esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Considerations for Expanding the Measurements in Space and Time  
There is a need to expand the measurements to additional sites, and to cover longer time intervals. For long-term monitoring it is necessary to ensure proper calibration at regular intervals to avoid drifts or degradation due to exposure of rain, snow, frost, wind and dust. We strongly recommend users to carry out compensation for dark-current drift. This can be problematic at high-latitude locations where there may be light even during the middle of the night. The dark drift effect on daytime measurements should be further analyzed. Another possible sensor drift is magnitude drift. Reflectance can be cross-calibrated by using a field spectroradiometer, as shown by [PERSON] [51]. However, calibrating the absolute values of irradiance and radiance in the field is not possible, and to ensure correct absolute values of these quantities sensors will have to be recalibrated in a laboratory at regular intervals. Another possible source of error is spectral drift. There is no easy way to calibrate this in the field, and calibration in a laboratory is needed. Our sensor manufacturer recommends calibration of their light sensors every two years.  
We suggest mounting additional downward and upward PAR sensors in the forest stands to enable computation of all four PAR fluxes at all sites, hence enable accurate determination of canopy PAR absorption. These data will aid in the development of light-use efficiency models, and for further understanding of satellite-data driven productivity models. For the deciduous stands, both NDVI and absorbed PAR were strongly related to phenology, though the NDVI signal was less noisy during the growing season. For the coniferous stands, transmitted PAR was not sensitive to seasonal canopy variations, whereas reflected PAR showed some seasonal variation. Based on only one year of data, NDVI appeared somewhat better for observing coniferous phenology than any of the other radiation quantities.
## 2 Principle and Design Analysis of Novel SSPP and SIW with Wide Passband Characteristics  
### The SIW  
Operating in the fast-wave region, the SIW exhibits a lower cutoff frequency, which inherently generates high-pass filtering behavior with cutoff frequency of TE\({}_{10}\) mode. Figure 1a illustrates the schematic unit structure of SIW. \(d\) denotes the via diameter, \(s\) denotes the via hole spacing, and \(W_{sinv}\) denotes the center-to-center distance between two rows of vias. The cutoff frequency decreases as the via-hole spacing increases. The analytical relationships governing the cutoff frequency of SIW and structural parameters are derived as follows [32]:  
\[f_{1}=\frac{c}{2\sqrt{\varepsilon_{r}}W_{r}} \tag{1}\]  
\[W_{r}=W_{sinv}-s\Big{(}0.766e^{\frac{0.4483\ell}{2}}-1.17e^{-\frac{1.214\ell}{2 }}\Big{)} \tag{2}\]  
in which \(f_{1}\) denotes the cutoff frequency of the SIW, \(c\) denotes the speed of light in vacuum, \(\varepsilon_{r}\) denotes the relative permittivity of the substrate material, and \(W_{r}\) denotes the effective width between the two rows of metallic vias. To mitigate bandgap effects, the structural parameters must satisfy the condition \(d<s<2d\). The SIW structure was designed based on the aforementioned equations and optimized using CST simulation software S2 2021, successfully achieving the low-frequency characteristics for the n257 band. This design employed Rogers 5880 as the substrate material, which has copper layer with thickness of 0.035 mm and conductivity of 5.8 \(\times\) 10\({}^{7}\) S/m, and the thickness of dielectric substrate was 0.508 mm, the dielectric constant was (\(\varepsilon_{r}\)) 2.2, and loss tangent was 0.0009. Figure 1b illustrates the dispersion characteristics of the SIW unit for different \(W_{sinv}\), in which \(d=0.4\) and \(s=0.38\). To obtain the cutoff frequency 24.25 GHz of n257, the value of \(W_{sinv}\) was chosen as 4.5 mm.
## 5 Group Communication Using Unicasts
### Entity Creation  
To facilitate the creation and manipulation of entities, the Entity Manager offers a CoAP resource \"/e\". We call this resource the Entity Management Resource. This interface only supports the CoAP POST request method. As payload of the request, it expects a collection of resources in CoRE link format [11], which together should form the entity. In the response, the Location-Path CoAP option is used to specify the name of the newly created resource. In the current design, the payload of the response is in plain text and describes the results of the validation tests performed by the Entity Manager on the collection of resources.  
Figure 6: Entity Manager (EM) high-level structure.  
Thus, when a client wants to create an entity consisting of several members, it has to compose a CoAP POST request and send it to the Entity Management resource on the Entity Manager. The EM creates the entity, assigns it a unique URI, and stores the entity in the entity database for future usage. Then the EM starts the entity validation process (explained in the next subsection). The client is informed about the URI to use in order to access or further customize the newly created entity and about the results of the validation of the entity. In addition, the new entity resource can be registered in a resource directory as well, making it available for lookup. If the entity did not pass the validation process the client should fix any errors and resubmit the entity for validation again before the client can use the entity.  
An example of the entity creation process is shown in Figure 7. In this simple example the client requests the creation of an entity consisting of two members: coap://[Sen5]/tmp and coap://[Sen8]/tmp, with Sen5 and Sen8 the IPv6 addresses of the two sensors. The Entity Manager creates the new entity, assigns it the URI \"/1\" and informs the client about the newly created entity. From now on, any client can access the newly created entity by accessing the \"/1\" resource on the EM. Please note the validation process is not shown in Figure 7 for simplicity.  
At creation time, the client can use optional URI-Query CoAP options with the POST request to specify the name of the entity to be created or to customize the default behavior of the entity. For example, a POST to coap://[EM]/e?path=\"/room_humidity\"&eo=\"min\" will create the entity \"room_humidity\" that returns by default the minimum value of all members when queried. We will discuss customization of the entity behavior in more detail in Section 5.5.
# Performance Analysis of the Pilot and Data Component of a CSS Signal for LEO-PNT +
## 2 Signal Model
### Ranging Capabilities  
The most important task of a PNT signal is to provide the means for ranging. The CSS signal considered in this paper is a complex signal so that it needs both I&Q components for the transmission and reception of the signal. Therefore, the transmission of a CSS signal can be used for either ranging (i.e., \(\{\tau,f_{\rm D}\}\) estimation) or data transmission. This is in contrast with traditional GNSS [14], in which the transmitted signal is usually real and then a pilot/data scheme is transmitted through the I&Q components. Then, in our case, we should consider some way to make the ranging (pilot) component coexist with the data component, e.g., waveform multiplexing [15] or time multiplexing [4; 11]. Note that for the pilot component, we have absence of data, so that we would be able to obtain the time-delay and Doppler frequency estimation using a BOK-chirp signal as defined in [4; 11]. Therefore, in the following, we will assume that a BOK-chirp signal is transmitted from the satellites for the purpose of ranging/synchronization. The signal processing and design of the pilot component was studied in [4; 11]. Here, in Section 3.1, we give the main ideas needed to understand the design of the data component.
# Single-Switch Non-Isolated Resonant DC-DC Converter for Single-Input Dual-Output Applications
## 4 Conclusions  
In this paper, a new configuration of the Cuk and SEPIC ZVS-QR combination DC-DC converter for bipolar output with a single switch has been presented. The topology proposed employs a single power switch with ground reference, which simplifies the design of the gate drive with a single resonant \(L\)-\(C\) circuit and provides a bipolar output voltage with good regulation, acceptable efficiency and a step-down/step-up conversion ratio. This combined configuration is an interesting alternative in many applications where small size, light weight and high power density are very important features. To verify its performance, a SEPIC-Cuk Combination ZVS prototype with a cost-effective commercial resonant-mode controller and gate driver was used. The prototype provides a dual-output voltage of \(\pm\)12 V from a single input in the range 10-14 V, operating in step/up and step/down modes. The experimental results show that this combination is suitable for applications where a Single-Input Dual-Output (SIDO) voltage is required. Four aspects of the prototype proposed can be highlighted: (i) simple structure, since it uses only one switch and fewer passive elements; (ii) the driver and the control circuit are simpler structures, due to the existence of only one power switch to be controlled; (iii) a bipolar output voltage is provided without a transformer; and (iv) the turn-off and turn-on processes of the power switch occur at zero voltage, which reduces the switching losses.  
Investigation, C.D.-M., E.D., S.P.L., J.L.A. and J.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Chemicals and Reagents  
Thyme essential oil (TEO) was obtained from Katyani Exports (New Delhi, India). According to the manufacturer's specifications, TEO was extracted using steam distillation from the entire flowering plant (herb) without the root. Ajwain essential oil (AEO) was purchased from the same supplier and was extracted from the crushed seeds through steam distillation. For the chemical analyses, TEO and AEO were diluted with hexane; 5 \(\mu\)L EO was added to 1 mL of hexane. After 10 min of shaking, samples were analysed using GC-MS. \(N\)-hexane (CAS: 110-54-3) was obtained from Penta (Prague, Czech Republic). Analytical standards with indicated CAS numbers and purity were purchased from Sigma-Aldrich, Prague, Czech Republic, namely, p-cymene (CAS: 99-87-6; \(>\)99.5%), thymol (CAS: 89-83-8; \(\geq\)98.5%), myrcene (CAS: 123-35-3; \(\geq\)90.0%), \(\alpha\)-pinene (CAS: 7785-70-8; 98%), \(\alpha\)-terpinene (CAS: 99-86-5; \(\geq\)90%), \(\beta\)-pinene (CAS: 18172-67-3; \(\geq\)98.5%), and \(\gamma\)-terpinene (CAS: 99-85-4; \(\geq\)98.5%)
# Optimization of the 3D Point Cloud Registration Algorithm Based on FPFH Features
## 4 Experimental Evaluation  
At present, the ICP algorithm and its variant algorithms are the most important methods used to deal with point cloud registration, among which three prominent algorithms are the original ICP algorithm [6], the ICP algorithm accelerated by kd-tree [19], and PL-ICP (point-to-line ICP) algorithm [20], which are very typical and powerful in the whole ICP algorithm series. At present, many scholars are constantly researching and innovating the above methods. This paper improves the ICP algorithm and compares the improved algorithm with three ICP-related algorithms. Because the wide application range of the ICP algorithm leads to the continuous updating of the ICP algorithm, many variant algorithms are more and more widely used. Therefore, this paper selects the above three algorithms for comparison. In this paper, MATLAB R2020b and the PCL programming library are used. the CPU is an Intel (r) core (TM) i5-11400 h @ 2.70 ghz, and the memory is 16 Gb.  
The experiment was divided into two parts. The coarse registration experiment analyzed the registration error of the selected data set, and the fine registration experiment verified the convergence speed of the proposed method. The selected point cloud data set comes from the Stanford 3D data set and a self-measured free-form surface data set. The self-measured point cloud data are shown in the following Figure 11.  
In the experiment, a self-developed three-axis CNC machine tool is used. The range of the \(x\)-axis is 0-270 mm, the \(y\)-axis is 0-245 mm and the \(z\)-axis is 0-225 mm. The measuring part of the information-collecting device adopts the Kinsey contact sensor, GT2-H50, with an accuracy of 3.5 \(\upmu\)m and a resolution of 0.5 \(\upmu\)m.
# Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet
###### Abstract  
The next-generation cellular systems, including fifth-generation cellular systems (5G), are empowered with the recent advances in artificial intelligence (AI) and other recent paradigms. The internet of things (IoT) and the tactile internet are paradigms that can be empowered with AI solutions and integrated with 5G systems to deliver novel services that impact the future. Machine learning technologies (ML) can understand examples of nonlinearity from the environment and are suitable for network traffic prediction. Network traffic prediction is one of the most active research areas that integrates AI with information networks. Traffic prediction is an integral approach to ensure security, reliability, and quality of service (QoS) requirements. Nowadays, it can be used in various applications, such as network monitoring, resource management, congestion control, network bandwidth allocation, network intrusion detection, etc. This paper performs time series prediction for IoT and tactile internet delays, using the \(k\)-step-ahead prediction approach with nonlinear autoregressive with external input (NARX)-enabled recurrent neural network (RNN). The ML was trained with four different training functions: Bayesian regularization backpropagation (Trainbr), Levenberg-Marquardt backpropagation (Trainlm), conjugate gradient backpropagation with Fletcher-Reeves updates (Traincgf), and the resilient backpropagation algorithm (Trainpr). The accuracy of the predicted delay was measured using three functions based on ML: mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).  
5G; IoT; tactile internet; machine learning; AI; traffic prediction Article  
Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet  
[PERSON], 1,2,*(), [PERSON], 3 Department of Communication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg, University of Telecommunications, 193232 St. Petersburg, Russia; [EMAIL_ADDRESS] (R.K.); alex-in-spbflyandex.ru (A.P.); [EMAIL_ADDRESS] (A.K.)
## 1 Introduction
Two optical system configurations, one with transmitted light and the other with reflected light, were created and tested for the analysis and classification of eggshells. In the case of transmitted light analysis on chicken eggs, they achieved an accuracy of 88.8%, specifically for white eggshells [26]. The measurements can be conducted without destroying the egg by utilising reflective light, making them more applicable in industrial settings. Eggshells from infected and non-infected hens exhibit distinct reflective properties. The study conducted by the authors [26; 27; 28; 33] demonstrated that it is possible to detect changes caused by MS infection in a chicken flock by analysing back-reflected signals from eggshells at selected spectral wavelengths of a white light source. By employing machine learning algorithms, the researchers were able to differentiate tested samples of various origins with a reasonable probability. In the case of white eggshells, the F-scores reached 95.75%, while for brown eggshells, the F-scores reached 86.21% [31]. When using modified machine learning algorithms, the F-scores for white eggshells were 86% while for brown eggshells, they were 96% [27]. The last two results, reported in [28; 33], were obtained using a portable multispectral fibre-optics reflectometer that uses selected single-colour LEDs instead of a broadband light source and an optical fibre bundle. Deep learning methods are sometimes employed in more complex scenarios that require information about molecules. This approach was utilised by [PERSON] et al. in their work, in which the researchers used deep learning to predict molecular excitation spectra [19]. Their results demonstrated that this type of network could achieve up to 97% accuracy in learning spectra and infer spectra solely from molecular data. [PERSON] et al. [34] presented a similar application of deep learning, in which they successfully predicted seven optical properties related to organic compounds. Additionally, this method has been proven effective in drug identification, as shown by [PERSON] et al. [35]. This approach enables efficient work in this field. However, as demonstrated above, less complex machine learning methods are predominantly used to analyse the spectra distribution for any material.
## 8 Results
### Importance-Performance Map Analysis (IPMA)  
The importance-performance map analysis (IPMA) was applied as additional support to standard results reported in PLS-SEM and to bring more clarity to the impact of exogenous constructs on the target construct in this model, Net Impacts [51]. In a practical sense, we detected the areas that have proven to be highly important but relatively underperformed on the CRISS platform. The indirect result of the IPMA is a priority list of potential improvements in the platform. In statistical terms, we discovered the total effects (importance) and average latent variables scores (performance) of the Net Impacts predecessors [51]. Since all indicators were measured with the same 5-point Likert scale, no corrections were needed during the analysis conducted in SmartPLS software version 3 [50]. The summary of the calculated IPMA data for Net Impacts is shown in Table 7.  
User Satisfaction had the strongest total effect over the target Net Impacts, followed by Information Quality, System Use, System Quality, and Service Quality. The lowest performance value for the Net Impacts was exhibited by System Use, then System Quality, User Satisfaction, Information Quality, and Service Quality. The values of predecessor constructs were plotted in an importance-performance map divided into four quadrants (see Figure 4). The cut-off values for the quadrants and interpretation were determined according to [54]. A cut-off value of 0.35 for the x-axis was a mean of importance scores. The value of 50 was used for performance (y-axis) as a midpoint of the 0-100 range.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Predecessor Construct** & **Importance (Total Effects)** & **Performance** \\ \hline Information Quality & 0.43 & 58 \\ Service Quality & 0.07 & 63 \\ System Quality & 0.30 & 49 \\ System Use & 0.34 & 47 \\ User Satisfaction & 0.61 & 54 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of the IPMA for Net Impacts.  
Figure 4: Importance–performance map of predecessor constructs.  
Information Quality and User Satisfaction fell under the first quadrant, which corresponds to \"keep up the good work\", meaning they both have high importance and performance levels. There were no constructs targeted in the second quadrant. These would be the constructs respondents considered essential and on which improvement should concentrate. On the borderline between the second and third quadrant, System Use should be targeted first with undertaken managerial actions. The second area to improve would be System Quality, which belongs to the third quadrant and is characterized as \"low priority\". However, since the second quadrant contained no construct, excess resources should be allocated to the third quadrant [55]. The fourth quadrant indicated \"possible overkill\" and included Service Quality. It means respondents mainly agreed that they had received proper support from responsible persons for using the CRISS platform. Still, they did not consider it very important for the Net Impacts.
## 2 Related Terms and Works
### Research Related to the Mirai Botnet  
Several studies have been conducted on the Mirai botnet.  
[PERSON] et al. proposed the Internet of Things Detection System (IoTDS), a botnet detection system [11]. It uses a network of running bots, including Mirai, to detect botnets by determining abnormalities in device behavior by modeling the CPU utilization, temperature, memory usage, and running tasks of devices on a host basis using a classifier. It is a method intended to detect botnets, not to exterminate them.  
[PERSON] et al. proposed a model that reproduces the spread of Mirai through mathematical simulations based on the stochastic SIR (Susceptible-Infected-Removed) [12]. Although the simulation takes into account various situations, such as the occurrence of variants and the use of zero-day vulnerabilities, it is only a simulation, discards some things, and does not fully reproduce the actual behavior of the worm.  
[PERSON] et al. observed infection trends of Mirai and its variants using 7500 IoT honeypots [13]. They found that specific malware trends are closely related and that malware authors have taken over competing strategies over time. They also found that epidemiologically, IoT botnets are not self-sustaining, and without a bootstrapping infrastructure to continuously spread reinfection to vulnerable devices, Mirai and its variants would be wiped out. On the other hand, the research was limited to the analysis of botnets and did not provide botnet disinfection or any findings on the subject.  
[PERSON] et al. investigated Mirai-infected devices and showed that an Internet Service Provider (ISP) can place Mirai-infected devices in a quarantine network to mitigate the botnet and notify the users of the targeted devices, thereby removing Mirai and preventing subsequent re-infection with a high probability [14]. The results showed that Mirai can be removed and subsequent re-infection can be prevented with a high probability. Although this is one of the methods to disinfect botnets, the cooperation of ISPs is important, and there is a limit to the number of applicable networks. The scope of the target network is also limited, making it difficult to apply to a scale, such as the Internet, that cannot be handled by ISPs.  
Most of the studies conducted on the Mirai botnet to date have focused on analyzing botnets, and even research aimed at disinfecting botnets has had limited real-world application.
## 5 Results
### Comparison of Predictive Performance of Models Using Dataset-1
#### 5.1.2 Experiments Using fastText subword  
A separate set of experiments is performed using the machine learning and deep learning models with the fastText subword word embedding. Table 4 presents the performance comparison of models using fastText subword for the sentiment analysis of in-text citations. A results comparison reveals that the CNN, LSTM, RF, SGD, and LR models show a marginal improvement in their performance, except for the voting classifier. The improvement is observed in terms of better accuracy, precision, recall, and F1 score. CNN achieves the highest precision value, of 85%, while LSTM achieves 84% precision. The voting classifier achieves the lowest accuracy of 85.52% and the highest recall of 86%. CNN and RF achieve the highest F1 score of 83% each.