esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 New Electronic Control System of the Receiver for the Installation on SRT
### Monitoring and Control Boards for the ALMA Local Oscillator at SRT  
As mentioned above, the adaptation of the ex-PdBI receiver to SRT involves the installation of the NRAO local oscillator developed for the ALMA Band 3 project. A new ad hoc prototype of an electronic board has been developed to remotely tune this local oscillator. This prototype is composed of two modules: one for managing the AMC and the other for controlling the PA, respectively. Since the two modules require high DC power supplies (i.e., AMC/PA: +6 V and 1.5 A, \(\pm 15\) V and 500 mA), each control board is equipped with a power supply stage that ensures high stability at high currents. In addition, a further protection stage to prevent malfunction is built on each board.  
The board for the control of the AMC module is designed to provide feedback for the voltages by the power supply stage. The board is based on two high-resolution ADCs, model ADS1115, fitted on Adafruit modules from Texas Instruments [44], which are capable of reading all AMC voltages with 16-bit resolution. The board is equipped with an inter-integrated circuit (I2C) bus for communication with the Arduino UNO REV3 microcontroller (see Figure 10).  
The PA module is controlled such that the gate and drain voltages can be set independently. The gate and drain voltages are set by two programmable potentiometers, one for each PA output channel. The remote control of these potentiometers and data sending are possible thanks to a serial peripheral interface (SPI) bus, which is directly connected to the Arduino microcontroller. The PA control board is also equipped with two high-precision ADCs [44] that provide feedback about the state of the amplifier junction's polarisation.  
A dedicate module of Tester100 GHz was developed to test the monitoring and control electronics boards for the ALMA Local Oscillator [31], as well as for the other electronic boards. This module permits the reading of all voltage values generated by the two boards and is useful for the tuning of the ALMA local oscillator. The whole system is  
Figure 10: Ad hoc electronic board for the control of the AMC and PA modules of the ALMA local oscillator.  
equipped with an I2C bus, which serves to the communication between the ADCs of the two boards and the Arduino microcontroller, and a serial peripheral interface (SPI) bus to communicate between the potentiometers and the Arduino microcontroller. The module provides real-time feedback to the users.
## 4 Outer Approximation Cutting Method for UFR
\begin{split}&\min_{y,\Delta w,s^{j}\geq 0}s^{j}\\ & s.t.\mathbf{B}y+\mathbf{C}\mathbf{\Delta}w\leq\mathbf{b}^{{}^{ \prime}}\\ &\mathbf{B}_{j}y+\mathbf{C}_{j}\mathbf{\Delta}w+s^{j}\geq b_{j}^{{} ^{\prime}}\end{split}  \tag{27}\]  
Here, \(s^{j}\) represents the slack variable for identifying umbrella constraints, while \(\mathbf{B}_{j},\mathbf{C}_{j},b_{j}^{{}^{\prime}}\) denotes the corresponding \(j\)-th row. At this stage, the initial space required for model analysis has been obtained, and the umbrella constraints within the model have been identified. However, Equation (24) cannot yet be directly solved using a linear programming solver due to the nonlinearity of its objective function. Therefore, further reformulation is required, resulting in problem (28):  
\[ \begin{split}& f=\quad\max_{z}\left(\mathbf{z}^{\intercal}\mathbf{b}^{{}^{ \prime}}+\max_{\Delta w}(-\mathbf{z}^{\intercal}\mathbf{C}\mathbf{\Delta}w) \right)\\ & s.t.\quad\mathbf{B}^{\intercal}\mathbf{z}=0\\ &\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad-1\leq \mathbf{z}\leq 0\\ &\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \mathbf{D}^{c}\mathbf{\Delta}w\leq\mathbf{d}^{c}:\mathbf{\
u}^{d}\end{split}  \tag{28}\]  
Here, \(\mathbf{\
u}^{d}\) represents the dual variable of the initial space constraints. When the inner optimization problem reaches its optimal solution, the constraints satisfy the constraint qualifications, and its KKT conditions can be expressed as follows:  
\[\left\{ \begin{array}{l}\mathbf{C}^{\intercal}\mathbf{z}+(\mathbf{D}^{c})^{ \intercal}\mathbf{\
u}^{d}=0\\ \mathbf{\
u}^{d}\geq 0\\ \mathbf{D}^{c}\mathbf{\Delta}w\leq\mathbf{d}^{c}\\ \mathbf{\
u}^{d}(\mathbf{d}^{c}-\mathbf{D}^{c}\mathbf{\Delta}w)=0\end{array} \right. \tag{29}\]  
To eliminate the nonlinear constraints in Equation (29), the big-M method is applied to transform \(\mathbf{\
u}^{d}(\mathbf{d}^{c}-\mathbf{D}^{c}\mathbfHere, M is a sufficiently large constant, and \(\mathbf{\alpha}\) is a binary variable used to decouple the nonlinear constraints. So far, the model is obtained as follows:  
\[ \begin{split} f=&\max_{\mathbf{z},\Delta\mathbf{w},\mathbf{v}^{ \text{d}}}z^{\text{T}}\mathbf{b}^{\text{'}}+(\mathbf{\mathrm{d}}^{\text{c}})^{\text{T}} \mathbf{v}^{\text{d}}\\ s.t.&\mathbf{\mathrm{B}}^{\text{T}}\mathbf{z}=0\\ &-1\leq\mathbf{z}\leq 0\\ &\mathbf{\mathrm{C}}^{\text{T}}\mathbf{z}+(\mathbf{\mathrm{D}}^{\text{c}})^{ \text{T}}\mathbf{v}^{\text{d}}=0\\ & 0\leq\mathbf{v}^{\text{d}}\leq M\mathbf{\alpha}\\ & 0\leq\mathbf{\mathrm{d}}^{\text{c}}-\mathbf{\mathrm{D}}^{\text{c}}\Delta \mathbf{w}\leq M(1-\mathbf{\alpha})\end{split}  \tag{31}\]  
Equation (31) can be solved using commercial software, and its solution determines whether the most critical point in the current space lies within the feasible region. The algorithm flowchart Figure 2 is as follows:
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 2 Related Work
### VANETs and 5G-Enabled Cloud-Assisted VANETs  
As an extension of mobile ad-hoc networks (MANETs) [23], the main entities in vehicular ad hoc networks (VANETs) include the vehicles, the fixed infrastructures along the roads, called road side units (RSU), and an over-viewer third party, called Trusted Authority (TA), in charge of registration, certification and revocation of all the entities within the VANETs architecture. Commonly, VANETs architecture is classified into two main communication means namely vehicle-to-vehicle (V2V) and vehicle to infrastructure (V2I) [24]. The computational cost for the value-added applications in VANETs requires huge computation capabilities, which led to the mixture of VANETs and cloud computing, called _VANETs using Cloud_[25]. _VANETs using Cloud_ is defined as vehicular networks equipped with smart devices which communicate with the cloud in the same way as our mobile phones connect to different servers located in the cloud. _VANETs using Cloud_ was introduced in [26] by [PERSON] et al. for the first time. [PERSON] et al. suggested an autonomous vehicular cloud (AVC) architecture as a special case of _VANETs using Cloud_. In [27], [PERSON] et al. presented additional services by combining cloud computing and VANETs: Computing as a Service (CompaaS), Storage as a Service (STaaS), Network as a Service (NaaS), Cooperation as a Service (CaaS), Entertainment as a Service (ENaaS), Information as a Service (INaaS) and Traffic-Information as a Service (TIaaS). [PERSON] et al. pointed out the feasibility of _VANETs using Cloud_ compared to convectional VANETs. Also, the feasibility of _VANETs using Cloud_ was approved by several researchers [28; 29; 30; 31; 32]. _Vehicular Cloud_ refers to the full utilization of vehicle devices as computers to form mobile servers. In this architecture, one could use the vehicle's OBU to make his/her personal cloud. As a combination of _Vehicular cloud_ and _VANETs using Cloud_, _Hybrid vehicle cloud_ was proposed. The proposed protocol is built on _VANETs using Cloud_ framework, i.e., cloud-assisted vehicular networks. In the following sections, vehicular networks and cloud-assisted vehicular networks are used interchangeably.
## 2 Dynamic Modeling
### Dynamic Modeling of Coaxial HAUV  
Referring to the relevant experience with underwater robots, the coaxial HAUV dynamics can be expressed in Newton-Euler equation form as  
\[\left\{\begin{array}{l}\dot{\eta}=J\
u\\ M\dot{\
u}+C_{v}\
u+D_{v}\
u+g_{v}=\tau_{v}+\tau_{d}\end{array}\right. \tag{23}\]  
where \(\tau_{d}=\begin{bmatrix}d_{p}^{\mathrm{T}}&d_{\Theta}^{\mathrm{T}}\end{bmatrix}^ {\mathrm{T}}\) is lumped uncertainty, which includes modeling uncertainty, model approximation error, and generalized external disturbance.  
To simplify the presentation, referring to the relevant experience with underwater robots, the above model (23) is translated into an inertial coordinate system [45]:  
\[M_{\eta}\ddot{\eta}+C_{\eta}\dot{\eta}+D_{\eta}\dot{\eta}+g_{\eta}=\tau_{\eta}+ \tau_{d\eta} \tag{24}\]  
where the transformed variables satisfy the following relationship: \(M_{\eta}=J^{-\mathrm{T}}MJ^{-1}\), \(C_{\eta}=J^{-\mathrm{T}}\Big{(}C-MJ^{-1}\dot{J}\Big{)}J^{-1}\), \(D_{\eta}=J^{-\mathrm{T}}DJ^{-1}\), \(g_{\eta}=J^{-\mathrm{T}}g_{v}\), \(\tau_{d\eta}=J^{-\mathrm{T}}\tau_{d\upsilon}\).  
In order to facilitate the subsequent controller design, the translational motion and the rotational motion in the dynamic model of Equation (24) are separated into two subsystems, as shown in Equation (25):  
\[\begin{split}\mathrm{A}:\left\{\begin{array}{l}\dot{\eta}_{1}=v_{1I }\\ \dot{v}_{1I}=\tau_{p}+f_{p}+\overline{d}_{p}\\ \dot{\eta}_{2}=v_{2I}\\ \dot{v}_{2I}=\tau_{\Theta}+f_{\Theta}+\overline{d}_{\Theta}\end{array}\right. \end{split} \tag{25}\]  
where subsystem A is the displacement subsystem. Subsystem B is the rotation subsystem. \(v_{I}=\left[v_{1I}^{\mathrm{T}}\quad v_{2I}^{\mathrm{T}}\right]^{\mathrm{T}}\), \(v_{1I}\), \(v_{2I}\) are the linear and angular velocities under the inertial frame, respectively. Each element in Equation (25) satisfies the following relation:  
\[M=\begin{bmatrix}M_{11}&0^{3\times 3}\\ 0^{3\times 3}&M_{22}\end{bmatrix}\text{ is the mass matrix; }M_{\eta}=\begin{bmatrix}J_{1}^{- \mathrm{T}}M_{11}J_{1}^{-1}&0^{3\times 3}\\ 0^{3\times 3}&J_{2}^{-\mathrm{T}}M_{22}J_{12}^{-1}\end{bmatrix}\text{ is the mass matrix in the inertial frame; }\tau=\begin{bmatrix}\tau_{p}^{\mathrm{T}}&\tau_{\Theta}^{\mathrm{T}}\end{bmatrix} ^{\mathrm{T}}\text{ is the control input under the inertial frame; }\tau_{p}=M_{\eta 11}^{-1}I_{1}^{-\mathrm{T}}\begin{bmatrix}0&0&u_{1} \end{bmatrix}^{\mathrm{T}}\text{, }\tau_{\Theta}=M_{\eta 22}^{-1}J_{2}^{- \mathrm{T}}\begin{bmatrix}u_{2}&u_{3}&u_{4}\end{bmatrix}^{\mathrm{T}}\text{. } \overline{d}_{p}=M_{\eta 11}^{-1}I_{1}^{-\mathrm{T}}d_{p}\text{, and } \overline{d}_{\Theta}=M_{\eta 22}^{-1}J_{2}^{-\mathrm{T}}d_{\Theta}\text{ are, respectively, the uncertainty of the position and attitude loop under the inertial frame; and \(f=\begin{bmatrix}f_{p}^{\mathrm{T}}&f_{\Theta}^{\mathrm{T}}\end{bmatrix}^{ \mathrm{T}}\) is the nonlinear function of the hydrodynamic composition expressed under the inertial frame, \(f=-M_{\eta}^{-1}\big{(}C_{\eta}\dot{\eta}+D_{\eta}\dot{\eta}+g_{\eta}\big{)}\).
# Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration
## 2 Background
### Standard Bayesian Model for Sets of Trajectories  
By extending an auxiliary variable \(u\in\mathbb{U}_{k|k^{\prime}}=\left\{0,1,\ldots,n_{k|k^{\prime}}\right\}\) over the single-target trajectory space, \((u,X)\in\mathbb{U}_{k|k^{\prime}}\times T_{(k)}\) is used. When \(u=0\) represents that the target trajectory was not detected, it corresponds to a PPP; when \(u=\left\{1,\ldots,n_{k|k^{\prime}}\right\}\), it indicates that the target trajectory corresponds to the \(i^{\text{th}}\) Bernoulli component. After adding the auxiliary variable, we use \(\widetilde{\mathbf{X}}\in\mathcal{F}(\mathbb{U}_{k|k^{\prime}}\times T_{(k)})\) to represent the set of target trajectories with the auxiliary variable. \(w^{a}_{k|k^{\prime}}\) denotes the weight, \(\Downarrow\) denotes the union of disjoint sets; \(f^{p}_{k|k^{\prime}}(\mathbf{Y})\) is the Poisson part probability density function, representing the trajectories of objects assumed to exist but undetected; \(\lambda_{k|k^{\prime}}(\cdot)\) is the Poisson intensity for trajectories; \(f^{m\Downarrow}_{k|k^{\prime}}(\mathbf{V})\) is the multi-Bernoulli part probability density function, representing the probability of latent trajectories being detected at least once by some time \(k^{\prime}\); in the multi-Bernoulli probability density Equation (5), \(n_{k|k^{\prime}}\) indicates the number of Bernoulli components that exist up to some time \(k^{\prime}\), with each Bernoulli component having \(h^{i}_{k|k^{\prime}}\) independent hypotheses; \(a\) represents the global hypothesis index. For the \(i^{\text{th}}\) Bernoulli component density, there are \(a^{i}\) local hypotheses and \(a^{i}\in\left\{1,\ldots,h^{i}_{k|k^{\prime}}\right\}\). For a global hypothesis \(a=(a^{1},\ldots,a^{n_{k|k^{\prime}}})\), a local hypothesis \(a^{i}\) is assigned to each Bernoulli component, and \(\mathcal{A}_{k|k^{\prime}}\) is the set of global hypothesis indices; that is, \(a\in\mathcal{A}_{k|k^{\prime}}\).  
\[w^{a}_{k|k^{\prime}}=\frac{\prod\limits_{i=1}^{n_{k|k^{\prime}}}w^{a^{i}a^{i}} _{k|k^{\prime}}}{\sum\limits_{b\in\mathcal{A}_{k|k^{\prime}}}\prod\limits_{i= 1}^{n_{k^{\prime}|k^{\prime}}}w^{i^{\prime}b^{i}}_{k|k^{\prime}}} \tag{4}\]  
where \(w^{i^{\prime}a^{i}}_{k|k^{\prime}}\) represents the weight of the local hypothesis Bernoulli component, and \(w^{a}_{k|k^{\prime}}\) represents the weight of the global hypothesis Bernoulli component. Normalization ensures that the sum of the probabilities of the global Bernoulli components equals 1; that is, \(\sum\limits_{a\in\mathcal{A}_{k|k^{\prime}}}w^{a}_{k|k^{\prime}}=1\).
# Relational Fisher Analysis: Dimensionality Reduction in Relational Data with Global Convergence +
###### Abstract
propose the probabilistic relational PCA (PRPCA) to build a probabilistic model associated with PCA and relational learning. RDA and PRPCA effectively integrate relational learning into the dimensionality reduction algorithms. Nevertheless, how to better inject relationships into traditional dimensionality reduction models is still worth exploring. Recently, a few deep relational learning algorithms have been proposed. Specifically, [PERSON] et al. design a deep learning model based on relational network for hyperspectral image few-shot classification [26], [PERSON] et al. apply local relation learning for face forgery detection [27], and [PERSON] et al. develop a weakly supervised anomaly detection method via context-motion relational learning [28]. In addition, some relational learning methods are used in the one-shot [29] or zero-shot [30] learning scenarios. However, many real-world applications have only very few data. Hence, shallow relational learning algorithms are still needed to be proposed and utilized. In this paper, we propose a novel and general framework for dimensionality reduction, called relational Fisher analysis (RFA) [31]. Besides the intrinsic and penalty graph in the graph embedding framework, we further construct a relational graph which captures the relational information encoded within data. Through this graph, the proposed RFA takes into account the impact of the relational information in the presentation learning process. An effective iterative trace ratio algorithm is proposed to optimize RFA. Futhermore, we use the kernel trick to extend RFA to its kernelized version--KRFA. Additiionally, we theoretically prove that the optimization algorithm of RFA converges. To evaluate the effectiveness of RFA, we conduct extensive experiments in many real-world applications. The results demonstrate that the proposed RFA outperforms most of the classic dimensionality reduction algorithms on the datasets we use. The effectiveness of KRFA is also tested. This paper is based on one of our previous conference papers [31], with significant improvements. For concreteness, we propose the KRFA algorithm and add more exclusive experiments with comparison to the related approaches. The rest of this paper is organized as follows: In Section 2, we introduce several related ideas, including graph embedding,trace ratio problem and relational learning, which are highly relevant to our work. In Section 3, we focus on our proposed method RFA, including the notation, the formulation and the iterative optimization method of RFA. Section 4 includes the proof of the convergence of RFA and we present how to extend RFA to a kernel version--KRFA. In Section 5, we compare our methods RFA and KRFA to other commonly used dimensionality reduction methods with extensive experiments, which demonstrate the effectiveness of our proposed methods. Finally, we summarize this paper in Section 6.
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 4 Video and Audio Distortions
### Video Degradations  
The video component of the source sequences was subject to three types of distortions: video coding, packet-loss, and frame-freezing. These types of degradations were applied to source sequences in Experiments 1 and 3 [48,49]. The processing algorithms used to generate the PVSs are described below.
## 2 Related Works
### Security and Vulnerability to Cyber-Attacks  
Most studies on the vulnerabilities and security of charging coordination in smart grids have focused on EVs [23; 24; 25; 26]. Existing systems are vulnerable to various cyber-attacks, including FDI and manipulation of SoC data, which can lead to low performance and unfair energy allocation.  
[PERSON] et al. [23] investigated how EVs that report false data to gain higher charging priority affect charging coordination systems that use optimization techniques. They assessed the consequences of such attacks on both dishonest and honest EVs, revealing that dishonest EVs are more likely to charge successfully, while honest EVs may experience delays or fail to charge altogether. To tackle this issue, the authors proposed an anomaly-based detector to identify dishonest EVs by analyzing deviations from typical driving patterns. The given experimental results show that the detector achieves high accuracy and low false alarm rates, even when tested against unseen attack types. Similarly, [PERSON] et al. [24] addressed the impact of FDI and hijacking attacks on EVs charging systems, particularly focusing on scenarios where a single point of failure can lead to significant operational disruptions. They conducted a simulation over a 24-h period involving 12 chargers and 34 realistic charging sessions to investigate the potential technical issues that could emerge from compromising a user's mobile application or the communication interface between the Distribution System Operator (DSO) and the charging coordinator. The given results demonstrate how cyber-attacks aimed at these components can result in mismanagement of charging sessions, breach operational grid constraints, and cause significant problems for the distribution grid. Additionally, [PERSON] et al. [25] proposed a feed-forward neural networks detection model to identify deceptive EVs, utilizing a unique dataset of real driving traces alongside a malicious dataset generated by an RL agent. The RL agent creates sophisticated attacks designed to evade simple detection methods while ensuring high charging priority for the malicious EVs. They evaluated the effectiveness of these RL-generated attacks against traditional methods, showing that their detection model is more resilient to intelligent and stealthy threats.  
Additionally, [PERSON] et al. [26] proposed a dual-scheme framework leveraging deep RL to address advanced cyber threats in EV Charging Stations. The first scheme uses deep RL to develop sophisticated attacks that manipulate SoC data, creating covert and challenging cyber-attacks. These attacks are designed to evade simple intrusion detection systems (IDS) by exploiting vulnerabilities in the EVs charging network. The second scheme is a deep RL IDS, trained using datasets generated by the first scheme, allowing the IDS to effectively detect these complex and stealthy attacks. The framework demonstrates enhanced robustness and accuracy compared to traditional approaches, with low false alarm rates even when encountering new and previously unseen attacks.
## 1 Introduction
Classifications of observed surfaces can be performed using various methods, and apart from the mentioned approach based on VIs, still, the most used approach is related to geographic object-based analysis (GEOBIA) [49; 50]. GEOBIA is defined as a subdiscipline of geographic information systems (GIS) focused on the processing and analysis of raster data [51]. Unlike pixel-based analyses, GEOBIA groups pixels into meaningful homogeneous units according to their spectral characteristics [49]. The main difference between the VIs approach and GEOBIA is the different use of MS images and the data they contain. VIs use pixel information to create certain VI model, which is later processed within the VITO tool. On the other hand, GEOBIA requires the use of randomly selected training samples to gather data about the landscape in order to successfully carry out the classification. Sometimes, the accuracy might well be determined only by the selection of inadequate training samples. Comparison and accuracy assessment of olive trees canopy extraction based on VIs and the GEOBIA approach is important in PA, intending to improve the sustainable management of olive groves and other plants with similar vegetative characteristics. Other methods in the process include deep learning classifier, which success is based on deep neural networks (DNN) [52]. Deep learning is a computer-based modeling technique that consists of numerous processing layers and is used to comprehend how data is represented at various levels of abstraction [53]. It is still debatable whether or to what extent deep learning techniques can outcompete other state-of-the-art and support vector machine (SVM) algorithms in the context of classification using GEOBIA, despite the recent explosion in popularity of DNNs [54]. Although a deep learning classifier was not utilized in this study, it will undoubtedly be considered in future research. The main goal of this research is to compare the accuracy in the extraction of olive tree canopies between the GEOBIA approach and the approach based on VIs. The secondary objectives are to determine the best of the three tested classification algorithms in the GEOBIA approach and to optimize and select the most representative VI to extract the olive tree canopy.