esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Setup and Measurement Campaign
### CNR Inspection and Jammer Position Determination
Sometimes there were not enough measurements near the jammer (as in Figure 9b) to give enough weight to the region of interest and the function fitted accordingly. In other cases, the  
Figure 8: Time development of CNR for different receivers and satellites with the color of the points representing the satellite elevation: (**a**) continuous acquisition on a smartphone; (**b**) typical acquisition on a geodetic device with many blank spots; (**c**) smartphone with some blank spots; and (**d**) acquisition on a geodetic device with unusually few (relative to geodetic devices) blank spots; the reasons could be multipath or non-line-of-sight due to vegetation. For other receivers, see [[https://gnss.fpp.uni-lj.si/2022-09-19](https://gnss.fpp.uni-lj.si/2022-09-19)]([https://gnss.fpp.uni-lj.si/2022-09-19](https://gnss.fpp.uni-lj.si/2022-09-19)) (accessed on 19 February 2022). non-jammed CNR was itself unstable, so the algorithm tried to fit that dependency instead of the jammer-induced variation (Figure 9c). Figure 9: Examples of CNR-fitting functions: (**a**) a good fit; (**b**) a bad fit due to a lack of measurements in the vicinity of the jammer; and (**c**) a bad fit due to the instability of the non-jammed CNR. After the elimination of the bad fits, the average \(t_{0}\) of all the satellites was calculated for each of the rides. Since the position of the receivers was known and the road section was straight, it was possible to calculate the closest point on the road to the receiver (for that purpose, the middle of the driving lane was assumed for the jammer's position). In such a way a few points where the jammer's position and time were known could be determined. Assuming the speed of the jammer was known, it was possible to reconstruct the path of the jammer for most rides by fitting a linear function to those points. Again, the Levenberg-Marquardt method was used, but this time the points were weighted such that the spread of the time of approach obtained by all the satellites and the number of satellites used for the calculation were considered like this:  
\[w=\frac{\sigma}{\sqrt{n-1}} \tag{3}\]  
where \(w\) is the weight for a particular point, \(s\) is the spread of the time of approach over all satellites involved in the calculation, and \(n\) is the number of satellites. The motivation for such a choice is that the smaller the standard deviation, the more accurate is the time of approach. It was also considered that the deviation of many measurements drops as the inverse square root of their number. Most fits gave satisfactory results (e.g., Figure 10a), yet some did not (e.g., Figure 10b). The latter were discarded from further investigations.
# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 5 Engineering Application  
The 1613 (1) working face of the Guqiao Coal Mine in Huainan is flat. The comprehensive mechanized coal mining method was adopted. The average mining height was 2.9 m, the working face was 1528 m long, the width was 251 m and the average mining speed was 5.56 m/d. The average dip angle of coal seam was 3\({}^{\circ}\), which belonged to a nearly horizontal coal seam. The average mining depth was 668 m, and the average thickness of unconsolidated layer was 420 m. Figure 11 shows the layout of the monitoring points, and half a strike line and a tendency line were set up. The surface movement observation time was from 2 May 2017 to 7 January 2019.The subsidence and horizontal movement values above the 1613 (1) working face were obtained by leveling and RTK surveying.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Prediction Model** & **M-CM-GA-ELM** & **CM-GA-ELM** & **GA-ELM** & **CM-ELM** & **ELM** \\ \hline MeaRE & 5.509 & 5.719 & 6.066 & 8.131 & 8.752 \\ RMSE & 0.050 & 0.052 & 0.054 & 0.074 & 0.079 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy of prediction model (unit: mm).  
In order to further illustrate the method of predicting mining subsidence by using the PIM parameters calculated by the M-CM-GA-ELM model, the surface movement and deformation values of the 1613 working face after mining were calculated by using the predicted PIM parameters in Table 2. The surface subsidence contour line is shown in Figure 13. The same method could be used to obtain other movement and deformation contours.
# An Assessment of CyGNSS v3.0 Level 1 Observables over the Ocean
## 4 Discussion
### Potential Origins of Observable Biases  
The primary effect on NBRCS and LES is shown to be associated with geometry (Figures 4-6), and the primary cause of this likely relates to inaccuracies in the knowledge of antenna gain patterns on both the receiving and transmitting ends. Because of the bistatic nature of the GNSS-R system, two separate antennas are involved in the signal calibration process: one of the two nadir antennas on each CyGNSS receiver, and one from the individual GPS transmitter where the signal originates [25]. The nadir CyGNSS antennas seem to be well inter-calibrated across the constellation, but the consistency of the NBRCS bias pattern might indicate that some of the geometry effect also originates from the receiver end (Figures 4 and 5). It should be noted that the current CyGNSS L2 ocean wind speed algorithm also accounts for the incidence angle [26]; therefore, some of the L1 biases found in this study might well be compensated in the L1-to-L2 inversion process, although L1 data users would need to implement their own corrections prior to inversion. However, calibration inaccuracies originating from differences between individual GPS transmitters appear to dominate (Figure 6). Part of this seems to be related to the GPS block, as similar patterns are seen in each block, which likely relates to differences in antenna design. There is a further component that shows as a bias of up to \(\sim\)1 dB between individual transmitters within a block (although with a similar relationship with geometry), which likely relates to differences in actual transmit power between the individual SVNs, perhaps due to differences in hardware age.  
One additional biasing effect is seen spatially; this, however, is only seen in the LES observable, which shows \(\sim\)1.5 dB variation linked to mean sea surface height (Figure 7). This is suspected to relate to inaccuracies in the identification of the waveform peak location within the delay Doppler map (DDM).
## 3 Numerical Results
### Impact of the Joint System Cost  
Figure 4 shows the joint performance of the delay optimal, energy optimal and joint algorithms. In Figure 4, we observe that the proposed algorithm outperforms the other two schemes in terms of the system cost. As expected, increasing backhaul capacities not only reduces the average power consumption but also decreases the average delay cost. Therefore, with the increase of backhaul capacities, the joint cost rapidly decreases.  
Figure 5 shows the delay and energy performance of the delay optimal, energy optimal and joint optimal algorithms. In Figure 5a, the delay optimal algorithm achieves the best delay performance at  
Figure 4: Joint system cost versus backhaul capacities.  
Figure 3: System performance versus popularity shape parameter.  
the maximum cost of energy, as shown in Figure 5b. Otherwise, the energy optimal algorithm achieves the minimum cost of energy at the worst delay performance. Obviously, both methods have very low energy efficiency. In Figure 5a, the performance of the proposed joint optimal is in close proximity to the delay optimal, which achieves the theoretical delay performance but much better than the energy optimal. In Figure 5b, although the performance of the proposed method causes a slightly larger delay than the delay optimal (increasing minimum delay by around 20%), energy consumption can be reduced up to about 63%.
# Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
## 3 Autonomous Vehicle Perception Sensors  
There are multiple sensors used for perception in autonomous cars that will cover the environment around the autonomous vehicle (AV). These sensors are: 1. Camera, 2. LiDAR (Light Detection and Ranging), 3. Radar (Radio Detection and Ranging), and 4. Sonar (Sound Navigation Ranging) to a lesser extent.  
In this paper, we stick with three primary sensors: camera, LiDAR, and radar. The main sensor's placement on a typical autonomous vehicle, their coverage and max range are shown in Figure 2. A camera is affected by environmental variations such as occlusions, illumination and weather variation; LiDAR is affected by bad weather such as snow, rain; and radar has low detection accuracy and resolution. Using these complementary sensors with the appropriate sensor fusion method suited for them addresses the ineffectiveness of each sensor. It also increases the fault tolerance and accuracy of the entire perception framework.
###### Abstract  
The objective of this paper was to design configuration parameters for a stepped-lap scarf joint repair, which can be used for spar cap damage of a wind turbine blade in service and to realize the post-repair monitoring. Two experimental studies were included. First, tensile test for the unidirectional tape specimens with a large aspect ratio repaired using a multiple stepped-lap scarf joint method was carried out. The results showed that the reinforcement layer could effectively improve the load-carrying capacity of the repaired zone. The stepped-lap joint surface was identified as the weak part of the spar cap repair, which should be monitored. Second, by embedding carbon nanotube buckypaper sensors on the stepped-lap joint surface of the repaired specimens, quasi-static tensile tests and fatigue tests were carried out. According to the resistance response of the sensors, the quasi-static tensile test confirmed the failure processes, namely the stiffness turning point, damage evolution, crack propagation, and fracture. The fatigue test could accurately identify the progressive failure, namely the initial damage, damage accumulation, initial cracking, and crack propagation to structural failure. The above tests provided an important configuration parameter basis for evaluating the spar cap repair scheme and presented a promising method for the health monitoring of a spar cap after repair.  
wind turbine blade; spar cap damage; stepped-lap scarf joint repair; unidirectional tape with large aspect ratio; carbon nanotube buckypaper sensor; tensile test; fatigue test; health monitoring
## 2 Problem Formulation and Preliminaries
### Kalman Filter  
Kalman filter is a recursive estimator that provides the optimal solution under the assumptions of a linear system and Gaussian noise. The Kalman filter operates in a two-step process recursively: prediction and correlation. In the prediction step, the filter predicts the state and error covariance estimates to obtain the prior estimates for the next time step.  
\[\hat{x}_{k+1} =F\hat{x}_{k}+Bu_{k} \tag{3}\] \[P_{k+1} =FP_{k}F^{T}+Q\]  
where \(\hat{x}_{k}\) is the state estimate at time \(k\), \(P_{k}\) is the state covariance matrix, and \(Q\) is the process noise covariance.  
In the correction step, the filter incorporates a new measurement into the prior estimate to obtain an improved posterior estimate.  
\[\begin{split} K_{k+1}&=P_{k+1}H^{T}(HP_{k+1}H^{T}+R )^{-1}\\ \hat{x}_{k+1}&=\hat{x}_{k+1}+K_{k+1}(z_{k+1}-H\hat{ x}_{k+1})\\ P_{k+1}&=(I-K_{k+1}H)P_{k+1}\end{split} \tag{4}\]  
where \(K_{k+1}\) is the Kalman gain, \(R\) is the measurement noise covariance.
## 2 Related Work
### Location Service for a Location-based Approach with Mobile Sinks
The Sink Location Service (SLS) [24] informs the source node about the sink location to discover sink locations with low overhead. Each senor node gets four anchor locations during network initialization phase. A source node and a sink send sink location announcement and query messages along two paths respectively by geographic routing. The sensor node located on the crossing point of the sink location announcement path and the sink location query path informs the source node of the sink location, then the source sends the data packet to the sink by geographic routing. No flooding or global grid structure is needed for sink location announcement or query. Although SLS provides sink locations to minimize the overhead incurred for the provision of source nodes with the location of sinks, it does not solve forwarding data from a source node to a mobile sink. This process decreases the resilience of the protocol. The main reason for this is if one query message fails, there is no other query message to retrieve sink location information. In addition, no sensor node might be located on the crossing point of these two paths. In this case, the source node cannot find the location information of the sink.
## Nomenclature  
\begin{tabular}{l l} Symbol & Description \\ \(P_{pp}(t)\) & Photovoltaic power at time t \\ \(P_{L}(t)\) & Load power at time t \\ \(\Delta P(t)\) & The power margin \\ \(P_{cs}(t)\) & Power of energy storage system \\ \(P_{pc}(t)\) & Power of PCC point \\ \(C_{es\_unit}\) & Cost per kWh of the ESS \\ \(C_{rrp}\) & Total cost of replacing the batteries \\ \(E_{\_}{\_}{\_}{\_}{\_  
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## 3 Results  
### Fabrication and Operation of the Glycerol Biosensor  
Figure 1 presents the steps involved in fabricating the glycerol biosensor. Initially, a mixture containing GLDH and NAD\({}^{+}\) in phosphate buffer was deposited on a base of MB-SPCE. This layer was allowed to dry overnight under vacuum at 4 \({}^{\circ}\)C. Next, a 10 \(\mu\)L aliquot of a solution containing GLDH in PBS was deposited onto the enzyme/NAD\({}^{+}\) layer and again left overnight under vacuum to allow crosslinking to occur. These biosensors were refrigerated until they were ready for use.  
Figure 2 shows the sequence of reactions that occur during the operation of the amperometric glycerol biosensor. The biosensor's operation principle is that glycerol is enzymatically oxidised by GLDH in the presence of NAD\({}^{+}\), which results in the production of NADH and dihydroxyacetone. NADH then undergoes electrocatalytic oxidation at an MB-SPCE, and the resulting electrocatalytic oxidation current constitutes the analytical response. This response is directly proportional to the glycerol concentration. It should be emphasised that the advantage of exploiting the electrocatalytic oxidation reaction ofNADH is that the operating potential is much lower (0.0 V vs. Ag/AgCl) than is possible by direct oxidation at bare carbon electrodes. This aspect is discussed later.
## 3 Methods
### Synthetic Data Generation Techniques
#### 3.4.4 Data-Free Model Extraction  
A technique called \"Data-Free Model Extraction\" [25] seeks to extract a working duplicate of a target model without having access to the initial training set of data. This technique raises significant privacy concerns, as it potentially enables an adversary to obtain a model's functionality solely through querying, without the need for any proprietary data from the model owner.  
The following actions are usually involved in the data-free model extraction process: Query Generation: In the absence of training data, the adversary generates a set of synthetic input samples to query the victim model. There are several ways to create these artificial samples, including gradient-based approaches, Generative Adversarial Networks (GANs), or other data synthesis approaches. Model Querying: The victim model is queried using the created synthetic input samples, and the matching output predictions or labels are gathered. Replica Model Training: The original training data are replaced with the input-output pairs that are gathered from the target model queries. The functionality of the target model is essentially extracted by using these query responses to train a replica model.  
The key challenge in data-free model extraction lies in generating synthetic input samples that can effectively capture the target model's behavior with a variety of inputs. Techniques like gradient-based methods or GANs can be employed to create synthetic samples that provide useful information and accurately depict the decision limits of the target model.
# Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities
###### Abstract  
The future Intelligent Transport System \"ITS\" is one of the major challenges of the smart city. It requires fast and efficient communication between vehicles (vehicle-to-vehicle \"V2V\"), to ensure information exchange in order to improve safety, which reduces accidents and consequently save lives, hence the need of the Vehicular Ad Hoc Network \"VANET\", which makes possible the inter-vehicle communication. This network is characterized by a variable topology. Therefore, MANET (Mobile Adhoc NETwork) routing protocols need a few tweaks to be available for the vehicle environment. In this paper, we start by exposing some works related to the evaluation of the most well-known protocols. After a comparative study, we deduce that the OLSR (Optimized Link State Routing) protocol outperforms other routing protocols in terms of End-to-End Delay (EED) and Packet Delivery Ratio (PDR). In addition, we note that the Greedy forwarding \"GF\" mechanism is suited for the VANET environment, which has been improved and called Greedy forwarding Advanced \"GFA\", to overcome the stationary node problem. Our approach improves the OLSR protocol to be more suitable and efficient for VANET by introducing the GFA mechanism. Moreover, we compare our approach to the OLSR classic version. In this work, we use a realistic scenario from Open Street Map (OSM), and simulations are performed using SUMO (Simulation of Urban MObility). The trace files generated from SUMO are used for further simulation in NS-3 (Network Simulator) to validate our proposition. The simulation results are analyzed and discussed. Our approach performs best compared to OLSR in terms of EED and PDR, especially for dense traffic.  
VANET; OLSR; routing; greedy forwarding; NS3; end-to-end delay; packet delivery ratio Article
## 2 Materials and Methods
### sEMG Signal Acquisition and Preprocessing  
Figure 1 details the experimental protocol for acquiring sEMG signals in six different hand gestures. On the basis of previous studies [21; 40], 6 surface electrodes (Ag/AgCl) were applied to 6 muscles of forearm and hand: abductor pollicis brevis (APB), flexor digitorum superficials (FDS), brachoradials (BRA), flexor carpi ulnaris (FCU), extensor carpiadialis(EC) and extensor digitorum communis (EDC).The sEMG signals from six muscles were simultaneously acquired and recorded by a Noraxon Ultium wireless EMG system with a sampling frequency of 2000 Hz. During the experiment, the subjects were asked to perform 20 repetitions for each of the 6 gestures, resting for 2-3 s between repetitions and 5 min between gestures.  
For the acquisition and digitization of the signals, the commercial Noraxon Ultium EMG Sensor and MyoResearch software were used. A previous characterization of the sensor was required since the data-sheet did not provide detailed information of the sensor specifications. The sEMG sensor had a 16-bit gain-adjustable analog output, 300 ms fixed analog output delay, 1000 M\(\Omega\) minimum input impedance and a maximum CMRR of 100 dB. EMG signal was digitized by 2 kHz sampling frequency and 24-bit ADC. A 20-500 Hz (FIR) band-pass filter, a 50 ms RMS smoothing filter and rectifier were used in the digital signal processing. The time domain feature values were calculated over an analysis window of the muscle activation state. The analysis window was 250 ms (contains 1000 MB) and 1000 MB (contains 100 MB). The time domain feature values were calculated over an analysis window of the muscle activation state. The analysis window was 250 ms (contains 100 MB) and  
the entire cycle of muscle activation). For each analysis window, a feature set was computed, and these features were provided to a pattern classifier. Once the EMG signal was filtered, we computed four features in time domains: Integral Electromyographic(iEMG), Mean Absolute Value(MAV), Input Contribution Rate(ICRi) and Variance(VAR), as detailed in Table 1.  
The feature set for the right hand side was considered to contain 24 (6 \(\times\) 4) features. For this huge feature set, two methods were used to reduce the number of features: dimensionality reduction and feature selection. Firstly, the ICRi value is the percentage of the iEMG value of each muscle in the sum of the iEMG values of the six muscles when the subject was asked to perform a certain movement. The ICRi value can be calculated from the iEMG therefore they can be excluded. Secondly, statistical analysis was performed using SPSS 23 (SPSS Inc., Chicago, IL, USA) to analyze the average correlation between the hand gestures and the time features. Of the three remaining time-feature values, only a strong correlation exists between iEMG and gestural patterns (APB: 0.622 **, FDS: 0.573 **, BRA: 0.488 **, FCU: 0.411 **, EC: 0.525 **, EDC: 0.475 **, ** represents \(p\)-value less than 0.01). Finally, it was decided to select 6-channel iEMG as the input signal of hand-gestures recognition.