esa-sceva/satcom-chunk-collection huggingface dataset:
# Mixed-Carrier Communication for Technology Division Multiplexing
## 5 Evaluation and Discussion
In Equation (25), BnPSK BER is derived based on Equation (12) and MCC structure embedding \(L_{1}-\text{BPM}\), \(L_{2}-\text{BnPSK}\), and M-QAM OFDM:  
\[\text{BER}_{\text{BnPSK}}\approx Q(sin\frac{\pi}{L_{2}}\sqrt{2\ \frac{\text{SNR}_{\text{MCC-LS}}\ L_{1}/L_{2}}{1+2\ \text{SINR}_{\text{OFDM}}}}) \tag{25}\]  
Hence, it is concluded that increasing both \(L_{1}\)-BPM and \(L_{2}\)-BnPSK modulation orders results in better power efficiency at the cost of less spectral efficiency gain. Figure 9: Impact of varying PWM frequency on maximum MCC spectral efficiency at target BER, varying MCC-LS and OFDM QAM modulation orders. efficiency. Initial values for the problem variables fed into the model are obtained from the plots in Figures 9-11. Additionally, the BER threshold of \(3.8\times 10^{-3}\) is set to be the minimum acceptable BER per MCC technology for solving this problem. A visualization of the optimum maximized MCC spectral efficiency power requirements is shown in Figure 12. The relation between \(\text{SINR}_{\text{OFDM}}\) and \(\text{SNR}_{\text{MCC-LS}}\) is almost linear. As \(\text{SNR}_{\text{MCC-LS}}\) increases from 4 dB to reach 14 dB, MCC maximized spectral efficiency range is 2.3-3 bits/sec/Hz. The required \(\text{SINR}_{\text{OFDM}}\) range is 30-38.5 dB. The optimum variables are shown in Figure 13 for the same range of \(\text{SNR}_{\text{MCC-LS}}\), 4-14 dB. At this range, the maximum spectral efficiency is achieved by embedding 12-13 bits per BPM symbol, and 2-BrPSK within the MCC PWM-like envelope of frequency around 78.125 kHz, as shown in \[T_{r}=150 ms,\]0 Hence, the M-QAM modulation order controls the maximum obtained MCC spectral efficiency with a range of 12-15 bits per symbol. \[T_{r}=150 ms,\]1 MCC power requirements for spectral efficiency optimization. \[T_{r}=150 ms,\]2 MCC optimized variables for spectral efficiency optimization.
# Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning
## 4 HSI Inpainting Algorithm Outline  
By analyzing the HSI inpainting model and the HSI inpainting mechanism in Section 2 and studying the online robust spectral dictionary learning approach and the robust sparse coding method in Section 3, an HSI inpainting algorithm based on online robust spectral dictionary learning, INORSDL, is proposed. Algorithm 2 shows the pseudo code of the proposed INORSDL approach.  
```
Input:\(\mathbf{Y}\in\mathbb{R}^{L\times n_{c}}\) (Observed HSI data), \(\mathbf{M}\) (inpainting mask matrix) Output:\(\hat{\mathbf{X}}\in\mathbb{R}^{L\times n_{c}}\) (Inpainted HSI data)
1 begin
2 select the complete observed HSI pixel spectral vectors \(\overline{\mathbf{y}}_{c,q}\in\mathbb{R}^{L},q=1,\ \ldots\,n_{c}\)
3\(\hat{\mathbf{D}}=\text{ORSDL}\big{(}\overline{\mathbf{y}}_{c,q}\in\mathbb{R}^{L},q=1, \ \ldots\,n_{c}\big{)}\) (robust spectral dictionary training, Algorithm 1)
4 solve the robust sparse code matrix \(\hat{\mathbf{x}}\)
5\(\hat{\mathbf{X}}=\hat{\mathbf{D}}\hat{\mathbf{\alpha}}\)
6 end
```  
**Algorithm 2** INORSDL.
## 2 Related Work
### Blockchain Storage Scalability  
The issue of blockchain storage scalability has been widely studied in existing research. [PERSON] et al. [20] proposed sharding in blockchain networks, where each shard is managed by an independent committee responsible for transaction management and storage. While this approach significantly reduces the amount of blockchain data stored on individual nodes, it introduces high processing latency when handling cross-shard transactions, thereby increasing the network consensus cost. [PERSON] et al. [21] proposed pruning the blockchain state, offloading old blocks to the cloud to reduce storage consumption in local networks. Although this method effectively optimizes local storage costs, it compromises blockchain integrity within the local network, making it unsuitable for systems with high backhaul latency. [PERSON] et al. [13] introduced the LightChain architecture, which employs a distributed hash table (DHT) for distributed blockchain storage allocation, allowing nodes in the network to store only a portion of the blockchain data. However, this approach primarily focuses on load balancing in block distribution and does not optimize access costs resulting from the allocation strategy. [PERSON] et al. [12] integrated the InterPlanetary File System (IPFS) into IoT blockchain systems, delegating storage tasks to distributed trusted third parties. However, the introduction of third-party storage raises additional latency issues that remain unresolved. [PERSON] et al. [22] proposed the concept of \"consensus unit\", which aggregates multiple different network nodes into one consensus unit, enabling nodes to jointly store and maintain blockchain data.  
Although these mentioned studies improve blockchain storage scalability in their respective contexts [12; 13; 20; 21; 22], their studies either lack comprehensive modeling tailored to the SIoV scenario or have optimization objectives that differ from the problem we address. Therefore, these solutions cannot be directly applied to the system we propose. The studies by [PERSON] et al. [20] and [PERSON] et al. [21] focus on blockchain data pruning as the core idea. However, the damage to blockchain integrity caused by this approach means that it is only applicable to specific scenarios. The study by [PERSON] et al. [12] needs to take into account the credibility and stability of third-party systems. The schemes proposed by [PERSON] et al. [13] and [PERSON] et al. [22] are similar to collaborative storage architectures, but there is still room for improvement in the detailed strategies for block allocation. In this paper, we design an RSU cluster-based collaborative storage scheme that accounts for blockchain storage demands in SIoV and the mobility characteristics of vehicle users. By employing RSU clustering and a distributed block storage strategy, our approach further improves blockchain storage efficiency while reducing block access costs for vehicle users.
## 3 Automated Calibration of Chaboche Parameter
### Results and Discussions--Automated Parameter Calibration of Chaboche Model  
The stress-plastic strain parameters determined using the PSO and GA schemes are calculated after setting 244 MPa as the elastic limit, which was obtained from incremental uniaxial test result in Figure 9 and listed in Table 3. The \(\alpha\) and \(\alpha_{NLK}\) curves are obtained from Equations (3) and (5) and presented in Figure 16. \(\gamma_{3}\) is considered to be zero.  
In contrast to GA, the particles in the PSO method have less mathematical operations. Thus, only a few parameters are left to be adjusted and this saves time compared to GA. After evaluation in every iteration, the cost function of each particle is compared with past, local and global values to select the most eligible particle as upcoming member. Each member iteratively gets its new position and velocity to be evaluated in order to minimize the cost function. In this study, both methods are compared for accuracy in satisfying the cost function. Since the PSO method had resulted in more accurate parameters (in terms of \(MSE\)) and less optimization time, the PSO scheme was adopted in generating the ratcheting parameters for the stress-plastic strain results in Figures 12 and 13.  
The ratcheting parameters for post-stabilized monotonic and hysteresis loadings, with different elastic limit adoptions, 167.2 MPa and 244 MPa, calculated using the PSO method are presented in Table 4. For simplicity, the set of parameters obtained from post-stabilized monotonic stress-plastic strain curve with adopted elastic limits 167.2 MPa and 244 MPa are named Set A and Set B, respectively. Parameters obtained from hysteresis stress-plastic strain with adopted elastic limits of 167.2 MPa and 244 MPa are named Set C and Set D, respectively, and shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Set A** & **Set B** & **Set C** & **Set D** \\ \hline \(C_{1-4}\) & 2446600, 12207, & 296870, 17958, 2118, & 140520, 29589, 2118, & 140220, 16725, 2118, \\  & 2118, 24246 & 20508 & 31040 & 15818 \\ \(\gamma_{1-4}\) & 27439, 291, 0, 796 & 21179, 335, 0, 1196 & 4471, 899, 0, 325 & 47864, 282, 0, 732 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PSO method—Calibration for Set A, Set B, Set C and Set D.  
Figure 16: Comparison of \(\alpha\) and \(\alpha_{NLK}\) curves using four Chaboche components for post-stabilized monotonic curve with elastic limit 244 MPa (**a**) PSO calibration method (**b**) GA calibration method.
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 1 Introduction
Accordingly, this paper explores the accuracy limit of globally-referenced mapping involving collaborating consumer vehicles whose sense of global position is based on standard code-phase-based GNSS receivers. Key parameters in this exploration are the asymptotic averages of the error sources that impair code-phase-based GNSS positioning: receiver thermal noise, satellite clock and orbit errors, ionospheric and tropospheric modeling errors, and multipath. One or more vehicles navigating through a digital map over time make multiple time-separated GNSS measurements of the same location. If these vehicles collaboratively update the map over multiple sessions, then the GNSS errors are averaged across all sessions with appropriate weighting. Are the GNSS errors at every map location--including deep urban locations--asymptotically zero-mean, or, on the contrary, do location-dependent biases persist in averages of time-separated standard GNSS measurements? Such is the question this paper seeks to address. To this end, it describes and demonstrates a stereo-camera-based digital mapping pipeline called GEOSLAM (globally-referenced electro-optical simultaneous localization and mapping) that achieves the accuracy limit of digital mapping with standard GNSS. GEOSLAM combines standard GNSS position estimates with a visual SLAM system in a tightly-coupled architecture. To achieve the accuracy limit, GEOSLAM merges and jointly optimizes maps over multiple sessions with time-separated GNSS position estimates. This paper details the techniques GEOSLAM invokes to smoothly transition between unmapped and previously-mapped regions, consistently fusing current and prior maps without the need for a six degrees-of-freedom (6-DoF) pose from an inertial navigation system (INS). GEOSLAM enables multi-agent collaborative mapping by storing and rendering its map in a global frame of reference, such as the World Geodetic System 1984 or the International Terrestrial Reference Frame. Multi-session operation of GEOSLAM is demonstrated using camera and GNSS data collected in a moderate urban environment, and the accuracy of global localization with the multi-session GEOSLAM map is assessed with respect to CDGNSS-based ground truth.
# An Established Theory of Digital Twin Model for Tunnel Construction Safety Assessment
## 5 Case Study
### Virtual Model Establishment  
Based on the construction information obtained by various information collection devices such as intelligent sensors and RFID, the digital twin virtual model of the tunnel construction process is established to realize the \"geometry physics knowledge\" data integration.  
The basic geometric information of the tunnel structure is obtained through CAD graphics, and the initial geometric model is established using BIM technology. On this basis, the Trimble TX5 3D scanner is used to conduct 3D scanning of the construction structure, to achieve the correction of the geometric model. This modeling method ensures that the geometric model can truly reflect the geometric information of the construction structure. Based on the established BIM geometric model and the collected construction information, the basic information of various components is supplemented. The MIDAS model was established by Revit Api from the geometric model [43]. Combined with the sensor collection information, the same working conditions are set as in the actual construction process, and the physical model is established. The mechanical property of the construction structure is analyzed, and the mechanical property parameters obtained can be directly used for the structural safety assessment, which provides a basis for the establishment of the knowledge model.  
The establishment of a geometric model and physical model is the application process of modeling software, which is not described in this paper. The practical application of algorithms in the knowledge model is introduced in detail. Based on the construction information provided by the geometric model and physical model and structural design specifications, a rule database is established. The data analysis of the twin model is conducted using the LSTM, and a knowledge model is established to endow the twin virtual models with the ability of self-evolution, to realize the simulation, analysis, and evaluation of the tunnel construction process.  
When the physical information acquisition and data integration model are completed, this paper selects the LSTM algorithm to analyze and process the construction data. According to the current situation and change trend of various risk factors, the prediction for the safety status of the construction process on the next construction day is realized. As mentioned above, this paper selects the settlement deformation of the tunnel as an example to analyze the safety state of the construction process. Through the knowledge model, the predicted settlement is compared with the relevant standards and regulations, and timely adjustment measures can be taken according to the settlement to ensure the overall safety of the construction process.
## 4 Experiments and Performance
### Experimental Results and Analysis
#### 4.2.1 Experimental Validation of MSS-YOLO-Based Miner Identification
Meanwhile, in order to further visualize the experimental results, some of the detection results in this paper are shown in Figure 18. The upper right number of the detection anchor box represents the actual distance measured by the method of this paper. As can be seen from Figure 18, the miner measurement method described in this paper for a fully mechanized mining face has good stability under the conditions of near distance, long distance, low dust concentration, high dust concent  
Figure 17: Personnel identification and localization results and error analysis. (**a**) The \(x\)-axis error, (**b**) \(y\)-axis error, and (**c**) \(z\)-axis error. The black straight line is where the actual ideal miner position is located, green points are measurement points with an error of \(\pm\)10% or less, and red points are measurement points outside the \(\pm\)10% error region. Figure 18: Miner binocular vision recognition and localization detection results map. (**a**) Original image data; (**b**) 3D depth map after being solved by the SGBM algorithm; (**c**) resultant map of the 3D spatial data of the miner bounding box detection center obtained by the MSS-YOLO fusion SGBM model. exposure from a mine lamp. It can meet the requirements of three-dimensional position measurement of miners in a fully mechanized mining face.
# Underwater Glider Propulsion Systems VBS Part 1: VBS Sizing and Glider Performance Analysis
## 1 Introduction
### Background
The results of [24] show the direct dependence on the hydrodynamic coefficients [25] and the flight angles in the VBS displaced volume, highlighting the importance of the design requirements and the hydrodynamic parameters of the vehicle to estimate the VBS budget. The equations and charts of [24] have been updated for the present research in order to propose a practical method to estimate the VBS budget as a result of the glider performance analysis and the main operation states. One important correction is the variability of the angle of attack as a function of the desired glide slope or glide angle, considered previously as a constant. [PERSON] and [PERSON] [26] in 2020 presented a methodology to select and analyze VBSs for AUVs through buoyancy control, which emphasizes the importance of defining the design requirements and subsequently selecting the most appropriate type of VBS. These factors are necessary to generate the conceptual design for the computational simulation model and its analysis; the VBS volume is one of the design requirements that need to be defined. The purpose of the present research on underwater gliders is to propose a method to define the VBS volume capacity for its design process, given that \"the design process of a VBS system is not known in full, and existing approaches are not scalable\" [26]. In 2020, [PERSON] et al. [27] presented the performance analysis of the Slocum glider based on polynomial coefficients to improve its navigation, comparing different historical hydrodynamic models of the legacy vehicle. Their method used the polynomial coefficients to estimate the performance with a variation of the angle of attack. Another glider performance study was presented in 2020 by [PERSON] et al. [28], using semi-empirical equations for each major component of the Slocum vehicle (body, wings, and vertical tail); assuming a constant angle of attack (optimal angle of attack) and increasing the error in the velocity estimations while the glide angle is incremented. The semi-empirical equations are dimensionless lift and drag coefficients for different components and wings that depend on the attack angle, and the Reynolds number.
# Investigating the Privacy and Security of the SimpliSafe Security System on Android and iOS
## 3 Methodology
### Device Setup and App Installation  
A typical SimpliSafe security system includes a base station which is the heart of the system that all sensors connect to. The base station sounds the alarm or alerts the monitoring station when a sensor is triggered. It also contains a wireless keypad that allows the user to arm or disarm the system and change settings. The system comes with a companion app where the user can monitor camera images or change settings. The SimpliSafe security system allows users to subscribe to monitoring services which enable users to record and store camera feeds to the cloud or connect the system directly to emergency response services. In this study, we used the free (unmonitored) subscription plan in which the user can only monitor everything through the app, but the system was not connected to an emergency response service. In our setup, we used the base station, the wireless keypad, an indoor security camera, two entry sensors, and a motion sensor (see Figure 1). All SimpliSafe devices (hub, keypad, sensors, and camera) were new so there was no need for reverting them to factory settings. Prior to activating the system, a Google email address ([EMAIL_ADDRESS]) was used to set up the SimpliSafe account. We also obtained a SIM card for cellular connection and the plan that needed to be associated with the security system.  
Figure 1: The SimpliSafe security system, from left to right: base station, keypad, motion sensor, two entry sensors, and camera.
## 5 V-HOE Based Solar Concentrators
### Commercial and Space Holographic Concentrators  
Regarding commercial systems that make use of holographic concentrators, the best performing solutions are offered by Prismsolar, which propose patented enhanced glass-on-glass holographic modules composed of highly efficient bifacial N-type silicon cells with holographic technology. A single module is made alternating strips of bifacial solar cells with parallel strips of holographic film. It allows to collect not only the light that hits the solar cells directly, as normally occurs but also the light that hits the holographic film, which diffracts the light and a portion of this light is guided to the cell through total internal reflection, thus increasing the total harvested sunlight [8; 58].  
Finally, as a possible future application, taking into account their thin, lightweight and flexible, holographic elements can be studied, designed and fabricated for space solar concentrators. In fact, the solar power conversion is the primary power source for most of the satellite and loss of power, even for a short time, can lead to catastrophic consequences [50]. Thus, an enhancement of the collected light is desirable. Of course, in the space the holographic materials must be able to withstand much more drastic conditions respect to earth applications, due to strong thermal excursions, high vacuum and the presence of high-energy gamma, electronic and protonic radiation originating from the solar wind [4; 46; 47].
## 2 Methods
### Degradation Models  
#### 2.3.1 Packets Losses  
We have considered a fixed frame size of 20 ms in all cases and simulate packet losses by making the signal zero for the whole duration of the lost packet. The amount of packets lost is tuned by a parameter that establishes the percentage of packets lost. In all experiments, the values used for this parameter have been 0%, 5%, 10%, 15%, and 20% for train and test data. The details of the three methods applied are described below:  
* Individual packet losses: Packets along the audio file are randomly chosen and removed, assuring they are not consecutive packets.
* Burst packet losses: Batches of three consecutive frames are randomly selected and removed (a total of 60 ms) until the packet loss percentage is reached.  
* Single and burst packet losses: The two previous modes are merged to have a more realistic simulation, since a real scenario can include both types of losses. Batches of one, two, or three packets are randomly selected and removed (segments of 20, 40 or 60 ms) until the packet loss percentage is reached.