esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Authentication between Entities
#### 5.2.2 Signaling Cost  
In the context of a communication protocol involving an edge device, a 5G service provider, and a fog node, this section compares different signaling messages involved in various protocols. The primary focus is on the number of signaling messages exchanged.  
Figure 12: TLS 1.3 handshake.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Protocol** & **Edge User Computational Cost (ms)** & **Total Computational Cost (ms)** \\ \hline
3-Tier AKA & \(2T_{AES,E}+2T_{AES,D}=0.920\) & \(7T_{AES,E}+9T_{AES,D}=3.706\) \\ \hline
5G-AKA [31] & \(T_{CICIS,E}+7T_{H}=1.915\) & \(T_{CICIS,E}+T_{CICIS,D}+15T_{H}=3.935\) \\ \hline
4G EPS-AKA [31] & \(8T_{H}=0.856\) & \(16T_{H}=1.712\) \\ \hline TLS 1.3 [58] & \(T_{ICICBE}+T_{IKDF}+T_{INC}=2.251\) & \(2T_{ICICBE}+2T_{IKDF}+2T_{INC}=4.502\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Computational cost comparison.  
Table 9 and Figure 13 summarize the findings of this comparison. The proposed protocol involves six different types of signaling messages that are exchanged between the edge device, the 5G service provider, and the fog node. These signaling messages serve different purposes, including authentication requests, response messages, and key agreement messages. The widely used TLS 1.3 protocol requires only three signaling messages. However, the size of each message in TLS 1.3 is comparatively larger than the messages used in the other protocols being considered. The 5G-AKA protocol involves nine signaling messages for its operation. This higher number of messages can increase communication overhead and potentially cause higher latency. Similarly, the 4G EPS-AKA protocol requires eight signaling messages. Again, this higher number of messages might lead to increased overhead. The protocol proposed in the paper, referred to as 3-Tier AKA, stands out in this comparison. It requires only six signaling messages for its operation, which is fewer than the 5G-AKA and 4G EPS-AKA. This reduced number of signaling messages in 3-Tier AKA makes it more efficient in terms of communication overhead and potentially contributes to lower latency compared to the other authentication mechanisms.
## 4 Conclusions  
The following main conclusions can be drawn, based on the analysis and generalization of the obtained data:  
1. A laboratory stand and a measuring system with the required speed for studying the gas dynamics and heat exchange of stationary and pulsating flows in straight pipes with different cross-sectional shapes were created;
2. It was found that gas-dynamic non-stationarity (the presence of flow pulsations in comparison with steady motion) led to an increase in the TI by 47-72% in a round straight pipe and by 36-86% in a triangular pipe;
3. It was revealed that gas-dynamic non-stationarity (flow pulsations in the range from 4.6 to 15.3 Hz) caused an intensification of heat transfer in a round straight pipe by 26-35.5%;
4. It was shown that gas-dynamic non-stationarity (flow pulsations in the range from 3.0 to 15.8 Hz) led to an increase in the HTC in a triangular pipe by 24-36%;
5. It was found that increasing TI from 0.04 to 0.12 led to a rise in the HTC of pulsating flows in a round pipe by 11-16%;
6. It was revealed that increasing TI (from 0.04 to 0.15) for a pulsating flow in a triangular pipe caused a suppression of heat transfer by 7-24%;
7. The obtained results confirm the significant influence of flow pulsations, TI, and the cross-sectional shape of pipelines on the gas-dynamic and heat-exchange characteristics of gas flows in straight pipes.  
The obtained results can be useful for designing power and heat exchange equipment, and for refining mathematical models for predicting the gas dynamics and heat exchange of stationary and pulsating gas flows.  
Obtaining detailed information about the structure of secondary flows in the corners of square and TrPs under conditions of gas-dynamic unsteadiness should be considered a direction for further research on this topic. These studies will improve the understanding of the physical mechanism of transformation of the heat transfer level of pulsating flows in pipelines of different designs. The use of modern optical methods (for example, the PIV method) will be required to solve this pressing problem.  
Conceptualization, L.P.; methodology, L.P. and L.O.; validation, L.P. and L.O.; formal analysis, L.P. and L.O.; investigation, L.P.; and features, L.P.; data curation, L.P.; writing--original draft preparation, L.P. and L.O.; writing--review and editing, L.P.; visualization, L.P. and L.O.; supervision, L.P.; project administration, L.P. All authors have read and agreed to the published version of the manuscript.  
The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) is gratefully acknowledged.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors would like to thank the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) for supporting this work.  
The authors declare no conflicts of interest.
# Evaluation of Rice Straw Ash as a Pozzolanic Addition in Cementitious Mixtures
## 2 Materials and Method
### Physical and Chemical Characterization of RSA  
A sequential X-ray spectrometer, model PHILPS MAGIX PRO, equipped with a rhodium tube and a beryllium window, was employed for the chemical composition determination (X-ray fluorescence, XRF).  
The insoluble residue was determined by obtaining the total silica and alumina proportion, and subsequently determining the content of the crystalline and amorphous silica phases [25]. Then, 1 g of RSA was weighed and 75 mL of HCl 3 M were added. The mixture was stirred and placed on a heating plate at 250 \({}^{\circ}\)C. It was left to boil for 15 min. The mixture was gravimetrically filtered by a tapered funnel with paper filter. The precipitate was repeatedly washed with hot deionized water until filtration was free of chlorides. The final clean precipitate was burned with a Bunsen burner and finally calcined in a muffle at 900 \({}^{\circ}\)C for 1 h. The obtained solid contained both crystalline and amorphous silica.  
After establishing the insoluble residue weight in HCl (or impure silica), it was moistened with a few drops of distilled water and two drops of concentrated H2 SO4. Next, 5 mL of concentrated HF was poured to remove silica by volatilization and the crucible was placed on a heating plate at 250 \({}^{\circ}\)C until total liquid evaporation. The remaining solid was calcined at 950 \({}^{\circ}\)C for 1 h. Total pure silica was calculated as the difference between the weight of the insoluble residue and that obtained after this second calcination.  
To determine the amorphous or reactive silica content, 1 g of RSA was taken and the above-described acid-attack process was repeated to leave the sample free of chlorides. Then, 100 mL of KOH was added and placed inside a spherical flask. When boiling started, the paper filter was placed into the flask and left to boil for 3 min. The suspension was filtered through filter paper. The retained solid, which contained crystalline silica, was washed with hot deionized water and 100 mL of HCl 0.1 M. The chloride-free solid was calcined and weight was recorded. The procedure explained above, using distilled water, H2 SO4 and HF additions, was carried out for the crystalline silica content determination. The difference between total silica and crystalline silica content was the quantity of the amorphous or reactive silica present in RSA.  
The used X-ray diffractometer was a Bruker AXS D8 Advance model. The intensity and voltage of the X-ray generator tube were adjusted to 20 mA and 40 KV, respectively. Diffractograms were recorded for the interval 20 between 5 and 70 degrees, with a pitch an  
Figure 2: Self-made burner to obtain rice straw ash (RSA).  
gle of 0.02 and an accumulation time of 2 s. The employed equipment for the field emission scanning microscopy (FESEM) was the ultra 55 model of ZEISS OXFORD instruments.
# Model Updating Using Frequency Response Functions Based on Sherman-Morrison Formula
## 2 Theoretical Analysis
### Sensitivity Analysis Based on SMW
Therefore, the sensitivity of FRFs to the perturbation parameters can be directly solved by only the initial FRFs and parameter perturbations to avoid repeated FE analyses and calculations. The FRFs \(\mathbf{H}^{*}(\omega)\) of the system changed by parameter perturbation can be obtained according to Sherman-Morrison formula:  
\[ \begin{split}&\mathbf{H}^{*}(\omega)=\mathbf{Z}^{*-1}(\omega)=(\mathbf{Z}( \omega)+\Delta\mathbf{Z}(\omega))^{-1}\\ &=\mathbf{Z}^{-1}-\mathbf{Z}^{-1}\mathbf{U}\mathbf{\left(}\mathbf{I}+\mathbf{V}^{T}\mathbf{Z}^{ -1}\mathbf{U}\right)^{-1}\mathbf{V}^{T}\mathbf{Z}^{-1}\\ &=\mathbf{H}-\mathbf{H}\mathbf{U}\mathbf{\left(}\mathbf{I}+\mathbf{V}^{T}\mathbf{H}\mathbf{U}\mathbf{ \right)}^{-1}\mathbf{V}^{T}\mathbf{H}\end{split}  \tag{15}\]  
Therefore, the sensitivity of FRFs based on Sherman-Morrison formula can be expressed as  
\[\mathbf{S}=\frac{-\mathbf{H}\mathbf{U}\mathbf{\left(}\mathbf{I}+\mathbf{V}^{T}\mathbf{H}\mathbf{U}\mathbf{\right) }^{-1}\mathbf{V}^{T}\mathbf{H}}{\Delta\mathbf{P}} \tag{16}\]  
where \(\mathbf{Z}^{*}(\omega)\) is the dynamic stiffness matrix changed by parameter perturbation, \(\Delta\mathbf{Z}(\omega)\) is the variation of the dynamic stiffness matrix, which is caused by the change quantity \(\Delta\mathbf{P}\), and \(\Delta\mathbf{Z}(\omega)\) can be decomposed into two matrices \(\mathbf{U}(\omega)\) and \(\mathbf{V}^{T}(\omega)\), \(\mathbf{I}\) is the \(n\)-order identity matrix.
## 4 Deep Learning Methods for HDR Reconstruction
### Transformers for HDR Reconstruction
#### 4.3.3 Convolution-Based Methods
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Category** & **Architecture** & **Strengths** & **Limitations** \\ \hline \multirow{2}{*}{Alignment-based} & HFT-HDR [156], [PERSON] et al. [157], HyHDRNet [159] & \(\bullet\) & Frame alignment & \(\bullet\) & Supervised learning \\  & HyHDRNet [159] & \(\bullet\) & Multi-scale refinement & [PERSON]bullet\) & High computational cost \\ \hline \multirow{4}{*}{Attention-based} & CA-ViT [17], HDT-HDR [161], PASTA & \(\bullet\) & Spatial or temporal-level attention & [PERSON]bullet\) & Complex processing \\  & CA-ViT [17], HDT-HDR [161], PASTA & \(\bullet\) & Emphasis on global context & \(\bullet\) & High memory usage \\  & \(\bullet\) & Enhanced missing- & \(\bullet\) & Information reconstruction & \\ \hline \multirow{4}{*}{Convolution-based} & SCTNet [27], SV-HDR [162], SSHDR & \(\bullet\) & Multiscale feature fusion & \(\bullet\) & Requires correctly exposed \\  & SCTNet [118] & SV-HDR [162], SSHDR & \(\bullet\) & Possibility of self- & \(\bullet\) & Images \\  & \(\bullet\) & Supervised approach & \(\bullet\) & High inference time \\ \cline{1-1}  & \(\bullet\) & Better reconstruction & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Transformer-based architectures, with their strengths and limitations.
## 1 Introduction
Consequently, higher values of implant stability can be observed during implant insertion due to improved interlocking between the implant geometry and the bone matrix, resulting in greater contact between the bone and implant surface (thus increasing BIC). The increased contact between the implant and bone promotes bone healing and remodeling processes, resulting in advantages such as improved secondary stability and osseointegration [21]. Additionally, the autograft generated during drilling facilitates the retention of autologous bone particulate matter, which serves as a source of mineralization nuclei at the periphery of the implant site. This has been confirmed through histomorphometric analyses conducted in vivo on sheep and bovine bone [22]. The objective of this in vitro study was to assess the implant stability values associated with site preparation using osteecondensation drills, specifically comparing the osteo-condensation direction with the cutting direction. The use of osseed densification drilling protocols in the preparation of implant sites is hypothesized to significantly enhance the primary stability of dental implants. A secondary objective was to evaluate the influence of the depth of implant site preparation on implant stability. The evaluation of bone density at the implant site enhanced by osteodensification drills was conducted using parameters such as the integral depth curve (I), Cp, average torque (Cm), and ISQ.
## 2 Overall Framework of the Model  
The proposed method is divided into three main stages, as illustrated in Figure 1.  
Stage 1 involves the selection of sensitive meteorological features from the source domain NWP data. The correlation between meteorological features and wind-solar power output is calculated using the Pearson correlation coefficient, Spearman correlation coefficient, and mutual information theory. The evidence-based optimization theory is then applied to select meteorological features with a cumulative contribution rate of over 85%.  
Stage 2 is the extraction of power generation features. In this stage, the preprocessed NWP data from the source domain serves as input for the TCN-ATT-LSTM model, with historical power data serving as the output. Initially, the attention-based TCN model is employed to extract deep, significant temporal feature vectors from the NWP data for wind  
Figure 1: Framework of the transfer learning modeling process.  
and solar power forecasting. Subsequently, the output vectors are transformed into time series inputs for the LSTM network, capturing both short- and long-term dependencies in the time series data. Finally, the standard deviation (SD) of the training error is introduced as an optimization objective for the prediction system.  
Stage 3 involves network knowledge transfer, which is the core of the algorithm. The pre-trained weights and bias parameters of the TCN-ATT-LSTM model from the source domain are preserved, and the shallow network parameters of the target domain model are frozen to match those of the source domain, without further training. The target domain TCN-ATT-LSTM model is then re-trained with target domain data, allowing fine-tuning of the unfrozen layers' weights. After training is complete, all network layers are unfrozen, and the model undergoes a final round of fine-tuning using a very low learning rate.
# Generalization Investigation for Artificial Intelligence-Based Positioning in IIoT
## 3 System Model
### Modeling the LOS/NLOS Probability Based on Clutter Parameter  
Due to the distribution of clutters in the factory, radio propagation would be impacted. In this paper, the clutter parameters include the density of the clutter, the height of the clutter, and the size of the clutter. High clutters would result in the potential NLOS propagation and dense clutters would further cause serious multipath effects as shown in the example of Figure 3.  
In this paper, we refer to the modeling of the LOS probability in TS 38.901 by considering the clutter parameters [30]. Additionally, the LOS probability is modeled as  
\[\Pr_{LOS,subsc}(d_{\text{2D}})=\exp\biggl{(}-\frac{d_{\text{2D}}}{k_{subsc}} \biggr{)} \tag{1}\]  
where  
\[k_{subsc}=\frac{d_{clutter}}{\ln(1-r)}\frac{h_{BS}-h_{UT}}{h_{c}-h_{UT}} \tag{2}\]  
Accordingly, for one specific link, the NLOS probability is \(1-\Pr_{LOS,subsc}(d_{\text{2D}})\).  
In the equation, \(r\), \(d_{clutter}\), \(h_{c}\) represent the clutter density, size of clutter, and height of clutter, respectively. In addition, \(h_{BS}\) and \(h_{UT}\) denote the height of BS and the height of the device, respectively.
# Calculation of Blasting Damage Zone Radius of Different Charge Structures in Burnt Rock
## 1 Introduction
In summary, the radius of the failure zone after the blasting crushing process of burnt rock is affected by joints and fissures and does not conform to the existing theoretical calculation formula, and the distribution law of the failure zone also changes. The crushing zone is larger, and the crack extension and expansion zone are smaller. Therefore, in view of this kind of blasting situation of burnt rock, it is necessary to combine the damage theory to deduce the radius of the damage area of burnt rock. The core goal was to deeply explore and accurately quantify the specific influence of different charge structures on the blasting effect of burnt rock. The purpose of this study was to establish an accurate mathematical model and calculation formula through scientific methods and means to predict and evaluate  
Figure 2: Internal action phenomenon of blasting. the boundary and range of key damage areas, such as the crushing area and fracture area generated by blasting with different charge structures. By comparing and analyzing the radius of the blasting failure area with different charge structures, this research aimed to reveal the internal relationship between the charge structure and blasting effect, and then guide the optimization of the charge structure in practice so as to improve the blasting efficiency and reduce the damage to the surrounding environment while ensuring the safety of the blasting operation. In addition, this research also aimed to provide a solid theoretical basis and technical support for mining, water conservancy construction and other related fields that need frequent blasting operations, as well as promote technological progress and sustainable development in these fields.
## 4 Results
### Comparison on an Interannual Scale for Different Seasons
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Region** & **Name** & **Regression Coefficient** & **RMSD (mm)** & **R** & **STD** & **Bias (mm)** \\ \hline \multirow{4}{*}{1} & MSWEP & **1.14** & 26.22 & 0.46 & 0.40 & \(-\)55.0 \\  & CHIRPS & 0.44 & **10.74** & 0.45 & 1.06 & \(-\)18.0 \\  & PERSIANN-CDR & 0.73 & 16.87 & **0.47** & 0.63 & \(-\)35.0 \\  & TMPA & 0.42 & 10.8 & 0.43 & **1.01** & \(-\)**8.0** \\ \hline \multirow{4}{*}{2} & MSWEP & 0.87 & 25.47 & 0.76 & 0.80 & 38.0 \\  & CHIRPS & 0.66 & 17.68 & 0.78 & 1.18 & 23.0 \\  & PERSIANN-CDR & 0.77 & 16.91 & 0.84 & 1.08 & \(-\)63.0 \\  & TMPA & **0.92** & **11.53** & **0.93** & **1.01** & **2.1** \\ \hline \multirow{4}{*}{3} & MSWEP & 0.67 & 10.04 & 0.74 & 1.10 & **2.0** \\  & CHIRPS & 0.49 & **6.55** & 0.78 & 1.58 & \(-\)3.0 \\  & PERSIANN-CDR & 0.57 & 10.35 & 0.67 & 1.18 & \(-\)5.0 \\  & TMPA & **0.82** & 9.9 & **0.81** & **0.97** & \(-\)7.0 \\ \hline \multirow{4}{*}{4} & MSWEP & **0.95** & 10.71 & 0.97 & **1.01** & \(-\)**3.2** \\  & CHIRPS & 0.69 & 11.56 & 0.94 & 1.30 & **3.2** \\  & PERSIANN-CDR & 1.245 & 14.95 & 0.97 & 0.77 & \(-\)47.0 \\  & TMPA & 1.13 & **10.46** & **0.98** & 0.87 & \(-\)12.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptive statistics of the interannual precipitation variability in selected regions. Regions 1 and 3 experience pre—monsoon precipitation, while regions 2 and 4 experience monsoonal precipitation. Figure 10: Interannual variability of pre—monsoon (**a–b**) and monsoonal precipitation (**c–d**) in selected regions for in-situ and GPPs. (**a–d**) are equivalent to regions 1, 2, 3 and 4 in Table 1.  
PERSIANN-CDR, [51; 54; 55] for CHIRPS and [56; 57] for MSWEP. MWSEP is a relatively new product; thus, very few studies have reported similar findings in such a diverse range of climate.
# Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication
## 4 Simulation Results
### Latency Calculation for Different Packet Sizes  
The OFSMA and C-OFSMA systems packet processing technique is similar, except for the subcarrier bandwidth requirement to achieve high transmission reliability within \(0.2\) ms air interface latency. Therefore, we chose to compare our proposed scheme with OFDMA for air interface latency calculation. In the OFDMA system, a data packet is divided into several subcarriers with the cyclic prefix appended to each data block. As the OFDMA system's packet splitting mechanism consumes more time during transmission, the average packet size is growing. However, in the C-OFSMA system, the existing data packet is directly sent to a single subcarrier, and the subcarrier selection is random and unbiased. For simulation, \(200\) bits packet size is considered. With the increase in packet size, the transmission delay is also increased. Transmission latency depends on the scheme's propagation delay and the system delay for a single packet. If the system delay of the C-OFSMA scheme is \(\delta\), and propagation delay is \(\beta\), then the transmission latency \(\alpha\) can be expressed as  
\[\alpha=\delta+\beta \tag{20}\]  
The system delay, \(\delta\) is estimated from the simulation platform and is equivalent to the time consumed to transmit the bits from the sensor's end to the receiver via Gaussian noisy channels. The propagation delay is calculated by the ratio of the packet size \(\rho\) and link rate \(R\) of the system. So the \(\alpha\) can be represented as  
Figure 12: Determine received power at various points.  
\[\alpha=\frac{\rho}{R}+\beta \tag{21}\]  
For different packet sizes (50-350 bits), we calculate the transmission latency in Figure 13. When considering the 200 bits packet size, the transmission latency is 0.1953 ms. With the expansion of packet size, the transmission latency is significantly increased. So the consideration of taking 0.2 ms latency as transmission delay for 200 bits packet size is justified.