esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results and Comparison
### Performance Metrics
#### 4.3.3 F-Measure  
The F-measure is used to deal with imbalanced datasets (i.e., when one of the predicted classes appears a lot more than the other). It is a way to combine the precision and recall of the model as a single measure. Precision is defined as the number of correctly classified flows divided by the total number of all the flows predicted as the same class. The F-measure for each class is calculated as follows:  
\[\text{F-measure}=2*\frac{\text{Precision}*\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{12}\]  
where recall is calculated as in (11) and precision is calculated as follows:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{13}\]  
By substituting Equations (11) and (13) in (12), we obtain:  
\[\text{F-measure}=\frac{\text{TP}}{\text{TP}+\frac{1}{2}(\text{FP}+\text{FN})} \tag{14}\]
## 3 Model Construction
### FA-PSOBP Construction of Optimized Model
Performance error analysis focuses on analyzing the prediction errors for the test set. The error metrics include the following:  
Mean Absolute Error (MAE): Measures the average absolute difference between the predicted values and the actual values. Mean Relative Error (MRE): Measures the average relative difference between the predicted values and the actual values. Mean Squared Error (MSE): Measures the average squared difference between the predicted values and the actual values. Root Mean Squared Error (RMSE): The square root of MSE, providing a measure of the standard deviation of the prediction errors. \[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|[PERSON]\right| \tag{11}\]  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}\left([PERSON]\right)^{2} \tag{12}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left([PERSON]\right)^{2}} \tag{13}\]  
\[\text{MRE}=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{Yi-yi}{Yi}\right| \tag{14}\]  
Specifically, \(\text{Y}_{\text{i}}\) denotes the \"actual value\" for a given data point, \(\text{y}_{\text{i}}\) signifies the corresponding \"predicted value\", and \(n\) stands for the \"total number of data points\". The linear fit assesses the degree of linear correlation between the predicted values and the actual values. By analyzing these error metrics and the linear fit, we can evaluate the prediction accuracy and the degree of fit of the model. This helps in assessing the performance of the FA-PSOBP model in predicting rock mass acoustic emissions.
# Virtual Gymnasium: Personalized Weight Perception Interface in Lifting Virtual Objects
## 1 Introduction
Here, we proposed a personalized weight perception interface based on the physical characteristics of users when lifting virtual objects. Toward this, a virtual gymnasium (VG) was created to provide the opportunity for gymnasium (gym) exercises using the proposed pseudo-weight perception method. The salient feature of this system is that it provides a personalized weight perception interface by controlling the lifting force and customizing visual feedback according to the individual user body size. The proposed VG method creates a visualization of a circular (green to red) bar to represent the difference between the standard force required to lift a virtual dumbbell and the current force applied by the user. When the user lifts a virtual dumbbell in any exercise scenario, the circular bar indicates the difference between applied and actual forces using a machine learning algorithm. The user must place the circular bar in the middle to obtain the sensation of the actual weight. Hence, the proposed method visualizes a circular arc interface to guide the movement trajectory of every lifting process. The position and size of the circular arc interface can be changed according to the body size of each user. The main contributions of this study are as follows:  
* The proposed system provides a novel, personalized virtual weight perception interface that allows users to perceive the optimized weights of virtual objects during the lifting process by recognizing the user's upper body size. * The system provides both one-handed and two-handed lifting algorithms to support various lifting exercises, such as dumbbell and barbell curls.
## 5 Performance Evaluation
### Experiment Setting  
The simulation experiment is based on the real edge environment data sets Telecom ([[http://sguangwang.com/TelecomDataset.html](http://sguangwang.com/TelecomDataset.html)]([http://sguangwang.com/TelecomDataset.html](http://sguangwang.com/TelecomDataset.html)), accessed on 3 August 2022) [48; 49; 50] and Taxi ([[https://cse.hkust.edu.hk/scrg/](https://cse.hkust.edu.hk/scrg/)]([https://cse.hkust.edu.hk/scrg/](https://cse.hkust.edu.hk/scrg/)), accessed on 3 August 2022) [51]. The Telecom data set records the geographic location information of 3233 base stations in downtown Shanghai, and the Taxi data set records the driving track information of 4316 taxis in downtown Shanghai. To compare the performance of different algorithms objectively, this section treats the two datasets as follows.  
1. As illustrated in Figure (a)a, 80 base stations in Pudong Central Business District are selected in the Telecom data set for simulation experiment, and 30 of them are randomly chosen as network routers, which are only responsible for forwarding data without calculation.The communication range of each base station and network router is set as 250 meters, and the initial failure rate is evenly distributed between 0.1 and 0.15. All base stations are equipped with the same ES specifications and support parallel computing. The processed Telecom dataset is illustrated in Figure (b)b, with circles representing network routers, rectangles representing base stations, and red connection lines representing data links.  
2. In the Taxi dataset, the driving trajectories of 100, 300, 500, 700, and 900 taxis in the Pudong Central Business District in the same period were selected as the movement trajectories of edge users. The taxi drives at a constant speed. The base station closest to the initial position of the taxi is set as the source base station, and the base station closest to the taxi position after the maximum tolerable time is set as the target base station. Each taxi only needs to migrate one task.  
All simulation experiments were carried out on a computer with an 3.2 GHz AMD Ryzen 5800H processor and 16 GB of RAM. All experimental codes were implemented on in the PyCharm software using Python3.9.
# DCS-ST for Classification of Breast Cancer Histopathology Images with Limited Annotations
## 3 Method  
### Overview of the Method  
To tackle the challenge of classifying breast cancer histopathology images with limited annotated data, we introduce the Dynamic Cross-Scale Swin Transformer (DCS-ST). This novel architecture enhances the Swin Transformer framework. DCS-ST incorporates three key innovations: a dynamic window predictor, a cross-scale attention module, and a semi-supervised learning strategy. These components work synergistically to enable robust feature extraction and classification, effectively capturing local and global image features despite scarce annotations. The dynamic window predictor adapts the model's focus to varying feature scales, the cross-scale attention module integrates multi-scale information for richer representations, and the semi-supervised strategy leverages abundant unlabeled data to improve generalization. This pipeline, illustrated in Figure 1, addresses the critical need for accurate classification in data-constrained medical imaging scenarios.
# Dynamic Torsional Stiffness of Reducers and Its Testing Method
## 5 Value and Application  
Through the research in this article, it was discovered that the proposed dynamic torsional stiffness can more accurately represent the changes in torsional stiffness of reducers under real working conditions. Additionally, the suggested testing method avoids the influence of loading rate dependence and provides more precise test results, opening up a new direction for studying the torsional stiffness of reducers. The benefits of this method are as follows:  
Figure 16: Average torsion evaluation curve of small-size reducers.  
Figure 17: Evaluation curve of a large reducer at the 180\({}^{\circ}\) position.  
Figure 18: Average torsion evaluation curve of large reducers.  
1. Torsional stiffness testing under different loads aligns better with the actual working conditions of the reducer.
2. The hysteresis curve method is inefficient and unsuitable for industrial applications, whereas the transmission error method offers higher testing efficiency and enables online full inspection.
3. Acquiring complete information about the reducer's torsional stiffness in various working conditions. This method's significant advantage lies in obtaining comprehensive information about the torsional stiffness of the reducer (demonstrated in Figures 16 and 18, etc.). This information empowers application personnel to adjust the reducer's speed and torque, ensuring that its torsional stiffness consistently meets usage requirements. For example, in Figure 19, the torsional stiffness, torque, and speed surfaces of the small reducer during normal operation are represented by P\({}_{1}\), while the fault surface is depicted as P\({}_{2}\). An overlap exists between the two surfaces. Consequently, as long as the reducer's torque and speed during operation fall within the overlap area, its torsional stiffness will meet the usage requirements, and there would be no need to replace the faulty reducer.
# Tuberculosis Vaccines: An Update of Recent and Ongoing Clinical Trials
## 3 TB Novel Vaccines
### TB Subunit Vaccines
#### 3.1.2 GamTbvac  
The recombinant subunit vaccine, GamTBVac, is also under clinical trial. It consists of two mycobacterial antigen fusions (Ag85A and ESAT6-CFP10) fused with dextran-binding domain (from _Leuconostoc mesenteroides_) immobilized on dextran, and mixed with an adjuvant consisting of a DEAE-dextran core, and with CpG oligodeoxynucleotides (TLR9 agonists) [14]. The vaccine was assessed for immunogenicity and protective efficacyin GamTBVac-prime/boost and BCG-prime/GamTBVac-boost murine and guinea pig TB models [14]. The results showed that GamTBVac had strong immunogenicity and significant protective effects against the _M. tuberculosis_ strain, H37 Rv, under inhalatory and intravenous routes, with a strong protective effect as a BCG booster vaccine [14]. Such results led to the completion of a phase I clinical trial evaluating the safety and immunogenicity of this vaccine against TB among 60 healthy volunteers, aged 18-49 years (ClinicalTrials.gov: NCT03255278). The study was designed as a comparative placebo-controlled study, with a two-fold increase of an applied dose among the participants. First, the safety of the vaccine was evaluated, followed by the optimal dose and vaccination administration scheme. The trial was completed in December 2017, but no results have been posted yet (ClinicalTrials.gov: NCT03255278). Recently completed was a phase II double-blind randomized (in 3:1 ratio vaccine: placebo) clinical trial to assess the safety, reactogenicity, and immunogenicity of GamTBVac in 180 healthy BCG-vaccinated adults (ClinicalTrials.gov: NCT03878004). Results are, however, yet to be posted. Although not yet recruiting, a multicentre double-blind randomized placebo-controlled phase III clinical trial is underway that aims to assess the safety and efficacy of GamTBVac against the development of primary respiratory TB not associated with HIV infection in healthy volunteers between the ages of 18 and 45 years (ClinicalTrials.gov: NCT04975737).
## 1 Introduction
Principal component regression (PCR), derived from the principal component, is a useful method for dealing with collinearity [38; 39]. However, the regression model obtained with principal components is not as easy to explain as the regression model established with the original independent variables. Partial least squares (PLS) regression [40] is a variant of PCR regression in which the projection is applied to both the independent variable and the dependent variables. Therefore, the PLS method is also considered as a bilinear factor model. Random sample consensus (RANSAC) [41; 42] is a method that can estimate the parameters of the mathematical model in an iterative way from a set of observation data sets containing \"outliers\". Beside linear regression algorithms, non-linear regression algorithms have received increasing attention in the spatial decomposition of demographic data, especially the random forest model. [PERSON] et al. [10] downscaled demographic data into a building-scale gridded population map based on the random forest algorithm by using various geospatial data sets, such as POI data, Tencent online user densities data, and so on. [PERSON]'s study achieved the best accuracy at the community scale through comparison with six other decomposition methods, including areal weighting, binary dasymetric mapping, interpolation with cokriging, and so on. [PERSON] et al. [43] integrated Luojia 1-01 night light remote sensing images, POI data, and Sina Weibo check-in data, and converted the Zhejiang province's demographic data into a fine grid scale by using the random forest model. The effectiveness and superiority of the random forest model was proven again by [PERSON]'s research, which found that its accuracy was higher than that of the WorldPop dataset and previous density-based studies. [PERSON] et al. decomposed Chongqing's demographic data into gridded population data by using the random forest regression model and multi-source geospatial data. The model evaluation results of [PERSON]'s study once again confirmed the superiority of the random forest model over other regression models. However, the application of non-linearregression models other than random forest in the spatial decomposition of demographic data still needs to be strengthened. In addition, most existing studies only use one or two regression algorithms, and comparative studies of different regression algorithms in the spatial decomposition of demographic data are still relatively rare. To solve the problem, we applied twelve commonly used regression algorithms to generate the gridded population data of Guangzhou city, China, with a resolution of 150 m, and compared their performance using several evaluation metrics. The findings of our study can be used for fine-scale population mapping in Guangzhou or another city.
## 3 Attitude Determination
### The Body-Frame Antenna-Geometry as Multivariate Constraints  
The strength of the above model can be improved by including information about the geometry of the antenna configuration. The known body-frame antenna-geometry can be included into the above model through the parametrization:  
\[B^{k}=R^{k}B_{0}^{k},\qquad R^{k}\in\mathbb{O}^{3\times q_{k}} \tag{7}\]  
with the unknown \(3\times q_{k}\) orthogonal matrix \(R^{k}\) (\({R^{k}}^{T}R^{k}=I_{q_{k}}\)), \(\mathbb{O}^{3\times q_{k}}\) denoting the set of orthogonal matrices of size \(3\times q_{k}\) and the known \(q_{k}\times n_{k}\) matrix \(B_{0}^{k}=[b_{0,1}^{k},\ \ldots,\ b_{0,n_{k}}^{k}]\) describing the known geometry of the antenna configuration in the body frame. Here, \(q_{k}\) is the degree of geometrical independence of the GNSS baselines, for example, \(q_{k}=1\) for co-linearly installed antennas, \(q_{k}=2\) for co-planarly installed antennas and \(q_{k}=3\) for antennas not installed in a single plane. For \(q_{k}=3\), \(R^{k}\) is related to the Euler attitude angles \(\vartheta=[\phi\ \theta\ \psi]^{\mathrm{T}}\) as follows:  
\[R(\vartheta)=\left[\begin{array}{ccc}c_{\theta}c_{\phi}&-c_{\psi}s_{\phi}+ s_{\psi}s_{\theta}c_{\phi}&s_{\psi}s_{\phi}+c_{\psi}s_{\theta}c_{\phi}\\ c_{\theta}s_{\phi}&c_{\psi}c_{\phi}+s_{\psi}s_{\theta}s_{\phi}&-s_{\psi}c_{ \phi}+c_{\psi}s_{\theta}s_{\phi}\\ -s_{\theta}&s_{\psi}c_{\theta}&c_{\psi}c_{\theta}\end{array}\right] \tag{8}\]  
with \(\phi\) the heading, \(\theta\) the elevation, \(\psi\) the bank and where \(s_{\alpha}=\sin(\alpha)\) and \(c_{\alpha}=\cos(\alpha)\). Note that for \(q<3\), only the first \(q\) columns of \(R\) are defined. For example, for a linear antenna array (\(q=1\)), only the first column is defined, and hence, only heading and elevation are estimable. For \(q>1\) (an array with more than two antennas that are not in a straight line), all three angles are estimable.  
Substitution of Equation (7) into Equation (1) leads to the constrained GNSS attitude model [19, 24]:  
\[\mathrm{E}(Y^{k}) = AZ^{k}+GR^{k}B_{0}^{k}\qquad Z^{k}\in\mathbb{Z}^{fm\times n_{k}} \tag{9}\] \[\mathrm{D}(\mathrm{vec}(Y^{k})) = Q_{Y^{k}Y^{k}}=P_{n_{k}}\otimes Q_{yy}\qquad R^{k}\in\mathbb{O}^ {3\times q_{k}} \tag{10}\]  
Our objective is to solve for the attitude matrix \(R^{k}\) in a least-squares sense, thereby taking the integer constraint on matrix \(Z^{k}\in\mathbb{Z}^{fm\times n_{k}}\) and the orthonormality constraint on matrix \(R^{k}\in\mathbb{O}^{3\times q_{k}}\) into account. Hence, the least-squares minimization problem that will be solved reads:  
\[\min_{Z^{k}\in\mathbb{Z}^{fm\times n_{k}},R^{k}\in\mathbb{O}^{3\times q_{k}}} \left\|\mathrm{vec}\left(Y^{k}-AZ^{k}-GR^{k}B_{0}^{k}\right)\right\|_{Q_{Y^{k}Y ^{k}}}^{2} \tag{11}\]with \(||\cdot||_{Q}^{2}=(\cdot)^{T}Q^{-1}(\cdot)\). This is a mixed integer nonlinear least-squares problem that does not permit a closed-form solution. We now describe how Equation (11) can be solved.
# PriSign, A Privacy-Preserving Single Sign-On System for Cloud Environments
## 3 Preliminaries
### Inner-Product Functional Encryption  
An inner-product functional encryption (IPFE) [35; 36] scheme over the set of attributes \(\Sigma\) consists of the following PPT algorithms:  
* \(\mathsf{IPFE}.\mathsf{Setup}(1^{\lambda})\to(sk,pk)\): On input of a security parameter \(1^{\lambda}\), this algorithm outputs a master private key \(sk\) and a master public key \(pk\).
* \(\mathsf{IPFE}.\mathsf{Extract}(sk,\vec{v})\to dk_{\vec{v}}\): On input of a master private key \(sk\) and a characteristic vector \(\vec{v}\), this algorithm outputs a policy key \(dk_{\vec{v}}\).
* \(\mathsf{IPFE}.\mathsf{Enc}(pk,\vec{u},M)\to C\): On input of a master public key \(pk\), an attribute vector \(\vec{u}\in\Sigma\), and a message \(M\), this algorithm outputs a ciphertext \(C\).
* \(\mathsf{IPFE}.\mathsf{Dec}(dk_{\vec{v}},C)\to M/\bot\): On input of a policy key \(dk_{\vec{v}}\) and a ciphertext \(C\), this algorithm outputs the message \(M\) if \(<\vec{u},\vec{v}>=0\); otherwise, it outputs the failure symbol \(\bot\).  
An IPFE [36] scheme is secure if it is attribute-hiding; we introduce the definition of attribute-hiding in Supplemental Material A.
## 4 Experiment and Discussion
### Discussion  
#### 4.5.1 Comparison with the Existing Methods  
We compared our tool DSPUIM with existing undocumented instruction mining tools in terms of the applicable processor types of the tool. As shown in Table 5, existing undocumented instruction mining tools are only applicable to general-purpose processors such as x86, ARM, and RISC-V, while only our tool applies to DSP processors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**DSP Processor Model**} & \multirow{2}{*}{\begin{tabular}{c} **Original Instruction** \\ **Search Space** \\ \end{tabular} } & \begin{tabular}{c} **Instruction Search** \\ **Space after the** \\ **Instruction** \\ **Generation Method** \\ \end{tabular} & \
\begin{tabular}{c} **The Undefined** \\ **Instruction Space** \\ \end{tabular} &
\begin{tabular}{c} **The Undocumented** \\ **Instruction Space** \\ \end{tabular} \\ \hline TMS320C6748 & 4294967296 & 4272 & 2054 & 21 \\ \hline TMS320C6416 & 4294967296 & 4240 & 2250 & 15 \\ \hline TMS320 DM6437 & 4294967296 & 4272 & 2143 & 19 \\ \hline TMS320C6678 & 4294967296 & 1784 & 513 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics on the number of instructions at each step.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tool** & **Processor Type** & **ARM** & **RISC-V** & **x86** \\ \hline \multicolumn{4}{c}{Sandsifter [24]} & & \(\surd\) \\ \hline CPU security benchmark [25] & & & \(\surd\) \\ \hline UISfuzz [26] & & & \(\surd\) \\ \hline iScanU [47] & & \(\surd\) & \(\surd\) & \\ \hline Armshaker [48] & & \(\surd\) & \\ \hline HEAM [27] & & & \(\surd\) \\ \hline Our DSPUIM & \(\surd\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Undocumented instruction mining tools for processor types.
# Overexpression of _RrGGP2_ and _RrDHAR_ Increases Ascorbic Acid Content in Tomato
## 4 Discussion
While other genes may not be as efficient as GGP, studies show that co-transforming genes such as GME, GPP, and GLDH can significantly increase the AsA content, ultimately leading to an increase in AsA accumulation in plants. Metabolic control analysis of known kinetic parameters in _Arabidopsis_ can explain why GGP is the key gene in this conclusion, while manipulating other genes can have minimal impact on AsA content. This is due to feedback inhibition of the GGP catalytic step that provides high flow control coefficients [52; 65]. Apart from physiological and biochemical evidence, bioinformatics analysis also identified tomato GGP as a key gene for AsA biosynthesis [33; 36]. AsA accumulation in 11 wild and cultivated tomatoes and QTL analysis for ascorbic acid content in strawberry fruit reveals a complex genetic architecture and association with GDP-L-galactose phosphorylase [66; 67]. As we have speculated, GGP was recently identified once again as a key gene in two kiwifruits with distinct AsA content based on transcriptomic data [60], further solidifying its role in AsA accumulation. Why does GGP play such a crucial and distinctive role in the regulation of AsA biosynthesis and metabolism? With the rapid advancement of genome sequencing technology, we now have some answers from the perspective of gene evolution. GGP genes are present in all plants, and due to their unique whole-gene replication mode, GGP is significantly amplified in angiosperms. The majority of GGP proteins have similar catalytic functions, which can be attributed to their conserved motif arrangement and composition. This may explain why angiosperms have a higher AsA content and can adapt more readily to environmental changes [68]. Previous evolutionary analysis has shown that _R. roxburghii_ and strawberries are closely related [39], and strawberries are known for their high AsA content, which may be due to their relatively conservative motif arrangement and composition. Furthermore, research has revealed that the expression of GGP in various plants is significantly regulated by light and undergoes drastic changes under stress [19; 69]. Analysis of the GGP promoter sequence has shown that it contains numerous cis-acting elements associated with light response and stress response [70]. Additionally, the light and photosynthesis-dependent rate-limiting enzyme GGP is activated and plays a critical role in the regulation of the ascorbate pool size [71]. Recent studies on the mechanism of GGP translation regulation propose a model that allows for a feedback response to regulate AsA synthesis under adverse conditions. Under rapidly changing conditions, uORF directly regulates GGP translation without gene transcriptional modification, and this points to a more dependable way to regulate AsA concentration [72]. All the evidence mentioned above once again confirms that, from the perspective of genetic structure composition, GGP plays a key role in AsA biosynthesis.