esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Potential Confounding Factors  
While the use of external learning tools such as ChatGPT is a common concern in AI-assisted learning research, our experimental design explicitly addressed this issue by embedding ITS-CAL into the exam environment under strict supervision. As described in Section 4.1, students were only allowed to use ITS-CAL during the exam, and proctors strictly monitored their activities to prevent the use of external AI-based tools. This design ensured that students relied solely on ITS-CAL for assistance within the experimental setting. Outside of the exam, students had full access to other learning resources, but these were not included as a part of the controlled experiment. Therefore, while we acknowledge that students' independent study time and prior exposure to AI tools before the exam could have influenced their learning outcomes, we can reasonably rule out the direct impact of external AI tools during the ITS-CAL usage period.  
Another potential concern is whether the grade reduction mechanism discouraged students from using ITS-CAL, introducing a bias in system engagement. While this is a valid consideration, our statistical analysis suggests that students who engaged with the system did so despite the grade penalties, likely prioritizing their exam result. Moreover, because the final exam and questionnaire were designed and validated independently, the grading mechanism does not impact the validity or reliability of these instruments. Nonetheless, future studies could experiment with alternative incentive models (e.g., bonus score-based systems instead of penalties) to encourage engagement without discouraging usage.
# A Real Time and Lossless Encoding Scheme for Patch Electrocardiogram Monitors
## 2 Design, Materials, and Methods
### Dynamic-Block Mode and Heartbeat  
The heartbeat ratio and sampling rate influence the quantity of samples for each interval, such as P, PR segments, QRS complexes, ST segments, T, U, and V in fixed-block mode, and an increment counter counts the quantity of the encoded data and appends an end tag to the high-bit output stream. The optimal CR has a valid end tag when using ECG data block segmentation with QRS complexes, and the sequence value following the QRS complexes in one heartbeat can be replaced by an end tag. Although many real-time QRS detection algorithms have been proposed, dynamic-block mode can simply modify the counter for QRS complex detection. In addition, counting continual zeros in high-bits can be used roughly to segment the ECG stream by QRS complexes. When the quantity of consecutive zeros exceeds the threshold, one-third of the sampling rate is calculated in the normal heartbeat range from 0.8-3 HZ [34]. The next non-zero has a high probability of being a QRS complex, which will thus determine the block size of the dynamic-block mode. The compression procedure is identical to that of the fixed-block mode; the only major difference is the block size K, which is generated through non-QRS-complex segmentation, and is inserted in the initial output (Figure 4). Figure 7 shows the output format for dynamic-block mode.  
Figure 6: Fixed-block mode output format.
# Application of DMLF in Pulse Ranging LiDAR System
## 3 Results
### Error Analysis  
We performed 1000 simulation experiments on the peak method and the proposed DMLF with 1 dB and 1 m steps, respectively. The difference between the measured value and the true value is called error; we analyzed the Root Mean Square Error (RMSE) and Relative Error (RE) in the experimental results, which is shown in Figures 5 and 6.  
Obviously, in Figure 5a, the proposed DMLF has a higher timing accuracy than the traditional method, no matter which levels of SNR are used, and with the increase in SNR, the RMSE of peak method and DMLF are decreased. When the SNR = 15 dB, the traditional peak method has an RMSE of about 0.3318 ns, while it is about 0.1661 ns for proposed DMLF, which means that the error could be reduced by about 50% if we use the latter one. When the SNR changes from 5 to 7 dB, the RMSE of the traditional method decreases sharply, almost from 1.1113 ns to 0.6264 ns, which shows that the effect of this method is very unsatisfactory in the case of a low SNR level. This sharp change in error in the traditional method is due to the peak area of the echo signal becoming severely damaged  
Figure 5: **(a)** The RMSE of DMLF with the change in SNR. **(b)** The RMSE of tradition PM with the change in Range. DM means the proposed DMLF, and PM means peak method.  
Figure 6: **(a)** RE of DMLF in different SNR. **(b)** RE of DMLF in different Ranges.  
under the condition of low SNR, and the sensitivity of the peak point's area is too low. On the contrary, our method is still lower than 0.3 ns. In Figure 5b, the proposed DMLF also shows good timing accuracy, and the fluctuation with distance is also smaller than in the traditional method. When the distance is 50 m, the traditional peak method has an RMSE of about 0.3625 ns, while, it is about 0.1999 ns for the proposed DMLF. This sufficiently proves that our proposed method is more robust to noise and has better timing accuracy.  
We also analyzed the RE of the proposed DMLF at different SNR and ranges. As shown in Figure 6, it can be seen that, under the experimental conditions, the RE can be maintained from 4 to 29 ppm. Considering other environmental or negligible errors, the RE of DMLF can be less than 30 ppm.
## 3 Proposed Method
### Softmax Weight Correction and Loss Function  
In our study, two modules are designed to compute the final model classification, handling the processing and loss calculation for the two classification results (Figure 1). During the training phase, the long-sequence repeat question learning module and the overall semantic perception module are treated as two independent classifiers. These modules will compute losses \(Loss_{cls}\) and \(Loss_{leader}\) for the classification and guidance outputs, respectively. Considering the structure and inputs of the two classifiers, we introduce a guiding factor \(\lambda\) for computing the final output loss. Our output loss function is expressed by the following formula:  
\[Loss=Loss_{cls}+\lambda*Loss_{leader}. \tag{21}\]  
The design of the loss function aims to leverage the most prominent correlated sequence in the overall semantically reinforced context to contribute to the final classification. These two losses will collaboratively influence the backpropagation process. In either side of the classifier, considering each other's loss serves as the optimization basis to enhance the model's performance.  
Similar to the design of the loss function, the final output of the model is a weighted average result from both classifiers. The sum of the weights is not directly used as the denominator here. In the two types of outputs \(Output_{cls}\) and \(Output_{leader}\), the probabilities for each classification are computed using Softmax. The total classification result of both classifiers is weighted and summed according to the guiding factor. The average operation is then performed across the classifier dimension for the final output \(Logits\):  
\[Logits=\frac{1}{2}\cdot(softmax(Output_{cls})+\lambda\cdot softmax(Output_{ leader})). \tag{22}\]  
Next, this work performs a second averaging operation on \(Output_{cls}\) and \(Logits\) to enhance the contribution of classification learning to the overall classification result and focus on optimizing the long-sequence repeat question feature learning module. This operation effectively combines the second amplification of \(Logits\) after an amplification by the guiding factor, resulting in the total weight amplification for each feature sample:  
\[\forall\ x_{i}\in Output_{cls},\ \ W_{x_{i}}=\left(\sum\frac{e^{x_{i,j}}}{ \sum_{j}^{M_{embed}}e^{x_{i,j}}}\right)/\left(\frac{\lambda}{2}+1\right). \tag{23}\]  
The output after the second amplification is obtained as \(Output\):  
\[Output=\frac{Logits+Output_{cls}}{2}. \tag{24}\]
# Advanced Control Strategies for Securing UAV Systems: A Cyber-Physical Approach
## 3 Results
* Flight trajectory (center): The 3D flight trajectory plot illustrates the helicopter's path, emphasizing the altitude changes at the specified times. The plot shows a controlled ascent and descent, indicating the SMC's effectiveness in managing the transitions between different altitudes and control modes. * Roll, pitch, and yaw (bottom-left): * Roll: The roll angle plot indicates minor fluctuations during the altitude changes, with the SMC maintaining stability and closely following the desired roll angle. * Pitch: The pitch angle plot shows consistent control during the flight, with the pitch remaining stable even during the altitude transitions at minutes 12 and 35. * Yaw: The yaw angle subplot highlights the controller's ability to maintain directional stability during the altitude changes and control mode transitions, with minimal deviations from the desired yaw angle. * Servo flap positions (bottom-right): * Roll servo flaps: The roll servo flap positions reflect the adjustments made by the SMC to stabilize the helicopter during the altitude transitions and control mode changes. * Pitch servo flaps: The pitch servo flap positions show the SMC's active management of pitch stability, particularly during the altitude changes, ensuring smooth control. * Yaw servo flaps: The yaw servo positions depict the control inputs made to maintain yaw stability, demonstrating the SMC's ability to handle directional changes effectively. Overall the strength points of Figures 2-5 rather than Figure 6 could be highlighted as follows:* Robust altitude control: The SMC shows excellent performance in maintaining the desired altitude, even with potential disturbances, demonstrating its reliability and robustness. * Efficient throttle management: The throttle control is stable, ensuring efficient power usage and smooth altitude adjustments. * Accurate trajectory following: The helicopter's flight path is well-managed, highlighting the controller's precision in following the intended trajectory. * Stable attitude control: The roll, pitch, and yaw angles are maintained with high accuracy, contributing to the overall stability and safety of the helicopter. * Responsive servo control: The servo flap positions indicate a responsive and precise control system capable of quick adjustments, essential for maintaining flight stability and performance.
## 3 Case Study
### Comparison of the Surge Tanks for the Same Structural Parameters  
The mathematical model for the TCST is a part of the previously developed, repeatedly tested and reliable original software [36]. The SPST model results were compared with the results obtained for the same structural parameters of the surge tank already installed in HPP Pirot. The mathematical model of the SPTCST (Figure 3b) has been defined with the aeration orifice diameter of 15 m which is equivalent to the upper chamber diameter. Namely, the structural characteristics of the SPTCST are the same as in the TCST model.  
Figure 3: HPP Pirot: (**a**) throttled chamber surge tank and (**b**) semi-pneumatic surge tank developed for the comparison (levels are indicated in m a.s.l.).  
Figure 2: HPP Pirot scheme.  
Comparative analysis for the load rejection (linear guide vanes closing law in 9 s and complementary opening law of the synchronous pressure regulation valves; rated discharge of 2 \(\times\) 20 m\({}^{3}\)/s) and simultaneous start (linear guide vanes opening law in 90 s from speed-no-load 2 \(\times\) 2.8 m\({}^{3}\)/s to the rated discharge 2 \(\times\) 20 m\({}^{3}\)/s) of both turbines is illustrated in Figure 4.  
Results of the load rejection and simultaneous turbine start for the chamber-type and semi-pneumatic surge tank practically coincide for the same structural parameters. Regarding the other parameters of importance, such as pressure envelopes along the disposition, turbine spiral casing and draft tube head, turbine rotational speed during load rejection and turbine discharge, there is also no difference.
## 1 Introduction
### Related Work  
Many recent studies focused on using multiple channels in _OMN_ networks to improve the network performance. In particular, the experimental results of [3, 4, 5, 6, 7, 8] show that using multiple channels can significantly improve the network throughput. One possible reason is that using multiple channels can separate multiple concurrent transmissions in frequency domain. On the other hand, the relationship between the number of channels and the network capacity were studied in [9, 10]. More specifically, it is shown in [9] that the capacity of _OMN_ networks has different bounds, which only depend on the ratio of the number of interfaces to the number of channels. Reference [10] studied the problem by using _linear programming_. More specifically, the _interference constraint_ and _flow constraint_ were defined in [10]. In addition, the link scheduling and channel allocation problem has been studied in [11]. Specifically, in [11], a _conflict graph_ was proposed to model the constraints, and the channel assignment problem was converted into a graph coloring problem. Furthermore, the general channel allocation problem in _OMN_ networks was studied in [12]. Moreover, [13] first applied stochastic optimization in channel allocation in _OMN_ networks.  
However, most of the current studies on the channel allocation are focused on _OMN_ networks. Recent studies such as [14, 15, 16, 17, 18, 19, 20, 21] found that applying _directional antennas_ instead of omni-directional antennas in wireless networks can greatly improve the network capacity. For example, the analytical results in [14] show that using directional antenna in arbitrary networks achieves a capacity gain of \(2\pi/\sqrt{\alpha\beta}\) when both transmission and reception are directional, where \(\alpha\) and \(\beta\) are transmitter and receiver antenna beamwidths, respectively. Under random networks, the capacity gain is \(4\pi^{2}/(\alpha\beta)\). However, these studies only considered the single channel in _DIR_ networks. There are few studies on the multi-channel allocation of _DIR_ networks, which are shown to have higher performance than_OMN_ networks [14, 20, 22]. In particular, the channel allocation in IEEE 802.11-based mesh networks with directional antennas was studied in [23]. However, the allocation scheme of this study can only apply for the specific network, in which a wireless station (or a node) can simultaneously transmit to a number of other stations, or simultaneously receive from a number of other stations, but a station cannot simultaneously transmit and receive (_i.e._, the half-duplexity is still in place).
# Noise Suppressed Image Reconstruction for Quanta Image Sensors Based on Transformer Neural Networks
## 4 Experimental Results  
In the following subsection, simulations and experimental validations were conducted for the QIS noise suppression method proposed in this paper. In this section, the effectiveness of the network architecture is first verified through ablation experiments, and then the proposed reconstruction method is evaluated across different datasets. All experiments were performed in a Python 3.11.4 environment on a computer equipped with an Intel 12400F/4.4 GHz 6-core CPU and 32 GB of memory and the neural network training was accelerated using an Nvidia Geforce RTX 3070 Ti with CUDA 12.3.  
The training dataset consisted of the following: the BSD dataset containing 432 natural images [25], the DIV2K dataset containing 800 natural images [26], the Flickr2K dataset containing 2650 natural images [27], and the WED dataset containing 4744 natural images [28]. This formed a synthetic noise training dataset with a total of 8626 natural images. To enrich the training dataset, each image was randomly cropped into 108 patches of size \(48\times 48\), resulting in 931,608 image patches for training the grayscale denoising model. The input images used for training were the \(\frac{S_{n}}{L}\), which are summed three-dimensional data matrices under temporal oversampling rate \(L\). During training, the temporal oversampling rate \(T\) was set to 16, the spatial oversampling rate \(K\) was set to 1, and \(\sigma_{\text{read}}\) was set to 0.2e-. The targets are the corresponding clean (ground-truth) images. For testing and evaluation, we adopt the standard benchmark datasets BSD68, Set12 [29], and Kodak24 [30].  
The parameter settings for training the QIS-SPFT denoiser were as follows: batch size was set to 8, epochs were set to 24, and the initial learning rate was set to \(1\times 10^{-4}\).
## 2 Methodology
### MHA-ConvLSTM Combined Prediction Model  
#### 2.2.1 Multi-Head Attention Mechanism (Multi-Head Attention)  
Traditional recurrent neural networks (RNNs) struggle to capture long-range dependencies in sequences when working with extended time domain temporal data. The model's performance can be enhanced by using the attention technique to assist the model in more accurately capturing these dependencies. The fundamental goal of the attention mechanism is to direct attention to the information that is most important for the current task among a great amount of incoming data while reducing or even removing attention to other aspects. By addressing the issue of information overload, this strategy increases the task processing's effectiveness and precision. The operations for the typical attention mechanism module are query (\(Q\)), key (\(K\)), and value (\(V\)). In order to get the overall weights and outputs, the attention weights must first be determined using \(Q\) and \(K\), and then they must be applied to \(V\). According to Equation (3), the output vector is specifically calculated for the input matrices \(Q\), \(K\), and \(V\).  
\[Attention(Q,K,V)=Soft\max\left(\frac{QK^{T}}{\sqrt{d_{k}}}\right)V \tag{3}\]  
Equation (3) in \(Q\in R^{n\times d_{k}},K\in R^{m\times d_{k}},V\in R^{m\times dv}\).  
The multi-head attention mechanism is optimized and improved from multiple parallel attention mechanisms, which not only improves the computational speed but also enables the network to adaptively select important data features to train the network model, increasing the diversity of extracted features; the collaboration between multiple heads helps the network to learn deeper data features, while the collaboration between multiple heads also improves the accuracy of the model prediction. The mathematical expression of the multi-head attention mechanism as Equation (4) is:  
\[MultiHead(Q,K,V)=Concat(head_{1},\cdots,head_{k},\cdots,head_{n})W^{O} \tag{4}\]  
In Equation (4), \(Concat(\cdot)\) denotes the vertical stitching operation of the matrix and \(W^{O}\) denotes the weight matrix \(W^{O}\in R^{d_{v}\times d_{m}}\).
## 2 Literature Review
### Classification of WBAN Routing Protocol
#### 2.1.5 Cluster Routing  
In WBAN, temperature and energy are the main factors to be evaluated. Cluster-based routing is the type of WBAN routing which aims to decrease the energy consumption and maximize the life-cycle of a network. In cluster-based routing, the node energy consumption is minimized by breaking the networks into portions, and each portion represents a cluster. These clusters are used for a communication head using a cluster head (CH). The CH is responsible for forwarding, aggregating, and collecting the data. The existing cluster routing protocols discussed in the literature include hybrid indirect transmission (HIT) [46], AnyBody [47], Cluster-based body are protocol (CBBAP) [48].
## 3 Results and Discussion
### Volatile Profiles of Vidal Grapes in Weixi Region after On-Vine Dehydration (T5)
#### 3.2.1 FADs  
The percentage of FADs in the total content (free + bound) of volatile compounds in Vidal grapes (T5) was 39.97%, and their percentage in free and bound forms of volatiles was 39.67% and 40.10%, respectively (Figure 2). The FADs were present mainly in glycosidically bound form, being 2.27 times higher than those in free form. Twenty-seven FADs were detected in Vidal grapes, including 12 straight-chain alcohols, 6 straight-chain aldehydes, 1 straight-chain acid, 7 straight-chain esters, and 1 lactone. Straight-chain alcohols and aldehydes were the two most abundant compounds, accounting for 95.82% of the total FADs. The C6/C9 alcohols and aldehydes derived from the enzymatic oxidation of fatty acids through the lipoxygenase pathway are the main constituents of this group of compounds, which are the main source of 'green' or 'herbaceous' odors in grape juice and wine [2]. Among the free-form volatile compounds, the most abundant FADs were butanol, hexanol, hexanal, and _trans_-2-hexenol, which accounted for 18.58%, 14.89%, 12.19%, and 11.60% of the FDAs, respectively. Yet, within the bound forms, the largest contents were observed for nonanol, butanol, _cis_-6-nonenol, and hexanol, with percentages of 46.16%, 13.41%, 11.79%, and 9.66%, respectively. Unlike the high level of C6 aldehydes in grapes harvested at regular maturity [2; 3], the content of hexanal in on-vine dehydrated Vidal grapes was not the highest of FADs, representing only 5.29% of the total FADs and much lower than the contents of nonanol and butanol, which accounted for 34.57% and 14.99% of the total FADs, respectively. In the study conducted by [PERSON] et al. (2007) [9], it has also been found that butanol was one of the characteristic odorants distinguishing sweet Fiano wine from base Fiano wine, the former being made from dehydrated grapes while the latter was made from grapes at normal maturity.
## 2 Literature Review
### Wireless Sensor Networks (WSNs)  
WSNs typically consist of spatially distributed micro-devices, known as sensor nodes, which collaboratively monitor physical or environmental conditions [36]. However, these nodes are inherently constrained in terms of energy supply, processing power, memory, and communication range. These limitations make it challenging to implement robust data management and security mechanisms, leaving WSNs vulnerable to cyber-attacks and data integrity issues [37; 38; 39; 40]. Unlike traditional blockchain architectures that rely on energy-intensive consensus mechanisms and centralized control, Integrating Holochain into WSNs employs a distributed hash chain model where each node maintains its own chain of validated data which aligns well with the limited resources of sensor nodes.  
In a Holochain-enabled WSN, each sensor node can independently validate, store, and share data with its peers, promoting decentralized trust and eliminating the need for a central authority. This enhances data integrity and network resilience against common attack vectors such as data tampering, spoofing, and DoS attacks. Holochain's architecture reduces the overhead typically associated with data synchronization and consensus, making it a scalable and energy-efficient framework for secure data exchange in resource-constrained sensor networks.