esa-sceva/satcom-chunk-collection huggingface dataset:
# A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis
###### Abstract  
Indoor mobile mapping techniques are important for indoor navigation and indoor modeling. As an efficient method, Simultaneous Localization and Mapping (SLAM) based on Light Detection and Ranging (LiDAR) has been applied for fast indoor mobile mapping. It can quickly construct high-precision indoor maps in a certain small region. However, with the expansion of the mapping area, SLAM-based mapping methods face many difficulties, such as loop closure detection, large amounts of calculation, large memory occupation, and limited mapping precision. In this paper, we propose a distributed indoor mapping scheme based on control-network-aided SLAM to solve the problem of mapping for large-scale environments. Its effectiveness is analyzed from the relative accuracy and absolute accuracy of the mapping results. The experimental results show that the relative accuracy can reach 0.08 m, an improvement of 49.8% compared to the mapping result without loop closure. The absolute accuracy can reach 0.13 m, which proves the method's feasibility for distributed mapping. The accuracies under different numbers of control points are also compared to find the suitable structure of the control network.  
control-network-aided SLAM; distributed indoor mapping; large-scale environment +
Footnote †: journal: Applied Sciences
## 2 Theoretical Analysis
### Respondruction with BP-ANN
As a result, the reconstruction accuracy may be compromised, making it challenging to differentiate between inaccurate reconstructions and divergences caused by potential damage. Therefore, for practical implementation, it is advisable to choose cross-sections that are less likely to experience damage, particularly stress concentration or fatigue failure, as the primary observation points for our input features. This strategic selection helps ensure the reliability and accuracy of the damage localization process by minimizing the interference from nonlinear effects associated with damaged areas. The network configuration follows the design of BP-ANN structure which has been validated in the previous study [26] of a field test. In the context of linear regression problems, our preliminary investigations have shown that other machine learning methods can also yield satisfactory results in reconstructing responses. However, given that the effectiveness of these alternative methods has not been field-tested, and considering that method selection is not the primary focus of this study, a potential comparison among these machine learning techniques is not explored here. The network comprises two hidden layers. The output layer is designed with a single neuron, which corresponds to the monitoring data at the target cross-section. The active function is ReLU (as illustrated in Table 1). Traffic conditions are excluded from the training process, as demonstrated in Section 2.1 and corroborated by previous research [26], which established that the time-varying traffic conditions do not impact the transfer matrix of the linear bridge structure. This fundamental physical characteristic suggests that it is feasible to train a general ANN using a small-scale database to simulate the transfer matrix. In this study, we have found that with the current configuration, it takes less than 2 min to train the target ANN, which is supposed to be acceptable. Figure 2: BP-ANN structure to model transfer matrix [27] (C-S: cross-sections).
# A Method of Accuracy Increment Using Segmented Regression
## 7 Conclusions  
This article presents a method of accuracy increment when segmented regression is used. The main problem for segmented regression model building is the estimation of the coordinates of the breakpoint between adjacent segments. To solve this problem, two types of multidimensional optimization paraboloids are used. The paraboloids contain information on standard deviations between the model output and the observed data for different sets of possible values of breakpoint abscissas. The minimum standard deviation of each paraboloid coincided with the optimal position of the breakpoints.  
A step-by-step procedure for the proposed method was described by examples based on statistical simulation and real data observation.  
Generally, the use of SLR, SQR, and SLQR models provides a mathematical model with high accuracy, more accurately describes the geometrical structure of the analyzed dataset, and has good predictive properties.  
The results of this research can be used during mathematical model building for statistical data obtained in various branches of human activity.  
Conceptualization, J.A.-A. and M.Z.; methodology, J.A.-A. and V.K.; software, R.O.; validation, J.A.-A., A.M. and M.Z.; formal analysis, R.O.; investigation, J.A.-A.; resources, A.M.; data curation, M.Z.; writing--original draft preparation, J.A.-A. and M.Z.; writing--review and editing, J.A.-A. and M.Z.; visualization, R.O.; supervision, V.K.; project administration, J.A.-A.; funding acquisition, J.A.-A. and A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used in the study are obtained in two ways: (1) using random number generator during statistical simulation; (2) using open source from The United States Geological Survey with citing corresponding link.  
The authors declare no conflict of interest.
## 2 Viscoelastic Parameter Model of the Multi-Layered CGS
### Viscoelastic Parameter Model of the Interface-Layer  
Figure 4 shows the calculation model of the interface-layer, where \(\Omega_{\text{p2}}\) is the region of gas phase (pore) as inclusion, \(\Omega_{\text{m2}}\) and \(\Omega_{\text{m3}}\) are the regions of two-layer solid phases and \(\Omega_{\text{eq2}}\) is the region of equivalent media. Since \(\Omega_{\text{m2}}\) and \(\Omega_{\text{m3}}\) are known from the above calculation (Equations (3)-(5)), the interface-layer model can be equivalent to superposition of the hollow sphere model (\(\Omega_{\text{p2}}+\Omega_{\text{m2}}\)) and generalized self-consistent model (\(\Omega_{\text{m3}}+\Omega_{\text{p2}}+\Omega_{\text{m2}}\)).  
According to the hollow sphere model, the equivalent bulk modulus (\(K_{e}\)) and shear modulus (\(G_{e}\)) of \(\Omega_{\text{p2}}-\Omega_{\text{m2}}\) region can be obtained as Equation (10):  
\[\begin{array}{l}K_{e}=4K_{o}G_{o}(1-p_{2})/(4G_{o}+3K_{o}p_{2})\\ A_{3}\big{(}\frac{G_{e}}{G_{e}}\big{)}^{2}+2B_{3}\big{(}\frac{G_{e}}{G_{e}} \big{)}+C_{3}=0\end{array} \tag{10}\]  
where \(K_{e}\) and \(G_{e}\) are the one-layered CGS effective bulk modulus and shear modulus, \(p_{2}\) is the volume fraction of \(\Omega_{\text{p2}}-\Omega_{\text{m2}}\) region, \(A_{3}\), \(B_{3}\), \(C_{3}\) are undetermined coefficients.  
According to the generalized self-consistent model, the equivalent bulk modulus (\(K_{i}\)) and shear modulus (\(G_{i}\)) of interface-layer (\((\Omega_{\text{p2}}+\Omega_{\text{m2}})-\Omega_{\text{m3}}\) region) can be calculated as Equation (11):  
\[\begin{array}{l}K_{i}=K_{s}+\frac{(K_{e}-K_{s})(3K_{s}+4G_{s})p_{3}}{3K_{s}+4 G_{s}+3(K_{e}-K_{s})(1-p_{3})}\\ A_{4}\big{(}\frac{G_{i}}{G_{e}}\big{)}^{2}+2B_{4}\big{(}\frac{G_{e}}{G_{e}} \big{)}+C_{4}=0\end{array} \tag{11}\]  
where \(p_{3}\) is the volume fraction of pore and \(A_{4}\), \(B_{4}\), \(C_{4}\) are undetermined coefficients.  
Similarly, effective elastic parameters of the interfacial layer (\(E_{i}\)) is shown as Equation (12):  
\[E_{i}=\frac{9K_{i}G_{i}}{3K_{i}+G_{i}} \tag{12}\]  
Similarly, the viscoelastic parameter (\(J_{i}\)) in Laplace space is shown as Equation (13):  
\[J_{i}^{T}=\frac{1}{sE_{i}^{T}} \tag{13}\]  
Figure 4: Calculation model of interface-layer.  
The viscoelastic parameter of interface-layer is shown as Equation (14):  
\[J_{i}=\mathcal{L}^{-1}(J_{i}^{T}) \tag{14}\]
# Compact Beam-Scanning Reflectarray Antenna with SLL Reduction Using In-Plane Panel Translations
## 3 Multi-Stage Design
### Scanning Range Enhancement
This forces a drop to 32.5 dBi in maximum gain, but it also increases the minimum gain within this optimized range from 29.8 to 31.4 dBi (or from 25.4 to 27.0 dBi, considering the final panel translation range). After nine iterations, the phase law has been optimized for all panel positions from \(d_{-12}\) to \(d_{11}\). This results in a new maximum gain of 31.4 dBi, and a minimum gain of 30.4 dBi. In other words, compared to the unifocal design, the minimum gain has been increased from 25.4 to 30.4 dBi, and scan loss has been reduced to only 1 dB.
## 4 Analysis of Influential Factors
### Damage Analysis  
To quantitatively evaluate the damage evolution rule for the composite columns exposed to high temperature, the damage variable parameter \(D\) is introduced. When \(D\) = 0, the specimen is in the non-destructive state. When \(D\) = 1, the specimen is completely destroyed. The damage variable parameter can be calculated according to the following formula:  
\[D=1-\frac{EA(T)}{EA_{0}(T)} \tag{4}\]  
Figure 10: Change laws of deformation coefficient of specimens. (**a**) Influence of \(T\). (**b**) Influence of \(d\) and \(a\).  
where \(EA_{0}(T)\) represents the initial stiffness of the specimen under the non-destructive state, that is, the secant stiffness corresponding to \(0.4\,N_{\text{u}}(T)\) at various high temperatures; \(EA(T)\) denotes the effective stiffness of the specimen after damage, that is, the secant stiffness at any time under different high temperatures.  
In the whole loading process, the damage value of the specimen can be intuitively reflected by the damage evolution curve, as shown in Figure 11. Figure 11 indicates that the damage evolution curves can be divided into four stages:  
(1) The horizontal line segment: the damage value at the initial stage of loading is an approximately horizontal line, and the specimen is in the elastic stage.  
(2) The rapidly ascending segment: the damage value has increased rapidly, as manifested in the rapid development of cracks and the continuous peeling of concrete cover. The corresponding load-displacement curve is between the elastic-plastic stage and the descending section.  
(3) The slowly ascending segment: the growth rate of the damage value has slowed, the concrete cover of the specimens has completely lost its function, and the corresponding load-displacement curve is between the descending section and residual section.  
(4) The stable segment: the damage value shows almost no further increase, the outer RC of the specimen has basically lost its function, that is, the load is mainly borne by the core CFST column, and the corresponding load-displacement curve is in the residual section.  
It can also be seen from Figure 11 that the damage evolution curves of the specimens were not significantly affected by the diameter of the steel tube or the thickness of the concrete cover, and far less so than by the heating temperature. The higher the heating temperature was, the more serious the initial damage to the specimen was. Before the average longitudinal strain reached \(3.3\%\), the damage evolution curve of the specimen was in the horizontal and linear segment, and the damage accumulation was very small. Subsequently, the rapidly ascending section of the curve became shorter with increased heating temperature. The failure strain corresponding to \(85\%\,N_{\text{u}}(T)\) of the specimens at \(T\leq 500\)\({}^{\circ}\)C varied between \(13.3\%\) and \(16.7\%\), while it was between \(10.0\%\) and \(13.3\%\) when \(T\geq 600\)\({}^{\circ}\)C. Finally, only the curves of specimens at \(T\geq 600\)\({}^{\circ}\)C could enter the stationary segment, and the deformation at the end of loading was relatively larger.
## Nomenclature  
\begin{tabular}{l l} \(T\) & Wavelet denoising threshold \\ \(\eta\) & The scaling factor \\ \(y_{i}\) & The signal before processing for the \(i\)th channel \\ \(\tilde{y}_{i}\) & The signal of the \(i\)th channel after dynamic processing \\ \(H(X)\) & The spatial information entropy of \(X\) \\ \(V_{i}\) & The \(i\)-th segment of signal \(y_{i}\) with length \(R\) \\ \(U_{i}\) & The \(i\)-th segment of signal \(\tilde{y}_{i}\) with length \(R\) \\ \(g_{i}^{\prime}\) & The signal after the flexible processing of the \(i\)th channel \\ \(A_{\Psi}\) & The matrix consisting of the column vectors represented by index \(\Psi\) in \(A\) \\ \(Gain\Big{(}A_{j},r_{i}\Big{)}\) & The spatial information gain of the inner product between \(A_{j}\) and \(r_{i}\) \\ \end{tabular}
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 8 Submerged HF Membrane Integrity and Failure
### Failure  
Membrane failure is defined as the loss of mechanical integrity leading to the inability to achieve the rated log-removal values (LRV) of pathogens [312; 313; 314]. Membrane failure can occur during two phases of the operational lifespan of a membrane, namely, damage during the manufacturing and installation process, and during membrane filtration. Inconsistent manufacturing and fabrication techniques as well as handling error during installation often cause failure in the former phase. This issue is kept in check via the implementation of rigorous product quality-control methods and integrity testing of membrane modules before commissioning. On the other hand, unlike failure during the manufacturing and installation process, membrane failure during the filtration operation can mainly be attributed to operating parameters and maintenance protocols [315]. During operation, the likelihood of damage to the membrane is high given the stringent nature of operating protocols such as vigorous mechanical cleaning, chemical cleaning using strong oxidants, and high-pressure backwashing. Although these measures ensure that the membrane performance is maintained, they indirectly put a strain on the membrane integrity, leading to membrane ageing and failure.  
According to [PERSON] et al. [313], fiber failure can occur via four different mechanisms: chemical attack; damage during operation due to improper installation; faulty membrane module design; and punctures and scores due to the presence of foreign bodies. Furthermore, membrane ageing, coupled with excessive fiber movement due to external loads, can also cause submerged HF membranes to fail. This is discussed further in Sections 8.4 and 8.5.
## 1 Introduction
The gantry is mounted by vacuum beam transport systems and a series of beam steering and focusing electro-magnets. An overall size of a RF linac may require roughly a 50 m long facility for RF linac-based external-beam radiation therapy. Furthermore, a rotating gantry for multi-directional beam delivery may contribute a weight of the order of 100,000 kilograms. Therefore, the costs of construction and operation of such a facility may be prohibitively large so as to hinder the usage of conventional accelerator-based very high energy electron beam therapy systems in small-scale hospitals. To date, active research has been carried out on the laser plasma acceleration concept [14] in order to achieve high-energy, high-quality electron beams with GeV energy in a cm-scale plasma [15, 16, 17, 18, 19], 1%-level energy spread [20], a 1-mm-mrad-level transverse emittance [21], and a 1-fs-level bunch duration [22], ensuring that the stability of reproduction is as high as that of the present high-power ultra-short-pulse lasers [23]. Recently, staged laser plasma acceleration [24, 25] has been successfully demonstrated in conjunction with ionization-induced injection [26, 27, 28]. Relativistic electron beams  
Figure 1: Percentage Depth Dose distributions from 200 MeV electron beams, referring to Monte Carlo simulation results carried out by [PERSON] _et al._[8]: (**a**) single-field; (**b**) parallel opposed two-field; (**c**) orthogonal two-field, in comparison with those from a 15 MV photon beam for (**d**) single-field; (**e**) parallel opposed two-field; and (**f**) orthogonal two-field arrangements; and (**g**) 160 MeV single-field proton beam with Bragg peak, illustrated by [PERSON] _et al._[13]. from ultraintense laser plasma interactions can be conceived of as compact particle accelerators, inspiring a wide range of applications of unique particle beam and radiation sources. In this context, we present a new embodiment of the external-beam radiation therapy robotic system for delivering VHE electron/photon beams. This leads to a more compact and less expensive system with higher performance compared to previous techniques. Our proposed system comprises a drive laser, a plasma accelerator wherein the laser pulse generates VHE electron beams, a beam focusing system transporting electron beams from the plasma accelerator to a target in a patient, a photon beam target system generating VHE photon beams, a rotary gantry wherein the laser pulse is guided through a vacuum transport and focused onto the plasma accelerator, and a laser beam stabilizing system monitoring beam positions in the laser transport optics and correcting their alignment. We organize the remainder of this article as follows. Section 2 presents a conceptual design for the laser-driven VHE electron/photon beam radiation therapy system. Section 3 presents a design example of laser in-frame robotic gantry. Section 4 describes discussions on feasibility, prospects for a new laser-driven radiation therapy system, and conclusions.
## 3 Results and Discussion  
### Volatile Compounds  
Regarding the volatile compounds identified in the olive oils, the compounds were grouped by class. Table 1 describes the volatile compounds detected in the extra virgin olive oils collected in the Douro Valley. The percentage of samples with a specific compound and the average concentration in those samples of that particular compound were determined. Figure 1 shows the percentages of compound classes identified in these same olive oils. Despite being identified, all compounds reported in less than 5% of the samples were not considered in this study, as they were not guaranteed to be characteristic of the olive oils. Thus, 62 volatile compounds were identified and quantified: 15 hydrocarbons, 14 terpen, 11 alcohols, 9 aldehydes, 5 ketones, 5 esters, 2 ethers, and 1 nitrile. The group of hydrocarbons included compounds such as alkanes, alkenes, cycloalkanes, and aromatic hydrocarbons. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Volatile Compounds** & **\% of Samples** & **Concentration (\(\mu\)g/g)** & **Identification a** \\ \hline \multicolumn{4}{c}{**Alcohols**} \\ \hline
1-Heptanol & 7 &
\begin{tabular}{c} 0.3 \(\pm\) 0.2 \\ (0.2–0.7) \\ \end{tabular} & MS/IK.DB \\ \hline
1-Hexanol & 89 &
\begin{tabular}{c} 44.4 \(\pm\) 71.8 \\ (1.4–516.9) \\ \end{tabular} & MS/IK.DB \\ \hline
2-Ethyl-1-hexanol & 7 &
\begin{tabular}{c} 0.7 \(\pm\) 0.3 \\ (0.4–1.2) \\ \end{tabular} & MS/IK.DB \\ \hline
1-Nonanol & 91 &
\begin{tabular}{c} 0.7 \(\pm\) 0.5 \\ (0.1–2.5) \\ \end{tabular} & MS/IK.DB \\ \hline
4,8-Dimethyl-1-nonanol & 30 &
\begin{tabular}{c} 0.4 \(\pm\) 0.2 \\ (0.2–0.8) \\ \end{tabular} & MS/IK.DB \\ \hline
1-Octanol & 96 & \begin{tabular}{c} 0.8 \(\pm\) 0.5 \\ (0.3–2.9) \\ \end{tabular} & MS/IK.DB \\ \hline (\(E\))-2-Hepten-1-ol & 28 & \begin{tabular}{c} 1.3 \(\pm\) 0.5 \\ (0.6–2.3) \\ \end{tabular} & MS/IK.DB \\ \hline (\(E\))-2-Hexen-1-ol & 19 & \begin{tabular}{c} 105.4 \(\pm\) 148.4 \\ (1.9–532.5) \\ \end{tabular} & MS/IK.DB \\ \hline (\(Z\))-2-Penten-1-ol & 100 & \begin{tabular}{c} 4.6 \(\pm\) 3.8 \\ (1.3–27.0) \\ \end{tabular} & MS/IK.DB \\ \hline (\(Z\))-3-Hexen-1-ol & 16 & \begin{tabular}{c} 156.8 \(\pm\) 108.6 \\ (62.9–439.9) \\ \end{tabular} & MS/IK.DB \\ \hline \hline \multicolumn{4}{c}{**Aldehydes**} \\ \hline (\(E\))-4-Oxohex-2-enal & 96 & \begin{tabular}{c} 6.0 \(\pm\) 4.9 \\ (0.6–22.9) \\ \end{tabular} & MS/IK.DB \\ \hline (\(E\),\(E\))-2-4-Heptadienal & 32 & \begin{tabular}{c} 1.2 \(\pm\) 0.3 \\ (0.7–1.8) \\ \end{tabular} & MS/IK.DB \\ \hline (\(E\),\(E\))-2-4-Hexadienal & 96 & \
\begin{tabular}{c} 4.3 \(\pm\) 2.0 \\ (1.3–8.9) \\ \end{tabular} & MS/IK.DB \\ \hline (\(Z\))-2-Heptenal & 30 &
\begin{tabular}{c} 0.9 \(\pm\) 0.6 \\ (0.3–2.6) \\ \end{tabular} & MS/IK.DB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Volatile compounds identified in Douro extra virgin olive oils.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 4 Analysis and Validation of Results
### Theoretical Maximum Throughput  
This section compares the theoretical maximal throughput obtained from TCP protocol window size with the achieved throughput in the simulation.
## 3 Research Methodology
### Variable of Sample Informative Degree  
In the feature space, variable \(D\) can only indicate the distribution of one feature which cannot refer to sample distribution characteristics. Since the Naive Bayes algorithm assumes that all features are independent for the given class label, the relationship between features are not involved in our method. Under this assumption, we propose a rule to sum up the informative variable \(D\) of each feature distribution to get the informative degree \(SUM\_D\) of each majority sample.  
The construction of the informative degree \(SUM\_D\) mainly consider two aspects. One is the difference between two conditional probabilities \(p(X\mid Y=0)\) and \(p(X\mid Y=1)\) might be too small to split samples with different labels. The other is \(D\) from different features might be offset after summation. To avoid the above two possible problems, we sum up the rank values of \(D\) instead of the variable itself. The steps proposed to calculate informative degree \(SUM\_D\) of each majority sample are as follows.  
1. Order \(m\) variables \(D_{i}\) to \(D_{order}=\{D_{i1},D_{i2},\cdots,D_{im}\}\) by absolute values from the smallest absolute difference to the largest, where \(\mid D_{i1}\mid\leq\mid D_{i2}\mid\leq\cdots\leq\mid D_{im}\mid\). Let \(ABS\_vector\) denote the absolute values and \(SIGN\_vector\) denote the signs of \(m\) variables.
2. Rank the \(m\) variables of \(D_{order}\) with the smallest as 1, if there are elements which have the same value, calculate average rank of these elements. Let \(Rank\_vector\) denotes the rank.
3. Sum the product of \(SIGN\_vector\) and \(Rank\_vector\) as \(SUM\_D\). Let \(SUM\_D(X_{i})\) denote the informative degree of majority sample \(X_{i}\).  
\(SUM\_D(X_{i})\) quantifies how informative a sample is, especially when the classifier is Naive Bayes, this variable denotes how difficult a Naive Bayes classifier learns information for classification from a sample. The rank value recorded in \(Rank\_vector\) can differ variable \(D\) from different features clearly and the product of \(Rank\_vector\) and \(SIGN\_vector\) can avoid offset of value \(D\) from different features efficiently.
# A Survey on Mobility Support in Wireless Body Area Networks
## 3 Mobility Model and Communication Protocol
### Individual Mobility  
Individual mobility scheme represents model and communication protocols for a person's movement. This means that movement is mostly dependent on posture, such as walking, standing, sitting, running and so on. As for this mobility, general mobility assumes movement according to a person's ordinary activity, while specific mobility does extraordinary movement with limitations in specific places such as hospitals. Of two mobility patterns, a lot of research assumes general mobility patterns rather than specific mobility since the latter demands high complexity for modeling. In this section, we present the mobility model and related networking technology.