esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Vehicle Detection Using Ultrasonic Sensors  
The important aspect of WSN-based traffic information acquisition systems is to minimize power consumption. Because WSN-based systems are battery driven, their total lifetime is restricted by the residual battery capacity. Typically, the largest power consumption occurs with ultrasonic bursts in an ultrasonic sensor. Figure 3 shows the power consumption of an ultrasonic sensor under a series of ultrasonic bursts.  
Figure 1: Mount positions of an ultrasonic sensor. **(a)** Overhead mount; **(b)** side top mount; **(c)** horizontal mount.  
Figure 3: Power consumption of an ultrasonic sensor.  
Figure 2: Dissemination and detectable angle of an ultrasonic sensor.  
Thus, minimizing the number of ultrasonic bursts will reduce the power consumption of the sensor. Using a long detection interval can reduce the power consumption caused by such bursts, but an overly long detection interval will increase the number of detection errors. This is because a certain amount of data is needed to guarantee accurate vehicle detection. Therefore, it is important to determine an appropriate detection interval for WSN-based traffic information acquisition systems. To do this, we constructed the analysis model illustrated in Figure 4.
# Fast Face Tracking-by-Detection Algorithm for Secure Monitoring
## 4 Experimental Results
### Qualitative Evaluation  
#### 4.3.1 Evaluation on OTB2015  
In this work, we would like to present the results of 33 trackers in 14 group sequences, in order to clearly visualize the results, besides those of the FFTD and KCF [10] algorithms; we selected the top four trackers (context tracker (CXT) [20], collaborative correlation tracker (CCT) [50], Struck [19], TLD [21]) from the whole 31.  
Figure 6 shows the results of the FFTD and KCF algorithms and the other four trackers. We present the main results for four challenging sequences from the OTB2015 dataset.  
1. Occlusion and out-of-view. In the dragonBaby sequence, when the object moves out of camera view, the KCF and CXT trackers failed. In the girl sequence, the girl was completely occluded by the man. The KCF method did not perform well.
2. Out-of-plane rotation. In frame 119 of the girl sequence, the girl turned her head such that we could not see her face clearly; TLD and Struck did not perform well.
3. Illumination. In the trellis sequence, TLD and CXT drifted off the object, while FFTD achieved the highest precision.
4. Fast motion and motion blur. In dragonBaby, only FFTD could track the fast-moving face. In the jumping sequence, KCF could not follow a blurred face. Overall, the proposed FFTD algorithm performed well in most sequences.
## 3 Key Technologies of UAV-Assisted SAGINs
### Routing Protocols
#### 3.1.4 Hybrid Routing Protocols  
A hybrid routing protocol combines the advantages of active routing protocols and reactive routing protocols, aiming to adapt to the complexity and dynamics of drone communication in SAGINs. This protocol uses an active routing protocol locally to maintain accurate routing information and can narrow the propagation range of routing control messages. When the target node is far away, it uses a reactive routing protocol to search and discover routes. In addition, hybrid routing protocols such as Zone Routing Protocols (ZRPs) [52] use multi-range technology and propose a hybrid routing protocol framework called a Regional Routing Framework. Active routing protocols are used within a region to maintain routing information at all times, while on-demand routing protocols are used outside a region for on-demand routing selection. The size of the region can be adjusted to adapt to changes in local or temporary networks, in order to achieve optimal overall network performance. The temporarily Ordered Routing Algorithm (TORA) [53] is a reactive routing protocol that uses a link-reversal distributed algorithm to limit the transmission of routing information to a small number of nodes closest to topology changes, and nodes only store routing information from neighboring nodes. This mechanism enables the TORA to respond quickly to topological changes in the network while maintaining low overhead. The Enhanced Interior Gateway Routing Protocol (EIGRP) [54] is an advanced distance vector routing protocol that combines the advantages of link state and distance vector routing selection protocols. By using the DUAL algorithm, it can achieve network convergence as quickly as possible. The EIGRP uses bandwidth, latency, reliability, and other metrics to evaluate the quality of routing paths. It supports variable length subnet mask, routing aggregation, and fast convergence and is suitable for complex network environments. It has high scalability and flexibility in routing selection.
# Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication
## 2 System Model  
### Conventional Burst OFDM Frame Structure  
The typical time slot arrangement for UAV networks based on time division duplexing-time division multiple access (TDD-TDMA) is illustrated in Figure 1. In this arrangement, each UAV (or user) is allocated a specific time slot for transmission, while the remaining UAVs are in the receiving mode during this period.  
The conventional frame structure of a burst OFDM communication system is illustrated in Figure 2. The guard interval is used for path delay protection, and the AGC guard interval is used for received signal detection and AGC adjustment; the preamble is used for both symbol and frequency synchronization. The OFDM payload is the data area of the frame, which contains the valid data that are being sent. Therefore, the sum of \(T_{G}\), \(T_{AGC}\), and \(T_{P}\) is considered the time overhead for a single transmission. These parameters are determined by the communication distance, AGC convergence time, and synchronization performance requirements, respectively. When the time overhead \(T_{G}+T_{AGC}+T_{P}\) is fixed, the proportion of overhead is larger when the total frame becomes shorter, which may severely reduce the data transmission efficiency. To address this issue, we need to design an efficient frame structure that can minimize the overhead proportion for a short-frame burst OFDM communication system.  
Figure 1: Time slot arrangement for an UAV network based on TDD-TDMA.
# Screening Mechanism and Properties of a Cantilevered Vibrating Sieve for Particles Processing
## 3 Screening Mechanism of Cantilevered Vibrating Sieve (CVS) Based on Motion Characteristics
### Motion Characteristics of Cantilevered Vibrating Sieve (CVS)
After substituting Equation (5) into Equation (4) and multiplying both sides of the equation by \(Yi(x)\), integration is performed within the range of cantilevered sieve rod length, and the orthogonality of the vibration mode function is used to obtain:  
\[C_{i}=\frac{D\omega^{2}\sin\alpha}{\left|\omega_{i}^{2}-\omega^{2}\right|}\frac {\int_{0}^{L}Y_{i}(x)\mathrm{d}x}{\int_{0}^{L}Y_{i}^{2}(x)\mathrm{d}x}, \tag{7}\]  
where \(\omega_{i}\) is natural circular frequency and determined by:  
\[\omega_{i}=\beta_{i}^{2}\sqrt{\frac{EJ}{\rho A}}, \tag{8}\]  
By substituting Equation (7) and the relationship of \(\alpha=\gamma+\beta\) into Equation (5), then, during the vibration process of the screen body, the additional bending vibration of a cantilevered sieve rod can be expressed as:  
\[y(x,t)=\left[\sum_{i=1}^{\infty}\frac{D\omega^{2}\sin(\beta+\gamma)}{\left| \omega_{i}^{2}-\omega^{2}\right|}\frac{\int_{0}^{L}Y_{i}(x)\mathrm{d}x}{\int_ {0}^{L}Y_{i}^{2}(x)\mathrm{d}x}Y_{i}(x)\right]\sin\omega t, \tag{9}\]  
Apparently, according to the analysis of Equation (9), the additional bending vibration of a cantilevered sieve rod is closely related to the length of cantilevered sieve rod \(L\), the sieve inclination angle \(\beta\), the vibration direction angle \(\gamma\), the vibration amplitude \(D\) and the vibration frequency \(\omega\). Such additional bending vibrations along the axial direction of sieve rod have different amplitudes. In addition, when the vibration frequency of screen body closely approaches \(\omega_{i}\), namely, the \(\omega_{i}^{2}-\omega^{2}\to 0\), the additional bending vibration of cantilevered sieve rod reaches its peak value. Generally, a smaller difference between \(\omega_{i}\) and \(\omega\) will achieve a larger vibration of the cantilevered sieve rod. It should be noted that the natural circular frequency with a unit of rad/s above can be converted to be the natural frequency with unit of Hz by multiplying \(2\pi\).
## 5 Digital Simulation Experiments and Analysis
### Simulation Case 1
Figure 11: Output power of PV systems with AFL and FL: (**A**) Output power (overall); (**B**) output power (detailed): (**a**) output power (0.05-0.15 s); (**b**) output power (0.15-0.35 s); (**c**) output power (0.35-0.40 s); (**d**) output power (0.40-0.45 s); (**e**) output power (0.45-0.50 s); (**f**) output power (0.50-0.55 s); (**g**) output power (0.55–0.74 s); (**h**) output power (0.74–0.95 s). Although the transients in the PV output current cause a power surge at the point of MPP voltage mutation, especially in the case of a wide range of mutations, the AFL controller eliminates the dynamic processes in a short period and stabilizes the output power. It can be seen from Figures 11 and 12 that in parts a, d, e, and h of the simulation process, which indicate MPP in the right peaks, the currents in regions A and B are equal because of low irradiances. As regions A and B of different lighting conditions result in different MPP voltages, the PV array output power comes from regions A and B together. In the simulation process of b, c, f, and g, the irradiance of region B is much higher than that of region A, and the output power of region B is larger than the sum of the powers of regions A and B. Therefore, the bypass diode of region A turns on, and the region A voltage is clamped by the diode conduction voltage. At this time, the PV output voltages and powers generated by the region B from 0.16 s to 0.4 s and from 0.5 s to 0.74 s are shown in Figure 13.  
The DC-link voltage and the output current of the PV grid-connected inverter are depicted in Figure 13a. It can be observed that the DC-link voltage is maintained at 700 V and that the fluctuations are within an acceptable range of approximately 15 V.  
The effectiveness of the hybrid MPPT method presented in this paper is shown in Figure 13b. The output current is consistent with the PV output characteristic curve in Figure 9a.
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 4 Flight Validation and Performance Analysis
### Flight Validation and Performance Analysis
#### 4.2.1 Experiment Process Overview  
The flight experiment was conducted in a mission field measuring 2000 m \(\times\) 1000 m \(\times\) 1000 m, as shown in Figure 13, with the aim of validating the performance of the MADDPG-VSC and MADDPG algorithms on multi-UAV formation control tasks. The UAV flight trajectories were planned according to the ground truth coordinates, as shown in Table 10. The total flight duration was set to 535 s, with UAVs taking off and landing at the end of the runway. The initial position error was 0 m, and the UAVs maintained an average cruising speed of 20 m/s during the flight. To simulate more complex combat scenarios, the mission field was equipped with three radar detection zones representing enemy anti-arxrafty or ground fire threats. These threat zones provided a realistic operational environment for the UAV formation and further evaluated the algorithms' stability and adaptability in challenging conditions.  
The entire experiment was divided into four phases, i.e., takeoff, formation maintenance, mission execution, and safe landing. During the takeoff phase, the three UAVs simultaneously took off from designated starting points and maintained a triangular formation. In the mission execution phase, the UAVs flew along the predefined flight trajectories, adjusting the flight parameters based on real-time sensor data to ensure formation stability and minimize trajectory deviations. After completing the mission, the UAVs safely landed at the designated locations, marking the end of the flight.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Waypoint** & **wp1** & **wp2** & **wp3** & **wp4** & **wp5** & **wp6** & **wp7** & **wp8** \\ \hline Lon/\({}^{\circ}\) E & 90.1437 & 90.1581 & 90.1767 & 90.1823 & 90.1618 & 90.1643 & 90.1520 & 90.1431 \\ Lat/\({}^{\circ}\) N & 38.3884 & 38.3939 & 38.4016 & 38.3901 & 38.3838 & 38.3763 & 38.3721 & 38.3882 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Coordinates of ground truth waypoints.  
To ensure the safety and reliability of the experiment, a series of comprehensive safety protection measures were implemented. The GCS closely monitored the UAV flight trajectories in real time using a feedback system, detecting any speed anomalies or heading deviations. Immediate adjustments or automatic return mechanisms were triggered, if necessary. In addition, a redundant communication system was employed to prevent signal loss or interference, ensuring stable data transmission between the UAVs and the GCS. During the flight, the UAVs were also equipped with parachutes and airbags to prevent collisions with other UAVs or ground obstacles. If any threats or disturbances occurred, the system would automatically deploy the parachutes, ensuring a safe landing.
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 4 Methodology  
### Rock-Fall Early Warning Framework Design  
This section presents a framework-based Internet of Things (IoT) for rock-fall early warning. Figure 1 shows our proposed framework for detecting and predicting rock-fall  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Type** & **Factor** & **Unit** & **Factor Class** \\ \hline Topographic & slope angle & degree & (range 20–60) \\ Hydrological & rain full & mmh\({}^{-1}\) & (range 0–46) \\ Weather & temperature variation & \({}^{\circ}\)C & (range 0–21) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rock-fall conditioning factors.  
incidents. The framework consists of five layers: the field layer, the edge layer, the fog computing layer, the cloud computing layer, and the data presentation layer.  
1. The Internet of Things layer includes the actual sensors to measure the physical parameters of interest; these include rainfall, air temperature, air humidity, a seismic sensor for detecting seismic waves, and a camera for rock movement detection. The Field layer was also expanded to accommodate an intelligent voice module and a light panel to run out the early warning action.  
2. Edge Computing Layer: In order to decrease network traffic and energy consumption, the edge nodes gather input from the sensors and conduct data and image processing algorithms. The main goal is to locally generate fundamental model properties of the specific process which are then passed on to the fog computing. Furthermore, it is necessary to execute commands incoming from higher-level systems to deliver an early warning action and make local decisions, thus preventing upper-layer latency.  
3. Fog Computing Layer: It bridges the gap between the cloud and edge nodes by enabling computations such as rock-fall monitoring, rock-fall prediction, deep learning models, rock-fall risk assessment, networking, data management, and decision making. Blynk Cloud was used in this study; it is a comprehensive software package needed to deploy and remotely manage linked electrical devices at any size, from small-scale home IoT projects to millions of commercially connected items.  
4. Cloud computing layer: The cloud was chosen to provide communication services by streaming data between the fog computing and data presentation layers and providing a medium for data storage. Blynk utilizes HTTPS (API) to report telemetry and regularly  
Figure 1: Rock-fall early warning framework.  
fetch data streams. Additionally, it offers open-source hardware libraries so that any device can connect to Blynk Cloud.  
5. Data presentation layer: This is concerned with the software systems that convey the data analysis results to end users or decision makers. In this study, web and mobile app dashboards are used to present the early warning alarm in the case of event detection, in addition to affording access privileges to the system administrator and decision making.
# Study on the Earth Pressure during Sinking Stage of Super Large Caisson Foundation
## 4 Analysis of Monitoring Data
### Distribution Characteristics of Earth Pressure at the Foot Blade  
More than 90% of total 119 earth pressure sensors worked well during the first soil excavation stage, which were arranged at the foot blade of caisson foundation. After removing abnormal field data, a three-dimensional histogram curve of earth pressure was drawn based on field data during soil excavation, which is shown in Figure 16. Different area was represented by different colors, which is the same with that in Figure 12. Figure 17 demonstrated the variation curve of the average soil pressure with soil excavation procedure. From Figure 17, before soil excavation, the average earth pressure in the outer bulkhead area was 1.512 MPa which is the biggest, it is about 1.0 MPa in the partition wall areas, and the average value of earth pressure in the inner bulkhead area was about 0.829 which is the smallest.  
Figure 15: Sinking amount comparison between numerical results and the field measurements.  
Figure 16: Distribution of measured earth pressure with soil excavation step (the color is for the same meaning in the Figure 12).  
After soil excavation step 1, the earth pressure of the outer bulkhead and the outer partition wall area slightly increased by 0.202 MPa and 0.143 MPa, respectively. The earth pressure of inner bulkhead remained basically unchanged, and the earth pressure of the inner partition area decreased by 0.388 MPa. For soil excavation step 2, the earth pressure of the outer bulkhead and the outer partition wall area increased continually, and the earth pressure of the inner bulkhead and inner partition wall area slightly decreased, which is caused by soil excavation from the inner compartment. After soil excavation step 3, the earth pressure of outer bulkhead significantly increased to 2.359 MPa, while the earth pressure of the inner partition area decreased to 0.273 MPa because of soil excavation in this area, and the earth pressure of outer partition wall and outer bulkhead area are, respectively, 1.081 MPa and 0.502 MPa.  
From the distribution characteristics of earth pressure during soil excavation procedure, it can be seen that the earth pressure of the inner bulkhead and inner partition wall area gradually decreased with soil excavation in the inner compartment, while the earth pressure of outer partition wall and outer bulkhead area continually increased. The earth pressure of the inner areas (inner partition wall and inner bulkhead) gradually transferred to outer areas (outer partition wall and outer bulkhead) with soil excavation from the inner compartment to outer compartment. When the soil was excavated from outer compartment, the earth pressure in outer partition area was evidently decreased and the end resistance was further transferred to outer bulkhead area.
# Classification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming
## 2 Materials and Methods
### Classification
#### 2.6.2 Spatiotemporal Beamformer Classifier  
Before calculating the spatiotemporal beamformer (STBF), the signal was downsampled to 32 Hz or twice the low-pass frequency 16 Hz, resulting in 17 time samples between 0.1 s and 0.6 s. According to the Nyquist theorem, more samples would not contain more information; hence, the minimum temporal resolution was chosen to reduce the dimensionality of the covariance. The activation pattern is the difference between the averages of epochs in response to cued targets and the averages of those in response to non-cued targets. We constructed three variations of the spatiotemporal beamformer: STBF with empirical covariance estimation (stbf-emp) as in Section 2.3.1, STBF with LOOCV-shrunk covariance estimation (stbf-shrunk) as in Section 2.3.2, and STBF with Kronecker-Toeplitz-structured covariance estimation (stbf-struct) with LOOCV shrinkage for the Kronecker factors as in Section 2.3.4.
# Plant Electrical Signal Classification Based on Waveform Similarity
## 4 Results  
### Classification Results of Our Proposed Method  
#### 4.1.1 Experimental Data  
The details of electrophysiological experiments were performed as described in those previous papers [30; 33; 36]. Briefly, cucumbers were grown in greenhouse for two to four weeks with 16 h light, 50%-70% humidity, 29 \({}^{\circ}\)C in the day and 22 \({}^{\circ}\)C at night. For extracellular potential recordings, Ag/AgCl electrodes were 0.2 mm in diameter. When recording, the Ag/AgCl electrodes were inserted into the stem, and the reference electrode was attached to the plant. The extracellular measurements were performed more than 4 h after electrodes inserting. The arrangement of the electrodes is shown in Figure 5.  
Two two-channel amplifiers (SWF-1B, ChengDu Instrument factory, Chengdu, China) with signal acquisition system (RM6240, ChengDu Instrument factory, Chengdu, China) were simultaneously used to record the extracellular potential at four positions with 0.2-0.3 cm apart. The plant was placed on an anti-vibration table in a faraday cage (Inbio Life Science Instrument Co., Ltd., Wuhan, China).  
Figure 5: Extracellular recording sketch. S1 and S2 are stimulating electrodes; S1 is the anode. E1, E2, E3, and E4 are measuring electrodes; R represents reference electrode and is placed in the node of stem, with respect to the ground. The distance between S2 and E1 is 2 cm, and there is 0.2–0.3 cm between adjacent measuring electrodes.  
The electrical stimulus was applied by current injection. Two platinum wire electrodes, 0.1 mm diameter, 1.5 cm apart were inserted in the stem, 2 cm apart from the recording electrode. 2-15 V, 0.05-2 s squared DC pulse were applied through the platinum wire. In fact, the strength-duration curve allows us to determine the rheobase and the chronaxy value [79; 80]. For the same plant, the next stimulus was applied for at least 3 h if there are multiple current injections.  
After current injections, 68 data files were recorded with 32 plant samples. The Aps' amplitude was from 10 mV to 80 mV with duration time from 2 s to 30 s. As the frequency of AP is lower than 1 Hz (Figure 6b), a low-pass filter was used to reduce the current injection artifacts (Figure 6c).