esa-sceva/satcom-chunk-collection huggingface dataset:
Applied Sciences  
Dual-Conversion Microwave Down Converter for Nanosatellite Electronic Warfare Systems  
[PERSON] 1,*\({}^{\copyright}\), [PERSON] 2, [PERSON] 2 and [PERSON] 1,*\({}^{\copyright}\)  
1 Department of Engineering, University of Messina, 98166 Messina, Italy 1  
Footnote 2 of commercial off-the-shelf (COTS) components, which are also easily accessible by university programs with limited budget. CubeSat projects might be completed within a very short time compared to traditional satellite schedules. They also represent great educational tools, e.g., for demonstrating technologies, novel techniques or scientific missions.  
In this scenario, the possibility to integrate EW systems in CubeSats represents a great chance to fuel the research and development in this field.  
In this contribution, a microwave down converter was designed for CubeSat EW applications. The complete down-conversion chain, including the double stage of local oscillator (LO) generation, was designed in a unique compact system that can be integrated in nanosatellites. Particular attention was paid to keep costs low and to the overall system reliability. The down converter involves a dual-conversion architecture to provide a severe reduction of both the number and level of spurious signals in the band of interest.  
This task is addressed by first up-converting the received signal to a higher frequency, whereby the filtering stage might be more effective, and therefore by down-converting the filtered signal to baseband [17].  
The system simulations exhibit spurious free dynamic range (SFDR) and linear dynamic range (LDR) equal to 40.2 dB and 52.6 dB, respectively, an overall noise figure (NF) lower than 3.2 dB and a flat conversion gain (CG) about equal to 28.3 dB.  
Another interesting feature of the proposed down converter concerns the possibility to be exploited for different applications. Indeed, its wide operating bandwidth includes the operating frequencies of a great number of applications, e.g., satellite communication, radars, etc., where it can used as receiving stage [18; 19; 20; 21; 22; 23].
## 3 Results and Discussion
### Hyperfine Interaction Spectroscopy (ESEEM and ENDOR Methods)
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**B\({}_{0}\)**} & \multicolumn{2}{c}{\(\Delta=1\)\(\
u_{\text{sq2}}-\
u_{\text{sq1}}\), **(MHz)**} \\ \cline{2-3}  & **B\({}_{1}-\Delta\)B; \(\Delta\
u\) \textless{} 0 (M\({}_{1}=+1\))** & **B\({}_{2}+\Delta\)B; \(\Delta\
u\) \textgreater{} 0 (M\({}_{1}=-1\))** \\ \hline B\({}_{0}=\) B\({}_{1,2}\) & 0 & 5.7 \\ \hline B\({}_{0}=\) B\({}_{1,2}\)\(\pm\) 0.25 mT & \(\approx\)0.5 & 4.1 \\ \hline B\({}_{0}=\) B\({}_{1,2}\)\(\pm\) 0.5 mT & 3.83 & 2.5 \\ \hline B\({}_{0}=\) B\({}_{1,2}\)\(\pm\) 1 mT & 5.53 & 0 \\ \hline B\({}_{0}=\) B\({}_{1,2}\)\(\pm\) 1.5 mT & 5.58 & 0 \\ \hline \end{tabular}
\end{table} 
Table 3: Quantitative values of quadrupole interaction, depending on the external magnetic field values. line with the discussion on the EPR parameters given above, this means that the radical(s) are not located in the majority of apatite crystallites (A- or B-type substitution) but in the hydrated layers [61]. Other features of the ESEEM and ENDOR spectra can be ascribed to the interaction with \({}^{23}\)Na nuclei (v\({}_{x}\)\(\approx\) 3.9 MHz at B\({}_{0}\) = 340 mT, \(I\) = 3/2 and, therefore, quadrupolar). The presence of a signal from \({}^{23}\)Na nuclei is probably due to the incorporation of sodium into the OCP lattice during the synthesis stage. Similar results without deep analysis have been reported in some papers devoted to apatite investigations (see [61], for example). Frequently, the quadrupolar interaction makes the \({}^{23}\)Na spectra too complicated for analysis. Using the EasySpin procedure _salt_ for ENDOR spectra simulations and assuming an isotropic interaction, we were able to estimate \(A_{23\text{Na}}\) = 1.5 MHz and e\({}^{2}\)Qq/h = 3.3 MHz (Figure 6).
## 5 Conclusions and Perspectives
**Data Availability Statement:** Publicly available datasets are analysed in this study and available via the links below: GHE data: [[https://www.noaa.gov/nodd/datasets](https://www.noaa.gov/nodd/datasets)]([https://www.noaa.gov/nodd/datasets](https://www.noaa.gov/nodd/datasets)), accessed on 19 November 2023; PDIR data: [[ftp://persiann.eng.uci.edu/CHRSdata/PDIRNow](ftp://persiann.eng.uci.edu/CHRSdata/PDIRNow)]([ftp://persiann.eng.uci.edu/CHRSdata/PDIRNow](ftp://persiann.eng.uci.edu/CHRSdata/PDIRNow)), accessed on 19 November 2023; IMERG data: [[https://doi.org/10.5067/GPM/IMERG/3B-HH-E/06](https://doi.org/10.5067/GPM/IMERG/3B-HH-E/06)]([https://doi.org/10.5067/GPM/IMERG/3B-HH-E/06](https://doi.org/10.5067/GPM/IMERG/3B-HH-E/06)), accessed on 19 November 2023 for IMERG-Early and [[https://doi.org/10.5067/GPM/IMERG/3B-HH-L/06](https://doi.org/10.5067/GPM/IMERG/3B-HH-L/06)]([https://doi.org/10.5067/GPM/IMERG/3B-HH-L/06](https://doi.org/10.5067/GPM/IMERG/3B-HH-L/06)), accessed on 19 November 2023 for IMERG-Late; CMORPH data: [[http://ftp.cpc.ncep.noaa.gov/precip/CMORPH_RT/](http://ftp.cpc.ncep.noaa.gov/precip/CMORPH_RT/)]([http://ftp.cpc.ncep.noaa.gov/precip/CMORPH_RT/](http://ftp.cpc.ncep.noaa.gov/precip/CMORPH_RT/)), accessed on 19 November 2023 for CMORPH-RT; [[https://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V0.x/](https://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V0.x/)]([https://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V0.x/](https://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V0.x/)), accessed on 19 November 2023 for CMORPH; and [[https://sharaku.eoc.jaxa.jp/GSMaP/](https://sharaku.eoc.jaxa.jp/GSMaP/)]([https://sharaku.eoc.jaxa.jp/GSMaP/](https://sharaku.eoc.jaxa.jp/GSMaP/)), accessed on 19 November 2023 for GSMAP data. The ERAS reanalysis used in this study was provided by the ECMWF ([[https://doi.org/10.24381/cds.abb2d447](https://doi.org/10.24381/cds.abb2d447)]([https://doi.org/10.24381/cds.abb2d447](https://doi.org/10.24381/cds.abb2d447)), accessed on 19 November 2023 and [[https://doi.org/10.24381/cds.bd09156](https://doi.org/10.24381/cds.bd09156)]([https://doi.org/10.24381/cds.bd09156](https://doi.org/10.24381/cds.bd09156)), accessed on 19 November 2023) and generated using Copernicus Climate Change Service (C3S). DATABOURG, PANTHERE, RADOME, and OPERA datasets are available on request to corresponding authors. We acknowledge the technical and scientific staff of LaMP and Weather-Measures for their helpful discussions and management. We acknowledge the Databourg Systems staff for their help in the providing the exploitation of the DATABOURG datasets. We acknowledge the NESDIS Data access staff of NOAA for their help in GHE exploitation. We acknowledge the OPERA database service.
## 4 Discussion
### Thermal Comfort Assessed in Laboratory Studies  
If, on the one hand, the use of wearables in field studies has the main limitation of not being able to control and monitor all areas exposed to environmental variables, then, on the other hand, the use of professional devices in laboratory studies has the limitation of only being able to consider a limited exposure time [28], with possible noises in terms of the so-called Hawthorne effect [66; 67]. However, the latter has the advantage of being able to control all variables around the users. Indeed, the definition of quality criteria for multi-domain indoor studies [28] clearly states that measurements should be made close to the occupant, whenever possible, based on the recommendations of the domain-specific guidelines, in order to correctly assess the effect of an environmental stimulus on another domain perception or behavior, as these are the actual environmental conditions affecting the occupant. The same critical review [28] reports that the location of measurements was the only information that was reported more frequently, in 66% of the studies. Environmental measurements near the participants were more common in laboratory studies than in field experiments, where sensors were usually used to measure the average indoor conditions. In terms of thermal domain, in most studies, the height at which the thermohygrometers were installed was set according to the specifications of the ISO 7726 Standard. However, it is common to read expressions such as \"in close proximity to the user\", which are usually used to express the horizontal spatial distribution of this type of sensor. As it turned out in the approach followed in this study, the definition of the height at which the thermohygrometers could be installed was not sufficient. To ensure the reproducibility of the results, it could be useful to clearly indicate the distance from the human body where the thermohygrometers are installed.  
For the above reason, the use of wearables has a double importance, both in the field and in the laboratory, as they always offer the possibility to monitor the SP of the ambient air temperature and relative humidity.  
As a future improvement, it would also be interesting to extend the approach taken so far to users who have the ability to provide feedback on thermal perception, so that the feedback can be correlated with the PMV values calculated from environmental data monitored at different distances.
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 2 Methodology
### LSPP Model Design
#### 2.2.3 Encoder-Decoder  
1. Encoder  
The tunnelling and geological parameters listed in Table 2 form the input to the encoder. The data are arranged and entered in batches to improve computational performance. When the batch size is \(n\), the time relationship of the sample data in the same batch is shown in Figure 3. The time range of sample 1 is \([t_{1},t_{k}]\), and the time range of sample 2 is \([t_{2},t_{k+2}]\); until \(n\) samples are obtained, the time range of sample \(n\) is \([t_{n},t_{k+n}]\). The data flow in the encoder is shown in Figure 4. The input features have 8 dimensions. If the batch size is 32 and the historical sequence length (seq_len) = 96, the initial data shape is \(32\times 96\times 8\). The data are first mapped to a higher dimension through the embedding layer. After the Encoding Stacking 1 mapping is completed, the shape of the data is \(32\times 96\times 512\). The length of Encoding Stacking 2 is halved. Then, the data flow into the Muti-head ProbSparse self-attention Block in the encoder to calculate the attention score, extracting the long-range dependency between the data and calculating the output feature map.  
Figure 3: Schematic diagram of the time relationship between samples.  
Decoder  
The input to the decoder consists of the feature map from the encoder, the history label sequence data of the predicted target, and the mask data, where the mask length is the same as the predicted sequence length of the model. When the label sequence length (label_len) is 48 and the predicted sequence length (pred_len) is 24, this part of the initial input data has the shape of \(32\times(48+24)\times 8\), which is mapped to \(32\times 72\times 512\) after embedding. Prediction results are computed by a fully connected layer in combination with the computational attention mechanism.  
Performance Prediction  
The prediction results are organized into an \(n\times p\) two-dimensional matrix, where \(n\) represents the number of predicted samples and each row represents a predicted sequence of length \(p\), which intuitively demonstrates the prediction ability of the model at different time points. By extracting the last column, the prediction results after \(p\) moments can be obtained.
# Relationships Among Origin, Genotype, and Oenological Traits of _Brettanomyces_ Yeasts
###### Abstract  
_Brettanomyces_ yeasts play a relevant role in the fermentation industry, showing controversial behavior. There is growing interest in these yeasts in the fermentation industry as beer and bioethanol production, while in winemaking, they are considered spoilage microorganisms mainly used to produce ethyl phenols. These compounds may alter wine's organoletic characteristics, leading to significant economic loss. In this work, 45 _Brettanomyces_ strains from seven different environments were genotyped and assayed for some eenological characters to investigate the possible relationship among sources of isolation, genotype characterization, and eenological characters. The results of biotyping showed four main clusters which were also distinguished according to the eenological characters. The oenological characters also distinguished the strains based on the isolation source, suggesting an overall relation between origin and biotypes. The negative correlation between fermentation rate and ethyl phenols production in the _Brettanomyces_ population may indicate the adaptation to hostile environments differently from crop strains that showed the opposite behavior. The overall results contribute to clarifying some features of _Brettanomyces_ yeasts, even if further investigations into the ability of these yeasts to colonize winemaking environments are needed.  
_Brettanomyces_; cluster analysis; biotyping; winemaking environments; fermentation characters +
Footnote †: journal: Molecular Sciences  
0
Footnote 0: _Brettanomyces_, 2020, 20  
Other studies have been carried out to investigate the adaptation and dominance of _Brettanomyces_ yeasts in different adverse environments such as wine [2; 8; 14]. The strong genetic diversity encountered in wine strains increases the chances of adaptation to all types of wine. Several investigations on _B. bruxellensis_ have been published [15; 16; 17; 18; 19; 20], displaying a wide intraspecific variability [7; 15; 21].  
Nevertheless, not all strains may present the same wine spoilage potential. A series of works conducted in terms of carbon metabolism, stress tolerance, or production of volatile phenols revealed significant differences between strains [2; 8; 11; 22; 23; 24; 25; 26; 27]. However, the relationship between genetic variability, origin, and enoological characters showed variable and contradictory results.  
In the present work, 42 strains of _B. bruxellensis_ and 3 _Brettanomyces anomalies_ strains from different habitats such as wine, beer, spontaneous process kombucha, and crops (cocoa, corrosion) were genotyped and tested for the enoological characters of interest. The aim was to investigate the relationship among sources of isolation, genotype, and phenotypic traits of oenological interest.
# Towards Automatic License Plate Detection
## 3 Proposed Method
### The License Plate Localization Module (LPLM)
#### 3.2.3 Dimensions Analysis  
Once the Connected Regions (CR) are generated and enhanced by morphological operations, we examine the dimensions of the extracted regions to locate the license plate. We calculate the dimensional parameters of the CRs and examine whether its values fall in the range of the targeted license plate dimensions by using the two following parameters:  
* **The Spatial Area (SA)**, which is a collection of pixels in a spatial region that are confined within bounding box. In this study, a premeditated SA of the CRs is compared to the learned SA of the desired license plate.
* **The Aspect Ratio (AR)**, which is defined as the relation of height-to-width in a license plate. After the SA confirmation, if the AR parameter equates with the learned license plate parameters, it endorses the existence of the license plates. Finally, the LPLM module draws the green rectangle on that connected region, which outlines the existed license plate in the input image.
# Privacy-Preserving E-Voting System Supporting Score Voting Using Blockchain
## 6 Performance Analysis
### Results
**Case IV and Case V.** The impact of increasing both the transaction send rate and the number of transactions on latency and throughput can be seen in Figures 5 and 6, respectively. Transactions of 5000 and 10,000 are each executed at 100, 200, and 300 tps. It is clear that the read transactions experienced the lowest latency; additionally, it can be noticed that in the case of 5000 transactions, increasing the transaction send rate has no significant effect on the latency while in the case of 10,000 transaction, there is only a slight increase in latency. The results for the write transactions were more diverse. Increasing the number of transactions increases the latency more significantly for _Register_ than for _Vote_ (by a factor of roughly 3); and the transaction rate had little discernible effect on the throughput of the write transactions. It is interesting to note that across all three of the four charts, the latency and throughput were best at 200 tps. This supports our earlier conclusion that 200 tps was the best send rate with which to conduct the simulation of _Case II_. Much as before, the error rate for transactions remained at 0% despite varying both the transaction send rate and the number of transactions. To summarize:  
* Varying the transaction send rates only has a marginal negative effect on the latency for read transactions while it includes a positive effect on the throughput for read transactions, most notably at a send rate of 200 tps;
* Increasing the transaction send rate (up to 200 tps) has no significant impact on the throughput and latency of the read and write transactions;
* A transaction send rate of 200 tps produces the highest performance from the SUT and regardless of the number of transactions or their send rate, Ethereum is able to maintain a 0% error rate while executing the proposed scheme. In conclusion, the proposed system is feasible with a transaction workload of up to 10,000 transactions before its performance starts to become compromised. This performance is maximized if the transactions are sent at a rate of 200 tps. Figure 4: The impact of increasing the number of transactions on latency and throughput.
# Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs
## 3 System Models and Problem Statement
### Energy Dissipation and Radio Models  
In this paper, we use first order radio model for a fair comparison with previously-published protocols. Figure 1 shows the radio model used to measure energy dissipation in a heterogeneous network, which consists of three parts: the transmitter, amplifier and receiver.  
Depending on the distance between the transmitter and receiver, both free space and multi-path fading models are used in the simulations [35]. Thus, to transmit a \(pl\)-\(bit\) message to a distance \(d\), the mathematical representation of this message will be:  
\[E_{Tx}(pl,d)=E_{Tx}(pl)+E_{Tx-amp}(pl,d)E_{Tx}(pl,d)=\begin{cases}pl(E_{elc})+ E_{fs}pl(d^{2}),&\text{if }d<do.\\ pl(E_{elc})+E_{amp}pl(d^{4}),&\text{if }d\geq do.\end{cases} \tag{1}\]where \(E_{Tx}\) is the energy consumption of processing the data by radio circuits and \(E_{fs}\) is the energy of radio amplification to compensate the path loss, while \(E_{amp}\) is the energy consumption by the amplifier and \(d_{o}\) denotes the distance threshold. In order to receive this \(pl\)-\(bit\) message, the energy consumed by the receiver will be:  
\[E_{Rx}(pl)=E_{Rx-E_{dec}}(pl)=E_{elec} \tag{2}\]
# Path Loss Model for 3.5 GHz and 5.6 GHz Bands in Cascaded Tunnel Environments
## 2 Path Loss Model for Cascaded Tunnels in Far-Field Region of Propagation  
As shown in Figure 1, the proposed research aims at cascaded tunnels composed of straight and curved tunnels. According to the proposed model, the transmitting antenna (T\({}_{\mathrm{x}}\)) is placed in the straight tunnel and the distance between T\({}_{\mathrm{x}}\) and the curved tunnel is \(d\), while the receiving antenna (R\({}_{\mathrm{x}}\)) is placed in the curved tunnel with the radius of curvature (\(R\)), and the distance of R\({}_{\mathrm{x}}\) from the straight tunnel is \(d^{\prime}\).
# Realizing Efficient Security and Privacy in IoT Networks
## 5 Numerical Analysis and Results
### Joint Power Assignment for Multiple Sources  
For a multiple transmission source scenario, considering the \(n\)th transmission source, we set the rate constraint as \(C_{n}=\chi C_{n}^{*}\), representing \(C_{n}^{*}\) as the optimal data rate attainable from _Source_ to _Destination_ when power \(\mathbf{p}_{s,n}=\frac{\mathbf{p}_{s}}{M}\) is assigned to _Destination_ for data broadcast from the \(n\)th _Source_. Figures 7 and 8 present the data rates and privacy capacities for all three different sources. The subsequent result shows that although a small measure of privacy capacity is lost in the absence of rate constraint, significant data rates are gained and maintained.  
Figure 6: _Source-to-Destination_ data rate for multiple destinations transmission.  
Figure 7: Maximum realized privacy capacity for multiple sources transmission.
## 1 Introduction
### Problem Description and Objectives  
In addition, preliminary research about MR-based training should be counted as another problem. VR was more commonly used than MR for safety training, and training was prominent in the maintenance of workplace safety. Due to the lack of research in MR-based training, there might not be a significant chance to incorporate IoT and CPS in the training applications. Enriching MR content using IoT requires new architectures to handle the complexity of MR integration within the IoT platform. The functionality of CPS lies in conducting cloud-based data analysis in the cyber layer [69; 70; 71]. An MR technology-based OSH training enables trainees to explore hazardous situations without exposure to real-life threats. Thus, a lack of research in MR-based training can be a significant obstacle to uplifting the training quality and effectiveness. Referring to the study by the Bureau of Economic Analysis [72], the estimated expenditure on work-related accidents and occupational illnesses is around USD 200-550 billion. The data disclosed that OSH is a fundamental challenge in the workplace worldwide. Hence, this research expects to achieve two objectives. The first objective is to minimize errors or accidents in the working environment to protect workers from exposure to health and safety risks. The second objective is to reduce the training time to ensure work sufficiency by using new technical apparatus. Adopting advanced technologies is of utmost importance to ensure the occupational accident rate can be declined. To overcome insufficient research in MR-based OSH training and the absence of MR application with CPS and IoT for OSH training, employing CPS and IoT techniques to establish a better-quality MR-based platform for OSH training should be considered as one of the suitable solutions. Therefore, the main research questions that need to be addressed are as follows:  
1. How could an MR-based environment be assisted for OSH training?
2. How could HCPS be adopted for OSH training under an MR-based environment?