esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of Optimal Distribution Strategy of Damping Force Controller  
### Lateral Load Transfer Ratio  
The lateral load transfer rate (LTR) is the mainstream rollover evaluation index, and it is commonly used in the rollover control field [39]. When the vehicle is rolling, its LTR can be expressed by vertical force as the following equation, as shown in Figure 4[12]:  
\[LTR=\frac{F_{zl}-F_{zr}}{F_{zl}+F_{zr}} \tag{14}\]  
where \(F_{zl}\), \(F_{zr}\) are the left and right vertical forces of sprung mass, which can be expressed as  
\[\left\{\begin{array}{l}F_{zl}=F_{zl1}+F_{zl2}=\frac{F_{zl1}}{D_{l1}}+\frac{F_ {zl2}}{D_{l2}}\\ F_{zr}=F_{zr1}+F_{zr2}=\frac{F_{zl1}}{D_{l1}}+\frac{F_{zl2}}{D_{l2}}\end{array}\right. \tag{15}\]  
It can be seen from Equation (14) that when the vehicle runs in a straight line without steering wheel input, the vehicle has no load transfer and the LTR is 0. When the input of the steering wheel increases gradually, the vehicle body will roll due to the effect of suspension and the vertical forces of outside wheels will gradually increase. Assuming that the lateral acceleration is constant, the vertical forces of the inside wheel will decrease correspondingly, and the LTR value will gradually increase. When the rollover critical moment is reached, the vertical forces of the inside wheels inside will be 0. The wheels on another side carry all the mass of the vehicle, and the LTR reaches the maximum value of +1 or the minimum value of \(-1\). At this time, if the input of steering wheel angle continues to increase, the rollover accident will occur. Therefore, the value range of LTR is [\(-1\), \(+1\)].  
Figure 4: Vehicle rollover diagram (front view).
# Kinematics and Statics of the Gough-Stewart Platform
## 4 Statics of the Gough-Stewart Platform in Plucker Coordinate
### Statics of the Gough-Stewart Platform  
Figure 7 illustrates a Gough-Stewart platform, which is a representative multi-loop spatial mechanism. For the sake of generality, suppose that there are three external forces, \(F_{E}^{x}\), \(F_{E}^{y}\) and \(F_{E}^{z}\), and three external torques, \(T_{E}^{x}\), \(T_{E}^{y}\) and \(T_{E}^{z}\), exerted on the moving platform \(E\).  
In the absolute coordinate system shown in Figure 7a, the force screws of the external loads can be expressed as:  
\[\mathbf{\xi}_{E}^{W}=\begin{bmatrix}F_{E}^{x}&F_{E}^{y}&F_{E}^{z}&M_{E}^{x}&M_{E}^ {y}&M_{E}^{z}\end{bmatrix}^{T} \tag{27}\]  
The force screws of each prismatic joint \(B_{i}\) (\(i=1,2,\cdots,6\)) can be expressed as:  
\[\mathbf{\xi}_{F_{B_{i}}}=F_{B_{i}}\begin{bmatrix}\mathbf{e}_{B_{i}}\\ \mathbf{r}_{B_{i}}\times\mathbf{e}_{B_{i}}\end{bmatrix} \tag{28}\]  
where \(\mathbf{r}_{B_{i}}\) is the absolute position vector of joint \(B_{i}\), and \(\mathbf{e}_{B_{i}}\) presents its posture.  
Figure 6: Procedure of kinematics and statics analysis of serial kinematics chain.  
Figure 7: Statics of Gough–Stewart platform: (**a**) statics of the Gough–Stewart platform; (**b**) statics of one fixed leg; (**c**) statics of the moving platform.  
The moving platform would maintain stable under the action of external loads expressed by these force screws. Therefore, we obtain the following equations:  
\[\mathbf{S}^{W}\mathbf{F}_{B_{i}}=\boldsymbol{\mathsf{S}}_{E}^{W} \tag{29}\]  
where \(\mathbf{S}^{W}=\begin{bmatrix}e_{B_{1}}&e_{B_{2}}&e_{B_{3}}&e_{B_{4}}&e_{B_{5}}& e_{B_{6}}\\ r_{B_{1}}\times e_{B_{1}}&r_{B_{2}}\times e_{B_{2}}&r_{B_{3}}\times e_{B_{3}}&r_{B_{4}} \times e_{B_{4}}&r_{B_{5}}\times e_{B_{5}}&r_{B_{6}}\times e_{B_{6}}\end{bmatrix}\) is the unit force screw matrix and \(\mathbf{F}_{B_{i}}=\begin{bmatrix}F_{B_{1}}&F_{B_{2}}&F_{B_{3}}&F_{B_{4}}&F_{B_{5 }}&F_{B_{6}}\end{bmatrix}^{T}\) represents the force vector.  
The mechanism is in singularity when it satisfies \(\left|\mathbf{S}^{W}\left(\mathbf{S}^{W}\right)^{T}\right|=0\). Otherwise, we could obtain the solution of \(\mathbf{F}_{B_{i}}\) through:  
\[\mathbf{F}_{B_{i}}=\left[\mathbf{S}^{W}\left(\mathbf{S}^{W}\right)^{T}\right] ^{-1}\left(\mathbf{S}^{W}\right)^{T}\boldsymbol{\mathsf{S}}_{E}^{W} \tag{30}\]  
Through Equation (30), the driven forces of each limb shown in Figure 7b,c can be computed. With projection equations, we immediately obtain:  
\[\mathbf{F}_{B_{i}}=F_{B_{i}}e_{i} \tag{31}\]  
Equation (31) provides the general equation of static equilibrium for a single rigid object, and also for a multi-rigid-body system. Force screws are particularly suitable for programming a computer for analyzing the statics of multi-body systems.
## 2 System Model
### LDPC Coding and Decoding Algorithms  
This section describes the LDPC codes and presents the simplified sum-product algorithm for LDPC decoding [48].
# Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems
## 4 A Comparison of Sparse Signal Recovery Algorithms
### Greedy Iterative Algorithms  
The typical representation of the greedy iterative is OMP algorithm. Its main idea is to find the most relevant column contained in the measurement matrix to the residue in the \(i\)th iteration [25].  
\[j^{(i)}=\operatorname*{arg\,max}_{j\in\hat{\mathbf{\Omega}}^{(i-1)}}\left\|\left( \mathbf{r}^{(i)}\right)^{H}(\mathbf{\Phi}\mathbf{\
abla})_{j}\right\|_{2} \tag{15}\]  
where \(\mathbf{r}^{(i)}\) is the residual vector, \((\mathbf{\Phi}\mathbf{\
abla})_{j}\) denotes the \(j\)th column of the measurement matrix and \(\hat{\mathbf{\Omega}}\) denotes the estimation of the common support. \(\hat{\mathbf{\Omega}}\) can be updated by  
\[\hat{\mathbf{\Omega}}^{(i)}=\hat{\mathbf{\Omega}}^{(i-1)}\cup j^{(i)} \tag{16}\]  
After the sparse common support \(\mathbf{\Omega}\) is estimated, the least square estimation algorithm can be used to estimate the full channel, which is given as follows:  
\[\hat{\mathbf{h}}=\left(\mathbf{\Omega}^{H}\mathbf{\Omega}\right)^{-1}\mathbf{\Omega} \mathbf{y} \tag{17}\]  
Except OMP algorithm, there are some modified greedy iteration algorithms such as subspace pursuit (SP) algorithm to solve such a problem [11]. For SP algorithm, the relevant \(L\) columns are selected in each iteration to reduce iterations. For greedy iterative algorithms, a suitable criterion to end the procedure is crucial. If the sparseness of channel is known in advance, the algorithms will be stopped when the iterations reach the sparseness; otherwise, the maximum iteration will be set larger than the real channel sparseness to ensure the recovery accuracy. However, with regard to SP algorithm, there is an extra threshold to exceed since numerous columns are selected in each iteration.  
The defect of greedy iteration algorithms is that they cannot avoid the quantification error of angle even when \(G\) is large enough. Especially at the high SNR range, the quantization error is a key factor that plays a decisive role. This indicates that the performance based on greedy iterative algorithms is similar because the quantification error of angle is a bottleneck for such a sparsity channel estimation problem.  
Although the performance loses a lot by replacing the real angle of arrival and departure by discrete grid angle, the computational complexity is relatively lower than convex relaxation algorithms. This is due to the fact that the greedy iterative algorithms do not involve the complicated matrix operations such as derivation. Hence, the greedy iterative algorithms are candidates to solve such a sparse channel estimation problem as the low delay requirement for next wireless communication can be satisfied well.
## 4 Energy Consumption
### Average Energy Consumption during Normal Cycles  
Recall that after a successful or failed PF transmission, SNs switch to the _sleep_ mode during the rest of the cycle to save energy. We refer to these cycles as _normal_ cycles. In this subsection, we determine the average energy consumed by an RN in a _normal_ cycle _exclusively due to being in the sleep mode._
# Multimodal Network for Object Detection Using Channel Adjustment and Multi-Scale Attention
## 4 Experiment  
### Experiment Setup  
To comprehensively validate the effectiveness of our proposed method, we conducted extensive experiments on both the public MSRS dataset and a private industrial dataset provided by SAIC, evaluating performance across urban road and industrial scenarios.  
The MSRS dataset is a well-established benchmark for urban scene analysis and contains 1444 pairs of aligned thermal infrared and visible light images, which are annotated for vehicles and pedestrians. Each image typically includes multiple objects, providing a complex traffic scenario that helps evaluate a model's ability to distinguish between different objects in multi-object detection tasks.  
The private SAIC dataset consists of 1689 multimodal images from real-world production workshops, with each image representing a specific type of industrial equipment, such as sensors, lathes, forklifts, and filters. These images capture equipment of varying sizes and complexities, from smaller sensors to larger forklifts, while also reflecting common challenges in industrial environments, such as occlusions and cluttered backgrounds. This makes the SAIC dataset particularly valuable for evaluating the model's robustness in industrial equipment detection and classification tasks. A detailed breakdown of the SAIC dataset's composition and category distribution is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Sensors** & **Lathes** & **Forklifts** & **Fiters** & **Total** \\ \hline SAIC & 590 & 367 & 465 & 267 & 1689 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SAIC dataset statistic.  
To demonstrate the effectiveness of our multimodal fusion approach, we first conducted image fusion experiments to evaluate the quality of feature integration across modalities. Subsequently, we performed object detection tasks to validate the practical benefits of our fusion strategy. For the fusion quality assessment, we employed five well-established metrics: Visual Information Fidelity (VIF), Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), Gradient-based Fusion Metric (Qabf), and Entropy (EN). These metrics collectively evaluate different aspects of fusion performance, including information preservation, structural consistency, and noise suppression.  
For the object detection evaluation, we adopted three standard metrics: mean Average Precision (mAP), AP@50, and AP@75. These metrics provide a comprehensive assessment of detection accuracy at different Intersection-over-Union (IoU) thresholds, effectively validating the robustness of our method across various scenarios. The combination of fusion quality metrics and detection performance indicators offers strong empirical evidence for the reliability and effectiveness of our proposed approach.
# PLORC: A Pipelined Lossless Reference-Free Compression Architecture for FASTQ Files
## 4 Results and Discussion
### Power and Resource Consumption  
Table 9 further demonstrates that the PLORC compressor delivers the highest power efficiency, achieving a superior processing speed with equivalent power and hardwareresource consumption. In addition, the power efficiency of our decompressor is better than that of the other tested decompressors.  
Table 10 indicates the resource consumption of the tested algorithms. Our PLORC compressor and decompressor consume relatively fewer LUTs and FFs. Moreover, our architecture consumes no BRAM. The high power and resource efficiency enable the implementation of multiple PLORC architectures on a single ZCU102 board to compress data from multiple sequencing platforms in the future. Considering LUTs and Flip-Flops, the total logic resource on a ZCU102 FPGA platform can support the implementation of about dozens of PLORC compressors or decompressors.  
As analyzed in Section 4.4, Gzip, LZ4, Zstd, and REBA demand considerable memory resources and frequent memory access. The BRAM usage in LZ4 and Zstd is detailed in Table 10, and the memory requirement for REBA is in the GB range according to [16]. Furthermore, applying different clock domains between memory devices and FPGA logic in REBA introduces circuit complexity and increases resource consumption.
# Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
## 4 Experiments and Results  
### Water Quality Dataset Acquisition  
The data used for the experiments were collected from Laizhou Mingbo marculture, based at Laizhou City, Shandong Province, China, by a research team at Chinese Agriculture University. A team of researchers from the Chinese Agriculture University visited Laizhou Mingbo farm in Autumn 2019 and collected water quality data. They were responsible for deploying the sensors and their necessary maintenance for the four-month duration of the data collection. The collected raw data consists of seven water quality parameters: DO, pH, water temperature, salinity, Ammonia, and Nitrogen. This choice of parameters is governed by the availability of suitable sensors and the monitoring needs of the marculture farm. In the presented study, we have investigated forecasting techniques using DO datasets. The reasons for our choice are already indicated in Sections 1 and 2 above.
## 3 Finite Element Analysis (FEA)  
### Designing FEA Models  
In this study, steel I-grider bracing systems (same as beam bracing systems) were designed based on the provisions of the AASHTO LRFD bridge design specifications [22]. The girder heights (\(h\)) of the bridge models are 1/25 times their span length (L) to satisfy the provisions in AASHTO [22], to ensure that the bridge girder sections considered in this study do not allow excessive flexural deflections under design loads. The flange widths (\(b\)) of the steel I-grider models are 1/2-1/5 times their girder height (\(d\)). Moreover, all flanges and webs have a compact thickness for structural steels with a yield strength of 345 MPa, which satisfies the provisions of AASHTO [22] to prevent local buckling behavior.  
In this study, two different types of cross-sections were designed. One is the W-section stipulated in the AASC design manual [1], with 900 mm height or above, with a flange width of 1/2.5 to 1/3.36 times the girder height, which is intended for designing bridges. Another group is built-up sections having 2 m and 2.8 m heights with a flange width of 1/3 to 1/5 times the girder height for designing bridges. Thus, six steel I-grider sections were selected for each group, as summarized in Table 1. For torsional bracing design, this study considered zero to thirteen z-type torsional bracings with various values of torsional stiffness, in which the torsional bracing stiffness ranged from zero to sufficient torsional bracing to produce LTB within the unbraced length (\(L_{b}\)), so as to ensure that the effects of torsional bracing on the bracing internal forces are considered in this study. Table 2 illustrates the elastic material properties used in the FEA models.  
To consider the bracing torsional stiffness (\(\beta_{T}\)) in the FEA models, this study considers 14 different torsional stiffness values for each I-girder section. The bracing torsional stiffness considered in this study is summarized in Table 3.
## 4 Results and Discussion
### Assessment of Monthly Marine Gravity Field from Dual Satellite Constellation  
Compared to radar altimeters, spaceborne GNSS-R payloads have the advantages of low power consumption, low weight, low cost, and low size, making them highly suitable for constellation formation [54]. Therefore, this study leverages the specular points trajectories of FY-3E and FY-3F to simulate GNSS-R interferometric altimetry data from a dual-satellite constellation for monthly marine gravity anomaly inversion. Still using a 30\({}^{\prime}\) resolution as an example, Figure 8 presents the inversion results of the monthly marine gravity field in the Operational dual-satellite constellation scenario. Compared to single-satellite results, the dual-satellite constellation significantly reduces data gaps and enhances coverage. Furthermore, the accuracy of the monthly marine gravity anomaly inversion in the dual-satellite GNSS-R interferometric altimetry constellation was evaluated, with results shown in Table 2. It can be observed that the dual-satellite constellation improves the coverage rate, revisits, and inversion accuracy of the monthly marine gravity anomaly compared to the single-satellite. At a 15\({}^{\prime}\) grid resolution, the GNSS-R dual-satellite constellation observations can achieve a coverage rate of over 90%, reaching 91.45%, with average revisits of 10.83 times. The monthly marine gravity anomaly inversion accuracy is better than 10 mGal in both the IOD and Operational scenarios, reaching 9.87 mGal and 5.93 mGal, respectively. At a 20\({}^{\prime}\) grid resolution, the accuracy in the Operational dual-satellite constellation scenario is better than 5 mGal, specifically 4.82 mGal. In the IOD scenario, at a 30\({}^{\prime}\) grid resolution, the accuracy also surpasses 5 mGal, reaching 4.68 mGal. Compared to single-satellite observations, the dual-satellite constellation not only improves inversion accuracy but also narrows the accuracy gap between the IOD and Operational scenarios with increasing  
Figure 7: Monthly marine gravity anomaly from single Operational GNSS-R satellite (30\({}^{\prime}\) resolution). revisits. Therefore, GNSS-R constellations are crucial in improving the precision of monthly marine gravity anomaly inversion using spaceborne GNSS-R interferometric altimetry. However, at \(10^{\prime}\) grid resolution, the Operational monthly marine gravity anomaly accuracy significantly surpasses that of the IOD scenario, whether with a single satellite or a dual-satellite constellation. This is due to the fact that as spatial resolution increases, the reduction in average revisits is approximately proportional to the square of the grid resolution enhancement. For example, the average number of revisits at a \(10^{\prime}\) grid is approximately one-fourth of that at a \(20^{\prime}\) grid resolution. Therefore, despite high monthly revisits and accuracy of the monthly marine gravity field at a \(20^{\prime}\) grid resolution, when inverting the monthly gravity field at higher resolutions like \(10^{\prime}\) or \(5^{\prime}\), the ability to enhance
# Grasping Assisting Algorithm in Tele-Operated Robotic Gripper
## 1 Introduction
Grasping an object with a tele-operated system requires communication between a \"master\" operator and a \"slave\" robot, based on a varied level of control and information provided. Various uses of system like this can be examined from different angles: [PERSON] et al. [21] predicted the movement of an object being manipulated with a tele-operated manipulator, and corrected the prediction in the virtual reality based on data from the \"slave\" unit. [PERSON] et al. [22] used a tele-operated arm and gripper to mimic humans in creating precision grasps, thus building a set of synergies to the gripper. An ability to control the \"slave\" gripper using visual input command from the human \"master\" was introduced by [PERSON] et al. [23] with the help of a deep neural network. [PERSON] et al. [24; 25; 26; 27] presented differences in the performances of novice and expert surgeons when using a tele-operated system to preform surgical cuts. In this study we tried to create an assisting algorithm for tele-operated gripper users, advising them how to grasp an object and checking if their grasp was improved in relation to the unassisted case. Although the algorithm is applicable in generic robotic grasping, the purpose of the research is to advise the operator on grasping, and not force a specific solution. The suggested grasp was calculated through an optimal grasp searching algorithm. In Section 2 we will analyze the calculations required for finding an optimal grasp, and in Section 3 we elaborate about our searching algorithm. Section 4 deals with the series of tests we conducted to examine the improvement of the users grasping performance, and in Section 5 a discussion on the results is presented.
# Intelligent Reflective Surfaces for Wireless Networks:
## 5 Practical Challenges, Open Issues and Future Research Lines
### Practical Challenges
#### 5.1.2 Implementation and Testbeds  
In the last years, a few promising testbeds and experimental activities have been reported [92], and some industrial developments have also been reported [10]. However, these reports are usually difficult to find and insufficient to practically judge the IRS technology.
# Can Defect Prediction Be Useful for Coarse-Level Tasks of Software Testing?
## 7 Conclusions and Future Work  
To alleviate the difficult resource allocation situation without execution information for coarse-level tasks, an approach, called DPAHM, is proposed in this paper. The method regards the resource allocation problem for ST as a multiple decision-making problem and combine AHP with a variation of incidence matrix to predict the defect situation of coarse-level tasks based on SDP techniques. Thus, the corresponding resource allocation strategy is born.  
The approach is divided into four phases: association hierarchy framework conduction phase, software defect prediction model establishment phase, positive incidence matrix from vertical and horizonal direction production phase, and resource allocation strategy output phase. We apply the proposed method to a true software _MC_ and the result indicates our method can provide ideas about resource allocation strategies for coarse-level testing tasks, such as system-level testing.  
In the study, we aim at resource allocation for coarse-level tasks of ST. Accordingly, we only depend on SDP to predict defect situation for coarse-level tasks. DPAHM provides guidance for allocating resource. In the future, we will collect more resource allocation information (such as the number of test cases executed by each person every day, proportion of system testing time, budget for each person) to optimize the allocation strategy. Moreover, we assume that the call relationship between fine- and coarse-level is known or obtained by analysis. However, for complex software, it is difficult to analyze the association or hierarchy structure. Therefore, it is another direction in the future to provide resource allocation strategies for the coarse-level tasks of complex software.  
Conceptualization, C.C. and P.X.; methodology, C.C.; software, P.X.; validation, C.C., P.X. and S.W.; investigation, C.C.; resources, P.X.; data curation, P.X.; writing-original draft preparation, C.C.; writing-review and editing, C.C., B.L. and S.W.; supervision, B.L.; project administration, S.W.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript. This research was funded by a grant from the Science & Technology on Reliability & Environmental Engineering Laboratory of China (Grant No. 614200404031117). Besides, the research is also supported by Foundation of No. 61400020404. The authors would like to thank the providers for NASA and Softlab data sets. Besides, the authors are very thankful for the reviewers for their time and energy to provide valuable advice and suggestions for the paper. The authors declare no conflict of interest. The following abbreviations are used in this paper:  
AHP analytic hierarchy process BBT black-box testing CPDP cross-project defect prediction DPAHM DP-based association hierarchy method PIM positive incidence matrix SDP software defect prediction ST software testing SUT software under test TCG test case generation TCP test case prioritization TCS test case selection TCM test case minimization WBT white-box testing