esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Many existing portable lighting systems are based on limiting current sources, leading to fluctuations in brightness as battery levels deplete or as environmental conditions change. This is also important in building integrated photovoltaic applications, where portable lights can be charged from LVDC buses or LEDs can be used directly from the power supply, so regulating the brightness is crucial to avoid changes in illumination when the irradiance of PV changes [15]. There are numerous emerging and existing applications in which constant luminous flux in portable LED lamps is highly valuable. In medical and humanitarian operations conducted in remote areas, stable illumination is critical to ensure precision during diagnostics and procedures [16]. Similarly, in emergency response scenarios such as natural disasters or nighttime search-and-rescue missions maintaining consistent light improves safety and task performance [10]. Furthermore, extended outdoor activities like camping, mountainering, or scientific fieldwork benefit from uniform illumination, as it reduces visual fatigue, improves orientation in low-light environments, and eliminates the need for frequent manual adjustments. Therefore, the aim of this research is to design portable LED lighting systems powered by batteries to mantain a constant luminous flux using the zero-average dynamic (ZAD) and the proportional-integral-derivative (PID) controllers. The controller regulates the current through the LEDs while maintaining a similar illumination. The novelty of the proposed topic lies in the fact that it has a portable lighting system that can adapt the current to maintain the luminous flux of the system with the ZAD and PID controllers. As the temperature of the LEDs is an important aspect in maintainingthe luminous flux [17; 18], a temperature compensator was added to obtain a reference current and adjust the signal for the current controller. In addition, the system can improve the user experience in activities requiring similar illumination over long periods, such as agricultural, medical, residential, commercial, industrial, and outdoor activities. The system is tested by changing the voltage of the source and operating with a low-charged battery to evaluate the luminous flux output. The following contributions are obtained in this paper:  
* Compared to other solutions, this paper presents a solution for maintaining constant luminous flux in portable lighting based on LEDs, using the ZAD and PID controllers. * The electronic control system demonstrates the feasibility of adaptive current regulation to maintain constant luminous flux in the lighting system. * The previous literature has not presented a detailed mathematical model that considers the required current variation and maintains similar luminous flux in a portable LED lamp. Therefore, this paper presents a mathematical model for the controller and the adjustment of the monitoring of the current is given in the paper. In addition, the results of tests that consider changes in operating conditions are presented to demonstrate the effectiveness of the proposed control.
# Differences in Body Fat in Athletes Categorized by Resting Metabolic Rate
## 2 Materials and Methods
### Procedures  
#### Resting Metabolic Rate  
RMR assessments were conducted using indirect calorimetry (ParvoMedics TrueOne 2400 Metabolic System, Salt Lake City, UT, USA). Participants were instructed to remain motionless in a supine position on an exam table for approximately 20 min prior to the start of testing. Resting oxygen uptake was determined using a clear, hard plastic hood and soft, clear plastic drape placed over the participant's neck, head, and shoulders to determine energy expenditure. Participants were instructed to remain awake and motionless throughout the duration of the 15-min testing period. A five-minute period was analyzed in which criterion variables (e.g., VO2 L/min) did not vary by more than 5%. Oxygen uptake and carbon dioxide production was used to calculate RMR using the Weir equation [15] and expressed as kilocalories (kcal) per day. The TrueOne 2400 indirect calorimeter has been previously shown to provide valid and reliable measures of O2 and CO2 when compared to methanol combustion techniques [16], with relative percent errors of \(-0.653\%\) and \(-0.525\%\), respectively, and a coefficient of variation of 1.56% for test-to-retest reliability.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Male (_n_ = 98)** & **Female (_n_ = 92)** \\ \hline Age (years) & \(20.1\pm 1.6\) & \(19.4\pm 1.1\) \\ \hline Body Mass (kg) & 92.7 \(\pm\) 17.5 & 65.2 \(\pm\) 11.0 \\ \hline Height (cm) & 181.6 \(\pm\) 6.2 & 168.0 \(\pm\) 6.6 \\ \hline BMI (kg/m\({}^{2}\)) & 28.0 \(\pm\) 4.7 & 23.0 \(\pm\) 3.6 \\ \hline Body Fat (\%) & 15.6 \(\pm\) 8.8 & 22.7 \(\pm\) 6.0 \\ \hline Fat-free Mass (kg) & 77.1 \(\pm\) 9.4 & 49.6 \(\pm\) 6.4 \\ \hline  & XC: 5 (5\%) & XC: 9 (10\%) \\  & T\&F: 6 (6\%) & T\&F: 23 (25\%) \\  & BASE: 8 (8\%) & DIVE: 2 (2\%) \\ Sport & FB: 62 (63\%) & SOC: 43 (47\%) \\  & WRES: 17 (17\%) & SWIM: 4 (4\%) \\  & & TEN: 2 (2\%) \\  & & VB: 9 (10\%) \\ \hline Training Volume (h/week) & 7.7 \(\pm\) 4.7 & 9.4 \(\pm\) 3.1 \\ \hline \hline \end{tabular} Values are mean \(\pm\) SD and \(n\) (%). BMI: body mass index; BASE: baseball; XC: cross country; FB: football; T\&F: track and field; WRES: wrestling: DIVE: diving; SOC: soccer; SWIM: swimming; VB: volleyball; TEN: tennis.  
\end{table}
Table 1: Physical characteristics of subjects.
## 3 Methodology
### Proposed Method
#### 3.3.1 Data Preparation  
In the data preparation phase, the features are extracted from satellite images using an algorithm. The extracted features are combined with the numerical features to form a new dataset. The new dataset is then split into the training set and test set with a ratio of 80:20. As a preprocessing step, the two sets of data were scaled with a standard scaler to eliminate magnitude difference amongst features, thereby improving generalization and reducing training time.
## 4 Results and Discussion
### Consensus on Challenges
#### 4.1.4 Battery Disconnection  
Disconnection of hardware physical components such as the battery was identified as a challenge by four papers. This challenge was mentioned considering the use of the application independent of site context and project type. Researchers mentioned that adapting to some requirements before the use of the devices (e.g., by making sure that the device is fully charged) could help overcome this challenge [11]. Others suggested the use of power banks during AR application to ensure battery charging [36]. This challenge is related to the type of hardware's battery, which may have implications for all types of AR devices (i.e., head-mounted displays, cell phones, and tablets).
## 4 Simulation and Analysis of Results
### Analysis of Selection Processing  
Taking ulysses16 data as an example, the optimization ability of TPSMA is verified by analyzing the optimization process. Figure 5 and Table 2 are simulations and partially obtained paths under ulysses16 data. In the Tables, red represents the current point and optimal result, green represents the selected point by SMA and TPSMA, and blue represents the remaining points in the set of candidate points by TPSMA. Figure 10 shows the flow and distance values from path point 12 to the remaining path points, and their ranking of each point.  
Figure 10: Data of flow and distance from point 12 to the remaining points, and the selection from 12 to the next point by SMA and TPSMA.  
Figure 9: The simulation of bier127.  
When the current point is 12, it can be found in Figure 10 that the points of the largest flow, the second largest flow, the shortest distance, and the second shortest distance in the remaining candidate points are 15, 16, 8, and 11, respectively. In Table 2, point 15 with the maximum flow is selected by SMA, and the path 5 is the optimization result of SMA with a path length of 103.174. There are four candidate points, 15, 16, 8, and 11, which are obtained by TPSMA. Moreover, the path 1 to path 4 in Table 2 can be realized by adding random factors, and path 1 is the optimal result of TPAMA with a path length of 77.8372.  
When the current point is point 3, it can be obtained in Figure 11 that the candidate points are 1, 2, and 7. Path 1 to path 5 in Table 3 can be obtained by random factors, and path 1 is the final result of TPSMA in this paper. Point 1 is selected after point 3 and the path length is 77.8372 in path 1. Furthermore, Table 3 shows that the next point of point 3
# Performance Analysis of Partitioned Step Particle Swarm Optimization in Function Evaluation
## 1 Introduction
### Particle Swarm Optimization Components
#### 1.2.2 Inertia Weight  
From the discussion of [PERSON] and [PERSON] [16], the velocity is intended to expand the search space, that the particles have in terms of ability to explore a new area. The inertia weight is responsible for controlling the previous velocity, thereby, controlling the movement of particles toward the previous velocity. In addition, this inertia weight is also responsible for balancing the global search (toward _gbest_) and local search (toward _pbest_).  
Because of the inertia weight's control on the exploration of the PSO, several PSO variants were performed to modify the value of \(\omega\). The crazy PSO (CPSO) [14] uses a probability of craziness to determine if the new velocity will be used or if it would be a random number from zero to the pre-set maximum velocity. For both CPSO and weight-improved PSO (WIPSO) [19], the value of \(\omega\) changes on the basis of the current iteration that decreases as the iteration increases. This decrease in the change of the \(\omega\) value is necessary to make sure that the algorithm converges. The generalized PSO (GPSO) [20], likewise, changes in the inertia weight depending on the iteration. However, instead of a craziness component, the GPSO uses the previous _gbest_ and current _gbest_ to decide how much of the previous inertia would be used. However, if the GPSO got stuck in a local minimum, the inertia would not change. The GPSO compensates this by including the position of a random particle and a velocity of another random particle as the fourth and fifth terms in their velocity update, leading to too much exploration.  
The adaptive PSO (APSO) has been presented in several pieces of literature as the consideration of the current state of particles or sparseness of the solution for exploration. [PERSON] et al. [21] used the distances between particles to compute the local sparseness degree to select the exemplars of each group of particles as _pbest_ particles. Although [PERSON] et al.'s APSO seems a good way to select _pbest_ particles, the presented algorithm itself uses long complicated steps. In the work of [PERSON] et al. [22], the APSO used additional constant parameters to adjust the degree of sparseness than then adjusted the value of \(\omega\). [PERSON] et al.'s variant seems to be promising because the particles were moved according to the current state of the solution rather than being limited by a range of \(\omega\) or relying on randomness as in like the CPSO and WIPSO.
## 2 Data Collection and Pre-Processing
### Basic Statistics and Data Pre-Processing  
#### 2.3.1 Basic Statistics  
Figure 3(a) shows the results of plotting the AIS raw data collected from January to April 2020 in the area corresponding to Busan New Port, according to the information listed in Table 2, into a geographic information system (GIS). The GIS software used for the analysis was ArcGIS Pro. Figure 3(b) shows the result of organizing only the ships docked at the target pier in the raw data. According to the AIS data, the number of target ships corresponding to the arrival and departure was 42 each, and as there were ships that berthed two or three times during the period, the total number of trajectories was 50 each. Table 3 shows the results of the basic frequency analysis.  
Both the arrivals and departures were divided into cases of passing Todo to the left or passing to the right. Table 4 shows the results of counting which way each pier-specific ship passed Todo. In the case of the arriving ships, the number of ships passing Todo on the left and right was similar at 23 and 27 times, respectively. However, in the case of departure, the number of times ships passed to the left was 31, and to the right, 19.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Categorization** & **Automatic Identification System Data** \\ \hline Period & 01 January 2020–30 April 2020 \\ Collection Area & Latitude 034.8 N–035.1 N, Longitude 128.7 E\(\sim\)129.0 E \\  & (Around Busan New Port) \\  & Pier 2 No.4 \\ Pier & Pier 2 No.5 \\  & Pier 3 No.1 \\ Ship Type & Container ship \\ Size of ship & Gross tonnage 100–220 K \\  & Ship’s position (latitude, longitude) \\ Information & Speed over ground (knots) \\  & Course over ground (degree) \\  & Ship’s position (latitude, longitude) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Automatic identification system (AIS) data characteristics for analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Status**} & \multicolumn{4}{c}{**Number of Ships**} & \multicolumn{4}{c}{**Number of Berthings**} \\ \cline{2-9}  & **1 Time** & **2 Times** & \(\geq\)**3 Times** & **Total** & **Pier 2** & **Pier 2** & **Pier 3** & **Total** \\  & & & & & **No.4** & **No.5** & **No.1** & & \\ \hline Arrival & 36 & 5 & 1 & 42 & 28 & 21 & 1 & 50 \\ Departure & 35 & 6 & 1 & 42 & 28 & 21 & 1 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of ships and berthings.
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 3 Quantum Trusted Platform Module (QTPM)
### Quantum Cryptographic Module Development on FPGA  
Building on the QTPM software implementation described in Section 3.2, we propose a hardware-oriented approach to realize QTPM functionality using FPGA technology. Field Programmable Gate Arrays (FPGAs) offer an ideal platform for the direct deployment of cryptographic functions due to their scalability, speed, and flexibility. By embedding a QRNG into an FPGA, IoT devices can benefit from the superior randomness of quantum entropy while maintaining the performance and efficiency advantages of hardware-based security. The resulting FPGA-based quantum cryptographic module (QCM) is designed to support secure key generation and encryption, representing a critical step toward a full hardware realization of the QTPM architecture and its deployment in real-world IoT systems.  
Figure 2 presents the design of QRNG integration with FPGA. The Xilinx Vivado platform and Verilog programming language can be used to design, simulate, and program the FPGA. The FPGA can directly integrate with the QRNG card via the PCIe interface, utilizing built-in FPGA drivers such as a PCIe controller to efficiently manage data exchanges with the QRNG. This seamless integration enables cryptographic algorithms, including AES and RSA, to be implemented within the FPGA while leveraging true random bits from the QRNG for dynamic and highly secure key generation. Each cryptographic function (e.g., AES encryption, RSA key generation) is encapsulated in an independent hardware module. QRNG-based random numbers can be used to generate keys/tokens to enhance secure communication between IoT devices. The FPGA handles all encryption operations in hardware, which can improve performance and resistance to cyberattacks. We plan  
Figure 1: QTPM testbed (integrating IDQ QRNG PCIe card with raspberry Pi 4 to simulate IoT device secure key generation).  
to use the AMD Kintex FPGA board. The exact HDL modules and the complete FPGA integration are under development and are part of our future planned work.  
Moreover, the QRNG-FPGA design can be converted to a custom application-specific integrated circuit (ASIC), and the QRNG can be directly integrated into a chip. While both the QRNG and cryptographic functions are directly embedded into the chip, latency can be significantly reduced since no external communication is required for random number generation or cryptographic processing. The chip can generate high-quality session keys, encrypt sensor data, authenticate IoT devices, and enable secure IoT network communication while ensuring data integrity and confidentiality.
## 3 Adaptive Carrier-to-Noise Ratio (C/N\({}_{0}\)) Estimation Method
In Equation (24), the estimation process of the measurement noise variance that is based on Allan variance and the Kalman filtering process are completely independent of each other, thereby effectively reducing the risk of Kalman filter divergence [20]. According to Equation (24), the strong tracking filter adjusts the one-step prediction mean square error matrix according to the measurement innovation. Compared with the adaptive method that is based on the innovation sequence, the filtering process that is based on the Allan variance is more stable. To reduce the influence of the old measurement noise, an exponential fading memory weighted average method can be utilized. The recursive estimation formula for measuring the noise variance is as follows [20]:  
\[\hat{R}_{k}=(1-\beta_{k})\hat{R}_{k-1}+\frac{\beta_{k}}{2}{(Z_{k}-Z_{k-1})^{2}} \tag{25}\]  
\[\beta_{k}=\frac{\beta_{k-1}}{\beta_{k-1}+b} \tag{26}\]  
where the initial value \(\beta_{0}=1\) and \(0<b<1\) is the fading factor. Figure 2: Time-varying fading factor calculation process. By combining the noise variance and the state estimate of the amplitude Kalman filter, the following \(c/n_{0}\) estimation formula is obtained:  
\[\left(\hat{c}/\hat{n}_{0}\right)_{k}=\frac{\hat{X}_{k}-2\hat{\omega}_{n,k}^{2}}{2 T_{\textit{col}}\hat{\omega}_{n,k}^{2}} \tag{27}\]  
The \(C\)/\(N_{0}\) estimates are averaged over multiple times to obtain more accurate results:  
\[\hat{C}/\hat{N}_{0}=10\log_{10}\left(\frac{1}{N}\sum_{k=1}^{N}(\hat{c}/\hat{n} _{0})_{k}\right) \tag{28}\]  
A detailed algorithmic flow chart of the ASTKF C/\(N_{0}\) estimation method is shown in Figure 3.
## 3 Hybrid Differential Evolution Particle Filter Track-Before-Detect Algorithm
### Improved PF-TBD-Based on the HDE Algorithm
#### 3.2.1 Hybrid Differential Evolution Algorithm  
The HDE algorithm relies on the initial population generation, mutation, recombination and the new selection to probe the search space through iterative progress until the termination criteria are met.  
Detailed steps are presented accordingly in the sequel.  
Step 1: Creating the initial population.  
The first step of HDE is to create the initial population samples (the number of generations is \(g=0\)) in \(n\) dimensional space as follows:  
\[x_{y}(0)=x_{y}^{L}+rand_{y}(0,1)(x_{y}^{U}-x_{y}^{L}) \tag{14}\]  
where \(i=1,2,...,\)\(NP\); \(j=1,2,...,\)\(n\), and \(NP\) is the population size. \(x_{y}^{U}\) and \(x_{y}^{L}\) denote the upper and lower limits of the \(j\)-th variable in the population respectively. \(rand_{y}(0,1)\) represents a uniformly-distributed random value within [0, 1].  
Step 2: Mutation operation.  
The function of mutation in HDE is to maintain the diversity of a population. A typical HDE mutation sample formulation is:  
\[h_{y}(g)=x_{y}(g)+F_{m}\cdot(x_{r1,j}(g)-x_{r2,j}(g)) \tag{15}\]  
where \(g\) represents the \(g\)-th generation, and \(h_{y}(g)\) are the mutated vector samples. \(r1\
eq r2\
eq i\) and \(r1\), \(r2\) are randomly-selected integers within \(NP\), \(r1,r2\in\{1,2,...,\)\(NP\}\). \(F_{m}\) is the scaling factor.  
Step 3: Crossover operation.  
The basic crossover process is a discrete recombination, which employs a crossover constant \(C\in[0,1]\) to determine if the newly-generated individual samples are to be recombined. The expression of the crossover process is given in Equation (16):  
\[v_{y}(g)=\begin{cases}h_{y}(g)&rand\ (0,1)\leq C\\ x_{y}(g)&rand\ (0,1)>C\end{cases} \tag{16}\]  
where \(v_{y}(g)\) are the trial vector samples.  
Step 4: New selection operation.  
The HDE algorithm adopts the Metropolis criterion of the SA algorithm to select whether to accept the trial vector samples. We construct the parameter about fitness function values \(\Delta f=f(v_{y}(g))-f(x_{y}(g))\), and then, we decide the trial vector samples by the Metropolis criterion.  
Metropolis criterion: if \(\Delta f<0\), the trial vector sample is accepted. If \(\Delta f>0\), we calculate an acceptance probability \(P(\Delta f)=\exp(-\dfrac{\Delta f}{T_{st}})\) and generate a pseudo-random number \(r\). If \(P(\Delta f)\geq r\), the trial vector sample is accepted; otherwise, the point is discarded.  
Step 5: Cool down operation.  
In this step, the cool down operation of the SA algorithm is executed. We defined \(T_{{}_{SA}}\!=\!T_{{}_{SA}}\!\cdot\!\rho\); here, \(T_{{}_{SA}}\) is an annealing temperature, and \(\rho\in\left(0,\!1\right)\) is an annealing control parameter.  
When the new population is propagated, Step 2 to Step 5 is repeated until the pre-specified temperature \(T0\) is reached.
## 2 Near Real-Time Remote Sensing Architecture
### Overall Scheme of NRRS
Actually, it is particularly well-suited for directly accommodating inter-satellite communication terminals on remote sensing satellites due to their high attitude control accuracy. We have calculated that nearly 100 Earth stations are required to serve the Belt and Road region. As a near-global-reach constellation, Starlink has built nearly 200 gateway stations. If real-time direct downloading of remote sensing data is to be achieved,  
Figure 1: Architecture of near real-time remote sensing based on satellite Internet. hundreds of Earth stations are required. Constellation networking through inter-satellite links can greatly reduce Earth station deployment requirements and improve system operational efficiency. Constellation networking through inter-satellite links can greatly reduce Earth station deployment requirements and improve system operational efficiency._  
**Remark 2**. : _Since the use of satellite broadband Internet and multiple access of users, there will be an increased communication requirement. The amount of data transmitted by ISL depends on the traffic demands as well as the in-orbit processing capacity. If the satellite has near real-time edge computing capability, the transmission rate of Mbps is sufficient. Otherwise, a Gbps channel is required to transmit the raw image data. On the other hand, an optimization design of beam coverage can be adopted to handle the non-uniform and dynamic distribution of the traffic demands. [PERSON] et al. [31] present a method of optimizing irregular beam coverage and beam pattern depending on traffic demands, which allows for minimizing the costs per Gbps in orbit, the normalized coverage error, and offered capacity error per beam._  
**Remark 3**. : _The equipment and use of laser terminals will pose higher requirements for satellite design and attitude control. The attitude stability is required to be maintained above 0.005\({}^{\circ}\)/s. To establish a stable ISL between remote sensing satellites and third-party ISL service providers, several technical conditions have to be satisfied. Firstly, the communication systems of both sides need to match each other, which is necessary for both laser and microwave links. Secondly, it is necessary to address the issue of the large dynamics in LEO satellites. A series of indicators need to be designed and confirmed in advance, including communication distance, capture and tracking angle range, maximum angular tracking rate, maximum angular acceleration, etc. Although the construction cost is relatively high, the potential benefits are still attractive. It is expected that the remote sensing satellites will be an important user of broadband communication constellations in the future._