esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Optimization Design and Control Methods for Non-Conventional Lanes
### Signal Control Optimization  
Signal control is key to the efficient operation of non-conventional lanes, requiring the formulation of corresponding control strategies for new traffic organization modes. Signalcontrol for non-conventional lanes must consider not only the optimization of individual intersections but also achieve coordinated control with upstream and downstream intersections.  
[PERSON] et al. [76] constructed a signal control optimization model using dynamic programming theory for the special needs of oversaturated intersections. [PERSON] et al. [77] proposed a multi-layer boundary active control strategy for oversaturated traffic networks. In signal control for non-conventional lanes, pre-signal control is an important research direction. Pre-signal settings need to consider coordination relationships with main signals, including phase difference optimization and green time allocation. For contraflow left-turn lanes, pre-signals need to be activated at appropriate times, ensuring both that left-turning vehicles can smoothly enter the borrowed section and avoiding serious conflicts with opposing through traffic. For variable lanes, signal control needs to achieve precise synchronization with lane function switching, ensuring the safety and efficiency of the switching process.  
Future research should strengthen adaptive signal control algorithms, enabling systems to automatically adjust control parameters according to real-time traffic conditions. Simultaneously, balanced control strategies for multiple objectives should be researched, finding optimal balance points between efficiency, safety, equity, and other goals. Furthermore, considering the intelligent connected environment, new signal control modes under vehicle-road coordination conditions should be explored to achieve more precise traffic flow regulation.
## 5 Importance of Ubiquitous Cellular Coverage in Hospitals
### Applications of NB-IoT in Sensor-Based Hospital Monitoring  
Although the planned strategy for asset monitoring at CHUCB involves the use of Bluetooth Low Energy (BLE) tags integrated with more than 300 existing Aruba (Meridian) access points capable of integrating BLE and supporting BLE beacons, allowing real-time device location through internal georeferencing solutions. Among the software compatible with these BLE solutions, Aruba Meridian and Cisco DNA Spaces stand out, allowing real-time tracking of devices and people, with simple integration into the existing infrastructure. Narrowband Internet of Things (NB-IoT) emerges as a promising technology for hospital monitoring and tracking applications in contexts where there is no dense Wi-Fi infrastructure or effective BLE coverage. Although NB-IoT coverage at CHUCB has been limited, this technology offers technical advantages such as long range, efficient building penetration, and low energy consumption, making it suitable for devices that reliably report data sporadically [33; 34; 35]. However, as [PERSON] et al. [36] caution, implementing NB-IoT networks in hospital environments introduces critical security challenges, including vulnerabilities in data interception, medical device spoofing, and authentication protocols. These concerns become particularly relevant as our measurement results identify unreliable NB-IoT coverage at CHUCB's points 17 and 21. In these areas, weak or absent signals not only impair communication reliability but also undermine the implementation of essential security measures. This dual challenge of connectivity and security underscores the necessity for future research addressing both coverage optimization and context-aware protection mechanisms for healthcare environments with constrained connectivity. The application of NB-IoT could be considered in outdoor areas, ambulances, or remote areas where such infrastructures are not available. This hybrid approach can extend the reach of tracking solutions, ensuring continuous coverage throughout the hospital perimeter.  
There are already specific proposals in the literature for the use of NB-IoT for asset tracking in hospital environments, showing the feasibility of this approach [37]. Its integration with cloud-based IoT management platforms can enable functionalities such as real-time location, predictive analysis, and alert generation, with a direct impact on the efficiency of hospital services. Not only is asset management envisaged but also localization of patients will be explored (e.g., timely guiding patients to the most convenient service during their visit to the Hospital or localizing vulnerable patients).  
Thus, although BLE continues to be the most promising technology in the hospital context, NB-IoT represents a relevant complementary technical solution, especially in future projects aimed at environments with limited indoor infrastructure or irregular coverage.
# A Heuristic Solution Method for Multi-Depot Vehicle Routing-Based Waste Collection Problems
## 3 The Merge-Head and Drop-Tail Strategy to Improve Initial Solutions
### Merge the Head Customer  
Next, the merging operation of the nearest customer pair \([v_{i},v_{j}]\) on the list must be performed. The merging rule is that the later customer \(v_{j}\) in the pair is added to the route of the vehicle \(k_{\rm d}\) where the  
Figure 6: Flowchart of the whole procedure.  
previous customer \(v_{i}\) is located, and another customer \(v_{k}\) of the vehicle \(k_{\text{d}}\) paired with other customers is recorded. Finally, the customers are rearranged with the CWS method and get the new customer sequence visited by the vehicle \(k_{\text{d}}\). The order of the two customers in the pair is switched and the merging operation on the new pair \([v_{i},v_{j}]\) is performed.  
The nearest customer pair in the initial solution shown in Figure 5 is [3; 5]. Customer 5 is firstly added to the route in which customer 3 is located, that is, the route of the vehicle 2. The tail customer of the route is customer 6. A feasible improved solution after the CWS method is the route of the vehicle 1: 4-12-8-9-13-10-7-1, the route of the vehicle 2: 6-3-5, the route of the vehicle 3: 2-11. It can be easily computed that the total demand of the customers in vehicle 2 is 77.0, which does not exceed the vehicle capacity. The total distance of the new routes is 187.11.  
Then, the order of the customers in the nearest customer pair is reversed, that is, [5; 3]. Similarly, customer 3 in the initial solution is added to the route in which customer 5 is located, that is, the route of vehicle 3. The tail customer of the route is customer 2. The current route is re-planned with the CWS method: the route of the vehicle 1: 4-12-8-9-13-10-7-1, the route of the vehicle 2: 6, the route of vehicle 3: 2-11-3-5.  
The total demand of the customers in vehicle 3 is 87.0, which exceeds the vehicle capacity.
## 3 Results
### Optimization Process
#### 3.1.2 Validation and Grid Independence Test  
The grid independence test was performed to reduce the influence of the quantity of grids on the calculation result, as shown in Table 4. The density of grids of the model was increased to investigate the influence by comparing the current density of the PEMFC with different quantities of grids. Five cases with five quantities of grids (421,838, 610,707, 824,345, 1,004,194, 1,188,828) were calculated. The largest error of current density between Case1 and Case5 was 0.49%. The quantity of 610,707 grids was applied considering the accuracy and resource of the calculation.  
To validate this model, the numerical results were contrasted with the experimental results of [PERSON] [9] because of the similar geometry and operating conditions. The numerical results fit well with the experimental results when the current density was less than 0.9 A/cm\({}^{2}\), as shown in Figure 3. The numerical results were slightly higher than the  
Figure 2: The grid of the model and the partial magnification (50\(\times\)).  
experimental results with the increase in current density. The reason was the difference in water formation and distribution between the simulation and reality.
# Assessing Groundwater Level with a Unified Seasonal Outlook and Hydrological Modeling Projection
## 2 Methodology
### Weather Generator (WGEN)  
The seasonal weather outlooks from the CWB were coupled with weather generators to produce seasonal rainfall predictions and used as the meteorological inputs for watershed models, as shown in the second stage of Figure 1. The weather generator (WGEN) was based on the model proposed by [PERSON] and [PERSON] [13], used to predict future daily temperature and rainfall. The theory involves either Markov chains or empirical distributions of wet/dry spells [33]. The WGEN requires historical statistical observations records, including the mean, standard deviation, lag 1 serial correlation coefficients, unconditional probability of a rainy day, and the mean precipitation [13]. The WGEN determines daily temperature using the monthly mean and daily records, which follows a first-order Markov chain process [13; 17; 34]. The daily temperature is expressed as a function of the monthly mean and standard deviation, and a random variable is used to generate an extraordinary occurrence. The Markov chain process, applying historical observations and a random variable between 0 and 1, is examined to identify a rainfall event. In order to generate daily precipitation, two steps are conducted. First, a day is classified as rainy or rainless, and then the amount of rainfall for a rain event is estimated using an exponential distribution. To conduct the prediction, this study used the weather generator, WGEN, supplemented by the rainfall and temperature data recorded from rain gauge stations. Then, on the basis of the long-term seasonal outlook forecast from CWB, the probabilities of monthly rainfall were issued for the upcoming 3 months as the rainfall data. These were taken as inputs for the WGEN to produce 100 sets of rainfall forecasts over the subsequent 3 month simulation. Then, the 25 th and 75 th percentiles were selected as future rainfall amounts and inputs applied to the hydrological model, WASH123D, for simulating groundwater levels for the next season. The seasonal outlooks in the rainfall forecast of WGEN were derived from CWB, and the historical rainfall observations were obtained from the data of rain gauges.  
Figure 2: Illustration of seasonal forecasts by the CWB.
## 3 Materials and Methods
### Data Collection  
Reflecting the geographical and educational diversity of Korea, this study selected the C area as the target region. The C area encompasses both metropolitan and rural educational environments, including schools located in urban areas and those in agricultural and mountainous regions. According to the Ministry of the Interior and Safety (2024), approximately 55% of Korea's administrative divisions are classified as urban areas (si) and about 40% as rural areas (gun), although more than 90% of the population resides in urban areas. Similarly, in C area, as of 1 April 2024, a total of 808 schools (kindergartens, elementary, middle, and high schools) were operating, with approximately 60% located in urban areas and 40% in rural and mountainous areas [70; 71; 72]. This distribution reflects the overall geographical and educational structure of Korea, making the C area a suitable and representative region for investigating teachers' AI Literacy.  
Although the survey was not conducted nationwide, the selection C area holds significance in that the region structurally mirrors the geographical and educational characteristics of Korea. Therefore, the findings of this study are expected to provide meaningful insights applicable to the broader Korean educational context. While the study was conducted within a single province, the structural alignment between the C area and the national educational landscape mitigates concerns about the generalizability of the findings.  
A census survey was conducted among all primary and secondary school teachers in the C area from 16 June to 30 June 2024 using an online questionnaire. Out of 1325 participating teachers, responses from 1172 teachers were considered valid after excluding 152 incomplete or unclear responses. The characteristics of the respondents are detailed in Table 2.  
Figure 2: Composition of Features and Targets.
## 2 Spectrum Sharing Processes  
Dynamic spectrum sharing can be considered (at least conceptually) a three-phase process. First, there is the negotiation phase, where nodes coordinate to come to a spectrum sharing plan. Then, the outcome of the negotiation process, _i.e._ spectrum access policies/rules, is disseminated to all interested parties. Finally, the aforementioned access rules need to be implemented by nodes themselves. It should be noted that there is a _monitoring_ process that spans the above phases and provides the necessary information. _Sensing_ the environment, either to receive information about spectrum usage and availability, or to monitor conformance to spectrum access rules, as well as receiving _feedback_ on the quality of prior spectrum allocations from nodes, is tackled in this process. The interplay among these phases is shown in Figure 1.
## 3 Results  
### Characterization of Geobacillus sp. G4  
The _Geobacillus_ sp. G4 strain is a Gram-positive, rod-shaped bacterium forming round, white-yellow colonies with an average diameter of 1.4 mm (Supplementary Table S4). Characterization revealed that _Geobacillus_ sp. G4 is aerobic, non-motile, and forms endospores. Optimal growth conditions (50 \({}^{\circ}\)C to 70 \({}^{\circ}\)C and pH 6.0 to 7.5) were determined in LB liquid medium based on maximal bacterial growth, as measured by optical density at 600 nm. Additionally, acid production from lactose, cellobiose, glycerol, sucrose, and xylose was observed (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phenotypic Characteristics** & _Geobacillus_ **sp.** & _G. kaustoophilus_ & _G. thermolevovans_ \\  & **G4 *** & **ATCC 8005 *** & **ATCC 43513 ** \\ \hline Gram Staining & Positive & Positive & Positive \\ Motility & Non-motile & Non-motile & Non-motile \\ Morphology & Rod shape & Rod shape & Rod shape \\ Oxygen Requirement & Aerobic & Aerobic & Aerobic \\ G+C (\%) & 52 & 51–58 & 52–58 \\ Endospore Formation & + & + & + \\ Voges-Prokauer & - & - & - \\ Methyl red & + & + & no data \\ Catalase & + & + & + \\ Oxidase & + & + & + \\ Hydrolysis of: & & + & Variable \\ Casein & + & + & Variable \\ Starch & + & + & + \\ Acid from: & & - & + \\ Lactose & + & + & Variable \\ Cellobiose & + & + & + \\ Glycerol & + & + & Variable \\ Sucrose & + & + & Variable \\ Xylose & + & + & Variable \\ Growth at/in: & & & Variable \\ pH range & 6.0–7.5 & 6.2–7.5 & 5.0–9.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Phenotypic characteristics for differentiation among _G. kaustoophilus_, _G. thermolevovans_, and _Geobacillus_ sp. G4.  
The ability of isolate G4 to produce extracellular hydrolytic enzymes was evaluated on agar plates. After incubation at 60 \({}^{\circ}\)C for 24-48 h, isolate G4 demonstrated positive enzymatic activities for cellulase, pectinase, xylanase, amylase, protease, and lipase, indicated by distinct clear zones around colonies. Among these, _Geobacillus_ sp. G4 showed the largest halo formation for amylase (starch), followed by pectinase, xylanase, and cellulase (Figure 2A).  
To further quantify these enzymatic activities, isolate G4 was cultured in liquid media containing potato starch, oat spelt xylan, pectin, carboxymethylcellulose (CMC), olive oil, and casein as carbon sources. Enzyme assays revealed measurable production of amylase (maximum activity: 1.09 U/mL at 12 h), xylanase (0.34 U/mL at 48 h), and pectinase (0.24 U/mL at 30 h), as well as low but detectable cellulase activity (0.02 U/mL at 48 h) (Figure 2B). In contrast, protease and lipase activities, although clearly evident in agar plate assays, were below detection limits in liquid media under the tested conditions.
## 4 Simulation Experiment and Analysis of desensitization Optimization
### Dessitization Optimization Based on Traditional Methods
Based on the above analysis, it can be obtained that the method in this paper has a wide correlation range between angle and sensitivity, which is 0.793\({}^{\circ}\). The optimal system obtained by the traditional method is NO. 1, and the standard deviation is reduced by 0.003. The optimal system obtained by this method is NO. 6, and the standard deviation is reduced by 0.015. Therefore, the optimization of a single variable angle can only achieve the effect of desensitization and performance improvement in a small range.
# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 4 Experiments
### Results
#### 4.4.2 Data Size Impact on Classification Model Performance  
The effect of varying data size performance in terms of accuracy is depicted in Figure 7. Three subsets of the dataset (having 400, 600 and 1000 tweet samples in each subset respectively) were generated to measure the performance of classifiers. It was observed that an increase in tweet observations resulted in gradual improvement in predictive ability of classifiers.
## 2 Structure and Main Parameters
### Main Parameters
#### 2.2.3 Electromagnetic Torque  
The electromagnetic torque of the motor can be expressed as  
\[T_{\text{em}}=\frac{\pi K_{x}m\alpha_{i}B_{\delta}A_{\text{av}}(\sqrt{3}+1)^{2 }(\sqrt{3}-1)}{4} \tag{8}\]  
The AFPMBLDC parameter preliminary design is completed. The rated speed, rated power, rated voltage, and rated current are 4800 r/min, 300 W, 24 V, and 10 A, respectively. The pole arc coefficient, permanent magnet thickness, permanent magnet arc radius, and air-gap length are preliminarily set as 0.6, 3.0 mm, 1.8 mm, and 0.6 mm. The specific data are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Initial Value** \\ \hline Rated speed, \(n\)/(r\(\cdot\)min\({}^{-1}\)) & 4800 \\ Rated power, \(P\)/W & 300 \\ Rated voltage, \(U_{\text{N}}\)/V & 24 \\ Rated current, \(I\)/A & 10 \\ Motor outer diameter, \(D_{o}\)/mm & 65 \\ Motor inner diameter, \(D_{i}\)/mm & 25 \\ Pole arc coefficient, \(\alpha_{\text{i}}\) & 0.6 \\ Thickness of permanent magnet, \(l\)/mm & 3.0 \\ permanent magnet arc radius, \(r\)/mm & 18 \\ Air-gap length, \(h\)/mm & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of AFPMBLDC.
# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 5 Experimental Results
### Experiment 1: One Degree and no Load  
In this experiment, the user performed the flexion and elbow extension movements illustrated in Figure 8. First, with an upright posture and the arm relaxed, the user achieved almost complete flexion. After that, he/she relaxed the gill biceps, performing the elbow extension. The user then performed another bicep contraction but aiming to reach a right angle compared to the relaxed arm. Finally, he/she performed a less intense contraction.  
Figure 9 shows a graph illustrating the variation in the angle of the elbow joint during the execution of the experiment. The graph contains two curves: (i) the actual angle of the articulation of the user's arm (value measured by the gyroscope), which is drawn with a solid orange line; and (ii) the output of the control system (the calculated value), which is drawn with a dashed blue line. It is possible to notice the similarity in the two trajectories presented in the figure; besides, the estimate declines slightly more sharply than the measured value.  
Another important feature is the consistency of performance in the three scenarios. The first contraction, which reaches 120\({}^{\circ}\) and is faster, has almost the same difference between the measured and estimated values as the last contraction, which reaches less than 40\({}^{\circ}\) and is slower. Figure 10 shows  
Figure 8: Movements made in the elbow test: (**a**) flexion; and (**b**) extension.  
Figure 9: Graph of the trajectory of the elbow joint test. The solid blue line is the values read by the gyroscope, and the dashed orange line is the value computed by the control system.  
the variation of the system error throughout the test. The system generates a maximum error of less than 6% of the value measured by the gyroscopes. The average error equals 2.45%, with the first and third quadrants having a distance less than 1% from the average. This behavior affirms the stability of the solution.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 2 System Model and Routing Evaluation Parameter
### Routing Evaluation Parameter
#### 2.2.4 Cache Queue Index  
Data transmission in WSNs based on static or event-driven data has distinct \"many-to-one\" characteristics; hence, in the case of traffic bursts, it is likely to cause network congestion and thus worsen the success rate of packet transmission. Since the cache occupancy of the sensor node is a reliable congestion detection, this property can be considered in the establishment of routing, which makes for the avoidance of those nodes with large cache occupancy and transfers the traffic load to idle or lightly loaded nodes. Finally, we can accomplish a fair utilization of the queue and improve network performance. Here, the cache occupancy rate of the next hop \(j\) can be expressed as follows:  
\[r_{c}(j)=\frac{Q_{i}}{Q_{m}} \tag{18}\]  
Figure 1: The forward distance.  
where \(Q_{j}\) is the current queue length of node \(j\). \(Q_{m}\) is the maximum queue length.  
Cache occupancy \(r_{c}(j)\) only reflects load for a single node. Although it is always better to select a low-occupancy cache node when choosing the next hop, this strategy does not take into account the impact of node cache on the load balance and energy balance of the overall network at different network sizes. Therefore, this article introduces an adjustment factor \(\alpha\) and defines the cache queue index \(\zeta\), taking into account the situation of the entire network. The adjustment factor \(\alpha\) is set based on the size and performance requirements of WSN. The cache queue index \(\zeta^{j}\) of node \(j\) is defined as follows:  
\[\zeta^{j}=\sqrt{1-\left[r_{c}(j)\right]^{\alpha}} \tag{19}\]  
Because the \(r_{c}(j)\) value interval is \([0,1]\), \(1-\left[r_{c}(j)\right]^{\alpha}\) causes the result to be non-negative. The square root operation makes that \(\zeta^{j}\) has a nonlinear relationship with \(r_{c}(j)\). When \(r_{c}(j)\) is smaller, \(\zeta^{j}\) is higher, that is, node \(j\) has more cache and should forward a large amount of data; conversely, when \(r_{c}(j)\) is close to 1, node \(j\)'s cache is approximately full. It almost should not forward to any packet to avoid overload. Therefore, the nodes with the higher cache queue index should be selected as much as possible to reduce the packet loss ratio and ensure the success of data collection. In Sections 3.3.1 and 4.1, we make a detailed analysis of the relationship between the value of the adjustment factor \(\alpha\) and the network size and performance.