esa-sceva/satcom-chunk-collection huggingface dataset:
## Integrated GNSS Attitude Determination and Positioning for Direct Geo-Referencing  
**[PERSON] 1, [PERSON] 2,* and [PERSON] 1,3**  
\({}^{1}\) Global Navigation Satellite System (GNSS) Research Centre, Department of Spatial Sciences,  
Curtin University, GPO Box U1987, Perth WA 6845, Australia;  
E-Mails: [PERSON]ESS] (N.N.); [PERSON]ESS] (P.J.G.T.)  
\({}^{2}\) Geodetic Institute, Leibniz Universitat Hannover, Nienburger Str. 1, 30167 Hannover, Germany  
\({}^{3}\) Delft Institute of Earth Observation and Space Systems (DEOS), Delft University of Technology,  
PO Box 5058, 2600 GB Delft, The Netherlands  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +49-511-762-3191.  
_Received: 17 March 2014; in revised form: 8 July 2014 / Accepted: 9 July 2014 / Published: 17 July 2014_  
**Abstract:** Direct geo-referencing is an efficient methodology for the fast acquisition of 3D spatial data. It requires the fusion of spatial data acquisition sensors with navigation sensors, such as Global Navigation Satellite System (GNSS) receivers. In this contribution, we consider an integrated GNSS navigation system to provide estimates of the position and attitude (orientation) of a 3D laser scanner. The proposed multi-sensor system (MSS) consists of multiple GNSS antennas rigidly mounted on the frame of a rotating laser scanner and a reference GNSS station with known coordinates. Precise GNSS navigation requires the resolution of the carrier phase ambiguities. The proposed method uses the multivariate constrained integer least-squares (MC-LAMBDA) method for the estimation of rotating frame ambiguities and attitude angles. MC-LAMBDA makes use of the known antenna geometry to strengthen the underlying attitude model and, hence, to enhance the reliability of rotating frame ambiguity resolution and attitude determination. The reliable estimation of rotating frame ambiguities is consequently utilized to enhance the relative positioning of the rotating frame with respect to the reference station. This integrated (array-aided) method improves ambiguity resolution, as well as positioning accuracy between the rotating frame and the reference station. Numerical analyses of GNSS data from a real-data campaign confirm the improved performance of the proposed method over the existing method. In particular, the integrated method yields reliable ambiguity resolution and reduces position standard deviation by a factor of about \(0.8\), matching the theoretical gain of \(\sqrt{3/4}\) for two antennas on the rotating frame and a single antenna at the reference station.  
keywords:global navigation satellite system (GNSS); attitude determination; multivariate constrained integer least-squares (MC-LAMBDA); carrier phase ambiguity resolution; direct geo-referencing; laser scanner +
Footnote †: journal: Journal of Scientific Computing
## 1 Introduction  
Thermal batteries are counted among the chemical power sources utilized in the supply of power for special devices. They are characterized by a high impact resistance as well as endurance against vibrations and overload. A single cell contains an electrolyte immobilized in a filler, besides an anodic and cathodic material. In the environmental temperature range from \(-\)50 to +70 \({}^{\circ}\)C, the electrolyte-filler mixture has insulator properties. After heating the cell to temperatures above 300 \({}^{\circ}\)C, the electrolyte undergoes melting and becomes an ionic conductor [1]. In a single cell, the electrolyte is mixed with a filler, which is intended to prevent the molten electrolyte from leaking out of the cell without limiting the ionic conductivity of the electrolyte. For this reason, the filler should possible have a low density, good wettability, and chemical resistance to the action of the molten electrolyte, while maintaining an appropriate mechanical strength [2]. The fillers applied in practice are: SiO\({}_{2}\), MgO, ZrO\({}_{2}\), BN, Y\({}_{2}\)O\({}_{3}\), and TiO\({}_{2}\)[3, 4].  
Magnesium oxide is a commonly used filler for the immobilization of the molten electrolyte (which is a mixture of alkali metal chlorides) at the operation temperature of high-temperature cells. Before activation, the electrolyte is a solid and this is why it does not self-discharge for at least 15 years. It is only after melting caused by a thermal stimulus at above 300 \({}^{\circ}\)C that the cell reaches the rated voltage and electric current intensity is no longer than 1 s--this process is called cell activation. Moltenelectrolyte leakage and cracks in the filler-electrolyte pellets result in a reduction in the cell efficiency and internal short circuits. The task of the molten electrolyte is to provide good ionic conductivity between the electrodes. Since the filler itself is an insulator at the cell operation temperature, the electrolyte/filler ratio must be limited.  
It was found in earlier studies that mixed systems of MgF\({}_{2}\)-MgO prepared by co-precipitation are characterized by better immobilizing properties than bare magnesium oxide [5; 6]. In the present study, we subjected MgO to a modification consisting in the generation of a magnesium fluoride layer on the MgO surface. This is a considerably simpler method for the preparation of MgF\({}_{2}\)-MgO systems compared to the co-precipitation [5] and sol-gel [7] methods.
## 6 Conclusions  
In this paper, a CZM-based FE model was developed to evaluate the fracture behavior of asphalt concrete. Two-dimensional models were obtained by cutting from a three-dimensional model based on the real aggregate shape as a way to obtain a numerical model that is closer to the microstructure of the asphalt concrete in the experiment. Numerical simulations of SCB tests were conducted and validated with the experiment. With the validated CZM-based FE model, the effects of some critical factors on the fracture behavior of asphalt concrete were investigated. The following conclusions were obtained:  
1. The damage of the specimens was divided into three stages according to the variation of crack length and load. At the peak load, the average damage of the adhesive elements was higher than that of the cohesive elements, indicating that the aggregate-asphal interface is the weaker part.
2. At lower temperatures, asphalt concrete tends to crack earlier, and the cracking path tends to be marginally closer to the aggregates.
3. Since the stiffness of asphalt mortar is larger under a higher loading rate, the CZM elements in asphalt mortar cannot bear much more stress through deformation. Therefore, a higher loading rate may induce more, but minor, element damage.
4. Angular aggregates may induce stress concentration at the aggregate-asphal interface and thus tend to induce a higher percentage of damaged elements, especially adhesive-damaged elements.
5. On average, each 10% increase in fracture energy allows the specimen to bear 2.31% more load and 2.82% more displacement. Sufficient fracture energy could improve the ability of asphalt concrete to resist fracture.  
However, the effects of a wider range of temperatures and the fracture of the aggregates were not considered in this paper. In future studies, a wider range of temperatures and more typical aggregate shape parameters will be considered.  
Conceptualization, J.C.; writing--original draft, X.O.; writing--review and editing, X.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 51908558 and 52278468), the Natural Science Foundation of Hunan Province (Grant No. 2020)[5717], and the Hunan Transportation Science and Technology Project (Grant No. 202017).  
The data presented in this study are available upon request.  
The authors declare no conflict of interest.  
Figure 17: Effect of fracture energy on asphalt concrete fracture: (**a**) crack length evolution; (**b**) peak load and displacement.
# An Optimization Model for a Desert Railway Route Scheme Based on Interval Number and TOPSIS
## 5 Conclusions  
This paper proposed a quantitative evaluation model of the route scheme for the desert railway. The factors affecting the railway design from the aspects of alignment design, sand hazard and environmental impact were analyzed, and the evaluation index system was constructed by selecting the major factors. The comprehensive weights of the index were obtained by combining subjective weights and objective weights. The quantitative method of the qualitative index using interval number was designed. An optimization model of the railway route scheme in desert areas based on interval number and TOPSIS was established. The model was verified by using a case study of the Hotan-Ruoqiang railway. The following conclusions can be drawn:  
1. The comprehensive weights reflect the actual importance of each index. Compared with the single weighting method, the combination weighting method takes into account both the attribute characteristics of the index and the experience and knowledge of experts.
2. The quantification of qualitative indicators based on interval number reduces the impact of subjective reasons on the evaluation results. The interval number takes into account the fuzziness and uncertainty in the quantitative transformation. The index operation expressed in interval number improves the convenience and reliability of the scheme decision.
3. The proposed method is an effective evaluation method for the railway route scheme. The obtained results reveal that the evaluation results are consistent with the actual project. This model can provide quantitative theoretical support for the decision-making of the desert railway route scheme.  
Conceptualization, methodology, software, writing original draft, Y.G.; funding acquisition, writing review and editing, X.D.; investigation and validation, F.H.; data curation, Z.L. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the Fundamental Research Funds for the Central Universities (Grant No. 2022 YJS075).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Gaslike Social Motility: Optimization Algorithm with Application in Image Thresholding Segmentation
## 4 GSM Algorithm Application to Threshold Segmentation
### Gaslike Social Motility for Image Thresholding  
In this paper, the GSM algorithm is applied as an efficient method to determine optimal threshold values for multilevel thresholding (MTH), which is essential for image segmentation. The process begins by loading a grayscale image (\(l_{G}\)) and generating its corresponding histogram (\(ht_{G}\)). The parameters, including the population size (\(r\)), maximum number of iterations (\(G\)), and number of thresholds (\(uth\)), are then specified. The initial population (\(r_{i}\)) is randomly generated, with each particle representing a potential threshold value within the range of 0 to 255. The fitness value for each particle is calculated by evaluating the thresholds using the MCET function (Equation (12)). Through iterative refinement, the positions with the minimum fitness values are identified, and the solutions are improved until the stopping criteria are met. The best position among the population of particles is selected as the threshold vector (\(th\)). Finally, the segmented image (\(l_{s}\)) is produced using the threshold vector \(th\). One of the most commonly used segmentation rules for two thresholds is defined as follows:  
\[I_{Sdam}(x,y)=\begin{cases}I_{Gr}(x,y)&if\,I_{Gr}(x,y)\,\leq\,th_{1}\\ th_{1}&if\,th_{1}\,\geq\,I_{Gr}(x,y)\geq\,th_{2}\\ I_{Gr}(x,y)&if\,I_{Gr}(x,y)>\,th_{1}\end{cases} \tag{12}\]  
Here, \(I_{s}\) represents the gray value of the segmented image, while \(I_{Gr}(x,y)\) denotes the gray value of the original image at pixel position \((x,y)\). The thresholds \(th_{1}\) and \(th_{2}\) are obtained using the Gaslike Social Motility optimization algorithm. Each threshold \(th\) corresponds to a decision variable within the population, which is defined as follows:  
\[r^{i}=[th_{1},th_{2},...,th_{N}],\,th_{i}=[th_{1},th_{2},...,th_{nt}] \tag{13}\]  
Here, \(N\) represents the size of the particle population, and \(nt\) denotes the number of thresholds applied to each particle in the image, where \(i=[1,2,\ldots,nt]\).
## 3 SSMA Broadcast Protocol
### Discussion on Key Protocol Parameter
#### 3.4.2 The Lower Bound of BSS  
The length of _BSS_ should be able to handle the largest possible competing nodes, while it should be optimized to decrease message broadcast delay. Then, the lower bound of _len_(_BSS_) is given as follows:  
\[len(BSS)\geq len(BS)\times N \tag{11}\]  
The integer number \(N\) can be determined by estimating the maximum number of competing nodes. Let _nCNodes_ and _nNbrs_ be the number of competing nodes and the number of neighbors for a given node, respectively. With SSMAb, _nCNodes_ is approximately the same as one third of _nNbrs_ since a node has its neighbors over three tree levels. Thus, _nCNodes_ is given as follows:  
\[nCNodes\ \approx\ \frac{nNbrs}{3}+1 \tag{12}\]  
From the probability mass function in Reference [20], the distribution of _Pr_(_nNbrs_ = _k_) is shown in Figure 6, where \(R\), \(a\), and \(M\) indicate the transmission range, the width or the length of network dimension, and the number of nodes, respectively. However, take a look at Figure 7, where node \(x\) at level \(i\) has \(2\times k\) neighbors, corresponding to the maximum number of neighbors that a node can have at the same level, and the big dotted circle indicates the coverage of node \(x\). When node \(x\) gets the channel, all its neighbors at the same level are blocked, showing the worst-case blocking. Note that if any node (say \(a\)1) other than node \(x\) gets one _BS_, one (_b_\({}_{k}\)) of the other nodes can use the same _BS_ simultaneously. Thus, after node \(x\) broadcasts a message, two nodes can broadcast a message within the same _BS_ in parallel. This continues until all nodes finish transmission. Thus, we get \(N\) from _nCNodes_ in Equation (12) approximately as follows:  
\[N\approx\ \frac{1}{2}\times nCNodes \tag{13}\]  
We can get the maximum possible number of neighbors from Figure 6. Thus, when \(R\) = 10 m, \(a\) = 30 m, and \(M\) = 30, _nNbrs_\(\approx 16\), and _nCNodes_\(\approx 7\) from Equation (12), and _N_\(\approx 4\) from Equation (13). When \(R\) = 28 m, \(a\) = 100 m, and \(M\) = 75, _nNbrs_\(\approx 25\), _nCNodes_\(\approx 10\), and _N_\(\approx 5\).
# Weak Multiple Fault Detection Based on Weighted
## 2 Background
### Majorization-Minimization for Ogs  
The solution of the sparse optimization problem is usually divided into two steps. The first step is to pre-process the objective function, and transform the objective function that is non-smooth, non-convex, and inseparable into a smooth, convex, separable function. The second step is to solve the transformed objective function. MM is suitable for solving difficult optimization problems directly, such as non-differentiable problems. MM does not need to directly minimize the objective function \(F(x)\) in Equation (2), but converts \(F(x)\) into optimization problem \(Q_{k}(x,x_{k})\), \(\{k=0,1,2,\ldots\}\). The advantage of this is that the solution of \(Q_{k}(x,x_{k})\) is simpler than optimization problem \(F(x)\). The constructed function \(Q_{k}(x,x_{k})\) needs to satisfy the following constraints:  
Figure 1: Coefficients with overlapping group structure.  
Figure 2: Coefficient selection results: (**a**) non-overlapping group sparse; (**b**) overlapping group sparse.  
\[\left\{\begin{array}{l}Q_{k}(x,x_{k})\geq F(x),\forall x\\ Q_{k}(x_{k},x_{k})=F(x_{k})\end{array}\right. \tag{6}\]  
Through the \(k\)-th iterations, \(Q_{k}(x,x_{k})\) converges to the minimum value of the objective function \(F(x)\). Because the MM method is simple to use and efficiently calculated, it plays a more prominent role in multivariable optimization. Using MM algorithm to solve the optimization function \(F(x)\) can be summarized as the following steps:  
1. Construct an appropriate function \(Q_{k}(x,x_{k})\) to satisfy the constraints in Equation (51);
2. Calculate the minimum of \(Q_{k}(x,x_{k})\), marked as \(x_{k}+1\): \[x_{k+1}=\operatorname*{arg\,min}_{x}Q_{k}(x,x_{k});\] (7)
3. Let \(k=k+1\); go to step (2). To solve the sparse optimization problem (2), we use the MM algorithm. For instance, if the group size is set to \(K=2\), then the optimization objective function \(F(x)\) in Equation (2) is expanded as: \[F(x)=\min\limits_{\theta\in R^{N}}\frac{1}{2}\|y-x\|^{2}+\lambda\left(\cdots+ \sqrt{\left|x(1)\right|^{2}+\left|x(2)\right|^{2}}+\sqrt{\left|x(3)\right|^{2 }+\left|x(4)\right|^{2}}+\cdots\right)\] (8) The derivative is expressed as: \[\frac{\partial F(x)}{\partial x(i)}=x(i)-y(i)+\lambda x(i)\left(\frac{1}{ \sqrt{\left|x(i-1)\right|^{2}+\left|x(i)\right|^{2}}}+\frac{1}{\sqrt{\left|x (i)\right|^{2}+\left|x(i+1)\right|^{2}}}\right),0\leq i\leq N-1.\] (9)  
In Equation (9), \(x(-1)\) and \(x(N)\) can be considered zero, as noted for \(i\
otin I\). The Equation (9) is rewritten as:  
\[\frac{\partial F(x)}{\partial x(i)}=x(i)-y(i)+\lambda x(i)r(i;x), \tag{10}\]  
where \(r(i;x)=\sum\limits_{j\in I}\left[\sum\limits_{k\in I}\left|x(i-j+k)\right|^{2} \right]^{-1/2}\).  
Setting the derivative to zero, we can formulated the optimization problem as:  
\[x(i)+\lambda x(i)r(i;x)=y(i),i\in I. \tag{11}\]  
Hence, rewrite Equation (11) as:  
\[x(i)=\frac{y(i)}{1+\lambda r(i;x)},i\in I. \tag{12}\]  
More details about the MM procedure can be found in [35; 36].
# TEDL: A Text Encryption Method Based on Deep Learning
## 10 Experiments and Performance Analysis
### Recovery  
As described in Section 6.4, both parties only send and receive the first-dimension hash in the time-varying codebook. The hash that can be restored to a word is called a valid hash. Obviously, the number of valid hashes is equal to the total number of word keys in the codebook, far less than \(2^{256}\). When the ciphertext is partially tampered with, the valid hash that has the most overlap with it is selected from all the hashes of the first dimension,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Key Bits** & **Key Space** \\ \hline TEDL & 296 & \(2^{296}\) \\ AES & 256 & \(2^{256}\) \\ Salsa20 & 256 & \(2^{256}\) \\
3 DES & 168 & \(2^{168}\) \\ DES & 56 & \(2^{56}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key space.  
the plaintext hence can be restored with a high probability. We define the recovery accuracy rate (RACR) to measure the anti-interference and recoverability of TEDL.  
\[RACR=\frac{n_{c}}{n_{t}} \tag{42}\]  
where \(n_{t}\) denotes the total number of tampered hashes, \(n_{c}\) stands for the count of tampered hashes which are successfully restored to correct words.  
We randomly select 1000 words as samples, whose corresponding hashes are tampered with. To test the recovery rate under different conditions, we set the total number of tampered bits from 0 to 256 bits, and randomly choose the tampering location. Experiments repeat to calculate RACR and we plot it versus the number of tampered bits as Figure 9.  
It shows that an invalid hash is recoverable if the count of tampered bits is less than 85, otherwise, the original word is not able to be restored.
## 2 Methodology
### Case Study  
#### 2.1.1 Building Characterization  
The reference building used as case study was built in 2003, it has a total floor area of 176 m\({}^{2}\) and is composed by one inhabitable floor and an attic (Figure 5). The building has a sunroom (_a_), used for storage and as a laundry room, a pantry (_b_), a kitchen (_c_), three bedrooms (marked as \(d\), \(e\) and _j_), an entrance hall (_g_), a living room (_i_) and two bathrooms (_f_ and _h_).  
Table 1 presents the building window-to-wall ratio by orientation. The largest glazed surfaces are North and West oriented but 17.81% of the window-to-wall ratio is South-oriented, which allows it to take advantage of solar gains in the living room and in the bedroom 3. In the summer, more efficient shading techniques are required in these rooms to prevent overheating.  
Figure 5: Rendered images of the case study building: (**a**) 3d model; (**b**) ground floor blueprints.  
The reference constructive solution is described as follows. The building's envelope walls are composed of a double hollow clay brick with air gap and insulation in the middle. The interior partition is made by single-layered brick walls, rendered with gypsum on both sides. The ground floor is supported by a thick layer of compacted gravel and concrete. A damp-proof membrane protects the flooring insulation board, to which the interior ceramic floor is glued. The pitched roof is composed by an exterior coating of ceramic tiles supported by a roof structure. A slab separates the indoor space and the roof structure, creating between both an attic. In these elements, the existence of thermal bridges was considered. The calculation method used to determine the thermal bridges depends on the type of bridge encountered (linear and punctual thermal bridges) and the _U-values_ of the constructive solutions presented in Table 2 already include linear thermal bridges in columns and beams. For windows, a thermal transmission coefficient of \(U_{\text{winstalled}}\) = 2.44 W m\({}^{-2}\) K\({}^{-1}\) and a solar heat gain coefficient of 0.56 were used. In turn, a thermal transmission coefficient of \(U_{\text{winstalled}}\) = 1.40 W m\({}^{-2}\) K\({}^{-1}\) was considered for external doors. These values consider the _U-_value of the frame (\(U_{\text{f}}\)) and the glass edge thermal bridge (\(\text{\text{\text{\text{\text{F}}}}}_{\text{g}}\)) in accordance with ISO 1007/7 [43] and the installation thermal bridge (\(\text{\text{\text{\text{\text{F}}}}}_{\text{install}}\)) in accordance with EN ISO 10211 [44]. In order to optimize the sensitivity analysis, an average value, \(U_{\text{winstallation}}\), was assumed.