esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Geometry and Discretized Model  
The present investigation is entirely in line with the expressed concepts in terms of the methodology, but it also proposes several elements of novelty.  
The parameters for the velocity, mass, layer thickness, and other factors were selected in close collaboration with the commissioning company, which specializes in vessel production for ice navigation. These parameters were chosen based on their practical relevance and alignment with real-world conditions experienced by the company's vessels. The velocity and mass represent typical operational scenarios, while the layer thickness reflects the standard design specifications for similar composite structures. This approach ensures that the model is both realistic and applicable to the actual engineering challenges faced in ice-ship interactions.  
The geometry of a sandwich structure and an ice block is given in Figure 4. The sandwich structure, representing the boat hull, has a quadratic shape with dimensions of 500 \(\times\) 500 mm and a total thickness of 12 mm (core with 10 mm and another 8 layers of 2 mm). On the other hand, the ice block has a specific geometry, as indicated in Figure 4, with a thickness of 200 mm (as predicted for young ice 1). This geometry was utilized in the numerical simulations to conduct drop tests on the sandwich structure. Two different masses of ice, 4 kg and 8 kg, were investigated.  
Figure 3: Numerical validation of the SPH model in comparison with article [52].  
The ice block has been discretized with the SPH method, as shown in Figure 5a. The diameter of each particle has been chosen as 5 mm to reduce the computational time. The sandwich structure has been discretized by a shell quadrilateral element, Figure 5a. The size was 10 mm to avoid penetration between the particles and structure. In both cases, explicit physical properties were selected during the Ansys LS-DYNA simulation.  
The boundary conditions were set as fixed support on the four faces of the sandwich structure, indicated in blue in Figure 5b. Gravity and velocity of 5 m/s and 10 m/s were applied to the ice block in the Y direction. An impact velocity between 1-10 m/s is usually treated as low-velocity impact (LVI), while the ship-ice interaction occurs at a lower speed, for example 5 knots (2.6 m/s), which falls into the category of LVI [53; 54; 55].  
The frictionless contact between the ice and the sandwich structure was defined as *CONACT_AUTOMATIC_NODES_TO_SURFACE. The explicit Ansys LS-DYNA numerical simulation was performed in 8 ms.  
Friction between the ice and the composite material was omitted in this study due to computational time constraints and natural friction reduction conditions, aligning with a comparative analysis of a similar experimental campaign [52] that also did not consider friction.
# Angular Trajectory Design for MR-SPS Using Bezier Shaping Approach
###### Abstract  
Solar power satellite (SPS) is a kind of large-scale on-orbit servicing spacecraft collecting solar energy in space and transmitting energy to the earth. The solar arrays of the SPS must point to the sun to collect enough solar energy and the antenna must point to the rectenna on the ground to transmit energy. But due to the limitation of the control effort, accurate solar and earth orientation may not be achieved. This paper focuses on the MR-SPS, and establishes the attitude kinematics and dynamics model of a MW-level MR-SPS. Angular trajectory based on Bezier shaping approach is generated at different time of a year to satisfy the control constraints. The simulation results demonstrate the effectiveness of the proposed method. Even if the control torque is limited to a small amount, the optimal angular trajectory can still ensure high average energy receiving efficiency.  
MR-SPS; Bezier shaping approach; angular trajectory Article
## 3 Processes for Bioenergy Production
### Biochemical Methods
#### 3.2.3 Aerobic Digestion  
This process involves the degradation of organic substances by microorganisms under aerobic conditions. The bacteria degrade complex organic substances into simple compounds, with the final production of CO\({}_{2}\), H\({}_{2}\)O, and heat, which is produced in proportion to the microbial metabolic activity [30], and can therefore be considered a bioenergy production process.  
One example of bioenergy production through aerobic degradation is composting. Composing consists of a spontaneous biological decomposition of solid organic material in a predominantly aerobic environment, during which primarily bacteria, fungi, and other microorganisms transform organic materials into a stable product called compost, useful as a fertilizer or soil amendment [31]. Composing is an exothermic and spontaneous process in which the microbial activity releases energy as heat, increasing the temperature of the material, which can then be recovered for the production of renewable energy as bioenergy [32; 33]. Although the ultimate energy available from a composting substrate is the same as that available from the combustion of the substrate, the actual amount of heat produced is determined by factors such as the feedstock energy content, biodegradability, duration of the composting process, and composting conditions [33]. In fact, composting does not lead to biofuel production, but it can be considered a source of renewable thermal energy. The heat produced from composting can be used directly as vapor for greenhouse heating, as hydronic heating through conduction of heat exchanges inside the pile for consumption of hot water, and by a condenser-type heat exchanger [33]. The last option is considered the most efficient for energy recovery. Different heat recoveries have been reported from the composting process: 1.9 MJ kg\({}^{-1}\) of organic matter from tomato plant residues using a rotary drum bioreactor [34]; 6 MJ kg\({}^{-1}\) of compost from kitchen and garden waste [35]; 17.7-32.9 MJ h\({}^{-1}\) during the composting of animal manure, animal bedding, and waste feed hay in a commercial-scale system with compost vapor temperatures of 51-66 \({}^{\circ}\)C [36].
# Measuring Student Engagement through Behavioral and Emotional Features Using Deep-Learning Models
## 6 Conclusions  
This study presents an effective method for monitoring student engagement in real-time online classroom environments (OCEs). Specifically, it incorporates both behavioral and emotional features to compute student engagement using a self-trained convolutional neural network (CNN)- and transfer-learning models. In terms of behavioral indicators, students who are focused (looking forward or asking the teacher questions) are classified as engaged, which is supported by a 92% survey consensus. Conversely, behaviors such as looking away (talking to peers or avoiding the teacher/board) and yawning are categorized as non-engaged. Regarding emotional features, the survey shows that happiness, neutrality, sadness, and anger are associated with engagement at rates of 88%, 77%, 75%, and 69%, respectively. For detecting behavior-related features, the self-trained CNN classifier outperformed transfer-learning models, achieving training, validation, and testing accuracies of 97%, 91%, and 83%, respectively. On the other hand, for emotion-based feature detection, the ResNet50 model outperformed both the transfer learning-based models and the self-trained CNN, achieving training, validation, and testing accuracies of 95%, 90%, and 82%, respectively. In conclusion, both self-trained and transfer-learning-based models demonstrate efficacy in monitoring student engagement. The overall engagement level is computed using the models' confidence scores for behavior and emotion classification, combined with survey data.  
Conceptualization, N.M., S.M.B. and H.A.; methodology, N.M. and H.D.; software, N.M.; validation, S.M.B., H.A. and M.R.P.; formal analysis, H.A.; investigation, N.M.; resources, M.R.P.; data curation, N.M.; writing--original draft preparation, N.M., S.M.B. and H.A.; writing--review and editing, N.M.; visualization, N.M.; supervision, S.M.B.; project administration, H.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: The data presented in this study are available upon request from the corresponding author by request of the volunteer participants (they requested only to share the data if demanded).
# A Linear Fitting Algorithm Based on Modified Random Sample Consensus
## 4 Algorithm Verification and Discussion
### Simulation Results  
To given a brief verification of our algorithm, we first set a line \(y=x\) with extra Gaussian noises, e.g., the parameters of the original line are \(a=1\) and \(b=0\) as shown in Figure 4(a).  
Figure 5: _Cont._Here the blue points are the line data with extra noise added. The Gaussian noise in the simulation has the expectation value 0 and variance 5. Figure 5b-e show the line fitting results with related algorithm. The LSH-RANSAC algorithm proposed in this paper fits a line that nearly perfectly coincides with the standard original line, while the fitting results of the other three algorithms exhibit minor deviations. The fitting results of all four algorithms are summarized in Table 1.  
The proposed LSH-RANSAC algorithm fits a line that almost perfectly coincides with the original line, while the fitting results of the other three algorithms exhibit minor deviations.  
In a real dataset fitting experiment, the dataset consists of the center points extracted from a specific laser stripe, as illustrated in Figure 6a. Figure 6b-e show the line-fitting results obtained by each algorithm.  
The technical metrics for each algorithm in fitting the given point set are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **WLS** & **RANSAC** & **MLE** & **LSH-RANSAC** \\ \hline Fitted (a,b) & (0.980,0.589) & (0.992,0.893) & (0.980,0.587) & (1.004,\(-\)0.089) \\ Euclidean distance & 0.589 & 0.893 & 0.587 & 0.089 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between our methods and commonly used ones.  
Figure 5: Comparison of the results of simulation. (**a**) Original data with Gaussian noise. (**b**) Weighted least squares method. (**c**) Random sample consensus method. (**d**) Maximum likelihood estimation. (**e**) LSH-RANSAC algorithm proposed in this paper.  
As shown in Table 2, compared to the WLS, RANSAC, and MLE methods, the LSH-RANSAC algorithm demonstrates lower values for both the Mean Absolute Error and Residual Sum of Squares, alongside a higher coefficient of determination. This indicates that the LSH-RANSAC algorithm is more effective at mitigating the influence of outliers when fitting datasets that contain such anomalies, resulting in superior fitting performance.
## 5 Results and Discussion
The results of the medium size instances are shown in Table 3, and in addition to the behaviors described above, it can also be observed that:  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Space** & **Requested** & **Assigned** & **Order** & **Scenario** & **First Optimality** & **Relative** & **Time** \\  & **(i)** & **Products** & **(o)** & **(p)** & **Scenario** & **Cap (\%)** & **Gap (\%)** & **(sec)** \\ \hline
16 & 32 & 8 & 8  
* For type 1 instances, when the proximity between the number of available spaces and the quantity of products assigned is approximately 25% (\(I\approx\frac{1}{4}O\)), in less than 3600 s, the Relative Gap is around 16%, 3 of them found the optimal solution and the highest of 36% (instances 16,17, 22-25, 28). However, for type 2, this ratio shows relative gap greater than 80%. * Nevertheless, when the proximity is approximately 50% (\(I\approx\frac{1}{2}O\)) the complexity increases; in this case, in comparison to small instances, it does not depend on the scenario to find the optimal solution; it is not even possible to find the first feasible integer solution in less than 3600 or 7200 s for type 1 or 2 instances, respectively, except for the case when only one order is requested (instance 32). * The most complex scenarios continue to be 3 and 4, unlike when only one order is requested, because under this criterion, it is possible to find an optimal solution (type 28 and 32 instances) for medium type 1 instances. Note that type 2 reaches the point of not finding the first feasible integer solution, regardless of the scenario or the number of orders. Finally, the results of the large size instances are shown in Table 4, in which we can observe that:  
* We can observe that in most cases, the first feasible integer solution was not found, though in cases with a relative gap, one of the repetitions of instances 74 and 75 yielded the results shown in the table, but the rest did not show any results. Instance 78 was under the criteria of a single order and the six repetitions show the average relative gap present. * On the other hand, from small and medium sizes instances Scenarios 3 and 4 are the ones that present the greatest complexity, except when only one order is requested; therefore, no more large size instances were considered, as they were expected to not show any results. * In instance number 79, although a longer runtime was considered, it was not possible to find linear relaxation; therefore, a gap was not obtained. Considering the information described above, we found that for some real instances, the model does not find an initial solution, and for others, its linear relaxation was not found; therefore, it is concluded that the proposed model is difficult to solve for this type of instance, as [49] specifies.
## 2 Materials and Methods  
A solution of 2.5% residual CMC from a paper manufacturing process in distilled water was made. For the analyses, a solution of 2.5% fresh CMC and 5 heat-treated solutions with 5 different cycles were obtained: CMC 10 (10 cycles), CMC 20 (20 cycles), CMC 30 (30 cycles), CMC 40 (40 cycles) and CMC 50 (50 cycles). The cycles were carried out by heating some blank samples at 800 \({}^{\circ}\)C and cooling in a specific environment. The cooling curves T = f (t) from Figure 1 were drawn with a standard sample of Ag with a diameter of 12.5 mm and a height of 25 mm, inside which was mounted a chromel-alumel thermocouple connected to a millivoltmeter indicator.  
The contact angle measurements were obtained using a Kruss Easy Drop goniometer. The sessile drop method was chosen to measure the static contact angles of distilled water and the six solutions of CMC on a metal surface. Steel A 1040 1035 (ASTM) was chosen (Table 1). Using drop shape analysis software (DSA version 1.90.0.14), the average between the right and left contact angles was calculated. Before each measurement, the sample was cleaned with isopropanol and dried in the atmosphere. The volume of the liquid dropped on the surface was 2 \(\mu\)L. For each sample, at least 7 measurements were made every 2 s for 40 s. It was observed that 10 s was enough for the drop to stabilize on the surface. After that, the results could be considered valid to create a mean value. The standard deviation was calculated.  
Fourier transform infrared (FTIR) was applied to identify the presence of metallic and organic oxides in the analyzed calibration media. FTIR spectra were registered on a Bruker Vertex 70 FT-IT spectrophotometer in total attenuated reflectance mode in the wavenumber range of 1900-650 cm\({}^{-1}\).  
Electro-corrosion resistance was obtained using a VoltaLab-21 potentiostat (Radiometer, Denmark) with a three-electrode cell. A Pt auxiliary electrode and a calomel-saturated one were used. A working electrode area of 0.8 cm\({}^{2}\) was exposed to the electrolyte solution. This experimental setup was used to analyze the linear and cyclic curve determinations. Linear plots were registered at a scan rate of 1 mV/s and the cyclic plots at a scanning rate of 10 mV/s.  
The corrosion potential at zero corrosion currents of Eo \(\equiv\) E (I = 0), Tafel slopes (ba and bc), polarization resistance (Rp), corrosion current density (Jcor) and corresponding corrosion rate (Vcor) were evaluated using the facilities offered by VoltaMaster4 software (version 6.0.225310).  
The material used for linear and cyclic voltammetry, the cylindrical samples, the quality of carbon steel and improvement steel and the chemical composition are listed in Table 1.  
After corrosion in the fresh and altered environments, scanning electron microscopy (SEM) (VegaTescan LMH II, SE detector, 30 kV) was performed. The chemical composition of the surface was established with an EDS detector (Bruker X-flash).
# Low Temperature Chemoselective Hydrogenation of Aldehydes over a Magnetic Pd Catalyst
## 3 Results and Discussion
### Time Course of the Product Distributions  
There were two pathways for the deoxygenation of aromatic aldehyde (Scheme 2): (A) hydrogenation/hydrogenysis, and (B) direct hydrogenolysis of the C\(-\)O bond. According to the results in Figure 4, benzaldehyde was almost completelyconsumedin 1 h with 68.8%toluene and 41.2% benzyl alcohol generated. As the reaction time was prolonged to 12 h, the yield of benzyl alcohol gradually decreased from 41.2% to 0.4%, meanwhile the yieldof toluene gradually increased from 68.8% to 99.6%. Actually, as the reaction time increased, the yield of toluene of all the reactions at different reaction temperatures increased, while the yield of benzyl alcoholde creased (Table S3 and Figure 4), which means thatobtaining toluene as the final product of the successive pathway was just a question of time. Thus, we couldconclude that the deoxygenation of carbonyl groups over the \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd catalysts followed Pathway A in Scheme 2.  
**Ar=Aromatic group**  
**Scheme 2. Reaction pathways for the deoxygenation of the aromatic aldehydes.**  
**Figure 3. Results of the reduction of benzaldehyde at different temperatures. Reaction conditions: Benzaldehyde (1 mmol), 2 wt.% \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd catalyst (10 mg), H\({}_{2}\)O (10 mL), 2.5 bar H\({}_{2}\), 1 h.
# Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks
## 3 SEBA Architecture
### Virtual OLT Hardware Abstraction (VOLTHA)  
In the central office, the white-box OLT will be used, incorporating Virtual OLT Hardware Abstraction (VOLTHA), allowing the Virtual OLT (vOLT) to be managed by the Software-Defined Networking (SDN) controller, i.e., Open Network Operating System (ONOS). The vOLT can have NetConf, OpenFlow Agent, OLT Application Programming Interface (API), and ONU Management and Control Interface (OMCI) stack-connected to the ONOS SDN controller. VOLTHA hides PON-level details from the SDN controller and abstracts each PON as a pseudo-Ethernet switch easily programmed by the SDN controller.  
Figure 3 illustrates the operation architecture of VOLTHA. The process begins with VOLTHA activating the OLT, which has Network-to-Network Interface (NNI) ports on one side and PON ports on the other. Once activated, VOLTHA abstracts the OLT, including the connected NNI port, which is connected to a logical switch within VOLTHA. VOLTHA then informs ONOS of the existence of this logical switch. The ONUs are attached to the OLT through its PON ports, and the User Network Interface (UNI) port at the ONU is also added to the logical switch within VOLTHA. As an example, the Edgecore ASFvOLT16 White box OLT, which is used in industrial settings, supports [32]. Other vendors, such as CIG, Tellabs, and Iskratel offer similar OLT solutions.  
Figure 3: VOLTHA operation architecture.  
Figure 2: Generic SEBA architecture.
# Reducing the Number of Sensors in the Data Glove for Recognition of Static Hand Gestures
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author** & **Ref.** & **Kind of Gestures** & **Highest** & **Number of Sensors** & **Method** & **\# Gestures** & **\# Users** \\  & **Num.** & & **Performance** & & & & \\ \hline [PERSON] et al. & [14] & static, based on daily activities & 99 & 15 piezoresistive & \(k\)-NN/RF & 8 & 9 \\ \hline [PERSON] et al. & [15] & dynamic, based on Australian Sign Language & 99.7 & 10 piezoresistive + 1 IMU & RF & 27 & 5 \\ \hline [PERSON] et al. & [16] & static, based on French & 91 & 5 IMUs & RF/ANN & 22 & 17 \\  & Sign Language & & & & & & \\ \hline [PERSON] et al. & [17] & dynamic, based on daily activities & 89.34 & 5 piezoresistive + Myo armband & LSTM & 10 & 10 \\ \hline [PERSON] et al. & [18] & static, based on daily activities & n/a & 5 custom made spiral steel wire based & n/a & 8 & 1 \\ \hline [PERSON] et al. & [20] & dynamic, based on Chinese Sign Language & 98.3 & 10 RGO piezoresistive & ANN & 9 & 2 \\ \hline [PERSON] and [PERSON] & [23] & dynamic, based on American Sign Language & 100 & 10 GaIn piezoresistive & LSTM & 11 & 1 \\ \hline This work & n/a & static, based on Polish Sign Language & 98.9/**94.5** & 10/**3** piezoresistive & \(k\)-NNiRS/ \(k\)-NN & 17 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the results presented in this paper to articles in Related Work. The proposed reduction in the number of sensors results in a decrease in the recognition efficiency of the tested gestures to 93-95%. Table 2 shows that the largest contribution to these errors comes from misrecognized gestures corresponding to letters \"W\" and \"U\". In its current form, the system can be used for simple fingerspelling interactions. It would only allow the user to provide simple information or to translate proper names to people without knowledge of sign language or write on a computer using the manual alphabet. A version of the system having a set of 10 flex sensors and inertial sensors will also be an excellent tool for creating freely available databases. The aim of a future study is to develop a system consisting of two hand overlays based on as few sensors as possible. A solution could be a system that combines hand motion recognition using a glove and a smartphone camera to track mouth movements, pose, and facial expressions of the speaker.