esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Dead-Reckoning Based on Lie Theory for Overcoming Approximation Errors
## 4 Experiments and Results
### Experimental Environment  
The proposed method was validated using the KITTI dataset, which is widely employed in autonomous driving research by scholars [35]. This dataset captures recordings of vehicle movements in and around Karlsruhe, Germany, incorporating various real-world factors such as noise, drift, and friction. It encompasses camera images, laser scans, precise GPS measurements, and IMU acceleration data collected via a combined GPS/IMU system. Figure 3 shows a map where the colors encode GPS signal quality. The red track is precisely recorded using real time kinematic (RTK)-GPS correction values, the blue track is without correction, and the black track indicates areas excluded from the dataset due to unusable GPS signals.  
The dataset scenarios provided by [35] are diverse, ranging from highways in rural areas around the medium-sized city of Karlsruhe to urban scenes with many static and dynamic objects. The data used in this paper are from a residential area, characterized by densely packed housing and commercial buildings, narrow roads, numerous intersections, and roadside parking spaces. This data is calibrated, synchronized, and timestamped, and includes both the rectified and raw image sequences. The experiments were conducted during the day.  
The vehicle used in the experiments is a VW Passat station wagon modified for autonomous driving research. It is equipped with LiDAR, GPS/IMU, and four video cameras. Our analysis utilized data from four distinct locations, as illustrated in Figure 3, with specifics outlined in Table 1.  
Figure 2: Overview of the Lie theory-based dead-reckoning algorithm.
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 4 Analysis of the Influence of Vital System Parameters on System Stability
### Influence of the Asymmetric Grid Impedance on the System Stability  
It is indispensable to analyze the influence of unbalanced grid impedance on system stability. The extra inductances of 0 mH, 1.5 mH, and 3 mH are added to the \(a\)-phase grid impedance, respectively, to simulate the unbalanced grid condition. The corresponding Nyquist curves of the inverter system with different extra \(a\)-phase grid impedances are plotted in Figure 13. It can be seen that as the \(a\)-phase grid impedance increases, the Nyquist curve begins to encircle the point \((-1,j0)\), which indicates that the greater the extent of the grid inductance asymmetry, the higher the risk of system instability.  
Figure 11: Experimental current and FFT analysis results when the current loop bandwidth is 500 Hz: (**a**) Current waveforms; (**b**) FFT analysis results for positive and negative sequences.  
Figure 12: Experimental current and FFT analysis results when the current loop bandwidth is 100 Hz (**a**) Current waveforms (**b**) FFT analysis results for positive and negative sequences.
## 3 Methods and Fault-Slip Data
### The Tensor Ratio Method (TRM)  
The TRM, as described in [24; 25; 44], is a simple graphical and semi-automatic method plotting the heterogeneous fault-slip data on TR diagrams and examining if the slips on the faults are simultaneously compatible with a specific orientation of the Andersonian stress axes and a specific stress ratio (R) value (for details, see [44]). Moreover, it can define possible spatial stress perturbations due to large fault structures, as shown in the case of the 1999 Chi-Chi earthquake [25]. The faults with slips characterized by TR compatibility are called Tensor Ratio Compatible Faults (TRCF), defining a specific stress ratio, i.e., the stress ratio of the enhanced Andersonian driving stress tensor. The optimal TRM stress tensor is the one that explains the largest number of the TRCF with MA \(\leq 20^{\circ}\), and secondly, their Mean Misfit Angle (MMA), which must be the smallest. Although in TRM, the default threshold angle for the MA is \(20^{\circ}\); in the present examination, for the sake of consistency, we elaborated the optimal TRM stress tensors using MA \(\leq 30^{\circ}\), i.e., the same threshold angle as that chosen by [32].  
The TRM uses the MA minimization criterion and additional compatibility criteria concerning the fault activation, as the latter resulted from the Slip Preference Analysis [24; 25; 26; 44]. More precisely, it has been shown that: (1) extensional and compressional enhanced Andersonian stress regimes could activate, respectively, only extensional and contractional faults; (2) a contractional or an extensional fault can be activated if only its slickenline is in the side where the fault dip direction has an acute angle with the horizontal \(\sigma_{1}\) or \(\sigma_{3}\) principal stress axis, respectively; (3) faults with dip directions at angles up to \(\pm 15^{\circ}\) with the horizontal principal stress axis, either \(\sigma_{1}\) or \(\sigma_{3}\), activate as dip-slip faults (either reverse or normal), and these faults have the highest slip tendency values; (4) the (sub)horizontal kinematic axes (either P or T) of the faults with pitch (pt) \(\geq 80^{\circ}\), trend along the horizontal \(\sigma_{1}\) or \(\sigma_{3}\) axis, and the faults with pt \(\geq 60^{\circ}\) trend very closely around it; and (5) enhanced Andersonian stress regimes, either extensional or compressional, of similar orientationsbut of different stress ratios activate different Tensor Ratio Compatible Faults with Slip Preferences outlining different plot regions on the TR diagrams.
## 3 Security and Privacy of Computing Paradigms
### Cloud-Related Aspects
#### 3.1.2 Cloud Data Privacy  
The public Cloud faces more privacy threats, although these threats are very different based on their Cloud model variants. Some of the concerns of the danger here are the proliferation of information, malicious usage by an unauthorized person, and incapability to control by clients [55]. Clients' sensitive documents stored in the Cloud can be reached by attackers using the file's hash codes, with the help of a mechanism used in duplicating information [56]. Risks about privacy are regarded from several angles, such as access control, Cloud systems, customers, and stored information [57]. Knowing data privacy and other relating privacy principles will enormously assist in dealing with the known threat concerns. One vital setback holding some organizations from moving to the Cloud is the fear of losing classified data through information leakage [58].  
Most often, people's privacy is breached either knowingly or unknowingly. Accessing a person's private data without their knowledge or authorization is strongly considered an invasion of privacy. Different trends can occur, such as open disclosure, privacy attack, data violation, and other means of attacks. Privacy leakage can be very damaging, but privacy issues can be better managed with the points mentioned below:  
* Trust: Disclosing data of an individual or organization is considered a breach of privacy. Trust plays a very pivotal role in decreasing or eliminating fear [59]. There are various trust standards every customer can agree to, but in general, their concern is to see minimal or zero breaches of privacy at a reasonable scale [60].
* Who? The privileged persons to access certain data and who not to.
- What? Some detailed data are not made accessible to every worker. So what specific files are permitted for whom?
- When? Some data are needed for a period of time, and that period must strictly be controlled when that information has been accessed. These can be made functional by establishing management policies, checks on multi-domain, and providing strong management keys.
* Encryption of data needs to be sufficiently strong to protect the privacy of the client's files. Weak encryption of data poses a serious challenge to Cloud privacy [61].
# Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin
## 1 Introduction and Review of Models
### Review of Models
#### 1.2.3 GJR-GARCH Model  
The GJR-GARCH model proposed by [PERSON] et al. [38] is one of nonlinear GARCH family models to allow for asymmetry effects by integrating a dichotomous variable into the GARCH model. This model allows the larger impact of negative shocks to have a more distinct impact on volatility than a positive impact. The model also presented improved forecasting ability [39].  
The conditional variance of GJR-GARCH \((p,q)\) model is defined as follows.  
\[y_{t}^{2}=w+\sum_{i=1}^{q}\left[\alpha_{i}+\gamma_{i}\mathbf{1}_{\{e_{t-i}<0\}} \right]\varepsilon_{t-i}^{2}+\sum_{i=1}^{p}\beta_{i}y_{t-i}^{2}, \tag{3}\]  
where  
\[\mathbf{1}_{\{\;\cdot\}}=\left\{\begin{array}{ll}1,&\quad\varepsilon_{t-i}<0,\\ 0,&\quad\varepsilon_{t-i}\geq 0,\end{array}\right.\]  
and  
\[w\geq 0,p\geq 0,q\geq 0,\alpha_{i}\geq 0,\beta_{i}\geq 0,\alpha_{i}+\gamma_{i} \geq 0\;\text{ and }\;\sum_{i=1}^{p}\alpha_{i}+\sum_{i=1}^{q}\beta_{i}+\frac{1}{2}\sum_{i=1}^{q} \gamma_{i}<1.\]  
where \(\alpha_{i}\) and \(\beta_{i}\) are similar to the coefficients in the EGARCH model, and \(\gamma_{i}\) means the asymmetric leverage coefficient. The parameters \(w,\alpha_{i},\beta_{i}\) and \(\gamma_{i}\) are estimated by the maximum likelihood approach.
## 5 Simulations and Assessments
### Comparative Analysis of Different Control Methods
#### 5.2.2 Jerk Analysis under Different Engine Starting Conditions  
Since the vehicle jerk produced by the ADCC method was significantly less than 10 m/s\({}^{3}\), which can be accepted by drivers (as shown in Figure 23) under cases A and B.  
Figure 21: Jerk results of three control methods under case D.  
Figure 20: Comparison results of three control methods under case D: (**a**) output torque of power source; (**b**) output torque of power source; (**c**) output torque of power source.  
B, the demand-angular acceleration of the engine before ignition was optimized for the other four cases (C, D, E and F) as shown in Figure 22. It can be seen from Figure 22 that different engine demand-angular accelerations are presented under different engineering conditions. It is mainly the influence of the engine-starting conditions that prevents the motor output torque from being limited by battery power and gives as much maximum torque as possible.  
Figure 23 shows J\({}_{\text{a-max}}\) values produced by the three control methods under six engine starting conditions. The J\({}_{\text{a-max}}\) values with the ADCC method in cases C, D, E and F are greater than 85 m/s\({}^{3}\). It is difficult for drivers and passengers to accept such large vehicle jerks. Compared with the ADCC method, although the ADCC-EAC method can effectively reduce vehicle jerks in cases A, B, C and F, the ADCC-EAC method could not effectively reduce jerks under cases D and E. Moreover, the J\({}_{\text{a-max}}\) value in the case E is 60.8m/s\({}^{3}\), which is also difficult to accept. For the J\({}_{\text{a-max}}\) values generated by the DTCC-ESC method, it could be seen that the proposed DTCC-ESC method can effectively controlled the J\({}_{\text{a-max}}\) values within 10m/s\({}^{3}\) in the six cases of engine starting. Compared with the ADCC-EAC method, the J\({}_{\text{a-max}}\) value of DTCC-ESC method at engine starting point E improved by 97.1%.  
Therefore, the DTCC-ESC method proposed in this paper can effectively reduce jerks and improve driving comfort under different engine-starting conditions.  
Figure 23: Jerk analysis under six engine starting conditions.  
Figure 22: Optimized engine demand-angular acceleration before ignition.
## 7 Results
In addition, the hybrid attacks with the least effectiveness were the WP-replay and rank-replay attacks. The hybrid WP-replay attacks had the least impact on QoS performance, network stability, and power consumption whereas the hybrid rank-replayattacks were the least effective in targeting network overhead. However, the composite replay attacks yielded less impact on RPL networks than these two combinations and any other hybrid attacks. Another critical consideration is investigating the effectiveness of routing attacks as the RPL network scales up. In single-attack scenarios, the average degradations in QoS measures and network overhead were very close in S1 and S2 whereas a difference of 5-10% was noticed in S3. Similarly, energy consumption and network stability measures were more affected by increasing the scale of the network. Similar observations can also be made for the composite-attack scenarios as the impact of the routing attacks on the large-scale setup of S3 was more apparent. Although this scalability effect was evident for all the routing attacks, the VN and rank attacks had a slightly higher impact as the network scaled up considering both single- and composite-attack scenarios. For example, the VN and rank attacks caused the QoS measures to degrade by 7-18% in S3 compared to the results of S1 and S2 whereas the QoS degradations were up to 15% in the cases of other routing attacks as indicated in Tables 4 and 5. In hybrid-attack scenarios, the impact of routing attacks was amplified as the size of the network increased. For example, the PPC rate and energy consumption increased by more than 11% as the network scaled up from S1 to S3 as shown in Figures 4c and 8c, respectively. This can be noticed for all the hybrid-attack combinations but was a bit more noticeable for the VN-rank and VN-WP attacks.
## 2 Error Transfer Function of INS-Aided PLLs  
Compared with the DLL, the PLL is more sensitive to dynamic stress and much easier to lose locking, since the carrier wavelength is much shorter than the code chip length. Therefore, this paper studies the tracking performances of the PLL as an example. Error transfer function derivation is the precondition of the steady-state error modeling used for INS-aided PLLs design. To derive the error transfer function of INS-aided PLLs, its mathematical structure should be obtained based on the deeply-coupled system principle. In this section, the detailed architecture of the scalar-based deeply-coupled system will be introduced first. Then, the mathematical structure of INS-aided PLLs is proposed, reflecting the effects of all known error sources on tracking error. Based on that, the error transfer functions between all error sources and tracking error are modeled.
## 3 Proposed Method
### Examples  
Consider a scenario in which we have two original pixels with values \((x_{i}^{1}=30\) and \(x_{i}^{2}=30)\), and the secret bits to be embedded are represented by \(m_{i}=(100)_{2}=4_{5}\) in binary and quinary (base-5) systems, respectively. The first step in the embedding process involves assigning the original pixel pair \([x_{i}^{1},x_{i}^{2}]\) to a vector denoted as \(y\). Next, we calculate the variables \(f\) and \(s\). The formula \(f=[30\cdot 1+30\cdot 2]\) mod \(5=0\) determines \(f\), which represents a weighted sum of the pixel values modulo 5. Subsequently, we compute \(s\) as \(s=m_{i}-f=4-0=4\), indicating the difference between the secret message value and \(f\). For \(s=4\), the pixel values are adjusted according to Algorithm 1, which suggests modifying the original pixel values to embed the secret bits, yielding \(y\leftarrow[y_{1}+1,y_{2}-1]=[31,29]\). Hence, the updated stego pixel pair encapsulating the secret bit becomes \(y=[31,29]\).  
During the extraction phase, to retrieve the hidden secret bit from the stego pixel pair, we recalculate \(f\) as \(f=[(31\cdot 1+29\cdot 2)]\) mod \(5=4\). The alignment of \(f\) with the message value \(m_{i}=4\) ensures the accurate extraction of the secret bit, which, when converted back to binary, yields \(m_{i}=(100)_{2}\). Lastly, to restore the original pixel values from the stego pixel pair \(y=[31,29]\), we apply an averaging technique as outlined in Equation (6). This approach accurately reconstructs the original cover pixel value \(x_{i}^{1}=30\), demonstrating the reversible nature of this steganography method.
###### Abstract  
In this paper, we consider motion-planning for multiple unmanned aerial vehicles (UAVs) that oversee cooperative target tracking in realistic communication environments. We present a novel multi-UAVs cooperative target tracking algorithm based on co-optimization of communication and sensing strategy, which can generate information-gathering trajectories considering the multi-hops communication reliability. Firstly, a packet-erasure channel model is used to describe the realistic wireless communication links, in which the probability of a successful information transmission is modeled as a function of the signal-to-noise ratio (SNR). Secondly, the Fisher information matrix (FIM) is used to quantify the information gained in target tracking. Thirdly, a scalar metric is used for trajectories panning over a finite time horizon. This scalar metric is a utility function of the expected information gain and the probability of a successful information transmission. With the combining of the sensing and communication into a utility function, the co-optimization of communication and sensing is reflected in the tradeoffs between maximizing information gained and improving communication reliability. The results of comparison simulations show that the proposed algorithm effectively improved estimation performance compared to the method that does not consider communication reliability.  
multi-UAVs; cooperative target tracking; information filter; radio frequency communication; Fisher information matrix; receding horizon optimizing +
Footnote †: journal: Applied Sciences
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 4 Performance Comparison
### Range  
The range is essential in IoT applications because it defines the maximum distance devices can communicate, directly impacting the network's scalability and deployment flexibility. A longer range allows for wider coverage and connectivity, especially in remote or large-scale environments. NB-IoT and LTE-M have far less coverage compared to the ISM band-based LPWAN protocols, with Sigfox having a maximum range of up to 40 km, LoRa having a good range of up to 20 km, and Z-Wave up to 30 m [60; 61; 62]. To place the matter into perspective, using Sigfox, we can cover a typical large city with just one base station. LoRa has a lower reach, and it can take up to three base stations to cover the entire city. The spreading factor, which determines how far and robustly the signal can travel, can increase LoRa's transmission range [6]. However, it also increases the power consumption. Compared to LoRa, NB-IoT offers wider coverage in vehicle traction in agricultural applications [64]. NB-IoT and LTE-M primarily focus on applications where the end devices have issues with typical cellular networks or ISM band-based technologies due to physical barriers (indoor applications). In contrast, Z-wave focuses on even shorter-range indoor applications compared to NB-IoT and LTE-M [60]. On the other hand, RedCap offers coverage of less than 30 m, mainly suitable for indoor deployment [65].
# Productivity Analysis of Documentation Based on 3D Model in Plant Facility Construction Project
## 1 Introduction  
The Shaping the Future of Construction--A Breakthrough in Mindset & Technology, jointly published by the World Economic Forum and the Boston Consulting Group, reports the impact of integrated building information modeling (BIM) on the future construction market is enormous [1]. In fact, most of the advanced project delivery methods such as construction management (CM) at risk, pre-construction services (Precon), and integrated project delivery (IPD)--which are being piloted in the domestic construction market--use BIM as a core technology. BIM is an intelligent information system that is more than a physical three-dimensional model [2]. In Korea, Ministry of Land, Infrastructure and Transport have recently announced the \"smart construction technology roadmap\" to innovate construction productivity and enhance safety [3]. This roadmap aims to automate design based on BIM.  
However, at the design stage, Despite the results of previous studies on productivity improvement based on 3D model, 2D-drawing-based documentation method is still commonly used due to high BIM investment cost and lack of skilled BIM staff [4]. This approach is difficult to intuitively check a large amount of drawings, which leads to frequent design errors and changes, as well as excessive work [5]. Existing studies have found the effects of various BIM applications, such as the effects of design methods to convert 2D drawings to 3D model [6], BIM-based design validation to review design quality [7], and the economic impact of design errors on construction companies [8]. These studies point out that the low quality of design drawings created with documentation method based on 2D drawing results in rework and schedule delay [7,8].  
The productivity of documentation method of a number of drawings that is relevant to all tasks is essential to the analysis of construction productivity [9]. In the previous study, BIM-based design validation was used to systematize the types of design errors and quantitatively analyze the effects [7,8]. Although high quality drawings are required for advanced construction projects with off-site construction [10], DfMA methodology [11], and additive manufacturing [12], productivity analysis studies on the documentation method have not been dealt with frequently. While some studies have compared the productivity of CAD and BIM, it is difficult to find specific productivity analysis data for documentation methods in these studies [13].  
The purpose of this study is to quantitatively compare the productivity of 2D-drawing-based documentation method and 3D-model-based documentation method through case analysis of plant facility. In addition, this study investigates the effects of recycling of object-based libraries in 3D-model-based documentation.