esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Power Flow Formulation: A Linearized Approach  
The methodology used to evaluate the electrical variables, e.g., voltage at the nodes of the power distribution network, corresponds to a linear approximation of power flow on the complex plane, which is proposed and validated in [51]. According to [52], the nodal voltages and currents flowing along the branches can be represented through the admittance matrix expressed in (38), which has been largely explained in the power systems literature. The reader is encouraged to consult about the physical representation via admittance matrix of a power distribution network [53]:  
\[ \begin{bmatrix}I_{s_{h,l_{e}}}\\ I_{u_{n},b,c}\end{bmatrix} = \begin{bmatrix}Y_{h,b,c}^{ss}&Y_{h,b,c}^{sw}\\ Y_{a,b,c}^{ss}&Y_{a,b,c}^{ss}\end{bmatrix} \cdot \begin{bmatrix}V_{s_{h,l_{e}}} \\ V_{w_{h,b,c}}\end{bmatrix}  \tag{38}\]  
The node at the substation or power source is defined as \(s\), and \(w\) stands for the remaining nodes of the power distribution system. In accordance with the \(ZIP\) model [54] that describes the nature of the electrical loads on the power network, these are representedas shown in (39). The tag ZIP accounts for constant impedance (Z), constant current (I), and constant power (P), load. An electrical load can have different approximations, depending on its nature. For example, a constant impedance load is referred to any load in which the impedance keeps constant, regardless of the change in the voltage and current. These types of loads can be those used for lighting. A constant power load refers to electric machines, e.g., motors trying to keep constant the output power (shaft power) with the variation of current or voltage. In addition, the constant current loads keep the current constant, for example: charging a battery is a typical case of constant current load:  
\[S_{w}=S_{w}^{\beta}\cdot\left(\frac{V_{w}}{V_{nom}}\right)^{\alpha}, \tag{39}\]  
\(S_{w}^{\beta}\) is the electrical load at the node \(w\), described with \(\beta\), referred to as constant power load \(p\), constant current load \(i\), or constant impedance load \(z\). The exponent \(\alpha\) takes the values of 0, 1, or 2 for the constant power, current, and impedance load, respectively, and \(V_{nom}\) is the nominal line to neutral voltage at any node of the power network. Recall that node \(w\) is composed of the phases \(a\), \(b\), and \(c\). For the constant power load (\(\alpha=0\)), \(S_{w}^{p}\) is expressed as follows:  
\[S_{w}=S_{w}^{p}\cdot\left(\frac{V_{w}}{V_{nom}}\right)^{0}\] \[S_{w}^{p}=V_{w}\cdot I_{w}^{p^{*}}\] \[I_{w}^{p^{*}}=\frac{S_{w}^{p}}{V_{w}}\]  
The \(\star\) symbol represents the conjugate of phasor.
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
## 8 Conclusions  
In this paper, we propose a new CSMA/CA mechanism for the IEEE 802.5.4e LLDN superframe to reduce the number of CCAs. In order to improve the efficiency of the system, a prediction model is proposed to predict the optimal number of retransmission slots so that the possibility to retransmit a failed packet can be guaranteed. We present a novel medium access algorithm that exploits two radios such that it reduces the number of simultaneous transmissions, thus leading to lower delay and overall energy consumption. Performance analysis for data transmission reliability, throughput and energy consumption are made based on our proposed models. Our designs are validated with the simulation and analytical methods, which are well matched. Performance comparisons between the proposed solution and the original 802.15.4e LLDN MAC protocol have been done and show that our design protocol is better than the IEEE 802.15.4e MAC in terms of throughput, energy consumption, delay and reliability. Hence, our design can be implemented in WSNs for different applications like fire detection and alarm systems, fall detection systems, factory automation, robots, automated dispensations, airport logistic and many more emergent automated applications.  
This work is partly supported by the Ministry of Science and Technology (MOST), Taiwan under Grant Numbers 106-2221-E-182-014, 105-2221-E-182-043, 106-2221-E-182-013-MY3 and 104-2745- 8-182-001.  
[PERSON], [PERSON] and [PERSON] conceived of and designed the protocol and experiments, analyzed the data and wrote the paper. [PERSON] performed the experiments.  
The authors declare no conflict of interest.
# Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings
## Materials and Methods
### Conventional Methods
#### 2.3.3 PCA K-Means Clustering  
There exist immense number of clustering algorithms. Few popular algorithms are fuzzy C-means clustering [37], subtractive clustering [38], Density-based clustering [39], and K-means [15]. For our model, we have chosen K-means as our clustering method as it is one of the most popular clustering algorithms used in literature [40]. K-means clustering is an iterative clustering method which represents each of \(k\) clusters \(C_{i}\), where \(i=\{1,\ldots,k\}\), by their mean, i.e., the centroid \(c_{i}\). As such, each data points are classified to the cluster \(C_{i}\) which results in the smallest Euclidean distance (1) between the corresponding \(c_{i}\) and the point.  
K-means, however, is sensitive to the dimensionality of the data where its accuracy drops and susceptibility to outliers rises with increasing dimensions [41]. Therefore, we have implemented a classical dimensionality reduction technique, namely PCA, that is commonly used before applying K-means [14]. PCA reduces dimensionality of data while conserving the correlation structure of the variables and capturing the variability in the data. Given \(X\) is the original data matrix and \(Y=(y_{1},\ldots,y_{n})\), \(y_{i}=x_{i}-\overline{x}\) where \(\overline{x}=\sum_{i=0}^{n}\frac{x_{i}}{n}\), the covariance matrix becomes \(YY^{T}=\frac{1}{n}\sum_{i=0}^{n}(x_{i}-\overline{x})(x_{i}-\overline{x})^{T}\). The principal components can be obtained through taking the eigenvectors of the covariance matrix where the eigenvalues rank the eigenvectors in the order of variance explained [14]. For our experiment, the data was reduced to 3-dimensions using three principal components with the highest principal directions, and the number of clusters, \(k\), was determined through manual inspection of the data projected on the three principal components.
## 2 Results and Discussion
### Optimization of HS-SPME Conditions
#### 2.2.1 Reconstitution Volume  
We investigated if the addition of aqueous NaCl to the dried-down SPME vials could increase signal and decrease detection limits, which has been previously reported for quantification of pesticide residues in soil [22]. We observed a significant decrease in LOD for all three thiols upon addition of aqueous NaCl (\(p<0.1\) for dry vial _vs._ 10 mL buffer), as shown in Figure 3. The largest effect was observed for 4-MMP, where the LOD decreased by five-fold when 10 mL of NaCl solution were added, as compared to the control with no added solution. A potential explanation for this effect is that the aqueous solution used for reconstitution can compete for active sites on the glass SPME vial, decreasing adsorptive losses  
Figure 2: Effects of pH, PTC concentration, and sample volume on limits of detection for each thiol. The optimization was performed using a multifactorial CCF model. The red dotted lines show the mean values within a factor (**vertical lines**) or for a thiol detection limit (**horizontal lines**). The blue dotted lines represent the error bars.  
of the thiol-PFB analytes. Alternatively, the presence of water could alleviate hot spots of the SPME vial during extraction, preventing thermal degradation of the analytes.
## 3 Results and Discussion
### Current Density-Voltage (J\(-\)V) Characteristics
The average power conversion efficiency (PCE) of the perovskite cell deposited on M-TiO\({}_{2}\) doped with FK209 has improved by 28% compared to perovskite deposited on M-TiO\({}_{2}\) doped with Li\({}^{+}\). There are slight variations found in the short circuit current density between all cells prepared with different dopants and the average Jsc was 26 mA/cm\({}^{2}\) in forward and backward scanning. It is widely accepted in the literature that the backward scanning gives higher efficiency than forward scanning. This is related to a phenomenon called J\(-\)V hysteresis. It is due to charge distribution effect when applying external electric field. It relates to slow transient capacitive current, charge carriers trapping, ion migration and ferroelectric polarization, which can also cause band bending [29; 30; 31]. Figure 12: The illuminated J-V characteristic curves of perovskite solar cells deposited on ETL of M-TiO\({}_{2}\) doped with FK209, FK209/Li\({}^{+}\) and lithium.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 3 5G Security Architecture: A Systematic Approach
### Core Network
#### 3.1.4. Vendor Trust  
5G has allowed for the incorporation of new technologies like NFV and SDN, which are a design departure compared to past cellular network architectures. Previously, cellular  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Citation** & **SDN** & **NFV** & **MEC** & \begin{tabular}{c} **Network** \\ **Slicing** \\ \end{tabular}  & \begin{tabular}{c} **User** \\ **Auth.** \\ \end{tabular}  & **Encrypt. ** & \ \begin{tabular}{c} **Attack** \\ **Vectors** \\ \end{tabular}  & **HetNets** & \begin{tabular}{c} **Vendor/** \\ **Trust** \\ **Models** \\ \end{tabular}  & **Blockchain** & **ZTA** \\ \hline [33] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✗ \\ \hline [28] & ✓ & ✓ & ✓ & ✓ & ✓ & ✗ & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ \\ \hline [34] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✗ & ✗ & ✗ & ✓  
operators needed to design their deployments in a more hardware-driven paradigm, as some components required specific hardware solutions that could only be established with often proprietary purpose-built equipment. This made it more difficult for operators to deviate from what was available on the market and for new players to enter that market, which passively ensured a sense of security due to very limited vendor flux in the solutions landscape. In the current 5G era, however, vendor diversity and interoperability are key features, as the networks are more flexible than ever, allowing the core to be run using off-the-shelf server hardware instead of requiring specific purpose-built hardware. As off-the-shelf solutions are now commonplace in 5G and constitute a significant part of the rollout and promise of 5G, additional questions are raised on whether all operators and equipment vendors are following security guidelines and whether they can be trusted. These concerns in recent years have entered mainstream media through increased geopolitical tensions amid a global marketplace for 5G technology vendors. Security for 5G core and backhaul networks is required to gain its users' trust and maintain confidentiality, integrity, and availability of the communications and the data both in flight and at rest. An example illustration of a possible untrusted network path is presented in Figure 6. Various standards have been created in the pursuit of realizing a secure system for all operators to implement [40]. The authors of [37] outline the major high-level security pillars for a backhaul network listed in Table 3.
## 2 Materials and Methods
### Precipitation Products
#### 2.3.1 TRMM 3B42 RT and 3B42V7s  
The TRMM is a joint mission between the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) to monitor tropical precipitation. The 3B42 RT (near real time) and the 3B42V7 (post-real time) are products from the TRMM Multi-satellite Precipitation Analysis (TMPA) [35]. The TMPA-3B42 algorithm calibrates and combines microwave (MW) precipitation estimates, then creates thermal infrared (IR) estimates by calibrating the IR with the MW precipitation estimates, and finally combines both MW and IR estimates to generate TMPA precipitation estimates. The 3B42 RT data are available approximately nine hours after real-time estimations, which makes this product compatible with flood prediction studies. The 3B42V7 product incorporates the Global Precipitation Climatology Centre (GPCC) monthly precipitation for bias correction, but data are only available 10-15 days after the end of each month. The 3B42 RT product uses TRMM Microwave Imager (TMI) for calibration, while TRMM Combined Instrument (TCI) precipitation products are used in 3B42V7. The TRMM 3B42V7 daily precipitation was estimated by summing up 8 sets of 3-hourly precipitations for a given day at 00:00 UTC. Evaluation of the latest version of the TMPA products (version 7) using rain gauges data is still limited in South Asian regions [36] like Malaysia. The TRMM 3B42 RT and 3B42V7 data used in this study were acquired using the GES-DISX Interactive Online  
Figure 2: Spatial distribution of mean annual precipitation for the period 2003–2007 estimated from **(a)** 3B42 RT, **(b)** 3B42V7, **(c)** GPCP-1 DD, **(d)** PERSIANN-CDR, **(e)** CMORPH, **(f)** APHRODITE, and **(g)** 342 rain gauges.  
Visualization and analysis Infrastructure (Giovanni) as part of the NASA's Goddard Earth Sciences (GES) Data and Information Services Center (DISC) [37].
# Adaptive TCP Transmission Adjustment for UAV Network Infrastructure
## 5 Performance Evaluation  
This section describes experiments and results to demonstrate the performance of \(ASRAN\). Empirical experiment to verify the effectiveness of \(ASRAN\) is described in the Section 5.1. In addition, the result of simulation experiment to increase the number of UAVs and add UAV mobility is written in Section 5.2.
# Recent Progress in Hybrid Additive Manufacturing of Metallic Materials
## 5 Effect of Heat Treatments in AM Processed Maraging Steels
### Strengthening Mechanisms' Models
#### 5.1.3 Precipitation Strengthening
Based on these observations, an ideal balance of precipitate size, reverted austenite, temperature, and aging time requires more understanding to tailor specific properties. Both alloys exhibited different microstructures and sensitivities to the applied heat treatments despite having similar compositions. The joining of dissimilar metals poses a significant challenge in the design of heat treatment schedules, as eluded earlier on [34,46]. Dissimilar joints of managing steel (250) and PH 13-8 Mo studied by [PERSON] et al. [50] exhibited the best strength of 1528 MPa, after a post-well HT of 510 \({}^{\circ}\)C for 4 h followed by air cooling, attributed to the presence of Ni\({}_{3}\)Ti, Ni\({}_{3}\)Mo, and \(\beta\)-NiAl precipitates. The presence of precipitates in the maraging steel was not systematically quantified, but inferred from the observed improvement in hardness after heat treatment, which  
Figure 17: Change of hardness with aging time. Reprinted with permission from [84]. Copyright 2021 Elsevier B.V.  
Figure 18: Heat treatment schedule for LPBF-EOS SS CX and PH13-8 wrought alloy [122]. peaked at 600 HVN. The PH 13-8 Mo HAZ (where the failure occurred) exhibited a mixture of martensite and reverted austenite, which lacked NiAl precipitates. The absence of precipitates enhanced the failure through dislocation lodging [96]. The low hardness (450 HV) and potential elemental segregation were identified as leading factors contributing to the challenge of joining dissimilar metals. A similar alloy system was modified by performing different welding techniques, such as GTCAW and Laser Beam Welding (LBW), which have similarities to LPBF and Electron Beam Welding (EBW). All joints using the different welding techniques demonstrated high tensile strengths above 1500 MPa after heat treatment [123]. The materials yielded the greatest strength of 1649 MPa and 1656 MPa for LBW and EBW, respectively, due to the presence of uniformly distributed Ni\({}_{3}\)Ti, Ni\({}_{3}\)Mo, and -NiAl precipitates (510 \({}^{\circ}\)C for 4 h). In contrast, a lower heat treatment of 485 \({}^{\circ}\)C for 3.5 h (with air cooling) resulted in lower strength measurements of 1490 MPa (GTCA), 1600 MPa (LBW), and 1610 MPa (EBW). The E/LBW failure occurred at the weld zone, with a fine dendritic structure. The failure was attributed to Ti and Mo elemental segregation near the interdendritic borders [118; 123]. According to this study, an increase in aging time and temperature increases the mechanical characteristics of the parent alloys, despite failure in the joined section. This work did not determine the upper heat treatment limit to obtain the highest mechanical properties. In addition, solution treatment can be applied post-welding to minimize elemental segregation at the interface.
# A New Algorithm for Computing the Distance and the Diameter in Circulant Graphs
## 4 Diameter Formulas for Circulant Graphs
### Diameter of \(C_{n}(1,s)\) When Both \(n\) and \(s\) Are Even
\begin{cases}\lceil\frac{\lambda}{2}\rceil+\frac{s-\gamma}{2}&\text{ if }\ \gamma\leq 2\lceil\frac{s-2}{4}\rceil,\\ \lfloor\frac{\lambda}{2}\rfloor+\frac{\gamma}{2}&\text{ otherwise.}\end{cases} \)  
In this case, \(\gamma\) is even and \(s\geq 4\) because if \(s=2\), then \(\gamma=0\) (see Theorem 2). **Case 1. **\(\gamma\leq 2\lceil\frac{s-2}{4}\rceil\)  
**Case 1. **\(\lambda\) is even  
In this case, \(\gamma\geq 2\), \(\lambda\geq 4\), \(s\geq 4\), and \(d=\frac{\lambda}{2}+\frac{s-\gamma}{2}\). Let \(i=(\frac{\lambda}{2}-\frac{\gamma}{2})s+\frac{s}{2}\) be a vertex of \(C_{n}(1,s)\). Table 3 represents a simple calculation of the lengths \(\ell^{1}(i)\) to \(\ell^{4,l}(i)\) provided in Lemma 3. Thus, by Lemma 4, we have \(d(i)=\ell^{1}(i)=d\). **Case 1.2. **\(\lambda\) is odd  
If \(\gamma=2\) then, \(\lambda\geq 3\), \(s\geq 4\), and \(d=\frac{\lambda+1}{2}+\frac{s-2}{2}\). Let \(i=\frac{\lambda-1}{2}s+\frac{s}{2}+1\) be a vertex of \(C_{n}(1,s)\). Note that since \(s\geq 4\), we have \(\frac{s}{2}+1<s\). By a simple calculation of the lengths \(\ell^{1}(i)\) to \(\ell^{4,t}(i)\) represented in Lemma 3, and by applying Lemma 4, we obtain \(d(i)=\ell^{2}(i)=d\). If \(\gamma\geq 4\) then, \(\lambda\geq 5\), \(s\geq 6\), and \(d=\frac{\lambda+1}{2}+\frac{s-\gamma}{2}\). Let \(i=\frac{\lambda+1}{2}s+\frac{\gamma}{2}+\frac{s}{2}+1\) be a vertex of \(C_{n}(1,s)\). Since \(s>4\) and \(\gamma\leq\lceil\frac{s-2}{2}\rceil\), we have \(\frac{\gamma}{2}+\frac{s}{2}+1<s\). Note that \(\frac{\gamma}{2}-2>-\frac{\gamma}{2}+1\) because \(\gamma\geq 4\). By a simple calculation of the lengths \(\ell^{1}(i)\) to \(\ell^{4,t}(i)\) represented in Lemma 3, and by applying Lemma 4, we obtain \(d(i)=\ell^{2}(i)=d\). **Case 2. **\(\gamma>2\lceil\frac{s-2}{4}\rceil\)  
If \(\lambda\) is even, then \(\gamma\geq 2\), \(\lambda\geq 4\), \(s\geq 4\), and \(d=\frac{\lambda}{2}+\frac{\gamma}{2}\). Let \(i=\frac{\eta}{2}\) be a vertex of \(C_{n}(1,s)\). Note that since \(s>\gamma\), we have \(s-\frac{\gamma}{2}>\frac{\gamma}{2}\). By a simple calculation of the lengths \(\ell^{1}(i)\) to \(\ell^{4,t}(i)\) represented in Lemma 3, and by applying Lemma 4, we obtain \(d(i)=\ell^{2}(i)=d\). If \(\lambda\) is odd then, \(\gamma\geq 2\), \(\lambda\geq 3\), \(s\geq 4\), and \(d=\frac{\lambda-1}{2}+\frac{\gamma}{2}\). Let \(i=\frac{\lambda-1}{2}s+\frac{\gamma}{2}\) be a vertex of \(C_{n}(1,s)\). Since \(s>\gamma\), we have \(s-\frac{\gamma}{2}>\frac{\gamma}{2}\). Similarly, by applying Lemma 4, we obtain \(d(i)=\ell^{1}(i)=d\). Thus, the following theorem follows from Lemmas 8 and 9. Theorem 4: _When both \(n\) and \(s\) are even,_  
\[diam(C_{n}(1,s))= \begin{cases}\lceil\frac{\lambda}{2}\rceil+\frac{s-\gamma}{ 2}&\text{if }\ \gamma\leq 2\lceil\frac{s-2}{4}\rceil,\\ \lfloor\frac{\lambda}{2}\rfloor+\frac{\gamma}{2}&\text{otherwise.}\end{cases} \]
# Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
## 3 Presented Method: ESC-MASD-Net
### Residual Conformer Block  
The residual conformer (ResCon) block, depicted in Figure 3, is a variation of the Conformer convolution block that increases the channel size to produce enriched channel representation. The primary branch of ResCon comprises a series of operations, including point-wise convolution with a channel growth factor of \(G_{0}\), batch normalization, gated linear unit (GLU), depth-wise convolution, another batch normalization, Swish activation, and point-wise convolution with a channel growth factor of \(G_{1}\). As a residual connection added to the main branch, the second branch consists of a point-wise convolution layer with channel growth factor \(G_{1}\). The ReLU activation is applied to further process the output. Below are some of the key features and advantages of the ResCon block.  
1. The branch of the ResCon block expands channels for information capture and enhancement. Furthermore, this branch uses depthwise separable convolution, which combines depth-wise and point-wise convolution to drastically reduce overall processing requirements when compared to a standard convolution network.
2. The ResCon block uses a GLU (Gated Linear Unit) which operates as follows: \[\mathbf{Y}=(\mathbf{X}*\mathbf{W}+\mathbf{b})\otimes\sigma(\mathbf{X}* \mathbf{V}+\mathbf{c}),\] (5) where \(\mathbf{X}\) is the input, \(\mathbf{W}\), \(\mathbf{V}\), \(\mathbf{b}\), and \(\mathbf{c}\) are learnable affine transformation parameters, and \(\sigma\) is the sigmoid function. The GLU assists in learning which features of the input to pass through and which to block, allowing it to focus on relevant features for the given task. In addition, the number of channels can be adjusted using GLU's affine transformations.
3. The Swish function is represented as \(y=x\sigma(\beta x)\), where \(\beta\) is a learnable parameter and \(\sigma\) is the sigmoid function. Swish has been demonstrated to outperform ReLU activation for deep neural network training. Unlike ReLU, Swish does not have the problem of terminating the learning process when the input is negative.
4. The ResCon block's residual connection branch preserves all input information, guaranteeing that the main branch only grasps information that will improve or sustain performance. Furthermore, the point-wise convolution applied to the residual connection aids in alternating the number of input channels with little computational effort.  
Figure 3: Flowchart of residual conformer block.