esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Classification Method
Based on an average processing time of 15-min per project, the regular processing time on one  
Figure 9: Mapping of the reef crest and back and fore reefs: (**a**) PS imagery showing the lagoon area and reef crest separating the deep ocean boundary; (**b**) Polygon boundary of the geomorphic zone overlaid onto the imagery that identifies the reef crest area and is used in the parental guidance technique to refine the reef type boundaries; (**c**) Classification of reef crest (red), fore reef (brown), and back reef (orange) using the parental guidance approach; (**d**) Comparison to the polygon boundary differences indicating the improvement of reef type mapping using the parental guidance technique. Yellow circles highlight areas where the back reef has expanded and where sandy areas have contracted. engine would have taken 150 h (~6 days and 6 h). To speed up the time of processing, a total of three eCognition Server engines were run in parallel, reducing the real production time to approximately 2 days and 2 h.  
After the automated classification was completed, a quality control review identified several issues that were manually corrected. These errors included: (1) straight line boundaries caused by remnant seamless between scenes; (2) overestimation of coral reef area, particularly in nearshore fringing reefs; (3) coral reefs that had been missed in the classification; (4) seagrass beds classified in areas that were too deep or exposed; (5) data gaps along the shoreline; and (6) incorrectly classified areas caused by cloud interference. The manual correction process focused on improving the location of coral reefs, using available global reef maps, such as the UNEP-WCMC Global Distribution of Coral Reefs v.4 [57; 58; 59] and existing national coral reef maps where available as guiding references. High resolution satellite imagery base maps from Esri, Google Earth, and Microsoft Bing also served as important references when fine-tuning and correcting these data. Seagrass beds were also reviewed, and incorrect boundaries were adjusted based on expert feedback and image interpretation. An example of the final PS image-derived benthic habitat classification for the Turks and Caicos Islands can be seen in Figure 10.  
Figure 10: (**a**) An example area of the final PS image-derived benthic habitat classification for the Turks and Caicos Islands; (**b**) Zoomed-in area on the west side of Grace Bay in the northwest corner of Caicos Island showing the detail of the PS imagery; (**c**) Subsequent benthic habitat classification of the same area.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 4 Survey of Network Protocols
Address space, throughput, security, and jitter values were examined, highlighting the advantages of IPv6 over IPv4, including efficient routing, simplified configuration, built-in IPsec security, and support for new services and mobility. In another study, [PERSON] and [PERSON] [99] compared the performance of 6 LoWPAN-CoAP and RPL-CoAP, evaluating average latency and Packet Delivery Ratio (PDR). Both protocols saw improved PDR, but RPL-CoAP outperformed 6 LoWPAN-CoAP in latency. In a similar study, [PERSON] et al. [100] investigated the pairing of RPL-CoAP and 6 LoWPAN-CoAP protocols. Their study showed that RPL-CoAP surpassed the 6 LoWPAN-CoAP combination in received packets, suggesting that the former is a superior option for efficient IoT communication. Table 2 shows a comparative analysis of IoT protocol from the application, transport, and Internet layer in the literature, highlighting performance metrics and the best-performing protocols from the analysis. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Year** & **Network Protocol** & **Experiment Setup and** & **Performance Metrics** & **Best-Performing Protocol** \\  & **Analysed** & **Anselved Environment** & **Testbed Environment** & **Performance Metrics** & **Best-Performing Protocol** \\ \hline [PERSON] et al. [82] & 2016 & CoAP, MQTT, and WebSocket & STM3E7411 RE, ESP8266, Wi-Fi, and RTIT & Protocol efficiency and CoAP \\ \hline [PERSON] et al. [85] & 2016 & AMQP, DDS, MQTT, and XMPP & Wi-Fi, Adobe Flash, HTML5, and Microsoft Silver & Latency and Throughput \\  & & & & Microsoft Silver & Server Utilisation \\ \hline [PERSON] et al. [83] & 2017 & CoAP, MQTT, and HTTP & 4G and Broadband & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline Kayal and Ferros [87] & 2017 & CoAP, MQTT, XMPP, and WebSocket & Eclipse Mosquito, Greenric and Smack & Average response Time & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline Barusal and Priya [84] & 2021 & MQTT and CoAP & Coqa simulator, NS-3, and OMNet++ & IoHT environment & \\ \hline Al-Qosab, and & & Wireless Sensor Networks, & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline Al-Qosab, and & & Wireless Sensor Networks, & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation of network protocol.
## 1 Introduction
### Related Works
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Deep-learning-based Methods** & **Operational Mode/Setup** & **Dataset Used** & **Eye/Pupil Tracking Method** & **Gaze Estimation and Calibration Scheme** \\ \hline [PERSON] et al. [22] & Visible-light mode /Non-Wearable & UT Multiview & Uses a facial landmarks-based design to locate eye regions & Multiple pose-based and VGG-like CNN models for gaze estimation \\ \hline [PERSON] et al. [24] & Visible-light mode /Non-Wearable & MPII Gaze & The CNN-based eye detection & Joint eye-gaze CNN architecture with both eyes for gaze estimation \\ \hline [PERSON] et al. [26] & Visible-light mode /Non-Wearable & Self-made dataset & Uses the OpenCV method to extract the face region and eye region & GoogleNetV1 based gaze estimation \\ \hline [PERSON] et al. [29] & Visible-light mode /Non-Wearable & MPII Gaze & Uses face alignment and 3D face model fitting to find eye zones & VGGNet-16-based GazeNet for gaze estimation \\ \hline [PERSON] et al. [32] & Visible-light mode /Non-Wearable & ShanghaiTechGaze and MPII Gaze & Uses the ResNet-34 model for eye features extraction & CNN-based multi-view and multi-task gaze estimation \\ \hline [PERSON] et al. [34] & Visible-light mode /Non-Wearable & Self-made dataset & Uses the OpenFace CLM-framework/Face++/YOLOv3 to extract facial ROI and landmarks & Infrared-LED based calibration \\ \hline [PERSON] et al. [35] & Visible-light mode /Non-Wearable & Self-made dataset & Uses YOLOv3 to detect the eyeball and eye’s corners & Infrared-LED based calibration \\ \hline [PERSON] et al. [36] & Infrared mode /Non-Wearable & Self-made dataset & Eye-Feature locator CNN model & 3D gaze-estimation model-based design \\ \hline [PERSON] et al. [28] & Near-eye infrared mode /Wearable & German center for vertigo and balance disorders & Uses a fully convolutional neural network for pupil segmentation and uses ellipse fitting to locate the eye center & 3D eyeball model, marker, and projector-assisted-based calibration \\ \hline Proposed design & Near-eye visible-light mode/Wearable & Self-made dataset & Uses the YOLOv3-tiny-based lite model to detect the pupil zone & Marker and affine transform-based calibration \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the deep-learning-based gaze tracking designs. location is higher than that of previous wearable gaze tracking designs without using deep-learning technologies. To enhance detection accuracy, we used the YOLOv3-tiny-based deep-learning model to detect the pupil's object in the visible-light near-eye images. We also compared its detection results to the other designs, which include the methods without using deep-learning technologies. The pupil detection performance was evaluated in terms of precision, recall, and pupil tracking errors. By the proposed YOLOv3-tiny-based deep-learning-based method, the trained YOLO-based deep-learning models achieve enough precision with small average pupil tracking errors, which are less than 2 pixels for the training mode and 5 pixels for the cross-person test under the near-eye visible-light conditions.
## 3 Geological Settings and Sampling  
The Qidashan Iron Deposit, with proven BIFs ore reserves of 1259 Mt and high-grade iron ore reserves of 11.45 Mt, lies in the Anshan-Benzi area, Northeastern part of the North China Craton [14]. The deposit belongs to metamorphic volcanic-sedimentary iron deposit, and the stratum exposed in the mining area is mainly Archean Anshan group and Proterozoic Liaohe Group (Figure 5a). The main lithology of Yingtaoyuan formation where the BIFs ore was located is chlorite quartz schist, chlorite talc schist, sercite quartztite, metagranite, amphibolite, and banded iron quartzite. Large areas of Neoarchean granite are distributed on both the east and west sides of the deposit (Figure 5b).  
The Qidashan Iron Deposit is a typical BIFs deposit. The BIFs are chemical sedimentary rock characterized by the commonly alternating presence of iron-rich layers and silica-rich layers [25]. BIFs-related iron deposits in China are mainly composed of magnetite (average 30.35% total Fe) with subordinate amount of high-grade magnetite ores (total Fe \(\geq\) 50%) [26]. Low-grade iron ores (BIFs) in the Qidashan Iron Deposit are composed of quartz band and magnetite band with a width of about 1 mm. The high-grade iron ores in the BIFs are mainly massive and composed of magnetite, quartz,  
Figure 4: Schematic diagram of OH vibrations in octahedral sheet of chlorite. The OH-bending motion is shown in blue and the OH-stretching motion in yellow (modified from Bishop [20]).  
Figure 3: The spectral absorption features of chlorite [15; 17; 18].  
and chlorite. They are considered to have resulted from hydrothermal enrichment of BIFs near the faults, which are consistent with the occurrence of the BIFs layer [14]. The main types of wall rock alteration of Qidashan Iron Deposit include chloritization, sericitization, biotitization, and prioritization.  
In this work, 15 samples of BIFs, high-grade iron ore, and wall rocks were collected in the mining area (Figure 5c). All the samples were measured using ASD FieldSpec-3 pro spectrometer. Among which, six representative samples (from sampling location 1 and 3) were selected for laboratory-based hyperspectral imaging. These samples include three for high-grade iron ore (Q-2, Q-9, and Q-11) and the remnant for BIFs (Q-4, Q-12, and Q-13).
# Quantitative Assessment Methods of Early Enamel Caries with Optical Coherence Tomography: A Review
## 2 Optical Properties of Teeth  
Biological tissues' optical properties have important theoretical significance and application value for the diagnosis of diseases. The variation in optical properties of tooth tissue during demineralization are the basis of OCT caries detection [29].  
(1) Refractive Index  
Refractive index describes the reflection and refraction of light as well as the temporal properties of light transmission within a tissue. The refractive index of tooth enamel is 1.62 to 1.65 [30; 31]. Demineralization in caries lesions alters the refractive index of enamel [32], and can be considered as an index of its scattering properties [33].  
Since hydroxyapatite in tooth enamel is an anisotropic crystal, tooth enamel has birefringent properties [18]. When polarized light propagates through biological tissues, its polarization state is altered by the scattering properties of the tissue. By detecting changes in the polarization state of backscattered light from dental tissue, microstructural information of the tissue can be obtained, and caries lesions can be detected.  
(2) Scattering Properties  
Dependent on the structure of hard dental tissue, two types of scattering occur when light is transmitted inside the tooth. Dental enamel is an ordered array of hydroxyapatite crystals surrounded by a protein/lipid/water matrix. The diameter of hydroxyapatite crystals is about \(30\sim 40\,\mathrm{nm}\), and the crystals are clustered together into enamel rods with a diameter of \(4\,\mathrm{\SIUnitSymbolMicro m}\)[34]. In the near-IR (NIR), the crystal diameter is much smaller than the source wavelength, so it is in accordance with the Rayleigh scattering law. The diameter of the enamel rods is comparable to the source wavelength, so it obeys the Mie scattering law.  
Absorption Properties  
The absorption of enamel is quite weak in NIR. The absorption coefficient \((\mu_{a})\) of enamel at 1310 nm is around 0.12 cm\({}^{-1}\)[36], which is much smaller than the scattering coefficient. Therefore, scattering plays a major role in light transmission through the tooth.
## 5 Conclusions  
A Hough-transform-based localization method was proposed to evaluate TDOA measurements for emitter localization purposes, where the target is located on a plane, with sensors deployed in the three-dimensional space. A new variant of the HHT was proposed along with a fast, branch, and bound-type calculation. The proposed F-HHT was proven to find the global maximum of the HHT on a predefined grid. The performance of the F-HHT was studied and compared to that of the LS solution, and earlier variants of fast HHT methods, using simulation examples.  
The results showed that the error of the F-HHT is close to the theoretical limit (CRLB), similar to the LS solution and the other HHT methods. It was found that the F-HHT was faster in good GDOP situations, where the HT had a narrow peak around the global maximum. In such cases, the speed of the F-HHT was higher than that of the LS solution, and much higher than that of other HHT solutions. In cases when the GDOP was lower (i.e., the HT has wide peak around the global maximum), the F-HHT was slower than the LS solution but faster than the other HHT solutions. It is important to note that the F-HHT guarantees to find the global optimum, a property that neither the LS nor other HHT solutions can provide. The construction of further acceleration techniques, which maintain the global convergence property, is the topic of future research.  
Another attractive property of F-HHT (and other HHT-based solutions) is that they are robust against outlier measurements. It was demonstrated that the F-HHT can tolerate faulty sensors (other than the reference sensor) well. In a large sensor system, F-HHT was shown to tolerate extreme situations, such as more than half of the sensors providing outlier measurements. The performance of the proposed method was also illustrated using real measurements.  
Figure 11: HHT and positioning error, as a function of reference sensor index.  
**Author Contributions:** Conceptualization, G.S.; methodology, G.S.; software, G.S. and F.L.; validation, G.S. and F.L.; formal analysis, G.S.; writing, F.L. and G.S.; visualization, F.L.; supervision, G.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 VMD-NRBO-Transformer-BiLSTM Hybrid Prediction Model
### Overall Framework  
The overall framework of the VMD-NRBO-Transformer-BiLSTM hybrid network model is illustrated in Figure 5. The model consists of three primary modules. First, the raw data are processed using VMD, which decomposes the data into multiple subsequences. These subsequences are then split into training and testing sets. Next, the data undergo normalization and flattening before being input into the Transformer-BiLSTM network for model training and testing. The predicted values for each subsequence are subsequently re-denormalized and combined to yield the final prediction. Simultaneously, the NRBO algorithm is employed to optimize the model's initial hyperparameters, thereby enhancing its prediction accuracy.  
In summary, the prediction model proposed in this paper provides a good solution in terms of input data feature acquisition, solving the long-term dependency and gradient explosion of time-series data, and the problem of hyperparameter selection for network models. The denoising of nonlinear complex signals is realized by using the VMD technique, the combination of BiLSTM and Transformer makes Transformer more adept at dealing with the prediction of time-series data, and the introduction of the NRBO algorithm makes the hyperparameter selection of the network model more suitable to the predicted data features and improves the model's training effect.
# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 3 Discussion
### Types of Data  
In many cases, the level of useful information that can be extracted from different types of data are asymmetrical. However, it is important to note that the usefulness of certain types of data in isolation is not an indicator of how it will perform in combination with other data sources. In fact, the most important indicator for including or not a variable is its degree of complementarity with respect to the other ones [113]. In this context, variables that perform poorly in isolation can greatly improve the effectiveness of other variables if they have a high degree of complementarity [19]. In some cases, even if all variables perform poorly individually, they can yield good results when fused together [64]. Conversely, variables that perform well in isolation can match poorly with other variables if they share a high degree of redundancy. Poor synergies [86; 90] and high levels of redundancy [102] between different types of data can even lead to the deterioration of the results. However, it is also important to consider that, in some contexts, even if results do not improve with the inclusion of new data types, the robustness to conditions not considered during the development of the models may increase [30]. Moreover, the complementarity of different types of data can be increased by the application of techniques capable of decreasing redundancy (e.g., PCA). In any case, determining if it is worth to include or not a certain type of data are not trivial and usually requires thorough experiments exploring different data combinations [33; 36].  
Different types of data can have peculiarities that need to be taken into account in order to maximize the performance of the data fusion process. For example, the amount and quality of the information contained in thermal images is highly dependent on the time of day and weather conditions at the time of collection [18; 19]. Moreover, some types of data tend to be noisy and prone to outliers, especially if generated from low cost sensors [16], which can potentially introduce error instead of improving the results. In cases like these, applying noise reduction and outlier removal techniques is recommended [33; 73]. A comprehensive analysis of several outlier removal techniques in the context of water management is presented by [PERSON] et al. [93]. Despite the problems associated with spurious data, many studies simply ignore this issue, thus limiting the level of detail and quality of the resulting fused information [68].
## 3 Results  
### Planning Process  
For setting up a co-simulation as in the application case described in Section 2.6, the typical first step is the definition of the objective. In the information model, the evaluation criteria of a co-simulation can be described (for more details about the information model see [23]). In the example application case, this would be the violation of a transformer limit, as shown on the right-hand side in Figure 5. On the left-hand side, the considered domains (in this example only the energy domain), domain objects, and attributes are shown. The arrows describe data flows between the attributes, simulation models, and the evaluation as described in Section 2.6. For the example application case, the modeling was not too complex, but for the planning of large-scale co-simulation scenarios, the manual development gets more complex and demands assistance by querying the information model, e.g., to find evaluation functions without input, get assistance in finding suitable simulation models or find dependencies between different simulation models.  
After the definition of the objective, suitable simulation models have to be found or developed. Therefore, the simulation planning process can assist the user in finding suitable simulators for their use case, mitigating the need to develop new simulation models. The query in Figure 6a shows how the information model and the co-simulation component catalog can be used for this. It is easily imaginable that for a representation of wind turbines no new simulation model should be developed, but an already existing one can be chosen. For that use case, a query can be run to find suitable simulation models in the co-simulation component catalog. The results are filtered by the function \"generation/conversion\" and temporal resolution of 1 or 15 min.  
Figure 5: Example of the high-level simulation planning information model.  
```
PREFIXrdf:<[[http://www.w3.org/1999/02/22-rdf-syntax-ns#](http://www.w3.org/1999/02/22-rdf-syntax-ns#)]([http://www.w3.org/1999/02/22-rdf-syntax-ns#](http://www.w3.org/1999/02/22-rdf-syntax-ns#))> PREFIXcosicoca:<[[https://mosaik.offis.de/cosicoca/](https://mosaik.offis.de/cosicoca/)]([https://mosaik.offis.de/cosicoca/](https://mosaik.offis.de/cosicoca/))> SELECT?component?energySectors?usedMosaikAPI?openSource?functions?temporalRes WHERE{?componentrdf:typecosicoca:Component.?componentcosicoca:functions?functions.?componentcosicoca:temporalRes?temporalRes.... FILTERregex(?functions_\"generation/conversion\") FILTER(?temporalRes=\"1_min\"||?temporalRes=\"15_min\") }
```  
(a) SPARQL code  
Additionally, the attributes for the considered energy sectors, the used interface technology, and the availability as open source software are shown to decide for a suitable model in the results (see Figure (b)b). The result of the query shows that four components meet the filter and the simulation model WindTurbine would be the suitable one for the application case.
## 5 Results and Discussion  
### Correlation Analysis  
We first examine the correlation between the variables using a correlation matrix, which is a statistical technique used to evaluate the relationship between a pair of variables in a dataset. As shown in Figure 5, the correlation coefficients of the total crash counts and other independent variables range from \(-0.15\) to \(0.43\). Specifically, the correlation coefficient of crash count and ME8 near-misses (CW_ME8) is \(0.43\), which is the highest among those correlation coefficients for crash count. This indicates that the crash count (both total crashes and injury and fatal crashes) and near-misses have a moderate positive linear relationship and near-misses may play an important part in crash prediction models.