esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Analysis
### Soil Strengthening Mechanism  
#### 3.7.1 XRD  
Calcium carbonate occurs in various crystal forms, including calcite, aragonite, and vaterite [36]. Among these, calcite is the most stable of the three crystal forms, and the crystal shapes are mainly plate and cube. The samples before and after the curing of MICP were analyzed by XRD. It can be seen from Figure 18 that the soil samples before solidification are mainly composed of kaolinite and silica. After solidification, it was found that only the internal soil samples consolidated with the soaking method did not exhibit a characteristic peak for calcium carbonate. This is because these samples were not effectively consolidated. On the other hand, the remaining groups of samples showed calcite characteristic diffraction peaks in the range of 29.4-29.6\({}^{\circ}\). The samples that were strengthened with multiple injections of bacteria into a peristaltic pump and air compressor show a significantly higher characteristic diffraction peak for calcite compared to those that were strengthened with a single injection of bacteria. This indicates a notable significant difference in the amount of calcium carbonate precipitation produced by the two consolidation methods.
## 2 Material and Methods  
Dry land surface state anomalies are studied using numerical comparative analyses of satellite information from Meteosat and numerical modeling of land-atmosphere interaction. The following drought indicators linked to the coupling between the energy and water cycles (and thus considered as biogeophysical indices) are used.  
Soil moisture availability index (SMAI), which is developed on the basis of soil moisture (SM) calculated with the Soil-Vegetation-Atmosphere Transfer ('SVAT_bg') model [42] of the National Institute of Meteorology and Hydrology (NIMH). SMAI has been produced operationally at NIMH since 2010 as a post-processing of the SVAT_bg model and is used in this study as reference information to identify dry anomalies.  
Skin temperature retrieval according to the LSASAF LST product, which is based on IR satellite information from Meteosat Second Generation, to be tested for utility in reflecting dry anomalies on a monthly/annual basis.
## 3 Problem Statement  
Our network model is described as a graph \(G=(V,E)\), where \(V\) is the set of vertices (sensor nodes) and \(E\) is the set of edges (links between nodes). An edge \(e=(u,v)\)\(\subset\)\(E\) exists if and only if any two nodes \(u\), \(v\)\(\subset\)\(V\), and they are in the transmission range of each other. If a node, \(u_{i}\), sends a message to its neighboring node, \(v_{i}\), within its transmission range, other nodes, \({}^{\forall}u_{j}\) (\(j\
eq i\)), around these two nodes within their transmission range cannot send a message to others \({}^{\forall}v_{j}\) (\(j\
eq i\)), because they are in an interference range. Many researchers assume that the interference range is the same as the transmission range [4; 11; 13; 25] or that the interference range is greater than the transmission range [26; 27; 27]. The interference range can be varied depending on factors, such as distance between nodes, power and the signal-to-noise ratio [26]. In this paper, we assume that the transmission range is fixed (meaning it is not controllable after all the nodes are deployed in Euclidian space), the interference range is the same as the transmission range and two different nodes can transmit a message simultaneously, unless they are in an interference range. Figure 1 explains interference range of two nodes.  
Figure 1: Interference range when Node \(u\) sent a message to Node \(v\).  
While Node \(u\) sends a message to Node \(v\), Nodes 1 through 8 cannot send a message to any neighbors, because they are in the interference range of Nodes \(u\) and \(v\).  
The problem can be defined as follows:  
Instance: Given directed graph \(G=\left(V,\,E\right)\), \(s\in V\), where \(s\) is a sink node, and a message set \(M=\left\{m_{1},\,m_{2},\,m_{3},...m_{i},\,\text{for}\,\,^{\forall}i\right\}\) for each node, \(v_{i}\), except the sink node, \(s\left(v_{i}\
eq s\right)\).  
Problem Definition: Find a path to the sink node, \(s\), from each node, \(v_{i}\left(v_{i}\in V,\,\,^{\forall}i\right)\), where each node, \(v_{i}\), sent a message, \(m_{i}\), to the sink node, \(s\), and assign a scheduling time to each link in the path to minimize the total time schedule in a given network.
# A Staring Tracking Measurement Method of Resident Space Objects Based on the Star Tacker
## 3 Results
### Simulation Settings of the Proposed STM Method
#### 3.1.1 Orbit Parameters Settings of the Proposed STM Method  
When the star tracker performs staring-tracking, its optical axis should always point at the real RSO, and keeps the projection locations of RSOs in the center on the image plane. However, the estimated initial RSO orbit elements from original TSA measurements are not accurate. In the ECI frame, the RSO position error caused by orbit elements is defined as displacement \(D\). To design reasonable orbits for the STM method, we specifically analyzed the relationship between the displacement and orbital elements. The actual RSO orbit elements and the errors of the orbit elements in the simulation are listed in Table 1, respectively.  
The estimated RSO positions are calculated according to the parameters in Table 1. The results of displacement \(D\) between the true RSO orbit and the estimated inaccurate RSO orbit are shown in Figure 6. The true and inaccurate RSO positions in 3D space are shown in Figure 6a. Meanwhile, the tendency curves of \(D\) with \((a,e,i,f,\omega,\Omega)\) at different times are shown in Figure 6b based on Equation (6), respectively. The simulation results show that \(D\) changes periodically in one period of the orbit and the maximum displacement error is caused by the semi-major, followed by the eccentricity. Additionally, the error caused by other orbital elements are much smaller.  
Therefore, the reasonable orbital elements of the three orbits simulated in the STM method are listed in Table 2. Apart from the semi-major axis and eccentricity, the other elements of the real and estimated RSO orbits are essentially the same. The high-performance of satellite attitude control is reflected in the optimal attitude pointing technology with a satellite attitude accuracy of 8\({}^{\prime\prime}\)/s and adaptive line-frequency matching technology for attitude motion estimation with a matching time of 3 \(\upmu\)s. Furthermore, the planned attitude  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number** & **Orbital Elements** & **Actual Value** & **Error Value** \\ \hline
1 & Semi-major axis/km & 6928.14 & 20 \\
2 & Eccentricity & 0 & 0.1 \\
3 & Inclination/(deg) & 97.5521 & 0.1 \\
4 & Longitude of the ascending node/(deg) & 203.282 & 0.1 \\
5 & Argument of periapsis/(deg) & 0 & 0.1 \\
6 & True anomaly/(deg) & 0.10826 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Orbital elements of RSO in simulation.  
Figure 6: Simulation results of the displacement \(D\): (**a**) positions of the true and inaccurate RSO orbits in 3D space; (**b**) curves of \(D\) with different orbit elements in one period of the RSO orbit.  
of the observation satellite in the STM method can be calculated according to Equation (12). In the orbit frame, the calculated satellite attitude angle and angular velocity are shown in Figure 7 while the satellite rotates in the 3-1-2 order.
## 5 An O(1)-Competitive Algorithm
### Multiprocessor with Bounded Speed Algorithm: MBS  
At time \(t\), the processing speed of \(u\) adjusts to \(s_{uu}(t)=(1+\
icefrac{{\Delta}}{{3m}})\cdot min\left(\left(\frac{imp_{uu}(t) }{\Uptau}\right)^{\
icefrac{{1}}{{\Delta}}}\), \(\eta\right)\), where \(0<\Delta\leq\left(\frac{1}{3\alpha}\right)\), \(\Uptau\geq 1\) and \(\alpha\geq 2\) are constants. The importance \(imp(j)\) of a job is uninformed and acknowledged only at release time \(r(j)\). The policies considered for the multiprocessor scheduling MBS are as follows:  
Job selection policy: The importance-based/weighted round robin is used on every processor.  
Job assignment policy: a newly arrived job is allotted to an idle processor (if available) or to a processor having the minimum sum of the ratio of importance to the executed size for all jobs on that processor (i.e., \(min\ \sum_{f=1}^{n_{uu}}\left(\frac{imp_{u}(j_{f})}{ex_{u}(j_{f})}\right)\)).  
Speed scaling policy: The speed of every processor is scaled on the bases of the total importance of active jobs on that processor. Every active job \(j_{i}\) on \(u\) obtains the fraction of speed:  
\[\textit{processor's speed}\left(\frac{\textit{importance of}j_{i}}{\textit{ total importance of all active jobs on that processor}}\right)\]  
i.e., \(s_{uu}\cdot\left(\frac{imp_{u}(j_{i})}{\sum_{k-1}^{n_{uu}}imp_{u}(k_{k})}\right)\) or \(s_{uu}\cdot\left(\frac{imp_{u}(j_{i})}{imp_{uu}}\right)\). The speed of any processor gets adjusted (re-evaluated) on alteration in total importance of active jobs on that processor. MBS is compared against an optimal offline algorithm Opt, using potential function analysis. The principal result of this study is stated in Theorem 1. The Algorithm 1 of MBS is given next and the flow chart for MBS is given in Figure 2.  
**Theorem 1.**_When using more than two processors \((\)i.e., \(m\geq 2)\) and each processor has the permitted maximum speed \((1+\
icefrac{{\Delta}}{{3m}})\eta\), MBS is \(c\)-competitive for the objective of minimizing the IbFt+E, where \(c=\left(\frac{9}{8}+\frac{3\Delta}{8}\right)\left(1+(1+\
icefrac{{\Delta}}{{3 m}})^{\alpha}\right)=O(1)\) and \(0<\Delta\leq\frac{1}{3\alpha}\)._
## 2 Omni-Directional SGM with Reliable Cost Propagation
### Omni-Directional SGM  
Traditional SGM only takes pixels on several scan lines into account, and MGM tries to remove streak artifacts in disparity image by incorporating messages from nodes visited in the previous scan line. However, pixels in the neighborhood of root node contribute to the aggregated cost of root node multiple times in MGM (there are multiple paths between these two nodes), making pixels in the neighborhood overweight compared to other pixels and inhibiting reliable information from being propagated across the reference image, as shown in the second row of Figure 1. Here we propose omni-directional SGM which owns several advantages: (1) all pixels in the reference image contribute to the aggregated cost of root node; (2) there is only one path between any two pixels; (3) information propagates along all directions to alleviate streak artifacts; (4) aggregated cost can be recursively computed in linear time in each pass. As shown in the last row of Figure 1, our method traverses the reference image along four directions, namely from left-to-right, right-to-left, top-to-bottom and bottom-to-top. In each pass, the aggregated cost of root node can be recursively calculated from leaf nodes to root node.
# Efficient Task Allocation in Multi-Agent Systems Using Reinforcement Learning and Genetic Algorithm
## 2 Related Work  
### Centralized Optimization Algorithms  
Many traditional algorithms rely on centralized optimization algorithms, leveraging global information to optimize task allocation. These algorithms often employ mixed-integer programming [22] or centralized RL models [23], achieving high-quality solutions under well-structured environments.  
For instance, [PERSON] et al. [24] proposed a centralized algorithm for UAV placement under high-altitude constraints, ensuring efficient coverage and communication. Similarly, [PERSON] et al. [25] applied a hybrid federated RL framework in a UAV-assisted mobile edge computing (MEC) network, improving computation offloading and resource allocation. These algorithms demonstrate that centralized optimization achieves high efficiency in controlled settings with predictable task demands.  
However, centralized algorithms face scalability and adaptability challenges. [PERSON] et al. [26] highlighted that centralized algorithms optimize path planning effectively but struggle with rapid environmental changes, necessitating frequent re-optimization. [PERSON] et al. [27] proposed an automated task reallocation system for multi-robot missions, yet the algorithm remains constrained by centralized control bottlenecks. Furthermore, centralized algorithms introduce single points of failure, increasing system vulnerability in large-scale and dynamic environments [28].  
**Comparison with GA-PPO:** Unlike centralized algorithms that rely on a single controller, GA-PPO employs a decentralized framework, where genetic algorithms enhance exploration, and PPO dynamically refines policies. Compared to [PERSON] et al.'s [24] UAV optimization algorithm, GA-PPO does not require predefined constraints, allowing greater adaptability to changing conditions. Furthermore, while [PERSON] et al.'s [25] federated RL algorithm improves computational efficiency, it still relies on a central coordinator. GA-PPO eliminates this dependency, making it more robust to network failures and large-scale applications.
# Augmented Reality, Mixed Reality, and Hybrid Approach in Healthcare Simulation: A Systematic Review
## 3 Results
### Implementation of the Virtual Component of the Simulators (FQ2-5)
#### 3.4.3 Artificial Intelligence Techniques Integrated in the Simulation (FQ5)  
The use of AI algorithms is raising in the development of surgical simulators as the AI potential is huge for automatic performance evaluation, metrics extraction, simulation level adaptation to the trainee performance, and realistic force feedback implementation.  
Actually, in the literature, AI methods are studied but not yet robustly integrated. Indeed, among all the studies analyzed, only four report the use of AI methods [25; 36; 37; 40]. In [25] and [37], the authors use the commercial ProMIS hybrid laparoscopic simulator in a training program. ProMIS integrates a machine learning algorithm for the tracking of the laparoscopic instrumentation. In particular, the instruments are optically recognized through color-contrasted adhesive labels that are affixed to their distal tips and through a proprietary formula that combines time and path length the simulator manages to record and evaluate the economy of motion, and then generates a numeric score for the execution time. The same approach is used in [36] with the scope to estimate the 3D pose of the surgical instrument with respect to the camera and follow its movements. In this paper the adaptive algorithm developed through the color tracking method is compared with a second algorithm that tracks the endoscopic shaft using a combined Hough-Kalman approach. Here the final aim is to achieve a robust interaction with the virtual world and to improve the realism of the rendering when the virtual scene is occluded by the instrument.  
In [40], AI is used to improve the realism of the force feedback provided in the AR simulator. In particular, the authors use a cyber security algorithm to anonymously and safely record actual force feedback from real surgical interventions, then they analyze the data recorded to provide precise muscle memory acquisition during the training procedure.
## 3 Results
### Application of the New CL System  
#### 3.2.1 Dynamic Range, Detection Limit, and Relative Standard Deviation  
The Ni(IV)-luminol CL system was applied to determine SAL and TEB. Linearities for the TEB and SAL determination were investigated under their optimum conditions described above. Linear ranges of TEB and SAL were 1.0 \(\times\) 10\({}^{-9}\) M to 1.0 \(\times\) 10\({}^{-7}\) M (r = 0.998), and 1.0 \(\times\) 10\({}^{-9}\) M to 5.0 \(\times\) 10\({}^{-7}\) M (r = 0.996), respectively (Figure 4). Regression equations were obtained as: \(I_{\rm t}\) = 1047.25 + 1220.65 \(\times\) 10\({}^{8}\) [TEB], and \(I_{\rm t}\) = \(-\)643.16 + 955.23 \(\times\) 10\({}^{8}\) [SAL]. The detection limits (3\(\sigma\)) for the regression equation were 1.0 \(\times\) 10\({}^{-11}\) M for TEB, and 1.3\(\times\) 10\({}^{-11}\) M for SAL. According to the precision of the experiment, quantification limit for SAL/TEB was set the lowest concentration of the linear range that is 1.0 \(\times\) 10\({}^{-9}\) M for SAL and TEB by the standard requirement. The precision values were less than 2% RSD (n = 11) for 5.0 \(\times\) 10\({}^{-8}\) M TEB and SAL.  
This new CL system is highly sensitive and has a detection limit much lower than that by CZE (4.47 \(\times\) 10\({}^{-6}\) M) [4], HPLC/MS (0.006 \(\mu\)g/kg) [5] for SAL, HPLC (8.43 \(\times\) 10\({}^{-6}\) M) [10], MELC  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Studied Range**} & \multicolumn{2}{c}{**Optimum Condition**} \\ \cline{3-4}  & & **TEB** & **SAL** \\ \hline
[Ni(IV)]/M & 2.0 \(\times\) 10\({}^{-8}\)–2.0 \(\times\) 10\({}^{-5}\) & 6.0 \(\times\)10\({}^{-7}\) & 2.0 \(\times\) 10\({}^{-6}\) \\
[Luminol]/M & 2.0 \(\times\) 10\({}^{-8}\)–4.0 \(\times\) 10\({}^{-5}\) & 2.0 \(\times\) 10\({}^{-7}\) & 2.0 \(\times\) 10\({}^{-6}\) \\
[OH\({}^{-}\) in Ni(IV)]/M & 0.0001–0.05 & 0.003 & 0.001 \\
[OH\({}^{-}\) in Lu]/M & 0.0001–0.01 & 0.001 & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimization of CL conditions for the TEB and SAL determination.  
(3.55 \(\times\) 10\({}^{-8}\) M) [11], and other CL systems (3.12 \(\times\) 10\({}^{-6}\) M) [18] for TEB, (1.0 \(\times\) 10\({}^{-7}\) M) for SAL [24] compared with other published methods.
# Unconstrained Estimation of Multitype Car Rental Demand
## 4 Case Study and Discussion
### Demand Forecast
#### 4.4.2 BP Neural Network Forecast  
The BP neural network used in this paper is divided into three layers: the first layer is the input layer, with input variables of \(i\), \(j\), and \(t\). The second layer is a hidden layer. In this paper, the same sample is used to train the models of different neuron numbers. According to the test error, it is reasonable to determine 17 hidden neurons. The hidden layer activation function uses the sigmoid function. The third layer is the output layer, with the demand as the output element, a total of one neuron, and the activation function uses a linear activation function.  
After the BP neural network model is established, samples need to be trained. In general, the training sample needs to meet four characteristics: (1) there is a close functional relationship between the input and object variable, and the latter will change significantly  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Car Type**} & \multicolumn{8}{c}{\(\mathbf{\Delta_{4}}\)} & \multirow{2}{*}{**Subtotal**} & \multirow{2}{*}{**Total**} \\ \cline{3-3} \cline{5-10}  & & & **1** & & **2** & & **3** & **4** & **5** & **6** & **7** \\ \hline \multirow{3}{*}{EM} & 1 & 7 & 12 & 10 & 14 & 11 & 11 & 14 & 79 & \\  & 2 & 7 & 8 & 8 & 7 & 8 & 18 & 15 & 71 & 175 \\  & 3 & 3 & 6 & 1 & 3 & 5 & 5 & 2 & 25 & \\ \hline \multirow{3}{*}{PD} & 1 & 11 & 3 & 10 & 10 & 8 & 13 & 12 & 67 & \\  & 2 & 16 & 2 & 3 & 12 & 12 & 9 & 15 & 69 & 168 \\  & 3 & 1 & 6 & 2 & 3 & 6 & 5 & 9 & 32 & \\ \hline \multirow{3}{*}{MSM} & 1 & 12 & 2 & 5 & 7 & 11 & 13 & 13 & 63 & \\  & 2 & 11 & 9 & 9 & 7 & 10 & 8 & 11 & 65 & 155 \\  & 3 & 5 & 3 & 5 & 3 & 2 & 4 & 5 & 27 & \\ \hline \multirow{3}{*}{IMSM} & 1 & 11 & 5 & 8 & 9 & 10 & 14 & 3 & 60 & \\  & 2 & 10 & 9 & 9 & 8 & 12 & 5 & 1 & 54 & 133 \\ \cline{1-1}  & 3 & 4 & 1 & 2 & 1 & 3 & 6 & 2 & 19 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Holt–Winters forecast results based on different estimation methods.  
Figure 6: Fitting effect and error analysis of Holt–Winters model.  
Using prediction samples and training samples, the calculated values of forecasting models are checked separately. The neural network forecasting model has a high degree of fitness for the training samples, and the average error is 0.066 (see Figure 7). The model is reasonable and can be applied to later predictions. Demands of different car types can be obtained, which are shown in Table 10.
## 4 Vehicle Storage Analysis
### p-Value Analysis  
To statistically validate performance differences between 25 \({}^{\circ}\)C and 50 \({}^{\circ}\)C, we conducted a \(t\)-test on throughput, IOPS, and latency. We define sample means and variances from multiple runs on three SSD units for each model. Most \(p\)-values were below 0.05, confirming statistically significant degradation in throughput and IOPS. Latency generally showed smaller or non-significant changes, although random operations showed more noticeable latency increases for Model A. The \(t\)-test, an inferential statistical method, is used to determine whether there is a significant difference between the means of two groups. In this study, a two-tailed  
Figure 3: Throughput Comparison Between Model A and Model B.  
Figure 2: Latency Comparison Between Model A and Model B. \(t\)-test with equal variance was employed to analyze the significance levels of the observed differences. The \(p\)-values are calculated using the following equation. \[P\_value=\frac{mean1-mean2}{\frac{(n1-1)*var2^{2}+(n2-1)*var2^{2}}{n1+n2-2}*\sqrt {\frac{1}{n1}+\frac{1}{n2}}}. \tag{1}\]  
In Equation (1), \(mean1\) and \(mean2\) represent the average values of the sample sets, while \(var1\) and \(var2\) denote their variances. The terms \(n1\) and \(n2\) correspond to the number of records in each sample set. \(p\)-values are employed to evaluate the probability that the observed data could have occurred by chance [25]. They range from 0 to 1, with smaller \(p\)-values providing stronger evidence against the null hypothesis, which represents the initial assumption [26]. In this study, the null hypothesis assumes that there is no difference in the QLC SSD's performance between 25 \({}^{\circ}\)C and 50 \({}^{\circ}\)C. A \(p\)-value below 0.05 is typically considered statistically significant, indicating a less than 5% probability that the null hypothesis is correct [27]. A \(p\)-value below 0.01 suggests even stronger significance, implying a less than 1% probability that the null hypothesis holds true [28]. The \(p\)-values for Model A and Model B SSDs are summarized in Tables 2 and 3, respectively. The results reveal that most \(p\)-values for both SSD models indicate significant performance degradation, with some benchmarks showing even higher levels of statistical significance. For Model A, the majority of throughput benchmarks yield \(p\)-values below 0.01, indicating high statistical significance, except for the \"write zero\" benchmark, which shows no significant change. Within the IOPS category, the \"read zero\" benchmark demonstrates no significant variation, while the remaining benchmarks are statistically
# Method for Recognizing Disordered Sugarcane Stacking Based on Improved YOLOv8n
## 3 Experiment  
### Dataset  
#### 3.1.1 Image Acquisition  
The sugarcane dataset used in this study originates from the National Agricultural Science and Technology Park in Guilin City, Guangxi, China (Coordinates: E25\({}^{\circ}\)06\({}^{\prime}\), N110\({}^{\circ}\)31\({}^{\prime}\)). The research subject is the \"Gui Sugar 44\" sugarcane variety, which includes parts such as buds, leaf scars, stalk nodes, and internodes. The research method involves cutting the sugarcane into sets and then stacking them randomly for image acquisition.  
The image data collection took place from October to November 2023, daily from 9 a.m. to 5 p.m. A smartphone was used as the collection tool, and all images were captured under natural light conditions. To enhance the diversity of the data and the generalization ability of the model, the target was placed in various ways across multiple environments, and data were collected under these scenarios. The original image library contains 2500 images