esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
### Limitations  
This study has several limitations that warrant consideration. First, the analysis is based on data from a single academic programme (BSc Actuarial Science) at one institution, which may limit the generalizability of the findings to other disciplines or universities. Future studies should extend this research to multiple institutions and a broader range of academic programmes to provide a more comprehensive understanding. Second, while this study highlights Grade 12 mathematics performance and socioeconomic factors as key influences, it does not fully account for other critical variables, such as individual student motivations, teaching quality, and peer influence, which may significantly impact academic outcomes. Future research could integrate these variables to build a more holistic model of academic performance. Third, this study does not explore the potential mediating role of socioeconomic status (SES) in the relationship between pre-university mathematics performance and academic success. This is a notable gap given SES's well-documented influence on education. Investigating SES as a mediating or moderating factor could provide deeper insights into the dynamics of academic success. Fourth, while the methodological approach used effectively captures variability in academic performance, the slight underfitting observed at the extremes of the performance distribution suggests that additional covariates or more complex models might better account for outlier performances. Future studies should consider these enhancements to improve the precision of predictions. Lastly, the cross-sectional design limits the ability to assess long-term academic trajectories, as the data reflect a snapshot of performance rather than changes over time. Employing a longitudinal design in future research could provide insights into how the observed factors influence academic outcomes across different stages of higher education.
## 5 Damping System
### Influence of Damper Arrangement Schemes on the Seismic Responses  
In the damping systems of super-long-span suspension bridges, the distribution of additional stiffness and additional damping can be adjusted through damper arrangement schemes. Hence, it is important to study and optimize damper arrangements to improve the seismic performance of a whole system exposed to strong earthquakes. In this paper, three types of damper arrangement scheme were proposed; full-uniform distribution (FUD), full-trilaminar distribution (FTD) and bottom-middle distribution (BMD), as shown in Figure 18. Based on the results of Section 4.2, a friction damper combined with a shear-link beam (FD-LSB) was used for this contrastive analysis. The displacement responses along the height of the tower with different damper arrangement schemes are shown in Figure 19.  
Figure 16: Test specimen of a lattice composite tower with viscous dampers.  
Figure 17: Displacement response of the lattice composite tower with viscous dampers.  
The damper arrangement schemes clearly affected the seismic responses. The BMD scheme exposed to both far-fault and near-fault earthquakes performed the best among the three damper arrangement schemes. This was because the dampers were mainly installed in the bottom-middle of the tower, where the shear force and bending movement were larger than those of the other locations in the tower. The relative displacement in the bottom and middle parts of the adjacent sub-towers was also larger than the top.
## 2 Materials and Methods
### Training Details
#### 2.4.2 Hardware and Software Setup  
The experiments were conducted on a high-performance computing setup to handle the demands of training deep neural networks. All models (GRU, LSTM, Transformer) were trained using identical hardware and software configurations, as summarized in Table 5. Each run utilized two NVIDIA (Santa Clara, California, United States) GH200 GPUs with 144 GB HBM3e memory each, leveraging CUDA version 12.6 for GPU acceleration. The data loading process was supported by 144 CPU workers. Key libraries included PyTorch for model implementation and training, Sentence Transformers for base embeddings, and scikit-learn for utilities like t-SNE.
## 4 Discussion  
After presenting all the results, some observations can be made. The test on the COSMO-SkyMed data highlights the benefits of spatial decorrelation compared to SAR processing without a window: in the latter case, speckle is white but targets are unfocused; in the former case, speckle becomes white, and targets, which are not decorrelated, remain focused. The reason the SAR processor usually employs tapering windows in both range and azimuth is that the induced spatial correlation of speckle is considered a lesser drawback because the focusing of targets is generally mandatory.  
The test on the TSX-TDX data highlights that in vegetated areas, even with extremely close acquisition passes, low coherence can still be observed due to leaves rustling in the wind. Other sources of low coherence include shadows and occlusions in built-up areas. Therefore, the accuracy of coherence measurements is important even with very close passes, which is crucial for topographic and especially tomographic applications of SAR.  
While the improvement in the coherence maps is mainly linked to the spatial decorrelation of the speckle, which recovers local ergodicity and avoids underestimation of local variances in the denominator of Equation (7), the reason why the interferogram, modulus, and phase improve when calculated from whitened data is that for the same number of looks, these are uncorrelated rather than correlated. This increases the number of effective looks (i.e., with information content equal to independent ideal looks), thereby decreasing the variance of the estimated phase [50].  
Figure 11: Maps of interferometric phases of Euskirchen estimated using NL-InSAR: (**a**) from non-whitened SLC pair; (**b**) from whitened SLC pair.  
Also, in contexts where phase information is not relevant, e.g., the analysis of changes in glaciers and snow cover [16] or rainforest mapping [17], the increased accuracy of coherence analysis carried out from whitened data is advantageous.  
Furthermore, in tomographic processing with whitened speckle, another effect beyond the benefits of an increased number of effective looks, similar to interferogram production, can arise. In fact, the final quality of the tomographic output may be improved by the increased range resolution intrinsically offered by the deconvolution of speckle. This could have some beneficial effects on SAR tomography implementations limited by a coarse range resolution, as seen in the upcoming P-band narrow bandwidth BIOMASS satellite system for global forest monitoring [51; 52].  
We wish to remark that the proposed whitening step works only on complex data. It cannot be used for incoherent multilook products. On the other hand, the downsampling operation inherent in multilooking reduces the autocorrelation function of speckle, so the whitening step is no longer necessary. Since coherent processing starts with SLC data, the relevance of whitening is guaranteed.  
Figure 12: Phase residues overlaid on the coherence map of Euskirchen estimated using NL-InSAR: (**a**) from non-whitened SLC pair; (**b**) from whitened SLC pair.
# Cyclic Consistent Image Style Transformation: From Model to System
## 2 Related Work  
### Background of GAN Research  
Generative Adversarial Networks (GANs) are a type of deep learning model based on game theory, where data generation is achieved through the adversarial interplay between a generator \(G\) and a discriminator \(D\). The generator \(G\) aims to create realistic data samples to deceive the discriminator \(D\), while the discriminator \(D\) tries to distinguish between generated samples and real ones.  
The training process involves adversarial optimization, where both models continuously adjust their strategies to optimize their respective objective functions. The generator \(G\) attempts to maximize the probability of the discriminator \(D\) making a mistake, while the discriminator \(D\) tries to minimize this probability. The objective functions for these models are defined as follows:  
For the discriminator \(D\):  
\[\mathcal{L}_{D}=-\mathbb{E}_{x\sim p_{data}(x)}[\log D(x)]-\mathbb{E}_{z\sim p _{z}(z)}[\log(1-D(G(z)))]\]  
For the generator \(G\):  
\[\mathcal{L}_{G}=-\mathbb{E}_{z\sim p_{z}(z)}[\log(D(G(z)))]\]In these equations, \(p_{data}(x)\) represents the distribution of real data, \(p_{z}(z)\) is the distribution of the latent variables, \(D(x)\) is the discriminator's probability that \(x\) is real, and \(G(z)\) is the generator's output when given a latent vector \(z\). The generator \(G\) aims to minimize the loss function \(\mathcal{L}_{G}\) by producing data that are increasingly similar to real data, while the discriminator DDD aims to maximize \(\mathcal{L}_{D}\) by accurately distinguishing between real and generated data.  
This adversarial training process continues until Nash Equilibrium is reached. At this equilibrium, the generator produces samples that are nearly indistinguishable from real data, indicating that the generator and discriminator are in balance. GANs have been widely applied in various domains, including image generation [10; 11; 12], video generation [13; 14], and natural language processing [15; 16]. For instance, Conditional GANs (cGANs) [17] have been used for posture-guided character image generation and facial expression animation [18]. GANs also demonstrate exceptional capabilities in image editing and reconstruction tasks, such as super-resolution [19; 20]. However, GANs often encounter issues such as mode collapse and training instability, which limit their application in more complex tasks. These challenges have prompted researchers to explore various optimization techniques and improvements to enhance GAN performance and applicability [21; 22]. The development and evolution route of GANs is shown in Figure 1.
## 3 Results and Discussion
### Analysis of Deformed Surfaces  
#### 3.4.1 Deformed Surfaces of Samples Tested at Ambient Temperature (25 \({}^{\circ}\)C)  
Figures 11-13 show the deformation features for samples tested at ambient temperature and at different strain rates. All three different forms of LPBF Ti6 Al4V (ELI), deformed at different strain rates, show narrow zones of highly localized deformation commonly known as adiabatic shear bands (ASBs). The formation of ASBs is usually attributed to plastic instability resulting from thermal softening, which overcomes the effect of strain hardening in a deformed region. Plastic instability occurs when the local rate of heat generated due to plastic strain exceeds the rate of heat dissipation to the surrounding material. Plastic instability usually occurs at high strain rates, but it has also been reported to occur at low strain rates in materials with a low strain hardening rate, low strain rate sensitivity, low thermal conductivity, and high thermal softening rate [44]. The Ti6 Al4V alloy usually possesses a relatively low strain hardening rate and high strength, as shown in Figures 8-10 and Table 3, as well as a low thermal conductivity of about 6.7 W/m.K [45]. Therefore, the alloy is susceptible to failure by adiabatic shear banding, as is shown in Figures 11-13.  
Figure 10: Effect of temperature on the strain hardening of samples C, D, and E at strain rates of 0.1 s\({}^{-1}\), 0.005 s\({}^{-1}\), and 0.001 s\({}^{-1}\).  
Figure 11: Typical microstructure of samples C deformed at ambient temperature and a strain rate of (a) 0.001 s\({}^{-1}\), (b) 0.005 s \({}^{-1}\), and (c) 0.1 s\({}^{-1}\).  
Figure 12: Typical microstructure of samples D deformed at ambient temperature and a strain rate of (a) 0.001 s\({}^{-1}\), (b) 0.005 s \({}^{-1}\), and (c) 0.1 s\({}^{-1}\).  
As seen in these figures, the cracks, whose longer axes are oriented along the longitudinal directions of the respective ASBs, propagate along the longitudinal directions of these ASBs, possibly as a result of the coalescence of voids. The propagation of cracks along the longitudinal directions of the ASBs is expected to cause the final fracture of the samples.
# Diagnosing Phosphorus Limitation in Subtropical Forests in China under Climate Warming
## 4 Discussion
### Comparison of Phosphorus Limitation with Other Studies  
Previous studies on P limitation revealed that the major sources of uncertainty are related to the estimates of ASP, the stoichiometry of plant organs and the proportion of NPP allocation to plant organs [33]. In this study, we obtained biome-specific C:P ratios for leaves, wood and roots, and NPP allocation to C pools (leaves, wood and roots) from literatures which reported field measurements in subtropical China. Furthermore, we conducted field observations in representative forest sites to estimate ASP, which could reduce the uncertainty of P limitation studies in subtropical China.  
If we define the P deficit at the end of this century as the difference between ASP in 2018 and the AAPD for the period of 2018-2099, then the P deficit will be 372, 300, 1714 and 865 mg\(\cdot\)m\({}^{-2}\) for S\({}_{1}\), and 883, 879, 2299 and 1465 mg\(\cdot\)m\({}^{-2}\) for S\({}_{2}\) for RCP8.5 in Shennongjia, Qian Yanzhou, Mt. Gongga and Dinghushan, respectively. [PERSON] et al. (2017) estimated the P deficit in these areas were in the range of -25,000 mg\(\cdot\)m\({}^{-2}\) to 15,000 mg\(\cdot\)m\({}^{-2}\) for S\({}_{1}\), and 5000 mg\(\cdot\)m\({}^{-2}\) to 25,000 mg\(\cdot\)m\({}^{-2}\) for S\({}_{2}\)[33]. Our study refines this estimation to the range between 300 and 1714 mg\(\cdot\)m\({}^{-2}\) and 879 and 2299 mg\(\cdot\)m\({}^{-2}\) for S\({}_{1}\) and S\({}_{2}\), respectively. In this study, we assumed 1950 as the year of steady status, which may lower our estimates of P deficit when compared with other studies which assumed 1860 as the year of steady status.
## 5 Transfer Learning Actor-Critic Framework for Data Protection in Cognitive Radio Networks
### Markov Decision Process  
The problem of the operation mode decision in this paper is first formulated as a framework of a Markov decision process that is defined as a tuple \(\langle\mathbb{S},\mathbb{A},\mathbb{P},\mathbb{R}\rangle\), where \(\mathbb{S}\) is the state space, \(\mathbb{A}\) is the action space, \(\mathbb{P}:\mathbb{S}\times\mathbb{A}\mapsto\mathbb{S}\) is a transition probability function, and \(\mathbb{R}\) is the reward space. The state of the SU at the \(t\)th time slot is defined as \(s(t)=(e_{r}(t),\rho(t))\), where \(e_{r}(t)\) is the remaining energy of the SU, and \(\rho(t)\) is the probability (also called _belief_) that the PU is inactive in that time slot. The action state space is defined as \(\mathbb{A}=\{ID,TR_{Nk}\}\). At the \(t\)th time slot, the SU can choose to stay idle (action \(a(t)=ID\)) or it can choose to transmit data encrypted by the AES algorithm with key length \(Nk\in\{128,192,256\}\) (action \(a(t)=TR_{Nk}\)). This action provides an immediate reward, and causes the SU to transit into a new state, \(s^{\prime}\), with the following transition probability:  
\[P(s^{\prime}|s(t),a(t))=\begin{cases}1,&if\ s^{\prime}=s(t+1),\\ 0,&otherwise.\end{cases} \tag{11}\]  
We denote as \(R(s(t),a(t))\) the reward (i.e., security level) achieved at the \(t\)th time slot when the SU is in state \(s(t)\) and taking action \(a(t)\in\mathbb{A}\), which is defined as  
\[R(s(t),a(t))\in\{0,S_{Nk}\}, \tag{12}\]  
where  
* \(\quad\)R = 0 if the SU stays idle, or the transmission is not successful.
* \(\quad\)R = 10 if the transmission is successful, and the data are encrypted by AES-128.
* \(\quad\)R = 12 if the transmission is successful, and the data are encrypted by AES-192.
* \(\quad\)R = 14 if the transmission is successful, and the data are encrypted by AES-256.  
The value function is defined as the total discounted reward from the \(t\)th time slot, when the SU's state is \(s(t)=s\), which is given as follows [22]:  
\[V(s)=\sum\limits_{k=t}^{\infty}\eta^{k}R(s(k),a(k))|(s(t)=s), \tag{13}\]  
where \(\eta\) is the discount factor. The objective of this paper is to find an optimal action for the SU in the \(t\)th time slot to maximize the value function as  
\[a(t)=\operatorname*{arg\,max}_{a(k)\in\mathbb{A}}\left\{\sum\limits_{k=t}^{ \infty}\eta^{k}R(s(k),a(k))|(s(t)=s)\right\}. \tag{14}\]  
The solution to the problem of the operation mode decision can be found by solving this equation.
## 2 Materials and Methods
### Optimization of the Stator Windings  
In order to reduce the torque ripple and cogging torque of the motor, the optimized structure adopts a fractional slot, and the slot number equals to 36. The stator windings of 36 slots could not be designed to a single-layer winding; meanwhile, considering the purpose of the winding materials and cost savings, a single-and-double-layer hybrid winding is proposed for the optimization, instead of a double-layer stacked winding [22; 23]. The single-and-double-layer hybrid winding is essentially a deformation of the double-layer winding. The hybrid winding is connected according to the shorter span, which has a good material-saving and energy-saving effect [24]. The double-layer stacked and hybrid winding arrangements are drawn in Figure 3.  
The comparison of the structural parameters for the double-layer stacked winding and single-and-double-layer hybrid winding is listed in Table 2. It can be seen that compared with the double-layered winding, the hybrid winding can effectively shorten the average pitch of the winding and furthermore save material usage of the end winding and reduce the number of coils per unit.  
\begin{table}
\begin{tabular}{c c} \hline
**Design Parameters** & **Values** \\ \hline Maximum efficiency & \(\geq\)96.7\% \\ Maximum torque & 200 N\(\cdot\)m \\ Maximum speed & \(\geq\)10,000 rpm \\ Stator outside diameter & 180 mm \\ Stator inside diameter & 118 mm \\ Rotor outside diameter & 116.6 mm \\ Range of stacked iron core length & 135–150 mm \\ Stator slots & 48/36 \\ Pole pairs & 4 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 3: The stator winding arrangement: (**a**) double-layer stacked winding; and (**b**) single-and-double-layer hybrid winding.  
In particular, the single-and-double-layer hybrid winding has two different slot types, the single layer and double layer, to be determined. Assuming that the number of conductors per slot in the single layer slot is \(N_{1}\), the number of conductors per slot in the double layer slot is \(N_{2}\). Three optimization schemes for the number of turns have been obtained. The motor-related parameters of each scheme are calculated and compared as shown in Table 3. It can be seen that as the proportion of single-layer turns \(N_{1}\) increases, the fundamental winding coefficient of the motor increases, which is beneficial to obtaining a higher large output torque. However, at the same time, it follows that a larger average pitch of the winding can increase the amount of winding material and motor copper consumption. In addition, different single-and-double-layer turn assignments also have a significant impact on the coil space factor.
# Classification of Shoulder X-ray Images with Deep Learning Ensemble Models
## 3 Methods
### Classification Models Based on CNNs for Shoulder Bone X-ray Images  
The CNN-based deep learning models currently available were used to classify X-ray images of the shoulder bone. In training the models, training was firstly carried out with data in the training section of the dataset without pre-training, i.e., with a random weight as the initial weight. However, the required level could not be reached in training the network or at the end of classification by this application. Therefore, the transfer learning method was applied.  
In this transfer learning method, two versions of each available deep learning model pre-trained with ImageNet data, i.e., the existing weights in the pre-trained models, were used. ImageNet is a dataset and benchmark that contains millions of images and hundreds of object categories, on which the procedures of image classification, single-object localization, and object detection are mainly performed [27]. The shoulder bone X-ray images dataset used in this study was used for input of the deep learning models, which are specified in Figure 2 and were trained with ImageNet data. The structure comprised of 1000 classes in the last layer of these models was made to have two classes in order to classify bone images specific to our study as normal and abnormal. Following the above-mentioned procedures, classification was carried out by training the network with the shoulder dataset. Moreover, the last layer of 13 built deep learning models were fine-tuned with SpinalNet/Spinal FC; with the newly acquired models, shoulder bone X-ray images were trained and classified.  
The currently available ResNet, ResNeXt, DenseNet, VGG, InceptionV3, and MobileNetV2 deep learning models, which can be used in classification, along with versions with different numbers of layers were some of the models used in the classification of shoulder bone X-ray images in this study. The required method-based details regarding these models and SpinalNet are described in the following subsections.
# Optimising Amber Processing Using 3D Scanning: New Perspectives in Cultural Heritage
## 2 Methodology and Research  
A comprehensive 3D scanning methodology for amber is presented in Figure 5.  
The technology proposed by the authors to support the processing of amber is three-dimensional scanning technology. The scanning setup consists of a handheld scanner using structured light, such as the Artec Spider, a laptop, and a turntable--Figure 6a. The basic parameters of the scanner are as follows:  
* Blue light technology scanner.
* Light source: blue LED.
* Three-dimensional resolution, up to 0.1 nm.
* Three-dimensional point accuracy, up to 0.05 nm.
* Data acquisition speed up to 1 million points/s.
* Texture resolution 1.3 MPX (full-colour 3D replicas).
* Working distance 0.2-0.3 m.  
Three-dimensional scanning and subsequent data processing require the use of a high-performance laptop. Its basic specifications are as follows: 12-core CPU, 16 GB RAM, 4 GB VRAM and 512 GB SSD. The software used for scanning and data processing was Artec Studio 15.  
In the initial scanning phase, it is necessary to select a suitable group of stones that potentially meet the requirements of shape, dimensions and inclusions. Such a group of stones should be both workable and structurally dense enough not to change shape during subsequent operations. Due to the limitations of structured light scanners in scanning transparent and light-reflecting surfaces, the surface of amber was dulled with talc to avoid any additional calculations related to the phenomenon of refraction [9; 27].  
Figure 5: Scanning methodology.  
The models were placed on a turntable, as shown in Figure 6b. The use of a turntable facilitates the complete scanning of the model from all angles. The process of scanning a single component of amber required several to a dozen individual scans, which were then assembled into a complete model during the post-processing stage. The data were collected at a rate of 30 frames per second. The scanning was conducted under natural daylight without the use of additional lamps [28].  
The scanning data of object surfaces was saved in the form of a point cloud. During scanning, each scan was subjected to an initial visual assessment. Moreover, the amber was documented through photographic means, and the fundamental dimensions were measured using calipers to verify the precision of the 3D scan. Other properties of amber (Appendix A), such as density distribution, clarity and colour, fraction or weight, were omitted.