esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Dynamic Testing
### Clean Rail
#### 4.1.2 Clean Rail: Statistical Analysis of Instantaneous Resistances  
We then proceed to the statistical analyses of the instantaneous resistances, which are presented in Figure 11. For each 'clean' rail considered, we have calculated the median and IQR for the different experimental conditions. These data are then plotted as a function of current intensity.  
For the recently cleaned rail with a microscopic oxide layer, the effect of current is noticeable: whatever the speed and force applied, IQRs are wider at 20 mA than at 50 mA or 100 mA. The voltage-current characteristics therefore become less dispersed as the current intensity increases. What is more, in most cases, with the exception of \(I=50\) mA, \(F=200\) N, and \(v=25\) cm/s, the median tends to decrease with the increasing current, and the contact resistance decreases with the increasing force.  
As the statistical maps also show, a force effect is clearly visible, with lower IQR widths at 400 N than at 200 N, independently of current and speed. The voltage-current characteristics tend to be less dispersed with the increasing force.  
The effect of velocity, less perceptible in distribution maps, becomes evident through a statistical analysis of \(R(t)\). As speed increases, irrespective of the intensity injected and the force considered, the median and IQR width tend to increase. Contact resistance tends to increase and the voltage-current characteristics tend to be more dispersed with the increasing speed.  
For the freshly stripped rail, the force and velocity effects correspond to those observed on the surface with a microscopic oxide layer. However, the variation as a function of intensity shows an unexpected trend: resistance increases during the transition from 20 mA to 50 mA, then decreases at 100 mA. This may seem paradoxical. It could be attributed to a thermal effect induced by the current, sufficient to cause an increase in the resistivity of the metal (steel), but not sufficient for the softening of the material to contribute to a decrease in resistance. The effect should be more or less the same for 100 mA, since the maximum voltages are of the same order. However, for this current value, a decrease in resistance is observed.  
To validate these observations, we repeated the experiment under the same conditions, stripping the rail again. We noted an identical trend at 50 mA, where the contact resistance reaches its maximum values. At present, we have no explanation for this.  
Figure 9: Statistical maps of current–voltage characteristics obtained for the recently cleaned rail, for different current intensities (20 mA, 50 mA, and 100 mA, with \(f\) = 2300 Hz), forces (200 N and 400 N), and speeds (10 cm/s and 25 cm/s).  
Figure 10: Statistical maps of current–voltage characteristics obtained for the freshly stripped rail before testing, for different current intensities (20 mA, 50 mA, and 100 mA, with \(f=2300\) Hz), forces (200 N and 400 N), and speeds (10 cm/s and 25 cm/s).
## 2 Numerical Modelling
### Dynamic Force Model  
Based on the fact that there is a direct relationship between force and vibration, dynamic forces are extracted from the numerical simulation. It can be seen in the bearing vibration model that the nonlinear dynamic forces are  
\[F_{six}=K[\delta_{i}]^{3/2}cos\phi_{i};\ F_{isy}=K[\delta_{i}]^{3/2}sin\phi_{ i}; \tag{12}\]  
\[F_{dix}=C[v_{i}]cos\phi_{i};\ F_{diy}=C[v_{i}]sin\phi_{i}; \tag{13}\]  
where \(F_{six}\) and \(F_{isy}\) represent the spring force in the \(X\) direction and \(Y\) direction of the \(i\)th ball, respectively; \(F_{dix}\) and \(F_{diy}\) denote the damping force in the \(X\) direction and \(Y\) direction of the \(i\)th ball, respectively.  
The schematic diagram of the dynamic force is shown in Figure 2. The resultant forces can be calculated according to Equations (14) and (15).  
\[F_{rix}=F_{six}+F_{dix};\ F_{riy}=F_{siy}+F_{diy}; \tag{14}\]  
\[F_{rx}=\sum\
olimits_{i=1}^{N_{b}}F_{rix};\ F_{ry}=\sum\
olimits_{i=1}^{N_{b }}F_{riy};\ F_{r}=\sqrt{(F_{rx})^{2}+\big{(}F_{ry}\big{)}^{2}} \tag{15}\]  
where \(F_{rix}\) and \(F_{riy}\) represent the resultant force of the \(i\)th ball in the \(X\) direction and \(Y\) direction of the \(i\)th ball; \(F_{rx}\) and \(F_{ry}\) denote the resultant force in the \(X\) direction and \(Y\) direction of all the balls, respectively; \(F_{r}\) denotes the sum of forces on all balls. It is noted that the \(X\) and \(Y\) directions represent direction with and without external load, respectively, named load and non-load direction in the following text of this study.  
Figure 2: Schematic diagram of the dynamic forces: **(a)** spring, **(b)** damping and **(c)** resultant force.
# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 4 Experimental Results and Analysis
### Parameter Tuning in Classification Using CNN  
After the selection of representative bands, it is necessary to perform classification. The detail of the split between training and testing samples of both datasets is presented in Table 2.  
Parameter tuning for common parameters of the CNN model is presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Total Number** & **Number of** & **Number of** & **Number of** \\
**Name** & **of Pixel** & **Training** & **Test Pixels** & **Background** \\  & **Samples** & **Pixels** & **Pixels** \\ \hline Indian Pines & 10,249 & 7679 & 2569 & 10,776 \\ Salinas & 54,129 & 40,593 & 13,536 & 56,975 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of training and testing samples per band for the data sets.  
Figure 7: Row-1: Indian Pines data (**a**) Entropy of visible region bands (**b**) NDVI of NIR region bands (**c**) MNDWI of SWIR region bands. Row-2: Salinas’s data (**d**) Entropy of visible region bands (**e**) NDVI of NIR region bands (**f**) MNDWI of SWIR region bands (**B**) on \(x\)-axis for (**b**,**c**,**e**,**f**) are the chosen bands from the respective region).  
CNN network model for Indian Pines data is presented in Table 4, and the CNN network model for Salinas data is presented in Table 5.
###### Abstract  
A terahertz (THz) detector based on thermoelectric thin films was simulated using the finite elements method. The thermoelectric circuit consisted of \(Sb\) and \(Bi_{88}Sb_{12}\) 150-nm films on the mica substrate. \(Sb\), \(Bi_{88}Sb_{12}\), and mica-substrate properties have been measured experimentally in the THz frequency range. The model of electromagnetic heating was used in order to estimate possible heating of \(Sb\)-\(Bi_{88}Sb_{12}\) contact. THz radiation power varied from 1 \(\mu\)W to 50 mW, and frequency varied in the range from 0.3 to 0.5 THz. The calculations showed a temperature difference of up to 1 K, voltage up to 0.1 mV, and responsivity of several mVW\({}^{-1}\). The results show that thin \(Sb\) and \(Bi-Sb\) thermoelectric films can be used for THz radiation detection at room temperatures.  
thermoelectric; detector; terahertz; FEM; finite element simulation; bismuth; antimony +
Footnote †: journal: Applied sciences
# Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services
## 7 Conclusions  
In this work, we presented the Maritime Platform, an open and flexible platform that allows for the deployment and testing of 5G-based logistic services in the context of maritime transport while exploiting massive IoT data. We focused in particular on the 5 GT System, i.e., a global tracking system based on massive IoT data collected from containers. The paper reports the results of measurements we performed on the 5 GT system using an experimental testbed to assess its feasibility. The obtained results indicate the capacity of the implemented prototype to handle a large number of IoT messages and to address the requirements in terms of latency and packet loss. In fact, the experimental testing campaign was performed under high traffic conditions with more than 3000 NB-IoT accomplished transmissions without any CTD break down, which shows the robustness of the 5 GT system. Moreover, in most of the experiments, data were correctly received and published on the OneM2M platform (i.e., only 0.9% of loss), thus confirming the proper design and deployment of all the components. As future work, more complex scenarios (i.e., open sea) including the satellite communication will be evaluated.  
Conceptualization, M.G., S.N., M.F. and B.M.; methodology, M.G., S.N., M.F. and B.M.; software, M.G. S.N. and M.F.; validation, M.G., S.N. and M.F.; formal analysis, M.G., S.N. and M.F.; investigation, M.G., S.N. and M.F.; resources, M.G., S.N. and M.F.; data curation, M.G., S.N. and M.F.; writing--original draft preparation, M.G., S.N. and B.M.; writing--review and editing, M.G., S.N., B.M., P.C. and P.P.; visualization, M.G., S.N., B.M., P.C. and P.P.; supervision, P.C. and P.P.; project administration, S.N.; funding acquisition, S.N. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by the European Space Agency through the 5G SENSOR@SEA project (contract no. AO/1-10285/20/NL/AF). The view expressed herein can in no way be taken to reflect the official opinion of the European Space Agency.  
Not applicable.  
The authors declare no conflicts of interest.
## 3 Research Methodology
### Research Questions  
This study is guided by eight research questions that explore how TPM is changing in the digital age. These questions look at both the academic and practical sides of TPM in the context of Industry 4.0. They cover trends in research, key technologies, challenges, and ways to successfully use digital TPM in industry.  
_Q1. What are the spatio-temporal trends in TPM research in the era of 14.0?_  
This question aims to chart the spatial and chronological spread of digital TPM studies. It examines which countries or regions are most actively contributing to the field and how the volume of related publications has progressed over time, thereby offering a perspective on the global momentum and maturity of the subject.  
_Q2. What are the most influential works and publication sources in the field of digital TPM?_  
The aim here is to identify the scientific contributions that have influenced the formation of digital TPM. This involves determining the most-cited papers, as well as the leading journals or conferences, in order to define the intellectual structure of the field.  
_Q3. What are the dominant and emerging research themes related to digital TPM?_  
This question explores the central and emerging themes that characterize the digital TPM research space. Using keyword co-occurrence and clustering analysis, it traces the thematic development and highlights areas of growing interest or potential directions for future research.  
_Q4. What are the main technological enablers of digital TPM?_  
This question seeks to identify the core technologies, such as the IoT, artificial intelligence, Big Data, and predictive maintenance, that are instrumental in driving TPM's transition toward a digital paradigm.  
_Q5. How does I4.0 transform the traditional foundations of TPM?_  
This question investigates the reinterpretation and redefinition of TPM's foundational pillars in light of contemporary technological advancements and evolving organizational practices.  
_Q6. What are the challenges and opportunities associated with the implementation of digital TPM?_  
This question aims to map out technical, human, and organizational barriers as well as expected or observed benefits. This question aims to map the spectrum of barriers, technical, human, and organizational, as well as to highlight the potential benefits and opportunities that accompany the adoption of digital TPM.  
_Q7. How can enterprises successfully integrate digital TPM?_  
This question explores the critical success factors, methodological approaches, and practical best practices that facilitate the effective and sustainable implementation of TPM in smart manufacturing environments.  
_Q8. How can a standardized framework for digital TPM be developed, based on existing research and industrial needs?_  
The aim here is to synthesize academic insights in order to formulate a reference model that is both theoretically robust and practically applicable within real-world industrial contexts.
# A Novel Coding Based Dimming Scheme with Constant Transmission Efficiency in VLC Systems
## 3 The Proposed Coding Scheme  
In VLC links, the brightness can be determined by the average light intensity of the signal. We can achieve dimming control by adjusting the average intensity of the optical signal by constructing the transmitted codeword set. In this section, we will demonstrate the process of the encoding and decoding structure and give the algorithms accordingly.
## 3 Results and Discussion
### Model Calibration and Validation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**2013**} & \multicolumn{2}{c}{**2022**} \\ \cline{3-6}  & **Balance Items** & **Thousands** & **\%** & **Thousands** & **\%** \\  & **m\({}^{3}\)/Day** & **\%** & **m\({}^{3}\)/Day** & **\%** \\ \hline \multirow{6}{*}{**Fresh**} & Storage recharge & 1884 & 49 & 1229 & 25.5 \\  & Recharge & 2433 & 50 & 3562 & 73.5 \\  & River leakage & 37.6 & 0.8 & 41.1 & 0.8 \\  & Influx from a brackish groundwater zone & 9.2 & 0.2 & 10.1 & 0.2 \\  & **Total input** & 4363 & 100 & 4842 & 100 \\  & Storage discharge & 1115 & 26.0 & 1479 & 31.9 \\  & Groundwater withdrawal & 72.9 & 1.7 & 36.3 & 0.8 \\  & Groundwater discharge to rivers & 389 & 10 & 366. & 7.9 \\  & Evapotranspiration & 2610 & 61.3 & 2717 & 58.8 \\  & Outflow to a brackish groundwater zone & 29.4 & 0.7 & 30.2 & 0.6 \\  & **Total output** & 4217 & 100 & 4629 & 100 \\  & **Input–output** & 146 & 3.3 & 213 & 4.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fresh groundwater distribution zones and groundwater flux balance calculated for 2013 and 2022. The total balance input characterizing the groundwater flow rate in the freshwater distribution zone is approximately 4500 thousand m\({}^{3}\)/day, or 65% of the groundwater flow rate flux throughout the Zhem Basin. The groundwater outflows into the brackish water zone are relatively small, amounting to approximately 30 thousand m\({}^{3}\)/day, which is approximately three times greater than the inflow from the brackish water zone into the freshwater zone. Almost all groundwater discharge to rivers is formed in the fresh groundwater distribution zone. The difference between the balance components calculated for the entire basin area and the freshwater distribution zone corresponds to the distribution zone balance components of predominantly brackish groundwater. The model accurately simulated the measured historical characteristics of the simulated system, including hydraulic conductivity, specific storage, recharge, and groundwater balance values that concurred with the values obtained from field investigations within the specified modeling accuracy. Figure 14: Groundwater head contours and flow path, 2022.
# Meshing Stiffness--A Parameter Affecting the Emission of Gearboxes
## 3 Results and Discussion
### Influence of Number of Teeth on Meshing Stiffness  
The influence of the number of teeth was analyzed for spur gears with the number of teeth \(z_{i}=17\), 19, 21, 27, 35, and 61, for the module \(m=1\) mm, where the width of the gears was \(b=20\) mm and load force was \(F_{tb}=1000\) N. Figure 5 shows the course of the teeth stiffness depending on the number of teeth for one pair of teeth in contact. As the number of teeth on the gear increased, the stiffness of one pair of teeth in contact increased as well.  
Figure 4: Scheme of static measurement of tooth deformation.  
Figure 5: Dependence of single-pair stiffness of teeth on the number of teeth.  
Figure 6 shows the course of the meshing stiffness of the spur gearing. It can be stated that with the increasing number of teeth, the meshing stiffness increases. This is the result of the shape of the lateral curve of the tooth. The higher the number of teeth, the larger the radius of curvature on the active tooth side.
# Effective Dereverberation with a Lower Complexity at Presence of the Noise
## 4 Experiments and Evaluation  
In this section, we give the experimental results. The proposed dereverberation method is compared with the reference methods including ISCLP and WPE, which have excellent performance at present. Firstly, we introduce the experimental settings used for the evaluation. Then, the reference methods are introduced briefly. Finally, the experimental results are evaluated and discussed.
# Solutions to Increase PV Hosting Capacity and Provision of Services from Flexible Energy Resources
## 3 Simulation Results and New Frequency Adaptive _Pu_-Droop
The total active power change is quite similar in Cases 5a and 5b, but it seems that in cases with PV and BESS at the same connection point, the _PU_-blocking (Case 5b) may lead to unnecessary large PV curtailment during under-frequency events if BESS unit participates in frequency control. This issue is further studied in Section 3.2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Load**} & \multirow{2}{*}{\begin{tabular}{c} **PESS 3, QRESS 3** \\ **Steady-State, \(t\) = 25 s** \\ **(kW, kVAr)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **PESS 3, QRESS 3** \\ **(kW, kVAr)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **PESS 3, QRESS 3** \\ **(kW, kVAr)** \\ \end{tabular} } \\ \hline
5a & Low & \(-93,-67\) & \(-52,-67\) & \(-165,62\) & \(-25,-67\) \\
5b & Low & \(-93,-67\) & \(-84,-62\) & \(-200,67\) & \(108,-67\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: BESS 3 active (\(P_{\rm BESS\_3}\)) and reactive (\(Q_{\rm BESS\_3}\)) power during over- and under-frequency events in different cases (Figures 1 and 2, Tables 2 and 3). Figure 4: PV 2’s active power (\(P_{\rm PV\_2}\)) values in different cases with very low load during (**a**) steady-state, (**b**) over- and (**c**) under-frequency events (see also (Figures 1 and 2, Tables 2–6)). Simulations with Real-Life Measurement Data  
In this Section, the aim was to study PV 2 and BESS 3 unit's (Figure 1) active (\(P_{\text{PV\_2}}\)) and reactive (\(Q_{\text{PV\_2}}\)) power as well as PV 2 connection point voltage (\(U_{\text{PV\_2}}\)) in Case 5 by utilizing measured real-life frequency data from SSG (Figure 5). Measured 30 min frequency data (Figure 5) was modified for PSCAD simulations to 240 s input data and was utilized in the new simulation Case 6 during \(t\) = 10-250 s (Figure 6a). From simulation results of Figure 6 it can be seen how PV 2 and BESS 3 units participate in frequency control when frequency exceeds 50.05 Hz. In a similar manner, BESS 3 participates in frequency  
Figure 5: Measured 30 min real-life frequency data from SSG (which is modified for PSCAD simulations to 240 s frequency input data between \(t\) = 10–250 s, see Figure 6a). Figure 6: (**a**) Input frequency (\(f\)) data modified from real-life SSG measurements (Figure 5), PV 2 and BESS 3 (**b**) active (\(P\)) and (**c**) reactive power (\(Q\)), as well as (**d**) local voltage (\(U\)) behavior at PV 2 connection point during simulated Case 6 (similar to Case 5, see also Figures 1 and 2, Tables 2–6). control during under-frequencies (i.e., when frequency is smaller than 49.95 Hz). In addition, during \(t=178.0\)-180.5 s also load demand response participates in frequency support by load disconnection when under-frequency limit 49.90 Hz is exceeded. Due to local voltage rise at PV 2 connection point during under-frequency events also PV 2 active power needs to be simultaneously curtailed as shown in Figure 6.
## 6 Conclusions  
In this paper, a noise and distance prediction method based on spectrum sensing signals using regression models was proposed. The conducted experiments have shown that the proposed methods hold promise for predicting noise levels, as well as the initial and final distances between the PU and SU. The correlation coefficient value for XGB is the highest and closest to one (Table 6), indicating a strong correlation between the predicted and actual noise values in the test database. As a result, the proposed methods can greatly benefit various applications, especially in telecommunication and networking, enabling the design of communication systems that meet appropriate requirements for ensuring reliable and efficient data transfer.  
Additionally, the predictions for the initial and final distances between the PUs and SUs are presented as results. The conducted experiments have demonstrated that the proposed methods show promise in predicting distances between users. The correlation coefficient values for transformer are the highest, exceeding 0.82 (Tables 7 and 8), which implies a good level of correlation between the predicted distances and the actual distances in the test database. It is important to highlight that the number of possible noise levels is limited to seven (\(-114\) to \(-174\) dBm/Hz), while the number of possible distances is unknown since the distance was chosen randomly within a certain range of the area. Therefore, there may be a difference in performance between the two approaches. Hence, the proposed methods hold the potential to benefit numerous applications, including signal attenuation and path loss, interference and frequency reuse, fading and multipath effects, localization and tracking, and power control.  
**Author Contributions:** Conceptualization, M.V.; methodology, M.V.; software, M.V.; validation, M.V.; formal analysis, M.V.; investigation, M.V.; resources, M.V.; data curation, M.V.; writing--original draft preparation, M.V. and D.A.; writing--review and editing, M.V., D.A., A.C., C.C. and W.S.; visualization, M.V., D.A., A.C., C.C. and W.S.; supervision, M.V., A.C., C.C. and W.S.; project administration, M.V.; funding acquisition, M.V., C.C. and W.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** In this article, we generate a database as described in Section 3.2. All data are available upon request. If you are interested, please contact the corresponding authors, [PERSON] and [PERSON].  
**Conflicts of Interest:** The authors declare no conflicts of interest.