esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy-Efficient Routing Protocol for Reliable Data Transmission in Wireless Body Area Networks
###### Abstract  
Wireless body area networks will inevitably bring tremendous convenience to human society in future development, and also enable people to benefit from ubiquitous technological services. However, one of the reasons hindering development is the limited energy of the network nodes. Therefore, the energy consumption in the selection of the next hop must be minimized in multi-hop routing. To solve this problem, this paper proposes an energy efficient routing protocol for reliable data transmission in a wireless body area network. The protocol takes multiple parameters of the network node into account, such as residual energy, transmission efficiency, available bandwidth, and the number of hops to the sink. We construct the maximum benefit function to select the next hop node by normalizing the node parameters, and dynamically select the node with the largest function value as the next hop node. Based on the above work, the proposed method can achieve efficient multi-hop routing transmission of data and improve the reliability of network data transmission. Compared with the priority-based energy-efficient routing algorithm (PERA) and modified new-attempt routing protocol (NEW-ATTEMPT), the simulation results show that the proposed routing protocol uses the maximum benefit function to select the next hop node dynamically, which not only improves the reliability of data transmission, but also significantly improves the energy utilization efficiency of the node and prolongs the network lifetime.  
W 2019 19 4238 129 September 2019 29 September 2019
## 4 The Design of Routing Protocol
### Node Status Knowledge Exchange  
To optimize the routing decision-making process, it is necessary for all sensor nodes to possess their neighboring nodes' status information to calculate the decision values using an SVM model. The proposed routing protocol utilizes two approaches for exchanging node status information.  
* Simultaneous Exchange with Data Packet Transmission: In this paper, the sender's status information is appended to the data packet header prior to its transmission. Consequently, a node can obtain its neighboring nodes' status information from the incoming data packets.
* Use of Hello Packets Containing Node Status Knowledge: Each node in the UASN periodically broadcasts a Hello packet, used solely for exchanging status knowledge. These broadcasts complement the approach whereby node status knowledge is exchanged. As each node can obtain the status knowledge of the neighboring node(s) from data packet transmissions, special control packets do not need to be used. Therefore, the broadcast period of the Hello packet can be configured with an adequate duration to eliminate the overhead.
###### Abstract  
Space-based solar-induced chlorophyll fluorescence (SIF) has been widely demonstrated as a great proxy for monitoring terrestrial photosynthesis and has been successfully retrieved from satellite-based hyperspectral observations using a data-driven algorithm. As a semi-empirical algorithm, the data-driven algorithm is strongly affected by the empirical parameters in the model. Here, the influence of the data-driven algorithm's empirical parameters, including the polynomial order (n\({}_{\text{p}}\)), the number of feature vectors (n\({}_{\text{SV}}\)), the fluorescence emission spectrum function, and the fitting window used in the retrieval model, were quantitatively investigated based on the simulations of the SIF Imaging Spectrometer (SIFIS) onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1). The results showed that the fitting window, n\({}_{\text{p}}\), and n\({}_{\text{SV}}\) were the three main factors that influenced the accuracy of retrieval. The retrieval accuracy was relatively higher for a wider fitting window; the root mean square error (RMSE) was lower than 0.7 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\) with fitting windows wider than 735-758 nm and 682-691 nm for the far-red band and the red band, respectively. The RMSE decreased first and then increased with increases in n\({}_{\text{p}}\) range from 1 to 5 and increased in n\({}_{\text{SV}}\) range from 2 to 20. According to the specifications of SIFIS onboard TECIS-1, a fitting window of 735-758 nm, a second-order polynomial, and four feature vectors are the optimal parameters for far-red SIF retrieval, resulting in an RMSE of 0.63 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\). As for red SIF retrieval, using second-order polynomial and seven feature vectors in the fitting window of 682-697 nm was the optimal choice and resulted in an RMSE of 0.53 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\). The optimized parameters of the data-driven algorithm can guide the retrieval of satellite-based SIF and are valuable for generating an accurate SIF product of the TECIS-1 satellite after its launch.  
solar-induced chlorophyll fluorescence (SIF); data-driven algorithm; Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1); parameter optimization +
Footnote †: journal: Physics Letters B
# Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt
## 2 Modeling of the Proposed System
### Battery Bank Model  
The battery bank serving as a backup system of storing energy in the event that the renewable sources are unable to deliver the needed power. The hourly total power generated by the PV, WTs, and biomass system \(\mathrm{P_{re}(t)}\) is obtained based on the below equation [19; 25];  
\[\mathrm{P_{re}}=\mathrm{P_{PV}}+\mathrm{WT_{P}}+\mathrm{BG_{P}}/\mathrm{\eta_{ inv}} \tag{7}\]  
The technical specifications of the battery bank model are illustrated in Table 4. The following equations explain the energy production and consumption of the battery system from time t-1 to time t [30; 38];  
\[\mathrm{During}\ \mathrm{charging}\ \mathrm{Bat_{CH}},\] \[\mathrm{Bat_{CH}(t)}=(\mathrm{P_{re}(t)}-(\mathrm{P_{L}}\ (t)/\mathrm{\eta_{ inv}}))\times\mathrm{\Delta t}\times\ \mathrm{\eta_{CH}} \tag{8}\] \[\mathrm{SOC_{Bat}(t)} =\mathrm{SOC_{Bat}(t-1)}\times(1-\sigma)+\mathrm{Bat_{CH}(t)}\] (9) \[\mathrm{During}\ \mathrm{discharging}\ \mathrm{phase}\ \mathrm{Bat_{DIS}},\] \[\mathrm{Bat_{DIS}(t)}=((\mathrm{P_{L}}\ (t)/\mathrm{\eta_{ inv}})-\mathrm{P_{re}}\ (t))\times\mathrm{\Delta t}\times\mathrm{\eta_{DIS}}\] (10) \[\mathrm{SOC_{Bat}(t)} =\mathrm{SOC_{Bat}(t-1)}\times(1-\sigma)-\mathrm{Bat_{DIS}(t)} \tag{11}\]  
In which, \(\mathrm{\eta_{CH}}\) and \(\mathrm{\eta_{DIS}}\) indicate the battery charging and discharging efficiencies, respectively, \(\sigma\) is self-discharge rate, and \(\mathrm{SOC_{Bat}}\) is the battery state of charge. \(\mathrm{\eta_{inv}}\) denotes the inverter efficiency.
## 2 Material and Methods
### Preparation and Immersion Treatment of Fillets  
Fresh live HTS (weight: 0.75 \(\pm\) 0.05 kg; length: 40 \(\pm\) 2 cm) were purchased from Luchao Port Market (Shanghai, China) in August and immediately transported to the laboratory with ice within 30 min. HTS were killed using percussive stunning and fillets with 6 cm \(\times\) 4 cm \(\times\) 1 cm, weighing approximately 40 g, were cut and prepared. The final concentrations of chitosan, RA, and \(\varepsilon\)-PL in coating solutions are listed in Table 1. The HTS fillets were each immersed in freshly prepared chitosan-based coating solution for 10 min at 4 \({}^{\circ}\)C with a fillet/solution ratio of 1:3 (_w/v_), then removed and allowed to drain in a sterile biochemical incubator with air flow at 4 \({}^{\circ}\)C for 60 min to form the coatings. After that, each fillet sample was individually packed in a sterile polyethylene bag and stored at 4 \(\pm\) 0.5 \({}^{\circ}\)C for subsequent assessments at 3-day intervals.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Codes for Coating Treatments** & **Chitosan (_w/v_)** & **RA (_w/v_)** & \(\varepsilon\)**-PL (_w/v_)** \\ \hline CK1 & 1\% & – & – \\ CK2 & 1\% & 30 mg/L & – \\ C1 & 1\% & 30 mg/L & 0.05 \\ C2 & 1\% & 30 mg/L & 0.10 \\ C3 & 1\% & 30 mg/L & 0.15 \\ C4 & 1\% & 30 mg/L & 0.20 \\ \hline \hline \end{tabular} CK1: Chitosan coating without rosmarinic acid or \(\varepsilon\)-PL; CK2: Chitosan coating with 30 mg/kg rosmarinic acid; C1/C2/C3/C4: Chitosan coating with 30 mg/kg rosmarinic acid and 0.05%/0.10%/0.15%/0.20% \(\varepsilon\)-PL.  
\end{table}
Table 1: The final concentrations of chitosan, rosmarinic acid (RA), and \(\varepsilon\)-polylysine (\(\varepsilon\)-PL) in coating solutions.
# Development Characteristics and Mechanism of Crack in Expansive Soil under Wet-Dry Cycling
## 4 Experimental Study on Mechanical Properties of Expansive Soil under Wetting-Drying Cycles
### Experimental Results and Analysis
According to the distribution characteristics of crack ratio and cohesion decay ratio during different wetting-drying cycle processes, depicted in Figure 11, the strength degradation process of expansive soil can be approximately divided into three stages:  
(1) The first stage (0-1 cycle) is characterized by the incubation period of cracks, with low crack development, high soil integrity, and minimal decay of cohesion;  
(2) The second stage (1-4 cycles) shows the rapid development of cracks in the soil, leading to an increase in cohesion decay. The slope of the curve steepens, and by the end of the fourth cycle, cohesion decay reaches approximately 50-70%;  
(3) The third stage (4-5 cycles) indicates a stable development of cracks in the soil, with the decay process of cohesion gradually slowing down, and the slope of the curve becoming stable. This finding further confirms the close correlation between the strength degradation of expansive soil under wetting-drying cycle conditions and the development characteristics of cracks in the specimens. Thus, when dealing with slope prevention and control in expansive soil, it is necessary to employ support measures to mitigate the impact of natural forces on swelling and shrinkage deformation in slopes, consequently improving slope stability. To gain further insights into the relationship between crack development and strength degradation in expansive soil, a linear fit is conducted to determine the decaying rate of cohesion for different crack ratios of expansive soil. The crack ratio of expansive soil is the primary quantitative evaluation parameter used to measure the cracking process of expansive soil. This parameter provides an accurate representation of the development and expansion process of micro and main cracks in the soil. The fitting results, as demonstrated in Figure 12, indicate a clear linear increase (\(R^{2}>0.91\)) in the decay rate of cohesion with the rise in crack ratio for expansive soil. This suggests that the degradation of strength in expansive soil is primarily attributed to the damage inflicted on the soil structure due to the development of cracks in the specimens. Using crack ratio as a controlling indicator to depict the degradation law of expansive soil strength under wetting-drying cycle conditions is reasonably justified. Figure 12: Fitting line of the decrease rate of cohesive strength in expansive soil with crack surface area ratio.
# An Efficient Rice Virus-Induced Gene Silencing System Mediated by Wheat Dwarf Virus
## 2 Materials and Methods
### Infection of Magnaporthe Oryzae  
The rice blast strain RB2 was provided by [PERSON] from China National Rice Research Institute. Inoculation of the rice blast was performed via spraying application, with an equal volume of conidia suspension (1 \(\times\) 10\({}^{6}\)/mL) uniformly sprayed on both wild-type and silenced rice seedlings at the four-leaf stage, respectively. Then, these seedings were transferred to the incubator for darkness at 25 \({}^{\circ}\)C for 2 days, after which they were incubated under a 16/8 h light/dark photoperiod at 25 \({}^{\circ}\)C for 5 days. Phenotypic observations were carried out according to the international standard of Paddy Rice Pestilence [23]. The relative lesion areas were measured after 7 days inoculation using Image-Pro Plus software [24].  
\begin{table}
\begin{tabular}{c c} \hline
**Primers Name** & **Primers Sequence** \\ \hline WF & TGGCTTACTCTTGCAGGACTCGGACTGCGACTCTG \\ WR & CCTCGCCCTTGCCTCACCATGATCCAGCTTAGCATAGATTACG \\ WDV-F & TCAACTCCTTGCCCTCC \\ WDV-R & GCTTGGCTTGCCTTACACTCTG \\ WDV-OsPDSi-F & CGACACGCGTACTAGTAGTAGAAAATC \\ WDV-OsPDSi-R & CCTAGAGACCCGGACCCTCCGAC \\ WDV-OsPi2 Ii-F & CGCGACTACTATGGTATATG \\ WDV-OsPi2 Ii-R & CCTCCTGTGTGCATC \\ WDV-Rep-F & TACGCCTGGATTCAC \\ WDV-Rep-R & TACGCCTGAGGTTG \\ qQSpI21-F & GGTAATTGGTGTCATCTITGG \\ qOsP21-R & ATACCCTGTTGTGTTTTTTT \\ qOsPDS-F & TGCCGTGTCATCATGAAC \\ qOsPDS-R & CCTAGAGACCAGGCTCCGAC \\ _UBQ5_-F & AGACCTACCCAAGCCAAGAAGAT \\ _UBQ5_-R & CCAGCCACCGCACTCAGCATTAG \\ \hline \end{tabular}
\end{table}
Table 1: Primer sequences used in this study.  
All the treated rice leaves were sampled at 0 h, 24 h, 48 h, and 72 h after spraying, and stored in a refrigerator at \(-80\)\({}^{\circ}\)C for later use. The total RNA of rice leaf was extracted with TRIzol Reagent according to the manufacturer's instructions. The first-strand cDNA was synthesized from 1 ug of total RNA using Hifair(r) II 1 st Strand cDNA Synthesis Kit (Nigbo, China). The qRT-PCR primers for _OsPi2I_ were referenced from [PERSON]'s work [25], and the primers for _OsPDS_ were designed using SnapGene Viewer 5.2.4 software. The rice Ubiquitin gene (_OsUBQ5_, _Loc_Os03g13170_) was used as internal reference for qPCR. The detection of rice blast fungus biomass is based on real-time fluorescent quantitative qPCR detection of _M. orgaze_ DNA [26]. The reaction procedure was as follows: pre-denaturation at 95 \({}^{\circ}\)C for 10 s, denaturation at 95 \({}^{\circ}\)C for 5 s, extension at 60 \({}^{\circ}\)C for 30 s, and 40 cycles.
## 2 Materials and Methods
### Data Source  
The research data primarily originates from three sources: (1) Geographic Information Systems. Traditional villages were represented as \"points\" on the geospatial data on the macro spatial scale, and the geographic coordinates of the traditional villages were extracted using Google Earth and imported into ArcGIS 10.8 software to build a geospatial database of the traditional villages and dwellings in the Jiangxi-Anhui junction area. The basic geographic information data mainly comes from the geospatial data cloud. ([[https://www.gscloud.cn](https://www.gscloud.cn)]([https://www.gscloud.cn](https://www.gscloud.cn)), accessed on 1 December 2024). (2) Literature and Historical Materials. Literature and historical data were collected from multiple sources: the Digital Museum of Traditional Villages in China ([[https://www.dmctv.cn](https://www.dmctv.cn)]([https://www.dmctv.cn](https://www.dmctv.cn)), accessed on 1 December 2024), county records, gazetteers, and ethnographies of Xin'an and Xiuming, as well as research findings pertaining to traditional villages and dwellings in the Jiangxi-Anhui junction area. Dialect data were extracted from the Chinese dialect map of the southeastern region found in the 1987 Atlas of Chinese Language. (3) Field Research. Attribute information, including village names, spatial configurations, street layouts, and dwelling types, was gathered through interviews, aerial photography, conventional photography, and mapping techniques.
## 4 Practical and Theoretical Implications with Examples to Enhance Decision-Making  
Decision Tree Regression (DTR) and Gradient Boosting Regression (GBR) models have shown significant potential in predicting the hygroscopic properties of Borassus Natural Fiber (BNF). These models offer both practical and theoretical implications for the industry.  
Figure 7: Evaluation metrics for decision tree and gradient boosting during the prediction of: (**a**) sorption rate of the BNF and (**b**) desorption rate of the BNF.  
Theoretically, DTR and GBR models provide valuable insights into the relationships between various factors influencing BNF hygroscopic properties. DTR, for instance, can identify the most critical parameters affecting hygroscopicity through its decision rules, while GBR can capture complex nonlinear interactions between variables. By understanding these relationships, researchers can develop a deeper understanding of the underlying mechanisms governing BNF behavior.  
Practically, these models have direct implications for the production and utilization of BNF. Accurate predictions of hygroscopic properties are essential for optimizing production and application processes. DTR models can be used to create simple, interpretable rules for rapid decision-making in the construction field, such as determining appropriate properties based on the application's requirement. GBR models, on the other hand, can provide more precise predictions, aiding in the development of advanced composite systems for strengthening processes or the design of BNF formulations with desired hygroscopic properties.  
To avoid problems like swelling or cracking, precise predictions of hygroscopicity, for instance, can assist in identifying the ideal moisture content for composite manufacturing. Additionally, choosing the right raw materials to create composites from BNF with appropriate characteristics can be influenced by considering how the various components affect hygroscopicity. In the end, these models help to increase the sustainability and efficiency of BNF manufacturing and utilization. Some examples of the impact on the decision-making process are shown in Table 5.
# Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey
## 7 Sensors Fusion Techniques Using Deep Learning
### Sensor Fusion  
Visible sensors are effective, however, they are less reliable in low-light situations [86]. The study in [86; 97], suggests combining visible and thermal cameras together to increase the detection accuracy. It should be noted that thermal sensors are not very effective under high-temperature conditions and clothing can affect the pedestrian's or cyclist's thermal footprint [124].  
There has been a large amount of research conducted surrounding the most reliable approach in using both colour information of the visible cameras and the thermal information of the thermal cameras [86; 97; 125; 126; 127; 128]. These studies discuss the drawbacks of visible sensors due to their dependence on illumination and the benefits of adding thermal data would provide for increased accuracy. The KAIST dataset is largely used for multispectral pedestrian detection evaluation due to its large amount of high-quality images in both the visible and IR spectrum. In [86], fusion techniques using a CNN as the detector were discussed. Figure 8 compares evaluation based on the KAIST dataset and typical vision-based datasets (Caltech Diamler, etc.). It demonstrates that, although recent vision-based approaches are efficient during the day, their accuracy decreases at night.  
Figure 7: Sensor Comparison.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Environmental conditions \& costs** & **Visual** & **Thermal** & **RADAR** & **LiDAR** \\ \hline Resolution & & & & \\ Illumination & & & & \\ Weather & & & & \\ Elevation & & & & \\ Temperature & & & & \\ Cost & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sensor Comparison Matrix.  
Therefore, using multispectral information can aid in reducing this inaccuracy, especially at night-time. For further information of the studies used to generate the graph see [88; 89; 129; 130; 131] for VS data for daytime, [106; 125; 128; 132] for VS data for night-time and [86; 97; 133; 134; 135; 136] for multispectral data. Figure 8 demonstrates, that although there has been advancements in pedestrian/cyclist detection, improvements are still required to reduce the miss rate and increase the accuracy of detection systems. Multispectral information can be used to achieve higher accuracy, however, further investigation is required.  
Vision-based pedestrian detection has been widely researched, which has provided over 60 methods evaluated on the Caltech dataset alone [25]. Despite sensor fusion techniques having been recently applied for pedestrian detection, there is not much research conducted for cyclist detection using the same multispectral approach. This is an important aspect to be considered as cyclists are among the VRUs that are affected by road traffic accidents.
## 3 CENTERA: Our Centralized Trust-Based Efficient Routing Protocol with Authentication  
Improving and upgrading our previous model, CENTER, the proposed routing protocol CENTERA implements a centralized trust-based routing protocol with an appropriate authentication scheme for WSNs placing most of the computational load on the more powerful sink BS. Constructing the global view of the network from minimal local information of the authenticated sensor nodes, the BS is responsible for calculating the nodes' trust information and distributing the routing information after isolating the \"bad\" nodes. CENTERA is divided into eight functional building blocks ensuring the creation of a secure, trusted, and efficient wireless sensor network environment.
## 3 Proposed System Architecture
### Object Detection
#### 3.1.2 Graph Cut  
The resultant foreground and background pixels are given as a set of inputs for the video sequences. We now seek binary labels that mark each vertex \(v_{vertex}\) as the foreground, set to 1, and the background set to 0. Then, these labels are computed by constructing a graph \(G=(v_{vertex},\,\varepsilon)\), where \(v_{vertex}\) is the set of vertices (i.e., pixels), and \(\varepsilon\) is the set of edges linking nearby 4-connected pixels [15]. Finally, the maximum-flow and minimum-cut algorithm is applied to find the vertex labeling with a minimum energy function [27]. In applying the theory of graph cut for refining the foreground, we here focus on the user-assisted case, but note that an _MoG_ mask is given every 100 th frame, instead of manually re-drawing the scribbles and region of interest (_ROI_) for every frame. A comprehensive research problem and solution is illustrated in Figure 2.