esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Lumped Parameter Model of V-Shaped IPMSM
### D-axis Inductance  
As shown in Figure 3, d-axis magnetic path in the core is strongly affected by saturation due to remanent flux if PM is strong enough like rare earth type. Therefore, initial states of core reluctances are already determined before d-axis MMF source is applied. The initial states can be expected from MEC for PM flux linkage mentioned above. Thus, utilizing nonlinear reluctances at previous state, d-axis inductance can be estimated using the Equations (7)-(12).  
\[F_{dsn}=\frac{3}{2}\frac{4}{\pi}\frac{N_{a}k_{w}}{p^{2}}l_{dn} \tag{7}\]  
\[F_{ds1n}=\frac{1}{\alpha_{1}-\alpha_{0}}\int_{\alpha_{0}}^{\alpha_{1}}F_{dsn} \sin(\theta)\;d\theta \tag{8}\]  
\[\phi_{dn}=\frac{F_{ds1n}}{r_{g(n-1)}+r_{m(n-1)}+r_{ry1(n-1)}+r_{ry2(n-1)}+r_{ truth(n-1)}+r_{\text{hack}(n-1)}} \tag{9}\]  
\[B_{gn}=B_{g(n-1)}-\phi_{dn}A_{g} \tag{10}\]  
Figure 2: Flux density and flux lines over one pole of magnet at no load from finite element analysis (FEA).  
\[\lambda_{dn}=N_{a}k_{av}\phi_{dn} \tag{11}\]  
\[L_{dn}=\frac{\lambda_{dn}}{I_{dn}} \tag{12}\]  
where \(I_{dn}\): \(n\)-th d-axis current, [A], \(F_{dsn}\): fundamental amplitude of MMF on airgap at \(I_{dn}\), [A], \(F_{ds1n}\): constantly distributed MMF value in between \(\alpha_{0}\) and \(\alpha_{1}\), [A], \(\phi_{dn}\): flux passing through d-axis path, [Wb], \(r_{ryl1,2}\): nonlinear reluctances in rotor, [H\({}^{-1}\)], \(r_{tenth}\): nonlinear reluctance of teeth in stator, [H\({}^{-1}\)], \(r_{tack}\): nonlinear reluctance of back iron in stator, [H\({}^{-1}\)], \(B_{gn}\): flux density excited by \(I_{dn}\), [T], \(\lambda_{dn}\): d-axis flux linkage at \(I_{dn}\), [Wb], \(L_{dn}\): d-axis inductance at \(I_{dn}\), [H]. The Equations (7)-(12) describe the formulations of linear and nonlinear reluctances, q-axis MMF source, resulting flux and inductance.  
MEC in Figure 3 is constructed by superimposing MEC for d-axis PM flux linkage onto MEC for d-axis inductance including MMF source and nonlinear reluctances in rotor and stator. Moreover, the flow chart in Figure 4 describes the sequence to obtain d-axis inductance based on the previous value of nonlinear reluctances in d-axis path. First, nonlinear reluctances of stator and rotor on d-axis path are substituted into initial values at \(I_{dn}=0\), which is based on air gap flux density obtained in MEC for PM flux linkage. In the next step, at the given d-axis current 1 A\({}_{rms}\) and resulting MMF source, the flux produced in opposite direction to PM flux linkage can be calculated substituting nonlinear reluctances into Equation (3), and the neutralized flux density of cores must be updated for the next step. This sequence is recurred up to the rated current.  
As a result, until neutralizing the saturated core and flux density reaches zero, d-axis inductance gradually increases from \(I_{dn}=0\). This trend will be apparent as more PM is inserted in cavities.  
Figure 4: Flowchart for D-axis inductance calculation.  
Figure 3: MEC for D-axis inductance.
# Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer
## 2 Methods
### Algorithm Principle
#### 2.5.4 Principle of CNN-BiLSTM-Transformer  
This study proposes a neural network model that organically integrates three deep learning architectures--convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and Transformer--to capture neighborhood deep sequences (Figure 7). As shown in Figure 7, the model first employs CNN to capture local spatial features in logging data. Then, the BiLSTM layer integrates past and future sequence information to extract the temporal sequence features of the logging data. Subsequently, the Transformer encoder, using the multi-head self-attention mechanism, captures global dependencies and feature interactions in the logging data. Finally, the extracted deep features are mapped to the porosity prediction space through a fully connected layer, completing the porosity prediction.  
This method enhances prediction accuracy and improves the model's generalization ability through the multi-scale integration of local, temporal, and global features. It provides a valuable reference for the extraction and prediction of temporal features in geophysical logging.  
Figure 6: Transformer network structure.
# An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals
## 3 Multi-Scale Dictionary Derivation
### Pre-Processing  
The block diagram of the pre-processing stage is shown in Figure 2. (Let us remark that we focus here on the QRS complexes because they are the most relevant waveforms that can be found in the ECGs. However, the proposed approach can also be applied to construct dictionaries of typical P and T waveforms.) Firstly, all the QRS complexes were extracted separately from each of the \(Q\) available ECGs, and those patients for which a significant number of QRS complexes cannot be reliably obtained were removed from subsequent stages. After resampling to the maximum length of all the QRS complexes found for each of the remaining \(Q^{\prime}\leq Q\) patients, an individual average QRS complex was obtained per patient. Then, a second resampling stage was applied to the average QRS complexes of all the \(Q^{\prime}\) valid patients to ensure that they have the same length, followed by a windowing stage to obtain initial and final samples equal to zero, and a normalization to remove the mean and enforce unit energy on all the signals. Finally, these \(Q^{\prime}\) waveforms were stored in a QRS complexes database and used to construct the desired overcomplete dictionary. In the sequel, we provide a detailed description of each of the blocks in Figure 2.
# Constant Transmission Efficiency Dimming Control Scheme for VLC Systems
## 4 Simulation Results
### Spectral Efficiency  
The dimming control scheme based on extensional constant weight codeword sets is proposed for indoor VLC systems with constant transmission efficiency in this paper. The other dimming control schemes widely utilized can not realize constant transmission efficiency when the dimming factor varies. Spectral efficiency indicates the effective bit rate \(R_{b}\) can be realized when the bandwidth \(B\) is fixed and can be expressed as:  
\[\
u=\frac{R_{b}}{B}. \tag{16}\]  
When the bandwidth \(B\) is fixed, the spectral efficiency is only related to the effective bit rate \(R_{b}\). Therefore, the spectral efficiency can reflect the transmission efficiency and the effective throughput. The proposed ECWCS scheme guarantee that the spectral efficiency is not affected by the variation of the dimming factor through the unique way of encoding. Therefore, we can say that the proposed ECWCS scheme realizes constant spectral efficiency.  
Figure 4 shows the spectral efficiency comparison between the proposed ECWCS scheme and other dimming control schemes without constant transmission efficiency when the length of the coded binary sequence \(n=8\).  
We can see from Figure 4 that the spectral efficiency of the VOOK scheme and the VPPM scheme is greater than that of the ECWCS scheme within a specific dimming range. However, when the dimming factor is close to 0 or 1, the spectral efficiency of the ECWCS scheme is greater than that of the VOOK scheme and the VPPM scheme. It can be construed that the constant transmission efficiency is guaranteed by reducing the spectral efficiency within a specific dimming range.  
We can assume a scenario that the users need to change the dimming factor of the indoor VLC system from 0.1 to 0.5. When we utilize the VOOK scheme or the VPPM scheme, the transmission efficiency varies according to the value of the dimming factor, and the communication quality would be influenced. On the other hand, for example, when the length of the coded binary sequence \(n=8\), the dimming factor's division value of the VOOK scheme and the VPPM scheme is \(1/8\). From the contents in Section 3, we know that the dimming factor's division value of the proposed ECWCS scheme is \(\frac{1}{n\times 2^{\text{EC-1}}}=1/32\). Therefore, based on the above two points, the proposed ECWCS scheme is a better choice for the indoor scene with variable brightness.
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## Appendix A Attenuation  
The attenuation underwater acoustic networks can be expressed as follows:  
\[A(r,f)=r^{k}\alpha(f)^{r}, \tag{1}\]  
where \(r\) is the distance between a transmitter and a receiver, \(\alpha(f)\) is an absorption coefficient of frequency \(f\), \(k\) is a spreading factor (ranging from 1 to 2). Note that the different values of \(k\) describe the different scenarios of geometry of propagation: \(k=1\) for cylindrical spreading; \(k=2\) for spherical spreading.  
Then, Equation (1) can be expressed in dB as follows:  
\[10\log A(r,f)=k\cdot 10\log r+r\cdot 10\log\alpha(f). \tag{2}\]  
Generally, if the frequency \(f\) is above a few hundred Hz, the absorption coefficient \(10\log\alpha(f)\) in dB/km for \(f\) in kHz can be expressed as follows [21]:  
\[\begin{split} 10\log\alpha(f)=0.11\cdot\frac{f^{2}}{1+f^{2}}+44\frac{f^{ 2}}{4100+f^{2}}\\ +2.75\cdot 10^{-4}f^{2}+0.003.\end{split} \tag{3}\]
# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 2 Related Works
### Video Offloading Based on DRL  
In the last few years, the development of artificial intelligence (AI) technologies has led to rapid progress of DRL in modeling, routing, and resource management with a model-free environment. An adaptive video configuration network was pursued in [38] based on a black-box approach, independent of a detailed analytical performance model. The authors presented and designed an intelligent system named Cuttlefish, a type of smart coder which can adapt to the needs of the users without using any pre-programmed models or specific assumptions. The authors in [39] dealt with the problem of joint configuration adaptation and bandwidth allocation in an edge-assisted real-time video analysis system. They presented a novel approach which could select the configurations for multiple video streams immediately based on the state of the network and the content of the video. To work out the collaboration in the MEC network, an AI-based task allocation algorithm was presented in [40], which was trained by using a self-play strategy. The algorithm could detect a change in the network environment and adjust the resource allocation decision simultaneously. The authors in [41] proposed a two-layer learning model based on a DQN and a back propagation neural network to solve the joint decision of task offloading, wireless channel allocation, and image compression ratio selection in video analysis, and balance the accuracy of image recognition and processing delay. The authors in [42] presented a new approach to allocating resources in MEC networks using a radio map and DRL. Then, they presented a collaborative offloading and resource allocation algorithm which was used to solve the problem of reducing system latency and energy consumption. The authors in [43] considered the real-time video analytics of cameras based on edge coordination. In order to realize highly energy-efficient video analysis in a digital twin, a mobile device and edge coordination video analysis framework based on deep reinforcement learning was proposed, which takes energy consumption, analysis accuracy, and delay into consideration. However, using a DQN network to train the multi-parameterproblem necessitates the calculation of the probabilities of various actions and the selection of the action with the maximum probability, which leads to a high training delay and reduces effectiveness. Therefore, it is worthwhile to study how the training efficiency can be increased, and the precision of the network can be guaranteed.
## 3 The Proposed SSP-AA Model
### Framework of the Model  
By incorporating the adaptive attention mechanism and jumping knowledge module into the SSP-AA model, we have enhanced the model's expressiveness. The adaptive attention mechanism allows the model to automatically distribute weights according to the relationships between nodes, thereby better capturing the information within the graph structure. This helps improve the quality of node representations, making them more accurately reflect the actual graph structure data. The jumping knowledge module effectively addresses the issue of over-smoothing that arises with increasing model depth. Over-smoothing can render node representations too similar, reducing the model's discriminative power. Therefore, the introduction of these two modules allows the model to more effectively integrate the information of neighboring nodes, thereby improving model performance. Figure 6 illustrates the architecture of the SSP-AA model.  
As shown in Figure 6, during the sparse subgraph sampling stage of the SSP-AA model, a random walk strategy is used to sample sparse enclosed subgraphs with high-order neighbors no greater than two. In the feature aggregation stage, we extract node features from the enclosed subgraphs with the target node, utilize the jumping knowledge module to capture hidden features in the nodes, and reflect node representations through different network layers. Then, the adaptive attention mechanism is used to assign different weights to each node for aggregation. This aggregation operation incorporating the two modules is added iteratively in each layer until the final target node is updated. In the prediction stage, we use \(\mathrm{q}_{\mathrm{v}}\) to represent the aggregated features of the two target nodes. Finally, the target representation \(\mathrm{q}_{\mathrm{v}}\) is transformed into the link probability \(\mathrm{p}_{\mathrm{v}}\) through a Multi-Layer Perceptron (MLP).
# Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System
## 3 Proposed Method
### Illuminant Estimation
Similarly, denoting \(d_{iif,jj}\) as the cosine distance from the IIFs of an input image \(I_{in}\) to cluster centroid \(c_{ij},i=1,2,\cdots,k_{1},j=1,2,\cdots,k_{2}\), the probability for the input image belonging to cluster \(SS_{ij}\), _under the premise_\(I_{in}\in S_{i}\), can be represented as a radial basis function:  
\[\omega_{ij}=\frac{e^{-d_{iif,jj}}/2\sigma_{2}^{2}}{\sum\limits_{l=1}^{k_{2}}e ^{-d_{iif,l}/2\sigma_{2}^{2}}},i=1,2,\cdots,k_{1},j=1,2,\cdots,k_{2}, \tag{23}\]  
where \(\sigma_{2}\) is the radial fall-off factor. Using these probabilities from Equation (23), we construct an IIF weight matrix \(\mathbf{\omega}\) as follows:  
\[\mathbf{\omega}=\big{[}\omega_{ij}\big{]},i=1,2,\cdots,k_{1},j=1,2,\cdots,k_{2}, \tag{24}\]  
which will be used to weight different illumination estimates from all ANFIS models of cluster \(SS_{ij}\). **ANFIS prediction**. We utilize the ANFIS models trained in Section 3.3 to inference the illuminant color of an image. For cluster \(SS_{ij}\), the corresponding ANFIS model set \(FIS_{ij}\)as in (20) is used as an illuminant color predictor. By inputing the given image's IIF vector, \(\theta(I_{in})\), the predictor produces the illuminant estimation \(p_{ij}\) as follows:  
\[p_{ij}=FIS_{ij}(\theta(I_{in})), \tag{25}\]  
where \(FIS_{ij}=\{FIS^{r}_{ij},FIS^{\delta}_{ij},FIS^{b}_{ij}\}\) includes three ANFIS models corresponding to the RGB color components, and \(p_{ij}=\{p^{r}_{ij^{\prime}},p^{g}_{ij}\}\) refers to the set of the predictive outputs from \(FIS^{r}_{ij^{\prime}},FIS^{\delta}_{ij^{\prime}}\) and \(FIS^{b}_{ij}\), respectively. Therefore, the final illuminant estimation is obtained by weighting all ANFIS predictor outputs:  
\[\hat{\ell}_{est}=\sum_{i=1}^{k_{1}}\sum_{j=1}^{k_{2}}p_{ij}\omega_{ij}\eta_{i}. \tag{26}\]
## 3 Results
### Results of Performance and Usability Test with Porcine  
A standard 15 mm trocar was utilized at the umbilicus to facilitate the smooth insertion of the prototype. Additionally, a 12 mm trocar was positioned, as indicated in Figure 7, to accommodate the insertion of a conventional laparoscope, enabling the observation of the prototype's movement. CO\({}_{2}\) gas was injected through the 15 mm trocar to establish pneumoperitoneum within the abdominal cavity (Figures 8 and 9).  
Utilizing the ICT laparoscopy system, tasks were executed in upward, downward, left, right, and diagonal directions, with data recorded within the abdominal cavity of a porcine model. Based on the surgeon's experience, these tasks facilitated evaluations of the system's operation, performance, and usability.  
During the camera's movement, no interference or collision was observed with the abdominal wall, suggesting that the length of the laparoscopic camera was appropriate. Furthermore, the actual delay time between the controller's action and the camera's response was assessed to be within 100 ms. Despite this slight delay, it did not impede surgical operation significantly.  
Figure 8: Trocar placements for the evaluation test of the ICT laparoscopy system prototype.  
A comparative analysis of the recorded data revealed that our camera, with a 1 MP resolution, 720 p video quality, and a frame rate of 30 fps, possesses less than half the pixel count of traditional laparoscopes. Nevertheless, the camera provided satisfactory results regarding resolution and brightness--key factors for distinguishing organs and tissues during surgery. Additionally, compared with a common laparoscope, our prototype exhibited less lens fogging, a common issue due to temperature differences between the inside and outside of the abdominal cavity, regardless of the camera's position during the test.  
Usability evaluations conducted by three clinical experts yielded the following results:  
1. The average usability score for the controller was 85 \(\pm\) 7, indicating a high level of satisfaction with its operation;
2. The foot switch operation in the ICT laparoscopic system received an average score of 90 \(\pm\) 10, suggesting excellent usability;
3. The average score for the operation and the delay between the controller and the camera was 81 \(\pm\) 6, reflecting a minor concern regarding the response time;
4. The average score for the driving speed and range of the camera was 88 \(\pm\) 10, demonstrating a favorable evaluation of these aspects.  
These scores collectively indicate a positive reception of the ICT laparoscopy system's usability and performance despite noted areas for potential improvement.
## 2 Materials and Methods
### Design of the Springs IICAD System  
The system was designed and developed in the Microsoft's programming environment Visual Studio with the Windows Forms Application module, where background programming was realized with the C# programming language. The Springs IICAD system was designed through multiple panels used to create a side menu from where four main forms may be accessed. Main forms represent the modules which include compression  
Figure 1: Basic spring geometry. (**a**): Compression spring, (**b**): Torsion spring.  
Figure 2: Springs IICAD system architecture.  
spring computation, compression spring trial and error method, torsion spring computation and torsion spring trial and error method. All of the system modules require input data, but there are slight differences in the manner of their use. Computing modules calculate the parameters for the new spring design, which are interlinked with the parametric 3D models, structural analysis files and prototype preparation environment through a set of specific buttons. Trial and error methods are used to check if the existing spring design will satisfy the working conditions. Standard spring arrangements are built-in into these modules, and with a few input parameters, a computation scan can be performed [30; 31; 32]. Trial and error modules are featured with the condition logs which prompt users if existing spring design is safe or not. Figure 3 shows the layout of the Springs IICAD system.  
The standard built-in spring arrangements only consider the constant pitch values. The Springs IICAD system is built with a classic spring's requirements assumption, and it  
Figure 3: Interface layout of the Springs IICAD system (top to bottom: computation module and trial and error method module, respectively).  
does not include the calculation of the unpredicted loads and dynamic conditions that may occur in the exploitation of the mechanism or the machine in which the springs are built in.
# Optimized Architecture for Efficient OFDMA Network Design
## 4 Validation and Robustness Study of the Analytical Model
### Channel Model
#### 4.3.2 Combined Channel Model  
Taking into account the quality of the radio link between the BS and MS, the previous assumption can be refined by considering that half of the MSs operate under \"poor\" radio conditions while the other half experience good conditions. The criteria for good or poor conditions are determined based on stationary probability while maintaining the same coherence time in both cases. It should be noted that during the simulation, the radio conditions assigned to a mobile station (MS) will remain the same throughout the simulation [27].  
The stationary probabilities for the three different channel models are provided in Table 4[24].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Channel State (\(0,\ldots,K\))** & **MCS and Outage** & **Bits per Slot** \\ \hline
0 & Outage & \(m_{0}=0\) \\
1 & QPSK-1/2 & \(m_{1}=48\) \\
2 & QPSK-3/4 & \(m_{2}=72\) \\
3 & 16 QAM-1/2 & \(m_{3}=96\) \\
4 & 16 QAM-3/4 & \(m_{4}=144\) \\ \hline \end{tabular}
\end{table}
Table 3: Wireless channel parameters.
## 4 Discussion
In August 2023, when searching \"radiomic AND PET/CT AND prostate\" on the Pubmed database, 82 results were found ( [[https://pubmed.ncbi.nlm.nih.gov/?term=ra_diomics+AND+PET/CT+AND+prostate](https://pubmed.ncbi.nlm.nih.gov/?term=ra_diomics+AND+PET/CT+AND+prostate)]([https://pubmed.ncbi.nlm.nih.gov/?term=ra_diomics+AND+PET/CT+AND+prostate](https://pubmed.ncbi.nlm.nih.gov/?term=ra_diomics+AND+PET/CT+AND+prostate)) (accessed on 27 November 2023)). Only 2 out of these 82 results pertain to studying [F18]F-flucicolvine radiomic features in prostate cancer [22; 23]. [PERSON] et al. [22] searched for an image biomarker using Haralick texture features in prostate bed, pelvic lymph nodes, and extrapelvic metastases in 28 prostate cancer patients with biochemical relapse. In particular, they found three textural features (contrast, variance, and correlation) negatively associated with the probability of BCR and incorporated them into their radiomic model to enhance the probability of predicting BCR. Our aim and results were slightly different, and it has to be noted that they did consider PSA as a clinical variable in combination with RFs, while, in our sample, PSA was not significant in predicting the outcome. On the other hand, [PERSON] et al. [23] studied 233 patients with BCR from Pca with [18F]Flucicolvine, proposing a deep learning approach when studying the pelvis. A 2D-CNN slice-based approach showed the best performance in detecting abnormal [F18]F-FACBC foci of uptake, with promising results. Hopefully, our knowledge and experience with radiomics tools can grow once trained. A similar study on the prognostic value of RFs in [F18]-choline PET/CT, carried by [PERSON] and coll. [24], proved that a radiomic approach could potentially be useful in a daily clinical setting of Pca BCR and found RFs useful for N, M, and T lesions. Our sample also showed distant metastases, mainly bony, that PSA alone could not predict. In particular, six bone metastases were registered in four patients but were excluded from the final analysis because it would have not significantly influenced our statistical results. As previously reported in other PET tracers employed in Pca [25; 26], a flare phenomenon was seen in 7/29 patients in our population, all ongoing ADT therapy (data not shown), and 4/7 were diagnosed with bony metastases in the same [F18]Flucicolvine scan, confirmed at subsequent follow-up. It might have been interesting to scan these seven patients with [F18]F-FDG in order to calculate metastases' aggressiveness.