esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Active Distribution Network Communication Demand Analysis
### Problems with SCADA and Its Protocols  
The SCADA system has a wide range of applications, including data acquisition, monitoring control, as well as and process control in the fields of power, metallurgy, petroleum, chemical, gas, and railway. Most SCADA systems are based on fieldbus control technology. In this paper, SCADA refers to power SCADA, with TCP/IP used more often for communication. Its application is primarily on power system dispatch automation and distribution network automation systems.  
For the traditional power grid, with the shortage of power supply, when the power system is operating at the edge of stability, it is difficult for existing SCADA and related systems to support the safe and stable operation of the power system. When a large number of DERs are connected to the distribution network, SCADA cannot effectively solve the DER access control problem. This is mainly because the SCADA has a low sampling density, remote terminal unit (RTU) data upload rate is low, often in the range of 0.5-2 frames/s [22], and the clocks of the stations are not uniform, resulting in inconsistent data time sections, an inaccurate system operation status [23], failures of the traditional radiation current protection scheme of the distribution network [24], a limited state estimation accuracy [25], and other issues. From the perspective of the protocol, since the IEC 60870-5-101/104 protocol does not include synchronization time and phasor data, the data time accuracy is not high, and the voltage phasor accuracy obtained by iteratively solving other measurement information is difficult to guarantee.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 2 Materials and Methods
### Optimization Problem Formulation
#### 2.1.3 Sensor Model  
Each sensor \(j\) (of center \(\mathbf{x}_{j}\)) is modeled as a line in space with two points, \(\left(\mathbf{p}_{1}\right)\) and \(\left(\mathbf{p}_{2}\right)\), separated by a distance, \(l\), as shown in Figure 3. This line can be created based on azimuth \(\Theta\) and elevation \(\Phi\) angles to find the path over which field quantities (in this case strain or temperature) should be evaluated [27]. Azimuth angle \(\Theta\) is the rotation around \(x_{3}\)-axis (in the \(x_{1}x_{2}\)-plane). Elevation angle \(\Phi\) is the rotation around the \(x_{2}\)-axis (in the \(x_{1}x_{3}\)-plane). Using the trigonometric relations of these angles, one can thus formulate the direction vector \(\mathbf{d}=\left(d_{1},\,d_{2},\,d_{3}\right)\) such that the line is correctly aligned in 3D space.  
\[\begin{array}{l}d_{1}=\cos(\Phi)*\cos(\theta)\\ d_{2}=\cos(\Phi)*\sin(\theta)\\ d_{3}=\sin(\Phi)\end{array} \tag{5}\]  
From the original point, the initial and final points of the line are extended along the direction vector multiplied by half of the sensor length. Then, the coordinates of points \(\mathbf{p}_{1}\) and \(\mathbf{p}_{2}\), which define a sensor, are given by the following:  
\[\begin{array}{l}\mathbf{p}_{1}=\left(x_{1}+\frac{l}{2}\!\cos\theta\!\cos \phi,\ x_{2}+\frac{l}{2}\!\sin\theta\!\cos\phi,\ x_{3}+\frac{l}{2}\!\sin\phi \right)\\ \mathbf{p}_{2}=\left(x_{1}-\frac{l}{2}\!\cos\theta\!\cos\phi,\ x_{2}-\frac{l}{ 2}\!\sin\theta\!\cos\phi,\ x_{3}-\frac{l}{2}\!\sin\phi\right)\end{array} \tag{6}\]  
Figure 3: Sensor model in 3D space.  
where \(\mathbf{x}_{j}=\left(x_{1j},x_{2j},x_{3j}\right)\) are the coordinates of the center of the sensor. These expressions incorporate sensor placement and orientation with respect to structural geometry.
# Virtual Streamline Traction: Formation Cooperative Obstacle Avoidance Based on Dynamical Systems
###### Abstract  
Formation obstacle avoidance is a critical aspect of cooperation among unmanned surface vehicles (USVs). In practical scenarios involving multiple USVs, managing obstacle avoidance during formation assembly and navigation is essential to ensure the success of cooperative tasks. This study devised a formation cooperative obstacle-avoidance scheme utilizing dynamical systems (DS). The traditional interfered fluid dynamical system (IFDS) applied in two-dimensional planes was enhanced to address local minima issues. Furthermore, robust virtual structure patterns were implemented to effectively decouple velocity vectors. Streamlines were optimized by adjusting velocity amplitudes within specific distance intervals, facilitating precise formation assembly amidst multiple obstacles. Additionally, a novel inter-vehicle disturbance method, distinct from the IFDS, was developed to enhance inter-vehicle collision avoidance. The effectiveness of the proposed method in enabling USV formations to adeptly navigate obstacles while maintaining formation integrity and collision-avoidance capabilities was analyzed theoretically and confirmed through simulation.  
unmanned surface vehicles (USVs); formation obstacle avoidance; dynamical systems (DS); interfered fluid dynamical system (IFDS); virtual structure patterns; velocity vector decoupling; formation assembly; multi-USV cooperation +
Footnote †: journal: Applied  
csciences
# Educational 5G Edge Computing: Framework and Experimental Study
## 3 5G Smart Campus System Architecture
### Top-Level System Design of Smart Campus
#### 3.1.1 Smart Educational Terminal Layer  
The terminal layer mainly uses all kinds of smart terminals to perceive the situation information of intelligent education in an all-around way to provide personalized services for teachers and students in schools. Smart terminals use 5G communication networks to achieve the interconnection and communication among people and things. In this way, the smart campuses can achieve intelligent perception, intelligent management, and intelligent services. The smart terminals are divided into personal terminals and educational  
Figure 1: System architecture of smart educational computing.  
equipment. Personal terminals include smartphones, notebook computers, smart pens, VR/AR equipment, and others. Using personal terminals, teachers and students can obtain learning resources anytime and anywhere as well as interact and share learning resources with their learning peers in real-time. Personal terminal devices can also collect user data in real-time, such as cognition, behavior, learning preferences, and learning styles, among others, to help the system provide personalized learning services. Educational equipment includes teaching instruments, security monitoring, and experimental instruments with sensors. Common teaching instruments include smart microphones, smart large screens, digital video display stands, audio and video collectors, etc., which are basic tools for teachers to teach face-to-face or remotely. Security monitoring adopts high-definition video monitoring and sensor equipment, such as pickups, to collect data in real-time to provide monitoring and management around the clock to ensure campus security. Experimental instruments with sensors refer to various digital intelligent instruments, such as digital exploration laboratories, etc., which can collect and transmit experimental data to perform analysis and security control. Moreover, personal terminals and educational equipment can aggregate campus data to provide data support for smart campus management and personalized services. Although the widespread use of smart terminals on campus can provide all these advantages, a large number of them accessing the campus network results in a sharp increase in network load. Fortunately, the network access method of edge-cloud collaboration can effectively address this problem by offloading the collected'small' data to the edge cloud close to a terminal. For example, the data collected from a smart wearable device can be directly transmitted to the near-end eMEC via Bluetooth.
# Current Trends in Fluid Viscous Dampers with Semi-Active and Adaptive Behavior
## 3 Semi-Active Fluid Viscous Dampers
### Rheological Fluid Viscous Dampers
#### 3.1.2 Magnetorheological Dampers  
Magnetorheological (MR) dampers are semi-active devices made of MR fluids, characterized by iron particles of 1-7 \(\upmu\)m diameter [119]. Whenever a magnetic field is applied, the MR fluid passes in a few milliseconds [112] from a Newtonian behavior to a chain-like structure along the direction of the magnetic flux (Figure 10), and the flow of the fluid through the orifices of the damper is triggered only if a minimum amount of shear stresses is generated by the motion of the shaft [46].  
The commercial applications of MR dampers work with the flow and shear modes (Figure 11a,b) [120], while the squeeze-flow and the magnetic gradient pinch modes are not commonly used because of their complex circuit design [46].  
Figure 14 shows an example of an MR damper developed by the Lord Corporation [46]. This device works according to the flow mode and is composed of two chambers and a piston rod where the electromagnetic coils are placed. The MR fluid flows from one chamber to the other through a gap between the piston and the wall inside the cylinder. The damping force of this device is controlled by the intensity of the magnetic field that is modulated depending on the excitation detected by the sensors [121].  
Figure 12: Schematic example of ER damper [46].  
Figure 13: ER fluid damper proposed by [PERSON] et al. [118].  
In the last decades, MR dampers working with the shear mode mechanism have also been developed [106; 122]. An example is the device proposed by [PERSON] et al. [106] and sketched in Figure 15. This system consists of two parallel steel plates, a moving plate coated with a thin foam saturated with MR fluid, and a single coil placed at one end of the device. The output force is generated by the sliding motion of the moving plate between the two parallel plates. By increasing the strength of the magnetic field, the device is able to produce a higher damping force and dissipate a larger amount of energy [123; 124].  
MR dampers guarantee high output forces and stable hysteretic behavior within large temperature fluctuations; specifically, the force varies less than 10% in the range of temperatures between \(-40\)\({}^{\circ}\)C and \(+150\)\({}^{\circ}\)C [125].
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
###### Abstract  
The emergence of fog-based Internet of Things (IoT) systems have played a significant role in enhancing the applicability of the IoT paradigm. In such systems, fog-nodes are proficient enough to retain, process and transmit the data coming from IoT devices. Nevertheless, as an extension of cloud computing, inheriting the security and privacy concerns of cloud computing is also inevitable in fog-based IoT systems. To deal with such challenges, a diverse range of security solutions are reported in the literature. However, most of them have several limitations (i.e., vulnerability to known security attacks and high computation overhead) that curtail their practical implementation applicability. Keeping these limitations in mind, this paper propose a privacy-preserving hash-based authenticated key agreement protocol using XOR and concatenation operations for fog-driven IoT systems. Using healthcare as a case study, the security of the novel protocol is evaluated by using informal and formal security analysis. In order to obtain the experimental results, the key cryptographic operations used at the user, fog node and cloud server-side are implemented on a mobile device, Arduino and cloud server, respectively. Findings from the performance evaluation results show that the proposed protocol has the least computation cost compared to several related competing protocols.  
authentication; authentication protocol; key agreement; fog-node; fog computing 2023
# Log-Based Fault Localization with Unsupervised Log Segmentation
## 2 Materials and Methods
### Context-Based Pitfall: Inversion of Fault Prioritization  
Although the context-based ranking approach improves fault localization by considering the surrounding context of log lines, it may lead to incorrect prioritization in certain cases. One potential pitfall arises when a critical error is followed by a series of dependent errors, normal operations are resumed, and then an isolated error unrelated to the main failure appears at the end. In such a case, the context-based method may invert the fault priority, resulting in a misinterpretation of the root cause. In some cases, a critical error early in the log may indirectly cause a failure much later in the execution flow. One example is when a failure to read from the disk leads to missing credentials, which subsequently causes a database connection failure. The context-based ranking may incorrectly prioritize the database error, although the root cause lies in the disk read failure.  
Consider a system where the following sequence of events occurs (Table 4):  
* Disk Read Error: The system attempts to read necessary configuration data, including credentials, from the disk, but the read operation fails.
* Related Errors: The system encounters a series of related issues, such as retry attempts or warnings about missing files.
* Normal Operations: Despite the disk read failure, the system continues with other tasks.
* Database Connection Failure: When the system tries to connect to the database, it fails because the credentials, which should have been read from the disk, are missing.  
Figure 4: HDFS-10453 lines related to templates with most suspicious score sorted by timestamp.  
Figure 5: HDFS-10453 lines related to template with most suspicious score sorted by context. Line from context are grayed out.  
In this case, the original error (disk read failure) is the root cause of the subsequent database connection failure. However, a context-based approach might misinterpret the situation, assigning higher priority to the final database connection error, given its proximity to otherwise normal operations. This can lead to an inversion of fault prioritization, where the final error is treated as the most suspicious, despite being a consequence of the earlier disk read failure.  
The table below illustrates this scenario, with log lines that show the sequence of events leading to the database connection error:  
This example illustrates a limitation of the context-based approach: it can fail to recognize causal relationships between distant errors. In large systems, where the effects of early failures manifest much later in the execution, this can lead to incorrect fault prioritization. Without proper attention to these causal links, context-based ranking might mislead the fault localization process, focusing on the symptoms rather than the true cause.  
To address this issue, a more nuanced understanding of dependencies between log lines could help improve fault ranking and ensure that root causes are given higher priority, even when separated by normal execution.
# Channel Characterization and SC-FDM Modulation for PLC in High-Voltage Power Lines
## 4 System Model
### Single-Carrier Frequency-Division Multiple  
As a consequence of the fading present in the PLC channel, a certain number of data subcarriers are susceptible to detection errors due to deep fades on their channel gains. Considering that the proposed receptor utilizes linear estimators, it is not limited to finite constellations, permitting the use of linear codifiers, i.e., the domain and the image of the estimator covers any complex \(\mathbf{\beta}\in\mathbb{Z}^{N_{D}}\) vector.  
The technique known as SC-FDM [23; 24; 25; 26] utilizes the Fourier \(\mathbf{F}\) matrix:  
\[[\mathbf{F}]_{n,n^{\prime}}=\frac{1}{\sqrt{N_{D}}}e^{(-\beta\pi nm^{\prime}/N _{D})}\quad, \tag{22}\]  
as the appropriate data coder/decoder (DFT/IDFT proccessing) for the following reasons:  
* Structure of complex exponential sequences implies that each information symbol at the input distributes its energy uniformly over the entire output vector bandwidth. Consequently, deep fades are spread across the entire bandwidth and no particular data symbols are greatly affected. This process whitens the channel and helps each data symbol to experiment SNR close to the average.
* Coding and decoding processes can be performed by using the fast Fourier transform (FFT) algorithm, which has reduced complexity.  
It is important to mention that this coding process does not cause any reduction in the spectral efficiency achieved in conventional OFDM. This precoding method is currently used in standards such as LTE in uplink.
# Modeling Activity-Time to Build Realistic Plannings in Population Synthesis in a Suburban Area
## 6 Algorithm for Time-Choices Setting
### End-Time Range Components
#### 6.1.3 Potential Activity End-Time Range  
After calculating the current (local) latest time \(LLT_{A}\), the next step is to calculate the end-time range, this range has two potential bounds: (a) lower bound, which references the earliest end-time \(ET_{A}\), and (b) upper bound, which references the latest end-time \(LT_{A}\). To generate this range, the following steps are applied.  
First, the start-time \(ST_{A}\) is initialized: if \(A\) is the first activity, then start-time of \(A\) is equal to \(EST\). The second step is to select a duration \(dur_{A}\) for the current activity \(A\) (from the duration distribution \(DDist_{A}\) of \(A\)). The selection process is as follows: a duration proportion is selected, and then a random duration (from this proportion) is selected. If the duration distribution is not available, \(duration_{A}\) is set to \(dur_{A}\). After calculating \(ST_{A}\) and \(dur_{A}\), the initial end-time \(IET_{A}\) is deduced (see Equation (11)).  
\[IET_{A}=ST_{A}+dur_{A} \tag{11}\]  
The next step is to calculate the duration delay. To do so, the dynamic duration delay coefficient \(DyDCoef\) is calculated. This parameter is used to keep the duration of activity within the duration distribution limits. To do so, \(DyDCoef\) must satisfy the following constraints: it must have a positive value (Equation (12)), and the selected duration, including its delay has to be greater than the minimum duration \(minDur_{A}\) and less than the maximum duration \(maxDur_{A}\) (see Equations (13) and (14)).  
\[DyDCoef\geq 0 \tag{12}\]  
\[DyDCoef_{A}\leq\frac{maxDur_{A}-dur_{A}}{dur_{A}} \tag{13}\]  
\[DyDCoef_{A}\leq\frac{dur_{A}-minDur_{A}}{dur_{A}} \tag{14}\]  
\(DyDCoef_{A}\)'s value is drawn from the available range.  
If the static delay coefficient (which depends on the type of the activity) is included, the duration delay \(DDCoef_{A}\) can be deduced, which equals to the minimum value of both delay coefficients (see Equation (15)).  
\[DDCoef_{A}=\min(StDCoef_{A},DyDCoef_{A}) \tag{15}\]  
Figure 15: Predicted travel time and time spent on the intermediate activities: the sum of the travel time from \(A\) to \(z\) is the predicted travel time \(pTT_{A-z}\), and the sum of duration of the intermediate activities from \(A\) to \(z\) is the time spent \(TD_{A\to z}\).  
Once the \(DDCoef_{A}\) is defined, the duration delay \(DD_{A}\) can be calculated (see Equation (16)).  
\[DD_{A}=DDCoef_{A}\times dur_{A} \tag{16}\]  
The end-time range bounds depend on: earliest end-time \(Ee_{A}^{*}\), initial end-time value \(IET_{A}\) and delay duration \(DD_{A}\). The range bounds can be deduced as in Equations (17) and (18).  
\[ET_{A}=max(Ee_{A}^{*},IET_{A}-DD_{A}) \tag{17}\]  
\[LT_{A}=max(Ee_{A}^{*},IET_{A}+DD_{A}) \tag{18}\]  
Figures 16 and 17 illustrate this process.
## 3 Methods
### Influence Factors of Water-Surface Area Variations
#### 3.2.1 Precipitation and Evaporation Estimate  
For Honghu Lake, \(Q_{in}\) in Equation (3) is mainly determined by runoff, which is closely related to surface precipitation within the catchment. Due to lack of hydrological stations, \(Q_{in}\) in this study is simply evaluated by calculating the precipitation from the meteorological stations within the catchment. Figure 1 illustrates the Honghu Lake catchment that is extracted using hydrological analysis of ArcGIS software. In addition to three meteorological stations within the catchment, four others adjacent to the catchment are also selected. Then the gridded precipitation data (\(0.05^{0}\times 0.05^{0}\)) within the catchment is interpolated by Inverse Distance Weighting (IDW) method [30]:  
\[P(s_{0})=\sum_{i=1}^{N}\lambda_{i}P(s_{i}) \tag{4}\]  
\[\lambda_{i}=\frac{\frac{1}{d_{i0}^{2}}}{\sum_{i=1}^{N}\frac{1}{d_{i0}^{2}}} \tag{5}\]where \(N\) is the number of measured sample points; \(P(s_{i})\) are the observed values at sampled locations \(s_{i}\). \(P(s_{0})\) are the unknown values in location \(s_{0}\). \(\lambda_{i}\) are the weights assigned to each measured point, and \(d_{i0}\) is the distance between \(s_{0}\) and \(s_{i}\). According to Equation (4), the measured values closest to the prediction location have more influence on the predicted value than those farther away.  
Evaporation is an important component of lake water balance, but estimation of this flux over Honghu Lake is challenging due to lack of direct measurements over the lake surface. In this study, meteorological elements (including temperature, sun hour, relative humidity and wind speed) measured at Honghu meteorological station are used to calculate evaporation of Honghu Lake base on simplified version for the Penman equation [31]:  
\[\begin{split}& E_{open}\approx 0.051(1-\alpha)\,\text{R}_{s}\, \sqrt{T+9.5}\\ &-0.188(\text{T}+13)(\frac{R_{S}}{R_{s}}-0.194)[1-0.00014(0.7T_{ Max}+0.3T_{Min}+46)^{2}\sqrt{\frac{RH}{100}}]+\\ & 0.049(T_{Max}+16.3)(1-\frac{RH}{100})(0.54\,\text{U})\end{split} \tag{6}\]  
where \(\alpha\) is solar radiation reflection coefficient of water-surface, which equals 0.08 for open water surface; \(R_{S}\) is solar radiation and extraterrestrial radiation, respectively, which are expressed in MJ/m\({}^{2}\) / \(d\) ; \(T\) is average temperature, \(T_{Max}\) and \(T_{Min}\) is the maximum temperature and the minimum temperature, respectively; \(RH\) is relative humidity; U is wind speed at 2 m height.