esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 5 Performance Evaluation
### Number of Hops for Packet Forwarding  
Figure 7 shows the number of packet hops forwarded between 10,000 pairs of randomly selected ground stations in 6 topologies at the beginning of each time slice.  
The hop count of STLS is about 3.82% less than that of MHD. Since STLS connects all satellites in the same and adjacent orbits, many hops are required for relaying if the randomly selected ground stations are far away. Still, the MHD network requires more hops than STLS to transmit packets to the location of the destination satellite.  
The comparative analysis shows that DTLS1 has 6.32% fewer hops compared to Distance and 36.72% fewer hops compared to Time strategy; meanwhile, DTLS2 has 2.70% fewer hops compared to Distance and 34.27% fewer hops compared to Time. The significant variation in the topology obtained according to the dynamic connection strategy is still mainly caused by the uncertainty in the distance between the randomly selected ground stations tested.  
Figure 7: Comparison of packet forwarding hops in the six topology cycles.  
Among the topologies mentioned in this paper, STLS, DTLS1, and DTLS2, when compared with STLS as the baseline, DTLS1 has reduced hop count by 30.87%, and DTLS2 has reduced hop count by 28.19%. Furthermore, the hop count of DTLS1 is lower than DTLS2 by approximately 3.72%. The data shows that STLS1 dynamic topology has the least average hop count. Since a lower hop count leads to better network performance, the designed DTLS1 in this paper is superior in this metric.
###### Abstract  
This article reports on the comparative study of the hot deformation behaviour of virgin (steel A) and rejuvenated heat treatment creep-exhausted (steel B) P91 steels. Hot uniaxial compression tests were conducted on the two steels at a deformation temperature range of 900-1050 \({}^{\circ}\)C and a strain rate range of 0.01-10 s\({}^{-1}\) to a total strain of 0.6 using Gleeble(r) 3500 equipment. The results showed that the flow stress largely depends on the deformation conditions. The flow stress for the two steels increased with an increase in strain rate at a given deformation temperature and vice versa. The flow stress-strain curves exhibited dynamic recovery as the softening mechanism. The material constants determined using Arrhenius constitutive equations were: the stress exponent, which was 5.76 for steel A and 6.67 for steel B; and the apparent activation energy, which was: 473.1 kJ mol\({}^{-1}\) for steel A and 564.5 kJmol\({}^{-1}\) for steel B. From these results, steel A exhibited better workability than steel B. Statistical parameters analyses showed that the flow stress for the two steels had a good correlation between the experimental and predicted data. Pearson's correlation coefficient (R) was 0.97 for steel A and 0.98 for steel B. The average absolute relative error (AARE) values were 7.62% for steel A and 6.54% for steel B. This study shows that the Arrhenius equations can effectively describe the flow stress behaviour of P91 steel, and this method is applicable for industrial metalworking process.  
P91 steel; rejuvenation heat treatment; creep exhausted; constitutive equation; hot deformation 2023
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 2 Related Work
### Ipbtl-Rdhei
#### 2.2.2 Data Extraction and Image Recovery  
In the data-extraction phase, the embeddable space can be determined according to the labels in the encrypted image. Then, the secret data are extracted and decrypted with the data-hiding key. To recover the image, the marked encrypted image is first decrypted with the image encryption key. Then, the original image can be exactly recovered according to the prediction errors indicated by the labels.
# Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time
###### Abstract  
We developed and validated a pseudo-range correction (PRC) modeling system that can prevent degradation of positioning accuracy even in situations where one cannot obtain PRC messages for Differential Global Navigation Satellite System (DGNSS). A PRC modeling scheme was devised based on the repeat time of GNSS satellites and previously-collected PRC data. The difference between the modeled and real PRC values observed at the reference station showed a bias error of about \(\pm\)1.0 m and a root mean square error (RMSE) less than 1.5 m. When we applied the predicted PRC to Differential Global Positioning System (DGPS) and Differential BeiDou (DBDS) positioning, horizontal RMSE values were at a level of 1.0 m, while vertical RMSE was in the range of 1.8-3.0 m. We found that modelled PRCs can provide positioning results similar to those based on real PRCs and can provide significant improvement over standalone positioning without PRCs.  
Keywords:DGNSS; GNSS; GPS; BeiDou; pseudo-range correction +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Device Setup and Data Collection
#### 2.2.4 Data Analysis  
Velocity Measurement: Velocity data were extracted from the videos using the free software TRACKER, a powerful tool for video analysis and modeling. This facilitated  
Figure 5: Referential image of mixing process. (**a**) Blue water load (channel 1), (**b**) yellow water load (channel 2), (**c**) mix process inside chamber (mix tank).  
the calculation of the fluid velocities within the microchannels. The videos and images obtained through the digital devices were loaded into the program, as shown in Figure 6b. With the images, the initial position of each fluid was established. Through detection algorithms, the software determined the position of the fluid in each sequence of images. The obtained data included the position in the \(\times\) and y coordinates, as well as the execution time and the relative velocity of the fluid compared to the previously captured point.  
Graphical Representation: MATLAB R2023a was employed for data processing and the creation of graphical representations. Graphs and figures were generated to visualize the experimental results, aiding in data interpretation and comparison. The data tables were loaded into the program, and, through plotting codes, they were represented graphically to present them in the results of this work.  
Color Analysis: Color analysis was conducted to evaluate the fluid mixing efficiency. The threshold color image tool within the MATLAB Toolstrip was used to precisely determine the color changes within the microchannel network. The final images captured by the USB microscope were loaded into the tool and filtered by color range. This analysis, as shown in Figure 6c, allowed the verification that the mixing process occurred adequately.  
Mixing Analysis: ImageJ, a freely available software program, was used for the analysis of the mixing patterns within the microdevice. Images were imported into ImageJ, and the data were subsequently exported to MATLAB for further comparison with the simulation data. The software allowed us to obtain data from the images by comparing the color intensity in relation to the position of the fluid. With the obtained data, we were able to compare the tonality of each stage, both experimentally and through simulations.  
By following this refined methodology, we ensured precise device preparation, optimal surface treatment, a controlled experimental setup, and precise data acquisition and analysis in our capillarity-driven microdevice mixer study. All of these are essential for accurate and replicable results in capillarity-driven microdevice mixing studies. The combination of advanced imaging techniques and powerful software tools enabled the accurate quantification of the contact angles, fluid velocities, color changes, and mixing patterns,  
Figure 6: (**a**) Threshold color image tool using MATLAB, (**b**) TRACKER reference image, and (**c**) IMAGE mixing index analysis.  
providing a thorough understanding of the device's performance and its comparison with the simulation data.
# Security Risk Analysis of LoRaWAN and Future Directions
## 5 Security Threat and Risk Assessment of LoRaWAN v1.1
### LoRaWAN v1.1 Threat Catalog
#### 5.2.2 MITM Attacks  
* **Bit-flipping or Message Forgery Attack:** As also mentioned earlier in Section 2, several security vulnerabilities of LoRaWAN v1.0 are presented in [29]. Especially, a specific version of MITM attack called \"bit-flipping attack\", in which an adversary (or a rogue \(NS\)) changes the content of the messages in between \(NS\) and \(AS\). Bit-flipping attack still constitutes a threat for v1.1 as also declared in the specification document [16].  
Figure 4: Impact of an attack.  
Figure 5: Risk assessment guideline.  
* **Frame Payload Attack:** Section 4.5 described that handover-roaming enables more possibilities for a MITM attack, as the unprotected _FRMPayload_'s are first transported from the \(sNS\) (serving-NS) to the \(hNS\) (homing-NS), and from there to the \(AS\).  
Henceforth, as also discussed in Section 4.4, \(NS\)s are considered as trusted entities by default. However, deploying engineers are recommended to use extra precautions (end-to-end security solutions) if they are wishing to have end-to-end confidentiality and integrity protection against MITM attacks.
optical communications is scarce [5]. Although it is true that FSO offers more robust physical layer security than traditional RF transmissions, it is not a strong enough argument to disregard data security [5]. The study in [6] delves into physical layer security in FSO for the \"difficulty of breach by a third party\" compared to cryptographic techniques. Whilst this is true, it is also the case that physical layer encryption techniques often require additional hardware devices which drive the costs compared to mathematical cryptographic techniques at the presentation layer. To the best of our knowledge, none of the previous studies investigated this type of cryptographic encryption in FSO but instead focused on the physical layer.  
Considering security is paramount to communication systems, the literature on the presentation layer security is plentiful. [PERSON] et al. [7] highlights some of the underlying problems with cryptographic libraries such as OpenSSL and addressed them with a new library called Networking and Cryptography library (NaCl) (also known as salt). This library has some core features such as \"No data flow from secrets to load addresses\" and \"centralizing randomness\" to achieve higher performance and security. In [8], salt password hashing was used to secure data storage and transmission over a cloud computing network. The \"salt\" represents a random string which is hashed and combined with a hashed private key. The combination is once again hashed to guarantee the data cannot be decrypted under any condition. Similarly, the work in [9] used salt cryptography to secure data transmissions by embedding the transmitted information into a video with promising results for how robust it is to attacks.  
Likewise, more advanced data security techniques, such as elliptic curve cryptography, exist. In [10] the performance advantages of elliptic curve cryptography (ECC) were compared to other public key systems such as Rivest-Shamir-Adleman (RSA) or Diffie-Helman. The study concluded that the reason for ECC's success was due to industrial adaptation, which is as important as the performance advantages of a data security proposal. Recently, a non-terrestrial satellite or UAV-based FSO system whilst employing quantum key distribution (QKD) to secure the transmission was proposed in [11; 12]. However, quantum-based security is cost ineffective because the technology is still in its infancy.  
A balance of security and performance is important for adopting technologies such as FSO. It has been shown that strong upper-layer encryption techniques already exist and have been used to ensure secure data transmissions; however, they are not usually applied to a communication system using an FSO channel.
# Accurate Modeling of Conductor Rough Surfaces in Waveguide Devices
## 4 Conclusions  
The gradient model and effective conductivity are introduced to model the effects of conductor surface roughness in metal-based waveguide components. The frequency dependent values for effective conductivity can be easily and efficiently integrated within commercial 3D field solvers to account for surface roughness. From the comparative study with the smooth surface, the increased conductor loss due to surface roughness for waveguide components is investigated accurately. To alleviate the conductor loss from the surface roughness, a suitable method is to reduce the field strength of the propagating signal near the rough conductor walls of the waveguide [22]. For future work, the correlation length of the real random manufactured surface roughness should be taken into account to model the conductivity gradient. In addition, in the sub-millimeter and THz band, defining conductor parameters with dispersive effects in numerical simulations is a challenge for accurately modeling surface roughness in metal-based THz waveguides.  
Figure 6: (**a**) Detailed views of the insertion losses of the four-pole filter with smooth and rough surfaces, (**b**) Magnitude of the scattering parameters of the four-pole inductive filter with smooth and rough surfaces.  
**Author Contributions:** [PERSON] developed the theory, surface roughness characterization, and wrote the paper. [PERSON] performed the waveguide devices simulations with software.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (Grant No. 61471293) and the China Scholarship Council (Grant No. 201706285142).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Novel Low-Complexity and Parallel Algorithm for DCT IV Transform and Its GPU Implementation
## 4 Implementation
### Overview of Nvidia Used Development Boards  
Due to the parallel nature of the developed algorithm, the NVIDIA graphical processing units (GPUs) were chosen to be the base support processors on which the performances of the developed algorithm were analyzed. As a direct result, two Nvidia Jetson development boards were used. The Nvidia Jetson boards contain an ARM processor, a GPU (composed mainly of different CUDA cores), memory, and various supporting circuits and interfaces.  
The NVIDIA Jetson systems have various computing powers, power efficiency, and form factors. As a rule of thumb, Jetson Nano systems are mainly designed for entry-level applications--even if the difference between Jetson Nano and Jetson Orin Nano is huge, the last family of development systems (Jetson Orin Nano) is the weakest from the entire Jetson Orin series, composed of Jetson Orin Nano, Jetson Orin NX and Jetson AGX Orin.  
Figure 1: A flowchart of the parallel execution of the proposed algorithm.  
The Jetson AGX (Xavier and Orin) systems offer exceptional computational performances, being the best from their families, e.g., providing up to 275 TOPS in the case of the Jetson AGX Orin developer kit.  
In our case, the proposed parallel algorithm was tested on two different development systems, as shown in Table 2. The new algorithm ran on the NVIDIA GPU (CUDA cores) in such a way that each section of the algorithm is executed on another core of the development system.  
Jetson Orin modules are the newest in the Jetson ecosystem series, being a big step in AI, with a significant impact in all related applications (robotics, smart city, life science, etc.) due to the huge computation power that brings to all of them, e.g., the performances of the less powerful systems, of the Jetson Nano series, from the Orin family of modules is similar or more powerful than the Jetson AGX development systems of the previous Xavier family.  
The development systems are based on different ARM processors paired with LPDDR4X, or LPDDR5 memory, which are connected using 128- and 256-bit memory interfaces. As a direct result of memory type and memory bus interface, the maximum data exchange rate varies between 68 GB/s and up to 136.5 GB/s--Table 2.
## 4 Results and Discussion
### Data Analysis
#### 4.2.7. Soil EC  
As can be seen in Figure 18, the average values of soil electrical conductivity data were observed to be 0.53 dS/m, 0.51 dS/m, 0.50 dS/m, and 0.63 dS/m, respectively. The average soil EC showed a decreasing trend from 0.53 dS/m in June to 0.50 dS/m in August but experienced a sharp increase to 0.63 dS/m in September. The minimum soil EC levels were observed to be 0.46 dS/m, 0.4 dS/m, 0.41 dS/m, and 0.41 dS/m, respectively, with July recording the lowest minimum soil EC. The maximum soil EC levels were observed to be 0.73 dS/m, 0.76 dS/m, 0.87 dS/m, and 1.17 dS/m, respectively, with September recording the highest maximum soil EC.
## 3 Results
### Multiplicative Fault Detection
#### 3.1.3 Multiplicative Fault Detection Using Sliding Integral Algorithm  
The following figures show selected results from applying the SIA to estimate amplifier parameters under different window lengths (\(m\)) and data lengths (\(K\)). In each case, multiplicative faults were introduced abruptly at 10 s. In Figures 19 and 20, the time interval from 0 to 10 s corresponds to the parameter estimates prior to the fault occurrence, while the interval from 10 to 20 s reflects the parameter estimation after the fault has been introduced. During this post-fault period, a transient response can be observed, lasting approximately \((K+m-1)\cdot T\) seconds, where \(T=0.01\) s is the sampling period.  
Figure 19 shows amplifier parameter estimation using the SIA in response to a multiplicative fault in the amplifier capacitor \(C_{8}\), while Figure 20 corresponds to a multiplicative fault in the motor armature resistance \(R_{\rm a}\).  
When applying the estimation algorithm for the detection of abrupt parameter changes, it has been observed that the length of the identification dataset \(K\) should be selected to be  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{\(B=1.8\cdot 10^{-6}\) Nm/rad/s} \\ \hline
**Fault** & \(b_{\rm 0d}\) & \(b_{\rm 1d}\) & \(a_{\rm 1d}\) \\ \hline
1.8\(\cdot 10^{-4}\) Nm/rad/s & 2.162 & \(-0.34\) & \(-0.172\) \\
1.8\(\cdot 10^{-5}\) Nm/rad/s & 2.177 & \(-1.062\) & \(-0.524\) \\ \hline \end{tabular}
\end{table}
Table 6: Estimation of motor parameters using the SWA for multiplicative fault in \(B\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{4}{c}{\(R_{\rm a}=7.41\)\(\Omega\)} & \multicolumn{4}{c}{\(J=1.43\cdot 10^{-6}\) kgm\({}^{2}\)} \\ \hline
**Fault** & \(b_{\rm 0d}\) & \(b_{\rm 1d}\) & \(a_{\rm 1d}\) & **Fault** & \(b_{\rm 0d}\) & \(b_{\rm 1d}\) & \(a_{\rm 1d}\) \\ \hline
0.741 \(\Omega\) & 1.9 & 0.356 & \(-0.06\) & \(1.43\cdot 10^{-4}\) kgm\({}^{2}\) & 2.12 & \(-2.13\) & \(-1\) \\
74.1 \(\Omega\) & 2.369 & \(-2.227\) & \(-0.94\) & \(1.43\cdot 10^{-8}\) kgm\({}^{2}\) & 2.34 & \(-0.192\) & \(-0.083\) \\ \hline \end{tabular}
\end{table}
Table 5: Estimation of motor parameters using the SWA for multiplicative faults in \(R_{\rm a}\) and \(J\).  
Figure 20: SIA amplifier parameters estimation: (**a**) \(b_{0}\), (**b**) \(a_{1}\), \(b_{1}\) during the change of \(R_{\rm a}\) from 7.41 \(\Omega\) to 74.1 \(\Omega\).  
This algorithm can be performed in the case of application of continuous fault detection procedure, i.e., SIA. Fault identification, in terms of determining the magnitude of the change in the corresponding physical parameter, is straightforward in this case. The actual faulty physical parameter can be directly determined from the relations provided in (4) and (7), thereby completing the identification.
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 5 Discussion  
Traditional landslide warnings are usually decided by thresholds. However, the complex landslide generation factors, slope internal structure and physical properties lead to great difficulty in determining an appropriate threshold. Besides, the threshold requires dynamic adjustment during the monitoring process. These warning algorithms are semi-empirical. Improper thresholds will lead to missing alarms and false alarms.  
In this study, a landslide warning algorithm based on K-means-ResNet is proposed for rapid and automatic identification of landslide acceleration. This method includes the steps of subsequence construction, feature extraction, data clustering, feature screening and landslide state classification. The method was validated on the Zhongma landslide in Guangxi Province and the Shangmao landslide in Hunan Province, China. The results show that the proposed algorithm can quickly and accurately identify the landslide acceleration and deformation process, and improve the accuracy and efficiency of early warning.  
Compared with the traditional warning methods based on thresholds, the proposed algorithm has the following two advantages. First, the proposed algorithm determines the landslide state quantitatively, which is more objective and reliable than traditional threshold-based methods. Second, traditional early warning methods only rely on displacement value. However, single sensors are highly susceptible to environmental influence, leading to serious noise. The proposed algorithm takes both the displacement value and disaster-causing factors as the input to the model to identify the landslide status, effectively improving the accuracy and reliability of early warning.  
The algorithm also has some limitations. First, the algorithm needs sufficient data for model training, so it cannot perform early warning in the early stage and cannot cover the whole monitoring cycle. Second, model training needs to collect data and the model parameters need to be optimized, increasing the complexity of the algorithm. Finally, the deep learning model requires training samples. The samples are generated from the original data through a tedious process. The original data are usually uneven, which reduces the classification accuracy.  
In summary, the proposed early warning method can effectively and reliably identify the landslide deformation state and realize the landslide warning. The deep learning technique is the first time used in the field of landslide warning, so further research work is needed, such as dataset production, feature selection and model interpretability.
## 3 MG Overview and Experimental Test Bench
### Lppt Control Strategies
#### 3.3.1 P&O with Fixed Step Size  
The P&O with fixed step size is the conventional algorithm that is widely used for the MPPT method. Some direct methods focus on perturbing the converter's variables (input voltage [13], input current [11] or duty cycle [31]) or the inverter input voltage [32], whilst others observe the mechanical power while perturbing the mechanical speed [3].  
In this study, the P&O with fixed step size method is used to perturb the DC-bus voltage \(v_{WT}(k)\) to track LPP1 and LPP2. Three different perturbation step size \(\Delta v\) (2 V, 4 V and 6 V) were applied to the system to analyze the stability and the speed tracking.