esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Progress in Fast Distributed Brillouin Optical Fiber Sensing
## 4 Developments of the Fast BOTDA Schemes
### Optical Chirp Chain Technique for Fast BOTDA  
Different from recovering the BGS in frequency-domain for the OFC-based BOTDA, OCC-based BOTDA schemes [1; 47] have been implemented to recover the whole BGS or BLS in the time-domain thanks to the time-frequency mapping of the chirp signal. As demonstrated in Figure 14a, with a high-performance AWG, the frequency-sweeping process can be compressed into an optical chirp segment with a few tens of nanoseconds and the frequency ranged from \(\
u_{1}\) to \(\
u_{N}\) by the OFA technique. The chirp distribution of this optical chirp segment is given by the equation below.  
\[f_{AWG}(t)=f_{1}+\eta\cdot t \tag{9}\]  
where \(f_{1}\) is the initial frequency and \(\eta\) is the frequency chirp value.  
Subsequently, the OCC-probe wave is formed by head-to-tail linking several segments in the time domain. The duration of the OCC-probe wave should be longer than the round-trip time of the pump pulse. When single-shot pump pulse interacts with every optical chirp segment and the frequency span of the OCC-probe wave covers the BFS of the FUT (see Figure 14b), the BGS can be revealed at every segment in the time domain. The parameters of the OCC-BOTDA scheme can be summarized as \(T_{switch}<<T_{round\_trip}\), \(N_{code}=1\) and \(N_{f}=1\). Based on Equation (6), the maximum sampling rate of the OCC-based BOTDA without averaging is only limited by the fiber length.  
The distributed BGS of a static strain measurement is demonstrated in Figure 15a. The BGS is excited every 20 ns [1]. A zoom-in view within the blue box is illustrated in Figure 15b, which clearly shows that the main peaks have frequency-shifts as the strain ranges from 0.0 \(\mu\epsilon\) to 700 \(\mu\epsilon\).  
Lastly, the OCC-based BOTDA scheme is examined by three different types of vibration: a periodic mechanical vibration, a mechanical shock, and a switch event. The vibrational waveforms (see Figure 16a-c) are measured at the sampling rates of 25 kHz, 2.5 MHz, and 6.25 MHz, respectively.  
Figure 14: The operation principle of the OCC-BOTDA [1]. (**a**) The time-domain relationship and (**b**) the frequency-domain relationship.  
Figure 15: The static strain measurement [1]. (**a**) The time traces and (**b**) the BGSs for a series of strains.
## 3 System Model
### Fault Model  
Failures are an inherent part of WSNs; different types of faults may affect them [38]. If, in between the CH selection rounds, the CH goes down, then either the data aggregated of that cluster will be lost or a new CH selection will be initiated immediately. The proposed Fault-tolerant Energy-efficient Hierarchical Clustering Algorithm (FEHCA) does not introduce immediate CH selection in between two successive rounds as such a practice leads to energy depletion. The CH going down in between two successive rounds is managed by the sCH node of each cluster, which is on standby in every cluster and takes over the task of CH temporarily.  
The algorithm ascertains the selection of the CH at the centroid or near the centroid of the clusters. As a random CH selection does not present an ideal scenario, as shown in Figure 2, unpredictable CH selection generally leads to non-uniform energy dissipation in the network, which ultimately reduces the network lifetime due to the rapid energy discharge. Election of the CH near the centroids makes it easily accessible and helps in achieving uniform energy dissipation, and, once the current CH's energy is depleted beyond a certain threshold, preference is given to the eligible sensors near the previous round's CH and the defined energy threshold is satisfied.
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 2 Current-Voltage Curves
### Normal Condition  
To simulate the current-voltage curves of normal string connections using solar modules of different specifications, the number of cells connected in series within each module ranges from 330 to 450, and each cell has an open-circuit voltage of 1.35 V. The resulting simulated current-voltage (I-V) curves exhibit open-circuit voltages ranging from 445.5 V to 607.5 V and short-circuit currents from 8 A to 12 A, thereby allowing the model to be applicable to different site specifications as shown in Figure 2. The irradiance is simulated from 600 W/m\({}^{2}\) to 1000 W/m\({}^{2}\) to mirror actual field conditions as shown in Figure 3. The remaining fault conditions will also be simulated following this method.  
Figure 1: Circuit of the solar cell’s single-diode model [14].  
Figure 2: Simulation circuit diagram of the normal current-voltage curve.
# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 3 System Configuration
### Thermal Image Processing
#### 3.4.3 Image Rectification and Individual PV Module Extraction  
The resulting image \(I_{reg}\) from previous section is rectified for easier thermal distribution analysis. A sample illustration of image rectification is shown in Figure 10a. After image rectification, the individual PV modules are extracted by using the mask \(M_{RGB}\) from Section 3.4.1. Particularly, the edges of the valid feature points \(M_{RGB}\) and \(M_{th}\) are used to extract the individual PV module area. An example of one of the extracted PV modules can be visualized in Figure 10b.
# Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy
###### Abstract  
Models for determining contents of soy products in ground beef were developed using near-infrared (NIR) spectroscopy. Samples were prepared by mixing four kinds of soybean protein products (Arconet, toasted soy grits, Profam and textured vegetable protein (TVP)) with ground beef (content from 0%-100%). NIR spectra of meat mixtures were measured with dispersive (400-2500 nm) and Fourier transform NIR (FT-NIR) spectrometers (1000-2500 nm). Partial least squares (PLS) regression with full leave-one-out cross-validation was used to build prediction models. The results based on dispersive NIR spectra revealed that the coefficient of determination for cross-validation (\(R_{\mathrm{cv}}\)\({}^{2}\)) ranged from 0.91 for toasted soy grits to 0.99 for Arconet. The results based on FT-NIR spectra exhibited the best prediction for toasted soy grits (\(R_{\mathrm{cv}}\)\({}^{2}\) = 0.99) and \(R_{\mathrm{cv}}\)\({}^{2}\) > 0.98 for the other three soy types. For identification of different types of soy products, support vector machine (SVM) classification was used and the total accuracy for dispersive NIR and FT-NIR was 95% and 83.33%, respectively. These results suggest that either dispersive NIR or FT-NIR spectroscopy could be used to predict the content and the discrimination of different soy products added in ground beef products. In application, FT-NIR spectroscopy methods would be recommended if time is a consideration in practice.  
PLS; SVM; quantification; classification; dispersive NIR; FT-NIR +
Footnote †: journal: Applied Sciences
# Method for Estimating the Optimal Coefficient of L1C/B1C Signal Correlator Joint Receiving
###### Abstract  
The design of a modern Global Navigation Satellite System (GNSS) has been exceptionally valued by the military and civilians of various countries. The inclusion of the pilot channel in addition to the navigation data channel is considered one of the major changes in GNSS modernization. Schemes of an equal weight combination (1:1 combination) and power ratio combination for data and pilot are primarily adopted by traditional receivers. With the emergence of the new data and pilot modulation signals with unequal power, such as L1C at Global Positioning System (GPS) L1 frequency and BIC at BeiDou Navigation Satellite System (BDS) B1 frequency, the traditional combination coefficient cannot achieve optimal reception performance. Considering the influence of the combination coefficient on the reception performance, the optimal coefficient of the correlator joint is estimated in this paper. The entire architecture of the data/pilot correlator joint tracking and positioning with unequal power is given. Based on the equivalence principle of the correlator joint and the discriminator joint, the optimal coefficient of the carrier loop is determined. A mathematical model of joint code tracking accuracy is established, and the optimal coefficient of the code loop is determined. The real-life satellite signal and simulation results show that the amplitude-ratio combined scheme is the best for receiving of correlator joints, followed by the power-ratio combination scheme and, finally, the 1:1 combination scheme. It is worth mentioning that the positioning accuracy of the amplitude-ratio combination is improved by 2% compared to the 1:1 combination, and by 1.3% compared to the power-ratio combination for BIC signal. The positioning accuracy of the amplitude-ratio combination is improved by 2.37% compared to the 1:1 combination, and by 1.6% compared to the power-ratio combination for L1C signal. The conclusions of this paper are validated for the traditional data/pilot with an equal power allocation. The techniques and test results provide technical support for GNSS high-precision-user receivers.  
GNSS; MBOC; correlator joint receiving; BIC/L1C; high precision positioning; combination coefficient +
Footnote †: journal: remote sensing  
Article
# Formal Safety Assessment and Improvement of DDS Protocol for Industrial Data Distribution Service
## 4 DDS Protocol Modeling
### Formal Modeling Process  
The CPN model describes a pair of communicating entities: a sender and a receiver. The two communication entities are connected by a network channel, which represents a communication channel. The static characteristics of the model, such as state, are represented by the distribution of tokens, and the dynamic characteristics, such as state changes, are described by transition enabling rules and the flow of tokens. Combined with the definition of CPN, taking the first message MSG1 as an example, the DDS protocol is formally defined as the following nine-tuple: _DDS_ = (\(\Sigma\), _P, T, A, N, C, G, E, I_):  
_Color set_ \(\Sigma\) _= closet_ _NONCE_ = with Nm \(|\)Ns; closet_ _CertM_ = _STRING_;  
_Place set_ \(P\) _= Nm, CertM, Msg1, S1;_  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Descriptions** \\ \hline EntityM/S & Participant M/S \\ Nm/Ns & Random number \\ CertM & Participant M’s certificate \\ CertS & Participant S’s certificate \\ K & Shared key \\ SKm & M’s private key \\ SKs & S’s private key \\ PKm & M’s public key \\ PKs & S’s public key \\ Enc & (K)PKs \\ Signs & (Nm)SKs \\ Num & Counter \\ Hash & Hash value \\ Validate & Verify and comparison \\ Decrypt & Decrypt data \\ SignMf & Sign the private key after hash calculation for \\  & Enc and random number Ns \\  & String concatenation \\ \hline \hline \end{tabular}
\end{table}
Table 2: Symbols and descriptions.  
Transition set T = NmCertM, msg1; Directed arc set A = Nm\(\rightarrow\)NmCertM; CertM\(\rightarrow\)NmCertM; NmCertM\(\rightarrow\)Msg1; Msg1\(\rightarrow\)msg1; msg1\(\rightarrow\)S1; Node function N = Nm\(\rightarrow\)NmCertM: (Nm,NmCertM); CertM\(\rightarrow\)NmCertM: (CertM,NmCertM); NmCertM\(\rightarrow\)Msg1: (NmCertM,Msg1); Msg1\(\rightarrow\)msg1: (Msg1,msg1); msg1\(\rightarrow\)S1: (msg1,S1); Color function C= Nm:NONCE; CertM:CertM; Msg1:MSG1; S1:MSG1; Alert function G = \(\phi\); Arc expression function E = Nm\(\rightarrow\)NmCertM:Nm; CertM\(\rightarrow\)NmCertMxertm; NmCertM\(\rightarrow\)Msg1:Nm,certm; Msg1\(\rightarrow\)msg1:msg1; msg1\(\rightarrow\)S1:msg1; Initialization function I = Nm:Nm; CertM:CertM; Msg1,S1:NULL.
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 5 A Case Study of UAV NTN Airborne Network in Mountainous Area
### Field Trial Results  
#### 5.2.1 Parameters  
The field experiments were conducted on 11 January 2023 with several measurement points in the coverage area of the UAV on the ground. We managed to conduct the field trial with one rainbow CH-4 fixed-wing UAV to provide ground communication coverage. In the field trial test, a commercial 5G base station is mounted on the CH-4 fixed-wing UAV with a frequency of 2.6 GHz. The transmission power of the base station is 260 W with a 64 TR massive MIMO antenna. The trajectory of the UAV is designed as a circle with a radius of 3 km with a ground speed of 180 km/h and the flight height is set to 2 km above the ground (Figure 7). The map of field trial is shown in Figure 7 above with three test points marked red on the map. The trajectory of the UAV is shown in a black circle.  
Figure 7: Field Trial Map of UAV: The cirlce trajectory on the map indicate the flight path of the UAV; A, B, and C denote the three measurement point under the coverage of UAV base station.
# Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
## 2 Related Work
### Hierarchical Path Planning  
To overcome limitations of path-planning methods based on A* and ground-level motion-planning methods, hierarchical path-planning approaches are widely researched in the literature [24; 25; 26; 27; 28]. In Reference [24], a two-level-based, goal-driven architecture is used to solve mobile robot navigation in real life with vision systems and infrared sensors. In Reference [25], a two-stage path-planning algorithm uses a variant of A* search to obtain a kinematically feasible trajectory in the first stage and improves the quality of the solution via numeric non-linear optimization in the second stage, for an autonomous vehicle operating in an unknown semi-structured (or unstructured) environment where obstacles are detected online by a robot's sensors. In Reference [26], to guarantee the consistency between high and low levels of planning, [PERSON] and [PERSON] proposed a hierarchical motion-planning framework with a novel mode of interaction between the geometric path planner and the vehicle trajectory planner. In Reference [27], a novel hierarchical global path-planning approach for mobile robots in a cluttered environment with multi-objectives was proposed. This approach has a three-level structure which combines triangular decomposition, [PERSON]'s algorithm, and a proposed particle swarm optimization called constrained multi-objective particle swarm optimization. In Reference [28], to find an optimal maneuver that moves a car-like vehicle between two configurations in minimum time, a two-phase algorithm firstly solves a geometric optimization problem and then finds the optimal maneuver with system dynamics and its constraints satisfied.  
In this paper, to overcome the abovementioned disadvantages of SSG and LSPI, we designed a suitable hierarchical framework to combine a global abstract path planner based on SSG with a feasible trajectory planner based on LSPI, which can improve the computational efficiency of hierarchical path-planning methods by optimizing the relationship between high and low levels of planning.
# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
## 2 Experimental Site and Data  
### Experimental Site  
In-orbit testing and calibration experiments of the C-SAR satellite were performed at the Xilinhot SAR satellite calibration and validation field from 15 June to 24 June 2023. This site was built under the support of CASPLOS_CCSI [42], and the radiometric, geometric, and polarimetric calibration and validation of space-based SAR satellites was realized through the stationary and mobile deployment of ARCs [45] and CRs. A polarimetric calibration experiment was carried out for the C-SAR/02 satellite on 19 June 2023, using ARCs and trihedral CRs. The predominant surface features in this area comprise grassland, farmland, and low houses, with an elevation ranging from 1090 to 1160 m. Notably, the study site is devoid of prominent targets or electromagnetic interference, thereby allowing for the effective reduction in background clutter interference [46]. The distribution of equipment in the experimental area is shown in Figure 1.
# CNN-BiLSTM-DNN-Based Modulation Recognition Algorithm at Low SNR
## 4 Experiment and Conclusion Analysis
### Analysis of Experimental Results
#### 4.2.4 Computational Complexity Comparison  
Calculating the total number of parameters used in neural network training allows one to determine the complexity of a model. There are 806,392 parameters in the model overall, which affect the memory usage of the model and also determine its size. In Table 6, we present a comparison of computational complexity against other models using the RML2016.10a dataset. While our computational demands are higher, they are justified by improved average and peak recognition accuracies, as well as faster convergence speeds compared to alternative models. By leveraging the unique strengths of each component, our model excels at extracting and categorizing spatiotemporal features from complex datasets. This innovative approach addresses the limitations of traditional networks, which struggle to capture both spatial and temporal information simultaneously.
## 4 Conclusions  
1. The SSIP method conducts multi-channel array measurements in both spatial and frequency domains, achieving an observational efficiency three- to five-times higher than conventional symmetrical quadrupole IP sounding. For a single survey line (1200 m), data acquisition time can be reduced to 1/3 of traditional methods, significantly lowering field time costs. Through surveys along two lines, an average exploration depth of 1200 m was attained, representing an approximately 50% improvement over conventional IP methods (typically <800 m).
2. Drilling verification results demonstrate high consistency in both spatial positioning and burial depth between the three ore veins delineated by SSIP anomalies and the orebodies intercepted by drilling. The predicted orebodies show comparable dimensions and strike lengths to those confirmed by drilling.
3. The inferred mineralization-related anomalies exhibit low-resistivity/high-chargeability or low-resistivity/moderate-chargeability characteristics in inversion sections. According to vein distribution patterns, greisen-type W-Sn veins (e.g., Veins A and C) tend to be concentrated at the top interface of intrusive granites and their contact zones, while skarn-type W-Sn veins (e.g., Vein B) predominantly occur in limestone and marble strata contacting granites. These findings provide quantitative references for deep prospecting in similar metallogenic geological settings.  
Conceptualization, X.L. and H.L.; methodology, H.L.; software, Y.Z. ([PERSON]); validation, X.L., H.L. and Y.Z. ([PERSON]); formal analysis, Y.Z. ([PERSON]); investigation, X.L. and D.Z.; resources, H.L.; data curation, Y.Z. ([PERSON]); writing--original draft preparation, X.L.; writing--review and editing, H.L. and Y.Z. ([PERSON]); visualization, Y.Z. ([PERSON]); supervision, H.L.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (41774149); Project of Hunan Provincial Department of Ecology and Environment (HBKYXM-2023032); Hunan Provincial Science and Technology Program (2024 SKY-003).  
Not applicable.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Adaptation in Gait to Body-Weight Unloading
## 3 Results
### Emg  
#### 3.4.1 Rectus Femoris Activity  
EMG activity during all seven gait phases remained unchanged after walking in the control condition. For the unloading condition, there was a significant effect of time on EMG activity during loading phase (F (3.07, 30.70) = 7.62, \(p\) = 0.001), mid-stance (F (3.24, 32.36) = 5.051, \(p\) = 0.005), pre-swing (F (3.15, 31.53) = 2.94, \(p\) = 0.046), mid-swing (F (2.94, 29.41) = 4.557, \(p\) = 0.01), and terminal swing (F (5.38, 53.85) = 8.90, \(p\) < 0.0001). There was reduced activity during unloaded walking (1.08 \(\pm\) 0.11) relative to T\({}_{0}\) (1.45 \(\pm\) 0.15) in the loading phase. Immediately after unloading (T\({}_{1}\)), the activity increased (1.80 \(\pm\) 0.18), following which the activity returned to baseline at T\({}_{2}\) (1.65 \(\pm\) 1.7) and remained unchanged through the subsequent epochs (T\({}_{2}\)-T\({}_{5}\)). EMG activity during the mid-stance phase was significantly reduced during unloaded walking (0.66 \(\pm\) 0.07) relative to T\({}_{0}\) (0.96 \(\pm\) 0.10) and returned to baseline at T\({}_{1}\) (0.97 \(\pm\) 0.11). However, it was subsequently reduced at epochs T\({}_{2}\) (0.83 \(\pm\) 0.08) and T\({}_{3}\) (0.86 \(\pm\) 0.09), following which it returned to baseline at T\({}_{4}\) (0.84 \(\pm\) 0.1) and remained unchanged at T\({}_{5}\) (0.83 \(\pm\) 0.09). During pre-swing, there was no significant change in activity during unloading and immediately after unloading at T\({}_{1}\), even though there was a major trend towards reduced activity. However, the activity was significantly reduced starting from T\({}_{2}\) (0.8 \(\pm\) 0.07) through subsequent epochs (T\({}_{3}\): 0.81 \(\pm\) 0.08, T\({}_{4}\): 0.80 \(\pm\) 0.07, and T\({}_{5}\): 0.80 \(\pm\) 0.06) relative to T\({}_{0}\) (1.08 \(\pm\) 0.11). The EMG activity during mid-swing was significantly reduced during unloaded walking (0.61 \(\pm\) 0.06) relative to T\({}_{0}\) (0.80 \(\pm\) 0.05) and returned to baseline immediately after unloading at T\({}_{1}\) and was not different from that of T\({}_{0}\). Subsequently, it was reduced from T\({}_{2}\) through T\({}_{5}\) (T\({}_{2}\): 0.66 \(\pm\) 0.05, T\({}_{3}\): 0.69 \(\pm\) 0.06, T\({}_{4}\): 0.66 \(\pm\) 0.07, and T
###### Abstract  
Earth observation with unmanned aerial vehicles (UAVs) offers an extraordinary opportunity to bridge the gap between field observations and traditional air and space-borne remote sensing. In this regard, ground landing stations (GLS) systems play a central role to increase the time and area coverage of UAV missions. Bluetooth low energy (BLE) technology and the received signal strength indicator (RSSI) techniques have been proposed for target location during UAV landing. However, these RSSI-based techniques present a lack of precision due to the propagation medium characteristics, which leads to UAV position vagueness. In this sense, the development of a novel low-cost GLS system for UAV tracking and landing is proposed. The GLS system has been embodied for the purpose of testing the UAV landing navigation capability. The maximum likelihood estimator (MLE) algorithm is addressed on an embedded microcontroller for the position estimation based on the RSSI acquired from an array of BLE devices. Experimental results demonstrate the feasibility and accuracy of the ground landing station system, achieving average errors of less than 0.04 m with the UAV-MLE target position estimation approach. This 0.04 m distance represents an order of magnitude increase in location precision over other currently available solutions. In many cases, this increased precision can enable more innovative docking mechanisms, less likelihood of mishaps in docking, and also quicker docking. It may also facilitate docking procedures where the docking station is itself moving, which may be the case if the docking unit is a mobile ground rover.  
ground landing system; MLE; RSSI; target position estimation; UAV +
Footnote †: journal: Applied