esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 3 Methodology
### Impacts of Single Satellite on the Accuracy and Integrity of Subset
#### 3.1.1 Impact of Single Satellite on the Accuracy of Subset  
Generally, the accuracy of the satellite subset will always be improved if the new valid satellite information is introduced. The accuracy effect of a single satellite on the subset can be represented by the _HDOP_ value reduced by its inclusion as in Equation (12) and has been used in [7; 27; 40]. Although it is used for evaluating the accuracy effect of a single satellite on the all-in-view subset in the references, it is modified here to show the accuracy effect of a single satellite on the in-progress subset which needs the introduction of a new satellite.  
\[\delta H_{i}^{2}=HDOP_{0}^{2}-HDOP_{i}^{2} \tag{12}\]  
The \(HDOP_{0}\) is the \(HDOP\) associated with the subset before the \(i\)-th satellite is introduced and \(HDOP_{i}\) is the \(HDOP\) after that. (Note that the two notations here have different meanings from those in the reference). The \(HDOP\) effect \(\delta H_{i}\) is positive and is also derived in [41] as:  
\[\delta H_{i}^{2}=\left(A_{1i}^{2}+A_{2i}^{2}\right)/S_{ii} \tag{13}\]  
where \(A=\left(G^{\mathsf{T}}G\right)^{-1}G^{\mathsf{T}}\). Multiply each side of Equations (12) and (13) by variance \(\sigma_{0}^{2}\), then we can get:  
\[HDOP_{0}^{2}\sigma_{0}^{2}-HDOP_{i}^{2}\sigma_{0}^{2}=\left(A_{1i}^{2}+A_{2i} ^{2}\right)\sigma_{0}^{2}/S_{ii} \tag{14}\]  
If the measurement errors for each satellite are independent and identically distributed with zero mean and variance \(\sigma_{0}^{2}\), i.e., \(\sigma_{i}=\sigma_{0},i=1,2,\cdots n\), according to Equations (6) and (9) we can derive that:  
\[\left(2 drms_{0}\right)^{2}-\left(2 drms_{i}\right)^{2}=4*Slope_{i}^{2} \tag{15}\]  
where \(2 drms_{0}\) is the accuracy associated with the subset before the \(i\)-th satellite is introduced and \(2 drms_{i}\) is the accuracy after that.  
Although it is not theoretically derived in this study, simulation results show that the inference in Equation (15) still work when the accuracy of the pseudo-range measurements is known to differ. This finally reveals the connection between the accuracy effect of a satellite and its slope. As the slope means the sensitivity of the horizontal position error to the bias in each pseudo-range measurement. If the slope is large, which means the positioning accuracy is susceptible to the error in the corresponding satellite measurement, then the satellite has a large impact on the horizontal positioning accuracy of the navigation system.  
As a result, the slope of a satellite can be a good mark of its accuracy importance. If each of the satellite in the subset has a large slope value, this subset may own a better horizontal accuracy performance than other same size subsets.
## 2 Spectral UAV Sensors
### Spectral 2D Imagers
#### 2.3.1 Multi-Camera 2D Imagers  
A multi-camera 2D imager uses several integrated cameras to record a multispectral or hyperspectral image. Often, this is done by placing filters with a specific wavelength configuration in front of the detector. The first popular camera for this type for UAV application was the MCA, which had four or six cameras. Some examples of applications that have been developed with this first bulky model (2.7 kg) carried out on-board a helicopter UAV include water stress detection and precision agriculture studies [45; 46; 47]. Further miniaturization of the MCA model into the mini-MCA camera enabled its use from lightweight platforms for vegetation detection in herbaceous crops [48] and weed mapping [49]. However, due to its technical configuration, calibration and post-processing of data was complex [50]. Additionally, the camera had a rolling shutter, where not all parts of the image are recorded at the same time. For moving scenes (e.g., due to the movement of the sensor), this results in \"rolling-shutter\" effects that distort the images. Thus, rolling shutter cameras are not suited for taking images during UAV movement. Newer Tetracam cameras now also use global shutter in the Macaw model.  
Recently, similar but more compact systems have appeared on the market. Among them are the MicaSense Parrot Sequoia and RedEdge-(m) [51; 52] with four and five spectral bands (blue, green, red, red edge, near-infrared), and the MAIA camera [53], with nine bands captured by separate imaging sensors that operate simultaneously. Such cameras were used to assess forest health [54], leaf area index in mangrove forests [55] and grapevine disease infestation [56]. In addition, self-built multi-camera spectral 2D systems have been used to identify water stress [57] as well as crop biomass and nitrogen content [58].
# Research on Renewable Energy Planning Considering the Flexible Region of the Microgrid
## 4 Example Analysis
### Model Parameter Setting  
The initial investment cost of the photovoltaic module is 1176.5 USD/kW, and the annual operation cost is 2.9 USD/kW. The initial investment cost of the wind turbine is 1470.6 USD/kW, and the annual operation cost is 14.7 USD/kW. The installation cost of the energy storage is 455.9 USD/kW, and the annual operation cost is 0.7 USD/(kW-h). The demand side response load in the distribution network is configured.  
The cut-in wind speed is 3.75 m/s, the cut-out wind speed is 23 m/s and the rated wind speed is 12 m/s; the output model parameters \(C_{1}\), \(C_{2}\) and \(C_{3}\) are 0.1203, \(-\)0.08 and 0.0128, respectively. Then, the data of wind speed are one year's wind speed data in Alberta, Canada [29]. The NaS battery has the advantages of high energy and power density, high efficiency, convenient maintenance and a good load fluctuation response characteristic. It is suitable for large-scale production and application [30]. In this paper, the NaS battery is selected as the energy storage unit. The diesel engine is installed with 50 kW at node 8, and with 20 kW at node 27. In this paper, there are several nodes that are allowed to allocate the renewable energy generator, and they are nodes 2, 4, 6, 8, 10, 12, 13, 16, 18, 20, 22, 24, 26, 28 and 31. The maximum installed capacity is 2000 kW for the wind turbine, photovoltaic generator and energy storage at each installed node. The total active load of the distribution network is 3715 kW, and the load parameters of the IEEE-33 bus standard system are shown in Table 1.  
\begin{table}
\begin{tabular}{c} **Size of the** \\ **Load/kW** \\ \end{tabular} \\ \hline
1 & 1 & 100 & 17 & 1 & 90 \\
2 & 1 & 90 & 18 & 1 & 90 \\
3 & 1 & 120 & 19 & 1 & 90 \\
4 & 1 & 60 & 20 & 1 & 90 \\
5 & 2 & 60 & 21 & 1 & 90 \\
6 & 1 & 200 & 22 & 2 & 90 \\
7 & 1 & 200 & 23 & 2 & 420 \\
8 & 1 & 60 & 24 & 2 & 420 \\
9 & 1 & 60 & 25 & 2 & 60 \\
10 & 1 & 45 & 26 & 2 & 60 \\
11 & 1 & 60 & 27 & 2 & 60 \\
12 & 1 & 60 & 28 & 2 & 120 \\
13 & 1 & 120 & 29 & 2 & 200 \\
14 & 1 & 60 & 30 & 1 & 150 \\
15 & 1 & 60 & 31 & 1 & 210 \\
16 & 1 & 60 & 32 & 1 & 60 \\ \hline \hline \end{tabular}  
* Type of load: 1 is the general load node, 2 is the transferable load node.  
\end{table}
Table 1: Load parameters of the IEEE-33 bus standard system.  
Figure 1: Grid structure of the IEEE-33 bus standard system.
# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 3 Evaluation Metrics and Benchmark Datasets
### Benchmark Datasets
#### 3.2.5 CrowdPose Dataset  
The CrowdPose dataset [52] is a benchmark specifically designed to evaluate the performance of human pose estimation algorithms in crowded multi-person scenarios. Released by Megvii Research in 2019, it comprises approximately 20,000 images and over 32,000 human instances annotated with keypoints. Each person is annotated with 17 keypoints in accordance with the COCO annotation format, covering major joints from head to foot. To assess algorithm robustness under varying levels of occlusion and crowd density, the dataset introduces a novel evaluation metric called the Crowd Index. The Crowd Index quantifies crowding and occlusion levels for each image and is used to divide the dataset into three subsets: Easy, Medium, and Hard. Each subset contains an equal number of images, ensuring balanced and fair evaluation across different levels of scene complexity. Figure 9 displays representative examples from the CrowdPose dataset.
## 1 Introduction
Another study also reported a reduced-GO/gold nanoparticle aptamer-based electrochemical detection system for GA measurement with a LOD of 0.07 ug mL\({}^{-1}\)[13]. However, it presents some drawbacks, such as the cost of fabrication and the application of glass carbon electrodes, which require cleaning and mechanical polishing prior to testing. In our study, a graphene-based electrochemical aptasensor was developed for the high-sensitivity, effective, and rapid detection of GA. Screen-printed carbon electrodes (SPCEs) modified by GO were used as disposable biosensing sensor elements. The application of SPCEs makes our aptasensor fabrication cost-effective and easy to operate compared to conventional glass carbon electrodes. The aptasensor was constructed by the immobilization of aptamer onto GO-modified SPCEs by an amide bond, which is stronger than physical adsorption. Covalent binding between the NH\({}_{2}\) aptamer and GO was achieved by simple modification requiring no additional nanomaterial or complicated GO carboxylation treatment. The binding of the target analyte (GA) to the aptamer was analyzed by a sensitive square wave voltammetry (SWV) technique. The sensitivity, specificity and reliability of the developed electrochemical aptasensor was evaluated for GA detection. To validate the aptasensor, the clinical samples from diabetes and non-diabetes were analyzed and compared with a commercial assay. This study indicates that the aptasensor we developed can be applied for the detection of GA levels for glycemic monitoring of DM.
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
###### Abstract  
Over the past decade, Multibeam Echosounders (MBES) have become one of the most used techniques in sea exploration. Modern MBES are capable of acquiring both bathymetric information on the seafloor and the reflectivity of the seafloor and water column. Water column imaging MBES surveys acquire significant amounts of data with rates that can exceed several GB/h depending on the ping rate. These large file sizes obtained from recording the full water column backscatter make remote transmission difficult if not prohibitive with current technology and bandwidth limitations. In this paper, we propose an algorithm to decorrelate water column and bathymetry data, focusing on the KMALL format released by Kongsberg Maritime in 2019. The pre-processing stage is integrated into FAPEC, a data compressor originally designed for space missions. Here, we test the algorithm with three different datasets: two of them provided by Kongsberg Maritime and one dataset from the Gulf of Mexico provided by Fugro USA Marine. We show that FAPEC achieves good compression ratios at high speeds using the pre-processing stage proposed in this paper. We also show the advantages of FAPEC over other lossless compressors as well as the quality of the reconstructed water column image after lossy compression at different levels. Lastly, we test the performance of the pre-processing stage, without the constraint of an entropy encoder, by means of the histograms of the original samples and the prediction errors.  
Multibeam Echosounders (MBES); lossless data compression; lossy data compression; water column; sea imaging +
Footnote †: journal: remote sensing  
0222142063390/rs14092063
## 3 Hotline-Based Reliable WSN Topology
### Inter-Node Communication  
Under traditional _ad hoc_ routing algorithms like AODV and DSR [28], when a node S has to send data to a destination D, routes are discovered using a flooding mechanism. As shown in Figure 3, sender S does not know the path to destination D so the source floods the network with a route request (RREQ). RREQ is then rebroadcasted by every node that receives the message, until it reaches the destination or an intermediate node that has a fresh route to the destination in its cache. The destination node or the intermediate node then replies with a route reply (RREP). As shown in Figure 3, the route with dotted line is one of the routes discovered using a traditional routing algorithm. Due to the large number of intermediate wireless hops, a message sent through that path would likely be dropped due to wireless channel errors and will also be incurring high end-to-end delay.  
In our proposed topology, as we have divided the network into different clusters and each cluster is associated with a default gateway, routing of packets and route discovery is done within the cluster using RREQs. Our approach of intra-cluster routing is closer to the approach suggested in [13]. (We recommend OSPF for gateway-to-gateway routing, as will be explained in section 3.4) All the traffic (inter/intra-cluster and to/from the Internet) is routed through the gateway. The only exception here is the border nodes which are discussed later. Instead of an end-to-end wireless path, a packet is now routed through wireless-wired-wireless path, where the wired path is the communication between gateways via hotline and the wireless paths are used for intra-cluster communication. This approach clearly enhances the reliability of an end-to-end transmission. Under traditional routing, the path discovery process will discover an end-to-end wireless path. This path is less reliable, as shown in Figure 3, because the packet will traverse many wireless hops and with every hop there is an inherent threat of packet loss due to a variety of reasons like channel errors, collisions, and dead or sleeping nodes [4]. Moreover, under this approach, a number of sensor nodes are acting as relaying nodes. Consequently, more energy is consumed on sensors which results in a shortened network lifetime.  
Figure 3 also shows the hotline-assisted path that provides more reliable routing and involves lesser number of hops than traditional routing. A path discovery (RREQ) message will now reach from sender S to the default gateway. The default gateway will route the RREQ packet to the destination gateway. The destination gateway will then route the packet to the destination. The resultant route is more reliable and helps to conserve the energy of individual nodes thereby increasing the network lifetime.  
Figure 2: Router Advertisement Message Format [27].
# Wheel-Based MDM-PON System Incorporating OCDMA for Secure Network Resiliency
## 3 Results and Discussion
### Influence of FWM, Dispersion and Interference
Table 4 indicates the measured pulse broadening and supported bit rate for distinct fiber lengths of the proposed scheme for uplink (at 2.5 Gbit/s) and downlink (at 10 Gbit/s) transmission. Table 4 indicates that the supportable bit rates for both downlink and unlink transmissions are less than 1 over 0.2-1.6 km fiber length. Thus, the proposed scheme performs well under the influence of ISI.
# Evaluating the Efficacy of Common Treatments Used for _Vairimorpha_ (_Nosema_) spp. [PERSON]
###### Abstract  
_Vairimorpha_ (formerly _Nosema_) _apis_ and _V. ceranae_ are microsporidian pathogens that are of concern for managed honey bee colonies. Multiple treatments have been proposed to be effective in reducing the prevalence and intensity of _Vairimorpha_ spp. infections. Here, we test the efficacy of these products in one lab-based experiment and three field experiments. In the lab experiment, we found no reductions in _Vairimorpha_ spp. prevalence (proportion of individuals infected with _Vairimorpha_ spp.) or intensity (number of _Vairimorpha_ spp. spores per individual), but we did find a decrease in honey bee survival after treatment with Fumagilin-B, Honey-B-Healthy(r), and Nozevit Plus. The first field experiment showed increased _Vairimorpha_ spp. intensity in colonies treated with Fumagilin-B and HiveAlive(r) compared to a negative control (sucrose syrup alone). The second field experiment showed a weak reduction in _Vairimorpha_ spp. intensity after 3 weeks post treatment with Fumagilin-B compared to Nozevit. However, _Vairimorpha_ spp. intensity returned to levels comparable to those of other treatment groups after 5 weeks post treatment and remained similar to those of other groups for the duration of the experiment. The final field trial showed no positive or negative effects of treatment with Fumagilin-B or Nosevit on _Vairimorpha_ spp. prevalence or intensity. These findings raise questions regarding the efficacy of the products currently being used by bekeepers to control _Vairimorpha_ spp. We argue that the observed reduction of _Vairimorpha_ spp. is more likely relevant to the phenology of spore prevalence and intensity in honey bee colonies than to chemical treatment.  
honey bee; _Vairimorpha_ spp.; Fumagilin-B; HiveAlive(r); Nozevit; treatment; prevalence; intensity +
Footnote †: journal: Applied Sciences  
doi:10.3390/app13031303  
Academic Editor [PERSON]
# Impacts of Climate Change on Blue Carbon Stocks and Fluxes in Mangrove Forests
## 2 Mangrove Blue Carbon: An Update  
### Carbon Stocks  
Mangrove forests have the largest organic carbon (CORG) stocks of any of earth's ecosystems, with a global mean total forest stock of 692.8 \(\pm\) 23.1 (\(\pm\)1 SE) Mg CORG ha\({}^{-1}\) (updated from [8] with [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]). Soil standing stocks average 516.4 \(\pm\) 19.8 Mg CORG ha\({}^{-1}\) over the upper 1 m depth, accounting for 74% of total ecosystem standing stocks. However, in areas of peat and silt/clay soil, CORG concentrations are considerable to a depth of several meters. In deep (1-6 m) peat soils in mangrove schinkoles on the Yucatan Peninsula, Mexico, massive amounts of soil CORG up to 2792 Mg CORG ha\({}^{-1}\) were measured [9]. In 3.5 m cores taken from Trat, Thailand, soil stocks up to 962 Mg CORG ha\({}^{-1}\) were measured [10] with no significant change in concentration over the depth profile. Over the upper 2 m in mangrove soils in Gabon, CORG inventories ranged from 618 to 707 Mg CORG ha\({}^{-1}\)[11] although there was a clear decline in concentrations with increasing depth. Thus, it is reasonable to conjecture that a more realistic contribution of soil CORG to the total ecosystem CORG stocks may be as high as about 80%. These large amounts of CORG reflect rapid rates of soil accumulation on the forest floor, sustained accumulation over long time periods, and high rates of primary productivity. In contrast, global mean aboveground and belowground mangrove biomass average 105.8 \(\pm\) 4.2 Mg CORG ha\({}^{-1}\) (\(\pm\)1 SE) and 68 Mg CORG ha\({}^{-1}\), respectively, as updated from Alongi [8] with new data [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28], which underscores the dominance of the soil component.  
Using the median of 627.8 Mg CORG ha\({}^{-1}\) and the most used estimates of global mangrove areas of 83,495 [29] and 137,760 km\({}^{2}\)[30], we derive a range of global stock estimates for mangroves of 5.23-8.63 Pg CORG which is much greater than for salt marshes (1.74 Pg CORG) and seagrass meadows (2.61 Pg CORG) and equivalent to 15%-24% of the global mean CORG stocks in the tropical coastal ocean [8]. Regionally, total ecosystem CORG stocks are, on average, greatest on the Pacific Islands and in Southeast Asia, followed by Central and North America and the Caribbean, and in Africa and lowest in the Middle East and in parts of South and East Asia (Table 1). As there are very wide variations within and between individual locations, countries, and regions, the best generalization that can be made is that mangrove CORG stocks tend to be greater in the wet tropics with lower stocks in the dry tropics and in subtropical and warm temperate areas. This conclusion is unsurprising given that 86% of observed variability in mangrove C\({}_{\text{ORG}}\) stocks is associated with the best predictor, annual rainfall [31].
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 3 Sensing Technologies
### Strain Sensors
#### 3.2.2 Strain Gauge  
A strain gauge sensor measures strain in an object's (deformation). It typically consists of a thin, flexible substrate with a metallic foil pattern. This foil is arranged in a grid pattern that deforms along with the substrate. When an object is subjected to external forces and deforms, the strain gauge converts this deformation into an electrical signal for measurement and analysis. Strain gauges are highly sensitive and can detect very subtle changes in posture, making them ideal for applications requiring detailed posture analysis. Also, they offer more precise measurements of deformation, which can be directly correlated to specific posture changes. However, strain gauges require more careful placement and calibration compared to FSRs, making them more complex to integrate into a chair or seating system. Also, more sensitive to environmental changes such as temperature, which might necessitate additional compensation measures. Furthermore, they tend to be more expensive, especially when high precision and durability are required.  
[PERSON] et al. [98] utilized eight BF350 high-precision resistance strain gauges (shown in Figure 7) strategically distributed across the seat cushion to collect detailed pressure data from the human buttocks. This arrangement allowed for the effective identification of eight distinct sitting postures through the deployment of a convolutional neural network. [PERSON] et al. [85] attached a strain gauge built using inverse piezoresistive nanocomposite sensors to the user's back to collect sitting posture data. A 3-layer BP neural network is then used to differentiate between four different sitting postures with an identification accuracy of 98.75%.
## 3 Results and Discussion  
In this section, we provide an overview of our coSIF data product by considering a large region over North America that is within the latitude and longitude boundaries of 22 to 58 degrees North and \(-\)125 to \(-\)65 degrees East, respectively. We illustrate coSIF for North America in this article, but our framework applies equally to other large continental regions, albeit with different estimates of spatial dependence parameters. In Section 3.1, we present fitted bivariate spatial dependence models for February, April, July, and October 2021 (representing the four seasons), and we find strong spatial cross-dependence between SIF and XCO\({}_{2}\) in July, the month with the most prominent SIF activity. In Section 3.2, we consider the spatial and seasonal patterns of the coSIF-derived predictions and their corresponding prediction uncertainties, with a focus on July 2021. Section 3.3 presents validation metrics for July 2021 where bivariate cokriging, univariate kriging, and trend-surface-only predictions of SIF are compared.  
We produce all results on a high-end server with 56 physical cores and 376 gigabytes of memory. For reference, compute times using this architecture are as follows. Fitting the bivariate spatial model takes roughly 10 min for each pair of months. Production of the coSIF data product takes on the order of 12 h for each pair of months; however, the procedure is highly parallelizable and this compute time could be reduced by using multiple servers. In a given validation block, procuring validation predictions and approximating their corresponding multivariate variance-covariance matrix (see Appendix A.3) takes 15 to 30 min, depending on the amount of data in the block.