esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Description of SST and Its Departure Noise Modeling
In addition, an additional assumption is made that the available engine thrust is a linear function of the aircraft velocity \(V\) and altitude \(H\) in the considered range of these parameters, so that  
\[T\ =\ \tau T_{0}(1-aV)(1-bH) \tag{4}\]  
where \(T_{0}\) is the engine thrust under static conditions at the sea level. Thrust setting coefficient \(\tau\) is introduced to account for thrust control by an automatic VNRS or by a pilot at the cutback point. The certification procedure described in Section 2 imposes a number of constraints that the variables of Equations (2)-(4) have to meet, which were conveniently tabulated in [12]. The present study also preserves the constraint that the indicated speed must be kept constant after clearing the obstacle height and before reaching the pilot-initiated cutback point. The condition of constant indicated speed is related to the flight speed \(V\) by the following expression:  
\[\frac{dV}{dt}\ =\ -\frac{V^{2}}{2\rho}\frac{d\rho}{dH}\sin\gamma, \tag{5}\]  
which can be used for simplification of the first equation of (2). Equations (2)-(5) are solved with TsAGI in-house code that was developed and extensively used for determining the optimal deflection of high-lift devices \(\theta_{haps}\) to minimize the certification noise of subsonic aircraft [41]. It has been compared with SOPRANO [38] results and agrees well both for subsonic and supersonic aircraft. For the purposes of the present study, it has been modified to allow for variable thrust control. The position of high-lift devices \(\theta_{haps}\), is kept constant during the departure procedure in the same manner as is done for subsonic aircraft; thus, the considered VNRS is limited to variations in thrust. The resulting trajectory is represented as a number of segments, and for each segment, aircraft noise is calculated based on the available engine noise data. Under the assumption that the aircraft noise source is compact, the noise is propagated from the position at the takeoff trajectory to the certification microphones. Certification regulations [1] require that the temporal resolution for the measured signal at the microphone should be not less than 0.5 s, so the condition has been always checked, and the number of segments has been increased if necessary to ensure a time step \(\Delta t\) < 0.5 s. Based on the calculated time series at the microphone, the effective perceived noise level (EPNL) was computed. A block diagram of the modeling approach used in the study for the calculation of certification noise of the SST is provided in Figure 4. Its input data consist of:
# Satellite Image Categorization Using Scalable Deep Learning
## 4 Results
### Evaluation Metric  
Different evaluation metrics are used for the analysis of the results. These evaluation metrics include accuracy \((\mathrm{A})\), recall \((\mathrm{R})\), precision \((\mathrm{P})\), the F1 measure \((\mathrm{F1})\), [PERSON]'s kappa \((\mathrm{\kappa})\), and area under curve (AUC). These are all common evaluation metrics used in the context of multi-class classification tasks.  
Accuracy measures the fraction of correctly predicted instances among all instances. In multi-class classification, we can calculate the overall accuracy by summing up the number of correct predictions for all classes and dividing it by the total number of instances.  
\[\mathrm{A}=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{Total}\text{ samples}} \tag{15}\]  
Recall measures the fraction of accurately projected positive occurrences among all genuine positive occurrences. In multi-class classification, we can calculate the recall for each class by treating that class as the true class and all other classes as false values.  
\[\mathrm{R}=\frac{\mathrm{TP}}{(\mathrm{TP}+\mathrm{FN})} \tag{16}\]  
Precision measures the fraction of accurately anticipated positive occurrences among all projected positive cases.  
\[\mathrm{P}=\frac{\mathrm{TP}}{\mathrm{FP}+\mathrm{TP}} \tag{17}\]  
The F1 score is the harmonic mean of precision and recall. It is a way to combine both precision and recall into a single metric that is useful when we want to balance the importance of correctly predicting both positive and negative instances.  
\[\mathrm{F1}=2\cdot\frac{\mathrm{P}\times\mathrm{R}}{\mathrm{P}+\mathrm{R}} \tag{18}\]  
where TP = true positive, TN = true negative, FP = false positive, and FN = false negative.  
[PERSON]'s kappa is a measure of inter-rater agreement between two annotators. In the context of multi-class classification, we can treat the predicted labels and the true labels as two annotators and calculate their agreement using [PERSON]'s kappa.  
\[\kappa=\frac{\mathrm{P_{o}-P_{e}}}{1-\mathrm{P_{e}}} \tag{19}\]  
where \(\mathrm{P_{o}}\) is the probability of agreement, and \(\mathrm{P_{e}}\) is the probability of random agreement.
# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 3 Background
### Gaussian Mixture Models
The Davies-Bouldin index measures the ratio of within-cluster scatter (\(S_{i}\)) to between-cluster separation (\(M_{ij}\)). The index is the average of the worst-case ratio for each cluster. A lower Davies-Bouldin index indicates better clustering, meaning that the clusters are compact (low \(S_{i}\)) and well separated (high \(M_{ij}\)). Furthermore, The Calinski-Harabasz index is used to evaluate the quality of clustering. It is defined as follows:  
\[\text{CH Index}=\frac{\text{Tr}(B_{k})}{\text{Tr}(W_{k})}\times\frac{N-k}{k-1}\]  
where  
* \(N\) is the total number of data points. * \(k\) is the number of clusters. * \(\text{Tr}(B_{k})\) is the trace of the between-cluster dispersion matrix. * \(\text{Tr}(W_{k})\) is the trace of the within-cluster dispersion matrix. The Calinski-Harabasz index measures the ratio of the sum of between-cluster dispersion to within-cluster dispersion. A higher value indicates that the clusters are well separated and more compact. Specifically,  
* High values indicate better-defined clusters (high between-cluster variance and low within-cluster variance). * Low values suggest poor clustering (high within-cluster variance or low between-cluster variance).
## 3 Results  
Figure 1 shows the study flow chart. Among 77 patients screened, 47 were initially enrolled. Of these, 13 were excluded after angiography, 1 withdrew consent for study participation, and 2 had missing cystatin C values. Therefore, the study population comprised 31 NSTE-ACS patients treated with PCI. Three patients refused to undergo FDG-PET but still agreed to participate to the study. Baseline characteristics are shown in Table 1. The median age of the study population was 65 (55-72) years, and 26 patients (84%) were male. They had a high prevalence of classical risk factor. Ischemic changes of ECG (ST deviation or inverted T waves) were observed in 21 cases (68%). The median values of creatinine and MDRD e-GFR were 0.94 mg/dL (0.77-1.02) and 87 mL/min/1.73 m\({}^{2}\), respectively. The median value of cystatin C was 0.95 mg/mL (0.88-1.08). There were no differences in baseline characteristics between the two study groups, with the exception of CRP values. Indeed, patients with low level of cystatin C showed a strong trend toward an increased level of CRP (0.52 vs. 0.34 ng/L, p = 0.05). Table 2 shows that there were no differences in terms of OCT-assessed features of coronary plaque vulnerability between the two study groups. Overall, patients with low levels of cystatin C were more likely to have a higher FDG uptake in the aortic wall and CAC score. When modelled as continuous variable, cystatin C levels showed a strong trend towards an inverse relationship with the number of active slices in DA (rho = \(-\)0.35, p = 0.06). Yet, after multivariable analysis, levels of cystatin C were independently associated with FGD uptake in DA as measured by either averaged max TBR or number of active slices (Table 3). Figure 2A depicts a \({}^{18}\)F-FDG-PET/CT scan from a patient in the high cystatin group showing no significant FDG uptake in the DA (max TBR = 1.3), whilst a further \({}^{18}\)F-FDG-PET/CT scan from a patient in the low cystatin C group shows an active (max TBR = 2.0) focus of FDG uptake in the DA wall (Figure 2B). There were no active slices in the AA, in both cases. Finally, no trend was found for the association of cystatin C and CAC score. Figure 3 shows that areas of FDG uptake in the aortic wall are not related to the presence of calcification.
# Predicting Road Traffic Accidents--Artificial Neural Network Approach
## 2 Methodology
### Cluster Analysis  
Figure 5 depicts the results of a cluster analysis performed on the observed samples. The analysis employed the complete linkage algorithm and the City block (Manhattan) distances to assess the proximity of the samples. The City block distances, displayed on the abscissa axis, are a measure of the average difference between the dimensions of the tested samples [53; 54]. The linkage distance, also shown on the abscissa axis, between the main clusters was substantial, approximately 60,000. The dendrogram generated by the cluster analysis revealed the presence of seven main clusters, as seen in Figure 5. The cluster analysis was performed according to the Table S2, which shows the data regarding AADT, section length, terrain type, speed limit, evident injury, disabling injury, fatality, property damage and traffic accident. The cluster analysis was applied to show the similarities in observed parameters between samples. Cluster 1 comprised of sites 1, 72, 57, 58, 59, 71, 60, 62, 61, and 70, Cluster 2 included sites 2, 55, 12, 14, 13, 53, 56, 3, 6, 52, 54, 4, 51, 73, 7, 8, 67, 69, 5, 9, 50, and 68, and Cluster 3 consisted of sites 10, 65, 11, 27, 64, 45, 47, 46, 75, 76, 28, 74, 66, and 49. Cluster 4 included 15, 16, 17, 37, 38, 39, 18, 40, 34, 35, 19, and 22. Cluster 5 included 26, 77, 44, 41, 48, 42, and 43. Cluster 6 included 20, 25, 32, 33, 36, 21, 78, and 24, Cluster 7 included 23, 31, 30, 29, and 63.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **SL (m)** & **AADT (veh/day)** & **TA** \\ \hline Mean & 8184.53 & 5013.53 & 14.24 \\ SD & 10,582.75 & 4436.44 & 18.51 \\ Min & 0.20 & 109 & 0 \\ Max & 49,492 & 25,581 & 144 \\ Var & \(1.1\times 10^{8}\) & \(2.0\times 10^{7}\) & 342.70 \\ \hline \end{tabular}
\end{table}
Table 2: The average data for roads 1–3.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **AADT** & **SL (km)** & **EI** & **DI** & **F** & **PDO** & **TA** \\ \hline Mean & 18,547.60 & 10.51 & 1.96 & 0.91 & 0.38 & 7.67 & 10.92 \\ SD & 11,078.48 & 6.79 & 2.02 & 1.03 & 1.31 & 7.00 & 9.11 \\ Max & 66,985 & 29.50 & 7 & 4 & 11 & 41 & 50 \\ Min & 5574 & 1.5
## 2 Regions and Methods
### Study Area
#### 2.1.1 Meteorology and Hydrology  
The research region is a part of the middle temperate continental monsoon climate zone and is situated in the mid-latitude zone. All four seasons see considerable climate change: the spring is drought and windy, the summer is hot and brief with concentrated rain, the fall is chilly and cooling, and the winter is chilly and long. The annual sunshine number is 2565 h, the annual average temperature is 4.2 \({}^{\circ}\)C, the extreme maximum temperature is 38 \({}^{\circ}\)C, the extreme minimum temperature is \(-\)37 \({}^{\circ}\)C. The annual average rainfall is 540 mm, and the rainfall period is concentrated in June to August, accounting for more than 60% of annual rainfall. The average frost-free period is 144 d. The ground temperature is lower than 0 \({}^{\circ}\)C for 5 months of the year, the permafrost layer is 1.5-2.0 m, the standard freezing depth is 2.0 m, and the maximum freezing depth can reach 2.2 m [13; 14; 15]. Strong sunlight and precipitation make it simple for the slope to be in the cycle of water-saturation and drying crack. Strong sunlight causes mudstone to weather quickly and lose shear strengths completely. In addition, precipitation causes mudstone to relax and soften, and changes the structure of the rock, all of which cause the slope to become unstable.
# Detecting Composite Functional Module in miRNA Regulation and mRNA Interaction Network
## 4 Experiments and Results
### Association between Structural Consistency and Functional Hierarchy of CMFMs  
The structural consistency of CMFM is associated with their functional annotation hierarchy. To evaluate function enrichment of the identified CMFMs, we employ GOstats [19] to calculate their enrichment in GO-BP terms. In order to visually display the level of functional enrichment, we implement GO enrichment score (GOES) to measure GO-BP enrichment degree for each module. The GOES is defined as \(\sum\limits_{i}^{I}-\log_{10}p_{i}\), here \(I\) represents the number of GO-BP terms the mRNAs enriched and \(p_{i}\) represents p-valve in the \(i\)th GO-BP term.  
The modules detected by SNMMNF have, on average, small density values, which indicates that the mRNAs in the modules of SNMMNF are sparsely connected with the miRNAs. In addition, miMEC reflects the regulatory strength between miRNAs and mRNAs. Since miRNAs are known to suppress mRNAs, the negativity of miMEC is a reasonable indicator for evaluating the quality of CMFMs [10; 11; 20]. However, the average miMEC value of SNMMNF module is positive, which further indicates that there is an amount of false positive in CR and PPI interactions in the modules. Therefore, there is not much necessity to compare MBCFM with SNMMNF in functional analysis. Thus, we here implemented functional analysis comparison between MBCFM and Mirsynergy. Accordingly, we adopt a functional enrichment comparative analysis just for the mRNAs lists involved in these modules identified by MBCFM and Mirsynergy.  
The average number of mRNA identified by MBCFM is 5.42, and 4.76 in Mirsynergy. Seventy-eight of the 84 modules (just 93%) from Mirsynergy are enriched in GO-BP terms. It is less than the ratio of MBCFM 89/94 (just 95%). As illustrated in Figure 5, our modules with the function enrichment scores intensively distribute between 0.2 and 0.5 of the density value, whereas in Mirsynergy its module distribution is messy and there are lots of dense modules with zero GOES. These CMFMs identified by Mirsynergy are not meaningful for function analysis.  
We further employed David website ([[https://david.ncifcrf.gov/](https://david.ncifcrf.gov/)]([https://david.ncifcrf.gov/](https://david.ncifcrf.gov/))) to calculate their enrichment in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Seven modules from MBCFM are enriched in KEGG pathways. Our method is three modules more than Mirsynergy. In conclusion, the observations are in agreement with the viewpoint that our modules are more functionally enriched than Mirsynergy.
## 1 Introduction
### Related Works  
In similar work, the authors of [26] presented a hybrid aerial FD relaying protocol consisting of a IRS mounted UAV system. To help the information transfer between the base station and multiple users, the UAV acts as a relay operating in the decode and forward mode. The study also presented the closed-form formulas of achievable throughput, outage probability and ergodic capacity. The authors in [27] studied IRS-assisted multi-user multiple-input single-output (MISO) wireless systems by considering the ergodic capacity in both uplink and downlink scenarios. They examined the realistic case of statistical instantaneous channel state information (CSI). Further, analytical expressions of the ergodic sum capacity were derived as the main contribution. In [28], a two-IRS system was studied by employing the centralized and the distributed modes corresponding to the reflecting elements being mounted at a single IRS, and multiple IRSs were designed with the same number of reflecting elements. To examine the benefits of the two-IRS model, the closed-form approximation expressions of the ergodic capacity were derived along with their tight upper and lower bounds to provide more necessary insights. Although the transmit power and the Rician-\(K\) factor have the main influences on the system performance, selected modes of the centralized IRS result in a better ergodic capacity as compared with the distributed IRS mode. It is worth noting that the location of the IRS has strong impacts on ergodic performance. Therefore, a multiple IRS design for IoT is necessary.
# Exploring Data Model Relations in OpenStreetMap
## 4 Experimental Analysis
### Relation Analysis
#### 4.2.2 Existence of Super Relations  
We investigated the presence of super relations in all four cities. Taking our guidance from the OSM Wiki pages [25], we considered a relation as a super relation if it had 300 or more members defined within it. Overall, the numbers of such relations are, as expected, very low. Madrid has a total of 33 super relations, Rome has 46 and Zurich has 55. The exception for the four cities is Vienna, with a total of 149 super relations, of which 86 of these relations have between 300 and 500 members. Taking Vienna as an example, some of the largest super relations (21 have over 1000 members) represent long distance bus routes, very long hiking trails of hundreds of kilometers, or parts of the International European route network. Only 8 of the identified super relations in Vienna do not have the type=route tag. Even when we consider a wider definition of super relation to include all relations with 100 or more members, we still find that the number of relations exhibiting this characteristic to the small. There are 154 (8.30%) in Zurich, 386 (6.20%) in Madrid, 556 (6.05%) in Vienna and 673 (5.08%) in Rome.
## 3 Results and Discussion
When the pore size is large, the main role is played by the membrane-gas interaction as the molecules characterized by the largest Van der Waals interactions are more likely to stick at the membrane surface, diffuse along the surface and then drop into the pore without needing much energy as the transition is almost barrier-less. When the pore size is small enough to give rise to a relevant barrier for its crossing, it is rather the gas-pore termination interaction and the temperature that become important as molecules need to approach the pore with the correct orientation and they need a sufficient amount of kinetic energy to overcome the barrier.
## 2 Data Collection
### Railway Bridge Damage Data Collection  
A total of 17 railway bridges were surveyed using UAVs, resulting in the collection of approximately 14,155 images that captured a wide range of complex and diverse damage types. As shown in Figure 1, a dual-camera top-mounted UAV was deployed to capture damage images from actual in-service railway bridges. During the image acquisition process, overview shots of the target bridges were also captured from a distance to provide contextual information, as summarized in Table 2.  
A key feature of this study is that all damage data were collected from operational railway bridges, ensuring the practical relevance and applicability of the model training and analysis.  
Table 3 presents the number of images categorized by damage type. As shown in the data, there is an abundance of images for cracks, concrete scaling/spalling, and paint peeling, whereas fewer samples were available for water leakage and exposed rebar. Given the characteristics of deep-learning-based analysis, a larger volume of training data generally leads to improved classification and detection performance. However, due to the regular maintenance practices of railway bridge management authorities who conduct ongoing  
Figure 1: Process of collecting damage images from a railway bridge using a UAV.  
inspections and repairs to ensure structural safety, it is inherently difficult to acquire large quantities of real-world damage data, especially for less frequently occurring types.  
Nevertheless, for the developed model to be effective in practical applications, it is critical to train on data that reflect environmental conditions and factors that may affect detection performance or lead to false positives. To maximize data diversity and volume, this study focused on bridges within the Korean railway network known to exhibit the highest frequency of damage occurrences. As shown in Table 3, the collected images have a resolution of 42.4 megapixels (7952 \(\times\) 5304 pixels). The dataset includes images with no visible damage, images with a single type of damage, and images containing multiple overlapping damage types. Processing 42.4 MP images posed significant computational challenges. To manage GPU memory efficiently, image patches of 320 \(\times\) 320 pixels were extracted with overlap during training and inference. Additionally, inference time was optimized through parallel processing across multiple GPUs.  
To address the class imbalance caused by the relatively small number of images for leakage and rebar exposure, an oversampling strategy was applied to these classes. Specifically, data augmentation techniques were employed, including random rotations and spatial relocation of damage patterns within the images. By synthetically increasing the  
\begin{table}
\begin{tabular}{c c} \hline \hline  & \\ \hline \hline \end{tabular}
\end{table}
Table 2: A view of railway bridges taken by a UAV.  
quantity and diversity of training samples for these underrepresented classes, the model's ability to recognize them in field conditions was significantly improved.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 3 Analysis of Resource Management in the MLSCS  
In the MLSCS, the global distribution of SBSs, users, and their services, along with the use of global spectrum resources, greatly affects the scheduling of system resources. How to allocate a greater amount of resources to the places where users and service are concentrated, so as to improve the utilization efficiency of system resources, is the most important issue concerning resource management in the MLSCS.
# Enhancing Data Integrity in Blockchain Oracles Through Multi-Label Analysis
## 3 Methods
### Evaluation in a Simulated Blockchain Environment  
In the final implementation phase, once the model has been trained and evaluated, Bagging with four different models, including Random Forest (RF), Extra Trees (Extremely Randomized Trees), Bagged SVM, Bagged KNN, and the proposed IMWV is implemented in a test system using Ethereum technologies, with a test network in Ganache and Truffle. The smart contract will use the model to validate data integrity in the context of futures contracts, as presented in the proposed application architecture, shown in Figure 8.  
We designed a test to validate the models in a 60-day scenario where various variables will be requested twice daily, including altitude, temperature, humidity, precipitation, variety, and coffee price. These variables, which tend to show high volatility and significant changes in the initial stages of cultivation, will be sent to the smart contract through an oracle. This evaluation aims to verify the integrity of the data transmitted to the contract, ensuring its accuracy and consistency. Table 5 asses the variables.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Variable** & **Description** & **Frequency** & **Data Type** \\ \hline Altitude & Altitude of the cultivation region (in meters) & Twice a day & Numeric \\ Temperature & Current temperature in the cultivation region & Twice a day & Numeric (\({}^{\circ}\)C) \\ Humidity & Relative humidity in the region & Twice a day & Numeric (\%) \\ Precipitation & Level of precipitation (in mm) & Twice a day & Numeric \\ Variety & Variety of coffee being cultivated & Static (once) & Text \\ Coffee Price & Market price of coffee & Twice a day & Numeric (COP) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Key variables for daily smart contract data requests.