esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Increases in Fuzzy Logic Criteria
### Adaptation of Recharging According to the Baggage Arrival Rate  
Heuristic 2, referred to as \(sc5\), simulates recharging adaptations based on the baggage arrival rate and the corresponding variation in induced activity (the number of tasks to be  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenarios** & **Sc4** & **Sc5** & **Sc6** \\ \hline Number of bags & 1000 & 1000 & 1000 \\ \hline Total recharge time (s) & 4606 & 4285 & 935 \\ \hline Total simulation time & 03:39:08 & 03:37:03 & 01:32:15 \\ \hline Maximum number of waiting bags & 468 & 659 & 159 \\ \hline Average baggage waiting & 234 & 327 & 99.62 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time results and configuration for 1000 bags for Sc4, Sc5, and Sc6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Indicators** & **AIV1** & **AIV2** & **AIV3** & **AIV4** & **AIV5** & **Global** \\ \hline
**Thresholds** & 15/15 & 20/15 & 25/20 & 30/20 & 35/25 & \\ \hline _nbMissions_ & 203 & 201 & 199 & 198 & 199 & 1000 \\ \hline _timeMission_ & 62 & 62 & 63 & 63 & 63 & 62.6 \\ \hline _nbRecharges_ & 68 & 67 & 78 & 76 & 80 & 369 \\ \hline _wtRecharging_ & 143 & 178 & 5 & 10 & 70 & 406 \\ \hline \hline \end{tabular}
\end{table}
Table 7: AIV results for Scenario 4.  
performed by the AIVs). As shown in Table 8, this heuristic enables the AIVs to complete their missions more quickly compared to \(sc4\). Specifically, AIVs complete one mission in an average of 58 s under \(sc5\), whereas it takes them 62.6 s under \(sc4\) (see Table 7).  
The input linguistic variables are \(EnergyLevel\), \(DistanceStation1\), \(DistanceStation2\), \(AvailabilityStation1\), and \(AvailabilityStation2\). The output linguistic variable is \(Decision\). The fuzzy rules for the distance and availability criteria for stations 1 and 2 are as follows:  
1. If \(EnergyLevel\) is \(Critical\), and \(DistanceStation1\) is \(Near\), and \(DistanceStation2\) is \(Far\), then \(Decision\) is \(Station1\).
2. If \(EnergyLevel\) is \(Critical\), and \(DistanceStation1\) is \(Far\), and \(DistanceStation2\) is \(Near\), then \(Decision\) is \(Station2\).
3. If \(EnergyLevel\) is \(Caution\), and \(DistanceStation1\) is \(Medium\), and \(DistanceStation2\) is \(Far\), and \(AvailabilityStation1\) is \(High\), then \(Decision\) is \(Station1\).
4. If \(EnergyLevel\) is \(Caution\), and \(DistanceStation1\) is \(Medium\), and \(DistanceStation2\) is \(Far\), and \(AvailabilityStation1\) is \(Weak\), and \(AvailabilityStation2\) is \(High\), then \(Decision\) is \(Station2\).
5. If \(EnergyLevel\) is \(Caution\), and \(DistanceStation1\) is \(Far\), and \(DistanceStation2\) is \(Medium\), and \(AvailabilityStation2\) is \(High\), then \(Decision\) is \(Station2\).
6. If \(EnergyLevel\) is \(Caution\), and \(DistanceStation1\) is \(Far\), and \(DistanceStation2\) is \(Medium\), and \(AvailabilityStation1\) is \(High\), and \(AvailabilityStation2\) is \(Weak\), then \(Decision\) is \(Station1\).
# Human Factors Analysis of Air Traffic Safety Based on HFACS-BN Model
## 2 Methodology and Materials  
### Human Factors Analysis and Classification System (HFACS)  
HFACS is valuable for systematically analyzing the causes of an accident and currently remains at identifying the core risk factors of accidents. It describes four levels of human error: Unsafe acts, preconditions for unsafe acts, unsafe supervision, and organizational influences (Figure 1) [20]. Since its initial development, HFACS has proved to be an effective tool for human error analysis in various fields, such as railway [13], mining [21], and maritime transport [22], as argued previously. This study utilizes the Bayesian network (BN) to construct a quantitative prediction network among different levels of risk factors in HFACS and between risk factors and ATC performance.
## 3 Results and Discussion
### Drained Shear Strenght  
Consolidated drained direct shear tests were used to evaluate the drained shear strength of the material and the influence of oven drying on it. Twelve specimens with different normal stress were tested for the undisturbed state whereas four were tested for the oven-dried state. For the undisturbed samples, eight circular (60 mm in diameter, represented with dashed lines in Figure 13) and four square (100 mm sides) represented with solid lines in Figure 13) specimens were tested. For the oven dried sample, four specimens were tested, all using a 100 mm square box. Results are shown in Figure 13.  
Figure 12: Box plots of Unconfined Compression Tests for undisturbed and oven-dried samples.  
For the undisturbed state, specimens with a normal stress lower than 52 kPa behave as over consolidated, while for higher stresses, a normally consolidated behavior is observed. However, for oven-dried samples, the specimen with a normal stress of 50 kPa behaves as normally consolidated and show neither peak resistance nor increase in volume. This could be an effect of dynamic compaction.  
Figure 13: Direct shear test results (**a**) Shear stress against shear displacement for undisturbed specimens. (**b**) Vertical displacement against shear displacement for undisturbed specimens. (**c**) Shear stress against shear displacement for oven-dried specimens. (**d**) Vertical displacement against shear displacement for oven-dried specimens.  
To study the drained shear strength of the material, the Mohr-Coulomb criterion was used. Figure 14 shows the linear envelopes obtained for undisturbed (red line) and oven-dried (blue line) samples.  
A friction angle of 29\({}^{\circ}\) and 33\({}^{\circ}\) was obtained for the undisturbed and oven-dried samples, respectively. As for the cohesion, for undisturbed samples, a value of 18.7 kPa was obtained while the cohesion of oven-dried samples resulted to be 4.3 kPa. Both envelopes showed an acceptable fit with a coefficient of determination (R\({}^{2}\)) of 0.962 for undisturbed material and 0.999 for oven-dried material. As demonstrated in other test results, oven-drying the material produces a change in its behavior, with oven-dried specimens showing more sand-like behavior, while undisturbed samples behave as a more cohesive soil.
# A Micro-Coordinate Measurement Machine (CMM) for Large-Scale Dimensional Measurement of Micro-Slits
## 2 Experimental Setup for the Micro-CMM
In order to prevent the motion error induced by the \(Z\)-servo motor stage, it is desired to move the probe in the \(X\)-direction without lifting the probe up in the \(Z\)-direction. For this purpose, the artifact is moved by the \(Y\)-slide table in the negative \(Y\)-direction until the moving path of the probe along the \(X\)-direction is not blocked by the artifact. After the probe is moved across the artifact by the \(X\)-servo motor stage, the artifact is moved back to its original position by the \(Y\)-slide table in the positive \(Y\)-direction. Finally, the probing at \(P_{2}\) is carried out by using the \(X\)-directional PZT stage. As a result, Equation (1) can be rewritten as follows. \[w_{s}=L_{34}+D_{e}+2s_{ts}=L_{34}+L_{12}-w_{b}-2\left(s_{tb}-s_{ts}\right) \tag{3}\]  
Since \(s_{tb}\) is almost the same as \(s_{ts}\), the influence of the water layer thickness can be cancelled. Figure 4 shows the vibration frequency shifts when probing the slot die and the gauge block, respectively. The vertical axis in Figure 4 indicates the frequency shift \(\Delta f\) and the horizontal axis indicates the probe displacement in the \(X\)-direction. When the frequency shift is extended to the trigger threshold, the probe position in the \(X\)-direction is measured by the laser interferometer to determine the trigger point of the probing. The probe tip is then retracted back from the measured surface immediately. As shown in Figure 4, if the frequency shift \(\Delta f\) of 0.1 Hz is set to be the trigger threshold for the probing, the difference of the probe displacements for the two results in Figure 4 is less than 2 nm. Consequently, the gap width of the slot die can be measured accurately by the on-line qualification of the effective diameter of the tip ball. Figure 4: Vibration frequency shifts when probing the calibrated artifact and the slot die. After the on-line qualification procedure, the probe tip is positioned inside of the micro-slit of the slot die for the gap width measurement based on Equation (1). The gap width uniformity can also be obtained by measuring the gap widths at different \(Y\)-directional positions through moving the slot die with the \(Y\)-directional linear slide.
# Design and Optimization of Piezoelectric-Powered Portable UV-LED Water Disinfection System
## 4 Optimization
### Feasible Objective Functions (OFs)
#### 4.1.2 Cost of the System  
The cost of the system is highly dependent on two main components, UV-LEDs and _PZT_ ceramic. Besides, these components have a major role both in piezoelectric energy harvesting and UV disinfection. The next OF, the cost of the system, can then be derived based on the market price variability of these components. Firstly, considering market prices of _PZT Navy Type_ II ceramic of $4-5, the cost of the ceramic can be formulated, as a linear function of the number of ceramics (\(N\)), as follows  
\[C_{1}=4.5N. \tag{16}\]  
Next, the market price of UV-LED is highly dependent on the flux power (\(\phi\)) of UV-LEDs which was discussed in the previous section. As per the dosages given in Table 3, and considering the market prices of UV-LEDs of $9-50, the cost of two UV-LEDs (one UV-LED per each band) can be given as  
\[C_{2}=529\phi. \tag{17}\]  
Then, the overall cost of the system can be given as  
\[C=4.5N+529\phi. \tag{18}\]
###### Abstract  
In this article, a computationally-efficient approach is presented for Bit Error Rate (BER) calculations in multi-user Multiple Input Multiple Output (MIMO) frequency selective fading environments, operating in spatial multiplexing transmission mode. To this end, theoretical expressions for the equivalent Signal to Interference plus Noise Ratio (SINR) per active user and transmission mode, as well as mean BER, are derived. The key idea is that all parameters related to BER calculations can be expressed as sums of identically distributed random variables (RVs). Hence, computational burden can be reduced, since summation formulas take into account the parameters of a standalone RV along with the correlation of an arbitrary pair of RVs. As results indicate, the proposed approach can accurately estimate mean BER in multiuser MIMO orientations with increased reception diversity order, for arbitrary number of transmit/receive antennas, a modulation scheme, and a number of resolvable multi-path components.  
MIMO; spatial multiplexing; CDMA +
Footnote †: journal: Accepted: 24 June 2019; Published: 26 June 2019
## 2 Materials and Methods
### Overview of the Proposed Model  
Figure 9 depicts the proposed framework that outlines its various phases, from data collection to conclusions.  
Figure 8: Adjacency matrix of NCM in Figure 7.  
Figure 7: Simple NCM digraph.  
Phase 1: Data collection. Data were collected from expert responses to a questionnaire designed to assess the amount of \"smartness\" of a port. Port and marine experts were selected from academia and business.  
Phase 2: Neutrosophic cognitive maps. The expert's results were reinforced by a directed neutrosophic graph that depicted the links between the various techniques. The study of smart ports' KPIs from the relevant literature led to the identification of six types of strategies: (i) operational efficiency, (ii) financial, (iii) technological advancements, (iv) environmental sustainability, (v) customer centricity, and (vi) safety and security, as detailed in Section 3.  
Phase 3: Group decision support environment. The matrix structure of NCMs facilitates the process of gathering information from multiple experts, enabling the rapid creation of a shared knowledge base for group decision analysis and support. In order to calculate the combined NCM, we simply add the neutrosophic adjacency matrices \(N(E_{1})\), \(N(E_{2})\),..., \(N(E_{p})\), of an NCM with nodes.  
\[C_{1},\ C_{2}\ldots C_{n},\ \text{i.e.,}\ N(E)= N(E_{1})+N(E_{2})+\ldots..+N\big{(}E_{p}\big{)} \tag{9}\]  
If analysis method 1 is selected:  
Phase 4: Dynamical systems of the NCMs. The NCM dynamic systems were created using the neutrosophic graphs given by the experts. Subsequently, each node was turned on and interacted with to reveal the hidden pattern.  
Phase 5: Finding hidden patterns for each dimension/factor. The NCM model discovered a hidden pattern for each approach, which was then aggregated and ranked.  
Phase 6: Ranking of dimensions/factors and arriving at conclusions. Based on expert opinions, the hidden pattern results were collated and utilized to establish the most successful techniques.  
If analysis method 2 is selected:  
Phase 4: Static systems of the NCMs. Each member of the decision group establishes causal linkages between the topics they have discovered. Each group member then communicates the strength of the causal relationships between concepts by assigning numerical weights to them.  
Phase 5: Finding total centrality. When aggregating causal weights, absolute values are used to lend equal weight to positive and negative factors.  
Phase 6: Ranking of dimensions/factors and arriving at conclusions. Based on the experts' perspectives and the findings of phase 4, ideas with high centrality should be given particular attention in any analysis designed to enhance decision-making.
## 4 Empirical Study and Result
### Analysis of Merging Behavior in Different Traffic States  
#### 4.2.1 Merging Location  
The five work zone sites in Scenario 1 were divided into two groups to analyze different merging behavior under light and heavy traffic conditions. The group with light traffic consists of Site A1, B1, B3, and B4. And C1 represents the group with heavy traffic. The merging location distribution is shown in Figure 6. In the group with a light traffic condition, the most frequently appearing merging locations lie in the remote part of the road, about 300 m away from the taper area at Sites A1, B1, and B4. At Site C1, merging maneuvers centralize to start in the area less than 25 m from the taper, as shown in Figure 6a. To compare other merging behaviors between the two groups, we narrowed the range of merging locations of the first group to less than 150 m so that the two groups had a similar range, as shown in Figure 6b. However, the distribution of merging locations is not precisely identical after the reduction. There is a left-skewed distribution at SiteA1, uniform-like distribution at Sites B1 and B3, and a normal-like distribution at Site B4. Compared to the group with a light traffic load, at Site C1, most vehicles merge within 50 m of the taper, which presents an opposite trend from the first group.  
The observed merging location trends under different traffic states can be attributed to the availability of gaps and drivers' decision-making processes. In light traffic conditions, as seen at Sites A1, B1, B3, and B4, drivers tend to merge earlier (around 300 m from the taper) because there are ample gaps and minimal pressure to find a merging spot. This behavior aligns with existing lane-changing theories, where drivers prefer early merges in low-density situations to maintain speed and minimize disruptions. In contrast, in heavy traffic conditions (Site C1), merging locations are concentrated much closer to the taper (less than 50 m). This is because higher traffic density reduces the availability of gaps, forcing drivers to merge later. This behavior corresponds with findings from previous studies, which suggest that drivers delay merging when traffic density increases and suitable gaps become scarce.  
Figure 5: Flow–density relationship for all sites. It reveals a linear pattern in free-flow conditions, while congestion at Site C1 highlights significant capacity drops at higher densities, emphasizing the impact of merging behavior and lane closures on traffic stability.
# Bot-Based Emergency Software Applications for Natural Disaster Situations
## 1 Introduction
### Contribution  
We present and evaluate three bot-based applications that can help organizations help citizens who suffered from natural disasters. Bots have been previously used for medical purposes [11], for communication and training [12], and also for crisis communication and management [13]. However, to the best of our knowledge, there is no previous work evaluating the performance of bot-based applications to show that those applications are able to cope with critical mission requirements. In particular, we focus on critical resources and critical tasks when stressing the systems to unexpected peaks of user requests in natural disasters situations.  
The three bot-based applications are named Jayma, Ayni, and Rimay. Jayma gives supports to people who are in dangerous situations during a natural disaster. For example, it informs a group of predefined contacts whether the person is in danger or not and the location where he/she is. Ayni coordinates teams (small and large) of volunteers in the field. Ayni assigns tasks to volunteers and receives status reports and additional information. Rimay coordinates tasks assigned to organizations that provide support in a natural disaster situation. Users access these applications through bots of Telegram and Messenger. This is relevant because bots are a mechanism to integrate into the software that people use as part of their lives. These bot-based applications can be used by victims and also by volunteers to coordinate relief work.  
We deploy the applications on a computational platform based on container technology, container orchestration, and virtual machines. Containers allow the deployment of applications independently of the operating system, since it encapsulates the applications and their dependencies in portable modules that run under the same operating system kernel. Containers facilitate the deployment of applications in a wide range of operating systems such as Linux, Windows, or Mac. This container orchestration technology guarantees application mobility and also ensures fault tolerance through data replication and volatile applications where data do not need to be recovered after a failure.  
We present a comprehensive performance evaluation study on a single processor and on multiple processors with different workloads. For each application, we detect the most critical tasks, meaning the tasks that saturate faster supporting fewer requests per unit of time. We also evaluate the utilization levels of the CPU, RAM, and communication network. This information can be used by the engineer in charge of the data center to efficiently deploy the applications. The results show that the CPU is the most critical resource in Ayni and Rimay reporting utilization levels close to 60-70%. Meanwhile, the Jayma application tends to saturate faster the RAM memory than other resources.
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 4 Results and Discussion
### Summary of Findings
#### 4.4.1 Comparison with Previous Experimental Studies  
To contextualize our results, we compared them with the findings of [PERSON] et al. (2018) and [PERSON] et al. (2018), both of whom evaluated visible-wavelength laser performance in short-range FSO or VLC applications [9; 14].  
* [PERSON] et al. analyzed four wavelengths (1550 nm, 850 nm, 650 nm, 532 nm) under simulated atmospheric conditions, evaluating the quality factor, bit error rate (BER), and received power [9]. The 650 nm wavelength demonstrated a higher Q-factor (4.87) and better BER than 532 nm (Q = 4.42), indicating improved transmission quality and stability.
* [PERSON] et al. examined analog voice transmission using 650 nm, 532 nm, and 405 nm diodes in varying atmospheric conditions [14]. Their findings showed that 650 nm consistently delivered the best clarity and efficiency, due to lower scattering and stronger detector alignment. In contrast, 405 nm and 532 nm showed degraded signal clarity, especially under misty or humid conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **650 nm (Red)** & **532 nm (Green)** & **405 nm (Violet)** \\ \hline Threshold Current (mA) & 85 & 135 & 100 \\ \hline Max. Optical Power (mW) & High & Low & Moderate \\ \hline Max. Efficiency (\%) & 55.1 & 19.9 & 34.0 \\ \hline Frequency Stability & High & Poor & Good \\ \hline Modulation Bandwidth & Excellent & Limited & Suitable \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative performance of laser diodes for short-range FSO systems.  
Our own results align with these conclusions, as shown in Table 3. Despite methodological differences, all three studies confirm that 650 nm offers superior performance in terms of energy efficiency, optical stability, and atmospheric resilience.
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
## 3 System Deployment and Motivation
### Intertidal WSN
The theoretical maximum transmission range is around 300 m with a transmission power of \(-\)10 dBm. However, the PDR (packet delivery ratio) drops severely when the distance between nodes is over 100 m according to our test (the PDR is less than 20% when the transmission distance is 150 m). To deploy sensor nodes in the intertidal area, we cover the sensor node (except electrodes) with transparent epoxy (not shown in the figure) for waterproofing. We also implement the software design of sensor nodes with CSMA (carrier sense multiple access) as MAC (multiple access control) protocol and CTP as a routing protocol. The IT-WSN is a self-organized multi-hop network, and nodes are synchronized with the base station, which is located on dry land. For each minute, nodes are operated in the active mode for the first second to sense the environment and communicate with neighbors, then they turn to the sleep mode (low power mode) for the rest of the 59 s for energy efficiency. The system cycle (System cycle is the system operating cycle and we set the system cycle as 1 min in our system. We refer to system cycle as \"cycle\" in the remaining of the pages.) is 1 min. The base station has infinite resources for power and buffer space. During the running procedure, nodes can read current status (above or under water) by reading the voltage difference between electrodes in Figure 1c.