esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Vehicle to Vehicle Communication Protocols for VANETs in the Urban Environment
## 4 Simulation and Analysis
### Results and Discussion
#### 4.2.1 Packet Delivery Ratio  
Figure 10 shows the impact of traffic density on packet delivery ratio. With an increase in traffic density, the packet delivery ratio starts to increase. This is because traffic density is the source of connectivity. High traffic density provides more connectivity. The packet delivery ratio depends on connectivity. It is observed from the figure that dynamical junction selecting routing protocols TFOR and E-GyTARD perform better than the static junction selection routing protocol DGSR. The main reason behind this observation is that DGSR selects statically sometime those streets that do not contain enough vehicles to carry packet towards the destination. This causes a reduction in connectivity which reduces packet delivery ratio. The dynamic junction selection routing protocol TFOR outperformed E-GyTARD, this is because TFOR uses both directional and non-directional routing paths efficiently. E-GyTARD selects junction on the basis of directional density and hence misses some important streets that provide the shortest path to the destination. These shortest paths sometime provide enough non-directional traffic density which are useful in relaying packet towards the destination [2].
# On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network
## 6 Conclusions  
This paper discusses the reliability of the communication between the unmanned aerial vehicle and the control station when such a communication uses the infrastructure of commercial mobile networks. The main contribution of the paper is a methodology to provide two-phase selection algorithm based on learning machines. The two phases consist of two independent selection processes that are a posteriori connected with a Utility function that optimizes the final selection. The idea is to get final results that are as close as possible to the result that would be selected by a hypothetical algorithm that would own all the information that are available at each phase.  
With the presented algorithm, the UAVs will select the mobile network operator that will serve the connection during the flight and, on the other hand, the control station will select also the operator for the connection based on the parameters of the radio links between the control station and the antenna. A later selection will take into consideration the two links and will take the final decision on the network operator that will connect the UAV with the control station.  
The results show that it is possible to optimize two-phase selection process based on Machine Learning.  
Conceptualization, methodology, formal analysis, software of machine learning algorithms and validation, J.M.B.; analysis of State of the Art and software of Prediction engines, C.X.M., G.M., E.K.M. and E.P.; investigation and edition of the work, T.W.; measurements of drone communication on mobile networks, P.L. and P.K. All authors have read and agreed to the published version of the manuscript.  
The work of the researchers of Warsaw University of Technology was funded by the POB Research Centre for Artificial Intelligence and Robotics of Warsaw University of Technology within the Excellence Initiative Program. Research University (ID-UB). The work of the other Polish researchers was funded by National Center of Research and Development (NCBiR) in Poland under \"5 GDRON: 5G-based control system for drones\" project with grant No. POIR.04.01.04-00-0002/17.  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.
## 3 Preliminary Results  
### Examples and Lower Bounds  
We first present examples of Nash equilibria that provide some intuition on the nature of the problem, as well as lower bounds on \(PoA\) and \(PoS\).  
**Example 3.1** (Mst).: _Given a graph \(G\) and destination \(t\), construct a minimum spanning tree \(T\) of \(G\) and direct its edges towards \(t\). Note that this forms a Nash equilibrium: If a node \(u\) has an incentive to switch from its current forwarding choice \(v\) to a new node \(v^{\prime}\), forming \(T^{\prime}\), then doing so does not introduce a cycle and \(w(u,v^{\prime})<w(u,v)\). But then \(T^{\prime}\) is also a spanning tree, with total cost less than that of \(T\), contradicting that \(T\) is a MST._  
Now consider the MST for the Euclidean \"Horseshoe\" graph \(G_{H}\) of Figure 1 given in [16]. This example immediately gives a \(\Omega(n)\) lower bound on \(PoA\) with respect to \(SC_{stretch}\), since both a clockwise and a counterclockwise path to \(t\) are Nash equilibria. Further, it can inductively be checked that the best Nash equilibria for this case with respect to both \(SC_{stretch}\) and \(SC_{md}\) is that of Figure 1, thus also giving a \(\Omega(n)\) lower bound on \(PoS\) for \(SC_{stretch}\). We note the contrast with [16]'s approximate solution for balancing MST cost and SPT cost which yields a constant bound for \(G_{H}\) (by actually connecting the dots as a horseshoe) but is not a Nash equilibrium. Thus, we have:  
**Example 3.2** (Horseshoe).: _The Euclidean \"Horseshoe\" graph given in [16] yields linear lower bounds on both \(PoA\) and \(PoS\) under \(SC_{stretch}\), the optimal Nash being the counter-intuitive one of Figure 1 (unlike [16]'s constant stretch approximation for a non-game-theoretic scenario)._  
Now consider a Euclidean Spiral graph \(G_{spiral}\) with \(t\) at center such as the nodes of Figure 2. The spiral is formed such that each new node has a distance from the previous node that is greater than the previous node's distance to its previous node, and also shorter than the new node's distance to the closest neighbor in the inner layer of the spiral. It can be checked that the unique Nash in such a class of instances is that of directing the Spiral inward towards \(t\) as shown in the Figure. Moreover, precise parameters may be set such that the number of spiral layers is proportional to \(\Omega(n^{c})\) for any constant \(0<c<1\), leading to the following (see [23] for details):  
**Example 3.3** (Spiral).: _The Euclidean \"Spiral\" graph of Figure 2 yields a \(\omega(n^{c})\) (for any constant \(c<1\)) lower bound on \(PoA\) and \(PoS\) under \(SC_{md}\)._
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
###### Abstract  
Effective and precise monitoring is a prerequisite to control human emissions and slow disruptive climate change. To obtain the near-real-time status of power plant emissions, we built machine learning models and trained them on satellite observations (Sentinel 5), ground observed data (EPA eGRID), and meteorological observations (MERRA) to directly predict the NO2 emission rate of coal-fired power plants. A novel approach to preprocessing multiple data sources, coupled with multiple neural network models (RNN, LSTM), provided an automated way of predicting the number of emissions (NO2, SO2, CO, and others) produced by a single power plant. There are many challenges on overfitting and generalization to achieve a consistently accurate model simply depending on remote sensing data. This paper addresses the challenges using a combination of techniques, such as data washing, column shifting, feature sensitivity filtering, etc. It presents a groundbreaking case study on remotely monitoring global power plants from space in a cost-wise and timely manner to assist in tackling the worsening global climate.  
emission; coal-fired power plant; remote sensing; machine learning; NO2
# The Use of Augmented Reality for the Management of Equipment Ageing with a Virtual Sensor
## 3 Development of a User-Friendly Interface of the Virtual Sensor
### Interface Testing  
To test the virtual sensor, a hydrocarbon storage tank included in a tank farm was selected. The vessel is an atmospheric tank with a fixed roof made of carbon steel and containing diesel. It has been operating for over 55 years. Two bottom inspection datasets from different years were available (1990 was the first inspection year and 2019 the second one). During both inspections, the thickness was measured at a number of points. The nominal thickness of the bottom is 8 mm, while the minimum detected thickness was 6.1 mm in 1990 and 2.8 mm in 2019. Figure 4 shows the location of the sampled points on the tank bottom.  
Figure 3: Flow diagram interaction between the inspector and the App Mobile.  
A 3D model of the vessel was created as a basic support for the AR, and was adapted to be overlapped on a miniature of the storage tank that is available in the laboratory of the TREES-MAT (Technology and Research on Energy, Environment and Safety Materials) Group of the University of Messina. Figure 5 shows the 3D model created.  
A safety walk was carried out in order to observe the inside of the tank in AR. A \"safety walk\" is an inspection of a unit of an establishment, which has the aim of checking the conditions from a safety point of view. In this manuscript, the AR results are shown by overlapping them on a photograph of the tank in its real location.
# Wideband Millimeter-Wave Perforated Hemispherical Dielectric Resonator Antenna
## 3 Measurements and Discussions  
To validate the simulation results, two prototypes of the HDRA with solid and perforated coatings were fabricated and measured. Each prototype was mounted on a Ro4350B dielectric substrate, which had a thickness of 0.42 mm, a permittivity of 3.48, and a loss tangent of 0.0037, provided by Wrekin Circuits. The antenna was fed using a microstrip line positioned beneath the Rogers substrate. A 2.4 mm SMA connector was employed to connect the feeding microstrip line to the Agilent Technologies E5071C mm-wave vector network analyzer via a 50 ohm coaxial cable. Radiation pattern measurements were conducted using an SNF-FIX-1.0 near-field system [26].
## 2 Background
### Technology Roadmap
[PERSON] and [PERSON] [26] invented a scenario-based TRM that integrates a TRM with scenario planning, demonstrating the encompassing key tasks, their interdependencies and the key decision points in the original TRM. [PERSON] et al. [27] integrated scenario planning and TRMs with a novel method. He first used scenario planning to find out reasonable pictures of the common environment and then used scenarios for technology roadmapping, followed by the advantageous use of 'flex points'--essential developments that signal transitions along certain pathways. Regarding the AHP, [PERSON] et al. [28] performed a technology assessment for critical techniques with a structuring AHP model. [PERSON] et al. [29] developed a two-stage multicriteria decision making (MCDM) method that helped to make a long-term systematic energy TRM and utilized the fuzzy AHP to allot the corresponding weights of the criteria effectively. [PERSON] [30] utilized Technology Development Envelope (TDE), an extension of hierarchical decision modelling and AHP into the future, in order to yield multiple paths for technology development enabling organizations to build roadmaps depicting their strategies. For the development of MA in TRMs, [PERSON] et al. [4] proposed the concept of MA-based TRMs to support planning and forecasting in SMEs with the criteria of data availability and tractability. [PERSON] et al. [9] proposed a methodology integrating MA with  
Figure 1: Structure of a technology roadmap. Delphi to refine the analysis results with the aim of providing good quality results for companies. [PERSON] et al. [10] constructed a four-dimensional science and technology planning model including nation, technology, industry and risks and impacts for a nation seeking to combine future-oriented technology analyses (FTAs) with a national science, technology and innovation (ST&l) policy framework with bibliometrics and TRMs. According to the previous literature, the development of TRMs reveal that their combination with other methods has been a crucial branch of study. This paper achieves cooperation between MA and TRMs by a novel method and applies simplified TRIZ in the process of seeking new opportunities to compensate for a chaotic innovation path.
# Digital Twins' Application for Geotechnical Engineering:
## 2 The Current Situation of Digital Twins in Geotechnical Engineering
[47] proposed a hybrid modeling approach combining data-driven and mechanism-driven methods, utilizing digital twin technology to achieve real-time optimization and predictive maintenance in energy systems. The application of digital twin technology in petroleum engineering has enabled enterprises to enhance production efficiency, reduce operational costs, elevate safety standards, and achieve resource optimization with environmental protection across multiple operational phases. Looking ahead, the deep integration of digital twins with cutting-edge technologies such as artificial intelligence (AI), big data, and the Internet of Things (IoT) is poised to propel the transformation of petroleum engineering toward greater intelligence, greener practices, and sustainable development trajectories. The advantages and disadvantages of digital twins in applications across various fields are shown in Table 1.  
Figure 8: The digital twin system of a drilling platform (reproduced from [PERSON], [PERSON] et al. [45]). In summary, digital twin technology, through real-time data interaction between virtual models and the physical world, helps resolve persistent challenges in geotechnical engineering while enhancing engineering efficiency and safety. Consequently, geotechnical digital twin platforms are being actively developed across various domains. This trend stems from the technology's demonstrated capacity to address the following critical issues in geotechnical engineering:  
1. Real-Time Monitoring and Data Analytics  
By integrating sensor networks (including seismic, pressure, and displacement sensors) with digital twin platforms, this approach enables continuous tracking of critical geotechnical parameters such as geological shifts, structural deformations, and subsurface water flow dynamics. The system facilitates rapid identification of geohazards (e.g., landslides, subsidence, surrounding rock instability) through predictive analytics-driven risk assessment, while implementing pre-failure alert mechanisms to substantially mitigate catastrophic engineering failures and associated economic losses. 2. Precision Modeling and Predictive Analytics  
During the investigation phase, digital twin technology can integrate heterogeneous information such as geotechnical exploration data and seismic data from the field, optimize work deployment, and establish precise virtual models by combining technologies like Building Information Modeling (BIM), Geographic Information Systems (GISs), and Finite Element Modeling (FEM). These computational models facilitate comprehensive mechanical simulations and stability assessments under diverse operational scenarios, empowering
## 3 Results
### Fabrication of the NPs
#### 3.3.3 The Influence of a Surfactant's Concentration in the Water Phase  
In these experiments two neutral AABB polymers **1F6** and **4F4**, and four of eight surfactants were used. The parameters (invariable and variable) of the NPs fabrication process are given in table below:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Polymer**} & \multicolumn{4}{c}{**Surfactant**} \\ \cline{2-5}  & **Tween 20** & **Triton X-100** & **Brij 010** & **Mowiol 4-88** & **Mowiol 8-88** \\ \cline{2-5}  & \multicolumn{4}{c}{**MPD (nm) \(\pm\) SD**} & **[PDI] \(\pm\) SD** & \\ \hline \multirow{2}{*}{**1F6**} & **143 \(\pm\) 6** & **148 \(\pm\) 6** & **160 \(\pm\) 10** & **321 \(\pm\) 4** & **328 \(\pm\) 2** \\  & [0.123 \(\pm\) 0.019] & [0.116 \(\pm\) 0.004] & [0.268 \(\pm\) 0.009] & [0.053 \(\pm\) 0.015] & [0.075 \(\pm\) 0.019] \\ \hline \multirow{2}{*}{**1L6**} & **132 \(\pm\) 7** & **140 \(\pm\) 8** & **136 \(\pm\) 1** & **218 \(\pm\) 6** & **260 \(\pm\) 10** \\  & [0.152 \(\pm\) 0.032] & [0.172 \(\pm\) 0.091] & [0.145 \(\pm\) 0.098] & [0.102 \(\pm\) 0.056] & [0.192 \(\pm\) 0.086] \\ \hline \multirow{2}{*}{**4F4**} & **158 \(\pm\) 7** & **139 \(\pm\) 7** & **163 \(\pm\) 10** & **173 \(\pm\) 9 \(\blacklozenge\)** & **240 \(\pm\) 10 \(\blacklozenge\)** \\  & [0.111 \(\pm\) 0.006] & [0.121 \(\pm\) 0.009] & [0.299 \(\pm\) 0.013] & [0.117 \(\pm\) 0.009] & [0.224 \(\pm\) 0.008] \\ \hline \multirow{2}{*}{**8L6**} & **159 \(\pm\) 3** & **154 \(\pm\) 9** & **166 \(\pm\) 5** & **144 \(\pm\) 3** & **181 \(\pm\) 6** \\  & [0.131 \(\pm\) 0.003] & [0.090 \(\pm\) 0.003] & [0.088 \(\pm\) 0.001] & [0.076 \(\pm\) 0.006] & [0.095 \(\pm\) 0.003] \\ \hline \hline \end{tabular} \(\blacklozenge\) Partial aggregation.  
\end{table}
Table 4: The influence of the AABB polymers on the fabrication of the NPs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**C\({}_{\text{pol}}\) (mg/mL)**} & \multicolumn{2}{c}{**Polymer**} \\ \cline{2-3}  & **1F6** & **4F4** \\ \hline
0.6 & Full Aggregation & Full Aggregation \\
3.0 & Full Aggregation & Full Aggregation \\
6.0 & Full Aggregation & Full Aggregation \\ \hline \hline \end{tabular}
\end{table}
Table 3: Nonoprecipitation of the AABB polymers **1F6** and **4F4** without surfactant.
# Cervical Cell Segmentation Method Based on Global Dependency and Local Attention
## 4 Experiment
### Dataset and Implementation Details  
In this study, a series of experiments are implemented on the Herlev dataset. This dataset was published by Herlev University Hospital. They collected a total of 917 color images of cervical cells, including two main categories: normal cervical cells and abnormal cervical cells. The abnormal cervical cells were divided into four subcategories: carcinoma in situ cells (150 cell images), moderate dysplastic cells (146 cell images), severe dysplastic cells (197 cell images), and light dysplastic cells (182 cell images), for a total of 675 images of abnormal cervical cells. Normal cervical cells were divided into three subclasses, corresponding to normal columnar cells (98 cell images), normal intermediate cells (70 cell images), and normal superficial cells (74 cell images), for a total of 242 diseased cervical cells. These images were collected with different equipment and staining techniques and manually labeled by a professional pathologist. The manual labeling was divided into four categories, namely, background, nucleus, cytoplasm, and unknown regions; unknown regions were considered as background in this study.  
The [PERSON] dataset is divided into 8:2, which means that 734 images are utilized for training, and 183 images are set for testing. Notably, the data are divided separately according to cervical cell types. For example, the carcinoma in situ cells contain a total of 150 images, of which 120 images are randomly selected as the training set, and the others are used as the test set. All images are scaled to a size of 256 \(\times\) 256 pixels. In addition, to enhance the model generalization, some general online data augmentations are utilized in the training phase, including flip, rotation, random affine transform, random brightness contrast, and image blur.  
The deep learning framework is PyTorch 1.7, with the experimental code in Python 3.8. Additionally, the experiment is implemented on a PC (Windows 10 operating system, Intel(R) Xeon(R) Gold 6226R CPU @ 2.90 GHz, 16 Gbyte RAM, and NVIDIA Quadro RTX 5000 GPU). The model optimizer uses SGD with a batch size of 16 and an initial learning rate of \(1\times 10^{-4}\).  
The model is trained for a total of 200 epochs, with the \"poly\" learning rate strategy, in which the initial rate is multiplied by \((1-iter/max\_iter)^{power}\) for each iteration, with power 0.9. The traditional cross-entropy loss function is used and the parameters are kept consistent for all experiments. Unless otherwise noted, the experiments in this work will be conducted with the channel compression ratio set to 2. To ensure the reliability of the results, five trials are performed for each experiment, and then the mean \(\mu\) and standard deviation \(\sigma\) of the results are taken. Moreover, all experiments employ the same random seed.  
Figure 4: Illustration of the network structure used with the GDLA module embedded in U-Net.
## 1 Introduction  
The root canal system is defined as the space enclosed within the root dentin; it is invisible and variable. The physiological description of this system implicates the dental pulp. When the latter is in pathological conditions, it contains the products of tooth decay, bacteria, toxins, and filling material. Henceforth, the endodontic treatment's main purpose is to resolve pain and lesions resulting from pulp or periapical pathologies by eliminating intra-root and extra-root infections [1; 2].  
Consequently, when selecting the material used for root canal obturation, the material of choice must comply with the biocompatibility criteria, and therefore should be harmless and preclude toxic tissue reactions or systemic complications [3].  
Modern endodontics has evolved in recent years, and recently it has been focused on the concept of minimally invasive treatments. Explicitly, the conservative approach begins from the conservative access cavity, minimally invasive shaping, 3D cleaning, and finally, three-dimensional obturation [4].  
In general, the root canal mechanical preparation is a vital phase on which the quality of the entire endodontic treatment depends. This phase removes all vital and necrotic pulp tissue, bacteria, and toxins from the main canal.  
Moreover, the proper shaping of the canal must respect the original anatomy without any alteration; above all, the shaping must not modify the foramen. Likewise, it should avoid causing any excessive dentin removal to evade the hazard of microcracks or stripping.  
Equally important, fracturing the rotating files during the shaping phase should be evaded by all means [5; 6].  
In conclusion, it is crucial to respect a series of general, mechanical, and biological criteria to assess mechanical preparation. Such standards turn out to be particularly difficult, especially in the presence of complex anatomies [7].  
For decades, the traditional two-dimensional radiography was used to examine the endodontic space prior to the treatment. Unfortunately, it does not permit us to thoroughly analyze the actual existing anatomical features of the tooth under treatment.  
With the spread and wide use of 3D radiological technologies, such as cone beam computed tomography (CBCT), the anatomical characteristics can be illustrated three-dimensionally without altering the actual geometry. Furthermore, in some cases where lower dose conventional radiography does not provide adequate or sufficient diagnostic information and additional information would potentially change or enhance the treatment plan, small FOV CBCT may be considered [8].  
The current work aimed to evaluate the anatomy of sections of the apices of maxillary premolars 1 mm from the radiographic apex utilizing high-resolution cone beam computed tomography. These data were collected by measuring the examined tooth's meso-distal and bucco-palatal length.