esa-sceva/satcom-chunk-collection huggingface dataset:
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 2 The Energy-Efficient Internet-of-Things Model
### Radio Model and Energy Budget Computation
#### 2.2.3 Total Energy Dissipation in a Cluster  
The sensor nodes of the WSN can be further divided into cluster heads and cluster members. Using Equations (2)-(4), we calculated the total energy dissipated by a sensor node based on its role in the network. The total energy dissipated by a sensor node acting either as cluster member or a cluster head (CH) of \(m\) cluster members which also relays the data of other \(l\) cluster heads is given by Equation (5). Note that for a node acting as a cluster member (CM), the total energy dissipated is the sum of sensing and communication energies. Therefore, a sensor node spends in total:  
\[E_{tot} =\left\{\begin{array}{ll}(m+l)E_{rx}^{CH}+E_{DA}+E_{tx}^{CH};& \quad\quad\quad\quad if\quad CH\\ E_{tx}^{CM}+E_{sens}^{CM};&\quad\quad\quad\quad if\quad CM\end{array}\right. \tag{5}\]  
where \(E_{tx}^{CH}\) is the energy used by a cluster head while transmitting data to the next cluster head or base station and \(E_{rx}^{CH}\) is the energy dissipated for receiving data from a cluster member. \(E_{tx}^{CM}\) is the energy used while transmitting data inside the cluster to a cluster member, and \(E_{sens}^{CH}\) is the energy spent for sensing the environment.
# Seizure Detection in Medical IoT: Hybrid CNN-LSTM-GRU Model with Data Balancing and XAI Integration
## 3 Proposed Seizure Detection for MIoT
### Stages of Proposed Framework  
Based on smart Medical Internet of Things (MIoT) systems technology which ensures the detection of seizures from EEG signals with maximum reliability, the proposed detection system is founded on a developed theoretical framework, consisting of, but not limited to, statistical learning, optimization theory, and deep learning techniques. We introduced a robust method for the automated identification and detection of seizures by a set of advanced Deep Learning (DL) methodologies, each adopted for its theoretical merits and its relevance to the identification problem while effectively addressing the challenge of imbalanced EEG data, all within the context of MIoT systems. The depicted system structure can be observed in Figure 2, encompassing a series of significant stages aimed at achieving accurate seizure prediction.  
The main steps of the proposed system are elaborated as follows:  
* **Step 1 (Data Acquisition):** At this initial stage, the imbalanced seizure datasets are collected, forming the foundation for subsequent phases within the proposed framework.
* **Step 2 (Data Preprocessing):** All instances of the dataset are imported to make them suitable for further processing within the framework. To effectively address the imbalance problem, the SMOTE technique is applied to handle seizure instances.
* **Step 3 (Model Training/Testing):** The preprocessed and rebalanced dataset is divided into an 80-20 ratio, where 20% is allocated for the testing phase and 80% for the training phase. This initiates the training phase for hybrid Deep Learning models (i.e., CNN-LSTM-GRU). To evaluate the proposed framework, a subset of randomly selected training data is used for the chosen DL for seizure analysis.
* **Step 4 (Feature interpretability):** In this segment of the framework, the SHAP (Shapley Additive Explanations) technique is employed to enhance interpretability, providing insights into feature contributions for the prediction. This makes it easier to comprehend how the model is attained and helps in the decision-making process  
Figure 2: The block diagram of the proposed system.
# A Real-Time Sound Field Rendering Processor
## 2 HO-FDTD Algorithm
### Boundary Condition
Equations (6) and (12) indicate that the sound pressures of grids are calculated by the sound pressure of their neighbors at previous time steps, and no data dependency exists during computation. Hence, the equation may be implemented through pipelining to improve performance in hardware. We observe that Equations (6) and (12) consist of the sum of the sound pressures of a grid and its neighbors at the time step \(n\), and the sound pressure of a grid at the time step \(n-1\). For different types of grids, the updated equations have similar formats except for the multiplicands for the sum and \(P^{n-1}(i,j,k)\). Thus, a uniform updated Equation (13) can be derived. \[ \begin{array}{l}P^{(n+1)}(i,j,k)=D1*[P^{n}(i-1,j,k)+P^{n}(i+1,j,k)+P^{n}(i,j -1,k)+P^{n}(i,j+1,k)\\ +P^{n}(i,j,k-1)+P^{n}(i,j,k+1)+2P^{n}(i,j,k)]-D2*P^{(n-1)}(i,j,k)\end{array}  \tag{13}\]  
The D1 and D2 are shown in Table 1. It is worth noting that the part of summing in Equation (13) is changed according to grid positions. For grids on boundaries, the sound pressures of the virtual grids are replaced by those of the related neighbor grids.
## 3 Development of Software-Based Router and Traffic Generator  
Nowadays the majority of infocommunication networks are constructed on the basis of the proprietary equipment whose functionality is realized by hardware means, which demands specialised knowledge from the system administrator and is closed for modification of functioning of a network directed to the requirements of users. Each addition or change of functions by the system administrator in the network infrastructure, as a rule, leads to difficult deployment tasks, which must be carefully planned in advance. Transition from traditional hardware-based to software-based networks will allow a qualitative leap in terms of flexibility, productivity and production and will become a new solution for future info-communication networks in the telecommunications market [47; 48].  
For this reason, a software router and software traffic generator have been developed in this chapter. The development was carried out using the Qt5.2 integrated development environment, which uses the programming language C++ (standard C++11, 2011). The main advantage of this environment is that the code written in it can be compiled to different platforms (for example, Windows, Linux, Mac OS).
## 2 Light Clock
### Time Measured by a Moving Clock with a Freely Set Arm  
The important question is whether the way the light clock measures time depends on its position in the inertial system \(U^{\prime}\), i.e., on the angle of inclination of the clock arm in relation to the vector of velocity \(v\) at which the clock moves in relation to ether. In Sections 2.3 and 2.4, only the case when the clock arm is parallel to the velocity vector \(v\) is analyzed (Figure 4). If the way in which the light clock measures time is dependent on the inclination angle, then for the clock to be stable, it cannot be rotated. In such a case, the practical application of a light clock would be difficult.  
The experiments of [PERSON] and [PERSON] essentially consist in comparing the indications of two light clocks, the arms of which are inclined towards each other. The officially recognized results of these experiments show that the average speed of light in a vacuum is always constant in inertial systems available for experiments. This proves that, under real conditions, a light clock measures time independently of the angle of arm inclination. However, if it turned out that [PERSON]'s and [PERSON]'s experiments provide some insignificant positive results [9; 10], then the way the time is measured by the light clock would depend to some extent on the inclination angle of this clock in relation to speed \(v\).  
In papers [1,2], all linear transformations (without revolutions) are derived, for which the average speed of light on the way back and forth is always constant. Each of these transformations is consistent with the zero results of [PERSON]'s and [PERSON]'s experiments. In kinematics based on such transformations, the time of light flow along the arm of the light clock (on the way back and forth) does not depend on the inclination angle of this arm to the vector of velocity \(v\). Therefore, in these kinematics, the time measured using a light clock does not depend on the position of the light clock in the inertial system. Such kinematics are Special Theory of Relativity and Special Theories of Ether.  
In the further part of this paper, we will assume that there is kinematics based on one of the numerous transformations derived from the paper [1]. In each of these kinematics, the light clock measures the time independently of the inclination of the clock arm. Therefore, in all such kinematics, the time dilation of Equations (12) and (13) is valid regardless of the position of the clocks (i.e., regardless of the directions in which their arms are positioned in space).
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 4 Our Construction
### Hybrid Encryption
#### 4.1.2 Decrypt  
The VCSS receives the ciphertext information sent by user1 and decrypts it using the key in table \(QT_{B1}\). Then, use the key in the table \(QT_{B2}\) to encrypt and send it to user2. User2 decrypts the ciphertext with the key in the table \(QT_{A2}\) and obtains the video plaintext data, and the decryption process of user2 is the same as the decryption process of the server, which is not repeated here. See Algorithm 2 for the server decryption process.  
```
Input: Video plaintext \(M\) Output: Video ciphertext \(C\)
1 Init \(quantum\_AES\_flag=FALSE\);
2 User1 sets the encryption mode \(quantum\_flag\);
3 Send flag \(Server\leftarrow\)User\(1(quantum\_flag)\);
4 if\(quantum\_flag==TRUE\)then
5 if\(quantum\_AES\_flag==FALSE\)then
6 for\(i=1\) to \(QT_{A1}.len()\)do
7\(C_{i}=En_{xor}(QT_{A1}^{l_{i}},M_{i})\);
8 Send ciphertext \(Server\leftarrow\)User\(1(C_{i})\);
9 if\(\mathit{Insufficient}\)quantum\(\mathit{keys}\)then
10 BREAK;
11
12 end if
13
14 end if
15
16\(quantum\_AES\_flag=TRUE\);
17\(ID_{AES}=i\);
18 Server\(\leftarrow\)User\(1(quantum\_AES\_flag,ID_{AES})\);
19\(C=En_{AES}(QT_{A1}^{l_{i}},M)\);
20
21 end if
22 if\(quantum\_flag==False\)then
23\(C=En_{AES}(ck,M)\);
24
25 end if
26 return\(C\);
```  
**Algorithm 2**Vector encryption algorithm
# Intra-Articular Bone Marrow Aspirate Concentrate Injection in Patients with Knee Osteoarthritis
## 4 Discussion
### Clinical Studies using BMAC for Knee Osteoarthritis  
To date, few clinical studies have reported the outcomes of intra-articular BMAC injections in patients with knee OA [16; 17; 18]. [PERSON] et al. [16] used autologous BMAC for intra-articular injection with or without adipose grafts. They reported that patients with K-L Grade I or II showed significantly better improvement in clinical outcomes than K-L Grade III or IV. This finding concurs with the results of the current study. Moreover, the results of the current study suggest that clinical improvement can be maintained over 5 years (mean follow-up of 6.3 years) with a single intra-articular BMAC injection. Therefore, intra-articular BMAC injection could be a viable and simple treatment method for knee OA. However, it is difficult to directly compare the differences because of heterogeneity across studies in the study design, sources and doses of cells, and administration of adjuvant therapy. According to one study [12], the optimal number of cells required for adequate cartilage regeneration is approximately \(1\times 10^{8}\) cells. In the current study, the estimated number of cells was calculated as \(2.4\times 10^{5}\) adult stem cells and \(1.8\times 10^{9}\) mononuclear cells [26]. Although this number was lesser than that required for regenerative action, it is reasonable to presume that the effect of MSCs could be maximized within the osteoarthritis joint because an adipose tissue that served as a scaffold was used in this study [1]. Moreover, adipose tissue-derived MSCs (AD-MSCs) are easy to harvest, plentiful in number, and less affected by donor age than other cells. Particularly, a recent study showed that AD-MSCs could function intra-articularly in patients with arthritic knee [24]. Ultimately, both BM-MSCs and AD-MSCs in the BMAC might produce synergic effects [17]. Based on this observation, it can be inferred that a relatively small number of cells in BMAC could maintain the clinical efficacy of this treatment over a 5-year follow-up. However, the optimal number of cells and dose of BMAC for the treatment of knee OA has not been standardized. Further research is needed to determine the optimal number and dose of cells that can maximize the effect of the BMAC injection.
# HISEA-1: The First C-Band SAR Miniaturized Satellite for Ocean and Coastal Observation
###### Abstract  
On 22 December 2020, HISEA-1, the first C-band SAR small satellite for ocean remote sensing, was launched from the coastal Wenchang launch site. Though small in weight, the images it produced have a high spatial resolution of 1 m and a large observation width of 100 km. The first batch of images obtained within the first week after the launch confirmed the rich information in the data, including sea ice, wind, wave, rip currents, vortexes, ships, and oil film on the sea, as well as landmark buildings. Furthermore, geometric characteristics of sea ice, wind vector, ocean wave parameter, 3D features of buildings, and some air-sea interface phenomena in dark spots could also be detected after relevant processing. All these indicate that HISEA-1 could be a reliable, remarkable, and powerful instrument for observing oceans and lands.  
HISEA-1; synthetic aperture radar (SAR); small satellite 2021 13 2076 13.390/rs13112076
## 4 Conclusions  
The spatial-temporal distribution characteristics of the runoff pollutants at urban pavement sites were investigated by analyzing the water quality indexes of pavement runoff samples collected from RRS. The following conclusions can be drawn:  
1. The concentrations of Pb and Zn at the intersection were much higher than those at other sections of the road. The level of suspended solids far exceeded the limitation near the site where frequent human activities occurred. The spatial distributions of dissolved pollutants were similar to that of SS because of their high attachment to particles.
2. For all pollutants, the cumulative concentration reached a high level in a very short time and then ascended slowly. They were finally stable with the extension of rainfall duration. This feature was consistent with the property of the natural logarithmic function.
3. Six materials showed different removal rates for different pollutants. Based on the spatial-temporal distribution characteristics of pavement runoff pollutants, combinations of purification materials applicable for different pollution situations were recommended.
4. Different purification materials were integrated into a source control measure for treating pavement runoff with high pollution potential.
5. Depending on area locations, surrounding environment and rainfall events, the spatial and temporal distribution characteristics of pavement runoff were different. Only on the premise of understanding the actual pollution situations can engineers integrate different purification materials in the PCMs to make the removal of pollutants more effective and efficient.  
Data curation, C.K. and P.M.; Formal analysis, C.K.; Investigation, C.K., P.M., H.B., L.S. and Z.W.; Methodology, A.K. and P.X.; Project administration, A.K. and P.X.; Validation, Z.W.; Visualization, C.K.; Writing--original draft, C.K.; Writing--review and editing, A.K. and P.X.  
This research was funded by the Natural Science Foundation Committee of China, grant number 51578481 and 51578480, and the Research and Innovation Plans for Graduates, grant number kyzz15_0363. The APC was funded by the Natural Science Foundation Committee of China\".  
The authors would like to acknowledge the financial support of the Natural Science Foundation Committee of China (51578481 and 51578480) and the Research and Innovation Plans for Graduates (kyzz15_0363). We would also like to thank the technical support from the Testing Center of Yangzhou University and the Centre for Pavement and Transportation Technology at the University of Waterloo in Canada.  
The authors declare no conflict of interest. The sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Reliability of Supply and the Impact of Weather Exposure and Protection System Failures +
## 2 Failure Bunching and Protection System Failures
Fault Type 4 (FT4) is a result of a fault on the neighboring line that is correctly cleared by the neighboring line's protection system but causes an unwanted non-selective tripping of the line in focus. An equivalent failure rate for each line is then constructed as a summation of these four failure type contributions. The method is a contingency enumeration approach, where an approximate system reliability evaluation is used to obtain reliability indices for predefined minimal cut-sets. A more detailed presentation of the approach can be found in [17]. Complex network theory and graph-theoretic approaches have been used in the literature on cascading failures to study the power system, e.g., to identify critical components [29; 30; 31]. Protection system failures and misoperation have a propagating effect between components, and it is similarly necessary to consider the adjacency between them. The power system lends itself to a graph-based representation. A graph (\(G\)) is an object consisting of an ordered vertex set (\(V\)) and edge set (\(E\)) joining the vertices through its two connected buses \(u\) and \(v\), as seen in (1)-(3), where \(n\) is the number of vertices in the graph and \(m\) is the number of edges in the graph, see e.g., [32; 33]. Intuitively, it is natural to think of buses, \(b\), as vertices and components such as transmission lines, \(l\), as edges when it comes to representing the power system as a graph. An illustrative graph representation of a 4-bus power system following these conventions can be seen in Figure 1.  
In the following, the graph-based representation of relationships between components is a useful tool to incorporate protection system failures into power system reliability analysis. \[G=(V,E) \tag{1}\]  
\[V=\{b_{1},b_{2},\ldots,b_{n}\} \tag{2}\]  
\[E=\{l_{1},l_{2},\ldots,l_{m}\}=\{\{u_{1},v_{1}\},\{u_{2},v_{2}\},\ldots,\{u_{ m},v_{m}\}\} \tag{3}\]
## 4 Discussion
### Nanoring Parameter  
As the volatile matter decreased, there was a tendency for the number of nano-rings in the samples to increase. To further explore the correlation between volatile matter content  
Figure 8: Correlation between Fractal Dimension and Volatile Matter of Samples Using Different Analytical Methods (**A**). Cubic Counting Method (**B**). Triangulation Method (**C**). Variance Partitioning Method.  
and the number of nano-rings, Figure 9A plots the correlation between volatile matter content and the number of nano-rings in the samples. With the decrease in volatile matter, the number of nano-rings exhibits a linear increase trend. Due to the presence of various nanoscale pores in coal, low-volatile coal has a more compact structure, allowing nano-rings to be densely distributed within a limited space, thus exhibiting a higher number of nano-rings. In contrast, high-volatile coal has a relatively unstable molecular structure and is prone to decomposition during heating, which disrupts the nano-ring structure, resulting in a relatively lower total number of nano-rings.  
Additionally, to investigate the effect of coal volatility on nanoring roughness, the study analyzed and calculated the average roughness of nanorings for each sample across different expected diameter ranges. The nanoring roughness for each sample was obtained, and the results are presented in Table 4. According to Table 4, the nanoring roughness of the samples ranges from 1.639 to 4.903. Figure 9B illustrates the correlation between sample volatility and nanoring roughness. As the volatility of the coal samples increases, the nanoring roughness exhibits a linear upward trend. This may be due to the decomposition of volatile matter within the samples during thermal evolution or oxidation, which produces gases and forms nanoscale pores, directly affecting the roughness of the nanorings on the coal surface. High-volatile coal, containing more hydrophilic groups, readily adsorbs water molecules and gases, leading to local spalling and further increasing surface irregularities and roughness.
## 3 System Model
### Transmission Delay
Although no viable route from \(U_{A}\) to \(U_{G}\) exists at \(t_{k}\), by \(t_{k+5}\) UAV \(U_{A}\) can reach \(U_{G}\) via the stable multi-hop path  
\[\mathcal{P}:A\ \rightarrow\ B\ \rightarrow\ C\ \rightarrow\ D\ \rightarrow\ G.\]  
Conversely, \(U_{E}\) becomes isolated, its neighbor set empties, and no path to \(U_{G}\) is available at \(t_{k+5}\). Consequently, its packet is proactively discarded. This anticipatory drop prevents wasteful transmissions and thus materialy improves queue efficiency. Figure 2: Queue optimization using trajectory knowledge and transmission delay model. (**a**) At time \(t_{k}\), \(U_{A}\) is not reachable to \(U_{G}\), while \(U_{E}\) is reachable to \(U_{G}\). (**b**) At time \(t_{k+5}\), \(U_{A}\) is reachable to \(U_{G}\), while \(U_{E}\) is not.
# Comparison of Dimensionality Reduction Approaches and Logistic Regression for ECG Classification
## 2 Dataset Description and Processing
\begin{table}
\begin{tabular}{c c c c} \hline
**Acronym** & **Rhythm Type** & **Frequency (\%)** & **Age (Mean \(\pm\) SD)** \\ \hline SB & Sinus Bradycardia & 3889 (36.53\%) & 58.34 \(\pm\) 13.95 \\ SR & Sinus Rhythm (Normal) & 1826 (17.15\%) & 54.35 \(\pm\) 16.33 \\ AFIB & Atrial Fibrillation & 1780 (16.72\%) & 73.36 \(\pm\) 11.14 \\ ST & Sinus Tachycardia & 1568 (14.73\%) & 54.57 \(\pm\) 21.06 \\ AF & Atrial Flutter & 445 (4.18\%) & 54.70 \(\pm\) 17.35 \\ SI & Sinus Irregularity & 399 (3.75\%) & 34.75 \(\pm\) 23.03 \\ SVT & Supraventricular Tachycardia & 587 (5.51\%) & 55.62 \(\pm\) 18.53 \\ AT & Atrial Tachycardia & 121 (1.14\%) & 55.72 \(\pm\) 19.30 \\ AVNRT & AV Node Re-entrant Tachycardia & 16 (0.15\%) & 58.47 \(\pm\) 13.74 \\ AVRT & AV Re-entrant Tachycardia & 8 (0.07\%) & 57.50 \(\pm\) 16.84 \\ SAAWR & Sinus to Atrial Wandering Rhythm & 7 (0.07\%) & 51.14 \(\pm\) 31.83 \\ \hline
**All** & Total Records & 10,646 (100\%) & 51.19 \(\pm\) 18.03 \\ \hline \end{tabular}
\end{table} 
Table 1: Class distribution and age statistics for the SPH dataset. Figure 1: Construction of models using the training data, followed by the evaluation of each model on test data. _Note:_ Feature standardization was performed before data splitting, to ensure unified scaling across training and test subsets.