esa-sceva/satcom-chunk-collection huggingface dataset:
# Application-Specific Integrated Circuit of an Inter-IC Sound Digital Filter for Audio Systems
## 2 Materials and Methods  
### Noise-Cancellation Algorithms  
Noise control is the process of reducing the adverse effects of acoustic noise with the purpose of improving the quality of human life in a determined environment or the user experience with audio devices. The noise control methods are divided into passive and active methods. Conventional passive noise control (PNC) utilizes absorbers and mufflers, but those provide degraded performance at low frequencies of acoustic noise. Relevant applied research has been performed in active noise schemes, such as [2], where active noise reduction was applied to diesel engines by using an adaptive algorithm with an artificial intelligence strategy. Another research proposal [3] was applied in the automotive industry, where the interest was to have low-noise vehicles. This work proposes an active noise reduction based on the analysis of the tire pattern noise. Therefore, active noise control (ANC) methods are mostly used at low frequencies. ANC is based on the principle of the destructive interference of the unwanted noise [4]. The least-mean-squares (LMS) algorithm is the most-popular adaptive algorithm because of its simplicity and robustness [5]; the objective of the algorithm is to generate an adaptive filter, whose coefficients are continuously modified in a way that the unwanted noise is removed from the audio signal [2]. An ordinary least-squares regression powered with a stochastic gradient descent method [6] estimates the error at the current time, and the new filter values are calculated in each iteration, given by  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\mu\mathbf{x}(n)[e(n)]^{*} \tag{1}\]  
where \(\mathbf{w}\) stands for the filter coefficient vector, \(\mathbf{x}\) the input signal vector, \(e\) the error, defined as the difference between the objective and actual output, \(\mu\) a sensitivity constant to control the convergence rate of the filter, and finally, \(n\) the number of the current input sample. The asterisk denotes complex conjugation. The control system is, thus, updated with each new sample acquired.  
The NLMS algorithm [7] solves the convergence problem of the traditional LMS, where, due to the unknown nature of the input signal, an appropriate value for the learning rate \(\mu\) is difficult to choose. This is solved by normalizing the input signal and reducing the sensibility factor of the input signal power. The new filter values for each iteration are obtained by  
\[\mathbf{w}_{N}(n+1)=\mathbf{w}_{N}(n)+\frac{\mu\mathbf{x}(n)[e(n)]^{*}}{|| \mathbf{x}(n)||^{2}+\beta} \tag{2}\]  
where \(||\mathbf{x}(n)||\) refers to the magnitude of the input signal vector and \(\beta\) is a sensitivity constant used to prevent the function from reaching an indeterminate value when the input signal power's limit approaches zero, and setting a large \(\beta\) if needed, the change in the filter's new coefficients will not peak.
## 4 Experiment and Results  
### Dataset  
The PDF files used in this study consist of 11,097 malicious document files, 9000 benign document files collected by Contagio malware dump, from November 2009 to June 2018. Contagio malware dump site provides a collection of the malware samples. Any researcher can download malware samples from the site. Our samples have a long coverage on the period. To design an adversarial attack for the validation, we collected 115 clean files containing JavaScript files in separate. We successfully injected malicious codes into 101 clean files except for encrypted files.
## 1 Introduction
### Related Work  
Recently, UAVs have been proven to be highly useful in a variety of applications, including smart agriculture [9], security, power line inspection, survey and mapping [10], surveillance [11], SAR [12; 13; 14], delivery [15], and disaster area coverage [16]. Moreover, UAVs play an interesting role in crop monitoring by enabling various activities that were previously available to on-site farmers only [9]. The auction-based technique allocates the most suitable UAV to a survivor in the UAV and SAR issue, based on the bidding value derived as the distance between a UAV and a survivor. There is a center for natural disasters where most survivors are situated, vital for our endeavor. As a result, SAR operations should be organized with a greater emphasis on the center, with decreasing priority as the distance from this point increases.  
UAVs can be deployed quickly, and their coverage can be dynamically modified in a disaster emergency communications system. They offer network assistance for disaster site SAR quickly and effectively, and real-time information from the disaster scene may be sent back to the SAR to help better rescue individuals who have been impacted [17]. As a result, communication between the disaster relief command center and SAR is critical throughout the disaster relief process, and a disaster frequently destroys the commercial network in the disaster area. Furthermore, a UAV network can also be placed at the epicenter of a disaster site to monitor the disaster zone, allowing SAR to seek and track people or animals in distress.  
AI plays a vital role in improving the performance of robots including UAV in many applications [18; 19; 20]. The authors of [21] introduce the air learning open-source simulator environment for UAVs using Deep Reinforcement Learning (DRL) techniques. Federated Learning (FL) was used for improving UAV swarm computing scheduling and power allocation without exchanging sensitive raw data [22]. FL has been used to enable privacy preservation for internet-of-vehicles applications such as parking and traffic prediction [23]. The authors of [24] discussed decentralized FL architecture over UAV networks. The authors of [25] presented a novel and high-performing FL technique for edge-aided UAV networks, which utilized edge servers in base stations as intermediate aggregators with generally shared data. For privacy and high-level security, the authors of [26]introduced blockchains for securing data sharing in B5G for UAV computing networks. Reference [27] highlighted blockchain-enabled FL in UAV edge computing networks. Furthermore, the authors of [28] discussed the combination of blockchain and FL for UAV edge intelligence in smart environments. Table 1 provides a summary of the most related work included in this paper.
## 4 Discussion
### Limitations and Implications of This Study  
In the last decade, the rapid development of vegetation remote sensing provided great convenience for our extensive monitoring of vegetation at a regional scale [6; 7; 21; 27; 28]. The emergence of SIF, in particular, made it possible to monitor the physiological state of vegetation in real-time on a global scale. As a direct proxy for photosynthesis, SIF can accurately capture the changes in vegetation caused by environmental changes [10; 12; 43]. However, the current widely used SIF products do not have advantages in either temporal continuity or spatial resolution, which makes it difficult for us to conduct more refined SIF studies. In this study, we used the GOSIF data with a finer spatio-temporal resolution, but the GOSIF belongs to the reconstructed data, and there may be some uncertainty. In the future, OCO-3 [59], the Geostationary Carbon cycle Observatory (GeoCarb) [60], the Fluorescence Detector (FLEX) [61], and more organizations will provide more accurate SIF products and observation data in the future. With the more refined SIF products, we can more accurately distinguish between different vegetation types and thus study the vegetation productivity of diverse ecosystems.  
Drought is frequent in southwest China, and the spatial and temporal distribution of rainfall is extremely nonuniform. In this area with a continuously changing environment, the nonlinear relationship between fluorescence and photosynthesis exists not only at the leaf scale but also at the canopy scale. This complex mechanism still needs further exploration and experimental research.
## 2 Data and Methodology
### Methodology  
The workflow of our research is illustrated in Figure 4. First, the downloaded S1 images were preprocessed, and the backscattering coefficient images in the study area were obtained. Second, the backscattering coefficients of each sample were extracted according to the latitude and longitude. Third, the downloaded S2 images were preprocessed, and the spectral index images covering the study areas were obtained. Moreover, the vegetation indices of each sample were extracted according to the latitude and longitude coordinates. In order to eliminate the influence of vegetation on SSM inversion, the VWC estimation model was established by using the S2 spectral index. This model was substituted into the original WCM, and an improved WCM with the spectral index was established. Finally, the SSM estimation model was developed based on the improved WCM.
# Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach
## 4 Methodology
### Tensor Construction  
In this paper, the spatiotemporal tensor composed from the STMs is proposed as a traffic data modeling method. The tensor is constructed by flattening STMs into matrix as a frontal slices, placing transitions as spatial components, and adding time intervals as temporal components, represented in Figure 4. Tensor \(\mathcal{T}\in\mathbb{R}^{m\times n\times t}\) is constructed, where \(m\) represents the flattened size of the STM, \(n\) represents the number of observed transitions in the road network, and \(t\) represents eight time intervals. Frontal slices of tensor \(\mathcal{T}\) can be represented with matrix \(\textbf{{T}}_{:t}\in\mathbb{R}^{m\times n}\), where every STM matrix \(\textbf{{X}}\) is flattened into a vector \(\textbf{{x}}\in\mathbb{R}^{m\times 1}\) and placed into the matrix \(\textbf{{T}}_{:t}\) as column. Dimension \(m\) had the value of 400 as STM size is \(20\times 20\). Instead of using one tensor with all the data, data is divided into several smaller tensors using the grid-based map segmentation, where \(n\) represents the number of the transitions inside one grid cell. Then, the final form of the tensors is \(\mathcal{T}^{(1)},\mathcal{T}^{(2)},...,\mathcal{T}^{(N)}\) where \(\mathcal{T}^{(i)}\in\mathbb{R}^{400\times n\times 8}\). With the proposed approach, anomalies can be captured from different parts of the road network, while smaller spatial dimensions of the cells allows capturing more diverse traffic patterns.
# Bridging the Gap: A Survey and Classification of Research-Informed Ethical Hacking Tools
## 2 Background
### Ethical Hackers  
Traditional media often portrays hackers as mysterious figures, typically depicted wearing hoodies in dimly lit rooms, perpetuating a stereotype prevalent in pop culture. Hackers are commonly seen as computer pirates who infiltrate systems for personal or financial gain. However, historical context reveals a more nuanced understanding. According to the classic definition reported by [PERSON] [26], hackers enjoy the intellectual challenge of overcoming programming limitations and seeking to extend their capabilities. This definition, prevalent until the 1980s and intertwined with notions of democracy and freedom, has evolved, as discussed by [PERSON] and [PERSON] [27]. _Hats_ of different colours are broadly used as a symbolic representation of individuals based on their intentions and actions related to hacking [15]:  
* _White Hat_ (ethical): Embodies the principles of hacker culture by employing technical skills to proactively enhance system security measures. These individuals focus on identifying vulnerabilities and developing defensive strategies to mitigate potential risks.
* _Black Hat_ (malicious): Represents individuals who maliciously exploit vulnerabilities within systems for personal gain or disruptive purposes. Their actions typically involve unauthorised access, data theft, and system manipulation, often resulting in financial losses or reputational damage for targeted entities.
* _Grey Hat_ (undecided): Occupies an intermediary role, engaging in activities that blur the line between ethical and malicious hacking. These individuals engage in operations as both Black Hat and White Hat, depending on the circumstances [27].  
In recent years, cybersecurity and privacy protection have emerged as central themes for all organisations, and professional roles have arisen to address these needs. Penetration testing and malware analysis are among the most sought-after roles in the cybersecurity job market, falling under the main category of EH.
## 3 Results  
### Reduction of Air Pollution  
Air pollution reduction was calculated for each municipality as the difference between a particular scenario and the baseline concentration. The results with the population data were then recalculated as PM\({}_{10}\) and PM\({}_{2.5}\) annual concentration weighted with a population. Some results from the air quality modeling are presented in Figure 1a-d.  
The spatial air pollution concentrations weighted with the population presented above are some of the results obtained for each of the four model runs (baseline and three reduction scenarios). The next step was a population-weighted concentration, and the results for whole regions are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**PM\({}_{2.5}\) [\(\mu\)g/m\({}^{3}\)]**} & \multicolumn{4}{c}{**PM\({}_{10}\) [\(\mu\)g/m\({}^{3}\)]**} \\ \hline
**Region** & **Base.** & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Base.** & **Scenario 1** & **Scenario 2** & **Scenario 3** \\ \hline Lower Silesia & 18.5 & \(-6.58\) & \(-4.57\) & \(-2.82\) & \(33.5\) & \(-10.2\) & \(-7.82\) & \(-5.01\) \\ \hline Lodzkie province & 28.4 & \(-9.01\) & \(-7.62\) & \(-5.98\) & \(42.5\) & \(-12.8\) & \(-10.6\) & \(-9.12\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Annual air pollution with population weighted concentration reduction with baseline results in Lower Silesia and Lodzkie region.  
The biggest reduction was obtained for Scenario 1 for every region and pollutant. Changing to the highest class of solid fuel boiler (Scenario 2) gave a visibly lower reduction than phasing out coal and biomass in the household sector (Scenario 1). The percentage reduction for Scenario 1 for PM\({}_{2.5}\) for Lower Silesia equals 35.6% and for Lodzkie province 31.7%, while for PM\({}_{10}\) it is 30.4% and 30.1%, respectively. The lower air quality in Lodzkie province compared to Lower Silesia produced a higher reduction of PM\({}_{2.5}\), since the emission scenarios were percentage based. It is worth noting that the data in Table 5 cannot be directly related to measurements from the monitoring station since they are based on population-weighted concentration. However, the statistics fit the health impacts of air pollution the best, because they provide better information on human exposure to PM\({}_{2.5}\) and PM\({}_{10}\).
## 1 Introduction
investigated the superiority of B1C/B2a observation quality for early BDS-3 [20], [PERSON] et al. performed a comparative analysis of BDS-3 orbit determination using B1I/B3I and B1C/B2a observations across nine MGEX stations. Their findings revealed that the accuracy of the BDS-3 orbit generated from B1C/B2a observations was improved by approximately 9% compared with the B1I/B3I-based orbits [21]. [PERSON] et al. evaluated BDS-3 orbits estimated using B1I/B3I and B1C/B2a observations, comparing them with orbits from other MGEX analysis centers. They also validated these orbits using Satellite Laser Ranging (SLR) data, demonstrating that the B1C/B2a-based BDS-3 orbits exhibit superior performance compared with the B1I/B3I-based orbits [22]. [PERSON] et al. further enhanced BDS-3 MEO orbits with an ambiguity resolution using B1C/B2a observations. Their evaluation results indicated that a higher percentage of ambiguity fixing, lower SLR residuals, and enhanced performance for PPP applications were achieved by using B1C/B2a combination [23]. [PERSON] et al. conducted a comprehensive comparison of data quality, precise orbit determination accuracy, PPP performance, and clock offset product accuracy based on B1C/B2a and B1I/B3I observations. They concluded that B1C/B2a-based results consistently exhibited better performance [24]. However, these studies based on B1C/B2a dual-frequency combination observations did not cover the analyses of the ERP estimates, which need be further investigated. As of November 2021, with over 140 globally distributed MGEX stations supporting B1C/B2a signals [24], there is a significant advantage for ERP estimates based on BDS-3 B1C/B2a observations. In this research, we present our results as follows. Initially, the observation quality is comprehensively analyzed. Subsequently, the orbit determination results are assessed through orbit overlapping, a comparison with external products, and validation using SLR data. Following this, the ERP derived from BDS-3 B1C/B2a-based observations are thoroughly evaluated. Finally, we provide conclusions based on the analysis results.
## 3 Results
### Effect of Char Loading  
As shown in Figure 6, the increased char loading exerted a subtle influence on the NO reduction rate with increasing CO/NO ratio. It was found that the difference between the reduction ratio with 0.02 g and 0.2 g char was gradually decreased as the CO/NO ratio increased in the range of 0-10. This difference between them even became unobservable when the CO/NO ratio increased up to 10. At a low concentration of CO, the enhanced NO reduction with increasing char mass mainly contributed to the strengthened carbon-consuming reactions (1) and (2). However, at higher CO concentration (CO/NO = 10), carbon could not be consumed, and enough carbon sites could be provided by 0.02 g char for the catalytic reduction reaction. Therefore, under this condition, it is not suggested that NO reduction can be improved simply by increasing the amount of char.
## 3 Protection Coordination Constraints
### Time-Overcurrent Protection  
In case of time-overcurrent protection [26; 72], which is typically used for recloser-fuse coordination, first the operating time of a protective device is determined:  
for \(\forall(i,j,k)\in\mathcal{P}:\)  
\[t^{p}_{ijk}=\min_{\phi\in\Phi}\frac{A\cdot T_{ijk}\cdot\max(\Re(t^{\phi}_{ijk }),0)}{(t^{\phi}_{ijk/f})^{I^{p}_{ijk}})^{B}-1}, \tag{53}\]  
where \(T_{cj}\) is the time-dial setting and \(I^{p}_{cj}\) is the pickup current setting, which are decision variables for the OPC problem (both are subjected to upper and lower bounds). \(\max(\Re(I^{-}_{ijkf}/V^{a}_{ij}),0)\) is a directional element that prevents the recloser from operating for upstream faults, even in the presence of distributed generation. This is illustrated for two different values of \(I^{p}_{ijk}\) in Figure 11.  
The operating time of the recloser that trips during a fault is that of the minimum \(t^{p}_{ijk}\) of the set of reclosers in the microgrid:  
for \(\forall(i,j,k)\in\mathcal{P}:\)  
\[\delta_{ijk}t^{p}_{(1)}\leq t^{p}_{ijk}-T_{c} \tag{54}\] \[t^{p}_{(1)}=\sum_{ijk}t^{p}_{ijk}(1-\delta_{ijk})\qquad\quad \sum_{ijk\in\mathcal{E}_{p}}(1-\delta_{ijk})=1. \tag{55}\]  
Note that (55) requires that exactly one recloser operate during a fault. Additionally, there is a constraint on maximum operating time:  
\[0\leq t^{p}_{(1)}\leq\tilde{t}^{p}. \tag{56}\]  
It is common practice in optimal protection literature to simplify the problem formulation by not explicitly considering post-fault power flow, but rather to group protective devices into pairs and require that the downstream device in each pair operate before the upstream device is subjected to a coordination time interval [26].
# Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
## 4 Active Learning and Object Detection: A Literature-Driven Multi-Perspective Analysis
### (RQ4) Query Strategies Taxonomy
#### 4.4.1 Informativeness-Based Methods  
According to [49], informativeness measures the effectiveness of an instance on reducing the uncertainty of a model. This sampling strategy identifies unlabelled items that are near a decision boundary of the current ML model, i.e., samples that are expected to be a less obvious classification for the model. The main concept is to sample labels with the highest uncertainty, which are considered to be the most informative. As such, selected samples can belong to the subset with the lowest prediction probabilities, simultaneously ensuring the presence of useful information to improve the model. In the uncertainty approach, a naive way to define uncertainty--based on black-box methods--is to sample data according to the model's predictions on the unlabelled data [51]. In black-box methods [46], the image uncertainty is computed from the confidence returned by the softmax layer regardless of the neural network architecture. On the other hand, the white-box methods [46] focus on the network architecture and its knowledge to compute image scores (see Figure 8).  
Uncertainty-based methods are widely used, but they are prone to the problem of data bias [60], since the selected data are hardly representative of the full unlabelled dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**References** & **U** & **FD** & **H** & **2D** & **3D** & **WS** & **SS** & **E** & **MC** & **CS** \\ \hline
[5, 9, 14, 34, 43, 47, 48, 55, 60, 62, 63, 66, 69] & ✓ & & ✓ & & & & & \\
[59, 68, 77] & ✓ & & ✓ & & ✓ & & & & \\
[56, 64] & ✓ & & ✓ & & & ✓ & & & \\
[46, 70] & ✓ & & ✓ & & & & ✓ & & \\
[17] & ✓ & & ✓ & & & & & ✓ & \\
[50, 78] & ✓ & & ✓ & & & & & & \\
[18] & ✓ & & ✓ & ✓ & & & & & \\
[65] & ✓ & & ✓ & & & & & ✓ & \\
[37] & & & ✓ & ✓ & & & ✓ & & \\
[71] & & & ✓ & ✓ & ✓ & ✓ & & & \\
[36, 44, 45, 57, 61, 67] & & & ✓ & ✓ & & & & & \\
[54] & ✓ & & & ✓ & ✓ & & & ✓ & \\
[53] & ✓ & & & ✓ & & & ✓ & ✓ & \\
[15, 16] & ✓ & & & ✓ & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: References grouped by the AL Taxonomy. Uncertainty (U), Feature Distribution (FD), Hybrid (H), Weak-supervised (WS), Semi-supervised (SS), Ensemble (E), Monte-Carlo Dropout (MC), and Core-Set (CS).  
Figure 8: Image adapted from [8]. Black-box methods propose the use of traditional uncertainty measurement. White-box methods use the network architecture and its knowledge to compute the image score, i.e., uses information from the feature extraction stage and the prediction stage.