esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
At the same time, the ability to export excess electricity to neighbouring countries is constrained, for instance, by the physical capacity of transmission infrastructure. If the system is unable to absorb surplus production through exports, increased consumption, or generation curtailment, prices begin to fall sharply. In extreme cases,they fall into negative territory; producers become willing to \"pay to produce\", either due to technical constraints or because they are incentivised by subsidies (e.g., guaranteed purchase schemes, feed-in tariffs, or contracts for difference). This price response is not linear, as the marginal utility of each additional megawatt of RES generation decreases once absorption capacity is exceeded. The result is a quadratic or even exponential increase in the frequency of negative prices beyond a certain tipping point. This non-linear effect is particularly pronounced in systems with  
- Weak storage infrastructure;  
- Low demand-side flexibility;  
- Regulatory barriers that prevent rapid generation curtailment and demand response. For these reasons, the relationship between RES generation and negative price occurrence differs across countries. In smaller and less interconnected systems such as Estonia or Slovakia, negative prices appear at relatively low generation levels (300-800 MW) due to very limited export capacity. In contrast, in France--where nuclear power plays a dominant role and the share of RES is moderate--such events are less systematic and depend on a combination of several variables (e.g., demand levels, export conditions, and grid configuration). Overall, the non-linear linkage between RES output and negative prices represents a structural consequence of the energy market transformation. It highlights the need to strengthen system flexibility, develop intelligent demand-side management, expand cross-border trading capabilities, and improve the prediction of risk periods associated with high RES output. From a scientific point of view, these results point to the need for more research into the non-linear and threshold relationships between RES supply and market behaviour. At the same time, the scientific results support the case for systematically increasing system flexibility and better planning of regulatory interventions. This paper contributes to deepening the understanding of how variable generation shapes market risks under conditions of increasing RES penetration.
# A Novel Dual-Strategy Approach for Constructing Knowledge Graphs in the Home Appliance Fault Domain
## 3 Methodology
### Deep Learning-Based Named Entity Recognition
#### 3.2.3 Multi-Head Attention Mechanism  
To better handle long-range texts with dependencies, such as those in appliance failure repairs, this study integrates the multi-head attention mechanism into the model to enhance its understanding of the relationships between different parts of the input text. Multi-Head Attention, based on the Transformer model, uses multiple parallel independent attention mechanisms to capture attention distributions of the input sequence across different subspaces. This approach enables a more comprehensive capture of various potential semantic associations embedded within the sequence. In multi-head attention, each input word is transformed into three vectors: the Query (\(Q\)), Key (\(K\)), and Value (\(V\)) vectors. The similarity between the Query and all Key vectors is then computed, yielding an attention score matrix that represents the degree of attention each word pays to other words. Typically, a softmax function is used to normalize the attention scores for probabilistic representation, and the Value vectors are weighted and summed to obtain the optimal output word.  
\[Q_{i}=XW_{Q^{\prime}}^{i}K_{i}=XW_{K}^{i},V_{i}=XW_{V}^{i} \tag{9}\]  
\[A_{i}=softmax(\frac{Q_{i}K_{i}^{T}}{\sqrt{d_{k}}}) \tag{10}\]  
\[O=Concat(A_{1}V_{1},A_{2}V_{2},\cdots,A_{h}V_{h})W_{O} \tag{11}\]  
In the formula, \(X\) represents the word embedding matrix of the input sequence, and \(W_{Q}^{i},~{}W_{K}^{i},~{}W_{V}^{i}\) represent the \(Q,K,V\) transformation matrices for the \(i\) attention head.  
is the dimension of the key vectors, used for scaling the dot product to prevent gradient vanishing or explosion. \(W_{O}\) is a linear transformation matrix used to map the concatenated vectors back to the required output dimension, while \(h\) denotes the number of attention heads. The final output vector is represented by \(O\).
# A Quantitative Study of Axial Performance of Rockbolts with an Elastic-Debonding Model
###### Abstract  
Full-length anchorage rockbolts are widely used in roadway reinforcement and rock controlling in underground mining. This article proposes using an elastic-debonding (ED) model to analyse the axial performance of rockbolts. The advantage of this ED model was that the full force-deformation curve of rockbolts comprised only three phases, which was relatively simpler to calculate. Its effectiveness was compared with experiment tests. Based on the ED model, a series of parameter studies was conducted. Results showed that for cross-section area of rock, there was a critical range. Once the cross-section area of rock was beyond that critical range, external rock had a mild impact on the axial performance of rockbolts. Rockbolt diameter significantly affected the axial performance of rockbolts. When rock diameter increased, the peak force of rockbolts increased linearly, while deformation at the peak force decreased non-linearly. The corresponding calculation equation between the peak force, deformation at the peak force, and rockbolt diameter was obtained. Borehole diameter had a mild impact on the axial performance of rockbolts. Increasing rockbolt length benefits improving the peak force of rockbolts. Rockbolt modulus of elasticity had a more apparent impact on the deformation at peak force. Mechanical properties of the bolt/grout (b/g) face affected the axial performance of rockbolts. Increasing the b/g face strength improved the peak force of rockbolts. Slippage at the ultimate load had a more apparent impact on the turning point between the elastic phase and the elastic-softening phase.  
quantitative study; axial performance; rockbolt diameter; full-length anchorage +
Footnote †: journal: Applied Sciences
## 3 Ellipsoidization of Particles of Arbitrary Shape  
### Calculation of Particle Shape Ellipsoidization  
The shape of the actual filter-layer particles varies, and it is almost impossible for them to have a regular spherical or ellipsoidal form. Therefore, it is necessary to characterize the particle shapes using specific shape parameters and attempt to approximate the irregular shapes of the filter-layer soil particles as ellipsoids.  
Here, the plane projection area of the filter-layer soil particles is denoted as \(A\) and the perimeter as \(P\). The projection is approximated as an ellipse with a constant area and perimeter, and the radii of its long and short axes are defined as \(a\) and \(b\), respectively. From the formulas for the area and perimeter of an ellipse, the following relationships can be derived from Equation (10), as follows:  
\[\begin{array}{l}\pi ab=A\\ 2\pi b+4(a-b)=P\end{array} \tag{10}\]  
The value of the radius \(b\) of the short axis can be obtained by Equation (11), as follows:  
\[b=\frac{P-\sqrt{P^{2}-4(2\pi-4)\frac{4A}{\pi}}}{2(2\pi-4)}=\frac{P-\sqrt{P^{2} -32\frac{(\pi-2)}{\pi}A}}{2(2\pi-4)} \tag{11}\]  
When the particle shape is spherical with a radius \(r\), the formulas for the area and perimeter of a projection circle can be substituted into Equation (11) to obtain \(b=r\), which confirms the validity of the formula.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Ratio of the Long Axis to** & & & & & & & & & & & & & \\
**the Short Axis, \(\lambda\)** & & & & & & & & & & & & & \\ \hline Porosity, n & 0.510 & 0.501 & 0.498 & 0.503 & 0.512 & 0.534 & 0.577 & 0.652 & 0.717 & 0.770 & 0.811 & 0.868 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The relationship between \(\lambda\) and n in a loose arrangement.  
Figure 7: The relationship between ratio of the long axis to the short axis and porosity in a loose arrangement.  
At this time, the long-axis radius \(a\) can be obtained by Equation (12), as follows:  
\[a=\frac{A}{\pi b}=\frac{2A(2\pi-4)}{\pi P-\pi\sqrt{P^{2}-32\frac{(\pi-2)}{\pi}A}} \tag{12}\]  
The prerequisite for deriving Equation (11) is Equation (13), as follows:  
\[P^{2}-32\frac{(\pi-2)}{\pi}A\geq 0 \tag{13}\]  
This can also be expressed by Equation (14), as follows:  
\[\frac{32(\pi-2)A}{\pi P^{2}}\leq 1 \tag{14}\]  
For a given area, a circle has the smallest circumference, resulting in the maximum value of Equation (14). Therefore, we can obtain Equation (15), as follows:  
\[\frac{32(\pi-2)\pi r^{2}}{\pi(2\pi r)^{2}}=\frac{8(\pi-2)}{\pi^{2}}\leq 1 \tag{15}\]  
Specifically, for the plane projection of any particle shape, Equation (11) can be applied. Consequently, the plane projection of any particle shape can be approximated as an ellipse using the semi-major and semi-minor axes defined by Equations (11) and (12).
## 4 RMSSA Problem Formulation in Spatial Channel Networks
### Network Topology  
Model the SCN topology as a graph \(G=(V,E)\), where \(V=\left\{v_{1},v_{2},\ldots,v_{|V|}\right\}\) denotes a set of nodes and \(E=\left\{e_{1},e_{2},\ldots,e_{|E|}\right\}\) denotes a set of unidirectional SDM links. Each SDM link \(e_{i}=\left(v_{i}^{m},v_{i}^{m},q_{i}\right)\) is defined by the nodes \(\left(v_{i}^{m},v_{i}^{n}\right)\) it connects and the link length \(q_{i}\). The 20 SLs on each SDM link are denoted as \(L=\left\{l_{1},l_{2}\ldots,l_{i},\ldots,l_{20}\right\}\), and 32 SBs on each SL are denoted \(F=\left\{f_{1},f_{2},\ldots,f_{i},\ldots,f_{32}\right\}\).
## 2 Kinematic Constraint Equations
### Constraint Equations for a Mecanum Wheel  
Compared to a classical wheel, a Mecanum wheel additionally features rollers fixed on its tread area, i.e., the outer rim. Each roller has the same angle \(\delta\) (as a rule 45\({}^{\circ}\)) that is formed by the rollers' axes and the wheel's plane and may rotate freely about its axis. To model the Mecanum wheel, a thin disk with radius \(R\) as depicted in Figure 3 is introduced. It is assumed that the dimensions of the densely attached rollers are much smaller than the diameter of the disk. The velocity \(V_{P}\) is observed at the point of contact \(P\) of the disk with the surface.  
The wheel moves without slip, which implies that \(V_{P}\) is orthogonal to the roller's axis \(\gamma\) (unit vector, see Figure 3) so that:  
\[\mathbf{V}_{P}\cdot\mathbf{\gamma}=0\,. \tag{3}\]  
The velocity of the wheel's center \(\mathbf{V}_{K}\) is  
\[\mathbf{V}_{P}=\mathbf{V}_{K}+\mathbf{\omega}\times\mathbf{r}_{K} \tag{4}\]  
Figure 2: Two- wheeled robot-trailer system (top view).  
where \(\omega\) denotes the wheel's angular velocity, \(\mathbf{r}_{K}=\overrightarrow{K\dot{P}}\), and \(\mid\mathbf{r}_{K}\mid=R\). The angle \(\varphi\) describes the rotation of the wheel perpendicular to the wheel's plane and through the wheel's center. Equation (3) is now represented as follows:  
\[(\mathbf{V}_{K}-R\,\dot{\varphi}\,\mathbf{\tau})\cdot\mathbf{\gamma}=0\,, \tag{5}\]  
where \(\mathbf{\tau}\) is the unit vector that is tangential to the wheel's circumference at the point of contact \(P\). Rearranging expression (5) yields  
\[\mathbf{V}_{K}\cdot\mathbf{\gamma}=R\,\dot{\varphi}\cos\delta\,. \tag{6}\]  
The velocity \(\mathbf{V}_{C}\) of the center of mass of the Mecanum robot and the wheel's velocity \(\mathbf{V}_{K}\) fulfill the kinematic property  
\[\mathbf{V}_{K}=\mathbf{V}_{C}+\mathbf{\Omega}\times\mathbf{r}_{C}\,, \tag{7}\]  
where \(\overrightarrow{CK}=\mathbf{r}_{C}\). The vector \(\mathbf{\Omega}\) describes the angular velocity of the rigid body of the Mecanum robot. Based on expression (6), the condition for rolling without slip along the roller's axis can be written as  
\[(\mathbf{V}_{C}+\mathbf{\Omega}\times\mathbf{r}_{C})\cdot\mathbf{\gamma}=R\,\dot{\varphi}\cos \delta\,. \tag{8}\]  
As \(\mathbf{\gamma}\) is the unit vector pointing along the roller's axis, the constraint (8) can be summarized as follows:  
\[\mathbf{V}_{C}\cdot\mathbf{\gamma}+(\mathbf{r}_{C}\times\mathbf{\gamma})\cdot\mathbf{\Omega}=R\, \dot{\varphi}\cos\delta\,. \tag{9}\]
# Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure
###### Abstract
[16] constructed a cascading failure model in command and control networks and analyzed the influence of load parameters, capacity parameters, and evolution step size on cascading failure invulnerability. [PERSON] et al. [17] investigated the dynamic load redistribution strategy based on the node's local load rate with respect to the cascading failure transmission of the equipment support network. These works are more focused on cascading failures in single-layer combat networks, similar to complex networks in general; there are also more complex relations among different types of combat networks and the coupled networks are well worth investigating. Hence, [PERSON] et al. [18] firstly explored the cascading failure characteristics and laws of information flowing in combat systems by abstracting network structure in a hierarchical way. [PERSON] et al. [19] proposed the model of the military information systems of systems based on function dependency and analyzed the center of gravity. These works only consider the robustness of the combat network under cascading failures in terms of topology structure but rarely address the functionality of the combat network. According to the above analysis, although existing research has deepened the theoretical understanding of cascading failures in combat networks, there are still some deficiencies that need to be addressed: (1) The common research mainly focuses on the single-layer network, and there are few kinds of research on the cascading failure model of the combat network with hierarchical structure and coupling characteristics. (2) The research on hierarchical structure modeling and dependent failure of combat networks needs to be further explored as well as the heterogeneity of combat networks. (3) The method of load redistribution needs to consider the actual situation, as the load is not simply distributed equally among all neighboring nodes. To address these problems, we establish a heterogeneous dependent network model with a double-layer structure according to the actual situation of the combat network, design the rules of dependent failure and overload cascading failure, and discuss the robustness of the combat network through simulation experiments. The aforementioned contributions can provide a more comprehensive and realistic perspective to the study of the robustness of combat networks. This paper is organized as follows. In Section 2, the double-layer combat network model with group-dependent characteristics is established based on real situations at first. In Section 3, the failure model combing conditional group-dependent failure model and cascading failure model is proposed. Attack modes are also classified according to different objects and intentions. In Section 4, some experiments based on the simulated combat networks are carried out to analyze the influence of different parameters and attack modes on system robustness. The conclusions and the future work follow in Section 5.
## 3 Results and Discussion
### Experiment of the Insulators with EL Coating
The combined experimental and simulation results demonstrate that this approach can significantly enhance the safety monitoring of high-voltage transmission lines, providing both immediate visual feedback and precise quantitative data for maintenance  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Electroluminous Brightness (cd/mm\({}^{2}\))** & **Voltage Subjected to the Coating (kV)** & \multicolumn{2}{c}{**Simulated Voltage Subjected to the Insulator (kV)**} & \multicolumn{1}{c}{**Actual Voltage Subjected**} & \multicolumn{1}{c}{**Error Rate**} \\ \hline
3.16 & 0.45 & 1.81 & 2 & 9.67 \\
5.82 & 1.11 & 4.42 & 4 & 10.45 \\
13.56 & 1.38 & 5.46 & 6 & 9.38 \\
25.94 & 2.18 & 8.92 & 8 & 11.54 \\
38.65 & 2.30 & 9.24 & 10 & 7.63 \\
51.23 & 2.76 & 11.21 & 12 & 6.59 \\
58.76 decisions. The robust performance under actual operating conditions (110 kV) and the consistent sub 10% error in voltage prediction highlight the technical viability of this solution for practical grid applications. Based on the above experimental phenomena, we also propose an innovative solution for future field applications. Field inspections deploy UAVs equipped with multispectral imaging to capture real-time electroluminescent responses (e.g., chromatic shift from blue-green to colorless under abnormal fields) in ZnSCu-epoxy coatings on live 110 kV insulators, enabling simultaneous defect localization with infrared thermography.
# Synthetic Approaches to 1,3,4-Oxadiazole-Containing Boronic Derivatives
## 2 General Approaches for the Synthesis of Organoboron Derivatives
### Method C: 1,3,4-Oxadiazole Ring Formation  
Another popular approach for the synthesis of 1,3,4-oxadiazole derivatives bearing a boronic acid/ester or MIDA moiety is the formation of the 1,3,4-oxadiazole ring from acyclic starting materials already containing a boronic acid or ester group. The boron-containing group is usually attached to a hydrazine or carboxylic acid reagent. Three of the most popular methods include the cyclization of diacylhydrazides (Scheme 3, method C1) [15], thiosemicarbazides (Scheme 3, method C2) [16], and the oxidative cyclization of acylhydrazones (Scheme 3, method C3) [17].
# Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems
## 4 Experiments and Analysis of Results
### Sensitivity Analysis
#### 4.3.5 Discussion of Experimental Results  
To validate the effectiveness of the proposed method, the DBTS algorithm was compared with several conventional recommendation algorithms, in metrics of accuracy and diversity. Figure 6 shows the results for the four algorithms.  
Figure 5: Execution time of (**a**) offline stage and (**b**) online stage.  
Figure 5(a) shows that the DBTS has higher accuracy compared to UCF (13%) and MoleTrust (40%), but has poorer accuracy than BPR (2%) and LDA (7%). The UCF has lower accuracy because only user similarities determined by ratings of corresponding items are used to predict user preferences. When UCF is applied in social networking environments, its performance is the worst among the three methods because it does not consider the social relations of users or the popularities of items.  
Figure 5(b) indicates that the DBTS algorithm achieves the highest diversity compared to other algorithms. The recommendations of BPR and LDA are highly biased towards popular items, which hugely limited their diversity performance. The UCF has lower diversity because, as the number of recommended items increases, UCF only considers user preferences regarding popular items. Thus, personalized diversity and aggregate diversity are decreased. MoleTrust treats the trust of users equally and ignores differences in user selections under different trust values, which decreases accuracy. The DBTS uses a diversity factor to adjust the proportion of re-ranked items, which simultaneously improves aggregate diversity and personalized diversity.
## 2 Materials and Methods
### WSN Simulation Setup
#### 2.1.2 Simulations in Different Wireless Technologies  
Aside from simulating the WSN in static and dynamic scenarios, we also conduct simulations by varying the wireless communication technology in each simulation scenario. This allows us to comprehensively assess the performance of different CH selection and routing optimization algorithms in various networking environments. Bluetooth, LoRa, Zigbee, and Wi-Fi were chosen as the wireless communication technologies in the simulation due to their varied data rate, energy consumption, and transmission range characteristics. Simulating under different wireless technologies allows us to gain insights into how each algorithm adapts to varying wireless conditions and the impact of each technology on key performance metrics.  
LoRa technology was developed for low-power and long-range wireless communication, making it ideal for applications like smart agriculture, remote sensing, and urban Internet of Things (IoT) systems [27, 28]. This technology utilizes the unlicensed radio spectrum in the ISM band, which are the 433 MHz, 868 MHz, and 915 MHz frequency bands. The data rate for this technology can reach up to 50 Kbps and reach up to a 10 km transmission range. Zigbee is another wireless communication technology designed by the Zigbee Alliance to support machine-to-machine (M2M) communications and other IoT networks that require low-cost, energy-efficient, and low-data-rate capabilities [28, 29]. Typically, this technology is used for short-range communication with nodes within a 10-100 m transmission range. Zigbee also operates on unlicensed ISM frequency bands such as 868 MHz, 915 MHz, and the 2.4 GHz band. Zigbee supports data rates up to 250 Kbps.  
Wi-Fi is known for its high data rates, supporting up to 54 Mbps for the 2.4 GHz band [30]. This technology is often used in applications requiring higher data throughput, such as video surveillance, large-scale monitoring, and intelligent building systems. However, its high data rate comes with higher energy consumption than other wireless technologies. Data transmission using this technology can reach a maximum of a few hundred meters. The Bluetooth low energy (BLE) is an improved version of the old Bluetooth technology with the special characteristics of low power, making it ideal for WSNs and IoT applications where power consumption is critical [31, 32, 33]. This wireless technology operates in the ISM band of 2.4 GHz. BLE supports transmission range from 10 m up to 100 m and data rates up to 1 Mbps. Table 2 summarizes the different parameters for each wireless technology that was used in this study.