esa-sceva/satcom-chunk-collection huggingface dataset:
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 7 Computation-Distortion Trade-Off  
Combining the results of Sections 5.4, 5.5 and 6.1, we can relate the mean-square distortion to the number of additions. Section 6.1 empirically confirmed that the distortion is given by  
\[D=m_{\epsilon^{2}}^{S} \tag{44}\]  
(at least for rates close to unity). Combining (40) and (21), we have  
\[m_{\epsilon^{2}}=4^{-R}=K^{-\frac{2}{R}} \tag{45}\]for matrices with large dimensions. Furthermore, (43) relates the number of additions per matrix entry \(\tilde{C}\) to \(S\) and \(N\) as  
\[S\approx\tilde{C}N-v. \tag{46}\]  
Combining these three relations, we obtain  
\[D\approx K^{-2\tilde{C}+\frac{v}{N}}=4^{Rv}K^{-2\tilde{C}}. \tag{47}\]  
This is a much more optimistic scaling than in Example 2. For scalar linear functions, [PERSON]'s CSD representation made the mean-square error reduce by the constant factor of 28 per addition. Due to linear computation coding as proposed in this work, the factor 28 in (5) turns into \(K^{2}\), the squared matrix dimension, in (47). Since we have the free choice to go for either tall or wide submatrices, the relevant matrix dimension here is the maximum of the number of rows and columns.  
We may relate the computational cost \(\tilde{C}\) in (47) to the computational cost \(|\mathcal{B}|\) in (5), the benchmark set by [PERSON]'s CSD representation. For given SQNR, this results in  
\[\frac{\tilde{C}}{|\mathcal{B}|}\approx\frac{\log_{k2}\text{SQNR}+Rv\log_{K2}4 }{\log_{28}\text{SQNR}}. \tag{48}\]  
For \(q\)-bit signed integer arithmetic the SQNR is approximately given by SQNR \(\approx 4^{q-1}\). Thus, we obtain  
\[\frac{\tilde{C}}{|\mathcal{B}|}\approx\frac{\frac{2(q-1)}{2\log_{2}K}+Rv\frac {2}{2\log_{2}K}}{\frac{2(q-1)}{\log_{2}28}}=\left(1+\frac{Rv}{q-1}\right)\log _{K}\sqrt{28}. \tag{49}\]  
Although this formula is based on asymptotic considerations, it is quite accurate for the benchmark example. Instead of the actual 77% savings compared to CSD, which were found by simulation in Section 6.3.1, it predicts savings of 79%. However, it does not allow to include adaptive assignments of CSDs.
## 1 Introduction
In particular, MLP has been verified as an excellent neural network algorithm at the pixel-level in remote sensing applications, such as cloud masking, image classification, water extraction, or change detection [31; 32; 33; 34]. However, the application of MLP to radiometric and phenological normalizations of high-resolution satellite images is relatively rare. That is, based on these motivations, the proposed method establishes a relationship through MLP to perform relative radiometric normalization. The experiments are conducted on multiple scenes to demonstrate the ability and performance of MLP. Afterwards, the proposed method is comprehensively compared with conventional linear relationship-based methods and nonlinear relationship-based methods. Furthermore, an additional dataset is constructed and applied to the proposed method to verify the robustness of the approach. The rest of this paper is structured, as follows. Section 2 describes the images that were used in this study and the proposed algorithm, including MLP. In Section 3, the results of the proposed method are presented, and detailed comparisons with conventional relative radiometric normalization algorithms and additional discussions are made. Section 4 then concludes this paper.
# A Novel Digitized Method for the Design and Additive Manufacturing of Orthodontic Space Maintainers
## 2 Materials and Methods
### Anatomical Data Acquisition  
The starting point of the process consists of performing an intraoral scan of the child's dentition. This procedure allows digital capture of the shape of gum and teeth by a digital dental scanner [22]. This approach has witnessed a significant increase in terms of diffusion and adoption among dentits in recent years [23]. One of its advantages is that the resulting output consists of a 3D mesh of the patient's mouth, that can be subsequently exported in a.stl file (Figure 3a).  
Figure 3: Initial teeth surface elaboration: (**a**) 3D intraoral scan of the child’s dentition and (**b**) selection of a portion of the two teeth that are required to be maintained at a certain distance.  
Figure 2: The digital design process of the personalized space maintainers.  
After the completion of the 3D scanning process, the subsequent step involves identifying and selecting the specific surfaces of the teeth model that require to be kept at a specified distance. The complete scan is imported in MeshLab, and the teeth portion, adjacent to the edentulous space that must be preserved, is selected, and then trimmed. To prevent interference between the appliance and the patient's chewing, the selected areas should maintain specified distances from the gingiva in the lower zone and the occlusal plane in the upper zone. Figure 3b shows an example of this selection for the space between the primary mandibular canine and the primary mandibular second molar.  
It is relevant to notice that, compared to the traditional solutions, the selection proposed in this work offers two advantages. By selecting a small partial surface on the mesial, distal, vestibular, and lingual teeth areas, which does not interfere with the child's mastication, mechanical locking of the space maintainer is achieved (highlighted in green in Figure 3b), preventing it from dropping significantly lower. Additionally, maintaining a distance from the teeth-gum border (highlighted in red in Figure 3b) significantly reduces the risk of gum irritation and plaque formation in that area.
## 2 Materials and Methods
### Model Calculation and Accuracy Evaluation
#### 2.4.3 Model Calibration and Validation  
First, the 36 soil samples contaminated by each heavy metal were sorted in ascending order of concentration. Then, they were divided into 6 groups according to the concentration. The samples in each group were randomly divided into a calibration set and a validation set at a ratio of 2:1. Thus, the calibration dataset included 24 samples, and the validation set comprised the other 12 samples.  
Regression models need to be evaluated prior to their application. The R\({}^{2}\) value, root mean square error (RMSE) and residual prediction deviation (RPD) are the important statistical parameters used to evaluate the estimation accuracies of calibration models. The evaluation criteria are: RPD \(<\) 1.5 and R\({}^{2}\)\(<\) 0.50 indicate unsuccessful models, which are not recommended; 1.5 \(\leq\) RPD \(<\) 2.0 and 0.50 \(\leq\) R\({}^{2}\)\(<\) 0.65 indicate fair models; 2.0 \(\leq\) RPD \(<\) 2.5 and 0.65 \(\leq\) R\({}^{2}\)\(<\) 0.81 indicate good models; 2.5 \(\leq\) RPD \(<\) 3.0 and 0.81 \(\leq\) R\({}^{2}\)\(<\) 0.90 indicate very good quantitative models; and RPD \(\geq\) 3.0 and R\({}^{2}\)\(\geq\) 0.90 indicate excellent models [15]. The smaller the RMSE value is, the higher the prediction accuracy of the model is. The calculation formulas for the three indicators are as follows:  
\[r^{2}=1-\frac{\sum\bigl{(}y_{m}-y_{p}\bigr{)}^{2}}{\sum\bigl{(}y_{m}-\overline {y}\bigr{)}^{2}}, \tag{6}\]  
\[\text{RMSE}=\sqrt{\frac{\sum\bigl{(}y_{m}-y_{p}\bigr{)}^{2}}{n}}, \tag{7}\]  
\[\text{RPD}=\frac{\text{SD}}{\text{RMSE}}, \tag{8}\]  
where \(y_{m}\) is the measured value of heavy metal content; \(\overline{y}\) is the average value of the measured value of heavy metal content; \(y_{p}\) is the estimated value of heavy metal content; \(n\) is the number of samples; and SD is the standard deviation of the measured value of the test set.
## 6 Ethical and Privacy Issues
### Data Privacy and Security in Healthcare  
In healthcare, AI chatbots like Llama's ChatDoctor handle sensitive patient data, thus making them targets for data breaches and unauthorised access. The ethical handling of medical information is governed by regulations such as HIPAA in the United States, GDPR in Europe, and the Australian Privacy Act of 1988 in Australia, which emphasize the need for stringent data protection measures. However, chatbots can inadvertently expose patient data through security vulnerabilities, as highlighted by the concerns raised in the evaluation of ChatGPT 4.0's performance in medical domains [1].  
Recommendations  
The following recommendations are proposed to enhance data privacy and security for AI chatbots:  
* Implement end-to-end encryption for all data exchanges involving AI chatbots to safeguard patient information, as suggested in [47]. Extend privacy policies to govern AI use such as anonymisation, securing data at rest, and rights of data owners to access their data and delete them.
* Conduct regular security audits and updates to the chatbot systems to address potential vulnerabilities and ensure compliance with healthcare privacy laws, as outlined in the study of the security and privacy risks associated with AI agents in [48].
* Employ secure data storage and access control mechanisms to prevent unauthorised third-party access to sensitive patient information, as highlighted in [49].
## 2 System Architecture, Spectrum Sharing Technique, and Interference Management
### Co-primary Shared Access
#### Static Spectrum Sharing by Renting
To avoid such co-channel interference between indoor macro UEs and small cells and its UEs, we consider employing the ABS based eICIC technique with the small cells within a building such that indoor macro UEs are only allowed to operate during ABSs, whereas small cell UEs operate during non-ABSs. However, small cells can serve their UEs in all TTIs of all APPs at the absence of all macro UEs within the building (Figure 6). The transceiver 2 of a small cell of MNO 1 operates following either the DySP or the SGR techniques as described before. A number of notable features of static sharing that seems to be beneficial as compared to dynamic spectrum sharing are as follows:  
* The strategy can help generate some revenues for MNO 2. * No coordination between networks of MNOs 1 and 2 is needed. * Unlike dynamic spectrum sharing, no CoRS is needed for real-time update and allocation of spectrum to the UEs of both MNOs. * No spectrum is wasted because of the coordination control signaling exchanging over the backhaul between MNOs' networks. * Less complex and cost-effective solution in realization and maintenance as compared to the dynamic spectrum sharing since there is no cost associated with the CoRS implementation. * No need for the interference management for static sharing since small cell UEs of MNO 1 and UEs of MNO 2 operate at the orthogonal spectrum of MNO 2. **Remark 1**. : _Note that the spectrum renting and spectrum pooling are the typical co-primary licensed spectrum sharing techniques to use the licensed spectrum of one operator exclusively at equal rights under the approval of NRA [6]._  
Figure 5: Interference management for DySP. For static spectrum sharing, there is no need for interference management.
## 3 Experimental Results
### Mechanical Properties of the Recycled Concrete Evaluated
Tensile strength \(f_{ct}\) is usually obtained from indirect tensile strength \(f_{ci}\), which for concretes made with CDWPrec has an average value of \(f_{ti}\) = 2.8 MPa, i.e., tensile strength \(f_{tk}\) = 2.5 MPa. The modulus of elasticity showed an outcome of 27.6 GPa, which is very similar to the experimental value obtained when strains in concretes made by conventional aggregates is calculated. The peak strain at the time of maximum stress was 0.0021 for cylindrical specimens and 0.0034 for cubic specimens. In this case, the conversion coefficient between the two types of specimens is approximately 0.6. While for cylindrical specimens the value is similar to the 2% generally considered for conventional concrete, for cubic specimens the strain is higher, also associated with the higher strength that this type of specimen achieves due to its geometry. The high CoV in the modulus of elasticity and ultimate strain results show the heterogeneous behaviour of these materials. Although heterogeneity is present in conventional concrete, particularly in the strains produced by the phase change between aggregates of different sizes, recycled aggregates accentuate this irregularity due to their own characteristics, such as in the case of greater disaggregation of aggregates and cement [61]. It should also be noted that the compressive and tensile strength data are unique to each specimen. Nevertheless, the ultimate strain and modulus of elasticity data also take into account the spatial variability of the material within the specimen itself. This is because DIC allows multiple measurements to be taken within a single specimen. Issues such as manufacturing heterogeneity, inaccuracies in the alignment of the press during the test, or edge effects can lead to higher variability and therefore increase the CoV of these data. \begin{table} \begin{tabular}{c c c c c c} \hline \hline
**Property** & **Mean** & **STD** & **CoV (\%)** & **Lower Bound** & **Upper Bound** \\ \hline \(f_{c\text{-}c\text{-}d}\) & 36.5 & 4.3 & 11.8 & 29.1 & 39.4 \\ \(f_{c\text{-}c\text{-}ab}\) & 40.5 & 2.2 & 5.4 & 37.8 & 43.5 \\ \(f_{ti}\) & 2.8 & 0.3 & 11.2 & 2.5 & 3.1 \\ \(E\) & 27.62 & 4.56 & 16.5 & 21.18 & 36.82 \\ \(\epsilon_{p\text{-}q\text{-}d}\) & 0.0021 & 0.0002 & 11.6 & 0.0017 & 0.0026 \\ \(\epsilon_{p\text{-}c\text{-}c\text{-}}\) & 0.0034 & 0.0006 & 18.3 & 0.0023 & 0.0048 \\ \hline \hline \end{tabular}  \(f_{c
## 3 Improved Inter-Node Load Balancing for Multithreaded Applications
### Enhancements for Selecting Tasks for Inter-Node Migration for Multithreaded Applications
#### 3.2.2 Evaluating Only Those Tasks with Thread Group Leaders on Other Nodes  
The observation presented in Section 3.2.1 shows that most of the memory pages allocated to a multithreaded process are on the node where the thread group leader is located. Therefore, ensuring that threads are executed on the same node as the thread group leader involves more local memory access.  
For the proposed design, during the task evaluation for inter-node task migration, each task in the runqueue is classified into three types, according to where its thread group leader is currently located. Different decisions are made as follows. If the thread group leader is on the destination node, migrating this task to the destination node may involve more local memory access. Therefore, it is migrated directly instead of evaluating the remaining tasks in the runqueue. Suppose the thread group leader is on the source node. In that case, it is not selected for the migration because migrating it to the destination node may involve more remote memory access after migration. Therefore, only those tasks for which the thread group leaders are currently located on other nodes are evaluated.  
Figure 7 illustrates several multithreaded processes running on a 4-node NUMA system. There are four thread groups and 19 tasks. The thread group leaders are denoted as \"TGLR.\" Because Core#3 is idle, the OS scheduler performs the load balancing mechanism to migrate tasks from the overloaded runqueue (Core#1 on Node#0). The thread group leader for Task#3 is on the source node, so Task#3 is not selected for migration. Similarly, Task#11 is the target task and is migrated immediately because its thread group leader is on the destination node. If more tasks must be migrated to achieve load balancing, the remaining tasks (Task#19, Task#6, Task#16, and Task#9) are evaluated to determine which is best suited to inter-node migration.
# Three-Dimensional Resource Allocation in D2D-Based V2V Communication
## 2 Network Architecture and System Model
### Requirements for Different User Equipment (UE)  
For a V2I link, the aim is to have high capacity; i.e., a high data rate for a range of applications from infotainment services to traffic-related services. In the case of V2V, security communication between the vehicles requires an ultra-reliable link since it is the most crucial type of communication. In case of CUE, we assume a minimum required signal-to-noise ratio (SINR) for voice communication. Furthermore, the instantaneous channel state information (CSI) is available in the case of CUE at the eNB, but in the case of VUEs, conventional resource management techniques are rendered infeasible due to the high mobility of the vehicles, which makes it hard to track the channel variations in a short time [10]. The channel power gain consists of both small- and large-scale fading parameters denoted by \(g\) and \(\alpha\), respectively. It is assumed that the base station has the information of large-scale fading components (pathloss + shadowing) since they vary slowly compared to small-scale fading components. Table 1 shows the requirements of different UEs. For the V2V case, the high reliability means that that the probability of an outage is less than a certain threshold (p0) [10]. In the case of V2I, we need a high data rate, and thus we need to maximize capacity; for CUE, we need the SINR to be above a certain threshold to provide the best effort service.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**V2V (\(\ell\))** & **V2I(k)** & **CUE(j)** \\ \hline \begin{tabular}{c} High reliability \\ (probability of outage under a \\ certain threshold) \\ \end{tabular} & \begin{tabular}{c} Large capacity \\ (minimum SINR guaranteed) \\ \end{tabular} \\ \
\begin{tabular}{c} Pr [\(\gamma_{1}\leq\gamma_{0}\)] \(\leq\) p0 \\ \end{tabular} &
\begin{tabular}{c} Maximize \\ \(\sum[\log_{2}\!\left(1+\gamma^{k}\right)]\) \\ \end{tabular} & \(\gamma_{j}\geq\gamma_{th}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Requirements for different user equipment (UE).  
Figure 2: Vehicular and cellular users.
# Research on the Simulation Method of HTTP Traffic Based on GAN
## 3 Materials and Methods
### AE (Autoencoder)  
The autoencoder [21; 22] is an unsupervised learning technique that utilizes backpropagation and optimization methods to learn a mapping relationship from input data (referred to as \"data\") to guide the neural network in generating a reconstructed output X that is similar or identical to the input.  
An autoencoder consists of two main components: an encoder and a decoder. The encoder aims to map the original data to a hidden space h, while the decoder tries to reconstruct the original data from this latent space. Autoencoders can be understood as a form of non-linear dimensionality reduction. Variational autoencoders (VAEs) [23], which share a similar principle to autoencoders, can not only perform dimensionality transformations but also generate new data. Additionally, VAEs can be combined with generative adversarial networks (GANs) to form VAE-GANs [24].  
Inspired by the principles of VAE (variational autoencoder), this paper utilized AE (autoencoder) as the pre-generator part to capture the true distribution of the data. In traditional generative adversarial network (GAN) architectures, the generator's input data is random noise, and the generator needs to map this input noise into an output that matches the real data. Throughout this process, the generator is unaware of the true distribution of the data, and parameter updates solely rely on gradients from the discriminator. The generator continuously guesses the possible distribution of correct data based on the discriminator's gradients. When the data distribution is complex, the training difficulty of the generator increases sharply. Therefore, this paper adopted the AE module to alleviate the training difficulty of the generator.  
Specifically, this paper pre-trained a set of well-designed encoders and decoders to achieve dataset reconstruction. The purpose of training the AE network was to minimize reconstruction errors. In this way, the AE learned all the patterns of the true data distribution. Leveraging the representational capabilities of AE guides the generator in the GAN to learn all the distributions of the true data, preventing mode collapse where the generator can only generate a certain type of data.
## 3 Results
### Physicochemical Properties of Natural and Synthetic Cosmetic Emollients
#### 3.1.2 Color Evaluation of Cosmetic Emollients  
The CIE Lab model is currently one of the most popular ways of describing color and is the basis of modern color diagnostic systems [48]. Color measurement is a method commonly used to qualitatively describe various types of cosmetic products and their ingredients. Among other things, synthetic and natural emollients may have different abilities to change the color of cosmetic products, e.g., emulsions.  
The results of the L (lightness) parameter presented in Figure 4A indicate small differences between the tested synthetic emollients, which are within the margin of error. The values range from 25.64 to 26.40. The highest value of the L parameter, 26.4, was recorded for Octyldodecanol, which means that it is lighter than the rest of the tested synthetic emollients. The values of parameter a*, defining the limit of red and green color, do not fluctuate between individual emollients (Figure 4A), the results of which are between \(-\)0.23 and \(-\)0.28, with the exception of Caprylic/Capric Triglyceride whose a* parameter is \(-\)0.37. The values of b*, which determine the border of yellow and blue, similar to the parameter a*, do not differ significantly. The parameter b*, however, unlike the parameter a*, is positive. We can also again distinguish Caprylic/Capric Triglyceride, which in this case has the highest value of the b* parameter, 0.48. For Dibutyl Adipate and Octyldodecanol, the same value of the b* parameter was observed, 0.39.  
Taking into account the results of colorimetric tests for natural emollients (Figure 4B), there was no significant influence of the type of emollient on the results of the L parameter. The results of the L parameter oscillated within the error range, from 26.18 to 26.98. As with synthetic emollients, the values of parameter a* are negative, indicating green color, and the values of parameter b* are positive, indicating yellow color. Grapeseed oil has the highest value of the a* parameter, \(-\)0.37, among natural emollients. Castor oil has the lowest value of this parameter (a* = \(-\)0.58). Analyzing the b* values presented in Figure 4B, which mark the boundaries of yellow and blue, it can be seen that grape seed oil has the highest value of 2.74, while sweet almond oil has the lowest value of 1.40.