esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Security and Performance Analysis
### Implementation Result
#### 6.3.2 Implementation Setup  
ABE schemes can be implemented using pairing-based cryptography. The Java Pairing Based Cryptography JPBC library [51] is extensively used in developing cryptographic solutions. While configuring JPBC, the following.jar files are included: jpbc-pbc-v2.0.0-m.jar, jpbc-plaf-v2.0.0-m.jar, jpbc-api-v2.0.0-m.jar, jpbc-crypto-v2.0.0-m.jar, and bcprov-jdk16-1.46.jar. JPBC includes various packages such as api, util, pairing, field, etc. Some interfaces for pairing functions, finite fields, and elliptic curves are provided by package api. Package util offers support for mathematical operations and other functions. Package pairing and field are the concrete realizations of the interfaces offered by package api. JPBC supports a variety of elliptic curve types. Type A, Type A1, Type D, Type E, Type F, and Type G are included. This experiment was conducted on the group of elliptic curves of Type A1. Their order is the product of three primes of length 517 bits. In the global setup method, while performing setup for the central authority, the Boneh-Lynn-Shacham signature scheme, called the BLS signature, was implemented. A BLS signature enables a user to validate the authenticity of a signer. Signatures are created as elements in elliptic  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(|G|\) & No. of bits needed to represent an element in group \(G\) \\ \hline \(|G_{T}|\) & No. of bits needed to represent an element in group \(G_{T}\) \\ \hline \(|\mathbf{I}|\) & No. of attributes of satisfying set \\ \hline \(n\) & The size of universe of attributes \\ \hline \(A_{C}\) & Attribute set used in encryption \\ \hline \(A_{U}\) & User’s attribute set \\ \hline \(E\) & One exponential operation \\ \hline \(P\) & One pairing operation \\ \hline \(n_{a}\) & Count of AAs in the system \\ \hline \(n_{v}\) & Number of values all the attributes in the system may have (an attribute may have multiple values) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of notations.  
curve groups and verified using a pairing function. The Junit testing framework was used for testing the implemented classes.
## 2 System Model and Problem Formulation
### Channel Model  
We consider a ray-tracing channel model widely used in mm-Wave communications with a limited number of L scattering paths, as, due to the spatial sparsity in the mm-wave channel, it is expected that the propagation paths are along a small number of directions [19,25,26].  
Therefore, each column of the channel matrix \(\mathbf{H}\) is defined as  
\[\mathbf{h_{k}}=\sqrt{\frac{1}{L\rho_{k,BS}}}\sum_{i=1}^{L}g_{k,i}\mathbf{a}( \theta_{k,i}) \tag{6}\]  
where \(g_{k,i}\) is the complex gain of the i-path due to small-scale fading, \(\rho_{k,BS}\) is the path loss between the \(BS\) and the UAV\({}_{k}\) and \(\theta_{k,i}\in[-\pi/2,...,\pi/2]\) is the angle of arrival of the \(i-th\) path of the \(k-th\) UAV.  
Without loss of generality, we assume a uniform linear array (ULA) at the \(BS\) for simplicity, and the steering vector can be expressed as:  
\[\mathbf{a}(\theta_{k,i})=\left[1,e^{j\frac{2\pi}{\lambda}lsin(\theta_{k,i})},...e^{i(N_{R}-1)\frac{2\pi}{\lambda}lsin(\theta_{k,i})}\right] \tag{7}\]  
where \(\lambda\) is the signal wavelength, and \(l\) is the distance among antenna elements, where \(l\leq\lambda/2\).  
Figure 2: Hybrid mmWave beamformer with DoAs estimation.  
In the following, we assume that in Equation (6) the variations of the channel are only caused by the path gains \(g_{kj}\), and the path angles remain unchanged according to the mmWave channel measurements in [27].
glues, adhesives, protective surface coatings, and electrical insulators [6; 7; 8]. Possible applications of these composites are also in microwave absorbers and EMI (electromagnetic interference) shielding applications [9; 10; 11; 12; 13; 14; 15; 16]. Specifically, the tailoring of composites' complex dielectric permittivity is crucial for these two last applications. Indeed, high values of dielectric losses (\(\varepsilon^{\prime\prime}r\)) stemming from both dielectric relaxation and conductivity ultimately lead to radiation absorption.  
Thus, the maximization of dielectric losses is achieved by embedding particular fillers such as carbon nanotubes, graphene, or metallic nanoparticles into a polymer matrix.  
In this paper, multi-wall carbon-nanotube (MWCNT) were dispersed in an epoxy resin polymer matrix in order to prepare a composite.. First, the morphology of the filler was investigated through field emission scanning electron microscopy (FESEM) and Raman spectra. The complex permittivity of pristine polymer matrix and composites with a given (4 wt%) was measured by means of a commercial dielectric probe. Experimental permittivity data were then used to compute the reflection coefficient of one-layer of a given thickness, backed by a metallic plate. Calculations were compared to the measured reflection coefficient of the composite. In addition, the effect of impurities, specific surface area and aspect ratio of the filler was also investigated. Experimental results confirmed the feasibility of epoxy resin-MWCNT (high aspect ratio) composites as microwave absorbers.
## 1 Introduction
One of the main challenges is to ensure the precision of the timing and magnitude of the impacts, as the system relies on gravity and inertial forces, which could affect test accuracy and repeatability. In addition, the complex assembly process and calibration requirements \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Technique** & **Description** & **Advantages** & **Limitations** \\ \hline Izod and Charpy Technique, [12; 20] & Evaluates impact toughness of materials using standardized test configurations. & Provides standardized and comparability across studies. & Limited to specific configurations; may not capture all material behaviors under varied conditions. \\ \hline Drop Weight, [21] & Involves elevating a mass to a specific height before releasing it to impact specimen. & Offers controlled and repeatable impacts for reliable data. & Limited adaptability to various geometries; may not represent real-world conditions effectively. \\ \hline Pendulum Impact, [16; 22] & Uses a suspended weight released from a height to achieve impact velocity. & Simple setup and widely accepted methodology in impact testing. & Limited control over impact parameters; may not accurately simulate all practical scenarios. \\ \hline Shock Tube, [18; 23] & Propels a weight towards the sample using compressed air or liquid released in a channel. & Versatile with the ability to achieve high-impact velocities and simulate dynamic events. & Complexity of setup requiring specific infrastructure; may involve significant calibration challenges. \\ \hline Hydraulic Actuator, [24] & Generates impact through hydraulic force applied directly to the sample. & Capable of delivering high forces, suitable for large-scale tests. & Requires hydraulic systems, which can be costly and may restrict test frequency. \\ \hline Spring-Loaded Actuator & Releases a spring-loaded mass to create an impact on the specimen. & Parameters can be adjusted for flexibility; simple design. & Limited to lower impact energies; frequent recalibration may be necessary for accuracy. \\ \hline \end{tabular}
\end{table} 
Table 1: Low-Velocity Impact Testing Techniques. of the system may pose practical challenges for implementation and maintenance, especially in resource-constrained environments. As a result, there is still a significant gap in existing methods for low-velocity impact testing. These include inadequate handling of impact load and support formation, inability to capture important data such as load-time profiles, limited versatility in accommodating different material and system experiments, and restrictive limitations on the interactions between weight, velocity, and impact area. To overcome these challenges, this study develops a novel experimental setup tailored to evaluate the dynamic response of materials under low-velocity impacts. With its universal design, it provides enhanced adaptability through tunable parameters, including impactor weight, velocity, surface geometry, stiffness, specimen size, shape, and orientation, thus providing a versatile platform for testing various specimens in low-velocity impacts.
# Experimental Investigation on Shear Behavior of Dune Sand Reinforced Concrete Deep Beams
## 5 Prediction of the Shear Strength of the DSRC Beam
### Prediction of the Shear Strength of DSRC Beams  
The above comparisons indicate that ACI was the most suitable for predicting the shear behavior of DSRC beams. Hence, ACI was modified to predict the shear strength of DSRC beams. The DSC strength was higher than the normal concrete strength due to the inclusion of the appropriate amount of DS to partly fill the pores in concrete. The coefficient \(\lambda\) of DSC can be calculated using the following equation:  
\[\lambda=1.7\frac{f_{\mathrm{t}}}{\sqrt{f_{c}}} \tag{5}\]  
where \(f_{\mathrm{t}}\) represents the concrete tensile strength, and \({f_{\mathrm{c}}}^{\prime}\) represents the cylinder concrete compressive strength.  
The method for predicting the shear strength of DSRC beams was improved as follows:  
\[V_{\mathrm{u}}=1.445f_{\mathrm{t}}\phi\delta\beta_{\mathrm{s}}\sqrt{f_{c}}b_{ \mathrm{w}}w_{\mathrm{s}}\sin\theta_{\mathrm{s}} \tag{6}\]  
where \(\phi\) represents the concrete strength effect factor, which is 0.75 at C30 and 1.0 at C40 and C50; \(\delta\) represents the dune sand impact factor, which is 0.85 for a dune sand replacement rate of 50% and 1.0 for 40% and below.  
The predicted results for experimental DSRC beams are presented in Table 7. It can be observed from Table 7 that the predictions of the shear load capacity of DSRC beams provided by the proposed model were direct and efficient with mean value and standard deviation of 1.018 and 0.066, respectively. Overall, it can be concluded that the proposed model significantly reduced the variability of predictions and accurately estimate the shear strength of the DSRC beams.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & \(V_{\mathrm{u}}\) **(kN)** & \(V_{\mathrm{pred,proposed}}\) **(kN)** & \(V_{\mathrm{u}}\)**/\(V_{\mathrm{pred,proposed}}\)** \\ \hline CC1 & 428 & 390.72 & 1.10 \\ A1 & 383.5 & 355.80 & 1.08 \\ A2 & 406 & 329.13 & 1.05 \\ B1 & 461 & 489.30 & 0.94 \\ B2 & 589.5 & 607.12 & 0.97 \\ C1 & 391 & 390.72 & 1.00 \\ C2 & 357.5 & 390.72 & 0.91 \\ D1 & 360.5 & 254.69 & 1.06 \\ D2 & 472 & 449.78 & 1.05 \\  & Mean & & 1.018 \\  & Standard deviation & & 0.066 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Validation results of the proposed model.
## 3 Experiments
### Feature Extraction  
Feature extraction aims to extract valuable information from raw data to reduce training time, minimize the risk of overfitting, and improve accuracy. PCA is one of the dimensionality reduction methods widely used in the field of data mining. This method projects data into a low-dimensional space, namely the principal component space, to reduce redundant information and features in the data and uses statistical methods to identify the data points with the largest variance [44]. The proposed method was based on PCA technology for feature extraction.  
PCA normalizes the data and then extracts the eigenvectors and eigenvalues from the covariance matrix \(CM\):  
\[x^{\prime}=\frac{\sum_{i=1}^{n}{(x_{i})}}{n} \tag{9}\]  
\[CM=\frac{1}{n-1}\big{(}{(X-x^{\prime})}^{T}{(X-x^{\prime})}\big{)} \tag{10}\]  
where \(x^{\prime}\) is the mean vector.  
The covariance between two features is  
\[C_{jk}=(\frac{1}{n-1})\sum_{n}^{i=1}{(x_{ij}-x^{\prime}_{j})(x_{ik}-x^{\prime }_{k})} \tag{11}\]  
\[Y=X*W \tag{12}\]  
Arrange the eigenvalues in descending order, select \(k\) eigenvectors corresponding to \(k\) eigenvalues, where \(k\) is the dimension of the new feature subspace (\(k<d\)), use the selected \(k\) eigenvectors to construct the projection matrix \(W\), and transform the original dataset \(X\) by \(W\) to obtain the \(k\) dimensional principal component space \(Y\).  
\begin{table}
\begin{tabular}{c c c} \hline
**Label** & **Number of Records** & **Percentage** \\ \hline Normal & 76,967 & 52.04 \\ Intrusion & 70,921 & 48.96 \\ DoS (Denial of Service) & 52,987 & 35.82 \\ Probe (Remote to Local) & 13,935 & 9.42 \\ R2L & 3880 & 2.62 \\ U2R & 119 & 0.08 \\ Total & 147,888 & 100 \\ \hline \end{tabular}
\end{table}
Table 4: NSL-KDD Dataset.
## 3 Online Root Fault Identification Method
### Feature Vector Extraction  
#### 3.1.1 Segmenting Alarm Lists  
The alarms are recorded continuously in chronological order. First of all, we need to segment continuous alarms into alarm lists. The information alarm, 'I2' is used to segment alarm lists in this paper.  
A SCADA system not only monitors process variables and triggers alarms, but also changes the operating condition of a wind turbine to deal with alarms. The actions in response to alarms are different according to the alarm levels. When the alarm level is low, no operation is performed, or the wind turbine is restarted to try to eliminate alarms. On the contrary, when the alarm level is high, the wind turbine is shut down, awaiting manual maintenance. Therefore, every time one wind turbine is shut down due to a fault, it needs manual maintenance.  
The information alarm 'I2' indicates that the wind turbine is started. When the flag of I2 is'start', the wind turbine is started from a shutdown. When the flag of I2 is 'end', the wind turbine is shut down from running; that is to say, the start of I2 indicates that the wind turbine has returned to normal operation, and the end of I2 indicates that the wind turbine is shut down due to faults. All the alarms generated between the start of I2 and the end of I2 are related to the following shutdown. Therefore, we segment alarms into alarm  
Figure 1: The flowchart of the proposed online root faults identification method.  
lists using I2. The alarms generated between the start of I2 and the end of I2 make an alarm list. The total number of alarm lists is expressed as \(M\).
## 1 Introduction
This paper is devoted to developing such a paradigm that enables wireless control over smart sensor and actuator networks in the presence of uncertainty in communication resource availability. We are not interested in (robust) controller design or designing novel network protocols for any control systems, which are the topics of most of the work in the emerging field of wireless control. In particular, we will present a flexible time-triggered sampling scheme for smart sensors that are used in WCSs. It attacks the problem of uncertain resource availability deriving from e.g. system reconfiguration and radio interference. The use of smart sensors allows adapting the bandwidth demands of control applications with respect to network conditions through dynamically adjusting the sampling periods. A new algorithm for sampling period adaptation will be developed based on feedback control technology. The QoC of the system that operates in dynamic environments is guaranteed through maintaining the deadline miss ratio (DMR) in each control loop at/around a desired level, since this reduces the impact of both delay and packet loss on QoC. There has been significant interest in event-triggered sampling that promises to increase resource efficiency, for example, [8-12]. In control applications, however, time-triggered sampling (also called periodic sampling) is still dominant. This is mainly because sampled-data control theory in existence basically originates from time-triggered rather than event-triggered sampling. The theory for event-based control is still under development [13,14]. In systems where the shared computing/communication resource is sufficient, which is usually assumed by control engineers, time-triggered sampling with well-designed fixed periods is able to deliver predictable performance that can be analyzed explicitly using sampled-data control theory. When the resource becomes scarce, however, fixed period based time-triggered sampling will result in worse than possible performance in underloaded conditions and degraded performance even instability in overloaded conditions. There is an obvious lack of flexibility in time-triggered sampling when the system operates in resource-constrained environments with variable workload. The proposed sampling scheme addresses this problem of time-triggered sampling through using flexible sampling periods at runtime. The idea of sampling period adaptation is not new, but we will present a new method for adjusting the sampling period, which improves the flexibility and resource efficiency of the system. The rest of this paper is organized as follows. In Section 2, we briefly review related work. Section 3 describes the system model to be considered. In Section 4 we present the flexible time-triggered sampling scheme, along with the algorithm for sampling period adaptation. Simulations are conducted in Section 5 to evaluate the performance of the proposed scheme in comparison with the conventional non-adaptive time-triggered sampling scheme. Finally, Section 6 concludes this paper.
## 3 Method  
### Principle of SRS  
The SRS algorithm performs scaling followed by ranked subsampling.  
1. Scaling: feature counts (such as OTUs (operational taxonomic units), ASVs (amplicon sequence variants), or clades) are scaled sample-wise so that the sum of the scaled counts (\(C_{\text{scaled}}\)) for each sample is equal to the desired number of counts (\(C_{\text{min}}\)).
2. Ranked subsampling: the scaling step produces fractional values that must be converted into counts (integers). To do this, the \(C_{\text{scaled}}\) for each feature is split into the floor (\(C_{\text{int}}\)) and fractional part (\(C_{\text{frac}}\)) of \(C_{\text{scaled}}\). Because \(C_{\text{min}}=\Sigma C_{\text{scaled}}=\Sigma C_{\text{int}}+\Sigma C_{\text{ frac}}\), it follows that \(C_{\text{min}}\geq\Sigma C_{\text{int}}\). Therefore, \(\Delta C\)\(C_{\text{frac}}\) values (where \(\Delta C=C_{\text{min}}-\Sigma C_{\text{int}}\)) must be converted into additional counts (integers) so that \(C_{\text{min}}\) can be reached. To do so, \(C_{\text{frac}}\) values are ranked. Next, from the highest to the lowest rank, a count for each feature is added until \(\Delta\)C counts have been added. After this step, all samples will have been normalized to C\({}_{\rm min}\) counts.
3. Special cases: (i) when C\({}_{\rm frac}\) values involved in picking \(\Delta\)C counts share the same rank across features, the counts are added for features based on the respective C\({}_{\rm int}\) ranks; (ii) when both C\({}_{\rm frac}\) and its respective C\({}_{\rm int}\) values involved in picking \(\Delta\)C counts share the same ranks across features, the counts are assigned randomly (without replacement). The specification of the seed that initializes the random process enables reproducible results.
# Alternating Direction Method of Multipliers for Generalized Low-Rank Tensor Recovery
## 5 Convergence Analysis
### Applications in Background Modeling  
In this subsection, we test our method on two real-world surveillance videos for object detection and background subtraction: Lobby and Bootstrap datasets Lobby and Bootstrap datasets Lobby and Bootstrap datasets Lobby and Bootstrap datasets (2016). For convenience of computation, we only consider the first 200 frames for each dataset and transform the color images into the gray-level images. The resolutions of each image in the Lobby and Bootstrap datasets are 128 \(\times\) 160 and 120 \(\times\) 160, respectively. We add Gaussian noise with mean zero and standard deviation 5 to each image. Hence, we obtain two data tensors of order 3 and their sizes are 128 \(\times\) 160 \(\times\) 200 and 120 \(\times\) 160 \(\times\) 200, respectively. For two given tensors, we execute random sampling on them with a probability of 50%.  
Considering the fact that MRPCA fails in recovering the low-rank components on the synthetic data with missing values, we only implement the method of GLRTR on the video datasets. Two tradeoff parameters are set as follows: \(\mathbf{\lambda}\) = 0.0072 and \(\mathbf{\tau}\) = 0.001. We can obtain the low-rank, the sparse and the completed components from the incomplete data tensors according to the proposed method. Actually, the low-rank terms are the backgrounds and the sparse noise terms correspond to the foregrounds. The experimental results are partially shown in Figures 7 and 8 respectively, where the missing entries in incomplete images are shown in white. From Figures 7 and 8 we can see that  
Figure 5: Relative errors _versus_ different Inverse Signal-to-Noise Ratio (ISNR) on sparse noise.  
Figure 6: Relative errors _versus_ different Inverse Signal-to-Noise Ratio (ISNR) on Gaussian noise.  
GLRTR can recover efficiently the low-rank images and the sparse noise images. Moreover, we observe from the recovered images that a large proportion of missing entries are effectively completed.  
To evaluate the completion performance of GLRTR, we define the Relative Approximate Error (RAE) as \(\text{RAE}=\ ||\mathbf{\hat{D}}-\mathbf{\mathcal{D}}||_{\text{F}}/||\mathbf{\mathcal{D}}||_{ \text{F}}\), where \(\mathbf{\mathcal{D}}\) is the original video tensor without Gaussian noise corruptions and missing entries, and \(\mathbf{\hat{D}}\) is the approximated term of \(\mathbf{\mathcal{D}}\). The RAE of the Lobby dataset is 8.94% and that of the Bootstrap dataset is 20.11%. These results demonstrate GLRTR can complete approximately the missing entries to a certain degree. There are two reasons for that the Bootstrap dataset has relatively large RAE: one is its more complex foreground and the other is that the entries of the foreground can not be recovered when they are missing. In summary, GLRTR is robust to gross corruption, Gaussian noise and missing values.
## 2 Sensory and Communication System Architecture: X-IoCA
### Internet of Robotic Things
#### 2.1.2 ROS-Based Sensor Network  
The ROS-based distributed network is suitable for obtaining audiovisual information from the agents' environment for monitoring and processing. Sets of sensory devices, e.g., IMU, global positioning system (GPS), video, audio, and lidar, embedded in the agents can be integrated into a distributed network of ROS nodes. To facilitate the inspection of terrain, regardless of the type of environments, smartphones (given their lightness and versatility) can be mounted on UAVs, so that zenithal images can be obtained through ROS, making use of the UMA-ROS-Android application. This application, developed by our group and free to access in [39], integrates ROS nodes for publishing the images captured by its rear camera, the audio captured by its microphone, and the rest of data from its internal sensors, such as inertial measurement unit (IMU) and GPS. ROS nodes running on the sartphones can communicate with other ROS nodes over the WAN, having established the public socket (IP address and port) on which the ROS master node is hosted. Moreover, some smartphones (running the ROS-Mobile application [40]) could also act as local edges, hosting subscriber ROS nodes. These local edges could process and monitor the published information (especially, the audiovisual one) within the area of operation.  
In addition to smartphones, other equipment on board robots, such as different kinds of cameras, LiDARs or GPS, could be used to communicate other sensory information via ROS [41]. Conversely, the X-IoCA architecture makes it possible to transmit this heavy information through the WAN, in real time, thanks to the 5G technology characteristics [42; 43; 44] that support it. These onboard devices include PCs that can perform processing, thus operating as local edges (green boxes in Figure 1), being able to apply algorithms (e.g., SLAM) and communicate useful information, depending on the use case in which it is applied.