esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Our Proposed Methods
### Concurrent Image Conversion and Adaptive Message Embedding
\] (8)  
In Equation (6), we first obtain the remainder \(r_{R}\) using the divisor \(b_{R}\), and then calculate the difference, \(v_{R}\), between \(d_{1}\) and \(r_{R}\) in Equation (7). Finally, referring to Equation (8), we produce \(P_{R}^{\prime}\) according to the magnitude of \(v_{R}\) with respect to \(b_{R}\). Note that the component \(P_{E}\) in the \(E\)-channel remains intact during the digit embedding. We can follow the same approach to embed other two digits, \(d_{2}\) and \(d_{3}\), accordingly. We now analyze the time complexity of the embedding process. Let \(H\times V\) represent the size of an HDR RGBE image. We remark that the message concealment consists of five steps, as described above, and it is performed in the Red, Green, and Blue channels in a pixel-by-pixel approach. Consequently, the time complexity of message concealment is \(O(H\times V\times 3)\). We now present an example for message embedding. We present an example to illustrate the message embedding process. Let \(\mathbf{X}=(0.161621,\ 0.083496,\ 0.016113)\) represent a pixel in the OpenEXR image. In step 1, we convert \(\mathbf{X}\) to be a pixel \(\mathbf{P}\) in the RGBE format, where \(P=(165,\ 85,\ 16,\ 126)\). In step 2, without loss of generality, we assume the optimal base has been determined, where \(\mathbf{B}=(b_{R},\ b_{G},\ b_{B})=(5,\ 5,\ 7)\). In step 3, we determine the real embedding rate as shown in Table 1a. In step 4, the message, \(S_{175}=89\), has been decomposed into \((d_{1},\ d_{2},\ d_{3})=(4,\ 2,\ 3)\). To embed these three digits in step 5, we first refer to Equation (6) and obtain the remainders at each component, where \((r_{R},\ r_{G},\ r_{B})=(0,\ 0,\ 2)\). We then calculate the difference, \((v_{R},\ v_{G},\ v_{B})=(4,\ 2,\ 1)\), in Equation (7). Finally, using Equation (8), we derive the stego components in the Red, Green, and Blue channels, \((P_{R}^{\prime},\ P_{G}^{\prime},\ P_{B}^{\prime})=(164,\ 87,\ 17)\). Thus, the stego pixel produced becomes \(\mathbf{P}^{\prime}=(P_{R}^{\prime},\ P_{G}^{\prime},\ P_{B}^{\prime},\ P_{E}^{ \prime})=(164,\ 87,\ 17,\ 126)\), as the E-channel is intact. All the other pixels can work in the same manner to convey the corresponding digits.
# Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
## 3 Experimental Design
### Description of the LSTM Model  
LSTM is an advanced RNN used to specify which feature should be memorized or forgotten when the network is being trained. Therefore, given a sufficient history of features and solar radiation, the LSTM can determine the required history for each feature to provide an accurate solar radiation estimation.  
\begin{table}
\begin{tabular}{c c} \hline Epochs & 500 \\ \hline Batch size & 16, 32, 64, 128, 256, 512, 1024 \\ \hline Learning Rate (LR) & [0.0005, 0.05] with step 0.005 \\ \hline \end{tabular}
\end{table}
Table 4: Hyperparameters of the ANN model.  
Figure 6: ANN model.  
In the proposed LSTM/DNN model, we allowed the LSTM to access up to 30 h in the past in order to predict the solar radiation after 48 h. Hyperparameters optimization via grid search was performed on the hyperparameters shown in Table 5.  
The loss function of LSTM was the mean squared error (MSE) and the model was implemented by Keras.
# Using a Cable-Driven Parallel Robot with Applications in 3D Concrete Printing
## 5 Mechanical Design
### Concrete Extruder  
Figure 10 is the concrete extruder (CEx) designed for extruding a cement mortar mixture. The extrusion material is a mixture of cement, sand, plaster, and Sikament R7N--an additive to reduce the condensing time and match the printing speed of the system. This CEx is constructed from a screw extruder connected to the motor's reducer through the system. The screw extruder is guided by two bearing placed on the MB and extrusion head. An 80 W DC gear motor is used to drive the screw extruder with a maximum speed of 35 RPM and maximum continuous torque of 10 Nm; these specifications are applied after a 1:100 gearbox.  
The extrusion flowrate of the system is up to 0.2 kg/s, corresponding to the speed of the MP. The flowrate of the extruder can be controlled by changing the speed of the screw extruder. A PID controller was designed to control the extrusion screw speed stably. With this design, the volume of the concrete tank is limited due to the payload capacity of the cable robot. In order to coincide the robot configuration, the total weight of the MP (mobile platform), CEx, and concrete should not exceed 80 kg. In this design, the maximum weight of the MP and CEx is 60 kg (without concrete) and 70 kg (full concrete). The dimension of the nozzle can be changed by replacing the extrusion head.  
Figure 10: The concrete extruder.
## 2 Materials and Methods
### Classification  
The general performance of the DLA relies mainly on the classifiers employed to classify the considered images into normal/tumor class. The traditional DLA considers the SoftMax, which provides a reasonable accuracy with using transfer learning approach. Further, the performance of the DLA can be enhanced by employing appropriate classifiers [50; 51; 52; 53].  
In this work, the SoftMax classifier is replaced with other classifiers, discussed below:  
* Decision tree: DT is one of the more famous methodologies used to categorize the linear and non-linear information with a sequence of testing methods, which expands like a tree. The DT utilizes a quality exploration situation as the root and internal nodes, and the class label forms terminal nodes. Once a DT has been shaped, categorization is accomplished by the conclusions taken in each branch of the tree. Other particulars of DT can be found in [43; 44; 45; 46].
* K-nearest neighbor: KNN is a well-known technique often considered to classify medical images based on an existing feature set. In this work, KNN is considered to classify the brain MRIs of varied modality. During the classification task, the KNN evaluates the space among new features to each training feature and discovers the best neighbor. The earlier works on the KNN can be found in [43; 44; 45; 46].
* Support vector machine: SVM categorizer uses a hyperplane for labeling of dataset based on features gathered throughout the training stage. SVM is one of the most frequently used to categorize MRI images. Radial basis function-based SVM (SVM-RBF) is used to sort the 2D MRI with the elected features. In SVM-RBF, the kernel value is controlled by a scaling parameter \"\(\sigma\)\"; and this value is varied from 0.2 to 1.9 with a step size of 0.1. Furthermore, the SVM with linear polynomial kernel (SVM-Linear) is also adopted to grade the MRI database [43].
## 3 Queuing Model and Dynamic Spectrum Algorithm
### SU State Transitions  
Since there are two types of queues at CBS, the channel state of the cognitive radio communication system with N channels can be described by an integer pair \((i,j)\), where \(i\) represents the number of channels occupied by \(SU_{q1},j\) represents the number of channels occupied by \(SU_{q2}\).  
When an SU of \(SU_{q1}\) arrives with rate \(\lambda_{q1}\), the state of the system becomes \((i+1,j)\). Similarly, when an SU of \(SU_{q2}\) arrives with rate \(\lambda_{q2}\), the state of the system becomes \((i,j+1)\). At the state \((i,j)\), when an SU in \(SU_{q1}\) leaves with rate \(\mu_{q1}\), the state of the system becomes \((i-1,j)\); when an SU in \(SU_{q2}\) leaves with rate \(j\mu_{q2}\), the state of the system becomes \((i,j-1)\). Furthermore, when \(i+j=N\), \(SU_{q2}\) enters the blocking state. The CBS will determine whether \(SU_{q1}\) preempts the channel of \(SU_{q2}\) according to priority factor \(p\), which characterizes the probability that \(SU_{q2}\) will refuse to stop using channels. Thus, when the CBS determines that \(SU_{q1}\) preempts \(SU_{q2}\)'s channel, the system state changes from \((N-i,i)\) to \((N-i+1,i-1)\), and the state transition rate is \(\lambda_{i}\), which is given as  
Figure 3: Queuing model with two queues and multiple servers.  
\[\lambda_{i}\ =\ \Big{(}1-p^{i}\Big{)}\lambda_{q1}. \tag{1}\]
## 3 Target Degradation Strategy (TDOS)
### Energy Reduction Method
2 When the transfer orbiting approach is adopted, it is assumed that the launch vehicle will be orbited at the apogee of the transfer orbit. When the perigee altitude is reduced to \(h_{p}=h_{p\min}\), if the launch vehicle still cannot enter the orbit of the transfer orbit, the minimum perigee altitude is maintained, and the apogee altitude is reduced; when the apogee altitude is reduced to \(h_{a}=h_{\kappa}+\delta_{0}\), if the launch vehicle still cannot enter the orbit of the orbit, the reduced payload mode will be adopted. Downgrading strategy when the original target mission is an elliptical orbit  
When the direct orbiting mode is adopted, priority should be given to maintaining the perigee height of the target orbit and reducing the apogee height so that the payload can enter the degraded orbit near the perigee of the original target orbit for secondary orbital transfer of the payload; when it is lowered to \(h_{a}=h_{\kappa}+\delta_{0}\) if the launch vehicle still does not have the orbit-injection capability, the minimum apogee altitude is maintained, and the perigee altitude is lowered; when the perigee altitude is lowered to \(h_{p}=h_{p\min}\), if the launch vehicle still cannot enter the orbit for the transfer orbit, it will switch to the payload drop mode. Since the launch vehicle has a high flight altitude in the second stage and the altitude adjustment margin \(\delta_{0}\) is reserved, there will be no situation where the apogee altitude after degradation is less than the perigee altitude at the last degradation. When the transfer orbiting approach is adopted, the perigee altitude of the target orbit is maintained, the apogee altitude is lowered, the fuel consumption for orbit transfer is calculated, and the launch vehicle's orbiting capability is judged; when it is lowered to \(h_{a}=h_{p}\) if the launch vehicle still does not have the orbiting capability of the target orbit, the minimum apogee altitude is maintained, the perigee altitude is lowered, the fuel consumption for orbit transfer is calculated again, and the launch vehicle's orbiting capability is judged; when the perigee altitude drops to \(h_{p}=h_{\kappa}+\delta_{0}\), if the launch vehicle still does not have the orbit-injection capability, it will switch to the payload reduction mode.
## 2 The Movable NDT Trainer: Experimental Design and Data Collection  
This section describes the movable trainer [16], which automatically repeats the NDT intervention. The trainer consists of a gait detection system and a motor control system, as shown in Figure 1, which equips with an adjustable handle for safety. The gait detection system analyzed the gait information obtained from two IMUs attached to the user's shanks. It could automatically recognize the HS events and send triggering signals to the motor control system to imitate the therapists' intervention. The motor control system could repeat the therapists' intervention patterns by stimulating the users through ropes. The ropes connected the motors and the user's pelvis to cue the user as the traditional NDT training. Load cells were implemented between the ropes and the motors to measure the applied forces for feedback control. Previous studies [14; 15; 16] showed that the therapists tended to cue the subject's right (left) pelvis when the subject's left (right) foot struck the ground. Therefore, an RNN model [24] was applied to the gait detection system, which detects the HS in real time to activate the motor control system. The motor control system then repeats the NDT intervention after receiving the triggering signals from the gait detection system [14; 15; 16].  
Stroke patients tend to have uneven gaits due to hemiparesis, and stroke patients have their own individual walking patterns and speeds. Therefore, we developed an RNN model to detect HS events for subjects who might have varied walking patterns and speeds [24]. We applied the APDM OPAL system [30] to measure each subject's kinematic data with a sampling rate of 25 Hz. Two IMUs were attached to the subject's legs, as shown in Figure 2, to measure gait data. The specifications of the IMU are illustrated in Table 1.  
Human gaits are regularly periodic. A complete gait cycle, as illustrated in Figure 3, contains three important gait events: mid-swing, HS, and toe-off. We applied the six-axial IMU data and marked the HS events to build an RNN model [24], as shown in Figure 4. The model was trained by the gait data measured in five healthy subjects who wore a rehabilitation gaiter with a 2 kg mass to limit their joint movement on one knee to increase gait varieties (see Figure 5). We segmented the gait data with a sliding window, as illustrated in Figure 4, which consisted of 50 samples of six-axial IMU data as the model input. The window was then moved by one sample each time, with an overlap of 98%. The model output was marked as 1 for an HS event or 0 for a non-HS event. We applied the leave-one-out cross validation method [31] to train the RNN model. We then applied the model to three groups of subjects: healthy elderly subjects, stroke patients, and patients with PD. The results illustrated in Table 2 confirm that the RNN model successfully recognized the HS events with an average accuracy of 99.65% and an average delay of 0.0256 s. Therefore, we applied the RNN model to detect the HS and to activate the NDT intervention in real time.
# Optical Helicity and Chirality: Conservation and Sources
## 2 Review and Motivation
### Integrated Helicity and Local Densities  
Here, we will say a few words about the purpose of extending the magnetic definition of helicity to include the extra gauge potential \(\mathbf{C}\), leading to the second term in (1). Indeed, the quantity \(\int\mathbf{A}\cdot\mathbf{B}\,d^{3}\mathbf{r}\), where \(\mathbf{B}\) is the magnetic induction, is sometimes used as a measure of the total electromagnetic helicity [27; 28], as, when time averaged, it is equivalent to the volume integral of (1). There is a sense in which the helicity is only meaningful when integrated over all space: the appearance of the gauge potentials in the definition implies that the helicity density at a point is explicitly gauge-dependent. However, the total helicity is physically meaningful, as the volume integral serves to pick out only the transverse parts of \(\mathbf{A}\) and \(\mathbf{C}\), which are gauge-invariant, meaning that the integrated helicity is in fact a gauge-independent quantity [14].  
Such gauge-related ambiguity in the definition of the local densities might suggest that the physically meaningful content of both definitions is the same. On the other hand, the manner in which the total integral becomes gauge-independent might suggest that a local helicity density could be unambiguously defined by explicitly using only the transverse parts of the potentials in (1). This is the approach adopted throughout this article, though it should be cautioned that such a helicity density still retains an element of a non-local character, as even the gauge-invariant parts of the potentials at a point are not only determined by the fields and their derivatives at that point; the values of the fields at other points are involved as well [17].  
Having said this, there are more than simply aesthetic reasons that the symmetrical definition of helicity might be preferred over the asymmetrical. For one thing, we will see that the symmetrical definition obeys an exact local continuity equation and is therefore locally conserved, at least in the absence of matter [6; 17]. Furthermore, it retains its form under both Lorentz and duality transformations [14], the latter being transformations that rotate electric into magnetic fields, and vice versa, encapsulating the symmetry between these fields in the absence of charges. An analogous continuity equation cannot be drawn up for the asymmetrical definition [17], and only its integral over all space is invariant under duality and Lorentz transformations. This feature is the origin of subtle complications in the use of \(\mathbf{A}\cdot\mathbf{B}\) in the study of plasmas [24], but such issues do not arise for the locally-conserved form. Finally, as will be shown in Section 2.3, the quantum helicity operator derived from (1) has a particularly intuitive form, in further support of this symmetric definition.
## 3 Application to the Trajectory Tracking of a Quadrotor
### Quadrotor's Dynamic Model
These equations are the following ones [27; 28]:  
\[\ddot{x}=\frac{cos(\phi)sin(\theta)cos(\psi)+sin(\phi)sin(\psi)}{m}u_{1}, \tag{24}\]  
\[\ddot{y}=\frac{cos(\phi)sin(\theta)sin(\psi)-sin(\phi)cos(\psi)}{m}u_{1}, \tag{25}\]  
\[\dot{z}=-g+\frac{cos(\phi)cos(\theta)}{m}u_{1}, \tag{26}\]  
\[\ddot{\phi}=\phi\dot{\psi}\frac{(I_{xx}+I_{yy}-I_{zz})}{I_{xx}}+\frac{u_{2}}{ I_{xx}}, \tag{27}\]  
\[\ddot{\theta}=\phi\dot{\psi}\frac{(I_{zz}-I_{xx}-I_{yy})}{I_{yy}}+\frac{u_{3}}{ I_{yy}}, \tag{28}\]  
\[\ddot{\psi}=\phi\dot{\phi}\frac{(I_{zz}+I_{xx}-I_{yy})}{I_{zz}}+\frac{u_{4}}{ I_{zz}}, \tag{29}\]  
where \(u_{1}=T_{f}\), \(u_{2}=\tau_{\phi}\), \(u_{3}=\tau_{\theta}\) and \(u_{4}=\tau_{\psi}\) are considered as control signals. It is possible to stablish a mapping between the control signals \(u_{1}\), \(u_{2}\), \(u_{3}\) and \(u_{4}\) and the angular velocity of each quadrotor's electric motor \(\omega_{1}\), \(\omega_{2}\), \(\omega_{3}\) and \(\omega_{4}\). In fact, each motor produces a vertical force \(F_{i}\) and a moment \(M_{i}\) that is related to the motor angular velocity \(\omega_{i}\) in the form [29]  
\[F_{i}=k_{F}\omega_{i}^{2},\quad M_{i}=k_{M}\omega_{i}^{2}, \tag{30}\]  
where \(k_{F}\) and \(k_{M}\) are positive constants that can be experimentally determined. The relation between the motor angular velocities and the control signals is then given by [29]  
\[ \begin{bmatrix}u_{1}\\ u_{2}\\ u_{3}\\ u_{4}\end{bmatrix} =K_{MF} \begin{bmatrix}\omega_{1}^{2}\\ \omega_{2}^{2}\\ \omega_{3}^{2}\\ \omega_{4}^{2}\end{bmatrix} , \tag{31}\]  
with \(K_{MF}\) being an invertible matrix of the form  
\[K_{MF}= \begin{bmatrix}k_{F}&k_{F}&k_{F}&k_{F}\\ 0&k_{F}L&0&-k_{F}L\\ -k_{F}L&0&k_{F}L&0\\ k_{M}&-k_{M}&k_{M}&-k_{M}\end{bmatrix} , \tag{32}\]  
where L is the distance from the rotation axis of the motors to the center of the quadrotor.
## 3 Landslide Monitoring
After a calibration of the Archie's equation, the ERT images were transformed in water saturated maps. The results of the controlled experiment highlighted that the landslide body becomes unstable at zones where the water saturation exceeds 45%, and the instability phenomena could occur at the boundaries between areas with different water saturations. [PERSON] et al., 2020 [83] approached the study of shallow landslides in a mountain landscape mainly triggered by rainfall extreme events. They designed and implemented a landslide simulator, and a dataset of 20 land mass failures induced by water infiltration were described and analyzed. Both hydrogeological and TL-ERT measurements were performed. The time series of the ERT images were used for mapping the waterfront time-dependent changes during three different rainfall simulations: (i) temporal increase in the rainfall intensity; (ii) constant rainfall intensity and (iii) two sequences for rainfall events. The results of downscaled experiments confirmed that the rainfall intensity, the hydraulic conductivity and the soil moisture gradient controlled the stability, while rainfall intensity and duration were the main predictors of the shallow soil slips. Furthermore, their results could be useful for implementing procedures for the validation of the rainfall thresholds. [PERSON] et al., 2021 [84] investigated one of the more critical problems that generally occur in the ERT monitoring of shallow landslides. It is common to observe low changes in the geometry of the electrode array due to ground movements. In a field experiment carried out at Hollin Hill site (UK), the variations of the distances between the electrodes were detected with topographic methods (UAV surveys and global position measurements) and considered in the TL-ERT inversion (Figure 6). Figure 6: (**a**) The baseline inverted image of the Hollin Hill landslide (southern Howardian Hills, UK) measured in May 2016; (**b**) surface resistivity in relation to a hill-shaded relief map from a UAV survey in May 2016. The six images on the right (**c–h**) report an overview of elevation and electrode coordinate changes at Hollin Hill, as captured by terrestrial LiDAR (after [84]).
## 5 Discussion  
In order to observe the impact of the considered VLC scheme and the propagation characteristics on the proposed V2I-VLC system, the performance of the system can be compared with similar studies [44; 47]. In [44], a general V2I-VLC model is proposed. Additionally, in [47], typical V2V-VLC models based on two-lane roads are considered.  
As shown in Figure 4 in [47], under clear weather conditions, the path loss in the LOS scenario is about \(-44\) dB when Tx is \(10\) m away from Rx. Furthermore, when Tx is \(50\) m away from Rx, the path loss is \(-62.5\) dB. Comparing the results in Figure 6a and [47], it can be observed that mobility has a large impact on the path loss performance of vehicular VLC systems. This is because, in the proposed V2I-VLC system, the path loss is only about \(-52.5\) dB (under clear weather conditions) when the Tx and Rx are \(50\) m apart. The biggest difference between the two models is that the Rx of the V2V-VLC system is moving, while the Rx of the V2I-VLC system is fixed.  
Comparing the channel capacity shown in Figure 6 in [44] with that of the proposed V2I-VLC system shown in Figure 10 (under clear weather conditions), it can be observed that the use of multiple PDs for signal reception at the Rx end can significantly improve the performance of the vehicular VLC system. This is because Equal Gain Combining (EGC) uses a direct sum of the branch signals from all PDs with equal weighting to all branches, which increases the overall received SNR [44].  
In summary, in future research, the focus could be on the real mobility of vehicles or infrastructure. In addition, it will be of great significance to investigate MIMO techniques applicable to vehicular VLC.
## 2 Methods  
### Data Sources and Search Strategies  
This systematic review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines [28]. The first step of this process was using the research questions to help shape and develop a search strategy to capture all related and eligible papers in the targeted database. In this case, the following keywords were identified: \"eHealth\", \"digital health\", \"telehealth\", \"health informatics\", \"electronic health\", \"mHealth\", \"mobile health\", and \"transplant\". A systematic search of the literature on eHealth in transplants was conducted through the databases of Business Source Complete, PubMed, IEEE Xplore, JSTOR, Scopus, SpringerLink, ClinicalKey, ScienceDirect, Wiley Online Library, OVID, and Web of Science. The search was conducted within the databases by selecting eHealth-related terms using an OR operator (e.g., \"eHealth OR electronic health OR digital health\" OR \"telehealth\" OR \"mhealth\" [Title/Abstract]) and using the Boolean operator \"AND\" to search the keyword transplant simultaneously. The review focuses on articles published from 2018 onward to ensure the inclusion of the most recent and relevant findings. This timeframe reflects the rapid evolution of eHealth technologies,such as virtual health, digital tools, and their increasing integration into transplant care practices. Since technology and healthcare delivery methods have advanced significantly in recent years, studies published before 2018 may not adequately capture the current state of eHealth applications, their capabilities, or emerging challenges. The initial search was conducted between September and October 2023 and yielded a total of 30,996 publications.