esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis
### Security Analysis
To prove this, we first show that the upper bound of \((\frac{\mathcal{Q}}{s})\) is given by \([\frac{\mathcal{Q}}{s}]^{s}\). To start with, we know that  
\[Pr[\mathbf{r}_{i}>2^{\eta}-2^{\kappa}\cup\mathbf{r}_{i}<2^{\kappa}]=\frac{2\cdot 2^{ \kappa}}{2^{\eta}}=\frac{1}{2^{\eta-\kappa+1}}.\]1  
Consider the expansion of \(e^{s}=\Sigma_{i=0}^{\infty}\frac{(s)^{i}}{i!}\). If we only consider the term with respect to \(s\), we have \(e^{s}>\frac{e^{s}}{(s)! }\), which immediately yields \(\frac{1}{(s)!}<[\frac{e}{s}]^{s}\). Substituting the term in Equation (5), we have \(\binom{\mathcal{Q}}{s}<[\frac{e\mathcal{Q}}{s}]^{s}\). Now, it is easy to see that  
\[Pr[\mathbf{r}_{i}>2^{\eta}-2^{\kappa}\cup\mathbf{r}_{i}<2^{\kappa}]=\frac{2\cdot 2^{ \kappa}}{2^{\eta}}=\frac{1}{2^{\eta-\kappa+1}}.\]2  
Next, we show that, with the careful parameter, we have \(Pr_{KRA}<\frac{1}{\mathrm{poly}(\lambda)}\). Specifically, we have \(Pr_{KRA}=[\frac{ep\mathcal{Q}}{s}]^{s}\), where \(s\) is the key pool size. Notice that \(\mathcal{Q}\) is polynomial in terms of \(\lambda\), where \(\mathcal{Q}=\text{poly}(\lambda)\). Since \([\frac{e}{s}]^{s}<<1\), we have \(Pr_{KRA}=[\frac{ep\mathcal{Q}}{s}]^{s}<\text{poly}(\lambda)\cdot p^{s}<\frac{ 1}{\text{poly}(\lambda)}\). If \(p\) is sub-exponential, then the inequality holds. We know that \(p=\frac{(s+T-1)!}{T!(s-1)! }<[\frac{1}{s}]^{T}<\frac{1}{2^{\lambda 1/3}}\). **Discussion** We now discuss the overall security of the protocol and the impact of the parameters. In our protocols, we have two important parameters: \(s\) and \(T\), where \(s\) denotes the number of secret keys, and \(T\) represents the number of keys used to generate the pad \(r\) for encryption. The key-pool size \(s\) is strongly related to the storage overhead, since our protocol needs to pre-compute the product of the keys and weights. \(T\) is related to the computation complexity, because the IoT device has to perform \(T\) additions for each encryption and \(T\) subtraction with a search operation for each decryption. To fulfill the security requirement while preserving the efficiency, the parameters shall be chosen carefully. Throughout our implementation, \(s\) is chosen to be 8192 under careful consideration of the storage overhead. Under this condition, to fulfill the requirement of being semantically secure with a security parameter \(\lambda=128\) (or \(\lambda=80\)), \(T\) is required to be at least 10 (or 6). Nonetheless, semantic security indicates that the adversary cannot distinguish between different plaintexts being encrypted. If such a security requirement can be relaxed by instead focusing on defeating the key-recovery attack, the protocol can be made more efficient. Following Theorem 3, when \(T\geq 2\), the probability that the adversary can launch a successful key-recovery attack is negligible. In real IoT systems, the trade-off between security and efficiency can be chosen based on practical security requirements.
# Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase
## 6 Conclusions and Limitation
### Limitations  
Among the various factors considered in this study, the 'total floor area' showed an overwhelmingly high Pearson correlation coefficient value. This is attributed to the fact that the budget was set based on the unit price per total area in the budgeting process at the \"pre-design\" stage, and the design and construction process was conducted based on the budget afterward. Although the total floor area is the biggest factor influencing the construction cost, there are limitations to predicting various construction cost changes, and it is still difficult to define accurate construction cost influencing factors.  
In addition, compared to other data, construction cost data that can be collected for one year are insufficient, and there are difficulties such as requiring a large amount of effort to investigate the factors influencing construction costs. In the future, if a plan is prepared to systematically collect and organize construction costs, it is expected that more accurate construction cost predictions will be possible.  
[PERSON] conceived the experiments, analyzed the data and wrote the paper; [PERSON] supervised the research. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2021-KA163269).  
Not applicable.  
The authors declare that they have no known competing financial interest or personal relationships that could have influenced the work reported in this study.
## 1 Introduction
### Related Work and Motivation
Consequently, the core necessity of this work arises: how to design a practical, low-complexity precoding strategy that effectively balances the competing interests of radar sensing accuracy and communication quality under severe hardware constraints.
# Plenty of Room at the Bottom: Nanotechnology as Solution to an Old Issue in Enhanced Oil Recovery
## 4 Nanocatalysts
### Factors Affecting Nanocatalysts in EOR Applications
#### 4.2.4 Porous Media  
The physical properties of the rock formation also affect the performance of the catalyst process. The permeability has a significant impact on the retention (Figure 44). The chemical composition of the porous media also plays a role in the performance of the process. The oil reservoir generally consists of several metallic carbonates. The presence of certain compounds, such as calcite (CaCO\({}_{3}\)) or siderite (FeCO\({}_{3}\)), increases the production of carbon dioxide at the steam injection temperature [134]. Clay mineral surfaces have a negative charge, so when the catalyst is injected, the metal cations (e.g., VO\({}^{2+}\) and Ni\({}^{2+}\)) will be adsorbed onto the surface via electrostatic forces. These reactions can generate products similar to amorphous silica alumina catalysts which are commonly utilized in catalytic cracking processes [134; 142].
# Study on Load Transfer Mechanism of Pile-Supported Embankment Based on Response Surface Method
## 3 Analysis of the Numerical Simulation Results
### Response Surface Optimization Test and Results Analysis
#### 3.2.1 Response Surface Test Design
The results of the variance analysis of the fitted regression equation are shown in Table 6. The value of Prob(\(P\)) > F of the regression equation of the pile-soil stress ratio was less than 0.0001, indicating that the model is highly significant. This shows that the equation can accurately reflect the relationship between the evaluation index and each influencing factor. The fact that the regression equation correlation coefficient, \(R^{2}\) = 0.959, was greater than 0.9 shows that the orthogonal experimental results and mathematical model fit well, and that the mathematical model can be used to infer the experimental results. The determination coefficient, \(R^{2}_{adj}\) = 0.943, indicated that 94.3% of the data can be explained by this equation, and the higher fitting degree can be used to predict the pile-soil stress ratio. The coefficient of variation, \(C_{V}\), reflects the confidence of the model. The smaller the \(C_{V}\), the higher the confidence of the model. When the \(C_{V}\) is less than 10, it is considered that the reliability of the test is higher. Precision reflects the accuracy of the model, and generally requires that the precision of the model should be greater than 4. The precision of the coefficient of variation, \(C_{V}\) = 0.598, of the pile-soil stress ratio regression equation was 26.784, showing that the reliability and accuracy of the test are high, and that the regression equation obtained by fitting is meaningful and can be used to predict the pile-soil stress ratio of the pile-supported embankment. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Sum of** & **Degree of** & **Mean** & \multirow{2}{*}{**F Value**} & **Prob(\(P\)) > F** \\  & **Squares** & **Freedom** & **Square** & & \\ \hline Model & 1.496 & 8 & 0.187 & 58.475 & \textless{}0.0001 \\ Cohesion \(A\) & 0.715 & 1 & 0.715 & 223.468 & \textless{}0.0001 \\ Internal Friction Angle \(B\) & 0.013 & 1 & 0.013 & 3.999 & 0.0593 \\ Elastic Modulus of & & & & & \\ Embankment \(C\) & 0.038 & 1 & 0.038 & 11.814 & 0.0026 \\ Geogrid Strength \(D\) & 0.095 & 1 & 0.095 & 29.687 & \textless{}0.0001 \\ \(AB\) & 0.025 & 1 & 0.025 & 7.681 & 0.0118 \\ \(AC\) & 0.078 & 1 & 0.078 & 24.270 & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Variance analysis of regression equation.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 2 System Model
### Channel Model  
To describe the channel model, we clarify the deployment of the cells and their attachment to the users. We consider 5G massive MIMO networks with \(K\) small cells. The sets of cells are denoted by \(\mathcal{K}=\{1,\cdots,K\}\). There are \(I\) users in the system, including \(I_{s}\) stationary users and \(I_{m}=I-I_{s}\) moving users, which are denoted by \(\mathcal{I}_{m}=\{1,\cdots,I_{m}\}\) and \(\mathcal{I}_{s}=\{I_{m}+1,\cdots,I\}\), respectively. Let \(\mathcal{I}=\mathcal{I}_{m}\cup\mathcal{I}_{s}=\{1,\cdots,I\}\) define the set of all users. Furthermore, we consider the scenario where only 1-standing users are associated with the only 1-cell to activate at least an 1 beam of all cells.
# On the Integration of VLBI Observations to GENESIS into Global VGOS Operations
## 4 Results  
### VGOS Schedule Optimization  
For the VGOS schedule optimization, we test variations in weight factors used in the scheduling to evaluate each set of weights based on the estimates of geodetic parameters. For all three networks, we generate schedules containing only geodetic sources. The tested weight factors refer to the duration of a scan, the number of observations per scan, and the improvement in sky-coverage per scan. Each weight factor was tested with the values \(\{0,0.33,0.67,1\}\). The idle time weight factor was fixed to 0.5 with an interval of 300 s. This leads to a total of 64 tested schedules for each network.  
We consider the schedules that show the best estimates for station positions, dUT1, polar motion parameters (XPO and YPO), and nutation parameters (NUTX and NUTY). Figure 8 shows the five best results from schedules for Network A. Schedule 1 shows the best repeatabilities for station positions and YPO. Schedules 2, 3, 4, and 5 show the best repeatabilities for dUT1, XPO, NUTX, and NUTY, respectively. Additionally, the numbers of scans and observations are presented. The results underline that large numbers of scans and observations do not necessarily lead to the best results. For example, schedule 2 shows about 100 fewer scans and \(10,000\) fewer observations than schedule 4; however, schedule 2 shows better results for station positions and dUT1. In general, there is not one schedule that provides the best results for all parameters. The results vary strongly between the smallest and largest value for each parameter. For the station position repeatability, the worst result of 2.94 mm for schedule 4 is an increase of about 7.3% with respect to the best result of 2.74 mm for schedule 1. The percentage increase between the smallest and largest values for the repeatabilities in dUT1, XPO, YPO, NUTX, and NUTY are about 11.9%, 8.7%, 11.9%, 9.8%, and 15.0%, respectively. This shows that among the best five of the 64 tested schedules, relative changes in the estimated parameters of about 7-10% are a normal variation. Although not shown here, this analysis was equivalently performed with networks B and C. For network B, the relative changes in the station position repeatability and dUT1 repeatability across the best schedules are 6.3% and 10.8%, respectively. For network C, they are 5.7% and 11.6%. We choose the best schedule based on the performance across all estimated parameters. Consequently, the best schedule for network A is schedule 1. The weight factors used to create this schedule were used in the further investigations of this work.
# TFBN: A Cost Effective High Performance Hierarchical Interconnection Network
## 5 Static Cost Effectiveness Analysis of a TFBN
### Static Cost Performance Trade-off Analysis  
Before going to prototype implementation or practical implementation of a huge expensive MPC system, many early stages of evaluation are crucially needed to investigate the suitability of the proposed interconnection network. In this study, we have studied the very first stage of evaluation and statically evaluated the suitability of the proposed TFBN using many static parameters. We have considered many static parameters and the last one is the static cost-performance trade-off factor (CPTF). It is mentioned earlier that the cost incurring parameters of an MPC system is a router in the node (i.e., node degree) and a number of communications links and their total length to layout the system design (i.e., wiring complexity). The most decisive parameter for the static network performance is the diameter; this indicates the upper bound of the communication latency. The diameter again depends on the network size, i.e., the total number of nodes. Considering all these parameters, the CPTF of an interconnection network topology is expressed by the following Equation (1). The higher the outcome of Equation (1), the more suitable the interconnection network:  
\[CPTF=\frac{Node\ Degree\times Wiring\ Complexity}{Diameter\times Total\ \#\ of\ Nodes} \tag{1}\]  
We have assessed the factor of static cost-performance trade-off of TFBN along with TTN, TESH network, 2D-torus, and 2D-mesh networks, and the results are tabulated in Table 2 for both 256 nodes and 4096 nodes network. It is stated that the CPTF of a TFBN network is far higher than that of Torus, Mesh, TESH, and TTN networks for both sized network (256 nodes and 4096 nodes).
## 2 Proposed Radio Link Model for Underground Mines  
An important aspect of underground propagation is the hypothesis that, at the UHF band, the wavelength becomes comparable to mine dimensions, and the mine acts as a waveguide, allowing the low-loss propagation of EM waves [2]. Waveguides have characteristic cut-off frequencies, below which EM waves cannot propagate. Frequencies that are above the cut-off can propagate via multiple bounces off the walls, though at the expense of reflection path losses [18; 19]. The loss increases at higher frequencies, which is why 433 MHz-based devices have been used for experiments within the proposed study for low data rates and high-range applications. The prior studies contain propagation measurements at the 433 MHz band conducted in various environments, such as underground to underground and underground to above-ground communication, but not in underground mine environments [1; 2; 4; 5; 12]. Prior research does not contain radio models for different cross-sections of underground mines and tunnels in different structural layouts [18; 19].  
It is very important to precisely and analytically analyze the behavior of radio waves in the presence of lossy walls so that the optimal separation distance between nodes and radio propagation zones can be estimated accurately. It is necessary to understand the mine environment and re-derive the path loss equations for wireless mine sensor networks which help communication engineers to estimate underground communication range and signal characteristics. The proposed model considers tunnel environments with different mine cross-sections, particularly cross-sections lower than \(2\times 2\) m\({}^{2}\), due to the availability of operational mines in Pakistan for practical experiments.
## 5 Conclusions  
The privacy-preserving federated learning optimization strategy for space-air-ground integrated networks proposed in this paper successfully addresses the challenges of efficient device selection, privacy protection, and model aggregation in complex space-air-ground integrated network environments. By combining two-tier reinforcement learning, transfer learning, hierarchical attention mechanisms, and meta-learning-based strategy optimization, this method effectively copes with dynamic topologies, latency, bandwidth limitations, and device heterogeneity in the network, enhancing the efficiency and flexibility of device selection. Additionally, the differential privacy-based privacy protection mechanism ensures that data privacy is not compromised, enhancing the security of the model. Experimental results validate the efficiency and feasibility of this method in multi-task environments.  
Future work will consider further research on handling anomalous nodes in the global aggregation phase and proposing more efficient robust aggregation algorithms. Through further investigation of these issues, it is expected that more efficient, stable, and secure solutions for federated learning in space-air-ground integrated networks can be provided.  
**Author Contributions:** Conceptualization, L.L. and L.Z.; methodology, L.L.; software, W.L.; validation, L.L., L.Z. and W.L.; formal analysis, L.Z.; investigation, L.L.; resources, W.L.; data curation, W.L.; writing--original draft preparation, L.L.; writing--review and editing, L.Z.; visualization, W.L.; supervision, L.Z.; project administration, L.L.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China under Grant 62371098.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Average Loss** \\ \hline PPFL-SAGIN (dynamic weighting) & 0.9502 & 0.9534 & 0.9502 & 0.9512 & 0.1204 \\ \hline Static weighting strategy & 0.8512 & 0.8567 & 0.8512 & 0.8525 & 0.2695 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of performance metrics for two methods after 10 rounds of training.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Tunable Mid-Infrared Graphene Plasmonic Cross-Shaped Resonator for Demultiplexing Application
## 2 Resonator and Analysis
Therefore, by increasing of the resonator length, the resonance wavelength increases. By increasing of E\({}_{\text{f}}\), as shown in Figure 3b, the resonance wavelength decreases causing a blue shift. It is because the real part of the \(\beta_{SPP}\) (Equation (4)) decreases as the E\({}_{\text{f}}\) increases (given in Figure 2d). At the same time, the losses of the SPP wave supported by the graphene layer decrease with the E\({}_{\text{f}}\) increase. Therefore, the transmission of the transmission peak increases with the E\({}_{\text{f}}\) increase, also discussed in [57; 58]. Real part of \(n_{SPP}\) depends on the width of the graphene ribbon \(W\). For \(W<<L\), by decreasing of \(W\), the real part of \(n_{SPP}\) increases. Therefore, the transmission spectrum of the resonator experiences a blue shift by change of \(W\) when \(W<<L\), also discussed in [56; 59]. By increasing W, as seen in Figure 3(c), the resonance wavelength decreases which shows a blue shift. As can be seen from Equation (7), when the rotation angle between the graphene ribbon and the horizontal line \(\theta\) changes, the length of the resonator \(L\) is constant, while the effective refractive index \(n_{SPP}\) will change slightly due to the variation of the coupling strength affected by change of \(\theta\). By increasing of \(\theta\), the coupling strength increases. Therefore, the real part of \(n_{SPP}\) increases which is also discussed in [60]. Therefore, by increasing of \(\theta\), the resonance wavelength tends to exhibit a blue shift (shown in Figure 3d). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Segment/Direction** & **x (nm)** & **y (nm)** & **z (nm)** \\ \hline Input waveguide & 6 & 2 & 0.1 \\ \hline Resonator & 2 & 2 & 0.1 \\ \hline Output waveguide & 2 & 6 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Assumed mesh sizes of the proposed resonator structure of Figure 1.  
Figure 3: The influence of the alternation of (**a**) resonator length L, (**b**) graphene Fermi energy E\({}_{\rm f}\), (**c**) resonator width W, and (**d**) the angle between the ribbon and the horizontal line of the resonator \(\theta\), on the transmission spectrum of the proposed structure of Figure 1.
## 6 Experimental Validation
### Recognition Accuracy Evaluation  
In the experiments, a Morse-encoded signal interpreting the string \"hello world\" is generated on the computer with MATLAB using the method in Section 2.1, and the durations and amplitude fluctuation are considered by controlling the \(\mu\) and \(\sigma^{2}\) and adding AWGN, respectively. We first investigate the offline performance. Figure 12 shows the off-line pretreatment results based on the mk-means clustering algorithm. One can observe that the obtained waveform is just the same as the original signal, which indicates that our method works well.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & **Manufacturer** & **Model** \\ \hline AWG & Tektronix & AWG 7051 \\ LED & Cree Inc & XLamp® XP-L2 \\ Lens & ZhiSai Technology & ZLKC-KM5012 MP8 \\ APD & Hamamatsu Photonics & S8664-50K \\ TIA & Linear Technology & LTC6268-10 \\ ADC & Texas Instruments & ADS 8866 \\ MCU & STMicroelectronics & STM32F446 \\ \hline \hline \end{tabular}
\end{table}
Table 1: System configuration parameters.  
Figure 11: Demonstration of proposed free space optical (FSO) prototype: (**a**) Transmitter side, (**b**) receiver side.  
Then, the prototype is used to test the real-time decoding accuracy. The optical signal emitted from LED arrays is received by the photo detector and then processed in the embedded system; the translation results are obtained by the serial port software on the computer. Figure 13 displays the real-time recognition results in the Serial Assistant software derived from STM32. It is also observed that the decoding results are exactly the same as the raw string \"hello world.\" It is worth noticing that the transceiver was originally designed for the long-range outdoor environment and can support 4.8 km FSO communication [31]. The transmitter consists of more than 30 LEDs, each at a DC forward current of 1500 mA and a corresponding electrical power of 4.35 W, which is enough for indoor optical wireless communication. Thus, the received optical power maintains a high level in the laboratory environment, and the recognition accuracy also remains the same after increasing the distance from 1 to 6 m.