esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Formulation and Control Approaches
### Control Approach Based on the Bounded-Disturbance: First Control Approach
Consequently, the positive-definite diagonal gain matrix \(\sigma_{1}\) guarantees \(\eta_{1}(0)>0\) based on the definition of \(\eta_{1}\) in (4); hence \(\|\eta_{1}\|>0\) according to the properties of the Euclidean norm [32]. Therefore, when the design parameter \(d\) is selected to be arbitrarily small compared to \((v_{b}+l_{b})\), the inequality in (16) will be satisfied and provides sufficient conditions for the stability. Thus, Equation (15) can be simplified to  
\[\dot{V}_{1}=\frac{1}{2}{j{{\dot{\eta_{1}}}^{T}M(q)\eta_{1}}}+\frac{1}{2}\eta_{ 1}^{T}\dot{M}(q)\eta_{1}+\frac{1}{2}\eta_{1}^{T}M(q)\dot{\eta_{1}}=\frac{1}{2} \eta_{1}^{T}\dot{M}(q)\eta_{1}+\eta_{1}^{T}M(q)\dot{\eta_{1}}. \tag{7}\]2  
The Lyapunov-like theorem of uniform and ultimate boundedness [31] can be applied to upper bound \(\|\eta_{1}(t)\|\). Using Property 1, the Lyapunov function in (6) can be upper and lower bounded by positive definite functions as follows  
\[\dot{V}_{1}=\frac{1}{2}{j{{\dot{\eta_{1}}}^{T}M(q)\eta_{1}}}+\frac{1}{2}\eta_{ 1}^{T}\dot{M}(q)\eta_{1}+\frac{1}{2}\eta_{1}^{T}M(q)\dot{\eta_{1}}=\frac{1}{2} \eta_{1}^{T}\dot{M}(q)\eta_{1}+\eta_{1}^{T}M(q)\dot{\eta_{1}}. \tag{7}\]3  
where \(\alpha_{2}(\eta_{1})=(1/2)\lambda_{m}\left\|\eta_{1}(t)\right\|^{2}\), \(\alpha_{2}(\eta_{1})=(1/2)\lambda_{M}\left\|\eta_{1}(t)\right\|^{2}\); \(\lambda_{m}\) and \(\lambda_{M}\) are defined in (2). The term \(\eta_{1}^{T}K_{1}\eta_{1}\) in (17) is a continuous positive definite function, \(\forall\ \|\eta_{1}(t)\|\geq\mu>0\), where \(\mu\) is defined in (16). Thus, according to the Lyapunov-like theorem,  
\[ \begin{split}\|\eta_{1}(t)\|&\leq\alpha_{1}^{-1}( \alpha_{2}(\mu))\\ &\leq\sqrt{\frac{\lambda_{M}}{\lambda_{m}}(\frac{d}{v_{b}+l_{b}}) ^{2}}\\ &\leq\sqrt{\frac{\lambda_{M}}{\lambda_{m}}(\frac{d}{v_{b}+l_{b}}) },\ \forall\ t\geq 0.\end{split}  \tag{19}\]  
From (19), we conclude that \(\eta_{1}\) converges to a small neighborhood of zero according to the value of \(d\). Furthermore, based on (4), \(\Delta q\) is simply a low-pass filter of \(\eta_{1}\) signal [33]. Therefore, \(\Delta q\) is UUB with the ultimate bound result shown in (19). In this way, the practical deliverable input torque in (12) yields an accurate tracking control for the robot joints.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 3 Methods
### Pre-Processing Techniques  
Pre-processing techniques attempt to extract and enhance specific features (e.g., patterns, structure, trends) within the dataset sample. In the non-sequential analysis, the model expects this stage to increase the variance between classes in the dataset. Hence, the output forms distinctive features among classes. Implicitly, it allows the ML models to distinguish the samples. The process begins with data pre-processing using windowed averaging, standardization, and Principal Component Analysis. All three techniques were applied to the input samples that were the output of the dataset preparation phase. The analysis used two telemetry data, namely, current and voltage. Figure 8 shows the initial data distribution patterns for the voltage parameter before applying the pre-processing techniques. The figure indicates the different sample distributions of the anomalous and normal data classes. Through visual observation, we can clearly distinguish the two categories. However, it is difficult to identify the anomaly class if the analysis is based on simple thresholding. It can be seen that the anomalous patterns are located in the center of the vertical axis. However, the problem with simple thresholding is that the process may incorrectly categorize a sample in the transition state as an anomaly, especially a type 2 anomaly. Thus, a data pre-processing step is crucial to extract the anomalous patterns so that the ML model has distinguishable input samples.  
Figure 7: Type 2 anomalies in (**a**) voltage and (**b**) current data.
# A Tri-Satellite Interference Source Localization Method for Eliminating Mirrored Location
## 5 Results  
In the following simulation, the number of localization moments in single-moment positioning is \(p=1\) and the number of positioning moments in multi-moment positioning satisfies \(p>1\). The proposed method is based on the multi-moment location results to remove the mirrored location. Then, the obtained location result near the correct location is used as the initial iteration for the single-moment localization method. The proposed method is the improved tri-satellite localization method based on the multi-moment TDOA localization.
## 3 Results and Discussion
### Hydrogen Production Performance of C. populeti FZ10
#### 3.2.2 Hydrogen Production Using Natural Lignocellulosic Biomass
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Microorganism** & **Substrate** & **Conditions** & **T (C\({}^{\circ}\)C)** & **H\({}_{2}\) Yield (mL/g)** & **References** \\ \hline _C. lentocellum_ Cell10 & CMC & Batch, Untreated & 37 & 121.41 & [41] \\ _C. sartaggorne_ FZ11 & MCC & Batch, Untreated & 35 & 77.28 & [26] \\ _C. acetbustylicum_ X9 & MCC & Batch, Untreated & 37 & 67.2 & [42] \\ _C. termitidis_ CT1112 & \(\alpha\)-cellulose & Batch, Untreated & 37 & 87.36 & [43] \\ _C. cellulolyticum_ & MCC & Batch, Untreated & 35 & 138.9 & [44] \\ _C. populeti_ FZ10 & MCC & Batch, Untreated & 35 & 177.5 & This study \\ \hline _C. lentocellum_ Cell10 & Cassava residues & Batch, Untreated & 37 & 91.39 & [41] \\ _C. sartaggorne_ FZ11 & Corn stalk & Batch, Untreated & 35 & 87.2 & [26] \\ _C. thermocellum_ 27405 & Dried distillers grain & Batch, Untreated & 60 & 23.97 & [25] \\ _C.butyrateum_ FS3 & Corn stalk & Batch, Untreated & 36 & 92.7 & [34] \\ _C. thermocellum_ 7072 & Corn stalk & Batch/Two stage & 55 & 74.4 & [45] \\ _C. thermocellum_ & Corn stalk & Continuous, Untreated & 55 & 61.4 & [46] \\ _C. populeti_ FZ10 & Corn stalk & Batch, Untreated & 35 & 92.5 & This study \\ \hline \hline \end{tabular} Note: MCC represents microcrystalline cellulose; CMC represents carboxymethyl cellulose.  
\end{table} 
Table 6: Comparison of biohydrogen production by different _Clostridium_ species from cellulosic substrates. Figure 7: Characteristics of H\({}_{2}\) fermentation from corn stalk by _C. populeti_ FZ10. (**a**) Chemical components of corn stalk, (**b**) enzyme activity of fermentation broth. difficult to compare the hydrogen yields between different strains. Fermentative hydrogen production from cellulosic biomasses under mesophilic conditions is a more promising technology that can reduce the production cost and improve the operating economics. Obtaining high-efficiency hydrogen-producing strains is the key to improving the efficiency of hydrogen production. In this study, the methods of microbial pretreatment and microbial acclimation were used for the screening of hydrogen-producing bacteria. These are effective strategies to obtain hydrogen-producing strains that efficiently convert lignocellulosic biomasses into hydrogen.
# Analysis of Scintillation Effects on Free Space Optical Communication Links in South Africa
###### Abstract
Computation of the scintillation profile for Gaussian beam FSOC signals in the nine cities under investigation based on the zero inner scale and infinite outer scale model and finite inner and finite outer scale model. To the best of our knowledge, the computation of the scintillation profile for Gaussian beam FSOC links transmitting at 1550 nm in the cities of interest, while considering periods not exceeded 50%, 99%, 99.9%, and 99.99% of the time have not been reported in open literature. 2. Aerosol scattering losses over various distances for FSOC links transmitting at 1550 nm with respect to events not exceeded 50%, 99%, 99.9%, and 99.99% of the time, for nine major locations in South Africa, are investigated. 3. Outage probabilities of Gaussian beam FSOC links based on the aforementioned scintillation models, while taking into account the effect of pointing errors for events not exceeding the previously mentioned time intervals, are presented for various locations of interest. 4. Analysis of the bit error rate (BER) performance for intensity modulation and direct detection (IM/DD) avalanche photodiode (APD) FSOC systems transmitting at 1550 nm and based on OOK, BPSK, square, and rectangular SIM-QAM schemes during weak, moderate, and strong atmospheric turbulence, with regards to average weather measurements and events not exceeding 99%, 99.9%, and 99.99% of the time are presented. The rest of this paper is organized as follows: Section 2 presents the ground wind speed distributions for nine cities in South Africa; Section 3 presents and analyses the modified Rytov theory based on zero inner scale and infinite outer scale model and finite inner and finite outer scale model for Gaussian beam waves. Section 4 presents aerosol scattering losses over various link distances for the nine cities under investigation. Weak, moderate, and strong atmospheric turbulence parameters during clear weather for the locations of interest based on the Lognormal and Gamma-gamma turbulence models are provided in Section 5, while outage probability analysis of FSOC links with respect to the effect of pointing errors is presented in Section 6. In Section 7, the average BER analysis, taking in account pointing error effects for various FSOC systems in weak, moderate, and strong turbulence regimes is derived and the results are analysed, while conclusions are provided in Section 8.
## 3 Results and Discussion
### Quality Control  
Figure 2 reports the bioaccessible concentration obtained adopting UBM and SBET procedures to the reference material BGS102. Furthermore, for quality control purposes, in the same figure, the maximum and minimum bioaccessible concentration of As and Pb reported in the Certificate of Analysis of this material adopting UBM are also reported. It is possible to observe that the values obtained in this work adopting the UBM on the d < 250 \(\upmu\)m fraction fall within the range reported for As and Pb in the BGS certificate of analysis. This result confirms that all the different steps of the UBM procedure were correctly carried out.  
Figure 1: Total inorganic content of fraction d < 2 mm, d < 250 \(\upmu\)m, and d < 150 \(\upmu\)m for samples **A1**, **A2**, **S1**, and **S2** (striped bars represent values below the limit of quantification).
## 1 Introduction
This is very important for coordinated LVRT control, because the LVRT operation can result in significant damages to the WT and ESS when the rotor speed and the ESS violate their operational limits. Compared to the previous methods the benefits of the proposed method can be described as follows. * The proposed method can effectively control both of a WT and an ESS without violation of operational constraints. * From the proposed method, the stability of the LVRT operation can be improved in any wind speed conditions and it can prevent failures in both a WT and an ESS. * The proposed method can be applied with previous coordinated control methods by improving its stability. * No additional device such as chopper is required for achieving stable operation by dissipating power during grid faults.
# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
## 4 Constrained Stochastic Game
### Action Space  
Let \(\mathbf{A}=\prod\limits_{i}\mathbf{A}_{i}\) denote a global action space, where \(\mathbf{A}_{i}\) denotes a local action space of player \(i\). \(\mathbf{A}_{i}\) can be represented as follows. (Note that the two types of decisions (i.e., the decision on whether to use the resource of the relay satellite and the decision on whether to use the resources of the observation satellite) can be considered by the defined one-dimensional action space.)  
\[\mathbf{A}_{i}=\{0,1,2,3,4\} \tag{4}\]  
where \(A_{i}(\in\mathbf{A}_{i})=0\) represents the situation where observation satellite \(i\) delays its transmission. In addition, \(A_{i}=1\) and \(A_{i}=2\) denote the situation where observation satellite \(i\) transmits its observation to the relay satellite and ground station without the compression, respectively. Moreover, \(A_{i}=3\) and \(A_{i}=4\) denote the situation where observation satellite \(i\) transmits its observation to the relay satellite and ground station after the compression, respectively (any kind of compression method can be exploited). Note that \(A_{i}=2\) and \(A_{i}=4\) can be conducted only when observation satellite \(i\) has a connection with the ground station (i.e., \(G_{i}=1\)).  
\begin{table}
\begin{tabular}{l l} \hline \hline \(E[Q]\) & Expected queue length when the observation arrives \\  & at the relay satellite \\ \hline \(L_{Q}\) & The unit latency to handle one observation in the relay \\  & satellite \\ \hline \(\zeta_{D}\) & Average delay to deliver the observation to the destination \\ \hline \(\xi_{C}\) & Average utilization of the computational resource \\ \hline \(\xi_{S}\) & Average utilization of the storage resource \\ \hline \(\theta_{C}\) & Target utilization of the computational resource \\ \hline \(\theta_{S}\) & Target utilization of the storage resource \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._
## 2 Materials and Methods
### Data Sources
#### 2.2.3 Radiation Data  
The downward shortwave radiation data and upward longwave radiation data were the most recent reanalysis data (ERA-Interim) offered by the European Center for Medium-Range Weather Forecasts (ECMWF); these data were subject to strict quality control [35]. The spatial resolution was 0.75\({}^{\circ}\), and the temporal resolution was monthly.
## 2 Preliminaries
### The Basic Theory of Wavelet Packet Energy Analysis  
In the process of data failure identification, the fault data are realized by calculating the energy difference between the wavelet packet component of normal data and the corresponding component containing the fault data. Some of the fault information is sometimes hidden in high frequency information and cannot be found in wavelet decomposition. The difference between wavelet packet decomposition and wavelet is that the former decomposes the wavelet decomposition into the same low-frequency part of the same layer in the high-frequency part of each layer. Therefore, in the fault diagnosis process, the wavelet packet algorithm is more suitable than the wavelet decomposition algorithm.  
In the Mallat algorithm, there are impulse response functions \(h(n)\) and \(g(n)\). Therefore, the scale function \(\varphi(t)\) and the wavelet function \(\psi(t)\) are defined as follows [23]:  
\[\left\{\begin{array}{l}\varphi(t)=\sum_{n}h(n)\varphi(2t-n),\\ \psi(t)=\sum_{n}g(n)\psi(2t-n).\end{array}\right. \tag{1}\]In Formula (1), \(g(n)=(-1)^{1-n}h(1-n)\), \(n\) is the wavelet packet decomposition channel number. Set a finite energy signal, using the Formula (1), the scale function and the wavelet function can be obtained, and the wavelet packet decomposition is obtained as follows [24]:  
\[\left\{\begin{array}{l}u_{2j}(t)=\sqrt{2}\sum_{k\in Z}h(k)u_{j}(2t-k),\\ u_{2j+1}(t)=\sqrt{2}\sum_{k\in Z}g(k)u_{j}(2t-k),\\ u_{0}(t)=x(t).\end{array}\right.\;\;1\leq j\leq J, \tag{2}\]  
In Formula (2), \(u_{j}(t)\) is a wavelet packet coefficient, and \(j\) is the decomposition scale, \(k\in Z,k\geq 2\), means the scale factor \(k\) is an integer not less than 2, \(h(k)\) is the low pass filter coefficient, \(g(k)\) is the high pass filter coefficient, \(x(t)\) is a finite energy signal.  
Wavelet packet energy formula is defined as:  
\[E=\int_{-\infty}^{+\infty}x^{2}(t)dt=\sum_{a=1}^{2^{j}}\sum_{b=1}^{2^{j-1}} \int_{-\infty}^{+\infty}x_{j}^{a}(t)x_{j}^{b}(t)dt=\sum_{n=0}^{2^{j}-1}E_{j}^{ n}. \tag{3}\]  
In the form: \(x(t)\) is a finite energy signal, scale parameter \(j\), scale factor \(a\) and displacement factor \(b\) of basis function \(\psi(t)\). \(E_{j}^{n}=\int_{-\infty}^{+\infty}x_{j}^{i}(t)^{2}dt\), and is the data energy of the \(n\)-th band in the \(j\)-th scale decomposition of the decomposed signal. Thus, the wavelet packet energy difference is as follows: \(\Delta E_{j}^{n}=E_{j}^{n}-E_{j}^{n(d)}\), \(E_{j}^{n}\) is the normal data of wavelet packet energy, and \(E_{j}^{n(d)}\) is the data to be seized wavelet packet energy.  
In any interval, when a fault occurs, the energy difference in the fault point and its vicinity will be abruptly changed, and the energy difference between the data points without fault occurring will be relatively gentle. Therefore, the wavelet packet energy difference is sensitive to the fault data, which can be used to analyze and identify the fault on multiple scales.
## 3 Results and Discussion
### SEM-EDS Analysis
#### 3.3.2 Bronze Samples
The morphology observed through the photo imaging at the SEM microscope showed a homogenous sample. This sample was mainly composed of CuO (Table 3) characterized by high variability of weight oxide (from 11.67 to 89.69). In areas in which CuO was prevalent, the presence of SnO was lower and vice versa. Si and Al oxides were observed at low-value percentages except for the area 41 (44.25 and 7.71, respectively), which was also characterized by anomalous high values of Na\({}_{2}\)O (20.23 as shown in Table 3) and CaO (5.77). CaO was also present with high values (9.49) in area 33. Area 31 was characterized by the highest value of SnO (27.59), Fe\({}_{2}\)O\({}_{3}\) (29.79) and PbO (1.65). Fe\({}_{2}\)O\({}_{3}\) was also observed in all the other areas analyzed but with lower values, instead of PbO only in two areas. Mg, P, S, Cl and K oxides were observed but with very low-value percentages. Zn was not detected. Sample 3. In sample n. 3, incorporated into the resin and sectioned, different spectra were analyzed for each area selected, but the paper showed only the representative spectra for each analyzed area. Figure 14 shows SEM images and EDS spectra of the representative areas analyzed. Figure 14a shows particles with a regular morphology characterized by high values of CuO (94.54, as shown in Table 3). The spectrum of Figure 14b was similar to the previous one. The spectrum of Figure 14c shows high contents of CuO (50.08) and SnO (33.14) and small quantities of Fe\({}_{2}\)O\({}_{3}\) (8.12). Similar but not equal was the spectrum of Figure 14d, with similar values of Fe and Pb oxides. These two last spectra shown also small values of PbO (2.97 and 1.39, respectively). Mg, Si, P, S and Cl oxides were observed but with very low values. Na, Al, K and Zn oxides were not detected. The scarce presence of Cl, S and P species as the main corrosion agents in most of the samples, both iron and copper, justifies the low degree of corrosion due to environmental agents. Although the excavation area is close to the coastline and the aquifer is affected by the ingression of saltwater, the presence of clay sediments has protected the artifacts from corrosion and the concentration of chlorine detected is very low so that the metal was protected from corrosion [45]. Figure 13: Schematic of the areas investigated by SEM-EDS for sample 25. Area 31, colored in black, presented the green patina. Detail 33 had been analyzed on the edge from which the underlying part was distinguishable. Area 35, colored in yellow, presented a white patina, of which the edge was then analyzed (detail 37). A copper-colored accumulation (area 39, colored in white) and detail 41 of green patina under the concentration mentioned above. SEM images are related to detail 33 (in black), detail 37 (in yellow) and detail 41 (in white). Figure 14: SEM images and EDS spectra of the sample 3: (**a-d**) detailed spectra for each area selected.
## 2 Related Work
### The Proposed Social-Based Routing Algorithm  
Existing social-based routing algorithms make full use of social contact patterns to design efficient data forwarding strategies for opportunistic social network. ETNS [27] was proposed, which is an effective transmission strategy based on node socialization to divide nodes in the network into several different communities. By considering nodes' global trust, the author can judge the behavior of nodes in the communities and establish a community reduction strategy to decrease the number of inefficient nodes. PIS [18] integrates three social features, namely physical proximity, user interests, and the social relationship, of users' daily routines into a unified distance function, so as to select optimal relay nodes for data forwarding. FCNS [28] makes use of mobile and social similarities to predict the transmission preference of nodes. GSI [29] fully exploits the geographical information, social features, and user interests to enhance transmission performance. FSF [30] assesses the friendship strength among the pair of nodes, then it determines the individual selfishness of the relay node.  
In addition, many mathematical methods exist such as graph theory, fuzzy inference logic, information entropy. [PERSON] [31] proposed an Efficient Data Packet Iteration and Transmission (EDPIT) algorithm, which selects data packets via iteration. This article made full use of information entropy to solve an actual question. FPRDM [32] uses an intelligent fuzzy decision-making system to reduce the average end-to-end delay. Although these papers all have made corresponding contributions to the opportunistic social networks, there is still room for improvement.  
Different from the previously stated data forwarding strategies in opportunistic social networks, our work fully considers the applicable attributes of working time and non-working time, respectively, assigns corresponding weights to each attribute through a large number of experiments, and makes decisions using a fuzzy control system. Our proposed algorithm can choose more optimum next hop nodes and reduce the average end-to-end delay.
## 3 Results and Discussion
### Completely Static Operative Mode of the Test Rig
#### 3.4.3 Calculation of the \(CoG\) Height  
To calculate the \(CoG\) height, the support platform has to be inclined of a known angle \(\alpha\) without rotating the turntable (i.e., for the same values of \(\beta\) or \(\beta^{\prime}\)), thus causing a rearrangement of the weights supported by each quadrant and a reposition of \(CoG^{\prime}\) on the support plane with reference to the vehicle reference frame (calculated using the same formula of Equation (12) used to position the \(CoG^{\prime}\) on the support plane when it is horizontal). By quantifying the shift of \(CoG^{\prime}\) downstream the inclined support plane, i.e., \(\Delta y_{SP}(CoG^{\prime})\), it is possible to quantify the \(CoG\) height \(h\) (i.e., the distance of \(CoG\) from the support plane; Figure 9):  
\[h=\frac{\Delta y_{SP}(CoG^{\prime})}{\tan\alpha} \tag{12}\]  
Figure 8: (**a**) Main dimensions characterizing the positions of the load cells on the support plane; (**b**) dimensions characterizing a vehicle (here: a conventional farm tractor) and position of the \(CoG\) projection on the support plane (i.e., of \(CoG^{\prime}\)); (**c**) real test situation.
# Research on Crack Width Measurement Based on Binocular Vision and Improved DeeplabV3+
## 3 Visual Method for Measuring Crack Width in Concrete
### Crack Parameter Acquisition Algorithm  
In the routine inspection and maintenance of concrete structures, the maximum crack width is usually used to evaluate the damage to the structure. In this paper, firstly, the centerline position of the crack in the image is obtained according to the skeleton extraction algorithm, and the crack edge area is divided according to the skeleton. The maximum crack width is taken as the shortest distance from any point on one edge of the crack to the other side of the crack, and its expression is as follows:  
\[w_{i}=\min(\sqrt{\left(X_{i}-X_{j}\right)^{2}+\left(Y_{i}-Y_{j}\right)^{2}+ \left(Z_{i}-Z_{j}\right)^{2}}) \tag{13}\]