esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
This paper has presented a novel method of autonomous robotised UT deployment that locates the part, plans a path over its surface, approaches the surface without collisions, and scans the surface. In doing so, the proposed methodology frees up operator time and enables flexible collision-free UT sensor deployment to unseen surfaces without the need for a digital twin and without the need for a lengthy pre-scanning and path planning step. The path planning methodology enables greater use of RGB/D cameras applied to robotised NDT, negating their relatively low accuracy without relying on limited-information primitives. Online sensor corrections of the robot's pose and directions of motion provide a significant bridge from local corrections to global coverage of parts using limited information. The approach further enables future part localisation methodologies that may use Artificial Intelligence approaches to segment work-spaces, potentially resulting in drop-out of surface point cloud positions due to mislabelling.  
Local changes in friction can be mapped to an accuracy within that of the CW probe's planar aperture, informing operators of potential issues with surface preparation and signal gating which is of great interest to the automation of NDT processes.  
Mentioned throughout the text are the process' limitations. Surfaces with inflections between way points may not result in scans that correctly identify way-points. This can be solved by introducing multiple frames within a single path planning step. Small parts undergoing scan procedures in highly reflective work cells may require operator awareness for successful path planning using the Structural Similarity method. Path planning over parts smaller than the work-frame may also result in the probe partially overhanging the surface's lip. In processing UT data, the operator needs to be aware of any double reflections that may cause UT-signal gating issues. For online corrections the laser-distance measurements may be too widely spaced apart, preventing accurate surface normal alignment. To avoid this issue, the width of their spacing should be minimised. Finally, the success of the path planning method is reliant on the camera calibration quality. The operator is responsible for accurately calibrating the camera tool, so that the produced paths are accurately placed within the robotic work-cell.  
Conceptualization, Methodology, Investigation, Software, Validation: A.P. Supervision: M.S., G.P. and A.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Engineering and Physical Sciences Research Council (EPSRC) as part of an ICASE PhD studentship, grant number S513908/1. This project is part of an initiative known as the Advanced Engineering Materials Research Institute (AEMRI), which is funded by the Welsh European Funding Office (WEFO) using European Regional Development Funds (ERDF).
# Structure of Manganese Oxide Nanoparticles Extracted via Pair Distribution Functions
## 3 Results and Discussion  
### Computational Details  
Note that the intensity of diffuse scattering, which is related to short and medium range order, is orders of magnitude smaller than that of the Bragg peaks. Fourier transformation of experimental total scattering data gives the pair distribution function, which is a direct probe of interatomic distances in real space. The window in \(Q\)-space, which depends on the radiation source, defines the accuracy of the data in the real space. For elastic scattering, the diffraction vector, \(Q\), has a magnitude  
\[|Q|=\frac{4\pi sin(\theta)}{\lambda}\]  
where \(\lambda\) is the wavelength of incident photons and \(2\theta\) is the scattering angle. Since sin\(\theta\leq 1\), the experimentally accessible range of \(Q\) is limited to less than 4\(\pi\)/\(\lambda\). For example, the CuK\({}_{\alpha}\) radiation, which is a common radiation source, has a wavelength of 1.54 A with a \(Q\)-range of about 8 A\({}^{-1}\), but for investigating nanoparticles, a \(Q_{\text{max}}\) larger than 20 A\({}^{-1}\) is needed. For our MnO data here, we have \(Q_{\text{max}}\) = 25 A\({}^{-1}\), which enables us to achieve relatively good real-space accuracy.  
We will use the PDF approach as a'small-box modelling', where one considers relatively small unit cells with periodic boundary conditions. For periodic model structures, the PDF, \(G(r)\), can be computed in real space as:  
\[G(r)=\frac{1}{Nr\langle b\rangle^{2}}\sum b_{i}b_{j}\delta(r-r_{ij})-4\pi r \rho_{0}\]  
where \(b_{i,j}\) are related to thermal movements of the scattering atoms, \(r_{ij}\) are the relative positions of the scattering atoms, \(N\) is the number of particles and \(\rho_{0}\) is the particle density.  
The PDF provides atomic-scale structural insights from the distribution of atom-atom distances ranging from the local coordination scale to several nanometers. In addition to atom-pair separations (peak positions), the PDF also provides direct information about coordination number (integrated peak intensity), static and dynamic disorder (peak shape), and the coherent scattering-domain size (peak attenuation). Modelling the PDF for known periodic structures allows us to assess the effects of modifying the lattice constant, thermal vibration parameters, atomic positions and site occupancies. In addition, PDF-dependent shape parameters can also be used for extracting the radius of a spherically shaped nanoparticle.
# An Analysis of MPTCP Congestion Control
## 7 MPTCP Congestion Control
### Coupled CC
#### 7.2.3 [PERSON] et al. [8] argue that there is an inevitable tradeoff among friendliness, responsiveness and window oscillation. As such, it is not possible to maximise the performance of all three metrics simultaneously.  
BALIA CC was proposed by [PERSON] et al. [8] to generalise existing algorithms and achieve a balanced performance in terms of friendliness and responsiveness. BALIA modifies both increase and decrease phases (AIMD) of the CA algorithm. The minimum SS threshold is set to one MSS when multiple paths are available.  
The BALIA algorithm is based only on the subflow paths _cvmd_ and RTT. The increment _cvmd_ for subflow \(i\) as per (6) on arrival of an ACK.  
\[\frac{\frac{cvmd_{i}}{RTT_{i}}}{(\Sigma_{k}\frac{cvmd_{k}}{RTT_{k}})^{2}}\,\times \,\frac{\alpha_{i}+1}{2}\,\times\,\frac{\alpha_{i}+4}{5} \tag{6}\]  
BALIA decreases TCP-Reno _cvmd_ in the range of \([1,1.5]\) using Formula (7).  
\[\frac{cvmd_{i}}{2}\,\times\,\min(\alpha_{i},1.5) \tag{7}\]  
Parameter \(\alpha_{i}\) is defined in Formula (8). When BALIA uses a single path, \(\alpha_{i}\) is set to 1, in which both increment and decrement algorithms become the same as for standard TCP.  
\[\alpha_{i}=\frac{\max_{k}\frac{cvmd_{k}}{RTT_{k}}}{\frac{cvmd_{i}}{RTT_{i}}} \tag{8}\]
# A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks
## 3 The Improved Clustering Scheme
### Cluster Formation  
The cluster formation process consists of the following steps: neighborhood discovery, selecting MCHs, selecting SCHs, determining cluster members, and selecting FCHs.
## 4 Experiments
### Experimental Settings
#### 4.1.2 Implementation Details  
All models in the experiments were implemented using the deep learning framework PyTorch. The experiments were performed on a workstation with 32 GB of memory and two 2.6 GHz ten-core CPUs. Two NVIDIA RTX Titan GPUs with 24 GB memory were used for acceleration. In the phase of SSL for pretraining, the images were randomly cropped to a size of 96 \(\times\) 96 pixels, the learning rate was 0.6, and the optimizer was LARS [54]. In the fine-tuning stage, the images were resized to 256 \(\times\) 256 pixels. Stochastic gradient descent (SGD) was used as optimization method. The learning rates for ImageNet pretraining and SSL-based pretraining were 0.001 and 0.01, respectively. The learning rates were first warmed up for 5 epochs, followed by a cosine leaning rate decay in the rest of epochs [55]. All networks were trained for 100 epochs in order to ensure fair comparisons. In order to have a better convergence of the CNNs, we only used Mixup for the first 80 epochs. To obtain reliable results, we performed each experiment 5 times. The means and standard deviations of the five experiments are reported.
# Utilizing Ensemble Learning to Improve the Distance Information for UWB Positioning
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Example** & \multicolumn{4}{c}{**Distance Error between Tag**} & \multicolumn{4}{c}{**Distance Error between Tag**} & \multicolumn{4}{c}{**Distance Error between Tag**} & \multicolumn{4}{c}{**Positioning Error (cm)**} \\ \multicolumn{1}{c}{
\begin{tabular}{c} **and Anchor A (cm)** \\ \end{tabular} } & \multicolumn{4}{c}{**and Anchor B (cm)**} & \multicolumn{4}{c}{**and Anchor C (cm)**} & \multicolumn{4}{c}{**and Anchor D (cm)**} & \multicolumn{4}{c}{**Positioning Error (cm)**} \\ \hline LR refinement with 10\% prefilter & 8.5619 & 14.7145 & 13.1708 & 3.1046 & 19.2614 \\ \hline Raw data & 38.7411 & 33.6501 & 36.0461 & 54.3135 & 16.7028 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The example to show the ill-advised point of error estimation. According to the results shown in the example in Table 3, since the trilateration algorithm had some error-tolerant design, and even though all the distance errors between the tag and anchors in the raw data row were higher, it still possessed a lower positioning error. Apparently, this may cause the misjudgment of the algorithm efficiency. Consequently, a fairer evaluation mechanism is to evaluate the distance error instead of the positioning error.
## 4 Datasets and Signals
### Signals
#### 4.2.2 Gyroscope Data  
Another type of sensor used for road surface anomaly detection and classification but to a lesser extent is the gyroscope. These devices can sense the angular velocity of an object when they are mounted on a frame while it is rotating. Several gyroscopes can be embedded in gyrocompass, inertial navigation systems, or inertial measurement units [53]. Like the accelerometer, an adequate preprocessing (i.e., correct sampling frequency and filtering) stage is needed to use this type of sensor for road surface anomaly detection and classification. Some of the studies that have used gyroscope data are the ones of [PERSON] et al. [40] that only study the \(y\)-axis of this device. Furthermore, similar to the accelerometer data, a reorientation process from the smartphone coordinate system to the vehicle coordinate system needs to be performed on the gyroscope data with the help of the Euler angles [42]. Despite that, gyroscopes have less use for road surface anomaly detection and classification, as depicted in Table 6, linear acceleration estimations can be computed through gyroscope and accelerator sensor data, as pointed out in Refs. [10,11]. Hence, its use in combination with other sensor readings could potentially improve the performance of road surface anomaly detection and classification systems.
# Computational Evaluation of Turbulent Supersonic Jet Impinging on Inclined Plate
## 2 Computational Model
### Spatial Discretization  
The discretization of the computational domain was performed by using hexahedral elements in the proximity of the plate, aligned with the solid surface, together with tetrahedral elements in the rest of the domain. The sketch of the overall computational grid is reported in Figure 2, where a couple of side views with different zoom levels illustrate the flow region of interest, including the diverging nozzle. Owing to the FV approach, the compressible flow governing equations were numerically solved while inherently satisfying the conservation principles over each computational cell, e.g., [20], which is particularly important for non-uniform grid arrangement [21]. The particular meshing strategy allowed the smooth transition from structured to unstructured meshes, with the computational complexity of the overall CFD model being alleviated. The mesh resolution at the wall was prescribed by controlling the size of the first computational cell away from the solid surface, while ensuring that the non-dimensional height was corresponding to \(y^{+}\approx 35\), in inner units. In fact, to save computational resources, the turbulence modeling procedure was supplied with wall functions, which allowed us to avoid using more refined grids in the wall region.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Patch** & **Velocity** & **Pressure** & **Temperature** \\ \hline Inlet & FixedValue & FixedValue & FixedValue \\ Outflow & waveTransmissive & waveTransmissive & advective \\ Nozzle surface & noSlip & zeroGradient & zeroGradient \\ Plate surface & noSlip & zeroGradient & zeroGradient \\ \hline \hline \end{tabular}
\end{table}
Table 2: Boundary conditions: selected types in OpenFOAM.  
Figure 2: Sketch of the computational grid, with zoomed-in pictures of the space region of primary interest.  
The robustness of the present computational model was first assessed by performing a numerical grid convergence analysis for a given inclination angle, namely, \(\theta=30^{\circ}\). Three different grid resolutions were prescribed on the plate surface, say \(N_{y}\times N_{z}\), with the resulting overall mesh sizes listed in Table 4. Note, that owing to the prescribed Courant-Friedrichs-Lewy (CFL) condition, the time integration step was consistently reduced by refining the spatial grid. Based on the CFD results that were obtained, the fine resolution could be considered sufficient to accurately predict the surface pressure load on the plate, as demonstrated in Section 4. Also, looking at the relative errors for the pressure peak value measured against the experimental findings, that are given in Table 5, this resolution was needed for a correct estimation of the complex compressible flow field.
# A Survey on Cybersecurity in IoT
## 3 Data Integrity and Privacy
### Privacy in Resource-Constrained Devices  
The resource limitations of many IoT devices pose significant challenges for implementing robust privacy-preserving mechanisms. Traditional encryption methods, while effective, are often too resource-intensive for small, battery-powered devices [130]. To address this, lightweight encryption algorithms, such as the Advanced Encryption Standard (AES) in its truncated versions or custom lightweight block ciphers, have been developed to provide a balance between security and performance [131; 132; 133; 134].  
Moreover, the adoption of hardware-based security features, such as Trusted Execution Environments (TEEs) and PUFs, has been explored as a means to enhance privacy in resource-constrained IoT devices. TEEs provide a secure area within the device's processor that can execute code and store data in a manner that is isolated from the rest of the system, thereby protecting sensitive information even if the device is compromised [135; 136; 137]. PUFs, on the other hand, exploit the unique physical characteristics of semiconductor devices to generate unique cryptographic keys that cannot be replicated, thus providing a hardware-based root of trust [138; 139; 140].
## 6 Experimental Results and Discussion
### Result and Discussion
#### 6.1.2 Classification by Using Sequential Forward Selection  
After applying sequential forward selection for the different kinds of feature set, different numbers of features were selected. The results are shown in Table 5. In SVM classification, we set one more condition for the SFS process. That is, the lower bound of selected features is four features because if the number of selected features is not large enough, the result of classification will be not good. For example, the process of SVM, which is used to train and test all samples with only one feature, only takes approximately 0.3 s.  
Regarding SVM, we see that SFS gives a slight improvement from 75.75% to 78% with GLCM, a significant change in the recognition rate with GLRLM from 54.53% to 88.13%, and an improvement with the combination of GLCM and GLRLM from 61% to 89.25%.
# Gathering Big Data in Wireless Sensor Networks by Drone +
## 6 Algorithm for Big Data Gathering during Drone Movement
### The Algorithm to Create the Subsets
#### 6.2.2 Calculating \(T_{moving}\)  
The Incremental and Decremental heuristics consider data gathering only when the drone is hovering. Hence, the time the drone spends hovering (\(T_{collecting}\)) is the time all nodes need to send data to a drone (\(T_{allmodes}\)). It considers multihop data paths from sensor nodes to the drone. The overall time to gather all data from the WSN (\(T_{total}\)) is the sum of \(T_{collecting}\) with the time the drone spends flying to reach each hovering point (\(T_{trip}\)), according to Equation (1).  
The proposed algorithm has a procedure to create the set \(D_{mov}\) composed of a subset of sensor nodes for each path between two consecutive hovering points on tour. \(T_{moving}\) is the time the drone spends receiving data during its movement. It reduces \(T_{collecting}\) since it decreases the number of sensor nodes to send data when the drone is hovering. This is the main contribution of this work since the state-of-the-art heuristics for the scenario considered here does not support data gathering when the sink is moving. The reduction in \(T_{total}\) can be noted by comparing the same heuristic to create tours with and without the proposed algorithm. These results are explored in the next section. The procedure described here calculates \(T_{moving}\) according to Equation (6). It sums the time each sensor node in \(D_{mov}\) would spend to send data to the drone when it is hovering. The Connecting Graph, defined in Section 4, is used to verify the data paths' length.  
\[T_{moving}=\sum_{k=1}^{|D_{mov}|}\frac{m}{b}\times pathlength(s_{k}) \tag{6}\]  
where \(|D_{mov}|\) is the cardinality of the set \(D_{mov}\), \(m\) is the amount of data storage in the memory of each sensor node, \(b\) is the link bandwidth, and \(pathlength(s_{k})\) is a function that is obtained from the Connect Graph, the data path length of the sensor node \(s_{k}\).
## 3 Proposed Approach
### Feature Extraction
#### 3.2.6 Feature Selection  
To obtain an effective and discriminating feature set and to reduce the redundancy of the feature space, feature selection can be performed [56]. Therefore, Principle Component Analysis (PCA) is applied to reduce the dimensionality of the texture features and to obtain informative features [90]. The first row of the obtained matrix is selected as the first principle components of PCA. Since some of the acquired features often have very small values compared to others, a normalisation process [93] is needed. Therefore, features are normalised using the zscore function (i.e., mean centring with a unit standard deviation constraint) [94].
## 3 Results and Discussion
### Antibacterial Performance  
To further investigate the antibacterial properties of MS/CID/ZrO\({}_{2}\), we evaluated the antibacterial activities of both MS and MS/CID/ZrO\({}_{2}\) using the plate counting method. _E. coli_ was inoculated into the LB liquid medium and cultured at 37 \({}^{\circ}\)C and 120 r/min for 12 h. After the culture, the bacterial suspension was diluted with sterile water to achieve a concentration of 1.5 \(\times\) 10\({}^{5}\) CFU/mL. Under sterile conditions, 5 mL of _E. coli_ solution was mixed with the MS/CID/ZrO\({}_{2}\) composites under continuous stirring for 10 min. Then, the mixed solution was treated by rapid vacuum filtration at a pressure of \(-\)0.1 MPa to collect the filtrate.  
Next, 100 \(\mu\)L of the filtrate and 100 \(\mu\)L of the 1.5 \(\times\) 10\({}^{5}\) CFU/mL _E. coli_ suspension were transferred and coated onto plates. Each sample is coated with the plate three times. The coated plates were placed in a 37 \({}^{\circ}\)C air bath constant-temperature incubator and inverted for 12 h of culture. After the culture was completed, the growth of _E. coli_ on each plate was observed. Compared with the plates of the control group coated with 1.5 \(\times\) 10\({}^{5}\) CFU/mL _E. coli_ bacterial solution, there was a small amount of bacterial growth on the MS sample plates, indicating that MS has a certain antibacterial ability. There was almost no growth of _E. coli_ on the MS/CID/ZrO\({}_{2}\) sample plate, indicating that there were no live bacterial residues in the filtrate after the mixture of MS/CID/ZrO\({}_{2}\) and the bacterial liquid and suction filtration. This proved that the MS/CID/ZrO\({}_{2}\) material has a significant antibacterial effect on _E. coli_ (Figure 7). We suggest that both CID and ZrO\({}_{2}\) nanoparticles in the MS/CID/ZrO\({}_{2}\) composites had a synergistic antibacterial effect. CID is a broad-spectrum antibacterial agent that has strong inhibitory effects on both Gram-positive and Gram-negative bacteria [49]. It acts on the cell wall of _E. coli_, causing morphological changes on the cell surface and disrupting the cell membrane, which leads to the leakage of intracellular contents. This disruption affects the normal physiological activities of the bacteria, ultimately resulting in cell death [50]. ZrO\({}_{2}\) nanoparticles can alter the permeability of _E. coli_ membranes, leading to the deposition of nanoparticles in bacterial membranes and cytoplasmic regions of cells. The surface of ZrO\({}_{2}\) nanoparticles carries a positive charge, while _E. coli_ carries a negative charge. Therefore, the electromagnetic attraction between ZrO\({}_{2}\) nanoparticles and _E. coli_ causes oxidation and bacterial death [51; 52].
# A Risk Assessment Analysis to Enhance the Security of OT WAN with SD-WAN
### Contributions  
The purpose of this paper is to investigate the risks and current mitigation (mitigation is defined as risk mitigation for OT WAN mitigating risks on DoV, DoC, DoS) efficiency of using SD-WANs, a manufacturing-owned cloud network, to increase the security and availability of OT Networks with a focus on DoV, DoC, and DoS attacks. In this paper:  
1. A new risk assessment approach is introduced based on NIST CSF and ISA/IEC 62443 standards. The proposed risk-scoring approach allows for predicting risk based on security standard requirements and present vulnerabilities.
2. A program has been implemented to automate the scrutinization of eight different WAN technologies against SD-WAN to understand the possible gaps of an OT WAN infrastructure and highlight the security advantages of using SD-WAN to secure such infrastructure. The selected technologies are Satellite WAN, LoRaWAN, Private LTE/5G Networks, MPLS (Multiprotocol Label Switching), Leased Line, DMVPN (Dynamic Multipoint Virtual Private Network), IPSec VPN (Internet Protocol Security Virtual Private Network), and VPLS (Virtual Private LAN Service).
3. The suitability of all studied WAN technologies in terms of risks and mitigations related to DoC, DoV, and DoS attacks is highlighted.
4. Two designs of an OT-WAN-based network using SD-WAN and following the PUR-DUE model are introduced.