esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
### The Novelty of the Proposed Method  
The novelty of the proposed discrete element modeling method based on the energy correlation coefficient is that it is unnecessary to determine the location of potential sliding surfaces before the iterative calculation of the DEM. The proposed method based on the energy correlation coefficient is a generalization of the method based on the displacement statistics. The proposed method considers the mass or weight and displacement of every block.  
In addition, it is no longer to count the displacement of all blocks as a statistic, but to consider the correlation between all blocks. The method considers both time correlation and spatial correlation, and the consideration is more comprehensive. Since the method based on displacement statistics needs to determine the potential sliding surface before the  
Figure 14: The statistical results after the excavation of the 9# and 10# coal seams. (**a**) The relationship between the Spearman-CC and step, (**b**) the relationship between inter-step difference and step.  
discrete element calculation, it is only suitable for jointed rock slopes that the position of the potential sliding surface is liable to determination. Compared with the displacement-based method, our proposed method is not essential to determine the location of the potential sliding surface in advance. Therefore, the method proposed in this paper has a wider application range and could be applied to general jointed rock slopes that the position of the potential sliding surface is hard to determine.  
Specifically, the displacement-variation-coefficient-based method is only suitable for the analysis of jointed rock slopes where the potential sliding surface is easy to determine before the DEM iterative calculation. The method takes the displacements of rock blocks above the potential sliding surface as statistics, not the displacements of all rock blocks. Therefore, the calculation is faster.  
The energy-variation-coefficient-based method is a generalization of the above method, and it is no longer necessary to determine the position of the potential sliding surface in advance. The method is suitable for complex jointed rock slopes where the potential sliding surface is difficult to determine. In this method, the energy generated by all rock blocks in the process of movement are taken as statistics. Not only the displacement of rock blocks, but also the mass of rock blocks are considered, so as to eliminate the influence of block size on the calculation results. This method considers the time correlation between rock blocks, but does not consider the spatial correlation.  
The comparison of the mentioned methods is listed in Table 4.  
The proposed method could be applied in different case studies, such as complex rock slopes, underground mining and so on. In addition, the proposed method is built on the block discrete element method, if similar numerical models such as DDA [16; 17] and NMM [18; 19] are employed, the proposed method could also be used to analyze the deformations and failures of jointed rock slopes.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 5 Results
### Mesh Tests
#### 5.3.2 Urban Environment  
In this experiment, the nodes were positioned in an urban environment on a step shape, as seen in Figure 11.  
All nodes were about 100/150 m from each other and used a spreading factor of 7. Table 4 contains the data gathered from this test. This test is somewhat similar to the previous one. Still, while in the straight-line topology test more nodes could be connected to a single gateway using a higher spreading factor, this is now not the case when the devices are positioned behind corners that obstruct the line-of-sight between nodes. While nodes \(S\) and \(R\) might still communicate with each other by using a high spreading factor, this could not provide an error-less connection and, as seen before, using a higher spreading factor comes with a high cost. Having the ability to have relay nodes with our solution might be more expansive since more devices are needed. Still, it will produce a better solution where more data can be transferred between the network's two end nodes and with no packet loss.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measurement** & **Value** \\ \hline PDR & 100\% \\ RSSI (db) & [\(-\)88; \(-\)65] \\ SNR & [5; 7] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results obtained from the urban environment test.  
Figure 11: Positioning of nodes in urban environment test.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measurement** & **Value** \\ \hline PDR & 100\% \\ RSSI (db) & [\(-\)78; \(-\)64] \\ SNR & [6; 8] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results obtained from the straight line test.
# Study on L-Bending Springback of 45 Steel Leather Cutting Tool Coupled with Local Induction Heating
## 1 Introduction
[16] regarded AA5182-O as an elastoplastic material and used the Swift hardening law and isotropic von Mises yield criterion for finite element modeling; the simulation results show that laser-local-heating-induced material softening led to a decrease in the forming force and improved the sheet forming accuracy. [PERSON] et al. [17] used the laser local heating method to dynamically heat the single-point incremental forming of the sheet, which significantly improved the forming limit of the sheet. [PERSON] et al. [18] used laser heating to assist the asymmetric incremental forming of titanium alloy sheets. The results showed that the sheet's forming performance was improved and that the springback increased as the ratio of the elastic modulus to yield stress rose. Despite the fact that numerous local heating techniques, including laser [17], infrared [19], resistance [20], and electric current [21] heating, have been used in the bending process, the application of induction local heating in bending process is rarely reported. Because of its rapid heating and even temperature distribution, induction heating has been widely used in non-isothermal stretching, injection molding, stamping, and other processes [22]. It is worthwhile to investigate the interaction between inductive local heating and L-bending. In this study, the induction local heating method was introduced to assist in the L-bending of the 45 steel sheet and compared with the cold-working method. The bending behavior under different heating temperatures and bending angles were investigated by both FEA simulation and experiment. Simultaneously, the stress distribution and springback mechanism were further analyzed to explain the bending behavior. Through these efforts, the L-bending process can be optimized to improve the forming accuracy of leather cutting tools.
# High Throughput and Acceptance Ratio Multipath Routing Algorithm in Cognitive Wireless Mesh Network
## 3 Multipath Routing and Spectrum Allocation Algorithm
### Fundamental  
The multipath routing and spectrum allocation algorithm (MRIR) is proposed in this section. The fundamental idea is as follows: first, for each link (\(u\),\(v\)), the set of available channels of the link and the remaining bandwidth of each channel is calculated. If the link (\(u\),\(v\)) is allocated a channel, then the remaining bandwidth of the allocated channel is less than the wireless request bandwidth constraint, or, the link (\(u\),\(v\)) is not allocated a channel, but the maximum bandwidth of all the available channels is less than the wireless request bandwidth constraint, then the link (\(u\),\(v\)) is hidden. This means that the link (\(u\),\(v\)) is not included in the path construction (Lines 1 to 13 in Algorithm 1). Second, the primary path routing construction and spectrum allocation is completed. If the primary path satisfying the QoS constraint is constructed, then the backup path that satisfies the QoS constraint is constructed. If the primary or backup path satisfying the QoS constraint cannot be constructed, then the wireless request is rejected (Lines 14 to 28 in Algorithm 1). Third, the hidden edges of G are shown (Line 29 in Algorithm 1).  
The primary path routing construction algorithm (PPC) and primary path spectrum allocation algorithm (PPSR) in Lines 14 and 16 of Algorithm 1 are presented in Sections 4.1 and 4.2, respectively. The backup path routing construction algorithm (BPC) and backup path spectrum allocation algorithm (BPSR) in Lines 19 and 20 of Algorithm 1 are presented in Sections 5.1 and 5.2, respectively.  
The weight of the link (\(u\),\(v\)) allocated channel is denoted as \(f_{1}^{k}(u,v)\), which is given by  
\[f_{1}^{k}(u,v)=\frac{\left(\left|\delta^{k}(u,v)\right|+1\right)}{\left(\left| \varepsilon^{k}(u,v)\right|+1\right)(\left|\varphi^{k}(u,v)\right|+1)} \tag{7}\]  
where \(\left|\delta^{k}(u,v)\right|\), \(\left|\varepsilon^{k}(u,v)\right|\) and \(\left|\varphi^{k}(u,v)\right|\) are the primary link interference degree, backup link interference degree, and idle link interference degree, respectively.  
If the link (\(u\),\(v\)) is allocated channel \(k^{*}\), then \(f_{1}(u,v)=f_{1}^{k^{*}}(u,v)\). If the link (\(u\),\(v\)) is not allocated a channel, then \(f_{1}(u,v)\) is the minimum of the \(f_{1}^{k}(u,v)\) on all of the available channels, which is given by  
\[f_{1}(u,v)=\min_{k\in K(u,v)}\left\{f_{1}^{k}(u,v)\right\} \tag{8}\]  
The allocated channel of link (\(u\),\(v\)) is denoted as \(x_{1}^{*}(u,v)\) when the minimum of the \(f_{1}(u,v)\) is obtained, which is given by  
\[x_{1}^{*}(u,v)=\argmin_{k\in K(u,v)}\left\{f_{1}^{k}(u,v)\right\} \tag{9}\]
# Stability and Failure of the Edge-Closed Honeycomb Sandwich Panels with Face/Core Debonding
## 5 Numerical Results and Discussion
### Effect of Imperfections on Honeycomb Failure  
As shown in Figure 16, the red area represents failed elements, and the blue represents non-failed elements. In the model CD0, the additional bending moment generated by the asymmetric load caused the structure to buckle along the inner skin. Observed from the sides of inner skin or outer skin, the honeycomb damage of the sloped area that was the weak link appeared first. As the buckling displacement of the structure increased, the damage expanded from the periphery to the center of the structure. Compared to the inner skin, the outer skin was closer to the loading plane of the edge closed structure, which resulted in more serious damage.  
As shown in Figure 17, the sloped area and the debonding area were the weak areas of CD40 model. Because the diameter of the debonding area was still small, the honeycomb damage in the defect area did not appear before that of the sloped area. Observed from the sides of inner skin or outer skin, the honeycomb in the sloped area bore carrying more stress relatively, so damage appeared there first. As the degree of buckling increased, the damage expanded toward the center of the structure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Mean Value of Experimental Results (kN)** & **Simulated Value (kN)** & **Error/\%** \\ \hline CD0 & 65 & 108 & 66.2 \\ CD40 & 60 & 68 & 13.3 \\ CD80 & 35 & 32 & \(-\)8.6 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Buckling loads of various honeycomb panels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Mean Value of Experimental Results (kN)** & **Simulated Value (kN)** & **Error/\%** \\ \hline CD0 & 96.0 & 90.7 & \(-\)5.5 \\ CD40 & 86.5 & 82.2 & \(-\)4.9 \\ CD80 & 74.7 & 69.3 & \(-\)7.2 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Fracture loads of various honeycomb panels.  
Owing to the large debonding diameter in the model CD80, local buckling appeared obviously in the debonding area of the skin, as shown in Figure 18. Observed from the inner skin, the honeycomb at the edge of the defect area carried more stress relatively, so damage appeared there first. As the degree of buckling increased, the damage extends to the sloped area in the vertical loading direction.  
Figure 16: Honeycomb damage propagation in the CD0 Model. (**a**) the damage propagation of inner skin; (**b**) the damage propagation of outer skin.  
Figure 17: Honeycomb damage propagation in the CD40 Model. (**a**) the damage propagation of inner skin; (**b**) the damage propagation of outer skin.  
Observed from the outer skin, the honeycomb of the inclined area appeared damaged first, and then the damage expanded along the vertical loading direction.
## 2 Methodology
### Path Propagation Using Motion Primitives
As an illustration, when fully defined end translational state is given, let end condition be \(\mathbf{s}^{*}(T)=[\,p(T),v(T),a(T)\,]^{\mathrm{T}}=[\,p_{f},v_{f},a_{f}\,]^{ \mathrm{T}}\) along an axis. Substituting \(\mathbf{s}^{*}(T)\) into Equation (7) and reordering the equation yields  
\[ \begin{bmatrix}\Delta p\\ \Delta v\\ \Delta a\end{bmatrix}  = \begin{bmatrix}p_{f}-p_{0}-v_{0}T-\frac{1}{2}a_{0}T^{2}\\ v_{f}-v_{0}-a_{0}\bar{T}\\ a_{f}-a_{0}\end{bmatrix}  \tag{8}\] \[= \begin{bmatrix}\frac{1}{120}T^{5}&\frac{1}{24}T^{4}&\frac{1}{6}T^ {3}\\ \frac{1}{4}T^{4}&\frac{1}{2}T^{3}&\frac{1}{2}T^{2}\\ \frac{1}{6}T^{3}&\frac{1}{2}T^{2}&T\end{bmatrix}\begin{bmatrix}\alpha\\ \beta\\ \gamma\end{bmatrix} .\]  
Then, the unknown coefficients \(\alpha\), \(\beta\), and \(\gamma\) are solved by the inverse of Equation (8) as follows:  
\[ \begin{bmatrix}\alpha\\ \beta\\ \gamma\end{bmatrix} =\frac{1}{T^{5}} \begin{bmatrix}720&-360T&60T^{2}\\ -360T&168T^{2}&-24T^{3}\\ 60T^{2}&-24T^{3}&3T^{4}\end{bmatrix}\begin{bmatrix}\Delta p\\ \Delta v\\ \Delta a\end{bmatrix} . \tag{9}\]  
Hence, generating the MPs requires only evaluating the matrix multiplication for each axis from Equation (9), and then the state trajectory along the MPs is obtained by Equation (7). In implementation, \(T\) in Equation (9) is computed with the desired average speed \(\bar{V}\) as follows:  
\[T=\frac{\|\mathbf{p}_{\bar{t}}-\mathbf{p}_{\bar{0}}\|}{\bar{V}}, \tag{10}\]  
where \(\mathbf{p}_{0}\) and \(\mathbf{p}_{\bar{f}}\) represent the initial and target position vector, respectively. For the details of other end conditions, see the Reference [32]. Since the MPs solve for a locally optimal trajectory with respect to allocated time, the trajectory represents time-parameterized polynomials. That is, the closed-form solution of the MPs converts the trajectory generation problem to a problem of finding polynomial coefficients. Thus, the MPs make it possible for the rapid generation and the frequent updates of paths.
# New Detection Paradigms to Improve Wireless Sensor Network Performance under Jamming Attacks
## 2 Related Work on the Jamming Detection Problem
### Proposed Method and Contributions  
Our work focuses on finding an alternative where the network can defend itself, without the need for techniques that use large amount of resources, such as cryptography. In our analysis, we emphasize the study of one of the most aggressive cases of jamming (i.e., the reactive), and we propose two techniques inherent to the network so that the nodes, taking advantage of the communication capabilities of a sensor network, can defend against an attacker and counteract its effect. Our proposal incorporates the information of the jammed nodes to the tables of the neighbor nodes so that the self-configuring mechanisms of the routing protocols avoid the jammed area. Moreover, in this work we also present an algorithm for measuring thresholds of performance metrics to note the level of affectation of a node. We intend to cover the best of the previously studied proposals in order to test protocols widely known in sensor networks (proactive and reactive protocols) by modifying their valid routes so that it can guarantee a reliable delivery of information and isolate a possible attacking node from the other nodes in the network. In other words, it makes an accurate detection of the affected area based on more reliable performance metrics.  
Specifically, the contribution of this work to the state of the art are as follows: (1) We study a reactive jamming that represents the worst-case scenario and the most aggressive attack. (2) We also present an algorithm for measuring thresholds of performance metrics to note the level of affectation of a node. (3) We add a parameter in the routing tables and a control packet for AODV and DSR protocols. (4) In contrast to other works that find the best route to a destination, in this work we isolate the affected area so that the protocol re-configures the valid routes and the information arrives reliably.
## 3 Case Study
### Monitoring and Calibration  
Since measuring the actual airtightness of the case study building was not feasible, we referred to former studies on the airtightness of residential buildings [68; 69], a parametric analysis was conducted using a building model with varying airtightness values, ranging from 0.0001 to 0.0009 m\({}^{3}\)/s per m\({}^{2}\)@4 Pa. The impact of these different scenarios on the building's performance was assessed, and the model's accuracy was validated by comparing the simulated results with the measured indoor temperatures on-site, allowing us to estimate the building's actual airtightness. Hourly indoor temperature data were recorded using the Testo 174H temperature testing instrument between 18 April and 26 April 2024 under free-running conditions. The data were collected in a south-facing bedroom, with measurement points situated 1 m from the windows and readings taken at 15 min intervals. NMBE and cv(RMSE) were used to quantify the error between the measurement and simulation data. This study followed the ASHRAE Guideline 14-2002 [70], a standard method for calibrating building models that provides acceptable calibration accuracy limits. For hourly data, the acceptable limits are \(-10\%\leq\text{NMBE}_{\text{hourly}}\leq 10\%\) and \(\text{cv(RMSE)}_{\text{hourly}}\leq 30\%\), while for monthly data, the limits are \(-5\%\leq\text{NMBE}_{\text{monthly}}\leq 5\%\) and \(\text{cv(RMSE)}_{\text{monthly}}\leq 15\%\). NMBE and cv(RMSE) were calculated using the following equations:  
\[\text{NMBE}=\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})}{\sum_{i=1}^{n}y_{i}} \tag{17}\]  
\[\text{cv(RMSE)}=\frac{\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} }}{\bar{P}} \tag{18}\]  
where \(y_{i}\) denotes the measured data; \(\hat{y}_{i}\) is the predicted value; \(i\) indicates the number of case studies in the cluster; \(n\) is the number of samples; and \(\bar{P}\) represents the average of the actual values of the sample.  
As shown in Table 5 and Figure 4, when the value of airtightness is 0.0006, the NMBE and cv(RMSE) of the measurement points are 1.85% and 4.87%, respectively, which are within ASHRAE's error tolerance. Therefore, it can be concluded that the model is well calibrated.  
Figure 3: Floor plan of the reference building.
# Energy Management of Virtual Power Plant Considering Distributed Generation Sizing and Pricing
## 3 Mathematical Formulation
### Energy Storage (Battery) Unit  
The battery source is used to provide the load in the absence of renewable energy sources. The difference between the power produced and the load power required indicates whether the battery should be charged or discharged. The amount of charge of the battery bank is obtained in time horizon \(t\) using the following Equation (10)  
\[E_{bat}(t)=E_{bat}(t-1)+P_{Gen\_\_bat}\times\Delta t\times\eta_{bat}-\left(P_{ but\_inv}(t)\times\Delta t\right)/\eta_{dis\_\_bat} \tag{10}\]  
where, \(E_{bat}\) (\(t\)) represents the amount of battery electric energy at time \(t\). By the way, \(\eta_{bat}\) and \(\eta_{dis\_bat}\), are the charge and discharge efficiency of the battery bank, respectively.
# Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm
## 4 Cursory Dendritic Cell Algorithm
### Modular Dendritic Cell Algorithm  
DCA specification and implementation, using an object-oriented approach, was motivated by the need to adjust and update the algorithm's source code. With an object-oriented implementation, experimentation and changes to the original algorithm become easier to develop and test. This modular approach is illustrated in Figure 7.  
The decomposition of the DCA presented was carried out by identifying the main steps of the algorithm and abstracting those steps into separate classes (modules). The rationale for each class is according to the following steps: (i) the _antigens_ and values are received by the system; (ii) their signals are extracted; (iii) the extracted signals are distributed into the _DC_ population; (iv) each _DC_ decides how it processes these _antigens_ and signals; and (v) the _antigens_ are somehow impacted by this processing. Considering this rationale, the following classes are defined:  
* **Interface**: to keep the usage of the algorithm simple, a single interface is used to interact with the system as a whole. Therefore, the interface can receive _antigens_, and output _antigen_ classification results. Indeed, the _Interface_, as implemented, orchestrates the remaining modules.
* **Signal Extractor**: defines how signals are extracted from data. It receives _antigens_ and associated data, while outputs _safe_, _danger_, _PAMP_, and inflammation signals from received data and the _antigen_ they are associated with.
* **Sampler**: decides on a strategy for signals and _antigen_ distribution in the _DC_ population, i.e., which _DCs_ are attributed to which _antigens_ and signals. It receives the _antigens_ and signals from the _Signal Extractor_ and chooses specific _DCs_ to receive them.
* **DC Population**: the _DCs_ themselves can receive _antigens_ and signals. Received _antigens_ are stored locally by each _DC_. Each _DC_ also has access to the _antigen_ repertoire (see next module), allowing the collected _antigen_'s state to be updated by the _DC_ that collects it.
* **Antigen Repertoire**: a database structure holding all _Antigens_ that are given to the system. It might be relevant to store in the _Antigen Repertoire_ changes in the _antigens_ state, for instance, when a _DC_ migrates, or store information about which mature/semimature _DC_ is associated with a given _antigen_ collected. The _Interface_ can query the _Antigen Repertoire_ to achieve classification.  
Each component can be updated separately with this modular approach, and different strategies in different modules can be implemented.