esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of a Signal Modulation Method to Improve eLORAN Data Channel Communications
## 3 Results
### Performance Comparison  
Both the 3S-PPM and the ninth pulse modulations are types of phase modulation and have poor anti-noise performance. The eLORAN signal propagates through a long distance, which easily causes phase blur and makes phase recognition difficult. It also causes the demodulation success rate of the eLORAN receiver to decrease. APM modulation is similar to OOK modulation, which is easy to identify and conducive to receiver demodulation.  
Assuming that the channel noise can be modeled by additive white Gaussian noise (AWGN), the probability of error can be computed as a function of the signal-to-noise ratio (SNR). Without considering the cross-interference, in this article, the probability of error of the aforementioned three modulation methods are analyzed and compared theoretically.  
Figure 6 shows the error probability of 3S-PPM given in [26], and the difference between the analytical and simulation results is mostly due to the use of an ideal band-pass filter for the analytical model and a second-order Butterworth filter for the simulation. As shown in Figure 6, the red line represents the simulation model and the blue line the analytical model.  
Figure 6: 3S-PPM probability of error vs. SNR.  
Figure 7 shows the error probability of ninth pulse modulation given in [27].  
It can be seen from Figures 6-8 that in the same SNR environment the bit error rate (BER) of ninth pulse modulation is the highest, while the BER of APM is the lowest.  
This means that under the same working environment the range of APM signals demodulated by the receiver is larger than those of the 3S-PPM and ninth pulse signals.
## 2 Materials and Methods
### Study Area
#### 2.1.2 Soil Sample Collection  
Soil samples were collected using the S-shaped sampling method in Tianlin County and Luzhai County, Guangxi Zhuang Autonomous Region, with the distribution of sampling points shown in Figure 1. All collected samples were naturally air-dried and finely ground in the laboratory. Some samples were filtered through a 0.2 mm soil sieve, and their Soil Organic Carbon (SOC) content was accurately measured using the potassium dichromate oxidation method. The SOC content was then multiplied by a coefficient of 1.724 to obtain the SOM content. Another portion of the samples was filtered through a 0.149 mm sieve, and detailed spectral data from the visible to near-infrared region (350-2500 nm) were captured using an ASD FieldSpec1 4 Hi-Res (Purchased by Beijing LICA United Technology Limited from Analytical Spectral Devices in Boulder, CO, USA) ground object spectrometer. To enhance the accuracy of the spectral data, each sample's spectral data were collected 10 times, and the arithmetic mean was taken as the final spectral data. Additionally, to eliminate noise caused by external factors during the operation, the noisy edge bands of 350-399 nm and 2401-2500 nm were removed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Yachang Forest Farm** & **Huangmian Forest Farm** \\ \hline Latitude and Longitude & 106\({}^{\circ}\)08\({}^{\prime}\)–106\({}^{\circ}\)26\({}^{\prime}\) E, & 109\({}^{\circ}\)43\({}^{\prime}\)–109\({}^{\circ}\)58\({}^{\prime}\) E, \\ Annual Average Rainfall & 24\({}^{\circ}\)37\({}^{\prime}\)–25\({}^{\circ}\)00\({}^{\prime}\) N & 24\({}^{\circ}\)37\({}^{\prime}\)–24\({}^{\circ}\)52\({}^{\prime}\) N \\ Annual Average & 1058 mm & 1750 mm \\ Evaporation & 1484.7 mm & 1426 mm \\ Annual Average & 16.8 \({}^{\circ}\)C & 19 \({}^{\circ}\)C \\ Temperature & 16.8 \({}^{\circ}\)C & 19 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tree farm information.  
Figure 1: Distribution of sampling points in the study area. (**a**) is Yachang Forest Farm. (**b**) is Huangmian Forest Farm.
## 6 Performance Evaluation
### Simulation Results
#### 6.2.4 Effect of the Number of Non-Spatial Attributes  
Finally, we investigated how the number of non-spatial attributes affects the performance of SR, MQM, QRT, BQRT, and GQRT by increasing the number of non-spatial attributes (from 1 to 10).  
Figure 14 shows the effect of the number of non-spatial attributes on the CPU-time. It is observed from the figure that the performances of BQRT and GQRT improve as the number of non-spatial attributes increases. This is due to the fact that as the number of non-spatial attributes increases, the server in BQRT and GQRT can utilize more non-spatial information when assigning resident domains to the moving objects. However, GQRT, which fully utilizes the non-spatial information, performs much better than BQRT. GQRT takes 64.3% of the server workload, as compared to BQRT for _Uniform_. On the other hand, GQRT takes 52.9% of the server workload, as compared to BQRT for _Skewed_. Note that SR, MQM, and QRT are practically unaffected by the number of non-spatial attributes.  
Figure 14: CPU-time _vs_. number of non-spatial attributes. (**a**) _Uniform_; (**b**) _Skewed_.  
Figure 13: Total number of messages _vs_. number of moving objects. (**a**) _Uniform_; (**b**) _Skewed_.  
Figure 15 shows the effect of the number of non-spatial attributes on the total number of messages. As expected, the performances of BQRT and GQRT improve as the value of the number of non-spatial attributes increases. However, it is observed from the figure that GQRT outperforms BQRT in all cases for _Uniform_ and _Skewed_. As compared to BQRT, GQRT incurs only \(66.8\%\) and \(61.1\%\) of the communication cost for _Uniform_ and _Skewed_, respectively.
# Quantum Image Steganography Schemes for Data Hiding: A Survey
## 3 Quantum Image Steganography Schemes
### Double-Layer Matrix Coding  
[PERSON] et al. [36] were the first to propose the concept of matrix coding in 1998, which was the foundation of linear-codes-based steganography and numerous other classic steganography schemes inspired by his procedure. Its key notion is to utilize the Hamming code by implementing it into a steganography procedure to obtain great capacity and high embedding efficiency.  
In association with linear-codes-based quantum steganography, [PERSON] et al. [37] proposed a quantum image steganography protocol based on matrix coding. The scheme proposed by [PERSON] was an expansion of the matrix coding steganography initially produced in the quantum steganography sphere, and it maintains the circuit design used to transmit secret information with respect to its corresponding notion about the quantum carrier image proposed by [PERSON] et al.  
To further advance the previously proposed scheme, research conducted by [PERSON] et al. [16] in 2022 established two protocols inspired by the double-layer matrix coding approach. The first protocol introduced by [PERSON] was derived from [PERSON], [PERSON], and [PERSON]'s method to implement steganography (ZZW) [38]. Furthermore, the ZZW framework originated by [PERSON] et al. [38] is considered a double-layer matrix coding protocol which has two main elements in its procedure, namely, the hamming code and the wet-paper code. In the proposed framework, the pixel blocks can hide the covert data through the two channels utilized for embedding with one alteration. By applying this approach with the use of a quantum carrier image, [PERSON]'s scheme achieved both high security and enhanced embedding efficiency.  
The second protocol proposes an enhanced version of the quantum image steganography protocol that holds a capacity twice as large as the first protocol and achieves an even higher embedding efficiency. To exhibit the benefits of using both protocols, [PERSON] et al. [16] designed specialized quantum circuits that implement both of the previously introduced frameworks to hide and withdraw covert data, proving that implementing both protocols together results in achieving high imperceptibility along with great embedding efficiency.
## 4 Experiment
### Hyperparameters  
A comprehensive series of parameter optimization experiments were conducted on a set of 340 training images and 20 test images to ascertain the most effective hyperparameters, as delineated in Table 6. Given the model's convergence with the original dataset via transfer learning, a reduced learning rate of 0.0001 was set to fine-tune the rail defect B-scan image data. Employing a cosine annealing approach, the learning rate was reduced from the 8000 th to the 9000 th epoch, with the total epochs set at 10,000. Batch size, a critical parameter correlating with training duration and GPU capacity, was configured at 16 to maintain an equilibrium between these factors. Models including YOLOv8, DETR, YOLOv5, and Faster R-CNN were employed for the training process. To facilitate a fair comparison, the image input size for all models was standardized at 608 \(\times\) 608 pixels. The primary objective of the training was to optimize model performance by minimizing the loss function.  
Figure 8: (a) mAP and loss–epochs curves of the YOLOv8 model. (b) YOLOv8 PR curve.  
The bounding box regression loss typically employs mean squared error (MSE) for direct regression on the bounding's box coordinates, height, and width [46], with binary cross-entropy loss applied to analyze confidence and classification losses. Compared to early YOLO, YOLOV5 innovates in bounding box regression by replacing MSE with CIOU [47]. Further advancements in YOLOv8 include the substitution of CIOU with GIOU [48], which optimizes boundary overlap, non-overlap issues, center distance, and aspect ratio scaling, as detailed in Equations (9) and (10).  
\[\begin{split} L_{CIOU}=1-IOU(A,B)+\frac{\rho^{2}(A_{Cbr},B_{br})} {c^{2}}+\alpha\cdot v\\ v=\frac{A}{\pi^{2}}\left(\arctan\frac{\omega^{gt}}{h^{gt}}- \arctan\frac{\omega^{gt}}{h}\right)^{2}\\ \alpha=\frac{v}{(1-IOU)+v^{\prime}}\end{split} \tag{9}\]  
Within this framework, \(\omega^{gt}\) and \(h^{gt}\) symbolize the width and height of the ground truth bounding box, respectively. Conversely, \(\omega\) and \(h\) correspond to the width and height of the predicted bbox. Additionally, \(\alpha\) and \(v\) are penalty parameters related to the aspect ratio, where \(\alpha\) is a positive scalar and \(\
u\) quantifies the aspect ratio consistency.  
\[GIOU=IOU-\frac{AreaC-AreaA\cup B}{AreaA\cup B} \tag{10}\]  
In this context, \(AreaA\cup B\) refers to the combined area encompassed by A and B. The Intersection over Union (\(IOU\)) metric remains at 0 in scenarios where boxes A and B do not overlap. Here, C signifies the smallest rectangle that completely encases both boxes A and B, with \(AreaC\) indicating the total area covered by rectangle C.
# Security Analysis of the MQTT-SN Protocol for the Internet of Things
## 1 Introduction
Consequently, this is the field on which we will focus in this work, showing that analyzing protocols from an experimental point of view and extracting their vulnerabilities is critical for IoT cybersecurity research, in addition to assessing the impact of different attacks that exploit the vulnerabilities exposed by [PERSON] et al. in their work [13]. Figure 1: Growth of malware targeting IoT Linux. This research is focused on MQTT-SN [17], which is based on its predecessor, namely MQTT. While MQTT is a lightweight protocol, which uses a publish/subscribe messaging transport scheme, MQTT-SN is the newest version and has been designed to be even lighter than MQTT. The reasoning behind the choosing of this version is due to it being optimal for environments with resource-constrained devices such as the Industrial Internet of Things (IIoT) [18] and yet not having received the focus of detailed research from the security research field. Under these circumstances, this paper aims to firstly find deficiencies in MQTT-SN, secondly to exploit them, and thirdly, to evaluate their impact on the whole IoT scenario. After this evaluation, we recommend an attack detector designed to discover attacks in IoT environments. The advantage of this threat detector is its ability to detect anomalies and unmodeled attacks, which is crucial in combating novel attacks such as those proposed in this work. This proposal is a substantial extension of the work _Security Assessment of the MQTT-SN Protocol for the Internet of Things_[19]. Schematically, the main contributions of this article are as follows:  
* A practical analysis of MQTT-SN protocol vulnerabilities. * An implementation of attacks based on the vulnerabilities discovered. * An evaluation of the impact of the different attacks. * A threat detector, which has already been published, is suggested and tested with the implemented attacks. This paper is organized as follows. Section 2 describes the MQTT-SN protocol and the main differences between it and MQTT [5]. The state-of-the-art is reviewed in Section 3. A presentation of the MQTT-SN system deployed is made in Section 4. The attacks discovered are described and evaluated in Section 5. Section 6 describes a novel threat detector designed for IoT environments. Finally, we draw our conclusions in Section 7.
## 3 Results Analysis
### Absorption Characteristics
The complex propagation constant of the substrate layer can be written by \(\widetilde{\beta}=\beta_{1}+i\beta_{2}=\sqrt{\epsilon_{d}}k_{0}d\), where \(K_{0}\) is the free space wavenumber, \(\beta_{1}\) is the propagation phase, absorption of the dielectric is represented by \(\beta_{2}\), \(d\) is the substrate thickness, and \(\epsilon_{d}\) is the substrate dielectric constant. The entire wave is reflected due to the blocking of transmission by the ground layer after propagation phase delay \(\widetilde{\beta}\). The wave propagates toward the patch (layer 1) and is partially transmitted and reflected with transmission coefficient \(\widetilde{t}_{21}(\omega)=t_{21}(\omega)e^{i\theta_{21}(\omega)}\)  
Figure 4: (**a**) Normalized impedance and (**b**) reflectance, transmission, and absorption plot. and reflection coefficient \(\widetilde{r}_{21}(\omega)=r_{21}(\omega)e^{i\varphi_{21}(\omega)}\), respectively. Multiple reflection and transmission occur with the substrate, and total reflection is the superposition of all orders, which can be calculated by Equation (6). Figure 5b shows sound agreement between the IT model and CST-calculated absorption. \[\widetilde{r}(\omega)=\widetilde{r}_{12}(\omega)-\frac{\widetilde{t}_{12}( \omega)\widetilde{t}_{12}(\omega)e^{2i\widetilde{\beta}}}{1+\widetilde{r}_{21 }(\omega)e^{2i\widetilde{\beta}}} \tag{6}\]  
\[A(\omega)=1-|\widetilde{r}(\omega)|^{2}\]  
The above calculation was performed for the normal incident angle. For oblique incident angle \(\theta\), the propagation phase delay was modified because the propagation length increases, \(d^{\prime}=d/\cos\theta^{\prime}\), and the phase delay becomes \(\widetilde{\beta}=\sqrt{\epsilon_{d}}k_{0}d^{\prime}\); the refractive angle \(\theta^{\prime}\) can be calculated by [PERSON]'s law \(\sqrt{\epsilon_{d}}\sin\theta^{\prime}=\sin\theta\).
## 6 Application in Hot Wire Calibration
### Influence of Temperature and Humidity Discrepancies
The errors and the corresponding relative errors are exhibited in Table 18, they are in a range of 33.46% to 67.73% at humidity \(H=20\%\)RH, 31.62% to 64.64% at humidity \(H=60\%\)RH, and 25.35% to 49.73% at humidity \(H=80\%\)RH. If restricted to the velocity range from 0.1 m/s to 1.0 m/s (corresponding to \(E=1.35\), \(\ldots\), \(1.47\)), the maximum errors are 56.72%, 49.43%, and 42.41%, respectively, at 3 humidity values. The maximum error per degree Celsius discrepancy might be in an order of magnitude from 4.241% to 5.672%.
## 3 Results and Discussion
### Torsional Stiffness Effect  
Torsional stiffness (TS) refers to a shoe's resistance to rotation along its longitudinal axis, influencing foot and ankle kinematics during rapid multidirectional movements [11; 23]. In badminton, TS plays a crucial role in determining ankle stability and the efficiency of lateral push-off maneuvers [10; 11]. However, the optimal range of TS remains debated, as different studies report contrasting effects on movement performance and injury risk [27; 28; 29].  
[PERSON] et al. [11] examined how different levels of TS (50D, 60D, and 70D) influenced lower-limb biomechanics during forehand clear strokes, 45-degree sidestep cutting, and consecutive vertical jumps. Their findings indicate that higher TS (70D) significantly reduced peak ankle inversion angles during sidestep cutting compared with 50D and 60D shoes (\(p<0.05\)), suggesting improved ankle joint stability. However, shoes with excessive TS (70D) constrained ankle dorsiflexion and reduced coronal knee ROM, potentially affecting movement flexibility.  
In contrast, intermediate TS (60D) reduced knee abduction angles while optimizing stance time and lateral push-off efficiency. These findings suggest that moderate TS may strike a balance between stability and mobility, whereas excessive TS may restrict joint freedom, potentially influencing agility and movement control.  
The reduction in ankle inversion angles with increased TS aligns with previous research suggesting that higher TS limits excessive foot eversion and inversion [30; 31]. This stabilizing effect is particularly relevant for sidestep cutting movements, where excessive ankle inversion may increase lateral ligament stress and risk of ankle strains [27]. However, excessive stiffness may also impair dorsiflexion, reduce shock absorption capacity, and potentially increase compensatory stress on the knee joint [32].  
The biomechanical trade-off observed in TS optimization was further supported by [PERSON] et al. [33], who reported that elite athletes demonstrate higher ankleeversion torque and medial plantar pressure during lunges. While higher TS may help control excessive inversion, restricting dorsiflexion could limit the foot's natural loading response, affecting reactive movements such as high-clear recoveries.  
The role of individual adaptation should also be considered. Elite players with stronger foot musculature may tolerate higher TS without compromising mobility, whereas less trained athletes may require lower TS for greater movement adaptability. Additionally, laboratory-based experiments may not fully capture real-game scenarios, where postural adjustments and fatigue influence ankle mechanics.  
While these findings provide insights into TS's role in badminton footwear design, further studies are needed to explore the long-term adaptation effects of TS, particularly under game-like conditions and across different athlete skill levels.
## 3 Results
### Validation  
To verify the calculation results of the proposed ACS-OAR system, a validation procedure was conducted to compare our results with the work in the literature. Based on the author's knowledge, the ACS-OAR system investigated here has not yet been reported. Accordingly, the ACS and OAR system have to be validated separately. The work of [PERSON] et al. [14] was selected here to validate the ACS system model, and the work of [PERSON] et al. [32] was selected to validate the OAR system model. Validation results are listed in Table 4. It can be seen that the deviation between the calculated results of this work and the data in the literature is less than 5%, which is acceptable and verifies the accuracy of the model.
# A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
###### Abstract  
The intersection of cybersecurity and opportunistic networks has ushered in a new era of innovation in the realm of wireless communications. In an increasingly interconnected world, where seamless data exchange is pivotal for both individual users and organizations, the need for efficient, reliable, and sustainable networking solutions has never been more pressing. Opportunistic networks, characterized by intermittent connectivity and dynamic network conditions, present unique challenges that necessitate innovative approaches for optimal performance and sustainability. This paper introduces a groundbreaking paradigm that integrates the principles of cybersecurity with opportunistic networks. At its core, this study presents a novel routing protocol meticulously designed to significantly outperform existing solutions concerning key metrics such as delivery probability, overhead ratio, and communication delay. Leveraging cybersecurity's inherent strengths, our protocol not only fortifies the network's security posture but also provides a foundation for enhancing efficiency and sustainability in opportunistic networks. The overarching goal of this paper is to address the inherent limitations of conventional opportunistic network protocols. By proposing an innovative routing protocol, we aim to optimize data delivery, minimize overhead, and reduce communication latency. These objectives are crucial for ensuring seamless and timely information exchange, especially in scenarios where traditional networking infrastructures fall short. By large-scale simulations, the new model proves its effectiveness in the different scenarios, especially in terms of message delivery probability, while ensuring reasonable overhead and latency.  
c 2024 16 56 1
# Hybrid Locomotion Evaluation for a Novel Amphibious Spherical Robot
## 6 Discussion  
Based on the results obtained from the experiments, on land locomotion and underwater locomotion were concluded. First, the LSWM was evaluated to make the robot stand up and slide on smooth flat ground. We obtained the relationship between the rising and falling velocity \(v\) and the duty factor \(s\), which assisted the robot realizing the automatic lifting and dropping of the LSWM. Second, the two parameters which affected the velocity of the robot on land were the stride length and frequency. After experiments, we found that the optimal rotary angle was 55\({}^{\circ}\). On smooth flat terrain, the maximum velocity of SSTGL (sliding locomotion) was 16.7 cm/s at 1.5 Hz and increased 13.6% than TTWG (walking locomotion), which proved that SSTGL on smooth flat terrain had higher velocity performance than TTWG for ASR-III. On rough terrain, the maximum velocity of TTWG is 18.5 cm/s at 1.9 Hz. In addition, the mean power with SSTGL and TTWG measured approximately at the maximum velocity were 40.5 W and 56.7 W, respectively, which testified the low power loss of SSTGL. Compared to the maximum velocity (8.5 cm/s) of the previous robot [37], the maximum velocity of TTWG was about twice higher. For the rotatory gait, the maximum rotatory velocity in previous robot [38] was up to 67\({}^{\circ}\)/s while the weight of the robot was only 2.1 KG. However, Now ASR-III's weight was up to 8.2 KG (about four times heavier than the previous robot), and the rotatory velocity was 44.5\({}^{\circ}\)/s (decline 33.6%). Third, for the waypoints tracking, the maximum trajectory offset with closed-loop PID only occupied 1/5 to the size (35) of the robot, which was really slight offset. Finally, underwater locomotion, such as forward motion, backward motion, turn left or right, floating and sinking were realized. Compared to the previous work [36] in forward locomotion, the average velocity of the robot was just 10 cm/s (0.29 body/s), and increased about 81%. For the heading angle control, the robot can keep the expected angle at 25 s, and the time was shortened about 34% compared to the previous work [36]. For the depth control, the robot has a sensitive response, and the control errors are in the acceptable range. Besides, with the comparison to the related works, Locomotion speed of ASR-III on ground was slower than AmphiBot II [12], and locomotion speed of ASR-III in water was almost equal to AmphiBot II. However, the robot can rotate with zero-radius both on land and in water, and have a stable heading angle control. Compared to the hexapedal robot [16], ASR-III on ground and in water was slower than the hexapedal robot. However, our robot can realize  
Figure 25: (**a**) Experimental results for heading angle control; (**b**) experimental results for depth control.  
multi-locomotion, such as sinking down and floating up in water, rotating with zero-radius both on land and in water. In addition, the robot can keep a stable depth in water for some complex tasks.