esa-sceva/satcom-chunk-collection huggingface dataset:
# IEEE 802.11 ax OFDMA Resource Allocation with Frequency-Selective Fading
## 8 Conclusions  
In this paper, we have studied the uplink OFDMA transmissions under conditions of frequency-selective fading. We have proposed a greedy approach of RU assignment for three different metrics: MR, PF, and SRPT. It has been shown that this approach works better in terms of throughput and average flow upload time than algorithms that do not divide the channel by frequency. We also have shown that the proposed approach retains its advantage if there are a lot of conventional devices in the system.  
We have introduced an algorithm to adaptively change the information about channel conditions when this information is updated with a certain period. We have shown that this algorithm also increases the throughput and reduces the upload time because  
Figure 9: (**a**) Upload time and (**b**) goodput for power supplement for \(R=40\,\mathrm{m}\).  
the AP has more relevant data on the quality of the channel for STAs when scheduling channel resources.  
We have also shown that an unconditional increase of transmit power to counter the possible effects of fading is almost useless when the proposed adaptation algorithm is used and can improve or worsen the performance depending on the scenario.  
As a direction of future work, we consider extending the proposed solutions to uplink OFDMA with MU-MIMO. Such an extension is complicated by many issues, including timely sounding, CSI estimation [27; 35] and overhead reduction. Furthermore, we plan to extend our results to a bit more flexible RU structure of 802.11 be [36], which is currently being developed.  
Conceptualization, E.K. and D.B.; methodology, S.T. and D.B.; software, S.T., validation, S.T. and D.B.; formal analysis, D.S.; investigation, S.T. and E.K.; writing--original draft preparation, D.B. and S.T.; writing--review and editing, S.T., D.B., D.S., and E.K.; visualization, S.T.; supervision, E.K. and D.S.; funding acquisition, E.K. All authors have read and agreed to the published version of the manuscript.  
The research has been carried out at IITP RAS and supported by the Russian Science Foundation (Grant No 20-19-00788).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Discussion
### Legibility of Prints
The number of correct answers was the highest for the texts in 8 pt (95.0%) followed by the texts in 10 pt (92.5%) and the texts in 12 pt (90.5%). Regarding the paper variants, the results showed that the highest percentage of correct answers was with Japanese knotweed paper (S2) (71.5%) followed by giant goldenrod paper (S3) (70.0%), black locust paper (S4) (69.5%), and commercial office paper (S1) (67.0%). Some similarity was found with previous research [35]. This could suggest that when inappropriate typographic design, e.g., poor contrast, obstructed the reading, the texts were remembered better than when the texts were easily legible [57; 58]. Nevertheless, our statistical analysis showed no significant difference between these percentages, suggesting that the participants' answers were affected neither by variations in type size nor paper properties. Studying prints on different papers with selected typeface and type sizes revealed that the substrate had more influence on the legibility than on the correctness of the participants' answers. Our results suggest that if the contrast between the paper and typography are not good enough, the legibility can be improved with a typeface with a higher TTD and larger x-height.
# Managing Access to Confidential Documents: A Case Study of an Email Security Tool
## 5 Results
### Security Settings  
To answer the research question, (_\"How do users use the email security tool's additional features to manage access control to their confidential information?\"_) the participants were asked to open secured emails that they sent earlier and to review what security options (e.g., set an expiration time, enable disable forwarding, add watermarking, or add persistent protection) they enabled based on the given scenarios, as shown in Table 6. They were also asked to provide their reasons for selecting or not selecting security features.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Content Type** & **Security Features** & **Selected** & **Not Selected** \\ \hline \multirow{3}{*}{Employee} & \multicolumn{2}{l}{Expiration Time} & 68.4\% & 31.6\% \\  & Disable Forwarding & 68.4\% & 31.6\% \\  & Watermarking & 36.8\% & 63.2\% \\  & Persistent Protection & 84.2\% & 15.8\% \\ \hline \multirow{3}{*}{W-9} & \multicolumn{2}{l}{Expiration Time} & 84.2\% & 15.8\% \\  & Disable Forwarding & 89.5\% & 10.5\% \\  & Watermarking & 57.9\% & 42.1\% \\  & Persistent Protection & 94.7\% & 5.3\% \\ \hline \multirow{3}{*}{Medical} & \multicolumn{2}{l}{Expiration Time} & 89.5\% & 10.5\% \\  & Disable Forwarding & 63.2\% & 36.8\% \\ \cline{1-1}  & Watermarking & 31.6\% & 68.4\% \\ \cline{1-1}  & Persistent Protection & 84.2\% & 15.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Percentage of participants who did or did not select security options for each sensitive content type.
## 3 Transcriptomics  
### Target Identification  
Understanding the mycobacterial transcriptome is key to connecting genome information to protein target expression, highlighting potentially druggable pathways and bacterial responses to drug exposure. With the development of whole-genome technologies, such as microarrays and more recently RNAseq, gene expression studies have been able to capture a snapshot of the total abundance and differential expression of transcripts present in an organism in various conditions. Transcriptomics has become an important tool for exploring the biology of _M.tb_, providing information about adaptive responses to understand mechanisms of pathogenicity, assign gene function, discover new drug targets and explore drug action. Transcriptomics in a discovery biology setting has uncovered induction of potentially druggable pathways involved in \(\beta\)-oxidation of fatty acids, the glyoxylate shunt and cholesterol metabolism in _M.tb_ replicating intracellularly in macrophages [50; 51] and in expectorated _M.tb_ in patient sputa [52], alongside expression of metal detoxification systems [53; 54] amongst others. Profiling in vitro models of persistence has revealed adaptations to respiratory and metabolic networks involved in the transition of _M.tb_ between different growth states [55; 56] highlighting target pathways for investigation. RNA signatures from animal models of TB infection [57; 58] and human tissue [59] provide important information on the expression of targets in human disease, enhancing the prospect of cidal drug action by targeting pathways active in vivo. This is valuable evidence for drug discovery decision making, since the bactericidal or bacteriostatic inhibition of an essential target in vitro does not necessarily predict in vivo drug efficacy. This was demonstrated by [PERSON] et al. who identified pyrimidine-imidazoles as potent antim mycobacterial agents in a whole cell screen against _M.tb_; lead compounds showed activity in vitro but failed to show any inhibition in a murine model of infection [60]. Compound efficacy was linked to the accumulation of glycerol phosphate and a reduction in ATP synthesis in the presence of glycerol. Glycerol metabolism is dispensable in vivo and thus inhibition of this pathway was not cidal in animal models of TB disease. Approaches combining transcriptomic and gene essentiality datasets offer a multi-omics solution to prioritizing pathways for further investigation.
# Nanoencapsulation of Essential Oils as Natural Food Antimicrobial Agents: An Overview
## 2 Synthetic and Natural Food Antimicrobial Agents
### Synthetic Antimicrobial Agents  
Although consumers have begun to worry about the safety of synthetic antimicrobials used in food products, they are still widely used in the food industry to date. The main synthetic antimicrobials used are sorbic acid and its salts, benzoic acid and its salts, parabens, propionic acid and its salts, and sodium diacetate (Table 1). They have the advantages of having strong antimicrobial activity at low concentrations, high industrial availability, and low cost, but most of the end products of their metabolism can lead to food color change and flavor and nutrient deterioration, and even endanger human health [5].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Chemical Compound** & **Application Range** & **Mechanism of Action or Application Effect** \\ \hline Benzoic acid and benzoates & Particularly used in acidic foods & Destruction of bacterial cell membrane, inhibition of cell membrane absorption of amino acid and respiratory enzymes, blocking condensation of acetyl-CoA, etc. \\ \hline Sorbic acid and sorbates & Dried meats and acidic foods & Binding to biological enzymes and inhibiting enzyme activity. \\ \hline Paraben (Hydroxybenzoate) & Bakery products, soft drinks, cosmetic & Destruction of cell membranes, denaturation of intracellular proteins, inhibition of the activity of respiratory enzymes and electron transport enzymes. \\ \hline Propionic acid and propionates & Baked products such as pastries, bread, etc. & Binding to biological enzymes and inhibiting their effected on molds and Gram-negative bacteria under acidic pH, especially to prevent the production of aflat. \\ \hline Dimethyl dicarbonate & Beverages & Passing through the cell membrane and interacting with enzymes in the microbial cells to block intracellular metabolism. \\ \hline Sulfur dioxide and sulfites & Dried fruits, wine making & Hydrogen ions generated by the decomposition of sulfite can cause damage of bacterial surface proteins and nucleic acids to kill microorganisms. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Commonly used synthetic antimicrobial agents for Food.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Wavelet Transform  
Wavelet Transform (WT) is an algorithm that effectively integrates different signals by decomposing them into various frequency components for analysis. Unlike the Fourier Transform, the Wavelet Transform possesses time-frequency localization properties, allowing for localized analysis in both the time and frequency domains. This enables the Wavelet Transform to observe global trends and local details at multiple scales, making it particularly suitable for analyzing and identifying the spatial range of urban agglomerations [67]. We choose wavelet transform as the data fusion method because of its powerful multi-scale analysis capability. Through wavelet decomposition, features from different data sources, such as the brightness distribution of NTL data and the hotspot distribution of population mobility data, can be extracted at multiple levels and fused with high quality through inverse transformation. This method effectively captures the economic and population dynamics within the Pearl River Delta region and shows strong applicability, especially in high-activity areas like the Guangzhou-Shenzhen corridor [67].  
The process of using Wavelet Transform for data fusion involves the following steps: First, each dataset undergoes multi-scale wavelet decomposition, breaking down the original data into detail coefficients and approximation coefficients at different scales to extract multi-level features. Next, according to a predefined fusion strategy (e.g., weighted averaging, selecting maximum values), feature extraction and fusion processing are performed on the decomposition coefficients across each frequency band to retain key informational features during fusion [68]. Finally, by applying the inverse wavelet transform, the fused frequency band coefficients are reconstructed back into the original space, resulting in a new dataset that integrates both brightness information and population distribution features, thereby producing higher-quality composite data. The formula for Wavelet Transform is as follows:  
\[WT(\alpha,\tau)=f(t)\varphi(t)=\frac{1}{\sqrt{\alpha}}f(t)\int\limits_{- \infty}^{+\infty}\varphi(\frac{t-b}{\alpha})dt \tag{1}\]  
where \(f(t)\) is the image signal vector, \(\varphi(t)\) is the wavelet transform function, \(\alpha\) is the wavelet transform scale, \(\tau\) is the translation of the image signal, and \(b\) is a parameter.  
Figure 4: Preprocessed Heatmap Data of the PRD Urban Agglomeration.
## 3 Results  
### Validation of the Single Electrode Coaxial Cable Model  
In Figure 6 the analytical and numerical solutions of electric potential within a central grid are presented for the voxel sizes 0.5 \(\times\) 10\({}^{-3}\) m, 1.0 \(\times\) 10\({}^{-3}\) m, and 3.0 \(\times\) 10\({}^{-3}\) m. A comparison between both solutions was illustrated as a relative error with respect to the analytical solution in Figure 7. The datapoints of the numerical solutions lined up with the analytical solution after the inclusion of Electric-Potential Estimation (EPE) for all voxel sizes. Specifically, the inclusion of EPE significantly reduced the maximal errors from 87.8%, 88.0%, and 47.4% for the voxel sizes 0.5 \(\times\) 10\({}^{-3}\) m, 1.0 \(\times\) 10\({}^{-3}\) m, and 3.0 \(\times\) 10\({}^{-3}\) m, to 0.1%, 1.3%, and 0.4%, respectively. Note that these maximal errors without EPE occur in the far field, but the maximal errors without EPE near the inner electrode are also very high, 7.5%, 3.3%, and 26.6% for the voxel sizes 0.5 \(\times\) 10\({}^{-3}\) m, 1.0 \(\times\) 10\({}^{-3}\) m, and 3.0 \(\times\) 10\({}^{-3}\) m, respectively, see Figure 7. These errors near the electrode also reduced to values smaller than 0.4% after inclusion of EPE.  
Furthermore, in Figure 8 the relative error of the numerically calculated resistance values with respect to the analytical value were shown as a function of voxel sizes and grid types. Inclusion of EPE significantly reduced the maximal errors from 7.2%, 2.4%, and 30.8% for the voxel sizes 0.5 \(\times\) 10\({}^{-3}\) m, 1.0 \(\times\) 10\({}^{-3}\) m, and 3.0 \(\times\) 10\({}^{-3}\) m, to 0.2%, 1.6%, and 2.8%, respectively, for the central grid. For the shifted grid, EPE also significantly reduced the maximal errors from 2.3% and 31.3% for the voxel sizes 0.5 \(\times\) 10\({}^{-3}\) m and 3.0 \(\times\) 10\({}^{-3}\) m, to 0.1% and 6.2%, respectively, except for the voxel size 1.0 \(\times\) 10\({}^{-3}\) m. While the error remains low, it slightly increased from 2.4% to 3.5%, possibly due to the fact that the voxel size has the same width as the diameter of the cylindrical electrode.  
Figure 6: The absolute electric-potential distributions of a coaxial cable model (central grid) along the x-axis excluding/including the Electric-Potential Estimation (EPE). The electric-potential distributions were calculated analytically (black line), and numerically with voxel sizes 0.5 \(\times\) 10\({}^{-3}\) m, 1.0 \(\times\) 10\({}^{-3}\) m, and 3.0 \(\times\) 10\({}^{-3}\) m (colored symbols). In (**A**) EPE was excluded, while in (**B**) EPE was included. The grey bar illustrates the inner electrode of the coaxial cable.
# High Throughput Data Relay in UAV Wireless Networks
## 2 System Model
Meanwhile, during each time slot, the non-DS supported victims are allocated with bandwidth \(W\) each. The supported victims can both transmit in the remaining bandwidth \(NW-(N-2)W=2W\) simultaneously [22]. Therefore, we denote the bandwidth scheduling variable as \(a_{n}[k]=2\) if victim \(n\) is supported by DS at time slot \(k\), and \(a_{n}[k]=1\) if victim \(n\) is not supported by DS, where \(k\in\{1,\ldots,K\}\). The following statements relate to the DS enabled scheme. The distance between the UAV and victim \(n\in\{1,\ldots,N\}\) at time slot \(k\in\{1,\ldots,K\}\) is given by  
\[d_{n}[k]=\sqrt{||\mathbf{c}[k]-\mathbf{c}_{n}||^{2}+H^{2}}. \tag{1}\]  
We use \(P\) to denote the transmission power of a victim. Furthermore, we assume that the channels from the victims to the UAV are dominated by LoS links. Thus, the channel power gain between victim \(n\) and the UAV in time slot \(k\) is given by  
\[h_{n}[k]=\frac{\beta_{0}}{d_{n}^{2}[k]}=\frac{\beta_{0}}{||\mathbf{c}[k]- \mathbf{c}_{n}||^{2}+H^{2}}, \tag{2}\]  
where \(\beta_{0}\) represents the channel power gain at a reference distance of unit length. The maximum achievable data rate in bits/s/Hz for victim \(n\) at time slot \(k\) with respect to the sub-carrier bandwidth \(W\) is given by  
\[R_{n}[k]=a_{n}[k]\log_{2}(1+\frac{Ph_{n}[k]}{\sigma^{2}}), \tag{3}\]  
where \(\sigma^{2}\) is the power of the Additive White Gaussian Noise (AWGN). For the DS supported victims, the transmission bandwidth is \(2W\). Since the noise power spectrum density is the same, the received noise power at the UAV is twice as that for a non-DS supported victim. However, the UAV treats the overlapping signal as the effective received signal [22], hence doubling the received signal power. As a result, the received SNR at the UAV for a DS supported victim is same as that for a non-DS supported victim.Thus, the average achievable data rate from victim \(n\) to the UAV is denoted as  
\[R_{n}=\frac{1}{K}\sum_{k=1}^{K}R_{n}[k]. \tag{4}\]  
Note that, for each victim, \(R_{n}\delta_{i}K\) is the overall data throughput. As \(\delta_{t}\) and \(K\) are constants, thus the average achievable data rate \(R_{n}\) is equivalent to the overall throughput. Additionally, in this problem, the bandwidth scheduling variables set is \(\mathcal{A}=\{a_{n}[k],\forall n,k\}\), and the UAV's trajectory location variables set is \(\mathcal{C}=\{\mathbf{c}[k],\forall k\}\).
# Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
## 4 System Architecture
### Frontier Manager  
The Frontier Manager node combines the information gain targets found by the Frontier Algorithm node and the sampling manoeuvre possibilities to adjust the flyby to the map configuration. Several mechanisms are embedded to reduce the time spent selecting the next goal of the UAV.  
Figure 5 contains an activity diagram of the decision-making process. The starting point is the local search. The local search is centered on the last successful flyby manoeuvre. The boundaries of the local region are always within the operator defined region while keeping a minimum volume. The Frontier Algorithm's node is requested to compile an ordered list of unknown points. If no unknown space exists, the Frontier Algorithm node is queried again, this time using the operator-defined boundaries (global search). Additionally, the analysis of the map in each global searches is circular as one global search starts where the last global search finished. The focus of the search is distributed among the whole tree, avoiding analysing one set of voxels more frequently.  
A flyby manoeuvre will only be considered valid for flying when three requirements are met. These requirements are ordered to minimise the number of line of sight checks. Firstly, the unknown point must be visible from the flyby, assuming the unknown space to be free. Secondly, the minimum distance from obstacles must also be observed when the UAV is sampling. The flight corridor of the flyby must only contain free space to be valid. Finally, the voxel that contains the start of the flyby must have at least one neighbour eligible to be part of the solution of the path planner. Since the octree is sparse, the flight corridor between the start and end of the flyby and the flight corridor between the start of the flyby and its neighbours can be substantially different. This requirement exempts the system from making impossible path requests. Moreover, a reduced amount of flyby options is considered for each sampling point. In order to keep the computation time per sampling candidate compatible with online, on-board execution only nine flyby options are considered for each with six options around the candidate and three below it. Flyby options above the unknown are not considered because the blind angle of the Hokuyo sensor points downwards. The relative position of the manoeuvre is calculated, taking into account the sensor pitch.
# Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Parana State, Brazil
## 4 Discussion
### Rainfall Anomalies between 2000 and 2018  
The northeastern area of the state is concentrated in the central part of the Parana River basin, close to the climate transition line (subtropical and tropical) that separates several active climate systems in an area of greater atmospheric instability [32]. Thus, the precipitation estimate by the satellite may present a greater limitation (overestimation or underestimation) in this area due to atmospheric conditions.  
As previously mentioned, high-intensity orographic rainfall occurs along the coast, which is estimated with less accuracy by satellite-based algorithms, resulting in poor performance in detecting anomalies. The southwestern region of Parana has favorable conditions for the formation of severe storms and hail, which occur very quickly [62; 63; 64]. In September and October, the highest amounts of hail formation are observed in days per month [64], which exactly coincides with the period of greatest rainfall in the region (Figure 3). Thus, as on the coast of Parana, the sensitivity of IMERG in detecting anomalies in this region may have lower performance compared to other regions.  
According to [38], positive anomalies in rainfall can occur during the summer under conditions of El Nino Southern Oscillation (ENSO) in the south of Brazil. Over the years considered in this study, two El Nino events classified as \"moderate,\" between 2002-2003 and 2009-2010, and one classified as \"strong,\" between 2015-2016 (Golden Gate Weather Services, 2020) occurred. During these periods, positive anomalies were detected by IMERG, and gauges in all stations shown in Figure 12.  
In the other years, IMERG and gauges detected some anomalies in all nine stations analyzed. However, only IMERG detected negative anomalies. [39] reported that IMERG version 5 products tend to underestimate precipitation amounts for rainfall rates 40-75 mm day\({}^{-1}\), but overestimate precipitation amounts for high rainfall rates (>80 mm day\({}^{-1}\)). Thus, the anomalies detected in this study by the satellite could occur due to the under or overestimation of rain events. Thus, the use of IMERG products in anomaly studies should consider their variable performance for this purpose, requiring calibrations and prior data assessments.