esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Global Strategy  
[PERSON] et al. [1] introduced an on-demand computation and cache-centric routing strategy and algorithm for satellite networks. The algorithm dynamically groups satellite network topologies, dividing routing into three stages: direction design, direction enhancement, and congestion avoidance. This algorithm boasts efficiency and flexibility, with congestion avoidance strategies enhancing the reliability and efficiency of data transmission. [PERSON] et al. [2] proposed a dynamic routing update algorithm based on real-time queue and routing state models to balance traffic loads and ensure the prompt transmission of data packets from each satellite, thereby avoiding congestion at the current node. They employ a load balancing mechanism to achieve equilibrium in information transfer across intra-network links, facilitating enhanced overall system throughput and congestion avoidance. [PERSON] et al. [3] proposed two routing algorithms to optimize the utilization of inter-satellite links. By scheduling low-priority traffic onto links designated for high-priority services, the algorithms reduce the number of links used by low-priority traffic. Additionally, they introduce a load balancing strategy to control the aggregation of network flows, thereby minimizing the total number of utilized links. This approach enhances the resource utilization of satellite networks and contributes to energy conservation. [PERSON] et al. [4], building upon the selective iterative Dijkstra algorithm, propose a selective diversion global load balancing strategy to address link congestion issues in low Earth orbit (LEO) satellite networks with low latitudes. [PERSON] and [PERSON] et al. [5] introduce a mechanism for quantitatively estimating the global network link state and dynamically adjusting queue delay weights. This mechanism efficiently and dynamically updates routing tables between two switches, significantly reducing routing overhead.
## 3 Sensor Fault Diagnosis
### Single Sensor Fault
As can be seen from Figure 9, the \(WSSR\) of the other filters also drifts, while only the fourth filter remains unchanged. Therefore, the designed Kalman filter bank can diagnose all kinds of single sensor faults effectively. Subsequently, this filter is used to re-estimate the degradation, and the result is shown in Figure 10, which is in line with the actual situation. Finally, the actual degradation is brought into the on-board AEMM to reconstruct the fault sensor signal, as shown in Figure 11, where the short red line is the fault sensor signal, and the solid black line is the corresponding sensor signal reconstructed from the on-board model. Figure 8: \(WSSR\) value under single sensor bias fault. Figure 9: \(WSSR\) value under single sensor drift fault.
# Automatic Control Optimization for Large-Load Plant-Protection Quadrotor
###### Abstract  
In this article, methods for the attitude control optimization of large-load plant-protection quadrotor unmanned aerial vehicles (UAVs) are presented. Large-load plant-protection quadrotors can be defined as quadrotors equipped with sprayers and a tank containing a large amount of water or pesticide, allowing the quadrotors to water plants or spray pesticide during flight. Compared to the control of common small quadrotors, two main points need to be considered in the control of large-load plant-protection quadrotors--first, the water in the tank gradually diminishes during flight and the physical parameters change during this process. Second, the size and mass of the rotors are especially large, which greatly slows the response rate of the rotors. We present an extended-state reinforcement learning (RL) algorithm to solve these problems. The moment of inertia (MOI) of the three axes and the dynamic response constant of the rotors are included in the state list of the quadrotor during the training process, so that the controller can learn these changes in the models. The controlling laws are automatically generated and optimized, which greatly simplifies the tuning process compared to those of traditional control algorithms. The controller in this article is tested on a 10 kg class large-load plant-protection quadrotor, and the flight performance verifies the effectiveness of our work.  
Quadrotor attitude control; large-load plant-protection quadrotor; reinforcement learning; deterministic policy gradient
###### Abstract  
Supplementary cementitious materials (SCMs) and fillers play an important role in enhancing the mechanical properties and durability of concrete. SCMs and fillers are commonly used in self-consolidating concrete (SCC) mixtures to also enhance their rheological properties. However, these additives could have significant effects on the viscoelastic properties of concrete. Existing models for predicting creep and drying shrinkage of concrete do not consider the effect of SCM/filler on the predicted values. This study evaluates existing creep and drying shrinkage models, including AASHTO LRFD, ACI209, CEB-FIP MC90-99, B3, and GL2000, for SCC mixtures with different SCMs/fillers. Forty SCC mixtures were proportioned for different cast-in-place bridge components and tested for drying shrinkage. A set of eight SCC mixtures with the highest paste content was tested for creep. Shrinkage and creep test results indicated that AASHTO LRFD provides better creep prediction than the other models for SCC with different SCMs/fillers. Although all models underestimate drying shrinkage of SCC with different SCMs/fillers, the GL2000, CEB-FIP MC90-99, and ACI 209 models provide better prediction than AASHTO LRFD and B3 models. Additionally, SCC mixtures with limestone powder filler exhibited the highest creep, while those with class C fly ash exhibited the highest drying shrinkage.  
self-consolidating concrete; viscoelastic properties; creep; drying shrinkage; supplementary cementitious materials; fillers; prediction models +
Footnote †: journal: Applied Sciences  
15 July 20  
F fly ash +15% limestone powder (LSP); and two levels of slump flow--low (22-26 in.) and high (26-30 in.). All SCC mixtures were air entrained and contained Portland cement Type I/II, which is the common practice in cast-in-place bridge construction. The laboratory tests were conducted according to AASHTO or ASTM methods. Measured properties of SCC mixtures were compared to predicted values using the five prediction models listed earlier.
# Characterization of Quality Properties in Spoiled Mianning Ham
###### Abstract  
Deep spoilage is a cyclical and costly problem for the meat industry. Mianning ham is a famous dry-cured meat product in Sichuan, China. The aim of this work was to investigate the physicochemical characteristics, sources of odor, and associated microorganisms that cause spoilage of Mianning ham. High-throughput sequencing and solid-phase microextraction-gas-chromatography (SPME-GC-MS) techniques were used to characterize the physicochemical properties, microbial community structure, and volatile compounds of spoiled Mianning ham and to compare it with normal Mianning ham. The results showed that spoiled ham typically had higher moisture content, water activity (\(\mathrm{a}_{\mathrm{w}}\)), and pH, and lower salt content. The dominant bacterial phylum detected in deeply spoiled ham was Firmicutes (95.4%). The dominant bacterial genus was _Clostridium_sensu_stricto_2 (92.01%), the dominant fungal phylum was Ascomycota (98.48%), and the dominant fungal genus was _Aspergillus_ (84.27%). A total of 57 volatile flavor substances were detected in deeply spoiled ham, including 11 aldehydes, 2 ketones, 6 alcohols, 10 esters, 20 hydrocarbons, 6 acids, and 2 other compounds. Hexanal (279.607 \(\pm\) 127.265 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg) was the most abundant in deeply spoiled ham, followed by Butanoic acid (266.885 \(\pm\) 55.439 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg) and Nonanal (165.079 \(\pm\) 63.923 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg). _Clostridium_sensu_stricto_2 promoted the formation of five main flavor compounds, Heptanal, (E)-2-Octenal, 2-Nonanone, Hexanal, and Nonanal, in deeply spoiled ham by correlation analysis of microbial and volatile flavor substances._  
Mianning ham; deep spoilage; high-throughput sequencing; volatile compounds; physicochemical properties +
Footnote †: journal: Physics Letters B
## 4 Simulation
### BPSK Modulation  
We then multiply a baseband signal by the M-sequence to get SPSP. The simulation result, as obtained using Matlab, is shown in Figure 14.  
Figure 12: The frequency domain model of the second-order digital loop filter.  
Figure 13: The simulation result of the M-sequence module.  
The SPSP sequence will be obtained after the baseband signal has been spread. BPSK modulation is then performed by passing the SPSP sequence through a modulator. The simulation result of the BPSK modulation is shown in Figure 15.  
Figure 14: Simulation result of Spread Spectrum (SPSP).  
Figure 15: The simulation result of BPSK modulation.  
After spreading the spectrum and modulating the baseband signal, we transmit it and demodulate the waveform to verify whether we can recover the original SPSP waveform correctly. The demodulated waveform is shown in Figure 16, from which we see that the demodulated waveform is as same as our initial one shown in Figure 14.
## 2 Data and Methods
### Extraction of Impervious Surface Distribution
#### 2.2.2 Comparison of Methods  
The NDBI, IBI, NDISI, NDII, and CBI methods were used for comparison. The segmentation thresholds of the NDBI and NDISI were both set to 0, which is the default threshold according to the authors who proposed these indices, whereas the thresholds of the other three indices were determined by the Otsu method here because the authors who proposed these indices determined the thresholds manually rather than setting a fixed threshold. In addition, in all the methods, water bodies were masked first so as to ensure the comparisons coincided.  
(1) NDBI: [PERSON] et al. proposed the NDBI for extracting impervious surface distribution [7]:  
\[NDBI=\frac{SWIR1-NIR}{SWIR1+NIR} \tag{5}\]  
(2) IBI: [PERSON] constructed the IBI to extract impervious surface distribution [8]:  
\[BI=\frac{NDBI-\frac{(SAVI+NDWI)}{2}}{NDBI+\frac{(SAVI+MNDWI)}{2}} \tag{6}\]  
where SAVI [31] is calculated as follows:  
\[SAVI=\frac{(NIR-Red)(1+L)}{NIR+Red+L} \tag{7}\]where L is a correction factor that ranges from 0 to l. In this study, L was set to 0.5, which can minimise noise due to soil [11; 31].  
(3) NDISI: [PERSON] proposed the NDISI for extracting impervious surface distribution [9]:  
\[NDISI=\frac{TIR-(MNDWI+NIR+SWIR1)/3}{TIR+(MNDWI+NIR+SWIR1)/3} \tag{8}\]  
where TIR denotes the thermal band, which we replaced with the TIR1 band (10.6-11.19\(\upmu\)m) in Landsat 8 imagery. MNDWI is calculated as follows [32]:  
\[MNDWI=\frac{Green-SWIR1}{Green+SWIR1} \tag{9}\]  
(4) NDII: [PERSON] et al. devised the NDII to extract impervious surface distribution [10]:  
\[NDII=\frac{VIS-TIR}{VIS+TIR} \tag{10}\]  
where VIS represents one of the red, green and blue bands. [PERSON] pointed out that the accuracy of extraction is highest when VIS represents the red band [10], and hence we also used the red band to calculate the NDII.  
(5) CBI: [PERSON] et al. developed the CBI to extract impervious surface distribution [11]:  
\[CBI=\frac{\frac{(PC1+NDWI)}{2}-SAVI}{\frac{(PC1+NDWI)}{2}+SAVI} \tag{11}\]  
where NDWI is calculated as follows [33]:  
\[NDWI=\frac{Green-NIR}{Green+NIR} \tag{12}\]
## Materials and Methods
### Burned Area Mapping Method  
The new burned area mapping method consists of four phases: data pre-processing, extraction of sample sets, spectral characteristics analysis, and training of the BPNN. The flowchart of the proposed method is depicted in Figure 2. In the first phase, we carried out pre-processing on the MODIS Level 1B data to obtain the data of reflectance, emissivity and land surface temperature (LST). In the second phase, sample sets of five typical types (burned area, vegetation, cloud, bare soil, and cloud shadow) were extracted by the empirical formula method [18], multi-threshold method [31; 42; 43; 44; 45], and visual interpretation method [18; 25; 46]. Thirdly, the separability analysis of spectral bands and spectral indices between the burned area and four unburned types was investigated to find the optimal feature vectors for the input layer of the BPNN. In the last phase, training samples were used to train the BPNN and then to derive the burned area map.
# LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios
## 4 Twin System Modeling and LSTM-DQN-APF Algorithm
### System Setup  
To verify the effectiveness of LSTM-DQN-APF in complex urban scenarios, a hybrid physical-virtual twin platform needs to be built. Through twin technology, a full-process closed loop of environmental perception, trajectory prediction, and autonomous decision making is enabled. The first step is to build an accurate and dynamically adjustable experimental digital twin, creating a hybrid physical-virtual testing environment. As shown in Figure 4a, this hybrid platform consists of the physical subsystem, virtual sub-system, data fusion, twin service layer, and the connections between them, forming a \"perception-simulation-decision-control\" closed-loop architecture. Figure 4b shows the sensor monitoring interface of the twin system. This system uses dynamic perception and information fusion to create a digital virtual image space of the experimental field environment, generating diversified samples that closely approximate the real physical world to verify the LSTM-DQN-APF algorithm. In the construction of the physical system, the physical UAV is responsible for real-time environmental perception and data collection. In the virtual system, the focus is on building the digital model of the urban experimental environment and the UAV being tested. Additionally, the twin service layer performs operational control based on task requirements and provides algorithmic support for specific tasks. Through the combination of physical and virtual systems, the experimental field achieves dynamic feedback and continuous optimization processes from environmental perception to multi-sensor information fusion and task decision making. Through a data-driven closed-loop system, the task strategy and behavior parameters are continuously optimized during the experimental process, thereby improving path planning accuracy.  
\begin{table}
\begin{tabular}{c c} \hline
**UAV status** & **Rewards** \\ \hline r1 is the reward for reaching the target & 100 \\ r2 is the penalty for having a collision or & \(-\)100 \\ going out of bounds & \(-\)5 \\ r3 is the negative reward for staying put & \(1/\text{max}(\text{N}(\text{s}),\,\varepsilon)-1/\text{max}(\text{N}(\text{s} ^{\prime}),\,\varepsilon)\) \\ Explore rewards & \(\text{D}(\text{s},\,\text{g})-\text{D}(\text{s}^{\prime},\,\text{g})\) \\ Heuristic reward & \(\text{D}(\text{s},\,\text{g})-\text{D}(\text{s}^{\prime},\,\text{g})\) \\ F(s) & Combined force \\ \hline \end{tabular}
\end{table}
Table 1: Table of incentive functions.
## 3 Problem Formulation
### Spectral Efficiency  
The average spectral efficiency of the system can be defined as  
\[C=\mathbb{E}[\log_{2}(1+\gamma)]=\int\limits_{0}^{\infty}\log_{2}(1+\gamma) \frac{\beta^{\kappa}}{\Gamma(\alpha)}\gamma^{\alpha-1}e^{-\beta\gamma}d\gamma, \tag{10}\]  
whose approximated solution is given by  
\[C=\frac{(-1)^{-\alpha}\beta}{\log(2)\Gamma(\alpha)}[(-1)^{\alpha}(\Gamma( \alpha-1)\,_{2}F_{2}(1,1;2,2-\alpha;\beta)+\frac{\Gamma(\alpha)(\psi^{(0)}( \alpha)-\log(\beta))}{\beta})+\pi\beta\csc(\pi\alpha)(\Gamma(\alpha)-\Gamma( \alpha,-\beta))], \tag{11}\]  
where \(\psi^{(0)}(.)\) is the digamma function, \(\Gamma(.)\) is the gamma function, \(\Gamma(.,.)\) is the incomplete gamma function, and \({}_{2}F_{2}(a,b;c,d;e)\) is the generalized hypergeometric function. It is noteworthy that, although this study did not find an explicit solution for the spectral efficiency, it does present a more generic solution for the integral in the reference [22].
## 5 Conclusions  
We implemented both PRESENT and GIFT block ciphers with bit-slicing technology on CUDA GPU. We optimize the algorithm of cipher by applying the bit-slicing technique and minimize the overhead caused by applying the bit-slicing technique in normal format. Proposed implementation was evaluated on the RTX 3060 with NVIDIA Ampere architecture. We are able to achieve 553.932 Gbps PRESENT-80, 529.952 Gbps PRESENT-128, 583.859 Gbps GIFT-64, and 214.284 Gbps GIFT-128 throughput for encryption, respectively. For exhaustive search, 24.264 Gbps PRESENT-80, 24.522 Gbps PRESENT-128, 85.283 Gbps GIFT-64, and 10.723 Gbps GIFT-128 throughput are achieved, respectively. As a result of comparing the proposed PRESENT method with previous works, the bit-slicing method showed higher throughput than the existing table implementation method. The GIFT implementation is the first on GPU architectures. The implementation of PRESENT achieved approximately 4x higher throughput than the state-of-art work. As a result of our implementation, we were able to confirm that a lightweight cipher was suitable for a high degree of bit-slicing because of its simple key schedule and small blocks. In this paper, we dealt with PRESENT and GIFT of SPN structure. We believe that applying the bit-slicing technique to other lightweight ciphers is also effective. In the future, we plan to apply it to other design ciphers to which bit slicing is applicable.  
Conceptualization, S.L.; Software, H.K. and S.E.; Supervision, H.S.; Writing--original draft, H.K.; Writing--review & editing, W.-K.L. and H.S. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by Hansung University.  
The authors declare no conflict of interest.
## 3 Statistical Model of SNR
### A. Statistical Model of SNR for Single Hop Links
\begin{array}{rl}F_{\gamma_{\text{ub}}}(\gamma)&=\prod\limits_{i=1}^{N}\frac{ \varepsilon_{\text{mod},i}^{2}\varepsilon_{i}^{k_{i}}}{\Gamma(a_{i})\Gamma( \beta_{i})}\\ &\times\frac{1}{2\pi j}\int\limits_{i}\frac{\prod\limits_{i=1}^{N}\Gamma\left( \varepsilon_{\text{mod},i}^{2}+\frac{2l_{i}}{N}s\right)\Gamma\left(a_{i}+\frac {2l_{i}}{N}s\right)\Gamma\left(\beta_{i}+\frac{2l_{i}}{N}s\right)\Gamma^{k_{i}} \left(z_{i}+\frac{2l_{i}}{N}s\right)\Gamma(-s)}{\prod\limits_{i=1}^{N}\Gamma \left(\varepsilon_{\text{mod},i}^{2}+\frac{2l_{i}}{N}s\right)\Gamma^{k_{i}} \left(z_{i}+1+\frac{2l_{i}}{N}s\right)\Gamma(1-s)}\\ &\left(N\gamma\prod\limits_{i=1}^{N}\psi_{i}\left(\frac{a_{i}\beta_{i}}{A_{\text{ mod},i}\sqrt{\gamma_{i}}}\right)^{\frac{2l_{i}}{N}}\right)^{-s}\!\!ds\end{array}  \tag{36}\]The above formula can be simplified by \(I\)-function. After simplification, we obtain:  
\[F_{\gamma_{\text{ub}}}(\gamma)=\prod\limits_{i=1}^{N}\frac{\varepsilon_{\text{ mod},i}^{2}z_{i}^{k_{i}}}{\Gamma(\alpha_{i})\Gamma(\beta_{i})}I_{2N+1,4N+1}^{N,1} \left[N\gamma\gamma\prod\limits_{i=1}^{N}\psi_{i}\left(\frac{\alpha_{i}\beta_{i }}{A_{\text{mod},i}\sqrt{\overline{\gamma}_{i}}}\right)^{\frac{\alpha_{i}}{N}} \right]^{\frac{\alpha_{i}}{N}}\left| \begin{matrix}(1,1,1),J_{1}\\ J_{2},(0,1,1)\end{matrix} \right| \tag{37}\]
## 3 Proposed Algorithms Explanation
### Localization Free Energy Efficient Routing Protocol (LFEER)
#### 3.1.2 Information Acquisition  
The nodes in UWSNs initially do not have any information about the neighbor nodes which is required for routing. The residual energy, number of hops and BER information are needed in the proposed protocols. For the purpose of getting the information of each node, a hello test bag is transmitted by the final DNN towards the sensor nodes. The volume of the hello packet is considered 8 bytes, which is responsible to exchange all the required information among the sensor nodes [32]. After each 35 rounds, the hello packets are repeatedly exchanged among the nodes to keep the routing information updated. The unique ID of the sink differentiates the hello packet that it sends. A node directly receiving the packet from the final DNN inserts the value of the energy its has in its battery and hop count (which is unity for a node receiving the packet directly from the final DNN). The node then broadcasts the packet. Another node receiving this packet inserts its own residual energy and increments the number of hops (the new value becomes two) after the packet is received from the node with a single hop from the final DNN. This sequence is repeated unless nodes exchange residual energy and number of hops information. To calculate BER, a node sends a test bag to its neighbors. The test bag contains a specific number of bits. This pattern of bits is by default known to all nodes. Therefore, upon receiving the test bag, every node checks the number of bits changed (corrupted) in the test bag due to channel properties. Every node then informs the sender node about the number of corrupted bits in the test bag. The hello bag format is provided in Figure 2.
## 3 Analysis and Results
### Impact Zone of Merging and Diverging Sections  
The spatial segmentation of speed-homogeneous areas reveals non-converging segments in merging and diverging zones with increased traffic volume, reflecting unstable flow due to lane-specific speed variations. As illustrated in Figure 9 for Expressway 50, we can observe a comparison between the non-converging speed-homogeneous spaces and the converging spaces upstream. The latter are influenced by exiting vehicles at interchange (IC) diverging points, which induce lane-specific speed differences.  
Figure 9 presents an analysis of an interchange section--a merging/diverging area on Expressway 50. In the upper portion of the figure, segments are represented by the spatial mean speed, while the lower portion categorizes these segments as either stable (homogeneous) or unstable (heterogeneous). The analysis identifies stable flow in sections with speeds of 15, 29, 31, 7, and 13 km/h, while speeds of 28, 38, and 40 km/h correspond to unstable flow conditions. Stability resumes following these unstable sections, suggesting that the reduction in speed for vehicles exiting at the interchange induces temporary traffic instability in affected segments, which in turn lowers the average speed of trailing vehicles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & **Spaces** & **Average DTG Data** & **Average of Representative** \\  & **Number** & **Number** & **Values (km/h)** \\ \hline
5 & 5 & 1032 & 82.41 \\
6 & 5 & 808 & 84.44 \\
7 & 3 & 486 & 80.85 \\
8 & 9 & 252 & 69.66 \\
9 & 8 & 66 & 81.38 \\
10 & 8 & 47 & 83.94 \\
11 & 10 & 36 & 67.10 \\
12 & 68 & 19 & 76.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis results of Expressway 50.  
Figure 9: Analysis of the impact zone of merging and diverging sections.  
During periods of lower traffic volume, such sudden decelerations may heighten accident risk. The primary objective of this study is to analyze influence zones through segmented average speed and stability, thereby enabling the prediction of accident-prone areas. By understanding the dynamics of speed and stability in merging/diverging sections, this approach contributes to more effective traffic management and enhances predictive safety measures, reducing the likelihood of accidents in high-risk zones.