esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Implementation and Simulations
### The Implemented Behaviours  
The simulated nodes were programmed to exhibit three different behaviours during the course of the simulations. The behaviours exhibited by these nodes are briefly described below:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Topographical area & \(900\) m \(\times\)\(900\) m \\ Simulation time & \(900\) s \\ Channel type & WirelessChannel \\ Radio-propagation mode & TwoRayGround \\ Antenna type & Antenna/OmniAntenna \\ Interface queue type & CMUPriQueue \\ Maximum packet in queue & 50 \\ Network interface type & Phy/WirelessPhy \\ Link layer type & LL \\ MAC type & 802.11 \\ Number of connections & 12 \\ Traffic type & CBR \\ Data packet size & 512 bytes \\ Number of mobile nodes & 20 \\ \(\lambda\),\(\rho\),\(\omega\) & 0.8, 0.2, 0.1 \\ Behaviour Types & Good, selfish, deceitful nodes \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the simulations. CBR, constant bitrate.  
* Good nodes: A good node in our model responds to all route requests and ensures that all of the data and routing control packets that are meant for other nodes are forwarded to the next hop node or the recipient node if they are the last hop in the path.
* Selfish nodes: These nodes do not respond to any route requests received from other nodes, this ensures that they do not forward data and routing control packets for other nodes. Their existence is only known to other nodes when they need to send their own packets.
* Deceitful nodes: Deceitful nodes sporadically reply to the route requests from other nodes, but ensure that all of the data packets that are meant for forwarding are dropped. For instance, a deceitful node drops one out of every three routing control packets that it receives and ensures that every data packet it receives for forwarding is dropped.
## 1 Introduction
To obtain MDF, small-diameter fibers are used, so that a degree of compressibility is achieved without additional energy consumption [29]. Plywood and medium-density fiberboard are dimensionally stable, as they have a density of about 750-800 kg/m\({}^{3}\), compared to wood chipboards with a density of 650-750 kg/m\({}^{3}\)[11; 20]. Medium-density fiberboard is used as a raw material for high-quality furniture, having a much denser composition than wood chipboards [31; 32; 33; 34]. MDF and plywood do not have moisture resistance, but painted or laminated, they behave well in wet or humid environments [8; 35; 36; 34]. In order to increase the moisture resistance of the two composite materials and to extend their field of use in humid environments, heat treatment at high temperatures between 170-220 \({}^{\circ}\)C in an oxygen-poor environment, or even at higher temperatures under a nitrogen environment, can be used [37; 38; 39; 40]. This treatment is called torrefaction [26]. Through the torrefaction process, the molecular structure of the wood is changed, its dimensional stability is improved by 30-50%, the hygroscopicity is reduced, and, implicitly, the calorific value is increased [41; 42; 43]. In addition to the advantages highlighted above, torrefied wood-based composites (MDF and PW) have also some disadvantages, like a slight decrease in density and mechanical resistance [26; 30]. The aim of this work was to study two of the properties of the most used wood-based composites, namely plywood (PW) and medium-density fiberboard (MDF), to reduce water absorption, in order to use them in wet environments such as bathrooms and kitchens. They were thermally treated at high temperatures, and for comparison, unmodified PW and MDF were used.
# Characterization and Reactivity of Natural Pozzolans from Guatema
## 2 Materials, Equipment, and Methods  
### Materials  
The studied pozzolans came from volcanic ash deposits in two regions of Guatem and were named according to the initial letter of the sampling place: Sample T (from Tecpan town) and sample R (from El Rancho town). These deposits are near eruptive areas in the country. Tecpan is a town of the department of Chimaltenango. The samples were collected at 2347 m above sea level (W 90\({}^{\circ}\) 58\({}^{\prime}\)32.8\({}^{\prime\prime}\), N 14\({}^{\circ}\) 45\({}^{\prime}\)23.7\({}^{\prime\prime}\)). El Rancho is a town of the department of El Progreso. In this case, the samples were collected at 350 m above sea level (W 90\({}^{\circ}\) 01\({}^{\prime}\)12.6\({}^{\prime}\), N 14\({}^{\circ}\) 54\({}^{\prime}\)46.4\({}^{\prime\prime}\))  
These sites were chosen, among other reasons, for their easy access, uniformity of material, volume, and location close to rural and urban areas. Natural pozzolans were collected, dried, and ground by a mill ball to obtain a representative sample of adequate fineness.  
To manufacture the lime-pozzolan pastes and to assay the pH and conductivity, calcium hydroxide (or lime) with 95% purity was used and supplied by Panreac Quimica SLU. The mortars and pastes of cement were manufactured with CEM II/A-L 32.5 N, supplied by Cemex (Bunol, Spain). The proportion of limestone in cement was 13%, and the cement had a mean particle diameter of 20.58 um. Siliceous sand, supplied by Silices Carrion S.L. (Luria, Valencia, Spain), was used to prepare the mortars. The sand had a fineness modulus of 4.1 and its humidity percentage was below 0.1%.
# Production and Maintenance Planning for a Deteriorating System with Operation-Dependent Defectives
## 4 Optimization Method Description
### Repair/Major Maintenance Switching Policy  
The repair/major maintenance switching policy is presented in Figure 6a. Once the machine is at failure we use the decision variable \(w(t)\) to properly synchronize the two available maintenance options. The logic behind the maintenance strategy is as follows:  
* When \(w(t)=1\), the variable is set to its maximum value, and it denotes the conduction of a major maintenance that completely mitigates the effects of the deterioration process, in this case reducing the failure intensity and the generation of defective units to initial conditions.  
Figure 5: Production policy. (**a**) Production rate; (**b**) Production trace.  
* When \(w(t)=0\), the variable is set to its minimum value, indicating that a minimal repair must be performed. This type of maintenance does not restore the machine, since it leaves it the level of deterioration in the same level before the conduction of the repair.  
At observing the results of Figure 6a, we note that the computational domain is clearly divided into two zones indicating the type of maintenance recommended. With the aim to facilitate the characterization of the maintenance-switching policy, we present its trace \(T_{m}(\cdot)\) in Figure 6b. This trace serves us to define the following zones:  
* Zone \(M_{0}\): this zone suggests the conduction of a minimal repair, hence the decision variable \(w(t)\), is set to its minimum value, \(w(t)=0\).
* Zone \(M_{1}\) : in this zone the recommendation is to conduct a major maintenance, since given the level of deterioration of the machine is not profitable its operation. Thus \(w(t)\) is set to its maximum value, \(w(t)=1\).  
Fundamentally, at analyzing the results of Figure 6b, it is evident that the level of deterioration of the machine is the key parameter to determine the maintenance strategy. In particular, we note that when the level of deterioration of the production unit is moderate, (i.e., between age \(a\ =0\) and \(a\ =35\) in Figure 6b) then the system opts to recommend a minimal repair and avoid further costs of the expensive major maintenance. However, when the machine reaches higher levels of deterioration (i.e., after age \(a\ =35\) in Figure 6b) it is preferable to conduct a major maintenance than to experience the effects of more frequent failures and the increase of defective units. Thus, in our results, the repair/major maintenance activities are triggered according to a machine-deterioration-depended policy with a bang-bang structure, and it is given by the following expression:  
\[w(2,x,a)^{*}=\left\{\begin{array}{rl}1&if\ (a,x)\in Zone\ M_{1}\\ 0&if\ (a,x)\in Zone\ M_{0}\end{array}\right.. \tag{26}\]  
In view of Equation (26), once the machine is at failure, major maintenance must be conducted only when the machine has reached a certain level of deterioration that justifies its expensive cost.
# Measuring the Centrality of DNS Infrastructure in the Wild
## 4 Methodology
### _CNAME Chain-Based Method_
#### 4.1.2 Results  
For each public DNS, we group IPs interacting with our ADNS among multiple _CNAME_ redirections. The _CNAME_ chain resolution process of public DNS can be categorized into three patterns: multi-RDNSIP, single-RDNSIP, and multi-Query, as shown in Figure 3. Only Google and Level3 followed the multi-RDNSIP pattern, which uses different RDNSes to resolve _CNAME_ records recursively. The single-RDNSIP pattern, which includes OpenDNS, Cloudflare, Dyn, Verisign, Norton, 114 DNS, DNS Watch, SafeDNS, Comodo, Clearbrowsing, Freenom World, and DNS Advantage, does not change the RDNS IP when recursively resolving _CNAME_ redirections. The multi-Query pattern, mainly adopted by Quad9, Tencent, AliDNS, Yandex, AdGuard DNS, and DNSDB, re-initiates the lookup with a different IP (e.g., IP1) when the current one (e.g., IP0) fails to walk through the _CNAME_ chain. Note that the length of _CNAME_ chain tracked by different public DNS varies, regardless of their resolution pattern. Our observation reveals that Google Public DNS typically terminates the resolution process after tracking ten _CNAME_ redirections. In comparison, Level3 stops after nine, OpenDNS stops after seven, and DNSDB stops after five.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Pattern** & **Public DNS** \\ \hline Multi-RDNSIP & Google, Level3 \\  & OpenDNS, Cloudflare, Dyn, Verisign, Norton, \\ Single-RDNS & 114 DNS, DNS Watch, SafeDNS, Comodo, \\  & Clearbrowsing, Freenom World, DNS Advantage \\ Multi-Query & Quad9, Tencent, AliDNS, Yandex, AdGuard DNS, DNSDB \\ \hline \hline \end{tabular}
\end{table}
Table 1: _CNAME_ chain resolution patterns of public DNSes.  
Figure 2: Excerption of _CNAME_ configuration. The direction of the arrow indicates the chain of different _CNAME_s. The red arrows represent _CNAME_ chains from a.com to b.com, while the blue arrows represent _CNAME_ chains from b.com to a.com.  
Apparently, The effectiveness of the _CNAME_ chain-based resolver pool discovery approach is limited to the multi-RDNSIP and multi-Query pattern, which will uncover multiple resolvers in the same pool. In order to evaluate the efficiency of the _CNAME_ chain-based method in the wild, we examined the public DNS list provided in [31]. Out of the 2441 responsive public DNS, only 270 were found to use multiple resolvers to track _CNAME_ redirections, in either multi-RDNSIP pattern or multi-Query pattern, yielding an unsatisfying coverage of only 11.06%. The experimental results prove that the _CNAME_ chain-based method has limited efficacy in identifying resolver pools on the modern Internet. Therefore, we propose a new approach to comprehensively measure the resolver pool.
## 12 Seismic Performance of Steel-Concrete Composite Bridges
### Seismic Performance of Steel-Concrete Composite Bridges: Key Findings  
The behavior of composite bridges under seismic loads is complex due to the interaction between steel and concrete elements. Generally, these bridges outperform traditional ones by offering increased strength, ductility, and energy dissipation capabilities. The composite act between concrete and steel enhances the overall stiffness of the structure and reduces deformations, resulting in better seismic resistance. Concrete-filled steel tube piers are known for their excellent ductility and energy absorption, which help to minimize earthquake damage. When designing composite bridges for seismic regions, the focus is on capacity design principles to ensure the formation of plastic hinges in designated areas while maintaining elastic behavior elsewhere. Proper detailing of connections between steel and concrete components is essential to prevent premature failure. Advanced analysis techniques, such as pushover analysis and time-history analysis, are necessary for accurately predicting the seismic response of composite bridges and optimizing their design for enhanced earthquake resilience.
## 3 Related Work
### Neural Network for GDOP and WDGOP Approximation  
#### 3.1.1 Neural Network for GDOP Approximation  
It is known from some related literatures that the calculation complexity of an inverse matrix is higher than that of neural networks. A back-propagation neural network (BPNN) is capable of learning and realizing for both linear and nonlinear functions [28]. [PERSON] and [PERSON] used BPNN to obtain a GDOP approximation [9, 10]. It is a function approximation technique that uses a training process to find a relationship between inputs and outputs. The BPNN learning process can be considered as a gradient descent method that minimizes some measures. BPNN here is applied to learn the relationship between the entries of a measurement matrix and the eigenvalues of its inverse. Another solution using BPNN for GDOP approximation depending on three other input-output relationships was proposed in [11]. Considering both effectiveness and efficiency, we present two novel architectures based on an alternative artificial neural networks method, namely, the Rprop method, to calculate GDOP [12]. Compared to the traditional BPNN, the Rprop algorithm offers faster convergence and is effective for escaping local minima [29]. The proposed Rprop-based architectures for GDOP always have a high degree of accuracy compared with other architectures. Simulation results show the proposed architectures using Rprop and the matrix inversion method provide nearly identical GDOP value. This can reduce the computational complexity required to compute the matrix inversion for calculating GDOP. The main disadvantage of neural network-based methods is that they can incur approximation errors and need input-output patterns for training.
## 2 Mathematical Model for a Small Wind Turbine System
### Gear Train Mechanism  
The dual input transmission mechanism is shown in Figure 5.  
In the figure, the planetary gear sets are marked by two circles of dashed lines. The proposed mechanism integrates the two planetary gear trains as inputs for a single output and introduces a variable inertial flywheel. The governing equations for the system can be derived as below.  
\[\text{Gear 1}:\ (J_{1}+J_{W1})\ddot{\theta}_{1}+F_{1,3}\times(-R_{1})+F_{1,2} \times R_{1}=T_{1} \tag{5}\]  
\[\text{Gears 2},\ 4:\ (J_{2}+J_{4})\ddot{\theta}_{2}-F_{1,2}\times(-R_{2})+F_{4, 6}\times(-R_{4})=0 \tag{6}\]  
\[\text{Gears 3},\ 5:\ (J_{3}+J_{5})\ddot{\theta}_{3}-F_{1,3}\times R_{3}+F_{5,7} \times(-R_{5})=0 \tag{7}\]  
Figure 4: The pre-phase equivalent circuit for synchronous generator.  
Figure 5: Dual input transmission mechanism.  
\[\text{Gear 12}:\Big{[}J_{12}+(m_{6}+m_{8})(R_{8}+R_{10})^{2}\Big{]}\bar{\theta}_{12}-F _{6\_8,12}\times[-(R_{8}+R_{10})]+F_{12,14}\times(-R_{12})=0 \tag{8}\]  
\[\text{Gear 13}:\Big{[}J_{13}+(m_{7}+m_{9})(R_{9}+R_{11})^{2}\Big{]}\bar{\theta}_{13 }-F_{7\_9,13}\times[-(R_{9}+R_{11})]+F_{13,14}\times R_{13}=0 \tag{9}\]  
\[\text{Gear 14}:\ J_{14}\bar{\theta}_{14}-F_{12,14}\times R_{14}-F_{13,14} \times(-R_{14})=0 \tag{10}\]  
where the subscripts are used for the gear numbers; \(J_{i}\), \(m_{i}\), \(\bar{\theta}_{i}\) and \(R_{i}\) are the polar mass, mass, angular acceleration, and pitch diameter for gear \(i\), respectively; \(J_{W1}\) is the polar mass of the turbine at the input \(1\); \(T_{1}\) is the input torque produced by the wind; \(F_{i,j}\) is the tangential force between gear \(i\) and gear \(j\); \(F_{6\_8,12}\) and \(F_{7\_9,13}\) are the tangential forces from the arms of the planetary gear trains. For the planetary gear trains, the equations of motion can be derived as  
\[\text{Gear 7},\ 9:\ (J_{7}+J_{9})\bar{\theta}_{7}-F_{5,7}\times R_{7}+F_{9,11 }\times R_{9}=0 \tag{11}\]  
\[\text{Gear 6},\ 8:\ (J_{6}+J_{8})\bar{\theta}_{6}-F_{4,6}\times R_{6}+F_{8,10 }\times R_{8}=0 \tag{12}\]  
\[\text{Gear 10}:\ (J_{10}+J_{W10})\bar{\theta}_{10}-F_{8,10}\times(-R_{10})=T_{10} \tag{13}\]  
\[\text{Gear 11}:\ (J_{11}+J_{G}+J_{F})\bar{\theta}_{11}-F_{9,11}\times(-R_{11})=T_{11} \tag{14}\]  
where \(J_{W10}\) is the polar mass of the turbine at the input \(2\) and \(J_{G}\) is the polar mass of the synchronous generator. Generally, \(J_{G}\) is negligible. The variable inertia of the flywheel is represented by \(J_{F}\). \(T_{10}\) is the second input of the dual input transmission mechanism. According to the angular speed \(\dot{\theta}_{11}\), \(T_{11}\) is the output torque induced by the synchronous generator, as shown in Equation (4).
## 3 WPT System: Experimental and Simulated Data
### WPT System Phase  
A second WPT system analyzed in this study, as described in [42], utilizes inductive coupling for receiver coil charging. This system operates with a variable frequency, necessitating a control mechanism to adapt to fluctuating load conditions and inter-coil distances. To evaluate this, the phase difference between the voltage and the current of the transmitter coil was measured. A proportional-integral (PI) controller, implemented on a microcontroller, was employed to adjust the WPT system's frequency and thus improve the power factor, \(\cos\phi\). The feedback control objective was to maintain a phase angle \(\phi\) below 5\({}^{\circ}\). The operating frequency range spanned 50 kHz to 90 kHz. The PI controller utilized fixed proportional and integral constants of 1 and 10, respectively. The phase difference served directly as the error signal since the target phase was 0\({}^{\circ}\). the control loop is interrupted when the phase goes below 5\({}^{\circ}\). The frequency of the WPT system was the variable that the control circuit controlled. As suggested in [42], the PI control scheme can be enhanced through the integration of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the dynamic adaptation of the PI constants, enabling a faster control response. In the experiments, a huge amount of data was acquired, but in this study, only four parameters are of interest: frequency, phase, load condition, and the distance between the two coils. Experiments were conducted 18 times, varying the load resistance, R, and the distances between the transmitter and the receiver, \(z_{0}\). Specifically, tests were performed with six load values, R = 100, 200, 500, 1000, 1500, and 2000 \(\Omega\), and three distance values, \(z_{0}\) = 0, 4, and 8 mm. This dataset is publicly available in [43]. Analysis of this dataset reveals phase trends as a function of frequency and R, as shown in Figure 9, and \(z_{0}\), as depicted in Figure 10.  
Figure 8: Imaginary part of impedance parameter with distance between the two coils, measured in case 1, for no misalignment between the two coils: \((\Delta x)\), \((\Delta y)\), and \((\theta)\) are zero. Measurements shown are taken from the transmitter side of the WPT–NFC system after the intervention of the AIMN for the impedance adaptation, which tried to minimize the imaginary part of the impedance.  
The measured data, presented in Figures 9 and 10, exhibit significant noise and some data anomalies. Rather than employing regularization techniques, this dataset was intentionally used as a validation benchmark to assess the robustness of the proposed method. This approach acknowledges the inherent challenges of real-world measurements and tests the NN's ability to perform effectively under less-than-ideal conditions. Furthermore, it is anticipated that the NN will be deployed in real-world scenarios where perfectly clean measurements are unlikely. Therefore, training and validation with this noisy dataset aim to ensure the NN's resilience to such imperfections.
## 2 Materials and Methods
### Weight Distribution  
The study's accuracy and dependability are enhanced by the inclusion of weight in the simulations through the addition of passengers and their luggage, which more precisely mimics real-world circumstances. The simulations can capture the changing weight distribution as the vehicle travels along its route by precisely simulating passenger boarding at bus stops. Researchers can examine how the influx of passengers affects the performance and energy efficiency of the vehicle using this dynamic representation. Additionally, it enables the assessment of methods to enhance operational efficiency in terms of passenger flow, seating configurations, and boarding procedures.  
Adding load to the vehicle is essential for examining how weight distribution affects the performance and efficiency of the battery electric bus in numerous ways. Using factors such as the location of heavy components, passenger distribution, and cargo placement, IPG CarMaker 12.0.1 software enables precise weight distribution across the vehicle. This level of specificity guarantees an accurate representation of actual situations and offers information on the precise regions where weight distribution can be optimized to increase energy efficiency.  
A mean value of 84 kg per person, including luggage, has been considered in the weight distribution process, as shown in Figure 7. This number represents an estimate of the weight that each passenger typically carries, taking into account both their body weight and any accompanying luggage [31].  
To estimate how many people will board the vehicles during the simulated scenarios, the study uses data on passenger demand and ridership patterns that are currently available, based on the crowd at the bus station. Capacity-wise, each autonomous module is still considered to be able to accommodate no more than twelve passengers, while the electric bus can take up to 60 people on board.  
Passenger loads for the electric bus are defined in TruckMaker ([[https://www.ipg-automotive.com/en/products-solutions/software/truckmaker/](https://www.ipg-automotive.com/en/products-solutions/software/truckmaker/)]([https://www.ipg-automotive.com/en/products-solutions/software/truckmaker/](https://www.ipg-automotive.com/en/products-solutions/software/truckmaker/)), accessed on 5 April 2024), the software solution from IPG Automotive GmbH that allows the modeling and simulation of trucks, buses, and other heavy-duty vehicles. The definition of the weight distribution is performed in an equivalent way to that in CarMaker. Up to four generalized loads are  
Figure 7: Load distribution example for an autonomous module.  
placed on a three-dimensional axis, simulating passengers and baggage inside the bus. In the Figure 8, load definition on an electric bus model is illustrated.  
The simulations accurately reflect the inherent variability and unpredictability of passenger positioning by incorporating randomization into the weight distribution process. This randomness accounts for elements such as passenger preferences, boarding order, and the distribution of available standing and sitting areas.