esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Fully Informed Particle Swarm (FIPS)  
The FIPS algorithm that was developed by [PERSON] et al. in 2004 [1] is a variant of PSO in which each particle is influenced by all of its \(K\)-neighbors. This idea is in contrast to the standard PSO algorithm, in which the particle is drawn to the best location it has visited as well as the best position found by the particle in its neighborhood that manages to produce the best result. The algorithm is motivated by how individuals in human society are influenced by a statistical summary of their world [30].  
Each particle in the FIPS swarm is affected by a group of particles from its surrounding neighborhood, which is not necessarily to the best particle's location [1]. The velocity equation for FIPS is modified based on canonical PSO. Each particle inside the swarm is affected by the achievements of all its neighbors, rather than pointing to just one best particle performance from its neighbors [30]. FIPS's velocity and position updates are defined by Equations (1) and (2), respectively.  
\[v_{i}(t+1)=wv_{i}+\sum_{m=1}^{|N_{i}|}\gamma_{m}(t)\frac{y_{m}(t)-x_{i}(t)}{| N_{i}|} \tag{1}\]  
\[x_{i}(t+1)\ =\ x_{i}(t)+v_{i}(t+1) \tag{2}\]  
where \(N_{i}\) refers to the set of particles in particle \(i\)'s neighborhood. \(\gamma_{m}(t)\) is in the range of \(U(0,c_{1}+c_{2})^{D}\) and \(D\) is dimension of the problem. \(y_{m}(t)\) refer to the best position previously visited by \(m\). Coefficients \(c_{1},c_{2}\) and \(w\) represent cognitive, social and inertia weight, respectively. There are five variants of the algorithm, as stated by [PERSON] et al. [1], which are FIPS, wFIPS, wdFIPS, Self and wSelf:* FIPS refers to a fully informed particle swarm optimization algorithm with \(w\) returning a constant value and the number of neighbourhood contributions being the same;
* wFIPS refers to a type of FIPS algorithm in which each neighbor's input is weighted by the goodness of its previous best particle;
* wdFIPS refers to a type of FIPS algorithm where the contribution of each neighbor is weighted by its distance in the search space from the target particle;
* Self refers to a FIPS algorithm in which the previous best particle received half the weight;
* wSelf refers to a FIPS algorithm where the previous best particle received half the weight and the contribution of each neighbor was weighted by the goodness of its previous best particle.  
The swarm optimization algorithm is a type of algorithm that is greatly influenced by the neighborhood. As a result, the efficiency of the FIPS algorithm is generally even more dependent on its neighborhood topology. Ten topology types selected by [PERSON] [PERSON] [30] to be implemented on FIPS include All, Ring, Four Clusters, Pyramid, Square, UAll, UFour Clusters, UPyramid and USquare. Topology with the \"U\" prefix refers to a similar topology without the prefix, but with the particle's own index omitted from the neighborhood.
## 7 Conclusions  
This paper proposes a cross-coupling control strategy based on the improved GFTSMC and DOB, aimed at improving the synchronization accuracy and system robustness of dual-motor systems. In practical applications, dual-motor systems are often affected by external disturbances, leading to synchronization errors between motors, which impact system stability and accuracy. The paper first uses the improved GFTSMC method to design the speed loop for a single motor, enhancing motor tracking accuracy and dynamic response by optimizing the sliding mode surface. In dual-motor system control, the improved GFTSMC is combined with the cross-coupling strategy to effectively coordinate the operation of both motors, reducing speed deviations. To further improve system stability and robustness, a DOB is introduced to accurately estimate frictional torque and compensate for these disturbances. Experimental results validate the effectiveness of the proposed control method, showing that it significantly enhances both the dynamic and static speed synchronization performance of the dual-motor system and effectively mitigates the impact of external disturbances.  
This paper primarily focuses on the control theory of dual-motor systems. However, due to experimental constraints and limited resources, actual experiments on a test platform could not be conducted. Although this study is mainly validated through simulations, the design of the control strategy fully considers the practical industrial application requirements. In subsequent research, we plan to extend this control strategy to high-power motor systems, with a focus on verification experiments in practical industrial scenarios, to further evaluate and optimize the performance of the control strategy in real industrial environments.  
[PERSON] designed the proposed control strategy and experiments; [PERSON] and [PERSON] conducted the experiments and organized the data; [PERSON] wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.
# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 3 Proposed Method
### Proposed Model
#### 3.2.6 Loss Function  
For depth estimation, different types of loss function are used. The majority of methods use the mean squared error loss (MSE) and, in many cases, it performs very well. In our case, the model did not converge on MSE. We tried different types of loss functions, but none of them gave promising results. Hence, we borrowed the loss function from [PERSON] and [PERSON] [9] and used it in our network. This loss function performed very well, and our model started to learn features and converged. During training, they defined the loss function \(L\) between the true label \(y\) and predicted \(y^{\prime}\) as the weighted sum of a three loss function.  
Mathematically, the loss function is summarized as follows.  
\[\big{(}y,y^{\prime}\big{)}=\lambda L_{depth}\big{(}y,y^{\prime}\big{)}+L_{ grad}\big{(}y,y^{\prime}\big{)}+L_{ssim}\big{(}y,y^{\prime}\big{)} \tag{7}\]  
The first term in Equation (7) is the \(L_{1 Pointwise}\) loss; similarly, the second term is the \(L_{1 Gradient}\) which is the loss over the image gradient; and the last term is the \(L_{SSIM}\) which is the structural similarity term. Each of the above-mentioned losses are shown as follows.  
\[L_{1 Pointwise}=L_{depth}\big{(}y,y^{\prime}\big{)}=\frac{1}{n}\sum_{p}^{n}|y_{ p}-y_{p}{}^{\prime}| \tag{8}\]  
\[L_{1 Gradient}=L_{grad}\big{(}y,y^{\prime}\big{)}=\frac{1}{n}\sum_{p}^{n}|g_{x }\big{(}y_{p},y_{p}{}^{\prime}\big{)}|+|g_{y}\big{(}y_{p},y_{p}{}^{\prime} \big{)}| \tag{9}\]  
\[L_{SSIM}\big{(}y,y^{\prime}\big{)}=\frac{1-SSIM(y,y^{\prime})}{2} \tag{10}\]  
The term \(\lambda\) in Equation (6) is the weight parameter for depth loss function. Empirically, the value was set to 0.1 as a reasonable weight for this term. Such loss terms have the inherent drawback of tending to grow as the ground-truth depth values increase. Researchers have considered the reciprocal of the depth to address this issue [52; 53]. For the original depth map \(y_{orig}\), they define the target depth map y as \(y=m/y_{orig}\) where \(m\) is the maximum depth in the scene. Other methods have considered transforming the depth values and computing the loss in the log space [5; 53].
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 3 Data Caching and Data Sharing Security in 5G  
This section discusses some of the concepts used in this paper. After being granted access to the service, the UE should able to access data, request further authorization to cache, and share data. It should also be able to perform its own activity such as delegating its access rights to another UE. This could be achieved with the assistance of the SP or independently depending on the service policies, network coverage conditions and the delegation permission [4]. We are more interested in the content access and retrieval process, which deals with content delivery. With content delivery and sharing between end-users in proximity of each other, caching at UE level is possible with the use of content-centric networking (CCN)-based framework to enable traffic offloading, caching at the edge and content dissemination from the SP to UE via baseband unit pool (BBU) [9; 35].
## 4 Experimental Evaluation
### Results and Analysis
As shown in Figure 16a, the size of our scene is 20 m \(\times\) 20 m, with dense obstacles. The robot achieves omni-directional motion by differential rotation of two wheels, and it carries a 2D lidar with the field of view (FOV) of 360 degrees and the range of 10 m. Then, we test our autonomous exploration in this platform. First, the robot builds the map through the Simultaneous Localization and Mapping (SLAM) algorithm Karto (2017), and the frontier waypoints are obtained from the map. The test result is shown in Figure 16b, in which the established map, the robot's historical trajectories, and the robot's current path obtained by Mobile Robot Local Planning Benchmark (MRPB) ([PERSON] _et al._, 2018) are visualized by Rviz. As we can see, our approach can guide the robot equipped with 2D lidar to effectively explore the unknown environment, which means the proposed method has great potential in actual application. Figure 15: The result of 50 exploration trials of Graph-STNN and the removal of its components, with the model trained in 20 m \(\times\) 20 m maps and the same initialized landmarks and robot’s start locations, on 40 m \(\times\) 40 m maps. (**a**) One of the test result of 50 exploration trials. (**b**) Average exploration rate (left), trajectory error (middle), and exploration efficiency (right).
# A Review of Lithium-Sulfur Batteries Based on Metal-Organic Frameworks: Progress and Prospects
## 2 Metal-Organic Frameworks for Cathode Applications in Lithium-Sulfur Batteries
### Metal-Organic Framework Composites
#### 2.2.1 Composites with Graphene  
The pioneering utilization of graphene as a cathode material for LSBs was reported by [PERSON] and [PERSON] et al. [46], who synthesized sulfur particles within the interlayer spaces of graphene sheets to serve as the cathode in LSBs. Graphene is renowned for its exceptional conductivity and charge carrier mobility, substantial specific surface area, outstanding transparency, remarkable flexibility, and high mechanical strength across various applications. These attributes render graphene a suitable candidate for enhancing the conductivity of electrodes [47]. Hence, the combination of MOFs with graphene in hybrid structures allows for capitalization on their unique strengths, resulting in the manifestation of synergistic effects.  
For instance, [PERSON] and co-workers [48] designed a high-density nanocomposite electrode with minimal carbon content by integrating Zr-based MOF-808 (employing sulfur as the active component) with a graphene/ethyl cellulose additive. In contrast to conventionally employed carbon/binder blends, these nanocomposite materials exhibit enhanced specific capacity and a more compact structure. The sulfur-laden MOF and graphene nanosheet composites are designated as MOF-808@S/GE'. As illustrated in Figure 6a, MOF-808@S/GEC demonstrates a more compact structure than the other materials. Furthermore, [PERSON] et al. [49] fabricated a three-dimensional composite consisting of an indium metal-organic framework integrated with reduced graphene oxide (In-IPA@rGO) as a sulfur host for LSB applications using a straightforward two-step hydrothermal method, as depicted in Figure 6b. The In-IPA@rGO composite boasts a three-dimensional hierarchical structure and abundant catalytically active sites, which are favorable for mitigating polysulfide volume expansion and the shuttle effect. The SEM images of In-IPA@rGO and the EDS analysis of In-IPA@rGO/S in Figure 6c,d clearly reveal the material's three-dimensional structure. The In-IPA@rGO/S cathode exhibited an initial discharge capacity of 1672.3 mAh g\({}^{-1}\), and the capacity was maintained at 898.7 mAh g\({}^{-1}\) with a Coulombic efficiency (CE) close to 100% after 100 cycles at 0.2 C. However, the initial discharge capacity of the In-IPA/S cathode was 1099.4 mAh g\({}^{-1}\), which decreased dramatically to 296.7 mAh g\({}^{-1}\) after 100 cycles. From these results, it can be seen that the battery performance of In-IPA@rGO composites is improved, which may be due to the synergistic effect between In-IPA and rGO.
## 5 Results and Discussion
### Comparison of Reducing Gas Demand in a Shaft Furnace with or without Waste Heat Recovery  
The reduction gas demand in the shaft furnace must satisfy both the requirements of the reduction reaction and the heat demand necessary to sustain continuous reduction reactions. This demand is calculated using Equations (8) and (18), respectively, with  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Temperature of cooling gas (N\({}_{2}\)) in/K & 323 \\ Temperature of cooling gas (N\({}_{2}\)) out/K & 573 \\ Temperature of DRI out of furnace/K & 353 \\ Recovery rate of waste heat/\% & 40 \\ Electric heating furnace efficiency/\% & 95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Other relevant parameter settings.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Temperature of top gas out/K & 623 \\ Temperature of reducing gas in/K & 1173 \\ Temperature of DRI out of reduction section/K & 1123 \\ DRI metallization rate/\% & 92 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main operating parameters of the HDRSF.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**TFe** & **FeO** & **CaO** & **SiO\({}_{2}\)** & **MgO** & **Al\({}_{2}\)O\({}_{3}\)** & **H\({}_{2}\)O** \\ \hline
66.79 & 0.13 & 1.24 & 1.69 & 0.14 & 0.27 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Iron ore composition/%.  
the theoretical minimum demand for reduction gas being the greater of the two values. The necessity of reducing gas to meet the reduction reaction is affected by the reaction temperature and the composition of the reducing gas. According to Equation (18), the heat source of the shaft furnace originates from the thermal energy carried by both the reduction gas and the iron ore. In the improved process setup, the iron ore is preheated by high-temperature cooling gas before entering the furnace, increasing the thermal energy delivered by the iron ore and consequently reducing the heat brought in by the reduction gas. Therefore, with waste heat recovery, the thermal balance can be achieved with a reduced volume of reduction gas required at the same temperature. Under the temperature condition of 900 \({}^{\circ}\)C, the impact of the shaft furnace on the demand for reduction gas with or without waste heat recovery, under various compositions, is illustrated in Figure 5. Obviously, iron ore preheating can effectively reduce the demand for reducing gas in the shaft furnace.
## 4 Proposed Path and Shape Planning Method
### A-Star Based-Zigzag Planning
Specifically, the global planning algorithm defines a range of \(\{\left\{W_{n}(k)\middle\{(x_{n}^{k},y_{n}^{k}),mo_{n}^{k}\right\}\right\}\) and \(\left\{W_{n}^{i}\left\{(x_{n}^{i},y_{n}^{i}),mo_{n}^{i}\right\}\right\}\), where a set of \(\{W_{n}(k)\}\) is the boundary or obstacle waypoints and a set of free space waypoints \(\{W_{n}^{i}\}\) with value \(i\) starting from number 1 is used to connect a set of \(\{W_{n}(k)\}\). The \(n\) represents the horizontal line \(y=y_{n}\) on map frame coordinate where the waypoints stay on. The successive waypoints \(W_{n}^{i}\) that the distance between two adjacent waypoints on the horizontal or vertical direction has the distance of one grid square will route the waypoints \(W_{n}(k)\) to create the complete path trajectory of unbroken zigzag line. The components of one \(W_{n}(k)\) include waypoint location \((x_{n}^{k},y_{n}^{k})\) and waypoint morphology plan \(mo_{n}^{k}\). Note that \(k=\{bl,br,ol_{i},or_{i}\}\) where \(b_{l},br,o_{l}^{i},o_{r}^{i}\) are boundary waypoint on the left side of map border, boundary waypoint on the right side of map border, waypoint on the left side of obstacle number \(i\), waypoint on the right side of obstacle number \(i\) respectively on the horizontal line \(y=y_{n}\). For each morphology \(m_{n}^{k}\) with the robot size in pixel unit defined as \(w_{m}\times h_{m}\) (or \(l_{m}\times d_{m}\) in meter unit after being converted by Equations (1) and (2)), the dimension which is perpendicular to the heading direction of the robot is defined as \(w_{m}\) and the \(h_{m}\) is the remaining dimension. As shown in Figure 3c,d for the same I configuration but \(w_{m}\) is defined differently for horizontal and vertical moving directions. Unlike fix morphology robot, the width size of hTetro can be reduced by half by changing the morphology such as from O to I during vertical moving or from I to O during horizontal moving. The length of cells which are occupied by a specific \(m_{n}^{k}\) of robot, width is defined as same as \(w_{m}\). In this paper, the default morphology is O shape with \(l_{m}=0.5\) m and \(d_{m}=0.5\) m The I shape has the \(l_{m}=0.25\) m and \(d_{m}=1\) m The first waypoint \(W_{0}(bl)\) is the origin of the map. The process of finding intermediate waypoints will start from the first waypoint \(W_{0}(bl)\). Note that, \(M(x_{n}^{k},y_{n}^{k})\) is the pixel value of the location \((x_{n}^{k},y_{n}^{k})\).
## 4 Discussion
### Correlation Analysis of SRNR Performance
At this time, an increase in the exposed aggregate area ratio reduces the contact between the rubber slider and part of the mortar texture, reducing the impact of the mortar texture on skid resistance performance, and the friction effect of the coarse aggregate on the rubber slider becomes the main source of skid resistance performance. Therefore, as the exposed aggregate area ratio increases, the pendulum value gradually increases. When the exposed aggregate area ratio exceeds  
Figure 12: The relationship between noise value and exposed aggregate depth. Figure 13: The relationship between pendulum value and exposed aggregate area ratio. 71%, the mortar texture distributed in the valleys between the coarse aggregates is almost completely blocked by the coarse aggregate texture, and the texture contacted by the rubber slider changes from the original composite texture of coarse aggregate and mortar to only contacting the coarse aggregate texture. Therefore, the pendulum value shows a downward trend once again. In summary, the SRNR performance of EAC is closely related to its surface texture characteristics. However, some data points in the fitting results exhibited relatively high dispersion, with root mean square errors (_RMSE_s) of 1.75 BPN for the pendulum value and 0.83 dB for the noise value. This indicates that while the exposed aggregate depth and area ratio can reflect the overall variation trends of SRNR performance for EAC, high-precision prediction remains challenging due to the influence of additional factors such as the exposure postures of coarse aggregates and surface microtexture. Based on the correlation analysis, under the condition of meeting the basic requirements for surface texture characteristics with an exposed aggregate depth of 2-3 mm and an exposed aggregate area ratio of 65-75%, the greater the exposed aggregate depth, the better the noise reduction performance of the EAC surface. When the exposed aggregate area ratio is controlled between 69% and 73%, the skid resistance performance of its surface reaches a high and stable level.
# A Numerical Model to Estimate the Soil Thermal Conductivity Using Field Experimental Data
## 2 Materials and Methods
### Numerical Models Calibration and Validation  
Data from \(G_{exp}\) and \(T_{soil}\) were selected for different depths on 3 random days (an entire day of data) of each month of the year, totaling 33 days (since during October, data were not collected), and were used to estimate \(\lambda\) by different methods. The following methods were used:  
* Method 1 (\(M1\)): Isolating \(\lambda\) in Equation (2) and calculating the average value of \(\lambda\);
* Method 2 (\(M2\)): Isolating \(\lambda\) in Equation (2) and calculating the average value of \(\lambda\) for the observed data whose temperature variation (\(\Delta\)T) is equal to or greater than \(|0.1|\);
* Method 3 (\(M3\)): Isolating \(\lambda\) in Equation (2) and calculating the average value of \(\lambda\) for the 13-h data (local time). This time was chosen because it presents, in general, a greater variation of the temperature in relation to the depth than the other times;
* Method 4 (\(M4\)): Least squares method applied to Equation (2);
* Method 5 (\(M5\)): Least squares method applied to Equation (3).  
The method validation was performed using the experimental dataset, excluding the days used to estimate \(\lambda\). Analyses were performed considering the whole period (day and night) and separating the periods into day and night. For this, the daytime period was considered as the interval between 06:00 and 18:00 and the night, the interval between 18:00 and 06:00.  
The \(\lambda\) values obtained by each calibration were entered into the respective equations of each method together with the experimental values of the soil temperature, thereby obtaining estimated \(G_{z}\) values that were used in the different methods to estimate \(\lambda\) (\(G_{M1}\), \(G_{M2}\),... ). The method validation was performed using the values of \(G_{z}\) with the values obtained experimentally (\(G_{exp}\)) through the statistical indices RMSE and \(R^{2}\). These indices were calculated by the following equations:  
\[RMSE=\sqrt{\frac{\sum\left(Y_{exp}-Y_{est}\right)^{2}}{n}} \tag{4}\]  
\[R^{2}=1-\frac{\sum\left(Y_{exp}-Y_{est}\right)^{2}}{\sum\left(Y_{exp}-\overline {Y}\right)^{2}} \tag{5}\]  
where \(Y_{exp}\) represents the observed values, \(Y_{est}\) represents the estimated values, \(\overline{Y}\) represents the mean of the observations and \(n\) represents the number of observations.