esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Trusted Content Authorization Security Framework for Social Media
## 3 SecToken Framework
### Integration Process and Challenges  
Firstly, the location and interface of the SecToken framework in the social media platform need to be determined. Then, the API interface for interacting with the social media platform needs to be designed and developed to realize the functions of the SecToken framework. Secondly, authentication and authorization are required to implement user authentication and authorization mechanisms to ensure that only authorized users can access the functions of the SecToken framework. Finally, data transmission and encryption are required to implement secure data transmission and encryption mechanisms to protect sensitive information transmitted between the social media platform and the SecToken framework. There are multiple challenges in the implementation of the SecToken framework. Firstly, the security challenge is to ensure the framework's security to prevent malicious attacks and data leakage. Secondly, the compatibility challenge is to ensure that the framework can be compatible with different versions and types of social media platforms, and the integration with other security frameworks and components needs to be compatible. The performance challenge is also an important consideration, with the need to ensure that the framework will not negatively impact the social media platform's performance and user experience, including the response time and processing capacity. Finally, the accuracy challenge also needs to be paid attention to, that is, to ensure the accuracy of content judgment and to continuously improve the technical level to ensure its accuracy.  
Although the described security mechanisms have achieved particular success in ensuring the authenticity of content posted on social media and the identity of users, there are still some potential areas for improvement. Firstly, collecting personal information, such as user timestamps, address tags, and network IP addresses, may raise privacy issues, and this information needs to be handled more carefully to protect user privacy. Secondly, although technologies such as double authentication and digital signatures enhance security, a balance must be found between security and user experience to ensure that the system is secure and easy to use. In addition, further research is needed to address the potential risks of cyber attacks and digital signature cracking. Data protection and compliance are also key issues to ensure that relevant regulations are followed when processing user information and content summaries. Finally, with the evolution of technology, security mechanisms need to be constantly improved and updated to adapt to new security challenges and threats. Therefore, it is necessary to seek more comprehensive and effective solutions by considering user privacy, the balance between security and convenience, cyber attack response, data protection compliance, and continuous improvement.
# Analysis of Collapse Mechanism and Treatment Evaluation of a Deeply Buried Hard Rock Tunnel
## 1 Introduction
[32] analyzed the collapse process and instability mechanism of a slate tunnel, and summarized the collapse prevention experience from the survey, design and construction stages. Based on catastrophe theory, [PERSON] et al. [33] predicted the possibility of tunnel collapse and proposed corresponding preventive measures. [PERSON] et al. [34] studied the mechanism of tunnel collapse and established the risk assessment system for mountain tunnel collapse. [PERSON] et al. [35] proposed a new algorithm of tunnel structural support pressure to evaluate the stability of surrounding rock based on the key block theory. Based on the block theory, [PERSON] et al. [36; 37] studied the influence of interaction between cutting tools and rock blocks on the stability of TBM shield tunneling face. [PERSON], [PERSON] and Jin, L.F. [38] and [PERSON], [PERSON] et al. [39] have applied support vector machine (SVM) methods to analyze and evaluate the stability of the surrounding rock of a tunnel. To sum up, the research on tunnel collapse in China and elsewhere has accumulated a lot of valuable experience and put forward many effective collapse prevention and treatment techniques. However, current research mainly focuses on the mechanism and influencing factors for the collapse of tunnels at a shallow depth, in soft rock, rich water, and other adverse geological conditions, while few studies have reported on the collapse of deeply buried hard rock tunnels. Due to the regional differences in tunnel construction, the reasons and scale of tunnel collapse are also different, so it is difficult to implement countermeasures systematically, and more of them still rely on engineering experience. Therefore, the Chen Jiashan tunnel was selected as a case study to analyze the arch collapse during the construction of the deeply buried section. Base on comprehensive analysis of the geological conditions, collapse characteristics and types, and the construction environment, the collapse mechanism was analyzed. Based on block theory, the stability of the key block was analyzed and predicted, which provided important information for further treatments. Finally, monitoring results were applied to evaluate the treatment quality of collapse area. The proposed method could provide engineering experience for similar projects.
# Online Optimization of Pickup and Delivery Problem Considering Feasibility
## 2 Pickup and Delivery Problem
### Constraints
#### 2.2.2 Constraints on Agent Movement  
The movement of the agent \(l\) is constrained by the following inequality:  
\[p_{l}(t)\leq\mathcal{A}p_{l}(t+1),\;\;t\in\{\tau_{i},\dots,\tau_{i}+T_{f}-1\}, \tag{4}\]  
where the inequality sign (\(\leq\)) holds element-wise. Equation (4) represents that the agent \(l\) moves only between vertices for which edges exist (see, e.g., [50; 51; 52]). We remark that (4) becomes the constraint on the agent movement when (3) is imposed.  
For example, consider the undirected graph shown in Figure 1. Suppose that at time \(t\), the agent \(l\) is located at the vertex \(s_{1}\). Then, the location candidates at time \(t+1\) are the vertices \(s_{1}\), \(r_{1}\), \(r_{2}\), and \(\tau_{5}\). Based on the notation of (1), the vertices  
\[y_{l,k}=\begin{cases}1&\text{if the agent $l$ is responsible for the order $o_{k}$,}\\ 0&\text{otherwise.}\end{cases}\]  
As variables related to the agent \(l\)'s order receipt and delivery at time \(t\), we also introduce binary variables \(z_{l,k}^{\text{pick}}(t)\) and \(z_{l,k}^{\text{deli}}(t)\in\{0,1\}\) as follows:  
\[z_{l,k}^{\text{pick}}(t) =\begin{cases}1&\text{if the agent $l$ receives the goods of the order $o_{k}$ at time $t$,}\\ 0&\text{otherwise,}\end{cases}\] \[z_{l,k}^{\text{deli}}(t) =\begin{cases}1&\text{if the agent $l$ delivers the goods of the order $o_{k}$ at time $t$,}\\ 0&\text{otherwise.}\end{cases}\]  
Assume that the vertex \(s_{k}\) in the order \(o_{k}\) corresponds to the vertex \(v_{l}\) (i.e., the \(i\)-th element \(p_{l,j}(t)\) of \(p_{l}(t)\)). Then, receiving the goods of the order \(o_{k}\) at time \(t\) can be represented by the following expression:  
\[[z_{l,k}^{\text{pick}}(t)=1]\Rightarrow[y_{l,k}=1]\wedge[p_{l,j}(t)=1],\ \ \forall l,\ \ \forall t. \tag{5}\]  
Similarly, if \(c_{k}\) corresponds to the \(i\)-th element \(p_{l,j}(t)\) of \(p_{l}(t)\), then delivering the goods of order \(o_{k}\) at time \(t\) can be represented by the following expression:  
\[[z_{l,k}^{\text{deli}}(t)=1]\Rightarrow[y_{l,k}=1]\wedge[p_{l,j}(t)=1],\ \ \forall l,\ \ \forall t. \tag{6}\]  
The relationship between \(y_{l,k}\), \(z_{l,k}^{\text{pick}}(t)\), and \(z_{l,k}^{\text{deli}}(t)\) is represented by the following expressions:  
\[[y_{l,k}=1] \Leftrightarrow\sum_{t=T_{k}}^{\tau_{i}+T_{f}}z_{l,k}^{\text{pick }}(t),\ \ \forall k \tag{7}\] \[[y_{l,k}=1] \Leftrightarrow\sum_{t=T_{k}}^{\tau_{i}+T_{f}}z_{l,k}^{\text{deli }}(t),\ \ \forall k \tag{8}\]  
We define two sets of orders \(\mathcal{P}\) and \(\mathcal{D}\) to represent the status of each order. The set \(\mathcal{P}\) is the set of orders where the agent has not received the goods at the store. The set \(\mathcal{D}\) is the set of orders where the agent has received the goods at the store and is in the process of delivery. Here, because there is only one agent responsible for each order, the following constraints must be imposed:  
\[\sum_{l}y_{l,k}=1,\ \ \forall o_{k}\in\mathcal{P}\cup\mathcal{D}. \tag{9}\]
# Indole-3-Acetic Acid Action in Outdoor and Indoor Cultures of _Spirulina_ in Open Raceway Reactors
## 3 Results
### Biomass and Biomolecule Production Costs  
While various factors, such as energy, harvesting, and labor significantly influence the production costs of biomass and biomolecules, this study focused solely on the impact of phytohormone supplementation. As such, other cultivation parameters were not considered when calculating production costs. Therefore, only the costs associated with the culture medium, with and without phytohormones, were considered. However, we emphasize that further research is needed to delve deeper into the study of costs to enhance the robustness of this cultivation technique in the future.  
The implementation of IAA supplementation in outdoor cultivation (IAA-O) resulted in a 55%, 86%, 44%, and 50% reduction in the cost of biomass, carbohydrate, protein, and lipid production, respectively, in comparison with control experiments conducted indoors (C-I) (Figure 1). This cost comparison was based on the conditions with the highest and lowest production costs (C-I and IAA-O, respectively).  
Outdoor experiments (C-O and IAA-O) yielded lower biomass and biomolecule production costs than indoor cultivation (C-I and IAA-I). Furthermore, supplemented conditions (IAA-I and IAA-O) demonstrated lower production costs compared to their respective controls (C-I and C-O) (Figure 5). Compared with C-I production costs, the IAA-I production cost of biomass, carbohydrates, and lipids was reduced by 12%, 56%, and 19%, respectively. Additionally, compared with C-O production costs, the IAA-O biomass production cost was 16% lower, the carbohydrate cost was 51% lower, and the lipid cost was 11% lower. However, the protein production cost was reduced by less than 10% compared to the control and supplemented conditions. Phycocyanin production costs were lower in the indoor control condition (USD 2.30).  
Figure 4: Yield and productivity of phycocyanin (dry basis) of indoor and outdoor _Spirulina_ sp. LEB 18 biomass supplemented with IAA and control experiments. C-I = indoor control culture; IAA-I = indoor cultivation supplemented with IAA; C-O = outdoor control culture; IAA-O = outdoor cultivation supplemented with IAA. Lowercase letters that are identical within columns signify no significant difference in the means of phycocyanin yield at the 95% confidence level (\(p>0.05\)). Similarly, uppercase letters that are identical on the line indicate that there is no significant difference in the means of phycocyanin productivity at the 95% confidence level (\(p>0.05\)). Data are represented as the means \(\pm\) standard deviation (\(n=6\)).
## 3 Input Data  
Input data for the machine turn detection algorithm was created based on gyroscope \(Z\)-axis readings from the IMU unit. The measurement unit was embedded in a machine that was following a certain route. The sampling frequency was set to 50 Hz. A single sample used in the analysis consisted of more than 4.5-h records of sensors' measurements from one working shift of the vehicle. During this time, a total number of 16 complete haulage cycles were recorded. Raw data from the gyroscope \(Z\)-axis are shown in Figure 3A. From this point, the Yaw angle (Figure 3B) can be calculated by integration of that axis using the trapezoidal method (Equation (1)):  
\[\psi(\tau)=\int_{0}^{\tau}\omega_{Z}(t)\ dt, \tag{1}\]  
where \(\psi\) is the Yaw angle at given time \(\tau\) and \(\omega_{Z}\) is the angular speed in rotation around the \(Z\)-axis. The Yaw angle is the most useful one from all of the Euler angles in case of turn detection. The angle describes the machine rotation around the \(Z\)-axis, that is, its movements when viewed directly from above.
# Single-Shot Time-Lapse Target-Oriented Velocity Inversion Using Machine Learning
## 3 Results
### Inversion in Non-Repeatability Scenarios
In the horizontal direction, the peak of the anomaly is centered at 10 km and this position is not perturbed by noise. However, in the vertical direction, the peak shifts from 6 km to 6.04 in the worst scenario. In this way, the non-repeatability noise produces a shift depth in the peak of the anomaly. In order to quantify the uncertainty of the inversion methodology, we compute the error in the prediction of the velocity anomaly. Figure 9 estimates the pixel-to-pixel difference between ML-predicted and true velocity anomalies. Indeed, Figure 9 is an error color map where the horizontal and vertical axes reproduce the same dimensions of the previous Figures 6-8. The first panel in Figure 9 shows the error for the perfect repeatability case, while Figure 9b,c for the repeatability noise cases corresponding to \(\pm\)0.1m and \(\pm\)0.5 m, respectively. The error magnitude is much larger in the noise scenarios, the error bars in the  
Figure 8: Inversion results of individual samples for a scenario with non-repeatability, modeled by randomly moving the receivers in the lateral directions, with maximum perturbations equal to \(\pm\)0.5 m. The yellow pattern in the panels represent the velocity anomaly; the horizontal and vertical dimensions reproduce the reservoir region indicated by the rectangle in Figure 1. Comparison of true (first row) and predicted (second row) time-lapse velocity anomalies in the target region for specific samples of a testing dataset contaminated with geometry non-repeatability noise. The illustrated samples are located at key percentiles of the SSIM distribution on the referred dataset: minimum (worst case), three quartiles, and maximum (best case). The last two rows compare, respectively, the central vertical and central horizontal velocity profiles of the velocity anomalies. panels are not in the same scale. The general pattern with and without noise is distinct, the noiseless case is characterized by an underestimation in the peak of the anomaly, while the noisy cases are characterized by an overestimation in this peak and an underestimation in the region above the anomaly. This information is important for the geophysical interpreter that in the field case will deal with a noisy environment. In this situation, the interpreter should consider that the anomaly peak, whose velocity anomaly is produced by a fluid replacement, could be overestimated.
# First Evaluation of Topography on GNSS-R: An Empirical Study Based on a Digital Elevation Model
## 4 Understanding the Effects of Rough Topography on GNSS-R Space-Borne Data
### Regional Scale
#### 4.2.2 Interpretation of the Empirical Results
The impact of surface slopes and curvature variables on both observables, TE and \(\Gamma\), was quite strong at low SMC \(\sim\) [0, 0.2] m\({}^{3}\)/m\({}^{3}\), over the following ranges: \(\beta\)- [0, 5] \({}^{\circ}\), TC- [-1, 2] rad/m, and PC- [-2, 1] rad/m. Increasing SMC levels reduced the gradients in agreement with the findings using the heterogeneity variables (Figure 5). The spreading of TE and the decrease of \(\Gamma\) showed a much more gradual evolution for increasing curvature levels up to (a) \(\theta_{\rm e}\sim\) [20; 30] \({}^{\circ}\): TE- 600 m and \(\Gamma\sim\) - 13 dB for SMC \(\sim\) [0.3, 0.4] m\({}^{3}\)/m\({}^{3}\), TE- 600 m and \(\Gamma\sim\) - 13 dB for SMC \(\sim\) [0.2, 0.3] m\({}^{3}\)/m\({}^{3}\), TE- 400 m and \(\Gamma\sim\) - 13 dB for SMC \(\sim\) [0.1,Surface slopes modify the scattering area with respect to that corresponding to a flat Earth assumption. They are a key parameter in GNSS-R studies over land surfaces, which have a certain degree of correlation with TRI and VRM because increasing topographic heterogeneity belongs to increasing surface slopes. On the other hand, the PC and TC have a much clearer differentiated impact on TE and \(\Gamma\). These topographic variables are directly related to water accumulation over the surface. Over flat surfaces, \(\Gamma\) is high and TE is low because the curvature is negligible, and the scattering is quite specular. However, the impact of SMC is lower compared to areas with higher curvatures. In these areas, TE and \(\Gamma\) have a clearer differentiated response to different SMC levels. Finally, it was found that the variability with SMC is higher in a nadir-looking configuration than at grazing angles. It increases for negative PC levels (concave), while for positive curvature levels (convex) there is a negligible of influence of SMC.
# An OCARI-Based Wireless Sensor Network for Heat Measurements during Outdoor Fire Experiments
## 3 Experimental Protocol
### Data Acquisition Systems  
#### 3.2.1 Wired System: Campbell CR3000 Datalogger  
On the wired network, the heat quantities--namely, temperature and radiant heat flux--are recorded using a portable CR3000 datalogger model (Campbell Scientific--Edmonton, AB, Canada (c)). This is dedicated to work in outdoor conditions and was already used to collect data during large-scale fire spreading experiments [2, 6, 22, 23]. It also serves as the reference wired datalogger to evaluate the response of the Zigbee WSN tested in Reference [6]. For a fair comparison, we used the same wired datalogger with an identical setup. Specifically, on the CR3000, there are 14 differential analogue channel inputs. Each channel exhibits a maximum scan rate of 100 Hz. The recorded voltage levels are digitised with a 16-bit resolution. We selected five voltage levels ranging from \(\pm 50\) mV to \(\pm 5\) V. In this study, the channels are configured at a \(\pm 50\) mV (analogue resolution: 0.67 \(\upmu\)V per digit). We selected a sampling rate corresponding to 1 Hz. The data were stored in 4 MB of memory. It is necessary to bury the wires from the devices to the datalogger into the ground to protect them from the effects of the fire. Furthermore, a thermal insulation composed of an assembly of ceramic fibre protects the off-ground section of the datalogger. This protection is intended to make the wires resistant to the fire.  
The analogue data were encoded on 16 bits on the wired datalogger and timestamped at the instance of the digitisation. We estimated the delay for transmitting the information from the transducer to the datalogger for a cable with a length ranging to a few metres as approximately 9 ns based on the CR3000 datasheet. We can therefore neglect the corresponding delay in transporting the signal from the sensor to the ADC, in the wired architecture at least, with the chosen 1 Hz sampling rate.
## 4 Discussion  
### Effect Due to Switching of the Three-Axis Probe  
Since the measurement in SRM-3006 is performed while switching each axis of the three-axis isotropic probe, it is not possible to measure all three axes simultaneously. This is one of the concerns related to measurement while driving. Therefore, four different measurements were repeated five times to investigate the effect due to switching of the three-axis probe. One of the measurements is normal measurement with SRM-3006, which automatically switches the three axes and combines the E-field strengths of the three axes. For the remaining three measurements, one of the three axes is held during the measurement. Then, the results are combined by the root sum of squares and compared with the results of the normal measurement. Two routes (Routes 1 and 2) of approximately 2.5 km without any traffic signals, including line of sight (LOS) and non-LOS conditions, were selected in Tokyo.  
The measurements were conducted while recording the GPS position, but owing to the accuracy of the GPS, it is not possible to obtain the same positional measurements. To compare the results between the normal three-axis measurement and the combined separate measurements of three axes, four types of measured results were first smoothed using a median filter, which is a nonlinear digital filtering technique used to remove noise from an image or signal. Then, interpolation was performed at 5 m intervals to obtain data at the same point and averaged for every five repeated measurements under the same conditions. Table 4 shows averaged differences between the normal three-axis and separate measurements along the two routes. The results show that there are no large differences compared with the results shown in Section 2.2, even though the measurement was performed by switching three axes.
###### Abstract  
The rapid growth of computationally intensive tasks in the Internet of Vehicles (IoV) poses a triple challenge to the efficiency, security, and stability of Mobile Edge Computing (MEC). Aiming at the problems that traditional optimization algorithms tend to fall into, where local optimum in task offloading and edge computing nodes are exposed to the risk of data tampering, this paper proposes a secure offloading strategy that integrates the Improved Polar Lights Optimization algorithm (IPLO) and blockchain. First, the truncation operation when a particle crosses the boundary is improved to dynamic rebound by introducing a rebound boundary processing mechanism, which enhances the global search capability of the algorithm; second, the blockchain framework based on the Delegated Byzantine Fault Tolerance (dBFT) consensus is designed to ensure data tampering and cross-node trustworthy sharing in the offloading process. Simulation results show that the strategy significantly reduces the average task processing latency (64.4%), the average system energy consumption (71.1%), and the average system overhead (75.2%), and at the same time effectively extends the vehicle's power range, improves the real-time performance of the emergency accident warning and dynamic path planning, and significantly reduces the cost of edge computing usage for small and medium-sized fleets, providing an efficient, secure, and stable collaborative computing solution for IoV.  
Internet of Vehicles; mobile edge computing; blockchain; offloading strategy; improved polar lights optimization algorithm 2020 IEEE/RSJ International Conference on Intelligent Transportation Systems (IWT), pp. 2018-2019, 2018.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
###### Abstract  
The mega-low Earth orbit (LEO) satellite constellation is pivotal for the future of satellite Internet and 6G networks. In the mega-LEO satellite constellation system (MLSCS), which is the spatial distribution of satellites, global users, and their services, along with the utilization of global spectrum resources, significantly impacts resource allocation and scheduling. This paper addresses the challenge of effectively allocating system resources based on service and resource distribution, particularly in hotspot areas where user demand is concentrated, to enhance resource utilization efficiency. We propose a novel three-layer management architecture designed to implement scheduling strategies and alleviate the processing burden on the terrestrial Network Control Center (NCC), while providing real-time scheduling capabilities to adapt to rapid changes in network topology, resource distribution, and service requirements. The three layers of the resource management architecture--NCC, space base station (SBS), and user terminal (UT)--are discussed in detail, along with the functions and responsibilities of each layer. Additionally, we explore various resource scheduling strategies, approaches, and algorithms, including spectrum cognition, interference coordination, beam scheduling, multi-satellite collaboration, and random access. Simulations demonstrate the effectiveness of the proposed approaches and algorithms, indicating significant improvements in resource management in the MLSCS.  
2020 IEEEexample:BSTcontrol