esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Wireless Sensor Networks in
## 3 Results
### Testing the WSN
#### 3.1.2 Testing the WSN Nodes  
1. _Point to point communication_: Firstly, the maximum distance between the sensor and sink was determined as the distance at which RSSI is 0 dBm (1 mW). Indoors this value was 50 m. Secondly, we consider data rates at different increments of the maximum distance (50 m), while keeping the data loss rates below 2%. The rate was 48 kbps up to approximately 50 m (maximum distance), 64 kbps up to approximately 40 m, 128 kbps up to approximately 30 m 256 kbps (maximum data rate) up to approximately 20 m.
2. _Energetic autonomy_: The test performed consists of communicating a'still alive signal' at 0 dBm to the sink every 10 s. Full Energetic autonomy was achieved during the test (8 weeks) based on a 10,000 mAh solar chargeable power bank. The power bank was still 75% charged at the end of the test. The solar panels of the power bank where positioned behind a window in North-East direction.
3. _Mesh network configuration_: An extended indoor lab test which consists of 15 sensors has been performed. The sensors have been distributed in a three-story building, with 5 sensors on each floor. The size of the building is approximately 30 m by 120 m. Each floor has a height of 4 m.  
The distance between the sensors was typically 30 m, the data rate was set to 48 kbps. The test was performed successfully with this configuration.
4. _Automated discovery:_ This test was performed to evaluate the speed and ability of the sink to discover new nodes as they are introduced. For a set of 15 newly introduced sensors, the discovery time was bellow 30 s.
5. _Data loss:_ In this test, we take a look at the number of lost data-packets due to interference, packet collisions or other unknown influences. We found that the results depend chiefly on the number of allowed transmissions (Table 1). The system was observed for 24 h with the sensoring duty cycle set to 1 sample per minute with packet size of 24 bytes. The proprietary routing protocol was based on time-division multiplexing over the network mesh.
## 2 Wind Data in Taizhou
### Wind Speed Data from Early Meteorological Stations  
There are eight meteorological stations in Taizhou that are over 40 years old. Five of them provide valid data with complete wind data records. Since 1978, the maximum monthly wind speeds and the corresponding wind directions have been collected at five stations, as listed in Table 2. The frequency of the maximum wind speed range and the frequency of the corresponding wind direction angle were counted. The wind rose diagram was plotted and graded by wind speed. Taking Hongjia weather station as an example, the frequency of occurrence in various wind directions and wind speed ranges was calculated according to the monthly extreme wind speed samples for 40 years, as reported in Table 3. Figure 2 shows the rose diagram of the monthly extreme wind speed at Hongjia meteorological station. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Site Name**} & \multirow{2}{*}{**Altitude(m)**} & \multicolumn{3}{c}{**Wind Pressure (kN/m2)**} \\ \cline{3-5}  & & **R = 10** & **R = 50** & **R = 100** \\ \hline Kuocangshan & 1383.1 & 0.60 & 0.90 & 1.05 \\ Hongjia & 1.3 & 0.35 & 0.55 & 0.65 \\ Xiadachen & 86.2 & 0.95 & 1.45 & 1.75 \\ Kannen & 95.9 & 0.70 & 1.20 & 1.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The reference wind pressure in the Chinese load code (_GB50009-2012_). Figure 1: The reference wind pressure sites for Taizhou in the _Load code for the design of building structures_ (_GB50009-2012_). Note: The circled numbers are the locations with a specified reference wind pressure in _GB50009-2012_. © --Hongjia; © --Xiadachen; © --Kanmen; © --Kuocangshan. As shown in Figure 3, the annual maximum wind speed series of Hongjia station was obtained based on the wind speed data from 1978-2021. From the figure, it can be seen that the annual maximum wind speed of Hongjia station over 43 years was concentrated between 10 m/s and 25 m/s. It showed an overall trend of gradually decreasing with time, which may have a relationship with the urbanization process. As urbanization has progressed, the buildings around the weather station have shown a tendency to be \begin{table}
\begin{tabular}{c c c c c c} \hline
**Meteorological Station Number** & **M1** & **M2** & **M3** & **M4** & **M5** \\ \hline Meteorological station name & Tiantai & Xianju & Linhai & Wenling & Hongjia \\ \hline Latitude and longitude & \(120^{\circ}58^{\prime}\) E & \(120^{\circ}43^{\prime}\) E & \(121^{\circ}12^{\prime}\) E & \(121^{\circ}22^{\prime}\) E & \(121^{\circ}25^{\prime}\) E \\  & \(29^{\circ}09^{\prime}\) N & \(28^{\circ}52^{\prime}\) N & \(28^{\circ}52^{\prime}\) N & \(28^{\circ}22^{\prime}\) N & \(28^{\circ}37^{\prime}\) N \\ \hline Measured maximum wind speed (m/s) & 21 & 19 & 20 & 23.5 & 23 \\ \hline Maximum wind speed in 50-year return period (m/s) & 21.53 & 21.69 & 23.74 & 26.40 & 27.22 \\ \hline Wind angle corresponding to maximum speed & ESE & NE & WNW & N & NNE \\ \hline \end{tabular}
\end{table} 
Table 2: The information of the M1 to M5 meteorological stations (referred as the five main stations).
# Diverse Humanoid Robot Pose Estimation from Images Using Only Sparse Datasets
## 2 Related Work
### Learning-Based Human Pose Estimation  
Human pose estimation is a well-established and extensively researched field with notable advancements. Numerous techniques for detecting 2D human joints and estimating poses from RGB video or image data have been widely explored [23; 24; 25]. [PERSON] et al. [23] introduced a real-time multi-person 2D pose estimation method using Part Affinity Fields (PAF), showcasing its ability to estimate the poses of multiple individuals in complex environments. OmniPose [24], a multi-scale framework-based method, employs multi-scale feature modules to effectively capture information at different scales, addressing occlusion challenges. ViTPose [25], based on a vision transformer, offers a simpler network structure while maintaining efficient inference speed and high accuracy.  
Various studies have extended beyond 2D to explore 3D human pose estimation [26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36]. Video-based methods have been used to incorporate temporal information for more accurate human pose and shape estimation over time [29]. Research using diffusion models for human pose estimation has significantly enhanced the accuracy and reliability of 3D human pose estimation without requiring extensive training data [32; 34; 36]. Additionally, recent transformer-based models have improved the accuracy of 3D pose estimation by integrating spatial and temporal features [31; 33; 35]. However, these methods are trained on data of human poses and shapes. While humanoid robots are designed to mimic human appearance and movements, there are clear differences in their structural characteristics and external form, which present significant limitations when directly applying these models to humanoid robots.  
Moreover, approaches using additional sensors such as IMUs [26; 27; 30] have shown promise in human pose estimation. but pose practical challenges when applied to humanoid robots due to differences in link proportions and the requirement for sensor attachment.
## 3 Radio Wave Propagation Analysis  
### Channel Analysis Based on the Ray-Tracing Method  
Figure 7 shows a simple 3D analytical model in which the rectangular parallelepipeds were combined to simulate S and C environments. Figure 7a,b are the analytical models of S and C environments, respectively. As shown in Figure 3a, the wall in the C environment has both a door and windows, but these are not considered in the analytical model. The wall painted red in the figure was made of bricks, and the other walls and the ground were made of concrete. As shown in Figure 4a, the buildings have many balconies and windows,  
Figure 6: Directivity of the transmitting antenna in vertical installation: (**a**) photograph of the UAV with the transmitting antenna and (**b**) amplitude radiation pattern in the \(zy\)-plane.  
Figure 7: 3D analytical model for the radio wave propagation simulation: (**a**) simple propagation environment and (**b**) complex propagation environment.  
but these are not considered in the analytical model, and the walls were modeled flat, so the path analysis does not account for building penetration losses. In addition, the trees between the measurement point and Building C are not considered. All the walls of the buildings and the ground were made of concrete.  
The analytical parameters are shown in Table 3. The relative permittivity \(\varepsilon_{r}\) of the brick was set to 3.91, and the conductivity \(\rho\) was set to 0.024 S/m, based on the ITU-R recommendation [44]. The relative permittivity \(\varepsilon_{r}\) and conductivity \(\rho\) of the concrete were set to 5.31 and 0.194 S/m, respectively. The maximum number of reflections and diffraction were set to three and one, respectively. The frequency in the analysis was 2.5 GHz, and half-wave dipole antennas were used as the transmitting and receiving antennas.
## 2 Related Works
### Depth Errors in 3D ToF Sensors  
Despite their advantages, 3D ToF sensors, such as lidar or 3D ToF cameras, are affected by various types of depth errors that can compromise the accuracy of pose and diameter estimation. Depth errors in 3D ToF sensors can be broadly categorized into outliers, depth biases, and random depth errors [19; 20; 21; 22]. Outliers are caused by multipath interference or low reflectivity. Depth biases result from systematic errors such as modulation inaccuracies and result in systematic deviation from the ground-truth depth while random depth errors occur due to the thermal noise or low reflective surface that is usually approximated as zero-mean Gaussian distribution. To remove outliers, the authors in [22] utilized the density-based spatial clustering of applications with noise (DBSCAN) while estimating the robot pose. To correct the depth bias, the authors in [19] proposed a joint calibration scheme to calibrate a 2D camera and a 3D ToF camera based on the checkerboard. This scheme eliminates depth biases without requiring ground-truth measurements. In [20], a machine-learning approach to correcting the depth bias for ToF cameras is proposed. It combines a particle filter and support vector machine to learn and correct the distance-dependent depth bias. On the other hand, to correct the random depth error, [PERSON] et al. [21] propose a correction scheme based on entropy-based multilayer perception. It is trained to correct the random depth error using multiple frames of the same scene and pose. Due to the continuous movement of the in-pipe robot, the scene as well as robot pose in the pipe also continuously changes. Therefore, this scheme is unsuitable for in-pipe robots. As a result, random depth errors remain a significant challenge for in-pipe robots, particularly in estimating robot pose and pipe diameter.  
This paper investigates the impact of random depth errors on robot pose and pipe diameter estimation. This estimation problem is modeled as a non-linear least-squares optimization which transforms the measurement points from the sensor coordinate system to the pipe coordinate system and fits the transformed points to the elliptic cylinder model. The commonly used canonical ellipse residual function is shown to overestimate the pipe diameter in the presence of random depth errors, as it is more sensitive to positive errors than negative errors, leading to estimation bias. To address this, we propose a novel error-based residual function that explicitly estimates depth errors. Unlike the canonical approach, this function is symmetric to positive and negative errors, ensuring unbiased estimation. The details of these residual functions and their application to addressing random depth errors will be presented in the following section.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 1 Introduction  
Mobile Ad Hoc Networks (MANETs) are wireless networks that operate without fixed infrastructure such as routers or servers. Each device (or node) in the network can act as both a sender and receiver. This allows communication between nodes even if they are not directly connected. MANETs are especially advantageous in scenarios where conventional networks are impractical, including natural disasters, military activities, and isolated or rural locations [1,2].
## 2 Theoretical Discussion
### Tile Application Methods Comparison
As opposed to the spot-bonding method, the troweled adhesion method has obvious advantages in the sense that there are no voids forming between the tile and the bonding material and, on average, uses less amount of adhesive (3-7 kg/m\({}^{2}\), 2-7 mm thickness), and has faster workability (20-30 m\({}^{2}\)/day). [16; 17]. However, as the adhesive must first be applied to the entire wall sections, tile application must follow directly after to ensure that the adhesive does not dry or cure. When compared in terms of worker convenience, the troweled adhesion method is less preferred, which leads to countries without strict regulations of tile quality assurances allowing the usage of spot-bonding more freely. A comparison of typical differences between the spot-bonding method and trowel adhesion method is summarized in Table 2 below. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Spot-Bonding Method** & **Trowed Adhesion Method** \\ \hline Adhesive/Bonding Material & Cemertitious adhesive (cement/mortar-based bonding material) & Dry mortar/adhesive application (polymer, acrylic, epoxy, etc.) \\ Installation Method & Mortar adhesive is applied to the rear side of the tile and, subsequently, installed on the wall & Adhesive is first applied to the wall, followed by the tile installation \\ Weight and Adhesive Thickness & 20–40 kg/m\({}^{2}\) & 3–7 kg/m\({}^{2}\) \\  & 10–25 mm & 2–7 mm \\ Workability & 7–10 m\({}^{2}\)/day & 20–30 m\({}^{2}\)/day \\  & Experienced workers (technicians) required & Generalized work level sufficient \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Differences between the spot-bonding and troweled adhesion method. Figure 4: Notched trowel application method procedure: (**a**) adhesive preparation; (**b**) application on substrate surface and creation of 5–7 mm deep channels; (**c**) tile installation; (**d**) securing adhesion with tile and adhesive. Figure 3: Facilities installed with tiles: (**a**) 1980s case; (**b**) 1990s case; (**c**) recent case.
## 1 Introduction
However, differences in power production from individual MFCs, as well as voltage reversal issues when connecting multiple MFCs in series, present a challenge in the overall efficiency of the system, since these variations dramatically worsen the power production of MFCs arrays [33]. The objective of this paper is to present a circuit topology for efficient energy harvesting from multiple MFCs to increase the total available energy of the system to meet the voltage and current specifications of a power-demanding application. A ranking algorithm was developed based not only on the voltage of the MFC, which does not contain information about MFCs internal resistance, but rather on the available power from each individual MFC to prioritize the charge extraction from the MFC that has the highest available power. At the same time, recovery time is a critical observed requirement for the MFC to recharge its internal capacitive elements as part of its power generation cycle, to avoid damaging the MFC, due to power over-depletion and extend its lifetime. The algorithm also includes an MFC-failure protection feature that isolates an MFC in the array when the algorithm notices a significant power drop during the energy harvesting process. The MFC output power is regulated by a step-up DC-DC converter that stores the extracted charges in an output supercapacitor. Finally, the energy harvesting technique presented in this work is tested and demonstrated through an internet-enabled smart node, capable of transmitting the collected data to the internet. A performance comparison to other power management works based on MFC is made in terms of charging time, topology features and efficiency. The paper is organized as follows: In Section 2, a brief introduction to MFC is presented and its electrical model is described. Section 3 discusses the choice of top level circuit architecture for multiple MFC power extraction and describes the MFC power ranking and power extraction algorithms. The experimental results are presented in Sections 4 and 5 summarizes this work.
# Long-Term Analysis of Wave Climate and Shoreline Change along the Gulf of California
## 3 Materials and Methods
### Wave Climate Analysis  
The wave trend analysis was performed using the least-squares regression technique, applied to the long time series of wave data. This methodology was previously used by [43] to analyze the interdecadal variability of the wave characteristics on the west Guangdong coast in China. Additionally, the absolute wave power level (\(P\)) was calculated by Equation (2):  
\[P=\frac{\rho g^{2}H_{s}^{2}T_{s}}{64\pi} \tag{2}\]  
where \(\rho\) is the water density, \(g\) is the gravity acceleration constant, \(H_{s}\) is the significant wave height and \(T_{s}\) is the mean wave period.  
The time series of the annual means of \(H_{99}\) (the 99 th percentile), \(H_{s}\), \(T_{s}\) and \(P\) were used to estimate the mean change rate per year (\(R\)) given by Equation (3):  
\[R=\frac{\sum(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sum(x_{i}-\overline{x} )^{2}} \tag{3}\]  
where \(x_{i}\) is the i-th year of the time series; \(y_{i}\) is the i-th observation of the time series of the annual means of \(H_{99}\), \(H_{s}\), \(T_{s}\) or \(P\); and \(\overline{x}\) and \(\overline{y}\) are the means of \(x_{i}\) and \(y_{i}\), respectively.  
The Mann-Kendall (MK) test [44; 45] was used here to determine the significance of the wave data trends. This non-parametric test of randomness versus trend was previously used in other studies related to wave climates such as those by [1] and [46]. The purpose of this test was to statistically assess whether or not there was a consistent upward or downward trend in the variable of interest over time. The variance obtained from the MK statistic was then used to define the value for a specific significance level.  
The multi-year means of \(H_{99}\), \(H_{s}\), \(T_{s}\) and \(P\) (\(H_{99}\)*, \(H_{s}\)*, \(T_{s}\)* and \(P\)*, respectively) were also calculated at the three locations (C1, C2 and C3), as the means of annual values, in order to obtain an overview of the spatial variation of these wave parameters along the GC.
## 2 Material and Methods
### Analysis of Fucoxanthin  
The total fucoxanthin contents were analyzed using UPLC with an Acquity PDA detector and UPLC C18 1.7 \(\upmu\)M \(\times\) 2.1 \(\times\) 50 mm column (Waters, Milford, MA, USA). The column temperature was set to 35 \({}^{\circ}\)C, and the mobile phases used were distilled water with 0.1% formic acid (Solvent A) and HPLC grade acetonitrile with 0.1% formic acid (Solvent B). The gradient of the mobile phase used was as follows (Solvent A: Solvent B): 0 min (90:10), 1 min (90:10), 2 min (70:30), 4 min (50:50). 6 min (30:70), 7 min (10:90), 9 min (0:100), and 10 min (90:10). The flow rate was 0.4 mL/min, and the injection volume was 4 \(\upmu\)L. Fucoxanthin was detected at 450 nm. The total fucoxanthin contents in the kelp were measured by a standard curve and expressed as mg/g dry weight (dw). The limit of detection (LOD) and limit of quantification (LOQ) were calculated as 3 \(\times\) standard deviation (SD)/slope of the standard curve and 10 \(\times\) SD/slope of the standard curve, respectively.
## Materials and Methods
### Methods  
#### 2.3.1 Gpp Estimation Model  
**(1) Light Use Efficiency model based on linear relationships (GPP\({}_{\text{LUE}}\))**  
SIF is responsive to alterations in photochemical processes and exhibits a notable correlation with vegetation GPP [41]. The GPP can be quantified using the following [42]:  
\[GPP=PAR\times fPAR\times LUE_{P} \tag{1}\]  
where PAR is the incident photosynthetically active radiation, fPAR is the proportion of photosynthetically active radiation to that radiation, and LUE\({}_{\text{P}}\) is the light use efficiency for photosynthesis. SIF is expressed as follows [42]:  
\[SIF=PAR\times fPAR\times LUE_{F}\times f_{\text{esc}}(\lambda) \tag{2}\]  
where \(\lambda\) is the spectral wavelength, LUE\({}_{\text{F}}\) is the light use efficiency rate of SIF--the percentage of SIF fluorescence quanta emitted from the canopy at wavelength \(\lambda\) after PAR absorption by the vegetation--and f\({}_{\text{esc}}\)(\(\lambda\)) represents the fraction of SIF fluorescence quanta that escapes from the canopy and is emitted into the atmosphere. When the vegetation canopy structure remains unchanged over a specified period within the satellite footprint, it is reasonable to assume that f\({}_{\text{esc}}\)(\(\lambda\)) is a constant value of 1 [43]. Therefore, the relationship between GPP and SIF is determined by the following ratio of the two light use efficiency rates:  
\[GPP=SIF\times\frac{LUE_{P}}{LUE_{F}}=SIF\times a \tag{3}\]  
where a is the coefficient of fit between GPP and SIF. The GPP can be estimated by solving for a.  
**(2) GPP\({}_{\text{XGBoost}}\)**  
XGBoost is an ensemble learning algorithm that builds on the principle of gradient-boosting trees [44]. Its core concept involves gradually adding trees to the model to ensure that the predicted values of the ensemble closely approximate the real values, thereby continuously reducing the model bias and enhancing the overall model estimation with improved interpretability. We first screened the latitude and longitude of the site data. Then, we extracted the image element values for each parameter (SIF, NIRv, LAI, PAR, FAPAR, NDVI, PRE, and TEM) at the corresponding locations. Subsequently, we established separate XGBoost estimation models for the GPP across various vegetation types using the R 4.4.0.  
\[GPP=f_{XGB}(SIF,NIRv,LAI,PAR,FAPAR,NDV1,PRE,TEM) \tag{4}\]  
**(3) GPP\({}_{\text{RF}}\)**  
The RF is an ensemble learning algorithm that integrates a multitude of regressions and decision trees. It has the advantages of high-speed and efficient processing of large datasets and a superior ability to mitigate overfitting [45]. Furthermore, the model eliminates data normalisation and requires minimal parameter tuning, rendering it robust and reliable. The methodology for constructing the GPP estimation model using RF and the selected indicators was aligned with the approach employed to construct the GPP estimation model using XGBoost. The relationships established in R 4.4.0 are as follows:  
\[GPP=f_{RF}(SIF,NIRv,LEI,PAR,FAPAR,NDV1,PRE,TEM) \tag{5}\]