esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 3 Navigation Signal Design Considerations
### Ranging Performance
#### 3.3.1 DLL Error  
The DLL error is a component of the UERE budget and depends on the signal reception environment (e.g., \(C/N_{0}\)) and the signal waveform. The DLL error refers to inherent code tracking error due to signal waveform and can be analyzed in terms of accuracy and stability. Assuming only white noise exists, the 1 sigma value of the DLL error \(\sigma_{DLL}\) is determined by the thermal noise \(\sigma_{tDLL}\) and the dynamic stress error \(R_{e}\).  
\[\sigma_{DLL}=\sigma_{tDLL}+\frac{R_{e}}{3} \tag{7}\]  
For the stable DLL tracking in the receiver, the value of \(\sigma_{DLL}\) should not exceed one-sixth of the linear pull-in range of the DLL discriminator. Typically, the pull-in range of the DLL discriminator is defined as \(\pm d/2\), where \(d\) is the correlator spacing in chips [40].  
The dynamic stress error depends on the loop order and user dynamics. Therefore, we consider only the thermal noise jitter to evaluate the DLL error for signal design candidates. The thermal noise jitter is determined according to the DLL discriminator type. When using coherent early-minus-late processing (CELP) and noncoherent early-minus-late processing (NELP) as discriminators, the DLL thermal noise jitter (in seconds) is defined as follows [41; 42; 43]:  
\[\sigma_{tDLL,CELP}^{2}=\frac{B_{L}\int_{-B_{r}/2}^{B_{r}/2}G_{s}(f)\sin^{2}(\pi fd )df}{(2\pi)^{2}\frac{C}{N_{0}}\left(\int_{-\frac{B_{r}}{2}}^{\frac{B_{r}}{2}}fG _{s}(f)\sin(\pi fd)df\right)^{2}} \tag{8}\]  
\[\sigma_{tDLL,NELP}^{2}=\frac{B_{L}\int_{-B_{r}/2}^{B_{r}/2}G_{s}(f)\sin^{2}(\pi fd )df}{(2\pi)^{2}\frac{C}{N_{0}}\left(\int_{-\frac{B_{r}}{2}}^{\frac{B_{r}}{2}} fG_{s}(f)\sin(\pi fd)df\right)^{2}}\left[1+\frac{\int_{-B_{r}/2}^{B_{r}/2}G_{s}(f) \cos^{2}(\pi fd)df}{T\frac{C}{N_{0}}\left(\int_{-\frac{B_{r}}{2}}^{\frac{B_{r} }{2}}G_{s}(f)\cos(\pi fd)df\right)^{2}}\right], \tag{9}\]  
where \(B_{L}\) is the DLL bandwidth and \(T\) is the coherent integration time.
## 2 System Overview
### oneM2M Technology Standard
In most cases, MNs are created in the IoT gateway. Application service nodes (ASNs) are lightweight common service layers and AE application services are utilized in a remote M2M-based IoT system. In a tiny or limited IoT device system, application dedicated nodes (ADNs) are used to offer sensor monitoring and information return [22]. For visualization of data collected from the sensor, the authors used an oneM2M browser application. This application represents how data sensors (temperature, humidity, PM0.3, PM0.5, PM1, PM2.5, PM5, and PM10) are stored in the MySQL database. As we can see in Figure 4, the green blocks titled cin represent updated sensor data received every second.
# Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering
## 5 Conclusions  
Designing effective routing protocols and network management for VANETs is a challenging task due to the dynamic nature of mobility and data delivery. This challenge highlights the importance of developing dynamic parameters for routing protocols and clustering. While ACO has been used in various ways to optimize solutions, it has often failed to adequately address dynamic constraints. This study introduces a heuristic-based clustering algorithm integrated with an improved ant colony optimizer, resulting in a model that establishes a scalable and stable topology for VANET systems. The proposed model begins by employing a novel approach to select an intelligent initial node and optimizing the search space. Additionally, it enhances the convergence speed of the traditional ACO by adjusting the evaporation factor. The TR-PED algorithm, introduced in this study, accurately estimates the distances between deployed vehicles, ensuring robust VANET connectivity. A comprehensive comparison with the conventional ACO highlights the potential of the proposed approach. The results demonstrate improved simulated performance in terms of packet delivery, packet dropout, delay, and throughput. These innovative design concepts effectively address the challenge of local search space limitations and ultimately achieve reliable, scalable, and efficient vehicle-to-vehicle (V2V) communication. As a future work, the proposed technique can be extended to the concept of cellular automata which helps to select the optimal route by detecting malicious activities.  
Conceptualization, K.C.P. and I.H.; methodology, A.B., K.C.P. and M.A.K.; software, A.B., validation, K.C.P.; formal analysis, A.B. and K.C.P.; investigation, A.B. and K.C.P.; resources, I.H.; data curation, A.B. and K.C.P.; writing--original draft preparation, A.B., K.C.P. and I.H.; writing--review and editing, A.B., K.C.P., M.A.K. and I.H.; visualization, A.B., K.C.P. and I.H.; supervision, K.C.P., M.A.K. and I.H.; project administration, M.A.K. and I.H.; funding acquisition, I.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No. of Vehicles** & **DA-TRPED** & **Conventional ACO** \\ \hline
20 & 5 & 7 \\ \hline
40 & 6 & 9 \\ \hline
60 & 8 & 12 \\ \hline
80 & 13 & 14 \\ \hline
100 & 15 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative results of cluster formation analysis for network performance evaluation.
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 2 Materials and Methods
### Backbone Pre-Training Datasets  
For backbone pre-training with SSL, we look at existing large scale Sentinel 2 datasets. Several have been published in recent years and usually focus on land cover classification or segmentation. Since we do not use labels for pre-training we can use these types of data easily and in large quantities. Some well-known datasets are:  
* EuroSAT [25], which is a 10 class land-cover classification dataset containing 27,000 multispectral patches of size 64 \(\times\) 64 pixels. We apply the (A)ugmentation pretext task on this dataset;
* BigEarthNet [28], which is a very large scale multi-label land-cover classification dataset. It contains 590,326 multi-spectral image patches of size 120 \(\times\) 120 pixels. We randomly crop 320,000 64 \(\times\) 64 pixel patches from the original dataset and we apply the (A)ugmentation pretext task for BigEarthNet;
* SEN12 MS [29], which is a very large curated land cover segmentation dataset, made of Sentinel 1, 2 and MODIS images. It contains 180,662 Sentinel 2 multi-spectral patches of size 256 \(\times\) 256. For this dataset, we apply both the (A)ugmentation and the region-wise and augmentation (RA) pretext task. For the first one we sample 1,337,360 patches 64 \(\times\) 64 patches from the 256 \(\times\) 256 patches in the dataset. For the (RA) and (RA-lo) tasks we sample patches \(x^{q}\) and \(x^{k_{+}}\) of size 64 \(\times\) 64 pixels randomly  
Figure 4: S2-SHIPS geographical distribution of image tiles. Points on Western Europe map (middle) represent image tiles in data set for ports of: Southampton, Portsmouth, Brest, Rotterdam (3 tiles), Toulon, Rome, Marseilles.  
Figure 5: S2-SHIPS dataset patch samples: Brest (FR), Toulon (FR), Rotterdam (NL), Colon (PA), Suez Canal (EG). Note that some images have partial cloud cover or rough sea conditions.  
from the same 256 \(\times\) 256 patch. The distance between these two \"positive\" patches can thus be at most 1.2 km for (RA) and 640 m for (RA-lo), while sometimes there can be an overlap.
# Mobile Broadband Performance Evaluation: Analysis of National Reports
## 3 Case Studies
### Communication Report: Australia  
Regulator_\(\rightarrow\) Australian Communications and Media Authority (ACMA) of Australia also used survey-based quality evaluation. The authority started gathering and analyzing the report in 2005. The report was released in 2020 with 2018-2019 evaluation results [15].  
Operators_\(\rightarrow\) There are three major MNOs in the country: Telstra, Vodafone, and Optus.  
Method_\(\rightarrow\) Each year since 2005, ACMA surveys a group of people using statistical sampling to represent the population. As of 2019, the Australian adult population aged \(>\)18 stands at 19.4 million, and accordingly, 2067 people have been surveyed for this fiscal year. The data from the survey are then weighted to reflect the population. However, the annual report is based on the number of users instead of coverage.  
Metrics_\(\rightarrow\) Although there are no specific metrics that are useful for technical evaluation, ACMA together with AMTA requested all MNOs to provide a coverage map. This coverage map will represent the population covered instead of the geographic area covered. However, here are some of the metrics used to evaluate the quality of mobile broadband services:  
* Customer satisfaction \(\rightarrow\) rating out of 5;
* Pre- vs. postpaid service;
* Market share.  
Evaluated services \(\rightarrow\) 3G, and 4G, mobile broadband services coverages are expected to be available at each MNOs website. The data are then expected to be refined by area.  
Result_\(\rightarrow\) Even though it is not technical, here are some data extracted from the report.
* Coverage of population from at least one MNO for 3G and 4G services reached 99.4%. In addition, there are now \(>\)400 5G-capable base stations in Australia.
* Among the total population, 96% have mobile phones and 83% have smartphones. There are around 35.9 million mobile subscribers from all MNOs, where 54%, 29%, and 15% of the total subscribers belong to Telstra, Optus, and Vodafone, respectively.
* Among the subscribers, 69% are postpaid while the rest are prepaid. This number is only slightly different from previous years.
* There are arrays of quality of services evaluated through customer satisfaction. Although coverage satisfaction has improved, it is still under 4. The same is true for data speed and voice quality.  
Policies_\(\rightarrow\) A revised consumers protection code is issued to protect consumers from bad selling practices, credit assessment, financial crises due to communication services. In addition, a revised mobile base station deployment policy is also issued to strengthen communication services in rural and under-served areas.  
The report by ACMA mainly focused on the market share of MNOs and revenues from communication services, which made it difficult to present more results. Although the authority demanded MNOs to provide the coverage map, the data available in each MNO are not sufficient enough to display.
# Deep Learning-Based Low-Frequency Passive Acoustic Source Localization
## 2 Method  
The methodology used for low-frequency ASL in this paper can be described in three steps. The first step involves collecting the synthetic acoustic pressure data using a simulated microphone array. These data are then processed, either in the time or frequency domain, to generate an input feature capable of being fed to a CNN. Finally, the CNN is trained with the generated input feature against the ground truth and is used to make predictions. The method is summarized in Figure 2.  
Figure 3 shows a flowchart of the three test cases for low-frequency ASL developed in this paper. Case 1 (inspired by [PERSON] et al. [18], [PERSON] and [PERSON] [27]) is regression-based two-dimensional ASL on a scanning plane parallel to the microphone array plane. This setup can be applied to the detection of aeroacoustic sources, such as wake vortices that emit low-frequency noise. Case 2 (source localization on the horizon), also two-dimensional, tackles ASL as a direction-of-arrival (DoA) estimation problem through a classification approach. Case 3, which is an extension of case 2, deals with three-dimensional ASL. Cases 2 and 3 are inspired by the work of [PERSON] et al. [11] on the development of an early-warning clear-air turbulence (CAT) detection system using a ground-based microphone array. All the cases are discussed in detail in Section 3.
# Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities
## 2 ICN-Based VANET  
The research community and academia world explored and successfully deployed VANET in the last two decades. Currently, it is gaining more popularity due to rapid increase in the number of vehicles worldwide. VANET emerges in our daily life through conferring vehicles and their related objects, for example, road side units (RSUs), with communication resources that lead to vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), infrastructure-to-vehicle (I2V), and more generically, vehicle-to-everything (V2X) communications. Sensors in vehicles allow the transmission of collected information such as live video streaming between two vehicles, V2X application servers, RSUs, and cellphones of pedestrians [2]. Vehicles may extend the insight of their network outside and can have more general view of the local settings. Remote driving permits an application concerning V2X to activate a remote vehicle located in a hazardous zone or for people who are unable to drive vehicles. For a scenario where roads are predictable such as public transportation, then driving on the basis of cloud computing may be used. For this kind of scenario, such a platform may be considered where the entrance is based on cloud services [2].  
VANET related applications have several advantages, for instance, it reduces accidents and harms identified with individuals and vehicles [3]. In addition, it saves individuals time by providing traffic related data such as a busy and congested road [4].  
Figure 1: Vehicular internet.  
ICN is an appealing applicant solution for vehicular communications due to its numerous benefits. First, it fits fine to the quality of usual VANET usages, such as route reports and accident messages [5]. These usages are probable to benefit from in-network content caching and strategies. Second, data caching is mainly favorable to speed up content retrieval via caching in different nodes [6]. In vehicles, caching may typically be deployed at fairly low cost, as the energy demands of ICN nodes are likely to be a small portion of the overall energy use of vehicles, hence agreeing on high-level computation, uninterrupted data processing, and enough caching space in vehicles. In addition, ICN mainly endorses asynchronous information sharing among end users. [5; 7].  
Beside several advantages, VANET also faces numerous challenges, for example, vehicles' mobility and network disruptions, i.e., if two vehicles are connected in a grid and after some distance they change their route, then it is a challenging task that how they can communicate with each other. To overcome these kinds of issues, a new clean-slate model is required so that the user quality of experience is achieved. ICN is believed to be the most suitable paradigm for smooth data transmission without extra retrieval delay in VANET environments.
## 2 State-of-Art Review  
### General Parameters Affecting the Performance of RFA in Concrete  
#### 2.1.1 Recycling Process  
The process by which CDW is turned into aggregates, whether coarse or fine, suitable for reintegration in concrete can be divided in several stages, namely separation by metal detectors, initial screening, crushing, secondary screening, secondary crushing and then sieving and separating [40]. This number of stages of crushing has a significant effect on the quality of the RFA produced and subsequently on the RAC which will include it. Source concretes subjected to a secondary crushing procedure in an impact crusher normally result in RFA with less attached mortar than those following only a primary crushing procedure. Thus, due to the hardened cement paste having higher porosity than that of NA, as the content of adhered-mortar increases, so does the RA's water absorption [41]. [PERSON] and [PERSON] [42] obtained RFA from demolished concrete of current compressive strength (21 MPa). By repeatedly crushing and sorting, the authors used water absorption ratios of 6% and 8% to evaluate the effect of water absorption ratio of the RFA on the bond strength between reinforcing bar and RAC. [PERSON] [43] investigated two different crushing processes using a jaw crusher and an impact crusher. The work resulted in oven-dried density of 2390 kg/m\({}^{3}\) and 2280 kg/m\({}^{3}\) and water absorption of 6.59% and 10.35%, respectively. The process of washing the RA also has a strong effect on their WA. A study by [PERSON] and [PERSON] [44] showed that, after RA were washed, the water absorption of the aggregates dropped by 35% to 55%. This can be explained by the fact that very small particles were removed by the water after washing the aggregates, which significantly affected the water absorption of the fine RA. [PERSON] and [PERSON] [45] washed fine RFA with different washing stages and used a combination of chemical and physical processes. The process of washing the RFA had several effects; the water absorption fell from 6% to 2%, the impurity content fell from 0.5% to 0.2% and the ratio of absolute volume increased from 62% to 65%. Hence, it could be concluded that it is advisable to use jaw crushers rather than impact ones and to do several cycled of washing and crushing the CDW. However, it should be noted that the more cycles done, the higher the energy use is, which will then reflect on the environmental performance of the resulting RFA as discussed in Section 2.3.
## 4 Discussion
Based on in vitro tests, they found that high concentrations of AgNPs (18 wt.% and 25 wt.%) inhibited osteoblast proliferation, whereas lower levels (2 wt.% and 6 wt.%) induced good cytocompatibility. The cell cycle distribution oh mouse fibroblast NCTC, clone 929, cultivated in the presence of tested PLA-based bionanocomposites was also investigated by flow cytometry. Overall, the dynamics of the cell were not blocked in any phase of the cell cycle and were prepared for a new cell division. This pattern observed in the dynamics of the cell cycle is in agreement with the cell cycle distribution of cells at an optimal density. G1 phase is very important because it influences the cell to divide or not, whereas in G2 phase cells grow in size and synthesize mRNA and proteins required for DNA synthesis. Silver nanoparticles were reported to be toxic to human cells at concentrations above 1% [53] and induce the reactive oxygen species generation [54]. It has been reported that the cytotoxicity of antimicrobial medical devices containing silver nanoparticles is dependent on the size and surface area of inorganic ions released from the devices [55]. Contradictory data showed that the AgNPs with a mean size of 15 nm induced the greatest loss in mitochondrial activity [56]. However, according to the Occupational Safety and Health Administration (OSHA) and the Mine Safety and Health Administration (MSHA), the permissible exposure limit for silver compounds are estimated to be 0.01 mg/m\({}^{3}\)[57]. The evidence related the commercial antimicrobial Luer-activated connector coated with silver nanoparticles, showed it is approved by the Food and Drug Administration [58]. However, the low concentration of silver nanoparticles is needed to ensure both non-cytotoxicity and bactericidal effect of elaborated materials. The values obtained in this experiment indicated that the cells were not blocked in any phase of the cell cycle and that most of these cells proliferated. All tests recommended the variant containing 0.5% AgNPs for biomedical applications. Future research will be conducted to investigate the release pattern of AgNPs in the medium, as well as their interaction with the cells. The effect of the silver nanoparticles on the antimicrobial activity of the prepared PLA-based bionanocomposites will be evaluated as the future perspectives of these materials to use as biomedical coating.
## 4 Design Methodology for SOA-Enabled HONoC
### Worst-Case Crosstalk Noise and SNR Analysis
#### 4.2.1 SNR Analysis with Multiple SOAs Using Segmented Regions in Mesh-Based HONoCs  
To facilitate the analysis of the SNR change resulting from SOAs and crosstalk, the entire on-chip optical network is partitioned into several sub regions. The criteria for region partitioning are that no sub region should contain any SOA inside and should be surrounded by the SOA-placed links. The sub region is denoted as \(REG(i,j)\) where \(i\) and \(j\) indicate the row and column position, respectively, as shown in Figure 7.  
It is apparent that the crosstalk noise introduced at the same \(REG\) with the source node of the optical signal to be routed will be subject to the same number of SOAs as the optical signal, whereas the other crosstalk noises introduced at nearby \(REGs\) to the destination node will be amplified to a lesser degree by the SOAs than by the optical signal under analysis. More specifically, we formulated the SNR of the optical signal path that travels from \(REG(1,1)\) to \(REG(n_{x},n_{y})\) in Equation (13a) for an SOA-enabled HONoC. For comparison purposes, the SNR for the non-SOA case is derived in Equation (13b).  
\[SNR_{1}=10\log\left(\frac{P_{5}L_{REG(1,1)(n_{x},n_{y})}}{\sum_{\forall i}\frac {N_{REG(1,i)}L_{REG(1,i+1)(n_{x},n_{y})}}{G^{(i-1)}}+\sum_{\forall j}\frac{N_{ REG(j,n_{x})}L_{REG(j+1,n_{x})(n_{x},n_{y})}}{G^{(n_{x}-1)+(j-1)}}}\right) \tag{13a}\] \[SNR_{2}=10\log\left(\frac{P_{5}L_{REG(1,1)(n_{x},n_{y})}}{\sum_{\forall i}N_{ REG(1,i)}L_{REG(1,i+1)(n_{x},n_{y})}+\sum_{\forall j}N_{REG(j,n_{x})}L_{REG(j+1,n_{x})(n_{x},n_{y})}}\right)\] (13b) \[i\in\{1,\ldots,\;n_{x}-1\},\quad j\in\{1,\ldots,\;n_{y}\}\]  
Here, the total crosstalk noise introduced into the optical signal when it passes through \(REG(i,j)\) is represented by \(N_{REG(i,j)}\) and the signal power loss that occurs when migrating from \(REG(i_{0},j_{0})\) to \(REG(i_{1},j_{1})\) is denoted by \(L_{REG(i_{0},j_{0})(i_{1},j_{1})}\). Additionally, \(n_{x}\) and \(n_{y}\) are the number of subregions divided by the SOA-placed links in the \(X\) and \(Y\) axes, respectively.  
In the non-SOA enabled HONoC, the SNR decreases as the routing path length increases as a result of increased signal attenuation and more involved crosstalk noises. In the proposed architecture, the effect of additional crosstalk noise according to the path length increase decreases by the appropriate placement of SOAs to boost the SNR. As shown in Equation (13a), the noise terms decrease by \(G^{0}\), \(G^{1}\), \(\ldots\) and \(G^{n_{x}+n_{y}-2}\), respectively. The SNR enhancement becomes more apparent as the number of \(REG\)s increases. Therefore, at the router level, we analysed a path that passes through the greatest number of \(REG\)s and a path inside an \(REG\).