esa-sceva/satcom-chunk-collection huggingface dataset:
# PrivacyGLUE: A Benchmark Dataset for General Language Understanding in Privacy Policies
## 2 Related Work  
NLP benchmarks have been gaining popularity in recent years because of their ability to holistically evaluate model performance over a variety of representative tasks. GLUE [5] and SuperGLUE [6] are examples of benchmarks that evaluate SOTA models on a range of natural language understanding tasks. The Generation, Evaluation and Metrics (GEM) benchmark [12] looks beyond text classification and measures performance in Natural Language Generation tasks, such as summarization and data-to-text conversion. The Cross-Lingual Transfer Evaluation of Multilingual Encoders (XTREME) [13] and Cross-Lingual Transfer Evaluation of Multilingual Encoders Revisited (XTREME-R) [14] benchmarks specialize in measuring cross-lingual transfer learning on 40-50 typologically diverse languages and corresponding tasks. Popular NLP benchmarks often host public leaderboards with SOTA scores on supported tasks, thereby encouraging the community to apply new approaches for surpassing top scores.  
While the aforementioned benchmarks focus on problem types such as natural language understanding and generation, other benchmarks focus on language domains. The LexGLUE benchmark [7] is an example of a benchmark that evaluates models on tasks from the legal language domain. LexGLUE consists of seven English language tasks that are representative of the legal language domain and chosen based on size and legal specialization. [PERSON] et al. [7] benchmarked several models, such as Bidirectional Encoder Representations from Transformers (BERT) [15] and Legal-BERT [16], where Legal-BERT has a similar architecture to BERT but was pretrained on diverse legal corpora. A key finding of LexGLUE was that Legal-BERT outperformed other models which were not  
Figure 2: Flowchart depicting our main contributions, that is, the PrivacyGLUE benchmark with its tasks and models, along with the model disagreement analysis proposed in our study.  
pretrained on legal corpora. In other words, they found that an in-domain pretrained model outperformed models that were pretrained out-of-domain.  
In the privacy language domain, we tend to find isolated datasets from specialized studies. The Refs. [4, 17, 18, 19] are examples of studies that introduce annotated corpora for a privacy-practice sequence and token classification tasks, while the Refs. [20, 21] release annotated corpora for privacy-practice question answering. [PERSON] et al. [22] is another recent study that released an annotated corpus of privacy policies. As of writing, no comprehensive NLP benchmark exists for general language understanding in privacy policies, making PrivacyGLUE the first consolidated NLP benchmark in the privacy language domain.
# Accuracy Analysis of GNSS Hourly Ultra-Rapid Orbit and Clock Products from SHAO AC of iGMAS
## Appendix A The POD Processing by Combined Serial and Parallel Threads (CSPT)
\tag{12}\]  
where \(\Delta_{\rm HF}^{\rm S}\) and \(\Delta_{\rm tr,IF}\) denote the satellite and receiver clock bias which eliminate the clock bias difference, respectively. \(\Delta_{\rm tr,IF,ref}\) is the clock bias of the reference receiver. The orbit and clock parameters are estimated through batch least square (LSQ) mode, in which linear algebra package (LAPACK) optimized function libraries are applied to decompose and inverse the normal matrix. The methods of generating prediction orbit and clock are introduced in Section 2.
## 3 Proposed Routing Algorithm
### Operation of the Radio-Aware Routing Algorithm
#### 3.2.3 The node selection algorithm
Firstly, in order to prevent a sender selecting long route or loop, RA chooses candidate nodes with shorter distance than the reference distance of the sender. Secondly, RA chooses an optimum target node among candidate nodes with highest link quality which can reflect on the current network's status.
# Smart Contract Vulnerability Detection Based on Hybrid Attention Mechanism Model
## 2 Background
### Security Vulnerability in Smart Contracts
#### 2.2.2. Arithmetic Vulnerability  
Integer overflow and integer underflow are a common type of error in many programming languages. Specifically in smart contracts, integer calculations are often closely related to the transfer of digital assets, which may cause very serious consequences. Integer overflow mainly occurs in the coin issuing contracts, and there are endless cases of attacks because of that in real contracts, which is a high-risk vulnerability. Taking Ethereum as an example, the unsigned integer in Solidity will be automatically truncated if the calculation result exceeds the range of representation during the calculation process, and the high-order numbers will be discarded, resulting in abnormal calculation results. As shown in Figure 2, the amount in line 2 is the multiplication of two unit 256 values. If a very large  
Figure 1: Reentrancy.  
value causes the calculation result to overflow and the value of amount becomes 0, then the check logic of require_value > 0&&balance [mag.sender] \(\geq\) amount) in line 3 will fail. This is because the amount should normally be a non-zero value. Attackers can exploit this vulnerability to transfer a large amount of tokens to an address with a small amount of tokens, causing serious economic losses. For the problem of integer overflow, Ethereum provides the SafeMath library. The numerical calculation operations in the library will detect integer overflow behavior and throw an exception when overflow occurs. Using the SafeMath library can effectively prevent integer overflow attacks. Nevertheless, irregular programming style and ignorance of integer overflow vulnerabilities still lead to frequent occurrence of integer overflow attacks.
# A Robust Localization System Fusion Vision-CNN
## 3 System Overview and Methodology
### Design of Convolutional Neural Network  
The PoseNet uses the SAD optimization method to optimize the Euclidean loss function. There is no problem with the position because the three position directions are independent of each other. However, for the orientation represented by quaternion \(q\), its modulus length is 1, and \(q\) and \(\
eg q\) are equivalent to represent the same rotation posture. Using the Euclidean loss function cannot guarantee that it is on the unit circle and defining the loss function in this way is over-constrained, which reduces the speed of training convergence and causes loss value fluctuations in the training process. Here we take the sum of the squares of the imaginary part of \(\hat{q}q^{-1}\) as the loss function of the orientation, which is \(\left|\hat{q}q^{-1}\right|_{x,y,z}\). In some papers, it is mentioned that adding geometric information can improve the positioning accuracy of the system. Here, the laser measurement data and a priori map are used as environmental constraints. For each laser measurement point, the predicted pose \((\hat{p},\hat{q})\) can be projected into the prior grid map and the grid coordinates can be calculated. The distance from the grid to the obstacle grid cell is defined as the loss function value, which is added to the loss function of the neural network, and the overall loss function of the neural network is:  
\[loss(I)=\alpha|\hat{p}-p|^{2}+\beta|\hat{q}q^{-1}|_{x,y,z}^{2}+\gamma\sum_{z} |dis(z,\hat{p},\hat{q},m)|^{2} \tag{1}\]  
where \(\alpha,\beta\) are scale factors used to adjust the displacement error, direction error, and geometric error approximately equal at the time of final convergence. The laser measurement point is \(z\) and \(m\) is the prior grid map. The prior map acquisition and the calculation of the map grid coordinates from the laser measurement points will be introduced below. The overall vision-based neural network structure is shown in Figure 2.  
Figure 1: System framework.  
The size of the weight file of the network is approximately 50 MB, which is nearly the same as the weight file of the PoseNet network. Since the environmental constraints are only added when training the network and not when predicting, adding geometric constraints will not affect the computational efficiency of the system. The system takes approximately 5 ms to process an image for pose prediction. In the following, the vision-based relocalization estimation is used as a rough estimate of the pose combined with laser measurement data to obtain a high-precision pose estimation.
## 1 Introduction
In response to the problems of low data quality and low sample utilization in the experience replay mechanism of previous algorithms, and in combination with the needs of autonomous driving, this paper designs dual prioritized experience replay, DPER, and uses it as the basis for sample classification. It gives different priorities to each experience through dual-layer selection, and based on this, proposes a method based on priority experience replay, DPER. The main contributions of this paper include the following:  
This study presents a streamlined decision-making framework for autonomous vehicles, utilizing a dual-network architecture that separates ego-vehicle and environmental vehicle state processing. The design decouples action selection from evaluation through distinct parameter updating protocols, achieving precise motion control while enhancing system stability. Experimental results confirm its effectiveness in reducing Q-value overestimation and maintaining operational consistency in dynamic traffic scenarios. The dueling network is incorporated into the double deep Q-network, merging their respective benefits and reconfiguring the original network's output layer. This integration enhances the driverless car's capacity to comprehend the current state and refines the precision of action value estimation. By separately assessing the state value and action advantage, the dueling network boosts the accuracy of these estimations. The double deep Q-network further reduces the overestimation bias, making the decision more robust. This structure improves the performance of traditional algorithms at multiple levels. A new dual-priority experience replay mechanism is proposed. This paper introduces an innovative dual-priority experience replay mechanism that combines TD error priority and segmented sampling priority. The mechanism significantly improves the learning frequency and quality of key states by giving higher priority to recent experience on TD error and dynamically adjusting the state importance on segmented sampling. A comprehensive priority--that is, dual priority--is calculated based on TD error and segmented sampling. Dual-priority experience replay effectively solves the problems of low sample utilization and sparse rewards in traditional experience replay and can more effectively utilize experience replay data in unmanned highway scenarios, especially when dealing with rare but important scenarios (such as high-speed lane changes and emergency braking), which can help the DPD3 QN better learn these key decisions. The remainder of this paper is organized as follows. Section 2 introduces related work in the field of deep reinforcement learning and autonomous driving. Section 3 presents the proposed DPD3 QN method, including the network structure and dual-priority experience replay mechanism. Section 4 describes the simulation environment and experimental setup. Section 5 provides the simulation results and comparative performance analysis. Finally, Section 6 concludes this study and outlines future research directions.
# Compact Model for L-Shaped Tunnel Field-Effect Transistor Including the 2D Region
## 1 Introduction  
With the power requirements of complementary metal-oxide-semiconductor (CMOS) technology surging beyond unreasonable levels to meet the high computing demands of today's world, there has been a desperate push for devices that can perform better for less power [1]. The tunnel field-effect transistor (TFET) is one such device among the potential candidate devices [2]. TFET works on the principle of band-to-band-tunneling (BTBT) and achieves a steeper subthreshold slope (SS) than a metal-oxide-semiconductor field-effect transistor (MOSFET) of equivalent dimensions and electrical parameters.  
However, the on-current (\(I_{\text{ON}}\)) of TFET is lower than that of MOSFET of equivalent dimensions/electrical parameters. To overcome this problem, different types of TFET architectures have been suggested, including the line-tunneling type [3] TFETs. The structure of line tunneling type TFETs has a gate-source overlap. This overlap increases the BTBT area and consequently increases the \(I_{\text{ON}}\). The L-shaped TFET (LTFET) [4] is an example of a line-tunneling TFET that features the channel region growth vertically in the form of an L-shape. The LTFET offers the same benefits as a conventionalline-tunneling type TFET, but with a much lower device footprint. More significantly, LTFET is the only example of a line-tunneling TFET that has been experimentally realized.  
Compact modeling is an important part of the circuit design process. While there are compact models available for the conventional TFET [5; 6; 7], the literature is almost completely lacking in compact models for the line-tunneling type TFETs. [PERSON] et al. [3] developed a compact model for conventional line-tunneling TFET. [PERSON] et al. [8] developed a compact model for LTFET. However, the line-tunneling TFET considered in [3] features a significant difference from the LTFET: The gate directly overlaps with the source region, as shown in Figure 1 in [3], whereas in the LTFET, there is a channel region present between the source and the gate. Direct overlapping of gate/source without any channel layer present in between completely changes the electrostatics of the device and makes the model presented in [3] inapplicable to LTFET. In [8], only the one-dimensional (1D) model is presented. In LTFET, both 1D and 2D BTBT components are present.  
This paper presents a complete model of the LTFET, including both the 2D and 1D BTBT, which is presented in Section 2. The model presented is continuous from the subthreshold region to strong inversion. The model is tested for LTFETs with varying geometries, and the results are presented in Section 3. A conclusion is presented in Section 4.
## 4 Stability Analysis  
According to Property 2 as described previously, the following inequality can be easily obtained:  
\[\ddot{W}_{1}\left[\text{Proj}_{\hat{W}_{1}}\left(\gamma_{1}\tilde{e}_{1} \tilde{W}_{1}^{T}B_{1}\left(X\right)-m_{1}\hat{W}_{1}\right)-\gamma_{1}\tilde {e}_{1}\tilde{W}_{1}^{T}B_{1}\left(X\right)+m_{1}\hat{W}_{1}\right] \leq 0 \tag{54}\] \[\ddot{W}_{2}\left[\text{Proj}_{\hat{W}_{2}}\left(\gamma_{2}S_{q} \tilde{W}_{2}^{T}B_{2}\left(X\right)-m_{2}\hat{W}_{2}\right)-\gamma_{2}S_{q} \tilde{W}_{2}^{T}B_{2}\left(X\right)+m_{2}\hat{W}_{2}\right] \leq 0\] \[\ddot{W}_{3}\left[\text{Proj}_{\hat{W}_{3}}\left(\gamma_{3}S_{d} \tilde{W}_{3}^{T}B_{3}\left(X\right)-m_{3}\hat{W}_{3}\right)-\gamma_{3}S_{d} \tilde{W}_{3}^{T}B_{3}\left(X\right)+m_{3}\hat{W}_{3}\right] \leq 0\]  
\[\ddot{F}\left[\text{Proj}_{\hat{F}}\left(\gamma_{2}\tilde{e}_{1}-m_{4}\hat{F} \right)-\gamma_{4}\tilde{e}_{1}+m_{4}\hat{F}\right]\leq 0 \tag{55}\]  
Then, substituting (53)-(56) into (52), we get  
\[\dot{V}\leq-k_{1}\tilde{e}_{1}^{2}-k_{2}S_{q}^{2}-k_{3}S_{d}^{2}-k_{d}\left|S_ {d}\right|-k_{q}\left|S_{q}\right|+\frac{1}{2}\sum_{i=1}^{3}\varsigma_{i}^{2}- \sum_{i=1}^{3}\left(\frac{m_{i}\tilde{W_{i}}^{T}\hat{W}_{i}}{\gamma_{i}}\right) -\frac{m_{4}\hat{F}\hat{F}}{\gamma_{4}} \tag{56}\]
## 3 Results  
Following the numerical analysis of evaluating the thermal behavior of the considered battery system in transient mode (using the MATLAB/Simulink 1D simulation model), the results are presented in graphical form in Figure 12, Figure 13, and Figure 14. Additionally, the performance of a non-sectioned model is also presented for comparison. In this case, the entire surface of a battery cell is considered as one and there is no conduction inside the cells (as shown in Figure 10).  
It is observed that using the initially estimated air flow, it is not possible to keep the temperature in the cells' core below the desired value of 50 \({}^{\circ}\)C, with the maximum temperature in the last module reaching 51.7 \({}^{\circ}\)C, according to Figure 12. In comparison, when using the non-sectioned model, the maximum temperature is 53.3 \({}^{\circ}\)C.  
Due to the high temperatures, the re-evaluation of heat transfer coefficients obtained by CFD simulations is necessary. The air flow is increased by 50% and 100%, representing 945 and 1260 m\({}^{3}\)/h, and the simulations are run again for all the analyzed heat generation rates. The results illustrated in Figure 13 represent the values obtained for the reference heat generation of 1 W/cell.  
Using these 3D heat transfer maps (h = f(Qcell, Vair)), the maximum temperature in the analyzed battery segment, the maximum temperature difference in the segment and the maximum temperature difference in a single accumulator are evaluated at the end of the endurance race, and the results are illustrated in Figure 14.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Density [kg/m\({}^{3}\)] & 2817 \\ Core heat capacity [J/K] & 50 \\ Surface heat capacity [J/K] & 3.25 \\ Radial thermal resistance [K/W] & 30.8 \\ Axial thermal resistance [K/W] & 10.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Battery cell characteristics.
_applied_  
_sciences_  
_Article_  
**The Effect of Contrast Agents on Dose Calculations of Volumetric Modulated Arc Radiotherapy Plans for Critical Structures**  
**[PERSON] 1,2, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 3, [PERSON] 1, [PERSON] 4, and [PERSON] 1,*  
1 King Fahad Medical City, Riyadh 12231, Saudi Arabia; [PERSON]DDRESS] (A.-A.E.); [PERSON]RESS] (S.A.); [EMAIL_ADDRESS] (R.A.); [PERSON]ESS] (H.A.); [PERSON]ADDRESS] (H.A.)
2 Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt
3 National Cancer Institute, Cairo University, Cairo 11796, Egypt; [PERSON]ADDRESS]
4 Department of Medical Physics & Biomedical Engineering, University College London, London WC1E 6 BT, UK; [EMAIL_ADDRESS]
*Correspondence: [PERSON]ESS]  
**Featured Application: This article investigates contrast-enhanced computed tomography imaging in radiotherapy, highlighting the impact of contrast materials on CT numbers and dose calculations in comparison to non-contrast CT imaging, justifying the use of contrast-enhanced CT imaging to improve utilization and efficiency in radiotherapy simulation.**  
**Abstract: Radiotherapy dose calculation requires accurate Computed Tomography (CT) imaging while tissue delineation may necessitate the use of contrast agents (CA). Acquiring these two sets is a common practice in radiotherapy. This study aims to evaluate the effect of CA on the dose calculations. Two hundred and twenty-six volumetric modulated arc therapy (VMAT) patients that had planning CT with contrast (CCT) and non-contrast CT (NCCT) of different cancer sites (e.g., brain, head, and neck (H&N), chest, abdomen, and pelvis) were evaluated. Treatment plans were recalculated using CCT, then compared to NCCT. The variation in Hounsfield units (HU) and dose distributions for critical structures and target volumes were analyzed using mean HU, mean and maximum relative dose values, D2%, D8%, and 3D gamma analysis. HU variations were statistically significant for most structures. However, this was not clinically significant as the difference in mean HU values was within 30 HU for soft tissue and 50 HU for lungs. Variation in target volumes' D2% and D8% were insignificant for all sites except brain and nasopharynx. Dose maximum differences were within 2% for the majority of critical structures and target volumes. 3D gamma analysis results revealed that majority of plans satisfied the 2% and 2 mm criteria. CCT may be acquired for VMAT radiotherapy planning purposes instead of NCCT, since there is no clinically significant difference in dose calculations based on either image set.**  
**Keywords: contrast agent; radiotherapy; CT simulation; dose calculation; VMAT**
## 2 Materials and Methods
### Modelling Scheme  
#### 2.2.1 Pre-Modelling  
The dataset for the study as documented in [PERSON] and Atzberger [22], was realized from the cooperation between BOKU and the NDMA. The data is used in an operational setting at the NDMA with statistics on drought generated monthly with an option for on-demand processing. Figure 3: Average Normalized Difference Vegetation Index (NDVI) (2003–2015) across months by county based on the National Drought Management Authority (NDMA) operational Early Warning System (EWS). Figure 2: Study area: Mandera, Marsabit, Turkana and Wajir counties (right) in Kenya. The map of Kenya groups its counties into arid and semi-arid lands (ASAL) and non-ASAL. The variables used in this predictive study (Table 1) comprises both precipitation and vegetation indices. Moderate resolution imaging spectroradiometer (MODIS) at 250 m ground resolution is the source of the vegetation data, while Tropical Applications of Meteorology using SATellite (TAMSAT)'s version 3 product [38] is the source of the precipitation data. Both the precipitation and vegetation data are directly provided by BOKU [22]. BOKU does only post-processing (for example spatial sub-setting and temporal aggregation) on the TAMSAT data. The vegetation datasets were smoothed using a modified Whittaker smoothing algorithm. For operational monitoring, the indicators and indices calculated at pixel level are then aggregated over the administrative units of interest at either 1 or 3-month time steps. In addition, uncertainty modeling is provided at each time step for each pixel or the MODIS derived vegetation data. The details and formulae for the computation of these indices are as provided in Table 1. The precipitation includes RFE, RCI and SPI. Vegetation conditions are characterized through NDVI and VCI. [PERSON] [22] and [PERSON] et al. [40] document the use of the VCI as a temporal and spatially aggregated anomaly of the NDVI. While the NDVI gives absolute vegetation status for a given spatial extent at a given time, the VCI scales the actual NDVI value in the range between a historical minimum (VCI = 0%) and maximum (VCI = 100%) for a given time unit. Widely used time units are dekads (10-day periods), months and seasons (3 months). The choice of and the definition of drought conditions using the VCI3M is two-fold. First, the VCI as a variable is appropriate for monitoring vegetation conditions. The northern parts of Kenya are mainly pastoral lands, therefore pasture conditions and their monitoring remains key. Second, the VCI is a direct measurement, as opposed to say RFE, which is a modelled output. Several studies have evaluated the BOKU dataset including comparisons against similar products [40; 41].