esa-sceva/satcom-chunk-collection huggingface dataset:
# Quad Element MIMO Antenna for C, X, Ku, and Ka-Band Applications
## 5 Conclusions  
The proposed design operates on three wide bands that cover bands 3-15.5 GHz and two mm-wave bands (i.e., 26.4-34.3 GHz and 36.1-48.9 GHz). The minimum port isolation and ECC over the bands 3-15.5 GHz, 26.4-34.3 GHz, and 36.1-48.9 GHz are observed as (15.1 dB, 0.11), (22 dB, 0.035) and (22.37 dB, 0.04), respectively. The diversity performance of the MIMO antenna is performed by analyzing diversity gain, average efficiency, total interference ratio, channel capacity loss, and ergodic channel capacity. The MEG and TARC in all operating bands (i.e., 3-15.5 GHz, 26.4-34.3 GHz, and 36.1-48.9 GHz) are less than 0.2 dB and \(-\)4.1 dB, respectively. The CCL in all operating bands is less than 0.3 bps/Hz and the range of CC in operating bands 3-15.5 GHz, 26.4-34.3 GHz, and 36.1-48.9 GHz are 12.13-19.13 bps/Hz, 16.55-19.18 bps/Hz, and 16.92-18.44 bps/Hz, respectively. The simulated time-domain analysis in two different orientations, which are face to face (FTF) and side to side (STS), are studied. In both orientations, the group delays (GD) lie between 1010 and 1085 ps, and the S21 magnitude is less than \(-\)75 dB in all operating bands. The S21 phase provides linear characterization in all operating bands. The simulated values of FF and SFF are (0.9359, 0.7584) and (0.9477, 0.7583) in FTF and STS orientations, respectively. In all operating bands, the measured values provide good agreement with respect to the simulation ones, which indicates a good candidate for operation in all investigated bands.  
Conceptualization, R.K.M.; Methodology, A.K.S.; Software, S.K.M.; Formal analysis, A.J.A.A.-G.; Investigation, R.S.; Resources, T.A.H.A.; Data curation, M.A.; Writing--original draft, R.K.M.; Writing--review & editing, M.A.; Visualization, S.K.M.; Supervision, A.K.S.; Project administration, A.J.A.A.-G.; Funding acquisition, T.A.H.A. All authors have read and agreed to the published version of the manuscript.  
This research is supported by Researchers Supporting Project number (RSPD2023R868), King Saud University, Riyadh, Saudi Arabia.  
The authors would like to acknowledge the support provided by the Researchers Supporting Project number (RSPD2023R868), King Saud University, Riyadh, Saudi Arabia. Furthermore, the authors would like to extend their appreciation to Universiti Teknikal Malaysia Melaka (UTeM) and the Ministry of Higher Education (MOHE) of Malaysia.  
The authors declare no conflict of interest.
# Real-Time Optical Flow Estimation Method Based on Cross-Stage Network
## 5 Discussion  
The proposed structure is designed based on the conventional optimization-based approach, and feature encoders extract pixel-wise features. The correlation layer calculates the visual similarity between pixels. The update operator mimics the steps of the iterative optimization algorithm. However, unlike traditional approaches, characteristics and motion dictionary probabilities are not created manually but are learned and replaced by characteristic encoders and update operators. Previous studies [9; 10; 11; 12; 13; 14; 15] design differently from the high-resolution upsampling method used.  
The following describes the advantages of the proposed method. First, by using a single high-resolution flow field, we solve the problem of low-resolution error recovery in detailed continuous calculations, missing small and fast-moving objects, and the large number of training times generally required for continuous calculations.  
These performances are shown in Table 2 and Figure 13. The results of the sintel (Final) in Table 2 confirm that the proposed algorithm performs well even in the presence of low-resolution images or noise. In addition, the diagram of training operations in Figure 13 shows that our method performs well even with a small number of learning times.  
Next, the update operator has a repetitive and light structure. Many recent studies [9; 10; 11; 12; 13; 28] include some form of repetitive improvement but do not weigh between them. Previous methods used recurrent units in FlowNet and PWC-Net. However, the size of the network, that is, the number of parameters, is limited to less than five times. However, in the proposed network structure, the number of parameters is 4.0 M, as shown in Table 1, and can be applied more than 100 times.  
Finally, the update operator consists of ConvGRU units that inspect a four-dimensional multi-scale correlation volume. On the other hand, the improvement module in the previous task uses only the general convolution or correlation threshold layer.  
Given continuous RGB images \(I_{1}\) and \(I_{2}\), a dense displacement field (\(f^{1}\), \(f^{2}\)) in which each pixel \((u,v)\) is mapped from the corresponding coordinates (\((u^{\prime},v^{\prime})=(u+f^{1}(u),v+f^{1}(v))\)), is estimated. After extracting the first feature, the second visual similarity is calculated, and the update is repeatedly performed. All the above steps are differentiable and have a structure that can be learned end-to-end.  
Figure 14: Optical flow results on tiger scene of MPI Sintel datasets.  
Figure 15: Optical flow results on wall scene of MPI Sintel datasets.
# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
## 3 Methodology  
The proposed approach comprises three modules: quad-tree look-up, landmark matching, and attitude control. First, the landmarks extracted offline from the reference orthophoto are saved in a quad-tree data structure. The landmarks extracted from UAS imagery are matched to landmarks retrieved from the quad-tree in real-time. We have tested with multiple landmark extraction and matching algorithms. The matching landmarks estimate the geometric transformation \(T\) and predict the UAS geoposition. The estimated geoposition is used within the quad-tree look-up module to update the dataset. The attitude control module uses the recovered transformation to estimate Euclidean platform rotation and altitude to update the UAS global rotation and altitude. These estimated values keep the acquired image and the landmarks extracted from the orthophoto with the same orientation and scale. The information flow of the proposed approach is shown in Figure 1.
# Biodegradable Contrast Agents for Photoacoustic Imaging
## 3 Nitrogen-Doped Carbon Nanodots as Biodegradable PA Agents  
Carbon-based nanoparticles have been investigated as therapeutic agents for PAI and photothermal therapy owing to their strong photoabsorbtion ability, biocompatibility, water solubility and photostability [44; 45; 46; 47]. However, biodegradability is the main challenge of these materials. [PERSON] et al. synthesized nitrogen-doped carbon nanodots (N-CNDs) to overcome this issue [48]. N-CNDs were produced by the solvothermal carbonization of citric acid in nitric acid (Figure 2A). First, a heated mixture of citric acid and nitric acid formed polymer-like intermediates by dehydration. Then, the intermediates underwent carbonization to generate carbogenic cores. Simultaneously, oleylamine was implanted on the surface of the core to inhibit undesired intergranular aggregation during the carbonization process. Finally, oleylamine was replaced with ethanolamine to improve the water solubility. The N-CNDs were determined to have a size of approximately 3 nm (Figure 2B). The amplitude of the measured PA signals of N-CNDs (2.2 \(\pm\) 0.2) was better than those of gold nanorods (1.0 \(\pm\) 0.1) and MB (1.3 \(\pm\) 0.1) (Figure 2C). As photothermal agents,  
Figure 1: Melanoidin nanoparticles. **(A)** Schematic of GG-melanoidin. **(B)** Transmission electron microscopy (TEM) image of GG-melanoidin/Fe\({}^{3+}\). **(C)** Comparison of the PA signals of GG-melanoidin, GG-melanoidin/Fe\({}^{3+}\), gold nanorods (AuNR) and methylene blue (MB) under irradiation at 680 nm. **(D)** Three-dimensional PA images and **(E)** depth-resolved two-dimensional PA images of sentinel lymph nodes. Reproduced with permission from [43]. Copyright American Chemical Society, 2016.  
aqueous solutions of N-CNDs showed temperature changes when irradiated with an 808 nm laser (Figure 2D). Time-course PA images of SLNs in a rat model showed that the signal increased 30 min after intradermal injection of the N-CNDs (Figure 2E). Subsequently, the PA signal of the SLNs gradually decreased until 180 min (Figure 2F).  
The PA signal of the bladder increased 100 min after the time of injection and an enhanced signal was observed until 450 min, indicating that renal clearance occurred in the rat body. Therefore, these biodegradable carbon nanodots are a promising PA contrast agent and photothermal therapeutic agent.
# Power System State Estimation Approach Considering Transmission Line Temperature
###### Abstract  
The transmission line parameters vary with the change of temperature, which has a significant impact on power system state estimation (SE). Based on the theory of electro-thermal coordination (ETC), this paper proposes two ETC-SE approaches with the consideration of transmission line temperature. The heat balance equation (HBE) is combined with the conventional weighted least square SE for establishing an ETC-SE model. Moreover, an augmented Jacobian ETC-SE approach is developed by integrating the HBE into pseudo measurements and the line temperature into state vectors. The Jacobian matrix is augmented correspondingly and the partial differential coefficients of measurements to line temperature are provided, which enables to calculate line temperature and voltage phasors simultaneously. Furthermore, in order to accelerate the solving process, an improved two-step ETC-SE algorithm is proposed, in which the SE and temperature estimation are decoupled and solved via alternate iteration. The effectiveness of the proposed ETC-SE approaches is verified by the IEEE 14-, 39-, and 118-bus systems. The results show that the proposed ETC-SE approach is effective to reduce the calculation errors and possesses good convergence performance with varying environmental circumstances and ill-conditioned branches.  
state estimation; electro-thermal coordination; heat balance equation; transmission line temperature +
Footnote †: journal: Applied Sciences
# Underwater Acoustic Target Tracking: A Review
## 3 Knowledge of Acoustic Target Tracking Instruments
### Acoustic Imaging Sensor  
Whether based on TASA or UWSNs, these algorithms are non-visual tracking. The development and maturity of underwater acoustic imaging or video processing technology provide an alternative approach for underwater target visual tracking using acoustic imaging sensor. The most widely used acoustic imaging sensor is the looking forward sonar which is usually installed on the front of AUVs. The imaging sensor transmits acoustic signals to the underwater area of interest and processes the received back-scattering echoes. By using acoustic imaging techniques, an initial acoustic image of the real environment is obtained from the image sequences, the underwater target tracking can be realized by determining the target area in each frame image.  
In conclusion, there are diversified alternative acoustic tracking instruments for underwater target tracking. However, published survey articles are only centered on WSNs, which is not comprehensive. Therefore, in this paper, a classification, which is based on three commonly used underwater acoustic tracking instruments including the acoustic imaging sensor, TASA, and UWSNs, is proposed in Section 5.
## 4 Discussion
### Uncertainties  
Several potential sources of uncertainty may impact the results presented. One significant source is the influence of various OACs, such as suspended solids and coloured dissolved organic matter (CDOM), which can directly affect water-leaving radiance and, therefore, can cause misestimations of Chl-_a_ concentrations [13]. Spatial heterogeneity of in situ Chl-_a_ concentrations is likely another source of uncertainty because small-scale variability of the Chl-_a_ concentration over small distances (<10 m) may not be captured at the spatial resolution of the satellite images [90]. Similarly, temporal mismatches between in situ measurements and satellite overpass introduces uncertainties, with longer time differences increasing the uncertainty in the models [20]. Only 11% of the measurements were synchronous (i.e., on the same day) with the satellite overpass. Additionally, field data, although commonly referred to as water-truth data, may not represent the absolute true state of the water and inherently involve errors associated with sampling and laboratory extraction [91]. Lastly, atmospheric errors are an unavoidable source of uncertainty in aquatic remote sensing studies. Despite significant improvements in atmospheric correction processors, none perfectly replicate the water-leaving radiance. Comparisons of calculated remote sensing reflectance with in situ water-leaving reflectance show that even high-performing models can have median errors of up to 30% for the green and red bands and up to 60% for the blue band [17], with mean absolute differences of up to 60% [19].
## 2 Related Work  
There are two strategies to offload cellular networks traffic: one is to offload through WiFi access points and another is to offload onto opportunistic networks [3]. In the study of offloading cellular networks traffic through WiFi routers (APs), [PERSON] studied how many WiFi routers were needed to guarantee a certain quality of service [3]. [PERSON] studied the delayed offloading [4]. When a user wants some data, his/her smartphone does not download it from cellular network. It waits to download the data until it can contact a WiFi infrastructure. With tens of minutes delay, 70%-90% of cellular network traffic can be offloaded. Car-Fi offloads data traffic from cellular networks to existing home Wi-Fi routers [5]. This traffic offloading method needs WiFi infrastructures, but the offloading efficiency of this method reduces if the WiFi infrastructure is insufficient [6].  
The first study about offloading cellular traffic through opportunistic communication was done by [PERSON] _et al._[7]. They studied how to select the initial k nodes as the target-set selection. [PERSON] _et al._ studied how to extend a publish/subscribe system to a mobile network [8]. Meanwhile, they also assumed that nodes in the mobile network are contacted. However, the mobile network composed of mobile smartphones is intermittently connected networks, not _ad-hoc_ networks. Push-and-track offloads traffic through opportunistic networks [9]. In push-and-track, the user sends acknowledgments to the server while receiving the content and the server decides whether to push the content into the network. Sometimes, the number of users is very large, and the server has to handle tens of thousands of subscriptions every day. Then the server will become the bottleneck of the system. In [PERSON]'s research on offloading with opportunistic networks, if a node cannot get the wanted content from the contacted nodes, it will download it through cellular networks [10]. After a node downloads its requested content, the node sends the content to some random neighbors in the same district. This method does not suit a scenario with low node density. [PERSON] _et al._ studied DTN-based mobile data offloading [11]. In their study, a service provider sends the data to some nodes and these nodes send the data to the subscribers with two-hop routing algorithm. [PERSON] _et al._studied the average delay equation for delayed offloading [12]. [PERSON] _et al._ studied the relation between the smartphone's energy and offloading efficiency, and proposed an energy-aware algorithm for opportunistic networks [13]. In addition, some researchers have begun to study the economics of mobile data offloading [14; 15]. Anyway, none of these works considers the distance between the subscriber and the node that has the content, but this distance may determine whether the traffic offloading works. Also, these works do not take the deadline of the subscription into consideration, which means the subscription table will soon grow hugely and the subscribing server cannot work efficiently.
## 2 Materials and Methods
### Construction of the B-Camera
#### 2.2.1 Sensor Array and Multiplexer  
The sensor array performs the spatial sampling of the magnetic field on an area, which is achieved by arranging the magnetometers into a two-dimensional array. The minimum spatial sampling size, or the size of a sampling grid, is related to the dimension of the sensor. Since the present implementation used an altered version of an HMC5883L magnetometer chip, we set the grid cell size to be 2 cm. The sensors were arranged into a \(4\times 4\) array, so that the total size of the sensed area was \(8\times 8\) cm\({}^{2}\).  
The HMC5883L sensor can measure a magnetic field up to 8 gauss (1 gauss = \(10^{-4}\) tesla) in three axes. The chip employs the I2C (IC-to-IC Communication) protocol to communicate with other devices, which in this case was a microcontroller. Since all of the sensors had an identical fixed address, we employed a 1-to-16 multiplexer to access the field values of each sensor, which were controlled by four digital channels of the microcontroller. Thus, the measured field values could be read sequentially by the microcontroller from each sensor's data registers.  
The sensors were calibrated according to the self-test protocol given in the chip datasheet. An internal current source inside the IC generated a direct current that created \(\pm 1.1\) gauss of artificial magnetic field bias on the sensors. By measuring these measured fields, one can test the sensor functionality and estimate its sensitivity for each axis. We excluded all sensors with more than 15% error compared to the expected readouts and adjusted all successive measurements based on the estimated sensitivity. Although this is not the best way to calibrate magnetic sensors, this setup was very useful to prevent us from obtaining poor-quality sensors that gave extremely incorrect data during experiments.
# Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery
## 2 Materials and Methods
### Image Preprocessing  
First, we created a 2020 study image by assembling a composite of cloud-free Sentinel-2 Multispectral Instrument (MSI) surface reflectance data, captured between 1 January 2020, and 31 May 2020. The composite included the blue (Band 2), green (Band 3), red (Band 4), and near-infrared (Band 8) spectral bands. For each of the four spectral bands, each pixel's spectral reflectance value was the median value of that pixel across all available Sentinel-2 imagery captured between 1 January 2020, and 31 May 2020. We used the median value for each pixel to minimize the influence of potential outlier values on the classification process. We used the date range of 1 January 2020, through 31 May 2020, because the entire range falls within Belize's dry season, reducing the potential influence of seasonality on the spectral reflectance values in the study image.  
Second, we created a series of exclusion masks to eliminate portions of the study image that we knew did not contain mangroves, and therefore did not need to be included in the  
Figure 1: Map of Belize and its location within the Mesoamerican Reef ecoregion.  
classification process. Eliminating extraneous portions of the study image before classification increases both the efficiency and the accuracy of the classification [32; 33]. We used the Normalized Difference Water Index (NDWI) [34] to mask areas of open water (NDWI value > 0.0) out of the study image. We then used Shuttle Radar Topography Mission (SRTM) elevation data to mask areas of greater than or equal to 20 m elevation out of the study image. Finally, we used the Normalized Difference Mangrove Index (NDMI) [35] to reduce the number of non-mangrove pixels (NDMI value > 0.1) in the study image.  
The SRTM data had a native resolution of 30 m. To calculate the NDMI mask, we used the shortwave-infrared band (Band 12) of the Sentinel-2 MSI surface reflectance data, which had a native resolution of 20 m. We resampled the SRTM and the Band 12 data layers to 10 m to create their respective masks, so that the masks would align properly with the 10 m study image. When integrating gridded data layers with different spatial resolutions, the conventional protocol is to resample the finer layer(s) to the coarsest resolution [36]. In this case, we resampled the coarser layers to the finest resolution because the SRTM and Band 12 data layers were not inputs to our classification algorithm. We used them solely to create masks with which to perform a \"broad-stroke\" reduction of the processing extent of our study image, before performing the more nuanced classification algorithm outlined below.
# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
## 4 GeoSOT-AS-Based Gridding Modeling Method for Airspace Information
### Longitude and Latitude Information Conversion Coding Algorithm  
After extracting the latitude and longitude information of the airspace target, the corner point coordinates of the airspace target location are calculated stepwise according to the selected planar dimensional profile range level according to the profiling method in Section 3.1, and the iterative operation was performed according to the rounding and remainder, with the assignment rules carried out according to the filling curve, and the specific algorithm flow is shown by Algorithm 1:  
```
Input: Location information for ADS-B data point source (Lng,Lat), level L Output: ADS-B data plane dimensional grid coding set
1. function EncodeLocation(Lng,Lat,L)
2. if\(L==1\)then
3. Step1: Obtain hemisphere identifiers in which (Lng,Lat) is located
4. Step2: Calculate the latitude and longitude direction identification code
5. Step3: Get the first level grid for the location
6. elif\(L>1\)then
7. for\(i\) in range\((1,\mathbf{L}):\)
8. Calculate
9.\(a_{i}\)=\(|(Lng-\lambda_{i-1})/(\Delta_{L}\lambda|+1\)
10.\(b_{i}\)=\(|(Lat-\phi_{i-1})/(\Delta_{L}\psi)+1\)
11.\(n_{ge}\)\(\leftarrow\)Lng\(-\lambda_{i-1}\)
12.\(Lat-Lat-\phi_{i-1}\)
13.\(\lambda_{i}\), \(a_{i}\) the longitude of the position angle point and column number
14.\(\phi_{i}\), \(b_{i}\) the latitude of the position angle point and row number
15.\(p_{i}\)=\(a_{i}a_{i}+\beta_{i}b_{i}\,a_{L}\,\beta_{L}\) is determined by the current fill curve
16.\(P_{code}\)=\(p_{1}p_{2}\)...\(p_{L}\)
17. End if
18. return\(P_{code}\)
19. End function
```  
**Algorithm 1** Grid set generation algorithm for latitude and longitude  
Figure 7: Data representation under GeoSOT-AS model.