esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### The State-of-the-Art Methods
#### 2.2.1 Magnetostatic Active Contour (MAC) Model  
The magnetostatic active contour (MAC) model is based on the level set representation with edge-based description [39]. MAC represents the active contour using an implicit model in which the contour, \(c\), is defined as follows:  
\[c=\{\bar{x}|\phi(\bar{x})=0\}, \tag{6}\]  
where \(\phi:\mathbb{R}^{2}\rightarrow\mathbb{R}\). For image segmentation MAC considers the following PDE:  
\[\frac{\partial\phi}{\partial t}=\alpha s(\bar{x})\
abla\bigg{(}\frac{\
abla \phi}{|\
abla\phi|}\bigg{)}|\
abla\phi|-(1-\alpha)F(\bar{x})\
abla\phi, \tag{7}\]  
where \(\alpha\), \(s(\bar{x})\), and \(F(\bar{x})\) are a real constant, the stopping function (Sobel filter), and the magnetostatic force, respectively. The advantages of MAC compared to other related works are as follows:  
* Significant improvement in initialization invariance;
* Significant improvement in convergence capability; the contour attracts into deep concave regions;
* Is not affected by stationary point and saddle point problems;
* It is able to capture complex geometries;
* It is able to capture multiple objects with a single initial contour.
## 4 The Layout Designing of the Collaborative Workplace Supported by a Modeling Paradigm
### Problem formalization  
It is possible to formalize the HRC workplace layout designing problem by expressing the elements of the paradigm in the following way:  
1. Set of passive resources \(P\) (previously defined in Section 4.1.1) located within the workplace floor with pose defined as \(\{x_{p_{i}},y_{p_{i}},\theta_{p_{i}}\}\forall p_{i}\in P\); each passive resource can be characterized by a set of points of interest, e.g., geometrical center of gravity, vertices, and points reachable by active resources;
2. Set of active resources \(A\), i.e., robots and human operators as stated in Section 4.1.1, each one characterized by a series of attributes and skills;
3. Set of elementary tasks that have to be performed by the active resources or by using passive resources (e.g., machine tools); each task is described by type and level of interaction as stated in Section 4.1.1;
4. Set of task centers (i.e., HTC, RTC, and CTC as defined in Section 4.1.1) within the workplace \(W\) with pose expressed as \(\{x_{w_{i}},y_{w_{i}}\}\forall w_{i}\in W\);
5. Set of basic locations within the workplace that correspond to the position where workpieces enter/exit the workplace (see the Logistic Spaces definition in Section 4.1.1).  
Therefore, considering the above definitions, the solution to the design of the layout of the collaborative workplace could be expressed in matrix form by the following entities:  
1. A matrix \(T_{lc}\) of task-task center assignments;
2. A matrix \(L_{cp}\) of task center-passive resource assignments;
3. A matrix \(F_{ap}\) of active resource-passive resource assignments.  
This means that the proposed approach helps the designer in defining the optimized and standard compliant layout by identifying the task centers and defining their stances within the workplace according to an optimization criterion.  
The optimization problem consists of finding a vector \(w\) that minimizes a nonlinear objective function _f(w)_ subject to nonlinear constraints and bound constraints. It is known in the literature as a constrained nonlinear programming problem (NLP) [56]. The constraints are expressed by means of inequalities or equalities.  
Therefore, it is possible to formulate the current problem as:  
\[\min_{w}f(w)\text{ subject to }\left\{\begin{array}{l}g(w)\leq 0;\\ geq(w)=0;\\ lb\leq w\leq ub.\end{array}\right. \tag{1}\]  
with  
\[f(w)=a_{1}E(w)+a_{2}S(w)+a_{3}G(w); \tag{2}\]  
where \(w\) is the vector containing the Cartesian coordinates of the identified task centers; _g(w)_ and _geq(w)_ represent the constraints as equalities and inequalities; _lb_ and _ub_ stand for lower bounds and upper bounds. In Equation (2), \(E(w)\) takes into account the ergonomicaspect, \(S(w)\) is the occupied floor space within the workplace and \(G(w)\) is an additional indicator; \(a_{1}\), \(a_{2}\), and \(a_{3}\) are weighting factors.
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 1 Introduction
Since this approach directly extracts information from the jamming waveforms collected in real-time, it eliminates the need to predefine the spectral shape and response time of the jamming, making it effective in handling various types of jamming signals;
* We propose a reactive jamming utilization scheme based on a Multi-Sequence Frequency Hopping communication framework (MSFH), referred to as IMSFH, which exhibits enhanced anti-jamming performance as reactive jamming power increases. This scheme streamlines the processing of communication signals in complex jamming environments, thereby ensuring real-time communication for users. It effectively harnesses intelligent reactive jamming in scenarios where multiple types of jamming coexist, making it applicable to more complex environments. * The proposed communication framework was compared with MPFH, FH, and WGMPFH in terms of communication performance against intelligent reactive jamming. Simulation results indicate that the proposed framework achieves significantly better bit error rate (BER) performance compared to MPFH, FH, and WGMPFH when countering intelligent reactive jamming. Additionally, the proposed framework was compared with IDFH in complex jamming environments. The simulation results demonstrate that the proposed communication framework outperforms IDFH significantly under such conditions. The remainder of this paper is organized as follows. In Section 2, we review the related work. Section 3 presents the system model and problem formulation. Furthermore, Section 4 provides a detailed description of the jamming exploitation method based on IMSFH, and Section 5 discusses the simulation results and performance analysis. Finally, conclusions are provided in Section 6.
# Improving Machine Learning Identification of Unsafe Driver Behavior by Means of Sensor Fusion
## 1 Introduction  
About 90% of road accidents occur due to human errors following fatigue, inattention, or drowsiness [1]. Recent advances in machine learning technologies have led to a whole new set of applications and systems designed to assist the driver in order to prevent accidents. An example of these systems is represented by advanced driver assistance systems (ADAS) which are being developed to enhance vehicle safety by assisting or automating some driving maneuvers. In particular, they are currently designed, for instance, to avoid collisions by implementing collision avoidance or pedestrian crash avoidance by means of radar technology [2]. Moreover, one of the fundamental tasks in avoiding dangerous situations is to identify unsafe behaviors while driving and alert the driver of the possible risks.  
Modern vehicles are equipped with several hundreds of sensors and electronic control units (ECUs) devoted to monitoring and optimizing several car functions, such as fuel injection, braking, gear selection, and so on. Each ECU is connected with others by means of a standard communication bus called the controller area network (CAN). The access to the CAN bus provides the ability to read a multitude of parameters and signals, coming from in-vehicle sensors, which can provide a snapshot of the driving performance [3].  
Recently it has been shown that machine learning technologies, applied to in-vehicle sensors signals, can successfully identify driving styles and recognize unsafe behaviors [4; 5].  
In this paper, a novel methodology to improve machine learning identification of unsafe driver behavior, based on sensor fusion, is presented. In particular, we investigated the use of different external sensors, a gyroscope and a magnetometer, to increase the classification performance. Moreover, unlike other approaches in the literature, our work makes use of an objective methodology which, starting from the work presented by [PERSON] et al. in 2016 [PERSON] et al. (2016), allows labeling each driving interval as safe or as unsafe by looking at the relationship between speed and lateral and longitudinal acceleration of the vehicle. Then, the performances of the machine learning algorithms were measured versus this ground truth.  
As a final remark, one of the strengths of the proposed sensors fusion is that our work does not use any tracking system such as GPS, or of any recording device, to capture video and audio, which if incorrectly used could compromise the driver's privacy.  
The rest of the paper is organized as follows: Section 2 provides an overview of the state-of-the-art. Section 3 describes the proposed method. In Section 4 we present the results of the experimental evaluation; in Section 5 we report some conclusive remarks and discussion.
# Online Distributed User Association for Heterogeneous Radio Access Network
## 3 System Model and Problem Formulation
If the BS schedules for equal resources for each UE in an OFDMA-based MAC protocol [17; 19; 27; 39], then the actual UE data rate will be given by the following equation:  
\[R_{i,j,k}=\frac{r_{i,j,k}}{|N_{j,k}|}, \tag{3}\]  
where \(|N_{j,k}|\) is the total number of UEs attached to BS \(j\) at tier \(k\). Note that if a scheme can evenly distribute UEs among the BSs, then the minimum data rate will also increase as the per-user resource sharing rate will be high. We assume that equal time-sharing will be used to schedule the UE, in case a BS is associated with multiple UEs. Hence, the throughput of an UE \(i\) depends on the total number of UEs attached to and sharing the same BS [17; 19; 39]. While defining the \(load_{1}\), we consider the current load of a BS to be the number of attached UEs. The \(load_{1}\) of a BS is defined as follows:  
\[\lambda_{j,k}^{load1}=|N_{j,k}|, \tag{4}\]  
Similarly, while considering the average data rate of a BS as \(load_{2}\), we have the following expression,  
\[\lambda_{j,k}^{load2}=\frac{\sum R_{i,j,k}}{|N_{j,k}|}. \tag{5}\]  
Note that both Equations (4) and (5) represent the load of a BS by considering different parameters. One important difference in \(\lambda_{j,k}^{load1}\) and \(\lambda_{j,k}^{load2}\) is that, the higher \(\lambda_{j,k}^{load1}\) value signifies a higher load of a BS, while a higher \(\lambda_{j,k}^{load2}\) value implies a lower load. The definitions of all the variables are given in Table 2.  
Before the problem statement is formally defined, we highlight the trade-off problem between throughput and fairness. The authors in Ref. [49] presented a family of fairness functions by considering five axioms of fairness measures. [PERSON]'s fairness [50], \(\alpha\)-fairness, and entropy are special cases of this family. Furthermore, authors in Refs. [51; 52] provide an analytical framework of a throughput-fairness trade-off, and show that an approach can be considered more or less fair compared to other ones, depending on the fairness definition. In this work, we maximize the throughput of the network by considering the log utility function. This ensures that allocation of more resources for a well-served UE has a low priority compared to the allocation of more resources to UEs with low data rates [17].
## 2 Problem Formulation  
The reverberant signal results from the convolution of the anechoic speech signal and a causal AIR. The anechoic speech signal can be expressed in the Short-time Fourier Transform (STFT) domain by \(S(k,l)\), where \(k\) and \(l\) are the frequency and frame indices, respectively. According to the convolutive transfer function (CTF) model [2], the reverberant speech signal \(Z(k,l)\) can be expressed as Equation (1)  
\[Z(k,l)=\sum\limits_{l^{\prime}=0}^{+\infty}H\big{(}k,l^{\prime}\big{)}S\big{(} k,l-l^{\prime}\big{)}, \tag{1}\]  
where \(H(k,l)\) represents the AIR and it can be split into three components as Equation (2)  
\[H(k,l)=\left\{\begin{array}{ll}H_{d}(k),&l=0\\ H_{e}(k,l),&1\leq l\leq N_{e}\\ H_{l}(k,l),&l>N_{e}\end{array}\right. \tag{2}\]  
where \(H_{d}(k)\) is the direct sound, \(H_{e}(k,l)\) consists of early reflections, \(H_{l}(k,l)\) represents later reflections, and \(N_{e}\) usually corresponds to approximately 20-50 ms. The late reverberant speech component \(Z_{l}(k,l)=\sum\limits_{l^{\prime}=N_{e}+1}^{+\infty}H_{l}(k,l^{\prime})S(k,l- l^{\prime})\) mainly decreases the speech fidelity and intelligibility [4] and needs to be suppressed. Hence, the main challenge is to derive an estimator for the spectral variance of the late reverberant speech component (i.e., LRSV) \(\lambda_{l}(k,l)=E\Big{[}\big{|}Z_{l}(k,l)\big{|}^{2}\Big{]}\), where \(E[\cdot]\) denotes the expectation operator. Once \(\lambda_{l}(k,l)\) is given, a spectral enhancement method [11] can be used to suppress the late reverberation.
## 3 Proposed Improved Hybrid SSA with an EDO (ISSAEDO) Algorithm
### Results of the Proposed ISSAEDO Compared with Other Recent Algorithms
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Algorithm** & \(I_{ph}\) **(A)** & \(I_{sd}\) **(in A)** & \(R_{s}(\Omega)\) & \(R_{sh}(\Omega)\) & \(\gamma_{1}\) & \(I_{sd}\) **(in A)** & \(\gamma_{2}\) & **RMSE** \\ \hline SSA & 0.7611237070 & 0.2553533450 & 0.0372915872 & 46.3936672000 & 1.4578304200 & 1.1351825925E-03 \\ EDO & 0.1494502860 & 0.6416363660 & 0.2519534990 & 85.2178070000 & 1.4772455100 & 8.7962970619E-03 \\ SSAEDO & 0.9631364110 & 0.0491793204 & 0.2857274100 & 79.7732647000 & 1.3100563200 & 1.5303729578E-03 \\ DE & 0.7607755300 & 0.3230207970 & 0.0363707928 & 53.718523000 & 1.4811835900 & **9.8602187789E-04** \\ SSAED & 0.7607755240 & 0.3230210790 & 0.036370790 & 53.7186219000 & 1.4811836700 & 9.8602187790E-04 \\ EDODE & 0.3842538840 & 0.099486933 & 0.16755090700 & 65.4048061000 & 1.0850875010 & 9.979333516E-04 \\ ISSAEDO & 0.7607755300 & 0.323020207850 & 0.0363707930 & 53.7185252000 & 1.4811835800 & **9.8602187789E-04** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparative summary of the diverse components of the ISSAEDO for SDM. DERL, and EJAYA) were capable of reaching the best minimum, best maximum, and best mean RMSE results for the SDM in comparison with other algorithms. The presented ISSAEDO obtained the minimum STD value. Therefore, the presented ISSAEDO has the potential to produce stable and reliable results when compared to other methods. Finally, ATLDE demonstrated the minimum execution time. Table 14 presents the values of the seven unknown parameters Iph, Isd1, Rs, Rsh, \(\gamma\)1, Isd2, \(\gamma\)2, and RMSE for DDM. Based on the values of RMSE shown in Table 14, ISSAEDO only obtained the lowest RMSE value (9.8248485179E-04). Table 15 shows the statistical outcomes (the minimum, maximum, mean, and STD) extracted from the presented ISSAEDO and the competitive algorithms. The proposed ISSAED, and IGSK algorithms are capable of reaching the best least RMSE values for the DDM compared to the state-of-the-art methods. The MTLBO is the only algorithm that achieved the best maximum RMSE values, the best mean RMSE values, and minimum STD values. Finally, ATLDE achieved the minimum execution time.
## 2 Problem Description
### Coordination between the VPP and the Power Market
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Primary Control** & **Secondary** & **Secondary** & **Tertiary Control** & **Tertiary Control** \\  & **Reserve** & **Control Reserve 1** & **Control Reserve 2** & **Reserve 1** & **Reserve 2** \\ \cline{2-6}  & **GF Control** & **Equivalent** & **LFC** & **EDC** & **EDC** & \\ \hline Response time & Within 10 s & Within 5 min & Within 5 min & Within 15 min & Within 15 min \\ \hline Continuous operation time & At least 5 min & At least 5 min & At least 30 min & 3 h & 3 h \\ \hline Clearing time & 1 week ago & 1 week ago & 1 week ago & 1 week ago & 1 day ago \\ \hline Minimum bid amount & 5 MW & 5 MW & 5 MW & 5 MW & 5 MW \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Japanese BC market’s requirement [25]. Figure 1: The structure of the VPP. RESs is expected to be utilized without any curtailment. It can be solved by adjusting the day-ahead operating schedule in the intraday market (Figure 2). However, this adjustment must ensure that the VPP has enough energy to provide reserve service during the next contracting period. From the VPP operator's point of view, a two-stage scheduling model is proposed to maximize the VPP's profit in the reserve capacity and energy markets. This model determines the optimal reserve capacity and the possible operating scenarios of the VPP in the day-ahead and intraday markets. The uncertainty in RES and demand and the probability of reserve activation will be considered in this model. The two stages are outlined as follows:  
1. In the first stage, the VPP decides its trading strategy in the BC market a few days before operating based on the long-term forecasted value. The reserve capacity \(SR^{t}\) should be higher than the minimum bid amount and be maintained over the required continuous operating duration \(\tau_{iON}\). The variable \(SR^{t}\) is a here-and-now decision made before the realization of uncertain parameters. 2. After determining \(SR^{t}\), the VPP should predict its energy trading scenarios \(P_{sell}^{t}/P_{buy}^{t}\) at hour \(t\) on the operating day. These scenarios are based on long-term forecasts and will be revised in the day-ahead market with the short-term forecast figures for load and RESs. At the same time, the scenarios of reserve activation are also taken into account to predict the amount of buying/selling energy that needs to be adjusted in the intraday market. To simplify the model, we consider only two extreme scenarios of reserve activation: the entire reserve/no reserve is activated, where the probabilities of these cases in hour \(t\) are assumed to be \(p_{SR}^{t}\), and \(\left(1-p_{SR}^{t}\right)\), respectively.
# MHBase: A Distributed Real-Time Query Scheme for Meteorological Data Based on HBase
## 2 Related work
### Optimizing  
As HBase only offers the rowkey-based index [18], it can quickly look up specific records by rowkey because of the rowkey dictionary sequence. The only way to query other columns is to use a filter to perform a full-table scan, which has the effect of degrading performance. Like RDBMS, it is impossible to retrieve data using only a primary key for the meteorological application. The index scheme for HBase has always been a hot topic in open source communities. IHBase [19] is a region-level index structure that uses Bitmap [20] to organize indexes and is suitable for read-only scenarios. Furthermore, IHBase stores indexes in memory; therefore, indexes must be rebuilt every time a _RegionServer_ crashes or restarts and this rebuild operation requires much time to complete.  
Some giant companies provide their own enterprise solutions for HBase optimizing. For example, Cloudera [21] integrates Solr with HBase to present a much more flexible query and index model of HBase datasets. However, this system is complicated to deploy and it will throw exceptions unless the servers are properly tuned. _The Intel(r)Distribution for Apache Hadoop\({}^{*}\) Software_[22] includes almost all of the Hadoop ecosystems from basic project HBase to data mining project Mahout. This release has enhanced the secondary index of HBase, but it must be deployed on a node that has more than two quad-core CPUs, 16 GB RAM and does not have RAID disks.  
Huawei's recent contribution [23], which uses coprocessors to implement secondary index, is recognized as the best solution and this has attracted the attention of the open source community. Ge proposes CinHBa [24], whose index model is similar to Huawei's contribution to provide a high-performance query capable of generating non-rowkey columns. Lastly, Apache Phoenix [25] integrates this scheme internally after version 4.1 and also provides long-term technical  
Figure 3: Vertical partitioning in HBase.  
support. In this paper, we use this idea as a reference to propose a real-time query scheme for meteorological applications.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 3 Satellite Networks Vulnerability Risk Assessment Method
### Comparative Analysis with Existing Assessment Method  
#### Overview of CVSS Framework  
The CVSS is a widely adopted standard for IT vulnerability assessment. Its methodology employs three hierarchical metric groups to calculate severity scores (0.0-10.0):  
1. Base metrics evaluate intrinsic vulnerability characteristics through two subcomponents. These include exploitability metrics (attack vector (AV), attack complexity (AC), privileges required (PR), and user interaction (UI)) and impact metrics (confidentiality(C), integrity (I), and availability (A), with scope (S) determining whether exploitation affects external components).  
The base score combines exploitability and impact subscores. In CVSS 3.1, exploitability is weighted as 8.22 \(\times\) AV \(\times\) AC \(\times\) PR \(\times\) UI, while impact depends on confidentiality, integrity, and availability losses. Scope alters the impact formula, elevating scores if vulnerabilities affect external components.  
Temporal metrics adjust the base score based on dynamic factors, including exploit code maturity (E), remediation level (RL), and report confidence (RC).  
Temporal Adjustments multiply the base score by temporal metrics (e.g., Temporal Score = Base Score \(\times\) E \(\times\) RL \(\times\) RC).  
Environmental metrics customize scores for specific organizational contexts by modifying base metrics using security requirements (CR, IR, AR) and adjusting attack vectors or complexity (e.g., MAV, MAC).  
The final environmental score adjusts the temporal score using these contextual parameters.
# A Bayesian Control Chart for Monitoring Process Variance
## 3 The Construction of the Shewhart-Type Variance Chart with the Bayesian Approach
### The Parameters of the Prior Distribution of \(p\) and Process Variance Are Known
#### 3.1.2 The Out-of-Control ARL of the Shewhart-Type Bayesian Variance Chart
Because a new statistic, \(M_{T+t}\), follows from a binomial distribution with parameters \(n\) and \(p\), the posterior predictive distribution of \(M_{T+t}\) is the beta-binomial distribution with parameters \(n\), \(\hat{\alpha}\), and \(\hat{\beta}\), written as:  
\[M_{T+t}\ \sim Beta-Binomial\left(n,\hat{\alpha},\hat{\beta}\right),\ \text{where}\ \hat{\alpha}=1+\sum_{t=1}^{T}m_{t},\ \hat{\beta}=1+nT-\sum_{t=1}^{T}m_{t}. \tag{21}\]  
Thus, the in-control expected value of \(p\) is estimated with:  
\[E_{0}(p)=\hat{\alpha}/\left(\hat{\alpha}+\hat{\beta}\right). \tag{22}\]  
The upper and lower control limits of \(M_{T+t}\) are obtained, respectively, as follows:  
\[UCL_{M}=\frac{n\hat{\alpha}}{\hat{\alpha}+\hat{\beta}}+3\sqrt{\frac{n\hat{ \alpha}\hat{\beta}(\hat{\alpha}+\hat{\beta}+n)}{\left(\hat{\alpha}+\hat{\beta }\right)^{2}(\hat{\alpha}+\hat{\beta}+1)}}\, \tag{23}\]  
\[LCL_{M}=\frac{n\hat{\alpha}}{\hat{\alpha}+\hat{\beta}}-3\sqrt{\frac{n\hat{ \alpha}\hat{\beta}(\hat{\alpha}+\hat{\beta}+n)}{\left(\hat{\alpha}+\hat{\beta }\right)^{2}(\hat{\alpha}+\hat{\beta}+1)}}. \tag{24}\]  
The \(M_{t}\) for the \(T\) preliminary samples are plotted on the resulting Bayesian variance chart. If no points fall outside the control limits, then the process is regarded as the in-control state. Thus, the \(M_{T+t}\) for the subsequent monitoring samples are plotted in order. If either \(M_{T+t}\geq UCL_{M}\) or \(M_{T+t}\leq LCL_{M}\), the process is deemed to show some out-of-variance-control signals. In the following discussions, we assume that the number of preliminary samples, \(T\), is large enough so that \(\vartheta^{2}\) and \(\frac{\hat{\alpha}}{\hat{\alpha}+\hat{\beta}}\) are almost the same as the true values of \(\sigma_{0}{}^{2}\) and \(E_{0}(p)\), respectively.