esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Effect of Vessel Depth on the Selection of Laser Fluence
The difference in \(E_{e,R134a}\) is important because its absolute value is not high, especially when vessels are buried at a depth of 0.25 mm (5-9 J/cm\({}^{2}\)). Figure 20: \(E_{\mathit{eff}}\) of laser at different melanin contents in vessels buried at a depth of 0.25 mm (marked by solid rectangles). The simulation of effective fluence with melanin, vessel depth, and curvature can be obtained by least square fitting as follows:  
\[E_{eff}=15.536f_{m}^{0.197}H^{1.048}stdGC^{0.0861}, \tag{16}\]  
where vascular depth \(H\) is measured in millimeters. The error between the simulation results of \(E_{eff}\) and the predicted values of the fitting formula is less than \(\pm 16\%\), as shown in Figure 22. This correlation can predict the laser fluence threshold of vessel hemorrhage in the treatment of vascular lesions by a 595 nm PDL. The application conditions of Equation (16) are \(1\%\leq f_{m}\leq 9\%\), \(0.25\) mm \(\leq H\leq 0.5\) mm, and \(20.5\leq\) stdGC \(\leq 64.5\). In the simulation of light transport in turbid medium like skin tissues, the establishment of scattering model is important to our TMC method. For the dynamic scattering medium, special consideration should be done to build an appropriate scattering model. In the animal experiments by which our CCPD model was validated, motional disturbances by the breathing of the anesthetized rats was mostly eliminated by the fixation of translator stage and the optical chamber. But the effect of blood flow to light scattering was neglected in this work. Recently, [PERSON] et al. [41] established an angular spectrum model to trace the field propagation during the entire optical phase conjugation process in the presence of dynamic scattering media, which can be used in the establishment of scattering model in our subsequent research. In addition, optical scattering prevents light from being focused to the target  
Figure 21: \(E_{eff}\) of laser at different melanin contents in vessels buried at a depth of 0.5 mm (marked by solid rectangles). Figure 22: Simulation results of \(E_{eff}\) with different parameters of vessel and epidermal melanin. vessel through thick dermal layer at depths greater than \(\sim\)1 mm [42]. Although the vessel is buried at a depth of \(\sim\)0.5 mm in our simulation, the scattering effect of the skin needs to be carefully considered. In our future work, the digital optical phase conjugation based wavefront shaping technique [42] will be introduced to improve the scattering model for simulation of light propagation in skin with deep buried blood vessel.
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 3 Uncertainty Estimation in Neural Networks -- Mathematics and Algorithm
### Mathematical Theory on Modeling Uncertainty
#### 3.1.2 Mathematical Model  
A universally accepted mathematical model for uncertainty in neural networks has yet to be established, and it is possible that one may never emerge. This is because, as [PERSON] et al. [64] argue, the specific model may be context-dependent. [PERSON] and [PERSON] [26] adapt the more common model proposed by [PERSON] and [PERSON] [65] to include distributional uncertainty, and arrive at Equation (1). Their approach is classification-based, and thus the predictive uncertainty is modeled as a distribution over the class prediction \(\hat{y}\) instead of the embedding vector.  
\[\underbrace{p(\hat{y}\mid x,\mathcal{D})}_{\text{Uncertainty}}=\int\int\quad p (\hat{y}\mid\xi)\quad\cdot\quad p(\xi\mid x,\theta)\quad\cdot\quad p(\theta \mid\mathcal{D})\quad\mathrm{d}\xi\mathrm{d}\theta \tag{1}\]  
Here, \(x\) is the input, \(\mathcal{D}\) is the training dataset, and \(\theta\) represents the model parameters. The Dirichlet distribution \(p(\xi\mid x,\theta)\) is a distribution over the categorical distribution \(p(\hat{y}\mid\xi)\), where \(\xi\) acts as a stand-in for the distribution \(p(\hat{y}\mid\xi)\) and can in practice be thought of as its parameters. This formulation is not ideally suited to embedding-based tasks, as it models uncertainty based on the class prediction \(\hat{y}\). Consequently, we adapt Equation (1) to be based in the embedding space, resulting in Equation (2), where \(z\) is the embedding vector as defined above.  
\[\underbrace{p(z\mid x,\mathcal{D})}_{\text{Uncertainty}}=\int\int\quad \underbrace{p(z\mid\xi)}_{\text{Data}}\quad\cdot\quad p(\xi\mid x,\theta)\quad \cdot\quad p(\theta\mid\mathcal{D})\quad\mathrm{d}\xi\mathrm{d}\theta \tag{2}\]  
Now, data uncertainty is modeled as a distribution in the embedding space, distributional uncertainty as a distribution over those distributions (i.e., their parameters), and model uncertainty as a distribution over the model parameters. This is consistent with the modelling approach employed in previous works that utilize probabilistic embeddings, i.e., distributions in the embedding space, to capture data uncertainty [20; 21; 25; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54] (see Section 2.1.1), as well as Bayesian Neural Networks, in which distributions are placed over the model parameters, to capture model uncertainty [22; 23; 24; 25] (see Section 2.1.2).  
Note that the color scheme for data, distributional, and model uncertainty, as well as the feature vector is used throughout this work to further indicate what is being displayed.
## 1 Introduction
### Objective  
This study focuses on bridge structural health monitoring by installing on slabs and piers the vibration-based power generation devices, which can generate power by converting environmental vibrations into electric energy by electrostatic induction and can detect slab damage and pier scours by the change in the generated energy. An energy generation index to detect slab damage and a reference-free method to detect uneven pier scour are proposed. To illustrate and verify them, numerical slab models and pier models are constructed, and their modal frequencies and mode shapes are obtained by eigenvalue analyses. Damage-induced change in modal frequency and mode shapes are investigated, in which the analytical details are given in Section 2. The energy generated from the vibrations of the numerical models are calculated and the proposed methods are introduced, with their verification in Section 3. Finally, several concluding remarks are provided in the last section.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(n\): number of comb electrode & 500 \\ \(\varepsilon_{0}\) [F/m]: permittivity of vacuum & \(2.13\times 10^{-15}\) \\ \(b\) [m]: thickness of comb electrode & \(3.00\times 10^{-4}\) \\ \(E\) [V]: charged voltage & 400 \\ \(d\) [m]: gap between the fixed and the movable comb electrodes & \(1.60\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural and material properties of MEMS vibrational power generator.
# Influence of Soil Parameters on Detecting Voids behind a Tunnel Lining Using an Impact Echo Method
## 4 Numerical Analysis
### Parameter Analysis  
#### 4.2.1 Void Influence  
First, the void influence on the vibration response was analysed. In the FE model, the soil had a Young's modulus of 0.8 GPa, Poisson's ratio of 0.24 and density of 1600 kg/m\({}^{3}\). A void with dimensions of 16 cm \(\times\) 16 cm \(\times\) 10 cm was considered. The results obtained were similar to those of the tests (Figure 25). If a void existed behind the plate, the vibration amplitude was larger and the vibration duration was longer. By calculating the box-counting dimension, the value was determined to be 1.2569 for the non-void condition and 1.2976 for the void condition.  
Figure 24: Comparison of FE and tested results in (**a**) time domain and (**b**) frequency domain.  
Figure 25: Void influence on vibration responses in (**a**) time domain, (**b**) frequency domain and (**c**) time–frequency domain.  
With increasing density, both the box-counting dimension and peak acceleration decreased sharply. Compared to the dimension value of 1.2976 for the void condition, density had a certain amount of influence on void detection. With an increasing Young's modulus, both the box-counting dimension and peak acceleration decreased slowly. Compared to the dimension value for the void condition, [PERSON]'s modulus had a small influence on void detection. Poisson's ratio had the smallest effect on both values and almost no influence on void detection.
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 4 Removal Techniques
### Chemical Processes
#### 4.2.2 Nanoparticles  
In the last few years, nanotechnologies have been widely applied in many fields, including soil remediation. The synthesis of nanoparticles (D < 100 nm) for heavy metal removal has demonstrated feasibility for cleaning soils. In general, nanoparticles can remove pollutants through different mechanisms: adsorption [125], redox reactions [126], precipitation [127], and co-precipitation [128], all enhanced by a large specific surface area.  
Studies making use of different nanomaterials are available. The results evidenced that an excellent removal can be achieved with zero-valent iron nanoparticles (nZVI), where the iron support can have different origins: for example, bentonite [72; 73], zeolite [74; 75], biochar [76; 77], vinegar residues [78], rhamnpid [79], carboxymethylcellulose [80], and starch [81]. In general, this kind of nanoparticle has shown very good results for the removal of all heavy metals and metalloids.  
Recently, attention has been paid to the use of nanoparticles based on a metal-organic framework. This kind of nanoparticle has found wide use in wastewater treatment [129]. However, preliminary tests have also demonstrated positive results for its application to polluted soil remediation [130]. Further studies are needed.  
However, nanoparticle techniques are the most controversial, since, at present, the effects on soil properties are not well known, especially in a real long-term scale. Considering the high reactivity of nanoparticles, the main problems to be solved are the potential toxicity of the nanomaterials, the interaction of soil-nanoparticles, the impacts on biodiversity and different ecosystems present in the soil, and the actions to regenerate nanoparticles. This requires a deeper knowledge of the question, given the transfer of this technique to a larger scale.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 3 USVs for Natural Disasters
### Floods
These measures can be used to understand and predict floods in case of extreme weather events in water channels--traditional methods to measure the current fixes the instrument at a specific location to collect a long time series of flow data. Another approach runs a vessel in a transect line repeatedly over a complete tidal cycle. In the former, the instrument cannot obtain accurate quantification of the total transport, and the latter is labor intensive and weather dependent, not feasible for long-term observations. To overcome such drawbacks and perform accurate measurements of the cross-channel structure and long-term estimate of the total transport, [PERSON] et al. develop a USV equipped with an ADCP sensor to operate continuously for covering both flood and ebb during a complete tidal cycle. Tests are performed in a tidal channel at Port Fourchon, Louisiana.
## 2 Small-Signal Modeling of the Distributed WEDES System
### Derivation of Transfer Functions
#### 2.3.2 Transfer Function of WPT Stage (Half-Bridge Inverter, WPT Coils, and Half-Bridge Rectifier)  
Figure 5 shows the circuit diagram of the WPT stage. By writing the [PERSON]'s voltage law (KVL) equations as given by Equation (17), the ratio between the output voltage \(V_{\mathrm{Rr}}\) and the input voltage \(V_{\mathrm{Tr}}\) at the resonance frequency can be calculated as given by Equation (18).  
\[\left[\begin{array}{cc}j\omega L_{Tr}+\frac{1}{j\omega C_{Tr}}+R_{pTr}&-j \omega M_{TR}\\ -j\omega M_{TR}&j\omega L_{Rr}+\frac{1}{j\omega C_{Rr}}+R_{pTr}+R_{Lr}\end{array} \right]\left[\begin{array}{c}I_  
where \(Z_{Tr}=j\omega L_{Tr}+\frac{1}{j\omega C_{Tr}}+R_{pTr}\) is the equivalent impedance of Tx side and \(Z_{Rr}=j\omega L_{Rr}+\frac{1}{j\omega C_{Tr}}+R_{pTr}+R_{Lr}\) is the equivalent impedance of Tx side. By substituting \(s=j\omega\) into Equation (19), the following transfer function can be obtained.  
\[G_{vTRr}(s)=\frac{V_{Rr}(s)}{V_{Tr}(s)}=-\frac{sk_{TRr}\sqrt{L_{Tr}L_{Rr}}R_{Lr }}{Z_{Tr}Z_{Rr}+s^{2}{k_{TRr}}^{2}{L_{Tr}L_{Rr}}}. \tag{19}\]  
Similarly, the ratio between the Tx current and Rx current can be derived as given by Equation (20).  
\[G_{iTRr}(s)=\frac{I_{Rr}(s)}{I_{Tr}(s)}=-\frac{s^{2}k_{TRr}C_{Rr}\sqrt{L_{Tr}L_{ Rr}}}{s^{2}L_{Rr}C_{Rr}+sC_{Rr}(R_{pTr}+R_{Lr})+1}. \tag{20}\]
## 4 Evaluation of the Impacts of Active Power Error on Reactive Power in the Proposed Controller  
Reactive power sharing of the proposed controller is affected by the active power error. In this section, the reactive power operation is evaluated through the step and ramp changes of active power error, and also different values of the virtual inertia. Based on Figure 2 and Equation (2), the transfer function of reactive power and active power error can be achieved as:  
\[Q/\Delta P=A_{P}/\Delta \tag{24}\]  
where, \(A_{P}\) and \(\Delta\) are given in the Appendix A. Figure 5 shows the Bode and Phase diagrams for \(Q/\Delta P\), as well as the step and ramp changes of active power error around the operating angular frequency. It can be seen from the Phase diagram of Figure 5, that the \(Q/\Delta P\) and ramp active power error generate a phase difference of 90 degrees for the reactive power of the proposed controller. But, by carefully analysing the magnitude diagram in Figure 5, it can be achieved that the magnitude of the reactive power generated by the active power error is extremely low. To complete the aforementioned analysis, the Root Locus diagrams of reactive power for step and ramp active power error are shown in Figure 6. It can be seen from Figure 6, that a step change of active power error leads to fluctuating reactive power responses and in another case of ramp change, the reactive power becomes completely unstable as shown in Figure 6b. Changing the virtual inertia is considered in Figure 7. As it can be seen, decreasing the virtual inertia makes a little difference in the magnitude of \(Q/\Delta P\) related to larger value and totally for all values of the virtual inertia and the same results are approximately achieved.  
Figure 4: The virtual inertia effects on the grid angular frequency.  
Figure 5: Bode diagrams for \(Q/\Delta P\) as well as the step and ramp changes of active power error.  
Figure 6: Root Locus diagrams of reactive power in \(Q/\Delta P\): **(a)** Step active power error and **(b)** ramp active power error.  
Figure 7: Various values of virtual inertia in \(Q/\Delta P\).
## 5 Conclusions  
For wired sensor networks, wiring sensors to the user may cause problems and inconvenience regarding mobility. This led to the use of wireless sensor networks to proceed with body sensor networks, which should take into account the problem of power efficiency. Specifically, in body sensor networks, using implanted sensors and optimizing the energy consumption can keep sensors alive long.  
The LEACH protocol is energy efficient before the first node is dead. However, there is a sharp drop in energy efficiency after FND. The proposed protocol, ELEACH-DFL, has the advantage of maintaining energy efficiency even after FND occurs, while considering cluster configuration and CH selection separately. In a cluster-based routing WSN protocol, network lifetime is severely affected by the configuration of clusters and the location of CHs. Without considering these, LEACH improved only the problem of one node being selected continuously as the CH by ensuring that all nodes are selected evenly.  
Therefore, ELEACH-DFL proposes using a fuzzy logic to improve CH selection issues. Thus, ELEACH-DFL allows the optimal CH to be selected by considering the energy of each node and the location or density of the nodes. The ELEACH-DFL (extended LEACH-dual fuzzy logic) proposes both CH selection and cluster configuration methods. When selecting a CH, the CH candidate was firstly selected using the threshold equation, and the node with a highest chance was determined as the CH by comparing the remaining energies of the nodes among the candidates and the distances of the near nodes together. After CH selection, it was decided that when each non-CH candidate node participates in a cluster, it should participate in the appropriate cluster based on the remaining energy of the CH, distance from BS, and distance to the CH.  
We compared the proposed ELEACH-DFL with LEACH in terms of energy efficiency, FND, and HND. In a field size of \(50\times 50\), the FND efficiency improvement rate of ELEACH-DFL versus LEACH protocol is approximately \(32\%\). In addition, in a field of \(50\times 150\), the FND efficiency improvement rate of ELEACH-DFL versus the LEACH protocol is approximately \(159\%\). The proposed algorithm has better energy efficiency characteristics than conventional LEACH, regardless of the location of the BS. It would be better for further research to consider the computational amounts and reliability of data transmission simultaneously.  
The authors of D.Y.Y. and T.K. conducted a basic survey on the research, and the overall study of ELEACH-DFL was led by the authors of J.-Y.L. and D.L., and the thesis research was conducted overtly by the author of S.Y.P. All authors have read and agreed to the published version of the manuscript.  
No external funding available.  
The authors have no conflicts of interest.
# Key Technologies for THz Wireless Link by Silicon CMOS Integrated Circuits
###### Abstract  
In terahertz-band communication using ultra-high frequencies, compound semiconductors with superior high-frequency performance have been used for research to date. Terahertz communication using the 300 GHz band has nonetheless attracted attention based on the expectation that an unallocated frequency band exceeding 275 GHz can be used for communication in the future. Research into wireless transceivers using BiCMOS integrated circuits with silicon germanium transistors and advanced miniaturized CMOS integrated circuits has increased in this 300 GHz band. In this paper, we will outline the terahertz communication technology using silicon integrated circuits available from mass production, and discuss its applications and future.  
terahertz; CMOS; BiCMOS; silicon; integrated circuit; wireless communication; transceiver; ultrahigh speed +
Footnote †: journal: _Photomics_
# Dealing with Low Quality Images in Railway Obstacle Detection System
## 5 Comparative Analysis of Proposed and Existing Methods  
### Image Training and Testing Dataset  
The majority of image blur assessment methods have been evaluated using open, publicly available, image datasets, which contain clear (non-blurred) and blurred photos. In this paper, for the BD problem in railway applications, we used our own database of images collected from different cameras installed in the integrated OD system demonstrator [10] and from static test runs aimed at creating a dataset. These pictures were obtained in varied shooting settings and were not edited in any way since they were taken, so this database is filled with different formats of photos; some of them are undistorted, while others are blurred. It was noticed that the majority of the blurred photographs belong to out-of-focus blur images, which can be explained by the fact that the installed cameras can cover a distance of up to 2000 m and the focus is frequently not correctly fixed during railway transport (track configuration, train speed, etc.). The installed vibration suppression system [10], on the other hand, considerably reduces the motion blur effect, allowing cameras to maintain their physical stability.  
The complete database we obtained from on-ground testing in real-world conditions consist of tens of thousands of images, but, for our application, we used a subset of 2180 images. Selected examples of images in our railway dataset is presented in Figure 6. The SMART2 project participants' assessment of the photographs provided the ground truth, and each image was marked as blurred or non-blurred. In our database, 1568 of the  
Figure 5: Blur classification.  
images are marked as non-distorted images (118 of them exhibit partial blur), while the remaining 612 are blurred images (468 possess out-of-focus blur and 144 motion blur). The resolution of the images in our dataset ranges from 1024 \(\times\) 768 to 4000 \(\times\) 2248 pixels, and we resized all the images to 1024 \(\times\) 768 pixels format.  
To train the algorithms considered in this paper, we used 60% of the images (1308) from the database (946 undistorted, 362 blurred). Subsequently, the remaining 40% of the images (872) of the image dataset were used for testing purposes (620 undistorted, 252 blurred). In all experiments, besides the training and the evaluation sets being disjoint, we made sure that the collection of images was taken by different cameras, so that the machine learning model in DSS did not acclimate to one type of camera.
# CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
## 4 Experiments and Results
### Basic Settings
#### 4.1.2 Implementation Details  
In this study, we fixed the number of clients (\(K\)) at 100 and conducted 1000 communication rounds. In each round, 10% of clients were randomly selected for training, with each client training its local model for 5 epochs. We used the Dirichlet distribution (Dir(\(\alpha\))) to simulate client data class distributions, where \(\alpha\) controlled the variation and imbalance across clients. This ensured a realistic representation of heterogeneous data due to diverse local environments.  
For the datasets NSL-KDD, CIC-IDS2017, and CSE-CIC-IDS2018, the warm-up rounds (\(R_{w}\)) were set to 700, 800, and 500, respectively. These rounds stabilized the model before  
Figure 7: Visualization Results of Original Data.  
Federated Learning began, improving convergence and performance, especially for imbalanced or sparse cybersecurity data. They also allowed for hyperparameter tuning and learning process optimization.In each communication round, clients shared model updates with a central server, which aggregated them using a center-based algorithm to form a global model. Updates were securely transmitted using SSL/TLS encryption, and client models were trained with differential privacy to ensure data confidentiality.  
This setup modeled a realistic Federated Learning environment with non-iid data distribution. It explored how Federated Learning handles datasets with varying imbalance and class distribution, providing insights into system performance and scalability in real-world scenarios.