esa-sceva/satcom-chunk-collection huggingface dataset:
# Fabricating Sustainable All-Cellulose Composites
## 3 Dissolution of Cellulose
### Non-Derivatising Solvents
#### 3.4.2 Aqueous Solvents  
It was discovered already in 1930s that by combining some additives to the aquatic NaOH or LiOH solution, the solubility of cellulose can be improved, and cellulose can be dissolved [117, 118, 119, 120, 121]. Such additives include urea [122, 123, 124], thiourea [125, 126, 127] zinc oxide (ZnO) [128, 129] and poly(ethylene glycol) (PEG) [130, 131]. Although these additives improve cellulose solubility, the role of the additives has not been fully understood. It is suggested that e.g., in aqueous NaOH-urea dissolution process, the hydroxyl (OH') anion breaks the inter- and intra-molecular hydrogen bonds of the cellulose, while sodium (Na+) ions stabilize the hydrophilic hydroxyl groups and urea stabilizes the hydrophobic part of cellulose [132]. Rapid dissolution of cellulose occurs in conditions such as aqueous 7 wt% NaOH/ 12 wt% urea solution, which has been pre-cooled to \(-\)12 \({}^{\circ}\)C [118]. Here, similar to many other dissolution processes, degree of dissolution is related to molecular weight, crystallinity and applied conditions [133]. Therefore, having comprehensive understanding of structure and property relationship of dissolution mechanisms will be essential and ease commercial development for the NaOH-urea solvent system in industrial scale. These solvent systems are particularly interesting since they are cost-effective, non-volatile, they possess of low environmental toxicity, and they show great potential when it comes to the usage of cellulose from agricultural and forestry wastes [41, 77, 134]. The only apparent disadvantage of these methods is the need to involve freezing-to-thawing [135], which requires specific cooling equipment.
# Research on Damage Localization of Steel Truss-Concrete Composite Beam Based on Digital Orthoimage
## 2 Structural Damage Identification Method Based on Digital Image Processing
### Image Matrix Similarity Damages Identification Method  
The structural image matrix can be considered as a matrix consisting of pixel coordinates containing structural morphological information, denoted by the symbol a, as shown in Figure 1.  
If a structure is damaged at a place, the degradation of its stiffness matrix will destroy the original deformation continuity, and the abnormal changes at the damage site will be more obvious compared with other parts, which will be characterized as discontinuous pixel distribution at the edge of the structure on the image, so the similarity test of the structure image matrix can be carried out for damage identification. Similarity analysis is an analytical method to evaluate the degree of similarity between two things, and the commonly used similarity analysis methods are the Euclidean metric, Pearson correlation coefficient, and cosine similarity [23]; in this paper, the Euclidean distance metric is used as the index of image matrix similarity analysis, and the similarity function can be expressed by Equation (6).  
\[\text{sim}(D_{0},D_{1})=\rho\Big{(}D_{0}\Big{(}x^{(0)},y^{(0)}\Big{)},D_{1} \Big{(}x^{(1)},y^{(1)}\Big{)}\Big{)}=\sqrt{\big{(}x^{(0)}-x^{(1)}\big{)}^{2}+ \big{(}y^{(0)}-y^{(1)}\big{)}^{2}} \tag{6}\]  
where \(\text{sim}(D_{0},D_{1})\) is the similarity between two image matrices \(D_{0}\) and \(D_{1}\).  
\(\rho\big{(}D_{0}\big{(}x^{0},y^{0}\big{)},D_{1}\big{(}x^{1},y^{1}\big{)}\big{)}\) represent the Euclidean distance between the corresponding elements of the two image matrices \(D_{0}\) and \(D_{1}\). \(x^{0},y^{0},x^{1}\), and \(y^{1}\), respectively, represent the horizontal and vertical coordinates of the elements in \(D_{0}\) and \(D_{1}\). From the relationship between structural damage and morphology mentioned earlier, it can be seen that when the structure is not damaged, the similarity distribution of the structure under different working conditions is a straight line or a continuous smooth curve. After the damage occurs, the matrix similarity curve will show an abnormal peak response at the damage site, and the principle is shown in Figure 2.  
Figure 1: Image matrix.
# HGTMFS: A Hypergraph Transformer Framework for Multimodal Summarization
## 4 Hypergraph-Based Multimodal Summarization
### Multimodal Hypergraph Group Transformer
#### 4.4.2 Heterogeneous Inter-Hyperedge Aggregation  
The attention mechanism for information propagation between hyperedges aims to learn the attention weights between vertices and their associated hyperedges. This mechanism accounts for varying importance levels of different vertices concerning their connected hyperedges and vice versa, thereby improving node representation by aggregating topological information from connected hyperedges. We employ a scaled dot product attention mechanism with a heterogeneous attention approach to propagate information from the hyperedge to the vertex. Specifically, consider the target node \(v_{i}\) with its associated hyperedge set \(\mathcal{E}(v_{i})=\{e_{1},e_{2},\ldots,e_{t}\}\). Each hyperedge \(e_{j}\in\mathcal{E}(v_{i})\) has a corresponding learned representation \(\mathbf{e}_{j}\). The attention score between node \(v_{i}\) and its connected hyperedges is expressed as  
\[\mathbf{q}_{i}^{v}=\mathbf{W}^{q}\mathbf{v}_{i},\quad\mathbf{k}_{j}^{ \epsilon}=\mathbf{W}^{k}\mathbf{e}_{j},\quad\mathbf{v}_{j}^{\epsilon}= \mathbf{W}^{v}\mathbf{e}_{j},\]  
\[a_{i,j}=\text{att}(\mathbf{v}_{i},\mathbf{e}_{j})=\text{softmax}_{e_{j}\in \mathcal{E}(v_{i})}\Bigg{(}\frac{\mathbf{q}_{i}^{v}\cdot\mathbf{k}_{j}^{ \epsilon}}{\sqrt{d}}\Bigg{)}, \tag{4}\]  
where \(\mathbf{W}^{q},\mathbf{W}^{k},\mathbf{W}^{v}\in\mathbb{R}^{d\times d}\) are trainable parameter matrices. The representation of each node \(v_{i}\) is updated based on a weighted aggregation of the projected hyperedge representation and the attention score:  
\[\bar{\mathbf{v}}_{i}=(1-\beta)\mathbf{v}_{i}+\beta\sum_{e_{j}\in\mathcal{E}(v _{i})}a_{i,j}\mathbf{v}_{j}^{\epsilon}. \tag{5}\]  
where \(\beta\) is a hyperparameter that controls the balance between the original node representation \(v_{i}\) and the aggregated hyperedge representation \(v_{j}^{\epsilon}\). Thus, the hypergraph representation \(\mathbf{r}_{HG}\) is  
\[\mathbf{r}_{HG}=\frac{1}{|\mathcal{V}|}\sum_{i=1}^{|\mathcal{V}|}\bar{\mathbf{ v}}_{i}. \tag{6}\]
## 3 Hybrid Computing Architecture of MSD Algorithm
### Correlation Calculation Based on SC
#### 3.1.4 The Correlation Circuit Architecture Based on SC  
Building upon the inverters, multipliers, and adders discussed earlier, along with the integration of SNGs and SNCs, this study presents the architecture of the signal correlation circuit based on SC. Considering that the fundamental computational units within SC exhibit low resource consumption but require a long computing time to achieve acceptable computational precision, a parallel computing framework is adopted to improve the computational efficiency.  
Figure 7 illustrates the circuit diagram for the correlation calculation between the received signal and a local signal, where N is the number of symbols utilized in the MSD algorithm and \(N  
Furthermore, the local signal serves as prior information in the MSD algorithm and, thus, is directly stored as random bit sequences in the correlation circuit based on SC. On one hand, the SNGs will consume a certain amount of computational resources. On the other hand, a certain inaccuracy arises during the data domain conversion of SNGs. The pre-storage of a random sequence of the local signal serves to efficiently mitigate the computational resources while simultaneously safeguarding the accuracy of the computed results. Nevertheless, in comparison to storing the binary data of the local signal, this strategy entails a higher consumption of storage resources.
## 3 Results and Validation
### Preparation of Landslide Susceptibility Mapping  
To produce the final resultant maps, the landslide susceptibility index (LSI) for each pixel was used to indicate the landslide probability occurrence in a certain area. In this work, the LSI measure is estimated for the study area, and the resultant maps were reclassified to five levels using the natural break method [133]. Figure 14 shows five resultant maps by the SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO methods. Results show that according to the SWARA-ANFIS-WOA model, the moderate class has the largest area (20.80%), followed by low (20.32), very low (19.82%), high (19.68%), and very high (19.38) classes. For the SWARA-ANFIS-GWO model, the percentages are 37.66%, 32.39%, 12.22%, 11.52%, and 6.1% for the very low, low, moderate, high, and very high classes, respectively. For the SWARA model, the  
Figure 12: The SWARA-ANFIS-PSO method. (**a**) Target and outputs for the training set; (**b**) target and outputs for the verification set; (**c**) MSE and RMSE for the training set; (**d**) frequency errors for the verification set; (**e**) MSE and RMSE for the verification set; (**f**) frequency errors for the verification set.  
Figure 13: Convergence plot of the SWARA-ANFIS-GWO, SWARA-ANFIS-WOA and SWARA-ANFIS-PSO methods.  
percentages are 19.60%, 20.22%, 19.95%, 19.95%, and 20.28% for the very low, low, moderate, high, and very high classes, respectively. For the SWARA-ANFIS model, the percentages are 0.24%, 32.23%, 20.60%, 17.02%, and 29.91% for the very low, low, moderate, high, and very high classes, respectively. For the SWARA-ANFIS-PSO model, the percentage are 15.72%, 22.55%, 22.86%, 18.50%, and 20.37% for the very low, low, moderate, high, and very high classes (Figure 15).  
Figure 14: _Cont._
# Navigation Path Extraction and Experimental Research of Pusher Robot Based on Binocular Vision
## 4 Conclusions  
1. In this study, a pusher robot based on binocular vision was developed for dairy farms, which was mainly composed of a vehicle navigation hardware system, feeding operation system and control system. Among them, the control system was used for the extraction and tracking control of the pushing operation path, which met the technical requirements of the pushing operation. The device adopted a modular hardware structure design and layered software structure design, which met the requirements for data throughput and response speed.  
2. This study proposed a method for extracting working paths in farms based on machine vision. The sky removal, noise removal and grayscale operations were performed on the collected images by using image transformation and smoothing. Then, a Gabor filter, PCA and global threshold segmentation were used to further extract the texture features, main feature components and feed belt features of the cowshed environment. ROI was used to extract contour boundary lines for irregular feed bands.
3. The navigation test results showed that the absolute deviations of the pusher robot at different initial lateral distances were all within 15 cm. Meanwhile, the maximum lateral deviation in different test areas was 8.9 cm, and the average maximum lateral deviation was 7.6 cm. The above test results showed that the pusher robot could operate stably without affecting the feeding of the cows, and the accuracy met the navigation requirements of the robot in the feeding operation. In the next study, the navigation deviation will be further reduced by means of algorithm optimization and assisted positioning.  
Conceptualization, Z.Y. and F.T.; methodology, F.L. and Z.W.; validation, Y.Y.; formal analysis, Z.S.; investigation, R.W.; resources, S.Y.; writing--original draft preparation, G.H.; writing--review and editing, Z.Y. and F.T.; project administration, F.T.; funding acquisition, F.T. All authors have read and agreed to the published version of the manuscript.  
This study was funded partly supported by National Natural Science Foundation of China, grant number 32072786, and the Shandong Agricultural University Research Foundation, grant number 030-72195.  
Not applicable.  
The data used to support the findings of this study are included in the article.  
The authors declare no conflict of interest.
## 2 Airborne SAR System Requirements  
A new L-Band antenna system is required for the next generation airborne SAR sensor of the German Aerospace Center. To fulfill the system requirements, a dual polarized antenna operating in L-Band with a center frequency \(f_{0}\) = 1.325 GHz and a bandwidth of 150 MHz is needed. The antenna system is a planar phased array of \(5\times 8\) elements with side-looking configuration by means of beamforming in elevation. The final antenna will be mounted on the DLR research aircraft Do-228 as it is shown in Figure 1.  
This new design exploits the restricted available space on the antenna carrier by maximizing the number of the array elements in comparison with previous sensors [12], thus enhancing the radiation properties of the antenna and achieving a better radiation pattern shaping. The new design allows an implementation of up to 66% more antenna elements than the previous L-Band airborne system, due to the extremely compact implementation of the single element, which is based on a cavity-box stacked microstrip patch with aperture coupling feeding.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 5 Fundamentals of Nonlinear Optimization
### Karush-Kuhn-Tucker (KKT) Conditions
#### 5.2.1 Geometric Interpretation of KKT Necessary Conditions  
The KKT conditions show the relation between the gradient of the objective function and the gradients of all the constraint functions. It shows that at a critical point \(\bar{\mathbf{x}}\), the gradient of the objective function must be a linear combination of the gradients of all the constraints of the problem, where the dual vectors \(\boldsymbol{\mu}\) and \(\boldsymbol{\lambda}\) represent the weights. The complementary slackness conditions in Equation (8d) enforces the Lagrangian multipliers of the inactive constraints to take zero values. As a result the vector \(\boldsymbol{\lambda}^{T}\
abla\mathbf{h}\left(\bar{\mathbf{x}}\right)+ \boldsymbol{\mu}^{T}\
abla\mathbf{g}\left(\bar{\mathbf{x}}\right)\) belongs to the cone of gradients of the active constraints (i.e., equalities and active inequalities). The geometrical interpretation of the gradient KKT conditions is that \(-\
abla f\left(\bar{\mathbf{x}}\right)\) belongs to the cone of the gradients of the active constraints at the feasible solutions \(\bar{\mathbf{x}}\). If \(-\
abla f\left(\bar{\mathbf{x}}\right)\) lies outside the cone of the gradients of the active constraints, then \(\bar{\mathbf{x}}\) is not a KKT point.
## 2 Materials and Methods
### Fiber Distribution Evaluation  
A quantitative evaluation of the fiber distribution including the fiber dispersion and fiber orientation was also carried out to investigate the effect of the rheological properties on the fiber distribution. If there is a flow velocity gradient, as shown in Figure 4, rigid fibers submerged in the fluid rotate due to the velocity gradient. The velocity profile of a fluid is obviously dependent on the rheological properties of the fluid, and then the rotational movement is naturally influenced by the rheological properties of the fluid [46; 47]. The fiber dispersion indicates whether the change in the rheology according to the amount of WRA causes any sediment or agglomeration of fibers.  
Figure 3: Set-up of three-point bending test with notched beam. (**a**) Test setup; (**b**) Dimensions.  
Figure 2: Placing method adopted for fabricating beam specimen. (**A**) Device; (**B**) Material flow.  
The fiber dispersion and orientation were evaluated using an image analysis technique [35]. After finishing the flexural test, the tested beam specimens were seen at locations near the flow starting point and near the midspan. The former is 35 mm away from the end of specimen where fresh UHPFRC was placed and the latter is 220 mm away from the end. To obtain a sound section without cracks, which are supposed to be formed along the notch in the flexural test, the images at the midspan section were not used for the analysis.  
The fiber dispersion was quantified with the coefficient (\(\alpha_{f}\)) defined as follows [40]:  
\[\alpha_{f}=\exp\left\{-\sqrt{\frac{\sum(x_{i}-1)^{2}}{n_{f}}}\ \right\}, \tag{1}\]  
where \(n_{f}\) denotes the total number of fibers detected on the image and \(x_{i}\) describes the number of fibers detected in the \(i\)-th sector when the whole image area was divided into several sectors of an equivalent square area. \(\alpha_{f}\) close to 1 indicates a homogeneous dispersion of fibers, and 0 for \(\alpha_{f}\) means a deeply biased dispersion of fibers.  
For the evaluation of the fiber orientation distribution, the fiber orientation coefficient (\(\eta_{\theta}\)) was introduced. The fiber orientation distribution was first measured by calculating the inclination of each fiber on the image. The coefficient was then calculated by the following equation [48]:  
\[\eta_{\theta}=\int_{0}^{\pi/2}p(\theta)\cos^{2}\theta\ d\theta, \tag{2}\]  
where \(p(\theta)\) is the measured fiber orientation distribution. If all the fibers are perfectly aligned in a direction, the fiber orientation coefficient (\(\eta_{\theta}\)) to the direction is equal to 1.
## 4 Discussion
### Limitations and Future Directions
#### 4.4.2 Controlled Experimental Conditions  
Technically, the traversing Wi-Fi signal between the sender and receiver is vague and not applicably filterable within an open environment. Therefore, it was necessary to conduct the study in a controlled environment to ensure the data was precise enough for accurate prediction. This controlled environment included careful placement of Wi-Fi antennas, minimal electronic interference, and avoiding rooms with heavy Wi-Fi traffic. Additionally, participants were instructed to follow specific guidelines, such as lying still and maintaining a consistent posture throughout the experiment. These measures aimed to minimize external variables and ensure the reliability of the collected data. However, we introduced deliberate noise during some parts of the experiment to test the robustness of our method, ensuring that the approach could handle real-world conditions effectively.
## 2 Related Works
### SM Based On FDA  
SM, as an emerging information modulation technique, has gradually been used in wireless communication in recent years. The basic idea of SM is to exploit spatial location information to transmit messages. Traditional SM is to map block of upcoming information based on transmit antenna number carrying unit and constellation diagram carrying unit (e.g., M-PSK). However, in this paper, we research wireless communication based on FDA with cooperative LUs. Therefore, unlike the traditional SM technique, we apply SM into FDA by directly using LUs number information as a carrying unit to transmit more information bits. At the receiving end, one or some LUs will be active and receive messages. The other LUs are inactive and receive zero power. In our scheme, only if we correctly estimate the LUs number informations and the received symbols will the block of information bits retrieved by the cooperative LUs. We achieve security communication in the case Eves are proximal to LUs or even in same locations.  
In general, our proposed scheme maps the block of information bits, which is determined by the number of LUs and digital modulation. The size of each block for a system that use M-PSK or M-QAM constellation diagram and K LUs can be calculated as  
\[B=\log_{2}(K)+Q\log_{2}(M), \tag{5}\]  
where the first term \(\log_{2}(K)\) is the number of information bits carried by LUs number. \(Q\) is the number of active LUs, and the second term \(Q\log_{2}(M)\) is the number of information bits carried by symbols of constellation diagram, which are received by \(Q\) active LUs, respectively. For example, we set the BPSK constellation diagram (\(M=2\)), the number of LUs is \(K=4\), and the number of active is \(Q=3\), which maps five message bits as a block to be transmitted at one time, as shown in Table 1.
## 6 Application Examples
### Example of Composite Laminated Plate  
The proposed method is also applied in the reliability analysis of an 8-layer composite laminated plate made of AS4 carbon/3501-6 epoxy obtained from [44]. The considered micro uncertainty parameters contain material property parameters (longitudinal elastic modulus of fiber \(E_{f1}\), transverse modulus of fiber \(E_{f2}\), elastic modulus of matrix \(E_{m}\), fiber volume ratio \(V_{f}\), major Poisson's ratio of fiber \(\
u_{f12}\), Poisson's ratio of matrix \(\
u_{m}\), in-plane shear modulus of fiber \(G_{f12}\)), and strength parameters (longitudinal tensile strength of fiber \(X_{fT}\), transverse tensile strength of matrix \(Y_{mT}\), compressive strength of matrix \(Y_{mT}\) and shear strength of matrix \(Y_{mC}\)), which are assumed to be Normal distribution functions according to [45]. The first uncertainty parameter (mean value) of \(E_{f1}\) is assumed to be uncertain. The insufficient data from Source 1 contain sparse sampling points of \(\{2.01,2.34,2.40\}\times 10^{11}\) Pa and multiple intervals of \(\{\left[2.01,2.21\right]\times 10^{11},\left[2.14,2.27\right]\times 10^{11}\}\) Pa, and the sub-intervals with their corresponding BPAs from Source 2 are listed in Table 3.  
The hybrid reliability of the 8-layer composite laminated plate is analyzed using the proposed algorithm. The reliability results of the proposed method and Monte Carlo method are compared in Figure 5; the reliability indices considering uncertainty information from single source are also calculated. Compared with the results under single data source, the proposed method can obtain a more accurate reliability result, and the compu  
Figure 4: Reliability analysis results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Interval** & **BPA** \\ \hline \(\left[1.99,2.10\right]\times 10^{11}\) Pa & 0.12 \\ \(\left[2.10,2.25\right]\times 10^{11}\) Pa & 0.41 \\ \(\left[2.20,2.31\right]\times 10^{11}\) Pa & 0.33 \\ \(\left[2.29,2.48\right]\times 10^{11}\) Pa & 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Intervals and BPAs from Source 2.  
tational effort is less than that of the Monte Carlo method, which shows the effectiveness of the proposed method.