esa-sceva/satcom-chunk-collection huggingface dataset:
# Synchronization of Heterogeneous Multi-Robotic Cell with Emphasis on Low Computing Power
## 4 Validation of the Proposed Solution
### Implementation of the Proposed Solution and Discussion
#### 4.2.3 Visualization--MATLAB Application  
For the visualization of the robotic cell behavior, any development tool supporting functionalities for sockets processing can be used. In this case, the visualization part of application was implemented using the MATLAB software tool since it was already used for simulation validation of the proposed solution. The capability of MATLAB to access existing system Java classes to be used in the MATLAB workspace [31; 32; 33; 34] was utilized in this application part. The communication with the master object was realized through the activated TCP/IP channel (Box 7).  
The received message from the master object consisted of cell object identifier (_station_), operation identifier (_index_), and operation duration (_value_). After decoding, the message was used as a data source for graphical representation (3D bar graph) of the production process in the robotic cell, as shown in Figures 18-20 (Box 8).  
The graph in Figure 18 was updated after each operation. Information on the operation duration of all participating executive elements was collected and subsequently distributed by the master manipulator.
## 3 Results and Discussion
### Improved Detection Limits through Design Optimization
#### 3.1.1 Sampling Head Design Optimization  
The sampling head, crucial for a fast and sensitive detection, was modified first. The novel design, utilizing a heated brass body, was aimed to further improve the sampling. Within this modification, the brass area exposed to the analyte is minimized. Theoretically, imaginable catalytic surface processes can be neglected due to the fact that the analytes pass only a small brass surface area on their way into the transferline and only very few applications of brass for catalytic surface processes can be found [32]. Because of the minimized adsorption processes at the inside of this desorption head, a faster analyte transfer into the IT-MS is enabled. This in turn should give more narrow signals and better limits of detection. Heating of the investigated sample surface is prevented by mounting of a thin spacer of polytetrafluoroethylene (PTFE) at the top of the sampling head (Figure 1b). The influence of the transferline was investigated by varying the length and the diameter of the transferline while keeping the temperature and parameters constant. The diameter was adjusted to ensure a nearly similar sample flow into the MS. As exemplarily visualized in Figure 2 for the investigation of tetryl, the version with the shortened transferline revealed an approximately four-times improved signal width. This effect is caused by the higher temperature and a shorter transferline. On that account, a minimum of ad- and de-sorption within the transferline was achieved and thus more narrow peaks should be obtained.
## 3 Results and Discussion  
### Total Phenolics and Oil Content  
Table 1 shows the amount of oil content, the total phenolic content (TPC) in mg of caffeic acid equivalents (CAE)/g seeds and mg of CAE/g oil, and data on GLA content in seeds (% of total FA) previously reported by our Research Team [7]. Notably, seed samples vary in their total oil content, with values ranging from 12.7 (_R. aluminum_ 1B) to 25.6 g/100 g of seeds (_R. hudsonianum_). A significant variability was observed in TPC amounts among different samples and sections. In sect. Berisia, _R. alpinum_ 1A has a lower oil content compared to 1B but shows higher TPC, while _R. pulchellum_ stands out with the highest content of oil and TPC. For sect. Coreosoma, _R. hudsonianum_ has the highest oil content and TPC in oil, and _R. nigrum_ 'Koksa' has the highest TPC in seeds. Concerning sect. Ribes, _R. rubrum_ has the lowest oil and TPC content in seeds. Regarding GLA values, _Ribes_ 'Myuryuchenee' shows the highest percentages of total FA, but unfortunately, neither its oil content nor TPC is notable. Conversely, _R. nigrum_ 'Koksa' has 17.0% GLA of total FA, and given its high TPC (15.5 mg CAE/g oil), this cultivar constitutes a promising source of GLA-rich oil containing good amounts of phenolics, thus, it hosts healthy properties related to such compounds.  
In short, a proper selection of _Ribes_ varieties can significantly influence the number of bioactive compounds in seeds, i.e., GLA and TPC, whose values in the species and cultivars focused here are among the highest reported for _Ribes_ taxa. Previous analysis on the seeds of other _R. nigrum_ cultivars revealed that 'Ben Tirran' and 'Ben Sarek' are good sources of GLA (15.2-16.7% of total FA), although these have very low TPC amounts quantified by the Folin-Ciocalteu (F-C) method: 1.99 and 2.31 mg of Gallic Acid Equivalent (GAE)/g seed residue, respectively [9]. However, [PERSON] et al. [15] indicated figures obtained by the F-C methodology in the range shown here for several _Ribes_ cultivars (5.6-11.3 mg CAE/g oil). Other works that focused on residues from the extraction of _Ribes_ fruits cannot be compared with the results obtained here since such works focused on the residual cake from the extraction of the fruit, which, in addition to the seeds, contains several other tissues of the fruit [16; 17].
## 3 Methods
### Audit and Threat Model  
We consider blind-signature schemes for which cryptographic proofs for blindness and one-more-unforgeability features already exist. For these proofs, the Adversary is either a malicious Signer (eager to learn what he signs) or a malicious User (aiming to derive more signatures of \(\mathcal{S}\) than requested).  
The scenario that we consider is different but motivated by privacy protection in the wild: we consider the case where the Signer \(\mathcal{S}\) and a device \(\mathcal{D}\) attempt to cheat the User holding \(\mathcal{D}\). In almost all cases, the User can neither inspect the Signer nor check \(\mathcal{D}\). All the User can do is eavesdrop on the messages exchanged between \(\mathcal{S}\) and \(\mathcal{D}\) and, possibly, inspect some security declarations and/or certificates for \(\mathcal{D}\) and \(\mathcal{S}\) issued by third parties. Persuaded by the certificates and provable but theoretical properties of a blind-signature scheme, the User may have a false sense of security and privacy. Namely, the certificates and declarations might be meaningless since the User may be given a malicious device different from those that undergo the certification process, and \(\mathcal{S}\) may run the services not as declared. As long as there are no observable protocol deviations, the User might be in a lost position.  
There are a few attack vectors carried out by the colluding Signer \(\mathcal{S}\) and device \(\mathcal{D}\):  
* **Unblinding known signatures:** This attack enables \(\mathcal{S}\) to decide whether a given signature \(\sigma\) has been created during a given session with \(\mathcal{D}\) (effectively showing which device has derived \(\sigma\)); the attack may require the message signed by \(\sigma\). If effective, this attack dismantles privacy protection for signatures that emerge on the market.
* **Immediate unblinding:** This attack enables \(\mathcal{S}\) to derive the same unblinded signature as that obtained by \(\mathcal{D}\) while running the blind-signature creation procedure;
* **Covert transmission to Signer:** In this attack, \(\mathcal{D}\) sends a covert message to \(\mathcal{S}\) during blind-signature creation. In particular, the covert message can contain the message to be signed blindly for immediate unblinding.
* **Covert transmission to device:** In this attack, \(\mathcal{D}\) sends a covert message to \(\mathcal{S}\) during blind-signature creation. In particular, the covert message can contain secret instructions for the device.  
In all the above cases, the Signer, which has been deployed as an important component of the privacy protection ecosystem, turns out to be a \"Big Brother\" spying on its Users.  
To prevent the mentioned attacks, one must either redesign the schemes so that the Users obtain more control over the situation or run an appropriate audit and certification framework together with a strict evaluation of the cryptographic schemes. Somehow, the second approach is the strategy more frequently implemented in practice.
## 2 Hybrid Wave Energy Converter and Test Method  
### Hybrid Wave Energy Converter  
As shown in Figure 1, the hybrid wave energy conversion device developed for nearshore at depths below 5 m is composed of a horizontal cylinder, a swing plate, a clutch gear and a yawing plate. The horizontal cylinder generates electricity by its rotation triggered by the water particle velocity and the current velocity. Moreover, electric power is also harvested by the heave motion caused by the height of the incident wave. The swing plate produces electricity by pendulum motion induced by the oscillation of the incident wave. Separate clutch gears are adopted for each horizontal cylinder and swing plate to minimize the energy loss of the incident wave and improve the capture width ratio of the wave energy converter. The clutch gears allow electricity to be generated by the horizontal cylinder when the rotation or heave motion velocity of the cylinder is higher than the swing velocity of the swing plate and by the swing plate when the opposite occurs. Thanks to such principle, the capture width ratio of the hybrid wave energy converter can be enhanced through the activation of the converter by instantaneously selecting the higher capture width ratio provided by the horizontal cylinder using the circular motion or kinetic motion of the incident wave or by the swing plate exploiting the linear oscillation. A period of suspension for the converter may occur when the water particle motion changes direction when the incident wave moves from wave crest to wave trough. However, the application of the clutch gears prevents such occurrence and enables the converter to rotate continuously in one direction, which in turn minimizes fundamentally any loss of the incident wave energy. The yawing plate floats, which makes it adaptable to water level change, and can rotate in both directions regardless of the ever-changing direction of the incident wave. The main supporting structure acting as the pivot of a seesaw and the added mass located as counterpart of the horizontal cylinder enable to control smoothly and easily the draft of the horizontal cylinder converter.
# A CMOS-Based Terahertz Reconfigurable Reflectarray with Amplitude Control: Design and Validation
## 4 Experimental Verification
### Comparison and Discussion  
In order to demonstrate the advantages of the proposed amplitude-control RRA, it is compared with other published works in terms of element performance in Table 3. The proposed element enables terahertz amplitude control, whereas previous studies [14; 15; 16; 17] focused on phase/frequency shift implementations. Featuring a single-switch topology, this work minimizes THz energy loss in switches while simplifying the theoretical analysis and extending low-frequency design methodologies to THz frequencies. The measured results of the proposed element demonstrate the lowest insertion loss among [14; 15; 16; 17]. Based on this topology, further optimization is expected to achieve better amplitude control performance for CMOS-based RRAs in THz applications.  
Figure 11: Measured and calculated amplitude change in the proposed amplitude-control RRA and the simulation results of the RRA element.
## 2 Materials and Methods
### Signal Pre-Processing and Data Extraction
To this aim four different feature sets were taken into account: the first was the Hudgins's set (H set), a classical feature set encompassing MAV, WL, SSC and ZC features [21]. The second one was another commonly used feature set, i.e., the Du's set (D set), which comprises the IEMG, VAR, WA, WL, SSC and ZC features [22]. The third feature set was obtained considering the Hudgins's and Du's sets together (HD set). Since neither the H set nor the D set encompasses frequency domain measure, the last feature set was build by adding to the HD set four features in frequency domain, i.e., the MNF, MDNF, TSP and MSP (HDF set). Then, the classification accuracy of each feature set was compared with the classification accuracy obtained for the same feature set when the FEn and PEn were included as an additional feature, one at a time. Finally, the accuracy of each feature set was also computed whether both FEn and PEn were added at the same time. The linear discriminant analysis (LDA) was used for the classification purposes, since it represents one of the most used classifier in the field of gesture recognition [2; 5; 9; 17], merging a relatively low computational cost and a high classification performances [18]. The LDA was performed by a 5-fold cross validation for each subject and the classification accuracy was obtained as the average value of the cross validation results from all the tested subjects.
## 3 Structure of the Adaptive Controller
### PID Gain Scheduler
#### 3.1.1 Gain Scheduler for the Frame of the Pitch Axis  
As shown in Figure 1, the tilting degree around the pitch axis of the loading device is mainly caused by climbing the stairs of the delivery robot and it is bigger than the tilt of the roll axis. Also, the frame of the pitch axis is designed as the outer frame of the 2-axes gimbal structure loading device. As the loading device aims to handle luggage up to 5 kg, the \(X\) set is composed of 6 cases of \(X_{3}\) that have risen from the empty loading device by 1 kg each. The \(X_{3}\) is related to the moment of inertia. Each \(X_{3}\) is determined by the expected properties of the loading device designed by SolidWorks. Each case of the \(X_{3}\) has 41 cases of the \(X_{2}\), which is the divided controllable range of the motor. The \(X_{2}\) is related to the horizontal position of the center of gravity. The optimized gains for each \(X\) set are obtained by the simulation results using 'fmincon()' function of MATLAB that finds optimized value by minimizing the sum of errors. The \(X\) set to gather the optimized gain of the frame of the pitch axis is Equation (16).  
\[0\ \text{kg}:\ X_{3}=10.32,\ X_{2}=\left[-9.5:\tfrac{9.5}{20}:9.5\right] \tag{16}\] \[1\ \text{kg}:\ X_{3}=9.699,\ X_{2}=\left[-9:\tfrac{9}{20}:9\right]\] \[2\ \text{kg}:\ X_{3}=9.017,\ X_{2}=\left[-8.2:\tfrac{8.2}{20}:8.2\right]\] \[3\ \text{kg}:\ X_{3}=8.190,\ X_{2}=\left[-7.2:\tfrac{72}{20}:7.2\right]\] \[4\ \text{kg}:\ X_{3}=7.236,\ X_{2}=\left[-6.5:\tfrac{6.5}{20}:6.5\right]\] \[5\ \text{kg}:\ X_{3}=6.215,\ X_{2}=\left[-5.5:\tfrac{5.5}{20}:5.5\right]\]  
Since the optimized gains show tendencies, the gain scheduler is constructed by simply interpolating the gathered gains into three-dimensional functions using the 'fit()' function of MATLAB. Figure 4 and Equations (18)-(20) are the gain-scheduling function for the frame of the pitch axis. The blue circles in Figure 4 are the optimized gains used to interpolate the gain-scheduling function. In interpolating the gains, the normalization function is included to consider cases of the \(X_{2}\) close to zero, and it is shown in Equation (17).  
\[x=\frac{X_{2}}{4.61},\quad y=\frac{X_{3}-8.446}{1.412} \tag{17}\]  
\[P_{\text{gain}}=2.376-0.0426\ x-0.257\ y+2.315\ x^{2}+0.02246\ x\ y+0.05377\ x^{3}-0.6863\ x^{2}\ y-0.6768\ x^{4} \tag{18}\] \[\qquad\qquad\qquad\qquad\qquad+0.01042\ x^{3}\ y-0.01188\ x^{5}+0.3 795\ x^{4}\ y\] \[I_{\text{gain}}=0.06586-0.001902\ x-0.01197\ y+0.03357\ x^{2}+0.00 06987\ x\ y+0.0003353\ x^{3}-0.01913\ x^{2}\ y\] \[\qquad\qquad\qquad\qquad\qquad-0.01175\ x^{4}-0.0006846\ x^{3}\ y+0. 0002325\ x^{5}+0.007949\ x^{4}\ y\] (19) \[D_{\text{gain}}=0.8144-0.008396\ x-0.1647\ y-0.002875\ x^{2}+0.0 02468\ x\ y+0.007287\ x^{3}-0.003321\ x^{2}\ y\] \[\qquad\qquad\qquad\qquad\qquad+0.02428\ x^{4}-0.001404\ x^{3}\ y-0. 0006309\ x^{5}-0.003238\ x^{4}\ y \tag{20}\]
## 2 Datasets and Methods
### Deep Learning Model  
The deep learning models selected in this paper are the ones with fewer parameters and higher accuracy trained on the ImageNet dataset, namely AlexNet, ResNet101, and DenseNet121 as the base network models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Drought Level** & **RJ** & **HF** & **FM** \\ \hline OM & \textgreater{}65\%FC & \textgreater{}70\%FC & \textgreater{}70\%FC \\ LD & 60\%\textless{}\textasciasciasciasciasci  
AlexNet, a deep convolutional neural network proposed by [PERSON] in 2012 [21], swept the ImageNet image recognition competition that year. The structure of AlexNet is shown in Figure 2, which includes convolutional layers, pooling layers, fully connected layers, and an output layer. Several convolutional and pooling layers are alternately stacked, followed by two fully connected layers and one output layer. AlexNet led the development of deep learning in the field of computer vision. It pioneered the use of deep convolutional neural networks for image classification and laid the foundation for subsequent deep learning models.  
ResNet (Residual Network) is a convolutional neural network model proposed by [PERSON] et al. in 2015 [22]. By introducing residual connections, it addresses the vanishing and exploding gradient problems encountered during the training of deep networks. The network structure of ResNet-50 is shown in Figure 3, characterized by its use of residual learning. By introducing \"residual blocks\", it addresses the gradient vanishing and model degradation issues [23], enabling the network to learn deeper feature representations. ResNet101, with its deeper layers and stronger expressive power, is suitable for complex visual recognition tasks. The advent of ResNet greatly advanced the development of deep learning, making it possible to train deeper neural networks and achieving significant performance improvements in various tasks.  
The Densely Connected Convolutional Network, a novel architecture within the realm of deep learning, was introduced by [PERSON] et al. in 2019. This network is characterized by its deep convolutional structure [24]. As shown in Figure 4, the network structure of the DenseNet series is quite different from the traditional convolutional neural network because it connects the output of every layer to all previous layers' output and forms a densely connected structure. This method of connection is good for feature transfer within the model and efficiently reduces the vanishing gradient problem, hence improving the model in terms of training efficiency and generalization capability. DenseNet-121 has 121 layers. This network is concise, and with a novel structure, it is efficient with better performance on the CIFAR index than the residual network.  
Figure 3: ResNet-50 network structure diagram. The asterisk (*) in the diagram indicates the size of the matrix at that layer.  
Figure 2: AlexNet network structure diagram.
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 4 Cislunar Dynamics (Constellation Scenario)
### Maximum Coverage  
Ultimately, full coverage with a minimum number of satellites will satisfy a major objective of this paper, which duly supports a seamless and robust development of autonomy for small satellite constellations for the Cislunar region. Hence, referring back to [29], the critical parameter to optimally organize and control will be the orbital velocity and period, which is optimally balanced with the orbital elements using the autonomous functions and arrangement of the given orbits. The equations for these parameters are given as:  
\[v_{o}(h)\approx\sqrt{\frac{GM_{E}}{R_{E}+h}} \tag{22}\]  
\[T_{o}(h)=\sqrt{\bigg{(}\frac{4\Pi^{2}}{GM_{E}}\bigg{)}(R_{E}+h)^{3}}\approx \frac{2\pi(R_{E}+h)}{v_{o}(h)} \tag{23}\]  
where \(G\) is the universal gravitational constant, \(M_{E}\) and \(R_{E}\) are the mass and radius of Earth, and \(h\) is the altitude of deployment.  
Also, referring to Figure 4 above, the three-body problem among the satellites (follower and target) is inspired by [37], which demonstrates the Rendezvous in the Cislunar space for elaborating the control and dynamics issue for NRHOs, which are the core positions for the missions to make a base at the Moon and sustain. These factors and essential dynamics are taken into thorough consideration while developing strategic autonomy and small satellite constellations in the Cislunar region of operations.  
Figure 4: Approach defining the dynamics in the Cislunar operations [16].
# Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science
## 4 Multidimensional Projection Approaches and Domains
### Sammon's Mapping and Related Projections
Then, the remaining samples are positioned by applying some interpolation method, such as triangulation, neural network, or linear transformation. With this change, the approach attained a good precision and improved the efficiency with the complexity of \(O(s^{3}+sn)\)[12]. On the other hand, the new approach needs representative samples of \"high quantity\" to perform the initial projection, with the authors suggesting half of the dataset, \(n/2\). \begin{table}
\begin{tabular}{c c c c} \hline
**Technique** & **Transformation** & **Nature** & **Complexity** \\ \hline PCA & Linear & Global & \(O(m^{3})\) \\ \hline Classical MDS & Linear & Global & \(O(n^{3})\) \\ \hline Kruskal & Nonlinear & Global & \(O(kn^{2})\) \\ \hline Sammon’s & Nonlinear & Global & \(O(kn^{2})\) \\ \hline FastMap & Nonlinear & Global & \(O(n)\) \\ \hline Chalmers & Nonlinear & Local & \(O(n^{2})\) \\ \hline Pekalska & Nonlinear & Global & \(O(s^{3}+sn)\) \\ \hline Isomap & Nonlinear & Global & \(O(n^{3})\) \\ \hline Chalmers Hybrid & Nonlinear & Local & \(O(n\sqrt{n})\) \\ \hline L-Isomap & Nonlinear & Global & \(O(sn\log n)\) \\ \hline SNE & Nonlinear & Local & \(O(n^{2})\) \\ \hline Force Scheme & Nonlinear & Global & \(O(n^{2})\) \\ \hline LMDS & Nonlinear & Global & \(O(s^{3}+sn)\) \\ \hline \end{tabular}
\end{table} 
Table 1: Multidimensional projection methods. The column “Complexity” presents the computational load related with the amount of instances (\(n\)), dimensions (\(m\)), iterations (\(k\)), samples (\(s\)), and graph edges (\(E\)).
## 2 Materials and Methods  
### Research Sites and Region  
The interest area covers regions from the upper-left corner at 45\({}^{\circ}\)N and \(-\)124\({}^{\circ}\)W to the bottom-right corner at 25\({}^{\circ}\)N and \(-\)89\({}^{\circ}\)W in North America (Figure 1). The AmeriFlux eddy covariance flux tower data were used in the study. Since the full year of available measurements by the satellite products is from 2017, we chose sites with data after 2017. The eight EC flux sites cover eight biome types, including evergreen needleleaf forests (ENF), deciduous broadleaf forests (DBF), mixed forests (MF), closed shrublands (CSH), open shrublands (OSH), woody savannas (WAS), grasslands (GRA), and croplands (CRO). Since the EC flux sites with the biome types of evergreen broadleaf forests (EBF), deciduous needleleaf forests (DNF), and savannas (SAV) are not matched in time with Sentinel-3 OLCI, this research did not estimate the GPP under these biome types at the site scale. Additionally, due to rich biome types, California was selected as the study area for regional GPP estimation.  
Figure 1: The geographical location of AmeriFlux sites and research areas employed in this study.