esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 2 Materials and Methods
### Quality Analysis
#### 2.6.2 Relative and Absolute Orientation Accuracy Analysis  
For comparing the accuracy and quality of the relative orientation, tie point and point cloud numbers and RMS re-projection were linked to determine the best solution and result. Each corresponding value was exported in an individual report by Agisoft Metashape and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sensor**} & **Camera** & **RMSE** & **Calibration** & \multirow{2}{*}{**Number of** **Point Clouds**} & **Number of Tie** \\  & **Orientation** & **Re-Projection** & & **RMS Residual** & **Point Clouds** & **Points** \\ \hline \multicolumn{6}{c}{Without pre-calibration (refined during relative orientation–self-calibration)} \\ \hline X2 & stereo & 0.136758 & 0.427 & 4,588,728 & 16,077/67,275 \\ \hline X3 & stereo & 0.126319 & 0.835 & 13,875,434 & 9601/54,016 \\ \hline X2 & sideways & 0.766283 & 1.377 & 18,676,576 & 43,782/62,556 \\ \hline X3 & sideways & 0.343216 & 0.742 & 30,471,652 & 21,968/118,537 \\ \hline \multicolumn{6}{c}{Calibration without splitting groups (in two separate chunks)} \\ \hline X2 & stereo & 0.176492 & 0.694 & 5,292,014 & 14.210/67.275 \\ \hline X3 & stereo & 0.200214 & 3.1 & 14,747,707 & 6850/54,014 \\ \hline X2 & sideways & 0.26795 & 1.28 & 14,242,671 & 17,238/62,431 \\ \hline X3 & sideways & 0.4989 & 3.762 & 39,937,968 & 37,810/58,581 \\ \hline \multicolumn{6}{c}{Calibration with splitting groups (in one chunk)} \\ \hline X2 & stereo & 0.27507 & 1.364 & 6,388,632 & 26,857/54,185 \\ \hline X3 & stereo & 0.37041 & 1.226 & 10,652,336 & 5514/57,984 \\ \hline X2 & sideways & 0.24312 & 1.083 & 14,118,170 & 35,312/57,520 \\ \hline X3 & sideways & 0.32965 & 2.357 & 37,484,289 & 32,946/56,378 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparing the results of the Insta360 spherical cameras’ (X2 and X3) fisheye images for different camera calibration and network design solutions. The optimal results underlined.  
is listed in Table 7. Similarly, for absolute orientation, two criteria were considered: the RMS error of control points (or GCPs accuracy) and the RMS error of control points (or control point accuracy) to also compare the quality (Table 8).
# A Method for Underwater Wireless Data Transmission in a Hydroacoustic Channel under NLOS Conditions
## 6 Conclusions  
Wireless data transmission in water environment is a non-trivial problem. Additionally, complications arise when NLOS conditions occur. Considering the demand for communication systems operating in such conditions, the possibilities of wireless underwater communication in the absence of direct visibility of the hydrophones were tested in this work. The tests were carried out in laboratory conditions simulating the situation which, for example, could be met in a wreck. By examining the impulse responses, it was found that NLOS conditions were indeed achieved in the experimental tank. The determined spectrum of received signals confirmed the presence of frequency-selective fading in the  
Figure 14: The impact of the number of replicas on the bit error rate.  
prepared artificial corridor in the laboratory tank. In the adopted measurement scenario, it was shown that the number of replicas increases with movement down the corridor, while the root mean square delay spread does not increase significantly. We found that the proposed technique of signal formation and diversity combining presented in this paper ensured a high quality of transmission compared to the DSSS technique. As expected, diversity combining greatly improves the transmission quality.  
The original qualities of this paper include: the impulse response studies in NLOS conditions, the evaluation of the quality of diversity combining in conditions of strong multipath propagation using the MFSK technique of forming the transmitted signal.  
The presented research results provide the basis for undertaking work leading to a communication system with autonomous underwater vehicles which operate in difficult propagation conditions, such as wrecks and ports.  
Conceptualization, R.S., A.C. and A.Z.; methodology, R.S.; software, A.C., A.Z.; validation, R.S., A.C. and A.Z.; investigation, R.S. and A.Z.; data curation, A.C.; writing--original draft preparation, R.S., A.C. and A.Z; writing--review and editing, J.M.; All authors have read and agreed to the published version of the manuscript.  
This research was supported financially from the Ministry of Science and Higher Education program \"Regional Excellence Initiative\" 2019-2022 project No. 006/PID/2018/19, the sum of financing 11,870,000 PLN.  
Not applicable.  
The authors declare no conflict of interest.
# A Period Training Method for Heterogeneous UUV Dynamic Task Allocation
## 3 MARL with the Attention Mechanism and Period Training Method  
To solve the UUV task allocation problem with emergencies, we propose a novel MARL algorithm with an attention mechanism. The algorithm consists of two parts; one is an encoder with deep feature extraction networks, and the other is a decoder based on the attention allocation mechanism. First, the encoder contains two linear projection networks and one self-attention network [13; 14; 15; 16], which is used to extract the high-dimensional features of the UUV and task data. Then, the obtained high-dimensional features are used in the decoder, and we introduce the attention mechanism to allocate task sets for each UUV sequentially. Finally, we design a period training method to optimize the parameters of the encoder and decoder.
# An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction
## 4 The Case Study Phases and Results  
### Company's Context Description  
The objective of this study is to design and test an algorithm that can work for an EWS that predicts the occurrence of a problem or an error as soon as possible. A particular part of a printing machine from \"Sofidel\" has been selected as the test scenario of the case study. It is focused on a specific facet of the printing machine, colloquially referred to as the \"embosser\". This integral component comprises an assembly of rolls and wheels, collectively responsible for exerting pressure and force on one or more paper substrates. The culmination of these actions yields intricate patterns on the paper. The embosser plays a critical role in the mass production of various paper products and is frequently prone to intricate paper jams. Anticipating those or related errors in advance would be valuable, enabling domain experts to proactively address and prevent potential disruptions, thereby saving time and money.  
The machine is equipped with integrated sensors at various levels that enable domain experts to understand whether its mechanical parts are failing or not. The sensor data generated revolve around the measurements of applied forces, pressure distributions, and rotational velocity of the rolls. Their acquisition hinges on the implementation of Internet of Things (IoT) sensors placed on the left, right, and middle parts, in multiple areas, to collect the mentioned physical information with the lowest energy consumption. This cascade of data forms the core of the explored analytical case study, helping to provide a comprehensive understanding and effective management of the embossing process. Once acquired, data are subsequently conveyed to a localised gateway through the Message Queuing Telemetry Transport (MQTT) protocol, known for its efficiency and reliability in data transmission, to an edge server. In the gateway, data are collected for advanced visualisation processes and monitoring, giving domain experts important insights into the production process. In addition, data are stored in a company-internal data storage system to enable engineers to monitor any malfunctions. In parallel, the collected data are marshalled and sent through the Internet as JavaScript Object Notation (JSON) packages to a cloud server for further analysis and elaboration.
# Regenerative Endodontics as the Future Treatment of Immutative Permanent Teeth
###### Abstract  
The regenerative endodontic procedure (REP) is an alternative solution for endodontic treatment of permanent teeth with incomplete root apex development. It results in angiogenesis, reinnervation, and further root formation. Indications for REP include immature permanent teeth with necrotic pulp and inflammatory lesions of the periapical tissues. The main contraindications comprise significant destruction of the tooth tissues and a lack of patient cooperation. We distinguish the following stages of this procedure: disinfection of the canal, delivery of the REP components, closure of the cavity, and follow-up appointments. For effective canal disinfection, the use of both rinsing agents and intracanal medicaments is suggested. Sodium hypochlorite and triple antibiotic paste are used most commonly. Light-activated disinfection is proposed as an alternative method. The prerequisite for the regeneration process of the pulp is the supply of its essential components: stem cells, growth factors, and scaffolds to the canal lumen. Blood clotting, platelet-rich plasma, and platelet-rich fibrin are used for this purpose. For a proper course of REP, it is also necessary to close the tooth canal tightly. For this purpose, mineral trioxide aggregate (MTA), tricalcium silicate (Biodentine), or types of glass ionomer cement are employed. The patient should attend regularly scheduled follow-up appointments and each time undergo a thorough interview, physical and radiological examination. The most important indicator of a successful REP is the continued growth of the root in length and thickness and the closure of the root apex visible on X-rays. Many different proposals for a management protocol have been published; the following paper proposes the authors' original scheme. Regenerative endodontics is the future of the endodontic treatment of immature permanent teeth; however, it still requires a lot of research to refine and standardize the treatment protocol. The application of tissue engineering methods seems to be promising, also for mature teeth treatment.  
Keywords:regenerative endodontic procedure; revascularization; apexogenesis; apexification; immature permanent teeth; regenerative endodontics +
Footnote †: journal: Applied Sciences
## 4 Results
### Results with Dual-Polarimetric Data  
The contrast enhancement processing is now applied to dual-polarimetric SAR data, and the improvements are evaluated across the following different polarization combinations: {HH, VV}, {HH, HV}, and {VV, VH}. In the case of PAZ, which lacks quad-polarimetric capabilities, the acquisition of all three possible polarization combinations can only be achieved on three separate dates (as shown in Table 1). In any case, to ensure a fair comparison among results obtained with different channel combinations, the three images were acquired using the same incidence angle (antenna beam). This way, only eventual changes in sea conditions and the presence of different ships would affect the polarimetric enhancement process. In contrast, since ALOS-1 and Radarsat-2 are quad-polarimetric, all three dual-polarization combinations could be extracted from the same image. Consequently, to test the performance of the method using ALOS-1 and Radarsat-2 dual-pol data, we manually selected two of the four available channels for each of the three cases.  
An important aspect of the contrast enhancement technique with dual-pol data is its very fast computation time, since the optimum channel is directly obtained by projecting the complete rank-1 image onto the second eigenvector of the reference patch, as detailed in Section 2.2.
## 4 Results and Discussion
### Multi-Spectral Optical Image  
Regarding the aim of this study, we begin by observing the performance of the multi-spectral optical image. First, the spectral signatures of the different land cover samples from the Landsat 8 image (Figure 4) for the built-up 1, built-up 2, built-up 3, bare land, and vegetation classes are shown in Figure 5. The spectral reflectance characteristics of bare lands and built-up features are remarkably similar, while the vegetation signature shows a different pattern. The geography of the study area makes it difficult to differentiate the urban area from the mountainous and desert areas that surround Tehran. ML and SVM classifications were applied to the Landsat 8 image using a total of fifteen training samples from five different land cover types. The results are shown in Figure 6. The ML classification result (Figure 6a) indicates that almost all bare land and mountain areas around Tehran were selected as built-up area (built-up 1). Additionally, the SVM result (Figure 6b) shows that some bare land areas in the southeast and west of Tehran were classified incorrectly as built-up areas (built-up 1 and 2).  
Figure 5: Spectral signatures of the five land covers obtained from the Landsat 8 image.  
To validate the classification result, we prepared the confusion matrix using the truth data. Figure 7a represents a closer look of the validation area, which is shown by a black frame in Figures 4 and 6b as well. Table 2 illustrates the confusion matrix results from Landsat 8 using SVM and ML classification. The SVM classification gave an overall accuracy and kappa coefficient of 41.1% and 0.26, respectively, while ML gave the values of 35.3% and 0.22. Moreover, the producer and user accuracy are shown in table below. The producer accuracy represents the correctly classified pixels out of the truth pixels of related land cover classes. The user accuracy, illustrates the correctly classified pixels out of the total classified pixels. The SVM algorithm provided higher accuracy than ML. A closer look at the SVM result is shown in Figure 7b. It is observed that some classes were incorrectly classified, such as built-up 2, which was mostly classified as built-up 1. Therefore, the Landsat 8 image does not seem appropriate for classifying the land cover of the study area, Tehran city.  
Figure 6: The ML (**a**) and SVM (**b**) classification results of Landsat 8 image, where the black frame shows the location of the validation area.  
Figure 7: Validation area containing the truth data for the five classes (**a**); and the result of the SVM classification from Landsat 8 image (**b**).
## 2 Literature Review and Conceptual Context
### Situation-Aware Route Planning
#### 2.2.2 Routing Using Weather  
There are a few applications of situation-aware routing using weather. However, an advanced Agent-Based Model ([PERSON], [PERSON], [PERSON], and [PERSON]) uses multiple agents that include alterations to a road-traffic network [44]. There are four different agents in this model: (1) traffic load, (2) weather condition, (3) type of road, and (4) travel estimation. While the first three agents perform updates on the network, the travel estimation agent runs an improved ripple-spreading algorithm (RSA) to generate the routing choices.  
Another study uses the same routing algorithm offline, where the optimization is c completed beforehand, and the weather is included in a coevolutionary algorithm [46]. One single routing optimization introduces the coevolutionary path optimization (CEPO) that simulates dynamic weather instead of static conditions. An upgrade on this study identifies the K-shortest paths [50] in a given dynamic environment without needing a memory-extensive hypergraph.  
A recent study used an Autoencoder Neural Network to promptly predict traffic conditions, considering the possible application of this method in logistics distribution [52]. The authors believe that traditional algorithms usually cannot predict extreme conditions for the logistics' environment, which yields a low accuracy for path optimization. Instead, they use variables such as historical traffic, weather, and distance of the road segment to make predictions.  
All these methodologies consider vehicle routing from its current position to the next but do not plan, since the models are not fed a set of destinations. In this case, applying some frameworks to last-mile delivery would need an improvement that can yield a set of routes per destination.
## 3 Results
### Forest Area Estimation  
Mean forest area estimates from the three image types for all of the study sites are displayed in Figure 4. Sites B, D, and E were the three densely forested sites with estimated proportions of forest area of 0.89, 0.77, and 0.60 from PlanetScope, 0.88, 0.76, and 0.56 from RapidEye, and 0.55, 0.48, and 0.41 from Sentinel-2, respectively. On the other hand, site F has the least forest cover, with an estimated proportion of forest area of 0.03, 0.07, and 0.05 from the PlanetScope, RapidEye, and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Observation** & **PlanetScope** & **RapidEye** & **Sentinel-2** \\ \hline Forest & 819 & 824 & 602 \\ Non-Forest & 774 & 772 & 947 \\ NA \({}^{*}\) & 3 & 0 & 47 \\ Total & 1596 & 1596 & 1596 \\ \hline \hline \end{tabular} \({}^{*}\) Not recorded due to clouds or missing imagery.  
\end{table}
Table 3: Observation results of forest and non-forest classes from the three image types.  
Sentinel-2 images, respectively. Forest area estimates from PlanetScope and RapidEye images showed similarities consistently over the seven study sites, although there were marginal differences, where estimates from RapidEye image were smaller in most cases. The estimates from Sentinel-2 images differed from the other two image types in most of the study sites, and these differences in mean forest area were much larger and statistically significant (\(p<0.01\)) for sites B, D, E, and G. However, the magnitude of the differences in mean forest area estimates decreased as the overall forest cover of the sites decreased. Thus, no significant differences were observed among the different image types for the three study sites A, C, and F, which had relatively smaller forest cover. It appears that when an area has a small proportion of forest such as sites A, C, and F, Sentinel-2 images, having relatively coarser resolution than PlanetScope and RapidEye images, would produce a reasonably comparable estimate.
# THz Rectennas and Their Design Rules
## 3 Frequency
It is also important to point out that as the frequency increases, insulators' behavior can also change greatly. The dielectric constant values reported for many oxides used today in THz diodes' fabrication are contradictory. For example, niobium oxide, Nb\({}_{2}\)O\({}_{5}\), has different reported values in [41; 42; 43]. Among many other factors, the frequency under which the material was tested plays a major role in its value.
## 2 Data and Methods
### Factor Analysis  
Factor analysis is utilized to represent the underlying information of a set of variables using a smaller number of variables [25]. An indices system was built and principal component analysis (PCA) was carried out to extract information from the original data sets regarding construction accidents, finance, labor and building construction scale and combine them into two composite indices called factors. A score function and a mathematical model based on the result of factor analysis were built. Statistical Product Service Solutions (SPSS 23.0) was utilized for completing factor analysis, ANOVA and other statistical functions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Subcategory** \\ \hline \hline  & Residential buildings \\  & Commercial buildings \\  & Hotels, restaurants and apartments \\  & Offices \\  & Schools and hospitals \\  & Passenger waiting rooms at airports, whaves, railway \\  & stations and bus stations \\  & Indoor stadiums and entertainment venues \\  & Workshops and warehouses \\ \hline  & Subway, light rail and tramcar subgrade track laying \\  & Urban municipal and ordinary highways \\  & Urban roads, streets, sidewalks, overpasses, underpasses, \\  & squares, parks and traffic barriers \\  & Subway and municipal road tunnels \\ Civil engineering & Municipal road bridges and urban flyovers \\ construction & Waterworks and sewage treatment works \\  & Water treatment system installation \\  & Gas and heat supply facilities \\  & Solid waste treatment works \\  & Urban landscapes, greenbelts and street lighting \\  & Urban pipelines and transfer stations \\ \hline  & Electric wiring, lighting and electric equipment \\  & Signal appliances \\  & Telecommunication lines and equipment \\  & Water pipes and equipment \\  & Gas supply lines and equipment \\ Mechanical installation & Heating pipelines and equipment \\  & Air conditioning equipment \\  & Fire detection devices \\  & Anti-theft devices \\  & Insulation and fireproof devices \\  & Elevators \\ \hline  & Doors, windows and glass \\  & Floor treatment \\ Architectural components & Wall and ceiling treatment and whitewash \\ installation & Paint \\  & Indoor woodworking and metalworking services \\  & Building repair and maintenance \\ \hline  & Preparation \\ \hline  & Construction equipment and operator service \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scope of building and civil construction.
# An Analysis of the Altitude Impact on Roots Compressor Operation for a Fuel Cell System
## 1 Introduction
### Knowledge Gaps  
Despite advancements made in understanding fuel cell system operation throughout the years, including the altitude impact, most of these studies were conducted using radial compressors. This technology can be particularly affected by the operation at certain altitudes due to surge and choke limitations, together with a high sensitivity of efficiency to the specific working point. Instead, other technologies such as screw or roots compressors, also under consideration for the future development of hydrogen fuel cell systems [44], may offer a more robust operation in a wider range of altitude conditions without such a severe impact on rated power operation and fuel consumption. However, the information available in the literature on the matching of such compressor technology with PEMFC systems is scarce, and the interaction with altitude operation is yet to be explored.