esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 What Could Have Been Done?
### Manual Detection of Duqu  
From a malware analyst's point of view, the detection of Duqu is not so difficult as it creates multiple Local Security Authentication Server (lsass.exe) processes, out of which only one exists on a clean MS Windows system. This fact on its own makes a well-prepared security practitioner suspicious about a possible infection. Hence, the first logical step is the examination of the newborn lsass.exe processes. For this purpose, we used Sysinternals Process Monitor [23], which logs information about any activity on processes, threads, the file system, the network, and the registry. Process Monitor registers kernel hooks to record registry activity, and uses Event Tracing for Windows (ETW) to get notifications about network events. By checking the stack trace of a newborn lsass.exe process with Process Monitor, one can notice function calls into.nls files, where NLS stands for National Language Support. These files normally contain a table for language translation, and no executable code, therefore their presence in the stack trace is suspicious. In addition, there observed paths do not exist in the file system. Table 2 summarizes the stack trace of an lsass.exe process created by Duqu. One can observe the abusive name sortEA74.nls, which resembles the name of the legitimate sortkey.nls and sorttbls.nls files.  
At this point, we could have used a ring 3 debugger such as OllyDbg, to attach to the malicious lsass.exe processes and to try to understand their detailed operation. However, an even more interesting question is how the code injection was achieved. This can be answered by using a very useful feature of Process Monitor: boot time logging. This feature allows us to record and interpret all relevant events that occurred during system startup. After creating such a bootlog file, one can search for various operations, events, processes and similar entities. In our case, we searched for any match in the Path field for the name sort*.nls. We found two matching events, where an svchost.exe process wanted to read IFEO (Image File Execution Options) entries from the registry under subkeys sortC1D1.nls and sortBA08.nls. This must be an anti-debugging technique of Duqu, as IFEO allows for debugging of specified processes at startup, or a way to start any (in this case malicious) executables via a built-in method of Windows. If we check the parent process of this malicious svchost.exe, we end up with services.exe, which also injected malicious payload into other processes such as lsass.exe, alg.exe, imapi.exe, spoolsv.exe and other svchost.exe instances. As this services.exe is the process where the initial installer was injected by a system module (e.g., jminet7.sys), we stopped our investigation at this point.  
While the method described above does not allow a system administrator to identify the malicious system module itself, it still identifies the fact of malicious code injection into system processes. Thus, it could have raised an alarm and triggered a deeper investigation.
# GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning
## 2 Scheme Statement
### System Architecture  
The system architecture of the proposed scheme is illustrated in Figure 2. The overall system consists of two entities: participants and cloud servers.  
Participants: Participants receive the initial model parameters, train the local model on their local datasets at a specified learning rate, and then upload the local model parameters along with auxiliary computation information to the cloud server for ag  
Figure 2: The proposed system architecture.  
gregation. During this process, participants may choose to apply masking techniques to safeguard local model parameters and related private information.
* Servers: The cloud servers are responsible for receiving the local model parameters and auxiliary information uploaded by participants and aggregating them based on the proposed aggregation rules. Subsequently, the global aggregation results are returned to each participant for iterative training until model convergence.
# LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios
## 2 Preliminary
### Apf  
The artificial potential field method, with its intuitive physical model and strong adaptability, has been widely used in UAV path planning for a long time. This method constructs a composite potential field by superimposing the attractive field of the target point and the repulsive field of obstacles, guiding the UAV to move along the gradient direction of the potential field, thus achieving efficient obstacle avoidance in simple scenarios. However, as UAV applications extend into complex urban low-altitude environments, the limitations of traditional APF gradually become apparent. Dense buildings and narrow alleys can easily fail to reach the target point due to the complicated layout of obstacles, as shown in Figure 1a, or cause UAVs to get stuck in local minima, as shown in Figure 1b.  
Although the APF algorithm provides an intuitive physical driving framework for UAV obstacle avoidance, its static potential field model struggles to meet the real-time decision-making needs in dynamic obstacle environments. This is particularly evident in complex urban scenarios, where the local optimum traps of traditional potential field functions can lead to the global failure of path planning. To address the challenge of coupling dynamics and uncertainty, this paper introduces the Deep Q-Network algorithm from deep reinforcement learning. By constructing a state-action value function mapping, the DQN enables autonomous strategy optimization for UAVs in complex environments.
## 4 Experiments
### Experiments on CMD-Net Dataset
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{(a) Chamfer Measure, (\(\sigma=0.01\))} \\ \hline
**Method** & **Airplane** & **Bench** & **Car** & **Chair** & **Pillow** & **Rife** & **Sofa** \\ \hline Noisy & 0.56 & 0.59 & 0.81 & 0.73 & 0.79 & 0.44 & 0.83 \\ Ours & 0.31 & 0.54 & 0.77 & 0.46 & 0.57 & 0.42 & 0.51 \\ \hline \multicolumn{6}{c}{(b) Chamfer Measure, (\(\sigma=0.015\))} \\ \hline
**Method** & **Airplane** & **Bench** & **Car** & **Chair** & **Pillow** & **Rife** & **Sofa** \\ \hline Noisy & 1.03 & 1.06 & 1.18 & 1.18 & 1.34 & 0.90 & 1.26 \\ Ours & 0.57 & 0.58 & 0.83 & 0.48 & 0.69 & 0.52 & 0.65 \\ \hline \multicolumn{6}{c}{(c) Chamfer Measure, (\(\sigma=0.02\))} \\ \hline
**Method** & **Airplane** & **Bench** & **Car** & **Chair** & **Pillow** & **Rife** & **Sofa** \\ \hline Noisy & 1.67 & 1.71 & 1.64 & 1.78 & 2.05 & 1.60 & 1.80 \\ Ours & 0.68 & 0.80 & 0.96 & 0.74 & 0.80 & 0.64 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Denoising performance of our method (CMD-Net) on the CMD-Net dataset is evaluated using the Chamfer Measure evaluation metric. The numbers in this table are C2C scores (see the metric in Section 4.1). Figure 6: C2C scores generated by CMD-Net are normalized by their _noisy_ counterparts. Left-most, middle, and right-most images are of \(\sigma=0.01\), \(\sigma=0.015\), and \(\sigma=0.02\), respectively. Figure 7: Error analysis for the normalized C2C scores. Left image explains the cumulative error, while the right one shows the weights of the errors for low-, mid-, and high-level noise. in turn, showed overperformance for the _car_ and _rifle_ categories. This clearly depicts the merit of our complete **self-supervised** methodology circumventing the dependency on pre-aligned data. Figure 8 presents several qualitative results for \(\sigma=0.02\). In the figure, the first row depicts rotated and noise-corrupted test input instances from the _airplane_ and _car_ categories. These instances were fed into CMD-Net, the canonical mapping part of which first canonicalized them. In the second row of the figure, the test inputs and canonicalization results are overlaid, and the third row depicts canonicalization only. The canonicalized inputs were then fed into the denoising part of the network, and the final results are given in the fourth row of the figure. This characteristic of CMD-Net alleviates the dependency on pre-aligned data for the denoising problem.
## 4 Impact Damage Shape and Modeling in 3D-FE Model  
Due to point impact damage and based on the ultrasonic imaging, the composite plate is seriously micro-cracked inside the impact damaged zone. In order to model the damage zone in 3D-FE model, we can reasonably assume that a uniform point impact in the quasi-isotropic plate would produce a damage zone, the shape of which would be a right circular cone due to the impact conditions and the transversely isotropic nature of the plate. Damage in composites is a collection of various types of cracks of different characteristics (matrix voids, delamination, broken fibers, etc.), depending on the applied stress and the composite architecture. At the time when the strength can be clearly defined in metals (ex. unstable growth of a crack at the origin of the brittle fracture), failure in composite materials is still an active research topic, which often generates lively debates in the scientific community [41].  
Figure 4: (**a**) A-Scan signals corresponding to the reflection of a longitudinal ultrasonic wave at the defect position of the impacted CFRC plate; (**b**) C- Scan image based on the A-scans performed at intact and damaged regions of the CFRC plate. The amplitude of the back-wall echo is strongly decreasing in the damaged area. The damage was found to be ~30 mm wide.  
The development of a simple modeling method can be proposed when the wavelength of a Lamb wave is at least one order of magnitude greater than the microscopic defects created within the composite. In such a case, the homogenization procedure of the elastic properties can be expressed through a degradation of the elastic properties. In the literature [42], it has been observed that a decay of 80% in the elastic properties is quite representative of a severely cracked and delaminated zone, in terms of wave scattering. The choice of 80% decay in the material stiffness rather than other smaller values is motivated by the fact that the damaged zone in FE model is considered as a homogeneous medium with mass density equal to that of undamaged material (unchanged thickness), although it would likely be a heterogeneous region made of cracks, voids and delamination, which are known to be efficient scatterers of elastic waves. In the present FE model, we considered an 80% decrease in the material stiffness properties within the cracked zone, which is modeled as a right circular cone with height (h) and diameter (2 r). Finally, note that it was not possible through the ultrasonic imaging experiment to see how deep the damage occurred penetrating the plate through the thickness from the above surface. However, as the impact did not penetrate through the whole thickness, and for comparison with experimental data, the undamaged thickness below the conical damage is considered to be \(\sim\)1 mm from the bottom surface of composite plate. Finally, we note that the effect of the damage depth on the scattering of the A\({}_{0}\) mode will be presented in the present contribution.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Proof That Our Method Uses the Minimum Number of Drones
#### 3.6.5. Wastage of Coverage in _InnerXDrectionFilling_ and _InnerYDrectionFilling_  
Both _InnerXDrectionFilling_ and _InnerYDrectionFilling_ make use of _ThreeNeighbours_ and _TwoNeighbours_. The last circle's center along each round of _InnerXDrectionFilling_ is set to be equidistant from \((m,y_{ref})\) and another reference point of the circle. Similarly, for _InnerYDrectionFilling_, the last circle's center is set to be equidistant from \((x_{ref},n)\) and another reference point of the circle. The reason for this is to focus on the circular area within the target region. Because _TwoNeighbours_ and _ThreeNeighbours_ are proven to equally distribute the wastage area, their combination minimizes the required number of drones.
## 4 Dataset Annotation and Filtering
### Annotation Process  
The annotating process for building custom NER for the Encyclopedia of A.S. Pushkin is a multi-stage process containing crucial steps for clearing, filtering, and preparing the dataset. In this subsection, we will delve into each step we followed to prepare the dataset and shed light on the methods and techniques we used. Figure 2 shows the pipeline for the process of data preparation:  
1. **Proofreading and Document Structuring:** The first step in preparing the dataset is having domain experts examine the raw dataset. Every single text within the dataset undergoes a thorough proofreading and review process to improve its quality and readable content. Text files are also separated into organized paragraphs to make processing steps easier. 2. **Data Cleaning and Formatting:** To make sure that the textual data can be analyzed by NER models, a data cleaning and filtering phase was carried out. This involves removing unnecessary punctuation, white spaces, and misspelled characters. The target of this stage is to deliver clear, clean, and organized texts that will enhance performance when training the NER system. 3. **Annotation Using Pre-trained NER Model**: Regarding the NER annotation procedure, an advanced Russian pre-trained NER model, such as DeepPavlov [24], is utilized. Named entities in the text can be recognized and annotated using this model. In particular, the annotated entity types are as follows: * **PER (Person)**: Recognizing people who are referenced in the book, such as historical figures, writers, critics, and commentators. * **DATE (Date)**: Identifying all the entities that give the text a chronological framework, such as periods and dates. * **LOC (Location)**: Identifying the regions and locales that are mentioned in the text, such as cities and countries.
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 5 Results and Discussion
### Inpainting Performance with Different Numbers of LSTM Layers
In summary, it was observed that, for each model with different LSTM layers, the average MOS decreased as the gap length increased. Across the different datasets, the model that obtained the highest average MOS varied. However, the differences among the five LSTM models were not significant, with only minor fluctuations. These slight differences in performance emphasise the impact of model architecture and training datasets on the performance of speech inpainting, which means that it is important to customise the model architecture according to different datasets and inpainting requirements.
# FMS Scheduling under Availability Constraint with Supervisor Based on Timed Petri Nets
## 2 Background  
### Timed Petri Net  
Formally, a TPN [33] is a five-tuple \((P,T,O,W,h(T))\) where:  
* \(P=\left\{p_{0},p_{2},\ldots,p_{|P|}\right\}\) : is a non-empty finite set of places;
* \(T=\left\{t_{0},t_{2},\ldots,t_{|T|}\right\}\) : is a non-empty finite set of transitions;
* \(O\subseteq(P\times T)\cup(T\times P)\) : are the direct arcs from places to transitions \((P\times T)\) or from transitions to places \((T\times P)\);
* \(W:(P\times T)\cup(T\times P)\rightarrow\mathbb{N}\) : is the affectation function of weights to arcs, where \(\mathbb{N}\) is the set of non-negative integers;
* \(h(t)\in h(T)\) : is the static time linked to \(t,t\in T\).  
\begin{tabular}{l l l} \(\bullet\) & \({}^{\circ}p\) (\(p\)') & : designs the set of input transitions (respectively output) of place \(p\); \\ \(\bullet\) & \(M\) & : is a marking and the initial marking is denoted by \(M_{0}\); \\ \(\bullet\) & \(M(p)\) & : is a place marking; \\ \(\bullet\) & \(M[t>\) & : indicates that transition \(t\) is fired at marking \(M\). \\ \end{tabular}  
It is important to note that transition \(t\) is enabled by marking \(M\) only if \(M(p)\geq W(p,t)\), \(\forall\ t\in p^{\circ}\). An enabled transition satisfying its time condition can be fired. Furthermore, \(P\cup T\
eq\varnothing\) and \(P\cap T=\varnothing\) where \(\varnothing\) designs the empty set. In the literature, several classes could be derived from TPN from where the Timed Marked Graph (TMG) is our endpoint of interest in this paper.  
Definition 1: The \(TMG\) is a particular type of \(TPN\) where every place possesses one input transition and one output transition, i.e., \(\left|{}^{\circ}p\right|=\left|p^{\circ}\right|=1\). Furthermore, all weights associated to arcs are equal to one, i.e., \(W:(P\times T)\cup(T\times P)\rightarrow\left\{0,1\right\}\).  
Figure 1 illustrates a TMG model. The place \(p_{0}\) is initially marked, and the enabled transition \(t_{0}\) can be fired after four time slices (\(t.s\)). The transition \(t_{1}\) is fired if \(p_{1}\) is marked with a delay of \(8\ t.s\).
## 4 Numerical Example
### Calculation Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Soil Type**} & \multirow{2}{*}{**Depth (m)**} & \begin{tabular}{c} **Unit Weight** \\ **(\(\mathbf{kN/m^{3}}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Cohesion** \\ **Yield Stress** \\ **(\(\mathbf{kPa}\))** \\ \end{tabular} & \multirow{2}{*}{**Friction Angle (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{   }}}}}}}}}}}}\))**} & **Poisson’s** &
\begin{tabular}{c} **Black** \\ **Modulus** \\ **(MPa)** \\ \end{tabular} \\ \hline
1 & Fill & 0–1 & 19 & 20 & 15 & 0.33 & 101.308 \\
2 & Holocene sand & 1–2 & 19 & 30 & 20 & 0.32 & 101.308 \\
3 & Holocene sand & 2–4.8 & 19 & 1 & 40 & 0.32 & 147.840 \\
4 & Pleistocene clay & 4.8–8 & 19 & 1 & 40 & 0.40 & 195.972 \\
5 & Pleistocene clay & 8–17 & 19 & 30 & 20 & 0.30 & 290.342 \\
6 & Pleistocene clay soil & 17– & 20 & 1 & 40 & 0.26 & 560.045 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Soil parameters of the site of Daikai subway station. fixed horizontal and vertical constraints at the bottom. The structural model is statically calculated, the results of which are imported to achieve stress balance. Subsequently, these constraints are removed, and ground motions are introduced at the bedrock for dynamic implicit calculations. Due to the large size of the soil relative to the structure and the soil's energy dissipation, damping effects are considered negligible. To validate the finite element model's accuracy, its dynamic responses during the Kobe earthquake are compared with analysis results from [PERSON] et al. (2018). This comparison includes the central column's horizontal displacement and internal forces, as summarized in Table 2. All errors are within a 15% margin, affirming that the finite element model realistically captures the structure's dynamic characteristics and is suitable for further investigation.
# A Hybrid Bald Eagle Search Algorithm for Time Difference of Arrival Localization
## 4 Experimental Design and Analysis
### Performance Evaluation
#### 4.2.2 HBES-Based Simulation of TDOA Localization  
In order to verify the performance of the HBES algorithm for TDOA localization, the combination of the HBES algorithm and other intelligent algorithms (PSO, GWO, COOT, BES, SSA [26], CPSO [27]) are compared.  
The simulation scenario and parameters are set as follows: 8 base stations are deployed in a 50 m \(\times\) 50 m range with base station coordinates of (0, 0), (0, 25), (0, 50), (25, 50), (50, 50), (50, 25), (50, 0), (25, 0), the superior limit of 2D plane coordinates \(ub\) = [50, 50], the inferior limit \(lb\) = [0, 0], and the number of iterations of all swarm intelligence algorithms The Root Mean Square Error (RMSE) was selected as the evaluation index for localization accuracy.  
\[\text{RMSE}=\sqrt{\frac{1}{P}\sum_{p=1}^{p}{(x_{p}-x_{pt})^{2}+(y_{p}-y_{pt})^ {2}}} \tag{31}\]  
where \(x_{p},y_{p}\) and \(x_{pt},y_{pt}\)) are the true and estimated positions of the test point \(p\), respectively, and \(P\) is the total number of test points. The performance of the localization algorithm is usually assessed based on whether the root mean square error is close to the Cramer-Rao Lower Bound (CRLB). The Cramer-Rao Lower Bound specifies the lower limit on the standard deviation that can be achieved by an unbiased estimator, representing the theoretical best estimation accuracy, and is calculated as:  
\[\text{CRLB}=\sqrt{trace[(G^{T}Q^{-1}G)^{-1}]} \tag{32}\]  
\[G=\left[\begin{array}{cccc}\frac{x-x_{2}}{r_{2}}-\frac{x-x_{1}}{r_{1}}& \frac{y-y_{2}}{r_{2}}-\frac{y-y_{1}}{r_{1}}\\ \frac{x-x_{3}}{r_{3}}-\frac{x-x_{1}}{r_{1}}&\frac{y-y_{3}}{r_{3}}-\frac{y-y_{1 }}{r_{1}}\\ \vdots&\vdots\\ \frac{x-x_{N}}{r_{N}}-\frac{x-x_{1}}{r_{1}}&\frac{y-y_{N}}{r_{N}}-\frac{y-y_{1 }}{r_{1}}\\ \end{array}\right] \tag{33}\]  
\[Q=\left[\begin{array}{cccc}2\sigma_{r}^{2}&\sigma_{r}^{2}&\cdots&\sigma_{r }^{2}\\ \sigma_{r}^{2}&2\sigma_{r}^{2}&\cdots&\sigma_{r}^{2}\\ \vdots&\vdots&\cdots&\vdots\\ \sigma_{r}^{2}&\sigma_{r}^{2}&\cdots&2\sigma_{r}^{2}\\ \end{array}\right] \tag{34}\]  
where the trace [] function is used to find the trace of the matrix, \(\sigma_{r}\) is the standard deviation of the distance noise, \((x,y)\) and \((x_{i},y_{i})\) are the coordinates of the unknown node and base station \(I\), respectively, \(r_{i}\) is the distance from the unknown node to the base station, and \(N\) is the total number of base stations.  
Figure 4: Simulation diagram of the test function: (**a**) comparison of convergence curves on \(F_{1}\); (**b**) comparison of convergence curves on \(F_{2}\); (**c**) comparison of convergence curves on \(F_{3}\); (**d**) comparison of convergence curves on \(F_{7}\); (**e**) comparison of convergence curves on \(F_{8}\); (**f**) comparison of convergence curves on \(F_{10}\); (**g**) comparison of convergence curves on \(F_{13}\); (**h**) comparison of convergence curves on \(F_{15}\); (**i**) comparison of convergence curves on \(F_{21}\); (**j**) comparison of convergence curves on \(F_{23}\).