esa-sceva/satcom-chunk-collection huggingface dataset:
# Federated Learning for Surface Roughness
## 5 Conclusions  
This study aimed to address the challenge of real-time surface roughness prediction in wire electrical discharge machining (WEDM) by proposing a predictive framework that integrates an enhanced Mini-GPT model with federated learning. The system enables local model training across machines while preserving data privacy. The experimental results demonstrate that the centralized model achieved high prediction accuracy (Absolute Error < 0.1) for 71.56% of samples, with a low RMSE of 0.074. The federated model, after data balancing and augmentation, reached comparable performance with 70% accuracy and an RMSE of 0.076, confirming its practical viability for distributed industrial settings.  
A key observation was that prediction accuracy is closely tied to sensor resolution and synchronization. Deviations were often associated with non-linear current patterns and millisecond-level timing offsets between sensor data streams. These findings suggest that incorporating high-precision time synchronization mechanisms could further improve model reliability and accuracy.  
The primary contribution of this work lies in offering manufacturers a non-intrusive, scalable solution for real-time defect detection that only requires external current sensors. The federated learning architecture also supports rapid adaptation to new machines, promoting broader industrial deployment. Future research will explore the integration of multimodal sensors and federated transfer learning to enhance the robustness and generalization of the proposed system across different manufacturing platforms.  
Conceptualization, K.-L.C., Y.-H.T., W.-R.J, S.-C.C. and Z.-W.Z.; Methodology, K.-L.C., Y.-H.T., W.-R.J., S.-C.C. and Z.-W.Z.; Software, K.-L.C.; Validation, K.-L.C., Y.-H.T., W.-R.J. and S.-C.C.; Writing--original draft, K.-L.C.; Writing--review & editing, Y.-H.T. and Z.-W.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company Moldintel Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
###### Abstract  
Frozen fractured rock mass is often encountered during the implementation of geotechnical engineering in cold regions. The tensile strength parameters of frozen rock play an important role in the construction of rock slopes involving tensile failure. In order to study the tensile characteristics of a frozen fractured rock mass in a cold region, original rock specimens were mined and processed in the Yulong Copper Mine, and artificial, frozen fractured marble specimens were made. The effects of different ice-filled crack angles, lengths, and widths on the force-displacement curve and the tensile strength of frozen rock were studied by laboratory Brazilian splitting experiments and RFPA3D, and the evolution law of the tensile strength of frozen rock was revealed. At the same time, wing crack initiation and cracking mode after tensile failure were analyzed by high-speed camera; the whole process of the Brazilian splitting of frozen rock was reconstructed, and the development of microcrack initiation in frozen rock was analyzed. The following conclusions were drawn from the test results: the frozen rock specimens have typical brittle-failure characteristics. The tensile strength of frozen rock decreases gradually with the increase in the width and length of ice-filled cracks, and decreases first and then increases with the increase in the angle of the ice-filled crack. The ice-filled crack incurs damage first, and then the wing cracks start from the tip of the ice-filled crack and extend continuously. The tensile strength of frozen rock is significantly affected by the angle and length of ice-filled cracks.  
Keywords:frozen rock mass; Brazilian splitting test; tensile strength; ice-filled crack ice; failure characteristics +
Footnote †: journal: Applied Sciences
# In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications
## 3 Results
### PUSCH Transmit Power  
A key parameter in EMF exposure is the transmit power of the device. Figure 6 shows the boxplots of the PUSCH transmit power (\(\rm P_{tx}\)) in dBm as a function of distance to the base-station antenna. Again, the path type between the smartphone and base-station antenna is mentioned in this figure. For each of the experiments, we plotted the median PUSCH Tx power (i.e., the reported transmit power independent of the use case, indicating a set value, not a time average), as shown in Figure 6. In order of magnitude, we observed similar PUSCH Tx powers for 5G NR and 4G LTE, ranging between \(-\)20 dBm and 20 dBm. As for the duty cycle and bandwidth, we observe that data-intensive applications show the largest values for the PUSCH transmit power. For 5G NR, we also observe that the transmit power increased with the distance of the smartphone to the base-station antenna. When communicating over LTE, irrespective of the network mode (4G or 5G), the PUSCH transmit power decreased up to Pos 2 (236 m from the base station) and increased with the distance for positions further away. Looking at the influence of the path type on the transmit power, there is an indication of a possible increase in transmit power for 5G NR at Pos 9 (NLOS due to buildings; see Figure 6a) and for 5G and 4G LTE at Pos 6 (NLOS due to buildings and vegetation).  
Figure 5: One-second-averaged uplink bandwidth fractions as a function of distance to the base-station antenna per use case and per technology: (**a**) 5G-NR, (**b**) 5G-LTE, and (**c**) 4G-LTE. The color of the labels on the horizontal axis indicates the path type, i.e., red, orange, and blue indicates NLOS (buildings), NLOS (vegetation), and NLOS (veg./build.), respectively.  
Figure 6: Boxplots of the one-second median PUSCH transmit powers as a function of distance to the base-station antenna per use case and per technology: (**a**) 5G-NR, (**b**) 5G-LTE, and (**c**) 4G-LTE.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 2 Underwater Communication Technology Overview
### Radio Frequency (RF) Communication  
From the physics perspective, for frequency ranges of satellites, TV, mobile, and radio communications, the conductivity in seawater is very high; thus, it strongly affects EM wave propagation. Due to this, establishing communication links in ultra and very high frequency (UHF and VHF respectively) for a distance more than 10m in ocean is not very likely. In the case of lower frequencies, i.e., at very low and extreme frequency ranges (VLF and ELF, respectively), EM attenuation can be considered short enough to allow reliable communication over few kilometers to occur Frequencies from 3 kHz to 30 kHz and 3 Hz to 3 kHz are not wide enough to allow transmissions at elevated data rates.
## 2 Data and Methods
### Bio-Optical Measurements  
By using a profiling package, bio-optical measurements were performed to measure various optical parameters. The package included a Seabird ABE911P conductivity-temperature-depth (CTD) profiler that was used to measure hydrological characteristics of the water bodies and some optical devices, including a WET Labs AC-S and a HOBI Labs Hydroscat-6 (HS-6). The particulate absorption coefficient (\(a_{\text{p}}(\lambda)\)) and beam attenuation coefficient (\(c_{\text{p}}(\lambda)\)) in the spectral range of 400-700 nm were  
Figure 1: Bathymetry map of the study areas (Bohai Sea and Yellow Sea) overlaid with the stations collected from the four cruises in November 2014, August 2015, July 2016, and January 2017. The red rectangles refer to those stations with remote sensing reflectance, \(R_{\text{rs}}(\lambda)\), data, and different colors indicate different water depths.  
observed with the AC-S instrument, and then the particulate scattering coefficient (\(b_{\rm p}(\lambda)\)) could be derived using the relationship \(b_{\rm p}(\lambda)=c_{\rm p}(\lambda)-a_{\rm p}(\lambda)\). The particulate backscattering coefficient, \(b_{\rm bp}(\lambda)\), was observed by the HS-6 instrument which has six spectral channels, i.e., 442, 488, 550, 620, 700, and 852 nm, while the blue and green bands (i.e., 442, 488, and 550 nm) were used as delegates for the analysis in this study. The detailed measurement methods for the AC-S and HS-6 instruments can be seen in the [PERSON] et al. (2016) [24] study.  
The remote sensing reflectance (\(R_{\rm rs}(\lambda)\)) spectra were collected with a Satlantic Hyper-Profiler II radiometer [25]. Meanwhile, water samples were simultaneously collected along with the above optical measurements and then filtered immediately in the lab onboard. The filtered particulates on GF/F filters were used to analyze total suspended matter (TSM) concentrations [24]. The collected bio-optical parameters during the four cruises are summarized in Table 1.
## 2 Data Quality Analysis
### DQA1: Satellite Position Difference When Ephemeris Is Updated  
DQA1 uses the ephemeris data to estimate the satellite position difference when the ephemeris is updated. When the ephemeris is updated, this analysis uses the original and new ephemeris to calculate the satellite positions. Ideally, the difference in satellite position before and after the ephemeris update should be small if the satellite control ability of the GNSS is stable. The results of DQA1 are shown in Table 2. In DQA1, both systems have the same level of stability most of the time. However, one of BDS IGSO satellites (PRN 10) and two of BDS MEO satellites (PRN 11 and PRN 12) exhibit dramatic differences for some ephemeris updates, and both the new and original ephemerides are declared as healthy. This sudden change in satellite position would degrade user positioning performance. Based on the DQA1 results, the orbit prediction for BDS is not as stable as that of GPS for the experiment period. For a standard GNSS receiver, it is difficult to identify which of the two inconsistent orbits is more accurate. As a result, the DQA1 method is proposed to be included in the BDS-GPS receiver design to validate each satellite position used for positioning and detect the irregular or large change in satellite position because of ephemeris update. If the DQA1 detects any irregularities when ephemeris updates, the corresponding satellites would be excluded from positioning.
## 2 Structure Overview and Modeling of a DDTV
### Models of the Track-Ground  
There are many mature tire-road models like the Burckhardt tire model [16] and magic formula tire model [25] for wheeled vehicles; however, the structure of the track and the contact form of the track-ground differ greatly from the tire-road [25; 37], so there are no suitable analytical models for it. Therefore, the best way to conform to the reality is to analyze and simulate with an experimental model. Experimental track-ground look-up-table models can be obtained through [36] and actual tests. Some typical track-ground conditions are shown in Figure 3.  
Figure 3: Schematic diagram of four typical track-ground conditions, where the adhesion coefficient can be indicated as \(\mu\) and slip ration can be indicated as \(\lambda\).
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 3 The NOMA System-Level Simulator
### Introduction to the NOMA System-Level Simulator  
The performance of an entire network was analyzed in system-level simulations. In conventional cellular networks, such a scenario comprises multiple cells with their respective base stations (BSs) that cover a defined area wherein many users are located. Simulations at the physical layer level enable the investigation of adaptive modulation coding (AMC) for NOMA and modeling of channel coding; however, estimating or measuring the impact of cellular planning, scheduling, or interference on a large scale with a larger number of BSs with mass connectivity is difficult. A practical way of performing system-level simulations is to abstract the physical layer using simplified models while maintaining high accuracy at low complexity. This is due to the vast amount of computational power required for performing system-level simulations of radio links between the BSs and their connected users.
## 4 The Blood-Brain-Barrier
### Structure  
Although endothelial cells of the CNS vasculature form the main barrier against the entry of xenobiotics in the brain, these cells do not function independently from others, but rather act as modules within the multicellular neurovascular unit. In fact, circulating immune cells, neurons, microglia, pericytes and astrocytes are intimately linked with the endothelium and play supporting roles in maintaining and functioning this barrier [43; 48; 49] due to an intricate network of molecular crosstalk between them [45] (Figure 3).  
Figure 3: Schematic representation of the cellular networks of the BBB. The paracellular pathway is governed by the tight junctions created by the brain endothelial cells. Foot processes from astrocytes form a complex network surrounding the capillaries and provide links to neurons. Pericytes are distributed discontinuously along the length of the brain capillaries and partially surround the endothelium. Microglia are CNS-resident immune cells (based on [50]).  
Indeed, in addition to the CNS endothelial cells, several functional layers exist between the blood and brain. This is the case of the basement membrane, which completely covers the capillaries and is made of laminin, fibronectin and type IV collagen, and contains pericytes; astrocytes also surround this basement membrane of the BBB. Each of these layers may contribute to solute movement through BBB [51].  
However, the endothelial cells lining the cerebral blood vessels still represent the main anatomical structure of the BBB and can be distinguished, both functionally and morphologically, from the peripheral endothelial cells. For example, they have a high concentration of mitochondria, which is an indication of a high energy expenditure [52].  
Moreover, endothelial cells from the CNS composing the tight junctions, greatly limit paracellular permeability [53; 54; 55]. They also display low rates of transcytosis when compared to peripheral endothelial cells, effectively reducing vesicle-mediated transcellular transport [56].  
As a result of this tight paracellular and transcellular barrier, endothelial cells are polarized, presenting distinct abluminal and luminal surfaces with efflux and influx transporters and receptors, which together control the movement between blood and brain [52].  
In addition, these endothelial cells have a net negative surface charge, repelling negatively charged compounds, and they also have no fenestrations (small transcellular pores that allow free diffusion), thus preventing fast exchange of molecules. Endothelial cells also have very low levels of leucocyte adhesion molecules, restricting the number of immune cells that can indeed enter the CNS [52].
## 2 Measurements and Quality Control Processes
### Statistical Relationships of Hourly Values  
The variables illustrated in Figure 8, can be expressed by quadratic equations. However, the coefficient of determination is relatively low:  
\[L_{dn}=-10.01+1.058*L_{up}-0.0006*L_{up}^{2},\ R^{2}=0.57 \tag{6}\]  
The quadratic equations of the relationships between the hourly longwave irradiances with air (\(T_{a}\)) and ground surface temperatures (\(T_{g}\)) are shown below:  
\[L_{dn}=251.9+2.035*T_{a}+0.068*T_{a}^{2},\ R^{2}=0.62 \tag{7}\]  
\[L_{dn}=258.3+3.195*T_{g}+0.021*T_{g}^{2},\ R^{2}=0.60 \tag{8}\]  
\[L_{up}=325.1+0.152*T_{a}+0.227*T_{a}^{2},\ R^{2}=0.88 \tag{9}\]  
\[L_{up}=329.2+1.969*T_{g}+0.172*T_{g}^{2},\ R^{2}=0.96 \tag{10}\]  
It is evident that \(L_{up}\) has strong relationships with both air and ground surface temperatures. The coefficient of determination of the relationship of \(L_{up}\) and \(T_{g}\) is closed to 1.
# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 7 Staggered Transition towards a Smart WMS
### Concurrency  
To make the system concurrent, so that it could be accessed by various managers at the same time, MQTT was again utilized. All the computers, as well as any devices that were used for the frontend display, etc., were made subscribers to the MQTT brokers. Different topics were setup that relayed different data from publishers to subscribers. The computers in every division of the factory were linked to the system using the same intra-network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & **No.** & **Bytes** & **Avg. Time** & **Avg. Time** \\  & **of Packets** & **Transmitted** & **Connection Cycle** & **for One Message** \\  & & & **(ms)** & **(ms)** \\ \hline MQTT & 1000 & 173,980 & 30,483 & 30.4 \\ HTTP/S & 1000 & 198,341 & 42,503 & 42.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MQTT vs. HTTP/s—latency.  
The data could be accessed by the authorized personnel using their login credentials, which made the whole system concurrent and accessible.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 4 Research on Multi-Branch Line Fault Ranging Technology Based on Fault Branch Judgment Matrix
### Simulation Verification  
The 220 kV multi-branch grid-connected circuit shown in Figure 6 was built in ATP/EMTP. All line parameters are set to be the same, the sampling frequency is 10 MHz, and the fault occurrence time is 2 \(\upmu\)s. The line parameters are highlighted in Table 3.
# Research on New Drilling Downlink Communication Based on Anisotropic Magnetoresistive Technology
## 4 Experimental Test and Analysis
### Test and Analysis of the Factors Affecting the Quality of Download
#### 4.2.1 Test under Different Transmitting Voltage  
With other parameters unchanged, BER tests are conducted under different transmission voltage conditions, and the relationship between BER and transmit voltage is obtained as shown in Figure 12.  
It can be seen from Figure 12 that the BER of information transmission decreases with the increase of transmitter voltage when other parameters are fixed. Increasing the transmitter voltage in the range of 6 V-16 V can significantly reduce the BER. When the transmitter voltage is increased in the range of 16 V-24 V, the BER still decreases, but the change is relatively slow. The result has demonstrated that the higher transmitting voltage of the transmitter, the greater current flowing through the simulated drill string, and the lower BER of the analog communication system, the more conducive to information transmission. In practical application, the transmitting voltage of the transmitter should be increased as much as possible within the range of allowable conditions.  
Figure 12: Comparison of communication BER under different transmission voltage.
# Fuzzy Adaptive Asymptotic Control for a Class of Large-Scale High-Order Unknown Nonlinear Systems
## 4 Simulations and Discussion
### Case Study: Two Inverted Pendulums on Connected Cars  
The MDSC is applied to control two inverted pendulums on connected cars, whose dynamics are given as:  
\[\dot{x}_{1,1} = x_{1,2}\] \[\dot{x}_{1,2} = \Big{[}\frac{g}{cl}-z(t)\Big{]}x_{1,1}+z(t)x_{2,1}+\frac{m\sin(x_{ 1,1})}{M}x_{1,2}^{2}+\frac{z(t)}{a(t)}(\sin(2t)-\sin(3t)-L)+\] \[\frac{1}{cml^{2}}u_{1}\] \[\dot{x}_{2,1} = x_{2,2}\] \[\dot{x}_{2,2} = \Big{[}\frac{g}{cl}-z(t)\Big{]}x_{2,1}+z(t)x_{2,1}+\frac{m\sin(x_{ 1,1})}{M}x_{2,2}^{2}+\frac{z(t)}{a(t)}(L+\sin(3t)-\sin(2t))+\] \[\frac{1}{cml^{2}}u_{2}\] \[y_{1} = x_{1,1}\] \[y_{2} = x_{2,1} \tag{52}\]  
where \(z(t)=\left[\left(ka(t)(a(t)-cl)\right]/\left(cml^{2}\right)\right)\), and the coefficients of the system are the same as in [35; 36]: \(M=m=10\) kg, \(L=2\) m, \(l=1\) m, \(g=9.81\) m/s\({}^{2}\), \(k=1\) N/m, \(c=m/(m+M)=0.5\). The system initial states are \(x_{1,1}(0)=x_{1,2}(0)=\pi/3.6\), \(x_{2,1}(0)=x_{2,2}(0)=\pi/4\). The control parameters are \(K_{k,1}=25,K_{k,2}=28,k=1,2\). The output reference trajectory is set as \(y_{1,d}=\sin(t)+0.5\sin(2t),y_{2,d}=0.5\sin(2t)\).  
Figure 3a,b perform the output tracking curves of subsystem 1 and subsystem 2; Figure 2c,d show the control input and virtual control law curves of each subsystem. The developed MDSC achieves asymptotic output tracking while ensuring the boundedness of all signals. The effectiveness of the developed algorithm has been well demonstrated.
## 3 Push-Pull Converter Sizing and Parameterisation
### Transformer Sizing and Parametrisation
#### 3.1.4 Leakage Inductances of Transformer Primary and Secondary Windings  
The calculation of the leakage inductance of a transformer (\(L_{lk}\)) is a complex task that is highly dependent on the transformer topology, the winding process and the physical separation between conductors. The influence of these factors determines the three fundamental components of the leakage inductance: the internal leakage inductance (\(L_{in}\)), the Yoke leakage inductance (\(L_{y}\)) and the equivalent leakage inductance (\(L_{eq}\)) [34].  
For a transformer with push-pull topology (separate primary and secondary windings), several works have proposed an analytical solution to determine the fundamental components of the leakage inductance of the transformer windings. A general expression for its calculation, successfully validated experimentally, is proposed in [34] and given by (11):  
\[L_{lk}=2\left(L_{in}+L_{y}+L_{eq}\right) \tag{11}\]The expressions proposed in [34] for the calculation of internal leakage inductances and yoke leakage inductances are complex and require previous tests on the transformer, so practical simplifications are required, which, with reasonable accuracy, favour their calculation. Thus, if it is assumed that the primary and secondary windings of the transformer are concentric throughout the window area of the central branch of the transformer core (usual in this type of power transformer) and conductors with reduced insulation thickness (insulating varnish) have been used for their manufacture, the terms associated with the internal leakage inductance and the yoke leakage inductance can be considered negligible with respect to the equivalent leakage inductance term [34]. Therefore, the leakage inductance of the transformer windings (\(L_{P_{1,2}}\) and \(L_{S_{1,2}}\), see Figure 2) can be calculated from the equivalent leakage inductance equation for this type of transformer topology, according to (12) [34]:  
\[L_{P,S}=\frac{2\cdot\mu_{0}\cdot N_{P_{2},S_{2}}^{2}\cdot l_{a}\cdot b_{w}}{3 \cdot l_{w}} \tag{12}\]