esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Prediction and Optimization of Building Wind Environment Using CFD and Deep Learning Method
###### Abstract  
CFD offers advantages over wind tunnel experiments in the prediction and optimization of building wind environment; however, the computational costs associated with optimizing architectural wind environment remain a challenge. In this study, an approach that combines deep learning techniques with CFD simulations is proposed for the prediction and optimization of the architectural wind environment efficiently. A dataset of wind field is constructed using CFD simulation, considering various wind directions, wind speeds, and building spacing. Subsequently, a U-net deep learning model is trained as a surrogate model to rapidly predict the architectural wind field under different conditions. The results indicate that the model can accurately predict the wind field in buildings. The prediction time of building wind field is only 1/900 of that of CFD simulations, making it a viable surrogate model for wind environment optimization. Furthermore, considering all the building layouts and inflow conditions examined in this study, the maximum and minimum uniform wind speed area ratios \(A_{uni}\) are 0.84 and 0.13, respectively. Under a single inflow speed, the maximum improvement in the \(A_{uni}\) is 0.4, with an improvement rate of 48%. The results demonstrate the effectiveness of the proposed method as an efficient approach for optimizing architectural wind environment.  
building wind environment; CFD; deep learning; U-net +
Footnote †: journal: Applied
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 3 Data Description: UWF-ZeekDataFall22
### Zeek and Its Role  
Zeek (formerly Bro) [6] is a prominent tool used for collecting Zeek logs that provides a structured, detailed view of network traffic and is highly suitable for post-processing with external software. Zeek logs capture a wide range of protocol interactions, including TCP, UDP, and ICMP traffic, along with in-depth transaction logs like DNS requests and responses. Its versatility and ability to monitor large-scale network environments make it the optimal choice for collecting data in this experiment. Zeek's output format is log files.  
Key Features Collected  
* Protocols: The dataset includes diverse protocol types such as TCP, UDP, and ICMP, with unique flows across thousands of IP addresses.
* Traffic details: Features like the number of bytes, packet counts, and connection states are meticulously logged, providing a comprehensive view of both normal and attack traffic.
* Network coverage: The dataset covers 254 unique source IP addresses and 4324 unique destination IP addresses.
* More details on the UWF-ZeekDataFall22 dataset are available at [5; 25].
# Enhanced Linearity in Intracranial Pressure Monitoring System Through Sample Isolation Bridge ROIC
## 5 Conclusions  
This paper presents a proposed ROIC that integrates a cross-connection IA with a sampled-data isolation-based SAR ADC. This configuration enables high-resolution performance while effectively mitigating signal distortion that could arise at the interface between the IA and the SAR ADC. The sampled-data isolation SAR ADC markedly lowers the loading capacitance and switching noise at the output of the IA in comparison to traditional SAR ADCs. The developed ROIC is capable of achieving a peak SNR of 54.6 dB and a peak SNDR of 51.8 dB, for a signal bandwidth of 300 Hz, with a total power consumption of merely 71 uW. Fabricated using a 0.18 um CMOS process, the ROIC chip occupies an area of 1.35 mm\({}^{2}\). This energy-efficient bridge readout IC is expected to find applications in intracranial pressure monitoring.  
Conceptualization, S.Y., Q.S. and S.H.; methodology, S.Y., Q.S. and Z.W.; software, S.Y. and J.X.; formal analysis, S.Y., Z.W., and S.H.; writing--original draft preparation, S.Y.; writing--review and editing, S.Y. and S.H.; supervision, S.H. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **This Work** & [37] & [40] & [41] & [42] \\ \hline Architecture & IA+SAR & CFIA+DT\(\Delta\)\(\Sigma\)M & CCIA+CT\(\Delta\)\(\Sigma\)M & IA+SAR & RC+CT\(\Delta\)\(\Sigma\)M \\ \hline Process (nm) & 180 & 700 & 180 & 350 & 350 \\ \hline Supply (V) & 1.8 & 5 & 1.8 & 3.3 & 5 \\ \hline Power (mW) & 0.071 & 1.35 & 2.16 & 0.201 & 4.3 \\ \hline Area (mm\({}^{2}\)) & 1.35 & 6 & 0.73 & 2 & / \\ \hline BW (kHz) & 200 & 0.004 & 1 & 200 & 2 \\ \hline SNR/SNDR (dB) & 54.6/51.8 & 91.2 */- & -/88 * & -/50 & 80/- \\ \hline \(FoM_{SNR}\) (dB) & 149.1(@32\(\times\)) & 125.9 *(@100\(\times\)) & - & - & 136.7(@32\(\times\)) \\ \hline \(FoM_{SNDR}\) (dB) & 146.3(@32\(\times\)) & - & 144.7 *(@100\(\times\)) & 139.9(@178\(\times\)) & - \\ \hline \end{tabular}  
* Estimated value.  
\end{table}
Table 2: Performance of the ROIC versus the state of the art.  
**Funding:** This work was funded by the National Key Research and Development Program of China under Grant 2023 YFC2410602.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
**Acknowledgments:** The authors express their gratitude to the professors and colleagues at both the Institute of Microelectronics, the Chinese Academy of Sciences, and the University of Chinese Academy of Sciences for their contributions to knowledge exchange. During the preparation of this work, the authors used ChatGPT in order to improve language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
## 2 Mathematical Model Formulation
### Multi-Energy Microgrids
#### 2.1.1 Gas Turbine (GT)  
The gas turbine (GT) burns natural gas to generate electricity and heat. The partial load characteristics of the efficiency of gas turbine power generation can be expressed as the following formula [10]:  
\[\eta_{GT}^{E}=(a\cdot\mathit{PLR}^{3}+b\cdot\mathit{PLR}^{2}+c\cdot\mathit{PLR}+d )\eta_{GT}^{En} \tag{1}\]  
where \(\eta_{GT}^{E}\) is the electrical efficiency of GT. \(\eta_{GT}^{En}\) is the rated electrical efficiency of the GT. \(\mathit{PLR}\) is the partial load rate (The ratio of actual load value to rated load value) of GT\(a,b,c,d\) are constants.  
The mathematical model of gas consumption and residual heat produced by the exhaust gas of GT can be expressed as:  
\[F_{GT}(t)=\frac{P_{GT}(t)}{R_{G}\eta_{GT}^{E}} \tag{2}\]  
\[H_{GT}(t)=F_{GT}(t)R_{G}(1-\eta_{GTJ}^{E}-\eta_{GT}^{E}) \tag{3}\]  
where \(F_{GT}(t)\) is the gas consumption at time slot \(t\). \(P_{GT}(t)\) is the electrical power of the GT at time slot \(t\). \(R_{G}\) is the calorific value of natural gas. \(\eta_{GTJ}^{E}\) is the loss factor of the GT. \(H_{GT}(t)\) is the waste heat generated by GT. The output electric power of the GT meets the operational constraints below:  
\[u_{GT}(t)P_{GT}^{\min}\leq P_{GT}(t)\leq u_{GT}(t)P_{GT}^{\max} \tag{4}\]  
where \(u_{GT}(t)\) is the binary variable of the working condition of GT. \(u_{GT}(t)=1\) means GT is operating at time slot \(t\). \(u_{GT}(t)=0\) means GT is shutdown at time slot \(t\). \(P_{GT}^{\max}\), \(P_{GT}^{\min}\) are maximal and minimal electrical power of GT respectively  
\begin{table}
\begin{tabular}{c c c c} \hline
**Symbol** & **Definition** & **Symbol** & **Definition** \\ \hline PV & Photovoltaic array & ESS & Energy storage system \\ WT & Wind turbine & EC & Electric chiller \\ GS & Gas station & AC & Absorption chiller \\ GB & Gas boiler & GT & Gas turbine \\ WH & Waste heat boiler & HX & Heat exchanger \\ \hline \end{tabular}
\end{table}
Table 1: Definitions of the symbols in Figure 2.  
Figure 2: Architecture of the multi-energy MG.
## 5 Proposed Scheme
### Censor Registration Phase  
The sensor sends a real identity to the \(CS\) for registration. The sensor adopts PUF technology to store secrets. Figure 5 presents the detailed registration steps.  
**Step 1:**: \(IoT_{SL_{j}}\) chooses identity \(SID_{j}\) and challenge value \(CH_{j}\). The sensor transmits its identity \(SID_{j}\) to the \(CS\) over a secure channel.
**Step 2:**: The \(CS\) generates random number \(r_{k}\) and computes shared secret value \(SI_{j}=h(SID_{j}||r_{k}||S_{k})\) with the master key. Then, the \(CS\) transmits \(SI_{j}\) to \(IoT_{SL_{j}}\) securely and stores \(SID_{j}\) and \(r_{k}\).
**Step 3:**: \(IoT_{SL_{j}}\) computes \(RE_{j}=PUF(CH_{j})\) and \(SA_{j}=SI_{j}\oplus h(SID_{j}||RE_{j})\). The sensor stores \(\{SID_{j},SA_{j},CH_{j}\}\).
## 3 Data and Methodology
### Methodology
#### 3.2.2 Dynamic FSM  
The dynamic FSM adds a global set to the quintuple FSM and attributes the following elements to the original quintuple: the \(i\)th FSM set, the \(i\)th entity label set of the hierarchical elements of the input address, the \(i\)th state transition function, and the \(i\)th initial state set and final state set. Notably, the generation process of the dynamic FSM is to generate a corresponding set of FSMs based on the elemental level features of each address, which achieves the dynamization of FSM. Unlike the quintuple FSM, the dynamic FSM is a sextuple. The \(i\)th dynamic FSM \(M_{i}\) is shown in Equation (1). \[M=(W,Q_{i},\Sigma_{i},\delta_{i},q_{i0},F_{i}) \tag{1}\]  
Figure 2: CHTopoNER model. where \(W\) is an already defined global FSM set, \(Q_{i}\) is the \(i\)th state set, \(\sum_{i}\) is the entity label set of the input address-level elements, \(\delta_{i}\) is the state transition function, and \(F_{i}\) and \(q_{i}\) are the \(i\)th initial and final state sets, respectively. Dynamic FSM is an algorithm that dynamically generates a model of a directed graph. It can ameliorate the excessive reliance of FSM on keywords in the address-level elements and avoid the uncertain threshold setting of the bidirectional FSM. Where the transition relationship between each state \(s\) and the next state \(s^{\prime}\) is added as a key-value pair into the state transition function \(\delta_{i}\), with \(s\) being the key and \(s^{\prime}\) being the value. Here, the set is used to store values because state \(s\) may have multiple successor states. The state transition function is constructed using range(len(sorted_E2)-1) in the loop to traverse every position of state \(s\). Each time, the two adjacent positions are taken out as one state and the next state. Finally, the state transition function \(\delta_{i}\) is added to the sextuple of the dynamic FSM \(E3\), which is returned.
# Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things
## 4 Case Study
The IR sensor captures the distance data and the date and time the distance is captured and transmitted via the TIGO module into cloud storage. MQTT or HTTP is the available protocol to transmit the data via the network connection. Cloud storage is used not only to store data but also to process the stored data for end-user use. Every IR sensor installed is differentiated by \"device ID\", programmed in the TIGO module. Based on the device ID, the data frequency can be analyzed. OIP cloud storage also provides the device's dashboard to ease the process. Figure 13 illustrates the installation of IoT devices in each toilet. When the data is processed as meaningful information, the system extends to provide the toilet availability for the end user, such as giving the toilet occupancy status. A mobile app was developed to show meaningful data status to the end user. The mobile application uses the Application Programming Interface (API) to POST and GET the data from cloud storage. POST is used to send data, while GET retrieves data. Two different applications were developed to monitor the Smart Toilet system. Firstly, the mobile application. Figure 14 shows a screenshot of the mobile application developed. A mobile application was developed to authenticate to the user the toilet occupancy status. Additionally, the mobile application allows users to rate each cubic based on toilet cleanliness by simply tapping on the star symbols to rate. Another application is the web application. The web application was developed for facility management to manage the peripherals and view the janitor scheduling system. Also, the overall diagram of faculty and the occupancy status is shown on the main screen. Figure 15 shows a screenshot of the web dashboard. To summarize, the Improved Eagle Strategy represents a significant advancement in service selection within distributed cloud environments. Its unique attributes, namely responsiveness and reliability, position it as a valuable tool in the evolving landscape of WS. The Improved Eagle Strategy (IES) builds upon traditional bio-inspired optimization methods, such as PSO and ABC, by integrating a dynamic service selection mechanism that optimizes both functional and non-functional attributes in real-time IoT environments. Un  
Figure 13: Illustration of the installation of IoT. like PSO, which focuses solely on convergence speed, or ABC, which is often constrained by limited multi-objective service selection efficacy, IES excels in both speed and adaptability. Its unique clustering-based reduction of search space further decreases computation time, positioning it as a novel and scalable solution for cloud-based IoT systems.
# Characterization of Shallow Sedimentary Layers in the Oran Region Using Ambient Vibration Data
## 1 Introduction
Furthermore, an empirical equation has been proposed for the region under study in order to predict the average shear-wave velocity in the upper 30 m of the soil column (\(V_{s30}\)) using only the dispersion curve. The present study is of great importance for the evaluation of the seismic hazard in the Oran region. The results of this investigation constitute an essential resource for ground motions prediction, the assessment of amplification factors, and the realization of various studies dedicated to the mitigation of seismic risk. These results open up ways of effectively reducing the potential impact of seismic events and strengthening protection measures in the zone.
## 5 SBS in a DOF
### Steady State Analysis  
A steady state condition implies that the interacting optical and acoustic fields are invariant with respect to time. Imposing the steady state condition, we can model the interdependence of their intensities through the following equations [35]:  
\[\frac{dI_{p}}{dz}=-gI_{p}I_{s}=\frac{dI_{s}}{dz}, \tag{47}\]  
where, \(I_{p}\) and \(I_{s}\) are the intensities of the interacting optical pump and Stokes waves. Here fiber losses were neglected and the Brillouin frequency shift was considered small compared to the pump and Stokes frequencies. \(g(\Omega,z)\) characterizes the Brillouin gain spectrum, which in this case is also a function of \(z\) owing to the longitudinal variation of the fiber profile. \(g(\Omega,z)\) is given as:  
\[g(\Omega,z)=\frac{g_{0}(z)\abs{\Gamma_{B}/2}^{2}}{\abs{\Omega_{B}(z)-\Omega}^ {2}+\abs{\Gamma_{B}/2}^{2}},\ \ \text{where}\ \ g_{0}(z)=\frac{4\pi^{2}\gamma_{e}^{2}}{n(z)\
u_{a}(z)\lambda_{p}^{~{}2}c \rho_{0}\Gamma_{B}}\langle A|O\rangle, \tag{48}\]  
where \(\lambda_{p}\) is the pump wavelength, \(\langle A|O\rangle\) stands for the acousto-optic overlap. Equation (47) implies \(I_{p}(z)=I_{s}(z)+C\), where \(C\) is an arbitrary constant. Thus substituting \(I_{p}\) back in Equation (47), we arrive at [41]:  
\[\frac{dI_{s}}{dz}=-g(I_{S}+C)I_{s}. \tag{49}\]  
Thus integrating and solving for \(I_{s}(z)\), we get:  
\[\ln\left\{\frac{I_{S}(z)\abs{I_{S}(0)+C}}{I_{S}(0)\abs{I_{S}(z)+C}}\right\}=- CM_{z}, \tag{50}\]  
where \(M_{z}=\int_{0}^{z}g(z^{\prime})dz^{\prime}\). Substituting the boundary conditions and enforcing the threshold condition of 1% reflectivity, we solve the steady state coupled differential equations for the evolution of the pump and Stokes waves [41]. The pump intensity \(I_{p}(0)\) corresponding to the threshold condition can be computed from the solution of the equation:  
\[I_{s}(L)-\frac{0.01I_{p}(0)\times 0.99}{e^{M_{L}I_{p}\times 0.99}-0.01}=0. \tag{51}\]  
The above equation provides the minimum pump power required to achieve a 1% reflectivity due to SBS. In the next subsection, we employ this method to compute an optimal longitudinal perturbation profile for SBS suppression in a fiber with longitudinally varying radius profile.
## 3 Resource Allocation Algorithm
### The Proposed Multi-Cell Resource Allocation Algorithm
Because the inverse function of \(g(x_{i})\) is not an elementary function, we approximate \(g(x_{i})\) as:  
\[\widetilde{g}(x_{i})=\frac{1+\frac{S_{i}}{l_{i}}}{\frac{g(S_{i}+l_{i})}{l_{i}( S_{i}+l_{i})}} \tag{19}\]  
lower bound of asymptotic complexity is \(\Omega\Big{(}\frac{N_{k}^{2}}{N_{A}}\Big{)}\) and the upper bound of asymptotic complexity is \(O\big{(}N_{u}^{2}\big{)}\). Because the asymptotic complexity of the conventional inter-point method is \(\big{(}N_{u}^{3.5}\big{)}\)[31; 32], the complexity of the proposed algorithm is lower than the inter-point method. ```
Input: the information of terminals and Aps Output: the approximate optimal normalized resource ratio factor \(\widetilde{x}_{i}\) of each terminal for\(i=1:1:Nu\)  calculate the break point \(\sigma_{i}\) for terminal \(i\)  end for for\(j=1:1:N_{A}\) do the following steps for \(Cell_{j}\)  constitute the break point vector \(\sigma_{j}\)  sort \(\sigma_{j}\) in descending order and obtain \(\sigma_{j}^{\prime}\) for\(k=1:1:N_{j}\)  calculate \(\widetilde{v}_{k}\) if\(\widetilde{v}_{k}\in[\sigma^{\prime}_{k+1},\sigma^{\prime}_{k})\) \(\widetilde{v}=\widetilde{v}_{k}\) break end for for\(i=1:1:Nu\)  calculate the approximate optimal normalized resource ratio factor \(\widetilde{x}_{i}\) end
```  
**Algorithm 1** Low-Complexity Multi-Cell Resource Allocation Algorithm.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Bio-Inspired Energy-Efficient Routing Protocols (Bio-Inspired E2 RPs)  
Different technological advancements have been influenced by biological concepts across a variety of scientific areas [127]. The listed below protocols are reviewed in terms of E2 RPs for UWSNs.  
Figure 11: Comparison of throughput between different ALOHA protocols.  
5.1.1. Firefly Mating Optimization Routing Protocol (FFRP)  
The authors of [128] presented the firefly mating optimization routing protocol (FFRP), which is dynamic and bio-inspired. In the process of optimizing firefly mating, pheromones play a vital role, which are generated from the body. There are two different kinds of fireflies: female fireflies and male fireflies. The leading Tx anchor node is selected by the firefly mating optimization routing protocol based on the highest value of these two kinds of fireflies. The maximal value of FFRP depends on the location of the communicating node in relation to the distance of the neighboring nodes, angle of departure, remaining energy, and water. The overflow duration of a buffer is a special parameter that reduces buffer overflow in communicating nodes. The FFRP uses the amount of remaining energy and the proportion of properly transmitted data packets over the connection to determine the connection quality. With the growth in data speeds, this technology assures robust and consistent connections. The simulation results are software based, as well as the densities of various numbers of nodes in the network in comparison with other routing protocols. As an example, see MERP in [129] and QERP in [130]. The simulation results demonstrate that, when compared to other current routing protocols, energy efficiency, network performance, and data packet delivery ratio are all improved by the FFRP.
## 6 Stability Evaluation of Slope under Reinforcement Scheme  
### Monitoring Setting  
A monitoring system was designed and installed to examine the stability of the slope during excavation and the performance of reinforcement scheme. It can be divided into three main components: slope surface deformation monitoring, shallow deformation monitoring, and anchoring force monitoring. Through using these measures, we were able to evaluate the deformation of the slope and the anchoring force loss in the anchor cables.  
The arrangement of the surface monitoring points aimed to monitor the overall deformation of the slope that was influenced by fault F12, F17, F18, F90, F113, and the interlayer shear zones exposed on the excavation surface. The TCA2003 automatic tracking total station Leica was employed to monitor the surface deformation of the slope. A total of 17 surface observation piers were installed on different levels of the cutting edge of the slope, as denoted from HD1-DBL to HD17-DBL in Figure 16. Amongst these, 6 points were on the excavated slope above 2000 m elevation, and 10 points were located between 1680 and 2000 m.  
Multi-point extensometers were employed to monitor the deformation of the rocks that was induced by stress relaxation in the slope at shallow depth (within 40 m beneath the excavated surface). A total of five sets of multi-point extensometers were arranged in the left bank slope, of which one set was located above 2000 m (numbered M1-DBL) and 4 sets were located between 1680 and 2000 m (numbered M2-DBL to M5-DBL). The layout of the multi-point extensometers is plotted in Figure 16.  
To monitor the loss of anchoring force, eight cable dynamometers were installed in the pre-stressed anchor cables. Among them, three sets of dynamometers were located above 2000 m, as illustrated in Figure 16. The others were installed below 2000 m elevation.  
Figure 15: Final failure state of the slope after adjusting the reinforcement scheme. (**a**) Under a magnitude 7 earthquake. (**b**) Under a magnitude 8 earthquake.
## 3 Validation and Numerical Analysis
### Time-Varying Effect of the Helical-Geared Rotor-Bearing System  
Although many dynamic models for the helical-geared rotor-bearing system have been presented, no researchers have simultaneously considered the time-varying effect due to rotating shaft deformation in their dynamic models. In order to simplify the dynamic model of the helical gear system, the previous models considered the helix angle, gear pair center distance, and contact ratio as constant values. The results that simulate a gear pair system by using constant dynamic characteristic values are usually rough and impractical. This section presents the results of time-varying numerical simulations for a helical-geared rotor-bearing system. This time-varying effect can lead to different dynamic characteristics, which in the previous models are usually ignored. The driving shaft was subjected to a fixed torque \(M=500\) N-m and \(\Omega_{1}=3000\) rpm. Other parameter values given in Table 4 were used in the calculations for the helical-geared rotor-bearing system. In Figure 6, solid lines show the time-histories of the gear pair helix angle, transverse pressure angle, center distance, and total contact ratio. As shown in this figure, the fluctuation magnitudes of the helix angle are around 20.079 degrees, of the transverse pressure angle around 20.67 degrees, of the gear pair center distance around 52.298 mm, and of the total contact ratio around 2.4476. The dashed lines are the values when these dynamic characteristics are assumed to be constant. The difference values between the time-varying and constant conditions present the influence of the time-varying effect.