esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Wavelength Digital-Phase-Shifting Moire Based on Moire Wavelength
## 3 Experiments and Results
### System Calibration  
To calculate the moire wavelengths, system calibration was performed using a flat plate mounted on a linear translation stage and translated to 200 positions in 1.25 mm increments over a 250 mm calibration depth with 0.007 mm translation precision. Four binary (black-and-white stripe input to projector) grid patterns with different grid pitches were projected onto the flat plate and four images with 8-, 10-, 12-, and 14-camera-pixel grid-pitches, respectively, were captured at each of the different known plate positions. The capture of multiple images at different known positions is not required during object measurement, but is required during system calibration to calculate the moire wavelengths. For each grid pitch, phase-shifted moire patterns (images) were generated by digitally phase shifting a computer-generated binary grid overlaid on the captured image (generated binary grid has the same pitch as the corresponding captured image), followed by grid removal, as described in Section 2.1[17; 19]. Finally, the procedure of moire wavelength calculation was performed as described in Section 2.2. The resulting minimum, maximum, and middle-calibration-depth moire wavelengths are given in Table 1 for different camera-pixel grid pitches. The moire wavelengths calculated at the middle calibration depth, which would tend to minimize phase unwrapping errors, were used in measurement: \(\lambda_{1}=21.850\) mm and \(\lambda_{2}=26.048\) mm for two wavelength phase unwrapping with 10 and 12-pixel pitch images, and additionally \(\lambda_{3}=30.573\) mm for the three wavelength phase unwrapping with 10-, 12-, and 14-pixel-pitch images, respectively; and \(\lambda_{1}=16.966\) mm and \(\lambda_{2}=21.035\) mm for two wavelength phase unwrapping with 8- and 10-pixel-pitch images, respectively. The use of these moire wavelengths in surface measurement is explained in more detail in the following section.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \
\begin{tabular}{c} **Grid Pitch** \\ **(pixels)** \\ \end{tabular} & \(\lambda\)**Min (mm)** &
\begin{tabular}{c} \(\lambda\) **Max** \\ **(mm)** \\ \end{tabular} & \(\lambda\)**Mid-Calibration Depth (mm)** \\ \hline \(8\) & \(15.88\) & \(19.08\) & \(16.966\) \\ \(10\) & \(19.62\) & \(24.09\) & \(21.850\) & \(21.035\) \\ \(12\) & \(23.78\) & \(28.31\) & \(26.048\) & \\ \(14\) & \(27.93\) & \(33.22\) & \(30.573\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculated moire wavelengths for different camera-pixel grid pitches (minimum, maximum, and at mid-calibration-depth).  
Note that in practice, grid patterns of different pitch are projected onto a flat plate, and experimentally tested for their ability to generate suitable extended wavelengths that cover the entire range of depth of the object if known, or the full calibration depth.
# Role of Nicotinamide in Genomic Stability and Skin Cancer Chemoprevention
###### Abstract  
Nicotinamide (NAM) is an amide form of vitamin B3 and the precursor of nicotinamide adenine dinucleotide (NAD+), an essential co-enzyme of redox reactions for adenosine triphosphate (ATP) production and for other metabolic processes. As NAD+ status is critical in maintaining cellular energy, vitamin B3 deficiency mainly affects tissues that need high cellular energy causing pellagra and skin sun sensitivity. In animal models, NAD+ deficiency leads to UV sensitivity of the skin, impairs DNA damage response, and increases genomic instability and cancer incidence. Furthermore, NAD+ depletion is associated with human skin aging and cancer. NAM prevents the UV-induced ATP depletion boosting cellular energy and enhances DNA repair activity in vitro and in vivo. Moreover, NAM reduces skin cancer incidence and prevents the immune-suppressive effects of UV in mice. Thus, NAM is involved in the maintenance of genomic stability and may have beneficial effects against skin aging changes and tumor development. Clinical studies showed that topical use of NAM reduces cutaneous aging. Furthermore, oral NAM administration reduces the level of UV-mediated immunosuppression and lowers the rate of non-melanoma skin cancers in high-risk patients. Therefore, NAM replenishment strategy may be a promising approach for skin cancer chemoprevention.  
k 2019; 20; 5946; doi:10.3390/ijms20235946
# A Replica-Selection Algorithm Based on Transmission Completion Time Estimation in ICN
## 4 Algorithm Description
### Overhead Analysis  
We will analyze the communication overhead, memory overhead, and computation overhead of running the TCTE algorithm on edge nodes. TCTE uses passive measurement to perceive the link status between the replica node and the edge node, and does not send additional packets into the network, so the TCTE algorithm does not bring additional communication overhead. The memory overhead of TCTE is O(n). The more replica nodes in the ENRS domain, the greater the memory overhead of TCTE. In our implementation, TCTE needs to occupy about 200 bytes of memory for a replica node. However, even maintaining the state of 10,000 replica nodes only requires a few MB of overhead, and within a reasonable ENRS domain, it will not cause a large burden on memory. The number of replica nodes in the domain is n. The maximum value of TCTE computation overhead for completing a replica selection is O(n). When a certain NDC has caches in all replica nodes, the computational overhead of TCTE reaches the maximum. However, most of the time only a few replica nodes have the same replica, so the maximum cost of a replica selection will be far less than O(n). Therefore, the additional overhead of TCTE is acceptable.
# Visualization of Distributed Plasticity in Concrete Piles Using OpenSeesPy
###### Abstract  
Lumped plasticity models available in commercial software offer a limited resolution of damage distribution along structural members. This study presents an open-source workflow that combines force-based fiber elements in OpenSeesPy with automated 3D post-processing for visualizing distributed plasticity in reinforced concrete piles. A 60 cm diameter pile subjected to monotonic lateral loading is analyzed using both SAP2000's default plastic hinges and OpenSeesPy fiber sections (Concrete02/Steel02). Although the fiber model incurs a runtime approximately 2.5 times greater, it captures the gradual spread of yielding and deterioration with improved fidelity. The presented workflow includes Python routines for interactive stress-strain visualization, facilitating the identification of critical sections and verification of strain limits. This approach offers a computationally feasible alternative for performance-based analysis with enhanced insight into member-level behavior. Because the entire workflow--from model definition through post-processing--is fully scripted in Python, any change to geometry, materials, or loading can be re-run in seconds, dramatically reducing the time taken to execute sensitivity analyses.  
visualization and post-processing; structural analysis; nonlinear structural modeling; soil-structure interaction (SSI); fiber elements; distributed plasticity; fiber-section frame elements; OpenSeesPy
# Recommendation System for a Delivery Food Application Based on Number of Orders
## 2 Literature Review
### K-Nearest Neighbors  
The k-nearest neighbors (KNNs) [44] algorithm is a learning methodology that can be applied to solve classification, regression, or clustering problems. The main idea is to find the k-nearest neighbors, or samples, to a specific sample. KNN is performed by defining a distance metric. For example, if we define a dimensional space, the metric could be the Euclidean distance. The main advantage of this methodology is that any distance metric can be used; in consequence, KNN can be used for all types of data, for example, images, text, and numeric data. In classification or regression problems, we can predict the value of the new sample by using the labels of the k-nearest neighbors (samples). Equation (2) shows an example of the prediction of a new sample, \(y_{new}\), in a regression problem as the mean of the samples' labels, \(y_{i}\), in the neighborhood, \(\mathcal{N}\), where k is the number of nearest neighbors. In clustering problems, the groups can be calculated using the neighborhood of samples. KNN performance depends on the value assigned to k. For that reason, it is recommended to test several values.  
\[y_{new}=\frac{\sum_{y_{i}\in\mathcal{N}}yi}{k} \tag{2}\]
## 3 Results and Discussion
### Classification Model  
The logarithmic correlation had R\({}^{2}\) = 0.7164 with y = 0.11424 ln(x) + 3.2598. This value indicated that there was a logarithmic correlation between the average value of UV emission area and the insulator string condition level status. The prediction result produced an accuracy of 82% with nine predictions that were the same as the results of the visual-based assessment. The mean absolute error (MAE) of this prediction was 0.182.
# Performance Analysis of a Cognitive RIS-NOMA in Wireless Sensor Network
## 5 Conclusions  
In this paper, we studied the performance of a cognitive RIS-NOMA network in terms of outage probability and throughput. In particular, we derived the closed-form expression for outage probability throughput. Furthermore, the asymptotic expression of outage probability is shown to give more insight into the proposed system. Numerical results showed validation of the derived expression and the impacts of the main parameters in the proposed system by conducting a Monte Carlo simulation. Furthermore, the results show that outage performance and throughput from RIS-NOMA were superior to those of  
Figure 6: System throughput versus \(\eta_{\mathcal{S}}\) in dB when varying the number of reflective elements.  
Figure 7: The system throughput versus \(\eta_{\mathcal{S}}\) in dB when varying \(\eta_{th}\).  
RIS-OMA. Most articles in the literature presume possession of perfect CSI in RIS-NOMA to easily analyze the user selection procedure. The CSI for user selection may be out of date for a variety of reasons, including feedback latency, movement, etc. Furthermore, with widespread frequency reuse in wireless networks, RIS-NOMA is susceptible to co-channel interference (CCI). These issues require more effort in future work.  
[PERSON] proposed the idea and the system model. [PERSON] derived the expressions to examine system performance. [PERSON]-[PERSON] and [PERSON] double-checked the manuscript and created the figures, while [PERSON] and [PERSON] wrote the final paper. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Regional Innovation Strategy through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-003).  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflicts of interest.
## 2 Problem Description and System Models
### Constrained Markov Decision Process
#### 2.4.2 Action Space  
The action space \(A=[A_{PV},A_{CB}]\) consists of control actions for reactive power compensation, including the reactive power output adjustments of PV inverters and switching operations of capacitor banks. Here, \(A_{PV}\) represents the action set of PV inverters. Since PV inverters support continuous control, the action of each inverter is defined within the range \([-1,1]\). In contrast, capacitor banks (CBs) are discrete devices, and the action variables in \(A_{CB}\) are binary values \([0,1]\). Moreover, the operation of discrete devices not only affects the system state, but also incurs corresponding action costs.  
Figure 2: Block diagram of the CMDP process for a voltage-secure distribution network.
# 1D CNN Based Detection and Localisation of Defective Droppers in Railway Catenary
## 6 Conclusions  
A defective dropper in a railway catenary system is a common fault which is not easily detectable in advance using existing monitoring methods. In this paper, a computational framework was used to model the health condition of the droppers and simulate the pantograph-catenary interaction. The impact of dropper damage on the system's dynamic behaviour was analysed and categorised in terms of contact force quality. The filtered Gaussian white noise was added to the raw data to improve the authenticity for classification.  
This paper also contributes a method to classify the damage degree and the location of the defective droppers. To detect and locate damaged droppers, a method integrating multiple 1D CNN models was developed and applied. The hyper-parameter tuning technique and K-fold cross-validation were used to prevent overfitting and to ensure model performance regardless of the training data subset selected. Metrics including accuracy, F1-score, precision and recall have been considered, and the classification results show better performance of the integrated 1D CNN model than a conventional 1D CNN model.  
Figure 10: Comparison of the results between integrated model and the conventional model through four metrics.  
For example, under a 20 dB noise level, the accuracy of the integrated 1D CNN model was approximately 5% higher, which means there would be one in twenty fewer false alarms. It is concluded that the integrated 1D CNN method can forecast the risk of defective droppers. Regardless of the form of damage, as long as the damage affects the pantograph-catenary contact force quality, the defective dropper can be identified by the integrated 1D CNN method, which can improve the competence of intelligent condition monitoring in the railway system.  
This paper was written by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. The catenary simulation model was contributed by [PERSON] The integrated 1D CNNs model was developed by [PERSON] and [PERSON]. The results analysis was contributed by [PERSON], [PERSON] and [PERSON]. Project supervision was provided by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the China Scholarship Council grant number 201908060338 and the School of Engineering at the University of Birmingham And The APC was funded by the University of Birmingham.  
All relevant data are shown in the paper or could be recreated by following the methodology in the paper.  
The authors are deeply grateful to the Birmingham Centre for Railway Research and Education (BCRRE) for the resources provided.  
The authors declare no conflict of interest.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 2 Related Works
### Localization Approaches Related to the Mobile Anchor Node  
Both range-free and range-based localization approaches require anchor nodes, which are equipped with GPS units to provide reference locations [10]. Since the cost of the anchor node is much higher than that of the sensor node, it is expensive to deploy a large number of anchor nodes in UWSNs [11]. Hence, using mobile anchor nodes to assist nearby sensor nodes in localization is an economical and flexible approach [12]. In [39], a Dive and Rise (DNR) positioning method is presented, in which mobile anchor nodes are used to replace static ones. While sinking and rising, DNR anchor nodes broadcast their positions. Unknown nodes are localized by passively listening to DNR anchor nodes' messages. DNR method reduces communication overhead and energy consumption by passively listening, but localization performance heavily depend on frequency of location updates and the number of anchor nodes.
## 4 Markov Chain Analysis  
In Section 3.2, the state transition of the SU was analyzed. The state of the channel is divided into busy and idle only; meanwhile, the system has two kinds of SUs and the integer pair \((i,j)\) can indicate the state of the channels occupied by SUs. A user will obtain the channel or join in the queue when it arrives, while a user will leave the system when it completes communication. In a cognitive radio communication system, arrival is random and the occupancy of channels is memory-less. The arrival and leaving of users constitutes a birth-and-death process, so we use a Markov chain to analyze the model [19; 42].  
The property of Markov chain is denoted as  
\[P\{X_{n+1}=j|X_{n}=i,\ X_{n-1}=i_{n-1},\ \cdots,\ X_{1}=i_{1},X_{0}=i_{0}\}=P \{X_{n+1}=j|X_{n}=i\}. \tag{17}\]  
The state of the system is only related to the previous state in the Markov chain. A one-dimensional Markov chain has only one class of states, while a two-dimensional Markov chain has two classes. Due to the state transition of the system, we can draw the state transition diagram of the two-dimensional Markov chain, as shown in Figure 5.  
Since the number of one class of states is \(N\), there are \((N+1)\times(N+2)/2\) states in the system, the steady state probability of state \((i,j)\) is represented by \(P_{i,j}\). According to the state transition diagram, the steady state equations for each state are listed:  
State \((0,0)\):  
\[P_{0,0}(\lambda_{q1}+\lambda_{q2})=P_{1,0}\mu_{q1}+P_{0,1}\mu_{q2}. \tag{18}\]  
State \((i,0)\), where \(1\leq i\leq N-1\):  
\[P_{i,0}(\lambda_{q1}+\lambda_{q2}+i\mu_{q1})=P_{i+1,0}(i+1)\mu_{q1}+P_{i,1}\mu_ {q2}+P_{i-1,0}\lambda_{q1}. \tag{19}\]  
State \((0,j)\), where \(1\leq j\leq N-1\):  
\[P_{0,j}(\lambda_{q1}+\lambda_{q2}+j\mu_{q2})=P_{1,j}\mu_{q1}+P_{0,j+1}(j+1)\mu _{q2}+P_{0,j+1}\lambda_{q2}. \tag{20}\]  
When using the fixed priority factor, we can obtain the congestion coefficients of system S. While using the adaptively priority factor, we can obtain S'. The improvement of the system's congestion coefficient means (S' - S)/S. In Figure 10b, we can see an improvement when the system is at the blocking state. The improvement is positively related to the size of the fixed priority factor and negatively related to the arrival rates of \(SU_{q1}\). When the parameter \(p\) is 0.8, we can obtain the minimum point in Figure 10b, so that the improvement of the algorithm corresponding to the lowest point tends to 7%. This means that, as the arrival rate \(\lambda_{q1}\) increases, the improvement of the algorithm will decrease, but the minimum still has about 7% improvement. The vertex of the graph indicates the maximum degree which the DSA algorithm can reduce when \(\lambda_{q1}\) is near the switch point.  
Finally, we can determine that the DSA algorithm, with an adaptive adjustment of the priority factor, can effectively improve the system efficiency. The improvement of the algorithm is about 7-26%, and the specific improvement value is negatively correlated with the SU arrival rate.
## 1 Introduction
### Research Challenges  
While the PD-NOMA is broadly considered a promising multiple access technique which enhances the existing 5G-NR framework and B5G, several challenges [10; 11] demand further investigation into implementing NOMA to improve capacity, spectral efficiency and latency. Considering resource allocation in NOMA, multiple users have sensors and actuators share the same RB. They are paired based on the distinct (strong and weak) channel gains, resulting in different PAs. At the BS, the user signals are superimposed and transmitted, whereas, at the user's location, SIC is applied to received user signals to decode and extract the original signal. However, decoding the superimposed signal using SIC considering perfect Channel State Information (CSI) can be challenging. A perfect CSI can pose real-time channel estimation errors, which may degrade the system's performance [12]. Therefore, new hybrid techniques and algorithms can be developed to yield a practical channel estimation.  
In addition, NOMA's system performance reduction may arise due to an imperfect SIC receiver [13]. This imperfection depends on building efficient hardware to reduce computational complexity. The efficient PA among multiple users can pose another challenge in the NOMA system. Inefficient PA may lead to interference in the received user signal, unfairness among paired users and higher outage probability, thus resulting in performance degradation. Moreover, most researchers focus on adopting a two-user pairing or sensor clustering for easy superimposition at the BS and to reduce SIC complexity at the receiver's end. To cater to the growing demands of mMTC devices, multi-user pairing strategies or sensor clustering must be formed to take complete advantage of NOMA systems. Finally, due to the heterogeneous wireless network, a need for hybrid-NOMA (NOMA with other multiple access techniques) arises to address the diverse needs of devices through the BS. Integrating NOMA with other multiple access techniques, optimising the system's performance, can be challenging.
## 3 Results and Analysis
### Simulation of Networked SAR Satellites and Multiview Ocean Wave SAR Synchronization Data  
In this paper, a virtual network of satellites consisting of three SARs was simulated, and the SAR synchronized observation data of different orbit directions were simulated by the networked satellite cooperative observation. The SAR that implemented the imaging simulation in the previous section is called SAR-1. Based on the parameters and working principle of SAR-1, the other two SARs with angles of \(30^{\circ}\) and \(-50^{\circ}\) from the flight direction of SAR-1 were simulated. The angle was positive in the same direction as SAR-1 and negative in the reverse flight. The satellites were named SAR-2 and SAR-3. The SAR-2 and SAR-3 working modes were also side-looking. Based on SAR-1, the position coordinates of the subsatellite point were changed, and the angle of orbit was adjusted according to the relative position between the subsatellite points while ignoring the Earth's rotation and ground bending. A schematic diagram of the three SAR observations in the same sea area is shown in Figure 7.  
Figure 5: Ocean surface echo signals as follows: (**a**) wind speed of 10 m/s and (**b**) wind speed of 15 m/s.  
Figure 6: Ocean surface synthetic aperture radar (SAR) images as follows: (**a**) wind speed of 10 m/s and (**b**) wind speed of 15 m/s.  
The backscattering coefficient images obtained by the networked SAR satellite observations of the ocean surface and the corresponding ocean SAR images are shown in the following figures.
## 3 Experiments and Results
### Qualitative Results  
A visualization of the 3D segmentations is used to display the similarities and differences between the ground-truth and each method's results (Figure 11).  
When comparing the manual annotation with the silver-standard mask, is is apparent that the latter considers the thalamus a much broader structure than the former. Other studies [7] have observed similar results. Another important consideration is that the segmentation performed by the model after fine-tuning exhibits only minor differences when compared with the specialist's annotation, with only a few voxels misclassified the borders of the thalami.