esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Stability Parameters for Wide Body Aircraft Using Computational Techniques
## 2 US DATCOM
### US DATCOM Results
#### 2.3.2 Longitudinal Aerodynamic Coefficients  
Results for longitudinal aerodynamic coefficients, i.e., CL\(\alpha\)dot, Cmadot, CD\(\delta\)e, and CD\(\delta\)f etc., with the variations in the angle of attack and control surface deflections, were obtained and plotted in Figures 5 and 6.  
Not only are simple stability coefficients plotted in DATCOM, but it also gives plots for the angle of attack rate. Figure 5d gives an increasing trend of lift coefficient at the start that subsequently decreases due to the angle of attack rate, which is in concurrence with the original aircraft behavior in flight. Since the Boeing 747-200 has an  
Figure 4: General dimensions of Boeing 747-200 along longitudinal, lateral, and vertical axes [1].  
the lift coefficient due to pitch rate is a positive value throughout the flight. The variation of the moment coefficient with the rate of change of angle of attack as shown in Figure 5e suggests a nonlinear trend. It is one of the pitch damping derivatives that shows an unsteady increase in the moment coefficient as soon as the angle of attack rate changes. The plot shows an initial decrease in moment coefficient at a negative angle of attack rate. Subsequently, a positive slope is obtained for the range of angle of attack rate, thereby increasing the pitching vibration in the aircraft structure.  
Elevator deflection affects drag, and a plot of drag coefficient is drawn in Figure 6a to observe the behavior of aircraft when subjected to the deflected elevator over a range of deflection angles. Another trend for drag coefficient is plotted in Figure 6b to study the effect of flap deflection on the drag, which can be explained in terms of pressure changes that ultimately affect the drag force.  
Figure 5: Analysis of longitudinal aerodynamic coefficients for different angle of attack (**a**) Variation of lift coefficient (CL) vs. angle of attack (Alpha) obtained using US DATCOM; the figure shows an increase in the lift with angle of attack until the stall angle is reached. (**b**) Variation of drag coefficient (CD) vs. angle of attack (Alpha) obtained using US DATCOM; the figure shows a decrease in drag at a negative angle of attack regime and then increase. (**c**) Variation of moment coefficient (Cm) vs. angle of attack (Alpha); the figure shows a negative slope in the curve with positive y-intercept fulfilling the longitudinal stability requirements. (**d**) Variation of CLadot obtained using US DATCOM; the figure shows an increase and then decrease in lift coefficient with the rate of change of angle of attack. (**e**) Variation of Cmadot obtained using US DATCOM; the figure shows the variation of the pitching moment coefficient due to the rate of change of angle of attack. The trends are similar to Boeing 737 commercial aircraft.
## 2 Study Area and Data
### Data Sources  
This study utilized remote sensing interpretation and water environment monitoring data from two different time periods in the study area. The baseline period was set in 2020, prior to the implementation of the restoration project, while the monitoring periods covered 2021 and 2022, following the project's execution. At the ecological restoration unit scale, remote sensing interpretation focused on three key aspects: ecosystem structure, ecosystem quality, and ecosystem service functions. The monitoring indicators are shown in Table 1. A combination of monitoring techniques, including satellite imagery, UAV aerial photography, and field surveys, was employed to conduct periodic assessments of the study area, forming a comprehensive multi-dimensional monitoring system integrating aerial, terrestrial, and satellite observations. Water environmental monitoring serves to evaluate aquatic ecosystem restoration effectiveness through the systematic assessment of 24 fundamental water quality parameters, including turbidity, conductivity, transparency, flow rate, and velocity, with triannual sampling cycles. Monitoring sections were strategically established in line with the following criteria: (1) at the hydrological boundaries of the study area, (2) within key conservation zones, and (3) in regions exhibiting abrupt water quality variation (e.g., wastewater discharge outlets). Stagnant zones and backflow areas were systematically excluded, with the preferential selection of sampling sites demonstrating stable hydrodynamic conditions and undisturbed substrate morphology.
## 4 Discussion and Prospect  
(1) In this study, we performed a comparative analysis of the \"PID + average distribution\" control and \"SMC + optimal distribution\" control against a no-control baseline to evaluate their impact on the stability of the spray machine's chassis. The findings indicate that under scenarios of linear acceleration, serpentine maneuvers, and low-speed turns, the spray machine's chassis stability was significantly enhanced with the application of control strategies compared to the no-control condition, resulting in improved maneuverability and safety.  
In particular, during direct acceleration in both transit and field operation conditions, the steady-state longitudinal speed error rate averaged 0.655% under the \"SMC + optimal distribution\" control, a 0.9% reduction compared to the \"PID + average distribution\" control. Both control approaches yielded similar outcomes in wheel slip rate regulation. Under serpentine and low-speed turning scenarios, the \"SMC + optimal distribution\" control resulted in an average peak yaw angular velocity deviation of 0.0045 and an average peak center of gravity lateral offset deviation of 0.051, representing reductions of 0.0245 and 0.005, respectively, over the \"PID + average distribution\" control.  
Thus, the \"SMC + optimal distribution\" control method notably decreased the yaw angular velocity and center of gravity lateral offset deviation during steering and linear driving while maintaining the wheel slip rate within an optimal range, thereby directly enhancing the spray vehicle's driving stability. Moreover, this integrated control method enhances adaptability across various operational conditions, ensuring superior stability control in diverse environmental settings.  
(2) Taking into account the significance of the suspension system for chassis stability, in subsequent research, the suspension model can be introduced to enhance the accuracy of the sprayer's dynamic model, allowing for a deeper analysis and optimization of its role in chassis stability control. Furthermore, considering variations in terrain types, weather conditions, and crop types, different terrains and weather conditions lead to discrepancies in ground friction coefficients, impacting wheel slip rates. This necessitates more frequent and refined adjustments by the control system. Moreover, diverse crop types impose different requirements on field operations, requiring control strategies to take these factors into account to ensure efficient and stable spraying operations without damaging the crops.  
(3) In future research, more complex models and sensor data fusion can be introduced to enhance the control system's capability to perceive environmental changes, enabling adaptive control under various conditions. Simultaneously, real vehicle tests should be conducted in a wide range of practical environments to collect performance data of the control system under different conditions, thereby evaluating its robustness and adaptability.
## 2 ML Methodologies
### Unsupervised Learning  
Unsupervised learning, also known as observational learning, is a branch of ML that focuses on analyzing and clustering unlabeled datasets. Unlike supervised learning, which relies on labeled data for training, unsupervised learning algorithms work with unannotated data to identify hidden patterns and groupings. This approach enables the algorithms to discover insights and extract meaningful information from the data without human intervention.  
The primary goal of unsupervised learning is to discern similarities and differences within the dataset, facilitating tasks such as data segmentation and image recognition [108]. By autonomously exploring the data, unsupervised learning algorithms can uncover underlying structures and relationships that might not be immediately apparent.  
There are two main methods commonly used in unsupervised learning: clustering and association.  
Clustering algorithms aim to group similar data points together based on their inherent characteristics [109]. These algorithms employ various techniques, such as density-based clustering, hierarchical clustering, or k-means clustering, to identify clusters or clusters of clusters within the dataset. By organizing the data into meaningful groups, clustering enables researchers and practitioners to gain insights into the underlying patterns and structures present in the data.  
Association algorithms, on the other hand, focus on discovering relationships and associations between different variables in the dataset [110]. These algorithms search for frequent co-occurrence patterns or associations among items, enabling the identification of rules or correlations. Association analysis has applications in various fields, including market basket analysis, recommender systems, and anomaly detection.  
Both clustering and association methods provide valuable tools for exploratory data analysis and knowledge discovery. Unsupervised learning algorithms play a crucial role in extracting useful information from unlabeled datasets, enabling researchers and practitioners to uncover hidden insights, generate hypotheses, and make data-driven decisions.
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 2 Background  
### NoSQL and MongoDB  
Although relational database management systems occupy an important position in the data management field, the large-scale data introduces a new challenge for data storage and management [17]. Bottlenecks exist in the process of managing massive data with traditional relational databases due to their ACID properties, and people have started to search for a more optimal solution. Consequently, NoSQL technology has emerged as a possible solution [18; 19]. For the management of massive data in the distributed system, NoSQL databases usually focus more on the availability and partition tolerance and realize the eventual consistency, wherein it achieves the applications in many special areas [20]. With its feasible data model, high read and write performance, and powerful expandability under big data, NoSQL is more suitable for the storage and management of massive data.  
In terms of its storage mode, NoSQL databases can be divided into four categories: key-value database, document-oriented database, column-family database, and graph-oriented database [21]. Presently, as a kind of document-oriented database, MongoDB is attracting much interest due to its free schema, support for complex queries, and powerful expandability. The fundamental data storage unit of MongoDB database is a document, while the BSON (Binary JavaScript Object Notation) format is used as the data storage structure, which is similar to the JSON (JavaScript Object Notation) format. Data is stored in the form of key-value in MongoDB. The document corresponds to the row in the table of the relational database, and many documents compose a collection that corresponds to the table of the relational database. Unlike the table, database users do not need to define the mode of the collection, while the structure of the table needs to be specified in advance in relational databases, and different types of documents can be stored in the same collection. Moreover, for large binary files, MongoDB provides a GridFS mechanism to store them.
## 1 Introduction
For example, this can be achieved by considering each image in isolation, without taking into account the layer to which it belongs, or by performing classification at the layer level [16]. Unlike previous reports in the literature, an approach is proposed to merge the images corresponding to the EIM layers into a single image by leveraging various methods for integrating this information. To the best of our knowledge, this is the first approach to handle this task by considering image merging for classification purposes. Having only one image comprising this information could help to improve not only the classification performance but also the understanding of the information captured using this method. For achieve this, different computational methods were analyzed: the Coefficient of Variation (CV), the Root Mean Square Contrast (\(C_{\text{RMS}}\)), and Contrast-Limited Adaptive Histogram Equalization (CLAHE) in combination with the CV. The impact of using advanced fusion methods, including weighted averaging, Gaussian pyramid fusion, and Wavelet-PCA fusion, was explored. The obtained results demonstrate that classification based on the variability map generated through the integration and fusion of the different depth layers achieved an accuracy of 85 \(\pm\) 0.07, showing an improvement in the reliability of the automatic classification process. Furthermore, this result highlights the impact of integration and fusion on optimizing EIM image analysis, enabling a more homogeneous and structured representation of the tissue and enhancing the extraction of relevant information, thereby facilitating its application in the early detection of breast cancer. In this sense, integration and fusion approaches not only enhance the accuracy and reliability of computer-aided diagnosis systems but also increase their applicability in clinical settings, providing a promising tool for medical imaging.
# Aspects of Cyber Security in Autonomous and Connected Vehicles
## 6 Defenses or Countermeasures for Autonomous and Connected Vehicles
### Defenses for Safety Systems  
#### 6.2.1 Countermeasures on Radar and GPS  
In [96], the filter method is explained to avoid undesired digital radio frequency memory (DRFM) signals to prevent jamming and spoofing attacks created using DRFM repeaters. In [97], a novel spatiotemporal challenge response (STCR) method was used to detect the spoofing attacks made with multiple-input and multiple-output (MIMO) antennae and multiple beam forming. If reflected signals cross the threshold value of noise those are considered undesired signals. In [98], a signal encryption approach used analysis of drift, and direction of the upcoming signal to prevent GPS spoofing. In [67], two ways were provided to protect from GPS spoofing checking of GPS signals for threshold value if exceeds it leads to malfunction, and cryptography for GPS signal.
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## Appendix A Format of the Frames of the Unicast Implementation  
### Format of the Wireless Frames  
The format of the MiWi LoRa frame is shown in Figure 11. More details are given in [4]. The fields are as follows:  
* _Frame Control_: a bitmap specifying the type of packet, some transmission parameters, and the lengths of other fields (e.g., the _Destination Address Mode_ switches between 2- and 8-byte addresses).
* _Sequence number_: it increases by one each time a datum or command frame is sent.
* _Destination PanID_: the identifier of the Personal Area Network. It should be noted that all communication is intra-PAN, so the frames do not have a source PanID field.
* _Destination address_: an 8-byte-long address is used for unicast frames. For broadcast messages, it will be fixed to 0_xFFFF_ (2 bytes). The _Destination Address Mode_ is the field that governs the length.
* _Source address_: it is always 8 bytes long.
* _Payload_: the format of the employed frame types is included in the next subsection.
* _Frame Check Sequence_: it verifies that the information is not damaged during transmission.  
As can be observed, if a unicast _Destination Address_ (8 bytes) is used, the total overhead will be 23 bytes (header plus FCS).
# An Overview of Machine Learning and 5G for People with Disabilities
## 5 5G Service-Based Architecture  
Next, the service-based representation of the 5G non-roaming system architecture, specified by 3 GPP [58], is depicted in Figure 5. We distinguish between the User Plane (UP) and the Control Plane (CP). The UP carries only user traffic while the CP is dedicated to network signaling. The 5G system architecture aims at separating the UP and the CP functions to allow scalability and flexible deployments.  
In the user plane, we observe that the User Equipment (UE) is connected to either the Radio Access Network (RAN) or a non-3 GPP Access Network (e.g., Wireless Local Area Network, WLAN) and to the Access and Mobility Management Function (AMF) as well. RAN refers to a base station with new radio access techn  
Figure 5: Service-based representation of the 5G non-roaming system architecture.  
The upper part of the figure is the 5G core network CP and has a 'bus'; network functions (NFs) (e.g., AMF) within the CP allow any other authorized NFs to access their services using service-based interfaces (SBIs) for their interactions.  
The 5G system architecture consists of the following NFs, which constitute the 5G core network:  
* _Access and Mobility Management Function (AMF)_: it is responsible for registration and connection management as well as mobility management; it also supports authentication and authorization.
* _Session Management Function (SMF)_: it supports session management as well as User Equipment (UE) IP address allocation and management; it also selects and controls User Plane Functions (UPFs) for data transfer.
* _User Plane Function (UPF)_: it is responsible for packet routing and forwarding; acts as an external PDU session point of interconnect to Data Network (DN), e.g., to operator services, Internet access or third party services; it is also the anchor point for intra- and inter- Radio Access Technology (RAT) mobility.
* _Policy Control Function (PCF)_: it provides policy rules to control plane functions (e.g., AMF, SMF)
* _Application Function (AF)_: it interacts with the core network to provide several services.
* _User Data Management (UDM)_: it stores and manages subscription data of the UE; it supports access authorization based on subscription data.
* _Authentication Server Function (AUSF)_: it acts as an authentication server and stores data for the authentication of UEs.
* _Network Slice Selection Function (NSSF)_: it selects the set of network slice instances serving the UE.
* _Network Exposure Function (NEF)_: it supports exposure of the services and capabilities provided by 3 GPP NFs and a secure provision of information from external application to 3 GPP network; it translates internal/external information.
* _NF Repository Function (NFR)_: it supports service discovery function, maintains NF profile, available NF instances, and their supported services.  
Furthermore, the NF modularization design enables flexible and efficient network slicing. This representation also includes point-to-point reference points (e.g., N4) for the interaction between pairs of NFs when necessary.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 8 Experimental Evaluation
### Lossy Data Reduction Algorithms
#### 8.1.1 Random Projection  
We used the Gaussian Random Projection from the scikit-learn 1.4.2 library in Python 3.12.0 [43]. The methodology involved loading the dataset and performing the necessary conversions, particularly in the handling date formats for consistency across datasets. We then initiated the processing using the Random Projection algorithm. To ensure reproducibility, we set a random state seed of 42, increasing the reliability and consistency of the results.  
The results indicate that Random Projection is better for complex datasets with numerous columns and distinct data types. In Table 6, the Random Projection algorithm underperforms on almost all datasets, especially when dealing with a reduced number of columns, where it consistently produces results opposite to expectations due to several factors, including the nature of the dataset, the parameters chosen for Random Projection, and the characteristics of the Random Projection algorithm itself. These issues indicate the unsuitability of this algorithm for datasets with minimal columns and simple structures. The best compression result achieved was on the numeric data type. The algorithm reduced the Accelerometer dataset by \(-20.27\%\) of its original size in 0.007 ms, meaning it processes 1 MB in 0.0019 ms. Regarding processing time per MB, the fastest dataset to be processed and reduced the original dataset size was Smoke Detection, a numeric dataset. The algorithm took 0.0012 ms to process 1 MB.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 3 Preliminaries
### Elliptic Curve Signcryption
#### Signcryption Phase  
In the signcryption phase, the message is encrypted and digitally signed. The sender [PERSON] wanting to send a message \(M\), randomly selects an integer \(d\) from \([1\ldots p-1]\) and performs the computations given below.  
* Generate the key \(K\) as: \[K\,=H(dP_{y})\] (8)
* Divide the key \(K\) is into two equal size subkeys \(k\) and \(k^{\prime}\).
* Encrypt the message \(M\) using subkey \(k\) as: \[c=E_{k}(M)\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Shortened Scheme** & **Signature** & **Signature** \\  & **(t,\(w\))** & **Verification** \\ \hline Scheme 1 & \(t=Hash\)\((\pi G,M)\) & \(Z=w(X+tG)\) \\  & \(w=(\
u/t+x)modq\) & \(Check\)\(if\)\(Hash(Z,M)=t\) \\ Scheme 2 & \(t=Hash\)\((\pi G,M)\) & \(Z=w(G+tX)\) \\  & \(w=(\
u/1+xt)modq\) & \(Check\)\(if\)\(Hash(Z,M)=t\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variants of elliptic curve digital signature scheme.  
* Compute \(t\) using subkey \(k^{\prime}\) as \(t=KH_{k^{\prime}}(M,\ Blind\_Info)\)
* Compute \(s\) as: \[s=\frac{d}{t+v_{x}}\ mod\ q\] (10)
* Then, [PERSON] creates the signcrypted message \(\{c,t,s\}\) and sends it to [PERSON].