esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 3 Cooperative Control of Variable-Sweep Wing UAV via MADDPG
### Optimization of the MADDPG-VSC Algorithm for Control Design
#### 3.2.5 Flight States of UAV and Control Strategies  
In the cooperative control of variable-sweep wing UAVs, the MADDPG-VSC algorithm dynamically adjusts control strategies based on real-time flight states, including velocity, attitude, and sweep angle [32]. These adjustments are crucial for maintainingformation integrity and optimizing overall mission performance [33; 34]. The control strategies are specifically tailored to each flight state, ensuring that UAVs can adapt to changing conditions and achieve precise formation assembly and reconfiguration when necessary. Table 3 presents the configurations for different flight states of the L-30A UAV, which are integral to executing cooperative maneuvers within the defined mission parameters.  
During the UAV formation process, specific control strategies are required for each flight state to ensure cooperation and stability. During takeoff, the primary focus is on generating sufficient lift with optimized thrust, while maintaining a minimal sweep angle for stability. This state is crucial for precise cooperation during formation assembly. As UAVs enter the climb state, the sweep angle is gradually increased to reduce drag, optimizing energy use while maintaining the integrity of the formation during the ascent.  
In the cruise state, the UAVs stabilize with a moderate sweep angle, balancing velocity and fuel efficiency. Steady thrust ensures that the formation remains intact over long distances, allowing for minor adjustments when needed. Maneuvering demands rapid adjustments in rudder control and sweep angle, enabling UAVs to quickly respond to environmental changes and reconfigure the formation as required.  
During dives, the UAVs adjust the sweep angle to reduce lift and increase velocity for a controlled descent. In the vertical attack state, UAVs adopt a high sweep angle to minimize drag and increase velocity, ensuring precision targeting while maintaining formation integrity. Finally, during landing, UAVs reduce velocity and lower the sweep angle to ensure stability and safety, with precise thrust and rudder control to facilitate a cooperative landing sequence for the entire formation.  
These strategies ensure that UAVs can effectively adapt to various flight states by selecting and executing appropriate actions within the defined state space \(S=\{x,y,z,v,\dot{v},\Lambda\}\), where each element corresponds to critical state variables such as position, velocity, acceleration, and sweep angle. The ability to dynamically adjust these control inputs in response to specific flight states is crucial for maintaining formation integrity and achieving optimal performance across different operational states.
## 5 Performance Evaluation  
Simulation results are given to verify the effectiveness of our proposed algorithm, which are averaged over 1000 independent experiments to ensure the scientific nature of the simulation. It is worth mentioning that if the velocity of vehicles is 30 (in m/s), then the average inter-vehicle distance is 2.5 \(\times\) 30/3.6 according to TR 36.885, i.e., 20.83. In addition, the cell radius CR of the model is 500 (in m). The major simulation parameters are summarized in Table 2.  
The performance of our proposed BR-CMCRA algorithm is compared with the following four algorithms about the total/completed processing delay, maximum processing time, average throughput of TaVs, number of devices executed in each mode, and lost rate of each execution mode.  
1. LSVE [8] : TaVs leverage Loc + SeV mode to obtain the allocation strategies;
2. LESE [13] : TaVs leverage Loc + Edge mode to obtain the allocation strategies;
3. LSVLESE [19] : TaVs leverage Loc + SeV mode or Loc + Edge mode to obtain the allocation strategies;
4. LEA: All tasks are executed locally.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Wireless bandwidth of the BS/RSU (B) & 2 MHz \\ Data size of a task (\(\text{D}_{n}\)) & [10, 20] Mbits \\ The required CPU cycles per bit of a task (\(\text{App}_{n}\)) & [100, 200] CPU cycles/bit \\ The maximum tolerable delay of a task (\(\tau_{n}\)) & [1, 2] s \\ Transmit power of the vehicles (P) & 23 dBm \\ CPU cycle frequency of the ES (\(\text{P}^{\text{old}}\)) & 10 GHz \\ CPU cycle frequency of a TaV (\(f_{n}\)) or a SeV (\(\text{f}_{n^{\prime}}\)) & [1, 2] GHz \\ Noise power (\(\delta^{2}\)) & \(-114\) dBm \\ The cell radius (CR) & 500 m \\ BS/RSU height & 25 \\ Vehicles antenna height (\(\text{H}^{\text{Vth}}\)) & 1.5 \\ Carrier frequency (\(freq\)) & 2 GHz \\ Antenna gain of BS/RSU and vehicles (\(\text{Bs}^{\text{Gain}}_{\text{Ant}}\) and \(\text{Vh}^{\text{Gain}}_{\text{Ant}}\)) & 8 dBi and 3 dBi \\ The noise figure of BS/RSU and vehicles (\(\text{Bs}^{\text{Rg}}_{\text{Nasi}}\) and \(\text{Vh}^{\text{Rg}}_{\text{Nasi}}\)) & 5 dB and 9 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulator parameters.
## 5 Discussion of Findings
As a result, the more resilient consumers are more likely to be able to modify their behavior rapidly to adapt to the change. Concerning the factor of cognitive instrumentals, it is evidenced that potential consumers exhibit a positive PU towards IoT products and applications. Attitudes are formed when consumers face the various behavioral patterns in their direct social network. Potential consumers of IoT products and applications are found to be more inclined to directly experience, in terms of enjoyment, the effect of social influence. Social influence is confirmed to lead to PEOU directly and to PU either directly or through the mediating process of PEOU, because a consumer who thinks of a system or an application as easy to use will tend to perceive it as being useful as well. The same flow on PEOU and PU is consistent with the last antecedent, trust. The mediating processes lead to positive attitude, which in turn is of paramount importance to the creation of behavioral intentions towards IoT products and applications. PEOU and PU affect consumers' initial intention to use and are the main determinants of the consumers' attitudes. In our research, it is observed that PEOU (\(\beta\) = 0.301) explains attitude stronger than PU (\(\beta\) = 0.283), a result that contradicts past research results in the wider sphere of new technologies. For example, PU is found to be more useful than PEOU in predicting consumer attitudes to using mobile commerce [62]. Additionally, according to [PERSON] [63], PU is a more powerful predictor of attitude towards a new technology than PEOU. This may be because in addition to the fact that PU is one of the fundamental antecedents of innovation usage, which is related to the utilitarian value that technology provides to customers [26], it refers to the extent to which target consumers believe that using IT will create significant value for them [64]. Moreover, in the context of a completely new concept, such as IoT, consumers may be more likely to adapt their behavior to the new technology if they perceive it to be easy to use, which refers to PEOU [65] and less to the utilitarian value that it presumably provides. PEOU (\(\beta=0.392\)) is found to exert a significant influence on PU, and both PU and PEOU influence a consumer's behavioral intention to use a new system. This complies with past research [66; 67; 68; 69], concluding that PU and PEOU are useful as predictive tools. This study enables us to draw several conclusions. The overall fitness indices for the IoTAM exhibit an acceptable fit of the model. All path coefficients were statistically significant. It was demonstrated that facilitated appropriation, PU and PEOU significantly influence consumers' attitude and BI towards IoT products and applications. User character, cyber resilience, cognitive instrumentals, social influence and trust exhibited a significantly indirect effect on attitude and BIs, through the three main mediators. The IoTAM is proved to be a practical and serviceable model of investigating how different variables influence BI towards IoT products and applications.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 3 Disruption Tolerant Distributed Routing Algorithm
### Alternate Path Direction Calculation  
When the laser link in the direction of the main path fails, it must be switched to the alternate path immediately to avoid packet loss. Therefore, the alternate path must be calculated in advance and stored in the space router. When the main path is the horizontal hop direction, if there is a vertical hop between the current satellite node and the destination satellite node, then the alternate path is the vertical hop direction, otherwise the alternate direction is the vertical hop direction away from the polar region, which is also the vertical hop direction in which the latitude decreases. When the main path is the vertical hop direction, if there is a horizontal hop between the current satellite node and the destination satellite node, then the alternate path is the horizontal hop direction, otherwise the alternate direction is the horizontal hop direction away from the seam. The calculation method of the alternate path direction \(rt1\) is as follows.  
1. If \(rt=d_{h},d_{v}\
eq-1\), then \(rt1=d_{v}\);
2. If \(rt=d_{h},d_{v}=-1\), then \(rt1\) is the vertical hop direction away from the polar region;
3. If \(rt=d_{v},d_{h}\
eq-1\), then \(rt1=d_{h}\);4. If \(rt=d_{v}\), \(d_{h}=-1\), then \(rt1\) is the horizontal hop direction away from the seam.  
For the alternate path direction here and the detour direction of the failure area, to be proposed in the next section, it is inevitable to encounter the problem of routing loops. For the solution of routing loop elimination, please refer to Section 4.2. \"Forwarding Process and Loop Avoidance\".
# mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm
## 3 System Design
### Target Imaging
#### 3.3.1 Windowing with Blackman  
Windowing is to optimize each chirp of the original 3D data using a Blackman window, and the optimized 3D data are obtained after all chirps are windowed. The windowing process is shown in Figure 4. The windowing can optimize the spectrum of the signal, reduce the influence of sidelobe, and effectively mitigate the adverse effects of spectral leakage.
## 3 Results  
This section reports the results obtained during the tests performed to evaluate the static and kinematic GNSS positioning, the accuracy and precision of depth measurements, and the quality of the 3D models produced. In the exposure, the values of Total Horizontal Uncertainty (THU) at a 95% of confidence level and Total Vertical Uncertainty (TVU) at a 95% of confidence level were calculated according to the sixth edition of IHO Standards for Hydrographic Surveys [17] by formulas:  
\[\text{TVU}=1.96\cdot\sigma\text{V} \tag{7}\]  
\[\text{THU}=2.45\cdot\sigma\text{2D} \tag{8}\]  
Figure 8: (**a**) Point cloud after synchronisation and correction operations; (**b**) interpolated pool bottom; (**c**) 3D model of the pool surveyed using traditional methods.  
Figure 10: (**a**) Primitives representing the walls and bottom of the pool; (**b**) final 3D model of the pool.  
Figure 9: (**a**) Pool edge contour line before regularization; (**b**) pool edge contour line after regularization.  
where \(\sigma\)V is the vertical Standard Deviation, e.g., in the case of the depth measurement, and \(\sigma\)2D is the horizontal Standard Deviation, e.g., in the case of the planimetric GNSS positioning. In cases where the positioning values were reported in the two coordinates North and East (\(\sigma\)N for the North and \(\sigma\)E for the East), the worst \(\sigma\) value of the two was used, since the standard assumes the THU expressed by a single value.
## 4 Multi-Threaded Control with C/S Architecture Incorporating AES Encryption and TLS Protocol
### Security Analysis  
This paper integrated TLS and AES encryption algorithms, mainly from the network layer, transport layer and application layer to ensure the security of the data. The data link layer and the physical layer are more inclined to the hardware. This paper is not focused on optimizing the role of the hardware. It mainly focuses on the three layers of the network structure for the security analysis.
# A Dynamic Measurement System Based on Adaptive Clustering and Multi-Classifier
## 4 Results
### Method Performance Analysis Experiment
#### 4.4.1 Target Localization Accuracy Experiment
In summary, in terms of positioning accuracy, our method shows a clear improvement over the currently best methods in the field, especially at longer distances. Among our method's components, the performance ranking of the three modules is feature responder > state determination module > decision function.
###### Abstract  
Postharvest fruit rot caused by _Fusarium incarnatum_ is a destructive postharvest disease of muskmelon (_Cucumnis melo_). Biocontrol by antagonistic microorganisms is considered an alternative to synthetic fungicide application. The aim of this study was to investigate the mechanisms of action involved in the biocontrol of postharvest fruit rot in muskmelons by _Tridoderma_ species. Seven _Tridoderma_ spp. isolates were selected for in vitro testing against _F. incarnatum_ in potato dextrose agar (PDA) by dual culture assay. In other relevant works, _Tridoderma asperellum_ T76-14 showed a significantly higher percentage of inhibition (81%) than other isolates. Through the sealed plate method, volatile organic compounds (VOCs) emitted from _T. asperellum_ T76-14 proved effective at inhibiting the fungal growth of _F. incarnatum_ by 62.5%. Solid-phase microextraction GC/MS analysis revealed several VOCs emitted from _T. asperellum_ T76-14, whereas the dominant compound was tentatively identified as phenylethyl alcohol (PEA). We have tested commercial volatile (PEA) against in vitro growth of _F. incarnatum_; the result showed PEA at a concentration of 1.5 mg mL\({}^{-1}\) suppressed fungal growth with 56% inhibition. Both VOCs and PEA caused abnormal changes in the fungal mycelia. In Vivo testing showed that the lesion size of muskmelons exposed to VOCs from _T. asperellum_ T76-14 was significantly smaller than that of the control. Muskmelons exposed to VOCs from _T. asperellum_ T76-14 showed no fruit rot after incubation at seven days compared to fruit rot in the control. This study demonstrated the ability of _T. asperellum_ T76-14 to produce volatile antifungal compounds, showing that it can be a major mechanism involved in and responsible for the successful inhibition of _F. incarnatum_ and control of postharvest fruit rot in muskmelons.  
muskmelon; biocontrol agent; volatile compounds; _Tridoderma_
# The Bolt Anchorage Performance of Fractured Rock under a Freeze-Thaw Cycle Load
## 4 Analysis and Discussion
### Stress-Strain Relationship  
The stress-strain curves obtained from our experimental and numerical simulation processes, as depicted in Figure 9, exhibit certain differences between specimens under different conditions. Unbolted specimens without fractures manifest a degree of plasticity near their peak, followed by a phase of incomplete brittleness during their failure stage. In contrast, unbolted specimens with a 45\({}^{\circ}\) fracture dip angle show minimal plasticity near their peak and exhibit a brittle failure with a relatively small strain at failure. Unbolted specimens with a 90\({}^{\circ}\) fracture dip angle display a certain degree of plasticity near their peak; however, their stress-strain curves exhibit significant fluctuations during the deformation process, with the failure stage ranging between brittleness and ductility. Unbolted specimens with anchors demonstrate a relatively prolonged plastic phase near their peak, yet exceptions exist, as the failure stage still presents characteristics of incomplete brittleness. Bolted specimens with a 45\({}^{\circ}\) fracture dip angle show a degree of plasticity near their peak compared to unbolted specimens, with less pronounced brittleness during the failure stage. Bolted specimens with a 90\({}^{\circ}\) fracture dip angle exhibit a plastic phase near their peak, followed by a certain degree of ductility during their failure stage. The presence of anchors enhances their plasticity near their peak and their ductility after the peak on the stress-strain curve.  
Utilizing the aforementioned parameters, simulations were conducted for specimens under six conditions (Figure 10): unbolted without pre-existing fractures, unbolted with a 45\({}^{\circ}\) pre-existing fracture dip angle, unbolted with a 90\({}^{\circ}\) pre-existing fracture dip angle, bolted with a 45\({}^{\circ}\) pre-existing fracture dip angle, and bolted with a 90\({}^{\circ}\) pre-existing fracture dip angle. The stress-strain curves obtained from simulations closely matched the experimental results in terms of peak stress, tangent modulus, and other key data.  
Figure 9: Stress–strain curves for experimental and numerical simulation results.
# Differential Proteomic Analysis of _Listeria monocytogenes_ during High-Pressure Processing
## Materials and Methods
### Whole-Protein Extraction for Proteomic Analysis  
The bacterial culture (8 log CFU/mL) with a 19-fold volume of sterile distilled water was treated by HPP at 200-400 MPa. After PBS washing and centrifugation, the treated bacterial pellets were suspended in a lysis buffer (12 mM sodium deoxycholate, 12 mM sodium N-laurovlsarcosinate, and 10 mM Tris; pH 9.0), the cells were mixed with 1 mm ceramic beads and 0.1 mm glass beads, lysed with 5 cycles at 1 min and at 6.5 m/s speed using a cell homogenizer (FastPrep-24; MP Biomedicals, LLC, Irvine, CA, USA), allowing to stand for 5 min on ice between each cycle [8]. After centrifugation (15,000\(\times\)_g_, 20 min at 4 \({}^{\circ}\)C), the supernatant was collected as the whole-protein extract and stored at \(-\)80 \({}^{\circ}\)C for further use. The protein concentration was determined by the BCA Protein Assay Kit (Thermo Scientific, Rockford, IL, USA) according to manufacturer instructions. The control group was labelled as C, and samples treated with HPP of 200, 300 and 400 MPa were labelled as H2, H3 and H4, respectively.
## 2 Study Area and Datasets
### Datasets
#### 2.2.1 Remote Sensing Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sensor**} & \multirow{2}{*}{**Band**} & **Spectral Range** & **Spatial** & \multirow{2}{*}{**Swath Width (km)**} & **Revisit Time** \\  & & **(\(\mu\)m)** & **Resolution (m)** & & **(Days)** \\ \hline \multirow{4}{*}{CCD} & 1 & 0.43–0.52 & \multirow{4}{*}{30} & 360 (single CCD) & \multirow{4}{*}{4} \\ \cline{2-2} \cline{5-5}  & 2 & 0.52–0.60 & & 700 (two CCDs) & \\ \cline{2-2} \cline{5-5}  & 3 & 0.63–0.69 & & & \\ \cline{2-2} \cline{5-5}  & 4 & 0.76–0.90 & & & \\ \hline \multirow{4}{*}{IRS} & 5 & 0.75–1.10 & \multirow{4}{*}{150} & \multirow{4}{*}{720} & \multirow{4}{*}{4} \\ \cline{2-2} \cline{5-5}  & 6 & 1.55–1.75 & & & \\ \cline{1-1} \cline{2-2} \cline{5-5}  & 7 & 3.50–3.90 & & & \\ \cline{1-1} \cline{2-2} \cline{5-5}  & 8 & 10.5–12.5 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of the main payloads of HJ-1B. MODIS products that cover the study area and contain LST-related parameters were downloaded and corrected for geometry and projection effects. The ASTER GDEM is a product generated from optical data collected by the ASTER instrument onboard NASA's Terra satellite. This dataset is the only DEM that covers \(\sim\)99% of the entire land surface at high resolution. In this paper, we mainly use the ASTER GDEM v2, which has an accuracy of 17 m at the 95% confidence level [43]. The three kinds of satellite data were registered and matched with each other in the same projection.
## 3 Methodology
### Conventional Control (PID)  
Proportional Integral Differential (PID) control, also known as three-term control, is a simple, yet effective control scheme for systems with no noise. PID controllers are used for controlling Single Input Single Output (SISO) systems, however, the RUAV plant used is a Multiple Input Multiple Output (MIMO) system. To control the system, a multi-loop approach is required where one PID controller is used to control each of the inputs to the system using the three corresponding measured outputs of the same state.  
The equation for the controller output (\(u\)) is shown in Equation (2) below, the signal error (\(e\)) is calculated as the difference in the system output from the reference signal. This error is used in the controller, first by multiplying by the proportional gain, then by taking the differential of the error and multiplying by a differential gain and lastly, by taking the  
Figure 3: State Space Block Diagram—\(\frac{1}{s}\) representing integrator block in MATLAB Simulink.  
integral of the error and multiplying by an integral gain, these three products are then added together to produce the final output of the controller.  
\[u(t)\ =\ K_{p}e+K_{d}\frac{de}{dt}+K_{i}\int_{0}^{t}e(t)\ dt \tag{2}\]  
The Proportional gain (\(K_{p}\)) is proportional to the current signal error (\(e\)), if set too low, the controller will settle slowly, potentially not creating enough controller output to correct the error before the RUAV fails. If set too high, the controller will overcompensate for the error and will oscillate about the setpoint potentially becoming unstable.  
The Integral gain (\(K_{i}\)) relates to the speed of response and to totally eradicate steady-state error for accurate asymptotic reference tracking, if set too low, the response will be slow and may not correct the error in time before the system fails, if set too high, the controller will produce a response that overshoots and oscillates about the setpoint.  
The differential gain (\(K_{d}\)) works to steady the response when gains for \(K_{p}\) and \(K_{i}\) are set incorrectly causing oscillations. If \(K_{d}\) is set too low, then there may still be oscillations in the system even when at steady state, if \(K_{d}\) is set too high, then the response may be delayed, the desired input may take a long time to reach the output.
# Temporal Instability Analysis of Injury Severities for Angle and Non-Angle Crashes at Roundabouts
## 6 Results and Discussion
### Analysis of the Characteristics' Effects
#### 6.1.5 Vehicle Characteristics  
The commercial vehicle indicator had a significant impact on non-angle collisions in all periods but did not affect angle collisions. More specifically, there was a stable positive correlation with no injuries and a stable negative correlation with minor injuries. However, the indicator showed unstable impacts on severe injury, as evidenced by negative coefficients for 2012-2013 and 2014-2015 and positive coefficients for 2016-2017 and 2018-2019. The results indicated that the injury severity of crashes involving commercial vehicles in roundabouts has increased in recent years. This may be due to the growing demands on the efficiency of commercial vehicle transportation in a competitive economic climate, which has led to higher speeds and more severe collisions involving commercial vehicles. The findings on the commercial vehicle are similar to the results of a previous study [21]. Notably, commercial vehicle involvement only affected injury severity in non-angle collisions. This may be attributed to the fact that drivers are less likely to perform cut-in maneuvers when the other vehicle is a large commercial vehicle, which affects the probability of angle crashes. Therefore, the commercial vehicle indicator only contributes to different injury severity outcomes for non-angle collisions, while no significant impacts were found for angle crashes.