esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Summary of the Considered Mobility Models and Routing Protocols
### The Mobility Models
#### 3.1.4 The Gauss-Markov Model  
GM, like ST, is motivated by the need to have a model that is closer to reality in the sense that a node, for instance, would accelerate, decelerate or turn progressively. The model was proposed by [PERSON] and [PERSON] [5]. The current movement of a node (speed and direction) is related to the previous movement through Gaussian equations, using average speed and direction, as well as Gaussian random noise. A parameter \(a\) controls the degree of dependency on past speed and direction. The model is therefore said to feature temporal dependency. At a pre-set instant \(t\), the direction and speed of a given node are calculated. The node moves with that direction and speed for a constant time interval \(T\), and the speed and direction are calculated again. Figure 4 shows an example of a GM trajectory in a 2D area.  
Figure 4: Trajectory of a UAV under the Gauss–Markov (GM) model.  
The behaviour of the model at the boundaries is addressed in different ways. In [2], [PERSON] and [PERSON] suggest a direction change at the boundary without specifying exactly how this is done. In [37], [PERSON] _et al._ propose a 180\({}^{\circ}\) turn. This strategy is problematic, since it goes against one of the main reason why the GM model was proposed in the first place; which was to avoid sharp turns. Furthermore, the temporal dependency of GM is lost here, since the next direction is now unrelated to the previous one in this case. [PERSON] _et al._[38] propose a strategy to force the MNs away from the simulation boundaries. They define a buffer zone around the boundary. When the MN enters that zone, the mean direction is changed, so that the node is progressively pushed toward the center of the region.
## 5 Conclusions  
In this work, all recent developments towards the definition of an architectural approach for 6G networks were described. In particular, 6G networks will enhance current 5G functionalities, such as service-based architecture and network function virtualization, and at the same time will introduce novel features such as THz communications, the organic concept for flexible application deployment and reconfiguration, and distributed and decentralized machine learning approaches. A key concept in 6G networks will be their ability to process a large amount of data from heterogeneous resources. Therefore, open-access interfaces are expected to leverage data collection and processing along with IoT-edge-cloud frameworks.  
Sixth-generation networks can alternately be viewed as a collection of networks, or a network of networks, where various novel services can be integrated via flexible network slicing. Efficient mechanisms for dynamic network deployment can leverage optimum hardware utilization and green computing. To this end, the combination of the digital twin concept along with advanced machine learning approaches (e.g., deep reinforcement learning or explainable AI) can provide a multitude of potential responses for various network configurations and a deeper understanding of important 6G functionalities.  
Conceptualization, P.K.G. and A.G.; methodology, P.K.G.; software, N.N.; validation, P.T., L.S., X.M.-B., and N.N.; formal analysis, L.S. and X.M.-B.; investigation, N.N.; resources, P.K.G.; data curation, P.K.G.; writing--original draft preparation, P.K.G.; writing--review and editing, X.M.-B. and L.S.; visualization, N.N.; supervision, P.K.G.; project administration, P.K.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
3 GPP & Third Generation Partnership Project \\
5G & Fifth generation \\
6G & Sixth generation \\ AI & Artificial intelligence \\ AP & Access point \\ API & Application programming interface \\ AR & Augmented reality \\ BS & Base station \\ CF & Cell free \\ CGC & City gateway cloud \\ CN & Core network \\ CU & Centralized unit \\ \end{tabular}
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 4 Research Challenges
### General Challenges of Connected UAVs
Support groups can evaluate the framework with low-cost UASs while staying on the ground, avoiding dangerous and time-consuming hikes. Take, for example, how energy firms assess the major arch framework [68]. On rare occasions, administrators may choose a helicopter inspection or require a support crew to attempt a climb to examine the arch foundation from the outside. Maintenance personnel can use UAS to conduct an initial inspection from the ground, avoiding perilous climbs and reducing casualties. Due to the fact that a UAS inspection takes less time and requires fewer people than an actual climb, groups can inspect arches more frequently or with a smaller team. If an irregularity is discovered, its severity and impact can be assessed on the spot. Although a human climb may be required to solve the problem, the maintenance crew can ensure that they have the replacement components, the right instruments, and the right people on hand to fix and complete repairs. Individuals can also be rescued from dangerous situations and accidents by implementing comparative risk reduction and mitigation strategies across numerous verticals [69; 70; 71; 72].
## 4 Experiment and Analysis
### Comparison Experiments of Algorithms on the FSCB-Dataset  
This section details the comparison experiment results of the improved RT DETR-18+LPE+DySample model and existing object detection models (YOLOv3-tiny, YOLOv5n, YOLOv8n) on the FSCB-dataset. All models were trained under the same hardware configuration and training epochs, using standard object detection metrics to evaluate performance, including precision, recall, mAP50, and mAP50-95. The results are shown in Figure 7.  
Experimental results showed that the improved RT DETR-18+LPE+DySample model performed excellently on various performance metrics. The precision increased to 0.853, and the recall rate was 0.841, indicating superior positive sample detection ability. The mAP50 and mAP50-95 metrics achieved 0.885 and 0.621, respectively, demonstrating consistency and high precision across different IoU thresholds. These optimizations significantly enhanced the model's ability to handle complex scenes and small target detection, suitable for practical applications like student classroom behavior detection. Overall, the correctness of the optimization strategies and their potential application value were validated.
## 3 Materials and Methods
### BeePIV
#### 3.3.7 Putting It All Together  
We can now define the BeePIV algorithm in a single equation. Let \(V=(F_{t},\ldots,F_{t+k})\) be a video. Let the video's color variation (i.e., \(\texttt{CLV}^{V}\)) be computed with Equation (14) and let the values of \(\Theta^{V}\), and \(d^{V}\) be computed from \(\texttt{CLV}^{V}\) with Equation (15). We also need to select a context size \(\Delta\) and the parameter \(\beta\) for the circle drawing operator \(J_{\beta}^{*}\) in Equation (24) to generate motion frames \(W_{l}\).  
\[F^{*}(V)=Q^{*}\Big{(}G^{*}\Big{(}I_{\beta}^{*}\Big{(}E_{d^{V}}^{*}\Big{(}M_{n} ^{*}\Big{(}T_{\Theta^{V}}^{*}\Big{(}H_{\Delta}^{*}\Big{(}D_{\Delta}^{*}\Big{(}V \Big{)}\Big{)}\Big{)}\Big{)}\Big{)}\Big{)}\Big{)}\Big{)} \tag{31}\]  
The BeePIV algorithm is defined in Equation (31) as the operator \(F^{*}\) that applies to a video \(V\). The operator is a composition of operators where each subsequent operator is applied to the output of the previous one. The operator starts by applying the video difference operator \(D_{\Delta}^{*}\) in Equation (8) to subtract from each contextualized frame \(F_{t,\Delta}^{V}\) its background frame \(B_{t,\Delta}^{V}\). The output of \(D_{\Delta}^{*}\) is given to the video smoothing operator \(H_{\Delta}^{*}\) in Equation (11). The smoothed frames produced by \(H_{\Delta}^{*}\) are given to the video maxima operator \(M_{n}^{*}\) in Equation (20) to detect positions of local maxima. The frames produced by \(M_{n}^{*}\) are given to the video erosion operator \(E_{d^{V}}^{*}\) in Equation (22). The eroded frames produced by \(E_{d^{V}}^{*}\) are processed by the video drawing operator \(J_{\beta}^{*}\) in Equation (24) that turns these frames into white background motion frames and gives them to the video motion count operator \(Q^{*}\) in Equation (30) to return the counts of incoming, outgoing, and lateral displacement vectors. We refer to Equation (31) as the BeePIV equation. Figure 15 gives a flowchart of the BeePIV algorithm.
# A Distance-Vector-Based Multi-Path Routing Scheme for Static-Node-Assisted Vehicular Networks
## 3 RDV: The Base Scheme
### An Example  
An example scenario of packet forwarding in RDV is depicted in Figure 3 with the routing table of \(n_{2}\) and \(n_{5}\) shown in Tables 2 and 3, respectively. Note that each entry of RDV's routing tables consists of a destination, a next-relay node, a previous-hop node, the distance in _carry_ to the destination, the delivery ratio measured by SVS, and the routing metric to the destination. Suppose that a packet is to be forwarded from \(n_{2}\) to \(n_{7}\). First, \(n_{2}\) finds the entry for destination \(n_{7}\) and found the corresponding next-relay node is \(n_{5}\). Since the previous-hop is \(n_{1}\), \(n_{2}\) passes the copies of the packet if vehicles from \(n_{1}\) visit \(n_{2}\). If APD determines to forward multiple copies of the packet, they are passed to different vehicles coming from \(n_{1}\). Note that the entry means that, if the packet is passed to the vehicles coming from \(n_{1}\), the probability of the packet to reach the next-relay \(n_{5}\) is \(72\%\). When \(n_{5}\) receives one of the packet copies, \(n_{5}\) actually receives the packet. Thus, even if other copies of the same packet are received thereafter, \(n_{5}\) silently discards them. Next, \(n_{5}\) forwards packets to \(n_{7}\) in the same procedure, i.e., \(n_{5}\) finds the routing table entry for destination \(n_{7}\), passes the packet copies to vehicles coming from \(n_{6}\), and finally the destination \(n_{7}\) receives the packet. RDV forwards packets in this way to achieve high delivery ratio.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dest.** & **Next\_-Relay** & **Prev.** & **Dist.** & **Del. Ratio** & **Metric** \\ \hline \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \(n_{7}\) & \(n_{5}\) & \(n_{1}\) & 2 & 72\% & 0.064 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Routing Table of \(n_{2}\) in Figure 3.  
Figure 2: Example of Adaptive Packet Duplication (APD): If the required packet delivery ratio is \(P=99\%\), distance is 3-carries, and the packet delivery ratio from \(n_{1}\) to \(n_{2}\) is 80%, \(n_{1}\) creates four duplicated copies of a packet to suffice the delivery ratio \(P\).
# Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark
## 3 Proposed Method
### Classification Algorithms
#### 3.2.5 Multilayer Perceptron  
A multilayer perceptron constitutes an artificial neural network model used to map a set of input vectors to a set of known classes. It comprises of layers with nodes, having a specific weight, that are fully connected to the nodes of the next level. With use of the back-propagation method, which changes the connection weights of each node to the ones of the next layer in order to minimize the output error, the multilayer perceptron for a given dataset can be trained.
# ICARUS--Very Low Power Satellite-Based IoT
## 3 Physical Layer and Doppler Compensation
### Joint Timing and Frequency Synchronization
Due to the properties of the FZC sequence, the timing difference of both peaks is directly proportional to the CFO/Doppler shift which allows it to generate a simple look-up table approach to estimate timing and CFO. A similar strategy of CFO estimation has been used in [5]. The challenge of using FZC sequences lies in its elements which are equally spread among the unit circle of the complex number plane. In practice, it appears too complex to store these complex numbers in a ROM look-up table (LUT) with low bit-width. Instead, it appeared beneficial to use unit circle approximations of the original FZC subsequences. In ICARUS we hence applied an 8-PSK mapping of the FZC sequence, as depicted in Figure 11.  
Each element of subsequences 1 and 2 are rounded towards one of the eight elements of the given 8-PSK constellation. System simulations showed only a negligible performance degradation when compared with the full-circle FZC implementation but saves LUT space since only three bits need to be stored per sequence element. Complex multiplication of 8-PSK constellations is easier to implement in FPGA logic compared to high-resolution complex multipliers which is also of great importance with respect to the real-time implementation on ISS. Figure 10: Algorithmic idea of ICARUS joint timing and Doppler sync. Figure 9: Subsequence ambiguity in time and frequency shift.
## 4 Materials and Methods
### VOC Sampling and GC-MS Analysis  
To extract the extensive range of polarities of volatile organic compounds (VOCs), compounds were collected for 20 min by exposure to the headspace of each treatment (maintained at 33 \({}^{\circ}\)C, 90 rpm) on a solid-phase microextraction (SPME) fiber with a 50 \(\upmu\)m/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS, gray) coating (SUPELCO, Bellefonte, PA, USA). Subsequently, VOC analysis was performed by desorption in the GC HP 6890 Series using an Agilent BP-5 capillary GC column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, SGE, Austin, TX, USA) coupled to an Agilent Mass Selective detector 5973 (Agilent Technologies, Palo Alto, CA, USA) with helium as the carrier gas at a flow rate of 1.3 mL/min. Volatiles absorbed by the SPME fiber were desorbed by heating to 250 \({}^{\circ}\)C using splitless injection. The column temperature gradient was programmed to start at 40 \({}^{\circ}\)C for 0.5 min and then heated at 6 \({}^{\circ}\)C/min from 40 to 60 \({}^{\circ}\)C. Then, the temperature was elevated to 150 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, finally to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, and held for 6 min (total run time = 40 min). The gas chromatography-mass spectrometry (GC-MS) filament source and the quadrupole temperature were set at 230 and 150 \({}^{\circ}\)C, respectively. Ions were generated using electron ionization (EI) (70 eV) and acquired in full scan mode at 2 scans/s over m/z 30-300. All samples, including linear alkanes, were run under the same chromatographic conditions. Linear alkanes of the series C8-C20 were used to determine retention indices (RI) and later for the tentative assignment of compounds [12].
## 3 Proposed Method
### Adjustable Subsquares Properties Algorithm  
To demonstrate the effect of the proposed method, the adjustable subsquares properties algorithm will be discussed first. The algorithm consists of dividing a square into a number of subsquares adequate to the specific size of the subsquare, chosen such that the sum of the sides of the subsquares in the square is equal to the side of the main square in which the subsquares are placed. In a variant of the method presented in this article, the size of the main square (hidden image) is assumed to be 128 \(\times\) 128 pixels, and the size of the subsquare is assumed to be 32 \(\times\) 32 pixels, making for a total of 16 subsquares. As digital images stored in popular colour palettes, such as RGB and YUV, have three channels of information, the main square also has three dimensions, such that the main square can be considered, in simple terms, to be a square image, with specific areas in it being smaller squares. Thus, by dividing the image in the above-described way, 16 areas of interest are obtained from an image with dimensions (128, 128, 3). At this point, it should be mentioned that the input and output of both the encoder and the decoder contain an image stored in the form of a YUV colour palette. Thus, the third dimension of the image, representing the individual channels gives three possible values to modify for each pixel; in the method under discussion, the modification of the two values of the U and V channels, representing chrominance, is adopted, while the Y channel, representing luminance, is not subject to modification. The strength of the adjustable subsquare property manifests primarily in the adopted number system underlying the capacity of the system, together with the adopted size of the subsquare. Assuming that each channel is a representation of a digit in the adopted numeral system, it is possible to obtain as many as three digits in a given numeral system. For example, the maximum hidden value, starting from 0, for the binary numeral system is 7. For the senary numeral system, this value would be 215, and for the octal numeral system, this value would be 511. As mentioned previously, for the research presented in this paper, a two-channel variant was adopted; thus, referring to the examples in the previous sentence, the maximum values for each numeral system will change accordingly: For the binary numeral system, the maximum value will be 3; for the senary numeral system, 35; and for the octal numeral system, 63. For the purpose of presenting the method outlined in this paper, the senary numeral system has been adopted. A graphical depiction of the  
Figure 3: Preview of encoding effects: The top line of the figure shows the expected effect, while the bottom line shows the actual effect. The columns from left to right show the encoded image, the watermark converted later into a text form applied to the (UV) channels of the YUV colour palette, the preview of the Y luminance channel, and the preview of the whole image applied to the encoded image (all channels of the YUV colour palette).
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## Appendix A Absolute Error Between theoretical and practical measurements
### Model Applicability and Complexity  
The proposed model distinguishes itself through its simplicity and practical applicability in real-world scenarios. It is founded on well-established theoretical principles, including Friis' transmission equation and large-scale path-loss models, requiring only a limited set of empirical signal measurements collected via commonly available tools. Unlike more complex methods that depend on detailed infrastructure data, 3D ray-tracing simulations, or advanced wave-domain processing techniques, this model facilitates mobility-aware signal estimation without incurring high computational costs or needing prior knowledge of environmental obstacles.  
This reduced complexity renders the model particularly suitable for environments lacking comprehensive information about base station locations, building geometries, or full network topology. As demonstrated in the previous sections, the model attains estimation errors below 8% while maintaining low computational overhead. Moreover, by enabling the identification of handover zones and analyzing signal power variations associated with user movement, the model offers insights for optimizing network coverage.
## 4 Examples
### Triangular Mesh Example  
The final example is a triangular mesh with two inner boundaries. The initial mesh is shown in Figure 8a. The mesh is generated by randomly moving the positions of interior nodes, and it has 5383 nodes and 10083 triangular elements, 4914 of which are inverted. The Size and Shape-Size untanglers in Mesquite and the proposed approach (EEGT) are used to optimize the initial tangled mesh. The Untangle-Beta untangler fails to output the optimized mesh within 1800s, so its optimization result is not given in this example.  
The output meshes are shown in Figure 8. Both the Size and Shape-Size untanglers fail to untangle the initial mesh, and the optimized meshes of these two untanglers contain 1825 and 2636 inverted elements, respectively. All of the inverted elements are successfully removed by the proposed approach. Table 3 shows mesh quality statistics and the number of inverted elements of the initial mesh and output meshes of different methods. It can be seen that the proposed approach is effective in eliminating inverted elements and producing meshes with high quality, even for highly tangled meshes.
## 6 Discussion on Gaps, Challenges, and Links to Future Works
### Application of Available Excuation Software to Wildfire Evacuation
#### 6.5.6 Validation and Calibration  
Validating and calibrating the simulation models with real-world evacuation data is essential for ensuring their accuracy and reliability. However, obtaining evacuation data from actual wildfire events can be challenging due to safety concerns, privacy issues, and the scarcity of such data. Consequently, adequately validating and calibrating the models for wildfire evacuation scenarios remains a gap in the current state of research.  
Addressing these gaps and challenges requires collaborative efforts among researchers, practitioners, and emergency management authorities. By improving data access, enhancing wildfire behavior integration, refining behavioral models, and advancing the interoperability of software packages, wildfire evacuation modeling can become more comprehensive, accurate, and effective in safeguarding communities during wildfire emergencies.  
In conclusion, this chapter identifies significant gaps and challenges in wildfire evacuation modeling. Decision-making modeling, data availability, understanding evacuee behavior, and realistic assumptions, in addition to considering the dynamic relation between wildfire behavior and evacuation process, are crucial aspects that require further attention and research. Addressing these challenges will enhance the accuracy and effectiveness of wildfire evacuation strategies, ultimately contributing to the safety and resilience of communities facing the threats of wildfires in wildland-urban interface areas. As researchers, policymakers, and practitioners collaborate to address these challenges, we move closer to developing comprehensive and reliable wildfire evacuation models that are better equipped to protect lives and property during wildfire events.
# Multi-Directional Displacement Threshold Energy and Crystal Irradiation Damage Model
## 4 Atomic Displacement Model under Radiation
### Development of Classical Models
Consequently, the number of defects does not increase linearly with PKA energy; instead, final PKA energies typically range around several dozen keV. The Arc-DPA (Athermal recombination corrected) model was released by the NEA Nuclear Materials Primary Radiation Damage Expert Group in 2015 [8]. It calculates DPA by considering the compound process within the cascade subsequent to its formation. The recombination process does not require thermal driving, meaning that there is no defect migration involved and defects remain fixed in place. This correction is based on molecular dynamics simulation data, likely obtained after the PKA produces a certain number of surviving defects for dozens of picoseconds. These surviving defects further contribute to permanent material damage and microstructure evolution under thermal motion drive,highlighting the significance of irradiation damage research. While still based on the NRT formula, modifications are made to account for PKA energy. \[N_{F-P}=\left\{ \begin{array}{ll}0&0<E<E_{\text{d}}\\ 1&E_{\text{d}}<E<2E_{\text{d}}/0.8\\ 0.8E_{\xi}(E)/2E_{\text{d}}&2E_{\text{d}}/0.8<E\end{array} \right. \tag{7}\]  
Here, \(\xi(E)=\frac{(1-c)E^{b}}{(2E_{\text{d}}/0.8)^{b}}+c\) is the correction coefficient under different energies and \(b\) and \(c\) in the equation are determined by the type of material. The equation quantifies the rate at which thermal recombination reduces the residual displacement damage to the saturation value of PKA energy, starting from the sub-cascade layer and increasing with energy.