esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Governing Equations and Numerical Methods  
### Target Internal Flow System: Critical Nozzle  
Generally, in a nozzle, when the pressure difference between upstream and downstream is larger than a specific value, the flow cannot be accelerated higher than the sonic speed at the nozzle throat, and the mass flow rate is kept constant. This phenomenon is called a choked flow. When the choke flow occurs, the mass flow rate through the nozzle can be determined using the only inlet flow field information such as total pressure, temperature, and velocity upstream of the nozzle and the nozzle area, regardless of the downstream flow conditions. The ratio of the theoretical mass flow rate to the actual mass flow rate is defined as the efficient coefficient, which is closer to one as the Reynolds number increases. The critical flow Venturi nozzle (CFVN) is widely used as a flowmeter by using the characteristics of the nozzle. According to ISO 9300, the CFVNs can be classified into cylindrical-throat Venturi nozzle and toroidal-throat Venturi nozzle depending on their throat shape [9]. In this study, the toroidal-throat Venturi nozzles, which are commonly used in flowmeters, were investigated. Figure 1 shows the shape of the toroidal-throat Venturi nozzle with its geometric parameter, d.
# Parameter Observer Useable for the Condition Monitoring of a Capacitor
###### Abstract  
Monitoring the condition of electrolytic capacitors in practical applications is a topic that has been and remains the subject of much research. This article is part of research in this area. It develops a parameter observer (PO) and proposes its use for the determination of the equivalent capacity and equivalent serial resistance of electrolytic capacitors. The observer is an integral-open-loop type second-order system, the input of which is the voltage at the capacitor terminals measured during a two-stage capacitor's discharging process through a variable resistor. The PO estimates the so-called time constant of the discharging circuit for each of the two stages from which values the capacitor's parameters are calculated. The use of PO is illustrated for determining the output capacitor parameters of a buck DC-DC converter. The experiments were performed with two electrolytic capacitors with the nominal values 100 uF and 470 uF. Compared with other monitoring methods that use observers, the proposed observer is faster in tracking error mitigation, e.g., \(10^{-3}\) s in comparison with 5\(\cdot 10^{-3}\) s or more. The low computational volume of the discrete-time PO allows the prospect of implementation in real time.  
capcapictor; parameter observer; condition monitoring; time-varying parameters; continuous-time model; discrete-time model Article
## 4 Cryptographic Unit System
### Diffusion and Confusion Blocks  
One of the main goals of encryption systems is that the output is not humanly readable. As in any electronic device, packets communicated wirelessly are a set of 1's and 0's. So we aim through this encryption system to prevent any eavesdropper on the communication channel from detecting patterns in different packets allowing them to understand the different parts of the message. For example, if an eavesdropper intercepts different packets and reads them [32], conventionally he/she will know that any message will start with similar headers allocated in the first four or maybe eight bits. Moreover,  
Figure 4: Example of 128-bit Encryption Key using 2Q30 Data Representation with a \(stop\_time=n\).  
Figure 5: Generation of the Encryption Keys from the Henon System.  
he/she can notice that packets sent from the same device all have the same set of exactly sixteen bits in the middle. The eavesdropper can conclude then that this is the ID number of the device. On the other hand, a simple XOR operation or a simple bit permutation does not prevent this problem. This bit flipping just makes the analysis harder, as the eavesdropper can deduct from a simple change of a set of bits the original bit location. Once deciphered, the attacker is able to modify the packets and reuse them for his/her own purposes. For this reason, our encryption system focuses on two main properties of secure ciphers [33]:  
* **Confusion:** which is the property of drastically modifying data from the input to the output. In other words, each bit of the ciphertext should depend on several parts of the system.
* **Diffusion:** which is the property responsible for changing many different bits of the output when a single bit of the input is modified.  
The combination of these two properties decreases the chance of statistical attacks and analysis to break the cipher.
## 2 Background
### Model Construction
#### 2.3.4 PSO-SVM-CSDPC Disease Prediction Model  
According to the description of the PSO optimization algorithm [6], the idea of the PSO algorithm is very simple, the overall process is clear and concise, and can be easily applied to practical problems, and the PSO actually optimizes the parameters of the model in the process of application. In this paper, we adopt PSO to optimize the three Key parameters of SVM: positive penalty factor, negative penalty factor, kernel function parameter, which determine the accuracy and generalization ability of SVM-CSDPC disease prediction model to a certain extent, and learn from PSO application in other predictions. The good results of problem parameter optimization, so this paper applies PSO optimization algorithm to SVM-CSDPC disease prediction model, optimizes parameters of SVM-CSDPC disease prediction model, and constructs SVM prospective population cohort system optimization prediction model based PSO, hereinafter referred to as the PSO-SVM-CSDPC disease prediction model. The model process is as follows:  
1. Input the CKB data, use the relevant rules to perform missing value filling, data format processing and data normalization processing on the data to obtain the training and test data sets of the model.
2. Initialize the population. Set the initial population as 100. Each particle has three dimensions, representing the positive penalty factor \(C_{+}\), the negative penalty factor \(C_{-}\), the kernel function parameter \(\sigma\), respectively. Then the particle parameters, such as velocity and position parameters are set, including inertia weight \(\omega\) = 0.8. According to the problems studied in this paper, the velocity ranges of one dimension are [0.01, 0.1], [0.01, 0.1], [0, 0.1], and the position ranges of each dimension are [0, 10], [0, 10], [0, 1]. The learning factor \(c_{1}=c_{2}=2\)[6], and setting the termination condition, the termination condition is generally that the optimal fitness value satisfies the set threshold or reaches the maximum number of iterations.
3. Using cross-validation to calculate the group fitness, and sequentially updating the individual optimal value \(P_{i}\) and the global optimal value \(G_{i}\).
4. Adjust particle velocity and position according to Formulas (4) and (5).
5. If the end condition is reached, the output parameter ends. If the end condition is not reached, go to 3.
6. Input the optimal solution of the output into the model, input the test data set into the model for disease prediction of the circulatory system, and then verify the prediction result and evaluate it according to the evaluation index.
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## 6 Conclusions  
In this paper, we have investigated the positioning method for IIoT with a mm-Wave communication. The design of PRS and SRS-Pos are fully discussed to provide the problem model. Under the real district of power limitation and IoT packet size, a joint UL time and angle estimation method is analyzed. By giving the general system model, the time and angle estimation are derived for positioning, respectively. Through combining the time and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Running Time** \\ \hline TDOA-only & 0.5856 s \\ \hline AoA-only & 3.0083 s \\ \hline joint TDOA + AoA & 3.5624 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation setting.  
Figure 10: Illustration of positioning accuracy vs. TRP number.  
angle together, the localization of an IIoT device is improved according to the simulation results.  
In future work, regarding the joint DL and UL positioning, we will further investigate the impact of the device number on the positioning method, since the measurement load at TRPs increases with the device number. Especially, in case of massive machine type communication (mMTC) with a density of 1 connection/m\({}^{2}\), it is a huge burden for TRPs to perform positioning.  
Conceptualization, H.-M.C., S.H. and P.W.; validation, H.-M.C. and S.H.; writing--original draft preparation, H.-M.C. and S.H.; writing--review and editing, H.-M.C., S.H., T.C. and C.F.; funding acquisition, S.L.; investigation, F.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China (2020 YFF0305401).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
Many thanks to reviewers for their positive feedback, valuable comments and constructive suggestions that helped improve the quality of this article. Many thanks to editors' great help and coordination for the publish of this paper.  
The authors declare no conflict of interest.
# Modeling of Motorized Orthosis Control
## 4 Results and Discussion
The major novelty of the current research is the implementation of an optimization algorithm based on differential evolution (DE) to improve the control and movement of the orthosis. This method, which is based on evolutionary principles, has been chosen due to its simplicity, small number  
Figure 8: Results with the optimization algorithm and with the application of a load. Figure 7: Results without the optimization algorithm and with the application of a load. of configuration parameters, and good performance. [PERSON] et al. [28] combined the use of different gains and passive elements to reduce the energy consumption while applying a static optimization. Additionally, [PERSON] et al. [29] proposed a PID adaptive controller based on modified particle swarm optimization algorithm, where the controller is initialized with the desired position and velocity instead of EMG signals, as made in this work. Experimental studies, as the one carried out with eight subjects by [PERSON] et al. [30], showed improvements in the upper limb functions with the use of a controlled robotic system with one degree-of-freedom. Recently, in the study of [PERSON] et al. [31], a clinical case of an upper limb orthosis can be found for use in assisting the user to perform the flexion/extension of the elbow demonstrating the feasibility of the implementation of an upper limb myoelectric orthosis. [PERSON] et al. [32] developed a novel device, the Active Joint Brace (AJB), which combined an exoskeletal robotic brace with EMG control algorithms. They confirmed the feasibility of using an EMG-controlled powered exoskeletal orthosis for exercise training. The control and optimization algorithm that was carried out in the current research to assist the elbow movement could contribute to the improvement of devices as the one studied by [PERSON] et al. [33], which enables the bidirectional control at the elbow into flexion or extension. New wearable devices, as the one developed by [PERSON] et al. [34], combining a neuromuscular electrical stimulation (NMES) and robot hybrid system, have also showed improvements in the coordination of agonist/antagonist pairs when performing sequencing limb movements. In the previous study that was made by [PERSON] et al. [17], a similar EMG based orthosis control was proposed. However, this work did not explain how the control parameters can be obtained. Recently, [PERSON] et al. [35] developed a modified computed torque controller to enhance the tracking performance of a robotic orthosis of the lower limb. Their control laws proposed have similar concepts, but with different objectives and approaches. In the current study, we follow the movements of the human being, whereas, in [35], they try to teach or show the correct forces and torques that must be applied to follow the desired trajectory.
# A Digitalization Algorithm Based on the Voltage Waveform of the Multifunction Vehicle Bus
## 2 The MVB Voltage Waveform Digitalization Algorithm
### MVB Voltage Waveform Digitalization Algorithm Extraction Algorithm
#### 2.2.2 Feature Extraction  
In Section 2.1, five waveform features of the MVB physical layer are described, which can be associated with the time indexes of the edges. In Section 2.2.1, it was described that all time indexes of numerical abrupt changes will be captured. Thus, the corresponding actual waveform edges will be captured, and they are the reference points used to calculate the steady-state amplitude, slew rate, edge distortion and other waveform features.  
* Steady-state amplitude: Actually, the waveform between two adjacent edges is a pulse. With the waveform function, steady-state amplitudes of every pulse can be calculated.
* Overshoot: Within a pulse, the ratio of the maximum amplitude to the steady-state amplitude is the overshoot.
* Positive and negative steady-state amplitude difference: Between three continuous edges, for example, the first bit cell in Figure 5(b), there are positive and negative steady-state amplitudes. Their difference value is the target feature.
* Edge distortion: An extraction diagram of edge distortion is shown in Figure 7. The slope of the voltage waveform edge is rapid and the duration is short. Here are the steps to obtain edge distortion: 1. Obtain the adjacent sampling points (green spots in Figure 7) of the waveform edge; 2. Fit the sampling points with a linear curve (red line) to replace the actual waveform; 3. Calculate the actual zero-crossing point (orange spots) of the curve; 4. Repeat steps 1-3 for the next waveform edge; 5. The interval can be obtained based on the adjacent estimated zero-crossing points, and the edge distortion is the ratio of the interval to 666.7 ns.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parity Order of Edges** & **Interval of Edges** & **Position of Edges** & **Decoding Result** \\ \hline Even number & 0.5 BT & Middle of bit cell & None \\ Even number & 0.5 BT & Edge of bit cell & \"1\" \\ Even number & 1.0 BT & Middle of bit cell & \"1\" \\ Even number & 1.0 BT & Edge of bit cell & “NL\" \\ Even number & 1.5 BT & Middle of bit cell & “NL” \\ Even number & 1.5 BT & Edge of bit cell & “1” + “NL” \\ Odd number & 0.5 BT & Middle of bit cell & None \\ Odd number & 0.5 BT & Edge of bit cell & “0” \\ Odd number & 1.0 BT & Middle of bit cell & “0” \\ Odd number & 1.0 BT & Edge of bit cell & “NH” \\ Odd number & 1.5 BT & Middle of bit cell & “NH” \\ Odd number & 1.5 BT & Edge of bit cell & “0” + “NH” \\ \hline \hline \end{tabular}
\end{table}
Table 1: Decoding table according to the information of edges.  
Figure 6: Start delimiter of master frame (**a**) and slave frame (**b**).  
* Slew rate: The algorithmic approach to calculating the slew rate is to find the first point more than 100 ns away from the actual zero-crossing point, divide its amplitude by the time interval (100 ns) and obtain the slew rate.
# A NavCom Signal Authentication Scheme Based on Twice Two-Way Satellite Time Transfer
## 3 Performance Analysis
### Cost  
The cost of the proposed authentication scheme mainly consists of the cost associated with the third-party link, computation, and storage complexities of both the ground terminal and the satellite.  
However, owing to this authentication scheme being designed to operate within a NavCom integrated system, only the arrival time of the satellite-measured signal needs to be transmitted to the user terminal via the communication component, thereby eliminating the need for any third-party link cost.  
Furthermore, the receiver is responsible for fulfilling the ranging function, thus minimizing any additional cost requirements. As a result, the cost of this authentication scheme primarily involves the satellite's ability to measure the uplink signal accurately.  
This scheme utilizes physical layer security for authentication, because there is no need for key distribution and security, and the key management system is greatly simplified by directly utilizing the motion characteristics of low-orbit satellites and the slow-varying characteristics of clock differences.
# Phase-Coded and Noise-Based Brillouin Optical Correlation-Domain Analysis
## 3 Brillouin Optical Correlation-Domain Analysis Based on Amplified Spontaneous Emission Noise
### Principle of Operation
The SNR of signal power gain measurements, over detector integration time \(T_{D}\), can be estimated as [31; 32]:  
\[\text{SNR}\approx g_{0}\sqrt{\frac{v_{g}}{2}}\left|A_{p0}\right|^{2}\sqrt{ \Delta z\cdot T_{D}}. \tag{10}\]  
ASE B-OCDA encounters an additional difficulty. Unlike phase-coding with periodic sequences, the ASE source forms only a single correlation peak at \(z=L/2\). The position scanning of high-order correlation peaks, which is routinely performed in phase-coded B-OCDA [37], is inapplicable. The correlation peak may only be moved through the introduction of physical variable delay lines in the fiber paths leading either the pump or signal waves into the measurement section of interest [31]. Despite the above issues, ASE B-OCDA was successfully demonstrated with a measurement range of 50 mm and a spatial resolution of 4 mm [31]. That first experiment was conducted over a commercially available small-core fiber with a \(g_{0}\) that is twice larger than that of standard single-mode fiber [31]. Figure 11 below shows previously unpublished follow-up results, in which a section of standard SMF-28 fiber is analyzed with a spatial resolution of 2 mm. Local hot-spots that are 3 mm and 2 mm wide are properly identified, whereas a 1 mm wide heated region is not clearly recognized. The spatial resolution in this experiment comes close to the state-of-the-art of B-OCDA over fiber [16]. Recently, another version of B-OCDA that shares some of the characteristics of the ASE-based approach was proposed and demonstrated by [PERSON] et al. from Taiyuan University of Technology [34; 35]. In this technique, a semiconductor laser diode is placed within an external feedback cavity, which drives the source into a chaotic regime of operation [34; 35]. Noise-like waveforms of GHz-scale linewidths are obtained and used in the generation of the Brillouin pump and signal waves. The proposition builds upon an earlier suggestion that was raised in the context of Brillouin dynamic gratings over polarization-maintaining fibers [50]. Similar to ASE-based B-OCDA, high-rate external modulation of the laser light is unnecessary, and \(u(t)\) is a complex-valued random process. Care was taken to minimize secondary peaks in the auto-correlation function of the chaotic source [34]. As in phase-coded B-OCDA, pulse amplitude modulation of the pump waves was successfully introduced to suppress off-peak Brillouin interactions [35]. In the latest demonstration of this method, a 10.2-km-long fiber under test was monitored with a spatial resolution of 9 cm [35]. Position scanning relied on a long-range programmable optical delay line.
## 4 Simulations and Results
### Attacker Model  
In our proposed system, security and privacy are the main concerns for data sharing among vehicles. Vehicles communicate with each other for sharing sensory information. The main concern is to prevent privacy leakage and to ensure security against potential threats. We consider an attacker model tested against following attacks, for our proposed system.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 3 Methodology
### Input Feature Construction  
The model input comprises multispectral image patches extracted from Sentinel-2 Level-2A surface reflectance products. For each ground inventory plot, a patch of size \(32\times 32\times 10\) was generated, corresponding to a \(640\times 640\,\mathrm{m}^{2}\) area centered on the plot location. The spatial resolution of all bands was unified to 20 m through bilinear resampling to ensure consistency.  
Ten spectral bands were selected based on their relevance to vegetation and biomass estimation: visible (B2-B4), red-edge (B5-B7), near-infrared (B8, B8A), and shortwave infrared (B11, B12). These bands capture key canopy attributes such as structure, pigment concentration, and moisture content.  
To normalize reflectance values and improve model convergence, min-max scaling was applied to each spectral band independently, transforming pixel values to the range \([0,1]\) based on their global minimum and maximum within the study area.  
Cloud and shadow pixels were masked using the Scene Classification Layer (SCL). Only cloud-free samples were retained. The final input set ensures accurate spatial alignment between Sentinel-2 imagery and ground-truth plots, providing reliable input for carbon stock modeling. It is important to note that geographic coordinates (latitude and longitude) were not included as input features in the model. They were used solely for spatial alignment between ground inventory plots and Sentinel-2 image patches during the preprocessing stage. This design choice ensures that the model relies entirely on spectral information rather than spatial proxies, thus improving ecological interpretability and avoiding the inclusion of location-dependent artifacts.
## 2 Overview of Cognitive Radio Sensor Networks
### What is a CRSN
#### 2.2.1 Main Features of a CRSN  
For a CRSN to gain any rational meaning, it must adopt the intrinsic characteristics of WSNs while still performing CR functions. Thus, a WSN is expected to benefit from the features of CR, such as DSA and the power consumption reduction achieved by adaptability. The nature of throughput is expected to be bursty due to opportunistic channel usage, which mitigates the problem of an increased probability of collision in densely deployed WSN environments. In the past, because of the low throughput of traditional WSNs, congestion and over-flooding were not significant design issues. However, with the bursty nature of throughput in CRSNs, these issues must be addressed, especially in real-time applications that consider quality of service (QoS).  
One of the pioneering studies in this field [16] clearly demonstrated how CRSNs outperform traditional WSNs based on a comparative protocol study between a standard ZigBee/802.15.4 sensor and a CR-based version of the same sensor. The results showed the superiority of CRSNs over WSNs based on hop count, throughput and end-to-end application layer latency, without incurring significant overhead.
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 3 Requirements of Internet of Drones (IoD)
### Communication Requirements
#### 3.1.1 Seamless Coverage  
Hot-spot coverage (stages, tourist areas, and industrial areas) is appropriate for aerial entertainment. Widespread coverage in suburban, urban, and rural areas is required for the inspection and logistics of power or base stations. In the future, seamless drone coverage will become more essential for network planning. Unlike conventional network coverage serving mostly land users, enhanced sky coverage is required to support drone users flying at different heights.  
Coverage of up to 10 m altitude is appropriate for plant protection (e.g., spraying of agricultural chemicals). Coverage of up to 50 to 100 m altitude is required for power line inspection. Coverage of up to 200 to 300 m altitude is sufficient for mapping of agricultural lands, while coverage of the upper air pipeline of up to 300 to 3000 m altitude may be needed. It is difficult for networks to serve this large spectrum of coverage scenarios at varying altitudes [19].