esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 BACS Improvement Scenarios
### Energy Efficiency Improvements Package
#### 5.3.2 Case B  
The base case results of the BACS auditing of Case B resulted in an overall energy efficiency score of 73%, with the electricity and domestic hot water domains scoring the lowest with 29% and 42%, respectively. The scores for the other domains are relatively high, highlighting that these domains are already equipped with functionalities enhancing the energy efficiency quotient. Therefore, the improvement package for energy efficiency in this building majorly targets the electricity systems and domestic hot water functionalities and control patterns. The results in terms of points for each domain are depicted in Figure 12b, where the package has led to an increase of 10 points in energy efficiency and thereby an estimated reduction in energy consumption of 5%. These improvements also increased flexibility and storage by 3 points and comfort by 2 points. Assuming electricity and heating costs similar to Case A, implementation of the package would result in an annual economic savings of approximately 1.04 k EUR. Thus, implementing these improvements might not be worth the investment cost compared to the comparatively low energy reduction and savings attained.
## 3 Preliminaries and System Model
### Analytical Modeling
Assuming equal power split in both interfering links as in [29], we have:  
\[|I_{S\to R}|^{2}P_{SS}\leq\frac{Q_{th}}{2}, \tag{6}\]  
\[|I_{R\to D}|^{2}P_{TRj}\leq\frac{Q_{th}}{2}, \tag{7}\]  
\[P_{SS}\leq\frac{Q_{th}}{2|I_{S\to R}|^{2}}, \tag{8}\]  
\[P_{TRj}\leq\frac{Q_{th}}{2|I_{R\to D}|^{2}}. \tag{9}\]  
Combining the two constraints, the required SINR will be as follows:  
\[\gamma_{RR}=|g_{S\to R}|^{2}\frac{Q_{th}}{2\times|I_{S\to R}|^{2}}\geq \gamma_{th}, \tag{10}\]  
\[|g_{P\to R_{i}}|^{2}P_{p}+|g_{R\to D}|^{2}P_{TRj}\leq\gamma_{ir}, \tag{3}\]0  
We require a certain minimum signal-to-noise ratio \(\gamma_{th}\) at the receiving relay and the destination to ensure the desired data rate \(R\). Here, \(\gamma_{RR}\) is the SNR at the receiving relay and \(\gamma_{D}\) is the received SNR at the destination. It may be noted that we may not have a \(\gamma_{RR}\) and \(\gamma_{D}\) equal, but they have to be greater than \(\gamma_{th}\) to ensure the desired data rate. Thus, the relay selection protocol based on [12] yields:  
\[|g_{P\to R_{i}}|^{2}P_{p}+|g_{R\to D}|^{2}P_{TRj}\leq\gamma_{ir}, \tag{3}\]1  
Substituting the value of \(P_{SS}\) in the above equation, we get:  
\[|g_{P\to R_{i}}|^{2}P_{p}+|g_{R\to D}|^{2}P_{TRj}\leq\gamma_{ir}, \tag{3}\]2  
similarly, we can find:  
\[|g_{P\to R_{i}}|^{2}P_{p}+|g_{R\to D}|^{2}P_{TRj}\leq\gamma_{ir}, \tag{3}\]3and substituting the value of \(P_{TR_{j}}\), thus we have:  
\[|g_{P\to R_{i}}|^{2}P_{p}+|g_{R\to D}|^{2}P_{TRj}\leq\gamma_{ir}, \tag{3}\]4
# Weekly and Longitudinal Element Variability in Hair Samples of Subjects Non-Occupationally Exposed
## 3 Results and Discussion
### Comparison with Studies on Adult and Teenager Population
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Country** & **Sex** & **Linear Regression** & **R\({}^{2}\)** \\ \hline _Pakistan_ & & & \\  & girls & \(\mathrm{y}=4.3399\)\(\mathrm{x}-8.841\) & 0.9596 \\  & boys & \(\mathrm{y}=2.2490\)\(\mathrm{x}-5.702\) & 0.9486 \\ _Russian Federation_ & & \(\mathrm{y}=3.2442\)\(\mathrm{x}-6.0669\) & 0.9962 \\  & boys & \(\mathrm{y}=2.1688\)\(\mathrm{x}+2.6307\) & 0.9770 \\  & & \(\mathrm{y}=0.4252\)\(\mathrm{x}+16.754\) & 0.2921 \\  & boys & \(\mathrm{y}=5.6558\)\(\mathrm{x}-26.022\) & 0.9887 \\ _Italy_ & & & \\ Rome (2000) & girls & \(\mathrm{y}=2.6415\)\(\mathrm{x}-8.3798\) & 0.9097 \\  & boys & \(\mathrm{y}=2.8262\)\(\mathrm{x}-5.304\) & 0.9436 \\ _Korean_ & & & \\  & girls & \(\mathrm{y}=0.1558\)\(\mathrm{x}+8.2496\) & 0.3186 \\  & boys & \(\mathrm{y}=0.1489\)\(\mathrm{x}+8.7558\) & 0.2621 \\ _Brazil_ & & & \\  & girls & \(\mathrm{y}=0.0685\)\(\mathrm{x}+0.0381\) & 0.8539 \\  & boys & \(\mathrm{y}=0.0768\)\(\mathrm{x}+0.0630\) & 0.6464 \\ _Italy_ & & & \\ Palermo & girls & \(\mathrm{y}=3.6786\)\(\mathrm{x}-3.6842\) & 0.9690 \\  & boys & \(\mathrm{y}=2.5653\)\(\mathrm{x}-2.4715\) & 0.9739 \\ _East Aral Sea_ & & & \\ Kazalinsk & girls & \(\mathrm{y}=3.4555\)\(\mathrm{x}+9.0975\) & 0.7891 \\  & boys & \(\mathrm{y}=2.1209\)\(\mathrm{x}+21.810\) & 0.6089 \\ Zhanakorgan & girls & \(\mathrm{y}=3.4786\)\(\mathrm{x}-4.0669\) & 0.9684 \\  & boys & \(\mathrm{y}=3.4353\)\(\mathrm{x}-6.7967\) & 0.9882 \\ _Italy_ & & & \\ Rome (1992) & girls & \(\mathrm{y}=2.7462\)\(\mathrm{x}+2.2731\) & 0.7651 \\  & boys & \(\mathrm{y}=2.9344\)\(\mathrm{x}-7.3893\) & 0.9395 \\ _Spain_ & & & \\ Madrid & girls & \(\mathrm{y}=1.7397\)\(\mathrm{x}-0.6410\) & 0.9506 \\  & boys & \(\mathrm{y}=1.8624\)\(\mathrm{x}-3.0717\) & 0.9646 \\ _Poland_ & & & \\  & girls & \(\mathrm{y}=4.2421\)\(\mathrm{x}-59.335\) & 0.9945 \\  & boys & \(\mathrm{y}=3.5639\)\(\mathrm{x}-48.761\) & 0.9952 \\ _Italy_ & & & \\ Sicily—urban & girls & \(\mathrm{y}=3.6786\)\(\mathrm{x}-3.6842\) & 0.9690 \\  & boys & \(\mathrm{y}=2.4794\)\(\mathrm{x}-2.5041\) & 0.9741 \\ Sicily—rural & girls & \(\mathrm{y}=4.7754\)\(\mathrm{x}-6.3693\) & 0.9532 \\  & boys & \(\mathrm{y}=3.7000\)\(\mathrm{x}+5.1878\) & 0.9541 \\ Sicily—industrial & girls & \(\mathrm{y}=3.1540\)\(\mathrm{x}-1.8459\) & 0.9286 \\  & boys & \(\mathrm{y}=3.1144\)\(\mathrm{x}-4.0461\) & 0.9461 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Linear regression and coefficient of determination (R\({}^{2}\)) between our data and each dataset for different girl teenagers hair determined worldwide [81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91].
## 3 Results
### Characterization of PS/OMMT Nanocomposite
#### 3.2.3 TEM Analysis  
As shown in Figure 7A, spherical particles could be observed for the pure PS. However, the size distribution of particles was broad, and the particle size was in the range of approximately 200-600 nm. In contrast, when the OMMT was introduced into the polymer matrix, the particle sizes of the PS/OMMT nanocomposites became much smaller, as shown in Figure 7B-D. Moreover, the size distribution of PS/OMMT particles was narrow and monomodal, and the particle size was approximately between 250 nm and 400 nm. These results indicated that the morphology of the polymer particles was changed by the presence of OMMT. The reason was probably that the movement of the styrene monomers and growth of the PS chains were effectively reduced via the shielding effects of the clay platelets.  
Figure 6: XRD patterns of PS, PS/P-MMT, PS/P-SLS-MMT, and PS/P-KH570-MMT.  
Figure 7: Transmission electron microscopy (TEM) images of (**A**) PS, (**B**) PS/P-MMT, (**C**) PS/P-SLS-MMT, and (**D**) PS/P-KH570-MMT.  
As denoted by the yellow arrows in Figure 7B-D, the existence of exfoliated clay layers encapsulated inside the polymer particles was clearly visible. The TEM images of PS/OMMT nanocomposites showed that the nanoclay layers were completely exfoliated and dispersed homogeneously within the PS matrix. It showed that a highly exfoliated structure of PS/OMMT nanocomposite was obtained, which was in accordance with the corresponding XRD results. This result implied that the clay layers of 3 wt % OMMT could maintain the polymer colloidal stability, thus resulting resulted in the complete exfoliation of the clay layers and formation of exfoliated structure of PS/OMMT nanocomposites [37].
## 3 Results
### Between-Group Comparison for Number of Fixations  
For any of the groups analysed, no within-group differences were found that would show any statistically significant interaction between shooting positions and the number of fixations. The between-group analysis of this variable showed statistically significant differences for position 7 (\(t_{(98)}=3.176\); \(p=0.002\); \(d=1.257\); large effect size) and for angle 135\({}^{\circ}\) (\(t_{(198)}=2.364\); \(p=0.019\); \(d=0.334\), moderate effect size) (Table 4). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{\(\bar{\mathbf{x}}\)\(\pm\)**SD**} & \multicolumn{1}{c}{**t**} & \(\boldsymbol{p}\) & \(\mathbf{d}\) \\  & **U16** & **Pro** & & & \\ \hline \multirow{9}{*}{positions} & 1 & \(1.96\pm 0.86\) & \(1.84\pm 0.74\) & \(0.750\) & \(0.455\) & \(0.150\) \\  & 2 & \(2.00\pm 0.90\) & \(2.04\pm 0.78\) & \(-0.237\) & \(0.813\) & \(0.047\) \\  & 3 & \(2.22\pm 0.95\) & \(1.92\pm 0.75\) & \(1.747\) & \(0.084\) & \(0.349\) \\  & 4 & \(1.86\pm 0.83\) & \(2.06\pm 0.74\) & \(-1.269\) & \(0.207\) & \(0.254\) \\  & 5 & \(1.96\pm 0.86\) & \(1.84\pm 0.65\) & \(0.789\) & \(0.432\) & \(0.157\) \\  & 6 & \(1.76\pm 0.74\) & \(1.86\pm 0.81\) & \(-0.644\) & \(0.521\) & \(0.129\) \\  & 7 & \(2.28\pm 1.01\) & \(1.72\pm 0.73\) & \(3.176\) & \(0.002\) * & \(0.635\) \\  & 8 & \(1.82\pm 0.92\) & \(1.80\pm 0.73\) & \(0.121\) & \(0.904\) & \(0.024\) \\  & 9 & \(2.14\pm 0.95\) & \(2.04\pm 0.81\) & \(0.568\) & \(0.571\) & \(0.114\) \\  & 10 & \(1.98\pm 0.87\) & \(1.96\pm 0.70\) & \(0.127\) & \(0.899\) & \(0.025\) \\ \hline \multirow{9}{*}{angles} & 0\({}^{\circ}\) & \(1.98\pm 0.88\) & \(1.94\pm 0.76\) & \(0.344\) & \(0.731\) & \(0.049\) \\  & 45\({}^{\circ}\) & \(2.04\pm 0.91\) & \(1.99\pm 0.75\) & \(0.425\) & \(0.671\) & \(0.060\) \\ \cline{1-1}  & 90\({}^{\circ}\) & \(1.86\pm 0.80\) & \(1.85\pm 0.73\) & \(0.092\) & \(0.927\) & \(0.013\) \\ \cline{1-1}  & 135\({}^{\circ}\) & \(2.05\pm 0.99\) & \(1.76\pm 0.73\) & \(2.364\) & \(0.019\) * & \(0.334\) \\ \cline{1-1}  & 180\({}^{\circ}\) & \(2.06\pm 0.91\) & \(2.00\pm 0.75\) & \(0.509\) & \(0.611\) & \(0.072\) \\ \hline \hline \end{tabular}  
* Statistically significant differences (\(p<0.05\)).  
\end{table} 
Table 4: Between-group comparison for number of fixations, according to position and angle.
# Compensation for Group Velocity of Polychromatic Wave Measurement in Dispersive Medium
## 5 Conclusions  
In this paper, we proposed the group velocity compensation method using the derived time and center frequency offset terms based on the estimation of instantaneous frequency (IF). The proposed method can be divided two part. The one was the transmitted signal detector algorithm based on TFCC and multiple inductive couplers system. The second is the compensation algorithm, and the compensation terms was obtained based on the IF curve which was derived from Hilbert transform and phase unwrap algorithm of the transmitted signal. The variation of group velocity of chirp signal by dispersion in lossy medium was verified using a simulation tool. Through the comparison experiments with compensation without consideration of sweep rate change and existing methods using TFCC, superiority of the proposed method was proved. Although the group velocity of a signal propagating a short distance has similar accuracy in both the conventional method and the proposed method, when the signal propagates over a long distance (120 m), the proposed method has 2% better accuracy than the conventional method. This paper proposes a new method to compensate the group velocity error due to the dispersion of the chirp signal in the lossy medium, and this method can be applied to the detection of defect, it is possible to localize the fault in long-distance lines, such as submarine HVDC cable, without error.  
\begin{table}
\begin{tabular}{l l l l l} \hline  & \multicolumn{4}{c}{**Group Velocity**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Cable Length: 40 m**} & \multicolumn{2}{c}{**Cable Length: 80 m**} & \multicolumn{2}{c}{**Cable Length: 120 m**} \\ \hline TFCC & \(1.82\cdot 10^{8}\) & \(\text{[m/s]}\) & \(1.81\cdot 10^{8}\) & \(\text{[m/s]}\) & \(1.81\cdot 10^{8}\) & \(\text{[m/s]}\) \\ accuracy & 100 & \(\text{[\%]}\) & 99 & \(\text{[\%]}\) & 97 & \(\text{[\%]}\) \\ \hline proposed method & \(1.83\cdot 10^{8}\) & \(\text{[m/s]}\) & \(1.81\cdot 10^{8}\) & \(\text{[m/s]}\) & \(1.76\cdot 10^{8}\) & \(\text{[m/s]}\) \\ accuracy & 99 & \(\text{[\%]}\) & 99 & \(\text{[\%]}\) & 99 & \(\text{[\%]}\) \\ \hline measurement & \multirow{2}{*}{\(1.82\cdot 10^{8}\)} & \multirow{2}{*}{\(\text{[m/s]}\)} & \multirow{2}{*}{\(1.80\cdot 10^{8}\)} & \multirow{2}{*}{\(\text{[m/s]}\)} & \multirow{2}{*}{\(1.77\cdot 10^{8}\)} & \multirow{2}{*}{\(\text{[m/s]}\)} \\ value & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Estimation results of group velocity.  
Figure 4: The results of **(a)** acquired signals, **(b)** TFCC graph.  
**Author Contributions:** [PERSON] made a theory, designed the experiments and wrote the paper; [PERSON] analyzed the data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Methodology
### Semantic Enhancement Module (SE)  
Semantic knowledge is the key to breaking out of the vocabulary barrier and digging deeper to find semantic information contained in words [43]. [PERSON] et al. [31] proposed the self-attention mechanism, highlighting its important role in capturing semantic information. In this paper, we make full use of the self-attention mechanism according to this point to capture the semantic-related terms of each word in a sentence and improve the comprehension of the text. The self-attention mechanism can learn the different levels of importance of each element of the sentence. We use self-attention to calculate the attention weights between words to generate an attention matrix A. Each element in the attention matrix stands for the attention weight between word i and word j. Using the attention matrix as a neighbor matrix, in GCN, the neighbor matrix describes the connection relationship between nodes, and in the SE, the connection relationship is determined by the attention weights computed by the self-attention, which can better catch the semantic information. The attention score matrix A as the neighbor matrix can be denoted as:  
\[A=softmax\left(\frac{XW_{Q}\times\left(YW_{K}\right)^{T}}{\sqrt{d_{k}}}\right), \tag{5}\]  
In the above equation, \(W_{Q}\), \(W_{K}\) is the learned weight matrix. \(X\) and \(Y\) represent the characteristic representation of the input sequence and \(d_{k}\) is the dimensions of the node feature, respectively. Our semantic enhancement module uses a single self-attention head to gain a matrix of attention scores for the sentence. In each layer of graph convolution, the depiction of a node is updated in accordance with the nodes that surround it and the edges between them. Our model uses a 2-layer graph convolution network, using the adjacency matrix A to define the connectivity between nodes and to fuse the information from neighboring nodes through weighted summation or other aggregation functions. Information transfer and aggregation of node features is performed through graph convolution layers. Each graph convolution layer is calculated as:  
\[H_{l+1}=ReLU\left(\frac{AH_{l}W_{l}}{E+1}\right), \tag{6}\]  
where A is the adjacency matrix, \(H_{l}\) is the node features at layer \(l\), \(W_{l}\) is the weight matrix at layer \(l\), \(E\) is the degree matrix. In this way, it is possible for the model to aggregate the characteristics of the nodes and their neighbors layer by layer and learn a higher-level representation. The semantic enhancement module obtains the final representation \(H_{se}=\)\(\{h_{sel},h_{se2},..h_{sem}\}\) denoting the hidden representation of all aspect nodes.
## 2 Theoretical Framework and Methods
### Analysis Process
#### 2.3.2. Constructing the Uncertainty Measurement Function  
Based on the resilience grading standards, an uncertain measurement function is constructed. In this study, the \(C_{k}(k=1,2,\ldots,5)\) classification space \(\text{C}=\{C_{1},C_{2},\ldots,C_{5}\}\) is classified into Level I, Level II, Level III, Level IV, and Level V. The standard values are set at 1, 3, 5, 7, and 9, respectively. That is, when the actual value of the indicator equals the standard value, the indicator will completely belong to the resilience level corresponding to that standard value; when the indicator value falls within the interval defined by two standard values, it will belong to the two adjacent resilience levels to varying degrees. A single indicator measurement function is constructed, as shown in Figure 3.
## 4 Experimental Results
### Compression of ResNet-18 and ResNet-152 Using Tensor Decomposition Methods  
In our experiments, we primarily focused on ResNet-18 and ResNet-152 architectures, using the CIFAR-10 [14] and Tiny ImageNet [15] datasets to evaluate the image classification accuracy of the compressed models compared to their uncompressed counterparts. Additionally, we measured the inference time for processing a single \(224\times 224\) image on a CPU to assess the computational efficiency of the compressed models.  
For compression, we targeted all of the convolutional layers within third and fourth blocks of ResNet. We applied CP and Tucker decomposition techniques to various layers within those blocks.  
All experiments were conducted with consistent training and fine-tuning protocols tailored to the dataset. For CIFAR-10, we trained the base (uncompressed) ResNet-18 and ResNet-152 models for 60 epochs. The compressed models were fine-tuned over 60 epochs as well, with compression applied iteratively every 10 epochs, resulting in six compression iterations. At the start of each experiment, we fixed the set of layers to be compressed and progressively reduced the compression parameter \(\alpha\) every 10 epochs, using the sequence \(A=(5,3,2,1,0.5,0.1)\).  
For Tiny ImageNet, the base models were trained for 20 epochs, and compressed models were fine-tuned for the same duration. Compression was applied iteratively every two epochs, resulting in six compression iterations. Similar to CIFAR-10, the sequence of \(\alpha\) values \(A=(5,3,2,1,0.5,0.1)\) was used to progressively increase the compression ratio.  
Other training parameters, including a batch size of 256, an initial learning rate of 0.05, and a linear learning rate scheduler, remained consistent across both datasets.  
The results of the proposed compression approach are summarized in Table 1. The table includes the number of parameters, compression ratios, model sizes in MBs, image classification validation accuracies measured on the CIFAR-10 and Tiny ImageNet datasets, and inference times in (s) for processing a single image. Inference time evaluation was conducted on a CPU using \(224\times 224\) input images.
# Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species
## 1 Introduction  
The analysis of animal telemetry data can help researchers identify locations popular to animals, called biological hotspots, and to clarify movement patterns that could improve outcomes of conservation programs protecting vulnerable wildlife [1; 2]. Movement analysis is also important for studying animals' search strategies and their behavioural ecology [3]. In addition, because animals obtain resources (prey, mates, etc.) through movements, their movement patterns reveal important information on species fitness [4].  
Given that the study of animal movement through telemetry technologies started more than 30 years ago, there now exists a wealth of data on animal movement [5]. Recent advances in thedevelopment of tracking technologies along with improved data analysis and visualisation techniques have dramatically changed how researchers can study the movements of free-ranging animals. Increased cross-disciplinary collaborations between mathematicians and ecologists have led to the development of new quantitative approaches and tools that have become pivotal in the study of animal movement and have allowed enhanced and broad interpretation of results [3].  
Recent analysis demonstrates that marine animal subgroups could have very different movement patterns. Some research suggests that there could be more uncertainty in determining the target locations of young, inexperienced individuals [3]. Other studies observed that the preferences of female seals for locations with mesoscale oceanic circulation are seasonally flexible [6]. Moreover, ocean currents can also impact a marine animal's motion patterns [7], and elephant seals typically forage near the edges of eddies or temperature fronts. On the other hand, pregnant female elephant seals prefer to forage near mesoscale fronts, perhaps to reduce inter-species competition [8].  
In this work, we propose a novel learning model, called a recurrent neural network (RNN) with confidence measure (RNN-CM), to analyze telemetry data. Our RNN-CM approach can identify unique segments corresponding to certain animal subgroups with confidence scores. As illustrated in Figure 1, for given inputs, segments with low confidence are common segments for all the subgroups, and those with high confidence are unique segments for particular subgroups.
## 1 Introduction
However, these conventional traffic prediction methods are no longer suitable for satellite networks like SAGIN due to their dynamic changes in topology and link status. Recently, emerging machine learning (ML) technologies have displayed remarkable advantages in optimizing dynamic routing. ML enables the real-time perception of dynamic network traffic conditions, facilitating on-the-fly adjustments to network routing policies. Looking ahead, artificial intelligence is poised to become an intrinsic feature of 6G networks. AI-based traffic prediction models can autonomously extract various features from wireless data and use them comprehensively for precise real-time wireless traffic predictions. In the context of 6G satellite networks, this AI integration enables enhanced network autonomy and intelligence, leading to intelligent operation and maintenance management of terminal devices, network routing policies, and efficient automated service deployments [8; 9]. The evolution toward an intelligent network underscores the importance of traffic analysis and precise demand forecasting. Anticipating user demands in advance enables the network to allocate resources more efficiently, ensuring swift resource distribution among users contending for these resources. The remainder of this paper is structured as follows. Section 2 provides an overview of related works closely aligned with our research objectives. In Section 3, we present a business load prediction model for 6G satellite networks, taking SAGIN as an illustrative example. Section 4 delves into traffic load prediction methodologies employing adaptive time stepping (ATS), incorporating machine learning and recurrent neural network (RNN) techniques. Within this section, we introduce the ATS-NLMS adaptive prediction method,which serves to transform time series prediction datasets, and the ATS-GRU algorithm for real-time network traffic prediction. Section 5 is dedicated to the presentation of simulations and an analysis of the obtained results. Finally, Section 6 concludes the full text.