esa-sceva/satcom-chunk-collection huggingface dataset:
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 4 The Proposed Method
### LBIA Algorithm
#### 4.3.2 The Details of the Algorithm  
In the first step, degree normalization and PKs are used to calculate the initial information of each node. The propagation ability is considered to be proportional to its degree value and positional information. The degree of a node can reflects its ability to disseminate information; the larger the degree value, the more paths the information may choose. PKs represents an optimized measure of the node's position. There is an area of the network consisting of multiple important nodes, which belongs to the core of the network, and nodes in the state of the core are more efficient at propagating information.  
Figure 3: A simple network: (**a**) Assign initial information \(M_{x}\) to each node. (**b**) Information propagation between nodes. (**c**) Aggregate information from neighboring nodes. Where the number represents the node ID. Yellow node is source node and green nodes are first order neighbours of the source nodes. The arrow pointing is the direction of information propagation.  
In a network, information propagation between nodes is achieved through connections and interactions between nodes. Information propagates from the source node through the network, first to the surrounding nodes and then outward. This propagation usually creates a wave propagation effect in the network, gradually affecting more nodes, and each node receives information from its neighbors. The Jaccard similarity can describe the common information between two nodes. The higher the similarity, the higher the number of common neighbors between the nodes; therefore, the information dissemination will also be limited to a small area. By comprehensively considering the Jaccard coefficient, we extend the previously mentioned local balance value (Equation (5)) as the information propagated between nodes.
## 4 Buried Electrode Scheme Design and Performance Analysis
### Ground Electrode Depth  
To calculate the impedance of the buried electrode, see Equation (11):  
\[Z_{r}=\frac{1}{4\pi\sigma l}\ln[(4l/a)-1+1/d] \tag{11}\]  
For this model analysis, the effects of the following variables on the electrode impedance formula are considered: where \(\sigma\) is the soil conductivity, \(l\) is the depth of the ground electrode, \(a\) is the radius of the ground electrode, and \(d\) is the separation distance between the two vertical electrode rods. The model reflects the effect of the insertion depth of the ground electrode on the impedance between electrodes.
# Risk Assessment of Drilling and Blasting Method Based on Nonlinear FAHP and Combination Weighting
## 5 Discussion  
### Rationality Analysis of Index Weight  
To validate the correctness of the proposed method, this study considered three additional weighting calculation methods while accounting for expert weight determination, as described below:  
1. Conventional FAHP [38] employs fuzzy numbers for pairwise comparisons by experts to construct judgment matrices and calculate weights, followed by consistency verification.
2. LFPP-FAHP [31] utilizes logarithmic fuzzy preference programming (LFPP) to determine the weights of evaluation indicators, incorporating field conditions to establish reliability functions for dynamic indicators and grading criteria for indicator rationality.
3. FAHP-SPA [39] applies triangular fuzzy analytic hierarchy process (FAHP) to determine the fuzzy weights of evaluation indicators. At the objective level, the set pair analysis (SPA) method compares the affiliation between indicator sets and standard sets to define the ranking scope of evaluation criteria. Finally, a combined subjective-objective approach determines the overall risk level of tunnel operational safety.  
Taking the expert weights at the second level of the indicator system as an example, Figure 6 illustrates the expert weights calculated by different methods.  
The comprehensive weight method adopted in this study demonstrates superior robustness and discriminative power in weight calculation results compared to the three  
Figure 6: Second-level index weight composition.  
alternative methods (FAHP, LFPP-FAHP, and FAHP-SPA). Its advantages primarily lie in the integrated consideration of consistency and compatibility in expert judgments. For key indicators such as t\({}_{35}\), both FAHP and LFPP-FAHP exhibit extreme values due to excessive reliance on fuzzification or single-matrix optimization, while FAHP-SPA tends to smooth out differences through set pair analysis. In contrast, the proposed method dynamically adjusts expert weights to avoid extreme deviations while maintaining reasonable weight differentiation. For highly conflicting indicators like t\({}_{23}\), significant discrepancies exist between FAHP and FAHP-SPA results, whereas our method achieves more precise aggregation through compatibility iteration, effectively balancing group disagreements.  
Furthermore, the proposed method demonstrates enhanced stability in calculating low-weight indicators, indicating superior noise resistance compared to other approaches. FAHP-SPA's over-reliance on uncertainty analysis may drive some weights toward zero, while LFPP-FAHP's neglect of compatibility can lead to deviations from group consensus. In comparison, our method comprehensively optimizes weight allocation by integrating judgment matrix quality and inter-expert compatibility. This approach not only mitigates the subjective fuzziness bias inherent in FAHP but also overcomes the extreme optimization tendency of LFPP-FAHP and the over-smoothing issue of FAHP-SPA, thereby exhibiting significant advantages in both accuracy and discriminative capability.
## 1 Introduction and Background  
This introductory section is structured in two parts: the first outlines the socio-environmental context and research motivations, while the second presents the scientific background and research framework supporting the experimental phase.  
The urgency imposed by environmental issues is progressively leading the fields of architecture and engineering to broaden their fields of interest, expanding toward new sectors, including agriculture and energy [1]. In this regard, the well-documented challenges associated with (and resulting from) energy procurement currently represent the driving force behind the global acceleration that, since the Kyoto Protocol, has increasingly influenced production sectors [2].  
Energy consumption in the residential and agricultural sectors constitutes a significant component of overall energy demand, both in Italy and in the European Union. In 2022, households accounted for 27.3% of final energy consumption in European countries, while agriculture represented approximately 3% [3]. In the agricultural sector, crude oil represents the main energy source, accounting for about 60%. Electricity and natural gas accounted for approximately 12% each, while renewable energy contributed 10% [4].  
This phenomenon is gradually shaping scientific research and fostering extensive and multi-layered academic attention to the development of new experimental applications for improving the energy efficiency of buildings, regardless of their intended use [5]. This trend has now become well established within the disciplines of architecture and engineering, which have recently begun to reconsider the role of industrial buildings. This shift has led to growing interest in the development of energy efficiency models and techniques for rural constructions, promoting sustainability in buildings used for agriculture and livestock [6].
## 2 Materials and Methods
### Proposed FIR Filter Architecture Using Truncated MAC Units  
The number of KSAs in the MAC unit can be reduced to one if we use the iteration of the circuit in Figure 1 and the operation principle of the MAC unit in Figure 2. The output of each internal MAC unit in Figure 1 is fed to the input of the CSA tree of the subsequent MAC unit. Instead, numbers \(A\) and \(B\) from the previous MAC unit can be fed to the input of the adder tree of the next MAC unit, without adding them by a KSA. We call this unit a truncated MAC (TMAC); its operation principle is shown in Figure 5.  
The input of each TMAC unit receives signal \(X(N-i)\), filter coefficient \(b_{i}\) and terms \(A_{i-1}\) and \(B_{i-1}\) from previous TMAC unit output. The output of the TMAC unit is a pair of numbers \(A_{i}\) and \(B_{i}\), which are fed to the next TMAC unit input or are added in a KSA if this TMAC unit is the last one in the FIR DF. The main difference between TMAC and MAC units is the absence of a KSA, which requires the most delay and area, and a slightly wider CSA tree, which transforms one more term.  
The FIR DF scheme based on TMAC units is shown in Figure 6. Two zero signals must be fed to the inputs of the first TMAC unit, and outputs \(A_{K}\) and \(B_{K}\) of the last TMAC block must be fed to the input of the KSA.  
To describe the device shown in Figure 6 in delay and area terms, we must first find parameters \(U_{delay}\) and \(U_{area}\) of the TMAC unit:  
\[U_{delay}(TMAC)\approx 6,8 log_{2}k+1 \tag{22}\]  
\[U_{area}(TMAC)\approx 8k^{2}. \tag{23}\]  
The delay and area of the FIR DF computational part shown in Figure 6 are equal to the sum of delays and areas of the TMAC units and the KSA, respectively. If we denote the \(K\)-th order FIR DF computational part with \(k\)-bit coefficients based on TMAC units by \(FIR_{TMAC}^{K,k}\), then  
\[U_{delay}(FIR_{TMAC}^{K,k})=(K+1)\cdot U_{delay}(TMAC)+U_{delay}(KSA)\approx \tag{24}\]  
\[\begin{split} U_{area}(FIR_{TMAC}^{K,k})=(K+1)\cdot U_{area}( TMAC)+U_{area}(KSA)\approx\\ \approx 3 klog_{2}k+8k^{2}K+8k^{2}+3k+1.\end{split} \tag{25}\]  
A comparison of Formulas (20), (21) and (24), (25) shows that the proposed blocks can reduce the FIR delay by about \(2 Klog_{2}k\) and reduce its area by about \(3 kKlog_{2}k\)
## 2 Materials and Methods  
### Study Areas  
The monitoring tests were carried out in 2019 and 2021 during the spring-summer period (from June to August) in the towns of Perugia and Terni and at the Monte Martano EMEP network station. Perugia and Terni are the main cities of Umbria, a region of central Italy. Umbria includes the upper and middle valley of the Tiber River, bordered on the west and east by low hills that gradually rise in the east to the Umbria-Marche Apennine chain. The geographical characteristic of the region is the presence of wide tectonic basins, some of which host lacustrine (Trasimeno Lake) and riverine (Tiber River) basins. Perugia is the capital city of Umbria and is crossed by the river Tiber. It is located on the top of a hill that reaches a maximum altitude of 493 m a.s.l. The city presents a humid subtropical climate due to its inland location and the hilly topography of Umbria. Typically, summers are warm to hot and humid, while winters are cold with occasional snowfall. Terni is a densely populated and industrialized city located at the margins of the Central Apennines. It lies in a vast plain area (170 m a.s.l.) surrounded by hills (800-1200 m a.s.l.). The local climate is classified as mid-latitude temperate, with warm, humid summers and cold rainy winters [41]. Monte Martano is a regional-background mid-altitude station for air-quality monitoring; it is distant from any local pollution source and influenced by the boundary layer only during summertime [39]. The Monte Martano site was included in the European Monitoring and Evaluation Programme (EMEP) in 2017 (Figures 1 and 2).  
Figure 1: Study areas (**a**) and location of air-quality monitoring cabins (**b**).  
Figure 2: Passive sampler used in the studio (**a**) and Monte Martano cabin (**b**).
# Influence of Different Freezing Modes on the Base Slab Displacement of an Upper Structure
## 3 Testing Results and Discussion
### Displacement in Simultaneous Freezing Test  
As shown in Figure 5, base slab above a frozen wall was lifted. After the process of simultaneous freezing was completed, the maximum displacement of base slab was about 1.2 mm in experiments, which is equivalent to 30 mm in the project. The deformation of base slab is larger than the permissible movement (10 mm). The lifting trends of six displacement sensors are similar. At the beginning of freezing, frost heave rises slightly, and with the freezing time increases, each frozen soil column formed by a single freezing pipe starts to connect into a frozen soil ring, causing the rapid increase of frost heave.  
To approximately estimate frost heave during a freezing process, a simple method is proposed below, which can be used to calculate the lift displacement caused by a frozen wall. In the engineering project, the frozen wall is usually assumed as being uniformly expanded [56], and thus the expanded value can be deter  
Figure 5: Displacement of base slab versus time induced by simultaneous freezing.  
The frost heave ratio \(\varepsilon_{f}\) provides a susceptibility index for laboratory experiments on frost heave, which is defined as:  
\[\varepsilon_{f}=\frac{\Delta h}{h}\times 100\% \tag{7}\]  
where \(\Delta h\) is the frost heave and \(h\) is the sample height at a certain time.  
Then, the frost heave of frozen soil can be calculated by:  
\[\Delta l=h_{f}\cdot\varepsilon_{f}, \tag{8}\]  
where \(\Delta l\) is the expanded value of a frozen wall and \(h_{f}\) is the distance from the top of a tunnel to the base slab of upper structures, as illustrated in Figure 6.  
When subjecting to the same load as that in the project, the frost heave ratio of a sample was 2.67%. Based on Equation (8), the expanded value of frozen soil was 37.4 mm, which is similar to the in-situ result.  
In such an underpassing project, a frozen wall is in direct contact with the base slab of an upper structure. Considering the compressibility of frozen soil, the expanded value of the frozen wall is the maximum deformation value of base slab. Thus, Equation (8) can be applied to determine the maximum lift displacement of upper structures in an engineering project.
developed DBFormer, a dual-branch multiscale model for detecting wheat yellow dwarf disease from UAV multispectral images, achieving a mean intersection over union of 88.51% and a mean pixel accuracy of 93.73% [31]. [PERSON] et al. combined convolutional neural networks and Vision Transformers for multispectral plant disease detection, achieving a precision of 90.1% and an accuracy of 83.3% [32]. These studies highlight the promising potential of multispectral remote sensing technology for plant disease detection. Despite the considerable body of research applying remote sensing technologies for plant disease detection, most studies have focused on image segmentation and object detection, with insufficient attention given to disease area classification. Additionally, existing multi-source image fusion methods for disease regional classification tend to be simplistic and struggle to effectively integrate information from different data sources. Moreover, due to the low spectral resolution of multispectral images and the use of relatively simple image classification models in current studies, spectral semantic information is often underutilized and poorly fused. To address the above challenges, a novel MSFNet was proposed in this paper, which effectively integrates multi-source data and leverages the concept of a visual pyramid to enhance the extraction and fusion of spectral semantic information at different levels, thereby improving the accuracy and performance of disease regional classification. The main contributions of this paper are as follows:  
* A novel MSFNet was proposed for the identification of wheat disease-affected regions from multispectral UAV images, which integrates multi-source features via a multi-source fusion module and enhances spectral variation perception through a hierarchical spectral semantic fusion module. * A novel multi-source fusion module (MSFM) was proposed, utilizing a dual-branch structure to separately process RGB and multispectral vegetation index images, thereby enhancing spatial-spectral semantics and effectively integrating the complementary features of multi-source data. * A novel hierarchical spectral semantic fusion module (HSSFM) was presented, which adopts a pyramid architecture and incorporates an attention mechanism to fuse hierarchical spectral semantics, thereby expanding the model's receptive field for spectral variations.
# Development of Energy-Based Impact Formula--Part I: Penetration Depth
## 3 Development of New Energy-Based Penetration Formula
### Involved Energies in Impact Mechanism  
In this section, the energies involved in the impact mechanism are defined using the theoretical background. As described, there are six energy types that occur in a penetration: Kinetic energy (\(E_{K}\)), deformed energy of a projectile (\(E_{DP}\)), elastic penetrated energy of a concrete panel (\(E_{EP}\)), overall deformed energy of a concrete panel (\(E_{SD}\)), spalling-resistant energy (\(E_{SP}\)), and tunneling-resistant energy (\(E_{T}\)).
## 3 Launch Trajectory Planning
Substituting the components of either possible solution for \(\mathbf{p}_{L}\) and \(\dot{\mathbf{p}}_{L}\) into Equations (9) and (11), we get two nonlinear equations in the two unknowns \(\omega\) and \(\Delta t_{LT}\), which are the last parameters that need to be computed:  
\[x_{T}-x_{L} =\omega\left(x_{rs}\cos\alpha_{Li}-x_{rc}\sin\alpha_{Li}\right) \Delta t_{LT} \tag{18}\] \[z_{T}-z_{L} =\omega\left(z_{rs}\cos\alpha_{Li}-z_{rc}\sin\alpha_{Li}\right) \Delta t_{LT}-\frac{1}{2}g\Delta t_{LT}^{2} \tag{19}\]  
for \(i=1,2\). Equation (18) can be rewritten as  
\[\omega=\frac{x_{T}-x_{L}}{\left(x_{rs}\cos\alpha_{Li}-x_{rc}\sin\alpha_{Li} \right)\Delta t_{LT}} \tag{20}\]  
Substituting Equation (20) into Equation (19), we find  
\[\Delta t_{LT}=\sqrt{\frac{2}{g}\left[\frac{\left(x_{T}-x_{L}\right)\left(z_{ rs}\cos\alpha_{Li}-z_{rc}\sin\alpha_{Li}\right)}{x_{rs}\cos\alpha_{Li}-x_{rc} \sin\alpha_{Li}}-\left(z_{T}-z_{L}\right)\right]} \tag{21}\]  
Substituting Equation (21) into Equation (20), two values of \(\omega\) can be found. Thus, we obtain the values of \(\omega\) and \(\Delta t_{LT}\) that ensure that the launched mass reaches the target point. The method above gives two solutions for \(\alpha_{L}\) and thus two solutions for the launch trajectory. A necessary and sufficient condition for Equation (21) to have solutions is  
\[\frac{\left(x_{T}-x_{L}\right)\left(z_{rs}\cos\alpha_{Li}-z_{rc}\sin\alpha_{ Li}\right)}{x_{rs}\cos\alpha_{Li}-x_{rc}\sin\alpha_{Li}}-\left(z_{T}-z_{L} \right)>0 \tag{22}\]  
We offer the conjecture (verified on a large number of numerical simulations) that Equation (22) holds if and only if the target point lies below the plane containing the ellipse \(\Gamma\).
# Identification of a Human-Structure Interaction Model on an Ultra-Lightweight FRP Footbridge
## 4 Numerical Study
### Modal Analysis  
Employing the described model, the first three numerical vibration modes of the structure were obtained by carrying out a modal analysis. Figure 7b-d displays the obtained results. As described in Section 3.2, Mode 1 is the vertical bending vibration mode, Mode 2 corresponds to a low torsional-high lateral mode, and Mode 3 is a high torsional-low lateral mode. The values of \(f_{s}\) are given in Table 4, where it is noticed that the relative difference between the experimental and numerical natural frequencies is quite high for the second vibration mode. To reduce the discrepancies between the results, the FE model was calibrated as explained in Section 4.3.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Notation**} & \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Units**} & \multicolumn{2}{c}{**Adopted**} & \multicolumn{2}{c}{**Statistical Properties**} & \multicolumn{1}{c}{**Limits**} \\  & & & **Value** & \multicolumn{1}{c}{**Distribution**} & \multicolumn{1}{c}{\(\mu\sigma\)} & \multicolumn{1}{c}{**(Lower/Upper)**} \\ \hline GFRP elements & & & & & & \\ \(\rho_{GFRP}\) & Mass density & kg/m\({}^{3}\) & 1800 & Normal & 1800/90 & 1710/1890 \\ \(E_{1-P}\) & Elastic modulus, dir. 1 & GPa & 24.0 & Weibull & 24.0/2.4 & 20.4/27.6 \\ \(E_{2-P}\) & Elastic modulus, dir. 2 & GPa & 7.0 & Weibull & 7.0/0.7 & 5.95/8.05 \\ \(v_{12-P}\) & Poisson’s ratio 12 & - & 0.23 & Weibull & 0.23/0.02 & 0.20/0.26 \\ \(G_{12-P}\) & Shear modulus 12 & GPa & 3.0 & Weibull & 3.0/0.3 & 2.55/3.45 \\ GFRP deck panels & & & & & & \\ \(E_{1-D}\) & Elastic modulus, dir. 1 & GPa & 20.5 & Weibull & 20.5/2.05 & 17.43/23.58 \\ \(E_{2-D}\) & Elastic modulus, dir. 2 & GPa & 8.0 & Weibull & 8.0/0.8 & 6.8/9.2 \\ \(v_{12-D}\) & Poisson’s ratio 12 & - & 0.23 & Weibull & 0.23/0.02 & 0.20/0.26 \\ \(G_{12-D}\) & Shear modulus 12 & GPa & 3.0 & Weibull & 3.0/0.3 & 2.55/3.45 \\ CFRP strips & & & & & & \\ \(\rho_{GFRP}\) & Mass density & kg/m\({}^{3}\) & 1550.0 & Normal & 1550/77.5 & 1473/1628 \\ \(E_{1-C}\) & Elastic modulus, dir. 1 & GPa & 139.0 & Weibull & 139.0/13.9 & 118.2/159.9 \\ Concrete blocks & & & & & & \\ \(\rho_{c}\) & Mass density & kg/m\({}^{3}\) & 2400 & Normal & 2400/120 & 2280/2520 \\ \(E_{c}\) & Elastic modulus & GPa & 34.5 & Log-normal & 34.5/5.2 & 27.6/41.4 \\ Steel connectors & & & & & & \\ \(E_{s}\) & Elastic modulus & GPa & 200.0 & Log-normal & 200.0/20.0 & 160.0/240.0 \\ Non-structural mass & & & & & & \\ \(m_{add}\) & Additional mass & kg/m\({}^{2}\) & 2.0 & Normal & 2.0/0.1 & 1.8/2.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characterization of properties for the numerical model of the footbridge.  
Figure 7: Numerical model of the FRP footbridge: (**a**) Bottom view. (**b**) First vibration mode. (**c**) Second vibration mode. (**d**) Third vibration mode.
## 2 Materials and Methods  
### Passive Gait Assist System  
We designed the passive gait assist system to assist with plantar flexion during the late stance phase and dorsiflexion during the swing phase. This was achieved using a specialized shoe, shown in Figure 1b, which consists of straps 1 and 2 anchored at the heel and above the metatarsal, represented as blue and green lines, respectively. When the pulling force is at a low angle, relative to the ground, more tension resides within strap 1 compared to that of strap 2 which provides plantar flexion torque. When the pulling force is at a high angle, the greater tension in strap 2 induces dorsiflexion torque (Figure 1c). Thus, a device that can change the angle of the pulling force in accordance with the gait cycle needed to be implemented. We approached this by adding a spring-bar system (Figure 1b). During late stance, the elastic cord is lengthened and the bar drops, causing tension to reside more in strap 1 than strap 2, which elicits heel-off (HO) torque. When the heel lifts off from the ground, the spring-bar system returns to its original height, which increases the angle eliciting dorsiflexion torque (Figure 1d).  
The height at which the spring-bar system is attached can be varied by adjusting the screws. During our treadmill gait experiments, they were adjusted to be 1.05 m from the ground, which is higher than that of overground gait in order to compensate for the height of the treadmill (5 cm). The length of the bar was 36 cm, and the stiffness of the spring was 11.77 N/mm. Also, the free length and stiffness of the elastic cord were 61 cm and 0.18 N/mm, respectively.