esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Analysis
### Slip-Litoff Configuration
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Normal Modes** & **Static Modal Derivatives** & **Error \% F\({}_{\mathrm{e}}\) = 111.29 N** & **Error \% F\({}_{\mathrm{e}}\) = 333.87 N** \\ \hline Rubin with SMD & 1V & 1V1V & 4.03 & 42 \\ Rubin with SMD & 1V & 1V1V1S, 1V2S & 0.41 & 4.2 \\ CB with SMD & 1V & 1V1V & 7.18 & 49 \\ CB with SMD & 1V & 1V1V1S, 1V2S & 0.57 & 26.3 \\  & & 1S2S, 2S2S & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selection modes’ effects on the response accuracy in slip–liftoff configuration. Figure 9: Accuracy of reduced models in response prediction to the size of reduced space in slip–liftoff configuration. Figure 10 shows that the first SMD, 1V1V, is an axial mode (\(u_{y}\) is zero in left plot). This SMD will be consistently selected via all criteria because 1V (the first transverse mode) is linearly excited by the applied harmonic force and plays a significant role in the response obtained by a linear run. Meanwhile, 2S is also a transverse mode and 1V2S is an axial mode that could be selected by the MMI criterion considering a very small threshold. On the contrary, 1S is an axial mode, which is geometrically coupled to the bending modes; 1V1S is a transverse mode (\(u_{\mathrm{x}}\) is zero in Figure 10 (left)) that will never be selected by the MMI approach, since the contribution factor of 1S in a linear run is zero, leading to a zero weighting factor \(W_{1V1S}=0\). This modal derivative, which is orthogonal to the first bending mode, plays a critical role in providing the best accuracy. The deformation of modal derivatives of the fixed interface vibration modes and constraint mode is shown in Figure 11, when the first fixed interface mode (1V) is the first bending mode. The first and second constrain modes are, respectively, axial and transverse modes. They are the same as free interface modes, the static derivative of axial modes which participate due to their coupling to transverse modes; these will not be selected by MMI criterion, resulting in higher numbers of errors.
###### Abstract  
This work investigates the inverse problem of reconstructing the continuous displacement field of a structure using a spatially distributed set of discrete uniaxial strain data. The proposed technique is based on the inverse Finite Element Method (iFEM), which has been demonstrated to be suitable for full-field displacement, and subsequently strain, reconstruction in beam and plate structures using discrete or continuous surface strain measurements. The iFEM uses a variationally based approach to displacement reconstruction, where an error functional is discretized using a set of finite elements. The effects of position and orientation of uniaxial strain measurements on the iFEM results are investigated, and the use of certain strain smoothing strategies for improving reconstruction accuracy is discussed. Reconstruction performance using uniaxial strain data is examined numerically using the problem of a thin plate with an internal crack. The results obtained highlight that strain field reconstruction using the proposed strategy can provide useful information regarding the presence, position, and orientation of damage on the plate.  
Keywords:shape sensing: inverse Finite Element Method; structural health monitoring; inverse problem; fiber optics; full-field reconstruction +
Footnote †: journal: Applied
## 2 Materials and Methods
### Soil Characterization  
The soil samples were analyzed for pH, organic matter, N and P contents, particle size distribution, effective cation exchange capacity (ECEC), oxide contents, and pseudototal metal content according to the following methodologies. The pH was measured in two suspensions: one prepared with deionized water and another with 0.1 M KCl, both in a 1:2.5 (_w/v_) ratio [29]. Organic matter content was assessed by loss on ignition, heating 2 g of soil at 550 \({}^{\circ}\)C for 5 h in a muffle furnace according to ISO 10694:1995 [30]. Nitrogen contents were determined in an elemental analyzer (Thermo Flash EA 1112, Waltham, MA, USA), by complete combustion of the sample inside a reactor with a high-temperature (900 \({}^{\circ}\)C) oxidizing catalyst. The phosphorus content was determined using the Olsen method, which estimates the available phosphorus in soils through extraction with sodium bicarbonate (0.5 M, pH 8.5) [31]. Particle size distribution was analyzed by wet sieving for fractions >50 \(\upmu\)m (sand) and using the Robinson pipette method for silt and clay fractions, based on Stokes' law. The soil was pretreated with hydrogen peroxide to remove organic matter and dispersed with sodium hexametaphosphate [29]. The ECEC was determined by the sequential extraction of exchangeable cations (Na\({}^{+}\), K\({}^{+}\), Mg\({}^{2+}\), and Ca\({}^{2+}\)) with 1 M NH\({}_{4}\)Cl, quantified by ICP-OES (Perkin Elmer Optima 4300 DV, Perkin Elmer, Waltham, MA, USA), and exchangeable Al\({}^{3+}\) was extracted with 1 M KCl and titrated with 0.01 M NaOH [29]. Oxides of Fe, Al, and Mn were extracted using the dithionite-citrate method [29] and quantified by ICP-OES (Perkin Elmer Optima 4300 DV). The pseudototal metal content was determined by the acid digestion of 0.2 g of soil using concentrated HNO\({}_{3}\) and HCl in a microwave oven according to EPA Method 3051A [32]. After filtration, metal concentrationswere measured by ICP-OES (Perkin Elmer Optima 4300 DV). All results were standardized to the dry soil weight.
## 2 Related Works
### Deep Reinforcement Learning-Based Fault Detection  
The Markov decision process (MDP) is a general framework of reinforcement learning (RL), which comprises an agent and environment where the agent receives reward signals, such as positive or negative feedback, caused by its action at each state [50]. The agent continuously learns what an optimal action is for each state through interactions with the environment. As decision makers, the advantage of RL agents is consideration of long-term planning while deciding on an optimal action. Therefore, they choose the actions that can maximize cumulative rewards.  
[PERSON] et al. first introduced a value-based DRL anomaly detector, in which a Deep Q-Function Network (DQN) algorithm was utilized, and two actions for classifying abnormal and normal states were given [51]. [PERSON] and [PERSON] proposed a policy-based anomaly detector, in which Asynchronous Advantage Actor-Critic (A3C) algorithm was used, and the action space had two actions [52]. The policy-based anomaly detector outperformed the value-based approach. [PERSON] applied a valued-based anomaly detector with state-space models to structural health monitoring, where Q-learning was used, and artificial anomalies were generated for test purposes [53].  
Although the existing RL-based approaches had decent performances, they only used discrete action spaces with fault labels, which was the same problem as classification problems. Thus, it is difficult to apply these approaches under the scarcity of fault-labeled data. Moreover, these approaches do not utilize the continuous action space of DRL agents. In this study, we aimed to mitigate the difficulty of assigning threshold confidence by using a DRL-based dynamic threshold scheme. This can be applied under the scarcity of fault-labeled data because this scheme operates in an unsupervised manner (without fault labels). In the proposed scheme, a setting of continuous state and action spaces is applied, and the threshold values of each state are outputs of the DRL agent.
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 3 System Model  
### LEO Satellite Network  
The topology of the LSN can be demonstrated by \(G=(V,E)\), where \(V\) denotes satellite nodes and \(E\) denotes inter-satellite links (ISLs) between satellites. Inter-satellite links include intra-plane ISLs and inter-plane ISLs. The former are used to connect front and rear satellites in the same orbit, while the latter are adopted to connect adjacent satellites in adjacent orbits. Assume that the number of satellites in the LSN is \(N\), and each satellite contains four ISLs including two intra-plane ISLs and two inter-plane ISLs, as shown in Figure 2.  
Due to the mesh topology of the LSN, it can be regarded as a Manhattan network [32]. Figure 3 demonstrates the 2D topology of an LSN with \(n\) orbits, where \(m\) satellites are distributed evenly. Each node contains two intra-plane ISLs (blue lines) and two inter-plane ISLs (black lines). The motion of satellites in orbit is periodic and predictable [33]. It is assumed that, over a small period of time, the LSN topology remains constant [34]. Obviously, the length of intra-plane ISLs is relatively stable. Inter-plane ISLs in low latitudes have a longer length while inter-node distances in high latitudes are relatively shorter.
## 1 Introduction  
The field of indoor positioning seeks the location of an individual, as we can have with GPS, but in a building [1]. As signals from satellites can't go through walls in those places, a typical indoor positioning system is composed of a set of emitter sending a periodic signal (many types of signals are used such as radio frequency [2, 3], ultrasound and infrared) [4, 5, 6] and a set of receivers. Based on the strength of the signal measured between an emitter and a receiver (called RSSI for Received Signal Strength Indication), the goal is to determine the position of the user within a room. Among all the signal used in such systems, the BLE (For Bluetooth Low Energy) became more and more popular since 2013, with the iBeacon technology designed specifically for indoor localization, with many applications such as [7].  
In recent years, a certain number of indoor positioning studies are aiming to bring tracking technologies to museums [8, 9, 10]. Besides allowing museum managers to have a better overview of their places and thus having a better feedback on visits (which pieces of art are popular, and which ones are left), this also makes it possible to study behavior of visitors in such environments [11, 12]. Moreover, museums are perfect experimentation grounds for indoor positioning technologies. Indeed, museum managers are always pleased to hold these kinds of events where it offers the opportunity to the public to be a part of such experimentation and allowing us at the same time to popularize our research with them. On top of that, visitors stay a long time within the same room and walk slowly, which allow us to better capture their path.  
But museums also bring with them constrains that indoor localization researchers have to deal with. First, some studies apply machine learning algorithm for indoor localization systems as explain in [13], offering incredible accuracy values and great results overall. It's very difficult to use such methods in museums, as it will take a lot of resources and time to collect data in order to train those models that will only work on one museum (As the architecture and room layout is not the same between museums). Second, we are limited in the number and placements of nodes for our indoor positioning systems. Indeed, our nodes should not disturb visitors during their tours (so we can't place them in the middle of the way) and we have to work around artwork and room layout that were not always designed to accommodate an indoor positioning system.  
In this paper, we will describe our indoor positioning experimentation and the system we used to get the position of a visitor, that can be used in real-time. Beside the data process architecture, we will propose a new algorithm which at our knowledge has not had an equivalent in the state of the art and that we named the minimal zone searching algorithm (MSZ). We will then show that this algorithm allows use to get the position of a visitor as a zone with an average surface of 3 m\({}^{2}\) even when the placement of the nodes is not optimal.
## 2 Adaptive Sensor Arrays  
Figure 1 shows a functional diagram of an adaptive sensor linear array with \(K\) elements. Each \(k\)-th element of the antenna array is spacing \(d\) should generally be equal to \(\lambda/2\) (\(\lambda\) is the wavelength). The signal \(x_{i,k}(n)\) received by the \(k\)-th antenna element is given by:  
\[x_{i,k}(n)=\sum_{i=1}^{M}\rho_{i}v_{i}(n)e^{-j(k-1)\left(\frac{2\pi}{\lambda} \right)d\,cos(\theta_{i})}+r_{k}(n) \tag{1}\]  
where \(v_{i}(n)\), \(\rho_{i}\), and \(\theta_{i}\) are the signal, the angle of arrival (AOA) and the attenuation of the \(i\)-th source, respectively. All sources have the same wavelength and \(r_{k}(n)\) is the noise associated with each antenna element. The signal, \(v_{i}(n)\), is modeled as a narrowband signal.  
In many cases, overall reception performance is measured effectively using the SINR in the output of the array. The SINR estimates the ratio between the signal of interest with a view to noise plus interference, providing a measure of the quality of communication.  
In an array of sensors, the adaptive process is conducted by means of adjusting the coefficients (weights) associated with each of the elements of the array. This adjustment is performed using a signal processor and considers a performance criterion established for the system, which could be the SINR, the mean square error, the bit error rate (BER), or any other parameter [28, 29, 30].  
The desired characteristics of irradiation/reception, as well as the spatial filtering in an sensor array, are configured by convenient manipulation of certain array parameters, such as the number of elements (sensors), the geometry (spatial arrangement and spacing between the elements), types of antennas, and the coefficients (weights) used to adjust the signal amplitude and phase in each element.  
In the system shown in Figure 1, the array output signal for the \(i\)-th user is given by:  
\[y_{i}(n)=\mathbf{w}_{i,K}^{H}(n)\mathbf{x}(n) \tag{2}\]  
where \(\mathbf{w}_{i,K}^{H}(n)\) is the vector of weights for the i-th user of the system, and the index H represents the Hermitian conjugate transpose. The array output signal \(y_{i}(n)\) is compared to the desired response \(d_{i}(n)\), the difference between them is called the estimation error, as illustrated in Figure 1. As presented in [28], the reference signal (or desired signal) is a training sequence understood by the sensor array, sent periodically by the sources.
## 3 Results and Discussion
### Bioactive Compound  
#### 3.2.1 Total Phenolics Content  
Storage duration and pretreatment had a significant impact on TPC for treated and non-treated fruit samples (\(p\leq 0.05\)). The TPC of fruit samples treated with AEW increased significantly from 67.94 mg GAE L\({}^{-1}\) (day 0) to 99. 39 mg GAE L\({}^{-1}\) (day 9), while the untreated samples (control) declined continuously and significantly from 67.94 mg GAE L\({}^{-1}\) to 42.99 mg GAE L\({}^{-1}\) (Figure 2). Samples treated with AEW had the highest total phenolic concentration at 9 days of storage (Figure 2). This result agrees with other studies [14; 21]. For instance, it was demonstrated that minimally processed apples treated with AEW treatments maintained a substantially higher TPC than both the control and organic acid-treated samples [21]. Moreover, [PERSON] et al. [14] demonstrated that NEW treatments improved the quality of minimally processed mangoes for up to 12 days stored at 3 \({}^{\circ}\)C, preserving nutritional components. Minimally processed kiwi fruit kept at 4 \({}^{\circ}\)C for 8 days showed a delay in the decline in total phenolic content when treated with SAEW [22].  
Figure 2: Total phenolics concentration for treated (AEW and AIEW) and non-treated minimally processed ‘Granny Smith’ apples during storage at 2 \({}^{\circ}\)C and 95% RH for 9 days. Error bars represent standard deviation (SD) of mean values of treatments (\(n=3\)) tested using Duncan multi-range test at 95% confident interval (\(p\leq 0.05\)) and different lower-case letter indicate significant difference in means (\(p<0.05\)). Similar lower-case letters are not significantly different. Continuous dashed line indicates baseline measurement. Descriptions of treatments: AEW = acidic electrolyzed water (200 mg L\({}^{-1}\)); AIEW = alkaline electrolyzed water (200 mg L\({}^{-1}\)); and Control = non-treated.  
In another study by [PERSON] et al. [23], SAEW-treated minimally processed eggplants stored for 8 days at 4 \({}^{\circ}\)C maintained a greater phenolic content with AEW-treated samples when compared to distilled water. The greater concentration of TPC observed during the storage of fresh-cut 'Granny Smith' apples treated with AEW in comparison with AIEW-treated could be ascribed to higher ACC and its lower pH. Thus, it can be explained that the variation in total phenolic content seen in the fresh-cut apple samples could have been caused by the ACC of AEW and AIEW influencing the activity of specific enzymes.  
In contrast, for control samples, there was a degradation of TPC from 82.94 mg GAE L\({}^{-1}\) on day 3 to 42.99 mg GAE L\({}^{-1}\) at the end of storage on day 9. [PERSON] et al. [14] showed that TPC for non-treated fresh-cut mango (control) stored for 12 days at 3 \({}^{\circ}\)C declined during storage compared to samples treated with neutral electrolyzed water treatments (NEW) that maintained the TPC of fresh-cut mango samples. The comparative influence of the treatments on TPC was AEW > AIEW > control in this study.
## 3 Results and Discussion
### Thermodynamic Parameters
#### 3.4.2 Energy Efficiency and Drying Efficiency  
Figures 4 and 5 show the effect of different treatments (temperature, thickness and ultrasonic radiation time) on energy efficiency and drying efficiency, respectively. The highest energy efficiency and drying efficiency values (7.69% and 11.09%) were obtained at 70 \({}^{\circ}\)C, 4 mm thickness, and 30 min ultrasonic radiation time, respectively. Increasing the temperature from 50 to 70 \({}^{\circ}\)C increases the energy efficiency and the drying efficiency. Drying temperature increases the rate of moisture removal from the product and reduces the drying time, and consequently, both efficiency values will increase with increasing temperatures. However, comparing the application times of ultrasonic pretreatment shows that the highest values of both efficiencies occur when the 30 min ultrasonic pretreatment  
Figure 3: Amounts of \(SEC\) under different conditions for kiwifruit slices.  
is used. According to Figures 4 and 5, it can be seen that reducing the thickness of kiwifruit samples will significantly (\(p<0.05\)) increase energy efficiency and drying efficiency, because less thickness will increase the gradient of the drying product and more moisture is removed from the product, and as a result, the drying time of kiwifruit at low thicknesses is reduced. The lowest energy efficiency and drying efficiency values can be seen in the control treatment with a thickness of 8 mm (1.54% and 1.88%), respectively. The results show that increasing the ultrasonic pretreatment time improves both energy efficiency and drying efficiency. This is because, by increasing the ultrasonic pretreatment time, the changes in the product texture increases and the hard layer does not form in the process of drying this pretreated product and the product dries faster.  
The results of this study are consistent with the results published by other researchers. The energy efficiency values for drying chamomile in hot air dryers ranged between 1.91% and 6.76% [44], for apple slices between 2.87% and 9.11% [46], for _Pistacia atlantica_ in a hot air dryer equal to 5.65% [47], and for peppermint leaves in a hot air dryer with ultrasonic pretreatment varying between 1.41% and 3.69% [12]. [PERSON] et al. obtained the drying efficiency and energy efficiency for barley drying in a hot air dryer ranging 8%-17.4% and 24%-34.2%, respectively [48]. In another study, the drying efficiency was calculated between 3.42% and 12.29% for apple drying and showed that the drying efficiency increases by increasing the temperature [46].
## 2 Dielectric Properties and Channel Modeling
### Channel Models
Two important underlying assumptions are usually considered: firstly, each scatter is independent of the other, and secondly, multiple scattering can be neglected. Under those assumptions, the attenuation suffered in rain [42] can be determined using the following equation  
\[\kappa_{rain} =4.343\times 10^{3}\int_{0}^{\infty}\sigma_{ext}(n_{r},r,\lambda)N(r )dr\ (\mathrm{dB/km}) \tag{5}\]  
where \(n_{r}\) is the refractive index of the rain particles, \(r\) is the particle radius, \(\lambda\) is the carrier wavelength, and \(N(r)\) is the residual size distribution of raindrops, which is one of the main roles influencing the accuracy of theoretical predictions [8]. The parameter \(\sigma_{ext}\) refers to the total extinction cross-section in the expression \(\sigma_{ext}\)= \(\sigma_{abs}\)+\(\sigma_{sca}\), with \(\sigma_{abs}\) and \(\sigma_{sca}\) as cross-sections of absorption and scattering, respectively. All three terms depend on the incident wavelength (\(\lambda\)), refractive index (\(n_{r}\)), and radius (\(r\)) of water droplets. The power attenuation due to falling rain is calculated and shown in Figure 2. Here, we use the Marshall-Palmer [M-P] raindrop size distribution, which was also found to be reasonable for snow [43]. The Joss distribution was first obtained by measuring the size distribution of raindrops in Switzerland using a dishorometer [44]. The specific model proposed by ITU-R is based on measured data and empirical conduction [45]. A moderate falling rain (rain rate ranging from 2.6 to 7.5 mm/h) exerts a higher attenuation than the atmospheric gases when channels operate below 200 GHz. This indicates that the influence of falling rain on the biothermal heating effect cannot be neglected from the viewpoint of channel propagation. The Third Generation Partnership Project (3 GPP) TS 38.104 has defined 24.25-52.6 GHz as frequency range 2 (FR2) for 5G NR [46]. In consideration of the significant potential demonstrated by certain terahertz frequency band windows, such as 140 GHz, 220 GHz, and 340 GHz, in various short-range wireless communication applica  
Figure 2: Atmospheric gaseous attenuation under different values of relative humidity (RH) (left side) and attenuation due to rain under a rainfall rate of 5 mm/h when M-P, Joss, and ITU-R models are employed (right side). tions (e.g., V2X, high-speed transportation communication, and backhaul/fronthaul), we have included calculations for the 140 GHz frequency in addition to the 33.5 GHz frequency used in [47]. This selection is based on several factors, including lower transmission losses and higher penetration depth in water and skin layers, as illustrated in Figures 1 and 2.