esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Simulation Experiments
### Average Rate Analysis  
This section analyzes and examines the impact of the average activity rates upon revenues of the system. Note that scenario 1 defined in Table 1 served as a basis for the conducted experiments. In a similar fashion to the previous subsection, RL(EK-CBM) is used here as well.  
Figure 11a,b show the profitability of the system depended on fluctuations of average production, failure, remanufacturing, and refurbishing activity rates. As indicated in Figure 11a, the revenues are increased, as both rates are increased as well. That is, the production activities and failures become significantly scarce, when their respective rates assume high values. This observation is highly correlated with the product inventory indicated in Figure 10, which shows that the system mainly contains refurbished products. Another remark is the scarcity of failures that result in decreasing faulty items. This is achieved by the authorization of CBM activities that mitigate any degradation occurred by failures and thus the good system condition is ensured. Furthermore, Figure 11b demonstrates that the increased values of remanufacturing and refurbishing rates contribute positively to the system's revenues. It indicates that the system is in position of manufacturing and offering standard products, as the remanufacturing and refurbishing activities are becoming noticeably scarce due to the assumed values of their rates.  
Figure 12a,b compare average manufacturing rate against remanufacturing and refurbishing ones in terms of system revenues. In Figure 12a, the increased rates of production improve the system's cost-effectiveness. In that case, the production process is not initiated recurrently and thus the standard products would be in small numbers. This indicates that the revenue stream of the two-stage system is purely dependable on the already stored products. In this regard, as demonstrated in Figure 10, the agents refurbish returned items to complete the placed customer orders. However, Figure 12b shows that the revenues are high when the average refurbishing activity rate is increased. In that case, the agents decide to remanufacture faulty goods as the refurbishing activities are scarce. Furthermore, when the manufacturing facilities recurrently produce items, the profitability of the system is low. This can be attributed to the associated costs listed in Table 3. In this regard, the production activities are costly for the system and therefore the overall revenues are low.  
Figure 10: Dissemination of salable products.
# Aggregate Type and Concrete Age Effects on Anchor Breakout Performance: Large Database and Insights
## 3 Results
### Anchor Results
#### 3.2.1 Tensile Loading--Summary of Experimentally Determined Concrete Cone Capacities  
An overview of the experimentally determined concrete cone capacities is shown in Figure 4, where the tensile load capacity is plotted against the L.A. coefficient of the aggregate used. The color code and marker code are consistent with the previously presented Figure 3 and subsequent figures. Each plotted marker in Figure 4 represents the mean value of four tests for CDL and five tests for CSTB.  
The plotted data clearly demonstrate that the L.A. coefficient is not a suitable parameter to evaluate the influence of aggregate type on concrete cone capacity [32]. For substantially different L.A. coefficients, almost similar concrete cone capacities can be obtained within one concrete class. The same lack of a trend was previously found in the case of concrete compressive strengths. Table 6 summarizes the mean concrete cone capacities and respective COVs of all investigated concretes and ages.  
Figure 4: Experimentally obtained concrete cone capacities for all tested concretes and ages (note: 1 kN = 0.225 kip).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & **Batch/** & \(f_{\text{c,150}}\) **at list Age** & \(f_{\text{f}}\) **at 1 st Age** & \(f_{\text{c,150}}\) **at 2 nd Age** & \(f_{\text{f}}\) **at 2 nd Age** & **MPa (ksi)** \\ \hline \multirow{6}{*}{low strength} & CSTB 1a/Agg. 4 & 33.2 (4.8) \(\pm\) 3.3\% & 2.6 (0.4) \(\pm\) 8.2\% & 38.8 (5.6) \(\pm\) 4.0\% & 2.7 (0.4) \(\pm\) 7.0\% \\ \cline{2-6}  & CDL 1/Agg. 1 & 29.1 (4.2) \(\pm\) 5.0\% & 3.0 (0.4) \(\pm\) 20.8\% & 30.6 (4.4) \(\pm\) 5.5\% & 3.4 (0.5) \(\pm\) 8.6\% \\ \cline{2-6}  & CSTB 2a/Agg. 5 & 28.5 (4.1) \(\pm\) 6.4\% & 2.5 (0.4) \(\pm\) 4.3\% & 32.0 (4.6) \(\pm\) 4.3\% & 2.7 (0.4) \(\pm\) 2.2\% \\ \cline{2-6}  & CDL 2/Agg. 2 & 35.3 (5.1) \(\pm\) 3.4\% & 4.1 (0.6) \(\pm\) 3.3\% & 40.0 (5.8) \(\pm\) 6.7\% & 4.4 (0.6) \(\pm\) 4.2\% \\ \cline{2-6}  & CSTB 3a/Agg. 6 & 27.2 (3.9) \(\pm\) 1.1\% & 2.3 (0.3) \(\pm\) 9.7\% & 31.9 (4.6) \(\pm\) 6.3\% & - \\ \cline{2-6}  & CDL 3/Agg. 3 & 35.0 (5.1) \(\pm\) 6.3\% & 3.4 (0.5) \(\pm\) 2.0\% & 39.7 (5.8) \(\pm\) 8.0\% & 3.5 (0.5) \(\pm\) 9.1\% \\ \cline{2-6}  & CSTB 4a/Agg. 7 & 27.9 (4.0) \(\pm\) 5.3\% & 2.0 (0.3) \(\pm\) 5.0\% & 36.0 (5.2) \(\pm\) 13.1\% & - \\ \hline \multirow{6}{*}{High strength} & CSTB 1b/Agg. 4 & 74.4 (10.8) \(\pm\) 1.2\% & 4.3 (0.6) \(\pm\) 4.8\% & 80.0 (11.6) \(\pm\) 2.6\% & 4.5 (0.7) \(\pm\) 1.7\% \\ \cline{2-6}  & CSTB 2b/Agg. 5 & 73.4 (10.6) \(\pm\) 2.8\% & 3.5 (0.5) \(\pm\) 4.6\% & 74.1 (10.7) \(\pm\) 2.1\% & 4.3 (0.6) \(\pm\) 11.9\% \\ \cline{2-6}  & CSTB 3b/Agg. 6 & 56.0 (8.1) \(\pm\) 1.1\% & - & 62.0 (9.0) \(\pm\) 1.3\% & - \\ \cline{2-6}  & CSTB 4b/Agg. 7 & 63.2 (9.1) \(\pm\) 1.0\% & 4.1 (0.6) \(\pm\) 6.2\% & 68.6 (9.9) \(\pm\) 2.9\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimentally obtained concrete cube compressive strength.
## 3 Types of Scanning Patterns
### Raster Scanning  
#### 3.2.1 Raster Scan Trajectory  
The raster scan begins in the region of low probability and progresses toward the region of high probability [47]. It sweeps along the horizontal axis, taking a vertical step at the end of each sweep to move to the next line. This process alternates between the two directions. If there are \(N_{h}\) horizontal lines, then \(N_{h}-1\) vertical steps are required to cover the uncertainty area. For an uncertainty region that spans from \(-u\) to \(+u\) in both the vertical and horizontal directions in the rectangular coordinate system, the total length of the raster path can be expressed as [23]  
\[L_{\rm total}=(N_{h}+1)\times 2u \tag{21}\]  
Since the raster path is scanned with a step size \(n\), the number of horizontal lines \(N_{h}\) can be expressed as  
\[N_{h}=\frac{2u}{n}+1. \tag{22}\]  
The total length of the raster path, in both horizontal and vertical directions, can be written as  
\[L_{\rm total}=L_{\rm horizontal}+L_{\rm vertical}. \tag{23}\]  
Finally, the total length of the raster path can be expressed as  
\[L_{\rm total}=\frac{4u^{2}}{n}+4u. \tag{24}\]  
Figure 2a,b show the raster path scanning for the uncertainty region spanning \(\pm 250\,\upmu\)rad in both the horizontal and vertical directions, a beam divergence of \(\theta_{b}=25\,\upmu\)rad, and overlap factor values \(a=0\) and \(a=0.2\). Similar to the spiral scan path, as the overlap factor increases, the step size decreases, but the number of scan points increases, which, in turn, increases the acquisition time. Similarly, increasing the beam divergence reduces the number of scan points, thus decreasing the acquisition time. The movement during the raster scan follows a triangular waveform in the horizontal direction and a step function in the vertical direction. After one complete scan, the process begins again from the initial position.
## 3 Methodology
### Multimodal Fusion Model
#### 3.2.1 Video Processing Module: Cascade Attention-Based Network  
In reference [33], we propose a cascade attention-based facial expression recognition network for classifying facial expression sequences within video content. During preprocessing, we segment the video sequence into \(T\) segments, choosing a random frame from each segment. This results in a selected T-frame image sequence \(X\), which serves as input to the neural network for facial expression feature extraction.  
\[X=\left\{x^{1},x^{2},\dots,x^{T}\right\},(T\in N) \tag{11}\]  
In Equation (11), \(x\) represents the random selection of image frames within each segment of the processed video sequence, while \(N\) signifies the total number of frames after video processing. The division into \(T\) segments is determined by the value of \(N\). In our study, we follow the frame attention network's experimental results and set \(T\) to 3 [34]. If the number of frames obtained after processing the video is three or more, the sequence is automatically split into three consecutive segments, each containing potential starting or peak frames of human facial expressions. Consequently, three frames are randomly chosen from these segments for further processing. If the number of frames is less than three, all extracted frames are processed. The objective is to achieve a robust representation and classification of the dynamic characteristics of the video frame sequences \(x^{T}\).  
Our proposed network model, i.e., a cascaded attention-based facial expression recognition network, is built on the fusion of multiple attentions and comprises three main modules: (i) a local and multi-scale stereoscopic spatial context feature extraction module, Msp, for spatial feature extraction, where we use three different scales (downsampling parameters: [1.0, 0.9, 0.8]) to generate a three-scale pyramid of features [35], (ii) a cascading attention module, Matt, to extract attention features; and (iii) a temporal sequential feature extraction module, Mtem, to extract features in the temporal dimension. The model's architecture is depicted in Figure 9. The local and multi-scale stereoscopic spatial context feature extraction module Msp employs a variant of the residual network, the ResNeXt network, as  
Figure 8: Framework of proposed multimodal fusion method.  
the feature extractor and inputs the extracted features into the cascading attention module Matt. Matt utilizes a two-layer attention cascade to learn attention weights from the local and multi-scale stereoscopic spatial fusion features. The temporal sequential feature extraction module Mtem takes the cascaded attention features as input and extracts temporal features using a GRU. These temporal features are then passed to a fully connected layer to produce the classification outcomes.
# A Coalition Formation Game Approach for Efficient Cooperative Multi-UAV Deployment
## 4 Simulation Results and Discussion
### Diagram Form  
Assume that the prior information of the mission area is known. In particular, a probability density function (PDF) which obeys the normal distribution is intended to describe the importance (data traffic) in the mission area. Figure 2a shows the color-coded display of the density map for the mission area, where the length of each mission cell is 10 m, and \(\sigma_{i}\) is denoted as the normalized density of overall data traffic.  
Figure 2b-d illustrates deployment and coalition formation diagram of 10 UAVs under different values of weight coefficient \(\rho\). The coloured circles represent the UAVs' detection range, that is, coverage ability. These figures depict the formation of UAV coalitions and the way data are transmitted. Coalition member UAVs (black dots) send their data to the coalition head UAVs (green triangles), which upload the data to the central UAV (red star). It can be seen that, as \(\rho\) increases, the UAVs' state is more inclined to forming a coalition to minimize the energy consumption, while a mission reward becomes lower. We analyze that, in Equation (18), UAVs' whole coverage utility is determined bymission reward and energy consumption. After taking the partial derivative of Equation (18), \(\rho\) is the indicator that determines the marginal utility of mission reward and energy consumption.
# A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence
## 2 Overview of Healthcare Applications  
The four main categories in which we can organize eHealth applications are: eHealth (implementations that do not belong in any of the following subdomains), mHealth, telehealth and telemedicine. Sometimes more narrow classifications can be made, such as:  
* Remote patient monitoring (RPM)--refers to patient monitoring and the transmission of medical data such as blood pressure, heart rate, heart rhythm, oxygen saturation, glucose levels, weight, etc. Clinicians can often monitor this data in real time.
* Interactive patient care--offers live interactive communication between patients and healthcare service providers via video, phone or other secure channels.
* Store and forward--consists of recording and transmitting multimedia data such as image, sound or video.  
Remote patient monitoring, interactive patient care and store and forward are all included in the telemedicine category.
# Automated Design of the Deep Neural Network Pipeline
## 2 Background
### Text Processing
#### 2.5.2 Spam Detection  
Spam detection attempts to differentiate between genuine, benevolent texts and unsolicited or malicious texts [38]. Spam detection is most commonly defined as a binary classification task where a textual data instance is either classified as spam or ham. A real world example of spam detection is seen in email clients. This research uses four datasets for spam detection: Enron emails dataset [39], SMS spam dataset [40], Spam Assassin dataset [41] and the YouTube comments spam dataset [42]. A full description of the aforementioned datasets is given in Section 3.3. If an email is classified as \"spam\", it is unsolicited or malicious [41]. If an email is classified as \"ham\", it is a benevolent text.  
The work carried out in [43] focused on spam filtering for emails, specifically for datasets that are balanced. A deep neural network pipeline is constructed in which the preprocessing stage consists of case conversion, tokenization, stop word removal and stemming. The feature engineering stage made use of the TF-IDF technique, and the neural network architecture chosen was a CNN. The Enron emails dataset [39] was used, as it contains a balance of spam and ham emails, and the deep neural network pipeline set a new state of the art for [39].  
The work carried out in [44] used a deep neural network pipeline to perform spam filtering for SMS messages. The author of [44] specifies that a preprocessing stage was included, but does not mention the techniques used. The feature engineering stage used basic word embedding and the chosen deep neural network architecture was a Recurrent Neural Network (RNN). The SMS Spam dataset [40] was used to validate the aforementioned deep neural network pipeline.  
The work in [45] performs a general study of information filtering, which includes spam filtering. A deep neural network pipeline was used as the solution. No techniques were used during the preprocessing stage. The feature engineering stage used the GloVe technique, and an LSTM was chosen as the deep neural network architecture. The Spam Assassin dataset [41] was one of the datasets included. The last study is [46], which constructs a general spam detection system that amounts to a deep neural network pipeline. The preprocessing stage consists of case conversion, removal of URI-like strings, conversion to ASCII, removal of punctuation, removal of date and time strings, removal of whitespace, removal of stop words, stemming, lemmatization, spelling correction and duplicate word removal. The feature engineering stage used a custom unspecified word embedding technique. The deep neural network architecture chosen was a basic Deep Neural Network (DNN). The YouTube comments spam dataset [42] was one of the datasets used in this study.
# Mobile Robot Path Planning Algorithm Based on NSGA-II
###### Abstract  
Path planning for mobile robots is a key technology in robotics. To address the issues of local optima trapping and non-smooth paths in mobile robot path planning, a novel algorithm based on the NSGA-II (Non-dominated Sorting Genetic Algorithm II) is proposed. The algorithm utilizes a search window approach for population initialization, which improves the quality of the initial population. An innovative fitness function is designed as the objective function for optimization iterations. A probability-based selection strategy is employed for population selection and optimization, enhancing the algorithm's ability to escape local minima and preventing premature convergence to suboptimal solutions. Furthermore, a path smoothing algorithm is developed by incorporating Bezier curves. By connecting multiple segments of Bezier curves, the problem of the high computational complexity associated with high-degree Bezier curves is addressed, while simultaneously achieving smooth paths. Simulation results demonstrated that the proposed path planning algorithm exhibited fewer iterations, superior path quality, and path smoothness. Compared to other methods, the proposed approach demonstrated better overall performance and practical applicability.  
path planning; NSGA-II algorithm; Bezier curve; mobile robot +
Footnote †: journal: Applied sciences
## 6 Comparison with the Classical PD and PID Controllers
### Comparison with the Classical PD Control Law  
Let us assume that the state feedback gain \(\mathcal{K}\) has the following expression:  
\[\mathcal{K}=\left[\begin{array}{cc}\mathcal{K}_{p}&\mathcal{K}_{\varphi}\\ \end{array}\right] \tag{96}\]  
where \(\mathcal{K}_{p}\) and \(\mathcal{K}_{\varphi}\) are gain matrices with appropriate dimensions.  
It is crucial to highlight that when applying the specified control law (25) by taking into account expressions (17) and (96), the dynamics of the knee exoskeleton robot (9) in the closed loop become as follows:  
\[\
u_{*}\ddot{\phi}=\mathcal{K}_{p}\phi+\mathcal{K}_{\varphi}\dot{\phi}-f_{ \varphi}^{*}\dot{\phi}-f_{s}^{*}\text{sgn}(\dot{\phi})+a_{*}\cos(\phi+\theta_{ d})-a_{*}\cos(\theta_{d})+\Delta_{t} \tag{97}\]  
After assuming \(\Delta_{t}=0\) and reaching the equilibrium state, where \(\ddot{\phi}=0\) and \(\dot{\phi}=0\), the final expression (97) may be rewritten as follows:  
\[\theta-\theta_{d}=\mathcal{K}_{p}^{-1}a_{*}(\cos(\theta_{d})-\cos(\theta)) \tag{98}\]  
In \(\theta=\theta_{d}\), it is evident that the preceding Equation (98) possesses a simple solution. Therefore, we may conclude that the intended affine state-feedback controller (25) successfully attains the required state configuration \(\theta_{d}\).  
Nonetheless, the following statement describes the classical PD controller:  
\[u=\mathcal{K}\mathcal{X}=\mathcal{K}_{p}\phi+\mathcal{K}_{\varphi}\dot{\phi} \tag{99}\]  
Hence, by utilizing the classical PD controller (99), the regulated dynamic model (9) is changed into the following:  
\[\
u_{*}\ddot{\phi}=-f_{s}^{*}\text{sgn}(\dot{\phi})+a_{*}\cos(\phi+\theta_{d}) +\mathcal{K}_{p}\phi+\mathcal{K}_{\varphi}\dot{\phi}-f_{\varphi}^{*}\dot{\phi }+\Delta_{t} \tag{100}\]  
At the state of equilibrium, where \(\ddot{\phi}=0\), \(\dot{\phi}=0\), and \(\Delta_{t}=0\), the preceding dynamics (100) can be expressed as follows:  
\[\theta-\theta_{d}=-\mathcal{K}_{p}^{-1}a_{*}\cos(\theta) \tag{101}\]  
It is obvious that the intended state \(\theta=\theta_{d}\) cannot be found as a solution for the previous Equation (101). Consequently, there will be an observable static inaccuracy between the equilibrium state \(\theta_{\infty}\) at which the exoskeleton robot is stable and the target state \(\theta_{d}\). The definition of this static mistake, \(\theta_{\infty}\), is as follows:  
\[\phi_{\infty}=\theta_{\infty}-\theta_{d}=-\mathcal{K}_{p}^{-1}a_{*}\cos(\theta _{\infty}) \tag{102}\]  
Therefore, the traditional PD control law (99) causes a static error \(\phi_{\infty}\) (102) compared to the developed controller (25) used in this work. The position control issue will not be resolved in such case.
# Experimental Investigation on Shear Behavior of Dune Sand Reinforced Concrete Deep Beams
###### Abstract  
An experimental study on the shear behavior of dune sand reinforced concrete (DSRC) deep beams was conducted to determine the feasibility of using dune sand (DS) in engineering. Nine DSRC deep beams were designed and thoroughly analyzed for failure modes, diagonal cracks, and load-deflection curves in this study. The results showed that the shear strength and ductility of DSRC deep beams increased when the DS replacement rate was 30%, but the opposite effect occurred when the DS replacement rate was 50%. To analyze the differences in the effects of the DS replacement rate, shear span-to-depth ratio, concrete strength, and stirrup ratio on the shear strength of DSRC and normal reinforced concrete (NRC) deep beams, a total of 227 shear experimental tests of NRC deep beams were conducted. Furthermore, four national codes were evaluated and compared based on experimental data. The evaluation results showed that the four codes underestimated the shear strength of DSRC and NRC deep beams. Among them, ACI 318-11 provided more reliable predictions for both NRC and DSRC deep beams. It is in this regard that a new empirical model for predicting the shear strength of DSRC deep beams is proposed, in which a reduction coefficient of the DS replacement rate is incorporated. The verification results indicates that the predicted results of the proposed model are in good agreement with the experimental results.  
d  
eurm sand; prediction model; reinforced concrete beam; shear strength Article
## 4 Numerical Results
### Dynamics  
The free dynamics of the grid beam is now analyzed. By letting \(\mathbf{p}=\mathbf{0}\) and \(\mathbf{u}=\hat{\mathbf{u}}\left(s\right)e^{\lambda t}\) in Equation (5), \(\left(\lambda,\hat{\mathbf{u}}\left(s\right)\right)\) being the eigenpairs of the system, a (spatial) boundary eigenvalue problem is obtained. Since the problem is conservative, the eigenvalues are purely imaginary, namely, \(\lambda_{j}=\pm i\omega_{j}\), where \(\omega_{j}\) is the angular frequency of the \(j\)-th mode. As already discussed, the boundary eigenvalue problem admits an exact solution, which, in what follows is compared with FE results, to check the effectiveness of the homogenized beam model in capturing frequencies and modal shapes of the real grid beam.  
Comparison is relevant to the first six modes of the fine and coarse models and it is made for all the case studies, already discussed in statics. The results of modal analysis are presented in Table 3, where the frequencies, \(\omega_{EQ}\) and \(\omega_{FE}\), relevant to the equivalent beam model and to FE solutions, respectively, are reported; moreover, \(\epsilon_{\%}=100\left(\omega_{FE}-\omega_{EQ}\right)/\omega_{FE}\) is the percentage error. It is seen that the equivalent beam model furnishes a very accurate approximation of the first six frequencies of the fine model, particularly in the case studies I-a (\(n=40\), \(100\)) and I-b (\(n=40\)), where the maximum error is about \(5\%\). Otherwise, in case study II-a, the percentage error increases, where just for the torsional modes (up to about \(20\%\)), this behavior is in agreement with what was already discussed in the static analysis.  
Finally, a very good accordance is confirmed also by analyzing the the modal shapes of the first six modes in case studies I-a (\(n=40\), \(100\)), and I-b (\(n=40\)), reported in Figures 9-11, respectively.