esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Secure Network Encryption Protocol
### Counter Exchange Sub-Protocol  
The counter exchange sub-protocol shown in Figure 3 bootstraps the initial counter values \(C_{A}\) and \(C_{B}\). In the first two messages, \(A\) sends its counter \(C_{A}\) and \(B\) sends its counter \(C_{B}\) to each other. Note that counters are sent without encryption, since they are not secret values. In these messages, counters are used as nonces to ensure strong freshness. In the second and third messages, \(B\) and \(A\) are being authenticated by \(A\) and \(B\), respectively.
# GAE and OBE Enhanced Interference Mitigation Techniques in LDACS
## 4 Nonlinear Estimators
### Joint OBE Enhanced Pulse Peak Attenuator and Limiter  
Joint OBE enhanced pulse peak attenuator and limiter is similar to the OBE enhanced attenuator and limiter when the absolute amplitude of the received signal rests in between the lower threshold value (\(\alpha_{th}\)) and threshold value (\(\alpha_{th}\)). Hence the non linear functioning of the joint OBE enhanced pulse peak attenuator is mathematically depicted as in (32),  
\[\hat{x}_{|kJOA|}^{*}(y)=\begin{cases}|y|e^{iarg(y)}&\text{if }|y|\leq \alpha_{th}\\ \beta_{o}(y)|y|e^{iarg(y)}&\text{if }\alpha_{th}<|y|\leq\alpha_{th}\\ 0&\text{for }|y|>\alpha_{th}\end{cases} \tag{32}\]  
Similarly the functioning of the joint OBE enhanced pulse peak limiter is expressed as in (33),  
\[\hat{x}_{|kJOL|}^{*}(y)=\begin{cases}|y|e^{iarg(y)}&\text{if }|y|\leq \alpha_{th}\\ \beta_{mod}(y)|y|e^{iarg(y)}&\text{if }\alpha_{thl}<|y|\leq\alpha_{thu}\\ 0&\text{for }|y|>\alpha_{thu}\end{cases} \tag{33}\]  
where \(\beta_{mod}(y)\) is the same as the defined section OBE PPL. Hence, it is possible to realize this estimator in two ways with different definitions of \(\beta_{mod}(y)\).
# AI Approaches in Computer-Aided Diagnosis and Recognition of Neoplastic Changes in MRI Brain Images
## 2 Materials and Methods
### Dataset Description  
Obtaining a dataset of a decent size and high annotation quality in the field of medical data is a challenging task. Despite a large number of medical examinations, including MRI scans of the brain in a search for tumors conducted every year, there is a lack of publicly available datasets containing this information. As MRI scans are a type of personal data, they need to be protected, which requires compliance with the procedures while working with data and their proper anonymization, which often leads to losing valuable details. These datasets that are publicly available are often relatively small for the task of training neural networks and are often poorly annotated, thus making a reliable CAD system challenging. Due to these restrictions, this work focuses on recognizing only three types of brain tumors: pituitary, glioma, and meningioma. Of the types mentioned here, the glioma tumor turns out to be the most common malignant neoplasm, which is one of the reasons tumor type recognition is essential. The created system also takes into consideration the no-tumor case.  
The dataset used for training came from the Kaggle platform and was created by [PERSON] et al. [9]. It consists of images coming from different internet sources that were collected, filtered, and annotation-checked by authors and then published under the Creative Commons 1.0 license. The dataset contains 3264 images from four categories: no_tumor (500), pituitary_tumor (901), glioma_tumor (926), and meningioma_tumor (937). Images come in all three different cross-sections: Sagittal, Coronal, and Transverse planes, although the dataset is not balanced regarding the type of plane visible on the image, and they are not labeled with that information. All the images are grayscale, and their resolution varies from 167 \(\times\) 174 to 1446 \(\times\) 1375 pixels, but the most common resolution is approximately 500 \(\times\) 500 pixels.  
Before training networks, all the images were processed with a set of operations: rescaling to the size of 160 \(\times\) 160 pixels, normalization to the (0, 1) interval, and addition of one-hot encoding annotation; every image was paired with a four-element vector of zeros and a single one, indicating from which class the image comes. The dataset was then split into training and testing in a proportion of 70%:30%, and the training dataset was subjected to data augmentation with operations: random rotation in the range of (\(-\)15,15) degrees, vertical and horizontal translation up to 10% of image's size, and vertical and horizontal flips. The test dataset was not augmented. A few random samples from the training dataset are shown in Figure 5.
# Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse
## 2 Materials and Methods
### Total Loss  
Signals at millimeter wave and microwave frequencies experience scattering and absorption caused by leaves and branches of vegetation randomly distributed [51]. Therefore, the total path losses are formulated by combining the losses of the PLFree-space model with the PLveg vegetation losses that are predicted by the different vegetation models: PLtot = PLFree-space + PEveg, where PLtot is the total path loss [4].
## 3 Results
### Levels of Proteins and Pigments in the Cultivated Ulva spp.  
In IMTA cultivation assays (AQUA 1 and AQUA2), levels of protein and pigments were also measured in the cultivated _Ulva_ spp. to assess their growth and development in the IMTA-pilot system. Freshly collected _Ulva_ spp. (_Mondego_ estuary, Figure 3, d0) showed significant statistical differences (\(p\) < 0.05), in terms of protein content, between specimens collected in late spring and late autumn. On the other hand, levels of chlorophyll \(a\) and \(b\) and of carotenoids were similar between seasons (Figure 4 (Estuary, d0) and Figure 5, d0).  
Figure 3: Protein content (mg/mL/g wet weight) of _Ulva_ spp. cultivated in fish effluent, for 22 (AQUA1) and 26 days (AQUA2), in an IMTA-pilot system. In AQUA1, _Ulva_ spp. was collected in _Mondego_ estuary and _Figueira da Foz_ coast in late spring. In AQUA2, _Ulva_ spp. from _Mondego_ estuary (late autumn) was used. Values are presented as mean \(\pm\) standard deviation, \(n\) = 3. Different letters correspond to significant differences among data (\(p\) < 0.05), in which small letters compare data regarding _Ulva_ spp. collected in coast and capital letters compare data from _Ulva_ spp. collected in the estuary. Percentage of estimated crude protein is represented above each graph bar.  
Figure 4: Levels of chlorophyll \(a\) (Chl _a_) and \(b\) (Chl _b_) and of carotenoids (Carot) (ug/g wet weight) obtained for _Ulva_ spp. collected in _Mondego_ estuary and _Figueira da Foz_ coast (late spring) and cultivated in fish effluent for 22 days. Values are presented as mean \(\pm\) standard deviation, n = 3. Different letters correspond to significant differences among data (_p_ < 0.05), in which small letters compare data regarding _Ulva_ spp. collected in coast and capital letters compare data from _Ulva_ spp. collected in the estuary.  
With regard to the cultivated algae, in AQUA1, levels of total protein and crude protein and pigments of _Ulva_ spp. from coast and from estuary were not statistically different during the cultivation period (Figure 3, 22 days). The total protein and crude protein content of _Ulva_ spp. from coast and from estuary decreased during the cultivation period, data registered in d8, d16 and d22 being statistically different from d0.  
Similarly, in terms of pigments, a statistically significant decrease was also registered, though the levels of chlorophyll \(a\) and \(b\) and of carotenoids tended to increase on d22 (Figure 4). In late autumn (AQUA2), total protein content also augmented on the cultivated _Ulva_ spp., although no statistical differences were found (Figure 3). Crude protein however tended to decrease during the experimental period. On the other hand, levels of pigments significantly increased on d6 (\(p<0.05\)), but generally decreased over the cultivation period (Figure 5).
## 4 Simulation
### Case Study for the Design Variables  
The predictive model in the proposed method for the HEV system is based on a linear model with the state-space representation to clarify the relationship between the model parameters and outputs. To evaluate the performance and adequacy of the design variables in the proposed method, we compared the results for three cases of the design variables, as shown in Figure 10. All the simulations were performed considering the same time margin of 10 ms in the proposed ESN-MPC. Figure 10a,d,g show the results for the original design as references. The engine inertia was changed to 0.08 kg m\({}^{2}\). The torque limitations of the motor remained constant in (b), (e), and (h). However, the limitations changed in (c), (f), and (i). Decreasing the inertia results in deteriorated control performance, as indicated by the increased torque oscillations observed in Figure 10b. Moreover, the difference in the rotational speed between the engine crankshaft and carrier, \(\omega_{e}-\omega_{c}\), begins to oscillate, as shown in Figure 10e, and the motor torque pertaining to the two control inputs attain the limit values, as shown in Figure 10h. In case (c), the torque limits of the motors were higher than those in case (b). This corresponds to an improved vibration control performance. The increased motor limits indicate that the control inputs can implement the appropriate control for vibration reduction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Simulation Condition**} & \multicolumn{3}{c}{**CPU Time (ms)**} \\  & & **Av.** & **Max.** \\ \hline \multirow{2}{*}{ESN-MPC} & \multirow{2}{*}{Time margin} & 1 ms & 1.2 & 6.1 \\  & & 10 ms & 1.2 & 7.7 \\  & & 20 ms & 1.2 & 5.1 \\ \hline \multirow{2}{*}{EDT-MPC} & \multirow{2}{*}{Setting dead-time} & 1 ms & 2.2 & 5.9 \\  & & 10 ms & 2.3 & 7.5 \\  & & 20 ms & 3.4 & 9.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The computation time for the optimization of 10 test samples.  
Figure 10: A comparison of control performance for different design variables. (**a**,**d**,**g**) correspond to the original design, whereas (**b**,**e**,**h**) correspond to the results when the engine inertia is changed to 0.08 kg m\({}^{2}\). (**c**,**f**,**i**) indicate the results when the torque limits of the motors are varied from those shown in (**b**,**e**,**h**). All simulations correspond to the same time margin of 10 ms while using the proposed ESN-MPC.
# Prediction of Theoretical Derailments Caused by Cross-Winds with Frequency
## 4 Evaluation of Derailment Safety under Cross-Wind
### Derailment Safety Due to the Cross-Wind Frequencies
#### 4.4.2 The 2D Dynamic Vehicle with Dampers  
The amplitude (\(X\)) and the phase angle (\(\delta\)) are obtained using the dynamic equation with a spring damper and an external force with a constant frequency. The dimensionless amplitude (\(X\)) and the phase angle (\(\delta\)) are expressed as shown in Equations (37) and (38).  
\[\frac{Xk}{F_{0}}=\frac{1}{\sqrt{\left[1-\left(\frac{\omega}{\omega_{0}}\right) ^{2}\right]^{2}+\left[2\zeta\left(\frac{\omega}{\omega_{0}}\right)\right]^{2}}} \tag{37}\]  
\[\tan\delta=\frac{2\zeta\left(\frac{\omega}{\omega_{0}}\right)}{1-\left(\frac{ \omega}{\omega_{0}}\right)^{2}} \tag{38}\]  
Here, the used symbols are defined in Table 14.  
The above equations show that the amplitude and phases are the functions of frequency ratio (\(\omega/\omega_{0}\)) and damping ratio (\(\zeta\)), and they can be shown in Figure 13.  
In general, an actual train has dampers, as shown in Figure 14, which is expected to reduce the amplitude at resonant frequencies as described above. The characteristics of dampers installed in the KTX were applied for accurate results because the dampers can affect the simulation results. Figure 15 shows the simulation results between the models with dampers and without dampers.  
\begin{table}
\begin{tabular}{c c} \hline Natural frequency & \(\omega_{0}=\sqrt{\frac{k}{m}}\) \\ \hline Critical damping & \(c_{c}=2\sqrt{mk}=2m\omega_{0}\) \\ \hline Damping factor & \(\zeta=\frac{c}{c_{c}}\) \\ \hline \(\frac{\omega\omega}{k}=\frac{c}{c_{c}}\) & \(\frac{c\omega}{k}=2\zeta\frac{\omega}{\omega_{0}}\) \\ \hline \end{tabular}
\end{table}
Table 14: The symbols used in the damped vibration equation.  
Figure 13: Nondimensional values of amplitude depending on damping factors.  
Although the ratios of the wheel unloading showed a little difference in frequencies other than resonance, the results showed a good agreement and exceeded the TSI criteria of 0.9 in the frequency near to the resonance regardless of dampers.  
Even though the characteristics of dampers can affirmatively affect the running safety, the phenomenon of a resonance is critical to derailments regardless of dampers. In a design stage, it is important to evaluate how much a cross-wind with frequency near to the natural frequency of a vehicle can affect the running safety regardless of dampers.
## 3 Proposed Algorithmic Approach
### Default Route  
The default route refers to the path that the data generated by the nodes will follow once the application performs the routing process, which is to say, the selection of the best path as shown in the example in Figure 7. The default route depends on the source and destination address of each node. Therefore, it is important to note that the source address cannot be modified, while the destination address can vary depending on the default route. The application assigns the destination address of each node based on the execution of each algorithm. Depending on the algorithm, this default route can be adjusted according to each node's battery level and received signal quality.
## 4 Box-Type Boom Design and Optimization
### Optimization Potential of the Box-Type Boom Design  
As a manual process, the engineering design of a BTB and the generation of production-ready documentation can take up to 80 h. For the automation of the design of a BTB, a Knowledge-Based-Engineering (KBE) application has been developed together with LWN. For details, the readers are referred to the work in [2]. From this KBE-application, two tasks with high optimization potential were identified. These two problems are:  
1. _Boom Configuration_: The structural analysis defines the metal sheet thicknesses as well as the number and type of stiffeners in each segment (boom configuration), to define a BTB that can safely lift certain load cases at a given length. Currently, these parameters are determined by the engineer, through experience and a trial-and-error strategy, and evaluated using a structural analysis tool.
2. _Plate Partitioning_: An appropriate partitioning of the hull of the BTB into single plates is required to be able to manufacture the boom. Initially, this task was performed manually by the design engineer, and later, in the course of the implementation of the KBE-application, replaced by a heuristic approach (termed Successive Plate Partitioning) [42].  
Typically, both the structural analysis and the plate partitioning have to be carried out anew for every ordered boom, since the boom length and load cases are customer-specific. Furthermore, both are cumbersome tasks with a rather large solution space. Thus, they both bear the potential to be further automated and optimized.  
Following this identification of the optimization potential, the optimization of the BTB design (cf. Figure 4) is carried out accordingly in two steps: (1) Boom Configuration Optimization (see Section 5); and (2) Plate Partitioning Optimization (see Section 6). The mathematical formulation of the optimization problems are given in the respective sections.  
Figure 3: Box-type boom design: Variables of a boom configuration and of a manufacturable boom (adapted from [PERSON] et al. [3]).  
As depicted in Figure 4, the boom configuration optimization uses an approximation of the cost function (lower left corner in the figure), since the exact costs can only be calculated once all manufacturing details are known. This, in turn, is only the case after performing the plate partitioning optimization of Step 2 (lower right corner). Thus, a comparison of the approximated cost function and the exact cost function has to be carried out to verify the accuracy of the approximation.  
According to [PERSON] et al. [33], it can be \"fruitful to consider [...] sequential optimization using a series of sub-problems or two-level approaches in which intermediate approximate models are employed\". We here select this two-step procedure due to the distinct nature of these two optimization problems and the benefit of significantly less computational costs when using the approximate cost function. However, it may be possible that the global optimal solution may not be found.
_Algorithms_**2013**, \(6\), 591-617; doi:10.3390/a6040591  
_algorithms_  
ISSN **1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
Local Search Approaches in Stable Matching Problems  
**[PERSON] \({}^{1}\), [PERSON] \({}^{1,\ast}\), [PERSON] \({}^{1}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{3}\)**  
\({}^{1}\) University of Padova, Padova 35131, Italy; E-Mails: [EMAIL_ADDRESS] (M.G.);  
[EMAIL_ADDRESS] (F.R.)  
\({}^{2}\) Tulane University and IHMC, New Orleans, LA, USA; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) NICTA and UNSW, Sydney, NSW 1466, Australia; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +39 049 827 7704 / +39 049 827 7799.  
_Received: 14 August 2013; in revised form: 4 September 2013 / Accepted: 22 September 2013 / Published: 3 October 2013_  
**Abstract:** The stable marriage (SM) problem has a wide variety of practical applications, ranging from matching resident doctors to hospitals, to matching students to schools or, more generally, to any two-sided market. In the classical formulation, \(n\) men and \(n\) women express their preferences (via a strict total order) over the members of the other sex. Solving an SM problem means finding a stable marriage where stability is an envy-free notion: no man and woman who are not married to each other would both prefer each other to their partners or to being single. We consider both the classical stable marriage problem and one of its useful variations (denoted SMTI (Stable Marriage with Ties and Incomplete lists)) where the men and women express their preferences in the form of an incomplete preference list with ties over a subset of the members of the other sex. Matchings are permitted only with people who appear in these preference lists, and we try to find a stable matching that marries as many people as possible. Whilst the SM problem is polynomial to solve, the SMTI problem is NP-hard. We propose to tackle both problems via a local search approach, which exploits properties of the problems to reduce the size of the neighborhood and to make local moves efficiently. We empirically evaluate our algorithm for SM problems by measuring its runtime behavior and its ability to sample the lattice of all possible stable marriages. We evaluate our algorithm for SMTI problems in terms of both its runtime behavior and its ability to find a maximum cardinality stable marriage. Experimental results suggest that for SM problems, the number of steps of our algorithm grows only as \(O(n\log(n))\), and that it samples very well the set of all stable marriages. It is thus a fair and efficient approach to generate stable marriages. Furthermore, our approach for SMTI problems is able to solve large problems, quickly returning stable matchings of large and often optimal size, despite the NP-hardness of this problem.  
**Keywords:** local search; stable matching; sampling; ties and incomplete preference lists
# GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification
## 3 Methodology
### Region Point Cloud Feature Extraction with 2D Wide Inception  
#### 3.2.1 Enhancing Region Representation  
We divide the original 3D point cloud into different regions depending on points' locations and convert it into the region's representation. Each point has three coordinates (x, y, z), and each coordinate has a positive or negative sign, so we have a total of 2 ~ 3 = 8 different regions for the original 3D point cloud.  
We have eight regions \(R_{1},~{}R_{2},\ldots,R_{8}\), as follows:  
\[\begin{array}{l}R_{1}=\big{\{}p_{s}\in~{}R^{3}\text{ such that }x_{s}>0,~{}y_{s}>0,~{}z_{s}\leq 0 \big{\}}.\\ R_{2}=\big{\{}p_{s}\in~{}R^{3}\text{ such that }x_{s}>0,~{}y_{s}\leq 0,~{}z_{s}\leq 0 \big{\}}.\\ R_{3}=\big{\{}p_{s}\in~{}R^{3}\text{ such that }x_{s}\leq 0,~{}y_{s}>0,~{}z_{s}\leq 0 \big{\}}.\\ R_{4}=\big{\{}p_{s}\in~{}R^{3}\text{ such that }x_{s}\leq 0,~{}y_{s}\leq 0,~{}z_{s} \leq 0\big{\}}.\\ R_{5}=\big{\{}p_{s}\in~{}R^{3}\text{ such that }x_{s}>0,~{}y_{s}>0,~{}z_{s}>0 \big{\}}.\\ R_{6}=\big{\{}p_{s}\in~{}R^{3}\text{ such that }x_{s}>0,~{}y_{s}\leq 0,~{}z_{s}>0 \big{\}}.\\ R_{7}=\big{\{}p_{s}\in~{}R^{3}\text{ such that }x_{s}\leq 0,~{}y_{s}>0,~{}z_{s}>0 \big{\}}.\\ R_{8}=\big{\{}p_{s}\in~{}R^{3}\text{ such that }x_{s}\leq 0,~{}y_{s}\leq 0,~{}z_{s}>0 \big{\}}.\\ \end{array} \tag{9}\]  
We create a colormap in Figure 3 based on eights regions, and the color values are chosen randomly. Figure 4 expresses the original 3D point cloud in eight different regions with eight corresponding colors. We use the color property because it helps 2D CNN to learn the region features easily. Next, we create three different 2D point clouds from three coordinates values of a 3D point cloud to better exploit the enhancing region features. The first 2D point cloud contains 2D points with the x-y coordinates of the original 3D point cloud. We can determine the regions \(R_{1},~{}R_{2},\ldots,R_{8}\) where the 2D points belong after removing the z coordinate. An enhancing region structure of the first 2D point cloud is encoded into a 2D color image with sizes 224 by 224 by 3 (see Figure 5). The encoding process is implemented by plotting the 2D point cloud with eight colors then exporting the result to an image. The color for each point in the 2D point cloud is decided by its region. For example, one point will have yellow if it belongs to the fifth region (see colormap in Figure 3). The process is the same for the other two 2D point clouds with the x-z coordinates, and the y-z coordinates, respectively. The 2D color image outputs are shown in Figures 6 and 7.  
Figure 3: Mapping eight regions into eight colors.  
Figure 4: The original 3D point cloud with eight different regions.  
Figure 5: A 2D point cloud in the x-y plane.  
Figure 6: A 2D point cloud in the x-z plane.  
Figure 7: A 2D point cloud in the y-z plane.
## 3 Results and Discussion
### UV-Vis Spectroscopy  
Figure 2 depicts the UV scan from 200-700 nm for the five different solvent extracts of _T. ficus_ fruit flesh. The water extract contained two major peaks at wavelengths of 230 nm and 248 nm. Peaks appeared in the ethanol extract at 282 nm, 304 nm, and 660 nm. Numerous peaks appeared in the methanol extract, ranging from 230 nm to 660 nm. The acetone extract had three significant peaks at 226 nm, 409 nm, and 669 nm, while the propanol extract had two significant peaks at 272 nm and 306 nm.  
Plants' colors are due to a combination of different compounds, including caroten, a member of the terpenids class. The appearance of one or more peaks in the UV-VIS spectra between 200 and 400 nm indicates the presence of unsaturated groups and heteroatoms such as S, N, and O. These peaks could be phenolics, flavonoids, alkaloids, polysaccharides, or glycosides (Table S1). The absorption at 278 nm represents the typical peak for flavonoids and their derivatives. The peak at 295 nm is an indication of triterpenes. However, the use of UV-visible spectrophotometry in the analysis of complex media is hampered by the inherent difficulties in assigning absorption peaks to specific constituents. To enable proper extract characterization and constituent identification, UV VIS findings must be supplemented with another analytical technique, such as FTIR [44].