esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Work  
The system presented in this manuscript provides a communication platform for the D-M and the ND-M to communicate with each other without the need to learn sign language. A unique feature of this work is the creation of a new dataset that combines three existing sign language datasets. The proposed system automatically detects the hand gestures of a D-M person and converts them into a voice for an ND-M person after going  
Figure 26: Similarities between different sign languages.  
through various processing stages. The system is reliable, easy to use, and based on a COTS LMD device to acquire hand gesture data which is then processed using CNN. Apart from CNN, a supervised ML algorithm is also applied for processing and automatically detecting sign language.  
The system provides an audio interface for the ND-M and a hand gesture capture interface for the D-M. The system currently recognizes ASL, PSL, and SSL datasets, and the total number of images is approximately 6000. The system includes a training mode that can help individuals review how accurately the system is detecting their hand gestures. The users can create their unique profiles, which the system can use when processing hand gestures. The proposed system is validated through a series of experiments using the combined dataset of ASL, PSL, and SSL, and results show an accuracy exceeding 95%. The results are encouraging and repeatable. Another key feature of this system is that the combined dataset can be used by millions of people who use English, Urdu, and Spanish sign languages. The proposed system is to be installed and can be upgraded. There is no annual licensing; hence there is a low initial expense and no ongoing reoccurring cost. The database is updated, which improves the system's performance and accuracy.  
In the future, more sign language datasets can be processed and added. Dataset size can also be increased based on the recommendations from ML-based algorithms. Datasets can also be improved by adding videos and other types of data, including word-level hand gestures. More work can be done in the undertaking of comparisons between different sign languages, understanding similarities between them, and afterward combining these to create larger datasets. The proposed communication system can be further customized for individual users by adding more training features.  
Conceptualization, M.I.S. and S.N.; methodology, formal analysis, programming, and validation, M.I.S.; data curation, S.N.; writing, M.I.S., A.S., S.N., M.-A.L.-N. and E.N.-B.; visualization and software architecture and development, review of machine learning algorithm, A.S.; supervision, M.-A.L.-N. and E.N.-B. All authors have read and agreed to the published version of the manuscript.  
This research has been partially funded by Universidad de Malaga, Malaga, Spain.  
Not applicable.  
No data is available.  
The authors would like to acknowledge the support provided by Universidad de Malaga, Malaga, Spain.  
The authors declare no conflict of interest.
## 4 Centralized optimization approach for the system reconfiguration
Obviously, executing such an algorithm at runtime for a system with a large number of components and with a significant number of potential states leads to degradation of the system responsiveness to a changing context and to the evolving requirements. For a partial scalability improvement, we may split the system into a number of subsystems with their own _Reconfigurators_[19], or introduce a few optimization constraints to the formula of \(Q\) for reaching only a sub-optimal configuration within the defined number of reconfiguration steps [3]. According to the research done earlier [3, 17, 19], tuning of the heuristic function and the sub-optimal search restrictions might improve the performance for particular applications; however in the general case, the widely used approach looks reasonable only for systems with a limited number of components. Therefore the novel solution is needed. The following sections address the issue and describe an alternative method that attempts to solve the bottleneck problem cased by centralization of the decision making process in the feedback controlled system at runtime based on the operational principles of brain-mind functioning.
# Multicriteria Autonomous Vehicle Control at Non-Signalized Intersections
## 3 Model Predictive Intersection Control Design  
### Intersection Scenario  
In the proposed intersection scenario vehicles can drive straight, turn left or right, therefore there is a probability of collision between vehicles tracking the same or different trajectories [22]. All features (e.g., velocity, driving intentions, position at the intersection) of autonomous vehicles are collected by a centralized controller transmitting measured data and control input for all participants of the traffic situation. The goal of the paper is to design an algorithm which guarantees collision-free passage of autonomous vehicles at intersections, while it minimizes the total traveling time or the energy consumed by the vehicles. Therefore, congestion at the intersection can be avoided and fuel consumption can be reduced while safety of the autonomous vehicles can be enhanced. In view of the intersection scenario, the goal of the centralized controller is to determine the ordering of autonomous vehicles as well as their accelerations with the consideration of total traveling time or energy consumption, and to guarantee collision-avoidance at the same time.  
The proposed Model Predictive Control (MPC) algorithm is based on some pre-assumptions. A four-directional type intersection shown in Figure 1 is examined, which is separated into different sections.  
Before reaching the control zone, each autonomous vehicle is self-controlled, supervised by its own cruise control system. When a vehicle enters the control zone, a velocity profile and a corresponding acceleration is calculated for it by the centralized intersection controller founded on the actual velocity and planned trajectory of the autonomous vehicle. The latter data along with the calculated accelerations are sent from the vehicle to the intersection manager and vice versa by \(V2I\) communication methods. Although the iterative calculation detailed later takes into account up to four vehicles at a time, the calculation is repeated with reconfigured initial conditions in case a new vehicle enters the intersection. Note, that in case the new vehicle enters while the preceding vehicle is still ahead, it engages adaptive cruise control with a predefined spacing until the preceding vehicle leaves the intersection conflict zone. In this manner, the proposed algorithm can be enhanced to handle intersections having bigger traffic density. Moreover, the proposed algorithm can be augmented to consider more complex intersections as well.  
Figure 1: Four-directional type intersection scenario.
## 3 Results and Discussion
### Effect of Soil Type
#### 3.1.2 Settlement of the Footing  
The footing settlements for different soil densities at different footing pressures are presented in Figure 6. It is clear from the figure that the increase of the soil density reduces the footing settlement. This reduction of the settlement could be attributed to the reduction of the facing displacement. Furthermore, the increase of the soil density is accompanied by an increase in shearing resistance and thus a reduction of the footing settlement. The difference of footing settlement of loose sand and dense sand is more than twice at all stages of loading, but the difference of settlement of footing on medium dense sand nailed slope reduces at higher foundation pressure.
## 4 Discussion  
### Pix2 Pix's Performance in Generating Examples/Images  
The evaluation of the images generated by the Pix2 Pix model yielded quantitative metrics of PSNR (18.04), SSIM (0.64), and FID (123.32). These values indicate adequate visual quality, although there is room for refinement, which is understandable given several factors. Generating realistic images from binary masks is inherently challenging due to the complexity of colonoscopy images and the limited volume of available annotated training data.  
Furthermore, the combination of six distinct public datasets likely introduced a significant domain shift, as variations in acquisition equipment and lighting conditions posed an additional challenge to the generative model. These factors collectively influenced the final image quality. Nevertheless, the choice of Pix2 Pix was motivated by its ability to control specific image characteristics, namely polyp size, which was central to the objective of analysing how such variations affect classifier predictions.  
Despite the results indicating room for refinement, the model generated images consistent in terms of texture and polyp contours. When directly comparing a real image with one generated from the same mask, a simplification in the structures was observable, with less detail in the synthetic images. However, this simplification does not compromise the visual quality and information required for the clinical context, as the generated images maintain a credible appearance. This shows that although the model does not perfectly reproduce all details, it effectively creates suitable examples for the proposed analyses.  
In addition, Pix2 Pix demonstrated a proficient ability to generate images with varying polyp sizes while maintaining an unchanged background. For these size variations, although a direct comparison with real images is not possible, a visual comparison was made with images generated from masks associated with real images. It was observed that, relative to these images, the size-varied polyps had a slightly lower visual quality but remained at an acceptable level for analysis, containing all necessary information. This control over size, while maintaining background consistency, was fundamental for generating examples to evaluate how classifiers respond to different polyp variation scenarios and to enhance the diversity and representation of different polyps in the dataset.
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 2 Materials and Methods
### Online Learning Direct-to-Satellite Packet Scheduling
#### 2.4.1 Algorithmic Problem Statement  
For a sensor placed in a remote location, a simple and _interpretable model_ is needed to be trusted to perform as expected [17]. To achieve this, a relatively simple algorithm inspired from reinforcement learning has been devised. The goal is to learn the probability of successful transmission, given three input variables: (1) the satellite pass duration (in minutes), (2) the maximum elevation angle of the satellite pass (in degrees), and (3) the RF background noise (in dBm).  
Borrowing the notation from reinforcement learning, the state space \(S\) is the set of all possible input variable combinations, and the action space \(A\) is the set of all possible actions [18]. In this case, \(A\) consists of the actions to transmit or not to transmit for each satellite pass with pass characteristics \(s\in S\). Let the function \(v\) be the mapping of \(S\) to a probability of successful transmission, \(v:S\mapsto[0,1]\), and let the policy \(\pi\) represent the conditional probability of choosing a particular action \(a\in A\) given a state \(s\in S\). Hence, the policy is the mechanism for choosing which satellite pass to select, given a set of passes and their characteristics.  
\[\pi(a|s)=P(A_{t}=a|S_{t}=s) \tag{1}\]  
In Equation (1), \(A_{t}\) represents the action at time step \(t\), and \(S_{t}\) represents the state at time step \(t\). Regarding the probability success mapping \(V\), a natural objective is thus to approximate it with collected experience: as the system runs and has successes and failures transmitting with different states \(s\in S\), it will converge to true probabilities of successful transmission for a given state, i.e., the value function \(v\)[18; 19].
## 3 Results
### Commercial Sprays
In this sense, this brand had also more minor elements, indicating a more complex mixture. Regardless, the elemental analysis helped us to better understand the composition of the sprays. For example, in \"Medium Yellow ZT107\", the major elements were bismuth and vanadium, which could indicate the use of bismuth vanadate (BiVO\({}_{4}\)), a common pigment used in the last decades [8]. However, in the Raman spectroscopy analysis, only acetoacetic arylide PY74, with the formula C\({}_{18}\)H\({}_{18}\)N\({}_{4}\)O\({}_{6}\), was identified as a pigment [15]. Considering the relative presence of the bismuth and vanadium, the mixture of both yellow pigments (PY74 and BiVO\({}_{4}\)) in this spray seems plausible. In the same way, in the cases of \"May Green ZT129\" and \"Cologno N. Green RV-237\", the presence of copper and chlorine as major elements could indicate the use of phthalocyanine green, which includes both elements in their structure. In addition, the PG7 pigment effectively was identified in both cases [15]. Regarding this point, it is necessary to remark that in the case of \"Cologno N. Green RV-237\", three Raman bands were observed at 1261, 1504, and 1590 cm\({}^{-1}\); these bands are not found in the literature [15; 16; 20]. In fact, some discrepancies were observed in the literature, but the spectrum collected in the IRUG database [16] finally presented broad bands that could contain the bands that appear in this study, thus, the resolution of the obtained spectra most likely allowed us to obtain more than one band instead a broad one. Continuing with the chlorinated pigments, in \"Bordeaux ZT148\", chlorine was identified as major element due to the presence of naphth AS PR112 pigment, a chlorinated pigment with the formula C\({}_{24}\)H\({}_{16}\)Cl\({}_{3}\)N\({}_{3}\)O\({}_{2}\)[15], but the presence of iron was also remarkable. Considering that the spray was red, the use of iron oxides mixed with the mentioned organic pigment was probable. The same case occurred with the \"Vivid Red RV-3001\" pigment, in which iron also was identified as major element, but when using Raman spectroscopy, another naphth AS was identified as a pigment, in this case PR170 (C\({}_{26}\)H\({}_{22}\)N\({}_{4}\)O\({}_{4}\)), without chlorine [15]. Another chlorinated pigment was assumed in the \"Orange 554\" spray by XRF, and was identified as pyrazolquinazolone PO67 (C\({}_{17}\)H\({}_{11}\)ClN\({}_{6}\)O\({}_{3}\)) pigment by Raman spectroscopy [15]. The same occurred for \"Yellow 529\", which was composed of diarylide PY13 pigment, with the formula C\({}_{36}\)H\({}_{34}\)Cl\({}_{2}\)N\({}_{6}\)O\({}_{4}\)[15]. Finally, the \"Violet ZT131\" and \"Fuchsia ZT134\" were very similar, as they were composed of the dioxazine PV23 pigment (C\({}_{34}\)H\({}_{22}\)Cl\({}_{2}\)N\({}_{4}\)O\({}_{2}\)) [15]. However, in the case of the latter, other bands were observed by Raman spectroscopy, but it was not possible to clearly identify the compound related to them. The presence of a monoazo or diazo red pigment could explain the bands 347, 1359, 1484, and 1553 cm\({}^{-1}\)
## 3 Results and Discussion
### Installation of the TIS-II GFP Imager into the ACMG  
The successful bench tests connecting KSC to the TIS-II Imager at HMP RS were followed by physical installation of the TIS-II into the ACMG and electronic connection to the ACMG control systems. The TIS-II GFP Imager then passed from active control by internet connection to automated control within the greenhouse sensor and data acquisition systems. Command and data packets were controlled by the embedded computer of the greenhouse. Images were processed and stored locally in the DAQ and control system before satellite transfer as part of the ACMG data stream controlled by the Linux single board computer. Figure 3 shows the imager set up against the northern wall of the greenhouse (left) and a closer view of the system as it is connected to its power supply and through a network cable to the ACMG systems.
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 4 Laser Crosslink Payload
### Optomechanical and Electronics Design  
To enhance the received optical power, deployment mechanisms were applied to the FEO, which has a large aperture that is difficult to achieve with the conventional 6U CubeSat platform. The clear aperture size was larger than \(\Phi\)190, and the main material was Zerodur. The distance between the primary and secondary mirrors was approximately 125 mm, given the constraints on the stowed and deployed configurations. Considering the on-orbit thermal environment, CFRP, which is a thermally stable material, was applied to the baffle and secondary mirror deployment mechanism. Figures 10 and 11 show the payload optics design with ray tracing and spot quality analysis. The field stop of the focused beam was located at the front of the back-end optics (BEO) Lens 1. With the tip-tilt control of the FSM within the FOR, spots on the CAM and APD are formed on the focal plane, and the edges of the spots do not exceed the pixel size of the detector. Decentering is required to distinguish the spot position in each section of the QC. When the segmented mirrors were misaligned after deployment, the spots were distorted, as shown in Figure 11c. This distortion degrades the performance of the PAT algorithm by blurring the spots on the detector. The spot quality with DST tolerance is considered in the PAT performance analysis, which determines the spot centers during the PAT sequence.  
The electronics handling LCT instruments are integrated in terms of instrument control electronics (ICE), as presented in Figure 12, which includes the optical train. The FPGA manages all electronics, such as the FSM, EDFA, detectors, and SLA. The CPU is utilized as an electrical interface with the nanosatellite bus, including power and thermal management, SPI data communication with the bus, and a deployment mechanism. Because the bus provides an unregulated battery bus voltage, the LCT has a latch-up protection switch. Furthermore, the FPGA executes the PAT algorithm by activating the FSM with an AOA estimation for feedback control.  
Figure 10: _Cont._Figure 11: Payload optical system design and spot distortion analysis for the deployable space telescope: (**a**) raytracing for the deployable space telescope with three segmented mirrors; (**b**) nominal spot configuration on tracking sensor (CAM); (**c**) distorted spot configuration on tracking sensor (CAM).  
Figure 10: Payload optical system design and analysis: (**a**) optical layout and beam paths; (**b**) spot position and quality analysis by defocusing and decentering with a tip-tilt platform (FSM).
## 4 Results  
### Results of Unmanned Aerial Vehicle Data  
A sub-area of a UAV hyperspectral image of 2000 \(\times\) 2000 pixels was chosen to verify the accuracy of NDCSI when determining the proportions of illumination and shadow of vegetation. Most of  
Figure 5: Scatterplot of RE’s reflectance and the NDVI for _Pinus_ (_a_) and _Larix_ (_b_).  
the area was covered with a thick _Larix_ canopy, which prevented the influence of a soil and water background. The original image and its NDVI and NDCSI results are shown in Figure 6. Figure 6c shows an explicit result calculated using NDCSI that shows illumination/shadow portions more clearly distinguished than those in the NDVI results (Figure 6b).  
Forty typical _Larix_ canopy subarea sample images with different illumination/shadow proportions were selected and their NDCSI values calculated. In addition, an accumulated sum reflectance of the visible spectrum (400 to 760 nm) were acquired to represent the measured brightness information of the corresponding pixels. Correlations between the NDCSI and the measured brightness values are shown in Figure 7.  
A single pixel's NDCSI and measured brightness values have a close relation, with a positive correlation of \(R^{2}=0.886\) (\(p<0.01\)), which indicates that the NDCSI has an adequate capacity for quantitative expression of the vegetation canopy illumination/shadow. However, the NDCSI changes astatically and does not fit the linear equation well when the illumination value is too high (circled in red in Figure 6). The illumination saturation phenomenon is very likely caused by the factor and similar formula structure of the NDVI, which is easily saturated and less sensitive to areas of high vegetation density because of nonlinear transformation.  
Figure 6: Sub-area of thick _Larix_ canopy (_a_) and its NDVI (_b_) and NDCSI (_c_) results for verification.  
Figure 7: Scatterplot of NDCSI and measured brightness values for sub-area samples. Five values (circled in red) had a poor linear correlation because of illumination saturation.
## 3 Results and Discussion
### Effect of pH and Ionic Strength on Sorption
The depression of Zn\({}^{2+}\) adsorbed by PPG-nZVI beads might be due to the competition of Na\({}^{+}\) with Zn\({}^{2+}\) for the same binding sites on the PPG-nZVI beads surface, which further confirmed that adsorption is main process to remove Zn\({}^{2+}\) but not that important for Pb\({}^{2+}\) and Cu\({}^{2+}\). When the concentration of NaNO\({}_{3}\) raised from 0.003 to 0.005 M, the adsorption capacity of Pb\({}^{2+}\) and Cu\({}^{2+}\) started to decrease, while it continually decreased for Zn\({}^{2+}\). It indicated the competition occurs on Pb\({}^{2+}\), Cu\({}^{2+}\), or Zn\({}^{2+}\). As literature reported, the much influenced by ionic strength on the uptake of heavy metal ions could be explained by the formation of iron oxide. With the concentration of NaNO\({}_{3}\) raised from 0.005 to 0.1 M, the uptake capacity increased for Cu\({}^{2+}\) and Pb\({}^{2+}\) which indicated that the electrostatic double layer of the PPG-nZVI beads is more compressed leading to the more adsorption. Based on the above analysis, it is concluded that the adsorption of Pb\({}^{2+}\), Cu\({}^{2+}\) by PPG-nZVI beads proceeds through both outer-sphere adsorption and co-precipitation and inner-sphere reduction. Adsorption and co-precipitation might play an important role on the interaction between Zn\({}^{2+}\) and the beads.
## 4 On-Board Unit Implementation  
This section describes the details of the hardware setup, the V2X communication protocols, the interface with the in-vehicle sensors and actuators network, and the software architecture of the experimental OBU that we have designed and implemented in this work.  
Figure 3: End-to-end propagation of detected hazardous events via Distributed Environmental Notification Message (DENMs) from/to the OBUs from two different vehicle OEMs.  
Figure 2: Authentication and subscription process for the connectivity establishment between vehicle on-board unit (OBUs) and the backend services on the edge, and between backend services.