esa-sceva/satcom-chunk-collection huggingface dataset:
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
###### Abstract  
The upcoming generation of communications can provide richer mobility, high data rate, reliable security, better quality of services, and supporting mobility requirements in the Internet of Things (IoT) environment. Integrating modern communication with IoT demands more secure, scalable, and resource-efficient mobility solutions for better business opportunities. In a massive 6G-enabled IoT environment, modern mobility solutions such as proxy mobile IPv6 (PMIPv6) have the potential to provide enhanced mobility and resource efficiency. For supporting richer mobility, a cost-effective and resource-efficient mobility solution is required in a massive 6G-enabled IoT environment. The main objective of the presented study is to provide a resource-friendly mobility solution for supporting the effective integration of future communication in the massive IoT domain. In that context, a location-based, resource-efficient PMIPv6 extension protocol is proposed to provide resource efficiency in terms of required signaling, packet loss, and handover latency. To compare and analyze the proposed model's effectiveness, mathematical equations are derived for the existing as well as for the proposed solution, and such equations are implemented. Based on the comparison among existing and proposed solutions, the results show that the proposed location-based service-oriented proxy mobile IPv6 extension is resource efficient for supporting mobility in 6G-enabled IoT.  
Proxy Mobile IPv6; IoT networks; next-generation IoT; location-based PMIPv6
# DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement
## 4 Experiments and Results
### Comparison with Other Methods
#### 4.3.2 Comparison Based on Decoding Time  
We compared DIR-Net with the traditional method in terms of BER, but also in terms of decoding time. In this experiment, we used Windows 10 system with 16G of running memory and an NVIDIA GeForce RTX3050 Ti Laptop GPU as the test environment.  
In order to compare the speed of each decoding technique more comprehensively, we test the denoiser speed, decoder speed, and denoiser-decoder speed of DIR-Net method with different code lengths, including (16, 8), (32, 16), (64, 32), (128, 64), and (256, 128) polar codes. To further evaluate the speed of DIR-Net, it is compared with traditional SC algorithms, BP algorithms, and deep learning-based methods only_fc_dec [27], FC_Decoder [28], and res_dec [29]. The decoding time for each method listed in Table 3.  
Benefiting from the parallel ability of deep learning algorithms and GPU, the deep learning approach outperforms the SC and BP algorithms in terms of running speed, as shown in Table 3. Additionally, the decoding speed is little affected with the code length increases.  
When utilizing only the decoder with the attention mechanism, the DIR-Net technique has a decoding comparable speed with that of the only_fc_dec and the FC-Decoder method. Since the attention mechanism for deep feature extraction is used in both the denoiser and decoder, we gain a significant improvement in decoding accuracy in exchange for a slight loss in speed.
## 4 Results  
Comprehensive experiments were conducted to evaluate our PDANet model. Firstly, we describe the experimental process and parameter settings of the baseline monocular depth estimation model using a single image as input. Secondly, the superior performance of the model on low texture and color fluctuation regions are presented by comparing our PDANet with the existing supervised and unsupervised models. Meanwhile, to demonstrate the effectiveness of each part of PDANet, we perform an ablation experiment by evaluating the performance of the Monodepth with and without perceptual consistency or data augmentation consistency.
## 3 Materials and Methods
### Simulation Conditions  
The simulation of the influence of road irregularities on the change in the displacement of suspension system components was simulated using the V-SIM 5.0 computer software. That is a programme enabling, in compliance with the principles of dynamics, the analysis of the vehicle movement and behaviour, the simulation of collisions as well as the analysis of the pedestrian's and driver's behaviour. In addition, the programme enables the reconstruction of different road incident scenarios, taking into account complex interactions in the human-vehicle environment system.  
In the simulation, it is assumed that the asphalt on the road is dry and has the following parameters:  
* coefficient of adhesion: 0.75;
* rolling resistance coefficient: 0.015.  
The roadway falls into the category of degraded roadways, with no repair or preventive work performed on them recently. On the roadway, there are cavities and ruts occurring alternately, as shown in Figure 5.  
Figure 5: Road conditions used in the computer simulation.  
Potholes in the road are marked red, and protrusions on the road pavement are marked green.  
The road surface condition in the simulation was designed based on observations of the actual condition that can be found on most roads with high traffic volume. The dimensions of the ruts were actually measured and adapted to the initial conditions of the simulation.  
In the simulation, two cars with the following technical parameters were involved:  
* length: 4365 mm;
* width: 1837 mm;
* height: 1639 mm;
* authorised mass: 2080 kg;
* engine capacity: 1997 cm\({}^{3}\);
* maximum power: 110 kW;
* tyres: 225/50 R17, load index: 94, pressure: 100%;
* The basic parameters of the suspension system are listed in Table 1. Data were read from the V-SIM 5.0 programme.  
The programme contains a database of real vehicle silhouettes along with their parameters and design solutions. After selecting the appropriate vehicle type, the user receives a ready model equivalent to the real vehicle, on which the user can simulate a road incident for any conditions.  
The cars were loaded in accordance with the following pattern:  
* In car No. 1 (blue--the one moving correctly), in addition to the driver, there is a complete set of passengers and a 100 kg load. The total weight of that car is 2059 kg;
* In car No. 2 (pink--the one with a damaged suspension system), there is only the driver and that car does not carry additional luggage. The total weight of that car is 1604 kg.  
In the simulation, five various car speeds were taken. The vehicles moved at 50, 70, 90, 120, and 140 km/h. The distance covered by the cars was 50 m, the roadway being degraded at the first twenty-five metres. The remaining part of the roadway was in a very good condition.
# Investigation of the Automatic Monitoring System of a Solar Power Plant with Flexible PV Modules
## 4 Conclusions  
The results obtained during this study showed a relationship between the efficiency of the used charging technologies (PWM and MPPT) and the overall performance of solar energy systems. After analyzing the experimental data, it was found that the MPPT technology achieved an average charging time of about 550 min to 12.8 V in the charging process, while the PWM method took about 650 min, indicating that the MPPT charging method was more efficient. This difference of 100 min shows that the MPPT technology is able to make better use of solar energy, especially in changing weather conditions such as cloudy days. In addition, this study found that MPPT technology consistently supported higher energy efficiency. The solar modules achieved up to 92% efficiency when working with MPPT compared to PWM which mixed out at 75%. This trend is particularly important when the sunlight angle is optimal, but the MPPT technology also maintains high efficiency under non-uniform sunlight conditions. The experiment showed that flexible solar modules significantly increased energy harvesting, even with uneven surface conditions. For example, flexible modules installed on an uneven surface were able to generate up to 15% more energy compared to traditional rigid modules, which lose efficiency if their angle is not optimal. In this way, these innovations allow for better use of solar energy and the optimization of energy resource management. The energy consumption analysis of the devices showed that the system with flexible modules adapted more to changing conditions, optimizing the management of energy resources. This confirms that continuous data monitoring and analysis is necessary to ensure the optimal performance and efficiency of solar energy systems. Finally, the data collected during this study, including voltage, current and energy quantities, allow accurate recommendations for system optimization and further research development.  
Methodology, G.B. Software, I.S. Formal analysis, G.G.; Writing--original draft, Z.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Optimizing IoT Video Data: Dimensionality Reduction for Efficient Deep Learning on Edge Computing
## 5 Discussion
### Single Frame  
For the _single-frame_ technique, Figure 8 illustrates the impact of the method used to select the single frame and the choice of image model on feature extraction. As shown, the mean image provides more information and achieves slightly better performance. Among the image feature extractors, the ResNet architecture produced the best accuracy on the test set.  
Although temporal information was lost, this approach demonstrates strong performance, even achieving the best results in certain metrics. Furthermore, while the reduction in data size was not the most substantial, the computation of the reduced representation was significantly faster, as it relied on a single image rather than processing an entire video sequence.
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 4 Results and Discussion
### Handover Latency  
Handover latency versus \(\rho_{f}\) is given in Figure 5, where \(\rho_{f}\) varied from 0 to 0.7 with a step value of 0.05. The likelihood of an erroneous packet transmission increased with a greater value of \(\rho_{f}\) on the wireless link. This in turn raised the number of retransmissions of mobility signaling, resulting in a larger handover delay. Consequently, as shown in Figure 5, the handover latency was relative to \(\rho_{f}\) for each mobility management protocol.  
In this analysis the proposed tunnel-free MIPv6 outperformed the other mobility management protocols in terms of handover latency due to the reason that the MN in the protocol utilized a tunnel-free approach and prepared its handover beforehand at the previous BMA before it actually moved to the new BMA. This analysis placed PMIPv6 second in terms of handover latency. PMIPv6 managed its MN locally. The LMA and MAG exchanged the mobility signaling, hence there was no need to perform the mobility signaling upon a wireless link leading to the minimized effect of \(\rho_{f}\) in the performance of PMIPv6.
# Reconstruction and Measurement of Irregular Karst Caves Using BLST along the Shield Metro Line
## 5 Case Study in Jinan Metro  
### Overview of the Project  
Jinan is a historical and cultural city famous for various springs, which can represent the city culture. Therefore, in line with the idea of being responsible for the history and future of Jinan, during the metro construction, the spring must be properly protected, which brings more challenging requirements for the safe construction of the metro tunnels. At present, the planned Jinan rail transit line includes two levels: the urban core area fast line (Line R) is under construction, and the central urban area general line (Line M) is still in planning. The Jinan rail transit construction line is shown in Figure 11a. There are a large number of caves in the karst areas along Line R, while the planned line M will pass through the exposed and sensitive areas of the spring core. A large number of springs are exposed in the sensitive spring area, which has a shallow limestone roof and rich karst water. This area is classified as an extreme water-rich area with a large number of karst caves, weathering fissures, and structural fissures in magmatic rocks. Considering the complexity of groundwater in spring areas and the significance of protecting the springs, there will be great challenges in the safe construction of the Jinan metro.  
Figure 11: Overview of the study area: (**a**) Jinan metro planning map; (**b**) geological cross-section of the tunnel between Wangfuzhuang and Dayangzhuang [27; 28]; (**c**) water inrush at the tunnel face.  
The section of rail transit line R1 from Wangfuzhuang to Dayangzhuang in Jinan was constructed using the shield method. This section is located in the most water-rich degree area in the central and western part of Jinan. In this area, the water supply source is sufficient, the water output of a single fractured karst water well reaches 10,000 m\({}^{3}\)/d, and the cave is pressure-bearing. The maximum water level elevation is about 8 m above the arch of the shield tunnel. In the middle of the weathered limestone under the K30 + 460.3-K31 + 362.2 mileage section, the karst in this section has been largely developed and is rich in water. The pressure-bearing karst caves were easily exposed in the shield tunnel construction process, which in the future may induce water inrush disasters, threaten the safety of workers, and impede construction progress. The interval geological profile is shown in Figure 11b. When the shield machine tunneled to K31 + 342.355, the phenomenon of high water pressure and large-flow water inrush appeared at the tunnel face, and it was found that the water inrush increased. This could result in a series of problems, such as poor slag soil properties and transportation difficulty, seriously reducing the tunneling efficiency [29]. The inrush of water from the excavation face is shown in Figure 11c, showing an obvious large flow rate that is pressure bearing.
## 1 Introduction
Key indicators used to assess the quality of satellite-borne GPS data include satellite visibility, multipath effects, data integrity, and more [15]. [PERSON] et al. [16] conducted an analysis of GPS data quality for F3/C and GRACE satellites, highlighting that larger multipath errors can lead to cycle slips, an increased number of estimated parameters during orbit determination, and ultimately reduced orbit determination accuracy. Phase center variation (PCV) represents a notable error source affecting orbit determination accuracy [17; 18]. While prior PCV values can be obtained before the launch, the complexities of satellite launch, including forces and fuel consumption, result in actual PCV values changing. Therefore, high-precision orbit determination necessitates the consideration of in-orbit PCV estimation [19; 20]. Currently, a comprehensive investigation comparing the orbit determination accuracy of HY-2B/2C/2D satellites is lacking. Building upon satellite-borne GPS data, this paper conducts a systematic exploration of reduced-dynamic precision orbit determination for HY-2B/2C/2D satellites. This exploration encompasses the analysis of observation data quality, precision orbit determination strategies and models, and orbit determination accuracy. The second section introduces fundamental information regarding the HY-2B/2C/2D satellites, including their attitude dynamics. Section 3 delves into an analysis of the quality of satellite-borne GPS data through metrics such as multipath errors, satellite altitude angles, and satellite visibility, providing insights into receiver performance. Section 4 discusses the influence of in-orbit PCV estimation on orbit determination. The orbit determination strategy is introduced, and the accuracy of reduced-dynamic orbit is evaluated from overlapping orbit validation, a comparison with Centre National d'Etudes Spatiales (CNES) orbits, Satellite Laser Ranging (SLR) orbit validation, and so on. Section 5 is the conclusion.
## 4 Fatigue Analysis and Life Modeling
### Fatigue Damage Model under Alternating Loads  
The frequent acceleration and deceleration of a vehicle during driving and the high-frequency alternation of rotational speeds can have a damaging effect on the motor rotor that cannot be ignored. The number of cycles at each level of load range and mean value is extracted by rainflow counting of the speed-time course using the range-mean counting method [23], which in turn allows us to perform a damage analysis of the load counting results at different operating conditions.  
The number of cycles \(n_{R,i}\) at a torque range \(S_{R,j}\) of level \(i\) is obtained by counting the rainflow cycles of the torque. Combined with the material S-N load life curve equation \(S_{R}^{m}\cdot N_{f}=C\), the fatigue life \(N\) at level \(i\) of the torque variation can be derived. According to the Miner linear cumulative damage criterion, the damage \(D_{s}\) generated by the motor rotor can be obtained, and the calculation formula is as follows:  
\[D_{s}=\sum_{i=1}^{N_{k}}\frac{n_{R,i}}{N_{f,i}} \tag{11}\]
# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 2 Related Work
The summary of the review findings are as follows: (i) Ethereum blockchain has been increasingly used for catering to the operational demands of various blockchain operations apart from its inherent security characteristic. (ii) Existing studies are more focused on accomplishing application targets and less towards resource optimization, with (iii) less inclusion of dynamicity of nodes and flexible architecture to support extensive network communication at low cost. Hence, a significant gap is observed between (i) increasing blockchain operations focusing on less flexible communication and low-focus cost efficiency and (ii) a need for more balance between security and resource management in IoT applications.
## 2 Materials and Methods
### Extreme Gradient Boosting (XGBoost)
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Factors/Sites** & **IT-Cas** & **JP-Mse** & **KR-CRK** & **PH-RiF** & **US-HRA** & **US-HRC** & **US-Twt** \\ \hline SWC & 60.4 & 42.6 & 40.7 & 62.1 & 41.7 & 47.8 & 0.4 \\ USTAR & 0.1 & 0.2 & 0.2 & 0.2 & 0.2 & 0.2 & \\ NETRAD & 125.8 & & 141.9 & 188.6 & 232.0 & 249.3 & 187.1 \\ TS & 12.9 & 19.5 & 14.3 & 28.5 & & & 18.6 \\ NDVI & 0.5 & 0.5 & 0.4 & 0.6 & 0.6 & 0.6 & 0.6 \\ DeltaTA & \(-\)0.4 & \(-\)0.6 & \(-\)0.3 & \(-\)0.2 & \(-\)0.3 & \(-\)0.4 & \(-\)0.4 \\ Number & 10,917 & 4207 & 23,594 & 13,844 & 1562 & 1813 & 55,854 \\ \hline \hline \end{tabular} Note: the blank indicates missing data. Input factors are air temperature (TA), \({}^{\circ}\)C, incoming shortwave radiation (SW_IN), W m\({}^{-2}\), outgoing longwave radiation (LW_OUT), W m\({}^{-2}\), vapor pressure deficit (VPD), hPa, atmospheric pressure (PA), kPa, wind speed (WS), m s\({}^{-1}\), wind direction (WD), soil water content (SWC), %, friction friction velocity (USTAR), m s\({}^{-1}\), net radiation (NETRAD), W m\({}^{-2}\), soil temperature (TS), \({}^{\circ}\)C, normalized difference vegetation index (NDVI), and the temperature difference between the previous day and the current day (DeltaTA), \({}^{\circ}\)C.  
\end{table} 
Table 3: _Cont._where \(l\) is the loss term of the \(t\)-th CART, C is a constant term, and \(\Omega\left(f_{t}\right)\) is the regularization term of the model, defined as:  
\[\Omega(f_{t})=\gamma T_{t}+\lambda\frac{1}{2}{\sum_{j=1}^{T}w_{j}^{2}} \tag{2}\]  
where \(\gamma\) and \(\lambda\) are customization parameters. In general, the larger these two values are, the simpler the tree structure will be. Then, the problems of over-fitting may be effectively solved. Taking a second-order Taylor expansion with Equation (1), it can be written as follows:  
\[O^{(t)}=\sum_{i=1}^{n}l(y_{i}\hat{g}_{i}^{t-1}+g_{i}f_{t}(x_{i})+\frac{1}{2}h_ {i}f_{t}^{2}(x_{i}))+\Omega(f_{t})+\text{C} \tag{3}\]  
where \(g\) is the first derivative, and \(h\) is the second derivative. They can be described as:  
\[g_{i}=\partial_{g_{i}^{t-1}}\cdot l\left(y_{i},\hat{y}_{i}^{t-1}\right) \tag{4}\]  
\[h_{i}=\partial_{g_{i}^{t-1}}^{2}\cdot l\left(y_{i},\hat{y}_{i}^{t-1}\right) \tag{5}\]  
Substituting (2), (4), and (5) into (3) and taking the derivative, the solutions can then be obtained from (6) and (7):  
\[w_{j}^{*}=-\frac{\sum g_{i}}{h_{i}+\lambda} \tag{6}\]  
\[O^{*}=-\frac{1}{2}{\sum_{j=1}^{T}\frac{(\sum g_{i})^{2}}{h_{i}+\lambda}}+\gamma T \tag{7}\]  
The XGBoost model has the advantages of high computational efficiency and high accuracy. More details about the XGBoost model can be found in [PERSON] et al. (2018).
# Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing
## 3 Materials and Methods
### In Vivo Human Brain HS Dataset
When possible, a first HS image was captured before tumor resection (e.g., P008-1 in Figure 1). After that, the HS acquisition system was moved out of the surgical area, and the tumor resection was performed by the surgeon. When the operating surgeon felt that it was safe to temporarily hold the surgery, a second HS image was captured while the tumor was resected (e.g., P008-2 in Figure 1). After acquiring the HS images, a specific set of pixels was labeled using a semi-automatic tool based on the spectral angle mapper (SAM) algorithm [33] developed in [32]. The surgeon, after completing the clinical procedure, selected only a few sets of very reliable pixels using the semi-automatic tool to create the ground-truth map for each captured HS image. After that, the SAM was computed in the entire image with respect to the pixel previously selected. Using a threshold manually established by experts, other pixels with the most similar spectral properties to the selected one could be identified. The pixels were labeled using four different classes: tumor tissue (TT), normal tissue (NT), hypervascularized tissue (HT) (mainly blood vessels), and background (BG). The background class involved other materials or substances present in the surgical scenario, but not relevant for the tumor resection procedure, such as skull bone, dura, skin, or surgical materials. In this work, only GBM tumor pixels from four different patients were labeled from the eight patients originally affected by GBM tumor in this study, due to inadequate image conditions to perform the initial labeling. The remaining images with GBM tumor were included in the database, but no tumor samples were considered. In total, six HS images (P008-01, P008-02, P012-01, P012-02, P015-01, and P020-01) were labeled with the four studied classes (NT, TT, HT, and BG) and were employed as test datasets. The studied datasets can be observed in Figure 1, where in the synthetic RGB images, the tumor areas are surrounded by a yellow line, and the ground-truth maps of each HS image are shown below. Due to the low number of HS images with labeled tumor pixels, the leave-one-patient-out cross-validation methodology was proposed to evaluate the algorithms. In this methodology, the training dataset was composed by all patients' samples except the one to be tested.
## 3 Theory  
### Photon Attenuation  
When photons enter a material with a certain intensity (\(I_{0}\)), it attenuates and its intensity after passing through a mass per unit area (\(x\)) layer of material is reduced to (\(I\)). The photon mass attenuation coefficient (\(\mu_{m}\)) depends on the material and can be calculated using Equation (1) [49]:  
\[I=I_{0}e^{-\mu_{m}x} \tag{1}\]  
The influence of different modifiers on the mass attenuation coefficients of the investigated glass is evaluated in this work.