esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Time Constant of PV Roof Tiles Working under Different Conditions
## 2 Test Object--Description of an Elastic PV Roof Tile  
Our test object was a Tegosolar PVL68 elastic PV roof tile (of Tegola, Vittorio Veneto, Italy), presented in Figure 2. The PV roof tile weighs about 4 kg and consists of two parts: bitumen substrate and a Uni-Solar elastic PV module. The PV module with a maximum power of 68 W consists of eleven serially connected solar cells. The cells are based on triple-junction amorphous silicon. Each cell is featured with a by-pass diode soldered in parallel, which enables current flow if a part of the module is shaded. Although the efficiency of cells based on amorphous silicon is fairly low (6%-10%), they are much cheaper to produce than crystalline silicon cells and can be manufactured in any shape and size [27]. The reference PV roof tile was composed of four main layers (shown in Figure 3), including [27]:  
* ETFE (ethylene-tetrafluoroethylene, also known as Tefzel)--durable polymer resistant to water and moisture, with high tensile strength, highly transparent and resistant to UV light; it is also used for the proper encapsulation of PV cells ensuring their proper electrical insulation. * PV cells--11 triple-junction PV cells made of amorphous silicon with different admixtures to improve sensitivity to absorption of light in the blue, green and red colour ranges; the total thickness of the PV cell was ca. 1 \(\upmu\)m, dimensions: \(239\times 356\) mm and ca. 10% efficiency. * Connection grid--stainless steel connections of PV cells. * PVDF bottom laminate (polyvinylidene-fluoride)--thermoplastic polymer with a high degree of PVDF crystallisation; it provides additional protection of PV cells from moisture and atmospheric conditions, proper electrical insulation, and mechanical, thermal and chemical protection. The construction of the reference photovoltaic roof tile, based on a triple-junction structure (with the addition of Ge in different amounts in individual parts of the PV cells) is presented in Figure 3, while its most important technical parameters are shown in Table 1. The structure which generated electrical energy consisted of stainless-steel foil, on which three layers of amorphous silicon, a transparent electrode and connection grid sockets were applied. The structure was coated with an  
Figure 2: Photovoltaic roof tile (Tegosolar PVL68) [27]. ETFE polymer film, which protected the module from water and prevented dirt deposition. Because the low efficiency of amorphous cells is mainly caused by the poor absorption of low-energy infrared radiation, each of the three layers of amorphous silicon was responsible for the absorption of different solar radiation wavelengths [28]. Individual parameters of the material layers making up the PV roof tile and roof structures on which the tiles can be installed, are presented in Table 2. The connection paths of PV cells were not considered in the deliberations with regard to their small size compared to the total area of PV roof tiles.
# Quantitative Assessment and Grading of Hardware Trojan Threat Based on Rough Set Theory
## 2 System Model and Assessment Formulation
### Information System and Attributes
#### 2.1.4 Effect  
The attack effects of HTs can be divided into _Denial-of-Service_, _Information Leakage_, _Function Change_, and _Degraded Performance_. After a Denial-of-service HT completes its attack, the whole chip becomes unresponsive; the threat of this HT is the greatest. HTs which leak core information of the IC are highly threatening. In a communication system, Function Change HTs usually change communication content. They pose a low threat, asthey only change specific functions of the chip; other functions can continue to operate normally. Performance-degrading HTs do not change the function of the chip, and have the lowest threat.
## 6 Results and Discussion
### Analysis for Permittivity and Conductivity (Frequency Is Fixed)  
The final numerical example is devoted to the case where the amplitude variation of the functional \(F(\epsilon_{r\,\mathrm{mm}},\sigma_{\mathrm{mm}})\) depends on the relative permittivity and conductivity parameters of the matching medium, which is illustrated in Figure 8.  
This image plot is obtained by assuming a constant frequency \(f=0.1\,\mathrm{GHz}\). The relative permittivity is varying in the range from 1 to 50 and the conductivity from \(0.01\,\mathrm{S}/\mathrm{m}\) to \(0.03\,\mathrm{S}/\mathrm{m}\), respectively. As labeled in Figure 8, two pairs reading \((\epsilon_{r\,\mathrm{mm}},\sigma_{\mathrm{mm}})=(45,0.028\,\mathrm{S}/\mathrm{ m})\) from the yellow-colored region and \((\epsilon_{r\,\mathrm{mm}},\sigma_{\mathrm{mm}})=(15,0.012\,\mathrm{S}/\mathrm{ m})\) from the darker blue-colored region, corresponding to respective highest and lowest amplitude values of the functional \(F(\epsilon_{r\,\mathrm{mm}},\sigma_{\mathrm{mm}})\), are chosen.  
Variations of the transmitted electric field in the innermost layer of the arm model along the line \(x_{2}=0\) are calculated using two parameter pairs from the image plot of Figure 8 and represented in Figure 9. The ratio of the areas under the transmittance curves presented in Figure 9 is 1.34, which demonstrates that the energy penetration into the arm structure is rather poor. Figure 10 shows the amplitude change of the scattered field by the internal object placed in the innermost layer of the arm model in the presence and absence of the matching medium.  
Figure 8: Amplitude variation of the functional \(F(\epsilon_{r\,\mathrm{mm}},\sigma_{\mathrm{mm}})\) with \(f=0.1\,\mathrm{GHz}\).  
The scattered electric field \(|E_{s}|\) is calculated on the observation circles of radii 0.3 m and 0.025 m. The amplitude changes versus observation angle are plotted in Figure 10 adapting the values of the pair \((\epsilon_{r\,\mathrm{mm}},\sigma_{\mathrm{mm}})=(15,0.012\,5/\mathrm{m})\) in the presence and \((\epsilon_{r\,\mathrm{mm}},\sigma_{\mathrm{mm}})=(1,0.012\,5/\mathrm{m})\) in the absence of the matching medium, both from Figure 8. The scattered field changes both inside and outside the arm model are shown in Figure 10 and they are dissimilar for different background spaces.
## 3 Results
### Identification of Volatile Organic Compounds by HS-SPME-GC-Ms  
#### 3.4.1 Characterization of VOCs in Different Fermentation Groups  
A total of one hundred and eleven VOCs were detected by GC-MS, including fifty-two esters, twenty-eight alcohols, eight acids, seven terpenes, five ketones, four aldehydes, and seven other compounds (Table 2, Figure 4A). The fermentation groups SY, EC-1118, NM-8, and ET008-c54 contained 74, 77, 80, and 80 VOCs, respectively. According to the data (Table 2, Figure 4B), the primary VOCs in the GAB were esters (1796.53-2640.94 \(\upmu\)g\(\cdot\)L\({}^{-1}\)), acids (1612.94-2214.16 \(\upmu\)g\(\cdot\)L\({}^{-1}\)), and alcohols (1420.56-2094.17 \(\upmu\)g\(\cdot\)L\({}^{-1}\)). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**RI \({}^{\delta}\)**} & \multirow{2}{*}{**Identification \({}^{\ast}\)**} & \multicolumn{4}{c}{**Concentration (\(\upmu\)g L\({}^{-1}\)) \({}^{\eta}\)**} & \multirow{2}{*}{**Threshold**} & \multirow{2}{*}{**Aroma**} \\  & & & & & & & & \\ \hline Ethyl Caprate & 1629 & MS, RI & 841.89 \(\pm\) 8.95 \(\pm\) 8.95 \(\pm\) 74 & 744.54 \(\pm\) 88.57 \(\pm\) 54 & 581.76 \(\pm\) 14.49 \(\upmu\)g & 889.98 \(\pm\) 15.46 \(\pm\) & 200 & brandy, fruit, grape \\ Ethyl Caprylate & 1426 & MS, RI & 529.24 \(\pm\) 3.3 \(\pm\) 4 & 412.26 \(\pm\) 8.44 \(\upmu\)g & 348.37 \(\pm\) 4.83 \(\pm\) 4. During fermented fruit alcoholic beverage fermentation, esters were mainly formed through esterification reactions between alcohols and acids [29]. Although there was no significant difference in the number of ester compounds across the fermentation groups, there was substantial variation in their concentrations (Figure 4B).
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 1 Introduction  
Intelligent Transport System (ITS) is the present and future of vehicular communications. It is a framework that incorporates various innovative technologies in the classical transport system to make it smarter, safer, convenient, and congestion-free [1]. A similar motive is behind the formation of Vehicular Ad hoc Networks (VANETs) where communication takes place among vehicles and various other units of the infrastructure [2,3]. Initially, VANETs were used for communication among cars with the help of roadside units. Later, this communication level extended to connect other vehicles and units such as pedestrians and grids. At present, these networks are deployed in the metropolitan cities only to provide information about accidents, tolls, parking areas, and charging spots. Another motivation for the formation of these networks is to establish communication among all vehicles and the global classical network, i.e., the internet that connects VANET applications with the other users.  
VANETs can be described as a hierarchy shown in Figure 1. It represents the relationship of the Internet of Things (IoT) with VANETs. It indicates that VANET is a sub-type of the IoTs and also adopts various attributes of IoTs and MANETs. With the rise in the availability of internet facilities and its speed, it is now possible to connect the devices that would not be possible earlier. Electric Vehicles (EVs) are also an important part of these vehicular networks. These vehicles can communicate with smart grids and charging spots. Vehicles having surplus electricity may pass to smart grids through charging spots (aggregators) [4]. This way a pool of energy from all the vehicles is created which can serve other vehicles. This creates a network of energy called the Internet of Energy (IoE) [5].  
Not only vehicles are connected in IoE, but other appliances are also connected to it that use energy. IoE is also connected with the solar panels for the energy storage process for futuristic IoTs. The sustainable development of the technology has produced EVs. IoE is well connected with these EVs. In the future, most vehicles will be electric and this will use facilities provided by IoE. EVs, IoE, and vehicular networks are now closely coupled with each other, and this combination is exploring further possibilities.
## 4 Electron Transfer-Based Assays
### Cupric Reducing Antioxidant Capacity (Cuprac)  
#### 4.2.1 Working Principle of CUPRAC Assay  
The CUPRAC assay was originally developed at the beginning of the XXI century to determine the antioxidant potential of plant compounds (e.g., ascorbic acid or quercetin) [58]. The tested extract is combined with an aqueous solution of copper(II) chloride (CuCl\({}_{2}\)), an ethanolic solution of neocuproine, and an ammonium acetate buffer at pH 7. This leads to the formation of the [Cu(Nc)\({}_{2}\)]\({}^{+}\) complex, which is responsible for the observed color change [59]. After an incubation period of 30-60 min, absorbance is measured at a wavelength of 450 nm. Upon addition of the extract or standard compound to the reagents, the color changes from green-blue to yellow-orange. The underlying principle of the method is the reduction of copper ions from Cu\({}^{2+}\) to Cu\({}^{+}\) by antioxidants. The intensity of the resulting yellow-orange color is proportional to the antioxidant content in the test sample. The reaction involved in the CUPRAC method is as follows [59]:  
n Cu(Nc)\({}_{2}\)\({}^{2+}\) + n-electron reductant \(\leftrightarrow\) n Cu(Nc)\({}_{2}\)\({}^{+}\) + n-electron oxidized product + n H\({}^{+}\)  
The CUPRAC assay is considered superior to other methods for measuring antioxidant potential because it operates at pH 7, which approximates physiological pH [60]. Furthermore, it has been demonstrated that CUPRAC is relatively unaffected by solvent effects in alcohol-water mixtures of varying compositions [46]. However, some studies have reported contrasting results, which were attributed to differences in the solvents used [61]. Methanolic and ethanolic extracts showed significantly higher antioxidant potential, as measured by the CUPRAC assay, compared to acetone-based extracts [61]. The short incubation time (30 min) at room temperature in the dark appears to be sufficient, yielding satisfactory results for most biologically relevant antioxidants, including ascorbic acid, gallic acid, and quercetin. Longer incubation times may be required for compounds such as naringin or naringin, likely due to their glycosidic forms [58; 60; 62; 63]. The CUPRAC assay is characterized by good linearity over a wide range of concentrations of standard substances, reagent stability, simplicity, and low cost. Additionally, it can be applied to both lipophilic and hydrophilic antioxidants [60; 64].
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 2 Background and Related Work
### C-Its  
A general overview of V2X systems will be given for a better understanding of C-ITS technology. V2X is a collective term incorporating several communication modes that enable communication among road elements. V2X communication systems can use technologies based on the IEEE 802.11p protocol that operates in the 5.9 GHz frequency band, having been designed to standardise vehicular communication systems. The IEEE 802.11p protocol is the basis of some standards for V2X communication [9], including:  
* Dedicated short-range communications (DSRC) with wireless access in vehicular environments (WAVE) as the upper layer in the United States of America.  
ITS-G5 with C-ITS as the upper layer in Europe.  
C-ITS refers to the integration of communication and information technologies with the support of transport infrastructures to provide an improvement in terms of traffic safety, mobility and sustainability, thus leading to more efficient and safer transportation [5; 18]. Moreover, C-ITS is composed of multiple sub-systems [10], such as handheld ITS sub-systems (such as smartphones); the central ITS sub-system; vehicle ITS sub-system present in vehicles; and roadside ITS sub-systems to be on traffic lights and other roadside infrastructures. An ITS-S is a functional entity specified by the ITS-S reference architecture [10]. The reference architecture follows the principles of the open systems interconnection (OSI) model for layered communication protocols [19].  
Many ITS applications require one of two communication strategies [20] or a combination of both: Periodic status exchange, where messages are needed by apps to know about the status of a vehicle or a roadside terminal, and event-driven information--messages informing about a specific event. Therefore, ETSI has defined two essential messaging services:  
* A cooperative awareness message (CAM) [21] provides awareness of the surrounding environment by periodically sending status data to nodes within a single-hop distance.
* Decentralised environmental notification message (DENM) [22] provides timely and relevant information about the driving environment and traffic events via multi-hop transmission to cover a specific geographic dissemination area.  
There are other messages in C-ITS [23], but DENM and CAM are the most widely used.
# Tire Defect Detection via 3D Laser Scanning Technology
###### Abstract  
Tire defect detection, as an important application of automatic inspection techniques in the industrial field, remains a challenging task because of the diversity and complexity of defect types. Existing research mainly relies on X-ray images for the inspection of defects with clear characteristics. However, in actual production lines, the major threat to tire products comes from defects of low visual quality and ambiguous shape structures. Among them, bubbles, composing a major type of bulge-like defects, commonly exist yet are intrinsically difficult to detect in the manufacturing process. In this paper, we focused on the detection of more challenging defect types with low visibility on tire products. Unlike existing approaches, our method used laser scanning technology to establish a new three-dimensional (3D) dataset containing tire surface scans, which leads to a new detection framework for tire defects based on 3D point cloud analysis. Our method combined a novel 3D rendering strategy with the learning capacity of two-dimensional (2D) detection models. First, we extracted accurate depth distribution from raw point cloud data and converted it into a rendered 2D feature map to capture pixel-wise information about local surface orientation. Then, we applied a transformer-based detection pipeline to the rendered 2D images. Our method marks the first work on tire defect detection using 3D data and can effectively detect challenging defect types in X-ray-based methods. Extensive experimental results demonstrate that our method outperforms state-of-the-art approaches on 3D datasets in terms of detecting tire bubble defects according to six evaluation metrics. Specifically, our method achieved 35.6, 40.9, and 69.1 mAP on three proposed datasets, outperforming others based on bounding boxes or query vectors.  
tire defect detection; 3D point cloud; object detection +
Footnote †: journal: Applied Sciences
## 5 Performance Analysis
### Evaluation and Simulation Results
#### 5.3.1 Model Accuracy  
Figure 2 presents how the accuracy of the model converges to rounds of communication in EdgeGuard compared to standard federated learning (FedAvg) and a centralized approach.  
This resulted in an average test error of 94.3%, which was higher than that of FedAvg at 91.7% and closer to the 95.5% error rate achieved by a centralized approach. The adaptive aggregation mechanism contributed to faster convergence toward this higher final accuracy in the federated setting.
# Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules
## 3 Channel Access
### FBE Variants
#### 3.2.1 Fixed-Muting FBE  
We begin with fixed-muting FBE, our first proposal to improve FBE channel access. This proposal extends the basic functionality of FBE in that after a successful transmission, the transmitting node refrains from transmission for the subsequent \(N\) (muted) periods. This means that CCA procedures are also omitted, both at the end of the period with the successful transmission and during the muted periods.  
Figure 3: Classification of FBE variants proposed in the literature to enhance FBE with contention resolution capabilities.  
Figure 2: FBE node synchronization issues in an example three-node network: (**a**) synchronous mode leads to constant collisions, (**b**) perfect node coordination—the FFP of each node is delayed by evenly spaced shifts ensuring round-robin transmissions (if CCAs do not overlap with COTs).
# Stochastic Modeling of the Al Hoseima (Morocco) Aftershock Sequences of 1994, 2004 and 2016
###### Abstract  
The three aftershock sequences that occurred in Al Hoseima, Morocco, in May 1994 (Mw 6.0), February 2004 (Mw 6.4) and January 2016 (Mw 6.3) were stochastically modeled to investigate their temporal and energetic behavior. A form of the restricted trigger model known as the restricted epidemic type aftershock sequence (RETAS) was used for the temporal analysis of the selected series. The best-determined fit models for each sequence differ based on the Akaike information criteria. The revealed discrepancies suggest that, although the activated fault systems are close (within 10 to 20 km), their stress regimes change and shift across each series. In addition, a stochastic model was presented to study the strain release following a specific strong earthquake. This model was constructed using a compound Poisson process and depicted the progression of the strain release during the aftershock sequence. The proposed model was then applied to the data. After the RETAS model was used to evaluate the behavior of the aftershock decay rate, the best-fit model was obtained and integrated into the strain-release stochastic analysis. By detecting the potential disparities between the observed data and model, the applied stochastic model of strain release allows for a more comprehensive examination. Furthermore, comparing the observed and expected cumulative energy release numbers revealed some variations at the start of all three sequences. This demonstrates that significant aftershock clusters occur more frequently shortly after the mainshock at the start of the sequence rather than if they are assumed to occur randomly.  
point process modeling; RETAS model; aftershock energy release; Al Hoseima; Morocco 2022 by the authors.  
Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Numerical Results
### Limits of the Proposed Solution and Future Works  
The proposed PLA scheme can be used in IoT scenarios with low environment mobility to enhance the authorization/identification in a network, especially when nodes have low computational capabilities and are not able to perform complex encryption algorithms. It was proven that this approach is able to correctly classify and authorize nodes with a high accuracy, even in the presence of challenging channel attributes variability; however, in the considered scenario, the nodes' position is assumed to be fixed, and hence, the mean values of delay and AoA do not change, but in a high mobility scenario, this could be not possible, thus reducing the ML classification accuracy. Different approaches should be considered in this case for classification.  
Moreover, the spoofing detection capability is achieved, thanks to the use of the node ID, and increases as the number of authorized nodes increases. This is suitable for a future scenario where a massive number of machines will require access to the network; however, in the case of small networks, the number of nodes is a limit that can be overcome with the introduction of sentinel nodes as we proposed. An alternative solution could be using different ML algorithms that, even if trained on \(N\) datasets, are able to detect \((N+1)\) classes, where the \((N+1)\)-th class is the one of an unauthorized node. Toward this goal, algorithms must be suitably selected and modified in order to work in a multi-class environment. These solutions could not only make the spoofing detection probability independent on the number of nodes in the network, but also avoids the use of the node ID.  
These aspects are currently under investigation for a future extension of this work.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Posture-Based Routing  
Posture-based routing is used to analyze the network topology of the human body in various dynamic postures to establish a fast and stable path. The analysis of various postures is of great significance. Many experiments have proved that all kinds of human body movements have some  
Figure 3: Routing classification for wireless body area networks.  
regularity [16]. This can greatly improve the development of the dynamic WBAN that is used. In the dynamic environment, the movement of limbs make the network topology good or bad, and also produce a shadow effect. The existing solutions can only be further processed, after the situation occurs, which was very passive and had a large delay. If the next action is predicted in advance in the current time-slot, the judgment and processing can be made in advance, which is very helpful to reduce the delay and improve the data transmission rate, successfully [17].