esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 3 Study Region and Data  
Bangladesh is a tropical country, situated mainly in the deltas of large rivers flowing from the Himalayas. The Brahmaputra River, known locally as the Jamuna, unites with part of the Ganges to form the Padma, which, after it merges with the third largest river, the Meghna, flows into the Bay of Bengal. No part of the delta area is more than 150 m above the sea level, with most of it being just a meter or two above sea level. During the rainy season, flood water covers most of the land surface, damaging crops and injuring the economy. The northwestern section of the country, drained by the Teesta River, is somewhat higher and less flat, but the only really hilly regions are in the east, notably in the Chittagong Hill Tracts to the southeast and the Sylhet district to the northeast. We collected observed data for soil temperature at different depths, air temperature, sunshine duration, rainfall, and humidity during 2001-2022 from 11 soil temperature observation stations in the Bangladesh Meteorological Department (Figure 1). Unfortunately, the observation of soil temperature is very limited in Bangladesh, which only takes place twice a day, so we could not simulate the diurnal temperature change in our study.
## 3 Results and Discussion
### DMTA Results for TPS Films Obtained via Thermocompression (Test 1)
The higher the content of U in the film is, the higher the temperature of a drop of E' for TPS/CCU value will be (Figure 2B). The drop of E' value for TPS/CCU 1:2 is at 6 \({}^{\circ}\)C and for TPS/CCU 1:5 is at 32 \({}^{\circ}\)C. The differences in drop of E' during heating are not so distinctive and all samples exhibited decreasing E' at ca. 20 \({}^{\circ}\)C. This drop in E' is related to glass transition of starch-rich fraction. A low storage modulus at higher temperatures indicated that the TPS materials, plasticized with the eutectics, are susceptible to thermoformation via e.g., compression molding, independently of the urea content. XRD Results for TPS/DES Films Obtained via Thermocompression and Extrusion and Thermocompression (Test 1 and 2)  
The starch structure was modified dramatically after thermal processing in the presence of plasticizing mixtures. Native potato starch granules exhibit characteristic peaks at 5.4, 17.1, 19.5, 22.5, 24.0 and 26.0\({}^{\circ}\), forming B-type semi-crystallinity [5]. As we can observe from Figure 3 after processing in the presence of the DESs, all these peaks were  
Figure 2: DMTA curves (**A**) Tan \(\delta\) for TPS/CCU; (**B**) Tan \(\delta\) for TPS/BaU and (**C**) Storage modulus \(-\) E’ for TPS/CCU; (**D**) Storage modulus \(-\) E’ for TPS/CCU; The analysis was performed on thermocompressed TPS films. flattened, indicating a highly amorphous structure independently of the processing method. It corresponds with high EB values (Table 2) related to a higher mobility of the amorphous structure of the polysaccharide. In the case of CCU mixtures, there is a slight difference in the diffractograms regarding the dependence of U content it can be noticed whereby the higher urea content is, the smoother the pattern of the sample becomes. The analysis was repeated after a certain time period of storage at ambient conditions. After 4 months of storage, there was no difference in the diffractogram. However, after 1 year, there was a barely noticeable peak at 16.8\({}^{\circ}\) that came from B-type crystals corresponding to the {1 2 1} crystal face (Figure 3B) [19; 42]. Comparing types of DES, for TPS with CCU eutectics, there is also a barely visible peak at ca. 17\({}^{\circ}\). However, this peak is not present in the extruded sample (eTPS), meaning that extrusion led to a more amorphous structure due to the presence of mixing and shear forces during processing. It corresponds with mechanical tests, where higher EB values indicated higher mobility of polysaccharide chains due to their highly amorphous structure.
## 3 Proposed Status-Byte-Assisted RDMA Transmission Mechanism
### Overall System Architecture  
Figure 2 provides an overview of the architecture of our proposed system, which includes a cloud-edge collaborative global management platform and multiple edge servers. The cloud-edge collaborative global management platform is a cloud-based system that manages and allocates resources across all edge servers, ensuring efficient use of resources and coordination among them. Each edge server is connected to several sensor devices such as radar and cameras. When the data processing requirements of an edge server exceed its processing capacity, the server sends a resource scheduling request to the cloud-edge collaborative global management platform. The platform then responds with a resource scheduling result based on the resource status of each edge server and the requested amount of resources.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method/Technique Name** & **Key Features** & **Advantages** & **Limitations** \\ \hline \multirow{3}{*}{Traditional RDMATransmission} & Dynamic memory block allocation, requires at least three network interactions per transmission & Simple implementation, high flexibility & High network latency and communication overhead \\ \hline \multirow{3}{*}{Sliding Window-based RDMA Transmission} & Controls data transmission using a sliding window mechanism, requiring only two RDMA interactions & Optimized for fixed window size, reduces network interactions & Memory blocks must be used in order, consuming excessive CPU and RNICA resources. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of various RDMA transmission mechanisms.  
Based on the resource scheduling results, the edge server that sends the request is designated as the data-sending end, while the edge server allocated for the collaborative processing function is the data-receiving end. The data transmission between the sending and receiving ends utilizes the proposed status-byte-assisted RDMA data transmission mechanism to accelerate data transmission and synchronization. Moreover, Docker and container technology, managed by K8s, are implemented at the data-receiving ends to achieve flexible and efficient data processing workflows. By enabling collaborative data processing among edge servers, the system can fully utilize the unique characteristics and resources of each edge server.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 3 MIMO Detection: A System Model
### Gauss-Seidel Detector  
The Gauss-Seidel method solves the linear system iteratively. For matrix \(\mathbf{A}=\mathbf{G}^{T}\mathbf{G}\), we decompose as follows:  
\[\mathbf{A}=\mathbf{D}+\mathbf{L}+\mathbf{U}, \tag{13}\]  
where \(\mathbf{D}\), \(\mathbf{L}\), and \(\mathbf{U}\) are the diagonal, strictly lower, and strictly upper triangular parts of \(\mathbf{A}\), respectively. The iterative update is  
\[\mathbf{\hat{s}}^{(n)}=(\mathbf{D}+\mathbf{L})^{-1}\Big{(}\mathbf{\hat{s}}_{ \text{MF}}-\mathbf{U}\mathbf{\hat{s}}^{(n-1)}\Big{)}, \tag{14}\]  
where \(\mathbf{\hat{s}}_{\text{MF}}=\mathbf{G}^{T}\mathbf{r}\) is the matched filter output. Despite improved convergence, GS is inherently sequential and, thus, not easily parallelizable [11; 32].
## 2 Background
### Feature Selection  
Feature selection methods can be categorized into three types: filter, wrapper, and embedded. The filter method uses predetermined criterions, such as Pearson correlation and mutual information between features, to select the optimal features [45]. In this case, a subset of the selected features was input into the classification algorithm. This method has the advantage of calculating quickly because it is independent of the classification algorithm [46]. The wrapper method uses a specific classification algorithm for feature selection. A subset of features optimized for a particular classification algorithm was selected by generating and evaluating various subsets [47; 48]. Although it is more accurate than the filter method, the computation is complicated because it requires executions of classification algorithms on subsets of all features. Embedded methods combine feature selection and learning procedures into a single process to reduce computational time [49]. Therefore, in contrast to other methods, feature selection and classification algorithms cannot be separated [50]. This study used genetic algorithm (GA), information gain (IG), and wolf search algorithm (WSA) as feature selection methods.
# Application of Modeling and Simulation in a Self-Reprogrammable Prototype of a Manufacturing System
## 4 Project Architecture
### Virtual Environment  
Initially, the development of the simulation model followed the steps of traditional modeling, where data collection and a thorough understanding of the system's practical functioning are fundamental to the initial stages of the project.  
The data collected were: Operating time for each part of the process; Transport speed at all points; Distance traveled; Number of parts transported in each cycle; Faults; and Actual system operating behavior.  
However, this specific model incorporates unique characteristics, with real-time data exchange being among the most significant. Consequently, it was essential to prepare the simulation model to receive data from sensors installed in the real environment. For instance, transport speed parameters were treated as variables depending on the status of production plan fulfillment, as determined by the following equation.  
\[Vt=\frac{\frac{Qi-Qe}{4}*Pp}{Tdi-Tg}*FC \tag{1}\]  
Vt = Transport speed;  
Qi = Parts initially planned;  
Qe = Parts delivered;  
Pp = Perimeter;  
Tdi = Initial available time;  
Tg = Time spent.  
This equation allows the transport speed to be adjusted according to the current condition of the process. The ideal transport speed that fulfills the delivery deadline, and it is based on the variable quantity of parts to be transported (Qi--Qe) and available working time (Tdi--Tg). These two variables are input into the simulation model through communication between the simulation and the real environment.  
Figure 4: Hardware to transport.  
Therefore, when the system identifies a delay in fulfilling the order, the transport speed is recalculated so that all parts are delivered on time.
## 3 Results
### Influence of the FRC Material  
To assess the influence of the type of FRC material, the force-deflection of PFRM80_c15_g35 vs. HFRC80_c25_g35 is compared in Figure 14a, and PFRM100_c15_g35 vs. HFRC100_c30_g35 in Figure 14b. Although the top and bottom concrete cover thickness have an impact due to the reasons already pointed out, mainly the top concrete cover thickness, the FRC type material, had a higher influence on the load-carrying capacity of these specimens. In fact, when using HFRC instead of PFRM, an increase of 69% in the \(P_{max}\) was registered in the specimens of 80 mm thickness (123 kN in PFRM80_c15_g35 and 208 kN in HFRC80_c25_g35), while an increase of 121% in the peak load was observed in the specimens of 100 mm thickness (158 kN in PFRM100_c15_g35 and 350 kN in HFRC100_c30_g35). The type of FRC also influenced the \(K_{Tl}\). In fact, when using HFRC instead of PFRM, an increase of 92% in the \(K_{Tl}\) was registered in the specimens of 80 mm thickness (17 kN/mm in PFRM80_c15_g35 and 33 kN/mm in HFRC80_c25_g35), while an increment of 32% in the \(K_{Tl}\) was observed in the specimens of 100 mm thickness (37 kN/mm in PFRM100_c15_g35 and 49 kN/mm in HFRC100_c30_g35). The smaller increase in this last comparison can be attributed to the larger top concrete cover thickness and internal arm of the GFRP grating in PFRM100_c15_g35 over HFRC100_c30_g35.  
Figure 14: _Cont._  
Figure 13: Force vs. centre deflection of HFRC100_c30_g35 and HFRC100_c25_g50.
# Research on Stagger Coupling Mode of Pulse Duration and Tool Vibration in Electrochemical Machining
## 2 Analysis of the Simulation of a Dynamic PVECM Process
### Physical Model  
Figure 2 shows a schematic diagram of the PVECM process. The electrolyte was pumped through the inlet (boundary 10) to the outlet (boundary 4). The inlet was 30 mm from the outlet, the initial inter-electrode gap was 0.1 mm, and the cathode and workpiece were both 20 mm long. To analyse how the coupling mode of pulse duration and tool vibration affected the gas void fraction, the following assumptions were made.  
1. The bubbles are evenly distributed in the liquid phase, and no mass transfer occurs between gas and liquid.
2. The cathode and workpiece surfaces are electric equipotential surfaces.
3. The current efficiency \(\eta=100\%\).  
The electric potential in the inter-electrode gap is given by the Laplace equation: [20]  
\[\
abla^{2}\varphi=0. \tag{1}\]  
The corresponding boundary conditions are  
\[\varphi|\Gamma_{6,7,8}=U, \tag{2}\]  
\[\varphi|\Gamma_{1,2,12}=0,\text{ and } \tag{3}\]  
\[\frac{\partial\varphi}{\partial n}\Big{|}\Gamma_{3,4,5,9,10,11}=0. \tag{4}\]  
The gas void fraction \(\beta\) is  
\[\beta=\frac{Q_{1}}{Q}=\frac{Q_{1}}{Q_{1}+Q_{2}} \tag{5}\]  
where \(Q\) is the total volume, \(Q_{1}\) is the gas volume, and \(Q_{2}\) is the liquid volume [21]. The relationship between electrolyte conductivity and gas void fraction is  
\[\kappa=\kappa_{0}(1-\beta)^{m}(1+\lambda(T-T_{0})), \tag{6}\]  
where \(\kappa\) is the electrolyte conductivity, \(\kappa_{0}\) is the initial electrolyte conductivity, \(\beta\) is the gas void fraction, \(m\) is the coefficient of gas void fraction on conductivity (typically, \(m=1.5\)), \(\lambda\) is the degree of temperature dependence, \(T\) is the electrolyte temperature, and \(T_{0}\) is the initial electrolyte temperature.  
The current density \(i\) is expressed by [PERSON]'s law:  
\[i=\frac{U\kappa}{\Delta}=\kappa\
abla\varphi, \tag{7}\]  
where \(U\) is the applied voltage and \(\Delta\) is the inter-electrode gap between cathode and workpiece.  
In the ECM process, the cathode electrochemical reaction is  
Figure 2: Schematic diagram of the pulse duration and tool vibration in electrochemical machining (PVECM) process.  
\[2\mathrm{H}^{+}+2e\rightarrow\mathrm{H}_{2}\uparrow. \tag{8}\]  
Compared with hydrogen, the volume of oxygen generated during the ECM process can be neglected. The hydrogen generated at the cathode can be described by [PERSON]'s law [22]:  
\[N_{\mathrm{H}_{2}}=\frac{i}{2F}, \tag{9}\]  
where \(F\) is [PERSON]'s constant.  
The equation describing the tool vibration is  
\[x(t)=A\sin(2\pi ft+\pi), \tag{10}\]  
where \(A\) is the amplitude of the displacement of the tool cathode, \(t\) is the machining time, and \(f\) is the vibration frequency.  
All models were analysed using the COMSOL software. The pulse duration was one-half of a vibration period when the cathode was near the workpiece, so the duty cycle was 50%. Table 1 lists the parameter values used in the calculations.
# Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography
###### Abstract  
Nowadays, eye fatigue is becoming more common globally. However, there was no objective and effective method for eye fatigue detection except the sample survey questionnaire. An eye fatigue detection method by machine learning based on the Single-Channel Electrooculography-based System is proposed. Subjects are required to finish the industry-standard questionnaires of eye fatigue; the results are used as data labels. Then, we collect their electrooculography signals through a single-channel device. From the electrooculography signals, the five most relevant feature values of eye fatigue are extracted. A machine learning model that uses the five feature values as its input is designed for eye fatigue detection. Experimental results show that there is an objective link between electrooculography and eye fatigue. This method could be used in daily eye fatigue detection and it is promised in the future.  
electrooculography signals; feature extraction; eye fatigue; machine learning Article
# Path Planning Optimization of the Load Transport Process Using Heuristic Algorithms
## 2 Description of the Problem
* Co-ordinates (in mm) for the endpoint \(P_{k}(1100,-300,1)\),
* The height of the obstacles: \(h_{1}=1360\) mm, \(h_{2}=650\) mm, \(h_{3}=600\) mm, \(h_{4}=560\) mm,
* The diameter of the obstacles: \(d_{1}=1500\) mm, \(d_{2}=4400\) mm, \(d_{3}=440\) mm, \(d_{4}=810\) mm,
* Working area (in mm): \(x\in[-1255,1255],y\in[-1255,1255],z\in[0,1360]\). In order to represent the trajectory of a load with smooth transitions, a B-spline curve was selected, which is characterized by introducing additional auxiliary points in addition to the start and endpoints. The smooth trajectory allows for uninterrupted transport of the load, which ultimately reduces the phenomenon of rope swinging. Auxiliary points are selected randomly, and during the optimization process, they can move according to the determined trajectory. However, they determine the final shape of the curve.
## 3 Numerical Simulation
### Grid Independence Study  
Mesh grid size is an important factor in numerical simulations because resolving the transitional flow depends significantly on the grid size to accurately describe the flow. Thus, a grid independence study at AoA = 0\({}^{\circ}\) was conducted to ascertain whether the selected grid density had sufficient resolution and to confirm that the spatial discretization errors were minimal. Moreover, the low AoA of 0\({}^{\circ}\) can achieve a better convergence than higher AoAs, and thus, only the grid size effect can be observed at the same condition. Table 1 shows the properties of four grids that were generated. The number of grid elements of Grid 2 in the lower and upper sides of the airfoil and the wake direction was doubled for Grid 1, and was halved for Grid 3. Grid 4 was the finest grid generated using 360,000 grid elements. Figure 5 depicts a representative example of the change in C\({}_{\rm d}\) (percent) as a function of the mesh number for the S809 airfoil. This serves to identify a mesh resolution that provides a reasonable tradeoff between accuracy and computational burden. By further increasing the number of grid elements, it is confirmed that using Grid 3 results in a value (\(-0.58\%\)) that is within the acceptable range of variation compared to Grid 4. This indicates that we have reached a solution that is independent of the mesh resolution. By considering the computation time and the minimal variation in the drag coefficient between the four cases, Grid 3 was found to be optimal in terms of accuracy and computation time. Therefore, the transition and standard \(k\)-\(\varepsilon\) turbulence models for Grid 3 were applied with the same elements by differently adjusting the distance of the first grid from the airfoil for the \(y^{+}\) value requirements of each model, as already described in Section 3.3. To the best of the authors' knowledge, compared to other S809 airfoil simulation results in the literature, the present simulation results for the airfoil aerodynamic performance can be regarded as highly accurate.
# Stability Conditions in Lignite Open Pits from Romania, Case Study: Olteu Open Pit
## 2 Materials and Methods
### Field Observations and Laboratory Tests
#### 2.2.3 The Lignite
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Values Determined in the Laboratory**} & \multicolumn{2}{c}{**Values Determined**} & \multicolumn{2}{c}{**Statistically**} \\ \multirow{2}{*}{**Type of Rock**} & \multirow{2}{*}{**Layer**} & \multicolumn{4}{c}{**LMP**} & \multicolumn{2}{c}{**from Documentations**} & \multicolumn{2}{c}{**Statistically**} \\  & & & \multicolumn{2}{c}{**GeoLogic Lab. [69]} & & \multicolumn{2}{c}{[56]} & \multicolumn{2}{c}{**Determined Values**} \\ \cline{3-10}  & & **[m]** & **w** & **Ysat/Ysat** & **c** & **\(\varphi\)** & **c** & **\(\varphi\)** & **c** & **\(\varphi\)** \\  & & **[\%]** & **[daN/m\({}^{3}\)]** & **[daN/cm\({}^{2}\)]** & **[\({}^{\circ}\)]** & **[daN/cm\({}^{2}\)]** & **[\({}^{\circ}\)]** & **[daN/cm\({}^{2}\)]** & **[\({}^{\circ}\)]** \\ \hline \multirow{2}{*}{Yellow-brown clay} & \multirow{2}{*}{5–15} & 46.15 & 1845/1920 & 0.23–0.63 & 0–10 & \multirow{2}{*}{0.22} & \multirow{2}{*}{15} & \multirow{2}{*}{0.31} & \multirow{2}{*}{10} \\  & & 39.66 & 1782/1825 & 0.33 & 11.18 & & & & \\ \hline \multirow{2}{*}{Sandy marl} & \multirow{2}{*}{3–4} & 22.35 & 1850/1970 & 0.43 & \multirow{2}{*}{22} & \multirow{2}{*}{0.32} & \multirow{2}{*}{20.5} & \multirow{2}{*}{0.45} & \multirow{2}{*}{21} \\  & & 16.13 & 1911/2000 & 1.72 & & & & \\ \hline \multirow{2}{*}{Lignite (Ind layer)} & \multirow{2}{*}{2.6–5.0} & 36.25 & 1180/1300 & 0.7’/1.6 ** & 18’/34 ** & 0.3/1/1.2 & & \\ \hline \multirow{2}{*}{Sandy marl} & \multirow{2}{*}{0.3–15} & 22.35 & 1850/1970 & 0.43 & \multirow{2}{*}{18’/34 ** } & \multirow{2}{*}{0.32} & \multirow{2}{*}{20.5} & \multirow{2}{*}{0.45} & \multirow{2}{*}{21} \\  & & 16.13 & 1911/2000 & 1.72 & & & & \\ \hline \multirow{2}{*}{Lignite (Ist layer)} & \multirow{2}{*}{2.3–3.2} & 36.25 & 11850/1970 & 0.43 & \multirow{2}{*}{18’/34 ** } & \multirow{2}{*}{0.3} & \multirow{2}{*}{30} & \multirow{2}{*}{1.1} & \multirow{2}{*}{26.5} \\  & & 16.13 & 1911/2000 & 1.72 & & & & \\ \hline \multirow{2}{*}{Sandy marl} & \multirow{2}{*}{-} & 22.35 & 1850/1970 & 0.43 & \multirow{2}{*}{22} & \multirow{2}{*}{0.32} & \multirow{2}{*}{20.5} & \multirow{2}{*}{0.45} & \multirow{2}{*}{21} \\  & & 16.13 & 1911/2000 & 1.72 & & & & \\ \hline \hline \end{tabular}  
* w—natural moisture; \(\gamma_{\text{var}}/\gamma_{\text{sat}}\)—natural/saturated volumetric weight; \(\text{c--}\) cohesion; \(\varphi\)—internal friction angle.
* Determined by shearing strength. ** Determined based on tensile and compressive strengths.  
\end{table} 
Table 1: Physical-mechanical properties of the rocks.
# Research on the Positioning Method of Steel Belt Anchor Holes Applied in Coal Mine Underground
## 5 Experimental Section  
To verify the accuracy of the proposed algorithm, a detection platform for steel strip anchor holes was constructed according to the hardware and software system introduced in Section 2. Initially, the actual coordinates of the steel strip anchor holes were determined using the coordinate paper laid out as shown in Figure 2. Subsequently, the steel strip was tested on the detection platform, and the detection coordinates were outputted.  
With the vertical distance between the detection platform and the steel strip fixed at 15 mm, the position of the steel strip was changed multiple times and subjected to detection. The results, presented in Table 1, show that the positioning error is within 5 mm, meeting the positioning requirements.  
In actual detection, the vertical distance between the detection platform and the steel strip may vary. To verify the accuracy of this detection method, the vertical distance was set at 17 mm and 19 mm, and multiple detections were carried out, with the detection results presented in Tables 2 and 3.  
The positioning errors in the above three tables are within 5 mm, meeting the design requirements. It can also be inferred that the closer the vertical distance between the inductive sensor and the steel strip, the smaller the detection error.  
\begin{table}
\begin{tabular}{c c c c} \hline No. & Actual Coordinates/mm & Detecting Coordinates/mm & Error/mm \\ \hline
1 & (670, 350) & (672, 351) & (2, 1) \\
2 & (566, 395) & (568, 393) & (2, 2) \\
3 & (645, 380) & (644, 382) & (1, 2) \\
4 & (505, 355) & (507, 356) & (2, 1) \\
5 & (510, 350) & (512, 348) & (2, 2) \\
6 & (525, 360) & (523, 362) & (2, 2) \\
7 & (508, 365) & (510, 367) & (2, 2) \\
8 & (530, 370) & (528, 371) & (2, 1) \\ \hline \end{tabular}
\end{table}
Table 1: Anchor hole identification and positioning results at a vertical distance of 15 mm.  
Figure 14: Hough circle detection.