esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Evaluation and Analysis
### Property Analysis of WFCS
(2) Hard clustering  
Figure 5 shows the fuzzy membership values for Cluster1 of 150 data points in WFCS when \(\beta\) is 1, 0.5, 0.05 and 0.005 respectively. When membership value is equal to 1, data point is hard clustered into Cluster1. When membership value is 0, data point is hard clustered into the other two clusters. In Figure 5a-c, there are 50, 31 and 12 data points hard clustered into Cluster1 respectively. In Figure 5d, all data point membership values are smaller than 1, then all data points are fuzzy clustered into Cluster1. As seen in Figure 5, the membership value becomes fuzzier when \(\beta\) is smaller. Hence, WFCS has the characteristics of both hard clustering and fuzzy clustering.
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 2 Methodology
### Heater Analysis  
The heater in this work is a pure resistive load, so every power (\(P_{H}(t)\)) produced on it is transformed into heat, given by:  
\[P_{H}(t)=\frac{(V_{B}(t)D(t))^{2}}{R_{H}}, \tag{34}\]  
where \(V_{B}(t)\) is the battery's voltage, \(D(t)\) is the duty cycle of a PWM (Pulse-width modulation) signal and \(R_{H}\) is the electrical resistance of the heater. Thus, by assuming the thermal resistance to be equal to \(R_{T}\) for every node of the satellite, as shown in Table 2, and applying the Laplace transform to Equation (31), the temperature at the battery (\(T_{7}\)) can be calculated by:  
\[T_{7}(s)=\frac{P_{H}+\frac{1}{R_{T}}\sum\limits_{w=1}^{6}T_{w}}{m_{7}\
u_{7}s +\frac{6}{R_{T}}} \tag{35}\]  
Notice that the \(T_{7}(s)\) has a time constant given by  
\[\tau=\frac{m_{7}\
u_{7}R_{T}}{6}, \tag{36}\]  
showing that the settling time of the battery temperature is proportional to its mass, specific heat and to the thermal resistance. Additionally, Equation (35) can be used to estimate \(T_{7}(t)\) by means of an Inverse Laplace transform, as  
\[T_{7}(t)=\frac{R_{T}}{6}\Bigg{(}P_{H}+\frac{1}{R_{T}}\sum\limits_{w=1}^{6}T_{w }-\exp\left(-\frac{6t}{R_{T}m_{7}\
u_{7}}\right)\Bigg{)} \tag{37}\]  
Given a certain \(D\) value, the steady-state value of \(T_{7}\) is obtained by Equation (38).  
\[T_{7}(\infty)=\frac{R_{T}}{6}\Bigg{(}\frac{(V_{B}D)^{2}}{R_{H}}+\frac{1}{R_{T} }\sum\limits_{w=1}^{6}T_{w}\Bigg{)} \tag{38}\]  
The duty cycle \(D\) can be isolated from Equation (38):  
\[D=\sqrt{\Bigg{(}T_{7}(\infty)\frac{6}{R_{T}}-\frac{1}{R_{T}}\sum\limits_{w=1}^ {6}T_{w}\Bigg{)}\frac{R_{H}}{V_{B}^{2}}}=\sqrt{\Bigg{(}\sum\limits_{w=1}^{6} \frac{T_{7}(\infty)-T_{w}}{R_{T_{w}}}\Bigg{)}\frac{R_{H}}{V_{B}^{2}}}=\sqrt{ \frac{Q_{T_{w}}}{P_{H_{max}}}} \tag{39}\]  
From Equation (39) it can be concluded that the cyclical ratio necessary to maintain a certain desired battery temperature is a ratio between its heat exchange and the maximum power input given by the heater.
## 4 Conclusions  
In summary, we demonstrated that the THz crescent cylinder metasurface sensor can be used to detect different concentrations of ibuprofen and recognize R/S chiral ibuprofen. In particular, from our experimental results and simulation analysis, we found that the higher-order resonance (MQ and EQ model) is more sensitive to changes in the dielectric environment compared with the lower-order resonance (ED and MD model). The sensitivity of the higher-order resonance in the ibuprofen concentration experiment was 60.42 GHz/mg, about 2.64 times that of the lower-order resonance. Meanwhile, by utilizing the higher-order resonance not only different concentrations of ibuprofen can be distinguished, but also the slight difference between R/S chiral ibuprofen can be recognized, even in a trace amount of sample, and the maximum differentiation can reach 18.75 GHz/mg when the sample volume is only 5 \(\mu\)L. Therefore, our results confirmed that this THz crescent cylinder metasurface sensor has potential applications in drug concentration detection and identification of enantiomers of chiral drugs with high sensitivity. We believe our approach will contribute to the development of THz biosensors.  
Conceptualization, F.F. and X.W.; methodology, F.F., W.S., and Z.Z.; software, W.S. and T.Z.; validation, F.F. and W.S.; formal analysis, F.F. and W.S.; investigation, W.S. and Z.Z.; resources, F.F.; data curation, W.S.; writing--original draft preparation, W.S.; writing--review and editing, W.S.; visualization, S.L.; supervision, S.C.; project administration, F.F.; funding acquisition, F.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (61971242, 61831012), the National Key Research and Development Program of China (2017 YFA0701000), the Natural Science Foundation of Tianjin City (19 JCYBJC16600) and the Key Program of the Natural Science Foundation of Tianjin (19 JCZD)C32700).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Polarization** & **Concentrations** & \(m\) **(mg)** & _\(\Delta f\)_ **(GHz)** & _\(\Delta T\)_ **(dB)** & _\(D_{M}\)_ **(GHz/mg)** & _\(D_{\Delta T}\)_ **(dB/mg)** \\ \hline \multirow{2}{*}{\(x\)-polarization} & 0.2 g/mL & 1 & 18.75 & 1.11 & 18.75 & 1.11 \\  & 0.4 g/mL & 2 & 25 & 2.47 & 12.5 & 1.235 \\  & 0.2 g/mL & 1 & - & 1.55 & - & 1.55 \\  & 0.4 g/mL & 2 & - & 3.09 & - & 1.545 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The calculated degree of differentiation of the R/S chiral ibuprofen by higher-order resonance for different concentrations.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Related Work
In 2023, the focus shifted towards ZTAs with the ZTRAN and OZTrust projects, which demonstrated the application of Zero-Trust in O-RAN environments using testbeds and prototypes [36; 38]. Further advancements in Federated Reinforcement Learning (FL RL) were made with the OpenAI dApp, which applied FL for optimization in O-RAN [41]. The trend continued into 2024, where new milestones such as FL for Jamming and FL for Anomaly Detection explored security aspects of O-RAN networks [39; 42]. The year also saw the introduction of ZTORAN, the first integrated solution combining ZT and FL in O-RAN, and TrustORAN, a decentralized Zero-Trust framework using Blockchain [37; 45]. Additionally, HSADR advanced secure aggregation techniques for FL, validating these through security experiments [44]. The most recent conceptual milestone, Blockchain-based FL Reputation, targets trust and reputation management for O-RAN using a Blockchain testnet [43]. Overall, the table highlights the evolution of these technologies and the increasing integration of security and optimization frameworks in O-RAN systems.
# Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic
## 2 Method of Gaze Guided System
### Eye-Gaze Tracking GGC System  
The GGC control system architecture is shown in Figure 1. In the first block (1), a user sitting in a chair and watches the live video from an overhead camera. The movement of the robot is monitored on this screen. Wherever the robot is required to move, the user looks at that side. The visual attention on that side is extracted from gaze data. To perform the robot navigation, the visual attention is converted from image space to 2D Cartesian space and produces control commands from this coordinate system. The point of gaze as the user observes the video frames is utilized for robot motion control inputs. The direction and speed are regulated by distance from the center point of the eye as seen in Figure 2. X-axis regulates steering and y-axis regulates robot speed.  
A high-resolution webcam [51] is used to track eyes where the user is looking. It is a video-based remote eye tracking system. A shape adapted mean shift algorithm [52] is utilized which is asymmetric and anisotropic kernels for object tracking which is process the image and calculates the point of gaze coordinates (see Figure 3).  
Figure 1: Interaction of the gaze-guided robot control system (1. User side, 2. Control system, 3. Robot side).  
Figure 3: Structure and functionality of real-time eye tracking algorithm.  
Figure 2: Robot control input parameters range (the X-axis regulate steering and the Y-axis regulates speed).  
A series of raw image continuously capture in image acquisition step (see Figure 4). The image tackled in this step is important in order to get the relevant Region of interest (ROI). The algorithms have been implemented using NI Vision Builder programming tools. The images are sent to the NI Vision system for machine vision and image processing applications. Image pre-processing makes easier to extract ROI. The analysis has purposed the removal of uninterested information to reduce the processing area. The original image is transformed into a grayscale and masked image that does not need to detect an eye feature point. A morphological operation has been applied for increasing the detection accuracy. A shape adapted mean shift algorithm has been applied which provides angular as well as a linear offset in object shapes.  
This algorithm allows tracking eye templates with changing shapes and size and a built-in function using the NI vision assistant. The tracking template position is well-considered as the reference and its coordinates are taken into account. In the final image analysis, the eye coordinate system is obtained (see Figure 5).
## 2 Related Work
### Span-Based Method  
The span-based method first identifies the boundary of entities, and then classifies entities according to the boundary identifier, so that overlapping entities can be identified due to different boundaries. [PERSON] et al. [21] propose a simple Bi-LSTM based model that generates a span representation for each possible entity and performs joint entity and relation extraction. Following this, DyGIE [22] shares span representations between multiple tasks via dynamically constructed span graphs. The MrMep model proposed by [PERSON] et al. [23] uses a variant of the pointer network to generate the boundaries (start/end) of all head and tail entities in turn, and then uses a multi-headed attention mechanism [24] to extract the relationships corresponding to each entity.  
With the advent of pre-trained models, some Transformer-based networks such as BERT, Transformer-XL, and RoBERT have achieved outstanding results in a number of natural language processing tasks. [PERSON] et al. [25] propose DyGIE++ on the basis of DyGIE, and replace the Bi-LSTM with the BERT pre-trained model, which further improves the performance of DyGIE. Following this, [PERSON] et al. [26] propose a simple and effective lightweight inference model SpERT, which is a typical span-based joint entity and relation extraction model. Based on SpERT, [PERSON] et al. [27] enhance the semantic representation of candidate entities and relations by attention mechanism, thereby improving the performance of joint entity and relation extract. TriMF is proposed by [PERSON] et al. [28], which employsa multi-level memory flow attention mechanism to enhance the bidirectional interaction between entity recognition and relation extraction.  
However, most of the current models lack the use of explicit semantic information. Specifically, they usually share context representation in name entity recognition and relation extraction, and they do not explore implicit semantic information fully and purposefully. In response to the above problems, we not only introduce explicit semantic information, but also extract more fully implicit semantic information. We propose a simple and effective separate encoding method based on joint learning. This method enhances the implicit semantic information of entity and local context, respectively, while ensuring effective information transfer between NER and RE.
## Materials and Methods
### Data Preprocessing and Multivariate Analysis  
A high-energy laser light source was used for generating the Raman signal. The generation of background fluorescence signals by the organic and inorganic samples is one of the major challenges in Raman spectroscopy. Various mathematical methods have been developed so far, such as polynomial fitting, wavelet transformation, Fourier transformation, and derivatives, to eliminate the baseline shift caused by fluorescence in Raman spectra [12]. In this study, the polynomial curve fitting method was employed for analyzing Raman spectra affected by fluorescence, as it is more convenient, faster, and more effective than the other methods [13,14]. This process works on the principle of obtaining a proper order of the polynomial to generate a baseline by using iterative calculations.  
The Raman spectral data are influenced by unintentional noise, which affects the spectral features and prediction performance of the model. Therefore, spectral pretreatment is necessary in order to acquire the required spectral information from the data. After the correction of the spectra using the polynomial fitting method, the spectra were further subjected to the multiplicative scatter correction (MSC) pre-processing method. The pre-processed spectral data were later used for developing the calibration model and then analysed using partial least squares regression (PLSR) in conjunction with a variable selection method using MATLAB (Version 7, The Mathworks, Natick, MA, USA).  
The principal component analysis was applied to the MSC pre-processed data to make the data easy to explore and visualize. The score plot is useful for the projection of data into a subspace, which is used as a basis for interpreting the relations between the variables.  
The PLSR model is used to determine the linear relationship between the dependent and independent variable matrices by predicting the properties of the dependent variable [15,16]. The PLSR model can be expressed by using the following equations:  
\[X=\text{TPT}+\text{E} \tag{4}\]  
\[Y=\text{UQ}^{\text{T}}+\text{E} \tag{5}\]  
where X and Y represent the independent and dependent variables, respectively; T and U are the score matrices; \(\text{P}^{\text{T}}\) and \(\text{Q}^{\text{T}}\) are the loading matrices of X and Y, respectively; and E is the error matrix.  
In this study, the independent matrix, X, consisted of spectral data for Grignard reagent, while the dependent matrix Y, consisted of nine different toluene concentrations: 0%, 1%, 2%, 3%, 4%, 5%, 10%, 15%, and 20%. During the calibration model development, the selection of the optimal number of factors or latent variables is crucial to ensure the satisfactory performance of the model. Inappropriate selection of LVs creates \"over-fitting\" or \"under-fitting\", which are responsible for the suppression of information from the developed model. Therefore, to prevent these issues in the developed model, the LVs were selected according to the lowest standard error values during the cross-validation process (CV) [17].
# Underlying Security Transmission Design for Orthogonal Time Frequency Space (OTFS) Modulation
## 5 Security Performance Analysis  
To evaluate the security of the proposed secure OTFS scheme, we consider its resistance to some effective attacks (including brutal force, chosen-plaintext, and statistical attacks). Here, we make the following assumptions about the eavesdropper, [PERSON]. First, for all data packets exchanged by [PERSON] and [PERSON], [PERSON] can completely overhear. Second, [PERSON] completely understands the secret key generation algorithm. Third, [PERSON] will not interfere with [PERSON] and [PERSON].
## 2 Methodology of the Study
### Quality of Products  
The quality of the obtained products was evaluated on the basis of water activity and the color of the samples. Water activity (\(a_{w}\)) was measured at room temperature (-23 \({}^{\circ}\)C) before and after drying experiments with a TESTO 650 multimeter (Testo, Titisee-Neustadt, Germany), equipped with a water activity measuring chamber. Prior to the measurement, approximately 5 g of ground sample was placed in a polystyrene container and subsequently inserted into a steel airtight measuring chamber. The measurement was conducted until a constant \(a_{w}\) value was reached, indicating no change at a value of 0.001 for a period of five minutes.  
The color of the samples was measured using a CR400 colorimeter produced by Konica Minolta (Tokyo, Japan) and quantified in terms of the three-dimensional color space, CIELAB, where \(L^{*}\) represents brightness, \(a^{*}\) denotes the chromatic component of colors ranging from red to green, and \(b^{*}\) signifies the chromatic component of colors ranging from yellow to blue. Based on the measured values for the raw material (index \({}_{0}\)) and the dried sample (index \({}_{d}\)), the color difference index, \(dE\), was determined according to the following equation [31]:  
\[dE=\sqrt{\left(L_{0}^{*}-L_{d}^{*}\right)^{2}+\left(a_{0}^{*}-a_{d}^{*}\right) ^{2}+\left(b_{0}^{*}-b_{d}^{*}\right)^{2}} \tag{11}\]  
The experimental data were processed using OriginPro 2024b [32]. The data presented in Section 3, Results and Discussion, are means \(\pm\) standard deviations.
# Research Status and Progress of All-Position Narrow-Gap GMAW for Pipelines
## 2 Current Status of Research on Narrow-Gap All-Position GMAW  
The all-position welding process of pipelines generally refers to the welding from the flat position to the vertical position and then to the overhead position. To address the issues of sidewall lack of fusion and interlayer lack of fusion, it is necessary to study the formation patterns and influencing factors of narrow-gap GMAW all-position welding seams, and explore methods to optimize seam formation. Currently, research by scholars both domestically and internationally on the formation of narrow-gap welds primarily focuses on three aspects: droplet transition behavior, arc morphology, and molten pool behavior.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 7 Challenges and Future Research Directions
### Distributed and Federated Learning  
Distributed ML algorithms create accurate models using multiple servers usually containing datasets of around the same size with independent and identically distributed samples. These algorithms aim to improve the learning process regarding time, memory, and bandwidth. Federated learning is a particular distributed learning approach in which ML algorithms build accurate models from vast decentralized and heterogeneous datasets residing on resource-constrained devices (e.g., gateways, edge devices, smartphones, and autonomous vehicles). A federated learning process can be coordinated by a centralized node (e.g., a 5G/6G network data analytics function or an SDN controller) or collaboratively by distributed nodes (e.g., in-slice managers or programmable switches). Multiagent DRL [180] and federated DRL (FDRL) [181; 182] have been proposed recently for enhancing DRL. It is worth exploring these DRL variations in 5G/6G networks since they could revolutionize the network-slicing resource management. Multiagent DRL and FDRL would learn deeply by interacting with the environment to meet XLAs and SLAs in multi-tenant and even multinetwork provider environments. Although some admission control and resource allocation approaches [183; 184; 185; 186; 187; 188] have touched on these DRL variations, many research challenges have arisen (mainly related to scheduling and orchestration phases): 1 Achieve an optimal trade-off between processing, memory, bandwidth, and accuracy requirements in the resource management solutions to facilitate their deployment in architectures based on microservices; 2 Support concurrent and coordinated decisions in solutions involving more than one resource management phase in multitenant scenarios. 3 Build up open solutions and experimentation platforms to facilitate comparison and evaluation of FDRL-based solutions; 4 Combine online and federated learning to obtain resource management models that learn distributed and continuously when new data appear; 5 Provide security to FDRL, including securing the central coordinator, collaborator nodes, and updates of the shared model.
## 4 Experiments and Analysis of Results
### Experiment Dataset  
This study utilizes the publicly available PCB defect detection dataset from Open Lab on Human Robot Interaction, Peking University. The dataset comprises 693 images featuring six distinct defect categories: missing hole, mouse bite, open circuit, short, spur, and spurious copper. To mitigate potential overfitting caused by limited sample size, the original dataset was augmented to 10,395 images through cropping, rotation, noise addition, and brightness adjustment. Subsequently, the enhanced dataset was randomly partitioned into training, validation, and test sets in a ratio of 8:1:1 through random sampling. The dataset processing images are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Environment** & **Configuration** \\ \hline Operating system & Ubuntu 18.04.5 LTS \\ CPU & Intel(R) Xeon(R) Gold 6226R CPU @ 2.90 GHz \(\times\) 4 \\ GPU & Tesla V100S-PCIE-32 GB \(\times\) 1 \\ Programming environment & Python 3.8.5 \\ Deep learning framework & Pytorch 1.10.0 \\ CUDA & 11.0 \\ RAM & 32 GB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment configuration.  
Figure 5: The diagram of Shape-IoU prediction frame and real frame.
## 7 Conclusions and Future Work  
This study proposed a novel FFT-DCNN model to identify structural damage detection and was verified on a three-story building structure and ASCE benchmark. The main contributions of this paper are summarized as follows.  
1. A novel data-driven method is proposed by combining FFT with DCNN, effectively handling vibration signals and accurately recognizing structural damage conditions.
2. Compared with traditional damage detection such as FFT-SVM and SVM, KNN, Random Forest, and XGBoost, the FFT-DCNN model automatically extracts features from the structure under different damage conditions and achieve higher accuracy in 93.38% for the three-story building structure and accuracy in 87.30% for the ASCE benchmark.
3. For time consumption, FFT-DCNN takes a short time on test datasets, compared with FFT-SVM, SVM, KNN, and RF. It indicates that the proposed method can be designed for recognizing structural damage in actual engineering.
4. This method also has some limitations. Compared with \"shallow\" ML methods, such as SVM and XGBoost, this method achieves high accuracy relying on a number of training datasets. Thus, we might utilize the transfer learning method to reduce training simple for accurately recognizing structural damage detection in the future.  
Writing--original draft preparation and visualization and writing--review and editing, Y.H.; data curation and investigation, H.C.; visualization, D.L.; methodology and software L.Z. All authors have read and agreed to the published version of the manuscript.  
The work described in this paper was supported by the Scientific Research Program of Chongqing College of Humanities, Science & Technology (Grant No. CQRKZX2020003), the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN2019018001), the Scientific Research Program of Chongqing College of Humanities, Science & Technology (Grant No.CQRKZX2020004), Foundation project of Scientific Research Platform of Hechuan for Chengdu-Chongqing Economic Area Research Base, and the Teaching Reform Project of School (Grant No. 19 CRKXJJG05).  
Not applicable.  
The authors declare that no conflict interest for the present study was found.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline FFT-DCNN & 87.30 \\ FFT-SVM & 21.29 \\ SVM & 18.19 \\ KNN & 60.57 \\ RF & 71.33 \\ XGBoost & 72.80 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison result between the proposed method and ML algorithms.