esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Lattices  
Based on [6], the definitions are shown as following:  
**Definition 1**.: _Let \(\mathbb{R}^{m}\) be the \(\mathfrak{m}\)-dimensional Euclidean space. A lattice \(\Lambda\subseteq\mathbb{R}^{m}\) is a set_  
\[\Lambda=\{\sum_{i=1}^{k}c_{i}\mathbf{b}_{i}|ci\ \in\mathbb{Z}\ \text{and}\ \mathbf{b_{1}},\ \ldots,\ \mathbf{b_{k}}\in\mathbb{R}^{m}\}\]  
_of all integral combinations of \(k\) linearly independent vectors \(\mathbf{b_{1}},\ldots,\mathbf{b_{k}}\) in \(\mathbb{R}^{m}\ (m>k)\). The integers \(k\) and \(\mathfrak{m}\) are called the rank and dimension of the lattice, respectively. The vector set \(\{\mathbf{b_{1}},\ldots,\ \mathbf{b_{k}}\}\) is called a basis of the lattice._  
**Definition 2**.: _Let \(\mathbf{A}\ (\mathbf{A}\in\mathbb{Z}^{m\times n})\) be a basis. The integers \(m\) and \(n\) are called the dimension and rank; \(\mathbf{y}\) and \(\mathbf{t}\) are column vectors; and \(q\) is a prime number._  
\[\Lambda(\mathbf{A})=\{\mathbf{y}\in\mathbb{Z}^{m}|\ \mathbf{y}=\mathbf{A}\mathbf{t}\ \text{mod}\ q,\mathbf{t}\in \mathbb{Z}^{n}\}\]  
\[\Lambda^{\perp}(\mathbf{A})=\{\mathbf{y}\in\mathbb{Z}^{m}|\ \mathbf{A}^{\intercal}\mathbf{y}=0\ \text{mod}\ q\}\]  
**Definition 3** (TrapGen(1\({}^{\sim}\)) [7, 8]).: _There is a probabilistic polynomial-time (PPT) algorithm that, on input of a security parameter 1\({}^{\sim}\), an odd prime \(q=\text{poly}(\sim)\), and two integers \(n=\Theta(\sim)\) and \(m\geq 6n\text{log}q\), outputs a matrix \(\mathbf{A}\in\mathbb{Z}_{q}^{n\times m}\) statistically close to uniform, and a basis \(\mathbf{T_{A}}\) for \(\mathbf{\Lambda}^{\perp}(\mathbf{A})\) with overwhelming probability such that \(\parallel\widetilde{\mathbf{T_{A}}}\parallel\leq\widetilde{\Theta}\big{(}\sqrt{m} \big{)}\)._  
**Definition 4** (The Shortest Vector Problem (SVP) [3]).: _Given a base \(\mathbf{B}\) (a set of vectors linearly independent) of a lattice \(L\), find the smallest possible nonzero vector of \(L\)._  
**Definition 5** (The Closest Vector Problem (CVP) [3]).: _Given a base \(\mathbf{B}\) of a lattice \(L\) and a vector \(\
otin L\), find a vector \(v\in L\) that is the closest to._
###### Abstract  
Current automatic exploit generation solutions generally adopt an 1-step exploit generation philosophy and neglect the potential difference between analysis-time environment and runtime environment. Therefore, they usually fail in evaluating exploitability for vulnerable programs running in an ASLR environment. We propose ExpGen, a 2-step vulnerability-exploitability evaluation solution for binary programs running in an ASLR environment, with three novel techniques introduced, separately partial-exploit sensitive-POC generation, exploitation context sensitive analysis-time exploit generation, and runtime exploit relocation. ExpGen firstly generates an analysis-time exploit that can carry out all the desired exploitation steps through applying the first two techniques in an iterative manner, then dynamically gaps the address-space layout differences between the analysis-time environment and runtime environment by adopting the runtime exploit-relocation technique, making the analysis-time exploit dynamically adaptable to the runtime exploitation session. Using a benchmark containing six test programs, 10 CTF&RHG programs and four real-world applications with known vulnerabilities, we demonstrate that ExpGen can effectively generate partial exploit input that carries out some address-leakage event and provide a complete automated exploitability evaluation workflow on vulnerable programs running in the ASLR environment.  
partial exploit; exploit generation; address leakage; runtime exploit relocation Article
## 3 Application Instance
### Data Collection  
The data used in this study were obtained from the 24-channel acoustic emission monitoring system at the Panlong lead-zinc mine in Guangxi. A significant amount of acoustic emission signals was collected from routine monitoring conducted between June and October 2022. Invalid signals and those with excessive repetition were excluded, resulting in a selection of 35,000 original acoustic emission signals categorized into seven types. Each type comprises approximately 5000 signals and includes signals from BO (blasting operation sound emission signals), SO (shovel operation sound emission signals), RD (rock drilling operation sound emission signals), MI (mechanical interference sound emission signals), OD (ore drawing operations sound emission signals), EI (electromagnetic interference sound emission signals), and RS (rock surrounding sound emission signals).  
The typical signal waveforms are illustrated in Figure 5; however, some signals were not fully recorded because their length exceeded the sampling length of the monitoring system. Furthermore, each type of signal has its own characteristics and patterns. Among them, the frequency of SO signals is the lowest, and they exhibit strong persistence. This is because shovel operations are typically carried out automatically by machinery and proceed relatively slowly and continuously. This simplicity makes the identification of shovel operation signals relatively straightforward. On the other hand, BO signals are more complex, often containing both high and low frequency components simultaneously. Additionally, their frequency and amplitude may exceed the threshold of the monitoring system, resulting in a slight distortion of the monitored signals. However, the higher amplitude is also beneficial for identification purposes. The patterns of EI signals are the least obvious, exhibiting greater randomness.  
Figure 4: Schematic diagram of ground pressure monitoring system.
## 4 Conclusions  
A simplified packed bed dielectric barrier discharge reactor is used to investigate the discharge currents, discharge images, and the reactive species (such as N\({}_{2}\)+ (B\({}^{2}\)X\({}_{\text{u}}\)+) and N\({}_{2}\) (C\({}^{3}\Pi\)\({}_{\text{u}}\))) under different gas compositions, pulse durations, and rising and falling times of the voltage pulse, experimentally. The parameters of voltage pulse have obvious regulations on the discharge currents. The discharge duration and the peak current of the discharge increase with the pulse duration, and the start time of the FP of the discharge current is delayed with increasing the rising time of the voltage pulse. There are bright discharge channels on the top of the reactor, while the discharge on the bottom of the reactor is diffuse, which affects the spatial distributions of N\({}_{2}\)+ (B\({}^{2}\)X\({}_{\text{u}}\)+) and N\({}_{2}\) (C\({}^{3}\Pi\)\({}_{\text{u}}\)). The generation of N\({}_{2}\)+ (B\({}^{2}\)X\({}_{\text{u}}\)+) has a strong connection with the existence of the streamer channel, and N\({}_{2}\) (C\({}^{3}\Pi\)\({}_{\text{u}}\)) can be produced in the entire discharge area, which can be proven by capturing the light emission from N\({}_{2}\) (C\({}^{3}\Pi\)\({}_{\text{u}}\)\(\rightarrow\) B\({}^{3}\Pi\)\({}_{\text{g}}\)) from the discharge area. Hence, the trends of N\({}_{2}\)+ (B\({}^{2}\)X\({}_{\text{u}}\)+) and N\({}_{2}\) (C\({}^{3}\Pi\)\({}_{\text{u}}\)) with pulse duration and pulse rising and falling time can reflect the changes of streamer channel distributions and discharge current, respectively. The reactor operated under pure nitrogen has a longer discharge duration and larger discharge area and discharge current, and as a result, the existence time of N\({}_{2}\)+ (B\({}^{2}\)X\({}_{\text{u}}\)+) and N\({}_{2}\) (C\({}^{3}\Pi\)\({}_{\text{u}}\)) are longer and there are more than those in air. In general, this simple model can reflect the discharge characteristics in a packed bed reactor well, and the gas composition and electric parameters play important roles in regulating the discharge and reactive species in a packed bed reactor, which can maximize the role of plasma in the application process.  
Writing--original draft preparation, Y.L.; Writing--review and editing, L.Q., L.Z., W.W. and D.Y.; Resources, D.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 15: Time evolution of the emission intensity of SPS of N\({}_{2}\) under different pulse rising and falling times in (**a**) air and (**b**) pure nitrogen.  
**Funding:** This research was funded by the National Natural Science Foundation of China (52077026 and 51977023), the Fundamental Research Funds for the Central Universities (DUT21 LK31), the Key Laboratory Fund of National Defense Science and Technology (6142605200303), and the Science and Technology Plan Project of the Ninth Division of the Crops (2021 JS003).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Efficient Semantic Segmentation Using Multi-Path Decoder
## 2 Related Works  
Image classification networks are used as the backbone in semantic segmentation task for extracting rich features. However, for yielding accurate segmentation, semantic segmentation models need semantic information and spatial information that backbone trained for classification cannot afford. Meanwhile, to segment objects with different scales and classify ambiguous pixels, multi scale features are needed. To handle these problems, multiple modifications are made for pixel-level prediction. Most modifications focus on how to get multi-scale context information, which can be summarized to two kinds.  
The first one is the modifications made for feature maps of same size but different receptive fields. Generally, models use feature maps with different receptive field sizes to represent different context information and then connect these feature maps to yield new feature maps which encode multi-scale context information. Many schemes are designed for combining feature maps of different receptive fields. Generally, many works first feed the last convolutional feature map of the backbone into a module that concatenates feature maps with different receptive fields, such as SPP(Spatial Pyramid Pooling) [14] or upgraded SPP, and then feed the feature maps output by this kind of module into the pixel-wise classifier [15]. DeepLab [16] use atrous convolution with different dilation rates to represent feature maps with different receptive fields. Combined with atrous convolution, DeepLab develops SPP to Atrous Spatial Pyramid Pooling (ASPP) module. By using different neurons to represent sub-regions with different sizes, pyramid scene parsing network (PSPNet) develops SPP to Pyramid Pooling module.  
The second one is modifications made for feature maps of different size. After copious operations of convolution with stride and pooling, the spatial size of feature maps put out by the backbone is very small compared to the original image and the receptive field of the neurons in the last layer is larger than the neurons in the shallower layers. By the same token, the neurons in the shallower layers have smaller receptive fields and the shallower layers encode less semantic information and more spatial information. This kind of models want to use feature maps in the shallower layers with high resolution to compensate the low resolution of high-level features and yield accurate segmentation results. To encode multi-scale context information, the collection of feature maps come very naturally. Specifically, when got feature maps from each layer, a \"U-shape\" architecture is built and feature maps from deep layer to shallow layer are gradually fused [17; 18; 19; 20].  
Besides, many works combine these two kind of modifications. Ref. [21] uses an \"U-shape\" architecture equipped with an atrous spatial pyramid pooling(ASPP) module. UPerNet [22] combines Feature Pyramid Network(FPN) with a pyramid pooling module (PPM) from PSPNet. Our proposed MPDNet also combines these two kind of modifications.
## 2 Materials and Methods
### Lead Ion Sorption Tests  
Pb(NO\({}_{3}\))\({}_{2}\) (99.5%) was purchased from [PERSON] (Milan, Italy) and the solutions prepared in distilled water (pH = 6). Perlite beads (1-2 mm) were packed into columns with diameters of 1 cm and heights of 50 cm. They were then eluted with lead ion solutions. Breakthrough curves were obtained from thermodynamic study. These curves represented the relative lead ion concentration (C/C\({}_{0}\)) vs. bed volumes (BV = V/V\({}_{0}\)). When the liquid-phase solution started to be leached by the column, the breakpoint was reached (i.e., the minimum lead ion retention capacity of the sorbent occurred and the final exhaustion of the bed was obtained, when the concentration of the effluent was approximately the same as that of the influent). The first set of experiments was carried out with 2-4 g of perlite at 0.3 L\(\cdot\)h\({}^{-1}\) and with 2 mg of Pb\({}^{2+}\) L\({}^{-1}\). Successively, experiments were carried-out at different influent concentrations (2-10 mg of Pb\({}^{2+}\) L\({}^{-1}\)), at 0.3 L\(\cdot\)h\({}^{-1}\), with 4 g of perlite. Finally, the process was evaluated at variable flow-rates (0.15-0.4 L\(\cdot\)h\({}^{-1}\)), with 2 mg of Pb\({}^{2+}\) L\({}^{-1}\) and 4 g of perlite. Table 1 shows all the experiments performed.  
Time-dependent breakthrough curves (kinetic study) were also reported. The sorption kinetics are dependent on the mass transfer phenomena [41]. Specifically, film diffusion control is the rate determining step, because functionalities (silico-aluminate groups) are essentially present on the surface of perlite. The half time of the reaction (t\({}_{0.5}\)) was obtained as follows [41]:  
\[\text{t}_{0.5}=1.33~{}\text{r}_{0}~{}\delta~{}\text{D}^{-1}~{}\text{C}_{0}{}^{ -1}~{}\text{S}^{-1} \tag{1}\]  
where t\({}_{0.5}\) is inversely proportional to the bulk specie concentration (C\({}_{0}\)) and diffusion coefficient (D), and is directly proportional to the particle radius (r\({}_{0}\)) and film thickness (\(\delta\)).
## 3 Method
### Intent Detection and Slot Filling
#### 3.2.1 Intent Information Adapter  
In order to carry out the task of intent detection in Chinese medical dialogues, this paper designs an IIA that, through the gate mechanism, ignores the unnecessary features in the sentence and focuses on the important information, which will improve the accuracy of the patient's intent detection. As shown in Figure 4. Firstly, the sentence representations of character information and word information are taken as inputs and both of them are spliced together, and through the gating mechanism, a common gating value is computed, then the important information of the sentence representations of both of them is obtained separately through the gating value, and finally, the fusion interaction is carried out to obtain the correct intent.  
Given two outputs \(S^{c}\) and \(S^{w}\) after the MLP attention Formula (3), \(S^{c}\) and \(S^{w}\) are the sentence representations of character information and word information on character and word sequences, respectively. Then, this paper employs an IIA to merge the character-word information with Equation (11). The gating mechanism, which ignores unnecessary features in a sentence and focuses on important information, helps the model to reduce the impact of word polysemy on intent judgment. The formula is as follows:  
\[\Pi\text{A}(C,W)=G((1-\lambda)\cdot C+W) \tag{11}\]  
\[\lambda=\textit{sigmoid}(C^{T}N_{f}W) \tag{12}\]  
where \(N_{f}\in R^{d\times d}\) is a trainable parameter and \(\lambda\) is an adaptively adjustable importance between character and word information.  
Finally, in this paper, the intention label of \(O^{t}\) is predicted using the summary vector \(V^{I}\), the probability \(P\) is obtained by _softmax_ function, which is given by (13):  
\[V^{I}=\Pi\text{A}(S^{c},S^{w}) \tag{13}\]  
\[P\left(\tilde{g}=j\mid V^{I}\right)\ =softmax\left(W^{I}V^{I}+b^{I}\right) \tag{14}\]
## 5 Application of Generative AI Models for Context-Aware Vulnerability Scoring
### Comparative Analysis of LLM Results
#### 5.4.2 Differences  
* Attack Vector Handling: ChatGPT treated ADJACENT as a separate class, leading to poor performance (28.57% precision, 40.00% recall) due to its low representation. DeepSeek and Gemini merged ADJACENT into the NETWORK class, which significantly boosted their NETWORK class performance (DeepSeek: 98.15% precision, 100.00% recall for NETWORK; Gemini: 97.14% precision, 98.08% recall for NETWORK). This methodological difference directly impacted reported metrics for the attack vector attribute.
* Overall Performance on Multi-Class Attributes: DeepSeek generally demonstrated stronger macro-average F1 scores for the three-class attributes (Privilege Required: 80.47%; Confidentiality: 74.42%; Integrity: 72.19%; Availability: 63.85%) compared to ChatGPT (Privilege Required: 57.44%; Confidentiality: 56.57%; Integrity: 56.33%; Availability: 49.69%) and Gemini (Privilege Required: 74.04%; Confidentiality: 76.31%; Integrity: 69.37%; Availability: 63.30%). DeepSeek's structured question-based approach appears to yield more balanced performance across these classes, especially for \"HIGH\" values, compared to ChatGPT's rule-based heuristics, which struggled significantly with minority classes. Gemini showed competitive performance with DeepSeek on Confidentiality and Availability but slightly lower on Privilege Required and Integrity.
* Recall for \"REQUIRED\" User Interaction: DeepSeek exhibited a slightly better recall for the \"REQUIRED\" user interaction class (44.44%) compared to ChatGPT (40.00%) and Gemini (43.75%), although all models still showed a \"critical gap\" in detecting these interactions due to imbalance.
* Approach to Ambiguity: Gemini's \"worst-case impact principle\" is a distinct feature, aiming to assign values based on maximum potential risk in ambiguous cases. While the direct impact of this principle on specific metrics is not explicitly quantified as a separate variable in the provided data, it represents a notable difference in its underlying decision-making logic compared to the other models.
## 4 Experimental Validations for Ship Stiffened Panel Strain Reconstruction
### Results and Discussion  
In Case 3, the strain across the monitored area of the plate surface exhibited an asymmetrical pattern. Notably, the amplitude of X-directional strain within the region linking the fixed end and the 2\(\#\) transverse T-section appeared to be more pronounced. This discrepancy arose from the variation in load amplitudes between Load D and Load F. This observation aligned with the strain response trend correlated with the simulated deformations of bending and torsion, as depicted in Figure 19a.  
In Case 4, owing to the fixed configuration of both ends of the panel by the platform and the application of load along the \(Z\)-axis, tensile strain was observed near the fixed end, while compressive strain prevailed in other areas. Furthermore, the overall strain distribution across the monitoring area exhibited an asymmetric pattern, stemming from the discrepancy in load amplitudes between Load C and Load E, as illustrated in Figure 19b.  
Figure 18: (**a**) The layout of fiber optic sensors located inside the stiffened ship panel; (**b**) the integration of optical fiber sensors.  
Based on the positioning of the reconstructed strain verification path depicted in Figure 16b, the data obtained from the OFDR sensor affixed to the outer side of the ship stiffened panel were employed as the reference strain for assessing the efficacy of the proposed method in strain reconstruction. Figure 20 illustrated the comparative analysis of strain reconstruction outcomes for Path 4, Path 5, and Path 6 in Case 3. The average relative error in strain reconstruction was determined to be 4.45%, with a root mean square error of 2.73 \(\mu\epsilon\).  
Figure 21 presented a comparison of the strain reconstruction effects for Path 4, Path 5, and Path 6 in Case 4. The average relative error in strain reconstruction was determined to be 4.87%, with a root mean square error of 1.85 \(\mu\epsilon\).  
Figure 20: Reconstruction of the strain on three paths in Case 3: (**a**) comparison; (**b**) relative error.  
Figure 21: Reconstruction of the strain on three paths in Case 4: (**a**) comparison; (**b**) relative error.  
application of the equivalent neutral layer calculation method. This configuration markedly simplified the complexity of sensor placement in ship health monitoring within the iFEM, ensuring effective reconstruction of structural strain.
# Optimal Base Station Density of Dense Network:
## 2 System Model  
We consider a two-tier heterogeneous networks consisting of the macro base stations (MaBSs) tier (e.g., cellular network) and the small base stations (SmBSs) tier (e.g., WLAN). MaBSs operate at licensed spectrum while SmBSs work in unlicensed bands. BSs at the same tier share the channel according to the corresponding medium access control (MAC) protocol. We denote the transmit powers of the MaBS (SmBS) as \(P_{\text{M}}\left(P_{\text{m}}\right)\) and the path loss exponent as \(\alpha\). All BSs are open access.
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 2 Background
### Open Challenges in Smart Contract Error Reporting  
Producing intelligible error messages is still an unsolved problem in Web3. The following intertwined factors make the task especially challenging:  
* **Heterogeneity of Error Messages:** Solidity compiles errors into different ABI payload patterns: (1) the standard Error(string) payload, emitted by both require and revert, (2) the Panic(uint256) payload produced by assert, (3) developer-defined custom error selectors with typed arguments, and (4) an empty payload by revert. An empirical study of 3866 verified contracts found that all the forms appear in the real-world smart contract code [9].
* **Gas-Cost Trade-Offs:** Developers often shorten or omit error messages to reduce deployment and runtime gas. Replacing a literal message, such as \"Unauthorized\", with a four-byte custom error saves 200-400 gas per call at the expense of human-readability.
* **Limited End-User Comprehension:** Crypto-wallet user interfaces (UIs) expose little contextual information. Thus, users often misjudge the transaction risk [2]. The same UI limitations affect authentication flows: users routinely sign opaque wallet prompts without grasping the associated danger [10].
## 1 Introduction
It is yet unknown whether 5G NR grant-free scheduling with \(K\)-repetitions and shared resources can satisfy critical applications and guarantee very high reliability levels with very low latency. In this context, this study presents an in-depth analysis of the reliability and latency levels that can be achieved with existing 5G NR grant-free scheduling solutions as a function of the number of UEs, the number of reserved radio resources, and the number of replicas \(K\). To this aim, the study analytically quantifies the probability of successfully delivering a packet when using grant-free scheduling with \(K\)-repetitions and shared resources. In addition, the study analyzes the impact of self-collisions. Self-collisions occur when a UE has to transmit a new packet, and the transmission of the \(K\) replicas of the previous packet has not finished. If this happens, the new packet must be stored, and its transmission is delayed until all replicas of the previous packet have been transmitted. This study demonstrates for the first time that self-collisions have a non-negligible impact upon the capacity of 5G NR grant-free scheduling to support stringent URLLC reliability and latency levels.