esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
## 2 Materials and Methods
### Algorithm Design
#### 2.4.3 Stage 3: Variance-Based Proximity Classification  
This algorithm is based on a simplified K-Nearest Neighbor (KNN) design. First, the optimal parameters are determined by training the model using KNN. Due to the imbalance in the training dataset, KNN employs oversampling methods to balance the data. Ultimately, training and testing results indicate that the algorithm performs best when \(k=3\) and \(p=2\). The algorithm then calculates the Euclidean distance between each data point and the training data, sorts these distances, selects the k-nearest points, and determines the most frequently occurring label among these nearest neighbors as the predicted activity level.  
Specific analysis methods:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variance Boundary Value** & **Classification Accuracy** \\ \hline
1.0 & 90.56\% \\
1.1 & 90.68\% \\
1.2 & 90.72\% \\
1.3 & 90.52\% \\
1.4 & 90.64\% \\
1.5 & 90.44\% \\
1.6 & 90.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Boundary of combined acceleration variance between low activity and medium activity, experimentally validated with the highest classification accuracy when the boundary value is 1.2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variance Boundary Value** & **Classification Accuracy** \\ \hline
20 & 87.23\% \\
25 & 89.83\% \\
27 & 90.37\% \\
28 & 90.79\% \\
29 & 90.87\% \\
30 & 90.72\% \\
31 & 90.68\% \\
32 & 90.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Combined acceleration variance threshold between medium activity and high activity, experimentally validated with the highest classification accuracy when the threshold value is 29.  
* Equation (6) calculates the Euclidean distance between a test point \(x_{test}\) and each training point \(x_{i}\): \[d_{i}=d(x_{test},x_{i})=\sqrt{\sum_{i=1}^{n}(x_{test}-x_{i})^{p}}\] (6)
* Equation (7) sorts the distances and identifies the closest \(k\) training points, forming the set \(N_{k}\). \[N_{k}=\{i_{1},i_{2},\ldots,i_{k}\,|\,d_{i_{1}}\,\leq\,d_{i_{2}}\,\leq\,\ldots \,\leq\,d_{i_{k}}\}\] (7)
* Equation (8) counts the occurrences of each label among the \(k\)-nearest neighbor labels. The label with the highest frequency is selected as the prediction. \[\hat{y}=\text{most\_common}\big{(}\big{\{}y_{i_{1}},y_{i_{2}},\ldots,y_{i_{k}} \big{\}}\big{)}\] (8)
# Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models
## 3 The Proposed Deep Crack Segmentation Approach
### Segmentation-Based Variant U-Net Models  
U-Net is an encoder-decoder convolutional network that utilizes skip connections for preserving features at multi-resolution, used to solve end-to-end semantic segmentation tasks. Skip connections are a critical component of conventional deep neural networks (DNNs) such as DenseNet, ResNet, ResNeXt, and WideResNet. Skip connections build a short-cut from shallow layers to deep layers by directly connecting the input of a convolutional block/the residual module to its output. Their task throughout the network is to speed up the learning process, preserve low-level features, and avoid the problem of vanishing gradient in deep models [24; 25; 26].  
In general, U-Net comprises two parts, namely the encoder part and the decoder part. The encoder part is responsible for taking an image as input and extracting features at multiple scales and abstraction levels, thus yielding a multi-level, multi-resolution feature representation. It is a simple down-sampling path consisting of stacked convolutional blocks with a max-pooling operator, used as dimensionality reduction, which can be replaced by a deeper network such as VGG or ResNet. Meanwhile, the decoder part controls the reconstruction of the probability segmentation maps, where its task is to take the feature representation and classify all pixels at the original image resolution in parallel. U-Net has the advantage of performing well on segmentation problems with limited amounts of data [24; 25; 26]. In this phase, three different U-Net based models along with two different loss functions, namely Dice and cross-entropy (CE), are utilized for pixel-level crack detection, as shown in Equations (9) and (10). Moreover, benchmark datasets of various crack types and severity are utilized for training the proposed model.  
\[Dice=1-\frac{2\sum_{i=1}^{N}\sum_{j=1}^{C}y_{i,j}p_{i,j}+\epsilon}{\sum_{i=1}^{N} \sum_{j=1}^{C}(y_{i,j}+p_{i,j})+\epsilon} \tag{9}\]  
\[CE=\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{C}y_{i,j}\text{log}\;p_{i,j} \tag{10}\]  
where \(p\) is a predicted map and \(y\) is its corresponding ground truth for class \(j\). \(N\) and \(C\) are the number of pixels and the number of classes (excluding the background), respectively. Finally, \(\epsilon\) is a smoothing constant to avoid dividing by zero.  
Moreover, contrast stretching is used as a prepossessing step during the testing time to handle the problems arising from different environmental conditions. In addition, the historical building's concrete has various surface structures, dust, ornaments, carvings, wood patterns, separators, corrosion on walls, bird droppings, fungi, detachment, color degradation, other damages, and weathering damages that hinder the ability to recognize the correct abnormality, mainly cracks.
## 6 Discussion, Implications, and Future Work  
This study co-designed, developed, and tested the usability of an application prototype that used smartwatch data and advanced analytics for health monitoring and anomaly detection. Our study highlights the potential of using smartwatch data to support HCPs in monitoring their patients/clients' health, and to motivate people to prevent chronic diseases and manage their health [5]. The co-design phase enabled us to initially gather information on HCP needs and expectations from these technologies to optimally support them in their work. In addition to HCPs' preferences about physiological parameters and the user interface, HCPs indicated their preference for healthcare administrators (someone with technical knowledge) to be the type of personnel to view the results of smartwatch data analytics, who can then notify the HCP when their intervention is required, thus possibly reducing burden associated with the additional data work. This feedback provides tangibility to introducing new roles in healthcare for the interpretation of technical results and as an interface between the technical and the clinical sides of the healthcare system [3]. In the evaluation phase, HCPs were particularly satisfied with the prototype's interface design and ease of use. The findings were comparable with prior studies on healthcare providers' perceptions about using smartwatch data for clinical decision making, in that HCPs recognized this technology's potential in preventive care [4; 9; 10]. Some participants left comments that would be useful to consider in future designs of similar applications including suggestions on the addition of other measurable parameters if the technology allows (participant 2), filtering anomaly detection to suit the requirements of the HCP (participant 6), as well as including a chat function with the notification feature. HCPs can also use this prototype for at-risk individuals, healthy individuals (as a preventive tool), and individuals diagnosed with chronic diseases (to manage their
## 3 Data
### Coordinate Systems  
Three coordinate systems are provided in this dataset: global frame, vehicle frame, and sensor frame. Some raw features are represented in unintended coordinate systems. In order to maintain consistency, it is crucial to transform data into the correct coordinate system. The dataset also providesvehicle pose **VP**, a \(4\times 4\) row matrix, to transform variables from one coordinate system to another. The formula below shows the most frequent transformation that is employed in our experiments [18].  
\[\mathbf{X}_{VF}\cdot\mathbf{VP}=\mathbf{X}_{GF},\]  
where \(\mathbf{X}_{VF}\) and \(\mathbf{X}_{GF}\) refer to the vehicle frame and the global frame, respectively.
## 3 Results  
### Structural Evaluation  
The X-ray diffraction (XRD) patterns used to evaluate the structure of the films are shown in Figure 1. In Figure 1a, the patterns of GdMnO650 and GdMnO750 are shown, where a comparison can be made. Notably, GdMnO650 displays only reflections associated with the substrate, explicitly from the planes (111), (200), and (220) of Pt and (211) of TiO\({}_{2}\). The GdMnO750 pattern also has these reflections and an additional one referring to the SiO\({}_{2}\) (202) plan. However, GdMnO750 clearly shows the growth of the GdMnO\({}_{3}\) perovskite phase, which was confirmed using an Inorganic Crystal Structure Database (ICSD) code collection card #95493. In this regard, it can be observed that the planes (121), (002), (022), (311), and (303) are associated with the peaks positioned at 33.2, 33.9, 41.7, 52.02 and 60.01\({}^{\circ}\), respectively.  
The pattern that most consistently displays the phase of interest is shown in Figure 1b, where a Rietveld structural refinement was performed to obtain the main refined structural parameters. A GdMnO\({}_{3}\) orthorhombic phase is observed associated with the Pnma spatial group and superimposed with reflections from the substrate is observed. From a resolution file obtained with a LaB6 standard sample, the peaks' widths were obtained using a model of spherical harmonics [32]. The structural refined parameters, lattice parameters, unit cell volume, average crystallite size, strain, and dislocation density are summarized in Table 1. From the obtained lattice parameters a = 5.31715(2), b = 5.78694(3), and c = 7.44901(6), it can be observed that a and c are relatively higher than those reported for a GdMnO\({}_{3}\) single crystal [49], a GdMnO\({}_{3}\) ceramic [14] and another system similar to ours previously reported [31]. The obtained unit cell volume 229.206(6) A is greater than the one reported by [PERSON] et al. [31] and less than those reported by [PERSON] et al. [14] and [PERSON] et al. [49]. Furthermore, the crystallite size and microstrain factor of the GdMnO850 lattice have magnitudes of 14.227 (7) and 7.1264 (4), respectively, where the dislocation density, which is a parameter associated with the degree of crystalline solid defects [50], exhibits a value of 0.00494(1) (Table 1). Comparatively, the crystallite size is smaller than the one reported by
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 5 Classification of Efforts for Evolving the Control Plane
### Efforts to Evolve the Control Plane of Inter-Domain Routing
#### 5.2.1 Brand New Design
For example, the network staff would need to be trained for new proposals or protocols, as well as acquire new network devices that support those clean-slate solutions. For the inter-domain routing system with the SDN solutions, those whose approaches are not compatible with the BGP have little practical appeal for wide adoption (such as MLV [60]), and solutions that propagate a detailed representation of the network state suffer from scalability limitations.
# A Unified Point Multiplication Architecture of Weierstrass, Edward and Huff Elliptic Curves on FPGA
## 5 Implementation Results and Comparison
### Comparison with the State of the Art
The last two columns in Table 3 show that the proposed accelerator is more efficient in clock frequency and computation time (latency) compared with a dedicated Huff curve architecture of [13]. Comparison with flexible/unified architectures of [18; 19].If we compare our unified accelerator with a flexible implementation of [19] on Virtex-6 FPGA, our unified implementation is 5.85 (ratio of 116,241 with 19,869) times more area-efficient (in terms of LUTs). We cannot compare the slices as the reference design does not give this information. It is essential to note that the architecture of [19] is flexible as it implements four different NIST-specified binary field key lengths of 233, 283, 409, and 571. In contrast, our design is flexible as we implement three different curves (Weierstrass, Edward, and Huff) using a single key length of 233 bits. The clock cycle comparison shows that the design of [19] requires 2609 cycles for 233-bit key-length while for the identical key-length, our unified accelerator takes 3607 cycles--that is, 1.38 times higher than [19]. Instead of the area and cycle requirements, the last two columns of Table 3 demonstrate that our accelerator can operate on the maximum circuit frequency and takes a lower computation time than [19]. For the identical \(GF(2^{233})\) field, a unified accelerator over Weierstrass and Huff curves is described in [18]. Similar to our unified accelerator, Montgomery and Double and ADD PM algorithms are implemented for the Weierstrass and Huff curves, as shown in columns two and three of Table 3. On the exact Virtex-7 FPGA device, our design is 1.19 (ratio of 8866 with 7423) and 1.05 (ratio of 23,017 with 21,917) times more efficient in slices and LUTs. Furthermore, columns nine to eleven of Table 3 show that we utilize lower clock cycles, achieve higher circuit frequency and take less computation time (latency) for implementing the Weierstrass and Huff curves. Our efficient replacement of storage elements (described in Section 3) for executing PA and PD instructions results in lower clock cycle counts. The efficient implementation of our Karatsuba multiplication architecture allows us to minimize hardware resources. In contrast, a digit-parallel multiplication design with a digit size of 32 bits is described in [18]. The efficient implementation of an FSM-based dedicated controller has the benefit of minimizing the critical path delay, resulting in higher circuit frequency. In addition to other design parameters (area, clock cycles, operating frequency, and latency), the last column of Table 3 shows that the proposed unified design outperforms the most recent state-of-the-art designs of Weierstrass, Edward, and Huff curves in terms of throughput.
## 2 Exosome Features
### Mechanisms of Exosome-Target Tissue Interaction and Exosome Transport  
Exosomes involve eukaryotic paracrine, exocrine, and autocrine communication [27] Exosomes interact with target tissues through precise and regulated mechanisms that ensure the delivery of bioactive molecules (proteins, lipids, and RNAs) to recipient cells, influencing cellular responses and tissue function. They can be found in many body fluids, including plasma, milk, urine, saliva, and others [28]. When exosomes circulate in these fluids, they reach the target cell directly due to their integral proteins and receptors that produce various signaling pathways triggering their internalization in the cells of the target tissues [29]. Receptor-receptor binding causes the exosome to fuse to the cell's plasma membrane, a process facilitated by SNARE proteins and their adhesion molecules [7]. This binding can occur through these ways: receptor-ligand interactions, endocytosis and macropinocytosis, direct membrane, phagocytosis, and tunneling nanotubes.
## 3 Data and Methods  
The analysis covers 71 companies, 46 of which operated in the purchasing group, and 25 were independent entities. The main goal of the analysis is to compare selected financial ratios between the two groups. The table presents the values of descriptive statistics (mean, median, standard deviation, as well as minimum and maximum) for selected financial indicators related to the profitability of enterprises in two groups.  
The significance of differences between the two groups of enterprises in the ROS distribution was assessed using the Mann-Whitney test. Test was used to assess the significance of differences in the level of selected financial indicators in groups. The authors choice of the non-parametric test was dictated by the lack of normality of the distribution of financial indicators in the groups.  
One of the goals of the statistical analysis was to examine the relationships between the selected financial ratios. For this purpose, the correlation analysis and the linear regression model were used. The form of the regression model (selection of the dependent variable and independent variables) resulted from the research problems posed in the paper.  
The purpose of the paper is to show:  
* why operating within purchasing groups allows companies to obtain high profits,
* whether there is a relationship between financial liquidity and profitability,
* how the operation of the industry purchasing group model in the renewable energy sector allows cost optimization.  
The paper attempts to verify the following Hypothesis:  
**Hypothesis 1**.: _It is assumed that in the case of companies operating within purchasing groups in the renewable energy sector, the level of financial liquidity increases with the growth in profitability._  
As for the selection of companies for the analysis, it was limited by the availability of data. The largest industry purchasing groups in Poland were tested. In the case of entities operating in the renewable energy sources industry, 46 out of 71 companies were analysed. In the case of independent units, 82 enterprises were selected. Financial data was obtained from 25 units.  
When analysing the data, it was assumed that companies that did not disclose their data did not differ significantly in terms of financial results from the companies from which the data was obtained. In fact, this assumption underlies all partial statistical surveys in which there was a \"refusal to respond\" in some cases.
## 2 Materials and Methods
### Gated Recurrent Unit (GRU)  
GRU is a newer generation of RNN architecture and is similar to LSTM. GRU does not have the cell state, instead it uses a hidden state to transfer information. Therefore, GRU has only two gates: reset and update gate (Figure 2).  
The key equations for GRU are shown below [24]:  
\[z_{t}=\delta_{g}(W_{z}x_{t}+U_{z}h_{t-1}+b_{z}), \tag{18}\]  
\[r_{t}=\delta_{g}(W_{r}x_{t}+U_{r}h_{t-1}+b_{r}), \tag{19}\]  
\[\hat{H}_{t}=\Phi_{h}(W_{h}x_{t}+U_{h}(r_{t}*h_{t-1})+b_{h}, \tag{20}\]  
\[h_{t}=(1-z_{t})*h_{t-1}+\hat{h}_{t}, \tag{21}\]  
where \(x_{t}\), \(h_{t}\), \(\hat{h}_{t}\), \(z_{t}\) and \(r_{t}\) are input, output, activation function, update gate, and reset gate. \(W\), \(U\), and \(b\) are parameter matrices and vectors.  
\(\delta_{g}\) and \(\Phi_{h}\) are sigmoid and hyperbolic tangent functions. GRUs have fewer parameters and thus may train faster than LSTM.
# An Overview of GEO Satellite Communication Simulation Systems
## 2 Coverage Analysis
### Fast Evaluation Algorithms  
In addition to conventional geometry-based methods, fast evaluation algorithms and approximate inference models have emerged to address the need for efficient coverage assessment. [PERSON] et al. [15] defined the coverage state function and the corresponding extension and intermediate modes for basic coverage analysis problems. Furthermore, an integrated coverage theory system is constructed by analyzing the properties of three basic spatial and temporal factors using parameter wildcard characters. To address large-scale connectivity challenges in IoT scenarios, stochastic geometry-based methods have been introduced for efficient availability analysis with low complexity and coverage performance estimates in [16].
## 4 Results and Analysis
### Reliability Analysis Considering LLD Affected by Reinforcement Corrosion
The main factor for the difference between the traditional and proposed methods is whether the bridge inspection data are used for analysis. If the corroded bridge reliability is calculated by the proposed method without bridge inspection, the reliability result will coincide with that of the traditional method. When the bridge inspection data are updated in time, the reliability assessment will be more accurate and reflect the real condition of the bridge. In the later stage, the bridge reliability calculated by the proposed method was also analyzed based on the inspection of concrete strength and concrete cover thickness, which was measured in 2020. As a result, the difference between the two methods is small for the later stage. This also demonstrates the effectiveness of the method proposed in this paper. The bearing capacity and load effect of the bridge will change substantially after the reinforcement corrosion in practical engineering. Beam 1 is located at the side of the bridge, which has a larger contact surface with the environment, and it is more susceptible to the disadvantageous influence of environmental chloride erosion and vehicle loads. Consequently, the deterioration process of the side T-beam is aggravated, which further leads to the reduction of stiffness. Therefore, the effective stiffness of Beam 1 decreases more than that of the inner T-beam, which is consistent with the degradation law of a degraded structure. This indicates that the proposed method can truly reflect the changing trend of degraded structure stiffness. The load effect of the T-beam increases with the increase in  
Figure 8: Time-dependent reliability of simply supported T-beam bridge: (**a**) Beam 1; (**b**) Beam 2.  
its stiffness according to the calculation principle of the modified eccentric compression method. Therefore, the effective stiffness of Beam 1 decreases more than the change of the internal T-beam. In addition, the resistance of each beam decreases gradually over time. As shown in Figure 8, although the load effect borne by Beam 1 is reduced, it is clear that the reliability index of the proposed method clearly remains lower than that of the traditional method. The results show that reinforcement corrosion not only reduces the bearing capacity of the structure but also causes the change of the transverse mechanical properties of the bridge, thus redistributing the load effect of each T-beam. Meanwhile, the calculation method of bridge bearing capacity based on the reinforcement corrosion model does not consider the influence of reinforcement corrosion degradation on load effect, which increases the error of bridge performance evaluation. The calculation method of LLD theory with reinforcement corrosion in this paper can fully consider the interaction between reinforcement corrosion caused by chloride erosion and load effect, which reduces the influence of calculation model error on bridge reliability analysis.
## 3 System Model
### The Establishment of Attribute Indexes  
#### 4.1.1 Energy Balance Factor  
Since the main energy consumption of sensor nodes is used for data transmission, the key to prolong the network lifetime is to balance the energy consumption of each source node \(i\) and its next hop. From the perspective of the source node \(i\), when its residual energy \(E_{i}\) is small, the energy consumption \(e_{ij}\) of single hop transmission is expected to be as small as possible. When the energy of node \(i\) is large, it is hoped that node \(i\) can transmit a longer distance, that is, the value of \(e_{ij}\) is as large as possible. Therefore, considering the residual energy of the node \(i\) and the energy consumption of data transmission, the energy balance factor is defined as follows:  
\[EB(j)=\frac{E_{i}}{E_{S}}\times(1-\frac{e_{ij}}{e_{R}}),j\in FN(i) \tag{5}\]  
where \(e_{ij}\) denotes the energy consumption of data transmission from node \(i\) to node \(j\), \(e_{R}\) represents the energy consumption of data transmission at the longest transmission distance \(R\).