esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The First and Second Parts of Making the Surgical Guide for TADs
### Part 1: Rationale for Using a Surgical Guide in the Implantation of TADs  
General consideration before using TADs as anchorage in moving teeth:  
1. The thickness of the mucosal layer in different areas of the mouth can affect the selection of the TAD's length [30]. When selecting a placement location, choose an attached gingival site rather than a flabby mucosa [31]. However, in some cases, implantation on a flabby mucosa cannot be avoided. Using a surgical guide makes locating the initial pilot hole made during the pre-drilling process easy. The surgical guide will save time and effort searching for the insertion point, as mucosa often covers it. For optimal results, it is recommended to utilize two surgical guides during the procedure: one for the pilot drill and another for the TAD. When placing them in the interdental bone between two teeth, having 0.5 mm of septal bone around the TAD is crucial [32]. In interdental placement, at least 2.4 mm of the interdental bone may be needed to avoid damaging the roots, considering that TADs usually have a diameter of 1.4 mm [33; 34].
2. Different TADs come in various head designs and sizes, so it is recommended to create a surgical guide to match the chosen TAD's dimensions. Laboratory technicians and clinicians should communicate to design an appropriate surgical guide. While the body of the TAD may be self-drilling, it is still recommended to use a pilot drill surgical guide to minimize any trauma to the soft tissue [35]. The surgical guide is particularly important for angulated insertion procedures to make it easier to enter the implantation site. The angle at which screws are inserted can vary depending on personal preference. Some people prefer an angle for stability, while others aim to avoid root collisions and position the screw tip apically. However, angled screw insertion is more challenging than straight insertion. A surgical guide can assist with accurate angulation insertion, and directional drilling requires a guide to prevent slipping. Designing the surgical guide insertion process for easy handpiece and bur manoeuvrability 1 during implantation is crucial.
3. Using a healthy tooth structure is necessary for a surgical guide to be in a stable position. If there is any doubt about the stability of the teeth in the intended placement, the number of teeth to be used as support for the surgical guide should be increased [36].
4. The force required to penetrate bone with a TAD varies from 20 N to 35 N, depending on the diameter. Placement typically occurs at a speed of 25 Rpm, while drilling occurs at 800 Rpm with water irrigation [37]. It is better to load immediately rather than waiting for weeks and leaving it unloaded [38; 39].
5. It is mandatory to maintain sterilization and follow the aseptic procedure during the implantation process.  
Although a surgical guide is an excellent support in the correct placement of the TAD, the clinician's experience is integral and crucial to the success of a TAD's placement. Researchers have mentioned the success and failure of TADs after their introduction [40].
## 4 Energy Balanced Strategies for Sparsely Deployed UWA-SNs
### EBH: an energy balanced hybrid data propagation algorithm  
We propose a hybrid data propagation algorithm EBH to achieve balanced energy consumption of individual nodes. The key insight of the algorithm is to combine the advantages of two data transmission methods.  
Figure 2: Linear sensor network.  
The sensor nodes are equipped with transmission power control module which has adjustable transmission power [24, 27, 28]. The node may transmit data via multi-hop route to the sink, which we defined as \(MODE0\) or directly to the sink with suitable transmission power, which we defined as \(MODE1\). Therefore, the nodes use a hybrid transmission strategy for data propagation, which is the core design of EBH. By alternately changing node's working mode based on the residual energy grade, the nodes dissipate energy evenly, so the lifetime of the network can be prolonged.  
We first overview the algorithm, and then we compute the optimum number of energy grade in terms of maximizing the network lifetime. At the end of this section, we present the practical algorithm in detail.
## 3 Mi-Sdp Reformulation
### Complete Mi-Sdp Model and Characterization  
Based on the SDP formulation presented in previous subsections, the MINLP model is transformed into an MI-SDP model with the structure presented below:  
**Objective function:**  
\[\min p_{\text{loss}}=\textbf{trace}\left(GX\right), \tag{16}\]  
**Set of constraints:**  
\[P^{s}+P^{gd}-P^{d}=\textbf{diag}\left(GX\right), \tag{17}\]  
\[p_{km}=g_{km}\left(X_{kk}-X_{km}\right),\ \forall km\in\mathcal{L} \tag{18}\]  
\[p_{km}^{\text{min}}\leq p_{km}\leq p_{km}^{\text{max}},\ \forall km\in \mathcal{L} \tag{19}\]  
\[1_{n\times n}\left(v^{\text{min}}\right)^{2}\leq X\leq 1_{n\times n}\left(v^{ \text{max}}\right)^{2}, \tag{20}\]  
\[1_{n\times 1}^{T}Y\leq N_{gd}^{\text{max}}, \tag{21}\]  
\[0_{n\times 1}\leq P^{gd}\leq Y\otimes P^{gd,\text{max}}, \tag{22}\]  
\[0_{n\times 1}\leq P^{s}\leq P^{s,\text{max}}, \tag{23}\]  
\[Y\in\left\{0,1\right\}, \tag{24}\]  
where \(\otimes\) is the Hadamard product between vectors [27]. Note that the classification of the mathematical model (16)-(24) is presented in Figure 2.
# Integration Between Serious Games and EEG Signals:
## 4 A Review of Technological Solutions That Integrate Serious Game and EEG Signals  
According to the literature review, research integrating serious games and EEG signals has several objectives, among which the following stand out: (a) test paradigms about BCI, (b) control video games, (c) design and implement neurofeedback games, (d) train or test video game users, (e) develop e-learning programs and medical applications (explore brain activities in certain disorders such as anxiety and autism), (f) activate EEG signals from serious games, (g) relate psychological components with physiological measurements, (h) compare traditional health equipment with low-cost equipment, and (i) test EEG signal analysis methods.  
On the other hand, in the research of existing works, three different categories of BCI systems are distinguished according to the experimental strategy implemented in the integration of the serious game system and EEG: (1) experimental strategy based on evoked potentials, (2) experimental strategy based on spontaneous signals, and (3) experimental strategy based on hybrid signals. The experimental strategy not only determines what the BCI user must do to produce the brain patterns that the BCI can interpret, but it additionally establishes restrictions on hardware and software and even defines the training required [47]. Experimental strategies are associated with different types of brain signals; the most common are evoked and spontaneous ones [47,48,56]. However, we include hybrid signals in this work [40]. Table 1 describes the technological solutions of 16 selected works related to serious games and EEG signals. Based on the above taxonomy, existing works are presented by separating them into three categories according to the experimental strategy. The analysis also includes characteristics such as the software platform for game development, the software for signal processing and visualization, the number of test subjects, the type of game, and the game commands.
# Automated Deep Learning Model for Sperm Head Segmentation, Pose Correction, and Classification
## 6 Discussion  
### Segmentation Result  
Table 3 presents a performance comparison between the two models. For the Chenwy dataset, the Dice coefficient improves from 0.905 to 0.975 during validation and from 0.906 to 0.974 in the test set, while the Hausdorff Distance reduces from 8.19 to 2.785 in validation and from 7.962 to 2.011 in the test set, highlighting significant gains in segmentation accuracy and boundary precision after retraining. Similarly, on the HuSHem dataset, the retrained model shows consistent improvements, with the Dice coefficient rising from 0.924 to 0.975 during validation and from 0.925 to 0.973 in the test set. The Hausdorff Distance also decreases from 3.615 to 3.145 in validation and from 3.867 to 2.615 in the test set. These results demonstrate that task-specific retraining effectively enhances model generalization and performance.  
Figure 6 presents the performance statistics across original and retrained models using box plots, where the median is represented by the central red line, the 25 th and 75 th percentiles are the bottom and top of each box, and outliers are shown as red points. It can be concluded that the retrained model consistently achieves higher Dice coefficients and lower Hausdorff Distances on both two datasets, with tighter distributions and fewer outliers, indicating more accurate and reliable segmentation results.  
In Figure 7, the image showcases the segmentation results of the retrained model (red) and the original model (yellow). In both datasets, the retrained model demonstrates significantly better segmentation accuracy, with the red contours closely following the sperm head boundaries, capturing even subtle and irregular shapes. In contrast, the original model's yellow contours exhibit less precision, particularly around edges and complex shapes, where deviations from the true boundaries are more pronounced.
# Performance Analysis of Cooperative Low-Power Wide-Area Network for Energy-Efficient B5G Systems
## 3 Error Performance Analysis
### Bit Error Rate (BER)  
In this subsection, we derive the BER of the uplink cooperative technique in LPWAN. First, we assume that the transmit signal of the ID is modulated to quadrature phase-shift keying (QPSK) modulation. The BER of QPSK modulation is known as \(Q(\sqrt{\text{SNR}})\).  
From (2), the conditional BER for given channel gain \(\|\mathbf{h}_{i}\|\) at the GW \(i\) in non-cooperative LPWAN is given as  
\[\Pr(\mathcal{E}_{b,i}\mid\|\mathbf{h}_{i}\|)=Q\left(\sqrt{d_{i}^{-\alpha}\| \mathbf{h}_{i}\|^{2}\rho}\right), \tag{12}\]  
where \(\mathcal{E}_{b,i}\) dedicates the bit-error event at GW \(i\). We easily can derive the non-cooperative BER of the \(i\)-th GW with \(K_{i}\) antennas as follows:  
\[P_{b}(i,K_{i}) \triangleq \Pr(\mathcal{E}_{b,i})=\ \mathbb{E}_{\|\mathbf{h}_{i}\|}\bigg{[}Q \left(\sqrt{d_{i}^{-\alpha}\|\mathbf{h}_{i}\|^{2}\rho}\right)\bigg{]} \tag{13}\] \[= \int_{0}^{\infty}Q\left(\sqrt{z\rho}\right)f_{Z_{\gamma_{i}}^{K_{ i}}}(z)dz\overset{(a)}{=}\frac{1}{2}\left(1-\sum_{k=0}^{K_{i}-1}\binom{2k}{k} \frac{(2\gamma_{i}+4)^{-k}}{\sqrt{1+2/\gamma_{i}}}\right),\]  
where \((a)\) is given in [18].  
Now, we analyze the BER of the uplink cooperative technique. As in Equation (12), The conditional BER for given all channel gains, \(\forall\|\mathbf{h}_{i}\|\) at the cloud server is given as  
\[\Pr(\mathcal{E}_{b}\mid\forall\|\mathbf{h}_{i}\|)=Q\left(\sqrt{\sum_{i=1}^{N} d_{i}^{-\alpha}\|\mathbf{h}_{i}\|^{2}\rho}\right), \tag{14}\]  
where \(\mathcal{E}_{b}\) dedicates the bit-error event at the cloud server.  
Finally, we can obtain the exact closed-form BER performance of the uplink cooperative technique in the presence of the cloud server connected with them as follows.  
\[P_{b} = \mathbb{E}_{\forall\|\mathbf{h}_{i}\|}\bigg{[}Q\left(\sqrt{\sum_ {i=1}^{N}d_{i}^{-\alpha}\|\mathbf{h}_{i}\|^{2}\rho}\right)\bigg{]}= \int_{0}^{\infty}Q(\sqrt{z\rho})f_{Z_{\gamma_{i}}^{i}}(z)dz=\ \int_{0}^{\infty}Q \left(\sqrt{z}\right)\sum_{i=1}^{N}\sum_{j=1}^{K_{i}}A_{ij}f_{Z_{\gamma_{i}}^{ i}}(z)dz \tag{15}\] \[= \sum_{i=1}^{N}\sum_{j=1}^{K_{i}}A_{ij}\int_{0}^{\infty}Q\left( \sqrt{z}\right)f_{Z_{\gamma_{i}}^{i}}(z)dz=\ \sum_{i=1}^{N}\sum_{j=1}^{K_{i}}A_{ij}P_{b}(i,j).\]
# An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs
## 3 Image Segmentation Based on an AGAN
### Agan Framework  
The AGAN framework is shown in Figure 3. An AGAN consists of three parts: a selector, a discriminator, and a feature extractor. The feature extractor contains multiple generators. The selector contains a controller and an adaptive mechanism. In this framework, the feature extractor extracts feature vectors of different dimensions. The extracted feature vectors are input into the corresponding discriminator to score the feature results. In this way, the feature extractor not only learns to extract dimensional characteristics through regular supervised training but also learns to extract features with better generalizability by deceiving the discriminator. To achieve more accurate feature descriptions and a faster iteration speed, the generators for different dimensions in the feature extractor are selectively promoted by the selector. The selector is used to adaptively coordinate the network. The selector is also used to adaptively select which dimensional features are considered.  
The settings of the feature extractor and discriminator in this algorithm refer in part to the deployment of a GAN. The main purpose of the original GAN model is to fit the corresponding generator and discriminator functions to generate images. There is no restriction on the specific structures of the generator and discriminator. Due to the remarkable achievements of deep neural networks in image processing [26], the feature extractor and discriminator in this paper are both designed on the basis of neural network models. At the same time, to better retain the image details, the features extracted in two dimensions are also combined to make the segmentation more accurate after adaptive selection. A detailed introduction to the composition of and algorithm for each component in the framework is given in the following sections.  
Figure 3: Adaptive generative adversarial network (AGAN) framework.
# Rain Monitoring with Polarimetric GNSS Signals: Ground-Based Experimental Research
## 3 Results and Discussion
### Statistical Results of G22 Satellite in 2015
#### 3.2.2 Comparison with Weather Radar  
For comparison, weather radar data is also processed and analyzed. Path-averaged rain rate is also calculated from radar reflectivity. Figure 12a,b displays the results from two rainy cases, which is obtained by the same method of Figure 7a,b. In these two figures, white corresponds to \"no data\". Since GPS ray does not come across cloud in this area, there is no data there. Figure 12c presents the sky view of G22 at this selected time. It is found that there are raindrops along the GPS rays, when the phase shift exists. In the case of 10 August, it is found that the radar reflectivity is quite strong. Especially, after 9 min, the path-averaged rain rate and phase shift appears almost at the same time. Furthermore, their values have some consistency. Therefore, this result still needs to be studied further.  
Figure 11: Examples of phase shift from two rainy days: (**a**) on 27 June 2015; (**b**) on 10 August 2015.  
Figure 12: _Cont._  
Figure 10: Examples of phase shift from two no-rain days: (**a**) on 15 June 2015; (**b**) on 30 August 2015.
## 3 Results and Discussion
### CNC/Alg/PEGDA Scaffold
#### 3.3.3 Morphology of CNC/Alg/PEGDA 3D Constructs  
The SEM micrographs show the morphology of the F8 [4% CNC: 4% Alg: 40% PEGDA] construct. The CNCs indicated by the arrows are attached to the hydrogel surface, resulting in an entangled network (Figure 10A). This unique entanglement seems to have a positive effect on the toughness and flexibility of the construct. The SEM micrographs also show that the hydrogel has a rough and wavy surface which indicates a tough network [49]. A rough surface is also more favourable for cell growth and attachment as it has a closer resemblance to the native tissue. According to a study done by [PERSON] et al., osteoblast cells effectively attached and increased proliferation and differentiation on rough surfaces as compared to smooth surfaces [50]. The pores within the CNC/Alg/PEGDA construct ranged from 3 to 20 \(\upmu\)m. The presence of pores within a scaffold is important to encourage cell proliferation and to promote the transport of gases and nutrients to the cells [51]. Upon further magnification (801\(\times\)), the SEM micrographs reveal a close-up view of the pores which have irregular oblong shapes as opposed to the conventional circular shape (Figure 10B). This observation indicates that there was shrinkage of the hydrogel leading to the distortion of the pore structures. According to previous studies, drying the hydrogel samples using liquid nitrogen before freeze-drying, also known as the cryo-fixing approach, for sample drying preparation is more favourable for maintaining the native structure of the hydrogel sample and, thus, should be taken into consideration for future SEM analysis [52; 53].
# Simulation of Fidelity in Entanglement-Based Networks with Repeater Chains
## 3 Entanglement Generation and Distribution
### Fidelity  
Fidelity is a measure of the amount of similarity between two quantum states \(\rho\) and \(\sigma\). It is defined as  
\[F(\rho,\sigma)=\mathrm{Tr}\sqrt{\sqrt{\rho}\sigma\sqrt{\rho}},\]  
where \(\rho\) and \(\sigma\) are density matrices. Fidelity is symmetric, has values \(0\leq F(\rho,\sigma)\leq 1\) and is maximal \(F(\rho,\sigma)=1\) whenever \(\rho=\sigma\). Thus, \(F(\rho,\Omega)\), where \(\Omega=|\Phi^{+}\rangle\langle\Phi^{+}|\) is a maximally entangled state, can be interpreted as a measure of the amount of entanglement of the state \(\rho\). For many scenarios of interest in quantum information problems, the calculation or measurement of entanglement involves only pure states, and in this case, the definition simplifies to \(F(|\psi\rangle,|\phi\rangle)=|\langle\psi|\phi\rangle|^{2}\). In this paper, fidelity will be taken as the metric for the quality of the entangled states created between two endpoints in the network.
# Review of Convective Heat Transfer Modelling in CFD Simulations of Fire-Driven Flows
## 2 Newton's Law and CFD Equations
### CFD Governing Equations
Some widely-used models in fire modelling include the constant Smagorinsky [27], the dynamic Smagorinsky [28] and Deardorff [29] turbulence models, the Eddy Dissipation Model (EDM) combustion model [30] and the Finite Volume Discrete Ordinates Method (fvDOM) combined with either the constant radiative fraction approach, the grey gas model or the Weighted Sum of Grey Gas Model (WSGGM) [31] for modelling absorption/emission. A detailed presentation of these models is considered outside the scope of the paper. The convective heat transfer models, presented later in the paper, are typically implemented in CFD codes as boundary conditions for the sub-grid scale thermal diffusivity, \(\alpha_{sgs}\), in the sensible enthalpy equation (i.e., Equation (5)) [22]. Hence, convective heat transfer will directly influence the diffusivity of enthalpy (i.e., temperature field) in the near-field region of a surface (i.e., as well as the diffusivity of species if a unity Lewis number is employed in the modelling). The procedure through which the convective heat flux, \(q_{c}^{{}^{\prime\prime}}\), is linked to the sub-grid scale thermal diffusivity, \(\alpha_{sgs}\), is presented below. The effective thermal diffusivity, \(\alpha_{eff}\), is calculated based on the convective heat flux predicted from the corresponding approach as follows:  
\[\alpha_{eff}=\frac{q_{c}^{{}^{\prime\prime}}}{\rho c_{p}\frac{d\tau}{dt}}, \tag{7}\]where \(\alpha_{eff}=\alpha_{sgs}+\alpha\), \(\rho\) and \(c_{p}\) are the density and the specific heat evaluated at the surface temperature, respectively, \(n\) denotes the direction normal to the surface and \(dT/dn\) is the temperature gradient based on the temperature of the first grid cell next to the surface and the surface temperature. The sub-grid scale thermal diffusivity, \(\alpha_{sgs}\), is then calculated by subtracting the molecular thermal diffusivity, \(\alpha\), from the effective thermal diffusivity, \(\alpha_{sgs}\), as:  
\[\alpha_{sgs}=\alpha_{eff}-\alpha. \tag{8}\]  
Finally, the convective heat fluxes, regardless of the method employed to estimate them, will be calculated as:  
\[q_{c}^{\prime\prime}=\rho\alpha_{eff}c_{p}\frac{dT}{dn}, \tag{9}\]  
where the term \(\rho c_{p}\frac{dT}{dn}\) is essentially cancelled between Equations (7) and (9).
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 3 Methods
### The Floodwater/Permanent Water Mapping Algorithm  
The execution of the floodwater/permanent water mapping algorithm follows immediately the execution of the preprocessing batch script described above; thus, the algorithm runs twice a day. It has been written in IDL language and takes advantage of the possibility to launch ENVI routines through specific IDL instructions. The algorithm is divided in four blocks; they are described hereafter.
## 4 Experimental Results and Discussion  
### Dataset Description  
The image dataset used for model training was obtained from Roboflow [35], and the images were captured using the FLIR T600 single-band infrared thermal imager (FLIR, Wilsonville, 2010, USA). This thermal imager operates within a spectral range of 7.5-14 \(\upmu\)m, belonging to the long-wave infrared (LWIR) region, which is well-suited for capturing thermal radiation emitted by objects with temperatures typical in industrial environments. With a spatial resolution of 640 \(\times\) 480 pixels, the thermal imager enables detailed thermal imaging of substation components. The dataset includes images from multiple substations, rather than a single site, focusing on seven types of equipment: isolator, circuit breakers, bushings, current transformers, isolation switch 1, isolation switch 2, and insulators. A total of 1670 images were collected, covering both normal operating conditions and fault states, thus reflecting a wide range of scenarios encountered in practical applications. The dataset used in this study is available for download from [36] for further research and applications.  
Due to the limited number of original samples, various data augmentation techniques were employed to expand the dataset, including rotation, flipping, brightness adjustment, and Gaussian noise addition. Additionally, to accommodate the input requirements of the network model, the resolution of the original images was adjusted from 640 \(\times\) 480 to 640 \(\times\) 640. After applying these data augmentation methods, a total of 10,020 infrared images were obtained. The augmented dataset was strategically divided into training, validation, and test sets in a ratio of 6:2:2 to ensure comprehensive model training and performance evaluation. Each image was meticulously annotated using the LabelImg tool to accurately identify the device types and their locations within the images. This rigorous annotation process ensures the reliability and consistency of the dataset, laying a solid foundation for effective model training.  
In addition, Figure 9 also visualizes the dataset. It can be seen from the figure that the number of images in the current transformer and insulator categories is greater than that in the other five categories. This is because these two types of power components are used most in actual substations, and the data distribution is also in line with reality. The main difference between isolation switch 1 and isolation switch 2 is that isolation switch 1 is used for larger circuit isolation needs and supports high voltage and high current, while isolation switch 2 is used for isolation