esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards NGGM: Laser Tracking Instrument for the Next Generation of Gravity Missions
## 3 LTI Units
### Instrument Control Unit (ICU)/Phasemeter
In addition, it provided internal power distribution to the ICU modules, as well as delivering the power supply to other LTI sub-systems such as quadrant photoreceivers and the steering mirror, laser and laser stabilization unit. **  
The development of precision laser ranging processors, or, in short, phasemeters, that was included in the BEE was pursued by the ESA and DLR for several years in the frame of the LISA mission [27]. The most critical functions and performance aspects of the phasemeter units were already proven in hardware, supported by technology development activities initiated by the ESA [28; 29]. Nowadays, this development is running towards a TRL6 unit demonstrator [30], in which the NGGM mission benefits from previous realizations. With respect to the NGGM, a TRL of four is currently being assessed, allowing for a reasonably fast development of the ICU towards TRL6. Figure 4: Baseline design for the ICU physical assembly (rendered image).
## 4 Experiments
### Dataset  
In this paper, the steel strip dataset NEU-DET [20] is used to verify the performance of the proposed DPN-Detector. The annotation type of the NEU-DET dataset is bounding box. Each annotation including a category label and the coordinates of the two points of the box (top left and bottom right), is stored in XML. There are six types of defects, namely crazing (Cr), inclusion (In), patches (Pa), pitted surface (PS), rolled in scale (RS), and scratches (Sc). There are a total of 1800 images, 300 images for each defect. The resolution of each image is 200 \(\times\) 200. We divide the dataset into training and testing sets, where the training and the testing sets contain 1260 images and 540 images, respectively. The distribution instances of the six defect types in the training and testing sets are visible in Table 1. Furthermore, in order to prevent overfitting, the training dataset is expanded by six times through different operations such as random horizontal or vertical flipping, cropping, zooming, and translation. Figure 6 shows some typical defect annotations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **Cr** & **In** & **Pa** & **PS** & **RS** & **Sc** \\ \hline Test & 197 & 306 & 252 & 136 & 189 & 161 \\ Train & 2947 & 4219 & 3762 & 1776 & 2633 & 2319 \\ Total & 3144 & 4525 & 4014 & 1912 & 2822 & 2480 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Defect instance distribution of six defect types in train and test sets.  
Figure 6: Examples of defective images with ground-truth annotations in the NEU-DET dataset: (**a**) Cr; (**b**) In; (**c**) Pa; (**d**) PS; (**e**) RS and (**f**) Sc.
## 4 Leak Detection Technologies
### Software-Based Methods
#### 4.2.1 Flow Variation Method  
The flow variation method is based on the premise that a significant increase in the flow rate at the inlet or outlet of the water distribution system indicates the presence of a leak. A leak alarm is triggered if the flow rate surpasses a pre-set threshold within a specific timeframe. [PERSON] [56] introduced a technique called the comparison of flow pattern distributions (CFPD), which can detect and measure modifications in the volume of water supplied during two distinct timeframes. The technique involves plotting one dataset against the other and identifying anomalies through the best linear fit with slope and intercept. Consistent changes, which may be caused by variations in weather or population size, are indicated by the slope, while inconsistent changes, attributed to increased leakage, are indicated by the intercept. This approach has proven effective in detecting leaks in water distribution systems (WDS). Additionally, it can be used to examine archived flow data for irregularities [57]. [PERSON] and [PERSON] [58] created Cuboid, an automated feature recognition tool for CFPD analysis of flow data in supply areas, to enable real-time detection of new leaks or identification of unregistered changes in valve status. The flow variation method faces limitations due to its sensitivity to minor fluctuations and potential false alarms from everyday water usage. Future research should refine the method by developing advanced algorithms to distinguish genuine leaks from other variations. Integrating complementary sensor technologies, such as pressure and acoustic sensors, could enhance the reliability, and scalability should be a key focus, especially for large water distribution networks.
## 6 Conclusions  
In conclusion, the OFDR system based on the probe Er-doped self-sweeping fiber laser is developed. It is demonstrated that the sensitivity of the system allows us to measure the RLS signal at level of about 20 dB above the NFL. The RLS results in the appearance of a complex structure consisting of alternating spatial peaks and dips in measured reflectograms. This structure does not change when the time domain averaging parameters are changed and remains unchanged even when each mode pulse is divided into several independent sections. This structure is associated with the interference of back-scattered signals originating from neighbor sections within a scattering volume, which is limited due to the spatial resolution of the OFDR system. The results of the interference depends on microscopic inhomogeneities in the LUT. In turn, the NFL changes with the number of averaging points: an increase in the number of averaging points from 1 to 430 points decreases the NFL by 30 dB. If the center frequency is tuned using the widowed Fourier transform within the probe laser scanning range, it becomes possible to detect the scattering spectra at each point of the LUT. If some spectral response distribution is recorded via an OFDR system and fixed as a reference, it may become possible to extract information about external impacts on the fiber state caused by heating or deformation. For this purpose, correlation analysis can be applied. This possibility will be investigated in our future research. In addition, averaging in the frequency domain can be performed to specify the information about the absolute value of the local scattering. However, the information about the spectral composition of the scattered signal will be lost in this case. The described analysis techniques can be useful in the development of distributed sensors based on Rayleigh OFDR systems.  
Conceptualization, I.A.L. and S.I.K.; methodology, N.R.P. and I.A.L.; software, N.R.P.; validation, N.R.P., I.A.L. and S.I.K.; investigation, N.R.P.; data curation, I.A.L.; writing--original draft preparation, N.R.P.; writing--review and editing, N.R.P., I.A.L. and S.I.K.; funding acquisition, I.A.L. and S.I.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was carried out as a part of the state assignment, state registration no. 122031600174-5.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The equipment of the Center for Collective Use \"High Resolution Spectroscopy of Gases and Condensed Matters\" at the Institute of Automation and Electrometry was used in this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols
## 4 Performance Parameters  
Opnet, Omnet, Matlab, QualNet, and other simulation tools, among others [39, 40, 41, 42], commonly take the design of UWSN into account. The proposed network is created in the QualNet simulator using several user-friendly UWSN design parameters. The performance indicators for the UWSN network in various applications are listed right away and shown in Figure 8.  
**Energy Consumption:** the energy used by nodes to send data from their point of origin to their point of destination.  
\[\text{Energy Consumption}=\frac{\text{E}_{\text{total}}}{\text{N}\times\text{Pt} _{\text{S}}\times\text{50}} \tag{1}\]  
where Pt\({}_{\text{S}}\) denotes the successful packet reception, N is the number of nodes and E\({}_{\text{total}}\) is the total energy consumption.  
**Average Transmission Delay:** the average transmission delay is the amount of time it takes for information to successfully travel from its source to its destination.  
**Average Jitter:** this measure concerns the time difference between individual packets as a result of route changes or network congestion. A routing protocol should be lower to function more efficiently. A network's congestion, routing modifications, or timing drift can all increase jitter by delaying the transmission of individual packets.  
\[\Omega=Sx_{time}-Rx_{time} \tag{2}\]  
where \(\Omega=\) average jitter, \(Rx_{time}\)= receiver time, \(Sx_{time}\) = source time.  
**Percentage of Utilization:** the proportion of packets that are successfully transferred from the transmitting node to the receiving one is known as the throughput of a communication channel.  
\[\text{Percentage of Utilization}=\sum_{i=1}^{n}\frac{\text{TxP}_{i}}{\text{Total time}} \tag{3}\]  
where TxP\({}_{i}\) = packet transmitted by ith node.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 4 Problem Statement
### Problem Definition  
Our deployment problem provides the optimum number and position of UAVs that are required to provide VoWiFi service to ground users. We aim to minimize the number of UAVs deployed over time (i.e., rate of drones per hour of service) while satisfying coverage and QoS constraints. For example, for a service duration of one hour, a solution with 3 drones that last 30 min (6 drones/h) will be preferred to that with 2 drones that last 15 min (8 drones/h). Therefore, our objective function minimizes the ratio between the number of UAVs deployed (\(D\)) and their average endurance expectation (\(\bar{T}_{\text{Flight}}\)). This can be formulated as follows:  
\[\begin{array}{ll}\underset{\mathcal{X}}{\text{min}}&\frac{D^{2}}{\sum_{i=1} ^{D}T_{\text{Flight}}(i)}\\ \text{subject to}&\sum_{i=1}^{D}|\mathcal{C}(i)|/U\geq C_{\text{min}}\\ &R(i)\geq R_{\text{min}}\ \,\forall i\in\mathcal{D}\\ &\mathbf{x}_{i}\in\mathcal{P}\ \,\forall i\in\mathcal{D}\\ &D\leq D_{\text{max}}\end{array} \tag{9}\]  
where \(D\) represents the number of UAVs deployed, \(T_{\text{Flight}}(i)\) accounts for drone \(i\) expected flight duration, \(C_{\text{min}}\) stands for the minimum ratio of users that must be covered, and \(R_{\text{min}}\) is a constant that defines the minimum acceptable speech quality (e.g., 65 as proposed in ITU-T G.107 [45]).
## 2 Materials and Methods
### Artificial Neural Network (ANN)  
ANNs have a rich and fascinating history that spans several decades. The roots of ANNs can be traced back to the late 1940s and the work of [PERSON] and [PERSON], who proposed a mathematical model of a neuron known as the McCulloch-Pitts neuron [PERSON] and [PERSON] (1953). They suggested that neurons in the brain could be modeled as binary on-off switches, which laid the foundation for ANNs. In the 1950s and 1960s, several researchers began to develop neural network models, including the 'perceptron' proposed by [PERSON] in 1958 [PERSON] (1958). The perceptron is a single-layer neural network that can learn to classify patterns by adjusting its weights. In the 1970s and 1980s, the development of the backpropagation algorithm by [PERSON] and others revolutionized the field of ANNs [PERSON] _et al._ (2016). This algorithm allowed for the training of multi-layer neural networks, which could learn to perform more complex tasks compared to single-layer perceptrons.  
Figure 5: Illustration of the selection process for the 25 sand particles for each sample (not to scale).  
ANNs can be used for various tasks, including classification, regression analyses and time-series prediction [55]. They have been successfully applied in different fields, such as finance, healthcare and image recognition. However, the accuracy of ANN models relies heavily on the quality and quantity of the training data [56]. The models may also suffer from the problem of vanishing gradients, where the gradients become too small to update the weights during backpropagation [57].  
To implement the ANN methodology, first, the data are divided into training and testing sets. The ANN model is then trained on the training set using a backpropagation algorithm to minimize the error between the predicted and actual outputs. The number of hidden layers and the number of neurons in each layer are determined using a trial-and-error procedure (or by a validation set). The performance of the ANN model is evaluated on the testing set using various statistical metrics, such as _mean squared error_ (MSE) and _mean absolute error_ (MAE). The hyperparameters of the ANN model, such as learning rate and momentum, are optimized using techniques such as grid search or randomized search.  
Moreover, before training the ANN model, the input data need to be preprocessed by normalizing the data to ensure that all features have equal importance. The activation function for each neuron is selected based on the problem being addressed (e.g., sigmoid, hyperbolic tangent (tanh), or rectified linear unit (ReLU)). Regularization techniques, such as dropout or weight decay, may also be applied to prevent overfitting during the training process.
## 3 Results and Discussions  
### Characterization  
#### 3.1.1 Texture Properties  
As seen from Figure 2, XRD patterns of prepared pure TiO\({}_{2}\) and RE/TiO\({}_{2}\) were all in accordance with JCPDS Card No.21-1272 of anatase TiO\({}_{2}\)[27; 28]. The characteristics peak of all RE/TiO\({}_{2}\) became broader and lower than pure TiO\({}_{2}\), suggesting a decrease in crystallite sizes (Table 1). The higher the rare-earth doping amount, the broader the XRD pattern peaks and the lower the intensity [29]. In other words, the doping of rare-earth elements slowed down the growth of TiO\({}_{2}\) nanoparticles, changed the surface structure of TiO\({}_{2}\) particles, and improved the diffusion barrier of TiO\({}_{2}\) grain.  
There are no new phases except anatase TiO\({}_{2}\) by XRD analysis for the doped catalyst. So, RE\({}^{3+}\) might highly disperse on the surface or enter into the lattice of TiO\({}_{2}\). Therefore, the growth of TiO  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **Size (nm)** & **Sample** & **Size (nm)** & **Sample** & **Size (nm)** & **Sample** & **Size (nm)** \\ \hline Pure TiO\({}_{2}\) (500 \({}^{\circ}\)C) & 19.0 & 0.10\%/Ce/TiO\({}_{2}\) & 16.5 & 0.10\%/Gd/TiO\({}_{2}\) & 14.9 & Pure TiO\({}_{2}\) (450 \({}^{\circ}\)C) & 14.2 \\
0.25\%/La/TiO\({}_{2}\) & 18.4 & 0.20\%/Ce/TiO\({}_{2}\) & 13.3 & 0.20\%/Gd/TiO\({}_{2}\) & 12.7 & 0.10\%/TiO\({}_{2}\) & 11.9 \\
0.50\%/La/TiO\({}_{2}\) & 15.6 & 0.30\%/Ce/TiO\({}_{2}\) & 13.1 & 0.30\%/Gd/TiO\({}_{2}\) & 12.3 & 0.20\%/TiO\({}_{2}\) & 11.7 \\
0.75\%/La/TiO\({}_{2}\) & 13.4 & 0.40\%/Ce/TiO\({}_{2}\) & 12.5 & 0.50\%/Gd/TiO\({}_{2}\) & 12.0 & 0.30\%/TiO\({}_{2}\) & 11.2 \\
1.00\%/La/TiO\({}_{2}\) & 11.5 & 0.50\%/Ce/TiO\({}_{2}\) & 12.4 & 0.70\%/Gd/TiO\({}_{2}\) & 11.9 & 0.50\%/TiO\({}_{2}\) & 10.9 \\  & & & & & 1.00\%/TiO\({}_{2}\) & 10.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Crystallite sizes calculated by Scherrer formula.  
Figure 1: Pure anatase TiO\({}_{2}\) ((**a**), unit cell) and anatase RE/TiO\({}_{2}\) ((**b**), supercell). The red, gray and green spheres represent oxygen, titanium and rare-earth atoms, respectively.  
particles was hindered by doped rare-earth elements. Besides, the rare-earth compounds might become light interception sub-trapping centers, which is important to retard the recombination of the electron-hole pairs and to affect the photodegradation activity [30].
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
## 2 Data and Methods
### Greenland
#### 3.2.2 RADARSAT-2 & Sentinel-1a  
In year 2013-2014, RADARSAT-2 provided a partial coverage of Greenland (Figure 3d), mainly limited by mode conflicts with interests from the Canadian government and commercial companies. Comprehensive mappings from Sentinel-1a for years 2014-2015 and 2015-2016 have been obtained from 677 and 2982 scenes (Figure 3e,f). The mapping coverage is similar to the 2014-2015 comprehensive map done by [19] and to its Landsat counterpart. Overall, previous mappings with C-band sensors, such as RADARSAT-1 (24 days repeat, [1; 3] or ENVISAT/ASAR (35 days repeat, [3]), displayed less correlation than Sentinel-1a/b, especially in the southern part of Greenland. This suggests that the shorter repeat cycle of Sentinel-1a/b, in addition to continuous mapping, is significantly useful to improve ice sheet mapping.
# Hybrid Wavelet-CNN Fault Diagnosis Method for Ships' Power Systems
## 3 System Description
### Running the Simulation  
Each simulation lasted 5 s, and the sampling rate of the monitored parameters, which were the 3-phase stator's currents, was 20.000 Hz. The IM was subjected to variable load torque that had been induced by a step function; the value, duration, and rotational speed for each load torque are summarized in Table 2. The time series of the 3-phase currents of the stator during healthy operation are depicted in Figure 2, and the maximal current flowed during the maximal load torque. When there was a change in the load, there was a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Block Name** & **Parameter** & **Value** \\ \hline  & Stator resistance & 1.4050 \(\Omega\) \\  & Rotor resistance & 1.3590 \(\Omega\) \\  & Stator inductance & 0.005839 H \\ Three-phase squirrel-cage IM & Rotor inductance & 0.005839 H \\ (4 kW, 400 V, 50 Hz, 1430 rpm) & Pole pairs & 2 \\  & Friction factor & 0.002985 N m s \\  & Inertia & 0.0131 J/kg m\({}^{2}\) \\  & Mutual Inductance & 0.1722 H \\ \hline  & Fault resistance & 0.1 \(\Omega\) \\ Three-phase block of fault & Ground resistance & 0.01 \(\Omega\) \\  & Snubber resistance & 106 \(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the IM and block of fault.  
Figure 1: Simulation model in MATLAB Simulink.  
proportional change in the waveforms of the currents, as expected. The time series of the 3-phase currents of the stator during faulty operation are depicted in Figure 3, where we can easily distinguish that the operation was faulty from 1.4 to 1.7 s. Our ultimate goal was to develop a method for both the accurate and automated condition monitoring of power systems. The research methodology that is presented in Section 4 was utilized for the diagnosis of stator winding faults.
# Decentralization of Virtual Linkage in Formation Control of Multi-Agents via Consensus Strategies
## 3 Decentralization of Virtual Linkage Approach
### Local Control of Each Agent  
After each agent has calculated the \(\dot{\xi}_{i}\), then each agent is able to update the value of \(\xi_{i}\) using Equation (5). Note that \(\lambda=[\lambda_{1x},\lambda_{1y},\ldots\lambda_{kx},\lambda_{ky}]\) represents the expansion/contraction rates of each virtual link along their coordinate frame's axis (See Equation (6)). Figure 5 shows the geometry definition of the virtual linkage. The position of a specified agent can be calculated using manipulation kinematics in Equation (7). Here, \({}^{vj}p_{i}\) is the relative position of Agent \(i\) in the corresponding virtual link \(j\) which it belongs to  
\[\xi_{i}(r_{i},q_{i},\Theta_{i},\lambda_{i})=\xi_{i}+\dot{\xi}_{i}\cdot dt \tag{5}\]  
\[{}^{vj}p_{i}=\left[\begin{array}{c}\lambda_{ij}\cdot{}^{vj}p_{ix}^{Ini}\\ \lambda_{ij}\cdot{}^{vj}p_{iy}^{Ini}\\ 1\end{array}\right] \tag{6}\]  
\[{}^{w}p_{i}^{d}={}^{w}T_{R}(r_{i},q_{i})\cdot{}^{R}T_{vj}^{d}(\Theta_{i}, \lambda_{i})\cdot{}^{vj}p_{i} \tag{7}\]  
Figure 4: Implication of virtual linkage states \(\xi_{i}\).  
Finally, the desired absolute position is passed onto the local controller to position the vehicle. Each agent is supposed to know its own position \({}^{w}p_{i}\), and the consensus control algorithm in Reference [22]  
\[u_{i}={}^{w}p_{i}^{d}-k_{p}\cdot\left({}^{w}p_{i}-{}^{w}p_{i}^{d}\right)-\sum_{j= 1}^{n}a_{ij}^{v}[({}^{w}p_{i}-{}^{w}p_{i}^{d})-({}^{w}p_{j}-{}^{w}p_{j}^{d})] \tag{8}\]  
is used, where \(a_{ij}^{v}\) are the elements of the adjacency matrix \(A^{v}=\left[a_{ij}^{v}\right]\in R^{n\times n}\). For a connected graph, consensus to the reference value is guaranteed.
###### Abstract  
The objective of this study was to evaluate the short-term and crossover effects of a percutaneous neuromodulation (PNM) intervention on the femoral nerve, regarding the pain, knee flexion motion (range of motion (ROM)), and functionality, in patients with unilateral anterior knee pain (AKP). Our study used a randomized clinical trial design. Thirty patients were divided into two groups: one asymptomatic knee group in which patients received stimulation in the femoral nerve corresponding to the nonsymptomatic knee; and one symptomatic knee group, in which patients received stimulation in the femoral nerve corresponding to the painful knee. Pain, knee flexion ROM, Victorian Institute of Sport Assessment-Patella (VISA-P) and Kujala questionnaires were evaluated. Twenty-eight patients completed the study. Compared to their baseline values, both groups showed an increase immediately at 24 h, and at 1 week for the knee flexion ROM variable. In addition, the symptomatic knee group showed an increase for the Kujala score and a decrease for the numeric rating scale (NRS) variable from baseline to 1 week. VISA-P score did not show statistically significant differences for the time-group interaction. After the intervention, there were no differences between the groups in any measured time. Conclusion: a single-shot ultrasound-guided PNM intervention per week in the femoral nerve may be an effective treatment for improving the pain, knee flexion ROM, and knee functionality. In addition, this technique produces crossover benefits in the nonintervention limb.  
anterior knee pain; femoral nerve; percutaneous electrical nerve stimulation; chronic pain; ultrasound; physical therapy; neuromodulation
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 5 Adversarial Ant Colony Optimization Algorithm for Solving Non-TT Routing Problem
### Working Pattern of AACO  
We added a non-TT routing program based on the AACO into the central control server of the smart factory TSN to optimize non-TT routing. When a new non-TT application sent a registration message to apply to set up a data flow with the data server, the transmission continued normally while the AACO process was created to perform offline route planning. The ES node where the application was running was treated as the source node \(v_{src_{k}}\) and the delay cost between it and other nodes was calculated to generate the new \(\{d_{ij}^{k}\}\). The updated \(G(V,E)\), \(\{d_{ij}^{k}\}\), and the priority of this data flow \(p_{k}\) were input to the AACO process to calculate \(m\) new routes. Starting from the next time period, all non-TT flows were transmitted with \(m\) new routes to complete the registration of the new non-TT application.
## 3 Test Results and Analysis
### Vertical Compression Performance of Bearings
#### 3.1.3 Pre-Pressure  
From the extracted test data from cases 1-12, the effect results of different pre-pressures (1700 kN, 2040 kN, and 2720 kN) on the three bearings were obtained, under the vertical pressure of 3400 kN and the loading frequency of 0.1 Hz. The obtained vertical compression load-displacement curves are drawn in Figure 7. As can be seen from the figure, the slopes of the hysteretic curves augment significantly with the growth of pre-pressures, indicating that the pre-pressure has a great influence on the vertical stiffness of the bearings, especially for HDRB-LSF and TLRB.  
Figure 4: Load–displacement curves of vertical compression: (**a**) HDRB-LSF; (**b**) TLRB; (**c**) LRB.