esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Chromatogram and Molar Mass Distribution of Cellulose Samples  
The four different cellulose samples were first dissolved into the solution of 10 mg/mL TBAF concentration. The chromatograms obtained presented two different behaviors depending on the celluloses nature (Figure 4A).  
Avicel(r) and cotton fibers, the lowest molar mass samples, had similar chromatograms displaying a well-defined peak between 8 mL and 9 mL, and a smaller one (especially for cotton fibers) between 6 mL and 7 mL, which is in accordance with their molar masses being close to each other.  
The well-defined peaks indicated that the dissolution was completed and the fibers were totally dissociated during the process. For both samples, the peaks were in the range of the calibration curve and the excluded volume percentage was negligible, allowing the calculation of the average molecular weight (Table 3).  
The obtained values were comparable to the viscosimetric ones and those obtained from the suppliers. Vitacel(r) and \(\alpha\)-cellulose, the highest molar mass samples, were also characterized by a similar DRI profile with two distinguished peaks identified on their respective chromatogram. But, the first peak was in the exclusion volume, whereas the second one, corresponding to very low hydrodynamic volume molecules, may be attributed to TBAF molecules complexed with DMSO, instead of cellulosic macromolecules (Figure 4A). Consequently, the average molecular weight were not calculated for these two samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **\%Excl. a** & **Dispersity** & **M\(n\) (kDa)** & **M\(w\) (kDa)** \\ \hline C DT 0 & 0.8 & 1.1 & 43.8 & 46.9 \\ CC DT 1 & 0.9 & 1.1 & 45.7 & 49.8 \\ \hline \end{tabular} a %Excl. is the no-retention percent of the sample.  
\end{table}
Table 2: Determination of molar masses among two different calibration curves of Avicel® samples dissolved with 10 mg/mL of TBAF concentration in solution.  
Figure 4: SEC chromatograms of cellulose samples dissolved with [TBAF] of 10 mg/mL and 20 mg/mL.  
A higher TBAF concentration (20 mg/mL) was then tested for \(\alpha\)-cellulose and Vitacel(r), in order to facilitate their dissolution and improve their fractionation. The obtained chromatograms (Figure 4B) presented again two distinct peaks. The first one was still eluted in the exclusion volume, but the second one was displaced to more reasonable elution volume (between 7.5 mL and 10 mL) corresponding probably to the totally dissolved cellulosic fraction. Unfortunately, the first peak accounted for more than 50% of the injected molecules (Table 2), preventing a correct calculation of the average molar masses, so the 20 mg/mL TBAF concentration did not seem sufficient to completely dissolve the high molecular weight cellulosic samples. For further investigation on analyses of high molecular weight cellulosic sample, it would hence be pertinent to test higher TBAF concentrations, keeping in mind that too high a concentration may lead to other artefacts during the fractionation.
## 3 Method
### UGVA Method and the Image Structure
We have identified five types of axes (see Figure 3):* Simple axis (type \(A\))--quantitative value of the key indicator (parameter) \(S_{p}\) is shown using one graphic primitive, e.g., a parallelogram that is a part of a limb with the student's age mapped to its length and student's average grade mapped to its height;
* Composite structural axis (type \(B\))--structural components of one quantitative parameter of a generalized indicator \(S_{p}\) are shown using a sequence of graphic primitives, demonstrating specifics of their composition, e.g., the average grade in humanities can determine the length of the first limb segment, the average grade in natural sciences--the length of the second segment, and the average grade of engineering courses--the length of the third segment;
* Composite heterogeneous axis (type \(C\))--quantitative parameters of different \(S_{p}\) are showing using a sequence of graphic primitives along one axis; they belong to different aspects of analysis, e.g., the length of the first primitive shows the average level of competency development for the given course, whereas the length of the second primitive--the average grade in the course;
* Composite cause-effect axis (type \(D\))--when the parameters of different \(S_{p}\), shown using a sequence of graphic primitives along one axis, is selected to demonstrate a cause-effect relationship between these parameters, e.g., the length of the first primitive shows the exam grade, whereas the length of the second primitive shows class attendance;
* Composite temporal axis (type \(E\))--each segment of an axis shows the value of the indicator \(S_{p}\) in different times \(t\), e.g., the average grade of the same student for different semesters. If a parameter's value is critically low, the relevant segment of the images can be omitted (which can lead to the omission of an entire axis or only a part of an axis) or marked by a special sign (e.g., in Figure 2a, the parameter coded by left forearm is missing). Figure 3: Graphic templates of different axes in UGVA images: (**a**) simple axis (type \(A\)), (**b**) composite structural axis (type \(B\)), (**c**) composite heterogeneous axis (type \(C\)), (**d**) composite cause-effect axis (type \(D\)), (**e**) composite temporal axis (type \(E\)). Figure 2: Four individualized images of complex systems in the UGVA notation: (**a**) a graduate student who studies courses using an online intelligent tutoring system; (**b**) an employee of a company, whose direct supervisor uses an intelligent decision-supporting system to receive advice on motivating employees; (**c**) a funding application for a scientific project according to their performance; (**d**) a laboratory researcher according to the university’s key performance indicators (KPI). When visualizing data about a complex, multiparameter system, axes of data mapping can be arranged using basic symmetry types [27].
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 2 Data and Preprocessing
### Multi-Parameter Selection
Therefore, this metric is an important measure for evaluating the goodness of fit of the G-R model. The definition formula is as follows:  
\[\sigma=\frac{\sum(log_{10}N_{i}-(a-b_{1}M_{i}))^{2}}{n-1} \tag{7}\]  
6. \(\Delta M\)  
\[\Delta M=M_{max,observed}-M_{max,expected} \tag{8}\]  
where \(M_{max,observed}\) represents the maximum observed magnitude among the most recent \(n\) events within the time window and \(M_{max,expected}\) denotes the maximum magnitude based on the G-R inverse power law relationship among these events within the time window. 7. _Lonmean_ and _Latmean_  
Lonmean and Latmean represent the longitude and latitude mean of n seismic events in the time window, respectively. The definition formula is as follows:  
\[Lon_{mean}=\frac{\sum Lon_{mean}}{n} \tag{9}\]  
\[Lat_{mean}=\frac{\sum Lat_{mean}}{n} \tag{10}\]  
8. \(Lon_{E}\) and \(Lat_{E}\)  
The energy-weighted mean longitude and latitude are parameter in seismology that more accurately reflect the spatial distribution of seismic activity by considering the relative importance of each earthquake event through a weighted averaging method. The definition formula is as follows:  
\[Lon_{E}=\frac{\sum\Bigl{(}Lon_{i}\times E_{i}^{1/2}\Bigr{)}}{\sum E_{i}^{1/2}} \tag{11}\]  
\[Lat_{E}=\frac{\sum\Bigl{(}Lat_{i}\times E_{i}^{1/2}\Bigr{)}}{\sum E_{i}^{1/2}} \tag{12}\]
# Federated Learning for Secure In-Vehicle Communication
## 4 Experimental Setup
### Simulation Parameters  
As the optimization algorithm, we use the Adam optimizer [40] because of its capability to adaptively adjust the learning rate, leading to quicker convergence and improved management of sparse gradients. We run the training for 1 and 5 epochs. For multi-class classification, the categorical cross-entropy [41] loss function is employed, which provides an effective way to assess the accuracy of probabilistic outcomes. A batch size of 64 samples is selected to balance computational efficiency and the ability to generalize. The learning rate is fixed to 0.001, a widely accepted value known to promote steady convergence in DL and FL applications.  
Table 2 summarizes the simulation parameters. Fraction Fit specifies the proportion of clients selected to train the global model for a round of FL; if equal to 1, all three datasets are chosen to engage in each round. Fraction Evaluation refers to the proportion of clients selected for model evaluation and is equal to 0.5, which means that two of the three datasets are chosen for testing in each round.  
We use the aggregation algorithm FedAvg [42], the global model update at round \(t\) is computed as a weighted average of the local models \(W_{t}^{i}\) (denoted as \(W_{t}^{(i)}\) for client \(i\)) based on the number of samples \(n_{i}\) (denoted as \(n_{i}\) for client \(i\)). The formula (1) [43] is given by:  
\[\frac{\sum_{i=1}^{N}n_{i}W_{t}^{i}}{\sum_{i=1}^{N}n_{i}} \tag{1}\]
## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Conventional Approaches
#### 8.1.3 Resource Management
The proposed framework considers three conflicting objectives, namely, maximizing the network sum rate, maximizing the number of IoT devices served, and minimizing carbon dioxide emissions. To solve the formulated mixed-integer nonlinear programming problem, the authors propose an outer approximation algorithm (OAA) and compare its performance with the two most known algorithms. Machine Learning Approaches  
The authors in [252] proposed an intelligent scheme based on \(Q\)-learning to determine the optimal power allocation and 3D position of multiple UAV-BSs during emergency situations to maximize the coverage of ground users. The proposed method led to a reduction in the number of UAV deployed by 56%, minimized the percentage of users in outage, and significantly reduced the transmit power of the UAVs. In [253], the authors addressed the problem of trajectory design and power allocation for multi-UAV networks by proposing a multi-agent DDPG algorithm to find the optimal strategy to jointly optimize the trajectory and transmission power. Numerical results reveal that the proposed method results in improved network capacity with reduced computational overhead. A DRL scheme for jointly optimizing the 3D-deployment and power allocation of a UAV-BS was proposed in [28]. The proposed scheme leverages DDPG and water filling to determine the optimal 3D position as well as the power allocation strategy while maximizing system throughput. The downlink power control problem in ultra-dense UAV networks was investigated in [254] for EE enhancement purposes. In this regard, mean-field game (MFG) was used to model the power control problem after which a DRL algorithm based on deep \(Q\)-networks was developed to determine the optimal power control policy that would maximize the EE of the UAV network. Simulation results proved that the proposed DRL framework outperformed the \(Q\)-learning and random approaches in terms of learning the optimal power control even at different UAV heights. An energy efficient resource allocation scheme for UAV-based edge computing systems that considers the power control, user association, computation resource allocation, and location planning for UAV was proposed in [255]. An iterative approach based on a fuzzy c-means clustering algorithm was proposed to solve the joint EE minimization problem. The performance evaluation showed that the proposed approach results in lesser power consumption compared with conventional methods during periods of low latency, low computation capacity, and high CPU cycles. Table 3 summarizes the CA and ML approaches that are applied for resource management in UAV-BSs in order to minimize their energy consumption.
## 4 Security Analysis of the MAPP Protocol
### Resistance to Attacks
#### 4.2.1 Replay Attack  
The proposed authentication method ensures the freshness of the transmitted message by attaching the current timestamp. For example, in the TCA method, the message format is \(\{L_{ZID},pid_{1},m_{i},T_{s},pk_{i},\sigma_{i}\}\), where \(T_{s}\) is the attached time stamp. All vehicles should be synchronized to provide accurate time and resistance against the replay attacks. The synchronization of vehicles can be provided using GPS devices.
## 4 Results and Discussion
### Addressing Research Questions
The radial axis represents the rating scale using the Likert scale (1 = totally disagree; 5 = totally agree), with higher values indicating better performance. familiarity levels. This could indicate that blurring is more universally suited for various AR environments, making it the most versatile option. Masking had a lower mean satisfaction score (2.81) and showed moderate variability in the satisfaction scores, with a standard deviation of 1.33. The highest satisfaction score for masking was at AR level 1 (3.67), and the lowest was at AR level 5 (2.47). This suggests that while masking may not be the most preferred method overall, it still has specific contexts (like AR level 1) where it performs relatively well. The moderate variability indicates that user experiences with masking can differ, which might imply that it is more context-dependent and may not provide a uniformly reliable experience across all AR familiarity levels. Pixelation had a mean satisfaction score of 3.13 and showed higher variability, especially with a score of 2.00 at AR level 2. This indicates that while some participants found pixelation acceptable, it may not be as effective across the full range of AR familiarity levels. The variability in user responses suggests that pixelation may be less suitable for diverse AR contexts.
## 4 Experimental Results  
Figure 14 shows a photograph of the fabricated antenna. Figure 15 illustrates the measured and simulated reflection coefficient characteristics of the fabricated UWB antenna. The fabricated UWB antenna satisfies a \(-10\) dB reflection coefficient bandwidth from 2.4 GHz to 12 GHz, which is wide enough to fully cover the desired UWB spectrum.  
The measured and simulated S-parameters of the three types of NB antenna are shown in Figure 16. NB Antenna #1 operates from 3.05 GHz to 5.35 GHz in the low-frequency band. NB Antenna #2 operates from 5.75 GHz to 9 GHz in the mid-frequency band, and NB Antenna #3 satisfies a \(-10\) dB reflection coefficient from 9.15 GHz to 11.2 GHz in the high-frequency band. Additionally, the three NB antennas maintain a high isolation level (greater than 17 dB) over the whole operating frequency band.  
Figure 16: _Cont._  
Figure 14: Photograph of the proposed antenna.  
Figure 15: Measured and simulated reflection coefficient characteristics of the fabricated UWB antenna.  
Figure 17 shows the simulated and measured radiation patterns of the fabricated UWB antenna at 3.5 GHz, 4.9 GHz, 6.52 GHz, 7.65 GHz, and 9.5 GHz. The fabricated UWB antenna has quasi-omnidirectional radiation patterns in the YZ plane.  
Figure 16: Measured and simulated S-parameters of NB antennas: (**a**) NB Antenna #1, (**b**) NB Antenna #2 and (**c**) NB Antenna #3.  
Figure 17: \(Cont.\)  
Figure 18 shows the simulated and measured radiation patterns of the NB antennas at 3.5 GHz, 4.9 GHz, 6.52 GHz, 7.65 GHz, and 9.5 GHz. The NB antennas have quasi-omnidirectional radiation patterns in the YZ plane.  
Figure 17: Simulated and measured radiation patterns of the UWB antenna for various frequencies: (**a**) at 3.5 GHz; (**b**) at 4.9 GHz; (**c**) at 6.52 GHz; (**d**) at 7.65 GHz; \(\xi\) at 9.5 GHz.  
Figure 18: Simulated and measured radiation patterns of the NB antennas for various frequencies. (**a**) NB Antenna #1 at 3.5 GHz; (**b**) NB Antenna #1 at 4.9 GHz; (**c**) NB Antenna #2 at 6.52 GHz; (**d**) NB Antenna #2 at 7.65 GHz; (**e**) NB Antenna #3 at 9.5 GHz.  
Table 2 shows a comparison of the simulated and measured peak gains of the proposed antennas. Recognizable discrepancies exist between the simulated and measured peak gains in the mid and high frequency bands. We think that the discrepancy is due to the cable loss and effect of the feeding structure, which was not considered in the simulation.  
Table 3 shows comparisons of various integrated UWB and NB antenna systems. From Table 3, it can be easily seen that the proposed antenna provides full coverage of the UWB communication band using only three NB antennas. This is attributed to the small-size broadband loop-type ground radiation antennas. This research also maintains relatively high isolation levels despite the compact ground size.
## 2 Materials and Methods
### Dexterous Grasping  
#### 2.3.1 Data Collection  
Achieving human-like dexterity and adaptability in grasping objects with varying properties is a challenging task. To address this challenge, we employ a learning-from-demonstration (LfD) approach, where the robotic system learns grasping strategies by observing human demonstrations [39]. The LfD data collection process involves an immersive teleoperation setup, where a Leap Motion Controller (LMC) [40] is used to capture finger movements [41; 42]. The LMC can track up to 60 frames per second, but an average of 10 frames per second is considered to reduce noise.  
Additionally, a Kalman filter was employed to further smoothen the tracking data for the dexterous hand. For the dexterous hand teleoperation, the hand was kept in the pre-grasp position. Later, the average direction vectors of the palm and individual fingers, \(\text{avg}_{\text{finger}}\) (thumb, index, middle, ring, and pinky) were calculated over the past 10 frames (N), as shown in Equation (2). The bending angle \(\left(\text{angle}_{\text{finger}}\right)\) for each finger was computed as the angle between the palm's average direction vector \(\left(\text{avg}_{\text{palm}}\right)\) and the finger's average direction vector (\(\text{avg}_{\text{finger}}\)), as shown in Equation (3).  
\[\text{avg}_{\text{finger}}=\frac{1}{\text{N}}\underset{i=1}{\overset{\text{N }}{\sum}}\text{finger}_{i}\text{-direction} \tag{2}\]  
\[\text{angle}_{\text{finger}}=\cos^{-1}\left(\frac{\text{avg}_{\text{palm}} \cdot\text{avg}_{\text{finger}}}{\left\|\text{avg}_{\text{palm}}\right\| \times\left\|\text{avg}_{\text{finger}}\right\|}\right)\times\frac{180}{\pi} \tag{3}\]  
The bending angles of the dexterous hand are mapped to the corresponding finger angle limits, enabling it to replicate the hand gestures of a human operator. The system illustrated in Figure 6 incorporates a gesture-based condition to achieve diverse grasping configurations, specifically 2-finger, 3-finger, and 5-finger grasp, depending on the number of fingers extended by the operator's left hand. The dexterous hand is equipped with force sensors set to a predetermined threshold, and the Kalman filter is employed to smooth out minor fluctuations in the finger positions received from the LMC. At this stage, the robotic arm is positioned for pre-grasping, while the operator teleoperates the dexterous hand to grasp various objects using combinations of 2, 3, or 5 fingers, as depicted in Figure 7. For certain objects, only data for 2-finger and 3-finger grasps were collected due to their size, while others were grasped using 2, 3, and 5-fingers.  
The teleoperation data, including finger positions, forces and their derivatives were normalized using Z-score normalization as shown in Equation (4):  
\[\text{z}=\frac{\text{x}-\upmu}{\sigma} \tag{4}\]  
where \(\upmu\) and \(\sigma\) are the mean and standard deviation of the feature, respectively. Later, the data were used to train the LSTM, GP, and Transformer models responsible for adaptive grasping.
# Mechanical Design of a Bioinspired Compliant Robotic Wrist Rehabilitation Equipment
## 3 Conception of the Rehabilitation Equipment
### Construction of the Rehabilitation Equipment  
Selected for the actuation of the rehabilitation equipment was a DMSP-10-300N-AM-RM pneumatic muscle manufactured by Festo, Germany, with an interior diameter of 10 mm, a 300 mm long active part and a maximum stroke of 60 mm. Taking into consideration the dimensions of the utilized pinion and the biological limits of the motions that have to be ensured by the equipment (flexion of \(-80^{\circ}\) and extension of +70\({}^{\circ}\)), the necessary axial contraction of the muscle (that is equal to the stroke of the rack) is of 39.25 mm.  
Initially, in the absence of compressed air, the pneumatic muscle is relaxed, the rack is placed maximum to the right and the position of the rehabilitation equipment corresponds to maximum flexion (Figure 9). In order to initiate rehabilitation exercising, the equipment has to be brought into zero position, when lever AB is horizontal. For this the pneumatic muscle has to be charged to a pressure of 2.72 bar causing a displacement of the rack of 19.62 mm. The horizontal position of the palm support is detected by an incremental rotation encoder placed in joint A. Once this position has been reached, the patient's hand is placed and strapped to the palm support by means of velcro tape.  
As the pressure in the muscle continues to be increased, its axial contraction grows and lever AB is rotated in the direction of extension. The flexion and extension motions that are specific to rehabilitation exercising are generated by varying air pressure in the pneumatic muscle. Figure 9 shows the start (zero) position of the equipment and the limit positions allowed by this.  
Figure 8: Variation of the force to be developed by the pneumatic muscle along with the rotation of the hand support.
# A Survey on Datasets for Emotion Recognition from Vision: Limitations and In-the-Wild Applicability
## 4 Discussion
### Application Scenarios  
**Smart environments and spaces**. A trending topic in the literature is extracting semantics-related people to improve decision making and extract insights from public and private spaces. Urbanists have been working on managing public spaces and proposing interventions based on how the local community relates to that place. Usually, this is performed in person by observing the region and registering the usage by residents. Researchers have been proposing the usage of pedestrian tracking [85] to understand this relation and plan interventions. This scenario could be improved by understanding emotion. Other research has also been proposed with the objective of correlating emotions and environments [9; 10; 12]. In these scenarios, surveillance cameras that are already installed could be reused to lower costs, for example. These cameras are usually placed with a top-down view, in which one can see the entire person, but only sometimes can their face be seen with clarity. In these scenarios, one should not depend too much on facial expression recognition but focus on other cues to extract emotion. Context is also not very representative since, in these views, the context is not prone to change and would usually only be the floor in the scene. GroupWalk and BoLD could be suggested for these application scenarios. According to the need for emotional representation, GroupWalk could be used to train or evaluate the model or pipeline, bearing in mind that the image quality could vary between recordings. The body language annotations in BoLD could be useful when a more detailed emotional representation is needed. For example, how groups of people are behaving in that region of the environment. Figure 7: Visibility of each individual body joint as keypoints for the datasets evaluated in this experiment: EMOTIC (**a**); CAER-S (**b**); BoLD (**c**). From top to bottom, we have face, arms and hands, legs and feet, following the 33 pose landmarks defined by Mediapipe and available: [[https://github.com/google/mediapipe/blob/master/docs/solutions/pose.md](https://github.com/google/mediapipe/blob/master/docs/solutions/pose.md)]([https://github.com/google/mediapipe/blob/master/docs/solutions/pose.md](https://github.com/google/mediapipe/blob/master/docs/solutions/pose.md)) (accessed on 4 April 2023). The size and heatmap color of each sampled point represent the visibility of the keypoint.