall system performance.
## 2 Related Work
### Wireless Sensor Network Localization  
The term \"localization\" refers to the process of calculating a node's physical position, or location, in space. In recent years, a variety of approaches have been proposed that fall into two general categories: range-based and range-free localization [4]. The former category refers to protocols that use absolute point-to-point distance or angle estimates to calculate the location of a wireless sensor node; the latter category refers to protocols that make no assumptions about the availability of point-to-point distance or angle estimates, but instead rely on the structure of the network.  
Although range-free techniques require less sophisticated hardware than range-based techniques [5], they typically assume an isotropic network where the hop count between two nodes is proportional to their distance [6]. Given the presence of non-isotropic conditions on the shop floor resulting from signal loss due to noise and blockage and the need to reconfigure the network configuration to adapt to change, we have chosen to use range-based localization in this research.  
Two commonly used approaches to estimate point-to-point distance between nodes for range-based localization are Received-Signal-Strength (RSS) and Time-of Arrival (ToA). RSS measures the power of the signal at the receiver and calculates the distance according to the propagation loss model. ToA measures the propagation time of the received signal (typically radio signals for large distances or ultrasound for small distances) and determines the distance by multiplying it by its own speed. In general, RSS is an easier parameter to implement, while ToA may achieve a higher accuracy [7]. For this research, we have chosen to use ultrasound-based ToA because of its higher accuracy, and also to align our current simulation work with our previous experimental work [8] using the Cricket platform [9].
# Deep Learning-Enhanced Spectrogram Analysis for
## 5 Discussion
### Challenges and Limitations  
Despite the promising performance of the proposed approach, it has several limitations. The generalization of unseen patient data remains a key challenge, as real-world biomedical signals exhibit significant inter-patient variability due to differences in body composition, lung capacity, and recording conditions [50]. Subsequent research should investigate transfer learning and domain adaptation methodologies to enhance model generalization among various patient demographics [51].  
Another limitation is the reliance on spectrogram-based features. While spectrograms effectively capture time-frequency characteristics, additional modalities such as electromyography (EMG) and electrocardiography (ECG) can enhance the classification accuracy and provide a multi-modal diagnostic framework [52]. The integration of these complementary signals can further improve the reliability of automated biomedical diagnostics.  
Moreover, although the model demonstrates high classification accuracy, its real-time deployment in clinical settings remains a challenge owing to computational constraints. Deploying the model on edge-computing devices or cloud-based platforms can facilitate real-time biomedical signal classification in telemedicine applications [53].  
To enhance the robustness and generalization of the model further, domain adaptation and transfer learning strategies should be explored. Pre-trained medical models trained on large-scale biomedical datasets can serve as a strong initialization for fine-tuning spectrogram-based biomedical signals. Additionally, semi-supervised learning approaches can be employed to leverage unlabeled patient data, thereby reducing dependency on large annotated datasets. These techniques could significantly improve the model's adaptability to new patient populations while maintaining high classification accuracy [54].  
Although the model demonstrated strong performance using empirically selected hyperparameters (e.g., learning rate = 0.0001, batch size = 32), a formal sensitivity analysis was not performed. In future work, systematic evaluations across different hyperparameter values will be conducted to examine the stability and robustness of the training process.  
Another limitation is the absence of dimensionality reduction or clustering visualization to assess the intrinsic separability of the anatomical region features. Future iterations of this study will incorporate statistical and visual techniques, such as t-SNE and PCA, to evaluate how well spectrograms from different regions form distinct clusters in the feature space. These methods can offer additional insights into the discriminative structure learned by the model and support the validation of region-specific representations.
## 2 Composition of the \"Target Situational Awareness\" Test System and Experimental Methods
### Carrier Aircraft and Pod Being Tested  
#### 2.2.1 Safe Hanging Design of The Pod Being Tested  
To ensure stable and reliable hanging of the pod under various maneuvering flight conditions, the mechanical interface between the pod and carrier was devised as \"TUTU four sliders\". The \"T\"- and \"U\"-shaped structures are shown in Figure 2.  
In the \"T\"-shaped structure, parameters D1-D6 are the length control variables, and A1-A3 correspond to the angle control variables. In the \"U\"-shaped structure, parameters D7-D11 correspond to the length control variables, and A5-A7 correspond to the angle control variables.  
TUMI 250 managing steel (\(\sigma_{m}\geq 1760\) MPa) was selected as the material for the \"T\"- and \"U\"-shaped structures, and the structures were fabricated via wire cutting. As shown in Figure 3, the results of the finite element analysis suggest that the maximum axial and normal stresses of the cabin body at the connection between the hanging mechanism and pod body correspond to 349.17 MPa and 285.73 MPa, respectively. These results indicate that this structure can effectively withstand the stress load during the hanging flight.  
Figure 3: Simulation analysis of the vibration stress distribution.  
Figure 2: Structural diagram of the hanging mechanism. (**a**) “T”-shaped structure, (**b**) “U”-shaped structure.  
To further verify the strength of the hanging mechanism, a functional vibration test of the pod was conducted using the vibration platform, and the vibration spectrum is shown in Figure 4.  
After the vibration test, the structure was sampled transversely and longitudinally. The metallographic morphology images are presented in Figure 5 at a magnification of 200. Typical morphology of cracks, such as stripes, was not observed. Hence, it was assumed that the structure can effectively cope with the stress load during hanging flight.