esa-sceva/satcom-chunk-collection huggingface dataset:
# Engineering Properties and Environmental Impact of Soil Mixing with Steel Slag Applied in Subgrade
## 3 Results and Discussion
### Mechanical Properties and Deformation Stability of Soils  
#### 3.2.1 Static Triaxial Test  
The stress-strain curves of intact soil (S), SSL and SSLC under the confining pressure of 50, 100, and 200 MPa are plotted in Figure 9. As shown in Figure 9, with increased deformation, longitudinal stress also gradually increased, while the slope of the stress-strain curve gradually decreased with increased deformation. When the stress-strain curve reached the peak value, it remained unchanged or decreased. The peak strain in the stress-strain curve was selected as the failure limit. The failure strain values of the test soil specimens were between 2.5% and 4%, which conforms to the normal failure law of soil. Figure 9 shows that the failure deviatoric stress corresponding to the failure strain was: SSLC > SSL > S.  
Based on the Lambe plane (s,t) method, the cohesion (c) and friction angle (\(\varphi\)) of the test soil were calculated, as shown in Figure 10 and Table 3. The cohesion and friction angle of intact soil were 42.24 kPa and 25.27\({}^{\circ}\), respectively, consistent with previous research [33]. The contribution of cohesion to shear strength was higher, especially under low stress. With the addition of SL, the shear strength of the soil improved as a whole. Under the same stress condition, the shear strength of the improved soil significantly increased by 1.75%-2.05%, which was mainly attributed to the increase of cohesion and friction angle caused by the addition of SL. Compared with the intact soil, the cohesion and friction angle of SSL increased by 20% and 66%, respectively, especially under high stress. The addition  
Figure 8: The CBR test results: (**a**) CBR values; (**b**) swelling capacity.  
of cement further improved SSL's strength, cohesion and friction angle, especially cohesion which was 1.5 times higher than that of SSL alone, highlighting the role of cement.  
The strength parameters of the test soil were used to estimate the strength and stability of the subgrade for practical engineering. The results showed that SSL performed well and improved the intact soil. The addition of cement further improved the strength of SSL. For this reason, cement, lime, etc. are often used as soil reinforce  
Figure 10: The relationship curve of (s,d).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strength Parameters** & **S2** & **SSL** & **SSLC** \\ \hline Cohesion (c) (kPa) & 42.24 & 50.77 & 128.06 \\ friction angle (\(\varphi\)) (\({}^{\circ}\)) & 25.27 & 42.14 & 48.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Strength parameters of test soil.  
Figure 9: The stress-strain curves at different confining pressures: (**a**) 50 MPa; (**b**) 100 MPa; (c) 200 MPa.  
The strength of the improved soil even meet the requirements of a road base, which would be another road application for SSL.
## 2 Related Work
Our methodology offers several key advancements: (a) We developed and compared four GAN variants (GAN, DCGAN, WGAN, and TSAGAN), customized for AE signal augmentation by incorporating gradient penalty to mitigate mode collapse. This approach generates diverse, high-fidelity synthetic signals, expanding the dataset from 900 to 4500 samples and balancing representation across 35 distinct source locations. (b) We adapted the Inception neural network for AE source localization prediction, modifying the final fully connected layers to predict continuous \((x,y)\) coordinates. This regression architecture preserves multi-scale feature extraction capabilities, which concurrently processes AE signals at three temporal resolutions: 1 us, 5 us, and 10 us simultaneously, enhancing the model's ability to capture complex AE wave propagation characteristics in bounded isotropic plate structures. (c) Our experimental setup employs a novel single fiber-optic AE sensor based on a tightly coiled fiber Fabry-Perot interferometer (FPI) formed by two fiber Bragg gratings (FBGs). This sensor, with a cavity length of 40 cm and center wavelength of 1557.1 nm, offers enhanced sensitivity and electromagnetic interference immunity, enabling precise localization without the need for a multiple sensor array. (d) We implemented a rigorous validation framework comprising t-distributed stochastic neighbor embedding (t-SNE) visualizations and Wasserstein distance analysis to quantitatively assess the quality and diversity of GAN-generated data. The Wasserstein distance between original and synthetic datasets decreased from 19.8 to 3.2 after 2000 training epochs, representing an 83.5% reduction. This numerically confirms the generation of high-fidelity synthetic AE signals and allows for the comparative analysis of different GAN variants' performance. The proposed approach improves AE source localization accuracy by 50% compared to traditional methods, reducing median error from six to three inches. It enables more precise damage detection and localization in plate-like structures, potentially enhancing structural health monitoring practices in various applications.
# Passive Solar Solutions for Buildings: Criteria and Guidelines for a Synergistic Design
## 4 Results
This last configuration highlights the possible impact of an integrated passive solar design on building energy consumption, as the combined use of direct and indirect system could generate a reduction of 50% of a common office building's energy demand: a higher share could be achieved taking into account ventilation, by exploiting the Trombe wall cavity as a solar chimney during the cooling season. Assuming the office module as a functional unit, we compared a 10-units building, as a benchmark of a small office facility, with the department building previously analyzed, which contains 32 office zones. Results in Figure 11b confirm the impact on cooling demand of the combined case, whereas neglectable energy savings in terms of heating demand have been achieved, thus implying that the reduction of heating load, around 15%, achieved in the whole building analysis was mainly related to common spaces and wide laboratories. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Energy** \\ **Demand** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Basic Case** \\ **[kWh/m\({}^{2}\)]** \\ \end{tabular} } & \multirow{2}{*}{**\begin{tabular}{c} **Fixed** \\ **Merg** \\ **[\%]** \\ \end{tabular} } & \multirow{2}{*}{**\begin{tabular}{c} **Adjustable** \\ **Blinds** \\ **[kWh/m\({}^{2}\)]** \\ \end{tabular} } & \multirow{2}{*}{**[\%]**} & \multirow{2}{*}{**\
\begin{tabular}{c} **Solar** \\ **Blinds** \\ **[kWh/m\({}^{2}\)]** \\ \end{tabular} } & \multirow{2}{*}{**[\%]**} & \multirow{2}{*}{**
\begin{tabular}{c} **Control** \\ **Glass** \\ **[kWh/m\({}^{2}\)]** \\ \end{tabular} } & \multirow{2}{*}{**[\%]**} \\  & & & & & & & **[kWh/m\({}^{2}\)]** \\ \hline HVAC & Heating & 47.59 & 47.81 & 0.46 & 48.20 & 1.29 & 39.75 & \(-\)16.47 & 43.39 & \(-\)14.91 \\  & Cooling & 45.66 & 45.07 & \(-\)1.28 & 30.51 & \(-\)33.17 & 46.26 & 1.32 & 33.05 & \(-\)32.74 \\ \hline Energy Per Total & \multirow{2}{*}{115.52} & \multirow{2}{*}{115.16} & \multirow{2}{*}{\(-\)0.32} & \multirow{2}{*}{100.99} & \multirow{2}{*}{\(-\)12.58} & \multirow{2}{*}{108.28} & \multirow{2}{*}{\(-\)6.27} & \multirow{2}{*}{93.48} & \multirow{2}{*}{\(-\)19.08} \\  & Building Area & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Energy demand for run simulations. Figure 10: Indoor zone air temperature of the office zone in free running conditions. The integration of the indirect system had a relevant impact on building energy saving: on heating demand, 65 to 70 kWh/m\({}^{2}\) can be saved yearly in unventilated and ventilated configuration, respectively, for the department building. On the cooling side, the unventilated solution reached the highest saving, around 20 kWh/m\({}^{2}\) for the small facility and 60 kWh/m\({}^{2}\) for the larger structure: the lower values of ventilated strategies are related to the lack of monitoring systems to prevent ventilation of warmed air from the Trombe wall cavity to cause overheating.
## 6 The Proposed RAWC Scheme
### Scheme Detail
#### 6.2.1 Link Weight  
The link weight is calculated according to the real-time remaining number of wavelengths available on each link and quantum keys available in the corresponding QKP:  
\[W(u,v)=\alpha\cdot\frac{\lambda_{ini}}{\lambda_{aaa}(u,v)}+\beta\cdot\frac{K_{ ini}}{K_{aav}(u,v)} \tag{1}\]  
where \(W(u,v)\) denotes the weight of \(\text{link}(u,v)\); \(\lambda_{ini}\) and \(K_{ini}\) represent the initial number of wavelengths on each link and the initial number of quantum keys stored in QKP for each link, respectively, i.e., the maximum number of wavelengths and the maximum number of quantum keys; \(\lambda_{aaa}(u,v)\) and \(K_{aaa}(u,v)\) represent the real-time remaining number of wavelengths available on \(\text{link}(u,v)\) and the real-time remaining number of quantum keys available in \(\text{QKP}_{uv}\), respectively; and \(\alpha\) and \(\beta\) are the weight adjustment factors (\(\alpha+\beta=1\)).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Definitions** \\ \hline \(G(V,E)\) & QKDON network topology \\ \hline \(V\) & the node sets of QKDON \\ \hline \(E\) & the bi-directional fiber link sets of QKDON \\ \hline \(W(u,v)\) & the weight of \(\text{link}(u,v)\) \\ \hline \(\text{QKP}_{uv}\) & QKP corresponding to \(\text{link}(u,v)\) \\ \hline \(\lambda_{ini}\) & the initial number of wavelengths on each link \\ \hline \(K_{ini}\) & the initial number of quantum keys stored in QKP for each link \\ \hline \(\lambda_{aaa}(u,v)\) & the real-time remaining number of wavelengths available on \(\text{link}(u,v)\) \\ \hline \(K_{ava}(u,v)\) & the real-time remaining number of quantum keys available in \(\text{QKP}_{uv}\) \\ \hline \(R_{i}(s_{R_{i}},\,d_{R_{i}},\,K_{R_{i}})\) & the QKD service request \\ \hline \(s_{R_{i}}\) & the source node of the QKD service request \(R_{i}\) \\ \hline \(d_{R_{i}}\) & the destination node of the QKD service request \(R_{i}\) \\ \hline \(K_{R_{i}}\) & the number of quantum keys required for the QKD service request \(R_{i}\) \\ \hline \(p^{i}_{j}\) & the \(j\)th alternative path of the QKD service request \(R_{i}\) \\ \hline \(\delta_{p^{i}_{j}}\) & the wavelength conflict degree of the \(j\)th alternative path for the QKD service request \(R_{i}\) \\ \hline \(\lambda_{aaa}(p^{i}_{j})\) & the total number of available wavelengths for path \(p^{i}_{j}\) \\ \hline \(\mu\) & wavelength conflict marker \\ \hline \(p_{R_{i}}/p^{i}\) & the communication paths of the QKD service request \(R_{i}\) in two cases \\ \hline SR & the success rate of QKD service requests \\ \hline WRU & wavelength resource utilization \\ \hline QKU & quantum key utilization \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations and definitions.  
As can be seen in Equation (1), the more wavelength channels and quantum keys are available, the smaller the corresponding weight value.  
When only one QKD service request arrives at a certain time, the Dijkstra algorithm is used to complete the routing calculation based on Equation (1). The path with the minimum weight value is taken as the communication path of the service.
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Nigella sativa L.
#### 3.3.6 Anticancer Potential  
Research into the anticancer potential of black cumin focuses on examining various types of extracts and compounds isolated from this plant. Thymoquinone is of particular interest in this field. Its antitumor effects include several mechanisms, e.g., effects on proliferation, apoptosis induction, and cell cycle arrest [106]. Thymoquinone displays selective toxicity against HepG2 (hepatocellular carcinoma) cells without adversely affecting normal hepatocytes. Thymoquinone treatment leads to inhibition of NF-kB expression, which may limit tumor growth, and increased expression of deleted liver cancer 1 (DLC1) and caspase 3 (Casp3), which supports apoptosis processes [107]. Thymoquinone demonstrates significant interactions with proteins implicated in the development of ovarian cancer and mechanisms of resistance to chemotherapy. Its capacity to attach to crucial amino acid residues within these proteins indicates that it could potentially serve as an adjuvant in ovarian cancer therapy, particularly in instances of resistance to cisplatin [108]. The combination of simvastatin and thymoquinone demonstrated an inhibitory effect on breast cancer cell viability, colony formation ability, cell migration, and promotion of apoptosis. Two breast cancer cell lines were utilized in the study: MCF-7 and MDA-MB-231 [109]. The other compound derived from black cumin, thymol, demonstrated a noteworthy inhibitory effect on MCF-7 cell proliferation in a concentration-dependent manner. At a 200 \(\upmu\)M concentration, a 50% reduction in cell growth was observed. Furthermore, when compared to the control group, thymol treatment resulted in decreased expression of cyclin D1 and PCNA (proliferative cell nuclear antigen), which are pivotal in the proliferation of cancer cells [110]. In a study conducted by [PERSON] and [PERSON] [111], an ethanolic extract of the aerial parts of black cumin was utilized. The extract underwent testing on various cancer cell lines, demonstrating significant inhibitory effects on their proliferation. The lowest inhibitory concentration of cell growth was 12.5 \(\upmu\)g/mL for MDA-MB-231 (breast cancer) cells. The extract led to apoptosis in MDA-MB-231 cells. Additionally, cell cycle arrest in the G0/G1 phase was observed [111]. The results of the presented studies seem to be very promising, but the authors agree that there is still a need for further research to determine the specific use of black cumin, its extracts, and isolated compounds in the context of cancer therapy.
## 2 Engineering Background and in Situ Monitoring Methods
### In Situ Monitoring Method of Surrounding Rock Pressure  
The in situ monitoring scheme primarily involves the selection of appropriate equipment, the layout of sensors, and the design of data acquisition protocols.
## 5 Conclusions  
In this study, we have presented an automated intrusion detection technique named XAIID-SCPS technique for the CPS platform. The presented XAIID-SCPS approach primarily concentrates on the detection and classification of intrusions in the CPS platform. In the XAIID-SCPS technique, several subprocesses are involved, namely, data pre-processing, HEGSO-based feature selection, IENN-based classification, and EFFO-based parameter tuning. Moreover, the XAIID-SCPS technique integrates the XAI approach LIME for better understanding and explainability of the black-box method for accurate classification of intrusions. The simulation values of the XAIID-SCPS technique are tested on a benchmark intrusion dataset and the outcomes prove the promising performance of the XAIID-SCPS technique over other recent approaches. In the future, data clustering and outlier removal methodologies can be designed to enrich the detection performance of the XAIID-SCPS technique. Moreover, the proposed model can be extended to the design of ensemble voting classifiers to improve the detection rate of the XAIID-SCPS technique.  
Figure 9: \(Accu_{y}\) outcome of XAIID-SCPS system with other systems.  
**Author Contributions:** Conceptualization, L.A.; Methodology, M.S.M.; Software, M.A. and A.A.A.A.; Validation, M.A., H.M. and M.A.H.; Formal analysis, A.A.A.; Investigation, M.A.; Resources, H.M.; Data curation, H.M.; Writing-original draft, L.A., M.S.M., H.M. and M.A.H.; Writing-review & editing, M.S.M., M.A., M.A.H. and A.A.A.; Visualization, A.A.A.; Supervision, L.A.; Project administration, M.A.H.; Funding acquisition, L.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number (134/44). Princess Nourah bint Abdulrahaman University Researchers Supporting Project number (PNURSP2023R349), Princess Nourah bint Abdulrahaman University, Riyadh, Saudi Arabia. Research Supporting Project number (RSP2023R787), King Saud University, Riyadh, Saudi Arabia. This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).  
**Institutional Review Board Statement:** This article does not contain any studies with human participants performed by any of the authors.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing is not applicable to this article as no datasets were generated during the current study.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.
# Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks
## 2 Previous Work
The PUE attack was highlighted as a critical threat undermining network performance in extensive CR-based 5G Internet of Things (5G-IoT) frameworks. This attack results in significant bandwidth wastage and substantial interference with primary user (PU) transmissions. A novel detection methodology with two key components was introduced to address this. First, secondary users (SUs) determine and share the transmitter's location with other authorized SUs. This localization is achieved by evaluating received signal strength (RSS) and integrating the spatial coordinates of neighboring receivers. Second, during the sensing phase, each SU collects and archives signal energy vectors in a database. When a new node is detected, its energy vector is matched against the stored vectors to discern potential malicious entities. Adversarial detection hinges on the degree of similarity between the newly acquired energy vector and those in the database. This approach effectively mitigates the missed detection rate of PU, thereby alleviating interference with PU communications [13]. Finally, with a single PUE attack, an approach includes a countermeasure strategy that leverages the entropy of signal data, DNN, and CSS to detect the attacks effectively. It creates a blacklist feature within the FC to save the MU data systematically. The proposed system is simulated and tested using an SDR testbed, demonstrating an improved method with the ability to detect PUE attacks. Furthermore, it facilitates the recording of attack-related data for future analysis and ensures the sharing of these data among all SUs within the network [14]. Although various techniques exist to detect PUE attacks, even with AI, only a few papers have implemented algorithms in MCRN devices for a single PUE attack. Nevertheless, in a natural environment, multiple PUE attacks on the network need to be studied, especially in a CSS scenario where a common control channel is required to share information and make a global decision about the presence of PUE.
## 5 Experimental Verification
### Uncontrollable Mode: AVC at (3,3) Modal Frequency  
For the (3,3) uncontrollable mode, the measured acceleration and the sensing voltage at the same location in the AVC experiment are shown in Figure 9, where the blue and red curves represent the uncontrolled and controlled signals, respectively. When the AVC is turned on, the sensing voltage level of PZT quickly decays by 20 dB, whereas the vibration acceleration level is enlarged by 5 dB. The active vibration suppression at the position of the PZT sensor is not achieved.  
In order to further evaluate the AVC effect, the global vibration response of the plate is measured and shown in Figure 10. It is clear that the controlled vibration at some non-control positions is greater than the uncontrolled vibration. In addition, the ratio of the closed-loop energy to the open-loop energy is calculated and shown in Equation (19). The controlled mode for AVC is verified to be uncontrollable because the ratio is greater than 1. Referring to Table 3, it can be found that the experimental results of the AVC effect in the (3,3) mode are consistent with the corresponding simulations. This validates the correctness of the theoretical results. As for the difference in Table 3 and Figure 10, it may be caused by the experimental inaccuracies in the boundary conditions of the plate or in the locations of PZT.  
\[\gamma=\frac{\sum 10^{0.1L_{a_{i};e}}}{\sum 10^{0.1L_{a_{i},e}}}=\frac{71.3}{39.8}=1.8 \tag{19}\]  
Figure 9: Control effect in the (3,3) mode. (**a**) Description of the sensing voltage of PZT; (**b**) description of the monitoring acceleration.
###### Abstract  
The aim of the present work was focused on optimising the yield and quality of the biocrude obtained by hydrothermal liquefaction (HTL) of _Nannochloropsis gaditana_. Temperature, reaction time and microalga concentration were the variables used to carry out an experimental factorial design with a central composite design. The responses chosen were the biocrude yield and the nitrogen and oxygen content in the biocrude phase. A second-order model was obtained to predict the responses as a function of these variables. Temperature is the most determining factor with a positive influence on biocrude yield. The maximum biocrude yield (42.3 \(\pm\) 0.8 wt%) was obtained at 320 \({}^{\circ}\)C, 10 min of reaction and 10 wt% microalgae concentration, and the nitrogen and oxygen content significantly decreased with respect to their corresponding levels in the initial microalgal biomass. The HHV value of the biocrude was 35.7 MJ/kg. The biocrude was composed of 30% of linear and branched hydrocarbons.  
experimental design; hydrothermal liquefaction; biocrude; biofuel; microalga; _Nannochloropsis_