esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 QoS Controller Implementation
### Practical Considerations  
The proposed controller is designed to be implemented on board the MQTT broker behind the MQTT-SN gateway. Here, we present some proposals for a seamless integration of the controller functionalities in the latest MQTT standard on the broker side.  
* _Controller activation:_ the QoS controller may be activated by any subscriber at will. Due to the limited options available in the current subscribe message packet header and payload, which must follow a very specific syntax according to the MQTT standard, we propose the following strategy. Upon subscription on any topic, a client may send to the gateway/broker a specific control message on the special topic $QoSControl/Activate, passing as a payload a JSON file containing (i) the topic over which the QoS controller should work, (ii) the operational mode and (iii) the operational parameters (constraints and thresholds). Examples are illustrated in Figures 7-9.
* _Feedback communication_: Regardless of the operational mode selected by subscribers, the controller needs to be aware of (i) the current hitrate and (ii) the average end-to-end  
Figure 6: Finite state machines used to model the controller behavior for the three different operational modes: (**a**–**c**).  
latency experienced by nodes in the current evaluation window. While for the latter metric the broker can perform an estimation based on the number of retransmissions performed, for what concerns the current hitrate each client needs to explicitly communicate the number of received messages in the previous evaluation window. Such an exchange may happen on a special control topic, such as, e.g., $HITRATE/topic. In such a way, the broker can be aware of the hitrate each client is observing on the topic topic.
* _Controller deactivation:_ A client has two choices for deactivating the controller: (i) unsubscribing from the topic over which the control is active, and resubscribing without activation or (ii) transmitting an explicit message on the special topic $QoSControl/Dectivate, passing as payload a JSON file where the value of mode is equal to off.  
[ \"topic\": \"/a/b/c\",  \"mode\": \"latency\",  \"param\": {  \"latency: 2s\"  \"delta_latency: 1s\"  \"delta_hitrate: 0.2\"  } ]  
We highlight that, to provide the maximum degree of flexibility, the proposed controller works independently on each subscriber requesting its activation. Indeed, each subscriber connected to the broker may: (i) not use the controller and rely on the standard QoS levels, (ii) activate the controller specifying its own latency or hitrate constraints or (iii) use the controller in autonomous mode.
## 3 Cooperative Communication Algorithms
### Model Validation
#### 3.3.3 The Effect of \(f\) and \(\mu\) on End-to-End Reliability  
In Figure 2(b), \(K\) varies from 1 to 10, \(\mu\) varies from 0.1 to 0.9, \(f\) is set to 0.3, hops are still set to 14. It is shown that the smaller \(\mu\), the larger \(K\) and the higher reliability \(W\). If FEC coding is applied, when \(\mu\) is set to 0.1, \(K\) should be greater than 4. When \(\mu\) is 0.5, \(K\) should be set to 9.
# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
## 2 Background
### False News and Rumors  
The issue of false information has existed since the invention of the first writing instrument [6]. Recent research [3] has recommended using the term \"false information\" rather than \"fake news\" because the latter term is inherently political, thus limiting its scope. False information is classified into two types: misinformation and disinformation, depending on whether it causes damage or espouses a particular interest. Misinformation is defined as false information spread without the intention of causing harm to a particular organization or the public generally. It can be caused by a variety of factors, including erroneous labeling (e.g., of people) and lax fact-checking processes, and it can spread quickly to users who are careless about the truth of what they receive or disseminate. Disinformation, on the other hand, refers to erroneous information disseminated with the purpose of confusing, hurting, or deceiving a specific group or the public in general [12; 13].  
Eight major categories of false information are identified by [14; 15], which include fabricated information, biased news, propaganda, conspiracy theories, hoaxes, rumors, clickbait, and satire. Rumors constitute the most popular type of false information and are extensively shared on social media. There are many ways to define rumors. For example, earlier studies have labeled them as false information [16], while recent research [17] has defined them as fake news or unconfirmed news items that have spread widely but require proof to determine their validity. Rumors can be categorized according to their veracity state (e.g., true, untrue, or unresolved) [18] or their trustworthiness ranking (high or low) [19].
## 3 Test and Verification  
### Test System Parameter Design  
#### 3.1.1 Introduction to Test Objectives  
In order to verify the rationality of the above testing plan, we test the thermal stability of the support frame of a certain dome multi-star simulator. The schematic diagram of the support frame is shown in Figure 7, and the temperature gradient conditions of its thermal index is shown in Figure 8. Through finite element simulation analysis, we have learned that the thermal deformation rate of the angle between the installation surface normals of the two single-star parallel light tubes in the upper left and upper right corners of the support frame is \(\
u\) = 0.359\({}^{\prime\prime}\)/\({}^{\circ}\)C, as shown in Figure 9. The physical definition of the rate of angular thermal deformation change is shown in Equation (17).  
\[\
u=\frac{\Delta\theta}{\Delta T} \tag{17}\]  
Figure 7: Schematic diagram of Support Frame.  
Based on the testing plan, it can be seen that the rate of change in the angle at a specific temperature is:  
\[\upsilon^{\prime}=\frac{\Delta\theta_{0}}{\Delta T}=\frac{\Delta\theta^{\prime}} {N_{matrix\ mean\ norm}*\Delta T} \tag{18}\]  
At the same time, we can see from Figure 9 the left and right mirror symmetrical structure of the support frame, and the relative angle between the two normals is 6.71\({}^{\circ}\), which means that the thermal deformation changes in its upper left and upper right mounting surfaces should be mirror symmetrical. Therefore, we only need to measure the change in normal direction of one of the single variables to simultaneously obtain the change in normal direction of the two variables.
## 2 Materials and Methods  
### Database  
In the present paper, as the focus is on CFSS members under both concentric and eccentric loading, the input data including the mechanical properties of steel and concrete (yield strength and compressive strength, respectively), the cross-sectional width, column length, steel tube thickness, loading eccentricities at the top and bottom of the member, were collected from the available literature. A total of 443 tests on CFSS members under compression served as a database, involving 314 concentric and 129 eccentric cases, respectively [41; 42; 43]. Figure 1 presents a diagram of CFSS members under eccentric loading, together with their geometrical parameters. A primarily statistical analysis of the database is shown in Table 1, including the min, average, max, standard deviation (StD) and coefficient of variation (CV) of all variables. It is apparent that the yield strength of the steel tube varies from 194.18 MPa to 835 MPa, with a mean value of 472.79 MPa and StD of 192.91 MPa. The compressive strength of the concrete core ranges from 7.9 MPa to 183 MPa, with a mean value of 59.84 MPa and StD of 36.50 MPa. The width of the cross section varies from 60 mm to 324 mm, with a mean value of 147.18 mm and StD of 46.86 mm. The member's length ranges from 195 mm to 4500 mm, with a mean value of 1426.55 mm and StD of 1110.43 mm. The thickness of the steel tube varies from 0.7 mm to 12.5 mm, with mean value of 4.88 mm and StD of 2.17 mm. The loading eccentricity at the top of the member ranges from 0 to 300 mm, with a mean value of 14.58 mm and StD of 37.10 mm. The loading eccentricity at the bottom of the member varies from \(-\)25 to 300 mm, with a mean value of 13.51 mm and StD of 37.02 mm. Figure 2 shows the corresponding histograms of variables in the database. It is seen that, for several ranges of values, not enough data have been collected to ensure a good homogeneity. Consequently, missing data should be completed in further researches. Finally, investigations dealing with problem of inhomogeneity in the data can be referred to [PERSON] et al. (2018), [PERSON] and [PERSON] (2018), and [PERSON] (2018).  
Figure 2: Histogram of the data: (**a**) yield strength of steel tube, (**b**) compressive strength of concrete, (**c**) width of cross section, (**d**) length of column, (**e**) thickness of steel tube, (**f**) loading eccentricity at the top, (**g**) loading eccentricity at the bottom and (**h**) load-bearing capacity.
## 4 Experimental Setup
### Framework Infrastructure  
The developed simulation framework is designed to evaluate the performance of the multipath approach. Note that we use the UDP streaming session as a demonstration for multipath over hybrid terrestrial-satellite network accesses. The reason is that the UDP has no flow and congestion mechanisms, and in this way, the obtained performance results, presented in the next section, of the intelligent controller are not shadowed by these mechanisms that are included in TCP-based or similar multipath solutions. For this purpose, the HUT is deployed on a PC, while the T/R and Channel modules are implemented on dedicated embedded platforms capable of hardware acceleration. The HUT operates on a PC equipped with an AMD Ryzen 9 5950X CPU and 32 GB of RAM. The T/R and Channel modules are implemented on the Zynq UltraScale+ RFSoC ZCU111 Evaluation Kit, which features the XCZU28 DR-2 FFVG1517E FPGA system on chip. This chip integrates a Zynq UltraScale+ MPSoC with ARM Cortex A53 processors capable of running the PetaLinux operating system. A 100 Mbps network connection is established between the PC host and the FPGA boards. Note that delay times are not induced on simulated link paths in our presented experiments. The reason is that these times would not affect the results because the intelligent controller uses only measured SNR values, and the UDP streaming session is also used without feedbacks that would be affected by delays. The complete system setup is illustrated in Figure 7.
## Abstract  
With its potential, extensive data analysis is a vital part of biomedical applications and of medical practitioner interpretations, as data analysis ensures the integrity of multidimensional datasets and improves classification accuracy; however, with machine learning, the integrity of the sources is compromised when the acquired data pose a significant threat in diagnosing and analysing such information, such as by including noisy and biased samples in the multidimensional datasets. Removing noisy samples in dirty datasets is integral to and crucial in biomedical applications, such as the classification and prediction problems using artificial neural networks (ANNs) in the body's physiological signal analysis. In this study, we developed a methodology to identify and remove noisy data from a dataset before addressing the classification problem of an artificial neural network (ANN) by proposing the use of the principal component analysis-sample reduction process (PCA-SRP) to improve its performance as a data-cleaning agent. We first discuss the theoretical background to this data-cleaning methodology in the classification problem of an artificial neural network (ANN). Then, we discuss how the PCA is used in data-cleaning techniques through a sample reduction process (SRP) using various publicly available biomedical datasets with different samples and feature sizes. Lastly, the cleaned datasets were tested through the following: PCA-SRP in ANN accuracy comparison testing, sensitivity vs. specificity testing, receiver operating characteristic (ROC) curve testing, and accuracy vs. additional random sample testing. The results show a significant improvement in the classification of ANNs using the developed methodology and suggested a recommended range of selectivity (_Sc_) factors for typical cleaning and ANN applications. Our approach successfully cleaned the noisy biomedical multidimensional datasets and yielded up to an 8% increase in accuracy with the aid of the Python language.  
pricipal component analysis (PCA); artificial neural network (ANN); multidimensional dataset; dimension reduction process; sample reduction process (SRP); receiver operating characteristic (ROC) curve; selectivity (_Sc_); sensitivity; specificity
# Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments
## 3 Methodology
### V-R-MATD3 Strategy
#### 3.2.2 Network Architecture  
The detailed network architecture design is shown in Figure 2. We propose a parameter-shared centralized actor architecture with decentralized critics. While each robot \(i\) nominally corresponds to an actor network \(\pi_{i}(\cdot|\theta)\), all actors share identical parameters (\(\theta_{1}=\theta_{2}=\cdots=\theta_{N}=\theta\)) and receive global observations to enable coordinated control. This design reduces training complexity while achieving full collaboration. The actor-critic neural network consists of an actor network \(\pi_{i}(o_{i}|\theta_{i})\), with weight \(\theta_{i}\), and two critic networks \(Q_{i}^{\pi}(o_{1:N},a_{1:N}|\phi_{1})\) and \(Q_{i}^{\pi}(o_{1:N},a_{1:N}|\phi_{2})\), with weights \(\phi_{1}\) and \(\phi_{2}\), respectively. Corresponding target networks \(\pi_{i}^{\prime}\) and \(Q_{i}^{\prime\pi}\) are also maintained.  
The actor network receives an input tensor of shape \((t,n,input\_dim)\), representing the state information of \(n\) collaborating robots over \(t\) time steps, where \(input\_dim\) is the state dimension per robot per time step. This input passes through a 256-unit ReLU-activated fully connected layer, a GRU layer, and another 256-unit ReLU-activated fully connected  
Figure 2: Connection between Voronoi partitioning and V-R-MATD3: the top left figure shows the robots’ action spaces partitioned by Voronoi; the bottom left figure depicts a single robot’s state space; the right figure presents the actor–critic neural network architecture of V-R-MATD3.  
layer. A final tanh-activated output layer produces the action vector \(A\) for all robots. The critic networks share a similar architecture but output a scalar \(Q\) value via linear activation to estimate the expected cumulative reward; the smaller \(Q\) value is chosen to prevent overestimation [29]. Incorporating the GRU allows the model to capture time-series dynamics, enhancing learning stability and efficiency.
## 2 Materials and Methods
### Hyperspectral Imaging System
#### 2.2.2 Hyperspectral Image Acquisition and Calibration  
Each file was placed on the linear translation stage and then conveyed to the field of view of camera with adjusted speed and exposure time to be scanned line by line. These individual lines of data were then compiled into a single, coherent, raw hyperspectral image and then saved in the computer before being processed. The raw hyperspectral image for each file contains both spectral and spatial information in the spectral range of 1000-2500 nm with a total of 198 bands. Thus, the final hyperspectral images have a dimension of 320 pixels \(\times\) 320 pixels \(\times\) 198 bands. The entire image acquisition process was controlled using inhouse software.  
To correct the raw, acquired hyperspectral images (R), two extra images for dark (D) and standard white (W) references were used to eliminate the influences from the bright and dark response. The dark image (0% reflectance) was acquired by recording a spectral image after turning off the light source and completely covering the camera lens. The white reference image was obtained by collecting a spectral image from a uniform white calibration tile (75% reflectance). The calibrated image (I) was then calculated using the following equation:  
\[\text{I}_{i}=(\text{R}_{i}\text{-D}_{i})/(\text{W}_{i}\text{-D}_{i}), \tag{1}\]  
where \(i\) is the pixel index (i.e., \(i=1,2,3,\ldots,\) n) and \(n\) is the total number of pixels. The final corrected spectral images were used as the basis for subsequent spectral extraction and data analysis.
## 2 Theoretical Background
### Mutual Information  
MI is defined as the measurement of the stochastic dependence between random variables, which allows the measurement of common information. Still, it reveals how the uncertainty about a random variable decreases when observing the other variable [29; 30]. This measurement also describes the relationship between the time series collected simultaneously from the analyzed system [19]. In this context, MI is used in electrical machine signals to quantify the association between variables and monitor motor operating conditions. Equation (1) shows how this measurement can be determined using the probability density functions of the signals in the study.  
\[I(X,Y)=\sum_{x\in X}\sum_{y\in Y}p(x,y)\log_{2}\frac{p(x,y)}{p(x)\cdot p(y)} \tag{1}\]  
where \(X\) and \(Y\) are the random variables; \(p(x)\in p(y)\) are the probability density functions (PDF) marginal of \(X\in Y\), respectively; and \(p(x,y)\) is the PDF between these two variables \(X\) and \(Y\). When the mutual information is calculated with the logarithm in base 2, it is measured in bits. However, when using the Neperian base, its unit is nats.  
In the proposed methodology, the signals analyzed are phases A and B of the stator current. Since these phases are out of phase with each other, it is necessary to use the Delayed Mutual Information tool (DMI). DMI is a measurement that has been used in the analysis of dynamic structures of complex systems, as it allows to estimate the similarity between random variables based on a time shift \(\tau\)[19]. This measurement can be defined using Equation (2) and has been widely used in feature extraction to help solve pattern recognition problems [4; 15].  
\[I(X,Y^{\tau})=\sum_{x_{n}\in X}\sum_{y_{n-\tau}\in Y}p(x_{n},y_{n-\tau})\log_{ 2}\frac{p(x_{n},y_{n-\tau})}{p(x_{n})\cdot p(y_{n-\tau})} \tag{2}\]  
Once the feature extraction stage is completed, the next step is to select the relevant mutual information values using the ABC algorithm, whose main characteristics are explained in the following section.
# Combining Optimization Methods Using an Adaptive Meta Optimizer
## 6 Experiments
### Experiments with Text Documents  
In this last group of experiments, we used the two datasets of text documents: CoLA and AG's News. As a neural model, we used a model based on BERT [17], which is one of the best techniques for working with text documents. To run fewer epochs, we used a pre-trained version [32] of BERT. In these experiments, we also introduced the comparison with the AdamW optimizer, which is usually the optimizer used in BERT-based models.  
For the CoLA dataset, we set \(\eta=0.0002\), momentum \(\mu=0.95\), and batch size equal to 100. We ran the experiments five times for 50 epochs. For the AG's News dataset, we set the same parameters used for CoLA, but we only ran it for 10 epochs because it achieved good results in the firsts epochs and also because the dataset was very large and therefore took more time. In these experiments with text analysis, we did not use the Dynamic ATMO approach because we used a very small number of epochs. We can see all the results in Table 4. Even for text analysis problems, we can confirm the results of the experiments done on images: although AdamW sometimes has better performances than ADAM, ATMO performs better.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & \(\lambda_{a}\) & \(\lambda_{s}\) & **avg acc.** & **acc max** \\ \hline CoLA & & & & \\ \hline AdamW & - & - & 78.59 & 85.96 \\ Adam & 1 & 0 & 79.85 & 83.30 \\ SGD & 0 & 1 & 81.48 & 81.78 \\ ATMO & 0.5 & 0.5 & 85.92 & 86.72 \\ ATMO & 0.4 & 0.6 & 86.18 & **87.66** \\ ATMO & 0.6 & 0.4 & 85.45 & 86.34 \\ ATMO & 0.7 & 0.3 & 84.66 & 85.78 \\ ATMO & 0.3 & 0.7 & **86.34** & 86.91 \\ \hline \hline AG’s News & & & & \\ \hline AdamW & - & - & 92.62 & 92.93 \\ Adam & 1 & 0 & 92.55 & 92.67 \\ SGD & 0 & 1 & 91.28 & 91.39 \\ ATMO & 0.5 & 0.5 & 93.72 & 93.80 \\ ATMO & 0.4 & 0.6 & 93.82 & 93.98 \\ ATMO & 0.6 & 0.4 & 93.55 & 93.67 \\ ATMO & 0.7 & 0.3 & 93.19 & 93.32 \\ ATMO & 0.3 & 0.7 & **93.86** & **93.99** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy results of BERT pre-trained on CoLA (50 epochs) and AG’s news (10 epochs) after 5 runs.  
Figure 5: Resnet18 test accuracies min, max, and avg computed every 35 epochs using all experiments.
## 1 Introduction  
Using high-strength concrete (HSC) with compressive strength exceeding 70 MPa and high-strength steel (HSS) with yield stress of 685 MPa or greater can reduce the member section size and the volume of concrete and steel bars for the entire building structure [1]. The durability of concrete can be improved, owing to lower water-to-cemetitious materials ratio of the HSC as well [2]. However, concrete becomes more brittle as its compressive strength increases and greater transverse reinforcement is required accordingly [2; 3; 4; 5]. Short and discontinuous steel fiber can be used as an alternative material to improve the ductile behavior of concrete [2; 6; 7; 8; 9]. Previous research studies have shown that adding steel fibers to concrete beams could enhance shear resistance, toughness, promote flexural failure and ductility, and potentially act as a substitute for conventional shear reinforcement [10; 11; 12; 13; 14; 15; 16; 17]. In addition, the randomly oriented steel fibers provide bridging action across the microcracks in the matrix and improve resistance to crack opening [2; 9; 18; 19; 20]. The shear stress on the interface between the steel fibers and the surrounding matrix (bond strength between steel fibers and matrix) is a key parameter in the bridging role and in providing more tensile strength during fracturing. Concrete fails once the steel fibers either break or are completely pulled out from the concrete.  
Despite the advantages of steel fibers as reinforcement in the concrete beams, the number of research studies on high-strength steel fiber-reinforced concrete (SFRC) beam with HSS longitudinal bar is limited. The general behavior of SFRC beams is presented in a literature review in order to fully describe the parameters affecting ultimate shear strength of the SFRC beams. In this study, the current shear strength equations for design and analysis of the SFRC beams derived from different research studies are summarized. A database is constructed from 236 beams that consist of normal reinforced concrete and SFRC beams. Every shear strength in the database is compared with the existing shear strength prediction equations. Furthermore, the high-strength SFRC beam specimens were provided to be subjected to monotonic loading. Accordingly, the current shear strength prediction equations can be verified. The new equation for predicting shear strength is proposed based on the evaluation of shear strength equations studied in this paper.