esa-sceva/satcom-chunk-collection huggingface dataset:
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 3 System Model and Assumptions
### Notations  
We represent a cryptographic hash function by \(H\), which is a function that maps an input of any length to a fixed length output, with the following characteristics [20]:  
* The mapping is deterministic, meaning that a given input will always result in the same output.
* It is infeasible to perform the inverse operation.
* It is infeasible to find more than one input that will result in the same output.  
Figure 1: Example topology with the network multicast manager (NMM), a border router (BR), a publisher (P), intermediate modes (I) and members of the multicast group (M).  
A small change in the input should result in a completely different output.  
The symmetric-key encryption operation of a message \(m\) under a key \(K\) to obtain the ciphertext \(c\) is denoted as \(c=E_{K}(m)\), and the corresponding decryption operation as \(m=D_{K}(c)\). Furthermore, the concatenation of values \(m_{1}\) and \(m_{2}\) is denoted by \(m_{1}\|m_{2}\) and the XOR operation by \(m_{1}\oplus m_{2}\).
# An Improved Self-Born Weighted Least Square Method for Cylindicity Error Evaluation
## 5 Experimental Verification and Literature Verification
### Experimental Verification
#### 5.1.2 Error Evaluation after Inserting Gross Error Points
(**b**) Insert one gross error point. (**c**) Insert two gross error points. (**d**) Insert three gross error points. point was not inserted, the cylindrical radius changed within 0.01 mm, and the algorithm had better resistance to gross error. Cylindricity error was 0.1336 mm when gross error point was not inserted. Cylindricity error was 0.1475 mm when one gross error point was inserted. The cylindricity error was 0.1875 mm when two gross error points were inserted. The cylindricity error was 0.2038 mm when three gross error points were inserted. With the increase of inserting gross error points, the cylindricity error changed within 0.08 mm, and the data change was small. This shows that the algorithm can reduce the influence of gross error on the evaluation results, had a certain resistance to gross error, and had good robustness.
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 5 Improved Harmony Search for Node Depth Adjustment
### Check Stop Criterion  
The whole process will be performed iteratively, and it will keep improvising new harmony vectors and update the HM matrix until the maximum iteration number (NI) is reached. Then, the harmony vector with the best value of objective function is selected as the final solution vector. It means that this vector will be used to adjust nodes to the optimal depth to improve tracking accuracy in each time interval.  
So far, we have finished the improved HS algorithm to determine optimal depth of activated nodes. In this method, the determinant of FIM at each interval is maximized to improve tracking accuracy. The detail pseudo code of the improved HS algorithm for depth adjustment is listed as Algorithm 1.  
```
1:1.Initialization:
2:if\(k=0\)then
3: Particle Initialization:
4:for\(i=1,2,\ldots,N\)do
5: draw particle \(x_{k}^{i}\) from the prior of target state \(p(x_{0})\);
6:endfor
7:endif
8:2.Tracking process:
9:for\(k=1,2,\ldots\)do
10:for\(i=1,2,\ldots,N\)do
11: sample \(x_{k+1}^{i}\sim p(x_{k+1}|x_{k}^{i})\);
12:endfor
13: {Start adjusting nodes' depth}:
14: Define parameters: \(HMS\), \(HMCR\), \(PAR\), \(BW\), \(NI\), \(d_{min}\), \(d_{max}\)
15: Initialize new harmony vectors using Equations (16) and (17)
16:while\(gn\leq NI\)do
17: Update parameters using Equation (21)
18: Check improvising a new harmony vector using Equation (18)
19:if\(rand\in(0,1)\leq HMCR\)then
20: Select from HM
21: Check pitch adjustment using Equation (19)
22:if\(rand\in(0,1)\leq PAR\)then
23: pitch adjust using Equation (20)
24:endif
25:else
26: Generate from moving range \([d_{min},d_{max}]\) using Equations (16) and (17)
27:endif
28: Update Harmony Memory
29: Calculate objective function value using Equation (14)
30: Compare new vector to the worst one in HM, and keep the better one.
31:endwhile
32: Select the best vector as optimal depth solution
33: {END adjusting nodes' depth}
34: Get the measurements from depth adjusted nodes and estimation using Particle Filter.
35:endfor
```  
**Algorithm 1** Node depth adjustment based target tracking scheme using improved harmony search
## 3 Case Study
### Evaluation Indicators  
This section discusses four evaluation indicators, namely the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (\(R^{2}\)), which are used to test the predictive abilities of different models. The formula of these four indexes are provided below.  
\[MAE(y,\hat{y})=\frac{1}{n}{\sum\limits_{i=1}^{n}}{|y_{i}-\hat{y}_{i}|} \tag{17}\]  
\[MSE(y,\hat{y})=\frac{1}{n}{\sum\limits_{i=1}^{n}}{|y_{i}-\hat{y}_{i}|^{2}} \tag{18}\]  
\[RMSE(y,\hat{y})=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}}{|y_{i}-\hat{y}_{i}|^ {2}}} \tag{19}\]  
\[R^{2}(y,\hat{y})=1-\sum\limits_{i=1}^{n}{(y_{i}-\hat{y}_{i})^{2}}/{\sum\limits _{i=1}^{n}{(y_{i}-\overline{y}_{i})^{2}}} \tag{20}\]  
where \(\hat{y}\) is the predicted value of the \(i\)-th sample, \(y_{i}\) is the corresponding true values, and \(\overline{y}=\sum\limits_{i=1}^{n}{y_{i}}/{n}\). These evaluation indicators can effectively observe the error between the predicted value and the measured value. It is worth mentioning that for all evaluation indicators except \(R^{2}\), the smaller its value, the better performance of the forecasting model.
## 1 Introduction
Even though solar modules sensitized with visible dyes are characterized by the highest photovoltaic efficiencies compared to UV and NIR absorbing dyes, they drastically reduce PAR inside the greenhouse with negative effects on plants. But, no less important and still unexplored, the manipulation of the light spectrum by the dye could provide solutions for controlling greenhouse parameters, reducing harmful radiation, and potentially lowering plant disease risks. It is well known that the partial absorption of UV radiation prevents the cellular damage of the plants [26], and that IR radiation obturation leads to a reduction of the air temperature inside the greenhouse, which leads to an increase in crop production. Also, within the greenhouse, in addition to controlling parameters such as light, relative humidity, and the amount of CO\({}_{2}\), it is very important to have a control over the excessive temperature inside, especially during the summer months, which can have a negative effect on agricultural production [27]. Therefore, to reduce the temperatures inside the greenhouse close to the ambient temperature, a traditional method such as ventilation, black cloth net shading, or whitewash on the roof surface of the greenhouse can be used [8]. In the context of inherent global warming, the design and development of new methods for inside temperature control should be imperatively considered. Thus, in accordance with the Intergovernmental Panel on ClimateChange (IPCC)'s Sixth Assessment Report (AR6), published in 2021, in the lowest-emission scenario, the global mean surface temperature is expected to increase between 1.0 and 1.8 \({}^{\circ}\)C relative to the preindustrial period by 2100 and between 3.3 and 5.7 \({}^{\circ}\)C in the highest emissions scenario. An increase in the global average temperature above 2 \({}^{\circ}\)C is predicted to affect many plants and to change agriculture patterns. In our previous studies, a DSSC based on UV dye with the requirements imposed by a greenhouse to have transparency over the entire PAR range, while having an efficiency of nearly 5% at an irradiance between 50 mW/cm\({}^{2}\) and 100 mW/cm\({}^{2}\), irradiance corresponding to maximum light intensity in all seasons, was demonstrated for the first time [28]. In this context, we started from the following premises: (i) the choice of affordable commercial dyes that absorb UV and NIR and are partially transparent to PAR, namely DN-F01 dye (Dyenamo Yellow, a broad absorption at around 355-470 nm) and DN-F15 (Dyenamo Transparent Green, two broad absorptions at around 337-480 nm and 580-742 nm) and (ii) a control over the excessive temperature inside of the greenhouse through the dyes. In our study, photovoltaic tiles based on yellow and green DSSCs for a wavelength-selective greenhouse were first tested in outdoor conditions to monitor their behaviour in terms of temperature, light intensity, and photovoltaic parameters. Based on the promising results obtained, the concept of photovoltaic tiles based on yellow and green DSSCs has been experimentally validated for the next implementation in a wavelength-selective greenhouse.
# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 2 Methodology Model  
### Overall Data Flow  
For the wind turbine short-term ahead power prediction problem designed in this study, the prediction process established is shown in Figure 1. The Supervisory Control and Data Acquisition (SCADA) data of the wind turbine was first acquired and then the raw data was pre-processed by digital filter for noise reduction. Next, the dataset pre-processing process for the supervised learning task was executed, and on the basis of this work, we will be able to implement the prediction of continuous time series. The final predictor was the LSTM neural network, but of course several other machine-learning methods were also used as predictors in this session to test the effect. With this complete set of processes, the short-term power output prediction of wind turbine can be effectively implemented.
# HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence
## 3 Use Case Definition  
The use case presented in this paper, which is intended to validate the HPC SELENE platform, has been titled as automatic accurate stopping and safe passenger transfer, and it consists of automatic functionality collection based on CV- and AI-enhanced techniques. Figure 1 shows the graphical representation of it. The use case specifically highlights the following three features:  
* Data collection and synchronization: this captures data from stereo vision-capable cameras in real time and it synchronizes and rectifies data of both video stream signals.
* Automatic station detection and accurate stop aligning the vehicle and platform: this detects the station platform and it accomplishes precise localization inside the platform area by detecting, recognizing and tracking visual patterns. The visual landmarks have been chosen to maximize the results of the detection and identification process in any possible lighting conditions. Visual stereo sensors that have been properly calibrated assess the physical distance.
* Safe passenger transfer: this captures data from rear cameras and it manages automatic safe door functionality preventing (a) door opening operation if the train and platform are not precisely aligned and (b) door shutting operations if any passengers are entering or exiting.  
Apart from the functional requirements, there are two key requirements to be met in this use case. The first is to keep the processing time as low as possible, since the accurate stop with a moving train (and its inertia) does not allow latencies that could turn the results of the visual analysis into obsolete data, as these would not be useful for an accurate control of the vehicle. On the other hand, the passenger detection functionality has to be secured by combinations of redundant executions over isolated resources.
# Residual Echo Suppression Considering Harmonic Distortion and Temporal Correlation
###### Abstract
A DTD is adopted to take account of residual echo estimates and microphone signals in the past frames without the adverse effect of the near-end speech and noise.
# An Iterative Guidance and Navigation Algorithm for Orbit Rendezvous of Cooperating CubeSats
## 2 Equations of Motion  
In order to write the equations of the relative orbital motion, a reference Keplerian circular orbit is defined in an inertial reference frame with axes \(\hat{N}\), \(\hat{M}\), \(\hat{h}\), as seen in Figure 1. Axes \(\hat{N}\) and \(\hat{h}\) are aligned respectively with the line of the ascending node and the angular momentum of the reference Keplerian orbit. The latter is circular and therefore has a fixed radius \(R_{R}\), inclination \(i_{R}\), and RAAN \(\Omega_{R}\). In the preceding frame, orbital motion of a space vehicle (subject to thrust and perturbing accelerations) is identified by the instantaneous radius \(r\), right ascension \(\xi\), declination \(\phi\), radial velocity \(v_{r}\), transverse velocity \(v_{t}\), and normal velocity \(v_{k}\).  
Using these variables the equations of orbital motion are:  
\[\left\{\begin{array}{l}\dot{r}=v_{r}\\ \dot{\xi}=\frac{v_{t}}{r\cos\phi}\\ \dot{\phi}=\frac{v_{k}}{r}\end{array}\right.\quad\left\{\begin{array}{l}\dot {v}_{r}=-\frac{\mu}{r^{2}}+\frac{v_{t}^{2}+v_{k}^{2}}{r}+a_{r}\\ \dot{v}_{t}=\frac{v_{t}}{r}(v_{k}\tan\phi-v_{r})+a_{t}\\ \dot{v}_{k}=-\frac{v_{t}^{2}}{r}\tan\phi-\frac{v_{r}v_{k}}{r}+a_{k}\end{array} \right., \tag{1}\]  
where \(\mu\) is the gravitational parameter of the main attracting body and \(\vec{a}=\{a_{r},a_{t},a_{k}\}\) is the vector containing the three components of the external accelerations, due to either thrust or orbital perturbations.  
The guidance law is obtained by linearizing Equation (1) around the reference orbit, assuming that the variables are perturbed by small displacements, denoted with \(\delta\), from the respective values, associated with the reference circular orbit, i.e.,  
\[\left\{\begin{array}{l}r=R_{r}+\delta r\\ \xi=\xi_{r}+\delta\xi\\ \phi=\phi_{r}+\delta\phi\end{array}\right.\quad\left\{\begin{array}{l}v_{r}= v_{rR}+\delta v_{r}=\delta v_{r}\\ v_{t}=v_{tR}+\delta v_{t}=\sqrt{\frac{\mu}{R_{r}}}+\delta v_{t}\\ v_{k}=v_{kR}+\delta v_{k}=\delta v_{k}\end{array}\right., \tag{2}\]  
At this point, it is useful to define a new set of coordinates:  
\[x=\delta r,\quad y=R_{R}\delta_{\xi}^{x},\quad z=R_{R}\delta\phi. \tag{3}\]  
Figure 1: Representation of the inertial and moving frames.  
By using the definitions in Equations (2) and (3), expanding Equations (1) to the first order, and neglecting higher-order terms, the well-known Hill-Clohessy-Wiltshire (HCW) equations are obtained, in terms of spherical coordinates \(x,y,z\),  
\[\left\{\begin{array}{l}\ddot{x}-3\omega_{R}^{2}x-2\omega_{R}\dot{y}=a_{r}\\ \ddot{y}+2\omega_{R}\dot{x}=a_{t}\\ \ddot{z}+\omega_{R}^{2}z=a_{k}\end{array}\right.. \tag{4}\]
# Cascaded Vehicle Matching and Short-Term Spatial-Temporal Network for Smoky Vehicle Detection
## 4 Approach
### Short-Term Spatial-Temporal Network  
After the vehicle matching stage, though detections that cannot connect to a certain vehicle were removed, those false positives from vehicle shadow regions could not be filtered effectively. We found that those vehicle shadow regions are difficult even for human beings if only the region of a frame is available. However, when we checked the original video stream, we found that it was possible for non-expert people to filter these false positives. We argue that the reason could be that more information is available in space time, see the top of Figure 8.  
Figure 8: Our (ST)\({}^{2}\)Net alternatives for spatial-temporal smoke recognition.  
Figure 7: Two common cases in our smoke–vehicle matching strategy. Case 1 refers to smoke regions overlapping with vehicles, while case 2 refers to there being no overlap between smoke regions and vehicles. Green boxes denote smoky vehicles.  
Inspired by the above fact, we extended the detected regions to larger square ones and designed two kinds of short-term spatial-temporal neural networks (ST)\({}^{2}\)Net to handle those shadow regions. Specifically, if the largest side of a detected region was less than 112, we extended it to an 112 \(\times\) 112 large region sharing the same center. Otherwise, we extended a detected region to a square region with the side as the largest side of the detected region. We also extended a detected frame region in time space, i.e., cropping the same region in \(K\) (e.g., 3, 5, 9, etc) frames. For the first (ST)\({}^{2}\)Net model, which is called (ST)\({}^{2}\)Net-prefix, we took the ResNet18 as a template and adjusted the first several layers to 3D convolutional layers according to the \(K\), see the left of Figure 8 when \(K\) = 3. For the second (ST)\({}^{2}\)Net model in the right of Figure 8, we added a 3D convolutional layer after the last 2D convolutional layer according to the \(K\), and then conducted adaptive pooling. We call this architecture (ST)\({}^{2}\)Net-suffix. Intuitively, (ST)\({}^{2}\)Net-prefix may be better at capturing low-level smoke motion features, while (ST)\({}^{2}\)Net-suffix keeps static high-level abstract features for further motion analysis. An evaluation will be presented in the next section.
# The Application of Intraoral Scanners in Orthodontic Care for Adolescents with Disabilities
## 2 Material and Methods  
The study participants were patients treated at a private orthodontic office in Szczecin, which had a contract with the National Health Fund for the treatment of disabled patients. All patients were qualified for orthodontic treatment using clear aligners. The participants were divided into two groups, each consisting of 42 individuals aged 8 to 12 years. Group I comprised patients with a disability certificate, with the level of disability classified as moderate. According to the criteria outlined in the Act on Vocational and Social Rehabilitation and Employment of Disabled Persons, moderate disability is characterized by impaired functionality of the body, resulting in the inability to work or perform professional duties, particularly in environments with limited work opportunities. In Group I, there were 11 boys and 31 girls. Patients with mild and severe levels of disability were excluded from this group. Group II (control) consisted of patients without a disability certificate. In this group, there were 21 boys and 21 girls.  
In order to develop a treatment plan and fabricate braces, it is necessary to take impressions with silicone material in a two-step process or scan the patient's teeth with an intraoral scanner. Maximilary and mandibular scans and bite registrations were performed with iTero Align Technologies (Cadent), software version 1.9 in Groups I and II. The scanner software enables time measurement throughout the scanning process. This functionality was used to evaluate the scan capture time in each group. An integrated portable scanner was used, consisting of a scanning camera (handpiece) and a computer with a monitor. The device allows for breaks to be taken during scanning. The multiple captured images are automatically stitched together into a whole. The digital impression is exported through a web portal to an external dental laboratory. Before scanning, teeth must be dried to accurately represent the oral situation.  
Patient cooperation in the course of scanning was also noted. The research team consisted of 2 people. All scans were performed by one person who had been working with the scanner for one year. A second person from the team kept an eye on the sequence of scanning procedures and recorded the scanning time.  
The statistical analysis of the results was conducted using Student's \(t\)-test, and significance was set at \(p<0.01\). Boxplots depicting the distribution of scanning times were generated using the R statistical program, version 4.1.2 (The R Foundation for Statistical Computing, Wirtschaftsuniversitat Vienna, Austria).
# Stimulation Montage Achieves Balanced Focality and Intensity
## 3 Results
### Robustness Study
#### 3.3.2 The Impact on Intensity by Conductivity Uncertainty  
Figure 5 and Table 2 show that the skull's conductivity uncertainty has the most powerful impact on electric field intensity. Figure 5B shows an example of intensity analysis in a single-target experiment with our SBFI method. An increase in skull conductivity induced a dramatic intensity growth on the target region. In contrast, the maximum intensity of the target region negatively correlates to the conductivity of the scalp, CSF, and cortex, on which the scalp has moderate effects stronger than the CSF and cortex. One possible explanation for the trends could be related to the conductivity ranges. The conductivity of the scalp/CSF is always much higher than the inner tissue layer skull/cortex. Therefore, the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tissue** & **Conventional** & **LCMV** & **SBFI** & **MI** & **CLS** \\ \hline Scalp & 0.003 & \(6.43\times 10^{-4}\) & \(6.14\times 10^{-4}\) & 0.008 & \(6.79\times 10^{-7}\) \\ Skull & 0.007 & 0.002 & 0.002 & 0.023 & \(2.80\times 10^{-6}\) \\ CSF & \(5.71\times 10^{-4}\) & \(6.23\times 10^{-5}\) & \(5.51\times 10^{-5}\) & 0.002 & \(4.49\times 10^{-8}\) \\ Cortex & 0.003 & \(9.41\times 10^{-5}\) & \(8.89\times 10^{-5}\) & 0.009 & \(3.12\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dissimilarity results of the robustness test (\(\left(\mathrm{V}/\mathrm{m}\right)^{2}\) ).  
Figure 4: (**A**) Mean square error (MSE) between expected electric field \(\mathbf{e}_{3n\times 1}\) and electric field \(\mathbf{e}^{\prime}_{3n\times 1}\) due to uncertainty in tissue conductivity. **Left**: single target. **Right**: synthetic multiple targets, from top to bottom are the results for target 1, 2, and 3, respectively. (Note: \(y\)-axis is log scale in the right column.) The effects on MSE in general: skull \(>\) scalp \(\approx\) cortex \(>\) CSF. (**B**) Example of the MSE changes across the conductivity distribution range at each layer. When the conductivity of a tissue is equal to that tissue’s original conductivity from the model, the MSE is trivially zero. However, as we shift the tissue’s conductivity away from its starting value, the error increases. Larger slopes on the graph above indicate a high sensitivity to conductivity changes, and therefore a low robustness to uncertainty. Within the possible conductivity changes, the model is most sensitive to skull, and least sensitive to CSF.  
current tends to be shunted through the layers of the scalp/CSF instead of the skull/cortex. If the scalp/CSF conductivity increases, current shunted though the scalp/CSF will increase, weakening the intensity at the cortex. If the skull conductivity increases, less current will be shunted at the scalp/skull boundary. At the skull/CSF boundary, the skull conductivity is still considerably lower than the CSF. The significant conductivity differences ensure an increase in the net current flowing into the CSF and cortex, resulting in an intensity increase at the cortex.