esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Reschargeable Sensor Networks
## 2 Related Work
### Energy Saving in WRSNs  
In the wireless rechargeable sensor networks, the energy efficiency issue is still a key problem. In [29], the potential mobility of sensor nodes has been exploited and a new metric Quality of Energy Provisioning (QoEP) is proposed, so as to characterize the expected portion of time that a mobile node can sustain normal operation in WRSNs, and to evaluate the impact of mobility on energy provisioning in WRSNs. Considering that some wireless chargers are not fixed, there are some studies based on the movable charger (MC), such as [30; 31; 32; 33]. In [30], [PERSON] et al. study addressing the MC scheduling, the moving and charging time allocation simultaneously to save total energy and formulate this problem as a mixed integer linear program, which is solved by a novel decentralized method based on Benders decomposition. [PERSON] considers the situation that the MC can collect packets as well as the static sink does in [31], and proposes a closer destination selection scheme, which improves the packet arrival rate and energy efficiency compared to the conventional scheme. As for [32], the researchers pay attention to large-scale WRSNs with multiple MCs and develop a heuristic algorithm to find the minimum number of MCs for the sensor nodes to work continuously. [PERSON] and [PERSON] [33] consider the scheduling of the charging mission to ensure that all sensor nodes will be working within a target lifetime.  
The sensor nodes and energy charger may be fixed in preset locations. In this situation, existing methods to improve the energy efficiency are mainly by optimizing the deployment of chargers. In [34], researchers present two greedy algorithms to deploy as few as possible chargers. In [35], the Particle Swarm Charger Deployment (PSCD) algorithm using the Particle Swarm Optimization (PSO) concept is proposed to optimize WRSN charger deployment. A layoff algorithm to combine simulated annealing-based algorithm in [36] is discussed to work on this issue. In addition, some power control and protocol joint design are also considered, e.g., [37; 38]. [PERSON] and [PERSON] [37] consider reducing overhearing by reducing the neighborhood size using transmission power control as well as route adaptations, and implements a cooperative joint Power COntrol and Routing (PCOR) scheme for rechargeable sensor networks. [PERSON] et al. [38] put forward a local algorithm, called SnapIt, to adapt the sampling rate with the objective of maintaining the battery at a target level.  
However, all the above studies on energy saving in WRSNs try to save energy through optimized deployment of wireless chargers. The energy saving scheme of traffic scheduling is still a blank for WRSNs.  
In summary, current studies on energy saving for WSNs or WRSNs are not suitable for hybrid SDWRSNs. New schemes are urgent to be developed to satisfy the energy saving requirement in hybrid SDWRSNs.
## 3 Parameter Optimization  
### Experimental Design and Results  
Various factors potentially affect the influence of geogrid reinforcement such as effective reinforced depth, crater depth, lateral displacement, lateral earth pressure, and deflection of first reinforced geogrid. Hence, the optimization of the design parameters of the reinforced geogrid is a critical prospect in engineering practice. Since reinforced geogrid properties also have certain influence on model experiment results, the elastic modulus and internal angle of friction are also taken into account in this chapter. Thus, the effect of different embedded ratio of reinforced geogrid, number of reinforced layers, interval of reinforced layers, elastic modulus, and internal angle of friction were investigated by orthogonal analysis. The orthogonal experimental scheme was designed with five factors at five levels (Table 6). By adopting the L\({}_{25}\)(5\({}^{\circ}\)) orthogonal table for 25-group scheme arrangement, the plan of orthogonal design is shown in Table 7, in which the factors A, B, C, D, and E are embedded ratio of reinforced geogrid, number of reinforced layers, interval of reinforced layers, elastic modulus of reinforced geogrid, and internal angle of friction, respectively. The five values of A are 2, 3, 4, 5, and 6, five values of B are 2, 3, 4, 5, and 6, five values of C are 2 cm, 4 cm, 6 cm, 8 cm, and 10 cm, five values of D are 25, 5, 1, 0.2, and 0.04, and five values of E are \(10^{\circ},20^{\circ},30^{\circ},40^{\circ}\), and \(50^{\circ}\).  
Furthermore, the values of the five aspects of each experiment selected by orthogonal experiment were defined as evaluating indicators and acquired by numerical simulation, which is effective reinforced depth, crater depth, lateral displacement, lateral earth pressure, and deflection of first reinforced geogrid. The results are presented in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & **Factors** & & \\ \cline{2-6}
**Level** & **Embedded** & **Number of** & **Interval of Reinforced** & **Elastic Modulus** & **Internal Angle of** \\  & **Ratio** & **Reinforced Layers** & **Layers (cm)** & **(GPa)** & **Friction (\({}^{\circ}\))** \\ \hline
1 & 2 & 2 & 2 & 2 & 25 & 10 \\
2 & 3 & 3 & 4 & 5 & 20 \\
3 & 4 & 4 & 6 & 1 & 30 \\
4 & 5 & 5 & 8 & 0.2 & 40 \\
5 & 6 & 6 & 10 & 0.04 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The factor and the factor level.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & **Lateral** & **Crater Depth** & **Effective** & **Lateral Earth** & **Deflection of** \\
**Group** & **A** & **B** & **C** & **D** & **E** & **Displacement** & **(cm)** & **Reinforced** & **Pressure (kPa)** & **First Reinforced** \\ \hline
1 & 2 & 2 & 2 & 25 & 10 & 20.61 & 37.74 & 134 & 67.4 & 1.61 \\
2 & 2 & 3 & 4 & 5 & 20 & 21.35 & 42.67 & 213.2 & 65.4 & 2.09 \\
3 & 2 & 4 & 6 & 1 & 30 & 20.56 & 43.6 & 277.5 & 60.6 & 2.95 \\
4 & 2 & 5 & 8 & 0.2 & 40 & 19.72 & 42
## 4 Model Verification and Comparison Based on 1 DCNN-BiLSTM-AdaBoost
### Model Verification Based on 1 DCNN-BiLSTM-AdaBoost  
The various performance metrics of the model are shown in Figure 8. By analyzing Figures 8-10, we observe that the performance of the second and fifth weak classifiers declines significantly during the AdaBoost iteration process. This phenomenon primarily stems from the core mechanism of the AdaBoost algorithm: in each iteration, the sample weights are updated based on the classification errors of the previous weak classifier, ensuring that subsequent iterations focus more on samples that are prone to misclassification. While this dynamic weight adjustment mechanism may cause temporary performance drops in certain weak classifiers, it ultimately helps the ensemble model reduce the overall error rate, thereby enhancing final prediction accuracy and robustness. This behavior is a natural characteristic of AdaBoost's weight adjustment process and reflects the model's increased focus on difficult samples.  
To better illustrate this process, Figure 9 presents the variation in the weights (Alpha values) of each weak classifier. The figure clearly shows how the weights change across different iterations. By observing the gradual increase in Alpha values, we can intuitively see that weak classifiers with higher overall weights play a more crucial role in the final decision-making process. This confirms that AdaBoost effectively emphasizes hard-to-classify samples, ultimately improving the model's robustness.  
Figure 10: (**a**) LSTM index graph; (**b**) BiLSTM index graph; (**c**) 1 DCNN index graph; (**d**) 1 DCNN-BiLSTM (single-channel data) index graph.  
Figure 9: Weights of each weak classifier.  
Additionally, since the fault dataset used in this study is relatively balanced across different fault states, the accuracy and recall values remain close. To avoid redundancy in data visualization, we do not separately plot the accuracy curve. By evaluating cross-entropy loss, confusion matrices, and multiple performance metrics, we can comprehensively assess the model's effectiveness. Overall, the introduction of the AdaBoost algorithm significantly enhances the model's ability to capture complex data features, improving its accuracy and robustness in wind turbine blade fault detection tasks.
## 4 Safety Control Measures for End-Suspended Piles
### Influence of Different Control Measures on Pile Deformation
#### 4.1.2 Influence of Formation Grouting Reinforcement  
When the excavation depth of the foundation pit was less than 4 m, the horizontal displacement of the end-suspended pile was small. Therefore, the soil behind the end-suspended pile was reinforced by grouting from 3.7 m below the ground with respect to the station top. The grouting reinforcement position is shown in Figure 11, where Ln denotes the grouting reinforcement thickness. By studying the effects of different grouting reinforcement thicknesses on the end-suspended pile, the best grouting reinforcement thickness was selected as Ln = 1-6 m.  
Moreover, when the excavation depth was less than 4 m, the displacement of the pile was small. Accordingly, the horizontal displacement curves of the end-suspended pile under different grouting reinforcement thicknesses at excavation depths of 8.7 and 12.2 m are extracted as shown in Figures 12 and 13. The figures show that the horizontal displacement of the pile gradually decreased with an increase in the grouting reinforcement thickness. When the reinforcement thickness was greater than 3 m, the control effect of the horizontal displacement on piles Z2 and Z3 decreased significantly. Based on a comprehensive analysis, the best grouting reinforcement thickness was determined as 3 m. When H = 8.7 m and the grouting reinforcement thickness is 3 m, the maximum decrease  
Figure 11: Schematic diagram of grouting reinforcement position.  
Figure 10: Comparison curve before and after connection to pile bottom: (**a**) pile Z2; (**b**) pile Z3.  
in pile Z2 is approximately 46% and the maximum decrease in pile Z3 is approximately 41%. When H = 12.2 m and the grouting reinforcement thickness is 3 m, the horizontal displacement of the bottom of pile Z2 is 1.42 mm, signifying a decrease of approximately 71%. whereas that of the bottom of pile Z3 is 1.76 mm, signifying a decrease of approximately 73%. The maximum horizontal displacement occurred in the piles. After grouting reinforcement, the pile displacement was significantly improved. Notice that reinforcing the grouting after the pile is constructed can improve the mechanical parameters of the soil, increase the bearing capacity of the soil behind the pile, and improve the anti-deformation capacity of the area behind the pile, which improves the ultimate bearing capacity of the end-suspended pile. Although reinforcing the grouting after the pile was constructed improved the displacement of the pile, there remained a risk of slipping at the bottom of the end-suspended pile when excavation depth reached the bottom of the pit. Therefore, a connection to the pile bottom was necessary to achieve the best control effect.
## 4 Experimental Plan
### Simulation Setup  
This simulation aimed to run the data collection experiments on a field of 5.5 km \(\times\)5.5 km. As Figure 3 shows, there are two parameters to simulate experiments: sensory device count and data density. That way, varying numbers of sensory devices, 256, 1024 and 4096, were randomly distributed throughout the network. This allowed us to observe and evaluate the route planning approach behaviour, scalability and performance if the node count changes.  
Each sensory device count was assigned by three data density proportions, including 25% (low), 50% (medium) and 100% (full). This means the network's node count consists of a proportion of source nodes reporting data samples to LDSs. For example, a network of 256 and 25% data density is comprised of 64 active sensory devices which transmit data records if a tie is found to a LDS, whereas other ones (192 nodes) have no relevant data to report and MA collection. This allows us to study the ability of route planning protocols to reactively detect event regions according to requested data samples (defined by the user on-demand) and forward MAs with minimum random walk to capture them. Under this simulation, each sensory device is assigned by a unique ID and randomly moves with a constant speed through a pre-defined road map, which is provided according to M-Grid mobility pattern.  
As Figure 4 shows, the data collection map was simulated according to a Manhattan model. The blocks (11\(\times\)11) had a size of 400 m. The roads were two-lane with 100 m width. LDSs were localised at the middle of junctions (500 m distance between each pair) and  
Figure 3: Parameter sweeping plan.  
formed a grid communication infrastructure. Each one was assigned by an ID containing grid row and column number. The sink was located in the centre (block) of the grid.  
This was a collision-free 2D simulation. The network nodes (including MAs and sensory devices ) were set up to communicate through wireless transceivers (IEEE 802.11) with a limited 200 m range of communications. A line of sight (LOS) wireless signal propagation model was used for all the simulation experiments. This model assumed no wireless interference by obstacles such as trees/buildings or ambient factors (e.g., noise and weather). A Carrier Sense Multiple Access (CSMA/CA) MAC protocol was used in the simulation experiments to provide wireless channel sharing and avoid collisions. MAs move with a constant speed of 30 m/s.  
The statistical power analysis technique [37] was used to compute experimental repetitions required in this research. For this, population standard deviation was calculated for a (randomly selected) subset of experiments. This helped to compute the number repetitions based on the experiment's confidence level. For this, the simulation results of 20 repetitions (the experiment subset) were collected and analysed. According to the subset results, at least 250 repetitions (sample size) are recommended to achieve 90% confidence for all the experiments. Table 2 summarises the simulation parameters.  
Figure 4: M-Grid mobility model.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps from Field Inventory Data and Optical and/or Radar Remotely Sensed Data
#### 3.2.5 The First Detailed Map of Aboveground Forest Carbon Stocks in Mexico (30 m)  
[PERSON] et al. [65] generated a spatially explicit map of aboveground carbon stored in Mexico's forests at 30 m spatial resolution through Random Forest modelling on the inventory data from Mexico's National Forest Inventory (INFyS) and space borne optical and radar data including the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) backscatter data, Landsat-based estimates of canopy density, MODIS vegetation index product, and topographic data from SRTM. Validation results with independent field data showed that the overall R\({}^{2}\) was 0.5 and the root mean square error (RMSE) was 14 t C/ha in flat areas, with low predicted precision on steep slopes.
# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 2 Materials and Methods
### Selection of Sensors  
The sensors were selected to monitor and assess the most likely hazards for workers in a heavy manufacturing plant involving metal cutting, grinding, and welding. Welding activities generate metal aerosols and gas pollutants such as carbon monoxide, nitrogen oxides, sulfur dioxide, and ozone. At the time the study was conducted, and after careful review of the available low-cost aerosol and gas sensors, our team selected a dust sensor (Sharp, Model DN7C3 CA006, Osaka, Japan) capable of detecting particulate matter with an average diameter of less than 2.5 microns (PM2.5), a 4-electrode carbon monoxide sensor (Alphasense, Model CO-B4 with sensor board 000-01 SB-02, Essex, UK), a 4-electrode oxidizing gas sensor (Alphasense, model OX-B431 with sensor board 000-01 SB-02, Essex, UK), a temperature and humidity sensor (Adafruit, model AM2302, New York, NY, USA), and a custom-built noise level sensor built from a Teensy 3.2 Microprocessor with an omnidirectional condenser microphone (CUI Inc., CMA-4544 PF-W, Tualatin, WA, USA).  
Sharp produces two configurations for its dust sensor: the GP series and the DN series. The GP series (e.g., model GP2Y10101 AU0F), commonly used in previous research, includes just the optical sensing arrangement with passive airflow. The DN series adds a fan on the outlet and a virtual impactor on the input that only permits particles smaller than 2.5 microns to enter the sensing zone. Initially, the Sharp DN sensor was selected after comparison with several other low-cost sensors [37]. This report describes the performance of the DN sensor. The original experiments with the DN series sensors are described here because the sensor is likely to be useful to others working in slightly less contaminated environments. To date, these experiments have not been reported elsewhere.  
The carbon monoxide and oxidizing gas sensors were selected based on their sensitivity and compactness. The oxidizing gas sensor measures the sum of O\({}_{3}\) and NO\({}_{2}\). At the time the oxidizing gas sensors were selected, the main commercial providers of electrochemical gas sensors only offered one sensor that responded to both ozone and nitrogen dioxide [41]. Subsequent to the construction of the monitors in this network, the manufacturer developed a sensor specific to nitrogen dioxide and proposed pairing an oxidizing gas sensor with a nitrogen dioxide sensor to obtain more accurate measurements of both gases [42]. The temperature and humidity sensors were included for their potential to improve gas sensor measurements.  
The noise sensor was purpose-built from components because we were unable to identify an appropriately packaged noise sensor that could be integrated with the rest of the equipment [38].
## 2 Materials and Methods
### Cell Viability (MTT Assay)  
Different human cancer cell lines of various origin was maintained in appropriate medium DMEM (Gibco, USA). The medium was complemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin (Gibco, USA). Cells were maintained in an incubator containing humidified atmosphere of 95% air and 5% CO\({}_{2}\) at 37 \({}^{\circ}\)C. MTT reduction assay was carried out to assess the antiproliferative activity as previously described [20]. Briefly, A549 (lung), LoVo (colon), MCF-7 and MDA-MB-231 (breast) cancer cells were plated in 24-multiwell culture plates at 5 \(\times\) 10\({}^{4}\) cells per ml and allowed to adhere for 24 h. Cells were incubated with extract at various concentrations (100, 50, 25 and 12.5 ug/mL) as well as DMSO as a vehicle control (0.1 \(v/v\) final concentration). At the end of incubation (48 h), 100 uL of MTT (Sigma Aldrich, St. Louis, MO, USA) was added to each well. After 4 h, the medium was discarded, and the formazan crystals were dissolved with isopropanol. The absorbance was measured at 570 nm with BioTek plate reader (USA). Extract concentration that decreases the number of viable cells into half (IC\({}_{50}\)) was computed using the program OriginPro 8.5 Software.
# A Joint Strategy for Fair and Efficient Energy Usage in WLANs in the Presence of Capture Effect
## 5 PHY Capture Induced Unfairness in WLAN and Proposed Solutions Based on Joint Strategy
### Adaptive Transmission Power Control (ATXPR)  
We defined a set of 10 transmission power levels as shown in Table 3. According to ATXPR, initially every node transmitted with the default transmission power denoted as DefaultTxPwrLevel. When a node suffered a transmission failure due to capture failure it invoked the adaptive transmission power control mechanism. In detail, when a transmission from a node in Zone 2 was unsuccessful because of capture failure at the AP, according to ATXPR this node stepped up its transmission power level for the next transmission attempt. This transmission power control strategy is only activated at the onset of transmission failure due to capture failure. This increase in transmission power level can be additive i.e., additive increase (AI) or multiplicative, i.e., multiplicative increase (MI). A node increased the level of transmission power as long as transmission suffers from capture failure or until the highest level of transmission power is reached at which point it will maintain the same highest power level as shown in Table 3.  
A node that has increased its transmission level decreases either additively, i.e., additive decrease (AD) or multiplicatively, i.e., multiplicative decrease (MD) only when its transmission was successful. However, after collision, a node neither increased nor decreased its  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Retransmission Counter** & **Power Level (dBm)** \\ \hline
0 & 10.54 \\
1 & 12.62 \\
2 & 14.91 \\
3 & 18.08 \\
4 & 20.23 \\
5 & 22.5 \\
6 & 24.62 \\
7 & 26.91 \\
8 & 27.08 \\
9 & 28.23 \\
10 & 30.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Transmission power levels for adaptive transmission power control.  
In summary, we had four different configurations of transmission power adjustment, i.e., AIAD, AIMD, MIAD and MIMD.  
The above four mentioned transmission power control techniques of ATXPR in combination with the CWAD jointly result in four distinct algorithms; additive increase and additive decrease (AIAD+), additive increase and multiplicative decrease (AIMD+), multiplicative increase and additive decrease (MIAD+) and finally, multiplicative increase and multiplicative decrease (MIMD+). The '+' symbol appended at the end of each of the above terms shows the addition of CWADJ to the four adaptive transmission power control algorithms. Note that all the four adaptive transmission power control algorithms operate in the presence of capture effect.  
These strategies helped the node suffering from capture failure to conserve its energy by staying in idle mode for a comparatively lesser duration during the contention and it also increased its chance of successfully capturing the channel in case another node transmits simultaneously in the next round of transmission.