esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modeling
### Decision Function  
The FCS-MPCC control strategy relies on the principle that a static power converter can only produce a finite set of switching states, each impacting the system variables differently. This is the case for the VSI described above, where we have eight possible switching states and the associated values of the  
Due to the lack of a modulator in the control design, the switching frequency is variable, leading to increased commutation between the consecutive sampling periods. As a result, this causes switching losses and reduces the converter's efficiency. To limit this issue, a second element  
\[J_{s}=\sum_{i=0}^{N-1}\underbrace{\sum_{j=1,3,5}\bigl{|}S_{j}(k+i+1)-S_{j}(k+i) \bigr{|}}_{\Xi(k+i+1)}=\sum_{i=0}^{N-1}\Xi(k+i+1), \tag{8}\]  
aiming to reduce the switching frequency is added to the decision function to reduce the commutation burden on the transistors at each sampling time. We introduce in (8) the quantity \(\Xi(k)\), corresponding to the number of legs of the VSI where a commutation occurred between two time instants \(k-1\) and \(k\).  
Remark: Following Remark 1, we have, in this paper, \(\Xi(k+i)=0\) for any \(i\in\{1,\ldots,N-1\}\).  
Finally, a third term  
\[J_{m}=\hat{f}\bigl{(}i_{d}(k+N|k),i_{q}(k+N|k)\bigr{)}, \tag{9}\]  
playing the role of a constraint on the maximum current that can pass through the SPMSM is added to the decision function. Indeed, the function \(\hat{f}\) is defined as  
\[\hat{f}(x,y)=\begin{cases}+\infty&\text{if }|x|>i_{\text{max}}\text{ or }|y|>i_{\text{max}}\\ 0&\text{otherwise}\end{cases} \tag{10}\]  
adding an infinite penalty to the cost in case the predicted currents get higher than the rated phase current.  
Finally, the total cost function has three objectives: reducing the deviation of the current from the reference, reducing the total amount of switches between transistor states, and limiting the maximum current through the SPMSM, and is expressed as  
\[J=\lambda_{i}J_{r}+\lambda_{f}J_{s}+J_{m} \tag{11}\]  
where \(\lambda_{i}\) and \(\lambda_{f}\) are weighting factors of current tracking and switching frequency regulation, respectively.  
We see that the cost function (11) is asymmetric. Indeed, reducing the switching frequency affects the current tracking error \(J_{r}\), as the chosen switching states influence the voltage sent to the motor. On the other hand, getting a better current tracking has an impact \(J_{s}\), as it imposes switching states and, as a consequence, a switching frequency on the VSI. To overcome this complexity, this paper presents an optimization algorithm using a multi-objective approach as well as a data-driven approach employing static and dynamic data to find the balance between current tracking error and variable frequency reduction, which gives overall enhanced drive performance.
# Satellite Network Task Deployment Method Based on SDN and ICN
## 4 Task Deployment Method Based on Genetic Algorithm
### Coding  
The number of chromosomes in the initial population is set as \(N^{c}\), and the set is used to represent the population. An \(m\times n\) task deployment matrix is used to represent an individual as a feasible solution to the problem. Let \(M\) be the number of satellites that can provide resources for calculation and caching when making decisions, and \(N\) is the  
Figure 3: The flowchart of the task deployment method.  
number of subtasks that the current task is decomposed into. An individual in a population is represented as:  
\[A=\begin{bmatrix}a_{1m_{a}}&a_{1m_{b}}&\cdots&a_{1m_{m}}\\ a_{2m_{a}}&a_{2m_{b}}&\cdots&a_{2m_{m}}\\ \vdots&\vdots&\ddots&\vdots\\ a_{nm_{a}}&a_{nm_{b}}&\cdots&a_{nm_{m}}\end{bmatrix} \tag{14}\]  
Each element in the individual matrix has a binary value, \(a_{mm_{a}}=1\), indicating that subtasks with task numbers \(m\) are deployed on the satellite node with task numbers \(m_{a}\). The computational and storage requirements of each subtask are different. Therefore, after the random generation of individuals (feasible solutions), it is necessary to detect whether the deployed nodes can meet the requirements of the computational amount and storage amount of the task. The individuals that cannot meet the requirements are then eliminated. Set is used to represent the population, and the number of individuals in the initial population is set as \(N^{c}\). \(N^{c}\) is a feasible solution set composed of the individuals.
# ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems
## 2 System Model and Overview
### ESCAPE Methodology  
A high-level overview of the overall framework proposed in this research work is depicted in Figure 2. In the following sections, its individual components are analyzed in detail. Specifically, as it is observed in Figure 2, we formulate and address the problem of distributed and autonomous evacuation planning, where evacuees are able to make the most beneficial actions for themselves. In a nutshell, the proposed problem and evacuation process formulation are as follows. Before evacuees start the evacuation process, they interact with the ESCAPE service through their mobile device to make an optimal decision regarding the evacuation route that they will follow. ESCAPE consists of two main components:  
1. evacuation-route selection, during which evacuees decide where they want to go, which is based on reinforcement learning, and
2. the decision-making process of whether evacuees will finally go or not go to the initially selected evacuation route, which is formulated via the concept of minority games.  
In the following subsection, we summarize some of the most recently proposed evacuation-planning methods in order to better position our work within the existing literature.  
Figure 1: Topology of the disaster area and system model.
# Influence of Body Weight Support Systems on the Abnormal Gait Kinematic
## 3 Results  
### COM Movement  
Figure 5 shows the comparison of the COM trajectories while subjects walked on the treadmill with different conditions. All the trajectories of all subjects were normalized, such that the gait cycle from 0% to 100%. In Figure 5, the mean trajectories are shown as the bold strokes, while the blurred area surrounding represents the variance of the COM trajectories (standard deviation). Row (a) shows the comparisons of the COM trajectories between normal walking and abnormal walking in the sagittal, horizontal, and vertical direction, respectively. The row (b), (c), and (d) represent the comparisons of the COM trajectories between the ABWS system and the CW system at the unloading level of 30%, 50% and 70%, respectively. The final row, row e, shows the comparisons of the maximum displacements of the COM in three directions: sagittal direction (anterior-posterior), horizontal direction (lateral), and vertical direction. From Figure 5, we can observe that the COM trajectories are modified due to hard walking under abnormal walking conditions. In the horizontal and vertical direction, the peak values of the COM trajectories appear sooner than the normal walking condition. The same results can also be observed in walking under the ABWS and CW system at all unloading force levels. In terms of the maximum displacements of the COM, the significant differences are observed in normal walking conditions for all directions (\(p<0.001\)). The maximum displacements of COM during walking under the abnormal condition were significantly different from the walking under ABW and CW system in both horizontal and vertical directions at all unloading force levels. In the sagittal direction, only at 30%, the significant difference of the abnormal walking versus unloading systems cannot be found (\(p=0.19\) for ABWS and \(0.99\) for the CW system). In comparison between the ABWS and CW system, we found that the maximum displacements of COM in sagittal direction are not significantly different. However, the significant differences are found in both horizontal and vertical directions (\(p=0.045\) at 30% unloading force in the horizontal direction, \(p<0.001\) in other cases).  
Figure 4: The range of motions (ROM) of the hip, knee, and ankle angle were calculated using the relative leg segments coordinations.
## 3 Results
### Temperature Correlations (Influent and Effluent)  
The values of each wastewater parameter shown in Tables 4 and 5 are discussed below in relation to the temperature. The temperature showed varying correlations with wastewater parameters in both the influent and effluent. For the influent pH, there was a moderate negative correlation with the temperature (r = \(-\)0.642, \(p\) = 0.002), indicating that rising temperatures are significantly associated with reduced pH levels, suggesting increased acidity in the incoming water. The total phosphorus (P Tot) concentrations in the influent exhibited a weak negative correlation with the temperature (r = \(-\)0.142, \(p\) = 0.509) which was statistically insignificant, indicating a minimal influence of temperature on the phosphorus levels.  
The chemical oxygen demand (COD) in the influent showed a weak negative correlation with the temperature (r = \(-\)0.166, \(p\) = 0.423), also statistically insignificant, suggesting only slight reductions in the COD with an increasing temperature. The biological oxygen demand (BOD5) in the influent exhibited a moderate negative correlation with the temperature (r = \(-\)0.357, \(p\) = 0.138), although this relationship was not statistically significant, indicating a potential but inconclusive reduction in the amount of organic matter with higher temperatures.  
The total suspended solids (TSSs) in the influent displayed a moderate positive correlation with the temperature (r = 0.38, \(p\) = 0.231) which was statistically insignificant, suggesting a slight tendency for the TSSs to increase with the temperature. The nitrogen (N-Total) levels in the influent demonstrated a strong negative correlation with the temperature (r = \(-\)0.642, \(p\) = 0.002), indicating that higher temperatures significantly reduce the nitrogen concentrations. Similarly, the ammonium (NH4) levels in the influent showed a moderate negative correlation with the temperature (r = \(-\)0.619, \(p\) = 0.003), reflecting a statistically significant decrease in the ammonium levels as temperatures rise, possibly due to an enhanced removal or reduced inflow of ammonium under elevated temperatures.  
These results highlight the influence of the temperature on wastewater parameters, with the pH, nitrogen, and ammonium levels showing significant reductions, while other parameters such as the phosphorus, COD, BOD5, and TSS levels exhibited weaker or statistically insignificant correlations.
# Impact of CoAP and MQTT on NB-IoT System Performance +
## 3 Results
### Service Availability  
Service availability is defined in this study in terms of delivery delay. In case the system has not been able to deliver the data transmission from the device to the cloud in 20 s, the device is considered as non-served. As can be seen in Figure 3, all stack options reach a service availability above 99% in the two lowest load points studied. At load 1.5 MTC requests per cell however, the MQTT service availability start rapidly to decrease reaching 95% at 2 MTC requests per second and cell then further lowering below 90% for the consecutive studied loads.  
CoAP survives the increase of load better but for the larger data size service availability drops below 99% at 2 MTC requests per second and cell. The curves shoot upwards in the order of generated load. Most load is generated with the larger file size over MQTT due to the TCP overhead. Second most load is generated by the smaller file size over MQTT, again due to the TCP overhead. After MQTT come the two plots for CoAP confirm, first larger file size and then smaller file size. Retransmissions cause the extra load. Best service availability is, perhaps counter intuitively, reached when no reliability measures are in effect and the load is thus the lowest.  
Figure 2: MTC application level throughput as a function of system load for (**a**) a single device; (**b**) on cell level.
# The Effects of 3D and 2D Imaging on Brain Wave Activity in Laparoscopic Training
## 3 Results
### Mental Workload  
#### 3.2.1 EEG Alpha Wave Activity  
There were no significant differences in EEG alpha wave activity between the simulated laparoscopic tasks (\(F_{1,11}=3.632\), \(p=0.083\)) and vision systems (\(F_{1,11}=1.842\), \(p=0.202\)). The EEG alpha wave activities for the circle-tracing and peg transfer tasks were 1498.58 (410.10) and 1966.17 (429.89) Bel-Seconds, and for the 2D and 3D vision systems, 1505.67 (377.70) and 1692.08 (461.73) Bel-Seconds.  
However, a significant interaction of simulated task \(\times\) vision system (\(F_{1,11}=11.072\), \(p=0.007\)) was found. For the interactions of the simulated task with EEG alpha wave activity and vision system, the results of one-way ANOVA indicated that in the peg transfer task, EEG alpha wave activity was significantly higher for 3D than for 2D (\(F_{1,11}=7.989\), \(p=0.016\)).
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 4 Results and Discussions
### The Measure of Data Distribution  
#### 4.3.1 A Multidimensional Approach to the Examination of the QoS Obtained  
Figures 8-13 compare the metrics individually in forty bin histograms. Figure 8 is the histogram of the RSRP for the three locations. A strategy that presents the result for the locations in subplots has been adopted to ensure that the trends are seen. Figure 9 shows the histogram for the RSRQ obtained at different distances from the base station. Figure 10 shows the signal strength distribution for the three locations as recorded. Figure 11 shows the ratio of the packet component carrier (PCC) to the signal-to-noise ratio (SINR) for UNILAG and Ikorodu. The measured data from Oniru VI was not sufficient enough to compute the histogram for the PCC/SINR. Figures 12 and 13 are the histograms for the download speeds (PCC-PHY and PCDP) obtained in all locations.  
Figure 8: Histogram of reference signal received power (RSRP).  
Figure 9: Histogram of reference signal received quality (RSRQ).  
Figure 11: Histogram of the ratio of the packet component carrier (PCC) to the signal-to-noise ratio (SINR).  
Figure 10: Histogram of received signal strength indicator (RSSI).  
Figure 12: Histogram of packet component carrier (PCC) physical (PHY) downlink (DL) throughput.  
Figure 13: Histogram of packet data convergence protocol (PDCP) downlink (DL) throughput.  
In the case of the RSRQ, the histograms seem to follow a normal distribution; however, on closer observation, it is clear that the values are pulled to the right for each of the subplots, indicating that more of the data is higher than the mean value. In Figure 10, the histograms are right-skewed, indicating that the median signal strength is lower than the mean value. It is also a sign that many of the values are near the lower end of the range, and higher values are infrequent. Figures 12 and 13 show that the download speeds shown by the throughput values are positively skewed. The bimodal shape of the PDCP throughput in the Ikorodu area shows that there are lower speeds recorded in that area compared to the mean values obtained for the area. In Figures 12 and 13, the download speeds obtained in Oniru VI show a wider spread of values and they are less skewed to the right compared to the speeds recorded at UNILAG and Ikorodu.
## 5 Results and Discussion
### Runtimes Consideration  
Most of the RO-PUF designs in the literature did not specify the RO runtime in detail. [PERSON] et al. mentioned that the RO runtime is 0.14 ms [32], and [PERSON] et al. enabled the series around 20 ms [28]. The analysis of the runtime was carried out by [PERSON] et al. [49]. The ROs were run in the range of 200 ns to 204.8 us. Therefore, this work expands individual RO (\(t_{\text{RO}}\)) runtimes with a duration varying from 100 ns to 1 ms. It is estimated that up to 1 ms, the number of pulses generated by RO has not exceeded the counter capacity. The ROs were run one by one with a pause of \(d_{i}\) = 100 ns. This technique was chosen to avoid interference between ROs or other adjacent circuits. Therefore, the total time (\(t_{total}\)) required to activate \(n\) ROs is formulated according to Equation (5).  
\[t_{total}=(n\ t_{RO})+(n-1)d_{i}\, \tag{5}\]  
In the previous study by [PERSON] et al. [49], an error would occur if the RO were run in a short duration. The author recommended using at least 12.8 us. Therefore, this study investigates this issue to determine the minimum runtime because of using different FPGA technology. In short duration runtimes, the study found that ROs generated pulses in close range. For example, upon realization using Pattern-1 at chip-1 at low temperature, for a runtime of 100 ns, ROs generated ranging from 30 to 45 pulses. The pulses generated by ROs were mostly equal. Then, for a runtime of 200 ns, the produced pulses were slightly different, ranging from 60 to 90. Upon further longer runtimes of 2 us, the number of pulses produced varied among ROs in close proximities. Some RO pairs resulted in equal pulses, such as RO-4 and RO-7, RO-5 and RO-9, RO-2 and RO-10, RO-27, and RO-29, which generated 690, 691, 696, 735 pulses, respectively.  
Whenever two or more ROs generate equal pulses, this leads to bit-flip when the ROs are run on different environmental conditions. For the use as a PUF, all ROs should produce different pulses that are scattered away from each other. Therefore, this work suggests using the minimum runtimes (\(t_{RO\_min}\)) of individual RO to guarantee at least \(s\) pulses difference among ROs using Equation (6).  
\[t_{RO\_min}=\
icefrac{{s}}{{\Delta f_{min}}},\ \text{where}\ s=1,\ 2,\ 3, \tag{6}\]  
where \(\Delta f_{min}\) refers to the minimum frequency difference among ROs based on the experiment, the minimum frequency of the recorded data in this work is 0.105 MHz, which means that the minimum runtimes guarantee at least s = 1 pulse difference is around 9.52 us. In the previous works [43; 44], a threshold of \(\Delta f_{min}\) was set to improve reliability. Any RO pairs with a frequency difference less than the set threshold are ignored, reducing the number of response bits. However, instead of setting a threshold of frequency difference of RO pairs, this work suggests increasing the runtimes until ROs produce minimum \(s\) pulses difference. Hence, all RO pairs may be used, and a maximum response bit may be generated.
## 4 Discussion
### Model Performance Results and Classification of AOI
Consequently, while certain variables may exhibit slightly higher significance, the cumulative contribution of all features makes specific feature selection less pivotal in this work context. After the sieving process, the ultimate AOI classification indicates a hazelnut coverage of 22,780 hectares, in close accordance with national statistics, which reported 23,900 hectares in total and 21,700 hectares in production for the year 2022. The mapping delineates hazelnut groves primarily within the PDO area (99.5%). Intensive cultivation is observed in the Monte Cimino area, especially around the volcanic crater of Lake Vico, with extensive and compact extensions on the eastern and southern slopes. The western flank exhibits mixed agricultural systems with hazelnuts facing environmental and social constraints. Some large parcels outside the core area indicate emerging transformations in agricultural landscapes. However, currently, hazelnut cultivation is confined mainly to the  
Figure 19: Density plot for SWIR variables for HZ and VEG in the training set during the summer period. PDO area. Despite the effectiveness of our multi-year optical approach in capturing the spectral and temporal patterns of the crop accurately, several limitations are acknowledged. Specifically, the method encounters challenges in accurately identifying young hazelnut orchards due to the temporal discrepancy, wherein the crop might not fully cover the ground three years prior, potentially leading to misclassification. Additionally, during the preprocessing stage of radar data, a three-pixel speckle filter is applied for noise reduction, which may induce confusion along the boundaries of classified patches. These limitations are predominantly attributed to the still coarse resolution of currently available satellite sensors. From a statistical perspective, it is evident that hazelnut cultivation may not always be declared or updated by producers, as these areas are not eligible for EU subsidies except for agri-environmental premiums or new installations co-financed by European Union aid. Therefore, relying solely on statistical data may lead to an underestimation of the areas dedicated to hazelnut cultivation. This aspect underscores the usefulness and effectiveness of remote-sensing techniques and satellite image classification for mapping hazelnut-growing areas. It is also crucial to have a methodology and analysis tool that enables the assessment of hazelnut orchard dynamics over the years in a replicable, scalable, and exportable manner.
# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
## 2 Comprehensive Survey
### Cross-Layer Routing Protocols in VANETs  
The available cross-layer schemes are classified depending on (a) cross-layer routing characteristics, (b) routing method, and (c) geographic measure. No geographic measure is being used by the cross-layer routing protocol. Independent cross-layer protocols have been designed that rely on routing characteristics from the PHY, MAC, and NET levels [21].
## 2 Literature Review
### Nighttime Utilization of RP Services  
Within this broader context, RP services exhibit distinctive temporal patterns, particularly at night. [PERSON] et al. [1] conducted spatial regression analyses using CleverShuttle data from Berlin and Munich, revealing a notable demand peak for RP services around midnight, precisely when PT services are unavailable or insufficient. [PERSON] et al. [29] observed similar peaks in the evening hours between 8 p.m. and midnight based on data from the MOIA RP service in Hamburg. [PERSON] et al. [30] investigated nighttime use of MOIARP during the COVID-19 pandemic, when the RP service supplemented PT. They found users to be significantly younger and that RP was mainly used for leisure purpose, even though nighttime curfew still existed during that time. An analysis of MOIA booking data from 2023 focusing on individuals with mobility impairments reveals that this user group primarily travels during the day, while usage is significantly lower in the evening and at night [31]. [PERSON] et al. [3] confirmed that RP usage intensifies during weekend nights, with up to 50% of trips being efficiently shared, using an offline utility-based algorithm and 1.5 million New York taxi rides. [PERSON] et al. [32] conducted an SCE in German cities to examine mode choice for shared modes and found that cost outweighed travel time for RP and carsharing. However, users' willingness to pay (WTP) for RP services appears to decline slightly during nighttime. [PERSON] et al. [33] report a 2.12% decrease in WTP at night, potentially reflecting heightened safety concerns or a higher subjective valuation of time.  
The reviewed literature highlights that nighttime mobility is shaped by a complex interplay of contextual, infrastructural, and psychosocial factors. Safety concerns, particularly among women, emerge as a consistent barrier across all modes of transport. While PT remains essential at night, its limited availability and perceived lack of safety often lead to increased reliance on private cars. In this context, RP services demonstrate potential as a flexible and demand-responsive alternative, especially during periods of low PT coverage. However, their effectiveness depends on a range of factors that are particularly sensitive to nighttime-specific conditions.