esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analyses
### Comparative Experiments with Tibia-Yolo
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Loss Function** & \(mAP_{0.50.95}\) **(\%)** \\ \hline YOLOv8s + ParNet + EMA + CARAFE (BaseLine) & 90.1 \\ BaseLine + CIoU & 90.1 \\ BaseLine + DIoU & 90 \\ BaseLine + EIoU & 90.5 \\ BaseLine + GIoU & 90.4 \\ BaseLine + WIoU & 90.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the use of different loss functions. of false detections and omissions. From the current point of view, Tibia-YOLO had the best performance in the test on the Tibia dataset. Finally, our model was tested on two public datasets. The two public datasets were the VOC2012 target detection dataset and the COCO2016 target detection dataset. In this test, we focused on YOLOv8-s. The performance improvements of the baseline and improved TB-YOLO included improvements in the \(mAP_{0.5}\) and \(mAP_{0.5;0.95}\) parameters. As in Figure 8, Table 7 shows the performance of TB-YOLO on three datasets, as well as a comparison with the basic YOLOv8s as the baseline. As can be seen in the table, the performance of our model on the public COCO2016 and VOC2012 datasets improved, but the improvements in the \(mAP_{0.5}\) and \(mAP_{0.5;0.95}\) parameters were relatively small, and the test results on the Tibia dataset improved. Notably, \(mAP_{0.5;0.95}\) increased by 6.1%. We analyzed this phenomenon. This might have occurred because our dataset was a single-category detection task that was relatively simple and more suitable for the TB-YOLO network. In the tests of the COCO2016 and VOC2017 datasets, although there was a certain improvement there were too many categories involved. As a result, the improvement was not as large as the improvement on the Tibia dataset. As can be seen in Figure 8, the test results of the improved Tibia-YOLO model on the three datasets improved in comparison with those of YOLOv8-s (baseline). In a test on the COCO2016 dataset, it could be seen that Tibia-YOLO could recognize people in the distance and had good small-target detection capabilities. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & \(mAP_{0.5;0.95}\) **(\%)** \\ \hline YOLOX & 18.6 \\ Fast R-CNN & 31.1 \\ YOLOv7 & 48.7 \\ YOLOv4-tiny & 52.3 \\ Faster R-CNN & 61.9 \\ RetinaNet & 71.0 \\ SSD & 71.5 \\ YOLOv5 & 82.1 \\ YOLOv8-s & 84.7 \\
**Tibia-YOLO** & **90.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of different network models on the Tibia dataset. Figure 7: The results of three groups of test data of broiler tibia with different models used for a comparison.
## 4 Experimental Results and Security Analysis
### Security Analysis
#### 4.2.3 Guess-and-Determine Attack  
According to [PERSON]'s principle [33], cryptanalysis should assume that the attacker knows the design and how the cipher works under attack. In addition, for stream ciphers, cryptanalysis usually assumes that the keystreams are known to the attacker. The purpose of the attack is to find out the key or the internal state. In guess-and-determine attacks, the attacker has to first guess the internal values and infers other internal state values [33].  
In our proposed improved CML-based keystream generator, the attackers need to guess the initial state variables, i.e., the parameters of the chaotic maps and the counter value. All of the system parameters and initial values are produced by the input 128-bit key. The attacker needs to deduce the input key to determine the system parameters and initial values. The guess-and-determine attack is as difficult as the brute-force attack because the key length is 128-bit. Therefore, the proposed improved CML has the ability to resist the guess-and-determine attack.
# Simulative Investigation of the Radar Cross Section of Wind Turbines
## 2 Method
### Wind-Turbine Parameters Influencing the RCS  
The parameters influencing a wind turbine's RCS can be classified by material:  
* Electromagnetic properties of the blade's construction material: fiber-reinforced plastic (FRP) is an isolator, while carbon-reinforced plastic (CRP) is a conductor [33; 34].
* Surface treatment, e.g., lacquer/varnish to protect against UV radiation, rain, or moss growth.  
It can be classifed by constructive design parameters:  
* Tower/nacelle height.
* Rotor diameter, blade length.
* Blade wall thickness.
* Aerodynamic shape.
* Position and size of lightning protection equipment.
* Inner structure: hollows, trimming weight location, and spar size and location.  
Also, it can be classied by parameters of operation:  
* Rotational speed (turns per minute): This is an important parameter for all Doppler radars, as the movement causes a Doppler shift in the signal, which can be misinterpreted (e.g., Reference [6]).
* Blade pitch angle: the blade's angle relative to the airflow (\"angle of attack\" in context of aircraft).
* Wind turbine azimuth angle: the \"look direction\" angle, i.e., the angle facing the wind.  
The rotor's elevation angle is usually fixed at about 2\({}^{\circ}\) upwards.  
Additionally, considering weather radars, polarimetric measurements are used to infer the shape of rain drops [7]; polarization studies should therefore be included.
# Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation
## 4 Analysis  
To analyze the performance of the LEO float solution to enhance PPP-AR, observation data of 258 stations in the MGEX observation network from January 1 to 3, 2022 were selected, as shown in Figure 2. The experiments of single-system and four-system combined PPP-AR enhanced using LEO ambiguity-float solution were carried out for these stations and compared with LEO-enhanced PPP. The detailed data processing strategy is shown in Table 1. In Figure 2, the red dots indicate that we conducted LEO-enhanced GPS PPP and PPP-AR experiments on this site, the green dots indicate that we conducted LEO-enhanced Galileo PPP and PPP-AR experiments on this site, the blue dots indicate that we conducted LEO-enhanced GLONASS PPP experiments on this site, and the yellow dots indicate that we conducted LEO-enhanced BDS-3 PPP experiments on the site. The presence of four colors in one dot indicates that we carried out LEO-enhanced four-system combination PPP and PPP-AR experiments on the site. In all experiments, the reference coordinates of the experimental results were the coordinate values in the SNX weekly solution file provided by the IGS. Supplementary information, such as the precise satellite orbit and the clocks (post-processed products), were provided by the IGS data center of the German Research Centre for Geosciences.  
Figure 2: The distribution of MGEX stations: GPS (red); GLONASS (blue); BDS-3 (yellow); and Galileo (green).
## 2 Mathematical Model and Numerical Method  
### Governing Equations and Numerical Methods for Fluid  
The governing equations for the two-dimensional incompressible flows with constant density and viscosity can be written as the following non-dimensional form:  
\[\
abla\cdot\mathbf{u}=0, \tag{1}\]  
\[\frac{\partial\mathbf{u}}{\partial t}+(\mathbf{u}\cdot\
abla)\mathbf{u}+\
abla p-\frac{1}{Re }\
abla^{2}\mathbf{u}=0, \tag{2}\]  
where \(\
abla\) represents the gradient operator, \(\mathbf{u}\) the velocity vector, \(p\) the pressure, \(t\) the physical time, \(Re=UD/v\) the Reynolds number with \(v\) being the kinematic viscosity of the fluid, and \(U\) the reference velocity, which is set to be the freestream velocity \(U_{\infty}\).  
The governing equations are discretized by the Galerkin finite element approach [26] on an unstructured collocated mesh. All variables are defined at the cell vortex. A dual-time-stepping scheme [27] is employed for temporal discretization. The method of artificial compressibility is employed to reduce the disparity in sound and convective waves. In the Galerkin finite element approach, the approximation of the convective term is equivalent to the central difference and an artificial dissipation operator [27] is adopted to prevent odd-even decoupling. For more details, one can refer to [23].
# An Overview of Bioactive Flavonoids from _Citrus_ Fruits
## 7 Industrial Application of _Citrus_ Flavonoids  
Flavonoids extracted from _Citrus_ fruits are already used as natural antioxidants in the following:  
* Pharmaceutical and nutraceutical supplements:  
Flavanones and polymethoxylated flavones extracted from Citrus fruits are mainly used as natural antioxidants in the formulation of pharmaceutical products. They are used in many vitamin complexes and as the active ingredient of certain drugs (circulatory system disease) [6; 90; 91]. Citrus byproduct processing could be a significant source of flavonoids due to the large volume of peel produced, in addition to being a source of D-limonene-rich essential oil. Fruit residues from _C. aurantium_, which are typically discarded as waste, could be used to make valuable nutraceuticals [92].  
* Agri-food industry:  
In the food industry, naringin is used to flavor beverages, sweets and baked goods, because of its typical bitter taste [35]. Moreover, due to their antioxidant activity, hesperidin and narirutin have protective effects against the peroxidation of lipids either in sunflower oil stored for 24 days at high temperature or in biscuits [33]. Citrus peel was also used to produce hesperidin and neohesperidin for the synthesis of dihydrohalcones. These compounds are used in the food industry as sweeteners and flavor enhancers [93]. In addition, anthocyanins derived from flavonoids are used as coloring agents (E163) in confectionery, dairy products and desserts or to compensate for fruit discoloration induced by certain processing steps [94].  
* Other industrial applications as a corrosion inhibitor:  
Several studies were conducted on the effect of flavonoids on carbon steel and copper [94; 95]. [PERSON] et al. 2017 [95] investigated the inhibition of carbon steel corrosion by neohesperidin and naringin in the presence of hydrochloric acid. In the paper of [PERSON], some flavonoid compounds, such as apigenin, luteolin and quercetin, were used to study the corrosion behavior of copper in nitric acid [96]. The authors reported that the inhibition of copper corrosion increases as the concentration of flavonoids increases.
# Transformer-Based Hybrid Forecasting Model for Multivariate Renewable Energy
## 1 Introduction
#### Objectives  
This study proposes a hybrid system that combines linear statistical and Deep Learning models for renewable energy forecasting. In the context of time series forecasting, hybrid systems combine the linear and nonlinear components in various ways, and have already been implemented by multiple authors [18; 19; 20; 21; 22]. The proposal combines linear and nonlinear models using the residual series, calculated from the difference between the time series and its forecast. The proposed hybrid system joins the strengths of the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model [23] with the state-of-the-art ML model, Transformer neural network [24]. So, the proposed system, named H-Transformer, is composed of three phases: linear forecasting using the SARIMA model, nonlinear forecasting of the residual series using the Transformer, and a combination of the linear and nonlinear forecasts using a simple sum. The H-Transformer employs the exogenous time series and Bayesian Optimization for ML model parameter selection in an innovative manner, aiming to improve its accuracy. In addition, the proposed hybrid system models the residual series with a brand-new approach, aiming to improve performance in the nonlinear phase.  
The investigation also analyzes the employment of deep learning methods and other Machine Learning architectures for nonlinear component modeling in the hybrid system framework. The performance of the H-Transformer is evaluated using two solar irradiance data sets and three wind speed time series. The proposed system is compared with traditional statistical methods, state-of-the-art ML models, and hybrid systems that combine the above-mentioned techniques. The performance is assessed by three metrics: the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE) and the Coefficient of Variation of the RMSE (CV(RMSE)).  
The proposed H-Transformer presents the following advantages:  
* Deals with the risk of inappropriate model selection by using the combination strategy;
* Provides an unprecedented combination of SARIMA with the Transformer neural network;
* Proposes a new method for residual modeling using a mapping between time series and error series;
* Increases the accuracy of residual nonlinear modeling using exogenous variables and Bayesian optimization.  
This work is organized as such: Section 2 contains a review of related works and how this work contributes to them. Section 3 introduces and explains the proposed model and its pipeline. Section 4 shows the experimentation process and a comparison between the results of other hybrid systems and single ML models with the H-Transformer. Finally, Section 5 contains the conclusion of the work and future research plans.
# Path Segmentation-Based Hybrid Caching in Information-Centric Networks
## 2 Related Work  
Cache placement strategy is the subject of much research. [PERSON] et al. [12] proposed a selection algorithm based on node centrality, in which the content is cached on the node with the highest betweenness centrality. In [13], the author proposed an auction-based caching, which allows the nodes on the data return path to coordinate caching decisions, with minimal overhead, through auctioning their \"right to cache\". [PERSON] et al. [14] proposed an age-based cooperative caching that aims at spreading popular content to the network edge based on lightweight collaboration mechanisms. In [15], the author proposed a caching strategy that considers nodes' social attributes and selects nodes with high social attribute scores to cache contents.  
The above approaches may lead to a situation where the content is too concentrated in a few nodes, which is not conducive to the full utilization of the network's overall storage resources. One solution to this problem is to select content for migration. [PERSON] et al. [16] proposed that the cache node can coordinate with neighbor nodes and migrate part of the content to the appropriate node in case of insufficient cache space. In [17], a cooperative caching policy is proposed, in which the on-path nodes optimally cache the requested content and are supported by a central off-path cache node for an additional level of caching. However, there is an additional communication overhead when choosing the migration node, and for Ref. [17], how to allocate the off-path cache node in a larger network is also a problem.  
More ideally, Ref. [18] introduces a cache manager to get a global view of all network states for ideal cache management. However, in the case of a high dynamic ICN network, it is not feasible to use this approach for obtaining the status accurately and timely. [PERSON] et al. [19] proposed a cooperative caching strategy called cooperative in-network caching (CIC). In CIC, content is divided into different chunks and is cached at more than one node, and CIC adds a cooperative router table and a cooperative content store to keep track records of the location and identity of all cached chunks. However, because the ICN cache networks are highly dynamic and the cached content is updated, this type of collaboration may incur additional communication overhead. A summary of the presented caching strategies in terms of their contributions is expressed in Table 1.
## 4 Understanding Centralised Identity Systems
### Inner Workings of Centralised Authentication  
All centralised identity management systems have several key components, such as identity providers (IdPs), which store and manage identity data; service providers (SPs), the applications or services that users interact with, acting as a bridge between the user and the IdP; an Authentication Server, which handles authentication requests (approving or denying access); and the single sign-on (SSO) feature, which allows users to access multiple applications with a single authentication [14]. The process of authentication in a centralised architecture is straightforward and can be understood as follows (see Figure 3):  
1. The user, through the SP (application or service), submits credentials to the IdP.
2. The IdP validates the credentials and generates an authentication token.
3. The authentication token, having approval and scope of access, is sent to the SP, which grants access to the resources.
4. The user can access the services without the need to re-authenticate, as the token serves as proof of identity for a specified duration [12].  
The CIMS provides consistency and simplicity in its approach by centralising user administration, enforcing policies across all SPs, and minimising the management cost associated with administering distributed systems.  
Figure 2: Centralised identity management systems.
# CFD Analysis of Subcooled Flow Boiling in 4 x 4 Rod Bundle
## 5 Results
### Model Validation  
Various initial conditions were used for the numerical analysis of the six cases, with inlet fluid velocity of approximately 0.260417 or 0.322917 m/s, heat flux of 148-200 kw/m\({}^{2}\), and inlet subcooled temperature of 20.2-25.2 \({}^{\circ}\)C. The graphs on the cross-section near the outlet from the rod can confirm the validity of the model in Figures 7-12. In particular, this model predicts the reasonable value on center of rod bundles in a wide flow path and the high inlet temperature or the large heat flux; the sub-cooled temperature is 20.2 \({}^{\circ}\)C or the heat flux is 200 kw/m\({}^{2}\). All the volume fractions and IACs in Figures 7-12 tend to be similar to the experimental values, which indicates that this study will be useful for understanding the vapor behavior of the entire steady-state flow in the simulations for predicting the boiling phenomenon. The results shown in Figures 14 and 15 are expected to aid in predicting the correlation between liquid and volume fraction, which is the most important factor considered in bubbly flow CFD research. These CFD results generally reproduced the trend of experimental values. However, this simulation has the same kind of error in all cases. Thus, we must describe the error. Numerical analysis results in higher errors as the mass flux is higher and the heat flux and temperature are lower. Moreover, it can be confirmed that the VF is smaller than the experimental value. The analysis of this cause from the model aspect shows that is due to the use of a constant value for each coefficient of the interface momentum transfer force models in this paper  
Figure 15: Distribution contour in Case 5: (**a**) velocity of liquid, (**b**) temperature of liquid, (**c**) volume fraction, and (**d**) velocity of vapor on the vertical plane at the wall edge.  
In each model, the coefficient was inputted as a certain and reasonable constant with reference to the papers, but it is not a value derived from the exact same experiment and the same working fluid, so an error occurs. In the interfacial momentum transfer force model that predicts the movement of bubbles, the most dominant force is the drag force, and then the lift force. When looking at the horizontal cross section of a vertical pipe, the lift force, which determines the location of the bubble, is one of the important factors in determining how well the bubble moves after generation and departure from the heating surface [38; 39; 40].
# Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm
## 2 Preliminaries and Notation
### MTTP for Space Robotics
#### 2.2.1 Objective Functions  
A. Minimum Maneuver Time  
In the MTTP for industrial robotics, minimum maneuver time [22; 23] has been considered to improve the work efficiency of robots, which is also a factor for space robotics. Another key factor is to reduce the accident risk in outer space. In some cases, an urgent task should be performed in ISS, while the functional completeness of ISS should be guaranteed in advance. Component maintenance, assembly, and refueling of ISS should be finished in the shortest time, contributing to the functional completeness of ISS before performing the urgent task. Thus, urgent tasks are performed in time and the accident risk is reduced. One of the developed objectives is that the space robot is required to execute a series of tasks such as refueling with minimum maneuver time. Therefore, the end effector is required to visit a set of predefined waypoints with minimum time, which is expressed as:  
\[\min F_{1}: = \sum_{j=1}^{N-1}T_{j} \tag{6}\]In Equation (6), \(N\) denotes the number of waypoints and \(T_{j}\) denotes the time period during which the end effector moves along the \(j\)th subpath, i.e., from the \(j\)th waypoint to the \((j+1)\)th waypoint. \(T_{j}\) is expressed as:  
\[T_{j}=\underset{i}{max}\left(t_{jf}^{i}-t_{js}^{i}\right) \tag{7}\]  
In Equation (7), \(i\,(i=1,...,n)\) denotes the \(i\)th joint. \(t_{js}^{i}\) and \(t_{jf}^{i}\) denote the initial and the final moving time instants of the \(i\)th joint, respectively, corresponding with the \(j\)th subpath. The \(n\) joints move for different time periods due to unequal angular distances and unequal angular velocities. The maximum of \(\left(t_{jf}^{i}-t_{js}^{i}\right)\,(i=1,...,n)\) is defined as the time period employed by the space robot for the \(j\)th subpath.