esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Soil Characterization  
The studied soil had a sandy texture, a basic pH, high concentration of organic matter due to the nature of the contamination, low electrical conductivity and low concentration of nutrients (Table 1). The nutrients are unbalanced with C:N:P of 100:1.5:0.08 far from the optimal 100:10:1 [4]. The results obtained for trace elements were below the limits established by the Spanish Legislation [32].  
\begin{table}
\begin{tabular}{c c} \hline MHWC (\%) & 13 \(\pm\) 4 \\ pH (H\({}_{2}\)O) & 8.66 \(\pm\) 0.05 \\ pH (KCI) & 7.42 \(\pm\) 0.04 \\ Electric Conductivity (dS/cm) & 0.7 \(\pm\) 0.2 \\ Total Limestone & n.d. \\ Sand (\%) & 91.20 \(\pm\) 0.01 \\ Silt (\%) & 2.5 \(\pm\) 0.3 \\ Clay (\%) & 6.3 \(\pm\) 0.3 \\ Texture & Sandy \\ Organic Matter (g/kg) & 23 \(\pm\) 4 \\ Assimilable Phosphorus (g/kg) & 0.011 \(\pm\) 0.001 \\ Nitrogen (g/kg) & 0.2 \(\pm\) 0.0 \\ Trace elements (mg/kg) & \multicolumn{1}{c}{Legislated values (mg/kg) \({}^{\star}\)} \\ Sb & 4 \(\pm\) 2 & 80 \\ As & 16 \(\pm\) 4 & 40 \\ Cd & 0.3 \(\pm\) 0.0 & 300 \\ Cu & 315 \(\pm\) 92 & 8000 \\ Co & 4.0 \(\pm\) 0.2 & 1500 \\ Cr & n.d. & 2300 \\ Sn & 6.75 \(\pm\) 0.07 & 100,000 \\ Hg & 0.285 \(\pm\) 0.007 & 15 \\ Mo & 8 \(\pm\) 1 & 1500 \\ Ni & 8.0 \(\pm\) 0.9 & 15,600 \\ Pb & 80.5 \(\pm\) 22 & 2700 \\ Zn & 290 \(\pm\) 42 & 100,000 \\ \hline \end{tabular}
\end{table}
Table 1: Soil basic characterization (mean \(\pm\) standard deviation) \(n\) = 3.
# Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion
## 3 Case Study
### Comparison of Graph Structures  
In this section, by comparing and analyzing the commonly used correlation coefficients, including Pearson correlation coefficient, Kendall correlation coefficient, and Spearman correlation coefficient, as well as their constructed correlation graph structure and probability graph structure, respectively, we compare the model accuracy and loss and select the graph structure construction method that is suitable for the proposed model in this paper. The activation function selected for the model is ReLU, the learning rate is 0.01, the number of iterations is 1000, and the number of multi-attention heads is 16. The ratio of the model training set, validation set, and test set is 7:1:2, which is selected randomly. Each graph structure construction method was subjected to 20 experiments, and the average accuracy of the 20 ornaments was used as the basis for judgment.  
As can be seen in Figure 5, the average accuracy of the Spearman correlation coefficient in the correlation graph structure is better than that of the Pearson correlation coefficient and Kendall correlation coefficient, and the average accuracy of the fusion graph obtained after the fusion of the graph structures is improved as a model input. This is due to the fact that different graph construction methods extract information from different aspects of the data, and the fused graph structure contains information from both aspects of the data, which can be input into the neural network with more information than a single graph structure. Especially when the probabilistic graph structure and the correlation graph structure constructed by the Spearman correlation coefficient are fused, the average accuracy of the model is greatly improved, reaching 96.36%. It can be seen that the model performance is optimal when the correlation graph structure is constructed with the Spearman correlation coefficient, which is chosen to construct the graphs in all subsequent experiments.  
Figure 6 shows the fused graph structure after fusion using the T_SNE probabilistic graph structure and the Spearman rank order graph structure. The edges between the nodes indicate that the nodes are neighbors to each other, as can be seen from the figure that the node relationships are only roughly delineated in a single graph structure, and after fusion of the graph structures, the neighbor relationships between the nodes have been clearly described.  
Figure 5: Test accuracy for each graph structure and total elapsed time.
## 3 LCI System Design Framework
### Design Exemplar  
This section contains a walk-through for component specification dependent on an exemplar set requirement list as outlined in the design framework shown in Figure 4:  
* Axial resolution: 5 \(\upmu\)m
* Operating range: 2.5 mm
* Measurement speed: 200 mm/s
* Lateral resolution: 20 \(\upmu\)m  
Axial resolution: The operating mediums absorption spectrum, degree of scattering present in the measurement and the required axial resolution will determine the potential range of \(\lambda_{0}\). It is also worth noting that some bandwidth regions have been more developed than others in terms of product availability and pricing. If a central wavelength of 1 \(\upmu\)m is chosen, then the corresponding bandwidth for a set axial resolution can be calculated using Equation (17)--for this example, \(\Delta\lambda\approx\) 88 nm.  
Operating range: For a required operating range and light source bandwidth, the minimum number of pixels sampling the wavelength range can be found using Equation (13) and solving for \(N_{s}\) (i.e., \(N_{s}=z_{max}\frac{4\Delta\lambda}{\lambda_{0}^{2}}\)). For the given example, this would come to \(N_{s}=880\) pixels.  
Measurement speed: The measurement speed is limited by the linescan rate of the camera. However, signal strength will impact the possibility of operating at such speeds which requires a knowledge of sample reflectivity and potential scattering or absorption of the operating medium; the amount of signal fall-off will also impact this. As shown by Equation (23), when relative transverse motion between the probe and sample occurs for a set exposure time, there will also be a reduction in the transverse resolution.  
Lateral resolution: To achieve a lateral resolution of 20 \(\upmu\)m at a translational speed of 200 mm/s with a linescan rate of 100 \(\upmu\)s, the maximum static FWHM of the beam profile should be \(\sim\)6.8 \(\upmu\)m from Equation (23), if the linescan rate is halved to 50 \(\upmu\)s, then the static FWHM should be \(\sim\)18.7 \(\upmu\)m. The static FWHM of the beam profile is dependent on the focusing optics used and will only be applicable over a certain axial field of view as described by Equations (18) and (19), respectively. The use of objective lenses can be employed to extend the field of view [64].
# Bioinspired Divide-and-Conquer Design Methodology for a Multifunctional Contour of a Curved Lever
## 2 Bioinspired Curved Lever Contour Design Methodology
### Curved Lever Mechanism in Salvia pratensis Flower  
The _Salvia pratensis_ flower has a staminal lever system that can dispense pollen to flower-visiting bees' abdomens for successful reproduction [17]. As shown in Figure 1a, the lever system is composed of a connective plate, a fertile anther lobe, and a rotational joint that has the role of a pivot. When a bee pushes its probocsis into the flower, the connective plate is forced to the left, as in Figure 1a. This force rotates the connective plate and fertile anther lobe clockwise. Therefore, the theca placed at the end of anther lobe makes contact with the bee's abdomen.  
The contour of the lever has a proper curved shape to achieve the successful dispensing of pollen. As shown in Figure 1b, if the contour of an anther lobe was a straight line, the theca could not make contact with a bee's abdomen due to interference with the bee's head. In addition, if the contour of the theca had the same curvature as the anther lobe shown in Figure 1c, the theca would make sharp contact with the abdomen and fail to dispense pollen effectively due to the narrow contact area.  
As illustrated in Figure 1d, the entire lever curve can be seen as a combination of partial contours with individual functional goals. The partial Contour 1 has a proper curve to satisfy the function of preventing interference with the bee's head. The partial contour 2 also has a shape that achieves the function of maximizing the contact area between the theca and the bee's abdomen.
# A LoRaWAN Architecture for Communications in Areas without Coverage: Design and Pilot Trials
## 4 Design of the TeamUp Network Based on LoRaWAN
### Bidirectional Message Exchange  
Once the network is implemented, regardless of the configuration used, the message exchange is checked. These messages are transmitted from node to node in the network using their corresponding technology. Figure 8 shows the bidirectional exchange necessary for a user to log in. Similarly, message exchanges between users occur within the network.  
Figure 7: FlowGraph of message processing on the TeamUp server.  
The network is deployed as a prototype for proof-of-concept testing. Currently, it does not implement timeout or retry mechanisms. However, it is intended to implement them as improvements to the proposed prototype.
# Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR
## 3 Methods
### Classification Algorithm
The selection of this threshold value is based on histograms of the ratio values for the two classes. Post-processing includes the application of a 3 \(\times\) 3 median filter to eliminate single outlier pixels, and the use of land cover maps to exclude dense forests and water surfaces. Forest cover and water bodies are extracted from the digital Landsat based maps of [PERSON] _et al._[33] with 30 m resolution. In addition, we use the SRTM Water Body Data Set [34] for complementing the water mask by adding the river courses.
# Uniform Circle Formation for Fully, Semi-, and Asynchronous Opaque Robots with Lights
## 2 Preliminaries
### Synchronization  
Concerning robot activation and synchronization, different modes are studied in the literature:In the _fully synchronous_ mode (fsynch), time is logically subdivided into global _rounds_, and all robots are activated at each round. Since _look-compute-move_ cycles are executed atomically, all robots terminate their cycle by the next round. Moreover, all robots start and finish each cycle step (_look_, _compute_, _move_) at the same instant.
* The _semi-synchronous_ mode (ssynch) coincides with the fsynch mode, except that not all robots are necessarily activated at each round. However, every robot is activated an infinite number of times (_fairness condition_).
* In the _asynchronous_ mode (asynch), the robots are activated independently from each other, and each robot executes its _look-compute-move_ cycle within an unpredictable but finite amount of time. In this mode, the snapshot taken by a robot during its look phase--used to compute its final destination--may not represent the real system configuration when the robot starts moving.  
For the ssynch and the asynch modes, an appropriate way to measure time is addressed by the notion of an _epoch_. An epoch is the amount of time within which each robot will be activated at least once. The fairness condition ensures each epoch lasts a finite time.
## 7 Results and Analysis
### Discussion
#### 7.12.3 Privacy Enhancements
However, these hybrid approaches introduce non-negligible energy overheads, increasing consumption by 18-35%, as shown in Figure 22. The trade-off between enhanced privacy and energy efficiency is particularly evident when comparing the three approaches across different privacy levels, where DP + HE consistently incurred the highest overhead, followed by DP + SMPC and DP-only configurations. These findings highlight the necessity of carefully balancing privacy requirements with system efficiency, especially in resource-constrained or latency-sensitive deployments.
## 3 Results and Discussion
### Empirical Experimental Results  
The results obtained for the four distinct empirical tests are plotted in Figure 7. The 0.1 m coaxial end-cable leakage at 868 MHz and 2.4 GHz shows a valid wireless communication that is possible towards the Rx sensitivity end-value since the BER and PER are 0. On the other end, the second scenario connecting the Tx and Rx only through the capacitive coupling PCB, indicates a stable communication at both frequencies, with a higher attenuation for the 2.4 GHz data. The attenuation for the highest frequency and data rate is expected since, in radio communication, a higher frequency and data rate means a shorter area coverage due to greater attenuation. The highest signal attenuation is not related to the message payload, but to the coupling capacitance; as the capacitance increases, the smaller the attenuation for 2.4 GHz, whereas for 868 MHz, the result is the opposite. When comparing the Li-ion parallel and series scenarios, the RSSI attenuation  
Figure 6: MATLAB simulation received signal strength (RSS) for 868 MHz and 2.4 GHz communication over the DC bus composed of four Li-ion cells organized in parallel (**A**) and in series (**B**). The square marker shows the average measurement interval value in the context of the min–max range bars.  
is similar for both cases with the exception of the 1 pF coupling, in which a variance of approximately 6 dB is recorded accordingly for the 868 MHz and 2.4 GHz transmissions. Compared to the simulation results that indicate a higher attenuation in the presence of a high current for the parallel scenario, the experimental results account for the phenomenon omitted in the simulation: at high frequencies, the wires conduct the signal through their surfaces rather than the volume, with a high proportion of the signal propagating on the surface and around the wire rather than inside it.  
Since the RSSI value for the 1 nF and 1 pF intervals experiences approximatively the same signal variation, the overall system's cut-off frequency behavior is observed when compared to the 1 \(\upmu\)F measurements. It acts similar to a low-pass filter for 868 MHz and as a high-pass filter for 2.4 GHz. The RC lumped element simulation cannot explain the behavior, since the results indicate the same tendency for both tested frequencies. Furthermore, the occurrence of this relationship cannot be attributed to the cable positions interconnecting the system or to the individual battery holders, since their relative movement when interchanging setups during the experiments did not lead to different results. The phenomena relate to the coupling PCB design since, at microwave frequencies, such as the ones investigated in the present study, the microstrip and strip-line filter theory may provide the answer for the unusual cut-off frequency.  
In summary, the empirical tests confirm the simulation suggestion that the high frequencies of 868 MHz and 2.4 GHz provide a stabile communication link over a short-range wired application, such as in the case of DC PLC.
## 3 Theoretical Framework and Hypotheses Development
### Hypotheses Related to Moderating Factors
#### 3.2.3 Hypothesis Related to Education Level  
This study adopts [46; 52; 65] to suggest the education level of the student (BSc, Master's, or Ph.D.). As a result, the following hypothesis is developed.  
**Hypothesis 11 (H11).**_Education level has a significant moderating effect on information adoption (IA) towards YouTube._
## 2 Microextraction Techniques  
Microextraction techniques (METs) are based on sorption processes in which the volume of the extraction phase is very small compared to the volume of the sample. METs can be divided into techniques based on extraction to the solid-phase SPME (solid-phase microextraction) and to the liquid-phase LPME (liquid-phase microextraction). In LPME liquid-phase microextraction techniques, the extraction system consists of microliters of an immiscible solvent and an aqueous phase containing analytes.  
Solid-phase microextraction methods can be broadly divided into static and dynamic. In the former methods, microextraction is conducted in a closed vessel with a stirrer, and mass transfer of the analyte, from the matrix to the extraction phase, is assisted by stirring (sample stir microextraction). In the latter case, the sample flows through a capillary or a needle with an inside deposited sorptive phase (sample flow microextraction) (Figure 1) [10].  
In sample stirred systems, the sorption surface is applied to the following:  
1. Thin glass fiber or metal rod (fiber SPME);
2. Rotating magnetic dipole (SBSE);
3. Solid substrate in the form of a thin film (TF-SPME).  
In the TF-SPME technique, the polymeric sorption phase can also be a self-supporting membrane.  
In sample flow systems, the sorption phase is placed on the surface of a capillary (in-tube SPME), inside a needle (in-needle SPME), or in a micropipette tip (in-tip SPME). Illustrative diagrams of solid-phase microextraction techniques are shown in Figures 2 and 3.  
Figure 1: Division of microextraction techniques to the solid phase (SPME).  
Figure 2: Solid-phase microextraction with sample stirring: (**a**) fiber SPME, (**b**) stir bar sorptive extraction—SBSE, (**c**) thin-film microextraction—TFME.
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 4 Problem Formulation  
In this section, we firstly give explicit characterizations of two metrics. After that, a problem of jointly optimizing data freshness and fidelity of the sensing system is formulated.
# Optical OFDM Error Floor Estimation by Means of OTDR Enhanced by Front-End Optical Preamplifier
## 2 Analysis
### OTDR Trace as Power-Delay Profile  
Now the task is to estimate BER floor from the normalized rms delay spread \(\frac{\sqrt{E[\tau^{2}]}}{L}\) of the fiber channel power-delay profile \(P_{\text{BS}}(z)\), which we consider to conform to the OTDR backscatter trace (as describing time dispersion alike).  
At the observation point that is \(z\) apart from the OTDR, the fiber response on the pulse (of amplitude) \(P_{\text{T}}^{1/2}(z)\), is its convolution with the fiber impulse response \(h(\tau)\) of an OTDR system, where:  
\[P_{\text{BS}}^{1/2}(\tau)=P_{\text{T}}^{1/2}(\tau)\ ^{*}h(\tau);\ \tau=(2z \cdot n)/c \tag{5}\]  
So, to obtain the impulse response \(h(\tau)\), we need to de-convolve it from the OTDR transmitted pulse \(P_{\text{T}}^{1/2}(\tau)\), i.e., solve (5) per \(h(\tau)\), which leads to [12]:  
\[h(\tau)=c/2n\cdot E_{\text{i}}\cdot S\cdot\eta_{\text{i}}\cdot\exp(-2\alpha z) \tag{6}\]  
where \(c\), \(n\), \(E_{\text{i}}\) and \(\eta_{\text{i}}\) are speed of light, laser energy, fiber average refractive index, and quantum efficiency of the avalanche photodiode detector, respectively, whereas \(S<1\) denotes the efficiency to confine the incident light (so only a certain part of the scattered light travels back to the OTDR), and \(\alpha\) stands for fiber loss.  
However, practically, for large dynamic range, we could consider the OTDR pulse (usually about 1 \(\upmu\)s long) to be still short enough (with respect to the reciprocal OTDR receiver bandwidth)--almost as an impulse described by delta function:  
\[P_{\text{T}}(z)=\delta(-2\alpha z) \tag{7}\]  
so that (5) modifies to:  
\[P_{\text{BS}}^{1/2}(z)=\delta(-2\alpha z)\ *h(z)=h(z);\ z=c\cdot\tau/2 \tag{8}\]This implies that the normalized power--delay profile of the fiber can be approximated by the normalized (to unity area) relative OTDR trace as [12; 13]:  
\[\left.\right|h(\tau\,\rceil^{2}=P_{\text{BS}}(z=c\cdot\tau/2) \tag{9}\]
# Guided Wave-Based Monitoring of Evolution of Fatigue Damage in Glass Fiber/Epoxy Composites
## 2 Experimental Test
In this study, a modulated five-cycle signal with center frequency of 250 kHz is employed as the diagnostic wave. Figure 3a,b \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Fatigue Cycles** & **0** & **1000** & **2000** & **3000** & **4000** & **5000** & **6000** & **7000** & **8000** & **9000** & **10,000** \\ \hline Specim S\_A & 25.30 & 23.70 & 23.32 & 22.90 & 22.67 & 22.58 & 22.55 & 22.49 & 22.51 & 22.50 & 22.46 \\ Specim S\_B & 18.14 & 17.60 & 17.19 & 16.88 & 16.67 & 16.65 & 16.63 & 16.62 & 16.61 & 16.60 & 16.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Longitudinal stiffness data for GF/EP specimens under fatigue cycles (Unit: GPa). Figure 2: Longitudinal stiffness degradation with fatigue cycles: (a) specimen S_A and (b) specimen S_B. shows the time trace and the frequency spectrum of the diagnostic excitation, respectively. The narrow frequency band can help to reduce distortion of the response wave signals due to dispersion effects. The response wave signals before fatigue test (zero fatigue cycle) is first acquired as the baseline signals and then measured with an interval of 1000 cycles when the fatigue tests are periodically paused. Figures 4a and 5a show a comparison of six sets of typical response wave signals under the fatigue state after 1000, 2000, 3000, 4000, and 5000 fatigue cycles, as well as the baseline wave signals obtained from the two specimens, respectively. The later acquired wave signals are not plotted together due to difficulty for clearly discerning each other. Combined with the stiffness information provided by Table 1 and Figure 2, it can be seen from the figures that with the increase of fatigue cycles, fatigue damage accumulate in the specimens, leading to the change of response wave signals, especially the reduction of amplitudes. Also, as illustrated in the locally zoomed signals in Figures 4b and 5b, at an early stage of the fatigue tests, the wave signals change rapidly, and then change slowly. This trend is similar to the degradation of longitudinal stiffness of the composite specimens, indicating the evolution of fatigue damage in composites can be characterized by analyzing the guided wave signals. Figure 3: Diagnostic excitation with center frequency of 250 kHz: (**a**) time trace and (**b**) frequency spectrum. Figure 4: Response wave signals after different fatigue cycles for specimen S_A: (**a**) six signals and (**b**) zoomed signals. Figure 5: _Cont._