esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Description of the Approach
### Prioritization of the Risk Treatment Paths with a Hybrid MCDM Approach
#### 4.1.2 Subjective and Objective Criteria Weighting
The inputs are assumed to be consistent if CR \(\leq\) 0.1 hold true; otherwise, the expert inputs must be revised. The CR is the fraction of the consistency index (CI) and random consistency index (RCI) as shown. The value of the RCI depends on the number of alternatives and was introduced by [PERSON] [50]. Small inconsistencies are accepted, as human inputs are subject to error, especially with an increasing number of alternatives. Furthermore, if inconsistencies are small, they do not have a decisive influence on the result of the AHP. In addition to the subjective weighting with AHP, an objective weighting takes place using the entropy (information theory) according to [PERSON] et al. [42]. The aim of the entropy is to calculate a weighting that reflects the information and uncertainty contained in an individual criterion. The entropy \(e_{j}\) is calculated using Equation (10) and is based on the normalized decision matrix D (Equation (7)). \[e_{j}=-k\sum_{i=1}^{n}r_{ij}^{*}ln\left(r_{ij}^{*}\right),\ j=1\ldots m \tag{10}\]  
where \(k=\frac{1}{\ln(n)}\) and \(n\) the number of alternatives per criterion. The higher the entropy, the higher the uncertainty of the criterion, and the lower the weighting \(w_{j}\) should be. This relationship is represented in the calculation of the objective weighting \(w_{Entr,j}\)[32, 40, 42]:  
\[w_{Entr,j}=\frac{f_{j}}{\sum_{j=1}^{m}f_{j}}\ \text{with}\ f_{j}=1-e_{j} \tag{11}\]  
The final weighting \(w_{j}^{final}\) for a criterion is generated by multiplying the objective weighting \(w_{Entr,j}\) and subjective weighting \(w_{AHP,j}\) similar to [32, 42]:  
\[w_{j}^{final}=\frac{w_{Entr,j}\cdot w_{AHP,j}}{\sum_{j=1}^{m}w_{Entr,j}\cdot w _{AHP,j}} \tag{12}\]  
The final weighting \(w_{j}^{final}\) combines the possibility of mapping individual preferences with the AHP and at the same time reduces the distortions by entropy due to the consideration of the contained information value of the different options. Since in this case there is sufficient data to calculate the entropy and an expert familiar with the use case is available, both weightings can easily be determined and combined.
## 2 Sensor Antenna Design  
### Theoretical Antenna Dimension  
The theoretical dimensions of the three patch antenna shapes--rectangular, circular, and equilateral triangular--were designed to operate at 2.4 GHz, based on their fundamental modes (\(TM_{10}\), \(TM_{11}\), and \(TM_{10}\), respectively), to achieve symmetric radiation characteristics. Alumina was selected as the substrate material due to its robustness in harsh environments, with a high melting point of 2072 \({}^{\circ}\)C. The substrate has a dielectric constant (\(\epsilon_{r}\)) of 9.8, a loss tangent (\(tan\)\(\delta\)) of 0.0003, and a thickness (\(h\)) of 2.45 mm [16]. The dimensions of the rectangular patch antenna were theoretically calculated using Equations (1)-(4) [17].  
\[W=\frac{C}{2\,f_{r}}\sqrt{\frac{2}{\epsilon_{r}+1}} \tag{1}\]  
\[\epsilon_{reff}=\frac{\epsilon_{r}+1}{2}+\frac{\epsilon_{r}-1}{2}\frac{1}{ \sqrt{\left[1+12\frac{h}{W}\right]}},\text{ for }\frac{W}{h}>1 \tag{2}\]  
\[\Delta L=0.412h\frac{\left(\epsilon_{reff}+0.3\right)\left(\frac{W}{h}+0.264 \right)}{\left(\epsilon_{reff}-0.258\right)\left(\frac{W}{h}+0.8\right)} \tag{3}\]  
\[L_{eff}=\frac{c}{2f_{r}\sqrt{\epsilon_{reff}}}-2\Delta L \tag{4}\]  
where \(c\) represents the speed of light in vacuum (\(3\times 10^{8}\) m/s). The effective physical length (\(L_{eff}\)) accounts for the fringing field effect, ensuring that \(L_{eff}\leq\lambda_{eff}/2\)[17]. The circular patch antenna is designed to operate in the \(TM_{11}\) fundamental mode, with its dimensions calculated using Equations (5) and (6).  
\[\alpha=\frac{c}{2\pi f_{r}\sqrt{\epsilon_{r}}}\;X^{\prime}_{np} \tag{5}\]  
\[\alpha_{e}=\alpha\sqrt{1+\frac{2h}{\pi\alpha_{e}}\left(\ln\frac{\pi\alpha}{2h }\right)+1.7726}\;\;\;(\ref{eq:2}) \tag{6}\]  
where \(X^{\prime}_{np}\) represents the root of the Bessel function for TM modes, \(X^{\prime}_{11}=1.8417\). This value corresponds to the \(TM_{np}\) mode used in the circular patch antenna design. \(\alpha\) denotes the radius of circular patch antenna while \(\alpha_{e}\) is the effective radius, which compensates for the fringing field effect using Long-Shen formula [18]. The equilateral triangular patch antenna is designed to operate in the \(TM_{10}\) fundamental mode, utilizing [PERSON]'s postulate which considers the extension of physical side length [19] as described in Equations (7)-(9).  
\[S_{e}=\frac{2c}{3f_{r}\sqrt{\epsilon_{r}}} \tag{7}\]  
\[S_{p}=(1-H\beta)\times S_{e} \tag{8}\]  
\[H=\frac{h}{\lambda_{g}} \tag{9}\]  
where \(S_{e}\) represents the electrical side-length; \(S_{p}\) denotes the physical length of the radiating part; H is the normalized height of antenna substrate, where \(h\) is the height of substrate; and \(\lambda_{g}\) is the wavelength within the substrate. \(\beta\) is the Bhatnagar constant that is equal to 1. The physical side length, \(S_{p}\), compensates for the fringing field effect and the boundary condition, resulting in a longer effective electrical length: \(S_{p}\geq\lambda_{eff}/2\).
## 3 Photonic Technologies  
### Photoelasticity  
#### 3.1.1 Working Principles  
Photoelasticity is one of the oldest methods used for experimental stress analysis. The method relies on the birefringence property exhibited by transparent materials, under the influence of external loading. The principles of photoelasticity can be succinctly described by:  
* For stress (or strain) induced birefringence, the normally-incident polarized light is split into two components along the principal stress directions in a plane perpendicular to the direction of light propagation and are transmitted only along these planes through the model.
* The velocities of light transmission along these directions are directly proportional to the intensities of the respective principal stresses.  
This means that the input linearly-polarized light emerges as an elliptical polarized light, where the eccentricity depends on the principal stress directions and their magnitudes. By the Maxwell Stress Optic Law, a change in load induced in two components, aligns with the principal stress directions, with different refractive indexes. The induced difference in the refractive indices leads to a relative phase retardation between the two components, which is linearly proportional to the principal stress components and is given by Equation (1) as:  
\[\frac{\alpha\lambda}{2\pi l}\frac{1}{C}=\left(\sigma_{1}-\sigma_{2}\right) \tag{1}\]  
where \(\alpha\) is the phase difference between the two components, \(\lambda\) is the wavelength of light, \(C\) is the relative stress optic coefficient; \(l\) is the distance the light traverses through the model and \(\sigma_{1}\) and \(\sigma_{2}\) are the two principal stress components. The number of fringe order \(N\) can be calculated by Equation (2):  
\[N=\frac{\alpha}{2\pi} \tag{2}\]  
The required phase difference or fringe order is determined by placing the birefringent model in a circular polariscope, as schematized in Figure 1.  
The fringe pattern can have dark lines, corresponding to points where the intensity of light is zero, called isoclinics and colored lines called isochromatics. Isoclinic lines provide information on the principal directions of stresses throughout the model. Isochromatics correspond to lines of constant principal stress difference (lines with the same color).  
The basic advantage of a circular polariscope over a plane polariscope is that in a circular polariscope setup we only get the isochromatics and not the isoclinics. This eliminates the problem of differentiating between the isoclinics and the isochromatics. In a standard plane polariscope both the isoclinics and isochromatics are shown.  
Figure 1: Schematic representation of a circular polariscope.
# Stability-Aware Geographic Routing in Energy Harvesting Wireless Sensor Networks
## 2 Paper Overview
### Motivation  
Motivated by the research mentioned in Section 2.1, this paper proposes a stability-aware routing method for reliable data transmissions on EH-WSNs, named stability-aware geographic routing in SAGREH, to show that performance improvements for EH-WSNs can be achieved by incorporating residual energy, energy harvesting, link quality, and location information in the routing decision. The motivation of the proposed protocol is illustrated by the network scenario shown in Figure 2. In this figure, there are two available routes between a source, S, and a destination, D, or \(p_{1}\) and \(p_{2}\), which are represented as (S, 1, 2, D) and (S, 3, 4, 5, D), respectively. Here, B is the maximum battery of each node \(v_{i}\). In \(e_{ij}(z,w)\), \(z\) indicates the amount of energy consumption, and \(w\) indicates estimated packet reception rates. The relationship between the estimated packet reception rate and the number of retransmissions was compared to the study by [PERSON] _et al._[24], who considered routing with acknowledgment. Specifically, a packet is retransmitted between two nodes until it is received and acknowledged correctly. They showed that the best value for the number of packet retransmissions, \(u\), was not a constant and could be solved by:  
\[u\times PRR=1 \tag{1}\]  
If \(p_{1}\) is chosen, the total hop count on this path is four, and the estimated PRR on route \(p_{1}\) is \(PRR(p_{1})=0.95\times 0.99\times 0.9\times 0.97=0.821\). If \(p_{2}\) is chosen, the total hop count on this path is three, and the estimated \(PRR\) on route \(p_{2}\) is \(PRR(p_{2})=0.79\times 0.9\times 0.89=0.633\). Moreover, the average energy consumption of \(p_{1}\) and \(p_{2}\) is equal to 0.51 B \(\times\)\(u\) or 0.49 B \(\times\)\(u\), respectively, and the residual energy of the network after transmitting one packet over \(p_{1}\) or \(p_{2}\) is 3.75 B \(-\) 0.51 B/0.821 = 3.141 B or 3.75 B \(-\) 0.49 B/0.633 = 2.98 B, respectively; 3.75 B is the total residual energy and energy harvesting at each node in the network. The reliable routing path from source to destination is \(p_{1}\) because its average consumed energy is lower and it provides a higher estimated packet reception rate, making it the best choice for a network. If only residual energy, consumed energy, and harvested energy nodes on the path are considered, then \(p_{2}\) should be chosen. However, compared with \(p_{1}\), \(p_{2}\) is an unstable path because the packet reception rate on this path is lower, so the total energy needed to transmit a packet successfully is higher.  
The objective of the proposed SAGREH approach is to select the route that not only has minimum energy consumption and maximum residual energy, but also gives high estimated packet reception rate to provide a reliable route. Because the performance of a network depends heavily on the \(PRR\), the \(PRR\) is used as an input metric to select a stable routing path.
# Exploring Gait Recognition in Wild Nighttime Scenes
## 5 Results and Discussion  
We conducted experiments on indoor gait datasets (CASIA-B and OU-MVLP), outdoor gait datasets (GREW and Gait3D), and our proposed GaitDN dataset, which includes nighttime data. We compared several current state-of-the-art pose-based methods with our GaitSAT approach. The experimental results demonstrate that GaitSAT exhibits superior recognition accuracy and generalization performance in nighttime scenarios while maintaining high-level performance on daytime data.
## 3 Machinability of AM Ti6 Al4V Alloy
### Surface Roughness  
The surface roughness (Ra) measurement refers to the average texture of a finished machined surface, and it plays an important part in the functionality and reliability of a final product. It is a predominant requirement for the machined part to perform satisfactorily and depends on cutting conditions, workpiece, and tool material properties. The surface roughness can be measured using different means, such as contact-based devices and digital holographic microscopes.  
High surface roughness can be desirable in certain applications; however, it is often undesirable, particularly when surface contact necessitates low friction for mating with another surface or when a smooth appearance is required to minimize wear on materials [74]. It was concluded that a cutting speed of 70 m/min and a depth of cut of 0.1 mm was insignificant in increasing the surface roughness, whereas a feed rate of 0.1 mm/rev contributed to an increase of 83.69% in surface roughness [75]. Surface roughness of less than 2 \(\upmu\)m can be achieved by selecting a suitable combination of a considerable depth of cut and high spindle speed [46]. It was reported that maximum surface roughness of 2.16 \(\upmu\)m and tool flank wear of 0.201 mm could be achieved with a cutting speed of 250 m/min, feed rate of 0.1 mm/rev, and depth of cut of 0.1 mm [76]. Another report shows that surface roughness decreased with increased nose radius. Therefore, the roughness value of a machined surface was reduced by 27.90% and 26.70% for the predicted and measured values, respectively [77; 78]. Cutting forces and tool wear variations for a machined part were insignificant in affecting roughness values of both wrought and smelled laser-manufactured Ti6 Al4V parts, even below 2 \(\upmu\)m [7; 79]. The surface roughness value increases with an increased cutting speed of up to 80 m/min and decreases with further cutting speed increases [80].  
It was observed that the surface finish can be improved by increasing the cutting speed. However, the built-up edge formation on the tooltip subsequently deteriorates the workpiece quality due to high surface roughness at low cutting speed. The quality of a surface finish decreases as the feed rate increases because of the high friction developed due to a large amount of chips flowing on the cutting tool edge.
## 3 Drone Routing Algorithms
### Trajectory Planning
#### 3.1.2 Joint Routing and Charging Strategy (JRCS)  
Planning a drone trajectory path for a long delivery mission effectively is a tedious question, considering the limited flight capacity of drones. In [12], the authors recognize this problem and address it by effectively dividing the delivery region into clusters. The centroid of the cluster is chosen as the CS to ensure maximum flight range. The ground users are clustered, and the proposed system ensures that all the customers are visited at least once. The formulated scheme ensures maximum flight distance in a single charge before recharging again. A MILP formulation is used to derive the drone-based delivery routing problem (DDRP), which minimizes the mission time of the drone-based delivery.
## 3 Traffic Models
### Proposed Model
At equilibrium \(\Delta V=0\), so  
\[a_{max}^{k}\left(1-\left(\frac{V_{k}}{V_{k}^{\prime}}\right)^{(-\frac{8868}{S }+80.02)(\frac{S}{S^{\prime}})R_{k}}-\left(\frac{j+V_{k}T_{k}^{\prime}}{S} \right)^{2}\right)=0 \tag{8}\]  
and rearranging gives  
\[S_{e}=(j+V_{k}T_{k})\left(1-\left(\frac{V_{k}}{V_{k}^{\prime}}\right)^{(-\frac {8868}{S}+80.02)(\frac{S}{S^{\prime}})R_{k}}\right)^{-\frac{1}{2}} \tag{9}\]  
This shows that with the proposed model, distance headway between vehicles at equilibrium is based on the reaction time of CAVs and is more realistic than the constant \(\delta\) in the ID model. The flow for the proposed model is then  
\[f=\frac{V_{k}}{(j+V_{k}T_{k})\left(1-\left(\frac{V_{k}}{V_{k}^{\prime}}\right) ^{(-\frac{8868}{S}+80.02)(\frac{S}{S^{\prime}})R_{k}}\right)^{-\frac{1}{2}}} \tag{10}\]  
CAV behavior can be affected by cyberattacks. In a connected vehicle environment, vehicle alignment relies on real-time data exchange, particularly the distance headway information communicated between vehicles. When this information is accurate and readily accessible, the system functions normally, enabling vehicles to effectively adjust their speed and behavior [57]. Conversely, if the distance headway is inaccurate or unavailable due to cyberattacks, the system does not function properly. This results in inefficient vehicle behavior and delayed reactions [57]. Thus, the impact of these attacks in the proposed model (7) can be expressed as  
\[a_{k}=a_{max}^{k}\left(1-\left(\frac{V_{k}}{V_{k}^{\prime}}\right)^{(-\frac{8 868}{S}+80.02)(\frac{S}{S^{\prime}})R_{k}}-\left(\frac{S^{\prime}}{S}\right)^ {2}\right)+c\left(\frac{S}{S^{\prime}}\right)R_{k} \tag{11}\]where \(c\) is the cyberattack intensity that ranges between 0 and 100. \(c=0\) indicates that the vehicles behave normally, whereas \(c=100\) signifies that the vehicles are affected significantly by the attack. During a cyberattack, the distance headway and reaction time are manipulated so the information communicated in the system is corrupted.
## 2 Problem Formulation
### Modeling and Sizing of PV and BES
#### 2.3.2 PV Modelling  
The solar radiation probabilistic nature can be designated according to the probability density function (PDF) of Beta as follows [36; 41]:  
\[\mathbf{f_{b}(s)}=\left\{\begin{array}{c}\frac{\mathbf{\Gamma(\alpha+\beta)}}{\mathbf{ \Gamma(\alpha)\mathbf{\Gamma(\beta)}}s^{(\alpha-1)}\mathbf{(1-s)^{(\beta-1)}}\mathbf{0}\leq s \leq 1,\ \alpha,\ \beta\geq 0}\\ \mathbf{0}\ \mathbf{otherwise}\end{array}\right. \tag{17}\]  
where \(\mathbf{f_{b}(s)}\) refers to the s distribution function of Beta and s refers to the arbitrary variable of solar radiation in kilowatt per meter square; \(\alpha\) and \(\beta\) refer to the parameters of \(\mathbf{f_{b}(s)}\) which are computed exploiting the standard deviation (\(\sigma\)) and mean deviation (\(\mathbf{\mu}\)) as shown in (18). The value of standard deviation and mean deviation are presented in [42].  
\[\beta=\mathbf{(1-\mu)}\left(\frac{\mathbf{\mu(1+\mu)}}{\sigma^{2}}-1\right);\alpha= \frac{\mathbf{\mu}\times\beta}{\mathbf{1-\mu}} \tag{18}\]  
The PV module output power output depends on the solar radiation and surrounding temperature as well as the PV module characteristics itself. The maximum output power related to solar radiation \(\mathbf{s}\), \(\mathbf{P_{o}(s)}\), can be expressed as [19]:  
\[\mathbf{P_{o}(s)}=\mathbf{N}\times\mathbf{F}\times\mathbf{V_{y}}\times\mathbf{I_{y}} \tag{19}\]  
where,  
\[\mathbf{F}=\frac{V_{MPP}\times I_{MPP}}{V_{oc}\times I_{sc}}; \tag{20}\]  
\[\mathbf{V_{y}}=V_{oc}-\mathbf{k}_{v}\times T_{cy}; \tag{21}\]  
\[\mathbf{I_{y}}=s[I_{sc}+k_{i}\times(T_{c}-25)]; \tag{22}\]  
\[\mathbf{T_{cy}}=\mathbf{T_{A}}+s(\frac{\mathbf{N_{OT}-20}}{0.8}) \tag{23}\]  
where, \(\mathbf{N}\) refers to the module's number; \(T_{cy}\) and \(T_{A}\) refer to the cell temperature and ambient temperature (\(\mathbf{C^{0}}\)), respectively; Ki and Kv refer to the coefficient of current temperature (\(\mathbf{A/C^{0}}\)) and coefficient of voltage temperature (\(\mathbf{V/C^{0}}\)), respectively; FF refers to fill factor; NOT refers to rated working temperature of cell per \(\mathbf{C^{0}}\); \(\mathbf{I_{sc}}\) and refer to short circuit current (\(\mathbf{A}\)) and open circuit voltage (\(\mathbf{V}\)), respectively; _VMPP_ and _IMPP_ refer to voltage at maximum power point and current at maximum power point, respectively; \(\mathbf{P_{o}(s)}\) refers to the PV module maximum output power at solar radiation (\(\mathbf{s}\)). The prospective output power at solar radiation (\(\mathbf{s}\)) is computed according to Equation (10). Therefore, the overall prospective output during the identified interval time \(\mathbf{t}\), \(\mathbf{P_{t}}\) (\(\mathbf{t}\) = 1 h in this study) can be expressed as follows:  
\[\mathbf{P_{t}}=\int_{0}^{1}\mathbf{P_{o}(s)}\mathbf{f_{b}(s)}\mathbf{ds} \tag{24}\]
# Seismic Design of Timber Buildings: Highlighted Challenges and Future Trends
## 3 Timber Buildings--Future Trends and Challenges in Seismic Design
### Tall Timber Buildings
Additional potential problems which should be addressed regarding the seismic provisions of tall timber structures are:  
* Acoustic insulation requirements (tendency towards physical separation of structural elements in order to prevent sound transmission [63; 64]) vs. seismic design principles requirements (tendency towards connection of structural elements in order to achieve necessary strength and stiffness) should be addressed, as some studies and technical documents show the reduction in strength and stiffness of acoustically-isolated connections in timber structures [65; 66]; in addition, improved acoustic performance of timber wall and floor elements can also be achieved with additional high-density layers [67; 68], resulting in higher building mass and consequently higher seismic forces;
* New types of connections and assembly tools are entering the market and their performance is critical for seismic behavior of timber buildings [22; 69; 70];
* Higher seismic forces in taller buildings require new types of connection systems that can accommodate strength, stiffness, ductility and energy dissipation needs [7; 22; 71];
* In addition to seismic actions, also wind-induced vibrations shall be furtherly addressed in tall timber buildings [72; 73; 74; 75];
* Numerical modeling of tall timber buildings--addressing problems such as determination of vibration periods and damping [76], modeling ductility in the joints [77; 78; 79], selection of appropriate seismic design assumptions and procedures [80], proper simplification assumptions in FEM models of tall timber buildings and hybrid timber buildings [81], etc. Figure 3: Tall hybrid timber building Brock Commons, Vancouver, Canada.
## 5 Initial Orbit Determination Methods
Based on this principle and in order to reduce the on-board computation workload, [PERSON] et al. utilized GPU to handle the increased computational demands caused by artificial intelligence algorithms in space situational awareness, and they achieved significant acceleration effects in addressing these concerns [42]. With the development of spacecraft control and cluster techniques, observing space objects through multiple observation stations, as shown in Figure 6, has become practical and attracted widespread attention [61; 73]. In the late 20 th century, [PERSON] et al. proposed the reference vector method which utilizes statistical characteristics of the observation data to achieve an approximate optimal estimation of the target state [74]. However, this method has low accuracy and cannot be used in practice [75]. Subsequently, [PERSON] et al. introduced a unit vector method to iteratively approximate the initial state of space targets by constructing two coordinate systems. This method has a wide application range and converges fast, but it may be inaccurate or even diverge when the observed arcs are too long [76; 77]. Building upon the unit vector method, [PERSON] et al. proposed a space target initial orbit determination method for multi-camera arraies. This method first establishes equations for solving the initial state of the targets, then incorporates measurements from different time into the equation to form a system of equations. By solving the equations using the least squares method, the dynamic state of the target will be ultimately obtained. By comprehensively utilizing the computational results from multiple measurement devices, this approach leverages the advantages of camera arrays, achieving higher computational accuracy compared to traditional methods such as the Laplace method and the Gauss method [78].