esa-sceva/satcom-chunk-collection huggingface dataset:
# Buckle Pose Estimation Using a Generative Adversarial Network
## 3 Methods
### Feature Extraction Module  
CNNs [35] are widely used to extract the features of objects and are trained by stacking multiple convolution kernels and pooling layers. Our feature extraction network uses a ResNet50 backbone to extract the main features. As shown in Figure 4, the network consists of three blocks: a low layer that contains low-level features, a middle layer containing transition features, and a deep layer containing high-level features. First, a convolutional layer with a convolution block and two residual blocks is used to obtain the most original low-layer features. The middle-layer features are used to obtain contour and edge information. Finally, two convolution blocks and eight residual blocks are used to obtain deep semantic features.  
An appropriate receptive field of deep features is key to the segmentation task. The receptive field size of each layer is calculated using Equation (1):  
\[R(i,j-1)=(R(i,j)-1)\times s+k. \tag{1}\]  
This formula iterates from the uppermost to the lowest layer, where \(R(i,j)\) represents the local receptive field of the \(i\)th to \(j\)th layer, \(s\) represents the step size, and \(k\) represents the size of the convolution kernel. The resolution of the training image is 1024 \(\times\) 768, and the width of the target buckle accounts for \(\sim\)40% of the entire image, which is approximately 400 pixels. Note that the ideal receptive field size cannot be lower than 400 pixels. However, the maximum receptive field of the feature extraction network is 267 pixels. The network cannot focus on the overall characteristics of the target after obtaining the deep features; however, using the pooling layer to improve the receptive field of the network loses part of the information. Therefore, dilated convolutions are added after the deep features to increase the receptive field of the network (see Table 1). The size of the receptive field in the final network is 427 pixels. To calculate the receptive field of the dilated convolution, the equivalent of the dilated convolution is first obtained, and the size of the receptive field is recalculated using the equivalent convolution. The conversion formula between the two is shown in Equation (2):  
\[k^{\prime}=k+(k-1)\times(d-1), \tag{2}\]  
where \(k^{\prime}\) represents the equivalent convolution, and \(d\) is the dilated ratios. In this study, dilated convolutions with void ratios of three and two and a convolution kernel size of three are used in correspondence to the convolution kernels with convolution kernel sizes of seven and five in the equivalent.  
Figure 4: Feature extraction module. Through different convolution blocks, the features of different layers are obtained, which are low-level features \(F_{3}\), mid-level features \(F_{2}\), and deep features \(F_{1}\).
###### Abstract  
It has been challenging to separate the time-frequency (TF) overlapped wireless communication signals with unknown source numbers in underdetermined cases. In order to address this issue, a novel blind separation strategy based on a TF soft mask is proposed in this paper. Based on the clustering property of the signals in the sparse domain, the angular probability density distribution is obtained by the kernel density estimation (KDE) algorithm, and then the number of source signals is identified by detecting the peak points of the distribution. Afterward, the contribution degree function is designed according to the cosine distance to calculate the contribution degrees of the source signals in the mixed signals. The separation of the TF overlapped signals is achieved by constructing a soft mask matrix based on the contribution degrees. The simulations are performed with digital signals of the same modulation and different modulation, respectively. The results show that the proposed algorithm has better anti-aliasing and anti-noise performance than the comparison algorithms.  
underdetermined blind separation; TF overlapped signals; TF soft mask; KDE algorithm; contribution degree function +
Footnote †: journal: Applied  
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Footnote 1: Copyright © 2022 by the authors. Licensee 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/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 5 Conclusions  
We studied the performance of the three diversity schemes: silencing, TAS with silencing, and MRT for transmission in a downlink cellular system to ensure ultra-reliable communication via CRAN. We obtained analytical closed-form expressions for the outage probability and expected value of SIR of these three schemes. The analysis showed that performance depends on the transmit rate, distance to the UE, path loss exponent, and the number of cooperating and interfering RRHs. We provided numerical results and discussion by showing outage probability and reliability analysis of the schemes when varying different system parameters. Our results showed that in the case of ultra-reliable operations, MRT schemes outperformed the TAS and silencing schemes with a minimal number of cooperating RRHs. In addition, for moderate reliability, TAS and silencing schemes are also feasible. The analysis showed that the outage probability improves by increasing the number of cooperating RRHs in an interference-limited scenario. The result showed that expected value and EE have significant improvement with increased cooperation of RRHs. In the case of EE, MRT seems beneficial compared to TAS and silencing. Meanwhile, expected values of SIR of MRT seems higher than silencing and TAS whereas silencing and TAS with silencing have very similar expected value as cooperation increases. MRT enables MC, which provides significant gain but at the cost of decreased average system sum throughput because resources are directed to the intended UE demanding ultra-reliability. Finally, as future work, we intend to extend our work focusing on the random RRHs location with Multiple-Input Multiple-Output (MIMO) scenario while considering coexisting UEs with heterogeneous quality-of-services requirements.  
B.K.: conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing original draft, review and editing, visualization; O.L.A.L.: conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, review and editing; H.A.: validation, formal analysis, investigation, resources, data curation, review and editing, supervision; M.L.-a.: review and editing, supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research has been financially supported by Academy of Finland 6 Genesis Flagship (grant 318927).  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
### Ultimate Strength of Optical Fibers  
The ultimate strength is the maximum stress that a material can withstand before rupture. The ultimate tensile strength of optical fibers varies among supposedly identical specimens. As with any brittle material, the ultimate tensile strength of optical fibers of silica depends on the size and distribution of flaws, which are random [49; 50]. Therefore, a breakage of an optical fiber cannot be attributed to a single strain or a single structural crack opening. Instead, a probabilistic rather than a deterministic definition describes the width of a structural crack when the crack is detected through a breakage of an optical fiber [49; 51].  
It should be noted that silica optical fibers are subjected to a tensile proof test at a specific stress of \(\sigma_{pt}\) = 0.69 GPa (100 kpsi) at production facilities. That is, the silica optical fibers available on the market are guaranteed to withstand a stress of \(\sigma_{pt}\) = 0.69 GPa. This stress corresponds to a tensile force of \(F\) = 8.6 N in the case of a silica-core silica-clad optical fiber with a cladding diameter of \(d_{2}\) = 125 um. Furthermore, the ultimate tensile strain, \(\varepsilon_{u}\), of thin silica optical fibers is much higher than that of brick masonry, with their values being about 0.035 and 0.0001, respectively [52; 53]. That is, masonry cracks are under a much lower strain than the silica optical fibers bonded to the masonry. Therefore, the optical fiber bonded to masonry initially stretches under masonry deformation and the onset of small structural cracks. It then breaks at a certain crack width, \(\delta_{c}\). A particular crack width, \(\delta_{c}\), depends on the ultimate strain of the optical fiber, \(\varepsilon_{u}\); the bond between the silica cladding and the plastic coating; the coating material and its stiffness; the bond between the coating and the binder; and the stiffness of the binder [54; 55; 56; 57; 58].
## 1 Introduction
When compared with the available approaches, the main advantages are:  
* Support of all possible topologies: line, star, cluster and mesh;
* Network failure conditions can be specified in a very flexible way, ranging from a single device to groups of devices;
* Failure and repair processes can be characterized using different types of time distribution functions;
* Network devices can have redundant (internal) architectures;
* Topology reconfigurations due to device failures are considered (e.g., self-healing routing protocols);
* Different types of dependability measures can be obtained from the same model (e.g., reliability, availability, MTTF) as well as the criticality of the network devices ([PERSON]'s measure). To complement the proposed approach we have also developed a software tool that automatically evaluates the reliability and availability of a WSN. The tool automatically generates a fault tree with the minimal set of events that leads to the network failure condition. After that, the fault tree is translated into a language understandable by the sharpe (Symbolic Hierarchical Automated Reliability and Performance Evaluator) tool [9], which is used to compute the desired dependability measures. The remainder of this paper is organized as follows: Section 2 surveys some of the most relevant research works on reliability and availability evaluation for Wireless Sensor Networks. In Section 3, we give an overview about Wireless Sensor Networks with a special attention to wireless industrial networks standards, such as WirelessHART and ISA 100.11a. Next, in the Section 4, is held a brief introduction to Fault Tree Analysis (FTA) and basic concepts used in the proposal. Section 5 describes the proposed methodology for the reliability and availability evaluation of Wireless Sensor Networks. In Section 6 several scenarios are evaluated using network topologies commonly adopted in industrial applications. Finally, Section 7 concludes the paper and presents directions for future studies.
## 2 Proposed Segmentation Methodology
### Clustering of Points
Here, the threshold is computed based on the noise level of the laser scanning data utilized (e.g., two times the accuracy of the laser data). After this process, the plane parameters are re-determined using all of the points in the cluster. The searching of the neighboring cells in the second iteration is carried out by checking the twenty four neighboring cells, as seen in Figure 11b. The eight neighboring cells that were checked in the first iteration are included in the search areas for the second iteration. Points having similar attributes to the points in the initial cluster but not sufficiently close (_i.e._, their proximity in the object/spatial domain is not satisfied) to that cluster might still remain in the eight cells; these points are considered in the second iteration. It should be noted that the method of point searching in the neighboring cells is designed to consider such points. Additional points are also included in the cluster after checking the proximity and co-planarity conditions; then, the plane parameters are updated definitively. Afterwards, the iteration proceeds to the next level. This iterative process is continued until no more points can be included in the cluster. Upon completion of this cluster growth, the points in the cluster are recorded and removed from the accumulator array. Now, the second-highest peak cell can be detected, and the same steps implemented for the first-highest peak are repeated. This process of moving from the highest peak to the next highest is repeated until the number of points located at the last peak is less than the number of points that have been pre-determined to make up the minimum detectable patch size in the object space. Figure 8d shows the clustered points categorized into two different groups using the above technique. As it has been mentioned earlier, the clustering process proposed in this research simultaneously considers the similarity in the attribute  
Figure 11: Searching of the neighboring cells in (**a**) the 1 st iteration and (**b**) 2 nd iteration. space and the proximity in the object space. Therefore, the cluster identification and plane parameter evaluation are quite robust since points with higher spatial and attribute similarity take priority over the less homogeneous ones (_i.e._, with lower degree of similarity and proximity).
## 3 Experimental Analysis
### Material Properties
#### 3.3.3 Adhesive  
Combining materials as diverse as timber and glass presents a unique challenge. The joint must strike the right balance between load-bearing capacity and ductility. In addition to the right choice of adhesive, the type of joint and the preparation and care of the surface are also important. When choosing an adhesive, it is important to know the criteria that each adhesive meets. These criteria are the strength of the adhesive bond, the deformability of the adhesive, the exposure to moisture and climatic changes, the temperature (in)resistance, the degree of adhesion, the type and bonding time of the adhesive, the viscosity, the humidity, and the fire resistance of the adhesive. One of the main advantages of a bonded joint is that it achieves uniform stress along the entire length of the joint, unlike the high local stresses of nailed or joints. [PERSON] and [PERSON] (2018) were among the first to investigate adhesives for joining timber-glass composites. They divided them into three groups, namely:  
- rigid adhesives--extremely high strength and stiffness (e.g., acrylic acrylate, epoxy);  
- medium stiff adhesives--balanced strength and elasticity (e.g., polyurethane);  
- elastic adhesives--extreme elasticity and low strength (e.g., silicone and some polyurethane).  
Combining two materials with significantly different values of coefficient of thermal expansion (\(\alpha_{\text{T}}\)), an elastic adhesive can be used to mitigate the relative deformation between the two materials due to temperature changes. The elastic adhesive bond performs a similar function in the event of a sudden change in relative humidity that affects the volumetric properties of the timber (i.e., expansion and contraction). Three types of adhesives were used: SikaSil SG500 silicone, SikaFast 5215 acrylate and Sikadur(r)-31 CF epoxy (Table 5).  
Figure 8 shows diagrams demonstrating the significantly higher strength of epoxy compared to silicone. The epoxy adhesive is shown to have elastic, nearly linear behaviour until failure. In addition, the stresses in epoxy adhesives can be up to 100 times lower than in silicone, which should be considered when selecting the type of adhesive. Acrylate adhesives exhibit bilinear behaviour.  
In practice, we are usually dealing with medium- to long-term loads. Therefore, we should refer to the results of [PERSON] et al. [36], who showed that the long-term strength of silicone is only about 10% of its short-term strength due to the highly creeping behaviour of silicone sealants. It is also worth mentioning the findings of [PERSON] et al. [37], based on the results of shear tests, showing that the failure mode of timber and glass elements depends on the strength of the adhesive. In general, it can be concluded that glass regularly fails in combination with highly resistant adhesives.
# Comparison of Measurement Possibilities by Non-Invasive Reflectometric Sensors and Invasive Probes
## 2 Materials and Methods
### Materials
#### 2.1.2 FP/Mts Sensor  
The FP/mts sensor presented in Figure 1 was intentionally designed for the in situ evaluation of soil moisture but for several years has been successfully applied for the moisture evaluation of soft building materials [42].  
Its main functional elements are the two 10 cm long sharpened acid-resistant steel rods (2 mm in diameter, separated by 14 mm), a sensor support made from a section of a PVC tube of 2 cm outer diameter and length, and a coaxial cable of length from 1.5 to 6 m, from the sensor to the terminating SMA connector.
## 6 Solutions for Security and Trust in IoV
### Machine Learning-Based Solutions for Security
#### 6.2.2 Unsupervised-Learning-Based Solutions  
The authors in [142] proposed an approach to defend against DoS attacks. The unsupervised deep learning choice was Deep Contractive Autoencoders (DCAEs). They compared actual and predicted data depending on mean square error and mean absolute error metrics. [PERSON] et al. developed a data-driven strategy for detecting DoS assaults as well as three additional types of in-vehicle network threats. Unsupervised learning is used to extract relevant features from data linked to the controller area network (CAN) bus. It reflects CAN behavior based on those traits, and any divergence from the learned behavior is regarded as a system attack. Additionally, by observing associated CAN bus metrics, this work adopts a data-centric scheme to narrow down the type of attack. For a car-hacking dataset, the suggested scheme's performance evaluation findings demonstrate a high performance [143]. The authors in [144] addressed the significant issue of DoS attacks in vehicular networks and investigated the viability of using unsupervised learning algorithms for DoS detection. Since DoS attacks pose threats to connected car functionality and safety, their detection and mitigation are of utmost importance. Future research should focus on enhancing the accuracy and adaptability of these algorithms, evaluating their performance in real-world scenarios, and developing hybrid approaches that combine unsupervised and supervised learning techniques. Detection of Anomalous Behavior in Smart Conveyance Operations (DAMASCO) is a security system described in Reference [145], which discusses the security challenges in VANETs and presents a system called DAMASCO. The system detects anomalies in vehicle-to-vehicle (V2V) communication using a statistical approach. The findings prove that the system is capable of identifying potentially malicious nodes while maintaining a low false positive rate. These studies show that unsupervised learning approaches can improve vehicle network security, notably by identifying DoS attacks. UL can improve the security and dependability of connected vehicles and IoT-driven transportation ecosystems by tackling the difficulties in these references and refining unsupervised learning algorithms. This adoption of unsupervised learning signifies a significant advance in enhancing the integrity and safety of current transportation systems.
## 5 MATLAB-Simulink System Model
### Vehicle and Motion Resistances  
The vehicle model captures both the inertia of the vehicle and its resistance to motion. These resistances include rolling resistance, aerodynamic drag, and gradient resistance (which can be either positive or negative). Transmission losses are also considered. The three types of motion resistance are modeled as follows [33]:  
\[F_{T}=F_{zero}+F_{roll}+F_{grav} \tag{10}\]  
\[F_{zero}=\frac{1}{2}\cdotp\cdotp A_{F}\cdot\mathrm{C}_{D}\cdot\left(v-v_{WIND} \right)^{2}\geq 0 \tag{11}\]  
\[F_{roll}=\mu_{roll}\cdot M\cdot g\cdot\cos\left(\alpha\right)>0 \tag{12}\]  
\[\mathrm{with}\ \mu_{roll}=\mu_{roll,0}+\mu_{roll,S}\cdot\mathrm{{}^{2.5}} \tag{13}\]  
\[F_{grav}=M\cdot g\cdot\sin\left(\alpha\right)\gtrless 0 \tag{14}\]  
where  
* \(\mathrm{F_{T}}\): total motion resistance [N].
* M: total mass of the vehicle [kg].
* \(\mathrm{F_{zero}}\): aerodynamic resistance [N].
* \(\mathrm{\mu_{roll}}\): rolling resistance coefficient [-].
* \(\mathrm{F_{roll}}\): rolling resistance [N].
* \(\mathrm{\mu_{roll,0}}\): constant rolling resistance coefficient [-].
* \(\mathrm{F_{grav}}\): gradient resistance [N].
* \(\mathrm{\mu_{roll,S}}\): incremental rolling resistance coefficient [-].
* \(\mathrm{\rho}\): air mass density [kg/m\({}^{3}\)] \(\alpha\): road slope (positive when climbing) [\({}^{\circ}\)].
* \(\mathrm{A_{F}}\): vehicle frontal area [m\({}^{2}\)].
* g: free fall acceleration [m/s\({}^{2}\)].
* \(\mathrm{C_{D}}\): vehicle drag coefficient [-].
* v: vehicle speed [m/s].
* \(\mathrm{v_{wind}}\): headwind speed (positive in the same direction than the vehicle) [m/s].  
The load torque in (9) is calculated from the total motion resistance in (10) using the following expression:  
\[\left\{\begin{array}{l}T_{load}=\frac{ERR}{i_{GEAR}\cdot i_{GEAR}}\cdot F _{T}\left(fraction\ mode\right)\\ T_{load}=\frac{ERR_{ICIER}}{i_{GEAR}}\cdot F_{T}\left(regenerative\ mode\right) \end{array}\right. \tag{15}\]  
where ERR [m] represents the effective rolling radius (distinct from the loaded tire radius) [38], i\({}_{GEAR}\) [-] is the transmission gear ratio, and \(\mu_{GEAR}\) [-] denotes the transmission energy efficiency (assumed to be constant at 0.95) [39]. Regarding the moment of inertia, the total inertia \(\mathrm{J_{vehicle}}\) [kg\(\cdot\)m] as experienced by the electric motor is given as follows:  
\[J_{vehicle}=J_{high}+\frac{J_{low}}{i_{GEAR}}^{2}+\frac{M\cdot ERR^{2}}{i_{ GEAR}}^{2} \tag{16}\]  
where J\({}_{\mathrm{high}}\) and J\({}_{\mathrm{low}}\) are the total rotational inertia in the high-speed shaft (motor shaft) and low-speed shaft (wheel shaft), respectively. The majority of the vehicle's inertia is determined by its mass M. The parameter values used in Equations (10)-(16) for the simulated vehicle are listed in Table 3.
# Fast Deployment of a UWB-Based IPS for Emergency Response Operations
###### Abstract  
A wide range of applications from multiple sectors already use ultra-wideband (UWB) technology to locate and track assets precisely. This is not the case, however, for first responder localization during emergency response (ER) operations, which are highly conditioned by procedural and environmental constraints. After analyzing these limitations and reviewing the current state-of-the-art solutions, this work presents a UWB-based indoor positioning system (IPS) that relies on the global navigation satellite system real-time kinematic (GNSS-RTK) technology to quickly, accurately, and safely deploy its required infrastructure on site. A set of tests conducted on a two-story building prove the suitability of such a system, providing an average accuracy of less than 1 meter for static targets and the ability to faithfully reproduce the path followed by a mobile target inside the building. The obtained results strengthen the presented approach and pave the way for more sophisticated UWB-based IPSs that would include unmanned aerial vehicles (UAVs) and/or mobile robots to speed up network deployment even more while offering additional ER services.  
IPS; UWB; IEEE 802.15.4a; GNSS-RTK; disaster management; first responders; emergency response +
Footnote †: journal: Sensors  
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