esa-sceva/satcom-chunk-collection huggingface dataset:
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 6 Discussion
### Root Causes of DoS Vulnerabilities  
Across all attack vectors, several core root causes emerged:  
* Insecure configuration access: Many attacks were made possible through access to the configuration channels or admin privileges (e.g., Raft settings, batch timeouts). Default settings often lack constraints against extreme or malicious values.
* Over-reliance on trusted components: The network assumes correct behavior from orderers, peers, and MSPs. When compromised, these components can cause cascading failures due to the absence of effective fault tolerance mechanisms.
* Architectural dependencies: CouchDB, an external component not governed by consensus, represents a critical weak point. Its failure affects not just data access but also overall peer functionality.
* Lack of resilience mechanisms: There is minimal redundancy, anomaly detection, or automatic recovery built into the current default deployment of HLF, making it vulnerable to performance exhaustion and DoS propagation.
# E-SAWM: A Semantic Analysis-Based ODF Watermarking Algorithm for Edge Cloud Scenarios
## 4 Experiments
### Steganography  
One of the fundamental requirements of an invisible watermark is its imperceptibility. The embedded watermark in the OFD document must remain completely hidden, ensuring that no noticeable alterations are made to the visible display interface of the document. Moreover, users should be unable to detect the presence of the watermark, making it challenging for attackers to identify its location or develop cracking methods.  
The most common watermarking algorithm employed in the industry is categorized as an explicit watermarking algorithm, relying on annotated files. When a highly transparent watermark is added, it becomes difficult to visually discern the watermark with the naked eye. Nevertheless, it is still possible to identify the watermark by converting the OFD page into an image and adjusting the image's contrast. In contrast, a low-transparency watermark is clearly visible to the naked eye. To the best of our knowledge, there is no existing research on watermarking of OFD files. While some studies have focused on watermarking PDF files using syntax- and format-based algorithms, these approaches tend to alter the original text content, which may not comply with the originality requirements for digital products.  
We propose an experiment to test the steganographic potential of E-SAWM. Figure 10 shows the comparison effect of OFD document watermarking. The two documents appear indistinguishable to the naked eye, and even after converting OFD pages to images, the watermark information remains hidden. In comparison to traditional watermarking algorithms that rely on annotated documents, the proposed OFD watermarking algorithm based on semantic analysis exhibits robust steganography capabilities.
# Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity
## 4 Simulation and Analysis
### Frequency Estimation of Scatterers Applying CS  
The noise corrupted data with SNR = 20 dB is generated by adding white Gaussian noise into the simulated passive radar echo. Then, the proposed CS method is applied to estimate the amplitudes and spatial frequencies of six scatterers ((0,0), (3,0), (3,9 \(\pi/16\)), (3,\(-9\)\(\pi/16\)), (3,15 \(\pi/16\)), (3,\(-15\)\(\pi/16\))) on the target. To provide a quantitative evaluation for estimation performance, the relative mean square error (MSE) is defined by  
\[\text{MSE}=\frac{\left\|\mathbf{\omega}^{\prime}-\mathbf{\omega}\right\|_{2}}{\left\| \mathbf{\omega}\right\|_{2}}, \tag{27}\]where \(\mathbf{\omega^{\prime},\omega}\) represent the spatial frequency vector and its estimation of the scatterers, respectively.  
Tables 1 and 2 give the MSE of the estimated spatial frequency from IO(A) and IO(B) using the proposed CS method, respectively, where the MSEs are calculated according to Equation (27). It can be observed that the estimated frequencies coincide with the theoretical values. This indicates that the proposed CS method can estimate the frequency parameter with high accuracy.
## 3 Proposed Redundancy Resolution
\(\mathbf{K}_{\xi}\) is a diagonal matrix whose elements belong to \([0,\infty)\). The larger the diagonal element of \(\mathbf{K}_{\xi}\), the greater the corresponding coupling motion. For instance, if the diagonal element of the secondary row of \(\mathbf{K}_{\xi}\) is larger, the vehicle will have a larger yaw angle according to the coupling effects. Similarly, if the diagonal element of the third row of \(\mathbf{K}_{\xi}\) is larger, the vehicle will have a larger pitch angle. \(\alpha\) is a coefficient belonging to \([0,1]\), which is used to adjust the values of \(\mathbf{K}_{\xi}\). To effectively utilize the self-motion during the entire UVMS motion, \(\alpha\) is defined as a nonlinear function related to time \(t\geq 0\), as given in (8). \[\alpha= \begin{cases}-0.5(1-e^{-\lambda(t-t_{s})})+0.5&t>t_{s}\\ 0.5(1-e^{-\lambda(t-t_{s})})+0.5&t\leq t_{s}\end{cases}  \tag{8}\]  
where \(t_{s}\) relatives to the time at which the system enters deceleration phase. \(\lambda\) is the coefficient and \(\lambda>0\). The curve of the nonlinear function is shown in Figure 2. It can be recognized that the smaller the value of \(\lambda\), the smoother the variations of the nonlinear function. Therefore, it is better to choose a small value of \(\lambda\) to ensure smooth movement of the UVMS.
# Multimodal AutoML via Representation Evolution
## 3 MuRE: Multimodal Representation Evolution  
We next discuss the proposed _MuRE_ approach. We begin by discussing the idea of _representation evolution_ (summarized in Figure 1), followed by the formal overview and the description of the final version of the proposed system.  
Conceptually, the AutoML we henceforth refer to as MuRE consists of two main conceptual steps: representation learning and configuration search. Both steps are simultaneously considered as part of _evolution_--we adopt the approach commonly referred to as _evolution strategies_[30] due to its compatibility with real-valued inputs (weights of feature spaces). This work builds on the recent implementation of this idea focused exclusively on text-based datasets [31]. The main reason the implementation was considered as the main building block is due to its capability for handling sparse and dense matrices simultaneously, and, further, considering learning algorithms that do not induce substantial memory overheads (which is a common caveat). For example, sparse matrices are common when dealing with symbolic representations (e.g., bags of tokens), however, dense ones are commonplace for document/image embeddings. The minimization problem addressed by the evolution can be in its _general form_ stated as follows:  
\[\text{Solution}\approx\operatorname*{arg\,min}_{\begin{subarray}{c}\Theta \in\text{algorithm\_space}\\ \text{$\wedge$ hyperparamSpace}\\ \text{$\times$ transformationSpace}\end{subarray}}\mathbb{E}\Big{[}\text{Loss}(\textit{learnerClass},\Theta,\text{data})\Big{]}.\]The stated problem is a generalization of the one discussed in [6], which considers the space of possible transformations; this space includes, e.g., dimensionality reduction and embedding construction. The proposed **representation evolution** process denotes the process of iterative re-weighting of different data representations and simultaneous evaluation for a given task (e.g., classification). The evolution here is the optimization procedure that enables exploration of differently-amplified representations and the effect of this amplification on the learning performance (fitness function outputs a score based on, e.g., cross validation). Intuitively, MuRE attempts to find the right _balance_ between different representations considered, enabling automated prioritization of feature spaces of different types (sparse and dense). Note that the expected value of the loss function for a given configuration \(\Theta\) is commonly estimated via cross-validation. Even if the stated criterion addresses the construction of the final learner, note that if the Loss is not representative, the solution obtained as the result of the minimization will not necessarily _generalize_/perform well on unseen data. Note that the Loss function is task-independent. We next discuss the core components in more detail.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
###### Abstract  
Here we introduce the new concept of computation coding. Similar to how rate-distortion theory is concerned with the lossy compression of data, computation coding deals with the lossy computation of functions. Particularizing to linear functions, we present an algorithmic approach to reduce the computational cost of multiplying a constant matrix with a variable vector, which requires neither a matrix nor vector having any particular structure or statistical properties. The algorithm decomposes the constant matrix into the product of _codebook_ and _wiring_ matrices whose entries are either zero or signed integer powers of two. For a typical application like the implementation of a deep neural network, the proposed algorithm reduces the number of required addition units several times. To achieve the accuracy of 16-bit signed integer arithmetic for 4k-vectors, no multipliers and only 1.5 adders per matrix entry are needed.  
Keywords:approximate computing; computational complexity; estimation error; fixed-point arithmetic; linear systems; rate-distortion theory; quantization +
Footnote †: journal: _Algorithms_
# Fully Open-Source Meeting Minutes Generation Tool
## 2 Related Work in Literature
### Abstractive Summarization  
Abstractive summarization allows for creating summaries with human-like qualities, generating new words and sentence structures while preserving the original meaning of the source text [15]. Several methods have leveraged transformer architecture due to its self-attention mechanism, which could preserve information from lengthy text data.  
The _text-to-text transfer transformer_ (_T5_) handles a wide range of _NLP_ tasks by framing them as text-to-text problems, benefiting from its training on diverse datasets [16]. However, this versatility comes at the cost of sub-optimal performance in specialized abstractive summarization tasks. The _pre-training with extracted gap-sentences for abstractive summarization (PEGASUS)_, explicitly designed for _summarization_ by Google, performs exceptionally well in this domain [17]. However, its specialized nature limits generalization across topics and requires computationally expensive fine-tuning for optimal results. In contrast, _bidirectional and auto-regressive transformer_ (_BART_) balances generalization and accuracy by leveraging diverse datasets and combining a bidirectional encoder with an auto-regressive decoder [18]. This dual approach improves word generation and preserves context over long sequences, although it increases computational cost and slows down processing due to its complex architecture.  
Given the diverse topics covered in the recorded presentation videos and prior evaluation [19], _BART_ was selected for its broad generalizability across different topics, making it suitable for generating summaries in this use case.
# Applicability of Infrared Thermography for the Detection of Phase Transitions in Metal Alloys
## 2 Materials and Methods
### IRT Method
\[S=U\Sigma V^{t} \tag{1}\]  
where \(S\) is a matrix containing \(n_{i}\) rows and \(n_{t}\) columns (where \(n_{i}\) is the number of pixels constituting the sample and \(n_{t}\) the number of frames of the infrared movie), \(U\) an \(n_{i}\times n_{i}\) matrix containing the space-eigenfunctions along the rows, \(V\) an \(n_{t}\times n_{t}\) matrix containing the time-eigenfunctions along the rows, and \(\Sigma\) an \(n_{i}\times n_{t}\) diagonal matrix containing the eigenvalues along the diagonal. Figure 2: Steps of the image treatment process: (**a**) raw infrared image; (**b**) distinction between samples and background through segmentation; (**c**) identification of individual samples through labeling. The transition detection is then made based on the observation of time-eigenfunctions (related to the time or temperature dependence of the signal). Eigenfunctions are given a rank according to their contribution to the raw data (the lower the rank, the higher the contribution). The rank of the eigenfunctions considered varies depending on the application. The first rank always represents the average evolution of the signals and subsequent ranks highlight deviations from it. In this work, the transition detection is made based on the third and fourth eigenfunctions. A detailed explanation of the process and its application to phase transition detection is available in [8]. The measurement uncertainty for the determination of transition temperature has been estimated at \(\pm\)1.5 \({}^{\circ}\)C in [11] through repeated measurements on known materials, comparison with standard methods and accounting for sources of error.
## 4 Experiments
### Evaluation Metrics  
In our experiments, we use accuracy (ACC), area under ROC curve (AUC), precision (PRE), recall (REC) and F1-score (F1) as the evaluation metrics. The formulas of these metrics are shown below:  
\[Accuracy=\frac{\sum_{i=1}^{N}f(x_{i})=y_{i}}{N} \tag{15}\]  
\[precision=\frac{\sum_{i=1}^{c}precision_{i}}{C} \tag{16}\]  
\[precision_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}},i=1,2,\dots,C \tag{17}\]  
\[recall=\frac{\sum_{i=1}^{c}recall_{i}}{C} \tag{18}\]  
\[recall_{i}=\frac{TP_{i}}{TP_{i}+FN_{i}},i=1,2,\dots,C \tag{19}\]  
\[\text{F1}=\frac{2*precision*recall}{precision+recall} \tag{20}\]  
where \(C\) is the number of classes and \(N\) is the number of total samples. \(TP\), \(TN\), \(FP\), and \(FN\) refer to true positive, true negative, false positive, and false negative.  
Accuracy (ACC) is the most commonly used metric among these performance metrics, but it does not indicate the true model performance when the classes are imbalanced. Area under the ROC curve (AUC) is less sensitive to class imbalance than ACC. Precision (PRE) and recall (REC) are related to the positive prediction. In our experiments, we use macro-precision and macro-recall, which are defined by Formulas (16) and (18). The F1-score (F1) is a metric that combines both precision and recall.
# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 2 Materials and Methods
### Target Carbon Fluxes and Predictors
#### 2.1.1 Flux Tower Site Data  
Three datasets, FLUXNET2015 [8], FLUXNET-CH4 [30], and Drought-2018 [31] were used as site datasets in this study, which include meteorological data and carbon flux variables. The FLUXNET2015 Tier 2, the FLUXNET-CH4, and Drought-2018 datasets contain data from 212, 80, and 52 sites, respectively. Since the FLUXNET-CH4 and Drought-2018 datasets have been produced more recently, only the new versions of data were kept for duplicated sites from different datasets. After removing duplicated sites, 280 sites around the world remained. The locations of these sites are shown in Figure 1. In this study, daily values of three carbon flux variables were extracted from these sites. The time coverages of these sites are different, varying from 1996 to 2018. Data after 1999 were used in the study to keep consistent with the time range of available remote sensing data. In order to keep consistent with the time interval of remote sensing data, site data were aggregated into a 10-day time step. Since GPP and RECO are always positive, we removed the negative values of GPP and RECO. We also removed the unrealistic continuous constant values of site measurements at some timestamps. Missing values were deleted for the machine learning modelling.
## 3 Results  
In this section, firstly, the results of the ship's behaviour affected by a single external force to which the ship reacts as a consequence of the initial restoring moment, defined by the metacentric height (GM), are analysed. As the GM is a parameter that depends on the ship's hydrostatic values, the present study considers the Td as the value which represents the ship's loading condition. Furthermore, the Td can be easily measured by the ship operators and is a parameter applicable to any type of ship. Afterwards, the influence of certain sea state conditions on the ship's rolling motion is studied, obtaining generic conclusions valid for the full wave spectrum. It is remarkable to note that in these initial simulations, beam waves are used because they are considered the worst condition for the general ship's safety.  
Regarding the ship's parameters used in the simulations, certain loading conditions (GM) were selected to be as representative as possible of all merchant fleets and even applicable to fishing vessels.  
The GM has a direct relationship with the Td and the ship's beam [30]. However, the GM is a very difficult to calculate parameter because it is variable for each ship during a certain sea passage; therefore, a range of Td values, which are usually found, is used. According to several empirical calculations included in the literature [31,32], Td values of 8 s, 10 s, 12 s, and 14 s were studied [33].  
The resistant environment during the rolling motion is produced mainly by the water. Air resistance can be underestimated due to its minimum effect, or directly included in the rolling damped factor, \(\lambda\).  
According to the literature review, a \(\lambda\) value of 0.015 was selected as representative of the average coefficient for the successive rolling angles with different amplitudes, where \(\lambda\) is changed [34].  
Regarding the maximum initial rolling angle, \(\theta_{\text{M}}\), 8.0\({}^{\circ}\) was selected as representative of what is considered, in the field of ship's theory, to be the initial intact stability.  
Therefore, the ship parameters and initial conditions used in the simulations of the two first sub-sections were as follows:  
* Ship in the upright position;
* An average maximum initial rolling angle (\(\theta_{\text{M}}\)) of 8\({}^{\circ}\);
* Ship's loading condition (Td) at 8 s, 10 s, 12 s, and 14 s;
* Rolling damping factor (\(\lambda\)): of 0.015;
* Ship at zero-speed condition.
# Research on Path Planning of Mobile Robot Based on Improved Theta* Algorithm
## 2 Algorithm Design
### Theta* Algorithm
If the two nodes are visible and the \(g(p)\) of node \(p\) plus the generation value from node \(p\) to node \(s\) is less than the \(g(s)\) of node \(s\), the parent of node \(s\) is updated to node \(p\), and the \(g(s)\) of node \(s\) is updated to the \(g(p)\) of node \(p\), plus the generation value from node \(p\) to node \(s\). If the two nodes are not visible and the \(g(n)\) of node \(n\) plus the cost from node \(n\) to node \(s\) is less than the \(g(s)\), then the parent node of node \(s\) is updated to node \(n\) and \(g(s)\) of node \(s\) is updated to \(g(n)\) of node \(n\) plus the generation value from node \(n\) to node \(s\). * The original \(g(s)\) of node \(s\) is compared with the updated actual generation value, and if the updated actual cost is smaller than the original actual cost, the estimated cost of node \(s\) is recalculated and the information related to node \(s\) in the open table is updated. 5. Remove the already traversed node \(n\) from the open table and add it to the close table, then return to step 2. Based on the description of the steps of the Theta* algorithm above, its pseudo-code can be written as shown in Algorithm 1. ```
1:start point:[\(\mathrm{s}_{\mathrm{start}}\)], map arrays:[map], goal point:[\(\mathrm{s}_{\mathrm{goal}}\)]
2:algorithm planning path: {path}
3:Initialize the open and close tables : {open : \(=\) closed : \(=\) \(\varnothing\)}
4:Initializing the cost function: {g(\(\mathrm{s}_{\mathrm{start}}\)) :=0}
5:Set the start node as the parent node: {parent(\(\mathrm{s}_{\mathrm{start}}\)) :=\(\mathrm{s}_{\mathrm{start}}\)}
6:open.Insert(\(\mathrm{s}_{\mathrm{start}}\), g(\(\mathrm{s}_{\mathrm{start}}\))+h(\(\mathrm{s}_{\mathrm{start}}\)))
7:whileopen \(\
eq\)\(\varnothing\)do
8:\(\mathrm{s}\) :=open.Pop()
9:if\(\mathrm{s}\) = \(\mathrm{s}_{\mathrm{goal}}\)then
10:return path
11:endif
12:closed := closed := \(\cup\) [s]
13:for\(\mathrm{s}^{\prime}\)\(\in\) nghbr\(\mathrm{vis}\)(s) do
14:if\(\mathrm{s}^{\prime}\)\(\
otin\) closed then
15:if\(\mathrm{s}^{\prime}\)\(\
otin\) open then
16:\(\mathrm{g}(\mathrm{s}^{\prime})\) := \(\infty\)
17:parent(\(\mathrm{s}^{\prime}\)) :=NULL
18:endif
19:\(\mathrm{g}_{\mathrm{old}}\) :=g(\(\mathrm{s}^{\prime}\))
20:if lineofsight(parent(s), s') then
21:if g(parent(s)) + cost(parent(s), s') < g(s') then
22:\(\mathrm{g}(\mathrm{parent(s)}^{\prime})\) :=parent(s)
23:\(\mathrm{g}(\mathrm{s}^{\prime})\) :=g(parent(s)) + cost(parent(s), s')
24:endif
25:else
26:if g(s) + c(s, s') < g(s') then
27:parent(\(\mathrm{s}^{\prime}\)) :=s
28:\(\mathrm{g}(\mathrm{s}^{\prime})\) :=g(s) + cost(s, s')
29:endif
30:endif
31:if\(\mathrm{g}(\mathrm{s}^{\prime})\) < \(\mathrm{g}_{\mathrm{old}}\) then
32:if\(\mathrm{s}^{\prime}\)\(\in\) open then
33: open.Remove(\(\mathrm{s}^{\prime}\))
34:endif
35: open.Insert(\(\mathrm{s}^{\prime}\), g(\(\mathrm{s}^{\prime}\)) + h(\(\mathrm{s}^{\prime}\)))
36:endif
37:endif
38:endfor
39:endwhile
```  
**Algorithm 1**Theta* algorithm.
# Assessment of Interventions in Fuel Management Zones Using Remote Sensing
## 3 Results
### Temporal Analysis  
The FMZs along roads were also analyzed using time- series data with three main structures: Sentinel-1 data, Sentinel-2 data, and with the combination of Sentinel-2 and Sentinel-1 data.
## 3 Numerical Methodology and Procedure
### Selection of the Turbulence Model  
To simulate the system, we utilized a pressure-based numerical solver with air as the working fluid. Considering the compressibility of air, it was modeled as a compressible ideal gas. Boundary conditions were set as a pressure inlet and a pressure outlet, in accordance with the specific flow dynamics observed within the RC-DTH hammer cylinder. The standard k-\(\varepsilon\) turbulence model [57] was employed for this numerical simulation. Due to the simplicity of the piston's movement in the cylinder, the standard k-\(\varepsilon\) turbulence model effectively characterizes the turbulent effects and provides an accurate representation of the gas flow dynamics inside the RC-DTH hammer.