esa-sceva/satcom-chunk-collection huggingface dataset:
# SARS-CoV-2 Treatment: Current Therapeutic Options and the Pursuit of Tailored Therapy
## 5 Chloroquine and Hydroxychloroquine  
Historically an antimalarial drug, chloroquine (CQ) has been used to synthetize hydroxychloroquine (HCQ), which is commonly used in the treatment of rheumatic disease, such as rheumatoid arthritis and lupus [54, 55, 56].  
The antiviral effects of CQ have been well known since 2003 [55], when its capacity to raise endosomal pH and to contrast viral fusion was described, alongside its immunomodulatory activity (Tables 2 and 3).  
Even if this drug is well-tolerated, concomitant use with drugs prolonging QTc (some antidepressants, macrolides, quinolones, antipsychotics), or oral hypoglycemic agents can increase the risk of cardiological symptoms or hypoglycemia. Older adults with COVID-19 may suffer from electrolyte disturbance and dehydration, and these conditions may favor the occurrence of arrhythmias if HCQ is co-administered [57]. Co-administration with azithromycin is not indicated [4]. HCQ can also increase the risk of seizures, reducing the activity of antiepileptic drugs [56].  
In vitro studies have demonstrated that CQ is effective against SARS-CoV-2 [58].  
Even if a minor number of studies reported a certain efficacy of HCQ [19], there is no indication that it improves clinical symptoms [20].  
A recent meta-analysis [59] evaluating Eight RCTs (with 6592 unique participants; mean age = 59.4 years; 42% women) documented that CQ/HCQ did not show any mortality benefit when compared with standard supportive therapy (Pooled Relative Risk [RR] 1.07; 95% CI = 0.97-1.18; I2 statistic = 0.00%). ADRs were significantly higher in patients randomized to CQ/HCQ (RR = 2.51; 95% CI = 1.53-4.12; \(n\) = 1818 patients), suggesting that the use of CQ or HCQ does not demonstrate any benefit in the treatment of COVID-19 patients.
# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
## 5 Multi-Well System Connectivity Analysis  
The fluid flow pattern between multiple wells significantly differs from that of a single well pair. The complex injection-production conversion relationship between multiple wells often influences recovery efficiency and is of more practical guiding significance [45]. In a multi-well system, synergies and mutual interference may cause the characteristics of inter-well connectivity to differ greatly from those of the single-well model. The relationship between pressure differential and liquid yield becomes more implicit for different well groups. Two typical well configurations were considered: multi-producer-one-injector and one-injector-multi-producer, whose basic settings are presented in Table 4.
# A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation
## 3 Efficiency Multi-Attribute Feature Generation Framework
### Notations and Problem Statement  
As mentioned above, person re-ID can be regarded as a retrieval problem. The core task of it is to seek the occurrences of a probe (query image) from the gallery (the candidate set), where they (probe and gallery) are captured from different camera views. Given the query image \(q^{i}\) (\(q^{i}\in\{q^{1},q^{2},\ldots,q^{N}\}\)) with the label \(y^{i}\) (\(y^{i}\in\{y^{1},y^{2},\ldots,y^{N}\}\)) and a set of gallery images \(G=(g^{1},g^{2},\ldots,g^{M})\) with labels \(y^{i}\) (\(y^{i}\in\{y^{1},y^{2},\ldots,y^{M}\}\)), the person re-ID aims to retrieve all the \(g^{i}\) (\(g^{j}\in G\)) that satisfy \(y^{i}=y^{i}\) (\(1\leq i\leq N,1\leq j\leq M\)). \(M\) and \(N\) denote the amount of the gallery and the query set, respectively. In this study, the person multi-attribute feature generation framework consists of a-ANet, a b-ANet, and a c-ANet, which generate the accessory, the body, and the color descriptors (denoted as \(d_{a}\), \(d_{b}\), and \(d_{c}\), respectively) for each person. The generated three kinds of descriptors are then via a tree-based feature selection method to construct a global person feature \(f\).
## 2 Damage Detection Using a Stationary Vehicle
### Instantaneous Amplitude of High Frequency IMF  
Hilbert transform (HT) is known to be used for exposing the instantaneous attributes of time-domain signals. The envelope of a time signal, which is known as the instantaneous amplitude (IA), shows the discontinuity of the signal, which indicates the location of the damage. Therefore, when the contact-point signal is decomposed into several IMFs, the IA of the high frequency signal is computed.  
The HT of a signal \(s(t)\) is defined as its convolution with a unit impulse function of \(1/\pi t\), as shown in Equation (5). The two signals \(s(t)\) and \(h(t)\) that form the analytical function \(z(t)\) are shown in Equation (6). Where \(A(t)\) represents the instantaneous amplitude (IA) and \(\theta(t)\) is the instantaneous phase.  
\[h(t)=H[s(t)]=\frac{1}{\pi}\int_{-\infty}^{\infty}\frac{s(\tau)}{t-\tau}d\tau \tag{5}\]  
\[z(t)=s(t)+ih(t)=A(t)e^{i\theta(t)} \tag{6}\]  
IA is the envelope of the original signal, and it varies with time. These parameters of the instantaneous phase and amplitude are physically meaningful when the signal is mono-component or narrow-banded as the IMFs that are obtained by the VMD method. IA is computed as follows:  
\[A(t)=\pm\sqrt{\left[s(t)\right]^{2}+\left[h(t)\right]^{2}} \tag{7}\]
# Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM
## 4 Experiments
To sum up, this study creates the Figure 3 which shows the average percentages of experimental methods over four datasets. We first determine the average for MSE, RMSE, MAE and MAPE over the minutely, hourly, daily and weekly datasets. Then, we scale these above values to percentage to show the graph better which are shown in Figure 3. Obviously, the proposed approach namely EECP-CBL is the best method for most above datasets including minutely, hourly, daily and weekly datasets derived from the IHEPC dataset in terms of four common performance metrics including MSE, RMSE, MAE and MAPE. Moreover, although our method has a larger training time than CNN-LSTM, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**\#No**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**MAPE**} & \multirow{2}{*}{**Training Time (s)**} & \multirow{2}{*}{**Predicting Time (s)**} \\  & & & & & & & \\ \hline \multirow{2}{*}{1} & Linear & & & & & \\  & Regression & 0.425 & 0.652 & 0.502 & 83.74 & 692.12 & 2.88 \\ \multirow{2}{*}{2} & LSTM & 0.515 & 0.717 & 0.526 & 44.37 & 2281.50 & 5.95 \\ \multirow{2}{*}{3} & CNN-LSTM & 0.355 & 0.596 & 0.332 & 32.83 & 820.70s & 2.31 \\ \multirow{2}{*}{4} & EECP-CBL & 0.298 & 0.546 & 0.392 & 50.09 & 1296.34 & 1.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performances of the experimental methods for hourly dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**\#No**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**MAPE**} & \multirow{2}{*}{**Training Time (s)**} & \multirow{2}{*}{**Predicting Time (s)**} \\  & & & & & & & \\ \hline \multirow{2}{*}{1} & Linear & & & & & & \\  & Regression & 0.253 & 0.503 & 0.392 & 52.69 & 27.83 & 1.32 \\ \multirow{2}{*}{2} & LSTM & 0.241 & 0.491 & 0.413 & 38.72 & 106.06 & 2.97 \\ \multirow{2}{*}{3} & CNN-LSTM & 0.104 & 0.322 & 0.257 & 31.83 & 42.35 & 1.91 \\ \multirow{2}{*}{4} & EECP-CBL & 0.065 & 0.255 & 0.191 & 19.15 & 61.36 & 0.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performances of the experimental methods for daily dataset. the prediction times of our method are a lot better than those of CNN-LSTM. Based on the above analysis, the proposed approach can be recommended to use in intelligent power management system to predict the electric power consumption in the future.
# On Beamforming with the Single-Sideband Transform
## Appendix A Properties of the Real Fourier Transform
\begin{split} A_{m}^{\prime}(-f)&=\frac{H_{1}^{ \prime}(-f)H_{m}^{\prime\prime}(-f)+H_{1}^{\prime\prime}(-f)H_{m}^{\prime\prime }(-f)}{H_{1}^{\prime 2}(-f)+H_{1}^{\prime\prime 2}(-f)}\\ A_{m}^{\prime\prime}(-f)&=\frac{H_{1}^{\prime}(-f)H_{ m}^{\prime\prime}(-f)-H_{1}^{\prime\prime}(-f)H_{m}^{\prime\prime}(-f)}{H_{1}^{ \prime 2}(-f)+H_{1}^{\prime 2}(-f)}\,.\end{split}  \tag{113}\]  
Assuming that \(h(t)\) is real-valued, then \(H_{m,\mathcal{F}}(f)=H_{m,\mathcal{F}}^{\ast}(-f)\), and, therefore, \(H_{m}^{\prime}(f)=H_{m}^{\prime}(-f)\), and \(H_{m}^{\prime\prime}(f)=-H_{m}^{\prime\prime}(-f)\). With this,  
\[ \begin{split} A_{m}^{\prime}(-f)&=\frac{H_{1}^{ \prime}(f)H_{m}^{\prime}(f)+\left[-H_{1}^{\prime\prime}(f)\right]\left[-H_{m}^{ \prime\prime}(f)\right]}{H_{1}^{\prime 2}(f)+\left[-H_{1}^{\prime\prime}\right]^{2}(f)}\\ &=\frac{H_{1}^{\prime}(f)H_{m}^{\prime\prime}(f)+H_{1}^{\prime \prime}(f)H_{m}^{\prime\prime}(f)}{H_{1}^{\prime 2}(f)+H_{1}^{\prime\prime 2}(f)}\\ &=A_{m}^{\prime}(f)\end{split}  \tag{114}\]  
\[ \begin{split} A_{m}^{\prime\prime}(-f)&=\frac{H_{1}^{ \prime}(f)\left[-H_{m}^{\prime\prime}(f)\right]-\left[-H_{1}^{\prime\prime}(f) \right]H_{m}^{\prime}(f)}{H_{1}^{\prime 2}(f)+\left[-H_{1}^{\prime\prime}\right]^{2}(f)}\\ &=\frac{-H_{1}^{\prime}(f)H_{m}^{\prime\prime}(f)+H_{1}^{\prime \prime}(f)H_{m}^{\prime\prime}(f)}{H_{1}^{\prime 2}(f)+H_{1}^{\prime\prime 2}(f)}\\ &=-A_{m}^{\prime\prime}(f)\,.\end{split}  \tag{115}\]  
Therefore, our frequency-to-frequency RFR \(A_{m}^{\prime}(f)\) is even, since \(A_{m}^{\prime}(f)=A_{m}^{\prime}(-f)\), and our conjugate-frequency RFR is odd, since \(A_{m}^{\prime\prime}(f)=-A_{m}^{\prime\prime}(-f)\).
# Characterization of Tropical Cyclone Intensity Using the HY-2B Scatterometer Wind Data
## 4 Results  
The best-track MSW is used to represent TC intensity in this section. As such, the goal of this study is to find the scatterometer-derived parameter that is the highest correlated with MSW in order to characterize the TC intensity. Both the maximum wind speed and the wind radii of HSCAT wind retrievals are examined.
# Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review
## 1 Introduction
The design of any radar system generally consists of two aspects: (1) radar hardware, including RF transceiver, baseband circuitry, processing unit, antenna, and so forth, and (2) digital signal processing (DSP) to parse the backscattered signal to recover the desired information. With growing interest of employing radar for short-range applications, many researchers have been pushing the technology boundaries in every aspect to try to make its applications ubiquitous in our daily lives. In the past two decades, various radar hardware architectures have been proposed, including direct-conversion quadrature radar receiver [48], digital intermediate-frequency (IF) receiver [24; 49], injection locking radar [50; 51], six-port radar [52], CW-FMCW hybrid radar [44; 45], and millimeter wave radar on-chip [46; 53]. Each of the architectures has its pros and cons and is suitable for certain specific applications. On the signal processing side, small angle approximation was first used to demodulate the phase-modulated motion information in CW radar [7; 8]. Researchers later developed more advanced demodulation techniques including the complex demodulation [54] and the arctangent demodulation [55]. Other signal processing methods such as adaptive DC calibration [17; 56], noise cancellation [57], distortion cancellation [58], and \(I/Q\) mismatch mitigation [59; 60] have been proposed to make radar systems robust enough for practical applications. This paper reviews the recent technical advances in CW Doppler radar sensors for healthcare applications, including system hardware improvement, digital signal processing, and chip integration. Emerging applications and future trends will also be discussed. This paper also discusses the hybrid CW-FMCW radars and the emerging applications and future trends. This paper is organized as follows: Section 2 introduces the basic theory of single-tone CW Doppler radar, and the DSP techniques to extract the desired information; recent advancements in various aspects were reviewed and summarized in Section 3; Sections 4 and 5 discuss the emerging applications and future trends respectively. Conclusions are presented in Section 6.
_applied sciences_  
_Article_  
Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems Using Transfer Learning  
**[PERSON] 1,2,4** [PERSON] 1,4** [PERSON] 1,3,4** [PERSON] 1,4**  
1 Institute for Geosciences, Kiel University, 24118 Kiel, Germany; [EMAIL_ADDRESS] (A.H.M.); [EMAIL_ADDRESS] (H.L.); [EMAIL_ADDRESS] (Z.R.)  
2 Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Via Dodecaneso, 35, 16146 Genoa, Italy  
3 Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada  
* Correspondence: frank.wuttkefig.uni-kiel.de  
These authors contributed equally to this work.  
4 Current address: GeoAnalysis Engineering GmbH, 24118 Kiel, Germany.  
**Abstract:** Physics-Informed Neural Network (PINN) is a data-driven solver for partial and ordinary differential equations (ODEs/PDEs). It provides a unified framework to address both forward and inverse problems. However, the complexity of the objective function often leads to training failures. This issue is particularly prominent when solving high-frequency and multi-scale problems. We proposed using transfer learning to boost the robustness and convergence of training PINN, starting training from low-frequency problems and gradually approaching high-frequency problems through fine-tuning. Through two case studies, we discovered that transfer learning can effectively train PINNs to approximate solutions from low-frequency problems to high-frequency problems without increasing network parameters. Furthermore, it requires fewer data points and less training time. We compare the PINN results using direct differences and \(L_{2}\) relative error showing the advantage of using transfer learning techniques. We describe our training strategy in detail, including optimizer selection, and suggest guidelines for using transfer learning to train neural networks to solve more complex problems.  
**Keywords:** PINN; transfer learning; damped harmonic oscillator; wave equation
# Exploring Attributions in Convolutional Neural Networks for Cow Identification
## 1 Introduction
Previous studies have shown that animal recognition models trained in one environment often fail under changed conditions, suggesting that CNNs learn dataset-specific artifacts rather than biologically relevant animal recognition features [33]. This issue necessitates the use of explanation techniques when training the model to understand which image regions contribute most to classification decisions. In order to help the model to generalize better, images of the animals in different environments should be taken. However, this is a challenging task due to the need to keep track of the individual cows; it appears to be also time-consuming. Hence, it would be useful if the research could apply attribution methods to detect the lack of generalization due to the background in the images. Models that do not generalize well have poor performance on unseen objects or environments. Identification systems that have poor performance are not useful in real farms. Data Shapley values are defined in [34] as a method to reliably assess the contribution of data records to the performance of the model. Based on the previous research in the application of machine learning in the field of cattle face recognition [18; 35; 36], the present paper focused on cow face identification with the aim of exploring the effect of the background on the generalization of an identification model trained on dairy cows from the Holstein breed. For this purpose, a procedure that quantifies the influence of the background in relation to the performance of the identification model was proposed. Some attribution methods were investigated in order to determine their capabilities to quantify the influence of the background without the need for intensive segmentation. The existence of such an attribution method would allow the builders of identification systems to assess the model's ability to generalize to different backgrounds by inspecting the attribution output. Attribution methods in image tasks aim to explain model predictions by identifying the importance of input pixels or regions that contribute to the output. Techniques like saliency maps, occlusion, and gradient-based approaches visualize these contributions, offering insights into how models process visual data. However, the research cited focuses either on classification tasks and/or evaluates the results only visually. The present paper proposes a test that can numerically compare the results of a model with the output of an attribution map. Within the current initial stage of the study, two related questions were posed: (i) Does the cow (foreground) or the surrounding background contribute more to the results of the identification task? (ii) Is there an attribution method that can quantify correctly the contribution of the foreground vs. the background for the performance of the model?
## 2 Feature Enhancement Point Cloud Data Resampling  
Aircraft components are generally large, and the data points can reach millions when the point cloud is scanned using measurement instruments such as scanners. The analysis of large point clouds is time-consuming, especially during the alignment process. In addition, the large point cloud also increases the burden of data storage. Therefore, the measured point cloud must be simplified to some extent. However, the simplification of the point cloud will undoubtedly affect the accuracy of the subsequent alignment. Traditional point cloud processing algorithms only consider the uniformity of sampling, such as the uniform resampling method with the help of voxel squares, but these methods cannot maintain the feature information of the original point cloud, which will eventually lead to worse alignment accuracy.  
To address this problem, we propose a feature enhancement method for point cloud resampling, so that the simplified point cloud can have uniformity while maintaining features such as edges. Specifically, we design a resampling strategy that consists of two steps. First, the point cloud is resampled using voxel mesh combined with geometric sampling to simplify the point cloud. Next, the point cloud is further simplified by transforming the point cloud resampling problem into an optimization problem while preserving the point cloud features.
## 3 OFDM Model
### Performance Metric  
As mentioned earlier, high-power amplifiers (HPAs) have non-linear responses causing undesired distortion effects which are poorly captured by the classical peak-to-average power ratio metric. The cubic metric (CM) [19] uses higher-order statistical informationgleaned from the OFDM signal to appropriately account for the signal's envelope fluctuations. The third-generation partnership project (3 GPP) defines the CM, \(\Omega\), as  
\[\Omega=\frac{\Gamma^{[dB]}-\Gamma^{[dB]}_{ref}}{\kappa}dB\,, \tag{2}\]  
where \(\Gamma^{[dB]}\) is the raw cubic metric (RCM) of the time-domain OFDM signal in (1) and is defined as  
\[\Gamma^{[dB]}=20\log_{10}\Bigg{\{}\sqrt{\left(\left[\frac{|x[n]|}{\sqrt{x[n]} }\right]^{3}\right)}\Bigg{\}}dB\,, \tag{3}\]  
where the factor \(\kappa\) is empirically determined and \(\Gamma^{[dB]}_{ref}\) is a fixed RCM reference level. Typical values for these two parameters are used later in the numerical evaluation of the system performance.
# Topology and Parametric Optimization-Based Design Processes for Lightweight Structures
## 4 Results
### MBB-Beam  
The results of the third and last case study of an MBB-Beam are presented. The same procedure was also followed in this case. The same material and mesh properties were applied. The MBB-Beam is supported with a fixed and roller support, as shown in Figure 16. Furthermore, two forces, F\({}_{1}\) = 100 N and F\({}_{2}\) = 100 N, are applied to the top of the beam.  
The following two parameters were used to define the placement of F\({}_{1}\) and F\({}_{2}\), respectively:  
\[\mathrm{d}_{1}=\mathrm{fcp}_{1}\times\mathrm{L/2}, \tag{22}\]  
\[\mathrm{and}\ \mathrm{d}_{2}=\mathrm{L/2}+\mathrm{fcp}_{2}\times\mathrm{L/2} \tag{23}\]  
where fcp\({}_{1}\) and fcp\({}_{2}\) represent the allowable range for the F\({}_{1}\) and F\({}_{2}\) placement in percentage. The allowable range of the used factors respected the following geometric rules:  
\[\mathrm{L}>\mathrm{d}_{1}+\mathrm{d}_{2}, \tag{24}\]  
\[\mathrm{d}_{1}<\mathrm{L/2}, \tag{25}\]  
\[\mathrm{and}\ \mathrm{d}_{2}<\mathrm{L/2} \tag{26}\]  
The chosen factors, as well as their value range and value increment (step), are shown in Table 6.  
Figure 16: (**a**) The initial design of the Messerschmitt–Bolkow–Blohm Beam (MBB-Beam), as well as the finite element model, (**b**) the FEA of the MBB-Beam (\(\sigma_{\mathrm{max}}\) = 32.9 MPa), and (**c**) the design space for the TO.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Parameter** & **Initial** & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** & **(7)** & **(8)** & **(9)** & **(10)** \\ \hline Mass (g) & 849.1 & 344.6 & 343.4 & 318.5 & 71.1 & 65.1 & 63.9 & 55.9 & 28.8 & 27.1 & 23.2 \\ \hline Max Stress (MPa) & 19.7 & 101.0 & 115.4 & 106.4 & 95.9 & 121.0 & 96.3 & 114.0 & 84.3 & 81.8 & 105.6 \\ \hline Optimization time (min) & - & 7.4 & 17.1 & 63.3 & 67.4 & 68 & 73 & 106.7 & 374.6 & 379.6 & 413.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of all design processes of the L-Bracket optimization.  
Moreover, in this case, there is a gradual mass reduction of the structure from processes (1) to (10), while the optimization time is increasing.
###### Abstract  
In order to solve the problem of low safety and efficiency of underground mine vehicles, a path planning method for underground mine vehicles based on an improved A star (A*) and fuzzy control Dynamic Window Approach (DWA) is proposed. Firstly, the environmental information is analyzed, and the proportion of obstacles is introduced into the A* algorithm to optimize the heuristic function to improve its search efficiency in different environments. Secondly, for the global path, the key node selection strategy is proposed, the node selection method is optimized, and the redundant nodes are deleted. The path is optimized by using a 3 times Clamped-B spline to improve the smoothness of the path. Then, based on the principle of fuzzy control, fuzzy rules are established, and a DWA fuzzy controller is designed to improve the environmental adaptability of the DWA algorithm. Finally, on the basis of the global path, the key node is used as the local target point of the DWA, and the fusion of the improved A* and DWA algorithm is realized. Finally, experiments are conducted to verify the effectiveness and feasibility of the proposed path-planning method. The average deviation of the path is controlled at \(\pm\)0.109 m, which basically meets the path planning needs of underground mine vehicles.  
coal mines; underground mine vehicle; path planning A* algorithm; fuzzy control DWA