esa-sceva/satcom-chunk-collection huggingface dataset:
# Active Control Loop of the BOROWIEC SLR Space Debris Tracking System
## 2 Satellite Laser Ranging Process
### Laser Beam Propagation  
An issue directly related to the laser measurement of the distance to an orbital object is the geometric shape of the laser beam. The laser distance measurement is based on the emission of a laser pulse towards the orbital object, where some of the photons are scattered from the object's surface. The moment of time when the returning photons are acquired in the photon detector ends the measurement procedure (Figure 5).  
As described in the Introduction, orbital objects subject to the range measurements by the SLR stations are divided into cooperative and uncooperative ones. Cooperative targets are equipped with retroreflectors mounted on the outer casing. A special feature of the solution is the ability to reflect the laser beam towards the point of its emission. Accordingly, there is no need to generate a high-energy laser beam in order to obtain a photon event caused by scattering from the measured object. Uncooperative targets are therefore all other orbital objects that are not equipped with retroreflectors. Therefore, in  
Figure 4: Timeliness of ephemeris data in the orbital object coordinate system (CryoSat-2 satellite).  
Figure 3: Positional uncertainty of the TLE data format relative to the CPF format using the SGP4 propagator (JASON3 satellite).  
order to achieve photon events from an uncooperative orbital object, high-energy laser sources and highly sensitive photon detectors must be used [17; 18].
# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 2 Materials and Methods
### Extreme Gradient Boosting (XGBoost)  
XGBoost is a supervised learning algorithm based on community trees for prediction and classification problems [41]. This algorithm is based on tree-based learning methods and generally performs classification tasks successfully on large and complex datasets [42]. XGBoost builds new models from previous model errors. It trains hierarchical trees using the boosting method, with the ensemble trees connected to each other, and by merging weak trees, more stable trees and complex data patterns are obtained. Compared to traditional machine learning methods, the advantages of XGBoost include regularization, parallel processing, high flexibility, handling missing values, and built-in cross-validation [43]. XGBoost provides powerful and flexible solutions for both regression and classification problems. It was chosen in this study because it performs particularly well on organized and structured data.
## 4 Related Work
### System Deployments  
Depending on the use case and the scope of the application, IoT applications can vary from local use up to worldwide deployments. The portability and access of the resulting applications are different in each use case. The system architecture has been designed to adapt itself to a variety of use cases and enables three ways of deployment (Figure 4):  
* cloud (3) : a cloud deployment of the PoCo enables the portability of applications and the access from anywhere with an Internet connection, e.g., access control in an international company. In this case, the discovery of the local CoAP networks by necessity has to be performed by the user applications since the PoCo could be deployed in a different network than CoAP devices and the IP multicast for discovering the CoAP networks has to be performed in the local network. Resource Directory (RD) [35] and CoAP devices will have to be located in the local network and they will have to be IP-accessible in order to enable their discovery and interaction respectively. The RD is an entity launched by the CoRE group which holds information on other servers such as list of resources and their characteristics. RDs are mainly used to indirectly discover nodes  
Figure 3: Architecture of the Adaptability.  
when it is not possible or practical due to the given network's specific characteristics, in this case RDs provide the resources and end points contained in the CoAP network to the PoCo.
* local (2): in this case, user applications and the PoCo have been deployed in different devices. The PoCo will run on a dedicated server, but in the same network as the user applications, e.g., controlling the sensors and actuators of a building on behalf of the employers. The application portability can done with the available CoAP networks in the local network. Discovering of RDs can be done by both user applications and the PoCo.
* embedded (1): user applications and the PoCo have been deployed in the same device. This enables a portable and embedded solution that can be applied to controlled areas, e.g., smart home controlled by the owners. CoAP networks are discovered by the PoCo. In this case, the portability is done in situ with the discovered CoAP networks.
# Comparison of Methods to Identify and Monitor Mold Damages in Buildings
## 2 Materials and Methods
### Bioinformatics Analyses  
The sequence data were processed using the bioinformatics pipeline described in [PERSON] et al. [22], which consists of seven steps: (i) quality checking using _fastqc_; (ii) demultiplexing of samples using _cutadapt_; (iii) quality filtering, trimming, dereplication, denoising, merging in contigs, removal of chimeras, and creating the amplicon sequence variants (ASV) table, all of them by using _data2_; (iv) clustering of ASVs into operational taxonomic units (OTUs) at 98% similarity using _vsearch_; (v) curation of OTU table by _lulu_; and (vi) taxonomic assignment of OTUs against the UNITE database.  
The included control samples revealed that: (1) there was no sequence in the PCR negative; (2) four OTUs were detected in the extraction blank, with high numbers of sequences, but almost absent in the study samples, and therefore, these OTUs were removed from the dataset; and (3) the sequences representing the species in the mock community were exclusively detected in the mock sample, confirming that tag switching was negligible in this study, as previously reported when we used the same approach, including library preparation and sequencing [22]. In addition, the technical replicates showed similar community profiles, confirming the reproducibility of the DNA metabarcoding workflow. Control samples and the technical replicates with lower number of reads were removed.  
In the final dataset, only OTUs including more than 10 reads, and with at least 70% identity in the taxonomic assignment to a member of the kingdom Fungi, were included. In addition to the automatic taxonomic annotation, the taxonomic affiliation of the top-200 most abundant OTUs with uncertain taxonomic affiliation were controlled by manually Blast search against both UNITE and the International Nucleotide Sequence Database Collaboration (INSDC) databases.
## 5 Experimental Results and Analysis of the Prediction Model
### Size Prediction Results and Analysis  
In this study, to more intuitively illustrate the error distribution of magnitude estimates across different models, we generated magnitude prediction result plots, with the result deviations recorded as shown in Figure 11. The red dots in the figure represent the predicted magnitudes, based on a total of 300 selected data points for prediction. For clear comparison, auxiliary lines \(y=(x+0.3)\) and \(y=(x-0.3)\) were added to the figure.  
Figure 11: Schematic diagram of magnitude prediction results of different network models.  
Figure 10: Bar graph of training accuracy evaluation indicators.  
To further validate the efficacy of the proposed model, we compared its prediction results with those of traditional microseismic magnitude prediction models, as well as CNN, AlexNet, VGGNet, and ResNet18 architectures. Traditional CNN models are commonly employed for earthquake classification with magnitude intervals of 0.5 or 1. However, our findings indicate that in more granular magnitude classification tasks (with intervals of 0.1), the traditional CNN model exhibited the lowest accuracy, with the majority of predictions deviating beyond \(\pm\)0.3 and displaying the highest degree of dispersion.  
While transfer learning implementations using AlexNet and VGGNet yielded marginally better results than the CNN approach for refined earthquake magnitude prediction, their predictions still contained substantial errors, characterized by consistent bias. In contrast, predictions from the improved residual network demonstrated the smallest deviations (within \(\pm\)0.3), minimal dispersion, and the highest concentration of data points clustered around the diagonal line, indicating superior accuracy.  
A comparative experiment with ResNet50 revealed that while its accuracy, prediction performance, and classification evaluation metrics were comparable to those of ResNet18, it incurred longer training times. Consequently, ResNet18 was selected as the optimal model for this study.
# Survey for Soil Sensing with IOT and Traditional Systems
## 3 RF-Based Soil Sensing Systems in Internet of Things (IoT)
### Wi-Fi Based Soil Sensing Systems  
In the study by [12], Wi-Fi technology is incorporated into the realm of smart agricultural soil sensing to measure soil moisture and electrical conductivity (EC). Their system, which is called Strobe, is devised to detect soil moisture and EC by leveraging RF propagation within prevalent Wi-Fi bands. The system overview of Strobe is shown in Figure 4.  
Strobe correlates the Wi-Fi time of flight (ToF) across several antennas and the amplitude ratios of these signals to the soil permittivity and EC, attributes influenced by the soil's moisture and salinity levels. The equation showcases the correlation between the RF signal's propagation speed and the soil moisture level, as depicted in Equation (4) [12].  
Figure 4: Wi-Fi-based soil moisture sensing system.  
Figure 3: Different types of soil surfaces.  
Using the speed of light and the Time of Flight (ToF) \(\tau\) over a specified distance \(d\) with the speed of light \(c\), the apparent permittivity \(\epsilon_{wifi}\) can be determined.  
\[\epsilon_{wifi}=(\frac{c\tau}{d})^{2} \tag{4}\]  
Their research effectively highlights the benefits of IoT devices, which are considerably more affordable than traditional commercial devices that can cost upwards of thousands of dollars.  
Another study [13] adopts the center frequency of 2.5 GHz for the soil moisture sensing task. They introduce CoMEt, an RF-based technique that gauges soil moisture across multiple depths beneath the ground surface without embedding any equipment into the soil or directly contacting the ground. CoMEt's primary insight is the dependency of an RF signal's phase on its wavelength in the transmitting medium, which in turn is influenced by soil moisture levels. They establish a correlation between the wavelength and the apparent dielectric permittivity, \(\epsilon\), which allows CoMEt to compute \(\epsilon\) using the deduced wavelength values \(l\), as illustrated in Equation (5) [13].  
\[\epsilon=(\frac{c}{fl})^{2} \tag{5}\]  
Subsequently, they employ the Topp Equation [70] to link the soil's volumetric water content (VWC) \(\phi\) with the apparent dielectric permittivity, as represented in Equation (6) [70]. CoMEt can estimate the moisture content in each layer of the soil.  
\[\phi=4.3\times 10^{-6}\epsilon^{3}-5.5\times 10^{-4}\epsilon^{2}+2.92\times 10^{- 2}\epsilon-5.3\times 10^{-2} \tag{6}\]  
The system is implemented with a software-defined radio paired with a Raspberry Pi, allowing real-time soil moisture measurement. In evaluations conducted in indoor and outdoor settings, CoMEt determined soil moisture across three soil layers with a median error of merely 1.1%.
## 4 The Proposed Work  
The proposed method is adaptable, reliable, and improves the general performance of the network. The methodology used in this research is based on the following three processes: (1) building and configuring the network settings based on space topology and the virtual devices used on the network, as shown in the proposed model in Figure 2, which is a simplified diagram of the proposed MCSDN architecture; (2) a mechanism of distribution and selection of controllers using the firefly algorithm (FA); and (3) the use of our proposed algorithm (SA-MCSDN) to choose the most suitable locations for the controllers. The current research is a continuation of previous work under the title Multi-Controllers Placement Optimization in SDN by the hybrid HSA-PSO algorithm, in which we used an algorithm to solve the same problem of MCSDN to select suitable locations for controllers. Table 2 shows the results from our previous work, as well as a comparison between our previous work and this research.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance Metrics** & **SA-FFCCPP** & **HSA-PSO** & **SA-MCSDN** \\ \hline Propagation delay & 31.3 & 15.6 & 4.80 ms \\ Average RTT & 10.95 & 7.5 & 6.05 ms \\ Matrix of time session & 246.5 & 203.5 & 113.13 \\ Average delay & 95.3 & 78.8 & 42.57 ms \\ Reliability & 79\% & 87\% & 99\% \\ Throughput (response/s) & 165.71 & 197.14 & 395.57 Kbps \\ Cost & 37.5 & 27.4 & 31.27 \\ Fitness value & 18.2 & 22.5 & 19.16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average results of the proposed SA-MCSDN algorithm.  
Figure 1: The iterative process of the scientific method [28].  
Figure 2 represents the system model, for which the mathematical equations in this section are formulated as follows: seven (\(n\)) controllers were used in the network topology, with twenty-eight (\(n\)) switches and forty-five (\(n\)) hosts.
# Choroidal Vascularity Index in Adult-Onset Foveomacular Vitelliform Dystrophy: A Pilot Study
###### Abstract  
This pilot study aims to investigate choroidal vascular status in eyes with adult-onset foveomacular vitelliform dystrophy (AOFVD), early age-related macular degeneration (AMD), and age-matched controls. In this retrospective study, choroidal thickness (CT) was measured manually using spectral domain optical coherence tomography images of the fovea, and 500 and 1500 \(\upmu\)m from the nasal and temporal regions in the fovea. The horizontal B-scan was imported into Fiji software. Choroidal vascularity index (CVI) and luminal and stromal areas were calculated. A total of 36 eyes from 36 patients, including 18 eyes with AOFVD and 18 eyes with CD, and 16 eyes of healthy subjects were included. CVI was significantly different among subgroups (ANOVA, \(p\) = 0.004). Eyes with AOFVD presented a higher CVI (+0.03 \(\pm\) 0.01, \(p\) = 0.001) than eyes with CD and controls (\(p\) = 0.03). No differences in CVI were detected between controls and eyes with CD (\(p\) = 0.25). AOFVD eyes accounted for the greatest luminal area, particularly significant in comparison with healthy controls (+0.27 \(\pm\) 0.11, \(p\) = 0.02). AOFVD eyes present a greater CVI than eyes with CD and controls. The major choroidal involvement is on the luminal component, further corroborating a possible role of the choroidal vasculature in the pathological manifestations of AOFVD disease.  
A 2021 11 10487 110487  
thy [4; 5; 6]. A few authors have reported increased choroidal thickness (CT) in eyes with AOFVD [7; 8], while [PERSON] et al. found similar CT in eyes with AOFVD and eyes of healthy age-matched controls. Ref. [9] [PERSON] et al. showed a reduction in the chorio-capillaris (CC) vascular density in eyes with AOFVD with differences in the vitelliform with respect to the vitelliruptive stage using optical coherence tomography angiography (OCTA) [10]. Even if the choroid is a predominantly vascular tissue, it is also composed of a connective structure, nerves, melanocytes, and extracellular fluid. Therefore, structural evaluation of thickness alone cannot inform on the component involved in CT changes. Furthermore, OCTA enables evaluation of the choriocapillaris but not the entire choroidal vasculature. Recently choroidal vascularity index (CVI) and quantitative assessment of the luminal and vascular area has been employed to further investigate the choroid and its role in retinal pathology [11].  
The objective of the present study was to evaluate CVI in patients with AOFVD compared with early AMD and in healthy age-matched control subjects.
## 4 Discussion  
### Impact of the Causes of Deficiency on Deviations  
Table 7 presents a classification of the planning deficiency causes according to their level of impact on schedule delays (Schedule Delay--\(\overline{\text{SD}}\)) and cost deviations (Cost Deviation--\(\overline{\text{CD}}\)), expressed through their average effect and relative ranking in each dimension. This table allows for the distinction between causes that predominantly affect time, those that mainly impact costs, and others that generate a dual effect. This differentiation is essential for understanding the nature of each cause and for proposing mitigation strategies with greater precision based on the type of impact they produce.  
The analysis of the causes of deficiency reveals significant differences in the extent to which each one impacts delays and cost overruns in road project scheduling. The cause of the Omission of road markings in planning (C\({}_{16}\)) ranks as the most critical in terms of schedule delays (Rank SD = 1), while its impact on cost deviations is notably lower (Rank CD = 5). This indicates that its effect is primarily associated with interruptions or miscoordination during execution rather than direct budget increases. In turn, Deficient estimation of material quantities (C\({}_{6}\)) and Lack of experience of the planner (C\({}_{10}\)), ranked second and third in schedule delays, respectively, also occupy high positions in cost deviation (Rank CD = 3 and 4, respectively), suggesting that both simultaneously affect time and budget. In contrast, Omission or deficiency in the definition of work packages (WBS) (C\({}_{13}\)) and High complexity of construction work (C\({}_{22}\)) stand out due to their greater influence on cost overruns (Rank CD = 1 and 2), despite being ranked lower for schedule delays (Rank SD = 5 and 8). This suggests that their impact is mainly reflected in the need for technical, administrative, or contractual adjustments. Additionally, causes such as Deficient coordination with public utilities (C\({}_{3}\)) and Inadequate estimation of permit acquisition durations (C\({}_{4}\)) show moderate impacts on both dimensions, indicating that their effects tend to be localized and manageable if addressed in a timely manner.
# Determination of Cellulose Degree of Polymerization in Historical Papers with High Lignin Content
## 2 Materials and Methods
### Determination of Monosaccharides  
Monosaccharide determination was carried out in the first filtrates after 3 or 6 h of delignification of paper samples. After 6 h of delignification, the paper sample was flushed and then dried, and a new portion of reaction mixture was added to the paper sample. After an additional 3 h of delignification (9 h altogether), monosaccharide determination was performed in the second filtrate. All filtrates were additionally filtered through 0.45 \(\upmu\)m filters (Chrom4 GmbH, Suhl, Germany) before injection into an ion chromatograph, Dionex ICS-5000 (Thermo Fisher Scientific, Waltham, Massachusetts, United States), consisting of a gradient pump and an electrochemical detector Dionex ED (Thermo Fisher Scientific, Waltham, MA, USA) with gold working electrode and Ag/AgCl reference electrode. A Dionex CarboPac PA20 3 mm \(\times\) 150 mm analytical column (Thermo Fisher Scientific, Waltham, MA, USA) was used, with the mobile phase composed of MQ (A), 10 mM NaOH (B), and 100 mM NaOH in MQ (C); 0.5 mL/min flow; and 25 \(\upmu\)L sample injection volume. Separation was performed at isocratic conditions at 20% B and 80% A for 20 min, followed by a wash step (increase to 100% C in 3 min, hold for 30 min, decrease to 20% B and80% A in 3 min) and finally re-equilibration to starting conditions for 14 min. Each sample was injected in duplicate, and standard quadruple carbohydrate pulsed amperometry waveform was used for detection (\(E_{1}=+0.1\) V from 0 to 0.4 ms, \(E_{2}=-2.0\) V from 0.41 to 0.42 ms, \(E_{3}=+0.6\) V from 0.42 to 0.43 ms, \(E_{4}=-0.1\) V from 0.44 to 0.5 ms). The monosaccharides were quantitatively determined in filtrates using calibration in the range 0.1-1 mg\(\cdot\)L\({}^{-1}\) (glucose, xylose, and arabinose), 0.5-1.1 mg\(\cdot\)L\({}^{-1}\) (galactose), or 3-6 mg\(\cdot\)L\({}^{-1}\) (mannnose). Limit of detection (LOD) was determined to be 0.03 mg/L for glucose, xylose, and arabinose; 0.17 mg\(\cdot\)L\({}^{-1}\) for galactose; and 1 mg\(\cdot\)L\({}^{-1}\) for mannose.
## 4 Distributed Hotspot Network Architecture
### Distributed and Decentralized Hotspot Network Layer
#### 4.2.7 PoC Challenge Creation and Target Selection  
Ordinarily, full hotspots or validators can construct a challenge for every block of the blockchain. However, increasing such a challenge request per block can be disastrous in terms of the computation load. Thus, a variable--\(\beta\)--can be used to control the PoC challenge rate so that the number of PoC challenges per block can be controlled. Increasing \(\beta\) can significantly increase the number of PoC challenges in each block.  
Firstly, a full hotspot or validator generates a short-term key pair--(\(p\),\(\mu\)) and a hash--_hash (publickey)_. Secondly, the (\(p\),\(\mu\)) and _hash (publickey)_ are included into the validator _txns_. Next, the _privatekey_ is stored in a _local_state_ (_validator_). Later, while absorbing the _txns_, if the proposed keys do not match to a consensus group member (CGM), such proposed keys are added to local cache--_lcache_. Later, each member of the CGM selects a number of keys--\(k\in\)_lcache_--so that the target \(\beta\) can be obtained. If a minimum (\(2k+1\)) number of nodes participate in a block, then \(\beta\) can be obtained by following \(\frac{\beta}{2(N-1)/3}\) by each of the validators in the group. The value of \(\beta\) may be fixed for any unnecessary changes of \(\alpha\), periodically, in order to reduce the network load. If more than \(2k+1\) number of nodes participate, then the public keys hashes are truncated so that the block metadata can be formed. A number of selected keys are removed from _lcache_, thus resulting in the adjustment and governance by the validators to serve the capacity of the network.  
Once, a block is successfully handled, every validator inspects the _publickey_ in the given block and finds out whether it matches with them. If a match happens, a new PoC is generated for each of such matching. Later, the _hash (publickey)_ is used with the _hash (block)_ to generate an entropy--for the respective PoC challenge in the \(H3\) region. Such an entropy--which is generated from a combination of _hash (publickey)_ and _hash (block)_, is used to identify the target node in the \(H3\) region for generating a PoC challenge. Among all the regions, the PoC challenge and target are locally persistent with each challenger validator.