esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Grid-Oriented Methods  
Hardware-oriented solutions use devices to monitor the characteristics of the electrical network system, such as power, current, and voltage [16]. These methods are very efficient when it comes to finding NTLs, but the main disadvantage is that they require additional equipment, making them costly.  
[PERSON] et al. present a technique for detecting energy theft by either manipulating or bypassing the energy meter. This technique requires the injection of high-frequency signals from smart meters, which represents a high cost for the utility company [83]. Another disadvantage is that energy theft through cyberattacks cannot be prevented. [PERSON] et al. developed a modified ammeter to facilitate the detection of fraud in low-voltage consumer units. This allows the operator to verify and compare differences between local and remote measurements to establish if there is fraud [84]. Another example is the development proposed by [PERSON] et al., who proposed a method based on the suitability of smart meters. This method involves evaluating the voltage drop in a segment. When the calculated voltage drop does not match the actual one, it is determined that there is unregistered consumption in that area, and the location will need to be inspected [85].
# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
###### Abstract  
With the rapid development of smart grids, the deployment number of transmission lines has significantly increased, posing significant challenges to the detection and maintenance of power facilities. Unmanned aerial vehicles (UAVs) have become a common means of power inspection. In the context of drone power inspection, drone clusters are used as relays for long-distance communication to expand the communication range and achieve data transmission between patrol drones and base stations. Most of the communication occurs in the air-to-air channel between UAVs, which requires high reliability of communication between drone relays. Therefore, the main focus of this paper is on decoding schemes for drone air-to-air channels. Given the limited computing resources and battery capacity of a drone, as well as the large amount of power data that needs to be transmitted between drone relays, this paper aims to design a high-accuracy and low-complexity decoder for LDPC long-code decoding. We propose a novel shared-parameter neural-network-normalized minimum sum decoding algorithm based on codebook quantization, applying deep learning to traditional LDPC decoding methods. In order to achieve high decoding performance while reducing complexity, this scheme utilizes codebook-based weight quantization and parameter sharing methods to improve the neural-network-normalized minimum sum (NNMS) decoding algorithm. Simulation experimental results show that the proposed method has a better BER performance and low computational complexity. Therefore, the LDPC decoding algorithm designed effectively meets the drone characteristics and the high channel decoding performance requirements. This ensures efficient and reliable data transmission on the data link between drone relays.  
power inspection; drone relays; data link; deep learning; LDPC decoding
## 2 Challenges in the Market Structure
### Data Requirements
#### 3.6.2 Geospatial Data  
For the examined Wind Power Plants (WPPs) and OHLs, the following GPS coordinates, of power system's components locations, including the routing of each tower's position, are provided:  
* Substations.
* Wind parks.
* Solar parks.
* 400 kV HVAC lines.
* 150 kV HVAC lines.
* 20 kV HVAC lines.
* HVDC lines and cables of interest.  
GPS data from the power system and GPS data from a few chosen POIs will be used for the visualization of GIS, energy forecasts, and weather localization:  
* Start and finish GPS positions for cables lines and OHLs, together with the names of the corresponding substations. If the same POIs are connected in parallel lines, it is required to specify whether the circuit is single or double. Dimensions, weight, and rated ampacity of the conductor, as well as the conductor's bundle type arrangement (two or three-bundle, etc.).
* Substations' GPS points: specifying the ends of the substation's surface and describing the plant's surface.
* GPS points of every WP tower in the examined wind parks.
* GPS points of each OHL tower of the OHL that is analyzed.
* Proposed interconnection lines list (TSOs).
# A Maximum Likelihood Estimation Method for Underwater Radiated Noise Power
## 3 Beamforming-Based Power Estimation Methods
### Conventional Beamforming  
Under the far-field assumption, the estimated channel transfer function and the weighting vector can be expressed as  
\[\begin{array}{l}\mathbf{\overset{\kappa}{G}}=\frac{e^{-ikr_{m_{0}}}}{r_{m_{0}}} \Big{[}\begin{array}{cccc}e^{-ik(1-m_{0})d\sin\theta}&e^{-ik(2-m_{0})d\sin \theta}&\dots&e^{-ik(M-m_{0})d\sin\theta}\\ \mathbf{w}_{CBF}=\frac{1}{M}\Big{[}\begin{array}{cccc}e^{-ik(1-m_{0})d\sin \theta}&e^{-ik(2-m_{0})d\sin\theta}&\dots&e^{-ik(M-m_{0})d\sin\theta}\\ \end{array}\Big{]}^{\mathsf{T}},\end{array} \tag{8}\]  
where \(m_{0}\) denotes the reference hydrophone index, \(r_{m_{0}}\) is the distance between the reference hydrophone and the sound source, \(k\) is the wavenumber, \(d\) is the inter-element spacing, and \(\theta\) is the bearing angle of the source relative to the array. Substituting Equation (8) to Equation (7) yields the Conventional BeamForming (CBF) estimate of radiated noise power  
\[\sigma_{s\,CBF}^{2}=r_{m_{0}}^{2}\mathbf{w}_{CBF}^{\mathrm{H}}\mathbf{R}_{x}\mathbf{w}_{ CBF}. \tag{9}\]
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
## 4 Evaluation of VRTHI
### Effectiveness in Reducing DoS Attacks  
We mention earlier that nodes under DoS attacks in the network obtain IP addresses that are not in the subscribed local cellular networks domain. The ratio of the total number of IPs the nodes received (from both inside and outside the network), and the total number  
Figure 8: Intensity of DoS attacks before VRTHTH. (**a**) Ratio of malicious IP; (**b**) DoS Hit Rate.  
of IPs received from outside the network, is an important metric to track the proportion of DoS attacks affecting the nodes. The nodes are observed for malicious IPs over one month, and this ratio taken for each week is plotted in Figure 9. The plot illustrates that, before implementing VRITHI, more than 50% of the nodes are under DoS attack in many situations, and the nodes become not useful for valid operations.  
Effectiveness of VRITHI is readily reflected in the reduction of DoS attacks, as illustrated in Figure 9. The modems used to connect to the cellular network have a default setting in which the control channel is active while the data transfer is going on. During the process of data transfer, socket connections are changed at run-time. The IP packets have the sequence numbers tagged to them such that they can be put in correct sequence at the destination. This helps in achieving a lower DoS attack ratio. From Figure 9, it can be noted that DoS attack after applying VRITHI is reduced from 65-82% to less than 28% and is achieved using hardware with as little as 2 KB of RAM.
## 4 Results
### Validation of the Approach  
To summarise the findings shown above, the developed numerical model associated with a joint stiffness of \(\mathrm{k_{n}}=\mathrm{k_{s}}=3\times 10^{7}\) Pa/m accurately models the experimental results, both in terms of collapse load multiplier and failure mechanisms. It is important to note that this agreement has been noticed for very different assemblages (WD), with a different distribution of voids (Figure 1), enhancing the consistency of the modelling. In particular, the block imperfections (imperfect contact area) noted in the experimental study ([PERSON] et al., 2017) can indeed be related to a softer joint stiffness, possibly even more marked because of the low stresses acting throughout the specimens.  
When facing such low contact stiffness, it has been noted that the contact stiffness significantly influences numerical results. Therefore, practical steps to assess these soft structures are described hereafter:  
* First, the joint stiffness of the studied block contacts must be evaluated. This can be done either using the proposed approach, i.e., calibrating the numerical value based on experiments conducted on structures (post-diction). Ideally, different structures should be investigated. Another option, which is even better, consists in characterising the joint stiffness itself, monitoring joints from a wall under vertical compression, as in ([PERSON] et al., 2016).  
Figure 8: Comparison of the observed collapse mechanisms between experiments and DEM simulations for \(\mathrm{k_{n}}=\mathrm{k_{s}}=3\times 10^{7}\) Pa/m for WD 5 until WD 7 ([PERSON] et al., 2017).  
The latter can be complemented by simpler joint closure tests [33]. Note that a combination of both experimental characterisation and numerical calibration/validation is ideal.
* Then, the numerical model with the calibrated parameters can be used in the engineering practice to assess every structure made of the same blocks (predictions).
# Role of Autologous Platelet Gel (APG) in Bone Healing: A Rabbit Study
###### Abstract  
Purpose: The aim of the present study is to evaluate the influence and efficacy of autologous platelets on bone regeneration in a rabbit defects model. Materials and Methods: A total of 12 critical size tibial defects were produced in six New Zealand rabbits: A total of six defects were filled with autologous platelet gel (APG) and six defects were maintained as untreated controls. No membranes were used to cover the bone osteectomies. The histology and histomorphometry were performed at four weeks on retrieved samples of both groups. Results: No complications were reported in any of the animals nor for the defects produced. A significantly higher lamellar and woven bone percentage was reported for the APG group with a lower level of marrow spaces (\(p<0.05\)). Evidence of newly formed bone was found in the superficial portion of the bone defect of APG samples where no aspects of bone resorption were observed. Conclusions: The evidence of the present research revealed that APG increases new bone formation restricted to the cortical portion and induces more rapid healing in rabbit bone defects than in untreated defects.  
autologous platelet gel; new bone formation; bone repair; platelet derivates +
Footnote †: journal: Applied Sciences  
doi: 10.3390/app11010395
## 3 Results and Discussion
### Partitioning of Cd, Pb, and Zn in Uncontaminated, Contaminated, and Remediated Soils
Since after the remediation the concentrations of Cd, Pb, and Zn in the water-soluble fraction were far below the limiting values for inertness [37; 38] and thus were their concentrations in the exchangeable fraction, the Cd, Pb, and Zn in the remediated soil composites do not present an environmental hazard. In an evaluation of the possible statistical significance of differences between the distribution of Cd, Pb, and Zn in contaminated and remediated soils, mean values (\(n=6\)) from distribution of Cd, Pb, and Zn between the fractions of the Tessier's sequential extraction were compared by computing Student's t-test. In the water-soluble, exchangeable, bound to carbonates, and fraction bound to Fe/Mn oxides and hydroxides, there were statistically significant differences between the means of Cd, Pb, and Zn concentrations, when a probability of \(\leq\)0.05 was considered as significant. In the fraction bound to organic matter and sulfide, no statistically significant differences between the means of Cd, Pb, and Zn concentrations were observed when a probability of \(\leq\)0.05 was considered as significant (Table S10). This was expected since, in this fraction, very low elements concentrations were found in contaminated and remediated soils. In the residual fraction, it was not possible to apply Student's \(t\)-test due to large differences in Cd, Pb, and Zn concentrations determined at different time intervals in this fraction.
# FTUC: A Flooding Tree Uneven Clustering Protocol for a Wireless Sensor Network
## 4 Problem Statements
### Global Minimum and Maximum Distances Problem  
For unequal clustering algorithms, the node competition radius evaluation is the most important parameter. In order to calculate this value, the nodes need three values: (i) Dist(S\({}_{i}\),BS) as the distance between the node S\({}_{i}\) and the BS; (ii) D\({}_{\text{max}}\) as the longest path between a node and the BS; and (iii) D\({}_{\text{min}}\) as the shortest distance between a node and the base station. D\({}_{\text{max}}\) and D\({}_{\text{min}}\) are global information calculated by the base station. If nodes exist that cannot communicate directly with the base station, it is therefore impossible to calculate the global minimum and maximum distances.  
A way to solve this problem is by flooding the network [21] to transmit long-distance information to the base station using a multi-hop strategy. In this approach, each node broadcasts the messages until reaching the BS. In this situation, the broadcasting implies network congestion, consumes energy on each node, and loses lots of useful information, which can in turn make the communication channels unsafe.  
To overcome this limitation, we propose a new approach called 'flooding tree'. Above all, we define the base station as a unique root node at the center of the network. Then, the root node broadcasts information to its neighboring nodes. In an iterative process, when the node receives the information it acknowledges the reception and broadcasts the information to its neighborhood until the end of the network. Finally, a tree type sub-network is constructed taking into account the fastest path in the network.  
In the built tree, the nodes between the end of the tree and the root node represent the route distance information. Then, the root node has to compare each depth of the tree branch to find the global minimum and maximum distances. If the network is static (there are no dynamic nodes), this phase has to be performed only one time at the first iteration of the algorithm.
# Effect of Temperature on Channel Compensation in Optical Camera Communication
## 3 Optical Camera Communication Link Model  
Complementary Metal Oxide Semiconductor (CMOS) cameras usually employ Rolling Shutter (RS) scanning. This type of scanning is based on exposing the sensor to the incoming light on a row basis. The row shift time is normally grasped to perform a time-to-space conversion, capturing the light source variations along the sensor's scanning dimension. This effect is one of the most important aspects of OCC and has enabled a multitude of communications techniques, such as Undersampled Frequency Shift On and Off Keying (UFSOOK), WDM and MIMO [31], CSK [34], and 2D-OFDM [35].  
Generally, most cameras possess a Color Filter Array (CFA) that is usually located over the image sensor. This allows the camera to split signals from light sources depending on wavelengths. Figure 2 shows both Bayer and Foveon RGB filter pattern. Bayer mosaic contains 25%, 50%, and 25% of red, green, and blue regions, respectively, while Foveon mosaic presents 100% of the color. Consequently, Foveon abolishes the de-mosaicing process, and its spectral response is based on the light absorption behavior of silicon [36]. These characteristics have prompted the use of cameras to carry out WDM, CSK, and MIMO systems by using RGB LEDs as a light source. Nevertheless, as mentioned above, those filters present an overlap between their RGB channels, causing channel cross-talk. Thus, an approach to compensate this interference between channels is needed.
# The Impact of Automated Vehicles on Road and Intersection Capacity
## 5 Capacity Calculation Model of Signalized Intersection in a Mixed Environment
### Case Analysis  
In order to verify the accuracy of the proposed calculation model of capacity correction for signalized intersections in a mixed environment, SUMO was used, to design simulation experiments involving different proportions of autonomous vehicles in different CACCs, with the number of vehicles passing the stop line in each lane counted, for purposes of comparison with the theoretical conversion results.  
The simulation section represents a standard intersection, with a lane width of 3.0 m. The simulation operation is shown in Figure 8. The detector is placed on the stop line, the red vehicle is an automatic vehicle, and the green vehicle is a manual vehicle. The traditional four-phase timing scheme is adopted, and the signal cycle is 120 s. Figure 9 shows the green time of each phase, wherein the yellow time is 3 s, the vehicle speed is 33 m/s, and the number of vehicles is the largest. The vehicle parameters are shown in Table 4. The simulation duration is 3600 s, the simulation step is 0.1 s, and the right-turn lane is ignored.  
Risk factor assessment and mitigation measures:  
1. Using a large range of road networks will increase the simulation time and calculation amount, resulting in inaccurate simulation results. In this paper, the standard signal control intersection was selected in the experimental verification stage.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**The Proportion of Autonomous Driving** & **Basic Saturation Flow Rate (pcu/h)** \\ \hline
0.0 & 1869 \\
0.1 & 1923 \\
0.2 & 1991 \\
0.3 & 2077 \\
0.4 & 2186 \\
0.5 & 2324 \\
0.6 & 2501 \\
0.7 & 2735 \\
0.8 & 3054 \\
0.9 & 3527 \\
1.0 & 4517 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Basic saturated flow rate of single lane under different automatic driving proportions.  
Figure 8: SUMO simulation diagram: intersection capacity in a mixed environment.  
2. The type and number of lanes at the intersection also impact the experiment. To this end, independent lanes were selected for each entrance direction of the intersection.  
The capacity of signalized intersections in a mixed environment is calculated according to Table 7 and Equations (1)-(3). The conversion coefficient is shown in Table 8.  
Take the east entrance straight lane as an example: the capacity of the lane converted by theory is compared with the actual traffic volume simulated by SUMO, as shown in Figure 10. The relative error is less than 16%, which proves that the deduced capacity of the signalized intersection in the mixed environment is more in line with the actual situation. In addition, with the increase of the proportion of autonomous vehicles, the capacity of the intersection is also increasing. When the intersection is full of AVs, its capacity is 2.40 times that of fully HVs.
## 2 Multiple Response Characterization and Modeling of DR Resources
### DR Resource Scheduling Decision Model  
As DR resources are involved in system regulation, the dispatch agency tends to integrate all DR resources as virtual output and make them cooperate with thermal units [22]. Similar to the constraints associated with thermal units, there are also relative constraints for DR resources with multiple response characteristics, including response duration constraint, response interval time constraint and response capacity constraint. The comparison of regulation constraints between thermal units and DR resources is listed in Table 2.  
Constraints of DR resources are modeled as follows.  
(1) Maximum response duration constraint  
\[\sum_{t=k}^{k+T_{m,\max}}X_{m}(t)\leq T_{m,\max},k=1,2,\ldots,T \tag{6}\]  
where \(T_{m-\max}\) represents the maximum response hours.  
(2) Maximum responding number constraint  
\[\sum_{t=1}^{T}X_{m}(t)[1-X_{m}(t)]\leq N_{m,\max} \tag{7}\]  
where \(N_{m-\max}\) represents the maximum hours in a dispatch cycle  
(3) Minimum response interval time constraint  
DR resources must have a certain interval time from the last time to receive the scheduling instruction due to their load characteristics. It can be expressed as:  
\[[T_{m,res}(t-1)-T_{m,resmin}][X_{m}(t)-X_{m}(t-1)]\geq 0 \tag{8}\]  
where \(T_{m,res}(t-1)\) represents the accumulated interval of the \(m\)th DR resource from the last response, and \(T_{m,resmin}\) represents the minimum response interval of the \(m\)th DR resource.  
(4) Load response amount constraint  
\[P_{m,\min}\bullet X_{m}(t)\leq\mid P_{m}(t)\mid\leq P_{m,\max}\bullet X_{m}(t) \tag{9}\]  
where \(P_{m-\max}\) and \(P_{m-\min}\) represents the maximum and minimum response amount of \(m\)th DR, respectively.
## 3 Methodology and Materials  
### Optimization Procedure  
The implementation of the presented ANN-PSO model can be executed easily. Generally, it can be summarized into four steps and the flowchart of the model is shown in Figure 2.  
Figure 1: Failure of the dry-mix shotcrete layer.  
(1) Preparation and preprocessing of data: The datasets used for the training and testing of the ANN model were collected by carrying out physical experiments and then the data samples were normalized in the \([0,1]\) range. This step is detailed in Section 3.2.  
(2) The establishment of the ANN model: Established the ANN model to capture the complicated mapping between UCS and the influencing factors of cement content, water-cement ratio, sand-coarse aggregate ratio, water reducer content and curing time, which can be adopted to the prediction of UCS of wet-mix shotcrete. The basis of the algorithm is presented in Section 3.3 and detailed results are shown in Section 4.1.  
(3) Definition of the objective function: The definition of the objective function can be achieved by combining the well-trained ANN model with specific requirements of the Jinchuan mine in engineering application. This step is detailed in Section 3.4.  
(4) PSO optimization: Taking objective function as the fitness function of PSO, PSO was run to perform a global search for the minimum fitness value. In this optimization question, the mix proportion corresponding to the minimum fitness value is the optimal mix proportion. This step is detailed in Section 3.5 and detailed optimization results are shown in Section 4.3.