esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Demand Charging Management Model and Its Optimization for Wireless Renewable Sensor Networks
## 3 Energy Consumption Analysis  
In this section, we use the WMCD to calculate the system's total energy consumption. To compare, a hybrid vehicle (PHEV) plug-in is used to transport the wireless mobile charging device battery. According to Pacific Northwest National Laboratory research data [41], a mid-size PHEV's energy consumption per mile (ECPM) is 675 J/m. If the WMCD travels at \(V=5\) m/s, the WMCD's energy consumption while traveling is approximately 3 kW. The sensor node's energy consumption, on the other hand, ranges from a few milliwatts to hundreds of milliwatts [42]. The energy required by the WMCD for traveling appears to encompass the majority of the system's total energy consumption. We introduce a particle swarm optimization-based energy renewable management system (ERMS-PSO). In ERMS-PSO, we first set \(T\) accurately, and we then calculate the charging period \(T_{i}\) for sensor node \(i\) based on its energy consumption rate \(pi\). As a result, the WMCD should visit a subset of nodes in each cycle, reducing the WMCD's journey travel distance. We employ two key steps to reduce the two portions of overall energy use. We wish to reduce the WMCD travel distance as much as possible because it accounts for the majority of the total energy use.  
1. The total power consumption has two parts; their sum is seen in Equation (4) and can be calculated as follows: \[P_{total}=\frac{1}{\lambda}*\sum_{i,j\in N}p_{i}+\frac{D_{total}*ECPM}{T_{total}}\] (4) where \(\lambda\) is the energy conversion efficiency of non-radiative energy transfer. \(T_{total}\) is the total time and \(D_{total}\) is the distance traveled by the WMCD throughout all cycles.
2. The ratio of vacation time of the WMCD (\(\mu_{vac}\)), which serves as the optimization goal in [13; 14; 15]. We describe \(\mu_{vac}\) in this study as the mean percentage of time in each cycle that the WMCD spent on vacation, and it can be calculated as follows: \[\mu_{vac}=\frac{\sum_{k}t_{vac}}{T_{total}},\mu_{vac}\in[0,1]\] (5) where \(\sum_{k}t_{vac}\) is the total amount of time that the WMCD spends on vacation across all cycles. Our goal in this research is to reduce the value of \(P_{total}\). In Equation (5), we can observe that when \(\mu_{vac}\) grows, the WMCD has a longer period to repair or replenish its battery at the RS, indicating greater network performance.
# New Vector-Space Embeddings for Recommender Systems
## 3 Proposed Approach
### Recommendation  
Recommendation is the problem of recommending items to the user based on the purchasing pattern of the user and other similar users. This corresponds to Term 3 in the formulation (2). The system uses a corpus of users' transactions where a transaction specifies the items purchased by a user. Representative vectors are to be learned for items such that items occurring together in transactions are placed close to each other in the vector space. The more transactions that include an item pair, the closer the vectors of these items are placed with respect to each other. This conveys the fact that these items are more likely to be purchased together following a purchase pattern. For example, items such as \"keyboard\" and \"mouse\" are more likely to be purchased together than items such as \"keyboard\" and \"cap\". After training is complete, the similarity between vectors is determined by computing the dot product.  
The input to the model is a corpus of users' transactions. The method begins by parsing transactions such that item pairs are extracted. Each item pair is assigned a score proportional to the number of transactions where this item pair occurs. The error function is given by:  
\[E=\sum_{i=1}^{N}\sum_{i^{\prime}=1}^{N}w_{ii^{\prime}}\Big{[}x_{ i}^{T}x_{i^{\prime}}-o_{ii^{\prime}}\Big{]}^{2}\] \[\text{subject to}||x_{i}||=1,i=1,\ldots,N, \tag{7}\]  
where:  
* \(x\) is the item's vector.
* \(o_{ii^{\prime}}\) is a number proportional to the number of transactions where the items \(i\) and \(i^{\prime}\) occur together, fed to the system as a scaled value where the maximum value is 1.
* \(w_{ii^{\prime}}\) is a weighing coefficient whose purpose is to put different emphases on different terms. Typically we take them to equal one.
* \(N\) is the number of items.  
Vectors are learned for the items such that the dot products between such vectors are as close as possible to the given similarity values \(o_{ii^{\prime}}\). The algorithm for training such a model is similar to the one described in the previous subsection.  
To recommend items for a given user, we use \"Average pooling\". A vector is computed representing the user's taste. This vector depends on the user's transactions' history. In our model, this user representative vector is computed using average pooling on vectors of the items that are present in this user's transactions' history, as in (8). The user's vector adequately represents the user's taste as it is placed in a middle ground between the different items that this user purchased.  
\[Vec_{user}=\frac{1}{T}\sum_{i=1}^{T}itemvec_{t}, \tag{8}\]  
where  
* _itemvec_ is the vector of the item.
* \(T\) is the number of items in the user's history.  
Next, recommendations for items are performed by obtaining the items that are close to the user's vector. This can be obtained by computing the dot product between the user's vector and the item's vector. Then, items that have the largest dot products are returned as the system's recommendation. Figure 1 summarizes this method.
# Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation
## 4 Experiments and Results
### Results
#### 4.3.3 Coronary Artery in Angiography Segmentation  
We chose Adam as the optimizer, and the initial learning rate was \(3\times 10^{-4}\). As for epoch and batch size, they were set to 250 and 4 respectively. The training time of the proposed model was about 3 h.  
Table 4 summarizes the quantitative results for the comparison between this experiment and other methods. As observed, in terms of SE, IoU, and DC indicators, Residual-Attention UNet++ was higher than UNet++ by, respectively, 2.66%, 1.46%, and 2.46%. In addition, compared with FCN, we can see that all evaluation indicators greatly improved, which further illustrates the superiority of the model.  
Figure 15 shows the qualitative results for the UNet++ and Residual-Attention UNet++ models, respectively. As observed, compared with UNet++, the definition of the coronary artery edge in the proposed model was closer to the ground truth.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **F1-Score** & **SE** & **IoU (\%)** & **DC (\%)** \\ \hline FCN & 0.5330 & 0.7250 & 55.12 & 68.34 \\ SegNet & 0.5631 & 0.7506 & 59.81 & 69.40 \\ U-Net & 0.5841 & 0.7865 & 64.15 & 69.17 \\ Attention UNet & 0.5904 & 0.8034 & 65.48 & 69.84 \\ UNet++ & 0.5907 & 0.8119 & 65.11 & 70.02 \\ Attention UNet++ & 0.5901 & 0.8135 & 65.51 & 70.47 \\ Residual Attention UNet++ & 0.6110 & 0.8335 & 66.57 & 72.48 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental performance of Residual-Attention UNet++ and other methods on Angiography datasets.  
Figure 14: The comparison between Residual-Attention UNet++ and other methods on F1-score after multiple experiments for cell nuclei dataset.  
Figure 16 shows the comparison between Residual-Attention UNet++ and other methods on IoU after multiple experiments, which further illustrates the excellent performance and robustness of the proposed model.  
Figure 16: The comparison between Residual-Attention UNet++ and other methods on IoU after multiple experiments for angiography dataset.  
Figure 15: Qualitative assessment of the performance of UNet++ and Residual-Attention UNet++ on coronary artery in angiography.
## 3 The Self-Adaptive Mobile Clustering Algorithm (SAMCA)
### Merging Clusters  
This subsection describes how the two neighboring clusters \(C_{i}\) and \(C_{j}\) combine together to form a new merged cluster. For any cluster \(C_{i}\), denote a set of clusters which is neighboring to cluster \(C_{i}\) as \(I_{nb}^{(C_{i})}\) and let \(N_{C_{i}}\) be the number of sensors in cluster \(C_{i}\). When \(N_{C_{i}}<LB\), clusterhead \(ch_{i}\) determines cluster \(C_{j}\) with the smallest cluster size as the candidate cluster for merging, where \(C_{j}\in I_{nb}^{(C_{i})}\). After determining \(C_{j}\) as the partner of merger, clusterhead \(ch_{i}\) sends a message to clusterhead \(ch_{j}\) to start the merge process. In order to update the cluster ID, the clusterhead in the merged cluster may be determined by the following ways: (1) Based on the cluster size. That is, \(ch_{i}\) is the clusterhead in the merged cluster if \(N_{C_{i}}>N_{C_{j}}\); otherwise, \(ch_{j}\) becomes the clusterhead in the merged cluster; (2) Based on the cluster ID. For instance, as \(N_{C_{i}}=N_{C_{j}}\) the clusterhead with a higher cluster ID is the clusterhead in the merged cluster. If \(ch_{i}\) is the clusterhead of the merged cluster, \(ch_{j}\) will broadcast a cluster ID update message to its cluster members in \(C_{j}\). Therefore, all cluster members are with the same cluster ID in the merged cluster and the merge operation is complete.
# Research on Location Selection Model of 5G Micro Base Station Based on Smart Street Lighting System
## 2 Smart Street Lighting System  
With the popularity of intelligent connectivity, light poles not only have lighting functions but also can connect to IoT sensors using existing electrical infrastructure and wireless technologies. It helps to create an ecosystem of intelligent city applications to help city operations and maintenance while building a robust network architecture to develop intelligent cities [21; 22]. A light pole as a platform can carry urban functions such as transportation, security, energy, and wireless communication. Figure 2 shows the proposed light pole platform. In the case of meeting basic lighting needs, it relies on LED lights and intelligent control platforms and integrates various functional devices such as cameras, sensors, electronic displays, and USB charging piles. Through information sensing and data interaction technology, we built subsystem modules such as intelligent lighting, intelligent transportation, intelligent security, intelligent monitoring, and intelligent energy, an essential source of IoT information collection. It can provide a variety of support for smart city big data applications and improve the city and municipal services.  
Smart street lighting system integrates various subsystem functional devices such as 5G micro base stations, security cameras, and traffic cameras. In particular, the 5G micro base station, as wireless communication subsystem equipment, is mounted in smart light poles to solve the pressure for large-scale construction demand on urban site resources. In addition, it can effectively deal with many problems such as significant investment and resource waste in the traditional base station construction model [23; 24].  
Due to the numerous and complex subsystems of smart light poles, selecting functional devices only from the perspective of function aggravates the confusion of light poles. On the premise of ensuring coverage requirements and construction economy in the location selection process of the 5G micro base station, the correlation between the 5G micro base station and the installed equipment was considered. For example, remote monitoring of micro base stations and ancillary equipment by the installed sensor devices such as cameras can reduce costs and increase efficiency in the daily operation and maintenance of base stations. It maximizes the synergy between functional devices to achieve intelligent  
Figure 2: Smart street lighting system.  
interconnection, reducing deployment operation and maintenance costs and beautifying the city's image.
## 3 The Proposed DRL + GNN-Based Handover Scheme
### MPNN-DQN Based Handover Scheme
Training  
while not DONE:  
Obtain the initial state of edges in the LEO satellite handover graph  
Calculate all candidate paths p and detailed path information  
For each edge p:  
Calculate the network state corresponding to the selected path p  
Choose action a according to the greedy strategy  
if np.random.rand() > self.epsilon:  
Based on Algorithm 1 (refer to Equations (14)-(16)), obtain the estimated value of q  
for the current state and action, and select the action with the maximum estimated value  
of q as the path to be selected  
else:  
Randomly select a candidate path as action a  
Generate new status, instantly reward r (refer to Equation (24)) and mark Done  
Store the information of state transition to the experience replay pool  
reward = reward + r  
steps += 1  
if steps % M == 0 and steps%15 == 0:  
Sampling batch data from experience replay pool  
Calculate loss and gradient (refer to Equation (18))  
Update network parameters w and \(\theta\)  
if episode%50 == 0:  
Update network parameters \(\theta^{-}\)  
**Algorithm 2** Satellite handover decision algorithm based on MPNN-DQN  
In the MPNN-DQN-based satellite handover algorithm, DQN agents interact with the LEO satellite environment during the training phase, using the robust characterization and generalization capabilities of GNNs to learn the optimal strategy for satellite handover paths; and during the testing phase, they utilize the trained GNNs to determine the optimal handover paths within the specified communication duration.
## 5 Performance Evaluation
### Impact of ICI by Comparing TCG and CGI
When the path loss exponent becomes larger, the difference of the channel conditions to picocells and the macrocell becomes larger, so the decrement of the spatial spectral efficiency can be very large when the PSNE collapses, which is clear by comparing Figure 6d, f with Figure 6b. Besides, there is a small augmentation in Figure 6b before the performance collapses, this happens occasionally when the transmission power of picocell devices, the ICI and the picocell radius coincide. In detail, when the picocell radius and the ICI are both large, but the transmission power is relatively small, devices on the edge of a picocell's coverage area should select the macrocell to guarantee system optimality.
## 1 Introduction
### The Multimodal Hidden Markov Model (MHMM): A Dynamic Solution  
To overcome the limitations of static and unimodal assessments, this research proposes a Multimodal Hidden Markov Model (MHMM). The MHMM is parameterized using published empirical data from surgical training studies ([PERSON] et al., 2025) [24], ensuring its parameters reflect real-world physiological, behavioral, and subjective metrics. It dynamically infers latent proficiency states (Novice, Intermediate, and Expert) using three complementary observation streams:  
* HRV (Physiological): Indicates stress and cognitive load.
* TCT (Behavioral): Reflects task fluency.
* NASA-TLX (Subjective): Captures perceived task demand.  
The MHMM leverages this integration and models temporal dynamics through a probabilistic framework, capturing transitions such as learning progression, fatigue onset, or proficiency decay. Grounded in classic learning theories ([PERSON]'s five-stage model and Fitts & Posner's three-phase framework) the MHMM is parameterized with empirical benchmarks drawn from surgical simulation studies and smart-factory task-analysis datasets [24; 25; 26; 27; 28]. In contrast to black-box architectures such as deep neural networks, the MHMM provides interpretable transition matrices and state-specific emission probabilities, enabling real-time insights into operator development and risk.  
Aligned with the core tenets of Industry 5.0, the proposed MHMM advances the following:  
* Human-centric AI by tailoring assistance to operator states.
* Operational resilience through the early detection of overload or regression.
* Sustainable workforce development by proactively mitigating burnout.  
The MHMM offers a scalable and interpretable framework for context-aware, real-time proficiency monitoring, supporting adaptive human-AI collaboration in safety-critical and cognitively demanding environments.
## 6 MIMO Antenna Performance Enhancement Techniques
### Defected Ground Structure (DGS)  
Different geometrical shapes, defects or slots are consolidated in the ground plane of the antenna. Low mutual coupling and wide bandwidth with maximum efficiency is the structure with defected ground in [88].
# Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights
## 4 Delayless Frequency-Domain ANC Algorithms  
The algorithm in Table 5 removes the delay in the weight update but does not remove the delay in the signal path. It is more essential to remove the delay in the signal path because this delay has a major effect on the performance of broadband ANC systems. Several delayless frequency-domain ANC algorithms have been presented to remove delays in the signal path [44; 46; 47]. This section first reviews several delayless frequency-domain ANC algorithms, and then, a new delayless algorithm with the modified filtered-x scheme is proposed.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 4 Preliminaries
### WiFi Fingerprint-Based Localization
#### 4.2.1 Parameter Settings  
According to the aforementioned computational process, multiple parameters influence both query accuracy and query performance, particularly in secure query scenarios.  
First, the number of access points (\(n\)) and the number of reference points (\(m\)) determine the vector length for each distance computation and the total number of distance computations, respectively. This paper followed the settings from multiple WiFi fingerprint-based designs [13; 14], where \(n\in[50,250]\) and \(m\in[100,800]\).  
Second, \(k\) determines the number of nearest neighbors selected. Studies have shown that a small \(k\) is sufficient to ensure query accuracy in indoor localization scenarios [36; 37]. Similar to prior works, we adopted \(k=3\) and \(k=4\).  
Finally, the bit length used to represent the signal strength value \(v_{i1}\) also impacts performance. Related studies indicate that quantizing \(f_{ij}\) to 4 bits achieves comparable accuracy [14] to the unquantized values. Therefore, we adopted this parameter setting in our work as well.
## 2 Results and Discussion
### The Binding Constants and the Number of Binding Sites  
For static quenching, the number of binding sites per protein molecule (\(n\)) and binding constants (\(K_{\mathrm{a}}\)) could be calculated according to Equation (3). The plots of log[(F\({}_{0}-\) F)/F] versus log[Q] for gardenin B and eupatorin are shown in Figure 10 and the calculations of slope and intercept are shown in Table 2. The double-logarithm curves were obviously linear and the fitting degrees were 0.9950 and 0.9962, indicating that the calculations were acceptable for this study [30]. The corresponding calculated \(K_{\mathrm{a}}\) (\(n\)) values between gardenin B and eupatorin and XO were 2.636 \(\times\) 10\({}^{6}\) (1.1124) and 4.612 \(\times\) 10\({}^{3}\) (0.9534) at 300.15 K, respectively. Accordingly, gardenin B showed stronger binding capacity than eupatorin.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Compounds** & **\(K_{\mathrm{q}}\) (10\({}^{13}\))** & \(K_{\mathrm{sw}}\) (10\({}^{5}\))** & **R\({}^{2}\)** & **Log\({}_{10}\)\(K_{\mathrm{a}}\)** & \(n\)** & **R\({}^{2}\)** \\ \hline
**1** & Gardenin B & 1.2194 & 0.7560 & 0.9988 & 6.4209 & 1.1124 & 0.9950 \\
**2** & Eupatorin & 1.0097 & 0.6260 & 0.9957 & 3.6639 & 0.9534 & 0.9962 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The affinity constants of gardenin B and eupatorin for XO. Abbreviations: \(K_{\mathrm{q}}\), \(K_{\mathrm{av}}\), R\({}^{2}\), \(n\), number of binding sites per protein molecule.  
Figure 10: The plots of log[(F\({}_{0}-\) F)/F] versus log[Q] for (**a**) gardenin B and (**b**) eupatorin at 300.15 K.  
Figure 9: The Stern–Volmer plots for XO fluorescence quenching by (**a**) gardenin B and (**b**) eupatorin at 300.15 K.
# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 3 Methodology
### Domain Discriminator and Gradient Reversal Layer  
In addition to using HOMM3 Loss to promote deep-feature alignment between the source domain and target domain, the LTS-SEI method also introduces adversarial learning to achieve shallow feature matching between the source domain and target domain. A domain discriminator is designed to compete with the feature extractor, as shown in Figure 2. The domain discriminator of the LTS-SEI comprises several fully connected layers and a sigmoid activation layer, as shown in Figure 5. Noteworthily, the last fully connected layer of the domain discriminator only has a neuron. The feature extractor and domain discriminator of LTS-SEI are similar to the generator and discriminator of a GAN. Unlike the generator, the goal of the feature extractor is not to synthesize samples but to generate domain-invariant features. We assume that the domain labels of the source domain samples are 0 (true) and those of the target domain samples are 1 (false). When the domain discriminator is unable to distinguish whether the output of the feature extractor originates from the source domain or target domain, it is indicated that the feature extractor and domain discriminator have reached the Nash equilibrium in the process of mutual game. We assume that the alignment between the source domain and target domain has been achieved, and the probability of the domain discriminator output being 0 or 1 should be 0.5 each. Therefore, the purpose of the domain discriminator is to minimize the domain discrimination loss and correctly identify the domain labels of shallow features. The purpose of the feature extractor is to maximize the domain discrimination loss and confuse the judgment of the domain discriminator. Here, the domain discrimination loss \(L_{D}\) is defined as  
\[L_{D}=-\frac{1}{K}\sum_{i=1}^{K}\left[d_{i}\cdot\log(p_{d_{i}})+(1-d_{i})\cdot log (1-p_{d_{i}})\right] \tag{18}\]  
where \(K\) represents the number of small batch samples, \(d_{i}\) denotes the real domain label of sample \(X_{i}\) and is represented as a one-hot vector, and \(p_{d_{i}}\) represents the recognition probability of sample \(X_{i}\) by the domain discriminator.  
The traditional neural network optimization method requires to minimize loss when updating parameters. We introduce a GRL between the feature extractor and domain discriminator because the purpose of the feature extractor is to maximize \(L_{D}\). GRL maintains the input unchanged during forward propagation and reverses the gradient by multiplying it by a negative scalar \(\lambda\) during reverse propagation. Based on this idea, the feature extractor and domain discriminator implement adversarial learning and ensure that the shallow features output by the feature extractor are domain invariant.