esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The main objective of this article was to identify the influence of Industry 4.0 on the evolution of logistics; subsequently to determine whether the impact of the relationship between the two paradigms goes in one way or in both ways; and then to list the main opportunities and risks of Logistics 4.0.  
In this paper, apart from the first dataset used for descriptive statistics, 21 articles published between 2018 and February 2022 were screened based on defined inclusion and exclusion criteria and fully read.  
The concept of Industry 4.0 is capable of significantly changing production processes and associated logistics processes.  
This paper presents the link between Industry 4.0 and Logistics 4.0, as well as the role of technologies and their contributions during their adoption in the logistics process. A few benefits of Logistics 4.0 are improved information visibility and connectivity, in combination with a physical network with fast and reliable delivery options, as well as logistics productivity efficiency and a reduced negative impact on the environment.  
We are aware that Logistics 4.0 is a relatively new concept, and little research is available in this area. Logistics 4.0 remains quite difficult to define, and digitalization within industrial logistics may also have other influences and is shaped by different actors.  
In this paper, we have restricted the choice of terms used to \"Smart Logistics\" and \"Logistics 4.0\" which could prove to be a limit to detecting the different opportunities possible with this recent paradigm.  
Another limit, which could also be the subject of a new scientific study, is the role of humans in Logistics 4.0. Several authors mention that certain job skills, mainly manual activities, will be replaced by machines due to problems of efficiency, safety, and quality, which induces a growing fear of job loss and therefore loss of income among many workers; this fear, whether justified or not, negatively affects staff motivation and causes resistance to change. On the other hand, the lack of qualifications of workers and the absence of computer security skills represent major risks to be considered more seriously.  
As a perspective of future research, we could focus on the environmental, economic, and social sustainability dimensions in logistics.  
Conceptualization, methodology, analysis, writing--review and editing, S.E.H. and Supervision, A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Overview of Polymer Additives in Crystallization
### Conjugated Polymers  
Incorporating conjugated polymers into small-molecule organic semiconductors fundamentally alters the thin film morphology, resulting in the formation of different crystalline structures and distinct electronic properties [62; 63; 64; 65; 66]. [PERSON] and colleagues documented that the crystal shapes and configurations of TIPS pentacene can be modified through the addition of two conjugated polymers including region-random pentacene-bithiophene (PnBT-RRa) and poly(3-hexylthiophene) (P3 HT) [67]. This resulted in previously unobserved crystal polymorphism and thin film morphologies. As shown in Figure 2a,b, the TIPS pentacene/P3 HT blend develops grasslike microwire bundles with enhanced long-range order. In contrast, the TIPS pentacene/PnBT-RRa blend crystallizes into a \"woven\" straight needle pattern, which significantly increases film coverage and extends long-range order up to centimeters. The TIPS pentacene based OTFTs incorporating PnBT-RRa and P3 HT as conjugated polymer additive showed enhanced hole mobility values of 0.14 cm\({}^{2}\)/Vs and 0.39 cm\({}^{2}\)/Vs, respectively. Besides, [PERSON] et al. reported another two conjugated polymers, poly[2,5-bis(3-alkylthio-phen-2-yl)thieno(3,2-b)thiophene (PBTTT) and poly(9,9-din-octylfluorene-alt-benzothiadiazole) (F8 BT), significantly altered the mor phology and charge transport properties of TIPS pentacene-based thin film transistors [68]. PBTTT, when blended with TIPS, leads to the formation of fibrous crystalline structures (Figure 2c). These fibers exhibit enhanced long-range order, which is beneficial for carrier mobility due to better \(\pi\)-\(\pi\) stacking and reduced grain boundaries. Consequently, the TIPS pentacene/PBTTT blend achieves a moderate mobility improvement to 0.3 cm\({}^{2}\)/Vs. F8 BT, on the other hand, induces the growth of rod-like crystals within the TIPS pentacene matrix, as shown in Figure 2d. These rod-like structures exhibit a high degree of crystallinity and uniformity, which substantially boosts the charge transport efficiency. The presence of F8 BT facilitates faster crystallization along the crystal axis, enhancing carrier mobility significantly to 3.53 cm\({}^{2}\)/Vs. The distinct morphologies presented from these two studies demonstrate that the crystalline habitat and solubility of the conjugated polymers play critical roles in determining the final film structure and electronic properties of organic semiconductors.
## 8 Conclusions  
This study finds the upper limit of the IDR for all lower channel gain users in the TDMA-RIS-NOMA scenario. The proposed GP-based power allocation method maximizes the power allocation coefficients and, therefore, improves the SINR to provide an upper limit of the individual data rates. Although the TDMA-RIS-NOMA scenario has a high chance of achieving the saturated channel due to its inherent characteristics of serving one user at a time, the proposed method could also be useful in non-TDMA scenarios to provide an upper limit of the IDR. The proposed method also helps in deciding the maximum size of the cluster based on the spectrum utilization factor and predefined quality of service requirements. The maximum IDR obtained from the analytical method for all lower channel gain users has also been verified with the Monte-Carlo-based simulation that showed a minimum dissimilarity. This provides an additional benefit over the previous studies that only focus on maximizing the overall sum data rate. This was the pilot study showing the effectiveness of using a GP-based power allocation method for obtaining maximum IDR and, can be useful in lower power requirement applications like NB-IoT systems. In the future, we intend to verify the study findings in complex scenarios by estimating the channel state information and investigating the effect of imperfect CSI and imperfect SIC on the maximum achievable individual data rate.  
Conceptualization, S.T. and J.S.; methodology, S.T. and J.S.; software, S.T., J.S. and N.D.B.; validation, S.T., J.S. and N.D.B.; formal analysis, S.T. and J.S.; investigation, S.T., resources, J.S. and N.D.B.; data curation, S.T.; writing--original draft preparation, S.T., J.S. and N.D.B.; writing--review and editing, S.T., J.S. and N.D.B.; visualization, S.T. and N.D.B.; supervision, J.S. and N.D.B.; project administration, S.T., J.S. and N.D.B.; funding acquisition, J.S. and N.D.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results
### Usability Validation Results of the Simulator
Table 8 shows the [PERSON]'s kappa statistical values for each category and the overall responses of the questionnaire. The kappa value ranged from a minimum of 0.467 (educational usefulness) to a maximum of 0.561 (usability). These values fall within the range of 0.41-0.6 on the general 5-range scale, indicating a moderate level of agreement in the responses. Additionally, the maximum asymptotic standard error value was 0.065 (usability), which is close to 0, suggesting that the kappa values are stable. Furthermore, Z-values exceeding the Z critical value of 1.96 at the 95% confidence level confirm that the kappa values are statistically significant, and the width of the 95% confidence interval being less than 0.256 (usability) further supports the reliability of the students' responses. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Kappa**} & **Asymptotic Standard Error** & \multirow{2}{*}{**Z**} & **Lower 95\% Asymptotic CI Bound** & **Upper 95\%** \\ \hline VR Simulator & 0.496 & 0.035 & 14.030 & 0.427 & 0.566 \\ \hline Usability & 0.561 & 0.065 & 8.606 & 0.433 & 0.689 \\ \hline Expertise & 0.520 & 0.064 & 8.083 & 0.394 & 0.646 \\ \hline Educational usefulness & 0.467 & 0.047 & 10.024 & 0.376 & 0.558 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: [PERSON]’s kappa results according to questionnaire category.
# A Novel Approach against Sun Glare to Enhance Driver Safety
## 3 Results  
In this chapter the results achieved will be presented and discussed. All the tests were done via MATLAB simulations. There are several scenarios that were created in order to test and highlight the principles described in the previous chapters.  
In Figure 12, the simulation flow chart is presented. The user shall configure the input data in a separate file then it can run the simulation. For readability, a simulation file for each use case was created. After the data were loaded into simulation, based on the data provided by the sun trackingsensor, the coordinates of S point were computed. Using Equation (11) and imposing some limitation like the distance between D and S or the fact that S must be on the other side of the windshield compared to D, the position of the sun is obtained. This step is used mostly for data visualization. In this way, the DS line can be plotted. Next, the intersection point was computed based on Equations (13) and (14). The user can provide input vectors of values for the sun's position (\(\alpha\)x and \(\alpha\)y) or for the coordinates of the driver's eye, D (\(x_{D}\), \(y_{D}\), \(z_{D}\)). After all the input data are processed and all the intersection points are available, the results are plotted in order to provide a good data visualization. All the computed data are also provided in numerical form if needed for an interpretation.  
The first test scenario is a simple one. The sun S and the driver D are considered as fixed points. As can be seen in Figure 13, an intersection point I was obtained. Therefore, the center of the dark spot was to be created there. The line between DS and ID is marked in purple, the plane is colored blue, and the intersection point I is marked with a green cross.  
For the next test scenario, the driver was considered a fixed point and the sun was mobile. A limitation that the points of interest are only on the top part of the windshield was applied. The practical use of this limitation is that in some cases the whole windshield is not covered by a HUD. Therefore, the points of interest were only the ones that were inside the display area. In Figure 14, the results of the simulation can be observed.  
The next test scenario covers the part where the driver is moving as well. Therefore, the sun was considered a fixed point and the driver is mobile. The driver's movement was described by a sphere with a fixed radius. The same limitations were applied as in the previous test. In Figure 15, the results of the simulation can be observed.  
Figure 12: Simulation flow chart.  
Figure 14: Test scenario 2—S is a mobile point and D is a fixed point.  
Figure 13: Test scenario 1—sun (S) and driver’s eye (D) are fixed points.  
Figure 15: Test scenario 3—S is a fixed point and D is a mobile point.  
For the final test scenario, the sun's position was changing, and the driver's eyes were moving as well. The driver's movement were described by a sphere with a fixed radius. The same limitations were applied as in the previous test. In Figure 16, the results of the simulation can be observed.
# Securing SCADA Energy Management System under DDs Attacks Using Token Verification Approach
## 3 Problem Definition  
As shown in Figure 1, the energy management system mainly consists of a power company system, a community energy management system, a building energy management system, a firewall, and IDPS and SCADA systems. The experiment of this paper was conducted in the energy management system of a Smart Green Energy Science City in southern Taiwan. When hackers use external attacks to break through a firewall and Intrusion IDPS, and there is no security defense mechanism, they can paralyze the SCADA system through DoS attacks. This can cause the system's control program to be unable to acquire the device's response and send repetitive requests, which further leads to errors due to the lack of device information and results in a hazardous impact on the energymanagement system. Therefore, this paper conducted a simulated experiment to prove the viability of the proposed Trusted Encrypted Validator module (TEVM), based on the token authentication architecture.
## 4 Simulation Environment  
To validate the performance of the proposed RALAR algorithm, an extensive simulation experimental work was performed by using the MATLAB environment. As shown in Figure 4, the plot used for simulation was the original image containing a part of Kuala Lumpur extracted from the GPS (Google map) application. Each of the routes has a speed limit that has a varying range from 60 to 100 Km/h. The time period of reading and refreshing the GPS application was set as 1 s The experiments were conducted for three different protocols (models), each of which contained 50 nodes that were distributed randomly in the map, and simulation time was set at 1500 s. The circular coverage zone of the node was fixed at 150 m of the radius. The first experiment was conducted with the LAR without any model (conventional LAR), whereas the other two contained the proposed RALAR (rectangle) and KALAR (benchmark) model. The chromosome of the GA was a scalar quantity, which ranges from 0.5 to 5 s. The population size was been set to 40, and the experiment lasted for 40 generations. The results were evaluated in terms of PDR, average E2E delay, routing overhead, and average energy consumption of nodes. Table 1 provides a summary of the simulation parameters.  
Figure 3: The Timeout Optimization.
# A Fast Ray-tracing Method for Locating Mining-induced Seismicity by Considering Underground Voids
## 4 Field Application  
The Huangtupo Copper and Zinc Mine is located in the southwest of Hami City, Xinjiang Uygur Autonomous Region, China. As the mine uses non-pillar sublevel caving, three large voids have been formed so far, as shown in Figure 14. The volumes of the first, second, and third voids are 120,068.60, 42,633.25, and 183,483.19 m\({}^{3}\), respectively. We have arranged a total of eight sensors in the middle of 210 and 260 m (Figure 14), with a sensitivity of 10 V/g, a sampling frequency of 10 V/g, and a microseismic system for continuous 24-h monitoring. The propagation velocity of the wave in the non-voids is about 5500 m/s.  
Figure 13: The relative error of the arrival time of the different step size s compared to the analysis solution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Effectiveness** & **Grid** & **Scope of Application** \\ \hline \hline \multirow{3}{*}{Proposed method} & \multirow{3}{*}{High} & \multirow{3}{*}{Fast} & \multirow{3}{*}{No} & \multicolumn{2}{c}{Underground mine with a lot of voids and the wave velocity} \\  & & & & in the non-voids is not much} \\ \cline{4-5}  & & & & different. \\ \cline{1-1} \cline{2-5  
As the existence of the three voids, the location of the microseismicity that propagates through voids using a constant velocity model will result in a large error. Thus, to show the improvement by integrating our proposed ray-tracing method into the location procedure, controlled blast experiments with small amounts of explosives were carried out in three different locations in the monitoring area. The coordinates of these blasts were pre-measured and listed in Table 4. The location method used in this study is proposed by [PERSON] [25]. The only difference is that the ray-tracing method mentioned in this article is used instead of FMM. The calculated blasting source location and location error are shown in Table 4. Then, we compared the location results based on the proposed method with those based on FMM and those using constant velocity. As shown in Table 4, the mean location error is 5.88 m by integrating our proposed ray-tracing method, while the mean location error is 7.22 m using the FMM-based location method, and the mean location error of the method with constant velocity is 22.14 m. The accuracy is greatly improved, which indicates that our method is effective in the location of mining-induced seismicity.
## 3 Results and Analysis of Influencing Factors
### The Impact of Casing Properties on Signal Attenuation
#### 3.6.3 The Impact of Casing Dimensions on Signal Attenuation  
Under different drilling conditions, the selected casing sizes may vary. Common casing inner diameters include 139 mm, 177 mm, 219 mm, and so on. To investigate the impact of casing inner diameter on signal transmission, we have specified a range of diameters from 11 cm to 21.9 cm with a wall thickness of 10 mm. For a transmission distance unit of 100 m, when the casing inner diameter L = 110 mm, the signal attenuation is 151 dB; at L = 130 mm, the attenuation drops to 124 dB; and at L = 219 mm, the attenuation further decreases to 91 dB. Clearly, signal attenuation gradually diminishes as the inner diameter increases.  
As shown in Figure 12, within a distance of 10 m, a smaller casing diameter provides larger values of the received magnetic field; however, beyond 10 m, a larger casing diameter can receive greater magnetic field amplitudes. This indicates that the casing's inner diameter plays a crucial role in the transmission of electromagnetic waves, affecting not only the degree of signal attenuation but also directly influencing the strength of the received signal.  
When the spacing between the casing and the drill string is close, electromagnetic wave attenuation is greater, but the signal received in short-distance transmission remains stronger; conversely, at greater distances, signal attenuation is less, enabling a farther transmission distance.  
Figure 11: The Impact of Casing Magnetic Permeability on Signal Attenuation.  
Figure 12: The impact of casing dimensions on signal attenuation.
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multirow{2}{*}{**Unit**} & \multicolumn{5}{c}{**MPC**} \\ \cline{3-8}  & & **for Domestic Drinking and** & \multicolumn{2}{c}{**for Surface Water**} & \multirow{2}{*}{**WHO**} & \multirow{2}{*}{**USEPA (USA)**} & \multirow{2}{*}{**EU Directive**} \\  & & & **Cultural Water Supply** & & & **for Fishery** & **for Sea** \\  & & & **Water Bodies** & & **Water * & & \\ \hline Cadmium & mg/L & 0.001 & 0.005 & 0.01 & 0.003 & 0.005 & 0.005 \\ \hline Cobalt & mg/L & 0.1 & 0.01 & 0.005 & - & - & - \\ \hline Manganese & mg/L & 0.1 & 0.01 & 0.05 & 0.5 (0.1) & 0.05 & 0.05 \\ \hline Copper (Cu) & mg/L & 1 & 0.001 & 0.005 & 2 (1.0) & 1.0–1.3 & 2.0 \\ \hline Nickel & mg/L & 0.1 & 0.01 & 0.01 & - & - & - \\ \hline Lead (Pb) & mg/L & 0.03 & 0.01 & 0.01 & 0.01 & 0.015 & 0.01 \\ \hline Zinc & mg/L & 1 & 0.01 & 0.05 & 3.0 & 5.0 & 5.0 \\ \hline Chrome (VI) & mg/L & 0.05 & 0.020 & - & 0.05 & 0.1 & 0.05 \\ \hline Chrome (III) & mg/L & 0.5 & 0.005 & - & - & 0.1 & - \\ \hline Petroleum & mg/L & - & 0.05 & 0.05 & - & - & - \\ \hline  & & & & & & & \\  biphenyls (PCBs) & \(\upmu\)g/L & 1 & - & - & - & - & - \\ \hline  & & & & & & & \\ \end{tabular}  
* Due to the fact, that there are no standards, especially for fish species in the Caspian Sea, seawater respectively marine water standards were used [27; 28; 29; 30; 31].  
\end{table} 
Table 1: Maximum permissible concentrations (MPC) of heavy metals and organic pollutants in water bodies, in mg/L
# Multi-Classifier Pipeline for Olive Groves Detection
## 2 Materials and Methods
### Satellite Images  
Our process is using Sentinel-2 Level 2A data downloaded from the Copernicus open access hub. Multiple images for the tile identified by the reference T32 TLP were used to properly identify the olive grove's reflectance spectrum evolution according to the phenological stages throughout the year. In the Mediterranean area, olive phenological steps are described by [PERSON] [14] and [PERSON] [15] and EO data were selected according to the main stage of development as described in Table 1.  
The RPG (Registre Parcellaire Graphique) dataset and the SCO-Live project citizen field observations were used to create a reference and to evaluate the resulting classification map. The RPG data is provided by the French National Institute of Geographic and Forest Information (IGN). It is a vector file containing a detailed description of the various vegetation and crop types in France. The document is freely available on the IGN web site and updated every year. Olive groves are identified within the RPG 2019 shapefile by the code \"OLI\". The SCO-Live project relies on citizen science to collect field information. Citizen science is a process that involves individuals to perform some experiments requiring a low scientific level [16]. Another aspect of citizen science is to involve individuals in field data collection by providing all the tools required for intuitive data collection. Within the SCO-Live project, an application has been developed and distributed to allow citizens to contribute by identifying and locating olive trees and groves. The resulting data base gathers all observation made and stores the information in a georeferenced point vector file which contains for each location various information on olive trees exploitation such as crops type, stage of growth, possible damage, etc. All olive observations are located within a global 5 m spatial accuracy linked with the individual phone GPS accuracy.  
All data set are georeferenced and projected to the coordinate reference system of EPSG:32632 (WGS84/UTM zone 32N).