esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### The Cellular Response to Elevated CO\({}_{2}\) Is Modulated by shNR4A2  
Given the evidence that elevated CO\({}_{2}\) modulates the transcriptional kinetics of _NR4A2_ and _NR4A3_ against a background of inflammation, we hypothesised that NR4A2 and NR4A3 may play roles as master regulators of the monocyte response to hypercapnia. To test this, we took an interventional approach using cells that are stably depleted of _NR4A2_ and _NR4A3_ using targeted shRNAs. The consequences of shNR4A2 and shNR4A3 in normocapnic monocytes [15] have recently been published. Here we report for the first time on the transcriptional response to elevated CO\({}_{2}\) in (short hairpin RNA targeting _NR4A2_) shNR4A2 and (short hairpin RNA targeting _NR4A3_) shNR4A3 cells, with comparisons to some of our previously published work. This approach is well suited to test our hypothesis that NR4A family members are regulated by hypercapnia and contribute to the cellular response to hypercapnia.  
We first investigated the CO\({}_{2}\)-responsiveness of shNR4A2 cells, both in the basal and LPS-stimulated states. Buffered hypercapnia (10% CO\({}_{2}\)) for 4 h was a modest stimulus in these cells, with 52 transcripts differentially expressed in the basal state (Figure 2A, Supplementary Table S1) and 646 transcripts in the LPS-stimulated cells (Figure 2B, Supplementary Table S2). Interestingly, there were markedly fewer transcripts in shNR4A2 cells compared to non-target short hairpin RNA (sh)-controls ((380 genes (basal) and 1889 genes (LPS-stimulated)) [26], suggesting that the depletion of _NR4A2_ attenuated the cellular response to hypercapnia. Of the 52 genes affected by hypercapnia in the basal state, 42 (or 80%) were also affected by hypercapnia in sh-control cells (Figure 2C). Of the 646 genes affected by hypercapnia in the presence of LPS, 488 (or 75%) were also affected by hypercapnia in non-target control cells (Figure 2D). Taken together with the principal component analysis Supplementary Figure S2A,B, indicate that (i) buffered hypercapnia is a robust stimulus to change gene expression in shNR4A2 cells, (ii) that the cellular response to hypercapnia is enhanced in the presence of LPS, and (iii) that the total number of transcripts affected is fewer than those seen in non-target control cells in the presence or absence of LPS. The top ten transcripts most affected by 10% CO\({}_{2}\) (up and downregulated, ranked by fold change) are included in Supplementary Figure S3.
# Influences of Sex on Muscle Architecture and Performance in Elite Field Hockey Players
## 2 Materials and Methods
### Change of Direction Speed and Sprint Testing  
All the results of pro-agility and sprint assessments are reported in Table 2. Performance was significantly lower in female players compared to male players in both pro-agility (\(p\)\(\leq\) 0.001; \(\eta^{2}\) = 0.390; CI: 0.176; 0.411) and 30 m sprint (\(p\)\(\leq\) 0.001; \(\eta^{2}\) = 0.650; CI: \(-0.763\); \(-0.456\)). The difference between the two groups was an average of 0.29 s and 0.61 s for the pro-agility and 30 m sprint tests, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Assessment** & **Male Group** & **Female Group** & \multicolumn{2}{c}{**Groups Comparison (\(p\); \(n^{2}\); CI)**} \\ \hline \%FM & \(9.69\pm 3.05\) * & \(20.51\pm 3.31\) & \(\leq\)0.001; 0.673; \(-12.622\), \(-8.989\) \\ VLMT (mm) & \(1.49\pm 0.29\) * & \(1.22\pm 0.31\) & \(0.007;0.182;0.811\), \(0.469\) \\ TrapFI (mm) & \(1.12\pm 0.21\) * & \(0.89\pm 0.15\) & \(\leq\)0.001; 0.284; 0.104, \(0.342\) \\ VLEI (a.u.) & \(41.22\pm 6.64\) * & \(52.01\pm 7.89\) & \(\leq\)0.001; 0.361; \(-15.575\) \\ TrapEI (a.u.) & \(30.75\pm 2.70\) & \(32.16\pm 6.19\) & \(0.375;0.021; \(-4.612\), \(1.778\) \\ VLCEI (a.u.) & \(52.64\pm 8.8\) * & \(77.35\pm 13.71\) & \(\leq\)0.001; 0.370; 18.008, \(33.172\) \\ TrapEI (a.u.) & \(38.93\pm 2.74\) & \(39.58\pm 9.72\) & \(0.798;0.002; \(-5.420\), \(5.070\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data of the body composition and muscle architecture assessments. FM = fat mass; MT = muscle thickness; VL = vastus lateralis; Trap = trapezius; EI = echo intensity; cEI = corrected EI; a.u. = arbitrary units. * indicates a significant difference (\(p\)\(\leq\) 0.05) from the Female group.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Assessment** & **Male Group** & **Female Group** & \multicolumn{2}{c}{**Groups Comparison (\(p\); \(n^{2}\); CI)**} \\ \hline Pro-agility (s) & 4.72 \(\pm\) 0.21 * & 5.01 \(\pm\) 0.16 & \(\leq\)0.001; 0.390; 0.176, 0.411 \\
30 m sprint (s) & 4.19 \(\pm\) 0.19 * & 4.80 \(\pm\) 0.25 & \(\leq\)0.001; 0.650; \(-0.763\); \(-0.456\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data of the performance assessments. * indicates a significant difference (\(p\)\(\leq\) 0.05) from the Female group.  
Figure 2: Ultrasonography image and pixel intensity histogram of the vastus lateralis from a typical male (**a**) and female (**b**) elite Field Hockey player participating in the present study.
## 4 Test Results and Analysis
### Ductility Coefficient  
Ductility is an essential feature in the seismic design of structures. The displacement ductility factor \(\mu\) was used to study the ductility properties of the structure. Two coefficients, \(\mu_{\Delta}\) and \(\mu_{\theta}\), were used to evaluate the ductility performance of the structure; \(\mu_{\Delta}\) denotes the displacement ductility factor, and \(\mu_{\theta}\) denotes the rotation ductility factor. Their expressions are as follows:  
\[\mu_{\Delta}=\Delta_{u}/\Delta_{y} \tag{3}\]  
\[\mu_{\theta}=\theta_{u}/\theta_{y} \tag{4}\]  
where \(\Delta_{u}\) is the yield displacement of the structure, \(\Delta_{y}\) is the damage displacement of the structure, \(\theta_{u}\) is the yield angle of the structure, and \(\theta_{y}\) is the damage angle of the structure.  
Each value is derived from the skeleton curve, and the ductility coefficients of the specimens are shown in Table 5.  
\"Seismic Design of Buildings\" (GB 50011-2010) specifies the limits of interlayer displacement angles for verifying the seismic deformation of steel structures. For the multilayer and high-rise steel structure, the elastic interlayer displacement limit [\(\theta_{e}\)] = 1/250 and the elastoplastic interlayer displacement angle limit [\(\theta_{p}\)] = 1/50. Table 5 shows the ductility coefficients (\(\mu\) = 2.84 \(-\) 3.07 for SF-1 and \(\mu\) = 3.34 \(-\) 3.47 for SF-2), elastic limit displacement angles (\(\theta_{y}\) = (3.29 \(-\) 3.51)\(\theta_{e}\) for SF-1 and \(\theta_{y}\) = (3.52 \(-\) 3.62)\(\theta_{e}\) for SF-2), and elastoplastic limit displacement angles (\(\theta_{u}\) = 2\(\theta_{p}\) for SF-1 and \(\theta_{u}\) = 2.38\(\theta_{p}\) for SF-2) of both specimens. Both specimens exhibit good ductility, but the ductility coefficient of SF-2 is approximately 13-17% higher than that of SF-1. Moreover, both elastic and elastoplastic displacement angles are higher for SF-1. Overall, the NEDC can retard the damage caused to structures and meet seismic requirements more effectively.  
Figure 18: Stiffness degradation curves of SF-1 and SF-2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Specimen** & **Direction** & \(\Delta_{y}\)**/mm** & \(\Delta_{u}\)**/mm** & \(\theta_{y}\) & \(\theta_{u}\) & \(\mu\) \\ \hline \multirow{2}{*}{SF-1} & + & 39.53 & 121.51 & 1/76 & 1/25 & 3.07 \\  & \(-\) & 42.41 & 120.34 & 1/71 & 1/25 & 2.84 \\ \hline \multirow{2}{*}{SF-2} & + & 42.19 & 146.23 & 1/71 & 1/21 & 3.47 \\  & \(-\) & 43.37 & 145.21 & 1/69 & 1/21 & 3.34 \\ \hline \end{tabular}
\end{table}
Table 5: Ductility coefficients of specimens SF-1 and SF-2.
# Performance Analysis of Cooperative Low-Power Wide-Area Network for Energy-Efficient B5G Systems
## 1 Introduction
In [13], the decoding weak transmission solution is presented with the use of the _coherent combining technique_, which enhances both the battery lives of client devices and the coverage of LPWANs in major urban deployments. It allows multiple LoRa wide-area network (LoRaWAN) GWs to pool their received signals in the _cloud_, coherently combining them in order to detect weak signals that are not decodable at any individual GW. It was implemented as a service running on a campus-wide LoRaWAN network installed at Carnegie Mellon University and evaluated through proof-of-concept experiments and a large-scale, trace-driven simulation. That is a coherent combining technique, which is similar to the macro-diversity technique in the cellular network. They evaluated it over a test network and through a simulation using traces from a LoRaWan deployment. However, the performance analysis is not presented in the literature. Actually, it is not trivial to attain tractable exact performance analysis for networks with multiple antennas. In [14], the cloud-radio access network with remote radio heads (RRHs) is analyzed in term of outage probability, for which RRHs are equipped with multiple antennas. The exact closed-form expression for the outage probability is obtained. However, It is derived from using the moment generating function approach with the approximation. With the finite limiting factor, it does not match well with the simulation result in the low signal-to-noise ratio (SNR) regime. This paper aims to exactly address the error performance analysis of the cooperative uplink LPWAN with multiple antennas GWs. The exact closed-form expressions for the performance analysis are derived using the exact channel distribution information (CDI) (_hyper-Erlang_ distribution) [15]. LPWAN is basically based on the low power transmission. It is certainly needed to analyze the performance exactly in the low SNR regime. The exact closed-form expressions of bit error rate (BER) and outage probability performances are obtained. Furthermore, the diversity order is obtained from the derived BER expression, which is the first theoretical result in the literature. Finally, the asymptotic expression of BER is addressed with the numerical results. The rest of the paper is organized as follows. The system model is explained in Section 2. In Section 3, the novel channel distribution is introduced and the analytical expressions for the error performances are derived. The numerical results are presented in Section 4. Finally, we draw conclusions in Section 5.
# Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning
## 5 Discussion
### Effect of Hyperparameters on Performance  
In this study, we generated corresponding pseudo-labels for unlabeled data, which allowed us to include unlabeled data in the training process. However, evaluating the accuracy or quality of pseudo-labels is challenging. Existing models to build pseudo-labels have a certain probability of generating false labels. If the samples have false labels or are located in the edge areas to supplement the imbalanced dataset, the classifiers can easily adopt a bias. Therefore, we introduced the nearest neighbor method and a consistency check in NearPseudo to evaluate the quality of the pseudo-labels and the remote sensing data, consequently improving the quality of the selected samples. Notably, the values of hyperparameters \(k\) and \(q\) in NearPseudo could influence the credibility and accuracy judgments of those pseudo-labels.  
Figure 4: Mean change in overall accuracy from baseline (unbalanced dataset) to balanced dataset after processing in the sub-training set **T2**. The dashed line shows a linear correlation between mean change and variance of F1-score for each class without balanced.  
Hyperparameter \(q\) refers to the search range of the supplemental sample. The larger the hyperparameter \(q\), the greater the search range for NearPseudo to screen suitable samples. Therefore, the accuracy of NearPseudo increases with \(q\). When the nearest neighbor method and consistency check are introduced, the selected samples can be filtered according to the quality of the sample. Hyperparameter \(k\) controls the filtering conditions. Smaller \(k\) values indicate that the selection conditions are harsher. The selected pseudo-labels have a higher quality under these conditions. Therefore, the accuracy of NearPseudo increases with the value of hyperparameter \(k\) decreases.  
Notably, the pursuit of maximized \(q\) and minimized \(k\) may help to improve the predictive accuracy. However, it is not always feasible. The run time may increase significantly when parameter \(q\) is extremely large or parameter \(k\) is extremely small. The full parameter time complexity can be utilized to reveal the relationship between various hyperparameters and run time [53]. The optimal full parameter time complexity of the balanced process in NearPseudo is \(O(n^{\prime}q(v+log_{2}k)/k+n^{\prime}M)\), where \(n\) is the number of training data points and \(n^{\prime}\) is the number of supplemented samples, \(v\) is the number of bands/features, and \(M\) is the time for initialization classifier to predict a sample. It can be seen that the run time of NearPseudo increases with \(q\). Simultaneously, the run time of NearPseudo increases as \(k\) decreases. Thus, these discrepancies should be noted when selecting the optimal hyperparameters.
## 2 Principles and Model Architecture of KANs
### Model Structure and Parameter Configuration  
Three initial KAN structures were designed for modeling, respectively, with their configurations listed in Table 1. A training protocol for KANs is proposed, accompanied by corresponding parameter specifications, as shown in Table 2.  
Different from classical artificial neural networks, the connection patterns between layers of KANs evolve dynamically during training. In this study, the KAN structure  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **KAN-A** & **KAN-B** & **KAN-C** \\ \hline \multirow{3}{*}{Each Layer of KAN} & 1 output variable & 1 output variable & 1 output variable \\  & 10 nodes & 3 nodes & 20 nodes \\  & 3 input variables & 3 input variables & 3 input variables \\ \hline \multirow{2}{*}{Total Number of Nodes} & \multirow{2}{*}{\(3+10+1=14\)} & \multirow{2}{*}{\(3+3+1=7\)} & \multirow{2}{*}{\(3+20+1=24\)} \\  & & & \\ \end{tabular}
\end{table}
Table 1: Three initial KAN structures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Training Stage** & **Procedures and Parameter Setting** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Network Structure Setup} & Grid structure configuration: grid size set to \\  & & 8, k B-spline order set to 5 \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{First Training} & Optimization algorithm: LBFGS \\  & & Number of iterations (steps): 500 \\  & & Regularization coefficient (\(\lambda\)): 0.01 \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Model Pruning} & Node attribution score threshold \\  & & (node_th): \(2\times 10^{-2}\) \\ \cline{3-4}  & & Edge attribution score threshold \\  & & (edge_th): \(2\times 10^{-2}\) \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{Second Training} & Optimization algorithm: LBFGS \\  & & Number of iterations (steps): 500 \\  & & Regularization coefficient (\(\lambda\)): 0 \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{Symbolic KAN Conversion} & R\({}^{2}\) threshold for goodness-of-fit. 07 \\  & & Preference weight for model simplicity \\  & & (weight_simple): 0.5 \\ \hline \multirow{3}{*}{6} & \multirow{3}{*}{Third Training} & Optimization algorithm: LBFGS \\  & & Number of iterations (steps): 300 \\  & & Regularization coefficient (\(\lambda\)): 0 \\ \hline \multirow{3}{*}{7} & \multirow{3}{*}{Output and Calculation} & Output symbolic expressions with 1-digit \\  & & and 2-digit decimal precision sequentially \\ \cline{3-4}  & & Calculate errors of KAN on training and \\ \cline{3-4}  & & test datasets \\ \hline \hline \end{tabular}
\end{table}
Table 2: KAN training protocols and parameters.  
immediately after loading the training dataset is defined as the initial structure. After completing the first training phase, the inter-layer connection patterns typically undergo changes, resulting in the post-training structure. The structure formed after model pruning is termed the pruned structure, which is generally much simpler than the previous two structures due to the removal of redundant network nodes and connecting edges.
## 1 Introduction
### Review of Related Works
#### 1.1.1 Transport Solutions
The work considered a common transport network in which optical and wireless network sections with computing/storage domains are integrated. Furthermore, the proposed optimization framework helps in the identification of the processing modules and optimal mix of the optical and wireless transport network technologies that are essential for supporting 5G BH and FH services in a cost-effective and energy-efficient manner. It was also demonstrated that with a suitable selection of transport network technologies and appropriate BBU functions allocation to proper compute modules, the system utilization can be enhanced and relatively higher energy efficiency can be realized. To meet the relatively higher throughput requirements in the 5G networks, a set of multicast techniques that can be employed for fixed and mobile optical wireless backhaul in such networks were presented in [34]. In an effort to attend to the traditional fixed backhaul limitations, a dynamic integrated satellite-terrestrial backhaul network based on the mm-wave band was proposed in [35]. The work was on seamless integration of the satellite and the terrestrial backhaul segments and exploitation of software-defined intelligent hybrid network management for the topology reconfiguration in accordance with the traffic demands.
# Object Detection Related to Irregular Behaviors of Substation Personnel Based on Improved YOLOv4
## 3 Improved YOLOv4 Lightweight Detection Model
### YOLOv4 Algorithm  
The YOLOv4 [20] model is an end-to-end object detection algorithm which has absorbed many excellent optimization strategies in the convolutional neural network (CNN) field compared to the YOLOv3 model, including to the feature extraction network, model training, loss function, etc., and which enjoys significant improvements in terms of accuracy and speed. Therefore, we used YOLOv4 as the basic network to realize a lightweight model, thus facilitating real-time detection in the substation. The YOLOv4 model is mainly composed of the three parts shown in Figure 3, namely, the feature extraction network (CSPDarknet53), feature enhancement network (SPP and PANet), and prediction network (YOLOHead).  
First of all, the images from the IBD dataset of substation personnel were input into the CSPDarkNet53, which consists of CBM module and five CSP modules. Then, features with three scales were extracted, including 52 \(\times\) 52, 26 \(\times\) 26, 13 \(\times\) 13, and fused using spatial pyramid pooling (SPP) [21] and the path aggregation network (PANet) [22] to enhance the feature information. Finally, the category and location of targets were predicted through the 3 \(\times\) 3 and 1 \(\times\) 1 convolutions in YOLOHead.  
Next, the predicted results of category and location must be decoded in order to display the detection results intuitively. Generally, prediction results are processed into S \(\times\) S \(\times\) \(M\) dimension feature information by 1 \(\times\) 1 convolution in YOLOHead; S represents a feature map size with a certain scale and \(M\) is the feature information, including location and category, the meaning of which is as follows:  
\[M=N_{\text{anchors}}\times(N_{\text{classes}}+L_{x,y,w,h}+C) \tag{1}\]  
where \(N_{\text{anchors}}\) is the three anchors allocated on each scale feature map, \(N_{\text{classes}}\) is the total number of categories, \(L_{x,y,w,h}\) are the four location parameters of the anchors, and \(C\) is the confidence score. The definition of confidence is  
\[C=\Pr(\text{object})\times\text{IoU}_{\text{pred}}^{\text{truth}} \tag{2}\]  
where IoU\({}_{\text{pred}}^{\text{truth}}\) is used to measure the consistency between the truth bounding box and the predicted bounding box and its value is the ratio of the intersection area and the union set  
Figure 3: The structure of YOLOv4.  
area. Pr(object) reflects whether the anchor contains a target. If there is a target, its value is 1; otherwise, its value is 0.
# Quality Analysis of Unmanned Aerial Vehicle Images Using a Resolution Target
## 3 Materials and Methods  
### Specifications of Resolution Targets  
#### 3.1.1 Bar Target  
The bar target in this study was developed based on the USAF 1951 test pattern, which is a resolution target used by the US Air Force to test the quality of sensors installed on UAVs, night goggles, and other image devices [21]. The USAF 1951 test pattern consists of 3 bars, and the distance between consecutive bars is fixed as a scale factor. Considering the characteristics of the unmanned aerial image, which has a higher resolution than the manned aerial image, in this study, the size of the bar pattern was successively reduced in 11 steps, as shown in Figure 7. The size was reduced by \(1/\sqrt[3]{2}\) (approximately 12%) times in each step; thus, Bar 11 was 15.75 cm wide and 3.15 cm long. Therefore, the shape of a small bar can be visually identified in the image because a high-resolution sensor is mounted [21].
# Polarization-Insensitive Phase Modulators Based on an Embedded Silicon-Graphene-Silicon Waveguide
The polarization dependence of the proposed waveguide has been studied for achieving the polarization-insensitive modulation with the finite element method. Based on the proposed polarization-insensitive waveguide, the polarization-insensitive PM is demonstrated with a wavelength range of over 500 nm, and a modulation bandwidth of 135.6 GHz through numerical simulations. The proposed PM also has a compact size and low insertion loss.