esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Voltage Stability in Distribution Networks:
## 5 Case Analysis
### Probabilistic Voltage Stability Assessment under Different Scenarios
Although the traditional 3 PEM can also identify the degree of instability in various scenarios, there are still discrepancies compared to the MCS method. Therefore, the better the fitting effect of \(I_{\text{LM}}\) probability distribution, the more accurate the results of the system stability assessment, which also verifies the precision of the method presented. Comparing values of each scenario, Scenario 3 shows the lowest risk due to its high and widespread \(I_{\text{LM}}\) values. This indicates that adopting distributed PV integration and ensuring higher connection capacities can improve the load margin and thus effectively reduce the risk of voltage instability. In contrast, centralized access systems, while simplifying management and maintenance, may face a higher risk of instability due to low load-margins.
## 3 Materials and Methods
### Electrochemical Measurements  
The S/KB composite (the sulfur content in the S/KB composite was ~77 wt% displayed in Figure S1 [42]) was obtained through the conventional melt-diffusion method. Ketjen Black (KB: ECP600 JD) and sulfur were uniformly ground in a weight ratio of 2:7 and then heated at 210 \({}^{\circ}\)C for 12 h in a closed autoclave filled with argon gas. The electrodes were prepared by casting the homogeneous slurry containing 90 wt% S/KB composites and 10 wt% PVDF binder onto a carbon-coated aluminum foil. The ordinary mass loading of sulfur in the as-prepared cathode was 1.3-1.5 mg cm\({}^{-2}\). A high-sulfur-loading cathode (3.0 mg cm\({}^{-2}\)) was prepared by pasting the same slurry on aluminum foils. The electrochemical performances were tested using CR2025-type coin cells with different functional separators, assembled in an Ar-filled glove box with S/KB-based electrodes as cathodes and lithium foils as anodes. The electrolyte was composed of 1.0 M bis(trifluoromethanesulfony)imide lithium (LiTFSI) dissolved in a binary of 1,3-dioxolane (DOL) and 1,2-dimethoxyyethane (DME) (1:1, \(v/v\)) containing 2 wt% LiNO\({}_{3}\) additive. The electrolyte volumes were kept at 20 \(\mu\)L mg\({}^{-1}\) sulfur for the ordinary-sulfur-loading electrodes and 12 \(\mu\)L mg\({}^{-1}\) sulfur for the high-sulfur-loading electrodes.  
Symmetric electrodes were prepared by pasting the homogeneous slurry containing Co@g-C\({}_{3}\)N\({}_{4}\)/G and PVDF binder in a weight ratio of 9:1 onto a circular carbon cloth (a diameter of 14 mm) with a mass loading of 0.5 mg cm\({}^{-2}\). The catalytic conversion was evaluated using a symmetric battery, which was assembled using two identical tailor-made electrodes separated by a PP separator with 40 \(\mu\)L of 0.2 M Li\({}_{2}\)S\({}_{6}\) electrolyte.  
For the other tested methods, we referred to them from our recently reported papers (e.g., material characterizations, entrapment experiments, and various electrochemical tests) [25; 42]. Additionally, it is pointed out that some of the physical characterization data of the pure g-C\({}_{3}\)N\({}_{4}\) sample were from our recent work reported in [42].
## 3 Results--HiL-Based Calibration and Validation of TWC and HEGO Diagnoses
### Phase #1--Diagnostic Run with an OK System
Besides a solid reproduction of the diagnostic run, it is all-important to have a reproducible diagnostic result of the corresponding diagnosis (Figure 4c) on the HiL platform, both for an OK and a nOK system over the entire relevant operating range of the respective component. Figure 7 shows OSC (a) and HEGO latency (b) results over the complete relevant exhaust gas mass flow range for OK as well as nOK components. In addition, the calibratable failure threshold and the exhaust gas mass flow-related release conditions are depicted in the figure as well. The OSC results in Figure 7a have been determined during so-called catalyst mappings on the HiL as well as in the vehicle, where each point represents the OSC result of one parallelization event, calculated according to Equation (1). The OSC value infers the diagnostic result of the TWC diagnosis. This parameter is a chemical property of the TWC and is used as an evaluation criterion for its ageing condition. A low OSC value thus correlates to a low conversion efficiency. Owing to this fact, the OSC of the TWC should be monitored during every driving cycle according to OBD legislation. The simulated OSC values in Figure 7a show good agreement with the results from the vehicle measurement over the entire operating range. The correct reproduction of the OSC values suggests an excellent modelling of the oxygen loading and unloading behavior of the TWC over the entire operating range. Figure 7b shows the HEGO sensor latency results over the complete relevant exhaust gas mass flow range for an OK (non-faulty HEGO sensor) as well as a nOK (faulty HEGO sensor) component. In addition, the calibratable failure threshold and the exhaust gas mass flow-related release conditions are shown in the figure as well. Similar to the OSC results, the HEGO latency results in Figure 7b have been determined during a mapping on the HiL as well in the vehicle, where each point represents the result of one parallelization event. The HEGO latency value implies the diagnostic result of the HEGO sensor latency  
Figure 6: Comparison of HiL and chassis dynamometer measurement—Diagnostic run of the parallelization for an OK system at \(\tilde{m}_{Exh}=90\frac{\sqrt{g}}{h}\) and \(T_{TWC}=700\,^{\circ}\mathrm{C}\). diagnosis, calculated according to Equation (2). According to OBD legislation, the dynamic behavior of lambda sensor signals should be monitored, as dynamically slow lambda sensors (e.g., HEGO) can lead to falsified results for diagnoses that heavily depend on a reliable sensor signal. The simulated HEGO latency values in Figure 7b show good accordance with the results from the vehicle measurement over the entire operating range.
# Factory Logistics Improvement: A Case Study Analysis of Companies in Northern Thailand, 2022-2024
## 4 Results and Case Study
### Transportation Management
#### 4.1.2 Transportation Management System (TMS)  
This case study examines a medium-sized medical device and equipment trader based in Chiang Mai. The company manages approximately 1000 SKUs and serves 450 customers, handling 1300 orders per month across Northern Thailand. While the company uses in-house fleets and personnel for deliveries, their distribution management relies on manual, experience-based assignment and paper-based processes. This approach has resulted in costly, time-consuming operations prone to errors, with a delivery-in-full-on-time (DIFOT) rate of 85%.  
To address these inefficiencies, a Transportation Management System (TMS) was developed and implemented. The TMS design process included system analysis, logical design, and physical design (as illustrated in Figure 3). The system incorporates various functions, including sales order management, pick-pack-ship job assignment, fleet and personnel management, stock cutting, GPS-based delivery tracking, return management, and monitoring and reporting.  
The implementation of the TMS yielded significant improvements, e.g., an 80% reduction in transportation management time, 25% cost savings, 100% error elimination, an increase in DIFOT to 97%, and 100% traceability of deliveries.  
Notably, the project achieved a rapid return on investment, with a payback period of just 5 months.  
The case studies of transportation management demonstrate significant improvements in efficiency and cost reduction. It is important to note the interdependencies between transportation and other key supply chain functions. Optimized transportation routes and schedules, as seen in the VRP case, directly impact inventory management by enabling more frequent, smaller deliveries, potentially reducing overall inventory levels. The implementation of TMS, as illustrated in the second case, not only improves transportation efficiency, but also enhances warehouse operations through the better coordination of pick-pack-ship processes. Furthermore, these transportation improvements can influence procurement strategies, allowing for more responsive and cost-effective sourcing decisions.  
Figure 2: Designed vehicle routes.  
These interconnections underscore the systemic nature of logistics improvements, where enhancements in one area often yield benefits across multiple supply chain functions.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 3 Results and Discussion
### Path Planning  
#### 3.2.1 Viewsed Map  
A path planning algorithm for UAV relay should not pass through areas with poor communication coverage. A viewshed analysis was, therefore, applied at a drone altitude of 100 m above ground level to determine the appropriate visibility region and avoid passing through areas with inadequate coverage, as shown in Figure 8. The viewshed map is used to construct a blockage map for the \(A^{*}\) algorithm to prevent the UAV from entering areas with no coverage.  
Figure 6: Points of interest reconnaissance across track waypoints.  
Figure 7: Elevation contour map.
# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 5 Methods for Congestion Learning
### Hierarchical Clustering  
Hierarchical clustering is considered when we assume a multi-level congestion prediction. There exist different ways of doing hierarchical clustering. We can start from a single cluster and divide it further, following a top-down approach, or we can follow a bottom-up process, starting from a high number of clusters that are combined. In this work we have followed the former alternative, to know at which extent we can split the samples in clusters, so that they actually represent congestion levels.  
To select the number of clusters, the different sets of data have been analyzed. As an example, Figure 8 shows the dendrogram of the dataset for the scenario with data rate of 50 Mbps and distance of 50 m; similar results were obtained for other configurations. In this plot, the similarity of two clusters is measured by the height where they join, so that the lower the clusters join, the more similar they are. As can be seen, many clusters are quite similar (they join at low levels), which would prevent algorithms from having a good performance. Considering these results, we decided to configure the classification with multiple clusters to 4 congestion levels. For the implementation of this algorithm we have used _k-means_ in an iterative way, so that it performs binary classification over the obtained clusters.
## 5 Conclusions  
A simulation framework based on the car-following model and CA was proposed in this work and then employed to simulate the traffic flow affected by the GPV information in the V2X environment. With the simulation, operation characteristics of the traffic flow were explored quantitatively. The research results suggest that the operation of the traffic flow has been effectively improved with the GPV information, and the improvement degree is up to 88.73%, which is calculated by the maximal volume of traffic flow. It is confirmed that the application of the V2X and other connected technologies has positive impacts on traffic flow and can effectively improve transportation efficiency.  
Writing--original draft, J.H.; Writing--review & editing, X.W.; Software, J.H., H.S. and J.Z.; Validation, J.H., C.B. and J.Z.; Visualization, J.H. and G.W.; Funding acquisition, X.W.; Project administration, X.W.; Resources, X.W.; Supervision, X.W.; Formal analysis, C.B.; Methodology, C.B.; Investigation, H.S. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Key Research and Development Project (Grant No.2018 YFB1601500), the Natural Science Foundation of Shandong Province (Grant No. ZR2020 MF082), the Qingdao Top Talent Program of Entrepreneurship and Innovation (Grant No.19-3-2-11-2 hc) and the Foundation of Shandong Intelligent Green Manufacturing Technology and Equipment Collaborative Innovation Center (Grant No. IGSD-2020-012).  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Open RAN--Radio Access Network Evolution, Benefits and Market Trends
## 5 Market Expectations, Requirements and Open RAN Benefits  
Open RAN is an awaited technology described by industry as a solution which brings a set of benefits from both operators and end-user perspectives. It is a specific RAN-related framework enhancing commercial-proofed 3 GPP solutions by introducing new interfaces and nodes from a scratch. From a mobile network operator (MNO) perspective, there are certain requirements towards Open RAN which need to be specified, implemented, and deployed to meet the MNO expectations. In the latest published technical specification [53] signed by selected European operators, there is a detailed prepared guidance for Open RAN indicated area and use cases which O-RAN should cover. The authors has stated that it is expected that from 2022 a wide Open RAN network roll-out should start. It is a strong expectation that Open RAN products will achieve a proper quality and security level as well as they will support standalone (SA) and non-standalone (NSA) modes, both 4G and 5G RAT also for legacy bands. The functionalities related to intelligent and programmable operation are perceived with minor priority, i.e., are expected to be deployed later. In [54], the operators under the open RAN MoU have defined in detail some priority levels (must have, nice to have) and priority types (how many operators have the corresponding priority) for Open RAN scenarios, infrastructure requirement, Open FH, RIC, RAN features, etc. Finally, an Open RAN architecture together with a wide radio access network decomposition may introduce many benefits, which will finally be evaluated after commercial deployment. In Table 3, a subjective extract of features perceived as benefits, gains, and game changers are presented based on common available resources.  
Figure 6: Example of Open Xhaul transport network.
## 2 Literature Review
### SHTES Systems in Greenhouses  
#### 2.2.1 Water Tank as SHTES System  
To control temperature and maximize energy efficiency, employing a water tank as a TES system in greenhouses, particularly in cold regions with harsh winters, is a sensible practice. You can use solar collectors or other heat sources to reheat the water in the tank on hot days or different periods of excess heat. The water efficiently stores heat because it acts as a thermal mass. The thermal energy stored in the water tank may be released to keep the greenhouse's temperature constant and comfortable when needed, such as throughout the night or during cold seasons. In addition to shielding plants from bitter cold, this technology helps maintain ideal growth conditions by reducing temperature swings. In the following, the previous works in the field of the applications of SHTES systems in greenhouses are reviewed.  
Chinese solar greenhouses intended for nighttime heating require a north wall with a high thermal capacity. Nevertheless, because of their poor heat conduction, which prevents heat from moving from the wall's surface to its depth, the north wall's capacity to store thermal energy using conventional construction materials is restricted. [PERSON] et al. [40] incorporated an active solar water wall made of hollow polycarbonate sheets into the north wall and an underground water storage tank to raise the temperature at night. Its abilities to gather and release solar heat, increase the greenhouse temperature at night, and function thermally in inclement weather were all considered during its evaluation. An average of 85.8% of the heat was captured, of which 80.4% was discharged for greenhouse heating. The average minimum nightly air temperature rose by 3.3 \({}^{\circ}\)C compared to the scenario without the water wall. They presented that by removing the need for additional heating, retrofitting Chinese solar greenhouses with water walls can enable the cultivation of warm-season crops all winter long. The schematics of the proposed system by [PERSON] et al. [40] are demonstrated in Figure 22.  
In a Chinese solar greenhouse, [PERSON] et al. [41] provided a theoretical foundation for the system's installation location and collector size based on solar radiation. A solar water heating system (SWHS) consisting of a sheet heating system was placed on the north wall of a Chinese greenhouse near Beijing. The findings showed an average rise of 3.1 \({}^{\circ}\)C in the lowest nightly interior temperature. In January, the time period for keeping the nighttime internal temperatures higher than 8.0 \({}^{\circ}\)C makes up 80.0%. The schematics of the proposed system by [PERSON] et al. [41] are depicted in Figure 23.
# Intelligent Space Object Detection Driven by Data from Space Objects
## 5 Conclusions  
With the constant advancement of space exploration, there has been an increase in the demand for various relevant technologies. However, it remains challenging to obtain real satellite data. In this paper, the data obtained from 3D satellite models is not technically processed to simulate the real space satellite images as much as possible. By applying deep learning to a novel domain, satisfactory results are obtained. Nonetheless, there remain certain limitations. Most of the realistic remote sensing images are large in size, which requires the consumption of a significant amount of memory during training. The application of compression techniques might lead to the loss of features for small objects and a decrease in the accuracy of detection. Due to the scarcity of genuine satellite data, the dataset lacks diversity in terms of object variations. Thus, there is considerable room for optimizing deep learning detection models, especially in the accurate identification of ultra-small objects. To sum up, further exploration is required in these aspects.  
Introduction, M.X.; Dataset Construction and Labeling, Q.T., X.L.; Space Object Detection Algorithm Model, Q.T. and M.X.; Experimental Results and Analysis, Q.T. and J.Z.; Conclusions, Q.T.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by Xi'an Institute of Optics and Precision Mechanics of CAS, grant number E33131D101.  
Not applicable.  
Restrictions apply to the availability of these data. Data were obtained from Beihang University and are available from Mr. [PERSON] with the permission of Beihang University.  
The authors declare no conflict of interest.
# Improved Sliding Mode Control for Permanent Magnet Synchronous Motor Speed Regulation System
## 1 Introduction
Based on this idea, a way to build a balance between the robustness and chattering reduction in SMC methods is presented, but only some simulations are performed. In Reference [26], an extended state observer is applied to estimate disturbances in SMC. In Reference [27], a fuzzy combined control using appropriate PID and SMCs is presented, and it provides infinite DC gain. The fuzzy combined control can guarantee a zero steady state error, and the disturbances can be rejected. The remainder of this paper is structured as follows. The second part describes the SMC design for the PMSM, and stability analysis is performed for the proposed control scheme with sliding mode and repetitive control. The third part presents the comparison of control results between simulation and experiment about SMC and improved SMC. In this section, the simulations and experiments were carried out to investigate the analysis in the second part. Finally, the conclusion is given in the fourth part. Furthermore, several assumptions are made to simplify the analysis. 1. The PMSMs have the same pole pairs. 2. The permeability of the back iron is infinite. 3. The magnetic field is unsaturated. 4. The influence of external environment on PMSM parameters is not considered.
## 2 Materials and Methods  
### The Study Area  
Marinduque, an island province in Region IV-B of Luzon, is known to have a \"heart-shaped\" boundary. The province's total land area is roughly 953 square kilometers, divided into six municipalities: Boac, Mogpog, Sta. Cruz, Torrijos, Buenavista, and Gasan. The focus of this study was on the Boac and Mogpog rivers, as shown in Figure 2. Annual rainfall in the province varies from 1700 to 2500 mm [41]. Its climate is classified as type 3 without a very pronounced maximum rain period, and a dry season during December to February or March to May.  
The total length of the major rivers in Marinduque is approximately 178 km, 40.7% of which are attributed to the Boac River (35.71 km) and Mogpog River (35.79 km). These two rivers are the longest in the province and are known to overflow during heavy rainfall [6].  
Marinduque is the site of two major TSF failure disasters in 1993 and 1996, considered the most extensive and worst environmental disaster in the Philippine mining industry [42]. The mining operation was stopped after the disaster, but no rehabilitation of impacted areas, including the TSFs and open pits, were implemented. The two abandoned TSFs, i.e., Maguilaguila and Tapian pit, are connected to Boac and Mogpog rivers. Without rehabilitation and management, these two TSFs located upstream of the rivers will continue to contaminate areas downstream of the mining areas, especially when flooding events and transport of mine drainage and tailings to the floodplains of the two rivers occur.
# Novel Thermal Management Strategy for Improved Inverter Reliability in Electric Vehicles
## 3 Proposed Thermal Management Strategy
### Temperature Constraint Tracking Algorithm  
Finally, the estimated highest \(T_{vj}\) value is sent to the TCT algorithm, whose block diagram is shown in Figure 4. This regulator aims not to exceed a given temperature limit \(T_{j,max}\) by computing the difference between \(T_{vj}\) and \(T_{j,max}\):  
\[\Delta T=T_{vj}-T_{j,max}. \tag{13}\]  
Figure 3: Equivalent Foster thermal network.  
The switching frequency of the PWM modulator, determined by the TCT algorithm for the \(k\)-th instant is:  
\[F^{*}_{sw,TCT}(k)=F^{*}_{sw}-\delta F_{sw}(k), \tag{14}\]  
where \(F^{*}_{sw}\) is the predefined PWM frequency of the inverter and \(\delta F_{sw}\) is a correction term that reduces the switching frequency when \(T_{vj}\) exceeds \(T_{j,max}\):  
\[\delta F_{sw}(k)=\delta F_{sw}(k-1)+\alpha\Delta T, \tag{15}\]  
where \(\alpha\) is a positive tunable parameter that sets the dynamics of the TCT algorithm.  
Finally, the correction term \(\delta F_{sw}\) is saturated as follows:  
\[\delta F_{sw}(k)|_{sat}=\begin{cases}0&if\ \delta F_{sw}(k)\leq 0,\\ F^{*}_{sw}-F^{*}_{sw,min}(k)&if\ \delta F_{sw}(k)>0.\end{cases} \tag{16}\]  
Saturation in (16) has two purposes. On the one hand, the predefined switching frequency \(F^{*}_{sw}\) cannot be exceeded; thus, \(\delta F_{sw}\) is saturated to zero when the compensation term is positive. On the other, the minimum switching frequency that ensures electric machine controllability (\(F^{*}_{sw,min}(k)\)) for the operation conditions of the \(k\)-th instant is established as follows:  
\[F^{*}_{sw,min}(k)=f_{e}(k)S_{p}=\frac{\omega_{m}(k)P}{2\pi}S_{p}, \tag{17}\]  
where \(f_{e}(k)\) is the electrical frequency of the IPMSM in the \(k\)-th instant, \(P\) is the pole-pair number of the machine, \(\omega_{m}(k)\) is the mechanical speed of the machine, and \(S_{p}\) is the number of samples per period required to ensure system controllability. In this work, \(S_{p}\) has been set to eight, while the minimum value that \(F^{*}_{sw,min}\) can acquire has been saturated to 2 kHz in order not to exceed a given torque ripple during near standstill operation. (Despite some recent research works [13; 40; 41] try to minimize the \(S_{p}\) to as low as 2 in order to fulfil the well-known Nyquist frequency (which must be half of the sampling one in order to guarantee stability); it is known that the conservative value should not be lower than 8.)