esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This showed that the accuracy of the proposed method is superior to that of traditional methods, such as random forests. [PERSON] et al. [25] compared the capabilities of various deep learning and machine learning models in rice mapping based on remote sensing; the results showed that the performance of deep learning models was significantly better than that of traditional machine learning models. Whether based on temporal [21, 26, 27], spatial [28, 29, 30], or spatial-temporal fusion features [20, 31, 32, 33], deep learning models show significant application potential in crop mapping based on remote sensing. Despite progress in crop mapping using deep learning models, their reliance on large amounts of sample data limits their application. This limitation becomes even more apparent when attempting to use deep learning models for accurately identifying crops on a large regional scale in areas with few reliable samples, creating an urgent problem. In response to this problem, some scholars have attempted to examine the spatiotemporal generalization and transfer abilities of deep learning models. [PERSON] et al. [29] divided a research area located in Arkansas, USA, into three subregions from south to north, analyzing the rice recognition ability of the deep learning model trained in one subregion in the other two subregions. Furthermore, [PERSON] et al. [34] used the phenological matching method to transfer a rice mapping model trained with the Arkansas dataset to a research area in Liaoning, Northeast China, with an overall accuracy of more than 87%. However, there is still insufficient research on crop model inter-continental transfer based on a very small number of samples, which cannot answer key questions, such as the required sample size for model inter-continental transfer, the accuracy and spatiotemporal generalization ability of the transferred model, and the influencing factors affecting this ability, thus limiting the application of deep learning models in rapid and accurate crop mapping in areas with a lack of samples. Building on the success of [PERSON] et al. [20] in applying the temporal feature-based segmentation (TFBS) model to rice mapping in the US, we conducted further research to explore the feasibility and effectiveness of inter-continental transfer for rice mapping models in areas with limited samples, which aims to answer the following questions:  
1. Is it feasible to achieve inter-continental transfer of pre-trained rice mapping models based on extremely limited samples from areas with a lack of samples, thereby reducing the cost of large-scale crop mapping and improving efficiency? 2. How does sample size impact the accuracy of the model transfer during the transfer process? Can the transferred model's accuracy match that of a model trained from scratch with a large number of samples? 3. What is the spatiotemporal generalization capability of the transferred model, and which factors influence its spatiotemporal generalization performance?
# Cognitive Model of the Closed Environment of a Mobile Robot Based on Measurements
## 3 Mathematical Model of the Mobile Robot  
The mathematical model of the robot's non-holonomic differential drive with two drive wheels is described in this section. The analysis contains a kinematic interpretation of the entire system.  
Model of the kinematics of a non-holonomic differential drive consisted of two wheels is illustrated in Figure 7, where:  
* L (m) is robot's track width;
* R (m) is wheel radius;
* C is robot's centre of mass (LiDAR's centre of rotation);
* P is wheel's axis middle point;
* d (m) is the distance between points P and C;
* (0, X, Y) is absolute coordinate system of a robot;
* (0, x, y) is relative coordinate system of a robot;
* \(\theta\) (rad) is robot's orientation to the \(X\)-axis;
* x (m), y (m), \(\theta\) (rad) are parameters of the robot's kinematics;
* \(\upsilon_{R}\) (m/s), \(\upsilon_{L}\) (m/s) are linear velocities of right and left wheel, respectively;
* \(\omega_{R}\) (rad/s), \(\omega_{L}\) (rad/s) are angular velocities of right and left wheel, respectively.  
The non-holonomic implies that the robot does not have ability to move sideways, and motion is based on wheels angular movement principle [33]. Three parameters (\(x,y,\theta\)) define the initial state of the mobile robot, which is represented with an equation q:  
\[q=[x,y,\theta]^{T} \tag{1}\]  
The kinematics model of a mobile robot is defined in matrix form as follows:  
\[\left[\begin{array}{c}\dot{x}\\ \dot{y}\\ \dot{\theta}\end{array}\right]=\left[\begin{array}{cc}\frac{R}{2}\cos\theta& \frac{R}{2}\cos\theta\\ \frac{R}{2}\sin\theta&\frac{R}{2}\sin\theta\\ \frac{R}{L}&-\frac{R}{L}\end{array}\right]\left[\begin{array}{c}\omega_{R}\\ \omega_{L}\end{array}\right] \tag{2}\]  
Figure 7: Mobile robot’s kinematic model of the non-holonomic differential drive.  
\[\left[\begin{array}{c}\dot{x}\\ \dot{y}\\ \dot{\theta}\end{array}\right]=\left[\begin{array}{cc}\frac{\cos\theta}{2}& \frac{\cos\theta}{2}\\ \frac{\sin\theta}{2}&\frac{\sin\theta}{2}\\ \frac{1}{L}&-\frac{1}{L}\end{array}\right]\left[\begin{array}{c}v_{R}\\ v_{L}\end{array}\right] \tag{3}\]  
The kinematic model described by the previous equations is well known and can be found in the standard mobile robotics textbooks, e.g., [34].
## 2 Time-Lag Effect  
In this section, the time-lag effect is demonstrated through the field measured data of a real bridge. Firstly, the general situation of the Lieshihe Bridge and its SHM systems will be introduced. Then the collected data is illustrated and the time-lag effect is put forward. Finally, the character of the time-lag effect in different seasons is discussed.
## 4 Simulation
### SOC Balancing Control with Adaptive Proportional Coefficients  
Considering both the SOC balancing speed and the safe operating range of the switches, the scaling factor a was set to 0.5, while b was set to 0.8. The supercapacitor energy storage system operated in a discharge state at 100 MW. Figure 9 illustrates the relationship between balancing variance and the corresponding adaptive proportional coefficients. The adaptive coefficients increased as SOC differences diminished.  
Figure 10 presents SOC simulation results for both constant and adaptive proportional coefficients. With constant proportional coefficients, the balancing speed decreased rapidly as SOC converged. By employing adaptive proportional coefficients, the system maintained a high balancing speed until SOC reached balancing, significantly shortening the balancing time. As shown in Figure 10, the implementation of adaptive proportional coefficients reduced the SOC balancing time of phases by 0.8 s compared to constant proportional coefficients, while the SOC balancing time of arms decreased by 2 s.  
The circulating currents under different control strategies are compared in Figure 11. The results demonstrate that the proposed adaptive proportional coefficient strategy  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\boldsymbol{\sigma_{\text{m}}}\)/\% & \(\boldsymbol{f_{\text{ave}}}\) /Hz & \(\boldsymbol{\Delta f_{\text{ave}}}\) /\% \\ \hline
2 & 352 & 85.77 \\
4 & 182 & 92.64 \\
6 & 135 & 94.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of average switching frequency and its rate of change.  
Figure 10: SOC for different proportional coefficients. (**a**) SOC with adaptive proportional coefficients. (**b**) SOC with constant proportional coefficients.  
Figure 9: The change between variance and adaptive proportional coefficients.  
achieved a 25% reduction in circulating current during the initial balancing time compared to constant coefficient methods. This improvement stemmed from the strategy's dynamic adjustment mechanism, which optimally resolved the fundamental trade-off between balancing speed and current stress. By automatically regulating the proportional coefficient in response to system conditions, the controller effectively mitigated excessive circulating currents while maintaining rapid SOC convergence.
## 6 Conclusions  
We conclude from the presented research that assigning dynamically the initial weights is significant regardless of the problem's size. Also, the mechanism's effectiveness and impact vary from one problem to another, as the problems in which the number of small clauses is enormous and the size of neighboring neighborhoods is minimal are affected positively by our presented mechanism. Simultaneously, the problems are affected less positively when having in their structure a more significant proportion of clauses of different or large sizes and the sizes of large adjacent neighborhoods. Furthermore, we would like to emphasize that our research was not to develop a new general-purpose clause weighting SLS but rather to look into a dynamic mechanism that could fill the gap between the size of the clauses and the neighborhood sizes within the problem structure. As a result of our research, we conclude that a general-purpose clause weighting SLS is developed, known as DDFW+Dynamic Initial weight assignment, that is very competitive to the state-of-the-art SLS, with a novel mechanism that dynamically assigns the clauses weights.  
Our research could be extended to be employed by non-weightings SLS solvers such that the division of the clauses and their neighborhood is utilized during the initialization phase prior to the search process. Furthermore, as the proposed mechanism within the paper is limited to handling problems that have complete solutions (satisfiable), a possible and logical extension of our work is to apply the mechanism to handling MAX SAT problems.  
Figure 3: The figure shows the ddfw+initWeight algorithm performance in comparison with other state-of-the-art algorithms.  
**Author Contributions:** Conceptualization, A.I.; methodology, A.I.; software, A.I. and W.H.; resources, A.I. and M.A.; data curation, F.A. and W.H.; writing--original draft preparation, A.I.; writing--review and editing, A.I., W.H., M.A., F.A. and N.J.; visualization, A.I. and N.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research has been partially funded by the deanship of the scientific research at petra university.  
**Institutional Review Board Statement:** Not applicable  
**Informed Consent Statement:** Not applicable.  
**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.
# Removal of Chromium(VI) by Chitosan Beads Modified with Sodium Dodecyl Sulfate (SDS)
## 2 Materials and Methods
### Synthesis of the Adsorbent  
Chitosan powder with a molecular weight (50-190 kDa) and degree of deacetylation (>80%) was used in this work. The viscosity of chitosan is 20 to 100 mPa\(\cdot\)s (in 0.5% acetic acid, 20 \({}^{\circ}\)C; degree Celsius) after drying. At first, 1.5 g of chitosan was placed in acetic acid solution (2.0%), and the solution was mixed for 24 h. The chitosan gel was prepared by dropping the above chitosan solution into 200 mL of 0.20 mol\(\cdot\)L\({}^{-1}\) NaOH. The obtained gel was rinsed with ultrapure water until attaining a pH of 7 after stirring for 24 h. Subsequently, 200 tablets of chitosan gel beads were placed in 100 mL SDS solution (including the fixed concentration of SDS), and then, left for five days. Therefore, chitosan gel beads modified with SDS were obtained. Finally, the SDS-modified chitosan beads were dried at 60 \({}^{\circ}\)C overnight for their use as an adsorbent. The procedure employed for the synthesis of SDS-chitosan beads is shown in Figure 1. It is considered that the prepared adsorbent has a bilayer of SDS over the surface of pure chitosan beads. This bilayer can have a higher ion capturing capacity [37].
# Macroscopic Traffic-Flow Modelling Based on Gap-Filling Behavior of Heterogeneous Traffic
Higher order models can better represent heterogeneous traffic conditions since they can also characterize the traffic acceleration, which is of prime interest in the case of heterogeneous traffic flow [24]. [PERSON] and [PERSON] [25] used the concept of lateral headway to explain the lane changing behavior in heterogeneous traffic. Their model, however, fails to characterize the heterogeneity in driver behavior. [PERSON] and [PERSON] [26] developed a second-order traffic-flow model for heterogeneous traffic. They incorporated the concept of area occupancy instead of traffic density. They also introduced traffic pressure as a function of the area occupancy. This model explains the bottleneck phenomenon better than the earlier models. However, it has the limitation of not characterizing the driver behavior in heterogeneous traffic conditions. It becomes evident from the discussion above that conventional macroscopic traffic-flow models cannot characterize heterogeneous traffic. Some of these models have been extended to heterogeneous traffic conditions, yet they are limited because of their inability to characterize the driver behavior that leads to the gap filling in heterogeneous traffic.
# A Survey of of Side-Channel Attacks and Mitigation for Processor Interconnects
## 7 Future Directions for Interconnect Side-Channel Attacks
### Integration of Interconnect and Traditional Processor Microarchitecture Side Channels  
Although processor microarchitecture side channels have received extensive attention and discussion, the vulnerabilities introduced by microarchitectural optimizations continue to emerge, with mitigation measures generally lagging behind. The rise of interconnect side channels provides new avenues for exploiting these vulnerabilities. Interconnect characteristics that have been or could potentially be used by side channels can enhance existing side-channel or other microarchitectural attacks, allowing them to bypass deployed defenses. For instance, reducing the timer precision required for cache side-channel attacks or transient execution attacks can circumvent restrictions on access to high-precision timers.
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 2 Materials and Methods
### Outage Probability  
Another primary performance indicator in communication techniques is the outage probability, as discussed in [20]. This is often expressed as a percentage and depends on the channel's state and the interference in the system. Outage probability measures the risk that a communication signal may not be received or may be lost. If the outage probability is low, the communication system is efficient in transmitting data. The outage probability can be determined as follows:  
\[P_{out}(C_{cs}<R) \tag{23}\]  
where \(R\) is the rate of capacity. As a consequence, the most favorable course of action for the transmitter is to employ data encryption. This decision is contingent upon the channel gain  
Figure 4: The process for CNN-SD.  
being sufficient to accommodate the desired rate denoted as \(R\). Under such circumstances, the attainment of dependable communication becomes feasible; otherwise, an outage is incurred. In the presence of a fading gain represented as h, one may conceive of the channel as permitting the flow of information. Provided that the volume of data surpasses the designated rate, the prospect of reliable decoding becomes attainable. The Saleh channel's outage probability, as a function of the transmission rate \(R\), can be expressed as follows:  
\[P_{out}(R)=1-\exp\left(\frac{-2^{R}-1}{SNR}\right) \tag{24}\]  
\(P_{out}\) signifies the outage probability of the system, characterized by how the destination performs detection when relying only on the received signals from the relay node.
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods
### Pond Objects Filtering (Post-Processing)
#### 3.4.1 Vector Attribute Filtering Using Geometric Features  
The compact shapes (mainly rectangular/circular) of aquaculture ponds are a characteristic and defining feature for the distinction between natural standing waters and managed aquaculture ponds. Without such information, the aquaculture areas, which are generally permanently covered by water, can hardly be distinguished from other (natural) water bodies. For this reason, we calculated four different geometrical parameters for the merged aquaculture pond outputs of the five Asian coastal macro-regions and added them to the mapping results. During the segmentation process, we converted the mapping results from raster to vector format. This resulted in self-contacts at the edges of some polygons, e.g., two vertices of the same polygon have the same coordinate (see Figure 8A), leading to invalid geometries. Therefore, a negative buffer at a distance of \(-10\)-\(6\) m was calculated and applied to the pond polygon file to eliminate invalid self-contacting polygons. At the end of the post-processing, a positive buffer with the same distance was applied in order to reconstruct the original geometry of the pond polygons. Holes inside the segmented aquaculture ponds posed another challenge and influenced the perimeter length of the polygon (see Figure 8(B1)). Since the perimeter is also used for the calculation of some compactness features, existing holes needed to be filled (see Figure 8(B2)) for a proper representation of the pond area as a polygon.  
To resolve these issues, the following steps were required as a preliminary process: (1) patching of holes, (2) reprojection to the global metric coordinate system (EPSG: 3857), and (3) elimination of polygon self-contact. For all aquaculture polygons we calculated the following geometric attributes, which indicate shape metrics of the pond polygon features and can be used to eliminate classification errors. The compactness, convex hull, and oriented minimum bounding box were calculated to describe the convexity, elongation,  
Figure 7: Histogram-based thresholding (OTSU) on the temporally smoothed SAR time series (median image).  
and rectangularity of a pond polygon. The ratio between area and area of the convex hull was found to be a good indicator to eliminate polygons with non-rectangular shapes. For example, lakes with dendritic shapes (see Figure 8(C2)) or rivers with curved shapes could be excluded after applying a filter of geometric attributes. We defined more than 10 test sites distributed across the study region and empirically determined threshold values based on the geometric parameters.
# Robustness Analysis for Multi-Agent Consensus Systems with Application to DC Motor Synchronization
## 2 Multi-Agent Consensus System Preliminaries
### Multi-Agent Forced Consensus Control  
Let us consider the following multi-agent system:  
\[\dot{\xi}_{i}=\bar{u}_{i} \tag{1}\]  
where \(\xi\) is the state and \(\bar{u}_{i}\) is a forced consensus control strategy given by  
\[\bar{u}_{i}=\sum_{j\in\mathcal{N}}-k_{p}\,(\xi_{i}-\xi_{j})+b_{i}u_{i} \tag{2}\]  
where \(k_{p}\) is a positive gain and \(b_{i}\) is defined as follows:  
\[b_{i}=\left\{\begin{array}{ll}0&\mbox{if the $i^{th}$ agent is follower}\\ 1&\mbox{if the $i^{th}$ agent is leader}\end{array}\right.\]  
Then, system (1) can be rewritten:  
\[\dot{\xi} = -\mathcal{L}\,\xi+B\,u \tag{3}\] \[y = C\xi \tag{4}\]  
where \(C=B^{T}\) are the output and input vectors and \(\mathcal{L}\) is the Laplacian matrix of \(\mathcal{G}\), the multi-agent information graph. Notice that, without loss of generality, agent 1 can be designated as the leader agent with access to the reference as shown in Figure 1. The leader control law considered here is based on the center of mass (average) of the states [4]  
\[u_{l}\equiv N\,k_{cm}\,\left(\xi_{cm}^{d}-\xi_{cm}\right) \tag{5}\]  
where \(k_{cm}\) is a positive gain, \(N\) is the number of agents in the multi-agent system, and \(\xi_{cm}^{d}\) and \(\xi_{cm}\) are the desired state center of mass and the calculated system state center of mass, respectively.
## 3 Materials and Methods
### Feature Fusion and Classification
#### 3.7.1 Feature Fusion Strategy  
To leverage the advantages of both deep CNN features and hand-crafted features, a feature fusion strategy was implemented. The extracted features from each method were concatenated to create a comprehensive feature set. Handcrafted features and deep features were concatenated into a single feature vector, and then reduced in dimensionality using PCA before classification. The fused feature vector is defined as:  
\[F_{\text{fused}}=\left[F_{\text{CNN}},F_{\text{Gabor}},F_{\text{GLCM}},F_{ \text{HSV}}\right] \tag{3}\]  
where:  
* \(F_{\text{CNN}}\) represents the deep features extracted from pre-trained CNN models (DenseNet201, EfficientNetB2 and ResNet50),
* \(F_{\text{Gabor}}\) denotes the textural features obtained from Gabor filters,
* \(F_{\text{GLCM}}\) consists of statistical texture descriptors from the Gray Level Co-occurrence Matrix,
* \(F_{\text{HSV}}\) captures the color-based characteristics of the maize leaf images.  
Since the feature vectors from different methods have varying dimensions, Principal Component Analysis (PCA) was applied to reduce dimensionality while preserving the most significant features. The PCA transformation is defined as:  
\[F_{\text{PCA}}=W^{T}\cdot\left(F_{\text{fused}}-\mu\right) \tag{4}\]where \(W\) is the matrix of eigenvectors, and \(\mu\) is the mean of the feature vectors. PCA helps mitigate redundancy, improves computational efficiency, and enhances model performance.
# A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories
## 3 Proposed Method
### Initialization
#### 3.1.2 Unscented Kalman Filter  
Every marker will be assigned an Unscented Kalman Filter (UKF) to keep track of its frame-by-frame movement state. We used the implementation of the FilterPy Python library ([[https://github.com/rlabbe/filterpy](https://github.com/rlabbe/filterpy)]([https://github.com/rlabbe/filterpy](https://github.com/rlabbe/filterpy)), accessed on 8 April 2021) which implements UKF as defined in [23] by using the formulation of [24]. The state vector (s) is defined by (1), where \(x,y\) and \(z\) correspond to the Cartesian coordinates of the marker's position at a given frame; \(v_{x}\), \(v_{y}\) and \(v_{z}\) correspond to its instant velocity, and \(a_{x}\), \(a_{y}\) and \(a_{z}\) correspond to its acceleration in the motion capture system's referential. Each marker's filter is initialized with a state transition function (\(F_{s}  
\[F_{s}=\begin{bmatrix}1&0&0&dt&0&0&\frac{dt^{2}}{2}&0&0\\ 0&1&0&0&dt&0&0&\frac{dt^{2}}{2}&0\\ 0&0&1&0&0&dt&0&\frac{dt^{2}}{2}\\ 0&0&0&1&0&0&dt&0&0\\ 0&0&0&0&1&0&0&dt\\ 0&0&0&0&0&1&0&0\\ 0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&1\end{bmatrix}\cdot s \tag{2}\]  
\[H_{s}=I_{9}\cdot s \tag{3}\]  
\[R=\varepsilon^{2}\cdot I_{9},\quad\varepsilon=0.5 \tag{4}\]  
where \(dt\) is the time between frames, \(\varepsilon\) is an error factor associated to the motion capture system and \(I_{9}\) denotes the identity matrix. The error factor was defined with basis on the accuracy values commonly reported for MoCap systems under dynamic conditions [25].  
Sigma points represent the state distribution and are required for the non-linear transformation in UKF [23,24]. In this work, sigma points were generated according to [26] by parameterizing alpha (\(\alpha\)), beta (\(\beta\)) and kappa (\(\kappa\)). According to [26], \(\alpha\) controls the spread of the sigma points and should be a small positive value between 0.01 and 1; \(\kappa\) is a secondary scaling parameter that can be set to 0.0; \(\beta\) incorporates prior knowledge of the data distribution and, assuming a Gaussian distribution, the value of 2.0 should optimally define \(\beta\). In our experiments, we used \(\alpha=0.1\), \(\beta=2.0\) and \(\kappa=0.0\).  
At each new frame, each marker's UKF is updated with a new position, velocity and acceleration measurement. This measurement can come straight from the marker's coordinates provided by the motion capture system--if the marker is available--or from the coordinates estimated by the gap filling algorithm--if the marker is missing in the new frame.