esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Extraction of Offset, Gain, Time Skew, and Bandwidth Mismatch
### The Extraction of the Gain, Time Skew and Bandwidth Mismatch  
To extract the gain, time skew, and bandwidth mismatch, the fractional delay filter is introduced to construct the reference. Additionally, the reference of the \(i\)-th channel can be represented as follows:  
\[y_{\text{ri}}[n]=y_{0}[n]*h_{\text{ri}}[k], \tag{15}\]  
Here \(y_{0}[n]\) is the output of ADC\({}_{0}\). And the DTFT of \(h_{\text{ri}}[k]\) is represented as follows:  
\[H_{\text{ri}}(j\omega)=e^{-j\omega T_{\text{s}}\frac{j}{M}}, \tag{16}\]  
where \(M\) is the number of channels. Hence, the error function of estimation gain, time skew, and bandwidth mismatch can be represented as follows:  
\[e_{i}[n]=y_{0}[n]*h_{\text{ri}}[k]-y_{\text{ical o}}[n]*h_{\Delta g_{i},\Delta \tau_{i},\Delta\omega_{i}}[k], \tag{17}\]  
here \(h_{\Delta g_{i},\Delta\tau_{i},\Delta\omega_{i}}[k]\) is the coefficients of an \(N\)-tap filter. And \(N\) is even. Hence, Equation (17) can be represented as follows:  
\[e_{i}[n]=y_{\text{ri}}[n]-\sum_{k=1}^{N}y_{\text{ical o}}\left[n-k+1+\frac{N}{2 }\right]\times h_{\Delta g_{i},\Delta\tau_{i},\Delta\omega_{i}}[k]. \tag{18}\]  
The minimum value of \(\sum(e_{i}[n])^{2}\) is obtained by the least square method. Hence, \(h_{\Delta g_{i},\Delta\tau_{i},\Delta\omega_{i}}[k]\) can be represented as follows:  
\[h_{\Delta g_{i},\Delta\tau_{i},\Delta\omega_{i}}[k]=\left(\mathbf{y}_{\text{ ical o}}^{\mathbf{T}}\mathbf{y}_{\text{ical o}}\right)^{-1}\mathbf{y}_{\text{ical o}}^{\mathbf{T}}\mathbf{y}_{\text{ri}^{ \prime}}, \tag{19}\]  
where \(\mathbf{T}\) is represented as matrix transposition and \({}^{-1}\) is represented as matrix inversion. Additionally, \(\mathbf{y}_{\text{ical}}\) can be represented as follows:  
\[\mathbf{y}_{\text{ical o}}=\begin{bmatrix}y_{\text{ical o}}\begin{bmatrix}1+ \frac{N}{2}\end{bmatrix}&\cdots&y_{\text{ical o}}\begin{bmatrix}N+\frac{N}{2} \end{bmatrix}\\ \vdots&\ddots&\vdots\\ y_{\text{ical o}}\begin{bmatrix}L_{\text{s}}+\frac{N}{2}\end{bmatrix}&\cdots&y_ {\text{ical o}}\begin{bmatrix}L_{\text{s}}+N+\frac{N}{2}\end{bmatrix}\end{bmatrix}_{L _{\text{s}}\times N}. \tag{20}\]  
Here \(L_{\text{s}}\) is the number of samples. Additionally, \(\mathbf{y}_{\text{ri}}\) can be represented as follows:  
\[\mathbf{y}_{\text{ri}}=\begin{bmatrix}y_{\text{ical o}}\begin{bmatrix}1+\frac{ N}{2}\end{bmatrix}\\ \vdots\\ y_{\text{ical o}}\begin{bmatrix}L_{\text{s}}+\frac{N}{2}\end{bmatrix}\end{bmatrix}_{L _{\text{s}}\times 1}. \tag{21}\]  
Hence, the gain, time skew, and bandwidth mismatch can be estimated with Equations (19)-(21). Based on Equations (10) and (19), Figures 2 and 3, the calibration of offset, gain, time skew, and bandwidth mismatch can be shown in Figure 4.
## 3 Results
### Effect of BuOH Fraction on LPS-Induced C2C12 Myotube Atrophy
C2C12 myotubes were treated with LPS (1 \(\mu\)g/mL) and the BuOH fraction of ISE (50, 75, and 100 \(\mu\)g/mL) for 48 h. (**a**) Cell viability of BuOH fraction in C2C12 myotubes. C2C12 myotubes were treated with 0–100 \(\mu\)g/mL of BuOH fraction to measure the cell viability. (**b**) Giemsa staining was performed to visualize C2C12 myotube morphology (magnification 20\(\times\), scale bar = 100 \(\mu\)m). (**c**) Myotube diameters (\(\mu\)m) were observed from randomly selected fields and were quantified using the ImageJ program. The mRNA expression of (**d**) FoxO3a, MAFbx, and MuRF1 was analyzed by RT-PCR. GAPDH was used as internal controls. Bars with a different letter are significantly different from control (\(p<\) 0.05).
# SDN Anomalous Traffic Detection Based on Temporal Convolutional Network
## 5 Conclusions
Advanced techniques, such as flow-based statistical analysis, entropy-based metrics, and graph-based representations can enhance detection capabilities by capturing higher-order dependencies within traffic flows. Furthermore, deep learning-based feature extraction methods, such as attention mechanisms and autoencoders, can learn hierarchical representations of network traffic, improving the adaptability of models to evolving attack patterns. In future research, integrating multi-modal feature extraction could provide a more comprehensive understanding of anomalous behaviors, leading to more robust and generalizable detection models. Third, real-time feasibility remains a challenge. While TCN's parallel computation offers an advantage in efficiency, achieving low-latency detection in high-speed SDN environments requires further optimization. Future work will focus on introducing knowledge distillation and quantization compression techniques to optimize inference speed while maintaining detection accuracy. Additionally, incremental learning mechanisms will be explored to dynamically adapt the model to new attack patterns, reducing reliance on frequent retraining. Finally, cross-domain collaborative detection will be investigated by integrating logs from northbound interfaces and state data from the control plane to provide a more holistic view of network security. By combining multi-modal feature fusion, real-time optimization, incremental learning, and cross-domain analysis, future research aims to construct a more robust and adaptive SDN security protection system, providing both theoretical support and practical implementation for intelligent network defense. Conceptualization, Z.W. and C.L. ; methodology, Z.W. ; formal analysis, Z.W. ; investigation, X.L., Z.G. and X.S. ; resources, Z.G. and X.L. ; data curation, Z.W. and C.L. ; writing--original draft preparation, Z.W. and Z.G. ; writing--review and editing, Z.W., X.L., Z.G., X.S. and J.L. ; supervision, X.S. and J.L. ; project administration, X.S. and J.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Basic Research Project of the National Defense Science and Industry Bureau (Project No. JCKY2022405C010). We would like to express our deepest gratitude to these organizations for their generous funding and support. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors. The authors declare no conflicts of interest.
## 3 Development of Virtual Sensor
### Virtual Sensor for Fault Detecting in Physical Sensor
#### 3.2.2 Virtual Sensor Modeling  
A virtual sensor based on an LSTM-autoencoder was developed to detect faults in an SCWT sensor. The hyperparameters and input variables for the LSTM-autoencoder are listed in Table 2. The input variables for the virtual sensor training were obtained using EnergyPlus, as described in Section 4.
# Comprehensive Document Summarization with Refined Self-Matching Mechanism
## 3 Method
### Problem Description of Extractive Summarizer  
Given an input document consists of \(n\) sentences \(\mathbf{D}=\{s_{1},s_{2},\ldots,s_{n}\}\), each sentence \(s_{i}=\left\{w_{1},w_{2},\ldots,w_{|s_{i}|}\right\}\) contains \(|s_{i}|\) words. The objective of summarization system is to produce a summary \(\mathbf{Y}\) by selecting m (m < n) sentences from the source document. We predict the label of the \(i\)th sentence \(y_{i}\) as (0,1). The labels of sentences in the summary are set as  
Figure 1: The model architecture of refined self-matching mechanism for extractive summarization (RSME), the embedding layer is responsible for transforming input text into continuous value vector; the hierarchical encoder is composed of convolutional neural networks (CNNs) and long short-term memory (LSTM), which are respectively used to construct state representation of the sentence and document; the self-matching module is responsible for establishing long-term dependency and locality for current sentence representation; the decoder is responsible for selecting sentences based on the current extraction status and the previously selected relative importance gain.  
\(y_{i}=1\). The goal of training is to learn a set of model parameters \(\theta\) to maximize probability \(p(Y|D,\theta)=\prod_{i}p(y_{i}|y_{i-1},\dots,y_{2},y_{1},D,\theta)=\prod_{i}p(y_{ i}|y_{<i},D,\theta)\).  
Existing summarizers usually contain the following modules: sentence encoder, document encoder, and decoder. Firstly the sentence encoder will be utilized to encode each word in the sentence and then assemble them into a sequential representation. Secondly, the document encoder contextualizes the sentential representation into document representation. Finally, the decoder selects the sentence according to document meaning representation until reaching the length limit.
## 2 The TASIPPS Conceptual Framework
### Network Security  
This component is geared towards safeguarding the foundation of the proposed framework. This is essential to the comprehensive security approach of the proposed framework, and a fundamental layer that ensures the protection and resilience of the entire network infrastructure upon which the framework is built. The implementation of robust network security measures is paramount to suppressing or eliminating potential threats, mitigating vulnerabilities, and maintaining the overall integrity of the framework's operations.  
The network security module forms a resilient shield for the framework's operational environment by incorporating IPSec, Network Access Control, and fortified firewalls alongside sophisticated IDS/IPS. These measures collectively enhance data protection, secure communications, and bolster the framework's overall resilience against modern cyber threats. As the bedrock of the framework's security infrastructure, the network security module plays an instrumental role in safeguarding the integrity and continuity of operations across the interconnected parties.
## 2 Theoretical Background
### Technology-Organization-Environment Framework (TOE)  
The Technology-Organization-Environment Framework (TOE), is a theoretical framework put forward by [PERSON] and [PERSON] in 1990 to explain the adoption of innovation by enterprises and describe how the process of innovation adoption is affected by technology, organization, and environment [43; 44]. Technical factors mainly refer to the characteristics of the technology itself that has been adopted or will be adopted related to the enterprise, such as the technical factors of collaborative robot innovation as new technology (comparative advantage, compatibility, complexity, observability, and trialability). Organizational factors mainly refer to the organizational characteristics of an enterprise, generally including enterprise scale, management structure, and human resources. Environmental factors need to consider the political, economic, and cultural environment in which the enterprise is located, such as competitors in the industry, technology suppliers, and government support.  
The TOE framework provides an excellent theoretical perspective for the study of enterprise innovation adoption. It has been widely used in the study of information system innovation adoption, such as enterprise resource planning (ERP) system adoption [45], RFID adoption [46], adoption of e-commerce applications [47], electronic supply chain management system (e-SCM) adoption [48], etc. In recent years, researchers have also applied the TOE framework to other innovative adoption studies. For example, they have used the TOE framework to explore the adoption of artificial intelligence in public organizations [49], and identified the determinants of Unmanned Aerial Vehicle technology adoption based on the TOE framework. Technical, organizational, and environmental factors of the TOE framework, as predictive factors of innovation adoption, can help enterprises figure out the determinants of adoption [50; 51]. The TOE framework is flexible to accommodate different factors, such as technology, organization, and environmental factors, to explain the adoption of enterprise innovation.
## 3 Results
### Optimization of the MSPE Parameters
#### 3.2.1 Optimization of the Extraction Process  
The present study aims to achieve satisfactory extraction performance for analytes in water samples. The effect of the amount of M-M-ZIF-67 on extraction was investigated using different amounts of the sorbent ranging from 2 to 10 mg. The experimental results (Figure 4A) show that the extraction recoveries of the OCPs rapidly increase when the amount of M-M-ZIF-67 is increased from 2 to 6 mg, and the recoveries then slightly decrease when the amount of sorbent is increased from 6 to 10 mg. Based on these results, 6 mg of M-M-ZIF-67 was chosen for the following experiments.  
The extraction time is an important factor for MSPE because it influences the adsorption equilibrium of the analytes between the sample solution and adsorbent. Therefore, the extraction time was varied in the range 5-90 min to investigate its influence on the extraction efficiency. As shown in Figure 4B, the recoveries of the target OCPs increase with increasing extraction time from 5 to 20 min, and they then remain almost constant until 30 min. The extraction recoveries for most of the analytes then decrease with a further increase of the extraction time. This is probably because of desorption of the analytes from the adsorbent, especially when the adsorption equilibrium is reached. Thus, 20 min was chosen as the extraction time for the following experiments.
# Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications
###### Abstract  
Recently, with the development of autonomous driving technology, vehicle-to-everything (V2X) communication technology that provides a wireless connection between vehicles, pedestrians, and roadside base stations has gained significant attention. Vehicle-to-vehicle (V2V) communication should provide low-latency and highly reliable services through direct communication between vehicles, improving safety. In particular, as the number of vehicles increases, efficient radio resource management becomes more important. In this paper, we propose a deep reinforcement learning (DRL)-based decentralized resource allocation scheme in the V2X communication network in which the radio resources are shared between the V2V and vehicle-to-infrastructure (V2I) networks. Here, a deep Q-network (DQN) is utilized to find the resource blocks and transmit power of vehicles in the V2V network to maximize the sum rate of the V2I and V2V links while reducing the power consumption and latency of V2V links. The DQN also uses the channel state information, the signal-to-interference-plus-noise ratio (SINR) of V2I and V2V links, and the latency constraints of vehicles to find the optimal resource allocation scheme. The proposed DQN-based resource allocation scheme ensures energy-efficient transmissions that satisfy the latency constraints for V2V links while reducing the interference of the V2V network to the V2I network. We evaluate the performance of the proposed scheme in terms of the sum rate of the V2X network, the average power consumption of V2V links, and the average outage probability of V2V links using a case study in Manhattan with nine blocks of 3 GPP TR 36.885. The simulation results show that the proposed scheme greatly reduces the transmit power of V2V links when compared to the conventional reinforcement learning-based resource allocation scheme without sacrificing the sum rate of the V2X network or the outage probability of V2V links.  
e 29 December 2022  
18 January 2023  
19 January 2023  
23 January 2023
## 2 Problem Description
The vorticity and stream function are stated as:  
\[u=\frac{\partial\psi}{\partial y},v=-\frac{\partial\psi}{\partial x},\omega=\frac {\partial v}{\partial x}-\frac{\partial u}{\partial y} \tag{9}\]  
We define the following dimensionless variables:  
\[Y=\frac{y}{L},X=\frac{x}{L},\Omega=\frac{\omega L^{2}}{\alpha_{f}},\Psi=\frac {\psi}{\alpha_{f}},V=\frac{uL}{\alpha_{f}},U=\frac{vL}{\alpha_{f}},\theta= \frac{T-T_{c}}{T_{h}-T_{c}} \tag{10}\]  
Subject to Equation (14), the governing equations are changed to the following non-dimensional forms:  
\[\frac{\partial\Omega}{\partial X}\frac{\partial\Psi}{\partial Y}-\frac{ \partial\Psi}{\partial X}\frac{\partial\Omega}{\partial Y}=\frac{\mu_{nf}}{ \mu_{f}}\frac{\rho_{f}}{\rho_{nf}}\text{Pr}\bigg{(}\frac{\partial^{2}\Omega}{ \partial X^{2}}+\frac{\partial^{2}\Omega}{\partial Y^{2}}\bigg{)}+\frac{\beta _{nf}}{\beta_{f}}Ra\text{Pr}\frac{\partial\theta}{\partial X}-\frac{\rho_{f}} {\rho_{nf}}Ha^{2}\text{Pr}\frac{\partial V}{\partial X} \tag{11}\]  
\[\frac{\partial\Psi}{\partial Y}\frac{\partial\theta}{\partial X}-\frac{ \partial\Psi}{\partial X}\frac{\partial\theta}{\partial Y}\frac{\partial \theta}{\partial Y}=\frac{k_{nf}}{k_{f}}\frac{\big{(}\rho C_{p}\big{)}_{f}}{ \big{(}\rho C_{p}\big{)}_{nf}}\bigg{(}1+\frac{4}{3}N\bigg{)}\bigg{(}\frac{ \partial^{2}\theta}{\partial X^{2}}+\frac{\partial^{2}\theta}{\partial Y^{2}} \bigg{)} \tag{12}\]  
\[\frac{\partial^{2}\Psi}{\partial X^{2}}+\frac{\partial^{2}\Psi}{\partial Y^{2}} =-\Omega \tag{13}\]  
Subject to the boundary conditions:  
\[ \begin{array}{ll}\theta=1&\text{left wall}\\ \theta=0&\text{right wall}\\ \frac{\partial\theta}{\partial Y}=0&\text{top andbottom walls}\\ \Psi=0&\text{all walls}\end{array}  \tag{14}\]  
In these equations, Pr, Ra, \(N\), and Ha denote the Prandtl number, Rayleigh number, radiation parameter, and Hartmann number, respectively, which are defined as:  
\[ \begin{array}{ll}\text{Pr}=\
u_{f}/\alpha_{f},\ Ra=g\beta_{f}(T_{h}-T_{c})L ^{3}/\alpha_{f}\
u_{f},\ N=4\sigma^{*}T_{\infty}^{*}/k_{nf}k_{nf}^{*}\\ Ha=B_{0}L\sqrt{\sigma_{f}/\mu_{f}}\end{array} \]  
Along the hot wall, the local and average Nusselt number can be determined by:  
\[Nu_{loc.}=\frac{k_{nf}}{k_{f}}\bigg{(}1+\frac{4}{3}N\bigg{)}\frac{\partial \theta}{\partial X},\ Nu_{ave.}=\int_{0}^{1}Nu_{loc.}dY \tag{15}\]
## 6 Conclusions  
This work presented a high-performance CP antenna array tailored for bistatic radar systems, aimed at enhancing space debris detection capabilities in LEO. The proposed design integrates 19 dual-CP slotted-patch elements as a compact and cost-effective FPC solution, achieving significant gain and polarization isolation. The feeding networks enable the reception of RHCP and LHCP signals. Simulation and measurement campaigns validate the electromagnetic performance of the SA array, confirming its suitability for SSA applications. Future developments will focus on the integration of active phase shifters to enable real-time dynamic beam steering up to \(\pm\)22.5\({}^{\circ}\), a capability already analyzed in terms of amplitude tapering and array optimization.  
Methodology, C.S. and G.N.; validation, G.N. and A.M.; investigation, C.S.; writing--original draft preparation, C.S.; writing--review & editing, C.S. and A.M.; project administration, G.N. and A.M.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript. Part of this research was funded by the framework of the SAURON (Sensors for Advanced Usage & Reconnaissance of Outerspace situation) EDIDP project.  
Figure 21: Measured normalized realized gain of SA prototype for RHCP: (a) E plane; (b) H plane.  
Figure 22: Measured normalized realized gain of SA prototype for LHCP: (a) E plane; (b) H plane.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed at the corresponding authors.  
**Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by the company Free Space SrI. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Results and Discussion  
### Runoff Generating Rainfall Recorded at Experimental Sites  
Rainfall amounts that generated runoff from the four tillage treatments ranged from 9 mm to 76 mm across the experimental sites (Figure 2). The 53 rainfall events recorded during the growing season generated surface runoff in all four tillage treatments (Supplementary Figure S2). There was no soil cover through mulching in all treatments, hence once rainfall intensity was higher than infiltration rate, overland flow was generated on the bare soil in the four tillage systems. Furthermore, the bareness of the soil surface was promoted by a lack of weed cover because the experimental plots were kept weed free. The light-textured soils at experimental sites have low water-holding capacity, and once saturation occurred in the top soil, overland flow was generated. The rainfall amount that frequently generated runoff in all tillage treatments was 30 mm and it occurred seven times during the experimental period. The 9 mm rainfall event generated runoff in all four tillage treatments at an experimental site with sandy soil. The rainfall event occurred five days after 52 mm of rain had been accumulated over two days, hence the sandy soil could have been moist by the time the 9 mm was received. Results from [12] showed that soil water was similar and near field capacity in the tillage treatments at the experimental sites. A study by [36] in the same Zhulube Catchment revealed that daily rainfall events of up to 10 mm could generate surface runoff on smallholder farms. The average rainfall amount that generated runoff was 30 mm and the same rainfall amount occurred more frequently over the period of experimentation. A 76 mm rainfall event occurred 15 days after a 17 mm amount had been received and soil was dry at the experimental sites. Despite the soil being dry, 76 mm of rainfall generated a large runoff from all treatments and this can be attributed to the soil being saturated during the rainfall event, as illustrated by soil water results from [12]. Rainfall intensity was higher than the infiltration rate of rainwater into the soil during the rainfall event.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Next-Hop Selection
#### 3.8.4 Probabilistic  
Probabilistic-based protocols assume that all nodes are homogeneous and randomly disseminated and select in a random way the next-hop among available sensors to enhance load-balancing and increased robustness. Authors in [110] present EAR, which associates a probability of use to each routing path according to the remaining energy of sensors that form it. Simulation results show the presence of some delays.