esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Verification Measurements
### Measurement Results  
In order to test and verify the above-described operation theory, five different scenarios have been set up and were investigated within the measurement campaign. The different scenarios are listed in Table 2, while the entries \"1\"and \"0\" represent the presence or absence of the corresponding targets T1-T5, respectively. For checking the field coverage and the setup's time gating operability, all targets were put into the field. As we did not perform any target classification in this scenario, it is labeled as scenario #0.  
Figure 15 shows the measured radar diagram of the scenario #0. Due to the length of the cables, all targets are located 80 cm further away than their corresponding field coordinates but can be clearly observed at their designated positions. Next to the range distribution, it is possible to investigate the targets' spectra by time gating the targets (see Figure 16) and feed the corresponding data to the inverse Fourier transformation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target No.** & **Coord. (x,y)** & **LOS-Distance \(R\)** & **Beam Area** \\  & **in m** & **in m** & **Beam Area** \\ \hline T1 & (0, 30.45) & 30.45 & CB2 \\ T2 & (27.4, 27.4) & 38.75 & OB \\ T3 & (27.4, 0) & 27.4 & CB1 \\ T4 & (12.9, 12.9) & 19.1 & OB \\ T5 & (19.3, 19.3) & 27.4 & OB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Target plan and properties.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenario No.** & **T1** & **T2** & **T3** & **T4** & **T5** \\ \hline
2 & 0 & 0 & 0 & 1 & 0 \\
3 & 1 & 0 & 1 & 0 & 0 \\
4 & 0 & 0 & 1 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measurement scenarios.  
Figure 15: Measured radar diagram of scenario #0.  
Figure 17a-c show the resulting spectra of the targets T1, T3, and T4, respectively. As can be seen, the notch at 11.5 GHz can be clearly detected for T1. The same is valid for the two notches at 8.5 GHz and 11.5 GHz for T4, which has both features due to its position in the overlap region. Unfortunately, no clear notch can be detected for the target T3, because the received power of the target is too low and too much affected by ground clutter. However, these results show that the concept works in principle since the notches in the main beam direction as well as in the overlapping area of both antennas can be extracted.
## Appendix A  
A multi-objective artificial neural network (ANN) was developed to invert three key mesoscopic parameters (\(E\), \(k_{s}/k_{n}\), and \(E_{d}\)) of the linear parallel bond contact model in PFC3D. Figure A1 shows the evolution of the mean squared error (MSE) loss over 1500 training epochs. Both the training and validation losses decreased rapidly during the first 600 epochs, then more gradually until reaching approximately 0.18 at epoch 1500. The close agreement between the training and validation MSE indicates that the ANN generalized well; accordingly, the number of epochs for the ANN training was set to 1500 in this study.  
The influence of the hidden layer size was evaluated by varying the number of neurons from 2 to 1350 in a single hidden-layer network (Figure A2). As the neuron count increased from 2 to 400, the MSE for training and validation decreased markedly from 0.06 to 0.01 and from 0.25 to 0.15, respectively (Figure A2a), while \(R^{2}\) rose from 0.85 to 0.95 (training) and from 0.72 to 0.78 (validation) (Figure A2b). Below 400 neurons, the network lacked sufficient capacity to predict the three parameters accurately; beyond 400 neurons, further increases yielded negligible gains. Therefore, 400 neurons per hidden layer were adopted.  
The impact of the network depth was then investigated by varying the hidden layer count from 1 to 6, with 400 neurons in each layer (Figure A3). Increasing the depth from 1 to 3 layers produced substantial improvements: the training and validation MSE fell from 0.23 to 0.01 and from 0.32 to 0.14, respectively (Figure A3a), while \(R^{2}\) climbed from 0.68 to 0.98 (training) and from 0.42 to 0.78 (validation) (Figure A3b). Additional layers beyond three yielded only marginal error reductions. Consequently, a three hidden-layer architecture (400 neurons each) was selected.  
Figure A3: Effect of hidden layer count on ANN performance: (**a**) MSE; (**b**) \(R^{2}\).  
Finally, with the network architecture fixed, four activation functions, i.e., ReLU, Tanh, Sigmoid, and Softmax, were compared over the same training regime (Figure A4). All four produced MSE values below 0.18 (Figure A4a), with Sigmoid and Softmax slightly outperforming the others. However, ReLU achieved the highest \(R^{2}\) on both the training (0.972) and validation (0.780) datasets (Figure A4b). Since our primary criterion was \(R^{2}\) performance, ReLU was chosen as the activation function for the final ANN model.
## 3 Results and Discussions
### Floc Stability and MP Entrapment Mechanism  
As mentioned above, flocs break up if they are subjected to strong agitation. The floc breakage may occur through erosion and rupture, with the predominant process depending on the floc size in relation to the Kolmogoroff microscale [57]. Therefore, we operated at a low speed of 30 rpm to prevent floc breakage.  
Additionally, the stability of the flocs depends on the combination of interactions that bind the bacteria and other components to the flocs. EPS, which make up more than 50% of the organic part of the flocs, are primarily responsible for floc stability [57; 58; 59]. The three-dimensional EPS network structure that encases the microbial cells and water in the floc creates a gel-like structure that encourages MP aggregation. In particular, it has been reported that MPs are mainly retained in flocs by binding to humic acids, which are part of EPS. Furthermore, it was observed that MPs act as environmental stressors that increase EPS production [34; 35]. The presence of MPs in the sludge decreases the size of the flocs and their hydrophobicity and increases their negative zeta potential [13]. Figure 9a shows two possible mechanisms of floc rupture.  
As shown in Figure 9b, fibre-MPs and fragment-MPs can interact with flocs in a variety of ways, becoming part of the flocs themselves and influencing the sedimentation rate. Most of the fragments were within the range of 50-150 um (Figure S4), a smaller size than the flocs after 30 min of stirring at 30 rpm (average size of 387 um), which is why they were mostly fully contained within the flocs. On the contrary, the length of most of the  
Figure 8: Size distribution of fibres (**a**) and fragments (**b**) in thickened sludge, before the experiment.  
fibres was within the range of 250-350 \(\upmu\)m (Figure 8). This larger size, combined with the gentle stirring (30 rpm), meant that the majority of fibre-MPs were only partially trapped in the flocs. In some cases, the same fibre was trapped by two or more flocs (see types II, III, IV, and V in Figure 9b) resulting in \"bridging\" that joined different flocs to form a \"Pangea shape\" macrofloc (see type VI in Figure 9b), which favoured the sedimentation process [60]. Furthermore, the electrostatic repulsion between coexisting MPs could be nullified, thanks to better collision and aggregation of the polymer particles with the sludge flocs [61; 62]. In addition to the shape and relative size of the MPs with respect to the flocs, other factors that are expected to affect the phenomenon of entrapment of MPs in flocs include the physicochemical properties of the MPs' surface and the possible presence of other substances that may facilitate or hinder the interaction. In this sense, it is expected that the use of flocculants and coagulants would result in a better MP removal, although further studies are required to confirm this [63].
# Preliminary Results of an Astri/UWM EGNSS Receiver Antenna Calibration Facility
## 2 Antenna Calibration Techniques
The robot's controller, together with a PLC (programmable logic controller), captures this time and waits until the beginning of following second to ensure time synchronization (Figure 2). After this period, the timestamp and current antenna orientation are recorded for post-processing. The communication between the receiver, PLC, robot controller, and personal computer (PC), which is used as a data storage device, is performed with the aid of an ethernet transmission control protocol (TCP). We also propose some modifications in robot arm movements scenarios. In the \"Hannower\" concept, the measuring program is satellite constellation-dependent and the average total number of different orientations in one calibration procedure is between 6000 and 8000 [27]. In our approach, we propose a regular robot arm movement scenario which is not dependent on any satellite constellation. Our main robot arm unit (RAU) movement scenario is based on a changing position for the zenith angle in the range from 0\({}^{\circ}\) to 60\({}^{\circ}\) in 5\({}^{\circ}\) steps and a changing position for the azimuth angle in the range from 0\({}^{\circ}\) to 350\({}^{\circ}\) in 10\({}^{\circ}\) steps. The use of these algorithms is very easy, and, on the other hand, they allow us to collect sufficient data for one calibration (in the case of a full satellite constellation) within about 1 h and 40 min. Our procedures have been extensively tested and this paper presents the initial results for an Astri/UWM calibration facility with the GPS L1 frequency.
# A Low-Cost Soft Robotic Hand Exoskeleton for Use in Therapy of Limited Hand-Motor Function
## 2 Materials and Methods
### Control System  
The Arduino Mega microcontroller was chosen as the main platform for all control, communication and signal processing as with 15 pulse-width modulation (PWM)-capable pins, it fulfills the requirements for the number of PWM pins needed to drive the DC motors, as well as an ample number of analog inputs (16) used for position monitoring. The Arduino primarily fulfills our requirement of being widely available at low cost. It also benefits from a large community of users and developers and access to code libraries available for a variety of interfacing options.
## 5 Conclusions  
In the presence of iron minerals with varying crystallinity, the degradation of toluene does not coincide with the reduction of iron; the electrons released during toluene degradation fail to fully reduce the iron. Aside from the losses incurred during the toluene experimental process, the assimilation by microbial growth, and the pre-measurement losses of iron, the results indicate the presence of incompletely degraded intermediate states. Consequently, a conceptual model delineating the mutual relationship between toluene degradation and iron reduction processes is established as follows: the heterotrophic iron-reducing microorganisms participate in two reaction stages of toluene and iron mineral heterotrophic iron reduction. The first stage corresponds to the dominant phase of toluene conversion to an inter-mediate state, while the second stage pertains to the dominant phase of intermediate state mineralization.  
This study delves into the relationship between toluene degradation and iron reduction, proposing a comprehensive conceptual model. It is helpful to deduce the coupling mechanism of toluene degradation and the ferrous reduction amount. It deepens our understanding of microbial involvement in degradation processes within complex environments, providing a significant theoretical framework and practical guidance for comprehending the degradation of toluene-like contaminants in subsurface environments.  
In practical field applications, it is imperative to consider the specific types of iron minerals in the subsurface of the site when evaluating the natural attenuation degree of pollution and the age of the contaminants. Moreover, the delayed electron transfer phenomenon between contaminants and iron also impacts the site assessments. Therefore, this factor should be taken into account during the assessment process.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app14125017/s1](https://www.mdpi.com/article/10.3390/app14125017/s1)]([https://www.mdpi.com/article/10.3390/app14125017/s1](https://www.mdpi.com/article/10.3390/app14125017/s1)), Figure S1: The gas chromatography spectra comparison between the experimental group and the control group.  
Conceptualization, H.D. and Z.N.; Formal analysis, Z.N. and C.L.; Investigation, H.D., M.Z., Z.N., Z.H., C.L. and J.S.; Methodology, H.D., M.Z. and Z.N.; Supervision, M.Z., Z.H. and C.L.; Validation, H.D.; Visualization, H.D.; Writing--original draft, H.D.; Writing--review and editing, H.D., M.Z., Z.N., Z.H., C.L. and J.S. All authors have read and agreed to the published version of the manuscript.  
The Central Leading Local Science and Technology Development Fund Project, grant number 236Z4204G.  
Not applicable.  
The data presented in this study are available in the article and Supplementary Materials.  
We appreciate the Nexis GC-2030 gas chromatograph and HS-10 automated headspace sampler manufactured by Shimadzu Corporation for ensuring the accuracy of our research data.  
The authors declare no conflicts of interest.
# Federated Learning for Edge Computing: A Survey
## 5 Applications
### Transport  
Vehicle networks have become more robust due to recent developments in sensing and communication technologies and the amount of data coming from in-vehicle sensors, embedded devices, and road cameras. Increasingly, AI and ML methods are also being used in transport to create intelligent transport systems. By directly running ML models in vehicles based on their datasets, such as road geometry, collision avoidance, traffic flow, and data from all the sensors available in modern vehicles, FL can support intelligent traffic systems. Since modern vehicles have many sensors at their disposal, they can generate a large amount of data that could be used to train a global model. FL would simultaneously address the protection and security of this data, and at the same time, this approach could be integrated with other computing power at the edge and IoT.  
The joint optimization problem of choosing a communication interface and the problem of choosing a route based on cooperative learning between vehicles can be solved using FL [29]. FL could facilitate the development of different types of IoT applications in vehicles. For example, in autonomous driving systems, each vehicle is trained online based on observations of a single vehicle, leading to limited knowledge of the environment. FL can provide more information for each vehicle through inter-vehicle collaboration. [PERSON] et al. [127] focus on using FL in autonomous vehicles. They propose a decentralized method for autonomous vehicles and simultaneously demonstrate through experiments that their FL-based methods are suitable for improving multi-object recognition performance in autonomous vehicles. [PERSON] et al. [128] focus on a novel FL framework that enables large-scale wireless connectivity for designing controllers of autonomous vehicles. The autonomous vehicle system must be continuously improved to react to unpredictable things in real-time and promptly, to react automatically to acceleration and deceleration, braking and stopping, or getting into the right lanes. [PERSON] et al. [129] explore applications within vehicle networks and the development of intelligent transport systems such as autonomous driving, infotainment, and route planning.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Environmental Monitoring and Smart Farming
#### 3.2.10 Smart Helmet  
The Smart Helmet [29] node is targeted for air quality monitoring in the mining industry. The Adafruit LoRa feather board with an inbuilt 48 MHz ARM Cortex M0+ (SAMD21) microcontroller by Microchip was chosen for the node. It contains 256 kB flash memory and 32 kB of static RAM. A LoRa module (RFM69) embedded in the Adafruit LoRa feather board was used for communication. It is capable of operating in the 868 MHz/915 MHz/865 to 867 MHz/923 MHz unlicensed bands in different countries. The module is capable of producing an RF output of +20 dBm. A communication range of around 500 m LOS is also achievable.  
A SparkFun SEN-16531 air quality sensor (SGP30) is incorporated for measuring levels of carbon dioxide and total volatile organic compound (TVOC). A battery is used to supply power to the node and the power consumption details are not specified.
## 2 Study on the Working Principle of an Online Vibrating Tube Liquid Densitometer
### Pressure Effects on Measurement Results  
The use of the environment of the online vibrating tube densitometer for the actual online measurement is more complex. The actual use of the vibrating tube system is not only affected by inertial forces; it is also affected by the impact of the liquid force. The interaction of these forces and the family force is caused by the unnecessary deformation of the vibrating tube, thus affecting the vibration of the tube system, causing the change in the intrinsic frequency of the system.  
The correction for the effect of pressure on the online vibrating tube liquid densitometer is made by measuring the vibration period, T\({}_{\rm p}\), and the density of the liquid in the pressurized state condition of the tube and inserting them into fitted Equation (8) [10]. The density value and compression factor, F, of the liquid at a standard pressure are obtained from the table, and the actual density values at different pressures are calculated as follows [10]:  
\[\rho_{px}=\rho_{0}\big{(}1+F\times\rho_{p}\big{)} \tag{8}\]  
\(\rho_{px}\): Density of liquids at different pressures (kg/m\({}^{3}\)); \(\rho_{0}\): density of liquids at standard pressure (kg/m\({}^{3}\)); \(F\): liquid compression factor; \(\rho_{p}\): density values for liquids under pressure testing (kg/m\({}^{3}\)); and \(P\): pipeline pressure (bar).  
Refer to the table to observe the density value of different liquids under different pressures. The complete pressure correction formula shown in (8) can be used for type correction to obtain P as the actual pressure in pipelines \(K_{20}\) and \(K_{21}\) by the method of least squares [3; 10].  
\[\left\{\begin{array}{l}\rho_{px}=\rho_{0}(1+K_{20}\times P)+K_{21}\times P\\ K_{20}=K_{20A}+K_{20B}\times(P-1)\\ K_{21}=K_{21A}+K_{21B}\times(P-1)\end{array}\right. \tag{9}\]
# Acidic Neutralization by Indigenous Bacteria Isolated from Abandoned Mine Areas
## 3 Results and Discussion  
### Physicochemical and Biological Characterization of the Soil Samples  
Table 1 lists the physicochemical properties including pH, moisture content, organic matter content, NH\({}_{4}\)+-N concentration, and particle size distribution of soil samples along with the concentration of heavy metals. The Y1 soil sample collected from a hillside located in Yongin was acidic at pH 4.51, while the C1 and C2 soil samples collected from farmland near the abandoned mine area in Cheonan were neutral at pH 7.8 and 7.1, respectively. Conversely, the concentration of ammonium nitrogen (NH\({}_{4}\)+-N) was higher in the acidic soil Y1 with 2.69 mg/kg than that in the other soils that were in neutral pH. It could be presumed that the microbial metabolism capable of producing NH\({}_{4}\)+ was further activated by an indigenous bacterial consortium, resulting in the accumulation of NH\({}_{4}\)+-N in an environment with a continuous supply of acidic precursors.  
In the meantime, C1 and C2 soil samples were found to be contaminated with a variety of heavy metals. Among them, arsenic was detected at 97.66 mg/kg and 56.08 mg/kg in C1 and C2 soils, respectively, exceeding the national soil pollution standard (i.e., 25 mg/kg or more). In addition, zinc was found at a rather high concentration of 252.6 mg/kg in C2 soil, but the concentration did not exceed the suggested contamination level. Other heavy metals were measured to be below the contamination levels in C1 and C2 soils, and it was confirmed that heavy metal contamination was not severe in Y1 soil.  
The microbial communities differed according to the soil samples (Figure 2). In Y1 soil, the dominant genera were found with _Rhodanobacter_ spp. (19.34%), _Paraburkholderia_ spp. (16.26%), and _Natranaerobaculum_ spp. (12.94%), as shown in Figure 2a. _Pseudomonas_ spp. was the predominant genus (C1: 82.91%, C2: 87.37%) in soils contaminated with arsenic  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Y1** & **C1** & **C2** \\ \hline Soil depth & Topsoil & Subsoil & Topsoil \\ pH & 4.5 & 7.8 & 7.1 \\ Moisture content (\%) & 25.15 & 9.92 & 10.61 \\ Organic content (\%) & 2.17 & 0.18 & 0.34 \\ NH\({}_{4}\)+-N (mg/kg) & 2.69 & 2.34 & 1.99 \\ \hline  & Sand (\%) & 73.40 & 86.26 & 85.62 \\ Particle size distribution & Silt (\%) & 19.71 & 9.62 & 6.91 \\  & Clay (\%) & 6.88 & 4.12 & 7.47 \\ \hline  & As (mg/kg) & - & 97.66 & 56.08 \\  & Cd (mg/kg) & - & 0.81 & 1.19 \\ Heavy metals & Cu (mg/kg) & - & 4.5 & 35.8 \\  & Pb (mg/kg) & - & 24.2 & 32.1 \\  & Zn (mg/kg) & - & 32.2 & 252.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physicochemical properties of the soil collected from the abandoned mine areas.  
(Figure 2b), suggesting that they had a similar microbial community structure at the genus level.
## 3 Results and Discussion
### Zeta Potential Characterizations  
The adsorption of reagents and ions significantly influences the surface properties of minerals. As a key parameter for characterizing mineral surface charge properties, zeta potential provides critical insights into the interactions between mineral surfaces and major species in solution [34]. In this study, the zeta potential of molybden was measured at pH 8.0 to assess the individual and combined effects of Cu\({}^{2+}\) and STG on its surface characteristics.  
Figure 6: Contact angles of molybden under different reagent conditions (Cu\({}^{2+}\): 8 mg/L, STG: 1.0 \(\times\) 10\({}^{-3}\) mol/L, and kerosene: 2.5 \(\times\) 10\({}^{-4}\) mol/L) at a pulp pH of 8.0.  
Figure 7 shows that the surface charge of molybden exhibited a noticeable positive shift with an increasing amount of Cu\({}^{2+}\). At a Cu\({}^{2+}\) concentration of 8 mg/L, the zeta potential stabilized at \(-\)11.33 mV, suggesting the surface had reached its adsorption capacity for Cu\({}^{2+}\). In comparison, the addition of STG alone caused only a minimal change in zeta potential, even at higher concentrations, suggesting weak interactions between STG and the molybden surface under these conditions. However, when molybden was pretreated with 8 mg/L Cu\({}^{2+}\) before the introduction of STG, a marked reduction in surface potential with increasing STG concentration occurred. At 1.0 \(\times\) 10\({}^{-3}\) mol/L STG, the zeta potential reached a steady value of \(-\)53.39 mV. These results indicate that the prior adsorption of Cu\({}^{2+}\) created reactive sites on the molybden surface, promoting subsequent STG adsorption.