esa-sceva/satcom-chunk-collection huggingface dataset:
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 4 Big Data Collection in LS-WSNs
### Architecture Based on Mobility
#### 4.2.3 Architecture with a Mobile Sink and Mobile Sensor Nodes  
In this architecture, mobility is considered at the level of the sensor nodes as well as at the level of the sinks. This architecture has the same advantages as those of the mobile sink and the static sensor node architectures. This architecture has been implemented in several scientific and industrial research projects. For instance, this architecture was implemented as part of a zebra tracking project in Kenya; the sensors were deployed on zebras from Sweetwaters Reserve to Mpala in central Kenya, and the sink was positioned on a vehicle. This project was one of the first studies carried out in the field of environmental monitoring that simultaneously took into account the mobility of nodes and sink. As a type of application using this type of architecture, we obviously mention this zebra tracking project (ZebraNet) [84].
## 3 Results and Discussion
### Transportation Sensitivity Analysis  
As illustrated in the discussion above, the transportation of Class A biosolids can have substantial environmental impacts. Five transportation scenarios were considered to illustrate the impacts of biosolids transport. Scenario 1 represents a WRRF that distributes biosolids on-site rather than performing land application, which effectively requires no transportation. Scenario 2 is representative of a rural WRRF in which biosolids can be applied to land within \(\sim\)10 miles of the utility. In Scenario 3, biosolids land application sites are \(\sim\)50 miles from treatment utilities. This situation is the baseline included in the analyses above and may apply to utilities located in smaller cities or suburban locations in states where Class A biosolids application is allowed. Scenarios 4 (100 miles) and 5 (200 miles) are representative of WRRFs that have to transport their biosolids out of state, such as in California. In accounting for transportation impacts, both the mass of the final Class A product and the transport distance affect the environmental impacts. The cumulative environmental impacts for the five biosolids transportation distances are summarized for the five treatment processes and shown in Figure 6.  
Direct Heat Drying produces a final biosolids product with a very high percentage of solids. Consequently, varying the transportation distance also has relatively little effect on the environmental impacts of Heat Drying (Figure 6). On the other hand, the biosolids produced by TPAD contain a low percentage of total solids, and the total mass of biosolids that must be transported is relatively high. Accordingly, changing the transportation distance has a substantial effect on the overall environmental impacts of biosolids treatment by TPAD. Interestingly, whereas TPAD has the lowest overall environmental impacts when a one-way travel distance of 10 miles is assumed, the environmental impacts of TPAD exceed those of Air Drying in Scenario 5 (200-mile one-way travel distance). Transportation also has a substantial effect on the environmental impacts of composted biosolids, except that in this case, the additional mass and volume of biosolids that must be transported is not due to moisture. Instead, it is due to the additions of sawdust and woodchips to biosolids during the Composting process and what proportion of those amendments is actually retained in the final biosolids product. For these calculations, it is assumed that the volume of sawdust will be reduced by 40% during composoting, and the woodchips are screened out with 70% efficiency.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 5 In-Body Antenna Applications
### Cochlear Implant  
A folded loop antenna built with a metal wire with the size of 38 mm \(\times\) 38 mm \(\times\) 2.2 mm and a wire radius of 0.3 mm is presented in Ref. [102] for a cochlear implant, as shown in Figure 20. The operating frequency of the proposed antenna is 2.45 GHz, with a bandwidth of 8.57%. It realizes a gain of \(-\)0.1 dBi.
## 2 State-of-the-Art: IAR Applications for Training and Assistance
### Developing IAR Training and Assistance Systems  
Both training and assistance systems use IAR features to deliver information related to the task execution. Such features can be categorized mainly into two main types [38]: embodiment/awareness and virtual fixtures. Embodiment enhancement [39] comprises information that can improve the overall illusion of presence, for example with the use of an HMD. Virtual fixtures [40] are additional objects (e.g., virtual images, text, arrows) overlaid on a remote scene, that help the operator/supervisor perceive the remote environment and the different points of interest. Such virtual fixtures are also helpful for training when highlighting important information regarding the task, thus guiding the trainee step-by-step to accomplish the task and/or to give graphical feedback. For instance, the benefits of haptic and graphical feedback on surgeon performance during a teleoperated palpation task of artificial tissues were presented in [41]. In addition, in [38], the researchers studied the effects of different AR features in an assembly scenario. In particular, the paper sought to understand to what extent excessive visual information can be detrimental; and what is the effect of users' previous expertise.  
There are several aspects that make both IAR assistance and training-based systems very complex, such as communication limitations like high latency or human factors like perceptual issues and visualization of the remote environment. Recent literature has studied the degradation of the functionality of the human visual system when using head-worn AR displays [42]. Some of the factors that have been considered in the literature are lighting conditions, decreased visual acuity, limited contrast, distorted perception of colors, identification of the different objects or parts, spatial perception, and the subjectively perceived distance (e.g., egocentric distance [43]). Moreover, additional consideration is given to the need for a short familiarization time with the AR system to perform assistance and training complex tasks. Regarding the performance evaluation of IAR applications, in [44], the authors studied the outcomes of the given instructions. Factors like task completion time, error rate, cognitive effort, and usability were assessed. For instance, it was also analyzed how people with previous AR/VR/gaming experience obtain better results at performingtasks in an AR scenario since they already possess skills such as judging distances or finding adequate strategies of motion [38].
# Minimum Variance Distortionless Response--Hanbury Brown and Twiss Sound Source Localization
## 2 MVDR-HBT Localization Principle  
To further enhance the positioning accuracy of the sound field HBT in low-SNR situations, this paper proposes an MVDR-HBT sound source positioning algorithm that combines MVDR with the sound field HBT. Firstly, the MVDR beamforming algorithm is applied to the sound field HBT. The MVDR-HBT algorithm is then derived, and to ensure its generality, it is extended from narrowband signals to wideband signals, with a theoretical study of the wideband MVDR-HBT. The MVDR-HBT positioning diagram is shown in Figure 1.
# RCA-PixelCNN: Residual Causal Attention PixelCNN for Pulsar Candidate Image Lossless Compression
## 2 Background
### Pulsar Candidate Image  
Pulsar signal search is a crucial scientific task in the sky survey observations conducted by the FAST telescope. Upon receiving pulsar signals, FAST employs search software (such as PRESTO) to undergo a series of data processing steps. For instance, pulse clipping is employed to reduce pulse interference, while dispersion removal mitigates dispersive delays. Subsequently, Fourier transformation is utilized to analyze the data in the frequency domain, thereby determining the signal period. Based on the established signal period, multiple received signals of the same period are combined to enhance the signal-to-noise ratio. The processed data are then transformed into image format, serving as samples of pulsar candidates [(23)]. Figure 1 illustrates pulsar candidate images processed by PRESTO.  
Pulsar candidate diagnostic images are a fundamental basis for scientists to assess the significance of pulsars, and they also serve as a data source for machine learning methods used in pulsar selection. The labeled subfigures in Figure 1 include (1) the pulsar profile curve subfigure, (2) time-phase subfigure, (3) frequency-phase subfigure, and (4) period-dispersion subfigure. These subfigures provide essential reference features for astronomers when evaluating candidate samples. Here, our work focuses on the compression processing of subfigures (2), (3), and (4), while for the other subfigures, we will convert them into binary images using WBS (skip-white-block) encoding [(24)]. In the time-phase subfigure and frequency-phase subfigure of positive samples in the pulsar candidate set, we can intuitively observe a prominent stripe. This is because the phase of the pulsar signal from the same point source radiation is the same, resulting in a significant vertical correlation in the images. The center of the period-dispersion subfigure is a bright point that radiates outward in all directions. These image pixels exhibit structural correlations, which can be a key factor in image modeling.  
With the activation of 19-beam observations and the utilization of parallel acceleration tools, FAST has significantly bolstered its search capabilities, resulting in the generation of a massive volume of pulsar candidate images. According to early statistics from the FAST data center, approximately 60,000 images are generated daily, accumulating to 60 GB per month and 800 GB per year. The exponential growth in the scale of astronomical scientific data stored in the form of image-based pulsar candidate images poses challenges in scientific data management.  
Researching data compression techniques for compressing candidate images holds paramount importance. This research facilitates efficient data storage, accelerates network transmission and sharing, and contributes significantly to advancing astronomical scientific exploration and research.
## 5 Evaluation Metrics and Results
### Results for the First Test Scenario  
In this test scenario, the designed exams include only VRL-based questions, which are C++ coding examples and need to run a C++compiler within the question. After collecting the mouse movement and performing the extraction of features. The preprocessed data are trained on several mentioned machine learning techniques, and the metrics are calculated as shown in Figure 13 and Table 5.  
Figure 13: The receiver operating characteristic curve (ROC) for the exam includes the VRL-based questions.  
As shown in Figure 13 and Table 5, the LightGBM algorithm is considered the best algorithm used in detecting cheating from un cheating students with a value of AUROC equal to 0.945, following the XGBoost, Random Forest, Logistic Regression, SCV, and KNN algorithms with values equal, 0.943, 0.93, 0.81, 0.81, 0.73, respectively. The calculated performance metrics for all AI algorithms used in classifying the student's behavior are shown in Table 5. As observed from the results in Table 5, simpler machine learning models like KNN performed weakly on the data with only 70% accuracy and 73% AUROC.  
More sophisticated machine learning algorithms such as SCV and Logistic Regression showed improvement in the overall accuracy, scoring 72% and 74 %, respectively, and having the same AUROC of 81%. From methods used with the ensembles, techniques are the bagging, stacking, and boosting methods. The bagging method is used to average the prediction of different samples of the same dataset. The stacking method fits many models on the same dataset, then uses another model to predict with it. The last method is the Boosting method, which is used to collect ensembles that are used in predictions made by previous models, then add them to the model sequentially and calculate the average prediction value.  
The ensemble techniques were found to work best for the dataset, where the ensemble learning machine provides us with better results as it uses multiple models to perform classification.  
From machine learning algorithm that uses the Bagging method is the Random Forest, which scores relatively well on the test dataset, with 86% of the predictions being correctly labeled and scoring 93% AUROC. The best scores were reached from the machine learning algorithms that were based on gradient boosting. Two of the most famous methods used are: Extreme gradient boosting and light gradient boosting machines, where both XGBoost and LightGBM scored the highest accuracy of 87% and 88%, respectively, on the test dataset. Furthermore, they showed robust improvement in the AUROC score resulting in 94.3% and 94.5%, respectively.
# Free Space Optic Receiver with Strongly Overlapped Photodetectors' Field of View
## 2 Free Space Optic (FSO) Receiver Design
### Selecting a Suitable Photodetector
A high background light level makes the \(I_{pb}\) current greater than the others. In an NIR photodetector and NIR application, \(I_{db}\) is almost zero (\(I_{db}=72.97\times 10^{-21}\) A for BPW34 and \(I_{db}=47.09\times 10^{-21}\) A for BP104F), and \(I_{dg}\) is negligible (\(I_{dg}=2\) nA, according to the datasheets of both photodiodes). The result is that the dark current \(I_{0}\) is determined only by \(I_{pb}\). The total \(I_{pb}\) current can be found from the relative spectral sensitivity curve integration. Please note that the narrower spectral bandwidth range has a lower current \(I_{pb}\) and lower current noise \(i_{nPD}\). Comparing the BPW34 and BP104F photodiodes, only the BP104F photodiode ensures lower \(I_{pb}\) currents and lower current noise \(i_{nPD}\). Lower BP104F currents were confirmed by measurements for several types of illumination sources, shown in Table 1. The true \(I_{dg}\) currents are also provided, confirming there is no better choice than the BP104F photodiode. With respect to the best photodiode dynamics, a photoconductive regime was selected. This means that the photodiode was biased in reverse with as high a photodiode voltage \(U_{PD}\) as permitted.
## 5 Simulations
### Simulation Results
#### 5.2.2 Step Height  
The energy consumption conditions with different step height are shown in Figure 14 and Table 5. The step heights \(H\) are chosen as 0.04, 0.08, 0.12, 0.16, and 0.20 m. According to Equation (37), when the step length \(S\) is set as a constant, the energy consumption is proportional to \(E_{cost}\).  
Based on the equations of the foot trajectory, the step height has no influence on the stance phase, which can be verified in Figure 14. With the augmentation of the step height, the movement distance of the leg endpoint as well as the joint energy consumptions will increase. Considering the obstacle crossing ability of the robot, a proper value of the step height needs to be chosen.
## 2 Research Framework  
The STIRPAT model, proposed by [PERSON] and [PERSON] [31], is widely used in analyzing the driving factors behind environmental pressure. What sets this model apart is its ability not only to allow coefficients to be estimated in a parametric form but also to decompose and quantify the impacts of different factors, thus enabling it to adapt flexibly to various scenarios according to research needs [32]. Due to its broad applicability and flexibility, the STIRPAT model has become a popular method for studying the factors affecting carbon emissions [22].  
The form of the STIRPAT model is as follows:  
\[I=aP^{b}A^{c}T^{d}e \tag{1}\]  
where \(I\) represents the environmental impact; \(P\), \(A\), and \(T\) respectively represent population size, affluence, and technological progress, which are considered key driving factors of CO2 emissions; \(b\), \(c\), and \(d\) are the exponents for the corresponding factors; \(a\) is a constant term; and \(e\) represents the error term. [PERSON], [PERSON], and [PERSON] [22] improved the STIRPAT model by incorporating transportation infrastructure (such as roads) into the model to explore its impact on urban carbon emissions. The improved model adopts a logarithmic form:  
\[lnI=\beta_{0}+\beta_{1}lnROAD+\beta_{2}lnP+\beta_{3}lnA+\beta_{4}lnT+\varepsilon \tag{2}\]  
where \(ROAD\) represents the scale of roads. Building on this, we further consider ports an essential component of transportation infrastructure and select the total factor productivity (TFP) of ports as a metric, since it can more comprehensively reflect the productivity and service capacity of ports, thereby more accurately assessing the impact of port activities on urban carbon emissions. Therefore, the model we construct is  
\[lnI=\beta_{0}+\beta_{1}TFP+\beta_{2}lnP+\beta_{3}lnA+\beta_{4}lnT+\varepsilon \tag{3}\]  
In this model, we did not take the logarithm of \(TFP\) because it is already expressed as a percentage change, which inherently represents a relative change. This makes it suitable to be directly used as a variable in the model, allowing for a more direct reflection of the impact of port TFP on urban CO2 emissions. We aim to determine the specific impact of port TFP (\(\beta_{1}\) coefficient) on urban CO2 emissions. We constructed an econometric framework based on panel data from Yangtze River inland ports and their corresponding port cities to achieve this. Initially, we use the global Malmquist productivity index [33] to measure the TFP of major Yangtze River inland ports. Then, we integrate these TFP data into our constructed econometric panel model, aiming to delve into the relationship between port TFP and the CO2 emissions of its city. To capture potential differences in this relationship across different levels of CO2 emissions, we employed a two-way fixed effects and panel quantile model. Moreover, to consider the impact of varying port sizes, we also introduced a panel threshold model to analyze the impact of size heterogeneity on the relationship between port TFP and CO2 emissions.
###### Abstract  
We analyzed 27 established and new simple and therefore perhaps portable satellite phycocyanin pigment reflectance algorithms for estimating cyanobacterial values in a temperate 8.9 km\({}^{2}\) reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense coincident water surface observations collected from 44 sites within 1 h of image acquisition. The algorithms were adapted to real Compact Airborne Spectrographic Imager (CASI), synthetic WorldView-2, Sentinel-2, Landsat-8, MODIS and Sentinel-3/MERIS/OLCI imagery resulting in 184 variants and corresponding image products. Image products were compared to the cyanobacterial coincident surface observation measurements to identify groups of promising algorithms for operational algal bloom monitoring. Several of the algorithms were found useful for estimating phycocyanin values with each sensor type except MODIS in this small lake. In situ phycocyanin measurements correlated strongly (r\({}^{2}\) = 0.757) with cyanobacterial sum of total biovolume (CSTB) allowing us to estimate both phycocyanin values and CSTB for all of the satellites considered except MODIS in this situation.  
keywords: cyanobacteria; total biovolume; blue-green algae; BGA; phyocyanin; algal bloom; harmful algal bloom; algorithm; aircraft; satellite; hyperspectral; multispectral; coincident surface observations +
Footnote †: journal: Journal of the American Statistical Association
## 2 Materials and Methodology
### Obtaining Images and Creating Datasets
#### 2.1.2 Preparing Images and Building Datasets  
This study used OpenCV (version 4.8.0) to process UAV-captured livestock films, and pictures were retrieved at 90-frame intervals. After OpenCV processing and discarding highly repetitive or blurred images, a total of 8750 valid images were obtained. A subsequent step involved utilizing the LabelMe data annotation tool (version 5.3.0) to annotate the livestock in the images. To fulfill the prerequisites of the subsequent study, the various livestock species were divided into two distinct groups: juveniles (from birth to 1 year old) and adults (over 1 year old). The YOLO standard format was used to record the annotation results in TXT files. These files contained the following information: category, relative center coordinates, relative width, and relative height. Figure 1 provides an example of a few of the labeled photographs out of the 105,852 livestock instances that were labeled in total. Ultimately, a ratio of 8:1:1 was employed to partition all images into training, validation, and test sets, respectively.
## 4 Simulation Results and Discussion
### BER Performances: JISB_DSICED3_versus SB_DSICED3_W/OF
Therefore, this indicated that the semi-blind scheme JISB_DSICED3 is an optimum solution for not only short-packet transmission, but also for a low region of the SNR, which are the operation characteristics of IoT transmission and green communication.
# Marine Bioactives and Their Application in the Food Industry: A Review
## 2 Marine Sources of Bioactive Molecules
### Marine Cyanobacteria  
There is a competitive and appealing interest in marine cyanobacteria compared to terrestrial cyanobacteria as they produce polysaccharides, and their EPSs possess unique biochemical properties. Polysaccharides produced by cyanobacteria are complex heteropolymers comprised of at least 10 different monosaccharides, including hexoses, pentoses, deoxyhexoses, and acidic hexoses. Moreover, the presence of pentoses is considered a unique property in cyanobacterial EPSs as it is often absent in prokaryotic origin acidic sugars and exhibits an anionic character due to the presence of anionic substances such as pyruvyl, acetyl, phosphate, and sulfate [2; 26; 27]. Since the 1950s, various EPSs have been discovered from different strains of cyanobacteria. For example, [PERSON] et al. [28] reported that EPSs produced by most cyanobacteria are present as sheaths, capsules, and slimes. These polysaccharides are mostly known as released polysaccharides (RPSs) from cyanobacteria, and most of the RPS-producing monosaccharides are obtained from _Chroococcus_, _Gloedhece_, and _Synechococcus_. Several studies have reported that glucose is the most abundant monosaccharide found in RPS, and arabinose, galactose, or fucose may also frequently be found in cyanobacterial RPS. Furthermore, cyanobacterial RPS has been studied for the presence of sulfated polysaccharides (SPs), and research remains in the developing stages [19; 27; 28]. However, according to [PERSON] et al. [18], industrial interest is more in microbial polysaccharides, mainly those composed of a lower number of monomers. The most common industrial use of microbial polysaccharides is as thickening agents for stabilizing the flow properties in aqueous solutions under different conditions, such as changes in temperature, ionic strength, and pH [29]. For instance, _Anabena_ ATCC 33047, _Cyanospira capsulata_, _Nostoc_, and _Cyanothece_, among other strains, are capable of producing RPSs that have considerable rheological properties. Furthermore, RPSs produced by cyanobacterial strains like _Aphanocapsa_, _Cyanothece_, _Nostoc_, _Phormidium_, and _Synechocystis_ are considered promising sources as stabilizers, emulsifiers, or bio-flocculants [30].