esa-sceva/satcom-chunk-collection huggingface dataset:
# Pattern Formation on Alloys by Cluster Ion Beam Irradiation
## 3 Results and Discussion
### _Sputter Yield and Displacement_  
First, we will analyze the magnitudes most closely related to the formation of surface ripples. Figure 2 shows the sputtering yield obtained for the different systems for the two angles of incidence. For intermediate angles (Figure 2a), the sputtering is lower for pure substances and higher for alloys of similar concentration. However, this trend reverses when the angle is grazing (Figure 2b), then the sputtering is lower for alloys of similar concentration. This result highlights a distinction in the mechanisms at play between pure metals and alloys.  
Figure 3 represents the horizontal and vertical displacements for angles of incidence of 50\({}^{\circ}\) and 70\({}^{\circ}\). Horizontal and vertical displacements have the opposite behavior to sputtering for intermediate angles. That is, for alloys of similar concentration, the displacement is lower, while for pure substances, it is greater (Figure 3a,c). This result is more visible in the case of Cu, the lightest species. This sputtering and displacement behavior points to the former as the fundamental mechanism in alloys for intermediate angles. For grazing angles, the issue is more complicated, since the horizontal displacement curves are ordered according to the Cu concentration (Figure 3b); therefore, the mobility of Cu is what determines this displacement. The vertical displacement curves are also minimal for  
alloys of equal concentration (Figure 3d). Finally, we must remember that the sputtering curves are minimal for alloys of this type. This fact leads us to believe that there is no clear dominant factor in pattern formation since no maximum occurs in alloys, either in sputtering or in redistribution. Recall that in pure metals, the predominant process for intermediate angles was redistribution, while for grazing angles, the predominant process was sputtering [(20; 21)].  
Figure 3: Horizontal \(d_{x}\) (**a,b**) and vertical \(d_{z}\) (**c,d**) displacements for bombardment of a surface with different concentrations (at.% Cu): 0, 20, 35, 50, 65, 80, and 100 and different angles of incidence: (**a,c**) 50\({}^{\circ}\) and (**b,d**) 70\({}^{\circ}\) as a function of time.  
Figure 2: Sputter yields for bombardment with Ar\({}_{6}\) clusters and different angles of incidence: (**a**) 50\({}^{\circ}\) and (**b**) 70\({}^{\circ}\) as a function of cluster number. In both cases, 2000 Ar atoms are launched onto the solid at the end of the process.
# Experimental and Modeling Study for the Solar-Driven CO2 Electrochemical Reduction to CO
## 2 Materials and Methods
### Photovoltaic Module  
Depending on the type of photovoltaic technology used, the physical phenomena driving photogeneration vary. In this work, DSSCs are utilized. Unlike conventional solar cells made from semiconductive materials, DSSCs are photoelectrochemical devices where charge photogeneration and charge transport are distinct processes. In these cells, electrons are photogenerated in a light-sensitive, ruthenium-based dye and then collected by a layer of TiO\({}_{2}\) nanoparticles deposited on a conductive fluorine-doped tin oxide (FTO) glass substrate. The electrons are subsequently regenerated by an iodide/triiodide redox couple in the electrolyte, with a platinum counter electrode facilitating the process. A single DSSC cannot produce sufficient power to drive the electrochemical cell. Therefore, a module comprising six DSSCs connected in series using conductive silver paste is employed. For the preparation of the DSSC, Pt films were sputtered on a conductive FTO glass substrate (TCO30-10, 3 mm, 10 ohm/sq., from Solaronix, Aubonne, Switzerland), with T\({}_{\text{dep}}\) = 20 s and I\({}_{\text{dep}}\) = 30 mA: the glasses were masked to form a 1 cm\({}^{2}\) circular shaped film; the FTO was also drilled to enable the electrolyte injection. For the photoanode, TiO\({}_{2}\) paste (Ti-Nanoxide from Solaronix, Aubonne, Switzerland) layers were deposited through the doctor blade technique and subsequently dried on a hot plate at 100 \({}^{\circ}\)C for solvent evaporation. After the photoanodes were fired (performed at 475 \({}^{\circ}\)C for 30 min), they were immersed in a 0.3 mM solution of Ru-based dye (N719, Rutheniumer 535-bis TBA from Solaronix, Aubonne, Switzerland) in ethanol for at least 17 h to ensure complete absorption on the TiO\({}_{2}\) surface. Photoanodes and Pt cathodes were then sealed through a thermoplastic sealant (Meltonix 1170-60, Solaronix, Aubonne, Switzerland) under a hot press at approx. 110 \({}^{\circ}\)C. Finally, the cell was filled with electrolyte, which is a mixture of 0.45M Sodium Iodide (NaI), 0.056M Iodine (I\({}_{2}\)), and 0.55M 4-tert-butylpyridine (TBP) in 3-Methoxyppropionitrile (CH\({}_{3}\)OCH\({}_{2}\)CH\({}_{2}\)CN), purchased from Sigma-Aldrich (St. Louis, MO, USA). The final module was characterized by measuring the current-voltage (I-V) characteristics under varying levels of irradiance (expressed in Suns, where 1 Sun = 1 kW/m\({}^{2}\)) with an LED solar simulator of class ABA from Oriel (model MiniSol LSH-7320, Irvine, CA, USA). A sketch of the final P--EC device is reported in Figure 1b, while a scheme of the electrochemical experiment is reported in Figure 1a.  
Figure 1: Schematic representation of the electrochemical test: two peristaltic pumps are used for electrolyte recirculation, and the catholyte reservoir is sealed and connected to the gas chromatograph for product analysis (reprinted with permissions under the Creative Commons Attribution (CCA) 4.0 from [21]) (**a**); sketch of integrated PV–EC device (**b**).
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 8 Research Gaps and Challenges
### Problem Solution Techniques  
The literature analysis shows that the research community largely adopts machine-learning solutions to solve the task scheduling problem. In this area, a challenge and gap to fill is the design of explainable ML models that allow the verification of the scheduling decision produced.  
Another challenge is the design of autonomous task scheduling algorithms. This challenge involves creating intelligent and responsive scheduling algorithms that can operate effectively and autonomously adapt their decisions in dynamic, real-time fog computing environments, ensuring that tasks are executed efficiently and meet application-specific needs [64]. This includes factors such as variations in device resources, network conditions, task workloads, and application priorities. Self-adaptive schedulers continuously monitor the environment and can dynamically reconfigure task assignments, priorities, and resource allocations.
## 3 Materials and Methods
### Federated Learning  
The overall federated learning procedure is illustrated in Figure 6. The procedural steps are outlined as follows:  
1. The server distributed the global model or global weight to each client (blue line);
2. The client utilized its own data to locally retrain the global model;
3. Each client transmitted its trained local model to the server (black line);
4. The server aggregated all the models into a new global model;
5. The server updated each client's model with a new global model or weight (blue line) and repeated steps 1 through 5.  
Algorithm 1, referred to as Federated Average (FedAvg) [62], stood as an aggregation algorithm extensively employed in FL. The algorithm incorporated various parameters, including the number of clients (\(K\)), local mini-batch size (\(B\)), number of local epochs (\(E\)), and learning rate (\(\eta\)). At the commencement of the training process, the server initialized the global model parameters (\(w_{0}\)). In each round, the server selected either a random set of clients or all clients for participation in the training, ensuring a minimum level of client involvement (\(m\)). Subsequently, the server distributed the current global model parameters (\(w_{t}\)) to the participating clients in parallel.  
```
1:
2:
3:
4:\(w_{t+1}^{k}\leftarrow\text{ClientUpdate}(k,w_{t})\)
5:endfor
6:\(w_{t+1}\leftarrow\sum_{k=1}^{K}\frac{n_{t}}{n}w_{t+1}^{k}\)
7:endfor
8:ClientUpdate(\(k,w\)): // Run on client \(k\)
9:\(B\leftarrow\) split \(P_{k}\) into batches of size \(B\)
10:for each local epoch \(i\) from 1 to \(E\)do
11:for each batch \(b\in B\)do
12:\(w\gets w-\eta\
abla l(w;b)\)
13:endfor
14:endfor
15:return \(w\) to server
```  
**Algorithm 1** Federated Learning (FedAvg) [62]  
On the client side, each participating client (\(k\)) received the global model parameters and performed local updates. The client divided its local data (\(P_{k}\)) into mini-batches of size \(B\) and iterated over the data for a specified number of local epochs (\(E\)). Within each epoch, the client processed each batch (\(b\)) and updated its local model parameters (\(w\)) using the learning rate (\(\eta\)) and the gradient of the loss function (\(\
abla l(w,b)\)). After completing the local updates, the client returned its updated model parameters (\(w\)) to the server.  
The server aggregated the received model updates from all participating clients by computing the weighted average based on the number of samples (\(n_{k}\)) used by each client. The aggregated model parameters (\(w_{t+1}\)) became the updated global model for the next round. This training process continued for multiple rounds until convergence or a predefined stopping criterion was met.  
The FedAvg [62] algorithm ensured that all clients contributed to the global model while preserving data privacy and computational efficiency. It served as a fundamental component in federated learning, facilitating distributed training across a network of clients while maintaining a shared global model.
## 4 Discussion
### Comparison with Other Methods  
To further determine the performance of the proposed MSMF-Net, comparison experiments with other state-of-the-art methods and generalization capability analysis were conducted to accomplish these experiments.  
Table 6 shows the proposed algorithm and the IoU values of the nine PointNet categories used in in the Vaihingen dataset (power line, Low_veg, Imp_surfaces, car, fence/hedge, roof, facade, shrub, and tree). The proposed algorithm performed significantly better in terms of accuracy than most PointNet methods in airborne LiDAR scanning to obtain large-scale cloud data of fields. This was a result of fusing the linearity, verticality, building facades, and fences to obtain a better classification; moreover, combining the roughness and the variance resulted in an improved classification of trees and shrubs, and, when adding in flatness, roofs and impervious surfaces were more likely to be distinguished from other features. However, due to the sparsity of airborne point clouds, there may not be enough points covering a surface when searching for neighborhood points with spherical neighborhoods. In addition, since the number of power line points was already small, the multiscale, multi-neighborhood algorithm was not as accurate in the classification of power lines as the PointNet method using only XYZ information.  
The ISPRS website provides the experimental results of different methods. In this study, the accuracy results of the proposed method were compared with some previously submitted results ([[http://www2.isprs.org/commissions/comm2/wg4/vaihingen](http://www2.isprs.org/commissions/comm2/wg4/vaihingen)]([http://www2.isprs.org/commissions/comm2/wg4/vaihingen](http://www2.isprs.org/commissions/comm2/wg4/vaihingen))WhatY4 [33] used a multiscale CNN to process feature images obtained from LiDAR point clouds. The features used included normalized elevation, intensity, normal vector, and local plane features. UM is a feature-based supervised machine learning classification method, which obtains the texture features of point clouds through local fitting surface and the k-nearest neighbor method, obtains the geometric properties and attribute information  
Figure 8: Details which were misclassified. (**a**) Zoomed-in image of the misclassification details of the model proposed in this paper on shrub; (**b**,**c**) zoomed-in details of the misclassification of the model proposed in this paper on tree.  
of the point cloud through the morphology method, and uses a multilevel machine learning method to classify the point cloud. NANJ2 [34] uses three normalized point cloud features, namely, altitude, intensity, and roughness, to generate multiscale point-cloud feature maps. It then designs a multi-foot CNN to extract multiscale deep-level features and classify them, so as to obtain the classification probability of each point. Then, a decision tree is constructed, and the obtained probability is used for training again to optimize the initial classification results.
###### Abstract  
(1) Background: The purpose of this study was to investigate the effects of low-dose non-thermal atmospheric plasma (NTP) on the proliferation and migration of human immortalized keratinocytes (HaCaT cells) and its molecular mechanisms. (2) Methods: The effects of NTP on HaCaT cells were detected by cell viability, wound healing assay, cell cycle, mitochondrial membrane potential detection, and western blot, and the role of reactive oxygen species (ROS) content in low-dose NTP was explored. (3) Results: Results showed that the proportion of cells in G1-S phase transition, cell migration ability, and the expression of Cyclin D1 and STAT3 reached the peak at 10 s treatment group, while the cell viability and the expression of PI3K, AKT, mTOR, ERK, WNT, and \(\beta\)-catenin proteins reached the peak at 15 s treatment group. (4) Conclusions: These results manifested that ROS produced by low-dose NTP promoted the proliferation of HaCaT cells by activating the PI3K/AKT/mTOR signaling pathway and also promoted the migration by activating the WNT/\(\beta\)-catenin signaling pathway. Therefore, these results will be useful for the application of low-dose NTP in the treatment of wound healing.  
non-thermal atmospheric plasma; HaCaT cell; reactive oxygen species; proliferation; migration; wound healing Article
# Experimental Study of Perfobond Rib Shear Connector under Lateral Force
## 5 Failure Modes of Shear Connections  
The overall resistance of the Perfobond shear connector comes from three sources: the reinforcement bars, rib plate, and concrete in rib holes. As observed in the series of tests and Table 1, failures can be caused by any of those three possible factors. For example, BH-10 and BH-11 specimens fail because of the low shear strength of the rib plate, as shown in Figure 20. The BH-3 specimen fails because of weak concrete within the rib holes, as shown in Figure 26. Specimens fail because of failure of reinforcement bars, as shown in Figure 27.
# Numerical Analysis on Spanwise Correlation of Vortex-Induced Force of Split Double-Box Beam
## 4 Results and Analysis
### Three-Dimensional Bypass of the Bridge in Static State
#### 4.2.3 Spanwise Correlation of Wake Wind Velocity When the Bridge Is in a Static State  
In this section, the spanwise correlation numerical simulation of fluctuating wind speed in the wake region is carried out for the bridge beam in the static state when the reduced wind velocity is 5.65, 6.21, 6.68, and 7.44. Figure 10 shows the variation of the spanwise correlation coefficient of the main beam in the wake region with spanwise spacing under different reduced wind velocities.  
It can be seen in Figure 10:  
Figure 10: Spanwise correlation of wake wind velocity of the main beam in the static state. (**a**) Spanwise correlation of along-wind fluctuating wind velocity; (**b**) Spanwise correlation of across-wind fluctuating wind velocity; (**c**) Spanwise correlation of vertical-wind fluctuating wind velocity.  
Figure 9: Correlation of pressure coefficient at each measuring point. (**a**) Spanwise correlation of pressure coefficient at 1# measuring point; (**b**) Spanwise correlation of pressure coefficient at 2# measuring point; (**c**) Spanwise correlation of pressure coefficient at two measuring points when \(\text{Vr}=6.21\).  
* The spanwise correlation coefficient of fluctuating wind velocity in along-wind direction and vertical-wind direction decrease exponentially with the increase of spanwise spacing in the wake region of the main beam of the split double-box beam;
* The spanwise correlation coefficient of across-wind fluctuating wind velocity approaches to 0 gradually after a precipitous decline, that is, the correlation between different sections of the across-wind fluctuating wind velocity is weak.
* The spanwise correlation coefficient of fluctuating wind velocity in each direction changes little under different wind speeds which means it does not change with the change in wind velocity.  
In general, the spanwise correlation of across-wind fluctuating wind velocity is the strongest, and the attenuation trend is slow. The spanwise correlation of along-wind fluctuating wind velocity is second and that of across-wind fluctuating wind velocity is the worst.
## 5 Performance Evaluation
### Experimental Results  
We have conducted three experiments. First, we compare the classification performance of a state-of-the-art DAE-based and a DAE+OCSVM ET anomaly detector. Second, we assess the vulnerability of the DAE+OCSVM ET anomaly detector to gradient-based adversarial evasion attacks using transferability. Third, we evaluate the proposed XAI-based DAE+OCSVM ET anomaly detector in terms of classification performance and its robustness against both gradient-based and optimization-based adversarial evasion attacks under a black-box threat model.
###### Abstract  
High-order modulations are necessary to improve the bandwidth efficiency of the satellite communication system. However, the non-linear characteristic of satellite channels limits the application of high-order modulations. In this paper, we propose a new 7-point constellation which is expected to be effectively applied to the satellite communication system, and combine it with non-binary low-density parity-check (NB-LDPC) codes over Galois field GF(7) to guarantee the reliability of the data transmission. The exact expression for the average symbol error probability (SEP) of 7-order quadrature amplitude modulation (7-QAM) over the Additive White Gaussian Noise (AWGN) channel is derived, and the non-linear distortion over satellite channels is also analyzed. Simulation results reveal that, compared with the traditional 8-order phase-shift keying (8-PSK), the 7-QAM method can achieve about 3 dB gain over the AWGN channel without channel coding at symbol error rate (SER) of \(10^{-6}\). Moreover, the proposed combined coded modulation scheme also has better SER performance than the NB-LDPC coded 8-PSK modulation scheme over the non-linear satellite channel.  
high-order modulation; symbol error probability; non-binary low-density parity-check code +
Footnote †: journal: Electronics Electronics
## 3 Dataset and Cluster Analysis
### Signal Analysis
First, the signal energy is lowered within gap sections (Gap patches) for both sensor signals than within zero gap ones (Zero Gap patches). The distribution of the RMS values, which is equivalent to the signal energy, is provided for different gap sizes in [14]. The presence of a large-sized gap means less availability of the material to be welded, which consequently reduces laser-material interactions and, thus, its acoustic emissions. On top of that, the structure-borne sensor (SB1) reveals that the frequency range is also different between the Zero Gap and Gap patches. While the signal in the Zero Gap patches is observable up to 400 kHz, the signal in the Gap patches is in a lower frequency range below 200 kHz and more frequency selective. Such change in the frequency range is, at least from the spectrograms, not observable in the airborne sensor signal (AB). It is also worth highlighting that the actual gap size in Zero Gap patches is not truly zero, and the largest gap was found to be 0.1892 mm. The possible causes and the effects of such unintended gaps are discussed in [14], reporting increased difficulty in distinguishing 0.1 mm Gap signals from Zero Gap ones. When it comes to ambient noise, multiple narrow-banded signals are prominently present in the entire frequency range. For a better understanding, only the ambient noise was recorded. Those recordings confirm that this narrow-banded interference was always observed when the robotic arm was moved. Since the environmental noise is innately wide-banded, it is highly likely that the robotic arm movement causes such narrow-banded acoustic emissions. Obviously, these multiple-band signals are harmonics of fundamental frequencies. However, the interval seems not to be constant, which makes it difficult to identify the fundamental frequencies. For instance, in Figure 3b two bands can be observed, one around 30 kHz and the other one around 60 kHz, yet the third one is not around 90 kHz but instead around 120 kHz. While the harmonics of 30 kHz are then barely observable between 250 kHz and 500 kHz, the trace of such harmonics can be perceived again above 500 kHz. Furthermore, some of the narrow-banded interference even fluctuates over the course of measurement, for instance, the band around 400 kHz in Figure 3b. These all suggest that canceling out such narrow-banded interference is not a trivial task. It requires either manual fine-tuning and selection of those bands, which needs to be repeated anew for different measurement setups, or an adaptive filtering technique.
# A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection
###### Abstract  
Different practical applications have emerged in the last few years, requiring periodic and detailed inspections to verify possible structural changes. Inspections using Unmanned Aerial Vehicles (UAVs) should minimize flight time due to battery time restrictions and identify the terrain's topographic features. In this sense, Coverage Path Planning (CPP) aims at finding the best path to coverage of a determined area respecting the operation's restrictions. Photometric information from the terrain is used to create routes or even refine paths already created. Therefore, this research's main contribution is developing a methodology that uses a metaheuristic algorithm based on point cloud data to inspect slope and dams structures. The technique was applied in a simulated and real scenario to verify its effectiveness. The results showed an increasing 3D reconstructions' quality observing optimizing photometric and mission time criteria.  
3D inspection; coverage path planning; point cloud analysis; optimization; UAV +
Footnote †: journal: Journal of Sensors
# Engineering Properties and Environmental Impact of Soil Mixing with Steel Slag Applied in Subgrade
## 3 Results and Discussion  
### Determination of Optimum Proportion (SL/S) between SL and Intact Soil  
#### 3.1.1 Compaction Test  
Figure 6 shows the compaction curves of intact soil (S) and SSL with various proportions of SL/S for two kinds of soil samples (i.e., S1 and S2). From Figure 6, the maximum dry density of SSL with S1 was 2.14-2.45 g/cm\({}^{3}\), and the maximum dry density of SSL with S2 was 2.10-2.35 g/cm\({}^{3}\), displaying an increasing trend with increasing SL content (i.e., from 30% to 70%). The optimum water content of SSL with S1 was 9.9-10.9%, and the optimum water content of SSL with S2 was 8.7-10.2%, showing a changing trend opposite to the maximum dry density. This phenomenon was closely related to the physical properties of SL. The specific gravity density of SL was 3.36, which was much higher than 2.68 and 2.64 for S1 and S2, respectively. Therefore, the optimum dry density of SSL was higher than that of intact soil (S). At the same time, as a low cohesion material, the content of viscous particles in SL was lower, so the optimum water content decreased.  
Greater compaction work is required during construction, especially for high-strength SL. A subgrade filler with higher SL content requires more energy consumption. Therefore, selecting the optimum proportion of SSL is of great significance for engineering applications.