esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Effect of Beam Inclination Angle \(\alpha\)  
To study the effect of beam inclination angle \(\alpha\) on the morphology/geometry of the weld, all other process parameters were kept constant. The beam was positioned 0.5 mm above the joint plane on the flange and focused 2 mm below the surface of the material. Focal point diameters on the surface were 0.82 mm, 1.00 mm and 1.61 mm for the 200 \(\upmu\)m, 300 \(\upmu\)m and 600 \(\upmu\)m process fibers, respectively. The cross sections of the welds are shown in Figure 3.  
It can be seen in Figure 3 that the penetration depth (measured from top of the weld) and the length of the joint fusion along the intersection decreased as the inclination angle increased. It can also be noticed that the penetration depth and area of the fusion zone correlate with the energy density of the beam. A full penetration weld was obtained only with the 200 \(\upmu\)m process fiber and 6\({}^{\circ}\) inclination angle. All of the joints followed the axis of beam propagation. Averaged dimensions from three welds produced with each inclination angle are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fiber Diameter** & **Penetration Depth** & **Bead Width** & **Fusion Zone** & **HAZ Area** & **Depth to** & **Max Hardness** \\
**[\(\upmu\)m]** & **[mm]** & **[mm]** & **[mm2]** & **[mm2]** & **Width Ratio** & **HV5 [FZ 1/HAZ]** \\ \hline
200 & 8.7 & 2.3 & 13.2 & 6.5 & 4.0 & 386/373 \\
300 & 7.8 & 2.4 & 12.6 & 6.0 & 3.3 & 392/359 \\
600 & 5.6 & 2.7 & 12.0 & 5.5 & 2.0 & 393/365 \\ \hline \hline \end{tabular} \({}^{1}\) FZ = fusion zone.  
\end{table}
Table 4: Effect of fiber diameter on the weld dimensions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Delivery Fiber Diameter (\(\upmu\)m)** & **200** & **300** & **600** \\ \hline \hline Nominal beam waist (mm) & 0.50 & 0.75 & 1.50 \\ \hline Measured beam waist (86\% pts) (mm) & 0.710 & 0.882 & 1.460 \\ \hline BPP (mm-mrad) & 9.079 & 12.000 & 23.800 \\ \hline Rayleigh length (mm) & 13.86 & 16.18 & 22.38 \\ \hline \hline \(P_{L}\) at workpiece (kW) & 6.0 & 6.0 & 6.0 \\ \hline Beam area at surface (mm\({}^{2}\)) & 0.396 & 0.611 & 1.674 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Beam properties.
## 4 Results and Discussion
### Parameter and Activation Sensitivity Analysis  
To comprehensively evaluate the model's stability and convergence under varying configurations, a dual-dimensional sensitivity analysis was conducted, focusing on the learning rate and the activation function. For learning rate analysis, four initial values were tested: \(lr=1\times 10^{-4},3\times 10^{-4},5\times 10^{-4},1\times 10^{-3}\). With all other settings held constant, the evolution of training loss and validation F1-score was monitored. The results show that the model maintained stable performance when the learning rate was within the range of \(1\times 10^{-4}\) to \(5\times 10^{-4}\), with F1-score fluctuations less than 1.5%. This indicates that the proposed method exhibited strong robustness with respect to optimizer parameter selection. In addition, an activation function substitution experiment was performed by replacing the standard ReLU function with SmoothReLU [34], a smooth, globally differentiable activation defined as SmoothReLU\((x)=\log(1+e^{x})\). Incorporating SmoothReLU across all neural units led to improved convergence behavior, reducing the number of training epochs required by 5 to 10. Furthermore, the final F1-score on the validation set increased by approximately 0.6%. These findings demonstrate that SmoothReLU not only mitigates the dead neuron problem but also enhances training stability and generalization capability. This analysis confirmed the framework's adaptability to hyperparameter configurations and activation design, supporting its applicability in complex, high-frequency prediction scenarios.
## 5 Performance Evaluation
### Simulation Results  
We first evaluate the performance of TLQoS considering the impacts of traffic load and bit error rates (BER). We further investigate the impact of delay deadline on QoS aware protocols such as TMQoS and TLQoS for the delay sensitive packets. The results are discussed in the following subsections.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline EWMA factor, \(\beta\) & \(0.2\) & EWMA factor,\(\alpha\) & 0.4 \\ Initial Value of \(\gamma\) & \(0.8\) & \(T^{thr}\) & 37.1 \({}^{\circ}\)C \\ \(\epsilon\) at 2 MHz & \(826\) & \(\sigma\) at 2 MHz & \(0.5476[\frac{S}{m}]\) \\ \(P_{c}\) & \(0.002\) & \(C_{p}\) & \(3600\,[\frac{J}{kg^{\circ}C}]\) \\ \(b\) & \(2700[\frac{J}{m^{3}\cdot s\cdot o}C]\) & \(\Delta_{t}\) & 5 s \\ \(T_{b}\) & \(37\)\({}^{\circ}\)C & \(I\) & \(0.1\)\(A\) \\ \(\rho\) & \(1040\frac{kg}{m^{3}}\) & \(K\) & \(0.498[\frac{J}{m\cdot s\cdot C}]\) \\ \(\Delta\) & 1 m & \(HC^{max}\) & 2 \\ \(HI^{min}(TLQoS)\) & 1 s & \(HI^{max}\)(TLQoS) & 10 s \\ HELLO Interval(other protocols) & 1 s & \(D_{up}^{th}\) & 0.1 s \\ \(R_{up}^{th}\) & \(0.1\) & \(T_{up}^{th}\) & 0.01 \({}^{\circ}\)C \\ MAC & \(802.15.4\) & Bandwidth & \(100\) kbps \\ Radio Range & 2 m & Payload Size & \(256\) bits \\ Retry Limit & \(5\) & Simulation Time & \(1000\) s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.
## 3 Results
### Properties
#### 3.2.3 Ionic Conductivity  
The ionic conductivity was computed based on the Green-Kubo correlation [31] via a programme compiled by language C in LAMMPS [13,15]. The ionic conductivity, \(\lambda_{z}\), can be calculated from the time integral of the charge flux autocorrelation function through the EMD method:  
\[\lambda_{z}=\frac{1}{3 Vk_{B}T}\int_{0}^{\infty}\langle J_{z}(0)J_{z}(t)\rangle dt\]  
where the charge flux vector, \(J_{z}(t)\), is defined  
Figure 8a shows the time evolution of the normalized charge flux auto-correlation function (ACF) and the running integral (integration of charge flux ACF) for a molten A-6 system at 900 K. The normalized ACFs all decayed to zero quickly. The running integrals reached a plateau after about 0.5 ps for all the systems. During ionic conductivity calculations, a time of 5 ps was adopted for the charge flux ACFs calculation.  
Figure 8b shows the simulated ionic conductivity results of ternary melts and the available experimental data. The ionic conductivities obtained here were in good agreement with the experiment, especially for molten LiCl. [PERSON] and coworkers [32] investigated the specific electrical conductivity of LiCl-NaCl-KCl experimentally, and the results are plotted in Figure 8b (marked in line and symbols). In the experimental temperature range, the results of the calculation agreed with the experimental data within the error of 8-12%. Further research, including the polarization effect, should improve the accuracy of the calculated results. Besides, take A-6 system as an example, the Nernst-Einstein fitting [33; 34], which neglects ionic correlations, was used to validate the simulation results here. The Nernst-Einstein relation, unlike the Einstein-Helfand relation, which is equivalent to the Green-Kubo relation at a long-time limit, is missing the cross term: D\({}_{+}\)D. [35]. Most of the ionic conductivity fitting for molten salts used the Nernst-Einstein formula, and the formula used here [33] is listed below:  
\[\lambda_{Z}^{NE}=\frac{e^{2}}{k_{B}TV}\Big{(}N_{Cl}q_{Cl}^{2}D_{Cl}+N_{Li}q_{Li }^{2}D_{Li}+N_{Na}q_{Na}^{2}D_{Na}+N_{K}q_{K}^{2}D_{K}\Big{)} \tag{7}\]  
In Figure 8b, when the LiCl addition was less than 60%, the ionic conductivity increased slightly. When LiCl addition was more than 60%, the ionic conductivity increased rapidly. Therefore, in practical industrial applications, it should be noted that the addition of LiCl largely increased ionic conductivity beyond a certain concentration. Furthermore, it also showed that the ionic conductivities of molten ACl increased with the ascending temperature.
# The Double Face of Metals: The Intriguing Case of Chromium
## 5 Chromium(III) in Diabetes and Supplements  
In the twenty-first century, the incidence of diabetes in the world population has increased dramatically. T2 DM is characterized by high glycaemia, resistance to insulin, and relative deficiency of insulin. T2 DM is related to different risk factors such as obesity, increased age, pancreatitis, cancer, family history, viral infections and immune diseases. Mechanisms such as oxidative stress, \(\beta\)-cell glucotoxicity, lipotoxicity and changes in gut microbiome compositions may influence insulin activity in T2 DM [77; 78]. Some dietary deficiencies, such as Cr(III) deficiency, have also been associated with diabetes: in fact, highly refined diets, that are poor in Cr(III), have been identified as a cause of diabetes incidence. T2 DM is largely preventable by staying a normal weight, eating properly and exercising regularly.  
Metabolic syndrome is related to a defect in insulin action and a compensatory increase in blood of this hormone [79]. Insulin resistance takes place when insulin is no longer able to metabolize glucose, thus leading to an increase of glucose and insulin levels in bloodstream. Recent studies showed that Cr(III) supplementation significantly decreases blood glucose concentration as well as lowers cholesterol and LDL in T2 DM [80]. NBC supplementation also increases AMP protein kinase (AMPK) and protein kinase Akt phosphorylation and facilitates the GLUT4 translocation in the cell membrane [81].  
[PERSON] et al. (2012) studied the results of CRDC and CrPic supplements (400 ug/day) in type 2 diabetic human beings and found that the concentrations of intercellular adhesion molecule-1 (ICAM-1), interleukin-6 (IL-6) and interleukin-8 (IL-8) did not change if comparing what happened before and after supplementation with chromium alone or placebo. The authors also observed a significant reduction in tumor necrosis factor- (TNF-) after 3 months of CRDC supplementation compared to baseline [82]. A few years later (2016), [PERSON] et al. also demonstrated that the supplementation of 400 ug/daily of CRDC for a same period (3 months) decreased TNF- levels compared to placebo [83]. [PERSON] et al. (2010) examined the influence of CrPic in myocardial ischemia-reperfusion injury (IRI) [84]. CrPic was suggested as a supplement in patients with T2 DM/impaired glucose tolerance with systemic hypertension, abnormal vascular function and ischemic heart disease. CrPic treatment determined an improvement of endothelium-dependent vasorelaxation and nitric oxide (NO) production. Moreover, it improved recovery of myocardium after IRI [85; 86]. Cr supplementation was also demonstrated to have a positive impact in heat-stressed buffalo calves and chickens. It also enhances insulin action in insulin-sensitive tissues, such as adipose tissue and muscles thus increasing feed intake, growth rate, carcass quality, reproductive parameters and ameliorating immune functions, finally leading to an increase in farm animal productivity [5].
# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms  
With the development of acoustic technology and tracking instrument, numerous research results have been applied into tracking underwater targets. To evaluate the state-of-the-art underwater acoustic target tracking algorithms, a classification criterion should be proposed and various algorithms should be compared and analyzed within the group with common characteristics. In this section, the classification of underwater acoustic target tracking algorithms is introduced. These algorithms can be classified based on the methods used such as instrument-assisted method, mode-based method, and the tracking optimization method. The categories of commonly used acoustic tracking instruments, namely, the acoustic imaging sensor, TASA, and UWSNs are put into the class called the instrument-assisted method. The mode-based method focuses on how to achieve better tracking results based on passive tracking and active tracking, which are classified according to the working mode of sensors. The tracking optimization method concentrates on improving the tracking accuracy and source consumption. In this paper, filtering techniques, arithmetic average, and the Sage-Husa model are taken as the noise-driven tracking optimization method. The sensor scheduling strategiesand quantized methods are taken as the source-driven tracking optimization method. Taxonomy of underwater acoustic target tracking is illustrated in Figure 5.
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 2 Materials and Methods
### Machine Learning and Fuzzy Logic Models for Soil Temperature Estimation
#### 2.2.4 Random Forest (RF)  
RF is an ensemble learning algorithm introduced by [PERSON] [51], which constructs a multitude of decision trees to enhance predictive performance. Each tree in the ensemble is trained on a distinct bootstrap sample of the data, and further randomness is introduced by selecting a random subset of features at each split, thereby promoting model diversity. In regression tasks, the final prediction is obtained by averaging the output of all trees; whereas, in classification tasks, a majority voting scheme is applied. In the present study, key hyperparameters--such as the number of trees, the maximum number of features considered at each split, and the maximum tree depth--were tuned to optimize model performance. RF models offer several advantages, including robust handling of complex, non-linear relationships, resistance to overfitting, and the ability to rank variable importance. However, they may require substantial computational resources in terms of training time and memory, particularly when applied to large-scale datasets.
## 2 Methodology  
### Feature Extraction  
Wavelet packet transform (WPT) can be viewed as a time frequency conversion technique of a non-stationary signal [36]. It complements the shortage of wavelet transforms that only decomposes the low frequency components but cannot extract high-resolution on high frequency components. The discrete wavelet transform of the discrete signal \(f(t)\) is given by [37]:  
\[w_{m,n}(t)=<f(t),\Psi_{m,n}(t)>=\frac{1}{\sqrt{m}}\int_{-\infty}^{+\infty}\Psi _{m,n}(\frac{t-n}{m})f(t)dt, \tag{1}\]  
where \(m\) is the scaling factor and \(n\) is the sifting factor, which are given, respectively, by:  
\[m=a_{0}^{m}, \tag{2}\]  
\[n=b_{0}a_{0}^{m}. \tag{3}\]  
When \(a_{0}=2,b_{0}=1\), (1) can be re-written as  
\[\Psi_{m,n}(t)=2^{-\frac{m}{2}}\Psi(2^{-m}t-n) \tag{4}\]  
In the wavelet transform, the signal \(u(t)\) can be separated in the Hilbert space by a scaling and by a wavelet function. The scaling function \(\Phi(t)\) corresponds to the low frequency part of the original signal while the wavelet function \(\varphi(k)\) corresponds to the high frequency part of the original signal with initial conditions:  
\[\Psi^{0}_{m,n}(t) =\Phi(t) \tag{5}\] \[\Psi^{1}_{m,n}(t) =\varphi(t) \tag{6}\]  
Figure 1 shows a 3-level decomposition of a WPT of a signal \(u(t)\). This is decomposed in a high-frequency part \(h_{k}(t)\) and in a low-frequency part \(g_{k}(t)\). Each part is computed by a filter, i.e.,  
\[h_{k}(t) =\frac{t_{k}+t_{k+1}}{2}, \tag{7}\] \[g_{k}(t) =\frac{t_{k}-t_{k+1}}{2}, \tag{8}\]  
The function using the above filters can be given by:  
\[u_{2n}(t) =\sum_{k}h_{k}u_{n}(2t-k), \tag{9}\] \[u_{2n+1}(t) =\sum_{k}g_{k}u_{n}(2t-k) \tag{10}\]  
As illustrated in the figure, this procedure can be recursively applied to both low and high-frequency parts. However, the number of decomposition levels will be limited by the actual application. Due to its smoothness and nonlinear characteristics, in this paper, we applied the Daubechies WPT with seven levels (Db7).  
We further compute an informative statistics from the WPT as follows [38]:  
\[p(m)=\sqrt{\sum_{n=1}^{N}(w_{m,n}(t))^{2}} \tag{11}\]  
where \(N\) denotes the number of data in each node of the 7 th decomposition level and \(w_{m,n}(t)\) and is given by (1). Hence, a feature vector \(\mathbf{p}\) can be defined for the signal \(u(t)\) as follows:  
\[u(t)\leftrightarrow\mathbf{p}=[p(1),p(2),\ldots,p(d)]^{\mathbf{T}} \tag{12}\]  
where \(d\) is the number of features, i.e., with seven levels \(d=2^{7}=128\).
# Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks
## 3 System Model  
Using collaborative relay can significantly improve system energy efficiency. In the traditional clustering method, the cluster head is already divided according to the fixed geographical position of the device before the network starts to operate, regardless of whether the device is in an active state or the sleep state. However, it is an unavoidable problem that cooperative relaying makes the cluster head consume more energy than other devices. In order to get rid of high energy consumption in a single cluster head, this paper draws on the idea of \"distributed\" and uses spatial and temporal methods for data aggregation to distribute high energy consumption behavior to all devices. As shown in Figure 2, the solid red circle represents the device that is activated and is the cluster head, the solid black circle represents the activated device, and the open circle represents the inactive device. During the Nth frame to the N +1 th frame, combined with different device activation or silence, the grouping and cluster head selection dynamically perform according to the number of activated devices and the distance between the devices. The number of devices activated at different points in time is less than the total number of devices. The density of devices that need to be clustered is relatively small, which reduce the complexity of the clustering algorithm.  
Based on the group and relay mechanism, research [19; 27] usually adopts the K-means algorithm to optimize the criterion function for evaluating clustering performance. In the adoption of this algorithm, it is necessary to specify the number of clusters and consider the optimal number of cluster heads [28; 29]. Also, it generally assumes that the algorithm will be used to perform the clustering step before the device starts to access. However, in practice, the access process of the device is a dynamic process including new device registration and device failure, which requires dynamic clustering. This paper proposes an adaptive cluster head selection method to solve this problem. This method reduces the collisions generated by the equipment from the access, thereby improving the energy efficiency and capacity of the system further.
# Low-Power Wireless Data Transfer System for Stimulation in an Intracritical Visual Prosthesis
## 6 Discussion
### Tissue and Safety  
It is essential to mention the safety of the system in regards to the SAR limits and tissue heating. While the scaled proof of principle demonstrator in Section 4 does not focus on low-power consumption, the designed receiver on integrated circuit shows simulation results of consuming below 1 mW. With this, a theoretical maximum of 1 mW of power is transmitted via the skin for data communication. In [21], 1000 mW was transmitted in SAR simulations illustrating that 10 g averaged SAR levels of maximum 1.92 W/kg are obtained. This value is below the specified 2 W/kg safety level. Our system's 1 mW through the skin data communication is very far below the SAR safety limit. In addition,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Reference** & [14] & [30] & [29] & [31] & [32] & [33] & **This Work** \\ \hline Comm, tech. & BPSK & BPSK & FSK & BPSK & OOK & ASK & BPSK \\ \hline Freq. (MHz) & 20 & 13.56 & 902–928 & 0.125 & 401–419 & 10 & 12 \\ \hline Datarate (Mbps) & 2 & 1.12 & 8 & 0.01 & 0.01–2 & 1 & 0.1–4 \\ \hline Pow. cons. (mW) & 6.2 & 0.7 & 0.6 & - & - & - & 0.95 \\ \hline Energy cons. per bit (\(\upmu\)W/Mbps) & 3100 & 625 & 75 & - & - & - & 238 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison with other biomedical implants.  
regarding heat dissipation into the tissue by the implanted receiver, to prevent damage at the electrode-tissue interface, the maximum temperature increase in the cortex has to be smaller than 1 \({}^{\circ}\)C. This corresponds to a maximum power density of 0.8 mW/mm\({}^{2}\) of exposed tissue area [18; 34; 35]. Our simulated implanted IC consumes below 1 mW in a 25 mm\({}^{2}\) package bringing the power density to 0.04 mW/mm\({}^{2}\), which is well below the safety limit.
## 1 Introduction
Therefore, in the near future, the demand for coal in China and even the world will still be very large, and coal production is still needed. Most coal production is carried out underground. Due to the harsh underground working environment, confined space, and complex structure, coal mine accidents occur from time to time. In real rescue situations, the probability of survival of the wounded trapped in rubble after more than 48 h will decrease [15]. After a coal mine accident, in order to be able to rescue the wounded during the critical period of rescue, it is necessary to have search-and-rescue robots that can replace or partially replace manual rescue. The main types of search-and-rescue robot are wheel type [16, 17, 18], leg type [19], crawler type, and the snake structure [20,21]. Of these, the wheel type is simple and the transmission rate is high, but its ability to climb over steps and other obstacles is poor. The leg type is highly adaptable, but the speed of movement is slow, and the operation is difficult. The snake structure is based on the bionic principle, and its stability is good, but too many joints increase the difficulty of modeling and manipulation of the robot. The crawler structure has a large support area, a small turning radius, and good maneuverability. The grousers attached to the track can increase friction and output greater traction [22]. Mobile rescue robots also include compound walking robots [23,24] and bio-robots [25,26]. The above analysis shows that more and more robots are being used in the post-disaster relief process. Crawler type search-and-rescue robots have been widely used in various environments due to their good adaptability to the terrain. However, there are problems such as their weak adaptability to the environment and poor stability in climbing obstacles. If these problems occur in real-world situations, this will seriously restrict the efficiency of disaster rescue. In order to address these problems of tracked robots, a new tracked rescue robot is designed in this paper. The main crawler and the front swing arm of the underground search-and-rescue robot can be equipped with a hydraulic mechanism [27], so that the swing arm has a certain degree of flexibility. In this way, the position of the center of gravity can be changed flexibly, enabling it to cross wider grooves and steps, improving the robot'sability to climb obstacles. At the same time, the main crawler and the rocker arm are driven by separate motors to enhance its driving capability.