esa-sceva/satcom-chunk-collection huggingface dataset:
# Secured Real-Time Machine Communication Protocol
## 4 Vulnerability Analysis
### Security Measures in SRMCP  
SRMCP employs advanced security mechanisms designed to counter these threats:  
* **Port Hopping:** Inspired by frequency hopping, SRMCP dynamically distributes communication across multiple TCP/IP sockets as encrypted, out-of-order segments. This mechanism obfuscates traffic patterns and mitigates threats from traffic analysis, MitM attacks, and eavesdropping.
* **Data Integrity and Authentication:** SRMCP uses Transport Layer Security (TLS) and AES-GCM to ensure data authentication and integrity. Any tampered or injected data fails both the TLS integrity check and the AES-GCM decryption integrity verification, effectively thwarting data injection and tampering attacks.
* **Replay Attack Prevention:** SRMCP employs unique transaction identifiers and strict timeout mechanisms to detect and reject replayed packets.
* **Post-Quantum Cryptography:** The use of the Kyber Key Encapsulation Mechanism (Kyber-KEM) for secure key exchange and AES-GCM for data encryption provides robust, quantum-resistant protection against future quantum attacks.
## 5 Results and Discussion
### AMPT Testing  
The dynamic modulus (E*) and phase angle were derived from AMPT testing in accordance with AASHTO [28] (Figure 9). The phase angle of Mixture 2 (3% GGBFS) was the highest at 35, followed by 32 for the 1.5% GGBFS mix. The phase angle of the mixture with 5% GGBFS was 29, which is lower than those of the 3% and 1.5% GGBFS mixtures. This is consistent with the results indicating better fatigue and rutting performance in the 3% GGBFS mixture. Figure 10 shows a sample of AMPT test results. A higher phase angle supports the idea of the mixture being more elastic. Master curve construction is based on the fact that the dynamic modulus presents an S-shaped format. The tests were conducted in different reduced frequencies. Figure 11 presents the results at 20 \({}^{\circ}\)C. Figure 11 presents master curves for the tested specimens, which are plotted as E* versus reduced frequency. The curves show that Mixture 2 (3% GGBFS) had the best performance, showing that the majority of 3% GGBFS tests presented a higher E*.  
Figure 8: Photograph of a wheel track test sample made with the 1.5% GGBFS asphalt mixture.  
Figure 9: Phase angles of different asphalt mixtures.
###### Abstract  
Unmanned aerial vehicle (UAV)-enabled vehicular communications in the sixth generation (6G) are characterized by line-of-sight (LoS) and dynamically varying channel conditions. However, the presence of obstacles in the LoS path leads to shadowed fading environments. In UAV-assisted cellular vehicle-to-everything (C-V2X) communication, vehicle and UAV mobility and shadowing adversely impact latency and throughput. Moreover, 6G vehicular communications comprise data-intensive applications such as augmented reality, mixed reality, virtual reality, intelligent transportation, and autonomous vehicles. Since vehicles' sensors generate immense amount of data, the latency in processing these applications also increases, particularly when the data are not independently identically distributed (non-i.i.d.). Furthermore, when the sensors' data are heterogeneous in size and distribution, the incoming packets demand substantial computing resources, energy efficiency at the UAV servers and intelligent mechanisms to queue the incoming packets. Due to the limited battery power and coverage range of UAV, the quality of service (QoS) requirements such as coverage rate, UAV flying time, and fairness of vehicle selection are adversely impacted. Controlling the UAV trajectory so that it serves a maximum number of vehicles while maximizing battery power usage is a potential solution to enhance QoS. This paper investigates the system performance and communication disruption between vehicles and UAV due to Doppler effect in the orthogonal time-frequency space (OTFS) modulated channel. Moreover, a low-complexity UAV trajectory prediction and vehicle selection method is proposed using federated learning, which exploits related information from past trajectories. The weighted total energy consumption of a UAV is minimized by jointly optimizing the transmission window (\(L_{\omega}\)), transmit power and UAV trajectory considering Doppler spread. The simulation results reveal that the weighted total energy consumption of the OTFS-based system decreases up to 10% when combined with federated learning to locally process the sensor data at the vehicles and communicate the processed local models to the UAV. The weighted total energy consumption of the proposed federated learning algorithm decreases by 10-15% compared with convex optimization, heuristic, and meta-heuristic algorithms.  
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# Towards a Heat-Resistant Tethered Micro-Aerial Vehicle for Structure Fire Sensing
###### Abstract  
The collapse of structures during firefighter intervention is one of the greatest risks that firefighters must face when entering buildings. To reduce these risks, situational awareness is key. Although many advances have already been developed in wildland and outdoor fires, there is still room for improvement in structure fires. The development of a heat-resistant micro-aerial vehicle for indoor fires poses a series of challenges such as component cooling, battery management, and protection from impacts. In this paper, a heat-resistant tethered micro-aerial vehicle is designed, modeled through thermal analysis, and successfully tested in real-world conditions. This platform has been equipped with a micro-sized thermal sensing camera and first-person-view (FPV) camera, optimized for thermal management, to allow for situational awareness in structure fires.  
heat-resistant UAV; structural fire; tethered drone; thermal sensing; situational awareness +
Footnote †: journal: Applied sciences  
article  
[color=gray!20, title  
methodology, the MAV must first be isolated from external conditions. Then, transient thermal analyses are performed to evaluate the heat flows. Given the thermal constraints and material properties, the design must be optimized until the imposed design requirements are satisfied.  
The most temperature-sensitive component is the battery, which must be placed outside the MAV to avoid rapid degradation. A suitable solution for this, which also allows for more extended flight autonomy, is using a tether. Thanks to this feature, the battery pack can be stored outside in a well-cooled area or carried around by the firefighter in a special enclosure.  
This work focuses on designing, developing, and evaluating a tethered micro-aerial sensing vehicle for situational awareness in structure fires. A transient thermal modeling approach traditionally used in satellite design is used to develop a novel heat-shield chassis for the drone. A micro-sized thermal camera and an FPV camera are equipped inside the chassis, and the battery is removed from the quadcopter to avoid degradation due to heat, reducing the drone size and weight. These contributions form a heat-resistant aerial sensing platform that withstands harsh conditions in an innovative way.
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 2 Related Works  
### Siamese Tracking  
Siamese tracking algorithms consist of two input branches with identical network structures, and they learn the similarity between inputs through a structure with shared weights. For example, SiamFC [18] replaces correlation filters with a Siamese neural network for similarity learning, overcoming the limitation of requiring the template region to match the size of the search region. Building upon this, SiamRPN [19] introduces a region proposal network for foreground-background classification and refinement. SiamRPN++ [20] employs a deep ResNet network instead of AlexNet, increasing the depth of the algorithms. Siam-Mask [21] adds extra branches and loss functions, unifying tracking and segmentation tasks. SiamDT [22] achieves accurate feature representation of the target object by enhancing ResNet-50 and adding an additional template branch, aiming to address issues related to template failure and model drift.  
In addition, some tracking algorithms further enhance accuracy by incorporating channel attention [23], adding extra template branches [24], and combining convolutional neural network (CNN) and Long Short-Term Memory (LSTM) networks [25].  
However, the feature extraction networks in the above algorithms are mostly convolutional neural networks, extracting primarily local features of the target object, lacking utilization of global features. Additionally, these algorithms need more utilization of higher-order statistical quantities and fully exploit the spatial information of features, reducing the discriminability of similar objects. Therefore, tracking results are susceptible to interference from complex environmental factors such as Fast Motion, Illumination Variation, and Scale Variation.
## 2 Problem Formulation  
In this section, the geometric description of the anthropomorphic hand and the scenario of the motion task are discussed. The anthropomorphic hand is designed according to the human hand anatomy. In this anthropomorphic hand, the wrist mechanism is designed to possess two degrees of freedom in order to mimic the motion of a human's wrist. For fingers, the thumb and index finger each has three joints, namely, the metacarpophalangeal (MCP), the proximal interphalangeal (PIP), and the distal interphalangeal (DIP) joints, as shown in Figure 1. Among these three, the PIP and DIP joints of a human finger are anatomically coupled by a tendon. As a result, to mimic a human finger's anatomy, the design of a four-bar linkage mechanism to create coupled motion by a ratio of 1:1 is adopted in this research, whereby \(\theta_{i3}\) is equal to \(\theta_{i4}\) and \(\theta_{i3}\) is equal to \(\theta_{i4}\), where subscript \(t\) stands for thumb and \(i\) stands for index finger. As many dexterous gestures and tasks are executed by the thumb and index finger, such as picking up small items and handling a key, the kinematics of the thumb and index finger of the anthropomorphic hand is the main focus of this paper. The other fingers, i.e., the middle finger, ring finger, and little finger, could also be taken into consideration for the IK problem by using the same procedure presented in this study. However, to fix ideas and to convey the concept clearly, the remainder of this paper uses only the thumb and index finger as examples for simplification.  
In this study, the target points of the index finger and the thumb are given at the same time as the inverse kinematic requests and the associated task space is restricted only  
Figure 1: Schematics of (**a**) the design and (**b**) the kinematic structure of the model anthropomorphic hand.  
to a translation of posture. Because both the index finger and the thumb are connected to the wrist, it is evident that the robotic hand has multiple serial kinematic chains, and each of them is dependent. In other words, the tips of both the index finger and the thumb comprise the same angle \(\theta_{w1}\) and \(\theta_{w2}\), where the subscript \(w\) represents the wrist. Mechanically, the inverse kinematics of the two fingers are highly coupled.  
With the problem described according to Cartesian coordinates, an IGA is then used to solve this highly nonlinear and coupled inverse kinematics. However, from theory, an IGA always returns a minimum value that it has computed even if the target point is out of the workspace. To avoid this ambiguous situation, a procedure to generate the workspace is proposed, and a technique to check the feasibility to assist the computation of the inverse kinematics is discussed in the next section.
# Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network
## 2 Material and Methods  
In pest monitoring, most approaches focus only on CV techniques [9; 10], not considering the process to obtain images automatically and in large numbers, which hampers the application of CV models in real contexts due to lack of information/images. Works that present the two technologies together, CV and IoT, in pest monitoring, are therefore the most promising [11; 12], since the images are obtained in a more real context and not under extremely controlled conditions, which could raise issues with the generalisation ability of the CV models.  
In the next subsections, we first introduce the most relevant aspects of the IoT, such as computation types, communications protocols, and network topologies; and then the most relevant CV models, such as faster Region-Convolution Neural Network (R-CNN) and You Only Look Once (YOLO).
# Mechanical and Dielectric Properties of Aligned Electrospun Fibers
## 6 Future Work  
While a few studies have methodically experimented with system parameter optimization for limited sets of parameters, none has studied the nonlinear effects of two or more factors with three levels per factor for obtaining the mechanical and dielectric responses. Consequently, parameter interactions and nonlinearities have were studied, in-depth, to optimize alignment for mechanical and dielectric properties. Very few studies have been conducted on the nanofiber orientation variations along the fiber direction, using a rotating mandrel and its effect on mechanical and dielectric properties. The variability of data in both dielectric and tensile tests needs to be quantitatively addressed, and therefore a modified improved system should be developed. The improved system will lead to consistencies in the behavior of nanofiber specimens and acceptable standard deviations in variability that indicate meaningful parameter levels and trends. Future work will focus on system improvements and methodical experimentation (Design of Experiments), to optimize system parameters for improved mechanical and dielectric responses and application of PAN nanofiber mat materials in advanced mechanical and energy applications. Future research work will determine the nature of the trade-off between mechanical strength and dielectric properties--whether it is linear or non-linear, synergistic, or detrimental.
# Automatic Configurable Hardware Code Generation for Software-Defined Radios+
## 3 Analysis of SDF-AP Model
The optimal buffer size of a channel \(c\) is then determined by finding the maximum element of \(\theta_{t,c}\) which in this case is 2. Generally, the variation in buffer size between successive throughput values largely depends on the structure of the access pattern and the average number of tokens produced and consumed over a period of iteration latency \(IL\). The are three possibilities regarding the buffer size results of the FIFO channel/s as the throughput \(\tau_{i}\) (\(0<\tau_{i}\leq\tau_{max}|i\leq N\) samples) increases; the buffer size either remains constant, increases or decreases with the increased throughput. Given the two throughput values \(\tau_{1}\), \(\tau_{n}\) such that \(\tau_{n}>\tau_{1}\) and their respective computed buffer sizes \(\theta_{1,c}\), \(\theta_{n,c}\) for channel \(c\), the buffer size values \(\theta_{k,c}\) (\(0<i\leq n-1\)) computed in the range \(\tau_{1}\leq\tau_{k}\leq\tau_{n}\) are constant if ratio of the last elements of _token counters_ (i.e., \(\frac{T_{C(IL-1),p}}{T_{C(IL-1),p}}\)) at \(\tau_{1}\) is respectively equal to the ratio of last elements of _token counters_ at \(\tau_{n}\), otherwise the buffer size from \(\tau_{1}\) to \(\tau_{n}\) increases or decreases. Our reason for why there is an increase or decrease when throughput goes high is attributable to the access patterns as well as the source and sink scheduling periods. Calculating the impact on buffering is not a straightforward operation due to needing to know these implementation-dependent aspects on these parameters. This aspect is out of the scope of this paper, but we do plan to take this study on buffer size further in our future research. Furthermore, the SDF-AP model example illustrated in Figure 1 is based on a simple acyclic graph whose analysis, scheduling and buffer computation are straight-forward. For a model with a cyclic graph, the same methodology for analysis, scheduling, and buffer computation can be used in the same way as for a model in Figure 1. However, this can only be possible on condition that the model source (\(u\in A_{src}\)) and sink actors (\(v\in A_{sink}\)) are not a subset of cyclic sub-graphs of the model graph. The limitation of a modeled source and sink actors that are not part of a cycle can be lifted by connecting these actors to virtual actors with infinite FIFOs. While this limitation aspect is out of the scope of this paper it will, however, be considered in the more complex examples that will be presented in our future work.
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 2 Materials and Methods
### System Architectural Design  
The 3D space target tracking simulator is designed to track a target initially located at a distance \"\(r\)\" from an observer located at the origin. The observer and target then move with velocities according to the initial data in Table 1. Range, bearing, and elevation measurements are taken at regular intervals, and noise is added. The simulation ran for a specified time interval of 10 s, the same time step used during training. For the first 10 s, the measurements, such as bearing, range, and elevation, were stored in a buffer, and the UKF did not operate. Afterward, the buffered measurements are combined with respective saved models and scalers to make predictions depending on the model used to make a prediction. The buffered data were first transformed into 3D data to meet the requirements of the GRU, LSTM, and CNN models, but not MLP, as it required 2D data. Predictions were made using each model separately, which were then used in the UKF to estimate the state of the unknown target. The block diagram in Figure 6 provides a visual summary of these steps. In this way, a data-driven approach of all mentioned algorithms (i.e., MLP, CNN, GRU, LSTM) was successfully implemented to predict the standard deviation effectively without relying on human experience or assumptions, which is crucial for the algorithm's better performance. The framework systematically produces scenarios, conducts simulations, and then compares the measured values with the actual values. An error metric is computed, and depending on a set of acceptance criteria, the procedure either halts or proceeds. While the text mentions deep learning models' involvement in standard deviation computation, the integration of these models with UKF remains unclear. Deep learning models have the ability to analyze raw data, such as time series, in order to provide early estimations of the state of a system. These estimations can be used as the initial conditions for the UKF.  
Overall, the proposed system significantly contributes to the field of target tracking by replacing the need for the assumption of a covariance matrix based on human intuition with the power of machine learning. The results from the above-mentioned algorithms may show that using deep learning algorithms outperformed the traditional method, demonstrating a promising approach to improve the accuracy and reliability of target tracking by eliminating the reliance on subjective assumptions. This method provides a more robust and efficient way to handle the complexity of target-tracking tasks.  
Figure 5: Basic Recurrent neural network block diagram.
## 2 Data-Cleaning Applications
Principal component analysis (PCA) is usually utilized in dimension or feature reduction and provides a significant increase in accuracy and efficiency along with other machine learning techniques in many applications aside from biomedical application [35; 36; 37; 38; 39]. Nevertheless, some of the information is lost during the process of dimension reduction [10]. Figure 2 shows a sample Excel dataset, and PCA reduces the dimensions vertically (by column), shown in red; for the proposed methodology, it reduces the noisy samples horizontally (by rows). We emphasise that the extraction of features, such as covariance, eigenvalues, eigenvectors, and dimension reduction, is not a novel technique that we propose here, but instead, we propose the implementation of sample reduction using PCA. Identifying the noisy samples that cause irregularity in the multidimensional datasets and omitting or reducing them is the main focus of this study. Figure 2: Sample Excel multidimensional dataset. The observation and the application of the PCA-sample reduction process are utilised for data cleansing of the noisy multidimensional datasets to increase the accuracy of classification problems in artificial neural networks and to identify recommended threshold ranges. To the best of our knowledge, we are the first to propose this unique approach. We discuss a specific technique for qualitative noise detection and omission of such. These techniques are explained with a motivating example highlighting deep learning classification problems of artificial neural networks in biomedical applications using publicly available biomedical datasets. The simplicity of this technique makes it portable and can apply to a variety of tasks for fast and accurate classification of typical or commonly used artificial neural networks. In terms of software implementation, developers and data engineers prefer Python programming. It is the best option for projects or programming involving AI and big data analysis as Python is a simple language with a mature and supportive Python community; an abundance of support from renowned corporate sponsors; an extensive and popular selection of libraries; and the ability to work with heavy-hitting frameworks such as TensorFlow, Sci-kit-learn, OpenCV, and Keras [40].