esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of the New Compound-Carrier-Modulated Signal for Future Navigation Signals
## 5 Conclusions  
In this paper, the NSCC modulation scheme was investigated. Its mathematical expression is presented to illustrate the signal structure and parameters. According to the NSCC modulation characteristics, we investigated the suitability for satellite navigation or navigation augmentation purposes through comparisons with the current GPS, Galileo and Beidou signals. The signal ACF and PSD were analyzed and show that the NSCC ACF profile possesses the narrowest peak to allow precise pseudo-range measurement, and its PSD is more compact over the frequency range and lower side lobes, exhibiting an efficient spectrum utilization. The NSCC modulation possesses a large set of constituent parameters, conferring it a great degree of design freedom and full spectral flexibility. For  
Figure 11: Multipath error envelop for NSCC(19) with different MDRs (**a**) and different correlator spacings (**b**).  
Figure 12: Multipath error(**a**) and average multipath error(**b**) for NSCCs with different code rates and its reference baseline.  
its structure characteristics and scenario constraints, a reference baseline consisting of a monomodal and bimodal signal is identified.  
Furthermore, the NSCC signal, together with legacy navigation signals, are comprehensively evaluated, employing the performance of interference, tracking accuracy and multipath mitigation respectively in terms of SSCs and anti-jamming quality factor, Gabor bandwidth and multipath error envelope. Both theoretical analysis and simulation show that the NSCC signal possesses less SSCs because of its efficient spectral utilization, to allow the coexistence of a large number of signals with minimal mutual interference. In addition, the tracking performance and multipath mitigation capability with the NSCC modulation scheme outperforms the other signal modulations. The high-frequency component can greatly improve both performances with same pre-filtering bandwidth, but it is not sensitive to the chip rate and number of subcarriers. The NSCC signal exhibits excellent anti-jamming reduction, while it is inferior to P code in the aspect of CT anti-jamming ability due to its flat power distribution. In conclusion, the NSCC modulation scheme provides viability for potential GNSS signal modulation schemes, especially for ground-based navigation augmentation systems.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61102130) and the National High Technology Research and Development Program (\"863\" Program) of China (Grant No. 2015 AA124101 and Grant No. 2015 AA124002).  
The work presented in the paper was carried out in collaboration with all authors. [PERSON] and [PERSON] conceived the ideas and concept. [PERSON] implemented the software and carrieried out the experiments and wrote the manuscript. [PERSON] and [PERSON] critically reviewed the paper.  
The authors declare no conflict of interest.
# Encoding the Intensity and Phase Gradient of Light Beams with Arbitrary Shapes
## 2 Materials and Methods
### Signal Processing Algorithms  
This proposal considers a black and white image of a closed curve as an input, drawn directly onto the screen or created from a predefined shape. In Figure 1, we present the flow diagram of the algorithm for image and signal processing required for extracting the harmonics of the horizontal and vertical waveforms that compose a given curve.  
As illustrated in Figure 1, the algorithm begins by reading the image that includes an arbitrary shape and applies image pre-processing techniques, resizing, binarizing, and centering the image within a larger background. Then, to obtain the contour of the desired curve, the 8-connectivity scheme of the Freeman chain code is extracted from the two-dimensional array [22]. This process returns an array containing the extracted code and a coordinate that represents the starting \((x,y)\) point. With this data, a pair of arrays is created, representing the horizontal and vertical coordinates that follow the shape of the given curve, both coordinate arrays are normalized and centered. Then, by applying the fast Fourier transform to each array and by adding sufficient harmonics to reconstruct at least 95% of the original shape, the parametric equations are estimated. These expressions are useful since they allow the generation of scalar beams with an intensity distribution that follows the prescribed curve. This is performed by extracting the phase term that shapes the phase of the beam along the curve, from the study of the expected complex field amplitude [12]. In the study, the method is limited to the realization of smooth optical patterns consisting of curves expressed by a super formula. In our proposal, we overcome this restriction by estimating the parametric equations \(x_{0}(t),y_{0}(t)\) of a given curve \(c(t)\)Estimating these expressions allows the use of the term \(\Phi\) that shapes the phase of the beam along the curve [12] as  
\[\Phi(\mathbf{r},t)=e^{\left(\frac{-i}{\omega_{0}}[yx_{0}(t)-xy_{0}(t)]+\frac{i\pi}{ \omega_{0}^{2}}\int_{0}^{t}[x_{0}(\tau)y_{0}(\tau)-y_{0}(\tau)x_{0}^{\prime}( \tau)]d\tau\right)}. \tag{1}\]  
In Equation (1), \(\mathbf{r}=(x,y)\) indicates the plane where the two-dimensional curve is drawn with \(t\in[0,T]\), \(\sigma\) is a free parameter for controlling the phase gradient along the curve and \(\omega_{0}\) is a constant that allows modifying the topological charge. Introducing this phase behavior in the complex field amplitude of a plane wave in the plane \(l=0\), results in the expression that preserves beam shape during propagation along the axial direction  
\[G(r,0|c(t),t\in[0,T])=\frac{1}{L}\int_{0}^{T}\Phi(\mathbf{r},t)\big{|}c^{\prime}(t )\big{|}dt \tag{2}\]  
Since we are interested in testing the phase of the designed light beams under a common path interference configuration, that additionally allows for the capture of their individual intensity, two phase patterns of the same size are displayed on the SLM, side-by-side. In the following section, we present the experimental setup for these experiments.
# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
## 2 Materials and Methods
### Data Acquisition  
The experiment occurred at the Urumqi Wild Horse group ranch in Xinjiang, China. The experiment was conducted from 29 June to 4 July 2023, during daily morning training sessions from 10:30 a.m. to 12:30 p.m. To ensure the comparability and stability of the research findings, healthy Akhal-Teke horses aged 5-8 years (Figure 3) were selected. Observers used timers to record the duration of each horse's low, medium, and high activity (Table 1), ensuring accurate timing. In this study, six horses were observed, and wearable devices were used to collect acceleration data on each axis and combined acceleration data. The six devices were configured with a range of \(\pm\)4 g (m/s\({}^{2}\)) for three-axis acceleration values and \(\pm\)2000 dps (\({}^{\circ}\)/s) for three-axis angular velocity values. The sensors had a sampling frequency of 2 Hz.  
Figure 1: Wearable device hardware includes STM32 main control chip, MPU6050, display module, 4G module, and power module.  
Figure 2: The overall framework of data collection, algorithm processing and analysis, and data transmission to end-users for monitoring.  
The experimental process involves the device collecting three-axis acceleration data, computing combined acceleration data, analyzing horse gait data, and outputting predicted activity levels (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Breed**} & \multirow{2}{*}{**Time**} & \multicolumn{4}{c}{**Activity Levels**} \\ \cline{3-5}  & & **Low Activity** & **Medium Activity** & **High Activity** \\ \hline \multirow{4}{*}{Akhal-Teke horses} & 10:38–10:43 & \(\surd\) & & \\  & 10:43–10:46 & & \(\surd\) & \\ \cline{1-1} \cline{2-5}  & 10:46–11:10 & & & \(\surd\) \\ \cline{1-1} \cline{2-5}  & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \cline{1-1} \cline{2-5}  & 12:11–12:19 & & \(\surd\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: An example of a horse training experiment record sheet documenting the training time for each horse’s activity level, displaying the training data for the horse with device ID 17. The symbol (\(\dot{\cdot}\)) indicates that some intermediate time intervals have been omitted. The symbol (\(\surd\)) corresponds to the Activity Levels for the respective time periods.  
Figure 4: Flowchart depicting the sequence of steps and procedures followed in the experiment.  
Figure 3: On-site data acquisition using a wearable device placed on the neck of the horse.
## 3 Dataset
### Noise-Network-Distorted Dataset
#### 3.1.2 Environmental Noise on Speech Quality  
In VoIP applications, the microphone that captures speech also captures environmental noise. The environmental noise captured with an intended speech in VoIP affects the quality of the far-end received speech. Several single-ended algorithms for estimating the speech quality of speech signals corrupted by noise have been proposed [14; 32]. In this study, in combination with the network condition parameters, the following noise types were examined: babble (multiple people talking), car, exhibition hall, restaurant, street, airport, train station, and train. The noise signals were artificially added to studio-recorded speech to generate signals at 0, 5, 10, and 15 dBs.  
Finally, the dataset parameters generated from environmental noise and network distortions are summarized in Table 1.  
The generated dataset comprised three female and three male speakers that read sentences selected from the IEEE database. The sentences include all phonemes in the American English language. The generated noise-network-distorted speech database was divided into training and test datasets. The training subset was used for training the single-ended speech quality prediction model in combination with the publicly available datasets, the noisy speech database for training speech enhancement algorithms and TTS models [28], and NISQA Corpus [29]. The noise-network-distorted speech testing subset was used to analyze the prediction model in different acoustic and network conditions, as this dataset contains acoustic and network condition parameters.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 4 Common Attacks within MANETs
### Delay Attack  
The Jellyfish Delay Variance attack is another sophisticated exploit that leverages the vulnerabilities found in the route reply (RREP) mechanism, commonly targeted in sinkhole attacks. While similar in its goal of disrupting network traffic, the Jellyfish attack provides the attacker with a broader set of strategies to achieve this disruption, thereby offering  
Figure 5: Black hole attack.  
greater flexibility and making detection more challenging. In particular, the Jellyfish attack can employ three distinct tactics [33]:  
* Jellyfish reorder attack: In this method, the malicious node deliberately rearranges the order of packets as they pass through the network. This reordering disrupts the intended sequence of data transmission, which can lead to issues such as out-of-order packet delivery, degraded application performance, and increased error rates in protocols that rely on sequential data, such as TCP.
* Jellyfish periodic dropping attack: Here, the attacker periodically drops packets rather than continuously. This intermittent loss of data packets degrades network performance by causing delays in communication, repeated re-transmissions, and reduced throughput. The periodic nature of the packet dropping also makes the attack harder to detect, as it mimics the natural packet loss that can occur in a congested or unstable network.
* Delay variance attack: This tactic involves introducing variable delays to the packets as they are forwarded through the malicious node. By causing inconsistencies in packet delivery times, the attacker increases overall network latency, which can severely impact time-sensitive applications. The unpredictable nature of these delays further complicates the detection and mitigation of the attack, as it can be mistaken for network congestion or other benign issues; see Figure 6.  
Collectively, these tactics allow the attacker to subtly and effectively degrade the performance and reliability of the network, all while remaining hidden within the legitimate traffic flow. The Jellyfish Delay Variance attack, with its multiple approaches, presents a significant challenge in ensuring the security and stability of MANETs and other wireless networks.
## 2 Materials and Methods  
Figure 1 shows the block diagram of the studied system, which is divided into software and hardware. In particular, from system schematics it is shown that the control implementation includes the hardware part with physical and electronic components, while the software part uses a DSP to acquire/process signals and to implement the control technique.  
The hardware consists of a permanent magnet DC motor that has the following characteristics: nominal power of 250 W, supply voltage of 42 V, nominal current of 6 A, and maximum speed of 4000 RPM. To measure the speed of the motor (\(W_{m}\)), an encoder with a rating of 1000 pulses per revolution was used. For sensing of the state variables, namely the capacitor voltage (\(v_{c}\)), inductor current (\(i_{L}\)), and a motor armature current (\(i_{a}\)), some precision resistors were used. The digital part is developed using the DS1104 board in dSPACE [34; 35], in which the ZAD-FPIC technique is implemented. The output of the centered pulse width modulation (CPWM) is calculated in real time by the DS1104.  
The state variables \(v_{c}\), \(i_{L}\), and \(i_{a}\) arrive at the controller through the 12-bit analog-digital (ADC) inputs. The controlled variable \(W_{m}\) is measured by an encoder that has 28 bits. All these variables are acquired at a sampling rate of 6 kHz. The control block admits the constant parameters defined for the DC electric motor, buck converter, and the ZAD-FPIC technique. The last required parameters are related to the time constants and the dynamics of the error that are imposed on the control system, for example \(K_{S1}\), \(K_{S2}\), and \(K_{S3}\) as described in following sections. During each sampling period, the microprocessor in the DS1104 board computes the duty cycle, namely \(d\), with 10-bit resolution, along with its equivalent in the centered pulse width modulation (CPWM) signal for control gate  
Figure 1: Diagram of the buck converter with zero average dynamics and fixed point induction control (ZAD-FPIC) techniques to control the speed of a direct current (DC) motor. CPWM, centered pulse width modulation.  
of the metal oxide semiconductor field effect transistor (MOSFET). Table 1 presents the values of the parameters of the power converter, the motor, and the controller. By using the method of [36], system parameters have been measured experimentally.
## 12 Simulation and Results
### Protocol Analysis  
This section discusses the routing schemes, Range-based Directional Beacon Forwarding (RDBF), Energy Efficient Probabilistic Depth Based Routing (EEFDBR), Using Directional Beacon (UDB) routing, Three-Dimensional Underwater Localization (3 DUL), Underwater Localization (UL), and Range-free scheme based on Mobile Beacon (RSMB) [10; 11; 12; 13; 14; 15], compared against the proposed protocol with various performance metrics. Deep and shallow water significantly impact node density, which increases with depth and distance. The mobility speed of the nodes also affects the performance of node density; therefore, we fix the mobility speed of the nodes, for example, 5 to 25 m/s of RREQ packets, for all compatible techniques.
## 2 Material and Methods
### Filling of Missing Data
#### 2.3.3 Imputation of Satellite Retrievals  
In this approach, we utilize the satellite retrievals from the pixel that encloses each of the target sites, as obtained from the IMERG-GPM product, for direct imputation, at both daily and monthly time scales. We note that such an expedient, albeit simple, has been disputed in the literature since it requires the downscaling of spatial-averaged precipitation in relatively large pixels to a point (i.e., the rainfall gauge) [42]. Moreover, satellite estimates are acknowledged biased, and the level of bias might be affected by long-term climate characteristics, seasonality, and topography [43; 44]. However, since bias correction on the daily time scale is usually ineffective along the entire range of precipitation amounts [33], and defining appropriate intervals for removing bias from rainfall estimates is not straightforward, we rely on the raw satellite data for filling missing values; at least for some ranges, the satellite estimate may closely resemble ground-based information. A potential advantage of this rationale is that it might make filling missing data easier for practitioners.
# Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines
## 4 Transmission Grid Model in Quality of Protection Modeling Language
### Equation Rules  
Equation rules play an important role in the quality of protection protocol analysis. Equation rules for a specific protocol consist of a set of equations asserting the equality of function calls. For instance: the decryption of the encrypted data with the same key, is equal to the encrypted data.
## Appendix A Gradient: Divergence, and Curl in Cylindrical Coordinates
### Gradient in Cylindrical Coordinates  
The _gradient_ of a smooth vector field \(U(\rho,\phi,z)\) in cylindrical coordinates is defined to be a second order tensor field,  
\[\
abla\otimes U =\frac{\partial}{\partial\rho}\left(U_{\rho}\dot{\rho}+U_{\phi} \dot{\phi}+U_{z}z\right)\otimes\dot{\rho} \tag{18}\] \[+\frac{\partial}{\partial\phi}\left(U_{\rho}\dot{\rho}+U_{\phi} \dot{\phi}+U_{z}z\right)\otimes\frac{\dot{\phi}}{\rho}\] \[+\frac{\partial}{\partial z}\left(U_{\rho}\dot{\rho}+U_{\phi} \dot{\phi}+U_{z}\dot{z}\right)\otimes\dot{z}\] \[=\left(\frac{\partial U_{\rho}}{\partial\rho}\dot{\rho}+\frac{ \partial U_{\phi}}{\partial\rho}\dot{\phi}+\frac{\partial U_{z}}{\partial \rho}z\right)\otimes\dot{\rho}\] \[+\left(\frac{\partial U_{\rho}}{\partial\phi}\dot{\rho}+U_{\rho} \dot{\phi}+\frac{\partial U_{\phi}}{\partial\phi}\dot{\phi}-U_{\phi}\dot{ \rho}+\frac{\partial U_{z}}{\partial\phi}z\right)\otimes\frac{\dot{\phi}}{\rho}\] \[+\left(\frac{\partial U_{\rho}}{\partial z}\dot{\rho}+\frac{ \partial U_{\phi}}{\partial z}\dot{\phi}+\frac{\partial U_{z}}{\partial z}z \right)\otimes\dot{z}\] \[=\frac{\partial U_{\rho}}{\partial\rho}\dot{\rho}\otimes\dot{ \rho}+\frac{\partial U_{\phi}}{\partial\rho}\dot{\phi}\otimes\dot{\rho}+ \frac{\partial U_{z}}{\partial\rho}\dot{z}\otimes\dot{\rho}\] \[+\frac{1}{\rho}\left(\frac{\partial U_{\rho}}{\partial\phi}-U_{ \phi}\right)\dot{\rho}\otimes\dot{\phi}+\frac{1}{\rho}\left(\frac{\partial U _{\phi}}{\partial\phi}+U_{\rho}\right)\dot{\phi}\otimes\dot{\phi}+\frac{1}{ \rho}\frac{\partial U_{z}}{\partial\phi}\dot{z}\otimes\dot{\phi}\] \[+\frac{\partial U_{\rho}}{\partial z}\dot{\rho}\otimes\dot{z}+ \frac{\partial U_{\phi}}{\partial z}\dot{\phi}\otimes\dot{z}+\frac{\partial U _{z}}{\partial z}z\otimes\dot{z}\]  
or, in matrix form,  
\[gradU=\left[\
abla\otimes U\right]=\begin{bmatrix}\frac{\partial U_{\rho}}{ \partial\rho}&\frac{1}{\rho}\left(\frac{\partial U_{\rho}}{\partial\phi}-U_{ \phi}\right)&\frac{\partial U_{\rho}}{\partial z}\\ \frac{\partial U_{\rho}}{\partial\rho}&\frac{1}{\rho}\left(\frac{\partial U_{ \rho}}{\partial\phi}+U_{\rho}\right)&\frac{\partial U_{\rho}}{\partial z}\\ \frac{\partial U_{z}}{\partial\rho}&\frac{1}{\rho}\frac{\partial U_{z}}{ \partial\phi}&\frac{\partial U_{z}}{\partial z}\end{bmatrix} \tag{19}\]
## 2 Materials and Methods
### Clinical Deployment Considerations
#### 2.8.3 Compatibility with Medical Systems  
For integration into clinical systems, the model can be embedded in electronic health record (EHR) platforms or decision support systems. It supports standardized input formats, such as CSV and JSON, making it easy to apply to data from mammography reports or lab results. Additionally, its modular design promotes adaptability to HL7/FHR standards, enhancing interoperability across hospital information systems.
###### Abstract  
In addition to various physiological parameters that affect athletes' performance and outcomes, anthropometric variables are also related to athletic performance. In particular, the length of the lower limbs is closely associated with human mobility and stride length, making it a crucial factor in various movement-based sports. Furthermore, favorable body proportions may vary depending on the sport, and a better understanding of how body proportions affect physical performance across different levels of athletes is needed. Therefore, the purpose of this study is to investigate the relationship between leg length and physical performance by measuring body dimensions (tibia and femur length) for athletes categorized by sports characteristics and performance levels. The study involved 312 athletes from 23 sports, divided into three activity levels. Anthropometric measurements of tibia and femur length were taken, and physical performance tests, including strength, muscular endurance, cardiovascular endurance, agility, explosiveness, flexibility, and anaerobic power, were conducted in the laboratory. The relationships between variables (leg length \(\times\) physical performance) were analyzed using [PERSON]'s correlation coefficients. Leg length via activity levels was verified through one-way analysis of variance (ANOVA) testing, including normality and homoscedasticity. Post hoc analysis ([PERSON]'s HSD test) was used to compare specific differences when significance was found. Statistical significance was accepted at the 0.05 level. As a result, an increase in lower limb length was found to have a relationship with physical performance components, including power (r = 0.302, \(p\) = 0.001), agility (r = \(-\)0.289, \(p\) = 0.001), endurance capacity (r = 0.168, \(p\) = 0.005), and anaerobic peak power (r = 0.265, \(p\) = 0.001). However, in the LD group, which consisted of athletes in static sports, no significant relationship was observed between lower limb length and physical performance components. However, in the LD group, which included static sports, no significant relationship was found between lower limb length and physical performance components. These findings may serve as foundational data for athlete talent identification and performance prediction.  
Keywords:lower limb; athletic performance; strength; power; anthropometric measurement