esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 4 Results and Discussion
### Optical Power vs. Modulation Frequency  
To assess frequency response, the modulation frequency was swept from 0.6 MHz to 6.0 MHz while maintaining a constant drive current of 200 mA. Figure 4 depicts optical output versus modulation frequency.  
Performance trends:  
* 650 nm (Red Laser): Maintained a stable and high optical output across the full frequency range, exhibiting minimal degradation and superior modulation stability. This confirms its suitability for data transmission over a wide range of bit rates with low distortion.
* 405 nm (Violet Laser): Demonstrated good high-frequency response but showed a moderate decline in output beyond 4 MHz. This can be attributed to the increased  
Figure 4: Optical power output (mW) as a function of modulation frequency (MHz) for 650 nm, 532 nm, and 405 nm laser diodes.  
thermal load, higher threshold current, and reduced QE at elevated switching rates. Although bandwidth performance was acceptable, the diode's high power consumption limits its viability for battery-powered or IoT applications.
* 532 nm (Green Laser): Exhibited the weakest frequency response, with output power decreasing markedly as frequency increased. A transient enhancement in the 1.8-4.2 MHz range was observed, forming a soft hump in the response curve. This anomaly may have resulted from internal resonances in the modulation circuitry or nonlinearities in the frequency-doubled laser architectures, such as relaxation oscillations or thermal-gain interactions. Further investigation is required to verify this hypothesis.  
In summary, while the violet laser supports high-frequency modulation from a bandwidth perspective, its elevated energy demand makes it less practical for energy-constrained applications. The red laser diode offers the most balanced combination of frequency stability, energy efficiency, and photodetector compatibility, making it the optimal choice for visible-light FSO systems in short-range environments.
# Feature Point Identification in Filtel Weld Joints Using an Improved CPDA Method
###### Abstract  
An intelligent, vision-guided welding robot is highly desired in machinery manufacturing, the ship industry, and vehicle engineering. The performance of the system greatly depends on the effective identification of weld seam features and the three-dimensional (3D) reconstruction of the weld seam position in a complex industrial environment. In this paper, a 3D visual sensing system with a structured laser projector and CCD camera is developed to obtain the geometry information of filler weld seams in robot welding. By accounting for the inclination characteristics of the laser stripe in filler welding a Gaussian-weighted PCA-based laser center line extraction method is proposed. Smoother laser centerlines can be obtained at large, inclined angles. Furthermore, an improved chord-to-point distance accumulation (CPDA) method with polygon approximation is proposed to identify the feature corner location in center line images. The proposed method is validated numerically with simulated piece-wise linear laser stripes and experimentally with automated robot welding. By comparing this method with the grayscale gravity method, Hessian-matrix-based method, and conventional CPDA method, the proposed improved CPDA method with PCA center extraction is shown to have high accuracy and robustness in noisy welding environments. The proposed method meets the need for vision-aided automated welding robots by achieving greater than 95% accuracy in corner feature point identification in filler welding.  
Keywords:fillet welding; feature point recognition; chord-to-point distance accumulation; laser light stripe; central extraction +
Footnote †: journal: Applied sciences
## 1 Background  
Most of the wrist motions occur in a diagonal plane of motion, termed the dart-throwing motion (DTM) plane [1], i.e., a wrist plane that ranges from radial extension to ulnar flexion. During DTM, the proximal carpal row undergoes minor sagittal translations and is therefore thought to be more stable [2]. This plane of motion is often exercised during daily activities performed at different heights [3] by both dominant and non-dominant upper limbs [4]. Since the DTM is our natural wrist motion, occupational therapists strive to incorporate it in the rehabilitation of the wrist [5; 6; 7]. However, although their interest in DTM has grown in the last decade and carpal kinematics have been documented, the stress distribution at the radiocarbon joint has yet to be explored, and thus a complete biomechanical analysis is yet to be reported. Additionally, since ligament mechanical properties might be influenced by aging [8; 9], this might be an important factor for consideration in the interpretation of biomechanical analyses of DTM.  
The biomechanics of the wrist was previously explored using finite element (FE) analyses to compare stresses during wrist flexion, extension, radial and ulnar deviation [10], for different purposes, e.g., to explain the mechanism of wrist injury [11] and to test the effects of surgical reconstruction techniques [12; 13]. These studies showed that the load distribution changed according to the posture of the wrist; however, no data exist regarding loads during DTM.  
The aim of this study was to calculate and compare the stresses between the radius and two carpal bones (the scaphoid and the lunate) in two wrist positions, extension and radial extension (position in DTM), and between an adult and an elder model.
###### Abstract  
Multi-armed bandit (MAB) models are a viable approach to describe the problem of best wireless network selection by a multi-Radio Access Technology (multi-RAT) device, with the goal of maximizing the quality perceived by the final user. The classical MAB model does not allow, however, to properly describe the problem of wireless network selection by a multi-RAT device, in which a device typically performs a set of measurements in order to collect information on available networks, before a selection takes place. The MAB model foresees in fact only one possible action for the player, which is the selection of one among different arms at each time step; existing arm selection algorithms thus mainly differ in the rule according to which a specific arm is selected. This work proposes a new MAB model, named measure-use-MAB (_muMAB_), aiming at providing a higher flexibility, and thus a better accuracy in describing the network selection problem. The muMAB model extends the classical MAB model in a twofold manner; first, it foresees two different actions: to _measure_ and to _use_; second, it allows actions to span over multiple time steps. Two new algorithms designed to take advantage of the higher flexibility provided by the muMAB model are also introduced. The first one, referred to as measure-use-UCB1 (muUCB1) is derived from the well known UCB1 algorithm, while the second one, referred to as Measure with Logarithmic Interval (MLI), is oppositely designed for the new model so to take advantage of the new _measure_ action, while aggressively using the best arm. The new algorithms are compared against existing ones from the literature in the context of the muMAB model, by means of computer simulations using both synthetic and captured data. Results show that the performance of the algorithms heavily depends on the Probability Density Function (PDF) of the reward received on each arm, with different algorithms leading to the best performance depending on the PDF. Results highlight, however, that as the ratio between the time required for using an arm and the time required to measure increases, the proposed algorithms guarantee the best performance, with muUCB1 emerging as the best candidate when the arms are characterized by similar mean rewards, and MLI prevailing when an arm is significantly more rewarding than others. This calls thus for the introduction of an adaptive approach capable of adjusting the behavior of the algorithm or of switching algorithm altogether, depending on the acquired knowledge on the PDF of the reward on each arm.  
c pacs: 2018 11 13 10.3390/a11020013
## 5 Simulation Results
### Joint Optimization Scheme
It is worth noting that when the number of task input bits is small, the energy efficiency of our optimization scheme is similar to LOC and EBW. As the number of task-input bits increases, the benefits of the RSB method gradually become apparent, which means our algorithm can adapt to  
larger computation tasks than other algorithms. It is also noticed that when the number of task-input bits is 80 KB-100 KB, the residual energy of the RSB scheme is close to EBW, indicating that in this case, the bandwidth setting of the joint optimization scheme is close to equal division. It can also be observed that the HOF curve declines slowly, indicating that the half offloading scheme is also more suitable for larger computation tasks. Figure 5 shows the residual energy of the WP-MEC system versus time block length \(T\). In the figure, the AP transmission power is 200 \(W\), the number of the task input bits follows the uniform in the range of 80 to 100 KB, and the time block length \(T\) varies from 1.6 to 3.2 s. The simulation result shows that the proposed joint optimization algorithm provides larger residual energy than the other four schemes. Besides, as the time block length \(T\) increases, the residual energy of all schemes increases. This is because the larger the time block length \(T\), the more energy each mobile device harvests, and the energy consumption of task computing decreases. Hence, the residual energy shows an upward trend. It is worth noting that when the length of the time block \(T\) is smaller, the difference of maximum residual energy between the RSB scheme and the other four benchmark schemes is larger. As the length of the time block increases, this difference between them gradually decreases. Figure 4: Residual Energy vs. Task Size. Figure 3: Residual Energy vs. Access Point (AP) Transmission Power. This result indicates that our joint optimization scheme is more suitable for the settings with the smaller time block length. It is also noticed that when the time block \(T\) is smaller, the maximum residual energy difference between the proposed joint optimization scheme and the LOC method is larger. As the time block length increases, the energy efficiency difference between RSB and LOC gradually decreases. This is because the local computing CPU frequency is affected by the time block length \(T\). The smaller the CPU frequency of each the mobile device cycle, the lower the local computing energy consumption will be. It can also be observed that when the time block length is larger, the residual energy of EBW is closer to RSB, indicating that the equal bandwidth allocation scheme is closer to the optimal solution.
# A Survey on Recent Advanced Research of CPS Security
## 4 Discussion
### Application Scenarios
#### 4.2.2 Health Care  
The health care system uses various wearable/implantable devices to monitor and assess a patients' health conditions. The data can be accessed by a doctor remotely to give medical advice. Nevertheless, cyber-criminals can steal personal health information or interrupt the process of healthcare delivery. To protect the data, security professionals built a remote monitoring framework to provide fine-grained access control protection [16]. Moreover, researchers also leveraged specification-based techniques to detect the intrusion behaviors of medical devices [17].
## 3 Results and Discussion
### Thermal Properties of the Electrospun Nonwoven Fabrics  
Weight loss as a function of temperature due to degradation is measured by thermogravimetric analysis (TGA). In the TGA curves, the onset temperature, where the deterioration process began, and the end temperature, where the combustion process ended, were both evaluated. The TGA profiles of PLA and HPO/PLA electrospun nonwoven fabrics are shown in Figure 3. Thermal degradation of PLA occurs in a single weight loss phase, as evidenced by the TGA curves. PLA has higher onset degradation temperatures than PLA/HPO blends. The profile for PLA degradation peak was at \(\sim\)368 \({}^{\circ}\)C [79] and it was completely decomposed at \(\sim\)444 \({}^{\circ}\)C, associated with the loss of ester groups by unzipping depolymerization [80]. On the other hand, the HPO/PLA samples of both the 3 HPO/PLA and the 5 HPO/PLA electrospun nonwoven fabrics showed nearly two steps of degradation, as shown in Figure 4. These degradations occurred at temperatures ranging from \(\sim\)360-354 \({}^{\circ}\)C to \(\sim\)500-550 \({}^{\circ}\)C, respectively.  
Figure 2: ATR-FTIR spectra of the HPO, the PLA, and the HPO/PLA electrospun nonwoven fabrics.  
The thermal properties of the two HPO/PLA electrospun nonwoven fabrics showed almost identical trends. The addition of HPO resulted in a slight decrease in onset temperature (\(\sim\)10-15 \({}^{\circ}\)C), indicating that blended samples have low thermal stability, accounted for by the presence of HPO dispersed in the polymer. When the HPO content is increased from 3% to 5%, the temperature of decomposition increased remarkably, while the temperature of onset thermal degradation decreased insignificantly. The decomposition temperature of the 5 HPO/PLA blend is \(\sim\)50 \({}^{\circ}\)C higher than that of the 3 HPO/PLA due to the presence of HPO in the matrix. The large difference in decomposition temperatures between the two starting materials may contribute to the degradation mechanism. This reassembly produces a physical barrier on the material's surface, reducing the permeability of volatile degradation products out of the blend and, as a result, delaying the blend's breakdown [77; 81]. This result is similar to results in the literature, which reported that the thermal behavior of epoxidized soybean oil (a simultaneous compatibilizer and plasticizer) modified PLA natural rubber-based compound [81]. The thermal properties indicate that the HPO/PLA electrospun fabrics have higher decomposition temperatures than PLA.
## 1 Introduction
[15] investigated the spatial and temporal history of vegetation cover on the Loess Plateau and its reaction to meteorological conditions using GIMS NDVI data, and they discovered that the study area's overall vegetation cover showed an increasing tendency and a high association with climate. [PERSON] et al. used 54 meteorological stations on the Loess Plateau from 1957 to 2012 to study the climate aridity of the Loess Plateau [16]. However, these studies focus on the Loess Plateau as a whole within a certain time period, or only compare before and after GGP, and rarely compare the changes of WUE in the Loess Plateau during different periods of fallowing. In fact, due to the different rates of vegetation restoration, different water-carbon coupling mechanisms are presented at different time periods, and balancing carbon uptake and water consumption at a regional scale is a challenge. Previous studies struggled to investigate the WUE of the entire Loess Plateau and its drivers but cannot be conducted for the complex spatial heterogeneity. Therefore, in this paper, we used PML remote sensing GPP and ET products, combined with normalizedvegetation index (NDVI) and meteorological data, to study the effect of different vegetation cover degrees on vegetation WUE for the Loess Plateau region, facing complex spatial heterogeneity and a long timeseries, and we also investigated the main driving forces affecting the WUE to provide a scientific basis for the sustainable development of vegetation on the Loess Plateau.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 6 Issues and Challenges
### Privacy and Security  
Privacy and security protection are the most important services to provide at the edge. Private information can be learned from the sensed data if the vehicle is deployed with the IoT. For example, a vehicle subscribing to IoT services via ITS can be tracked by reading its sensor data. That's why supporting services without harming privacy is a challenging issue. Nevertheless, the edge seems to have the potential of supporting privacy compared to cloud computing. Instead of publishing user data to the cloud, processing is performed at the edge, and further cloud-based computations can rely only on aggregated data. Privacy and security challenges in edge computing include remote data access, remote computation, and context-aware computing [46].  
In the context of smart cities, different ITS applications would require vehicle drivers to give access to some of the potentially private local data to untrusted vehicles which are trying to connect as edges. Efficient trust management systems along with data isolation mechanisms can effectively increase security at the edge. An attempt to ensure a secure collaboration in an edge computing environment is proposed in [47], however, a preliminary generalized framework is provided, which does not consider the challenges of vehicular networks in particular. In [48], an improved anonymous authentication scheme is proposed for vehicular networks based on secure multi-party computation theory and privacy protection protocol. The proposed scheme was theoretically analyzed, but with no evaluation in realistic scenarios. In conclusion, security and privacy are still two open issues for further research in the context of VEC.
# Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks
## 3 Materials and Methods
### Error-Level Analysis  
Error-Level Analysis is a passive detection method traditionally used by human forensics specialists to make differences in format artifacts of jpeg images more evident, it works by taking the difference between a jpeg image at different quality levels, making any difference in compression rate more evident on the resulting image [48], an example of this process can be seen in the Figure 2 and the code used to generate the images is presented in Listing 1.  
In this example, the resulting image on the bottom is mostly black, but edited areas have more color in them, however, ELA results aren't always so easily interpreted and traditionally need a forensics specialist to look at the results, however, this paper proposed using ELA as a feature extraction step and feeding it to a Convolutional Neural Network in order to perform the authenticity analysis of an image automatically.
## 6 Conclusions  
In this paper, we have introduced a low-power and lightweight RD/SD mechanism, denoted as DiRPL, particularly suited for constrained and duty-cycled SOs forming IEEE 802.15.4-based multi-hop IoT networks. First, we have described the main components of DiRPL and the functionalities that these elements must implement to perform SD and RD. Then, we have proposed a solution for automatic local RD/SD that allows (i) to discover resources advertised and available in constrained SOs and (ii) to publish them into the local resource directory (e.g., at the BR) with no need for any prior configuration. An extensive performance evaluation of the proposed RD/SD mechanism characterizing DiRPL has been performed, using Contiki-based nodes organizing in 6 LoWPANs with RPL in the Cooja simulator. The obtained results show that the time required for RD/SD is a quadratically increasing function of the number of hops in the path between a joining SO (i.e., a LEN) and the BR. Moreover, for increasing values of the CCR, delay and energy tend to become linear functions of the number of hops between the LEN and the BR.  
**Author Contributions:** Conceptualization, L.D., M.A. and G.F.; Data curation, L.D., M.A. and G.F.; Formal analysis, L.D., M.A. and G.F.; Investigation, L.D., M.A. and G.F.; Methodology, L.D., M.A. and G.F.; Supervision, G.F.; Validation, L.D., M.A. and G.F.; Writing--original draft, L.D., M.A. and G.F.; Writing--review & editing, L.D., M.A. and G.F.  
Figure 13: Detail on the ISO layers to be traversed by traffic flows with the proposed DiRPL mechanism.  
**Funding:** The work of [PERSON] and [PERSON] is partially funded by the European Commission H2020 Framework Program, under Grant No. 783221, AFarCloud project - \"Aggregate Farming in the Cloud.\" The work of [PERSON] is also funded by the University of Parma, under \"Iniziative di Sostegno alla Ricerca di Ateneo\" program, \"Multi-interface IoT sYstems for Multi-layer Information Processing (MIoTYMIP)\" project. The work reflects only the authors' views; the European Commission is not liable for any use that may be made of the information contained herein.  
**Conflicts of Interest:** The authors declare that there is no conflict of interest regarding the publication of this paper.
## 4 Application and Discussion
### Assessment of the Coupling Relationship Based on the DEMATEL Model  
1. Establishment of the Evaluation Scale and the Direct-Relation Matrix  
The evaluation scale established for the degree of causal relationship of risk factors is defined as 5 levels, i.e. 0, 1, 2, 3, and 4, where 0 represents no causality and 4 represents strongest causality. There are 14 kinds of risk factors based on the classification of 21 risk factors, and the degree of direct causal relationship will, based on expert judgment, be evaluated through pair-wise comparison, and the direct-relation matrix \(M\) will be obtained after averaging.
2. Normalization of the Direct-Relation Matrix  
\[M1=M\times\alpha=M\times\frac{1}{142.1} \tag{14}\]  
The normalization of the direct-relation matrix \(M\) is based on the formula 14, with the results shown in Table 3.  
Figure 4: The classification of risk factors.  
The initial conclusions that can be drawn from Figure 5 are as follows:  
* High importance, cause category: \(F_{11},F_{12},F_{41}\), and \(F_{42}\) belong to the cause category, which have a great impact on other risk factors and are less susceptible, but vary in the degree of relationship, with a strong relation on \(F_{41}\) and \(F_{42}\) and a weak relation on \(F_{11}\) and \(F_{12}\). All of them are important factors in the mission.
* High importance, result category: \(F_{23}\) and \(F_{24}\) are more important than \(F_{13}\) and \(F_{21}\) throughout the mission, all of which belong to the result category, showing that the factors are vulnerable to other factors.
* Low importance, cause category: \(F_{31}\), \(F_{32}\), \(F_{33}\) and \(F_{43}\) belong to the cause category with a low value of \(F_{31}\) and \(F_{32}\), which indicates less impact on other factors. The above factors are less important throughout the mission.
* Low importance, result category: \(F_{22}\) and \(F_{34}\) belong to the result category with a low value, which indicates less causality and less importance throughout the mission.