esa-sceva/satcom-chunk-collection huggingface dataset:
# RoseCliff Algorithm: Making Passwords Dynamic
## 5 Experimental Results and Discussion
### Potential Attacks
#### 5.1.2 Man-in-the-Middle (MitM) Attacks  
The RoseCliff algorithm goes beyond the conventional approach of merely transmitting a dynamically generated password after encryption over the network. This distinctive feature ensures that even if an eavesdropper is actively monitoring network traffic or gains unauthorized access to the password file, deciphering the original password remains an insurmountable challenge. One of the unique strengths of the RoseCliff algorithm lies in its utilization of a one-time number for randomization purposes. This implies that the dynamic password, which is derived from the amalgamation of the original password and this one-time number, undergoes constant and unpredictable changes. The dynamic nature of the password adds an extra layer of complexity, thus significantly heightening the difficulty level for any potential adversaries attempting to compromise the security of the system.  
In essence, the algorithm's design not only focuses on secure transmission but also incorporates a dynamic element that contributes to the continual transformation of the password. This continual change ensures that the authentication process remains robust against various forms of cyber threats, thus making it inherently more challenging for malicious actors to intercept, decipher, or exploit the cryptographic data being transmitted.
# High-Fidelity Fin-Actuator System Modeling and Aeroelastic Analysis Considering Friction Effect
## 3 Modeling of the System  
The electromechanical actuator installed on a missile provides sufficient torque to rotate the control fin against aerodynamic loads and allows the fin to follow guidance commands. The actuator must have appropriate stiffness to offset the disturbance to meet the stability and accuracy requirements. Different support stiffnesses will change the natural mode of the fin. Therefore, the normal mode cannot effectively represent the fin. To model the motion of the system, the substructure technique was introduced.
# Probabilistic Seismic Hazard Assessment for United Arab Emirates, Qatar and Bahrain
## 5 Selected Ground-Motion Attenuation Models
This attenuation model is applicable for earthquakes in the range Mw 3.0 to Mw 8.5, at R\({}_{\rm{JB}}\) distances up to 300 km, and for PGA and spectral periods from 0.01 to 10.0 s. It includes a regional variability in the source, path, and site effects as well. In favor of using this relationship, for instance, is the fact that NGA-West2 database [90] includes large Iranian events, as the 2003 M 6.6 Bam earthquake, and that several recent seismic hazard studies in low to moderate seismic areas, e.g., [91; 92; 93] have considered to use this ground motion prediction equation. The last selected ground-motion model used for the intermediate-depth seismic sources is that published by [PERSON] et al. [32] (Figure 4). This attenuation model was developed for interface/interplate, inslab/intraslab, and crustal earthquakes, using a strong-motion database from Japan, Iran, and Western United States. It has a valid spectral period range from 0 to 5.0 s, and a valid R\({}_{\rm{rup}}\) up to 300 km, within a magnitude range from Mw 5.0 to 8.3. This model also considers the style of faulting, as well as the soil characteristics. In the current assessment, the SCI site class (V\({}_{\rm{S,30}}\) > 600 m/s) has been chosen to be compatible with the previously selected attenuation models. The ground-motion attenuation models by [PERSON] et al. [81], [PERSON] et al. [82], and [PERSON] et al. [75] are used together as three alternatives, in a logic-tree framework, to model the ground-motion attenuation for those shallow-depth earthquakes in active crustal regions, sources from Sh-01 to Sh-10 in the [PERSON] et al. [2] zoning model, and all sources included in the model by [PERSON] et al. [65] (see Figure 2c). For the remaining sources (IN-01 and IN-02 in the model by [PERSON] et al. [2]), we implemented the model by [PERSON] et al. [32] for the intermediate-depth seismicity within the Zagros region and the Makran subduction zone (see Figure 5). The compatibility among the four selected ground-motion attenuation models was also considered for the computation of the seismic hazard values. All models are expressed in terms of the moment magnitude. Even though the models by [PERSON] et al. [81] and [PERSON] et al. [75] use the R\({}_{\rm{JB}}\) distance as a definition for the distance term, while the other two models use the R\({}_{\rm{rup}}\) distance, the used software code (R-CRISIS; [PERSON] et al. [94]) allows the application of different distance definitions. Thus, no distance conversions are required in the current assessment. Finally, all chosen attenuation models consider different styles for the faulting mechanisms. In this study, the different faulting mechanisms have been considered when using R-CRISIS [94], with the aid of the built-in rupture parameters by [PERSON] [95], to reflect the tectonic environment of each individual seismic source (see Table 3). Figure 5: Logic-tree design. Considered weights are shown below each individual branch (FPZ19: [82]; Zetal06: [32]; ZLLS18: [81]; ASK14: [75]).
## 3 Results and Discussion
### Discussion  
The study presented numerically the details of CO\({}_{2}\) distribution inside the chamber and the greenhouse. The performance of model was validated by comparing measurement and simulation results obtained from CFD model considering photosynthesis model. The model reproduced the distribution of measured CO\({}_{2}\) concentration in the middle of plant, decreasing due to CO\({}_{2}\) absorption by photosynthesis. As proven by [PERSON] et al. [9], the CFD model could simulate photosynthesis accurately.  
The chamber simulation results at the bottom of plant showed good agreement with the measurement results. The middle and upper left cross sections of the canopy showed that the simulation results were overestimated. This is likely due to the fact that the turbulence intensity near the fan located at the top of the chamber tends to be stronger than in other parts of the chamber, and thus the CO\({}_{2}\) concentration near the fan tends to be lower due to turbulent mixing.  
Otherwise, turbulence at the right side of the chamber is small (laminar), and because the simulation adopted a laminar model, the simulation achieved a better agreement with measurement for right side of the chamber.  
The greenhouse simulation results at the bottom of canopy showed good agreement with the measurement results. In position of 3.4 m from north wall showed simulation results were overestimated. The position of 3.4 m from the south wall showed simulation results that were slightly underestimated for heights of 1.2 and 2.4 m from the ground, while the results for heights of 1.8 and 4.2 m from the ground were overestimated.  
This may be because the temperature condition was neglected in the simulation. As reported by [PERSON] et al. [14] that unequal distribution of CO\({}_{2}\) depends on temperature and location. However, the simulation was still able to predict the detailed of CO\({}_{2}\) distribution considering CO\({}_{2}\) absorption at the plant by photosynthesis and the error values were still reasonable compared to previous study [9; 11].  
A few simulations of greenhouse were conducted to know the effect of various environmental conditions to the CO\({}_{2}\) distribution inside of the greenhouse. Cases with open and closed side vents showed that closed side vents have slightly more even of CO\({}_{2}\) concentration than those with open side vents inside the greenhouse. In contrast, the variability of CO\({}_{2}\) inside the plant, open (8.8%) and closed (8.7%) side vents, induced almost no significant improvement.  
Additionally, cases of a rainy- and sunny-day model showed that photosynthetically active radiation possibly compensated CO\({}_{2}\) absorption through photosynthesis to be lower at low light (rainy day) and higher at high light (sunny day). Nonetheless, we found that there was almost no significant difference in the variation of CO\({}_{2}\) concentration in the plant between rainy and sunny days.
# Multi-Band Polarization Imaging in a Harsh Sea Fog Environment
## 4 Experimental Results and Analysis
### Analysis of the Effect of Multi-Band on Polarization Imaging
The figure shows that the information entropy, mean gradient, and standard deviation of the polarization images at 670 nm are higher than those of the rest of the bands.
## 8 Conclusions  
Aiming at taking advantage of the existing onboard technologies for ground vehicle navigation in challenging environments, this paper has developed an integrated camera/IMU/GNSS system based on the EKF design. The estimated motion parameters from successive image frames were utilized to compensate the drifting IMU sensor biases and maintain the accurate stradown mechanization between the update of image data. For resolving the scale ambiguity problem of a monocular camera, GNSS differential technique provided an effective manner to extract the baseline magnitude of position change. Based on the EKF design, the proposed integrated system is able to provide 15-state high-bandwidth navigation solutions. Our implementation has been validated through a 5-minute long drive test which provided raw camera and IMU data as well as centimeter-accuracy truth reference. For a reasonable simulation of challenging GNSS environments, we gradually decreased the number of visible satellites with lower elevation angles over time. The experiment has provided results with high accuracies, a majority to the sub-degree level in the yaw angle and meter level in the horizontal plane. Furthermore, it was demonstrated that under the condition of sparse GNSS observations, the camera/IMU/GNSS integration potentially outperformed the tightly-coupled GNSS/IMU scheme which has yielded diverging navigation solutions. The camera/IMU/GNSS integration has also been tested with different image frame rates and the results revealed that the accuracy deteriorates with decreasing frame rates. Future work will further integrate GNSS measurements into the filtering architecture for better error growth control and automatically enable the loosely/tightly coupled GNSS/IMU integration in case relatively favorable GNSS conditions arise. Future work also includes a target objective of real-time functionality with revised computer vision algorithms on the GPU platform.
# Unsupervised Learning for Concept Detection in Medical Images: A Comparative Analysis
## 2 Methods
### Evaluation  
The previously described methods for representation learning were aimed towards addressing the domain of biomedical images. A proper validation of these features was made with the use of the data sets from the ImageCLEF 2017 caption challenge [27], specifically the portion corresponding to the concept detection task. In 2017, the focus of the ImageCLEF Caption challenge was on automated medical image understanding for quicker decision-making in clinical diagnoses. As the first sub-task of the caption prediction challenge, the goal of the concept detection task was to conceive a computer model to identify the individual concepts from medical images, from which full captions could be composed.  
This task was accompanied by a collection of images, originally seen in open access biomedical journals from the PubMed Central and encompassing a wide variety of modalities (radiology, biopsy, pathology, and general diagrams, to name a few). Images were formatted in JPEG, with resolutions varying from 400 to 800 pixels in either dimension. Each image was also accompanied by its respective caption (which was not used in this work), along with the extracted list of biomedical concepts. The concepts were written as concept unique identifiers (CUIs) from the Unified Medical Language System (UMLS ([[https://www.nlm.nih.gov/research/umls](https://www.nlm.nih.gov/research/umls)]([https://www.nlm.nih.gov/research/umls](https://www.nlm.nih.gov/research/umls)))). A few examples from the collection are shown in Figure 5, and small insights into the concepts present in these images may be obtained from Table 3. The full data set was divided into three parts: the _training set_ (164,614 images), the _validation set_ (10,000 images), and the _testing set_ (10,000 images). The testing set's ground truth was hidden during the challenge but was provided to the participants later on.  
Figure 4: Diagram of the adversarial autoencoder.  
Each of the set of features, learned from the approaches described in the previous section, were used to train simple classifiers for concept detection. In both cases, the same training and validation folds from the original data set were considered, after being mapped to their respective feature spaces. In addition, data points in the validation set with an empty list of concepts were discarded.  
These simple models were used to predict the concept list of each image by sole observation of their respective feature set. Therefore, the assessment of our representation learning methods is made based on the representation's descriptiveness without learning additional complex feature spaces, and so, on the effectiveness of capturing high-level features from latent codes alone.
## 3 Results
### GG vs. SC Cycle for Given Mission  
The primary aim of comparing GG cycle and SC cycle with the same mission requirements is to aid the mission designer in decision making when faced with the task of selecting a particular engine cycle for a mission.  
For GG cycles, all techniques using GLOW OF returned a minima between 52 and 112 bar \(p_{c}\) and an average \(F\) of 259 kN. The same techniques on SC, however, resulted in the minima at a relatively higher \(p_{c}\) of 98 to 102 bar with a resulting average increase in \(F\) to 272 kN. This higher \(p_{c}\) is still within the operating range for an SC cycle engine.  
Therefore, for this mission, a GG cycle would be more beneficial as the same GLOW can be achieved at a relatively lower \(p_{c}\) without a significant loss of \(F\). In addition, the additional design, manufacture and operational complexity of a SC cycle engine do not need to meet the requirements of this mission.
# Calculation of Nonlimit Active Earth Pressure against Rigid Retaining Wall Rotating about Base
## 4 Derivation of the Analytical Solution for Active Earth Pressure  
### Method of Determining Potential Slip Planes  
As shown in Figure 1, the retaining wall height is H, and the slip angle is \(\alpha_{\rm s}\).  
In practice, the rotation angle of walls in RB is very small, which makes it extremely unlikely that the horizontal displacement of the backfill will reach the critical displacement, resulting in most of the soil behind the wall remaining in a nonlimiting state [19]. However, the basic assumption based on [PERSON]'s earth pressure theory is that the soil within the wall height reaches a fully active state, that is, the shear strength of the soil behind the wall is fully developed. The concept of a quasi-slip cracking surface comes into play when the shear strength of the soil behind the wall is partially developed, assuming that a quasi-slip cracking surface appears behind the wall when the soil behind the wall has not reached a fully active state, and the angle between the quasi-slip cracking surface and the horizontal plane is the quasi-slip cracking angle.  
The studies [36; 37; 38] revealed that interface friction angle is less than the soil friction angle and has little effect on the slip angle. Moreover, tests [5; 7] show that the sliding surface essentially obeys [PERSON]'s theory.  
When the retaining wall rotates, the inclination of the wall back is negligible, which is only suitable for vertical wall back. Therefore, the slip plane angle in [PERSON]'s formula is introduced as a quasi-slip crack angle for simplicity of calculation:  
\[\alpha_{\rm s}=45^{\circ}+\frac{\varphi}{2} \tag{5}\]  
where \(\varphi\) is soil friction angle, and \(\alpha_{\rm s}\) is crack angle.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 2 Problem Modeling
### Interference Caculation  
EPFD is a method adopted by the International Telecommunication Union Radiocommunication Sector (ITU-R) to evaluate radio frequency interference. To avoid unacceptable radio interference from NGSO systems to GSO systems, Article 22 of the ITU Radio Regulations places corresponding restrictions on EPFD from satellite system to a GSO satellite system. In accordance with the clause, the EPFD between the GSO satellite system and the NGSO satellite system is presented as  
\[epfd=10 log_{10}(\sum_{i=1}^{N_{a}}10^{\frac{p_{i}}{10}}\times\frac{G_{t}(\Psi _{i})}{4\pi d_{i}^{2}}\times\frac{G_{r}(\varphi_{i})}{G_{r,max}}), \tag{3}\]  
where \(N_{a}\) denotes the number of transmitting stations of the NGSO satellite system, and \(P_{i}\) is the input power of the antenna of the \(i\)th transmitting station. \(G_{t}(\Psi_{i})\) is the antenna gain of the \(i\)-th transmitting station of the NGSO satellite system with an off-axis angle of \(\Psi_{i}\), and \(d_{i}\) denotes the distance between the \(i\)th transmitting station of the NGSO satellite system and the victim receiving station. \(G_{r}(\varphi_{i})\) denotes the antenna gain of the receiving station with the off-axis angle of \(\varphi_{i}\), and \(G_{r,max}\) is the maximum antenna gain of it.
## 2 Related Work
A study of the voice signals of patients with Parkinson's disease, in order to determine the ability to distinguish these people from healthy ones by their voices, has also been carried out [15]. The authors created a complex hybrid intelligent system that includes pre-processing features using clustering based on Gaussian models, principal component analysis methods, linear discriminant analysis, the least squares support vector method (LS-SVM), a probabilistic neural network (PNN), and common neural network regression (GRNN). A voice digital biomarker for identifying and quantifying symptoms of [PERSON]'s disease and determining a course of treatment has been described in [16]. The authors analyzed a database of PD patients and non-PD subjects containing voice recordings which were used to extract paralinguistic features, which served as inputs to machine learning models to predict PD severity. A descriptive correlation study comparing 20 subjects with PD and 20 healthy controls has been described in [17]. The subjects with PD completed the VHI-30 instrument and performed sustained phonation of different vowels in Spanish. The stage of the disease was evaluated using the Hoehn and Yahr scale. One article [18] has described a study on the correlation of the values of the selected acoustic parameters, with an assessment of the neurological state in patients with Parkinson's disease up to 3 h after taking medications that alleviate the symptoms of the disease. The analysis, including 5-s recordings of the vowel \"a\" and the syllable \"pa\", as well as text with different emotional shades in patients with Parkinson's disease and healthy people, has been described in [19]. Studies of the correlation between individual sound parameters of the voice and the severity of the condition of PD patients have also been carried out [20; 21; 22]. Another article [23] described the results of applying a statistical method based on the chi-squared distribution to confirm that 90% of PD patients have voice deviations. Many publications have focused on the selection of neural network architectures and machine learning methods for assessing the deviation of the voice of a patient with Parkinson's disease from the voice of a healthy person, such as [24; 25; 26; 27]. A distinctive feature of our study is the completeness of data collection from mobile phones, which can be used to classify the condition of patients and the availability of mobile phones. At present, 70% of PD patients have smartphones, and talking on the phone is the most commonly used feature. Therefore, the voice, hand movements with the phone, and the peculiarities of using the phone's sensors are sources of data that are always available and can help in monitoring the condition of patients with PD. In addition, the voice is an individual characteristic of a person. Using data on the sound of a voice, it is possible to identify a person and exclude their entry into the database of information that does not belong to a patient with PD.
## 4 Results and Discussion
### Scenario 3: Hydrogen Tracking  
The integration of the carbon capture process and electrolysis stage occurs through output tracking. [PERSON] et al. [48] described in detail the novel mixed-integer linear program electrolysis model that aims to efficiently convert renewable energy into hydrogen with a maximum capacity of 450 Nm\({}^{3}\)/h and 99.9% purity. The model enables us to deal with intermittent energy and electricity price as input while maintaining model constraints to yield an optimal number of electrolyzers and production schedule. The continuous variable hydrogen flowrate was calculated for an entire year, but a representative 24 h scenario was extracted from the results. The hydrogen-tracking scenario was executed assuming partial renewable load. By partial renewable, we are considering a base load from the grid of 220 MW being fed into the electrolyzer while utilizing any curtailed wind energy. In our study, we assumed a 20 MW load following scenario to represent the curtailed wind power as shown in Figure 12, where the yearly wind production for 20 MW capacity was obtained. Figure 13 demonstrates the results from the representative 24 h operation. The carbon capture model was able to track the hydrogen production accurately and fulfill the stoichiometric requirements for the succeeding methanol reactor.  
Figure 11: Reboiler duty (MW) stabilizing time.  
It was also observed that the desorber parameters were maintained within the desired limits as depicted in Figure 14, showing 10 h of the complete run. The output pressure and temperature were within the set limits, and the CO\({}_{2}\) mass fraction remained >98%, ensuring that the desired purity was delivered throughout the entire transient operation. By operating the carbon capture plant flexibly, we were able to improve their economics when retrofitted with power plants. Power outputs could be increased during peak electricity prices by reducing the CC operation and thus increasing revenues. On the other hand, when carbon prices go high or when the carbon credits are consumed, the CC plant could be turned up to capture more CO\({}_{2}\) and reduce emissions, which would translate into higher carbon costs.  
Figure 12: 20-MW wind energy, Heide (Germany) 2019. Generated from the model by [PERSON] and [PERSON] [49].  
Figure 13: Hydrogen production from the electrolyzer stage and the equivalent CO\({}_{2}\) capture flowrate.
## 3 Network Resource Awareness and Resource Status Prediction
### Construction of Resource Awareness Dataset
#### 3.2.2 Preprocessing of Dataset
Although normalization obscures features of data such as multi-peaks and shifts in resources, it preserves the relative positioning of the data. In other words, the model can still capture trends in data changes over time, including linear growth or cyclical fluctuations. More importantly, there exist differences in the dimensions and orders of magnitude among bandwidth, computing, and storage resources. Furthermore, the maximum available resources vary between satellites. Adopting a normalization method therefore facilitates the rapid processing of multi-dimensional data by the model, without the necessity of extensive modifications to training parameters, thereby enhancing the model's generalization capability.