esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Computation of Monthly Land-Water Masks with Data Gaps
#### 3.2.4 Masking  
Based on the performed classification and threshold computation of the five indexes, the resulting land-water masks are now combined to achieve a final monthly land-water mask including remaining data gaps caused by voids, clouds, cloud shadows, or snow. Therefore, all five binary land-water masks (0: land, 1: water) are accumulated leading to values between 0 (land in all 5 indexes) and  
Figure 8: Calculation of the thresholds for the land-water classification and its derived land-water masks using five water indexes shown exemplary for the Lake Ray Roberts in January 2007.  
5 (water in all 5 indexes) which is shown in the first column of Table 3. Figure 9 shows the result for Lake Ray Roberts in January 2017 where only pixels which were classified by at least one index as water (\(\geq\)1) are shown. For the case that all five indexes are classified as land, the pixels are not highlighted which affects 82.66% of the pixels. 17.11% of the pixels have been classified as water by all indexes shown in red. There are also 0.04% data gaps. Only 0.2% of all pixels were not classified uniformly by all indexes as water or land. Figure 9 clearly shows that only smaller sections near the lake shore are not uniformly classified by land or water. The reason therefore is mainly the different color in shallow water. This influence is very small as we work with a spatial resolution of about 10 m.  
Then, the final binary land-water mask with remaining data gaps is created by using Table 3. All pixels which are indicated as water by four or five indexes will be set to 1 in the final mask. All pixels which are classified as water by zero or one index are set to 0 in the final mask. In cases of disagreements between land and water where two or three indexes indicate water, the pixels are set to data gaps which will be filled in the next step. Existing data gaps are taken over in the land-water mask. In the final area error assessment, also errors caused by the indexing are considered. Therefore, pixels which do not match clearly (one or four water indexes) go into the final area error as index error.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Input Value (based on 5 Water Indexes)** & **Monthly Land-Water Mask** & **Index Error** \\ \hline
5 Water pixels & Water (1) & No \\
4 Water pixels & Water (1) & Yes \\
3 Water pixels & Data gap & No \\
2 Water pixels & Data gap & No \\
1 Water pixel & Land (0) & Yes \\
0 Water pixels & Land (0) & No \\ Data gap & Data gap & No \\ \hline \hline \end{tabular}
\end{table}
Table 3: Look up table for creation of final land-water mask and index error based on 5 water indexes.  
Figure 9: Combination of all land-water masks using five different water indexes for Lake Ray Roberts in January 2017.
## 2 Materials and Methods
### Evaluation Methodology
#### 2.1.1 Evaluation Preparation  
The preparation of the evaluation requires several points:* cooperation with all relevant partners in the project on: \(\bigcirc\) & selection of an area for evaluation within the pilot site, \(\bigcirc\) & selection of specific use-case(s), \(\bigcirc\) & evaluation date, \(\bigcirc\) & preparation of evaluation route, \(\bigcirc\) & specific requirements related to the service concerned, \(\bigcirc\) & provision of drivers to independently assess C-ITS,
* preparation of specific questionnaires related to the evaluated services,
* provision and preparation of a special \"evaluation\" vehicle equipped with HMI and other sensors for future assessment.  
Drivers addressed to carry out evaluation routes should be diverse in age, sex, mileage covered, driving experience, etc. This will make the evaluation assessment more relevant.
## 7 Conclusions  
Currently, there are several numerical methods along with commercially-available software to solve the non-linear equations describing the compressible gas flow through the gas network. In this paper, a novel, unique mathematical method has been developed for simulating a steady-state non-linear gas network to quantify the main characteristics, pressure and flow rate through the transmission pipeline system.  
Using two approximate models of the Irish natural gas transmission network as a case study the novel method was compared and benchmarked against a numerical modelling solution using the Newton-Raphson method carried out in MATLAB and the commercial software SAINT. The comparison of the results from all solutions showed that the new model is much faster than the alternative methods, and the average deviation between the methods was less than 0.31% (0.3% and 0.32%) for pressure and less than 2.1% (2.95% and 1.38%) for flow rate. Based on these key findings, the new method can be applied as a reliable fast method to model various conditions in a gas network even when there is a large number of nodes and branches. The method also lends its self ideally to the modelling of integrated energy systems as it is fast, simple, and can be easily integrated with linear models of electrical networks. The networks presented here only contain boundary conditions associated with pressure for supply nodes and load for demand nodes; in reality, there may be other real-world constraints that impact gas supply or demand. These constraints need to be included when modelling real-world systems, so that in the future, the results from the novel method can be validated with accurate data from a real gas network.  
I.D.; methodology, I.D., A.E. and M.L.; software, I.D. and A.E.; formal analysis, I.D., and A.E.; original draft preparation, A.E., and M.L.; data curation, I.D., E.S. and A.E.; editing.  
This work is supported by the Science Foundation Ireland, by funding [PERSON] and [PERSON] under Investigator Programme Grant No. SFI/15/IA/3074; and [PERSON] and [PERSON] under Strategic Partnership Programme Grant No. SFI/15/SPP/3125  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & Novel Method & MATLAB Model & SAINT Model \\ \hline
13 Nodes (small network) & 0.058 & 0.4 & 0.25 \\
109 Nodes (big network) & 0.32 & 2.8 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Elapsed for calculations in seconds for each model.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{2}{c}{**SAINT**} & \multicolumn{2}{c}{**MATLAB**} \\ \hline  & Pressure & Flow Rate & Pressure & Flow Rate \\  & Deviation \% & Deviation \% & Deviation \% & Deviation \% \\ \hline
13 Nodes (small network) & 0.3 & 2.95 & 0.32 & 1.38 \\
109 Nodes (big network) & 0.77 & 3.48 & 0.07 & 1.23 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Deviations of the novel method from the MATLAB and SAINT outputs.
## Challenges in Operational Estimation of ET/SSM
### Challenges in Operational Estimation of ET
The global datasets were designed for ET estimation over large spatial scales, often as input to community land surface models, rather than to assess crop-specific ET. In heterogeneous irrigated landscapes in semi-arid climates, extreme spatial variability in soil moisture and ET means that extremes of low and high ET may occur in a single 1 km pixel, which significantly reduces ET estimates in the 1 km aggregated average. This could result in an underestimation of ET from irrigated cropland if no further disaggregation technique were applied. Therefore, more complex algorithms that use all visible and NIR bands to model ET in a moving window may be more successful in irrigated landscapes [88]. Techniques such as image sharpening can also be attempted by combining imagery from different satellite platforms, for example by using Landsat to sharpen MODIS imagery, though this has similar problems as the intra-platform sharpening technique in areas where temperature-NDVI relationships are complicated by surface irrigation [39]. Furthermore, from an algorithmic point of view, nearly all available methods in deriving estimates of LE and/or H fluxes have been developed essentially for cloud-free conditions. In addition, there are significant challenges in evaluating the accuracy of derived ET operational products. This is due to a lack of appropriate \"reference\" data for different ecosystems worldwide. Evidently, ground instrumentation itself can be limited and our ability to measure ET is dependent on the type of the instrumentation used and the characteristics of the site on which this instrumentation is installed (e.g., an error of 20% is typically expected in the ET measurement by eddy covariance in rugged terrain [14]). The spatial representativeness of the \"reference\" measurement and how does that compare to the satellite-derived prediction of ET from the operational product is also a question that is hard to answer. To our knowledge, few studies have so far been concerned with establishing the accuracy of such operational products, particularly so at continental or watershed scales [18].
## 5 Concluding Remarks and Future Outlook  
In summary, the data reviewed above illustrated information about the pigments produced by fungi that can colonize paper cultural heritage, synthetic dyes, ligninolytic and decolorizing enzymes, and their application. During the past few years, enzyme-based catalytic engineering has been used in the bioremediation of industrial pollutants. In this regard, the development of enzyme-based immobilized systems suggests a great potential for biotechnological applications for cultural heritage bioremediation purposes. Ligninolytic and dye-decolorizing enzymes have demonstrated features to degrade, decolorize, or detoxify several types of synthetic dyes and pollutants. When these dyes are compared with the fungal pigments found in the books, we hypothesized that the described enzymes have the ability to also degrade these structures. Depending on the side groups of the pigments' structure, with the gathered action of lactose that can catalyze oxidation or demethylation, DyP that can cleave the -C(=O)- group and MnP that catalyzes oxidation-reduction reactions, specific fungal pigments could be degraded, which further leads to the cleaning of the stained paper. Similarly, water-based stamp inks can also be removed by the same principle since these inks are composed of azo dyes, previously shown to be removed by ligninolytic enzymes secreted by different microorganisms.  
Even though the costs of biocleaning methods are comparable to those of chemical-physical techniques, the biological approach is more favorable in terms of both environmental and human health safety. Thus, future research should focus on the development of new protocols for the large-scale production of decolorizing enzymes and their application on paper and perhaps also on architectural cultural heritage since the direct application of living microorganisms can leave residues on the surfaces that are about to be restored and later serve as nutrients to other dangerous microbes. Additionally, the discovery of new chemical reactions catalyzed by ligninolytic and dye decolorizing enzymes represents a great rationale for their further utilization in bioremediation processes.  
Conceptualization, J.P. and D.P.; methodology, Z.F. and L.K.; resources, J.P., Z.F. and L.K.; data curation J.P.; writing--original draft preparation, J.P. and D.P.; writing--review and editing, J.P., Z.F., L.K. and D.P. All authors have read and agreed to the published version of the manuscript.  
Authors acknowledge the projects APVV-19-0059 and VEGA 2/099/2021 which financed this study.  
Not applicable.  
Figure 9: Degradation of pigment 3-acetyl-2,8-dihydroxy-6-methoxy-anthraquinone.
## 3 Results
### Association of SA-MIPs and Phagocytosing Cells  
Flow cytometry revealed an unambiguous cellular association between monocytic/macr ophage cells and SA-MIPs after 24 h of incubation. The association is displayed in flow cytometry histograms showing the association of the NBD fluorophore-containing SA-MIPs (red histogram) or the lumogallion fluorophore-labeled Alhydrogel (blue histogram) with each cell line (Figure 1). The distribution of cells according to their fluorescence is seen on the x-axes with the black histograms in Figure 1 showing the background/autofluorescence of the cells in the absence of particles). Upon association with the fluorescent particles, the cellular fluorescence will increase, shifting the histogram to higher FL1/FL2 values, and the percentage of cells associated with particles can be estimated. The results show that 100% of the cells displayed an increased fluorescence upon incubation with Alhydrogel and hence are associated with the Alhydrogel particles (blue histogram Figure 1), whereas only 20-30% of the cells became associated with the SA-MIPs (red histogram Figure 1).  
Figure 1: SA-MIPs had a lower association with phagocytosis cells compared to Alhydrogel. THP-1 (**a**,**b**) and RAW 264.7 (**c**,**d**) cells cultured for 24 h in medium (control = black lines), or in the presence of 100 \(\upmu\)g/mL SA-MIPs (**a**,**c**; red lines) or 100 \(\upmu\)g/mL Alhydrogel (**b**,**d**; blue lines). The histograms show the fluorescence intensity. SA-MIPs (NBD) are measured in the FL-1 channel (530 \(\pm\) 15 nm) and Alhydrogel (lumogallion) was measured in the FL-2 channel (585 \(\pm\) 20 nm). One representative experiment out of at least three independent experiments is shown.  
Fluorescence microscopy was used to visualize a potential intracellular location of the SA-MIP particles. Membrane staining of RAW 264.7 cells using actin filament staining with rhodamine-phalloidin, SA-MIPs, and nuclear staining using DAPI is shown in Figure 2.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 6 Attacks that Require Advanced Detection
### Replay and Suppression Attacks  
These attacks involve the strategic manipulation of the BGP's operational mechanisms, such as the handling of withdrawal messages and the mitigation of route flapping, rather than the direct falsification of routing information. Understanding these attacks' effects on the global routing table requires insight into the complex interactions between ASs, and especially how routes are propagated and withdrawn across the network. This supports their inclusion in the analysis. These attacks do not inherently involve collaboration between ASs but can have a distributed impact on the Internet's routing infrastructure.  
While these attacks might not initially seem to benefit from early detection from multiple vantage points, the temporal aspect of replay attacks (retransmitting previously announced routes) and suppression attacks (intentionally delaying or not propagating BGP withdrawal messages) can indeed be better understood and detected with a comprehensive view of routing behavior over time. Grouped AS anomaly detection from multiple vantage points might help identify inconsistencies in route announcements and withdrawals.
## 2 Proposed Architecture
### RSP16T Cell
#### 2.1.3 Read Mode  
For reading, both word lines are set to VDD, and bit lines are precharged to VDD. Through the discharging path N4 and N6, BLB discharges while BL retains its initial precharged state, since the pull-down paths via N3 and N5 remain closed.  
Figure 1: Schematic of the proposed RALP16T cell.  
Figure 2: Layout of the proposed RALP16T cell.  
When the potential difference between the two bit-lines exceeds 50 mV, the read operation is performed [17]. Since the access transistors P6, P5 are in a closed state, the internal nodes S1, S0 are electrically isolated from the bit lines during the read operation.
## 3 Evaluation Methods of the Performance of GSP
### Fatigue Life Performance  
The fatigue life of GSP has raised more concern in recent years, because of its different fatigue life behavior from asphalt and concrete [116]. The results of fatigue life of GSP are summarized in characteristics and correction factors, which are attained by the Indirect Tensile Fatigue test (ITFT), Fatigue Bending test, Cyclic Wheel Load test, Proportion-scale Accelerated Road test, and Full-scale Accelerated Road test. Test methods and fatigue equations of these studies are also shown in Table 11.
## 3 Methodology
### Data Preparation  
In the first stage, structured crash attributes (e.g., severity, lighting, injuries) are combined with corresponding unstructured narrative descriptions to create a single input suitable for training and inference with LLMs.  
The data preparation process involves three steps:  
* Tabular Data Conversion: Key structured features, such as crash severity, light condition, and construction zone status, are extracted and formatted as part of a user prompt.
* Narrative Integration: The structured prompts are merged with crash narrative text to create context-rich inputs for the LLM.
* JSONL Format Creation: Each crash report is converted into OpenAI's required JSON Lines (JSONL) format using the system-user-assistant structure. A sample record is formatted as follows: { \"messages\": [ {\"role\": \"system\", \"content\": \"You are a helpful assistant for crash report classification\".}, {\"role\": \"user\", \"content\": \"Light: Daylight, Weather: Clear, Injuries: 2, Narrative: Vehicle 1 struck a barrier in a marked construction zone...\"}, {\"role\": \"assistant\", \"content\": \"severity: Minor, czone: 1\"} }
# Development of Enabling Technologies for Ku-Band Airborne SATCOM Phased-Arrays
## 2 Antenna Requirements  
The main requirements for the Ku-band aeronautical market, in terms of antenna characteristics, are summarized in Table 1.  
The operational band is the complete Ku-Band (10.7 - 12.75 GHz in receive and 14.0 - 14.5 GHz in transmit). The realignment of the polarization in respect to the satellite for both receiver and transmit signals, with the capability to operate also with the circular polarization in receiver mode, is required.  
The minimum antenna requirements in terms of operative field of view (e.g., antenna gain-to-noise temperature [G/T] over RF band in the entire field of view and for every selectable polarization, Equivalent Isotropic Radiated Power [EIRP] over RF band in the entire field of view and cross-polar discrimination (including pointing error)) are reported in the Table 2.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**ITEM** & **Value** \\ \hline Frequency band—RF received signal & 10.7 - 12.75 GHz \\ Frequency band—RF transmitted signal & 14.0 - 14.5 GHz \\ Polarization—RF received signal & Dual orthogonal linear or dual orthogonal circular \\ Polarization—RF transmitted signal & Single adjustable linear \\ \hline \hline \end{tabular}
\end{table}
Table 1: Antenna operative requirements.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**ITEM** & **Value** \\ \hline Radiation gain pattern & In agreement with [7] \\ \hline Field of view & \(0^{\circ}<\phi<360^{\circ}\) \\  & \(0^{\circ}<\theta<90^{\circ}\) \\ \hline G/T & \(>\)8 dB/K \\ \hline EIRP & \(>\)43 dBW \\ \hline \hline \end{tabular}
\end{table}
Table 2: Antenna performance requirements.  
It has to be underlined that an antenna in transmission mode has to respect the applicable regulations [7] for the EIRP antenna patterns expressed in terms of power density per bandwidth as shown in Figure 1.  
Moreover, complexity and cost of the antenna are other key factors to take into account together with practical feasibility and ability to operate simultaneously in receiver and transmit modes.
# Occlusion Avoidance in a Simulated Environment Using Reinforcement Learning
## 2 Previous Work
### Object Detection in Occluded Scenes
They then trained a semantic segmentation network to predict the occlusion masks directly from RGB images, and found--quite surprisingly--that the network had a tendency to learn the true occlusion mask, despite being only provided with the inaccurate mask.
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 4 Results
### Model Development and Performance Evaluation  
#### 4.3.1 Feature Selection  
In our pursuit of extracting crucial features and streamlining our feature set, we employed a feature selection method based on feature importance. This approach involved working with a dataset comprising 763 stratified samples (532 focused and 231 distracted samples), with the minority distracted group augmented using SMOTE to address class imbalance [35]. To reduce the variability inherent in individual RF iterations, we trained an ensemble of 30 distinct RF models and aggregated their average feature importance scores. These averaged scores were subsequently ranked, and for clarity in visualization and to simplify chart complexity, only the top 40 features were depicted in the feature importance ranking graph, as shown in Figure 10.  
Given the pronounced disparities in feature importance, it is essential to streamline and optimize the input features to avoid redundant information that could undermine model performance. Therefore, we selected the top 40 features based on their average importance rankings and incrementally incorporated them into the training model to examine how model accuracy evolves with increasing feature counts (Figure 11). As illustrated in the figure, the RF model initially exhibits low accuracy levels with a limited feature set, indicating insufficient critical feature support. As the number of features increases, model accuracy significantly improves, reaching 85.1% with 15 features. This suggests the model effectively captures the essential features for classification performance. Beyond this point, accuracy stabilizes with minor fluctuations, indicating marginal or negligible gains from additional features. In some cases, an excess of features even reduces accuracy, suggesting the detrimental effects of superfluous or noisy features. Consequently, we selected the top 15 features by importance as the optimal set for our classification model.  
Figure 10: Ranking of the top 40 features by importance. The naming convention for features is: Indicator_Channel.