esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
The research results have shown that there are areas in which the higher level of virtual immersion given by the AR technology through AR goggles is considered by experts to be more supportive than the lower level available on a smartphone: * The presentation of detailed shipment data, law regulations, results of the comparison of graphical labels and shipment parameters with acceptable labels and limits are better evaluated for AR goggles than for a smartphone. Operators especially appreciate the connection of a virtual picture with the controlled DG shipment that is automatically identified by the goggles. * From the operators' points of view, the filling out of an acceptance list in its electronic form is more difficult with AR goggles than with a smartphone. Operators have declared that \"pressing buttons\" in virtual space requires more experience, and sometimes it happens that AR goggles incorrectly recognize the acceptance gesture made by an operator. * There have been a few observations mentioned in the operators' answers which provide insights into technical aspects and present arguments for using the AR application in a smartphone rather than using it in AR goggles: * The tested AR goggles, which cover the personal glasses of a person with a visual impairment, can cause discomfort when worn. * When the same pair of AR goggles are used by a few DG operators in operational conditions, the goggles should be calibrated individually for every of them (or even for every pair of glasses used by a person with a visual impairment) due to differences in the visual quality, while a smartphone picture is easier to adjust for different people. * Communication with AR goggles (e.g., decision choice, accepting, distracting) in virtual reality, which was carried out by operator's gestures, has been considered to be more difficult because of incorrect and/or delayed interpretations of a gesture happening from time to time. * The computer processing power required for the recognition of graphic signs (letters, symbols) in real time during the DGA process is more limited in AR goggles than in smartphones. AR goggles cannot execute this function in warehouses without a stable and constant Wi-Fi access, and the recognition must be--in fact--executed by a remote computer. * The total operating time of AR goggles resulting from the battery capacity is shorter than that of a smartphone, which may be considered as a drawback for the goggles. * Due to the limited angle of view, longer work periods using AR goggles involving controlling DG shipments are considered to be less comfortable (more tiring) than the same work periods using a smartphone. * The results of the experiments are presented in the following.
## 4 Experimental Results  
### Building Extraction Results  
In this paper, the U2-Net network uses 3600 samples (512 \(\times\) 512) as the training set, 1200 samples (512 \(\times\) 512) as the validation set verification set, and 1200 samples (512 \(\times\) 512) as the prediction set. Its hyper parameters are set to default (initial learning rate lr = 1 \(\times\) 10\({}^{-4}\), betas = (0.9, 0.999), eps = 1 \(\times\) 10\({}^{-8}\), weight decay = 0), epoch = 350. Its extraction results are shown in Figure 6. _Precision_, _Recall_, _F1-Score_, and _IoU_ as accuracy indicators are selected for verification, where _Precision_ reaches 75.45%, _Recall_ reaches 62.54%, _F1-Score_ reaches 68.39%, and _IoU_ reaches 56.58%. From the results, the U2-Net network model detection results can reduce the workload of manual vectorization and improve the efficiency. The data in the red box in Figure 6 (the township site of Moxi) are of a densely built-up area, which is used in the later experiment.  
\[precision=\frac{TP}{TP+FP} \tag{5}\]  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]  
\[F1-Score=2\times\frac{precision\times Recall}{precision+Recall} \tag{7}\]  
\[IoU=\frac{Ap\cap Ar}{Ap\cup Ar} \tag{8}\]  
where _TP_ denotes the area of correctly predicted buildings. _FP_ and _FN_ are the mispredicted correct and incorrect areas of the buildings. _Ap_ and _Ar_ represent the predicted and true areas of buildings, respectively, in each damage grade.
# Rate of Penetration Estimation with Parameter Correction
## 1 Introduction
### Depth Corrections
In [6], the authors challenge the assumption that reference hook load remains constant by identifying and analyzing seven key factors affecting it: wellbore friction, added weight in air, well inclination, flow-induced lift, nozzle jet lift, cuttings in suspension, and back pressure in managed pressure drilling operations. By integrating these factors into correction models, they develop a systematic approach to improve WOB accuracy and minimize errors arising from fluctuating hook load values. A more direct method for WOB correction is the use of downhole measurement techniques to obtain real-time force readings at the bit. In [9], the authors propose a Downhole WOB measurement approach based on the force-strain principle, addressing the shortcomings of surface-based WOB estimations. Their method models the linear relationship between axial force, fluid pressure, and temperature difference while incorporating the effects of nozzle jet force, flow friction force, and lift force. This refined DWOB equation is validated through finite element simulations and field experiments, achieving measurement errors within \(\pm\)1000 N and \(\pm\)5000 N, respectively. The study highlights the benefits of DWOB correction in improving bit life and increasing ROP, demonstrating the significance of integrating real-time downhole measurements into WOB estimation frameworks.Additionally, various other studies have explored different WOB correction approaches, including advanced modeling techniques and real-time measurement strategies, see, for example [10; 11].
# Research on Named Entity Recognition Based on Gated Interaction Mechanisms
## 3 Results and Discussion
### Evaluation Indicators
#### 3.2.3 F1-Score (F1)  
The F1-score is the harmonic mean of precision and recall, used to comprehensively consider the performance of precision and recall. The calculation formula is as follows:  
\[F1=\frac{2 PR}{P+R} \tag{9}\]  
where P represents precision and R represents recall. The F1-score balances the trade-off between precision and recall, providing a single metric to evaluate the overall performanceof the model. A high F1-score indicates that the model has both high precision and high recall, achieving a good balance between the two indicators.
# New Flame Retardant Systems Based on Expanded Graphite for Rigid Polyurethane Foams
## 3 Results and Discussion
### Apparent Density of RPUFs  
The apparent density is a parameter that determines the physical properties of foams. It is closely related to such foam properties as dimensional stability, thermal insulation as well as all mechanical properties. By analyzing the results presented in Table 3, it can be stated that for all tested foams, the apparent density increased from 41 kg/m\({}^{3}\) for the reference foam to a maximum value of 48 kg/m\({}^{3}\) for the series containing graphite and silica. The increase in apparent density is the result of increased system viscosity, hindered expansion of bubbles, as well as an additional mass of fillers with higher densities compared to the PU matrix.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{\
\begin{tabular}{c} **T\({}_{\text{max}}\)** \\ **[\({}^{\circ}\)C]** \\ \end{tabular} } & \multicolumn{3}{c}{**Processing**} & \multirow{2}{*}{
\begin{tabular}{c} **Cell Size** \\ **Times [s]** \\ \end{tabular} } & **Cell Size** & **Wall** & **Total** & **Apparent** \\  & & & & **[\(\mu\)m]** & **Thickness** & **Porosity** & **Density** \\  & & **Start** & **Rise** & **Gelling** & **[\(\mu\)m]** & **[\% ]** & **[\(\text{kg}\) m\({}^{-3}\)]** \\ \hline PU–0 & 140 & \(49\pm 3\) & \(262\pm 8\) & \(448\pm 14\) & \(472\pm 10\) & \(62\pm 4\) & 91 & 41 \\ PU–EG & 134 & \(56\pm 4\) & \(277\pm 8\) & \(498\pm 13\) & \(296\pm 7\) & \(67\pm 3\) & 83 & 46 \\ PU–EG+SiO\({}_{2}\) & 133 & \(54\pm 3\) & \(338\pm 9\) & \(564\pm 11\) & \(282\pm 7\) & \(69\pm 3\) & 81 & 48 \\ PU–EG+SiO\({}_{2}\)+IL & 132 & \(58\pm 4\) & \(342\pm 8\) & \(594\pm 12\) & \(304\pm 8\) & \(71\pm 4\) & 82 & 47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Growth kinetics and structure parameters of RPUFs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{\begin{tabular}{c} **Apparent Density** \\ **(kg m\({}^{-3}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Hardness** \\ **(\({}^{\circ}\)Sh)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Compressive** \\ **Strength** \\ **(Parallel)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Compressive** \\ **Strength** \\ **(Perpendicular)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Flexural Strength** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Impact Strength** \\ **(kJ/m\({}^{2}\))** \\ \end{tabular} } \\ \hline PU–0 & 41.1 & 67.6 & 162 & 122 & 0.41 & 141 \\ PU–EG & 45.7 & 74.7 & 243 & 153 & 0.51 & 227 \\ PU–EG+SiO\({}_{2}\) & 47.3 & 75.6 & 261 & 164 & 0.49 & 222 \\ PU–EG+SiO\({}_{2}\)+IL & 46.4 & 74.4 & 258 & 163 & 0.46 & 215 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mechanical properties of PU foams.
## 3 Simplified Beamforming by High Efficient CSI Estimation
\right],\quad\mathbf{Y}=\left[ \begin{array}{c}\vdots\\ \phi_{m}(k^{\prime})\\ \vdots\end{array} \right] \tag{20}\]  
\(k^{\prime}\) represents the subcarrier indices used for the training signal. Above operation is performed through the \(m\)-th element. Fourth, reception weight vector for the \(i\)-th UE is obtained as \(\mathbf{U}_{i}=\phi_{i}=[\phi_{1}(k),...,\phi_{Nu}(k)]^{T}\). Assuming time division duplex (TDD), UE should calibrate CSI for transmission beamforming since uplink and downlink signals go through different circuits, e.g., HPA and low noise amplifier (LNA). CSI for transmission can be obtained by applying a reciprocity calibration coefficient [29; 30]. Last, UE transmits training signal via beamforming and BS estimates CSIT to calculate MU-MIMO precoding weight. Here, BS can use an existing CSI estimation scheme since the link budget is improved by the beamforming gain at the UE side. As a result, BS can obtain effective MIMO channel matrix, \(\mathbf{H}^{\prime}\), as shown in Equation (14). If necessary, the highly efficient CSI estimation shown in Figure 3 can also be applied to the uplink where each UE uses different subcarrier/antenna combination without overlap. Figure 3: Proposed high efficient channel state information (CSI) estimation. Table 1 summarizes the computation complexities defined as the required number of complex multiplications. Complexity of the proposed scheme is derived from Equation (19), where _Nc_ and _Np_ indicate the subcarrier number for signal transmission and for training signal transmission in the proposed scheme, respectively. That of SVD is based on QR-decomposition and Householder reflections [31].
## 2 Material and Methods
### Deriving Uncertainty Estimates  
#### 2.6.1 MODIS Sea-Ice Concentration Uncertainty  
Several uncertainty-afflicted parameters enter the MODIS SIC retrieval. The MODIS SIC uncertainty, \(\sigma_{SIC_{MODIS}}\), is given by Gaussian error propagation applied to Equation (2):  
\[\sigma_{SIC_{MODIS}}=\sqrt{Unc_{IST}+Unc_{tp_{water}}+Unc_{tp_{ice}}}, \tag{5}\]  
where \(Unc_{IST}\), \(Unc_{tp_{water}}\) and \(Unc_{tp_{ice}}\) are the uncertainty contributions of \(IST\), \(tp_{water}\) and \(tp_{ice}\), respectively. They are given by  
\[Unc_{IST} =\left(\frac{1}{tp_{ice}-tp_{water}}\right)^{2}*\sigma_{IST}^{2} \tag{6}\] \[Unc_{tp_{water}} =\left(\frac{IST-tp_{ice}}{(tp_{ice}-tp_{water})^{2}}\right)^{2}* \sigma_{tp_{water}}^{2}\] (7) \[Unc_{tp_{ice}} =\left(\frac{tp_{water}-IST}{(tp_{ice}-tp_{water})^{2}}\right)^{2}* \sigma_{tp_{ice}}^{2}, \tag{8}\]  
where \(\sigma_{IST}\), \(\sigma_{tp_{water}}\) and \(\sigma_{tp_{ice}}\) are the uncertainties of \(IST\), \(tp_{water}\) and \(tp_{ice}\), respectively. We assume that \(\sigma_{IST}\) and \(\sigma_{tp_{water}}\) are equal to the measurement uncertainty of the MODIS IST, which amounts to 1.3 K [10]. For \(\sigma_{tp_{ice}}\), we assume that it is equal to the standard deviation of the 48 iterations done for the retrieval of \(tp_{ice}\) in Section 2.4. It is typically between 0.1 and 0.6 K (see Figure 4d).
# Hybrid Precoder Using Stiefel Manifold Optimization for Mm-Wave Massive MIMO System
## 3 Problem Formulation
### Algorithm for Precoder Design  
This Algorithm 1 can be used for narrowband systems and also for wideband systems.  
```
Input: 1: Assuming s is Gaussian distributed: Singular Value Decomposition:

2:\(\textbf{H}=\textbf{U}\,\sum\textbf{V}^{\textbf{H}},\,\textbf{F}_{opt}=\textbf{ V}^{\textbf{H}}\)

Proposed with RSD (Riemannian Steepest Descent):

3:Require \(F_{opt}\)

4:For \(0\leq k\leq K-1\)

5:Set \(F_{RF}\) using inverse vectorization of \(\textbf{X}^{\textbf{k}}\)

6: Compute \(\hat{\textbf{F}}_{BB}=F_{RF}\textbf{F}_{opt}\) for each \(k\)

7: Compute Euclidean gradient of \(\
abla\,f(\textbf{X})=vec(-2(\textbf{F}_{opt}-F_{RF}F_{BB})F_{BB}^{H}\)

8: Compute Riemannian gradient \(\xi=P_{x}\
abla f(\textbf{X})\), where \(P_{X}\) is the projection operator onto tangent space.

9: Find step size \(\alpha\) and update \(\textbf{X}^{k+1}=R_{X}(-\alpha\,\xi)\)

10: End for

11:output \(\textbf{F}_{RF}=vec^{-1}\Big{(}\textbf{X}^{k+1}\Big{)}\)

and \(\textbf{F}_{BB}=\frac{\sqrt{N_{t}}}{\sqrt{1\,F_{RF}F_{BB}\,1\,1\,t_{T}}}\hat{ \textbf{F}}_{BB}\)
```  
**Algorithm 1** RSD (Riemannian Steepest Descent)
## 4 Results and Discussion
### Lightweight AI Model  
PQUM-DNS drastically reduces the model size while maintaining a similar AUC performance to that of the non-lightweight DAE model. This is achieved by removing the near-zero weights from the original DAE-trained model and compressing the model data format from float32 to int8. Consequently, the model size is significantly reduced. The application of PQUM-DNS to various datasets demonstrates that the lightweight model size is reduced by approximately 60% with AUC performance maintained at similar levels, as shown in Table 8 and Figure 18.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Retraining**} \\ \hline  & **Chiller Vibration** & **Paderborn Open** & **Paderborn Open** & **SECOM Open** & **Average** \\  & **Data** & **Dataset Current** & **Dataset Vibration** & **Dataset** & **Dataset** & **Average** \\ \hline  & \multicolumn{4}{c}{PQUM-DNS} \\ \hline Retraining data & \multirow{2}{*}{586} & \multirow{2}{*}{54} & \multirow{2}{*}{50} & \multirow{2}{*}{248} & \multirow{2}{*}{235} \\ quantity (fine-tune) & & & & & \\ AUC & 99.99 & & & & \\ \hline  & \multicolumn{4}{c}{DAE} \\ \hline Retraining data & \multirow{2}{*}{2346} & \multirow{2}{*}{219} & \multirow{2}{*}{204} & \multirow{2}{*}{993} & \multirow{2}{*}{941} \\ quantity & & & & & \\ AUC & 99.99 & & & & \\ \hline  & \multicolumn{4}{c}{Improvement} \\ \hline Retraining data & \multirow{2}{*}{75.02} & \multirow{2}{*}{75.34} & \multirow{2}{*}{75.49} & \multirow{2}{*}{75.03} & \multirow{2}{*}{75.07} \\ quantity decrease & & & & & \\ AUC increase & 0.00 & \(-\)1.49 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of PQUM-DNS and DAE retraining performance for different data types.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Model Size**} \\ \hline  & **Chiller Vibration** & **Paderborn Open** & **Paderborn Open** & **SECOM Open** & **Average** \\  & **Data** & **Dataset Current** & **Dataset Vibration** & **Dataset** & **Dataset** & **Average** \\ \hline  & \multicolumn{4}{c}{PQUM-DNS} \\ \hline Model size (bytes) & 5776 & 6969 & 5973 & 5866 & 6146 \\ AUC & 99.31 & 93.16 & 99.34 & 69.69 & 90.38 \\ \hline  & \multicolumn{4}{c}{DAE} \\ \hline Model size (bytes) & 18767 & 14765 & 19466 & 11866 & 16216 \\ AUC & 99.31 & 93.16 & 99.34 & 69.69 & 90.38 \\ \hline  & \multicolumn{4}{c}{Improvement} \\ \hline Model size & \multirow{2}{*}{69} & \multirow{2}{*}{53} & \multirow{2}{*}{69} & \multirow{2}{*}{51} & \multirow{2}{*}{60} \\ decrease & & & & & \\ AUC increase & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of PQUM-DNS and DAE model sizes for different data types.
# Future of IoT Networks: A Survey
## 2 IoT Overview  
In this section, we briefly describe the overview of the IoT structure, which Figure 1 presents. The structure of the IoT consists of four layers: service layer, platform layer, network layer, and device layer. Many research institutions adopt the IoT classification standard shown in Figure 1 to maintain specialty and consistency for IoT development.  
The service layer, which is on the surface, provides the interface and communicates with the users. Examples of the service layer are autonomous driving, health care, smart industry, personal devices, and door security. These services are connected with a platform layer to provide customized services to the users.  
The next layer of the IoT structure is the platform layer. The platform layer is located under the service layer and supports the IoT applications and services. There are many types of platforms, including the device platform, data analysis platform, service development platform, and service platform. For instance, the device platform provides an execution environment of services and development for users. Context awareness and prediction, cooperation among things, and connection between the service layer and other layers with the translation of natural language to machine language are examples of the data analysis platform. Furthermore, the service development platform provides development toolkits to users for them to easily develop IoT services. Finally, the service platform supports the generation and execution of a variety of applications.  
Along with the service and platform layers, one of the core layers of the IoT environment is the network layer. It serves to transmit the data among devices, contents, services and users. The network layer should be able to process, control, and manage enormous amounts of network traffic. A detailed elaboration of the network layer will be further described later in this paper.  
Finally, the device layer is a layer that perceives the environment with various sensing devices, processes it to send to the sink node or gateway, and responds to it if necessary. The device itself must be smart by applying autonomic actuation and a smart control algorithm. The device layer should be able to acquire and control the IoT devices.  
In addition to the four layers, security and privacy are important in IoT. Instead of identifying security as its own layer, each layer should incorporate a security solution to protect it from threats. Security issues should be handled as an important functional entity for each layer, and their current or prospective solutions should be customized according to specific properties and operations of each layer.  
Each layer is important and has its own roles and capability to enable IoT. Although each layer of IoT is important and should be discussed in depth, the aim of this paper is to describe the IoT network in detail. We will primarily focus on the IoT network in terms of its challenges and provide insights for the future IoT network.
## 3 Results and Discussion
### Changes in the Structure of the Pore Space of Concrete during Leading Experiments
Pores with sizes of <2 nm (micropores, according to the IUPAK classification [66]) were absent both in the original concrete sample and after leaching under the specified conditions. As can be seen from the data presented in Figure 3c (concrete before leaching), the pore diameter of the original concrete ranged from 8 to 80 nm, and pores with a diameter of 10 to 40 nm predominate. In this case, the maxima in the pore size distribution were observed in the ranges of 19-22 nm and 27-31 nm. Thus, the surface of the original concrete contains mainly mesopores, as well as a number of macropores. Comparing the diagrams in Figure 3c, one can note an increase in the volume of pores with a width of 10-20 nm, while the volume of the remaining pores does not change. Thus, under the influence of model waters, new pores with sizes of 10 to 20 nm were formed, which led to an increase in the volume of the pore space and the surface area of the concrete. This indicates the leaching of concrete components. Summing up the impact of the model water of the crystalline rock of the Yeniseyskiy site on concrete, it can be noted that in concrete, as a result of leaching, increases in the numbers of both macro- and mesopores occurred, and their size distribution changed towards an increase in the pores in the range of 10-100 \(\upmu\)m for the macropores and 10-20 nm for the mesopores. This happened due to the dissolution of part of the phases, mainly in the marginal part of the concrete samples. However, it should be noted that there were no large changes in the surface characteristics of the concrete, which may indicate a low intensity of the leaching processes, confirming the research data [16]. Based on the composition of the model waters before and after leaching (see Table 2), it can be assumed that the main leaching mechanism was the dissolution of the concrete components, as shown below. Figure 3: The change in the porosity of concrete samples. (**a**) Histogram of pore size distribution obtained from SEM image analysis. (**b**) Dependence of the probability density of distribution on the pore shape coefficient. K\({}_{\text{f}}\) is the degree of roundness, with 1 being isometric round pores and 0.01 being strongly elongated—elements obtained from SEM image analysis. (**c**) Change in the pore distribution of concrete obtained from nitrogen adsorption.