esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application to Steel Buildings in Downtown Area  
### Frame Analysis According to Levels of Gravity Load  
The feasibility of implementing the proposed optimal design method using the LM index was investigated through the quantity-optimized design of the underground structure of the steel buildings under various gravity loads. The dead load was 5.2 kN/m\({}^{2}\) including the slab thickness and the floor finishing. The live load was divided into five categories considering the usage of the underground space in the downtown area: 2.5, 4.0, 6.0, 8.0, and 12.0 kN/m\({}^{2}\) (see previous work [19] for detailed information about the live loads). The dimensions of the structural plan for the steel double-beam floor systems were summarized in Figure 2, and MIDAS-Gen [32], a commercial structure analysis program, was used for frame analysis. In addition, to examine the reduction of material quantity and the improvement of the environmental performance of the DBO system, the quantity-optimizations for the GBG and DBX systems were performed using the proposed optimal design method under the same design conditions.  
Figure 9 shows the moment diagram of the structural members in each structural system with the locations of the maximum moment (\(M_{u}\)). Depending on the load and boundary conditions of the structural members, the locations of the maximum moment were varied. From the results of the frame analysis for each structural system, the \(M_{u}\), used as demand in the code-based design, and the \(LM_{D}\), used as demand in the proposed optimal design, are summarized in Table 1.  
In the code-based design, if the initially assumed cross-section of the H-beam does not satisfy the safety-related constraints, the cross-section of the H-beam must be changed because the constraints are mandatory in structural design. On the other hand, the proposed design method can directly select a cross-section of the H-beam with \(LM_{\text{C}}\) that minimizes the difference from the \(LM_{D}\) summarized in Table 1. For example, the \(LM_{D}\) of the double  
Figure 8: Optimization process in code-based design and proposed optimal design methods.  
beam in a DBO system with a live load of 8 kN/m\({}^{2}\) is 1075.8 kNm\({}^{2}\). In this condition, a cross-section of H-400x200x8x13 having the \(LM_{\text{C}}\) of 1233.7 kNm\({}^{2}\) is simply selected without iteration because it has the minimum \(LM_{\text{C}}\) that satisfies the objective function. In addition, the \(\phi_{p}M_{\text{n}}\) of the selected double-beam (356.3 kNm) exceeds the \(M_{u}\) (336.2 kNm), so it can be seen that the cross-section selected by the LM index also meets the safety-related constraint in this case.
## 4 Results
### Uncertainty Evaluation of Geolocation of BNU-1 L1A Product  
Figure 5 shows the three-dimensional scatter diagram of the influence of the change in the satellite's attitude angle--roll, pitch, and yaw--on the geolocation change. The results show that when the angles of roll, pitch, and yaw change from \(0.1^{\circ}\) to \(0.5^{\circ}\), the geolocation change in the upper left corner point of the image changes from \(-6256\) m to \(6594\) m in the longitude and from \(-13\),\(915\) m to \(-112\) m in the latitude. Similarly, the geolocation change in the center point of the image changes from \(-6431\) m to \(4998\) m in the longitude and from \(-11\),\(889\) m to \(-251\) m in the latitude. These geolocation changes are non-linear (Figure 5). Under imaging conditions in polar regions, the random error of attitude measurement cannot be eliminated due to the lack of ground control points in polar regions [28]. To obtain high-precision geolocation products, it is necessary to add CPs to the image for geometric correction. Since there are few textures observed on high-reflectance ice and snow surface in polar regions, the extraction of CPs is the key to geometric correction.  
Figure 4: Displacement vectors of the CPs for the four sample BNU-1 L1A images and the corresponding MODIS images in Figure 3. The vectors start and end at the CPs’ coordinates in polar stereographic projection on the BNU-1 L1A images and the corresponding MODIS images, respectively. The color of the vectors represents the error magnitude according to the legend.
## 2 System Models
### The 5G Signal Model  
In 5G systems, CP-OFDM waveforms are widely used in the physical layer transmission of the air-port interface, providing high-speed and reliable data transmission capability for 5G. CP-OFDM waveforms stand for Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) waveforms. CP-OFDM waveforms are a multicarrier modulation method used in 5G communication systems to achieve high-speed data transmission and resistance to multipath interference in wireless transmission. The CP-OFDM waveform is a multicarrier modulation method used in 5G communication systems to achieve high-speed data transmission and resistance to multipath interference in wireless transmissions. CP-OFDM employs the cyclic prefix technique to counteract inter-symbol interference due to multipath propagation by adding a repeating data point at the beginning of each OFDM symbol. This makes it easier to synchronize and demodulate the signal at the receiving end, thus improving the performance of the system. According to the literature [15], the CP-OFDM time domain signal can be expressed by the following equation:  
\[s(t)=\sum_{n=0}^{M-1}\sum_{k=0}^{N-1}c_{n,k}p(t-n(T_{s}+T_{c}))e^{-2\pi k\frac{1 }{N}(T_{s}+T_{c})} \tag{2}\]  
where \(c_{n,k}\) is the nth OFDM symbol modulated on the kth subcarrier, \(T_{s}\) and \(T_{c}\) are the duration and protection interval, respectively, and \(p_{t}\) presents the gate functions.  
\[S(f)=\frac{P_{s}}{T_{tot}}\sum_{k=0}^{N-1}\left|P(f-\frac{k}{T_{s}})\right|^{2} \tag{3}\]  
\(p_{s}\) is the power of a single subcarrier of OFDM with subcarrier spacing \(\frac{1}{T_{s}}\). The power spectral density of CP-OFDM is obtained by performing a Fourier transform on the gate function:  
\[S^{CP}(f)=P_{s}T_{tot}\sum_{k=0}^{N-1}\left\{\frac{\sin[(f-\frac{k}{T_{s}})T_{ tot}]}{[(f-\frac{k}{T_{s}})T_{tot}]}\right\}^{2} \tag{4}\]  
According to the literature [16], many different 5G candidate waveforms have been proposed, such as Filter Bank Multi-Carrier (FBMC), Universal Filtered Multi-Carrier (UFMC), Generalized Frequency Division Multiplexing (GFDM), etc., and application examples and performance comparisons of these waveforms are carried out. In this paper, the most commonly used OFDM waveforms for 5G are selected for power spectral density simulation, while FBMC waveforms are added for comparison, as shown in Figure 4  
Figure 3: Power spectral density of BeiDou-S outbound signals.  
below. The blue part of the figure indicates the OFDM waveform and the red is the FBMC waveform PSD.  
As can be seen in Figure 4, OFDM has higher out-of-band leakage and is more likely to cause interference to systems adjacent to it, so in this paper, the 5G waveforms are identified as OFDM waveforms for analysis.
# Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT
### Related Work  
There have been many research results on the combination of RIS technology and information and energy co-transmission technology [28; 29; 30; 31; 32]. For example, [28] investigated the base station radiant power minimization problem in SWIPT-based RIS-assisted MIMO communication networks in the hope of improving the energy utilization of the communication system to meet the needs of 6G green communication. Further, [29] investigates a multi-user RIS-assisted MISO communication network based on SWIPT and achieves base station (BS) transmits power minimization based on a nonlinear energy-harvesting model, generalizing the results in [27]. Ref. [30] proposed a novel RIS-enhanced SWIPT system based on an EMC framework, which improves energy efficiency by optimizing the impedance parameters of the RIS elements with the active-shaped beam at the base station. Ref. [31] investigated the problem of co-optimization of information rate and energy harvesting in a SWIPT-based RIS-assisted MISO communication network. To solve the problem, the optimal solution was obtained by using pricing-based, sequential, and alternating optimization methods. Ref. [32] combines RIS-assisted SWIPT technology with Unmanned Aerial Vehicle (UAV) technology and develops iterative algorithms using methods based on successive convex approximations and alternating optimization to maximize the minimum average achievable rate for multiple devices. However, due to the defect of P-RIS \"multiplicative fading\", the actual capacity gain that can be brought by the existing P-RIS is insignificant for typical communication scenarios with strong direct paths, which leads to a significant reduction in the energy harvested at the user end and the lack of existing research on the SWIPT-based multiuser RIS-assisted scheme for maximizing energy harvesting at the user end under MISO communication networks.
## 4 Description of the System
### The Onboard Computer (OBC)  
The onboard computer is responsible for obtaining, processing and storing all the relevant data provided by the positioning system and controlling the transfer of data to data storage units in the transportation network. The computer is able to operate in adverse environmental conditions (temperature and vibrations), has a small size and has sufficient computation and storage capacity to execute all the processes required in this intelligent system. The more relevant characteristics of the computer used by the system are as follows: reduced dimensions (11.5 \(\times\)10.1 \(\times\)2.7 cm), a weight not greater than 330 grams, a 2 GHz CPU, a 2 GB DDR2 memory, power consumption not greater than 10 W, an operating temperature that varies between 0 and 70 \(\mathrm{\SIUnitSymbolCelsius}\) and a voltage range between 8 and 14 \(\mathrm{V}\). These parameters facilitate its installation given that its voltage levels can be provided by a conventional vehicle electric system (which provides voltage of up to 25 V). Finally, the computer is equipped with a Wi-Fi communication interphase and four USB 2.0 ports. This computer has been fitted with a 64 GB compatible 2.5\" solid-state hard drive (SSD). A solid-state drive is used because these storage devices can tolerate more intense vibration levels than conventional magnetic devices and do not increase the ambient temperature.
## 2 Materials and Methods
### Docking-based Homology Models' Comparison  
To appreciate the qualitative and the quantitative differences derived by using the two alternative templates, we decided to retrospectively compare the performance of molecular docking in posing and scoring for a selection of known potent and selective either agonists or antagonists. For each ligand and each homology model, 20 docking poses were retained and the representative one has been selected using the combination of two conditions: (1) the best match in reproducing the two crucial and conserved ligand-receptor interactions (hydrogen-bond with N\({}^{6.55}\) and \(\pi\)-\(\pi\) stacking with FEL\({}^{2}\)); (2) for all poses in agreement with the previous condition, the best combination of the force field-driven energetic Ele and vdW contributions.  
The EM correlation coefficient (whose value ranges between -2 and 2) was recently implemented as a statistical tool to evaluate the correlation between two variables dependent on one or more of interest, and to estimate the commutative influence of the latter without statistical bias [52]. It quantifies simultaneously (Equation (3), Figure 4): if two variables (numerical results), \(x\) and \(y\), have the same absolute value and the same sign (positive or negative); the sign of each variable (_sgnx_, _sgny_); the ratio between x and y ( \(\mid\)x/y\(\mid\)). Here, we demonstrate that the EM coefficient can be solved as a function of the ratio \(x\)/\(y\):  
\[\begin{array}{c}EM=\frac{\sqrt{(\mid xy\mid)}(sgnxsgny)-\left[\left(\frac{ \mid x\mid+y\mid}{2}\right)(sgnx)\right]}{\left(\frac{\mid x\mid+y\mid}{2} \right)}\\ \qquad\qquad\qquad=\frac{-x\left(y-\sqrt{\mid xy\mid}\right)}{\mid xy\mid(\mid x \mid+y\mid)}\\ =(sgnxsgny)\left(\frac{2\sqrt{\mid x/y\mid}}{\mid x/y\mid+1}-sgny\right)\\ EM_{\textit{gauss}}=(sgnxsgny)e^{(\frac{-2N^{2}}{3})}-(sgnx)\end{array} \tag{3}\]  
In this work, \(y\), specifically, represents the energy contribution of the ligand (Ele or vdW) calculated on the reference model complex (A2a-based model), while \(x\) represents the energy contribution of the same ligand calculated on the model subjected to the comparison (A1-based model). Positive energy values indicate disfavored binding poses, negative values in contrast indicate favorable binding poses. Four combinations of x and y energy values are possible, giving four different possible behaviors:  
1. one pose is better than the other (x model pose is better than y model pose, blue spots, \(-+\); y model pose is better than x model pose, red spots, \(+-\));
2. both the poses are disfavored (++, orange spots);
3. both the poses are favored (\(--\), white spots).  
In the latter case of white spots, the exponential form of the EM coefficient is expressly related to the ratio of \(x\) and \(y\), with a view to individuate the most favored pose. In this way, we determined the models' influence on the binding mode of ligands and, finally, their wellness.  
Figure 4: Graphical representation of the EM correlation coefficient (Equation (3)), a Gaussian function of the logarithmic ratio between \(x\) and \(y\) absolute values (\(N\)).
# Prediction of Dry Mouth Condition Using Radiomics Features from Tongue Diagnosis Image
## 1 Introduction
The absence of standardized tests for saliva flow and salivary gland function adds to the inconsistency in diagnostic accuracy. External factors, such as stress and environmental influences, can affect saliva production, potentially leading to misdiagnosis. Additionally, certain diagnostic procedures, such as sialography, are invasive and may cause discomfort, limiting their routine application. Given these challenges, there is a pressing need for ongoing research and the development of more objective, standardized diagnostic tools. In particular, recent studies have reported using tongue images, which are easily accessible and can be obtained in an automated manner, to predict various health indicators--ranging from general health assessments to the diagnosis of coronary artery disease based on deep learning [19]. Therefore, advances in this area are crucial in improving the accuracy of dry mouth diagnosis and enhancing patient care. In this paper, we propose a novel, non-invasive method for predicting xerostomia through the analysis of tongue images.
# Fast Loop Closure Selection Method with Spatiotemporal Consistency for Multi-Robot Map Fusion
## 4 Proposed Method
### Graph Theory for Clique
Nodes in an undirected graph represent inter-robot measurements, and two nodes are connected by an edge if and only if the corresponding element in the matrix \(\mathbf{M}_{c}\) is not zero. Considering that the clique is fully connected, all inter-robot measurements represented by nodes in the clique are pairwise internally consistent. In other words, the topologically correct and consistent subset with the largest cardinality in the graph is the solution to the weight function, and this subset has the highest cumulative sum. Finally, the inter-robot loop closures corresponding to  
Figure 2: A small case for the proposed loop closure selection method. (**a**) Diagram of inter-robot measurement based on continuity of similar scenes. The solid black line in the local trajectory of the single robot connects two sequential pose nodes, while the dotted line connects two non-sequential pose nodes. (**b**) Topologically correct matrix. (**c**) Cliques Topologically correct and consistent matrix. (**d**) corresponding to the overlapping trajectory regions. (**e**) Selected subset. the selected subset \(\mathbf{Z}^{*}\) are represented in the pose graphs, as shown in Figure 2e. Now, the solution of the weight function has been transformed into the maximum clique problem. In graph theory, finding the maximum clique in a given undirected graph is the maximum clique problem. We select the method proposed in [37] for the maximum clique problem because it is easy to perform and has been open-sourced. Once the proper loop closure selection is solved, the trusted subset of inter-robot measurements can be inserted into the existing SLAM back-end optimizer for map fusion, such as factor graph and graph optimization. Although the focus of this paper is to discuss map fusion in terms of two pose graphs, this solution can seamlessly be extended and applied to multiple pose graphs. We first compute the topology and consistency matrices of each pair of local maps, and then construct the corresponding adjacency matrix and solve for the maximum clique to determine the proper subset of the inter-robot loop closure set. Next, the two trajectories with the highest degree of coincidence and consistency are fused into a new map according to the selected subset, and the newly fused map is used as a new local map. This process can be performed iteratively until all local maps are merged into a global map. In the process of map fusion, it should be noted that the local maps with the highest degree of coincidence and consistency are preferentially selected each time, which can maintain the certainty of map fusion.
## 5 Data Fusion  
Although all the illustrated methods certainly represent suitable tools to achieve the purposes they are conceived for, sometimes, the complexity of an analytical problem requires to be regarded from several different perspectives to be comprehensively embraced. As an example, many present-day food chemistry issues cannot be easily addressed unless distinct instrumental techniques (e.g., Gas Chromatography-Mass Spectrometry--GC-MS--and MIR spectroscopy) are combined for a more extensive characterization of the samples at hand. Similar strategies are commonly defined _multi-block_ and the chemometric approach used for the concerted analysis of multi-platform data is known as _Data Fusion_ (DF).  
Broadly speaking, DF enables the simultaneous extraction of meaningful and useful information from diverse analytical sources. In recent years, DF has been found to provide more exhaustive descriptions of studied systems compared to when single datasets are assessed separately. For this reason, in the last decades, numerous DF methodologies have been proposed in the literature with the three tasks surveyed in the previous sections of this article in mind: data exploration, regression and classification. Nonetheless, despite the ultimate purposes behind their development, such methodologies are usually classified according to the _level_ at which the data fusion is implemented. In the so-called _low-level_ DF, for instance, raw datasets are simply concatenated and analyzed at the same time as a unique global ensemble. _Mid-level_ DF extracts specific features (e.g., principal components or latent variables) from each considered dataset which are subsequently gathered and investigated concurrently. Finally, in _high level_ DF, the outcomes of individual models constructed for every concerned dataset are pooled in a unique solution. Another common distinction among DF strategies is based on the nature of their underlying algorithms (_sequential_--if joint information is retrieved iteratively--or _parallel_--if joint information is retrieved collectively) [176].  
Due to the large number of existing DF approaches, the following subsections will offer only a basic and brief description of those most commonly used in the field of food authentication. In Table 3, a detailed list of applications of DF in this domain is also reported.
## 2 Aroma Volatile Composition and Their Biological Characteristic of Major Fruits
### Classification of Volatile Compounds in Fruit Flavor  
Various types of fresh fruits produce distinct volatile profiles. Flavor volatiles are derived from an array of compounds including phytonutrients such as fatty acids, amino acids, carotenoids, phenols and terpenoids [10]. Fruit volatile compounds are mainly comprised of diverse classes of chemicals, including esters, alcohols, aldehydes, ketones, lactones, and terpenoids. However, some sulfur compounds, such as \(S\)-methyl thiobutanoate, 3-(methylthio) propanal, 2-(methylthio) ethyl acetate, 3-(methylthio) ethyl propanoate, and 3-(methylthio) propyl acetate, also contribute to the flavor of fruit such as melons (_Cucumis melo_ L.) [9]. Bound volatiles are recognized as a potential source of aroma compounds in fruits such as kiwifruit (_Actinidia deliciosa_) [17]. Although an overwhelming number of chemical compounds have been detected as volatile compounds in fresh fruit, only a fraction of these compounds have been identified as impact components of fruit flavor based on their quantitative abundance and olfactory thresholds [18]. Many C\({}_{10}\) monoterpenes and C\({}_{15}\) sesquiterpenes compose the most abundant group of compounds present in the aroma profile. In some cases, these are also the key compounds determining the characteristic aroma. For example, the terpenoids \(S\)-linalol, limonene, valencene and \(\beta\)-pinene are key aroma compounds of strawberry _(Fragaria x ananassa_), kubo (_Cereus peruvianus_ L.) and citrus sp.) [19; 20; 21]_. Volatile terpenoid compounds, potentially derived from carotenoids, are important components of flavor and aroma in many fruits. Of particular interest are a group of terpenoid flavor volatile compounds generally present at relatively low levels but possessing strong effects on the overall human appreciation. Among these are \(\beta\)-ionone, geranylacetone (6,10-dimethyl-5,9-undecadien-2-one), pseudoionone (6,10-dimethyl-3,5,9-undecatrien-2-one), \(\beta\)-cyclocitral, gerani, theaspirone, \(a\)-damascenone and \(\beta\)-damascenone. Their structures reveal an isoprenoid-based origin, and they were long assumed to be the products of the oxidative cleavage of carotenoids [22].