esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 6 GCIoHE System Paradigm  
### The Next Leap Evolution  
As shown in Figure 8, the Tactile Internet adds a new dimension to P2M interaction by tactile and haptic sensations, enabling an unforeseeable plurality of new applications, products, and services. Ubiquitous availability, better reliability, and high security with extremely low latency will define the character of the emerging 6G enabled Tactile Internet, introducing numerous new opportunities.  
The 6G vision connectivity, as shown in Figure 9, is to brace the development of a global, ubiquitous intelligent mobile society (GUIMS) with the following achievements: (i) solve the limitations of 5G including system coverage and IoE; (ii) achieve up to a hundred times higher data rate, greater system capacity, better spectrum efficiency, and lower latency than 5G to serve the interconnection of everything; (iii) introduce ubiquitous, intelligent, and integrated network with holographic, broader, and deeper coverage, including terrestrial communication, satellite communication, and device-to-device communications; (iv) serve airspace, land, and sea, realizing a global mobile broadband communication system; (v) work on higher frequency to achieve wider bandwidth, such as better mmWave, terahertz, visible light, etc.; (vi) produce a personalized intelligent network combined with artificial intelligence (AI), especially tiny ML, to offer virtualized personal mobile communication with an endogenous security scheme or function security to offer the capability of self-awareness, real time dynamic analysis, and confidence evaluation, realizing cyberspace security; (vii) merge computation, navigation, and sensing with communications; (viii) adopt a more open architecture with software defined network (SDN), virtualized network functions (VNF) and radio access network (RAN) to realize self-intelligent development and rapid dynamic deployment of network functions; (ix) generate massive data through the IoE combining with novel technologies such as cloud computing, edge computing, tiny machine language (ML), blockchain, etc., realizing group collective intelligence (swarm intelligence); (x) act as a \"global wireless power grid\"; and (xi) develop a better massive MIMO.  
In the emerging 6G and beyond of wireless communication, an increasing number of ultra-scale intelligent factors will result from interference exploitation. To manage this exploitation, challenges for detection algorithms in uplink MIMO systems exist, especially for higher-order quadrature amplitude modulation (QAM) signals.
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 4 Tree Formation and Cost Function Evaluation  
### Tree Formation  
The first step in the initialization phase is the tree formation with the available set of sensor nodes and the central coordinator (CC) or the root node. Initially, the root node broadcasts the CC announcement to all the neighboring nodes. The CC announcement includes a sequence number, number of the visited devices, available energy, queue length, and all the necessary parameters to select the parent node. CC announcement is broadcast based on a sink timer. Initially, the one-hop neighboring nodes will receive the announcement from the root node. Based on the received sequence number and hop count, the tree is formed with selected parents and children. The detailed psedocode for the tree formation is given as follows:  
1. The root node broadcasts a CC announcement using a sink timer
2. One-hop connected devices receive the message
3. If the received sequence number is new add the previous hop forwarder in the tentative parent list
4. If the received sequence number is not new but if its hop count is less than the previous one, then add it to the tentative parent list
5. Execute MCPS algorithm (Algorithm 1) to select the best parent node
## 3 Motivation and Research Contribution  
The available research on VANETs lacks advancements in link reliability, especially in the context of software-defined networking. The primary motivation of this work is to exploit the flexibility and adaptability of SDN architecture to maximize link reliability and, hence, the packet delivery ratio. The enhancements in wireless technology, like LTE and high-speed Wi-Fi technology, make it easier to separate data transmission from traffic control transmission. Through GPS technology, it is easier to keep track of the global view of the network. Other researchers have implemented SDN in VANETs, but these approaches lack the full potential of SDN as they use a single interface to find the route and data dissemination. Also, the work carried out with VANETs has not paid attention to the heterogeneity of the vehicle hardware. The above discussion motivated us to carry out this research.
## 2 System Model
### Receiver  
A block diagram for the receiver is shown in Figure 1b. The SC-FDE demodulator in Figure 2 consists of a down-converter, PN remover, FFT, channel estimator, frequency domain equalizer, and inverse Fast Fourier Transform (IFFT). In the receiver, the data is converted to the frequency-domain from time-domain via FFT operation, and then frequency-domain equalization is conducted. The equalized signal is converted back to a time-domain signal. The frequency domain expression of Equation (5) is given as  
\[\mathbf{R}=\mathbf{D}\Lambda\mathbf{X}+\mathbf{W}. \tag{8}\]  
In summary, the FDE part consists of a rotated-phase compensation, channel estimation, and frequency domain equalization.  
Channel estimation plays a critical role in underwater acoustic communications [24; 25; 26]. We carried out channel estimation using the known PN symbols block-by-block. The underwater channel often exhibits sparse characteristics, which cause the channel to have a large delay spread but many of the channel taps to have negligible values. We used the improved proportionate normalized least mean squares (IPNLMS) method in the receiver because it has been shown to yield a robust estimation for sparse channels [27; 28]. We took this sparseness into account to apply the IPNLMS algorithm to the UWA channel estimation. The IPNLMS algorithm showed not only robust performance in non-sparse channels but also better performance than LMS in sparse channels.  
For the \(i\)-th block, the estimated channel impulse response vector is denoted by \(\hat{\mathbf{h}}_{i}\). In practice, we accounted for the time-varying property of the underwater acoustic channel by averaging the estimated channels from the \((i-1)\)-th and \(i\)-th blocks:  
\[\hat{\mathbf{h}}_{i}=\frac{\hat{\mathbf{h}}_{i-1}^{\prime}+\hat{\mathbf{h}}_{i}^{\prime}}{2}. \tag{9}\]  
We achieved diversity in the combination of different transmissions by maximal ratio combining (MRC) [19; 27]. The basic FDE assumes the channel is invariant in one block. For channel variations in one block or channel estimation errors, there is residual ISI in the MRC output of multichannel frequency domain equalizers. We then used a single-channel adaptive time domain DFE to combat the residual ISI, as shown in Figure 3. We use the recursive least square (RLS) algorithm to update the tap coefficients of DFE.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 2 Image Encryption Algorithms
### Chaotic Systems
#### 2.2.4 Tent Maps  
The tent map is a chaotic system that maps real numbers in the range (0,1) to another real number in the same range. The tent map can be represented by the following formula:  
\[x_{n+1}=f_{\mu}(x_{n})=\begin{cases}\mu x_{n}&\text{if }x_{n}<\frac{1}{2}\\ \mu(1-x_{n})&\text{if }\frac{1}{2}\leq x_{n}\end{cases}, \tag{4}\]  
where \(x_{0}\) is the starting value and \(x_{n+1}\) is the mapped value. \(\mu\) is the control parameter, which has a range of (0, 2). However, chaotic behavior is observed only when the control parameter \(\mu\) is within the range of (1, 2). The bifurcation diagram for the tent map is depicted in Figure 3.  
[PERSON] et al. presented an algorithm in [15]; it utilizes a keystream generated by a tent map. The keystream is subsequently XORed with the plaintext image to produce the cipher image. Notably, the proposed algorithm does not perform any shuffling or permutation on the image's pixels.  
In [16], [PERSON] et al. proposed an image encryption algorithm based on the tent map. The proposed algorithm has two cases. In the first case, a chaotic sequence is generated based on the tent map; this sequence is then XORed with the plaintext image to produce the cipher image. The second case is similar to the first case, with the addition of a permutation step. The resulting cipher image is then permuted using the sequence generated from the tent map in one of four variants (only rows are permuted, only columns, first rows then columns, or first columns then rows).  
Tent maps are easy to implement, feature simple control of initial parameters, and require minimal computational power. However, they suffer from a small range of chaotic behavior, similar to logistic maps.
## 2 Preliminaries
### Task AuD  
To better understand the task AuD, we use a simple example comprising two UEs and an edge server for low-latency MEC network. Figure 3 shows the task AuD of the network.  
As shown in Figure 3, the system's initial AuD is \(\Delta_{d,0}\). As time is divided into frames, if no task completion decision is made, the AuD for the next frame will be  
\[\Delta_{d,1}=\Delta_{d,0}+T_{f} \tag{9}\]  
Then, a task arrives at the UEs at \(t_{a}\). This task consists of two packets \(m_{1}\) and \(m_{2}\), which are individually cached in the queues of UE1 and UE2. After waiting in the queue, the packets at both UE1 and UE2 start transmitting to the edge server (may transmit in different frames). Specifically, the packet at UE1 starts transmission at \(t_{s,1}\) and the packet at UE2 begins transmission at \(t_{s,2}\). According to the system model, packets must be completed within the frame when starting transmission. After the two packets have completed their transmission (i.e., the task transmission is completed), the task begins to compute on the edge server at a subsequent moment, represented as \(t_{s,c}\), and the computation ends at \(t_{e,c}\). As shown in Figure 2, the computation phase of the task consumes a fixed time length. The computation finishes at the end of the frame with making the task decision. Since the task is decided, we have the new task AuD of the network in the current frame:  
\[\Delta_{d,c}=t_{e,c}-t_{a} \tag{10}\]  
Equation (10) shows that the new task AuD is determined by the time that all data packets of the task complete transmission and the task completes computation. Thus, for the task AuD \(\Delta_{d,t}\) in frame \(t\), the task AuD \(\Delta_{d,t+1}\) in the next frame is determined by:  
\[\Delta_{d,t+1}=\begin{cases}\Delta_{d,t}+T_{f}&\text{if no task decided in frame $t$}\\ t_{e,c}-t_{a}&\text{if the task decided in frame $t$}\end{cases} \tag{11}\]  
Figure 3: Task AuD of the low-latency MEC network.  
where \(T_{f}\) is the frame duration, \(t_{e,c}\) is the timestamp when the task computation is completed and the decision is made, \(t_{a}\) is the timestamp when the task arrives at the UEs.  
Therefore, for an MEC network that has gone through \(N\) frames, its average task AuD is defined as:  
\[\overline{\Delta}_{d}=\frac{1}{N}\sum_{t=1}^{N}\Delta_{d,t} \tag{12}\]  
From the example, it can be seen that, compared to the AuD of a single data packet, which only depends on when the packet is used for decision-making after being transmitted to the edge server, Task AuD requires that all packets of a task reach the edge server before they can participate in decision-making together. The Task AuD of a task is largely influenced by the packet that arrives last at the edge server.
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 3 Related Works  
The development of efficient handover decision-making algorithms is crucial for ensuring seamless connectivity in 6G mobile networks [40]. Various studies have explored different methodologies for optimizing handover processes, focusing on parameters such as signal strength, network conditions, mobility patterns, and QoS requirements. This literature review examines key contributions in this area, with an emphasis on fuzzy logic-based approaches and their improvements.
## 2 Classical Topologies
### Bifurcation Phenomenon

Table 4: New design parameters to minimize the bifurcation phenomenon. Figure 3: Input current as a function of frequency variations: (**a**) SS; (**b**) SP; (**c**) PS; (**d**) PP. phenomenon, a study was undertaken to evaluate the feasibility of the previously estimated coupling factor (\(\kappa\)). This assessment was conducted using the student version of Ansys Maxwell Maxwell (2016). The inductances \(L_{1}\) and \(L_{2}\) were designed in a spiral-planar geometry, as depicted in Figure 5. This particular geometry was selected due to its associated beneficial characteristics, which include the ease of implementation, symmetrical behavior, and a robust coupling factor, especially in scenarios involving misalignment. Figure 4: Input current without bifurcation. (**a**) SS. (**b**) SP. (**c**) PS. (**d**) PP. Figure 5: Transmitter and receiver coils simulated in the software Ansys Maxwell®. The study demonstrated that, for the coils designed, a coupling factor of 0.25 was achieved at a distance of 4 mm. This value aligns with the anticipated separation distance between the transmitting and receiving coils, specifically considering their application in implantable medical devices. Consequently, the obtained value of the coupling factor was validated and endorsed for this specific use.
# Study on DID Application Methods for Blockchain-Based Traffic Forensic Data
## 3 Forensic of Digital Data Collected from the Accident Vehicle
### Forensic of ECU(Electronic Control Unit) Vehicle Sensor, ADAS Vehicle Sensor, and LIDAR
Because laser uses microwaves, it can measure distance with stability night and day and complements the camera. LiDAR (Light Detection And Ranging) works on the same principle as RADAR but uses high-powered pulse laser so the distance information it gains is different. It sends out millions of laser beams in one second to measure distance, so it combines all the collected information and can reconstruct it in 3D. RADAR and image sensors create approximately 100 MB of data per second, and the internal ECU creates 50 MB of fusion sensor data per second. For example, the data collected by 5 RADAR sensors and 2 image cameras per second is estimated to be about 1 GB. If an autonomous driving vehicle were to drive forone day with a generated data rate of 1 GB per second, the total amount of data needed would be approximately 30 TB [10]. Advances in ADAS technology are made with the goal of fully autonomous driving, and since the amount of data collected by cameras and various sensors for fully autonomous driving is increasing, it would be difficult to store it within the vehicle. Therefore, Samsung Electronics-Harman is developing autonomous driving technology based on cloud serving without a computer within the vehicle while Microsoft Corp. is developing a cloud and AI-based autonomous driving service (e.g., Volkswagen Automotive Cloud, Baidu's Apollo Alliance etc.). As such, there is a tendency to shift to external storage without storing driving data internally. Accordingly, the collection of vehicle data for vehicle digital forensics also needs to change to a method of analysis that utilizes external data.
# Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase
## 3 Purpose and Methodology
### Research Methodology  
This study was structured around the following steps. First, the influencing factors that can affect the total construction cost were investigated and classified into two groups: those entered in the construction outline, and those obtainable from the BIM model. Second, data on the influencing factors and the total construction costs, which are dependent variables, were extracted and compiled from completed construction projects. Third, the influencing factors collected were summarized into several combination datasets to facilitate the machine learning process. Fourth, a machine learning model for predicting construction costs was prepared. Finally, datasets were applied to machine learning models to analyze error rates for each model. Details of these steps are shown in Figure 1.
## 2 Background
### Obstacles Avoidance (Braitenberg Algorithm)  
The obstacles avoidance algorithm block (light green) is shown in Figure 4. It implements the Braitenberg algorithm for calculating new velocities (\(v_{L}\)', \(v_{R}\)') when obstacles are present nearby [30]. If no obstacles are detected, the output velocities remain the same as the input velocities for the block (\(v_{L}^{\prime}=v_{L}\), \(v_{R}^{\prime}=v_{R}\)).  
The Braitenberg algorithm uses sensor inputs (such as infrared or ultrasonic sensors) to control the motors of a robot. The basic idea is to connect the sensors to the motors in a way that the robot can react to its environment. Broadly speaking, when the left sensor detects an obstacle, it reduces the speed of the right motor, causing the robot to turn left.  
Similarly, when the right sensor detects an obstacle, it reduces the speed of the left motor, causing the robot to turn right. This simple mechanism allows the robot to navigate around obstacles effectively. The adjustment between the sensors and the motors can be empirically calibrated by conducting a series of tests in different environments to observe the robot's behavior. This helps in fine-tuning the sensor thresholds and motor responses to ensure smooth and effective obstacle avoidance [31].
## 5 Conclusions  
A dynamic PMP based on the tricolor binary fringe combined time-division multiplexing (TDM) principle is proposed. In the proposed method, only one tricolor binary fringe, designed by three monochromatic binary fringes with the same fringe width but shifted 1/3 periods one another in R, G, and B channels, is needed; no defocusing projection is needed. The tricolor binary fringe is easy to be designed and encoded. Under the control of a special TMD timing sequence, the HDLP projects the tricolor binary fringe as three grayscale fringes onto the measured dynamic object separately and sequentially at 234 fps with the monochrome light projection mode, the high frame rate monochrome camera synchronized with the HDLP to capture the corresponding deformed patterns in R, G, and B channels. It guaranteed that sufficient 3D shape information of measured real-time changing or dynamic object in different states can be obtained at 78 fps. Just by FFT, filtering and IFT operations, the three nearly unbroken sinusoidal deformed patterns with an equivalent shifting-phase of \(2\pi/3\) extracted from the captured deformed patterns can be used to reconstruct the 3D shape of dynamic object with three-step PMP. The experimental results proved the effectiveness and the practicability of the proposed method for measuring real-time changing or dynamic object. The proposed method can effectively avoid the color cross-talk problem and the grayscale imbalance problem completely. It can also greatly improve the refresh frame rate of the HDLP. We must admit that the proposed method may be limited to measure objects with sharp edges.  
Conceptualization, Y.C. and G.F.; Methodology, Y.C.; Validation, G.F., Y.W., and L.W.; Investigation, Y.W. and C.L.; Resources, Y.C.; Writing--Original Draft Preparation, G.F.; Writing--Review and Editing, Y.C. and G.F.; Visualization, G.F.; Project Administration, Y.C.  
This research was supported by the Special Grand National Project of China under Grant No. 2009 ZX02204-008.  
The authors would like to thank the colleagues made contributions to this research work in our research lab.  
The authors declare no conflicts of interest.  
Figure 13: (**a–c**) Captured deformed patterns of swinging “heart” model at state 1, state 2, and state 3 from Video 1, respectively. (**d–f**) Corresponding reconstructed 3D shape results at the above three states from Video 2. Video 1 shows the captured deformed patterns of the swinging “heart” model in different states. Video 2 shows the corresponding reconstructed 3D shape.
# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 5 Prognostics
### Data-Driven Methods  
With the ability of data-driven methods to obtain predictive information on systems' time series operational data with access to prior historical data, performing fault prognosis on the studied agents becomes feasible. However, given the unrealized potential of data-driven methods in prognosis and unexplored areas of satellite and UAV prognostics, not many works are available in the literature about these agents' fault prognoses using data-driven methods. The following outlines the studies at hand regarding this area.  
[PERSON] and [PERSON] [12] presented the first part in a double-module RW prognosis scheme using a data-driven approach. The first module is responsible for estimating the future values of the accessible system states. An AutoRegressive Integrated Moving Average (ARIMA) model and an LSTM network are tested for time series prediction, and the latter is shown to fare better in the first module on the input datasets. Later, [PERSON] and [PERSON] [5] expanded upon this scheme by augmenting it with the second module. The second module uses the first module's estimated states to predict the RW's inaccessible parameters. Another LSTM network is employed to evaluate the nonmeasurable system parameters and their future values that could give us insight into the health state of the RW. Relating these parameters to the RW's RUL is left for future research by the authors.  
[PERSON] and [PERSON] discussed fault prognosis in CMGs in [69] using a predictive data-driven method that solely involves attitude rate sensor responses to circumvent the problem of unavailable measurement data for component-level variables. Component-level information includes temperature, vibrational and rotational measurements, and lubricant specifications. [PERSON] and [PERSON]'s method consists of a Chebyshev neural network to simulate healthy system dynamics, along with a general path model, historical failures, and real-time measurements to estimate the remaining operating life of the system. According to performance results, the proposed method provides 96.25% accuracy, assuming 70% of the real-time satellite data are unavailable.
## 3 System Model and Problem Description
### Problem Description  
Based on the above system model, this paper aims to solve the following problems: given an area where multiple ESs and NRs are deployed, the task will pass through \(h\) nodes in the migration path \(p\) in this area, among which there are \(m\) ESs and how to choose the appropriate migration path to reduce the failure rate of service \(r_{i}\) migration to minimize migration time, the problem can be described as:  
\[Min:\prod_{x=1}^{m}P_{i}^{x}(F\mid t_{i}^{x^{\prime}})\prod_{y=1}^{h-m}f_{y}^ {n} \tag{10}\]  
\[Min:MT_{i}^{p} \tag{11}\]  
\[s.t:g(r_{i})\in M \tag{12}\]  
\[h(r_{i})\in M \tag{13}\]  
\[MT_{i}^{p}\leq td_{i}^{r} \tag{14}\]  
\[0\leq P_{i}^{x}(F\mid t_{i}^{x^{\prime}})<1 \tag{15}\]  
\[0\leq f_{y}^{x}<1 \tag{16}\]  
Functions \(g(r_{i})\) and \(h(r_{i})\) represent task \(r_{i}\) arriving at source ES and migrating to target ES, respectively, \(M\) represents the resource pool composed of \(m\) ESs, and \(MT_{i}^{p}\) represents the migration time of the task. As shown in (10) and (11), the problem to be solved in this paper is to minimize the failure rate and migration time during task migration, where (12) and (13) limit the data transmission between the user and the ES to the base station's within the signal coverage, (14) indicates that the task should be migrated within the maximum tolerable time of the user, (15) and (16) indicate that the failure rates of ES and NR are within a reasonable range.