esa-sceva/satcom-chunk-collection huggingface dataset:
# Albatross-Like Utilization of Wind Gradient for Unpowered Flight of Fixed-Wing Aircraft
## 3 Method for Sustainable Climbing  
### Equilibrium Points and Sustainable Climbing  
An equilibrium point is an important concept of a nonlinear system \(\dot{x}=f(x)\). A point in the state space is an equilibrium point if, when the state of the system begins at the point, it remain there at all future times. Therefore, at an equilibrium point, \(\dot{x}\equiv f(x)\equiv 0\) must be satisfied. In the dynamic soaring system represented by Equations (4)-(6), the equilibrium points are defined by the constant velocities that can be maintained by the aircraft, namely, \(\dot{v}_{x}\equiv 0\), \(\dot{v}_{y}\equiv 0\), and \(\dot{v}_{z}\equiv 0\). For a constant bank angle \(\varphi\), [PERSON] et al. [39] proved the calculability of the equilibrium points of a dynamic soaring system. The equilibrium also indicates the force balance during flight through a gradient wind. If the value of \(v_{z}\) at an equilibrium point is negative, the aircraft would continue to dive at the corresponding velocity. This type of equilibrium point is thus referred to as a descending equilibrium point. Conversely, if the value of \(v_{z}\) at an equilibrium point is positive, the aircraft would continue climbing. This type of equilibrium point is referred to as an ascending equilibrium point. The existence of a descending equilibrium point has been proved to be trivial, while an ascending one cannot be guaranteed to exist. Should an ascending equilibrium point occur, the aircraft could utilize it for sustainable climbing. [PERSON] et al. [39] proved that the sufficient and necessary condition for the existence of an ascending equilibrium point was the satisfaction of the following inequality:  
\[\max_{0\leq v_{z}\leq v_{z_{\text{max}}}}f_{2}(v_{z})\geq\frac{1}{\sqrt{\big{(} L/D\big{)}^{2}+1}} \tag{12}\]  
where  
\[f_{2}(v_{z})\triangleq v_{z}\frac{\eta\sqrt{R_{gam}\Big{(}\sqrt{R_{gam}\Big{(} \sqrt{R_{gam}\mu^{2}+R_{gam}\mu^{2}{v_{z}}^{2}}-{v_{z}}^{2}\Big{)}}-R_{gam}}}{ \big{(}R_{gam}+R_{gam}\mu^{2}{v_{z}}^{2}\big{)}^{\frac{3}{4}}} \tag{13}\]  
\[v_{z\text{max}}\triangleq\sqrt{\frac{R_{gam}\Big{(}\eta^{2}+\sqrt{\eta^{4}+4} \Big{)}}{2}} \tag{14}\]  
where \(R_{gam}\) and \(\eta\) are abbreviated parameters defined as  
\[R_{gam}\triangleq\frac{g}{\sqrt{P_{Lm}{}^{2}+P_{Dm}{}^{2}}}\text{ and} \tag{15}\]\[\eta\triangleq\frac{G_{w}}{\sqrt{g\sqrt{P_{Lm}{}^{2}+P_{Dm}{}^{2}}}} \tag{16}\]  
The above equations constitute the initial criterion for sustainable climbing. It is, however, difficult to apply this criterion because it requires the determination of the maximum of \(f_{2}(v_{z})\) at every time. In Section 3.2, we propose a simplified criterion based on Equation (12). The derivation of the proposed criterion is presented in Section 3.3.
# Accelerating Image Classification using Feature Map Similarity in Convolutional Neural Networks
## 2 Cached CNN Structure  
In this section, we propose a cached CNN structure. Because a CNN consists of a large number of convolution layers, a large amount of time is required to classify input images and learn weights. However, assuming that the CNNs used in the actual field receive similar images consecutively, the input image can be classified by comparison with the previously input image, thus reducing classification time. Each convolution layer in a CNN generates feature maps from the input image, which represents the intensity of each feature. Input images with similar intensities can be classified in the same class. Therefore, we classify the input image by comparing its feature maps with the feature maps of the previously input images. To achieve this, feature vectors are extracted from feature maps generated from each convolution layer. Figure 1 shows this structure, which can be used to classify input images using a pretrained CNN model. It caches the feature maps of the images classified by the CNN, and classifies the newly input images by comparing them with the cached feature maps. This can be done at the convolution layer stage. If the classification is completed based on the similarity with the cached feature maps, the remaining CNN operations need not be performed. The steps of caching feature maps and image classification using them are as follows:  
1. The input image is classified into its class by the pretrained CNN.
2. Feature vectors are extracted from the feature maps generated at each convolution layer, and they are written into the cache with a class label.
3. A new image is input for classification, and the CNN operation is begun. After the convolution layer operation is completed, a feature vector is extracted from the feature maps and compared to the feature vectors of the cached feature maps.
4. The class label of the most similar feature vector is output, and the remaining convolution layer operation is terminated.
# A Highly Integrated Millimeter-Wave Circularly Polarized Wide-Angle Scanning Antenna Unit
## 2 Design of a Circularly Polarized Wide-Angle Scanning Antenna Unit
### Analysis of Antenna Unit Performance  
The antenna unit structure undergoes modeling in the three-dimensional electromagnetic simulation software ANSYS Electronics 2020 R1, where full-wave simulations assess both individual components and the overall antenna configuration. Initially, simulations focus on the electrical performance of the filter and matching load modules. Figure 6 presents the simulated electrical characteristics of the filter, demonstrating a voltage standing wave ratio (VSWR) below 1.11 within \(\pm\)300 MHz around the center frequency, with an insertion loss of less than 0.57 dB. Beyond this range, up to \(\pm\)500 MHz around the center frequency, performance shows a slight decline, with a maximum VSWR of 1.48 and an insertion loss below 0.8 dB.  
Figure 4: Model of the matching load structure.  
Figure 5: Model of the antenna unit structure from different perspectives: (**a**) Perspective; (**b**) Front view; (**c**) Side view.  
By optimizing the coupling patch and the shape of the buried resistor in the matching load, a design achieving a VSWR of less than 1.25 across the entire bandwidth is achieved. Similar to the filter, the entire matching load is integrated into the SIW, eliminating the necessity for separate load devices. Consequently, the antenna's volume and weight are reduced. Figure 7 illustrates the VSWR of the matching load.  
Following the integration of all antenna components to form the complete antenna unit model, electromagnetic performance simulations are performed. The active VSWR performance of the antenna unit is depicted in the Figure 8a,b for angles \(\theta=0^{\circ}\), \(20^{\circ}\), \(40^{\circ}\), and \(60^{\circ}\), with \(\varphi\) values of \(0^{\circ}\) and \(90^{\circ}\).  
Figure 6: Electrical performance of the filter: (**a**) VSWR; (**b**) insertion loss.  
Figure 7: The VSWR of the matching load.
# A Designed Calibration Approach for the Measurement -While-Drilling Instrument
## 4 The Experimental Verification
### Experimental Process and Result Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Known Attitude**} & \multicolumn{3}{c}{**Solved from Raw**} & \multicolumn{2}{c}{**After Calibration**} \\ \multicolumn{2}{c}{**Angle (Unit: \({}^{\circ}\))**} & \multicolumn{3}{c}{**The Raw Reading from Sensors**} & \multicolumn{3}{c}{**Reading (Unit: \({}^{\circ}\))**} & \multicolumn{3}{c}{**(Unit: \({}^{\circ}\))**} \\ \hline
**Azimuth** & **Pitch** & \multicolumn{3}{c}{**Magnetometer**} & \multicolumn{3}{c}{**Accelerometer**} & **Azimuth** & **Pitch** & **Azimuth** & **Pitch** \\ \hline \(\psi\) & \(\theta\) & \(\
u_{mx}^{s}\) & \(\
u_{my}^{s}\) & \(\
u_{mx}^{s}\) & \(\
u_{ax}^{s}\) & \(\
u_{ay}^{s}\) & \(\
u_{ax}^{s}\) & \(\psi\) & \(\theta\) & \(\psi\) & \(\theta\) \\ \hline
1.5 & 0 & 26.98 & 6.055 & 40.3675 & \(-\)0.0311 & 0.091 & 0.9961 & 355.2305 & 1.7788 & 1.7260 & \(-\)0.1345 \\
60 & 0 & 14.0425 & \(-\)16.935 & 43.0425 & \(-\)0.0316 & 0.0928 & 0.9956 & 53.6498 & 1.8115 & 60.2163 & \(-\)0.1011 \\
120 & 0 & \(-\)12.285 & \(-\)17.525 & 43.6325 & \(-\)0.0319 & 0.0944 & 0.9957 & 116.9278 & 1.8256 & 119.9135 & \(-\)0.0863 \\
178 & 0 & \(-\)25.735 & 5.2 & 41.55 & \(-\)0.03119 & 0.0937 & 0.9954 & 183.0086 & 1.7866 & 177.5677 & \(-\)0.1256 \\
302 & 0 & 13.8625 & 28.645 & 38.37 & \(-\)0.0308 & 0.0926 & 0.9955 & 301.1897 & 1.7617 & 302.1324 & \(-\)0.1510 \\ \hline \multicolumn{2}{c}{The maximum error (Max[ \(\mid\)\(Attitude_{Output}-Attitude_{Known}\mid\))]} & \multicolumn{3}{c}{6.35} & 1.8256 & 0.4323 & 0.1510 \\ \hline \multicolumn{2}{c}{The mean of attitude deviation} & \multicolumn{3}{c}{\(-\)2.2987} & 1.7928 & 0.0112 & \(-\)0.1197 \\ \hline \multicolumn{2}{c}{The Standard deviation of attitude deviation} & \multicolumn{3}{c}{4.7009} & 0.0257 & 0.2780 & 0.0259 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Test verification of the calibration strategy. Figure 11: The 2D contrast maps of the trajectory deviations. (**a**) Map of the upper-lower deviation of the three sets of trajectory data. (**b**) Map of the left-right deviations of the three sets of trajectory data. We found that the tri-axial non-magnetic turntable is not necessary from the whole experiment. But if we do not use this auxiliary equipment, the data collection steps for the raw datasets \(D_{k}\) and the dataset \(D_{a-x}\) need to be conducted separately. Meanwhile, a special bracket is required to help the MWD instrument rotating around the \(x_{b}\) axis of the _car_ frame, to ensure the reliability of the dataset \(D_{a-x}\).
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 5 Challenges and Future Research Directions
### Future Research Directions  
To overcome these challenges, future research must focus on making Generative AI-based IDS more practical, efficient, and adaptable for real-world IoV environments. One important direction is the development of lightweight, resource-efficient generative models that can be deployed at the edge without compromising detection accuracy. Techniques such as model compression, quantization, and knowledge distillation can significantly reduce the computational load, making it feasible to run IDS directly on vehicle control units or roadside infrastructure.  
In parallel, privacy-preserving ML methods should be adopted to address data confidentiality concerns. Federated learning, for example, allows vehicles to collaboratively train models without sharing raw data, thereby protecting sensitive information while still benefiting from collective learning. Incorporating mechanisms like differential privacy or encrypted model updates can further strengthen security and trust in data handling.  
Improving the quality and diversity of training data is another crucial area. Researchers should invest in creating large-scale, realistic datasets that include a variety of driving conditions, traffic densities, and attacker behaviors. These datasets should reflect the complexity of real-world scenarios to improve model generalizability and robustness. Furthermore, integrating data from multiple modalities--such as LiDAR, cameras, GPS, and V2X communication--can help enhance the system's ability to detect context-specific threats and reduce false positives.  
The development of standardized evaluation frameworks and real-world testbeds is also essential. Conducting long-term field trials in diverse vehicular settings will help validate IDS performance and reliability under realistic operational conditions. These platforms can serve as benchmarks for comparing different approaches and guiding future improvements.  
Lastly, future IDS should be designed with adaptability in mind. By incorporating continual learning, meta-learning, and online training techniques, systems can dynamically evolve in response to new threat patterns and changing network conditions. Additionally, the integration of technologies like blockchain for secure logging and software-defined networking (SDN) for flexible traffic management can further enhance the scalability, transparency, and effectiveness of IDS in complex IoV infrastructures.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 2 System Architecture
### System Design  
The system design focuses on the following key aspects:  
1. Link budget analysis: The link budget is analyzed to determine the maximum range and data throughput achievable under various conditions, including altitude, speed, and weather. This analysis informs the selection of communication technologies and antenna configurations.  
Figure 1: Communication system for UAM.  
Signal handover strategy: An effective signal handover strategy is developed to ensure seamless communication continuity as UAM vehicles transition between base-station coverage areas. The entropy method is used to evaluate signal parameters and determine the optimal handover point.
## 3 Security Aspects Regarding Wireless Body Area Framework
### Special Consideration
#### 3.4.3 Physical Layer Attacks  
Utilizing HBC for secure, short-range communication enhances security at the physical layer but is limited in range and dependent on the physical proximity of devices. In securing a WBAN system, particularly for healthcare applications, a multi-layered, sophisticated approach is required. This approach must carefully balance the need for strong encryption to protect sensitive health data with practical considerations of system performance, energy consumption, and user experience. The integration of HBC adds an innovative layer of security, particularly useful for key exchange and device authentication within the network. Selecting specific encryption methods and security strategies must align with the WBAN's operational requirements, considering the nature of the data, the computational capabilities of the network devices, and the overall system architecture. This balance is crucial in ensuring that the WBAN not only remains secure but also functional and user-friendly. A basic overview of added security, benefits, and considerations across the WBAN protocol is given in Table 1.  
The integration of authentication mechanisms and other security measures discussed earlier can effectively address security threats in WBANs, as outlined in [32]. These include service interruption, data interception, and tampering, as well as user, hardware, and software attacks. To further enhance security, intrusion detection systems can be deployed. These systems swiftly detect and respond to anomalies, preventing service disruptions, unauthorized access, and data breaches such as DoS attacks or tampering. By continuously monitoring network activity, intrusion detection systems help preemptively address vulnerabilities, ensuring the integrity and confidentiality of WBAN data.
# Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory
## 1 Introduction
The proposed CDS can take some actions such as changing the data rate so that the bit error rate (BER) is under the predefined threshold. In [20], a CDS with a simple executive was presented. The simple executive cannot predict the outcome of the actions before applying actions on the environment. Besides, the simple executive cannot control the modeling  
Figure 1: Block diagram of cognitive dynamic system (CDS). configuration of the perceptor or predict the outcome of actions using a virtual environment. In [23], a CDS is presented for NGNLE applications using advanced executive that can predict the outcome of the actions. However, the perceptor in [20; 23] can only extract the environment is model without memory. In [24], a CDS was presented for real-time health situation understanding for smart e-Health home applications. Reference [24] uses a perceptor that can extract the model using the adaptive decision-making tree. However, in [24], the executive cannot predict the outcome of actions using virtual actions before applying them on a NGNLE. In this paper, a general-purpose algorithm for the CDS for a CDM system in a NGNLE with finite memory is presented. The proposed CDS uses an advanced executive that can predict the outcome of multiple actions before applying an action to the environment. In addition, the advanced executive can change the modeling configuration of the perceptor through internal commands. Also, the perceptor can extract the model of a NGNLE with finite memory. In addition, a case study of new CDS and an algorithm are presented for a long-haul fiber-optic link as example of NGNLE. It is demonstrated that this new design can provide ~43% data-rate efficiency enhancement and 7 dB Q-factor improvement. This CDS can replace the typical soft decision forward error correction (SD-FEC) in a fiber optic link and increase the data rate efficiency. To ensure that we have a reliable communications link, the SD-FEC uses 20% overhead (OH) in the optical communications system [25]. However, the typical function of the CDS is to keep the CDM error under the system threshold. A comparison between the proposed method and some related works [20; 21; 23] for our long-haul fiber optic communications case study is given in Table 1.  
The paper is organized as follows. In Section 2, the NGNLE with finite memory is discussed. In Section 3, the architectural structure of CDS and the proposed algorithms CDM for a general NGNLE are presented. In Section 4, we discuss the simulation results for the case study of a long-haul fiber-optic orthogonal frequency division multiplexing (OFDM) link. Finally, in Section 5, we summarize the main contributions of our work.
# Mechanical Behavior of Sand Mixed with Rubber Aggregates
### Environment Impact  
The mixed of rubber aggregates to the sand for geotechnical engineering applications is of great value for the mechanical properties of soils. However, it must be ensured that the rubber granulate do not have a negative environmental impact (soil and groundwater contamination). Indeed, the decomposition of tire rubber give lot of chemical elements such as aluminum, zinc, chromium, iron, lead, manganese, sulfur, black carbon and pigments [19; 20]. Many researchers have shown that leaching from rubber occurs as a function of the environment PH and harmful metals are released more under acidic PH conditions [21; 22; 23]. Other research has shown that the degradation of tire rubber is related to two main factors: the first one is the action of bacteria that feed on the petroleum products in the rubber and the second one is the exposure of the rubber to the ultraviolet rays.  
Concerning the effect of temperature on the mechanical behavior of rubber, [PERSON] [24] showed that the oxidation of the metal filaments present in the tires is the main cause of the exothermic reaction. It is also noted that [PERSON] et al. [25] carried out a monitoring of temperature measurements for one year, in a construction of an embankment based on a sand-rubber mixture, in order to check the development of exothermic reactions. This work concluded that no internal heat generation was detected whatever the weather conditions.  
According to the art state discussed above, tire aggregates associated with soils are far from being hazardous to the environment. Indeed, decomposition of these aggregates cannot occur because the action of microbes is inhibited by the presence of zinc oxides in the rubber [26] while exposure to ultraviolet light is completely avoided because the aggregates are buried in the subsoil [27; 28].  
In all problems of soil stability, it is necessary to know its shear strength. This resistance is very complex because it depends on several parameters including the grains shape, saturation and others.  
The objective of this work is to study the mechanical behavior of two types of sands (Hostun and Dune sands) mixed with rubber aggregates at different content ranging from 0 to 30%. The two sands have a grain different morphology: angular grains for Hostun sand and sub-rounded for the Dune sand. Shear tests are performed on 120 samples to study the effect of the angular and sub-rounded character of the grains on the mechanical behavior of the sand-aggregate mixtures.
# Comparative Analysis of COL9A1 Genotyping in Oral Squamous Cell Carcinoma Diagnosis: A Pilot Study
## 1 Introduction
Currently, somatic mutations that can be identified by next-generation sequencing and that are listed in the Cancer Genome Atlas have provided an important pathway for the potential diagnosis of OSCC. The somatic mutation of gene COL9A1 (collagen type IX alpha chain I) was previously found to be associated with the occurrence of oral malignancy. COL9A1 is localised in chromosome 16q13. The gene is responsible for the encoding of the three alpha chains of collagen type IX, and several studies have reported that a somatic mutation can be involved in the pathogenesis of OSCC [10]. The use of a biomarker that can be linked to the presence of OSCC could be a ground-breaking approach for an early diagnosis. Currently, tissue samples and histopathological examination represent the gold standard for diagnosis, focusing on cellular and tissue alterations [10]. The perspectives that a molecular marker in the body's fluids have to offer in the screening, diagnosis and prognosis of OSCC are appealing. Blood samples have proved to be a reliable source that can provide valuable information related to genetic and epigenetic changes. The identification of a biomarker in serum samples can provide a fast diagnosis, a monitoring tool and a future prognostic modality [10]. The incidence of OSCCsuggests an important interaction between multiple genes and proteins, being the result of a complex number of factors that determine a series of molecular genetic events [7 ]. The aim of our study was to identify the presence of a COL9A1 gene variant in patients diagnosed with OSCC, comparatively using both tissue samples and blood samples. The evaluation and quantification of the results based on the presence or absence of gene variants in the samples provided were also associated with existing risk factors, tumour grading as well as the differentiation status. By also using blood samples to identify the genetic variants in patients diagnosed with OSCC, we targeted a non-invasive approach for an early diagnosis and screening method based on a personalised analysis.