esa-sceva/satcom-chunk-collection huggingface dataset:
# Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
## 5 Experimental Results and Analysis
### Time Performance  
Since superpixel methods are used as a preprocessing step to reduce the complexity of segmentation, they should be computationally efficient so that they do not negatively impact the overall performance. Because these methods are used to partition RGBD images, and the resolution of Kinect-produced images is fixed, we only need to quantify the segmentation speed when increasing the number of superpixels. However, VCCS uses multi-threading technology in this experiment, while others use single threading technology. Therefore, while offering the sixteen-threaded runtime, the single-threaded runtime of VCCS is estimated using the product of the execution time and the number of threads. Here, the computational times of four superpixel methods were recorded on an Intel Xeon E5 2.1 GHz processor and are shown in Table 1.  
Weighted coplanar feature clustering showed a performance speed that was competitive with SLIC and was the fastest among the RGBD superpixel methods. The performance of WCFC was slightly slower than SLIC because SLIC is an RGB superpixel algorithm that does not handle the depth data. Additionally, the computational time did not rise rapidly with the number of superpixels because the same framework was used. In contrast, the speeds of DASP and VCCS were greatly  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & & & & \\
**Superpixels** & & & & \\ \hline
500 & 0.532629 & 13.2797 & 1.844004 & 0.115253 & 1.742108 \\
1000 & 0.534678 & 14.1963 & 2.935915 & 0.183495 & 1.766105 \\
2000 & 0.543063 & 14.6144 & 6.327876 & 0.395492 & 1.804487 \\
3000 & 0.554729 & 16.4923 & 9.318771 & 0.582423 & 1.824409 \\ \hline \hline \end{tabular}  
* Speed of segmentation for increases in image size and number of superpixels. The runtime is measured in seconds. In addition, the algorithms are run on 640 \(\times\) 480 images.  
\end{table}
Table 1: The runtime of superpixel methods.  
Figure 6: The underestimation errors of the four superpixel methods.  
affected by the number of superpixels. In conclusion, WCFC is the best choice for RGBD superpixel segmentation tasks.
# A High Latitude Model for the E Layer Dominated Ionosphere
## 1 Introduction
The presence of an enhanced E layer ionization during ELDI occurrences may affect the propagation of HF radio waves and, therefore, has an effect on telecommunication and navigation applications. The use of an ELDI model in such applications could help predict signal propagation perturbations and their extent.
## 1 Introduction
### Input Data  
#### 2.2.1 Landsat Analysis Ready Data  
The Landsat Analysis Ready Data (ARD) products from the US Geological Survey are designed to reduce the amount of data preparation for scientists and to facilitate time series analysis by generating data at the highest scientific standards required for direct use in applications [29]. Landsat Collection 1 Level-1 scenes serve as the input for generating all ARD products. The ARD dataset is defined in the Albers Equal Area (AEA) projection and World Geodetic System 1984 datum (WGS84). The products are distributed in \(150\times 150\) km tiles instead of the traditional Landsat swaths in the WRS-2 path-row coordinate system. Both Landsat 7 and Landsat 8 images in the ARD surface reflectance (SR) product were used. On average, there are 22.8 and 19.5 ARD observations per pixel for 2016 and 2017, respectively (Figure 2).  
Figure 1: The 2016 reclassified Cropland Data Layer for Roberts County, South Dakota.  
Figure 2: Number of good observations for Analysis Ready Data (ARD) and Harmonized Landsat Sentinel-2 (HLS) data in 2016 and 2017.
## 5 Data Processing and Simulation Experiment Setup
### Simulation Experiment Setup  
To define the experimental objects and scope, a reference coordinate system consisting of at most seven vehicles was established. The target vehicle's coordinates were set at (0, 0), and the interaction vehicles are selected within a range where the center of the vehicle is located within a rectangle defined by X (\(-\)10, 10) and Y (\(-\)20, 80). The target vehicle is denoted as SV, the lead vehicle is marked as L, and the following vehicle is marked as F. The lane of the target vehicle is marked as 1, the left lane as 0, and the right lane as 2. The system consists of the target vehicle as the reference point and up to 6 surrounding interaction vehicles. The positions were recorded every 0.1 s, and each set of records was defined as one observation. The observations of non-interacting vehicles were filtered out.  
After conducting a continuous 10 min recording of the Expressway A section from the CitySim dataset, 12,284 observation data points were selected and divided into two groups: 80% of the data, after processing, was used as the training set for training the deep learning trajectory prediction model, and 20% was used as the test set.  
During the setup of the test scenario, the SUMO software was used to create a -km long, one-way, three-lane highway. It included two typical vehicle interaction scenarios (car-following and lane-changing) to evaluate the applicability of the risk quantification model. In the vehicle following scenario, as shown in Figure 7a, the target vehicle was behind the interaction vehicle and remains in the same lane, with a longitudinal conflict risk. In the lane changing scenario, as shown in Figure 7b, the target vehicle was ahead of the interaction vehicle and was in an adjacent lane. The target vehicle was changing from its lane to the lane of the interaction vehicle, and both longitudinal and lateral conflict risks were present.  
Figure 6: Comparative analysis of raw data and denoised data. (**a**) Comparative analysis of acceleration data. (**b**) Comparative analysis of speed data.  
Figure 7: SUMO test scenarios. (**a**) Car following scene. (**b**) Changing lane scene.
# From Iris Image to Embedded Code: System of Methods
## 3 Eye Segmentation Methods
### Base Radii Detection
This can be written as:  
\[r_{P}>0.15r_{I}\,\ \ r_{P}<0.75r_{I}\,\ \ d<r_{P}\,\ \ 2(r_{I}-r_{P}-d)>r_{I}-r_{P}+d\, \tag{12}\]  
where \(\vec{c}_{P}=(x_{P},y_{P})\), \(\vec{c}_{I}=(x_{I},y_{I})\) are centres of pupil and iris, \(d=\|\vec{c}_{P}-\vec{c}_{I}\|\) is a distance between these centres. From all possible variants of pupil-iris pair given by (9)-(11) we select those satisfying conditions (12). The quality of combination is a sum of pupil and iris qualities (10) and a weighted quality of fitting to conditions (12):  
\[ \begin{split}& Q=q_{P}+q_{I}+\gamma q_{fit}\,\\ & q_{fit}=\min\biggl{\{}\frac{r_{P}-0.15r_{I}}{r_{P}},\frac{0.75r_ {I}-r_{P}}{r_{P}},\frac{r_{P}-d}{r_{P}},\frac{r_{I}-r_{P}-3d}{r_{I}-r_{P}} \biggr{\}}\.\end{split}  \tag{13}\]  
The combination with the best quality is selected. If \(Q\) is below the given threshold, it is supposed that the eye in the image is squinted and upper and lower eyelids cover a big share of the iris border. In this case, the variant with absent top and bottom iris local maxima is tested. The formulas (9) and (10) are modified accordingly, iris center vertical position is taken equal to that of the pupil: \(y_{I}\equiv y_{P}\). If \(Q\) is below the threshold again, it is decided that there is no feasible iris ring and in the image. Other types of occlusion are not treated, the iris images are considered too bad for processing in this case. Thresholds for \(Q\) and value of \(\gamma\) in (13) are estimated experimentally so as to reject the biggest share of erroneously detected irises while preserving good outcomes. So, the method runs in six steps:  
Step 1. Gradient calculation. This step is common with center detection. Step 2. Candidates selection. This step is similar to Step 2 of center detection. In addition to gradient value condition (1) angular condition (4) is imposed. Step 3. Circular projecting. Calculating circular projections (6) in four quadrants. Step 4. Enumeration of maxima. Finding local maxima (7) in projections. Prior to this the projections are smoothed with a Gaussian filter to suppress redundant local maxima originating from noise. Step 5. Enumerations of hypothetical irises. Finding coordinates and radii of inner and outer circles from combinations of maxima (9), which hold conditions (12). Step 6. Selecting the best iris. Pair of circles is selected according to the qualities (8), (10), (13). If no feasible iris is detected in step 5, the result is \"no eye detected\". A sample of the projection combination is presented in Figure 3. Real positions of pupil and iris borders, taken from expert marking are depicted by arrows. There is no local maxima corresponding to the iris border in the top projection \(\Pi_{T}(r)\) since the iris border is occluded. Such minor obstacles do not prevent choosing correct combination.
# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
## 4 LoRaWAN Protocol  
Smart cities, utilities, companies with fleets on the ground (transport, maintenance, delivery, etc.), agriculture, health and many other applications are thriving for radio protocols that help them better manage and control their work as well as provide better service for users [34; 35; 36].  
IoT is connecting objects everywhere, where LPWAN is a type of wireless telecommunication protocol for IoT. LPWAN characteristics such as long range, low power, low cost and multi-usage are the main features. Many mature networks such as LoRa [37], Sigfox [38], Wi-SUN [39] and many others were proposed as LPWAN solutions to meet the desired targets.  
LoRa is an LPWAN technology developed by Cycleo and then acquired by Semtech [40]. It is based on spread spectrum modulation techniques derived from the Chirp Spread Spectrum (CSS) [41]. LoRaWAN defines the communication protocol and system architecture for the network, while the LoRa physical layer enables a long-range communication link. LoRaWAN protocol and its network architecture are main contributors in preserving the node's battery lifetime, network scalability, security, and the variety of applications served by the network.
# A Fairness Index Based on Rate Variance for Downlink Non-Orthogonal Multiple Access System
## 6 Conclusions  
Aiming at the resource allocation problem of a downlink NOMA system, this paper proposes a fairness index based on the sample variance of users' transmission rates, and designs a fairness constrained intra and inter cluster joint power allocation algorithm based on the proposed fairness index, which can maximize the system throughput under the minimum fairness constraint. The simulation results show that the proposed fairness index has a fixed value range and good sensitivity, which is suitable for comparing the fairness between systems with different user numbers. Different from the traditional power allocation scheme, this algorithm can take into account intra cluster fairness, inter cluster fairness, and system fairness, and has greater flexibility.  
Software, [PERSON]; Writing--original draft, [PERSON]; Writing--review & editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
Figure 4: Fairness varies with the number of users.  
Figure 5: Throughput varies with intra and inter cluster fairness.  
**Funding:** This work was supported by the National Natural Science Foundation of China No. 61701221 and 61901211, Jiangsu Intelligent Perception Technology and Equipment Engineering Research Center Open Fund Project ITS202106, China University Industry University Research Innovation Fund Project 2021 FNA0500, Jiangsu Future Network Fund Project FNSRFP2021 YB26, and Nantong Science and Technology Project, No. JC2018127.  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Review on the Use of Microalgae for Sustainable Aquaculture
## 3 Microalgae-Assisted Aquaculture
### Technologies for Biomass Production
#### 3.3.2 Algal-Bacterial Cooperation  
As mentioned above, to control the total cost, open systems are commonly used for microalgae-based wastewater remediation. In some cases, bacteria and microalgae in wastewater may form synergistic consortia, which perform much better in nutrients recovery than individual microbial system [54; 56; 61]. First, some substances secreted from microalgae and bacteria contribute to the formation of a synergistic relationship. [PERSON] et al. (2005) reported that vitamins released by bacteria have beneficial effects on microalgae growth. Besides, some intermediate metabolites of microalgae could be partly released to the extracellular environment, thus providing organic carbon for bacteria growth. Second, bacterial metabolisms may accelerate the breakdown of solid organics in aquaculture wastewater and provide more digestible nutrients for microalgae growth [54; 55]. It was discovered that extracellular enzymes, such as lipase and protease, released by microbes are critical to the yield of digestible nutrients [62]. Third, gas exchange between microalgae (O\({}_{2}\) producer) and bacteria (CO\({}_{2}\) producer) is beneficial to both biomass production and wastewater treatment. Such a cooperation mechanism has been fully documented by previous studies that used microalgae to treat wastewater rich in suspended solids [50; 54]. With the accumulation of dissolved oxygen in wastewater by microalgal photosynthesis, an aerobic environment is created in the aquaculture wastewater, promoting the bloom of beneficial bacteria, such as nitrifying bacteria, _Bacillus subtilis_, and yeast. Thus, after microalgae-based treatment, waste, containing beneficial microorganisms, reused for aquaculture may have positive effects on the health of aquatic animals [63; 64].  
To establish the interspecies cooperation, competition between microalgae and bacteria in nutrients utilization should be mitigated. The study of [PERSON] et al. (2014) studied the effects of inoculation concentration and inoculation ratio on microalgae growth in wastewater, revealing that the maximum biomass yield was obtained when inoculation concentration was set as 0.1 g/L. In addition, physical or chemical sterilization methods can be used to control bacteria in wastewater and adjust inoculation ratio. In the coming future, cost-saving technologies for bacteria control in aquaculture practice will be developed and widely used for microalgae-based aquaculture wastewater remediation.
## 3 A Monochromatic, Multi-Focal MFZP
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Design Parameters**} \\ \hline Parameters & Foci & Wavelength & Diameter & Slit Resolution & \multicolumn{2}{c}{Tolerance} \\ \cline{5-6}  & 10, 15, 20 \(\upmu\)m & 650 nm & 50 \(\upmu\)m & 10 nm & 0.5\% & 3\% \\ \hline \multicolumn{6}{c}{Optimized Parameters} \\ \hline Parameters & Relative Intensities (\(l_{1}\), \(l_{2}\), \(l_{3}\)) & Initial Phase-Offset (\(\phi_{1}\), \(\phi_{2}\), \(\phi_{3}\)) & Position \(|\Delta f_{m}|\) & Intensity \(|\Delta l_{m}|\) \\ \hline Value & 0.13, 0.36, 1 & 1.05 \(\upmu\), 0.70 \(\upmu\), 2.00 \(\upmu\) & \textless{}0.5\% & \textless{}2.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters for a monochromatic, multi-focal MFZP. a red/green/blue (RGB) laser system for another experiment to be discussed in the next section. We utilized an optical microscope (BH, Olympus, Tokyo, Japan) with a complementary metal-oxide semiconductor (CMOS) camera (UCMOS05100 KPA, ToupTek, Hangzhou, Zhejiang, China) as the imaging device to detect the transmitted light through the MFZP sample, varying the distance between the MFZP sample and the objective lens of the optical microscope. In Figure 4a,b, we illustrate the scanning electron microscope (SEM) image of the fabricated MFZP and the images of the transmitted light obtained at some specific distances by means of the optical microscope, respectively. One can see that the fabricated MFZP effectively pr  
Figure 3: Schematic of the experimental arrangement for the characterization of MFZPs. Figure 2: Simulation results on the monochromatic, multi-focal MFZP specified in Table 1: (**a**) Shape of the calculated MFZP pattern. (**b**) Calculated intensity pattern formed through the MFZP. (**c**) Calculated intensity profile on the center axis of the MFZP, i.e., the \(z\)-axis. at the locations as closely as designed. In Figure 4c,d, we also illustrate the whole spatial distribution of the transmitted light in both the longitudinal and transverse directions, from which one can clearly see that the three hot spots were well separated and centered at the targeted locations. However, we could not resolve out even finer intensity distributions than detected owing to the limited sensitivity of the CMOS sensor array of the optical microscope system we used. We observed that the peak locations of the three hot spots took place exactly at 10, 15, and 20 \(\upmu\)m with a measurement resolution uncertainty of 0.2 \(\upmu\)m and that the relative peak intensity levels of their average values were 0.97, 0.96, and 1.03, respectively. We emphasize that, overall, the experimental results were in good agreement with the numerical results shown Figure 2c, indicating that the designed MFZP pattern was also completely feasible in terms of fabrication.
# System Architecture Design of IoT-Based Smart Cities
## 2 Background  
### Smart City  
A smart city is a concept that refers to the use of technology and data to improve the quality of life, efficiency, and sustainability of urban areas. Smart cities aim to optimize the use of resources, reduce waste and pollution, and improve the overall livability of urban areas by making them more responsive to the needs of citizens. Smart cities leverage technology to improve the services they offer to citizens, and there are many different applications of smart city technology, depending on the specific needs and priorities of a particular city:  
* Intelligent transportation systems which can optimize traffic flow, reduce congestion, and improve public transportation;
* Smart building management systems that can reduce energy consumption and improve the overall comfort of citizens;
* Smart lighting systems that can adjust lighting levels based on the presence of people and vehicles;
* Smart parking systems that can help drivers find available parking spaces quickly and easily;
* Smart waste management systems that can optimize the collection and disposal of waste;
* Smart water management systems that can improve the quality and distribution of water.  
As stated before, one of the key components of a smart city is the Internet of Things (IoT) technology. In general, the goal of a smart city is to use technology and data to improve the quality of life, efficiency, and sustainability of urban areas. By making citiesmore responsive to the needs of citizens and by using advanced communication and IoT technology to optimize the use of resources, smart cities can help to create more livable, sustainable, and resilient communities.
# 5G Positioning: An Analysis of Early Datasets
## 2 The GINT05G Experiment  
As part of ESA's GINT05G project, a number of field tests were carried out in 2021 with the aim of collecting and analyzing 5G signals with the specific aim of extracting pseudoranges to estimate the location of the device being tested. For the 5G experiment, an adaptation of the sounding reference signals (SRSs) [24], configured on the downlink instead of the uplink transmission, has been used. This signal was transmitted on 3.7 GHz, spanned 100 MHz in bandwidth, and consisted of a custom frame structure (further details are in Section 2.4). To check the trajectories estimated by 5G the benchmark trajectory was estimated by a total station. It should be specified that although the survey was performed in 2021, this paper summarizes [26] and reports the first experimental results. Technical details of the GINT05G experiment are described in [27; 28; 29; 30; 31] and are just briefly summarized here.
# Mitigating Urban Congestion: A Cooperative Reservation Framework for Automated Vehicles
## 3 Infrastructure Map Recognition
### Algorithmic Implementation  
In Algorithm 1, the whole process to generate the set of _Survey Cluster_ can be found. This algorithm prunes, with the _check_distance_ parameter, non-relevant roads from a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Intersection** & **Roundabout** \\ \hline Generation & From intersections & From entries \\ Compatibility & With other intersections & With own intersections \\ Crossings & Usually in between & Often on approaches \\ Traffic Flow & Intermittent, stop-and-go & Continuous, circulating \\ Conflict Points & High & Low \\ Speed & Potentially higher & Lower \\ Space & More on approaches & Less on approaches \\ \hline \hline \end{tabular}
\end{table}
Table 1: Differences between intersections and roundabouts.  
Figure 5: Cluster compatibility generation.  
\(gms\_emu\) position extracted from the GNNS solution deployed at the emplacement and converted to local infrastructure coordinates given an ENU origin. Then, the algorithm iteratively constructs each control area from the intersection type lane, aggregating each Entry, Exit and Survey area to a new _Survey Cluster_ given its logical contacts (successor and predecessor lanes). Therefore, when adding the next lane to each cluster, the control area is expanded with the logical contacts (successor/predecessor,) and then, a _max_distance_ parameter is added as a threshold to customize the length of the generated area. Upon generation of the operational monitoring environment, depicted in Figure 4, the subsequent step entails the definition of the connection protocol and the implementation of a tracking mechanism for cluster resource allocation. For these specific features, the system generates several virtual landmarks like the Entry and Exit points, as well as the survey lane in which the Cooperative Connected Automated Vehicle is going to be tracked.  
```
1:\(cluster\_list\leftarrow[]\)
2:procedureGenerateCluster(\(check\_distance\))
3:\(cluster\_list\gets new\ Cluster()\)
4:for each\(lane\gets map\_lances\)do
5:\(distance\leftarrow lane\_distance(gms\_enu)\)
6:if\(distance<check\_distance\)then
7:\(max\_area\_distance\gets from\_param()\)
8:for each\(contact\in lane\_contacs()\)do
9:while\(max\_distance>accumulated\_distance\)do
10:\(area\gets lane\_expand(contacts)\)
11:\(accumulated\_distance\) += \(area\_length()\)
12:endwhile
13:\(cluster\_assign(area)\)
14:endfor
15:\(cluster\_list.push(cluster)\)
16:endif
17:endfor
18:endprocedure
```  
**Algorithm 1** Cluster generation from the map  
Finally, the proposed algorithm exhibits favorable computational efficiency, achieving an average processing latency of \(\approx\)75 ms with a \(\mathcal{O}(n)\) space compElxity to generate the _Survey Cluster_ set from the provided map data. These results may depend on the hardware used and the configuration of the Map recognized, but they were consistent with the test cases proposed in Section 7.