esa-sceva/satcom-chunk-collection huggingface dataset:
# FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks
## 5 Performance Evaluation
### Attack-Based Network  
#### 5.1.1 Attack Model and Assumptions  
We chose to utilize the Blackhole with CTS rushing attack model because it is considered a spatio-temporal form of attack due to the following features:  
* The attackers can analyze a network's packet propagation behavior, thereby obtaining information regarding vicinity and position of other nodes in the network.
* The attackers can also monitor the timing semantics of a routing protocol. This capability enables them to know when a node is about to communicate.  
These two distinct capabilities enable the attacker to perform an effective intersection attack with no regard for the protocol timing semantics, especially when CTS responses are being sent to the sending node during the collection window period. Thus, presenting itself both in position (Spatially) and time (temporally) as the most suitable forwarding candidate, which if selected, drops all received packets and eventually halts the routing process. For the experiments ahead, we targeted a static network where we tested the impact of attackers using two scenarios:  
1. Strategic Placement of Attackers.
2. Random Placement of Increasing Attackers.
# Smart Grid Architecture for Rural Distribution Networks: Application to a Spanish Pilot Network
###### Abstract  
This paper presents a novel architecture for rural distribution grids. This architecture is designed to modernize traditional rural networks into new Smart Grid ones. The architecture tackles innovation actions on both the power plane and the management plane of the system. In the power plane, the architecture focuses on exploiting the synergies between telecommunications and innovative technologies based on power electronics managing low scale electrical storage. In the management plane, a decentralized management system is proposed based on the addition of two new agents assisting the typical Supervisory Control And Data Acquisition (SCADA) system of distribution system operators. Altogether, the proposed architecture enables operators to use more effectively--in an automated and decentralized way--weak rural distribution systems, increasing the capability to integrate new distributed energy resources. This architecture is being implemented in a real Pilot Network located in Spain, in the frame of the European Smart Rural Grid project. The paper also includes a study case showing one of the potentialities of one of the principal technologies developed in the project and underpinning the realization of the new architecture: the so-called Intelligent Distribution Power Router.  
intelligent distribution power router; rural distribution networks; smart grid technologies Article
# Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation
## 2 Materials and Methods
### Methods  
#### 2.2.1 Maximum Likelihood Codes  
We use three existing codes for maximum likelihood (ML): FastTree 2, RAxML-NG, and IQ-TREE 2; for the sake of simplicity, we will subsequently refer to these methods as FastTree, RAxML, and IQ-TREE. All of these methods are run under the GTR+G (i.e., GTR with gamma-distributed rates across sites) model of evolution.  
FastTree does not make a strong attempt to find the best solution to ML and, instead, stops the search (using nearest neighbor interchange, or NNI, moves) after a polynomial number of moves. This approach makes it faster than the other methods, which continue searching until there is evidence of having found a good local optimum. Moreover, in our study, FastTree completed on all datasets. The other methods did not complete on some of the datasets within the allowed run-time limit (24 h for the datasets with at most 2341 sequences, and one week for the datasets with 10K or 50K sequences), using the available memory (generally limited to 64 Gb). In such cases, we used the best tree (i.e., the tree with the best ML score, according to the code) found for that method on that dataset.  
Figure 1: DTM Pipeline. Here, we show a step-by-step overview of the pipeline we study that uses Disjoint Tree Merger (DTM) methods to infer a tree. In Step 1, a tree is computed on the input multiple sequence alignment, which serves as a starting tree as well as providing auxiliary information used by the DTM. In Step 2, the starting tree is decomposed into multiple disjoint subsets; each subset defines a sub-alignment of the input alignment. In Step 3, a tree is inferred on each disjoint subset (i.e., alignment), which subsequently serve as constraint trees. In Step 4, these constraint trees are combined using a DTM (along with the auxiliary information provided by the starting tree) to form the final output tree.
## 5 Application Cases and Effect Analysis  
### Support Plan for Engineering Test Points  
Working face 21405 of the Hulusu coal mine was taken as an engineering test site, and its basic conditions were as described earlier. According to the requirements of Equation (1), the excavation work of the withdrawal roadway should be completed at a distance of 171 m from the coal mining face. In fact, the excavation time of the withdrawal roadway was advanced, and when the excavation was completed, the working face was still 300 m away. Therefore, the withdrawal roadway was free from the disturbance of the coal mining face during the excavation period.  
According to the requirements of Equation (4) and the parameters provided in Section 4.1, the thickness \(h\) of the roof support varies with the tensile strength \(\sigma_{\text{t}}\) of the roof, as shown in Table 3. The tensile strength range of the roadway roof at this test point is 1.8-2.4 MPa. In order to ensure the safety of the mining production from the most unfavorable perspective, the tensile strength of the roof should be calculated as 1.8 MPa, and the thickness of the roof support should not be less than 3.4 m.  
A support plan for the withdrawal roadway was designed, as shown in Figure 10, as follows: (1) The first layer of support for the roadway roof adopted a \(\Phi\) 21.8 \(\times\) 4300 mm flexible steel strand support, with a pre-tension of 200 kN. The second layer of support adopted a \(\Phi\)21.8 \(\times\) 8300 mm flexible steel strand support, with a pre-tension of 240 kN. (2) The first layer of support for the non-mining rib of the roadway adopted a \(\Phi\) 22 \(\times\) 2500 mm threaded steel anchor bolt support, with a pre-tightening force of 60 kN. The second layer of support adopted a \(\Phi\)21.8 \(\times\) 5300 mm flexible steel strand support, with a pre-tension of 200 kN. (3) The mining rib of the roadway adopted a \(\Phi\) 22 \(\times\) 2500 mm threaded steel anchor bolt support, with a pre-tightening force of 60 kN. When the working face arrived, the shearer cut away the mining rib, so the flexible steel strand support was not used. (4) The threaded steel anchor bolt and flexible steel strand were both anchored with MSCK2370 resin coil. (5) Metal mesh protection was employed for the roof and roadway sides, and W-shaped steel strips were also used to strengthen the protection.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \(\sigma_{\text{t}}\) (MPa) & 0.2 & 0.4 & 0.6 & 0.8 & 1.0 & 1.2 & 1.4 & 1.6 & 1.8 & 2.0 & 2.2 & 2.4 & 2.6 & 2.8 & 3.0 \\ \hline \(h\) (m) & 10.2 & 7.2 & 5.9 & 5.1 & 4.6 & 4.2 & 3.8 & 3.6 & 3.4 & 3.2 & 3.1 & 2.9 & 2.8 & 2.7 & 2.6 \\ \hline \end{tabular}
\end{table}
Table 3: The thickness of the roof support varies with the tensile strength of the roof.
# Cocrystal Prediction Using Machine Learning Models and Descriptors
## 2 Materials and Methods
### Materials  
Since we basically solve this problem using a data-driven approach, we first had to prepare a data set. The compounds pairs of the data set were mainly obtained from the work of [PERSON] et al. [16], [PERSON] et al. [41; 52] and [PERSON] et al. [32] and supplemented with an extensive literature review on cocrystal screening of different APIs. Duplicate records were removed from the data set, resulting in a total of 1476 molecular compound pairs. Of these 1476 pairs, 753 were positive pairs (experimentally verified cocrystals) and the remaining 723 were negative pairs (unsuccessful formation of cocrystals). Labels are converted to a numerical form (e.g.,'success' = 1, and 'fail' = 0). The raw data is a \(D\times 3\) matrix and samples are shown in Figure 2. We primarily develop models that predict which pair of API (i.e., 'compound 1') and coformer (i.e., 'compound 2') will successfully result into a new cocrystal formation (i.e., 'label' = 1) and which will not result in a new solid form (i.e., 'label' = 0), from the set of chemical experiments based on the two columns (e.g., 'compound 1' and 'compound 2') as shown in Figure 2. Prediction models use a combination of features known as molecular descriptors from each pair for the classification task.  
Figure 1: Development process of machine learning models for cocrystal prediction.  
Molecular descriptors often used to develop quantitative structure-property relationships (QSPR) models, and _Mordered_ is one of the attractive tools to extract the molecular descriptors [44]. We have chosen Mordred because of its advantages: (1) it provides a comparable number of PaDEL descriptors while fixing some bugs within the PaDEL descriptor calculator, and (2) it is easy to install and use since it is provided as Python 2 & 3 libraries. As shown on the left in Figure 1, the Mordred tool used to extract feature vectors from the raw data. Canonical SMILES strings for each compound were retrieved from PubChem ([[https://pubchem.ncbi.nlm.nih.gov/](https://pubchem.ncbi.nlm.nih.gov/)]([https://pubchem.ncbi.nlm.nih.gov/](https://pubchem.ncbi.nlm.nih.gov/))) as shown in Table 1, and were used as input to the Mordred tool to generate molecular descriptor values. We found that some descriptor values are missing due to implementation issues of the tool; for example, a part of autocorrelation descriptor of a small molecule is known to be missing even if there is no bug [44]. Since the missing values did not occur at random, we simply filter them out instead of using any imputation algorithms. We obtain a \(F_{ALL}\) dimensional real-numbered feature vector from a single pair of compounds. After adding a label column, we have \(D\) feature vectors of \(F_{ALL}+1\) dimension.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 4 CR-Based UAVs
### The Need for CR-Based UAVs
#### 4.1.2 Energy Efficiency  
Although CR devices may represent a computational overload to UAVs, they may be capable of actually reducing energy consumption to these aircraft. Because the UAVs operate in overcrowded spectrum bands, they are susceptible to a high number of loss, thus increasing packet retransmission. The energy consumed for packet retransmission may be greatly reduced when a UAV is equipped with a CR.  
[PERSON] et al. [30] proposed a method to maximize energy efficiency based on a joint optimization with medium access control (MAC) and physical layers, considering CR networks. In their scenario, a CR user senses different channels simultaneously and uses some idle ones for data transmission. The authors showed that the more channels the CR device is able to use, the more efficient is its bits/joule ratio throughput. The bits/second ratio also increased with the number of channels used.
# Control of an Omnidirectional UAV for Transportation and Manipulation Tasks
## 6 Interaction Wrench Compensation  
In tasks involving contact with the environment, e.g., manipulation or cooperative transportation, the capability of exerting both forces and moments without modifying the orientation of the load make the omnidirectional platforms preferable with respect to standard multirotors [8,40]. Moreover, even in simple transportation tasks, it is of the utmost importance to react to external disturbances due to contact with the object by compensating the wrench exerted by the object. Namely, an estimation of the wrench exerted by the object on the vehicle is fed back to the controller as an additional term in (18), (28), and (29). In fact, in the presence of contact with an object, the dynamics (5) of the ODQuad becomes  
\[\mathbf{M}(\mathbf{q})\ddot{\mathbf{q}}+\mathbf{C}(\mathbf{q},\dot{\mathbf{q}})\dot{\mathbf{q}}+\mathbf{g}(\mathbf{ q})=\mathbf{\eta}+\mathbf{J}_{C}^{\mathrm{T}}\mathbf{h}_{C}, \tag{47}\]  
where \(\mathbf{J}_{C}\) is the Jacobian matrix that relates the velocity of the contact point to the velocity of the generalized coordinates \(\mathbf{q}\), and \(\mathbf{h}_{C}\) is the wrench exerted by the object on the contact point of the ODQuad. Given an estimate, \(\hat{\mathbf{h}}_{C}\), of \(\mathbf{h}_{C}\), the control outputs (18), (28), and (29) become  
\[ \begin{array}{rcl}\mathbf{f}_{0}&=&\mathbf{M}_{pp}\tau_{p}+\mathbf{M}_{p\phi}\tau_{\phi}+ \mathbf{C}_{p}\dot{\mathbf{p}}_{0}+\mathbf{g}_{p}-\mathbf{J}_{C,f}^{\mathrm{T}}\hat{\mathbf{h}}_{C },\\ \mathbf{\mu}_{0}&=&\mathbf{M}_{p\phi}^{\mathrm{T}}\tau_{p}+\mathbf{M}_{\phi\phi}\tau_{\phi} +\mathbf{M}_{\phi\phi}\tau_{\delta}+\mathbf{C}_{\phi}\dot{\mathbf{\phi}}_{0}+\mathbf{g}_{\phi}- \mathbf{J}_{C,\mu}^{\mathrm{T}}\hat{\mathbf{h}}_{C}\\ \mathbf{\tau}_{c}&=&\mathbf{M}_{p\phi}^{\mathrm{T}}\gamma_{p}+\mathbf{M}_{\phi\phi}^{ \mathrm{T}}\gamma_{\phi}+\mathbf{M}_{\delta\delta}\tau_{\delta}+\mathbf{C}_{\delta} \dot{\delta}+\mathbf{g}_{\delta}-\mathbf{J}_{2}^{\mathrm{T}}\mathbf{h}_{2}-\mathbf{J}_{C,\tau}^ {\mathrm{T}}\hat{\mathbf{h}}_{C},\end{array}  \tag{48}\]  
where \(\mathbf{J}_{C,f}\), \(\mathbf{J}_{C,\mu}\), and \(\mathbf{J}_{C,\tau}\) are obtained from \(\mathbf{J}_{C}\) by selecting, respectively, the first three columns, the forth to sixth columns, and the last two columns. In order to measure the wrench exerted by the object on the ODQuad, a force/torque sensor could be mounted on the contact point robot-object [6]. This is usually capable to provide a reliable measure, but increases both the cost and the weight of the robot. In the aerial robotics field, a more viable solution is the adoption of a wrench estimator, which can provide a sufficiently accurate estimation [8]. In this work, the interaction wrench, exerted by the manipulated object on the vehicle, has been estimated via a momentum-based observer [41]. With reference to the system dynamics (47), the angular momentum \(\mathbf{\
u}\) can be computed as  
\[\mathbf{\
u}=\mathbf{M}(\mathbf{q})\dot{\mathbf{q}}\,. \tag{49}\]  
Let us recall the following property of dynamic model (5) [34,38]:  
**Property 2**.
# The Vibration Response Characteristics of Neighboring Tunnels Induced by Shield Construction
## 3 Monitoring Data Validation Analysis
### Monitoring Results
#### 3.3.2 Vibration Response of Neighboring Tunnel  
At the shield construction site, 10 sensors were installed on the opposite side of the tunnel, which had already been constructed, to monitor the vibration response at intervals of 1.5 m. The vibration displacements of the neighboring tunnels along the radial direction of the tunnel are shown in Figure 8.  
As shown in Figure 8, the vibration response of the neighboring tunnels is less intense compared to the vibration response of the tunnel on the construction side. Specifically, the vibration displacements at 0 m, parallel to the shield head, are the most pronounced in the radial direction of the tunnel, with a peak value of 0.02 mm. This phenomenon indicates that radial vibration remains significant near the vibration source (i.e., at the shield head), even though the neighboring tunnels are farther from the source. As the distance increases, after 5 m, the vibration displacement response rapidly decreases to 0.01 mm, and the vibration signal becomes much sparser and nearly negligible. This suggests that while the neighboring tunnels are less affected by the vibration, the vibration energy still propagates radially and reaches these tunnels, producing some vibration response in the region. The vibration displacements of the neighboring tunnels along the axial and gravitational directions are shown in Figures 9 and 10.  
From Figures 9 and 10, it can be observed that the vibration displacements of the neighboring tunnels along the axial and gravity directions are relatively sparse, and the vibrations are not pronounced. The Fourier transformations of these vibrations were converted into power densities, which are shown in the power density spectrograms in Figure 11.  
The peak power density of the adjacent tunnel-side lined pipe sheets is concentrated in the range of 3.7-3.8 Hz, with a peak of 0.00333 ((mm/s\({}^{2}\))/Hz) at 0 m (the same horizontal position as the cutter plate), 0.00059 ((mm/s\({}^{2}\))/Hz) at 4.5 m in front of the cutter plate, and 0.00053 ((mm/s\({}^{2}\))/Hz) at 4.5 m behind the cutter plate. This result indicates that, although the peak power density of all the lined pipe sheets is concentrated in the same frequency range (3.7-3.8 Hz), there is a significant difference in the vibration intensity of the lined pipe sheets at different locations at this frequency.  
Figure 8: Vibration displacements along the radial direction of the tunnel in the neighboring tunnels.  
Figure 9: Vibration displacements along the tunnel axis in the neighboring tunnels.
## 5 Applications of Wireless EM Energy Transfer and Propagation in SCs
### IPT Batteries Charging in EVs in SCs
#### 5.2.1 Wireless IPT Structure  
A wireless IPT charging tool is inserted between the power source and the battery load. The IPT central element responsible for wireless charging is an inductive coupler transformer (ICT) connected to the source and load through power electronics converters. The ICT is composed of a transmitter and receiver coils having inductances of L\({}_{1}\) and L\({}_{2}\) and parted by an airgap characterizing a mutual inductance M\({}_{12}\). The airgap size reflects a weak coil coupling, and hence, the transfer of the required power implies a significant absorbed reactive power. So, the two coils are compensated by capacities C\({}_{1}\) and C\({}_{2}\). Thus, an IPT tool can perform a galvanic split wireless power transfer and a capacitive  
Figure 4: MRI RF magnetic field \(B_{1}\) distribution with the insertion of a piezoelectric coated by electrodes (**a**) material outline; (**b**) field distribution with electrodes perpendicular to the field; (**c**) field distribution with electrodes parallel to the field [36].  
Figure 3: MRI RF (at 63.87 MHz, B\({}_{0}\) 1.5 T) magnetic field \(B_{1}\) (vertically directed) distribution in the case of no material [36].  
compensation that permits the electronics connected to an ICT to function at resonance. Such compensations on both sides of the ICT ensure consistent efficiency [74; 75; 76; 77; 78; 79; 80; 81; 82] and can use different topologies (series S and parallel P) contingent on the character of the load, such as for the two sides, SS, SP, PS, PP, etc. [75; 82]. The SS compensation topology seems to be an economical option [81; 82]. Magnetic ferrite sheets are usually employed to coat the outer external surfaces of the coils of the ICT, thus enhancing the transfer efficiency that is correlated to a better coupling coefficient. Figure 5 illustrates the schematics of an IPT involving its ICT details. The IPT includes its ICT-ferrite sheets sandwiched between the grid, via an AC-DC-AC-filtered adjusted frequency-voltage conversion, and the battery, via an AC-DC-filtered conversion [42].
# An Accelerated Method for Protecting Data Privacy in Financial Scenarios Based on Linear Operation
## 4 Results
### Precision Quantisation  
Due to the way floating point numbers are stored in the computer, there is an inevitable loss of precision in the encryption and decryption process, which accumulates over time as the number of calculations increases. Therefore, this section discusses this possible loss of model accuracy due to the computation of the encryption and decryption process. In this section, we compare the inference accuracy of the inference model in the non-privacy-protected environment with that of the inference model using this method. The results are shown in Tables 3-5.  
As can be seen from the figure, although the accuracy of the model is reduced in some models, the reduction is very low at 0.4%. Even in the ResNet series, this method results in a slight improvement in model accuracy. This result was observed on ResNet 50, an inference model with 50 linear computation layers, and this number of computation layers did not lead to any reduction in inference accuracy. This result shows that although this method accumulates accuracy errors in the computation process, these errors do not necessarily affect the final results. Therefore, it can be concluded that this method has little impact on the final progress of the inference model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Inference Model** & **Local CPU** & **HE** & **Our Method** \\ \hline VGG 13 & 4.3 min & 6.5 min & 43.1 s \\ VGG 16 & 6.1 min & 7.3 min & 44.8 s \\ VGG 19 & 7.4 min & 10.5 min & 46.5 s \\ ResNet 50 & 4.4 min & 5.4 min & 37.3 s \\ ResNet 101 & 10.7 min & 11.8 min & 54.2 s \\ MobileNet & 4.1 min & 5.7 min & 35.6 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Execution time for different methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Inference Model** & **Without Protection** & **With Our Method** \\ \hline VGG 13 & 83.6\% & 83.6\% \\ VGG 16 & 85.7\% & 85.3\% \\ VGG 19 & 86.3\% & 86.1\% \\ ResNet 50 & 86.5\% & 86.6\% \\ ResNet 101 & 85.8\% & 85.8\% \\ MobileNet & 73.4\% & 73.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Precision quantisation on our method.