esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Orbit Results and Attitude Analysis of the SNUGLITE CubeSatellite
## 3 In-Orbit Results
### Satellite Status
#### 3.1.3 Power Generation  
Figure 7 describes the level of power generation according to the latitude in December 2018. We classified the power generations into two groups: north and south directions in orbit. Given that the orbit of the SNUGLITE CubeSat is sun-synchronous, the movements were consistent such that the north and south denote eclipse and daytime, respectively. Since the CubeSat does not use sun-pointing attitude control and the number of solar cells at each surface is different, the power generation changes  
Figure 5: Altitude records calculated by on-board GPS receiver from launch date to late June 2019.  
Figure 6: Battery voltage level.  
depending on the CubeSat's attitude. In addition, the high level of battery voltage also decreases the power generation. When the battery is fully charged, the power generation level is near zero, as described in Figure 7. Though the level of power generation varies, we deduced that the power generation has generally been above 2 W.
# Communication-Efficient Vertical Federated Learning
## 3 Proposed Method
### Second Step  
Once all the clients have generated the latent representations of their local data, in the next step, these latent data from all the clients are aggregated to train the global model. In typical federated settings, the aggregation of local models is performed by a central server, but in this case, the data aggregation is performed by the guest client that has the target variable. The aggregated latent data are subsequently used to train the final global model which makes predictions for new data in the future. Each time the guest party needs to perform a prediction on new data, it requests from the other clients their latent data. The guest party receives the latent data, aggregates them and can perform the task using the final global model.  
The latent data differ significantly from the original data of the clients but still contain relevant important information. As a result, sharing latent data from clients poses no risk of raw data exposure, while also improving performance.
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 6 Efficiency
### Storage Requirements  
In our protocol, each more must store four auxiliary parameters and two critical parameters of 16 bytes each. Critical parameters are those needed to be stored preferentially in tamper proof hardware and whose initial values are pre-installed. This key material allows the node to efficiently construct any type of keys, on the fly and ad-hoc. This is the same for both the unicast and multicast approaches.  
Different buffers need to be reserved to calculate all required values and to send and receive all packets. In ContikiOS, a single buffer to send and receive packets is available, shared between all layers. The size of this buffer determines the largest packet size that can be sent/received by a more. In our protocol, the largest packet that needs to be received is the packet from the NMM to the \(P\), containing the identities and keys of all the motes in the group. This packet is \(32\cdot n\) bytes long, with \(n\) the number of members in the group.  
To calculate all values, only two additional buffers are needed, since they can be reused. The largest value that needs to be calculated is the packet containing the group key information, which needs a \(36+16\cdot n\) byte buffer for the multicast approach and a 56 byte buffer for the unicast approach.  
After the group has been approved by the NMM, each mote needs to store two 16-byte values (the group key \(k_{g}\) and the one-way key chain \(h_{w}\)) and the group ID.  
This results in the memory consumption found in Table 3. Both implementations fit in the Zolertia RE-Mote platform [26], a popular development platform for WSANs, which has 512 KB flash and 32 KB ROM. In addition, for each packet being sent in the group, the members need to store the 16 byte value from the one-way key chain. This results in an additional 1.6 kB (about 12%) RAM usage. However, the value can be calculated on the fly, to trade off memory usage with processing time.
### Literature Review
A machine-vision-based approach was adopted to utilize its advantages of measurement flexibility, high resolution, and nondestruction. With a closed-loop feedback control architecture, the inspection procedures are fully integrated with the production process to detect and recover abnormal operations in a real-time mode. In [14], the authors dealt with the general state of the art in machine vision. The authors presented the principle of image processing, the components of the system, and possible applications of machine vision in the present. Despite the authors' efforts, it was not possible to find an occurrence of a problem similar to our laboratory in the studied literature. Based on the mentioned literature, it is possible to compile general requirements that must be met for the successful use of a camera system for shape and color recognition. Based on the knowledge from [3], multi-stage image processing and a suitable source of diffuse light are needed. The speed of image processing depends on the hardware used. These conditions at our workplace are met. The findings of [4; 11] confirm the previous claims. In addition, it can be seen from Figure 1 that the camera's sensing space is closed to outside light. This condition is not met at our workplace (Figure 4), nor can it be, because the staff must have constant access to the work area (learning activities). In [9; 10], the authors emphasized the \"training\" of shape and color recognition software. We have dealt with this area repeatedly and thoroughly with each new camera setup at our workplace. In the paper [11], two cameras were used simultaneously to eliminate unwanted light phenomena, and in addition, the entire area was covered. This solution is unsuitable for us. Edge detection technology and multi-stage image processing are being developed in several studied literature sources ([3; 12]). We use these technologies in our workplace, as shown in Figure 7.  
Many study articles deal with various algorithms for image recognition, color, or edge detection ([15; 16; 17; 18; 19; 20; 21; 22; 23]) and their various modified variants. We decided to use standard algorithms built into our camera and which our software cooperating with the camera can solve reliably.
## 2 Materials and Methods
### Decision Tree Algorithm and Performance Evaluation  
The C4.5 decision tree algorithm was performed to analyze the data inferentially. Two models were built with five basic attributes plus 12 disease attributes. The performance evaluation parameters for the C4.5 decision tree algorithm were precision, recall, F-measure, ROC area, and PRC area. The C4.5, originally developed by [PERSON] [20] and considered an improvement of [PERSON]'s earlier ID3 algorithm, is used to generate decision trees. The decision trees generated by C4.5 are used mainly for classification. As a result, C4.5 is often referred to as a statistical classifier and a landmark decision tree program that is probably the machine learning workhorse most widely used in practice to date [21]. The C4.5 builds decision trees from a set of training data in a similar way as the ID3, using the concept of information entropy. The training data are a set of pre-classified samples. At each node of the tree, C4.5 picks the data attribute that best splits its sample sets into subsets enriched in one class or the other using the splitting criterion normalized information gain where the highest gain is chosen as the decision criterion. [PERSON] [20] suggests using the following equation to calculate the gain ratio:  
\[\text{GainRatio}(\text{X},\text{ T})\ =\ \frac{\text{Gain}(\text{X},\text{T})}{ \text{SplitInfo}(\text{X},\text{T})}\]  
Considering the information content of a message that indicates--not the class to which the case belongs--but rather the outcome of the test on feature X, we used the following equation for Split Info:  
\[\text{SplitInfo}(\text{X},\text{ T})\ =\ \sum_{\text{i}}^{\text{n}}\left(\frac{| \text{Ti}|}{|\text{T}|}\log_{2}\frac{|\text{Ti}|}{|\text{T}|}\right)\]  
The GainRatio(X, T) is thus the proportion of information generated by the split that is useful for the classification.  
The performance evaluators used for the C4.5 decision tree algorithm were precision, recall, F-measure, ROC area, and PRC area. These are defined below:  
Precision (positive predictive value (PPV)): \(\frac{\text{TP}}{\text{TP}+\text{FP}}\);  
Recall (also known as sensitivity, or true positive rate (TPR)): \(\frac{\text{TP}}{\text{TP}+\text{FN}}\);  
F-measure (also known as F1 score, which is the harmonic mean of precision and recall): \(\frac{2\times\text{PPV}\times\text{TPR}}{\text{PPV}+\text{TPR}}\);ROC area: A ROC area is an area under the ROC curve (AUC), one of the common evaluators for machine learning algorithms. A ROC curve is a plot of the false positive rate (\(x\)-axis) versus the true positive rate (\(y\)-axis) for a number of different candidate threshold values between 0 and 1; PRC area: A PRC area is the area under the PRC curve, another common performance evaluator for machine learning methods. A PRC curve is a plot of the precision (\(y\)-axis) and the recall (\(x\)-axis) for different thresholds similar to the ROC curve.
## 7 Results  
### Preliminary Design Phase: Waste Management and Selective Demolition Planning within a IMM Approach  
During the preliminary design phase, the output of field surveys supported by close-range photogrammetry were image planes of the to-be-demolished facilities (Figure 8). The image planes were used for measuring, material recognition, and for conducting analysis in remote mode. Figure 8: Image planes of two sides of the abandoned 32-m-high vegetable oil refinery building slated for demolition, obtained via close-range photogrammetry. Figure 7: Design configuration of the new school complex in Inveruno, Italy. All the data and information regarding construction techniques, materials, and related data were gathered and digitized in the digital material inventory, some examples of which are shown in Table 7. The digital material inventory enabled the maximization of the quantity and quality of the recyclable materials by obtaining the fractions of mono-materials that were suitable for reuse or recycling as secondary raw materials. In addition, it supported the definition and adoption of selective demolition strategies (Table 7), by including the following data:  
* Types of structures to be demolished;
* Safety measures to be adopted during the demolition activities;
* Material types and their EWC code classification;
* Quantity of materials and related management strategies, i.e., on-site reusing, recycling, or landfilling. A central aspect that emerged from the field surveys and from the digital material inventory definition was the serious state of abandon of the entire vegetable oil refinery facility. None of the building elements could be dismantled and reused. In addition, the disassembly of the metallic components before the demolition would have been dangerous for the workers. Therefore, the sorting of the metal components was planned for after the demolition of the concrete and brick parts. In addition to detailed demolition planning, it was possible to quantify the costs associated with the selective demolition activities by linking each activity to the related expected cost of execution. Consequently, it was possible to obtain a reliable prediction of the total costs of the selective demolition phase, which were compared to the costs associated with non-selective demolition procedures. Non-selective demolition procedures consider the demolition of an entire building without any interest in the materials involved and, consequently, does not allow the proper separation of the elements or the fractioning of mono-materials that are suitable for reuse or recycling as secondary raw materials. For the present case study, selective demolition costs identified with the support of the digital material inventory accounted for EUR 514,381. On the other hand, non-selective demolition costs could be estimated at an amount of about EUR 405,020 by considering a unique parametric cost for the demolition of the total volumes. Consequently, the selective demolition costs represented 127% of the estimated non-selective demolition ones.
## 2 Structure Design and Theoretical Analysis
### Numerical Simulation for the Sensor  
The optical response of the designed metasurface sensor was numerically investigated by time-domain simulations, which were performed by commercial CST Microwave Studio(Computer Simulation Technology AG, Darmstadt, Germany); the permittivity of the silicon was set at \(\varepsilon=11.8\), the \(x\) and \(y\) directions were chosen to the periodic boundaries, and the open boundaries were set at the \(z\)-axis. The linear polarized plane wave was incident into the device. We simulated the transmission spectra of the metasurface filled with simulative materials of different refractive indices. Assuming that the material filled in the grooves of the metasurface, and that the height was 150 \(\upmu\)m in addition, the simulative materials were considered to be loss-free.  
The simulated transmission spectra of the metasurface filled with materials of different refractive indices \(n\) are shown in Figure 2. There are two resonance dips located at different frequencies in the transmission spectra. When the incident wave was \(y\)-polarized, the first resonance appeared at around 0.42 THz and the second resonance was located at 1.07 THz. For the \(x\)-polarized wave, the resonances were located at 0.42 THz and 1.03 THz, respectively. Besides this, according to the spectral shift in resonances, the sensing performance of the higher frequency resonance was better than the resonance located at a lower frequency. Different sensing performance originated from the diverse electromagnetic modes hidden within the metasurface; thus, it was necessary to determine the electromagnetic properties inside the unit cell.
# Design and Lateral Stability Analysis of an Attitude Adjustment Tractor for Moving on Side Slopes
## 2 Design Concept
### A Wheel Tractor with Attitude Adjustment Function
#### 2.2.3 Hillside Tractor  
By installing both the modified rear and front axles, this tractor is shown in Figure 4. As a kind of four-wheel-drive/front-steering one, it can keep contact with the ground on uneven terrains; it also can adjust its posture/configuration when necessary to enhance its mobility.  
Figure 4: Tractor: (**a**) Whole model; (**b**) Front axle; (**c**) Rear axle.  
Here are several advantages of this tractor:  
* It can enhance the tractor's stability on lateral slopes by actively adjusting the adjustment mechanisms on the rear axle.
* It can move on small obstacles while ensuring all the wheels keep in contact with the ground without active control, which provides a simple control system for this tractor.
* It can level its body on side slopes, to ensure comfort for tractor drivers.
# Thermodynamics Modeling for Actinide Monocarbides and Mononitrides from First Principles
## 2 Computational Methods
### Lattice-Dynamics Method  
For calculations of lattice dynamics, we employed the self-consistent ab initio lattice dynamics (SCAILD) methodology [43]. The idea behind this method is to, in a first step, apply a small displacement to an atom (or atoms, depending on crystal structure) in the supercell of the perfect crystal and calculate the forces on all the atoms. Second, using these forces, in analogy with the finite displacement method, to calculate the phonon density of states in the harmonic approximation. In the third step, one can determine, at a given temperature, the associated atomic displacements from standard lattice-dynamics equations. Next, again calculate the atomic forces on the crystal with thermally excited atoms and lastly, establish the new phonon density of states. The scheme is iterative because it returns to step 3 and self-consistent as it eventually converges so that the phonon density of states and associated properties are no longer changing. For example, the free energy is the most fundamental property that we are focusing on in this report. The convergency of the free energy is thus very important and in [8; 15] we showed the lattice contribution to the free energy as a function of SCAILD iterations.  
In the present case, for the actinide monocarbides and mononitrides the number of iterations required for convergency of the SCAILD scheme is about 100-200. The entire procedure is carefully explained in mathematical detail [43] and is not repeated here.  
We thus applied SCAILD for the six compounds: ThC, ThN, UC, UN, PuC, and PuN. The supercell we chose for this purpose is a 54-atom (3 \(\times\) 3 \(\times\) 3) cell of the NaCl structure. Technically, SCAILD assumes fixed lattice constant and temperature so to include volume and temperature dependencies, it is repeated on a volume-temperature grid. We typically executed SCAILD for about 4-6 temperatures and 3-4 volumes per compound. For the calculation of the forces on the atoms we apply density-functional theory as implemented in an efficient plane-wave pseudopotential code, described above. The total number of such DFT atomic-force calculations for this report, including all six compounds, was about 20,000. Albeit a considerable effort, the alternative, quantum molecular dynamics simulations, are far less manageable from a computational standpoint.
## 4 Land-Streamer vs. Conventional Data
### Installation Time  
Land-streamer seismic surveys require less installation time when compared to conventional seismic surveys since the receivers and cables are already assembled, and no receiver planting is required. In this field test, it took around 20 min to set up the land-streamer system, while planting 96 channels and connecting them to the recording system took around 120 min.  
Figure 2: A photo of the data recording process shows the land-streamer, the conventional profile, and the weight-drop used as seismic source.  
In addition to the installation time, we also save a lot of time in collecting the equipment after the data acquisition. Land-streamer only needs a few minutes (less than 15 min) to collect the streamer and move to the next acquisition site, while the conventional system requires at least one hour to collect all cables and receivers from the site. Hence, in total, land-streamer survey saves about 80% of the acquisition time, which is used for planting/collecting equipment in the field.  
In the case of recording long seismic profiles, land-streamer is very efficient in operation. To record a very long conventional data set, we need to divide the profile into several segments, plant the receivers and connect the cables for each segment, collect receivers and cables after shooting each segment, which is very time consuming, while all of this is not required in the land-streamer survey.  
In summary, (1) the time required to install and collect the land-streamer system is 4 to 6 times less than the time required to install and collect the conventional system. (2) in the case of long profiles where the conventional system must be installed and moved several times to cover the whole profile, the land-streamer is much more efficient and will save days of field-work.
## 4 Discussion  
### Validation of Diffuse Transmittance PLSR Model  
To evaluate the performance of the prediction models established, an independent validation set was employed. This validation set comprised 58 samples, covering a broad range of SSC values from 4.85 to 16.80 \({}^{\circ}\)Brix, as detailed in Table 9. These samples were used to assess the model predictive capability across different SSC values, ensuring that the model performed effectively not only during the training and feature selection phases but also maintained high predictive performance in practical applications. This step is crucial for validating the model real-world applicability, as it simulates actual conditions and provides an objective assessment of model effectiveness.  
As shown in Table 10, the validation analysis using the independent validation set indicated that while data processing with MSC alone yielded an \(R_{\text{C}}^{2}\) of 0.99 and an \(R_{p}^{2}\) of 0.94, there was still room for improvement. The model RMSEC was 0.98 \({}^{\circ}\)Brix, and the RMSEP was 1.00 \({}^{\circ}\)Brix.  
Figure 12: Diffuse reflectance PLSR model prediction results for kiwifruit physicochemical indices.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample Sets**} & **No. of** & \multirow{2}{*}{**Min. (\({}^{\circ}\)Brix)**} & \multirow{2}{*}{**Max. (\({}^{\circ}\)Brix)**} & \multirow{2}{*}{**Mean (\({}^{\circ}\)Brix)**} & \multirow{2}{*}{**STD. (\({}^{\circ}\)Brix)**} \\  & **Samples** & & & & \\ \hline Whole & \multirow{2}{*}{251} & 5.00 & 17.60 & 11.18 & 4.33 \\ sample & & 4.85 & 16.80 & 11.59 & 4.34 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Diffuse transmittance prediction set and independent validation set of SSC distribution.  
However, significant improvements in model performance were observed when MSC was combined with CARS. After applying CARS for feature wavelength selection, the \(R_{C}^{2}\) for the calibration set slightly decreased to 0.98, while the \(R_{p}^{2}\) for the prediction set increased to 0.95, indicating enhanced performance in prediction. More notably, the RMSE values showed substantial decreases. The RMSEC decreased from 0.98 to 0.61 \({}^{\circ}\)Brix, and the RMSEP was reduced from 1.00 to 0.93 \({}^{\circ}\)Brix. These improvements demonstrated that CARS feature wavelength selection significantly enhanced the model predictive accuracy and stability.  
In summary, while MSC alone offered some modeling effectiveness, the combination of MSC with CARS for feature wavelength selection significantly enhanced the overall model performance. Validation analysis using the independent validation set further reconfirmed the model superior performance in practical applications. Notably, the feature wavelengths selected through CARS markedly improved the model detection accuracy, making it highly effective for SSC prediction tasks.