esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
## 2 Materials and Methods
### Evaluating Indicators of Generated Data
#### 2.4.5 Receiver Operating Characteristic (ROC) Curve  
The ROC curve is a graphical representation of the performance of a binary classifier. It is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various classification thresholds. TPR, also known as sensitivity or recall, represents the proportion of positive instances that are correctly classified as positive, while FPR represents the proportion of negative instances that are incorrectly classified as positive. Each point on the ROC curve corresponds to a specific threshold setting.  
Area under the ROC curve (AUCROC) is a widely used evaluation metric in machine learning for assessing the performance of binary classification models. It provides a single scalar value representing the overall quality of the model predictions. AUCROC quantifies the overall performance of a classifier across all possible threshold settings. The AUCROC value ranges from 0 to 1, with higher values indicating better classification performance. An AUCROC of 1 denotes a perfect classifier, while an AUCROC of 0.5 indicates a random classifier, signifying poor performance [31; 32].
# Research on Medical Text Parsing Method Based on BiGRU-BiLSTM Multi-Task Learning
## 4 Experiments and Results Analysis
### Experimental Results and Analysis
For the intention recognition task, we compared the proposed model with CNN, BERT, BiLSTM, TextCNN, and TextRNN, and the results are shown in Table 7. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1** \\ \hline BERT & 0.7999 & 0.7271 & 0.7617 \\ BERT-CRF & 0.7901 & 0.7752 & 0.7825 \\ BERT-BiLSTM & 0.7703 & 0.7395 & 0.7545 \\ BERT-BiLSTM-CRF & 0.8254 & 0.8196 & 0.8224 \\ Ours & 0.8607 & 0.8135 & 0.8364 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative experimental results of entity recognition. As can be seen from Table 7, the method proposed in this paper shows good results in accuracy rate, recall rate, and F1 value, and the recall rate was maintained at 88% while the accuracy rate reached 89%. Through comparative experiments, the following can be concluded:  
(1) From the introduction of BERT, the CNN and BILSTM models provide relatively low accuracy, and these results show their limitations in capturing contextual information and capturing sequential dependencies. The CNN model mainly extracts local features through convolution operations, which may lose long-distance dependent information. BILSTM is good enough to capture better sequence-dependent information, but its handling of long-distance dependencies may be inadequate. With the introduction of the BERT model, the effect is significantly improved, possibly because the BERT pre-trained model is better able to understand contextual information and capture the bidirectional dependencies of words in a multi-layer bidirectional structure to improve the understanding of the global context of the sentence. (2) By combining BERT, BiGRU, BiLSTM, and DPCNN, the model can take advantage of the advantages of various frames. BERT provides powerful context understanding, BiGRU and BiLSTM enhance the capture of long- and short-term dependencies, and DPCNN further extracts deep textual features. This multi-layered, hybrid architecture makes the model perform well in intention recognition tasks. In particular, the introduction of DPCNN improves the feature extraction ability of the model, meaning the model can more accurately identify complex intentions.
## 2 Dynamic Nuclear Polarization in Semiconductor Nano-and Microstructures
### Microwave-Irradiation-Induced Nuclear Polarization  
#### 2.2.1 Microwave-Induced Optical Nuclear Polarization  
The hyperpolarization of \({}^{29}\)Si nuclear spins is usually generated by solid-state DNP at low temperatures and high magnetic fields to spin-polarize an electron to near unity. This spin polarization is then transferred to nearby nuclear spins through microwave-mediated dipolar interactions, similar to those occurring in the liquid phase with \({}^{13}\)C [46].  
A combination of the DNP and the photoexcitation of the electron-pair spin to the triplet state is known as _microwave-induced optical nuclear polarization_[47]. The polarization from the electron triplet states is transferred via microwaves to the nuclear spins. The hyperpolarization of electron spins can be prepared at low magnetic fields, since it is generated due to selectivity in crossing from excited singlet states to hyperpolarized triplet states. Because the triplet states have a longer relaxation time as compared to the singlet state, the hyperpolarized nuclear spins can be generated by DNP at room temperature. Feasibility of the nuclear hyperpolarization at low magnetic field and room temperature provides an advantage to microwave-induced optical nuclear polarization as compared to the DNP. The unpaired electron spins do not perturb the NMR signals because the decay time of the triplet state to the diamagnetic state is shorter than the relaxation time of the longitudinal nuclear magnetization [47]. While the microwave-induced optical nuclear polarization was demonstrated for organics as fluorene C\({}_{13}\)H\({}_{10}\) crystals [48], it seems to be a possible mechanism for inorganic semiconductor systems as NP/MPs with nc-Si, where triplet excitonic state are rather probable (see Section 3).
# Prefetching Method for Low-Latency Web AR in the WMN Edge Server
## 1 Introduction  
Studies have been conducted on augmented reality (AR) services in web browsers such as the WebXR Device API (Web eXtended Reality Device Application Programming Interface) [1], AR.js [2], and Three.js [3]. Users can access AR services through the web browser on their smartphone without installing a separate application; this helps improve user accessibility [4]. However, there are limitations in that users must always be connected to the services [5], and fetching causes delays when using an AR service [6].  
The Institute of Electrical and Electronics Engineers (IEEE) has defined a standard called 802.11s, which is related to the mesh for a wireless mesh network (WMN). 802.11s conducts traffic forwarding via 802.11 ac [7, 8, 9, 10]. Internet wireless fidelity (Wi-Fi) can be provided without a shadow area throughout the service area that provides the AR service. Web data are saved on an edge server with a short physical distance hop. The client fetches the saved web data, which helps reduce latency. When downloading AR data, the edge server communicates with the client without a wide area network (WAN), and therefore, it is not affected by WAN latency [11, 12]. Further, it reduces the latency required to access the server and operates at a stable and constant speed. The AR data can be downloaded relatively quickly [13]; however, if there is a considerable amount of high-capacity AR data, latency can still occur [14].  
This study attempted to reduce user latency in AR services. To this end, we proposed a prefetching method that uses the Markov model. The Markov model enables predictions via the partial matching algorithm used for prefetching [15]. The proposed method considers the priority of each AR dataset to reduce latency, and it allows the prediction of the AR data that the user will request next. According to the proposed method, AR data are sequentially downloaded [16]. In this study, the following experiments were conducted: (1) The latencywas measured according to the use of an edge server; (2) a comparative analysis of network traffic was performed; (3) the hit ratio and latency for each request, user, and data in the time order were measured; and (4) the waste ratio results for each user were calculated.  
The remainder of this paper is organized as follows: Section 2 explains the background theory. Section 3 describes the proposed methods. Section 4 presents the experimental environment, evaluation method, and results. Finally, Section 5 presents the conclusions, limitations, and future research directions.
## 4 Application to the RG8 Test
### Results
#### 4.2.3 Final Stage: 4-Point Bending Loading  
After 700 h, the struts were removed, and a few days later the beam was placed on a test bench for a monotonic 4-point bending loading. The supports were spaced 5.1 m apart and located at the end of the beam part where the section was constant and the loads were spaced at 1.6 m, as shown in Figure 8.  
With regard to the global behavior of the beam, Figure 8a compares three curves, namely:  
* The experimental curve (dashed black line): the beam was only loaded up to the serviceability limit state (SLS).
* The calculated curve for the beam after the restrained shrinkage loading program (blue curve).
* The calculated curve for the same beam considered to be structurally sound (red curve). This loading triggers plasticity on the rebar; the steel behavior considered here is an elastoplastic model with linear hardening (\(f_{e}=500\) MPa). It should be emphasized that:
* The calculated RG8 curve is in good compliance with the experimental curve.
* The two calculated curves (RG8 and structurally sound beams) are very close during most of the loading evolution.
* A tremendous difference is observed between the initial stiffness values. The initial stiffness is strongly reduced (about 50%) due to concrete cracking produced by the restrained shrinkage. An additional calculation has shown that the first vibration mode is equal to 31.7 Hz instead of 59.2 Hz for the sound beam, which leads to a very different dynamic response (Figure 8c). This decrease must be taken into account in cases of dynamic loading, such as an earthquake.  
Figure 9 presents the cracking evolution observed during loading compared to the damage evolution obtained by calculation at the same level of loading.  
Figure 8: The four-point bending RG8 test: (**a**) total force-displacement responses (experiment, RG8 beam, and structurally sound beam); (**b**) close-up on the first 3-mm displacement showing the large difference between the two original stiffnesses (RG8 beam and sound beam); and (**c**) the corresponding free vibrations responses.  
It should be emphasized that:  
* At a low loading level (205 kN), cracking appears in the center of the beam between cracks due to restrained shrinkage, while the diffuse damage, as developed during the restrained shrinkage phase, is located in the same area and remains relatively unchanged during the first loading phase.
* For the following phases (685 kN and 1100 kN), cracks and damage develop on the upper part of the beam (loading occurs from the bottom up); moreover, the damage contour, which is always diffuse, is quite consistent with the crack pattern. The main conclusions drawn are as follows:
* The initial stiffness is heavily influenced by the restrained shrinkage treatment. The ultimate beam strength however remains the same, with or without this treatment.
* The calculations have yielded consistent results in terms of both global behavior (load-displacement) and local indicators (damage vs. crack pattern).
# O-Band Multimode Interference Coupler Power Combiner Using Slot-Waveguide Structures
## 3 Simulation Results
It can be seen from Table 1 that the main benefits of the proposed design in this work compared to other combiner devices are the low back reflection and insertion loss. These advantages could be very useful for integrating this combiner into a transmitter system that operated at the O-band spectrum. Figure 6: Normalized optical power of the MMI combiner versus wavelength over the O-band spectrum. Figure 7: FDTD simulation setup: (**a**) schematic sketch of the FDTD setup; (**b**) the calculation of the back reflection based on FDTD simulation setup.
# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
## 5 LoRaCog Protocol
### LoRaCog Mechanism  
Maintaining the star topology of LoRaWAN, LoRaCog will operate in the same way. The EDs will send frames to NS through GWs in the range. As shown in Figure 4, the CR job will be split between GW for sensing, NS for deciding, NS and ED for acting. Hence, the EDs will not be doing any CR jobs except the transmission on the licensed channel selected by the NS in order to avoid any additional energy-consuming actions.  
The GWs are doing spectrum sensing (it can be defined according to the country's regulations) and feeding this information to the NS. The NS has pre-knowledge of EDs transmission time. This is ensured during the join-activation request. Modification will be done for the RX2 window, and prior to it, the NS will instruct the GWs to send the licensed spectrum holes with MAC command containing new channel's parameters throughout their coverage of EDs. Spectrum holes in this case refer to the available unused licensed spectrum. Moving RX2 to just before TX (uplink transmission) is essential for the CR operation to inform the ED of the available channels to use. Using RX2, NS sends the available channel's parameters to the ED through GW just before the transmission in order to maintain the relevance of the channel status, since the primary user may become active again at any time. Therefore, each time ED wants to transmit, a spectrum sensing operation should be done in order to provide it with the currently available channels.  
The default channels defined in LoRaWAN will remain the same for each ED (for example, EU: three channels for uplink and one for RX2). They will be used for two purposes:  
Figure 4: Cognitive Radio Cycle managed by LoRaCog.  
the first is to receive any MAC command (such as the channel's new configuration) since default channels are known and fixed for both GWs and EDs. Secondly, in case the ED does not receive the new configuration, it will continue operating on its uplink default assigned channels as per LoRaWAN regional parameters [47]. This will ensure successful transmission over the unlicensed ISM bands (for example, EU default channels).  
LoRaCog will embed modification to the receive windows for classes A and B in LoRaWAN (see the next section); the NS will be able to send a synchronization frame (or new configuration frame) to inform and instruct the EDs (SUs in CR terminology) with available spectrum holes that the last can reconfigure and transmit accordingly.  
Therefore, before each transmission, the EDs will open to receive a new configuration using RX2. Taking into consideration licensed user sudden appearance; the uplink (TX) will occur straightforwardly after RX2 without delay. Otherwise, the ED will transmit using its default channels within the unlicensed spectrum. Figure 5 illustrates the logic by flow chart demonstration.
## 4 Conclusions and Future Research
### Limitations  
The review process was conducted with high-quality standards; uncertainty and bias still exist which are related to the evaluation of studies from different perspectives. The studies were analyzed based on the inherent content that suits the focus of the study. Therefore, the limitations mentioned in those studies and discussions on other non-related areas in some of those studies may not have been considered. The study included literature published between 2010 and April 2021. Therefore, some recent studies may have been missed in this review. As the collection of literature focused on the databases available at the university, some industry literature covering the scope of this review might have been omitted.
# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 3 Online Rendering and Tracking
### Formal Description
#### 3.2.2. Keypoint Detection and Matching  
For each new real image \(I^{r}\) captured by the camera, we detect a set of keypoints \(\mathcal{K}^{r}=\{\mathbf{k}^{r}_{j}\}_{j=1}^{N_{r}}\), where each keypoint \(\mathbf{k}^{r}_{j}\) is defined by its 2D coordinates \(\mathbf{u}^{r}_{j}=[u^{r}_{j},v^{r}_{j}]^{T}\) and an associated descriptor \(\mathbf{d}^{r}_{j}\). Similarly, a set of keypoints \(\mathcal{K}^{\mathrm{s}}=\{\mathbf{k}^{s}_{k}\}_{k=1}^{N_{s}}\) with coordinates \(\mathbf{u}^{s}_{k}\) and descriptors \(\mathbf{d}^{s}_{k}\) is extracted from the rendered synthetic image \(I^{s}\).  
Correspondences between the two sets of keypoints are established by finding pairs of descriptors that have the minimum distance under a chosen metric (e.g., Euclidean distance for SIFT (Scale-Invariant Feature Transform) or Hamming distance for ORB). This results in a set of matched pairs \(\mathcal{M}=\{(\mathbf{k}^{r}_{j},\mathbf{k}^{s}_{k})\}\). For each matched pair, we have a 2D point \(\mathbf{u}^{r}_{j}\) from the real image and a corresponding 3D point \(\mathbf{X}_{k}\) from the world point cloud, as the synthetic keypoint \(\mathbf{k}^{s}_{k}\) was rendered from \(\mathbf{X}_{k}\).
## 2 The Phase-Locked Loop and Normalized Fokker-Planck Equation
### Cyclic Slipping Density Function  
A cycle slip is defined as an increase (positive cycle slip) or decrease (negative cycle slip) of the phase error by \(2\pi\). Cycle slipping in a phase-locked oscillator occurs where the dynamic phase error is larger than \(2\pi\). If the error should become too large that the VCO skips cycles, the loop is considered to have lost lock. The integral of this density becomes  
\[\int_{-2\pi}^{2\pi}q(\phi,\tau)d\phi\to 0\,\text{ as }\tau\rightarrow\infty \tag{20}\]  
As long as the loop phase error remains within the limits of a specified subinterval, the density function \(q(\phi,\tau)\), accompanied with the initial condition \(q(\phi,0)=\delta(\phi)\), will satisfy the FP equation  
\[\frac{\partial q(\phi,\tau)}{\partial\tau}=\frac{\partial}{\partial\phi}[( \sin\phi-\gamma)q(\phi,\tau)]+\frac{1}{\alpha}\frac{\partial^{2}q(\phi,\tau)} {\partial\phi^{2}} \tag{21}\]  
and thus  
\[\frac{\partial q(\phi,\tau)}{\partial\tau}=(\sin\phi-\gamma)\frac{\partial q (\phi,\tau)}{\partial\phi}+q(\phi,\tau)\cos\phi+\frac{1}{\alpha}\frac{ \partial^{2}q(\phi,\tau)}{\partial\phi^{2}} \tag{22}\]  
The use of \(q(\phi,\tau)\) for the bounded case is to distinguish it from the unbounded nonmodulo-\(2\pi\) function \(p(\phi,\tau)\). The numerical algorithm used to generate \(q(\phi,\tau)\) is the same as for generating \(p(\phi,\tau)\), except that the end points \(q(\phi_{l},\tau)\) and \(q(-\phi_{l},\tau)\) are forced to be zero, whereas the end points for \(p(\phi,\tau)\) were found by extrapolation, which simulates an infinite interval.
# Unveiling the Role of Minerals and Trace Elements of Thermal Waters in Skin Health
## 2 Minerals, Trace Elements, and Other Compounds of TSW in Skin Health
### Cations  
#### 2.2.1 Calcium  
Many mineral spring waters are classified as calcium spring waters, since this cation is found in solutions linked to bicarbonate and sulfate waters.  
Calcium is well known for its participation in numerous skin processes, such as wound healing, the regulation of keratinocyte proliferation, the formation of certain proteins, the regulation of hyaluronic acid expression, and its role in the homeostasis of the epidermal barrier through calcium channels, among others [73; 74; 75; 76; 77; 78].  
Examples of calcium-rich TSW are Avene and Comano, which have been already discussed in Section 2.1.3.  
La Roche-Posay TSW is a spring water rich in calcium and selenium, with specific anti-inflammatory and antioxidant properties. Several in vitro and clinical studies have been carried out in the last few decades; some examples are described below and in Sections 2.3.1 and 2.3.2.  
La Roche-Posay TSW spray was effective in treating scars after pediatric plastic surgery, showing a reduction in the inflammatory aspect of scars, easing of itching, facilitating the removal of crusts, providing a careful cleaning method after non-traumatic surgery, and preventing infection [41].
## 3 Principles of Signal Transmission and Processing
### Symbol Decision  
After completing clock synchronization, the symbol decision is performed by comparing the data \(y_{c}\) from the four channels. The symbol start position is determined based on the clock synchronization time \(t\). The maximum \(y_{c}(t)\) value is sampled and compared with a set threshold \(B\). When it exceeds \(B\), the output corresponds to the frequency \(f_{c}\) associated with the maximum value \(y_{c}(t)\). The specific implementation is as follows:  
\[f_{c}(t)=f_{c}(t)\cdot\epsilon(\max(y_{1}(t),y_{2}(t),y_{3}(t),y_{4}(t))-B) \tag{21}\]  
The function \(f_{c}(t)\) denotes the frequency value corresponding to the maximum processed data at time \(t\), while \(f_{c}(t)\) represents the frequency associated with y at time \(t\). Acting as a step function, it yields 1 when exceeding 0. Leveraging the interplay between carrier frequencies and symbols, information transmission can be decoded, facilitating symbol decision making.
## 4 Experimental Evaluation
### Text Prompted Verification
#### 4.3.1 Bimodal Embeddings Matching  
We followed the d-vector verification framework to carry out the embeddings matching [31]. This approach requires that each target client has a representative template that can be matched with an embedding that was generated during an identity claim. Matching a template with an embedding allows us to establish how similar they are and whether the client that made the identity claim is the same person as the target client who owns the template or not. In order to generate the template of a target client we picked 25 pairs of speech and face data; these pairs represented the combinations of 5 SNR values and the 5 image transformations. The pairs were fed to the multitask network and the resulting bimodal embeddings were averaged to obtain the template.  
The verification score is obtained using the cosine similarity defined in Equation (7) as the matching function:  
\[S_{cos}(T,E)=cos(\theta_{TE})=\frac{T\cdot E}{\parallel T\parallel\parallel E \parallel}, \tag{7}\]  
where \(T\) is a template belonging to a target client and \(E\) is the embedding produced by a client during an identity claim.