esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Functional Properties: Lipid and Water Holding Capacity  
In previous studies performed by [PERSON] et al. on the same raw material of cactus pear wastes used in this work, the amount of dietary fiber was reported as follows: for the green residues, 34.9 g/100 g insoluble fiber and 7.9 g/100 g soluble fiber; for the red residues, 19.3 g/100 g insoluble fiber and 8.1 g/100 g soluble fiber [53]. Thus, the fiber content of each tablet is 0.24 g with green residues and 0.15 g with red residues, approximately. According to the recommended daily intake of fiber for adults (25-30 g/day) [54], in the present study, the daily portion of consumption would be six tablets per day; therefore, the tablets would cover 5.7% (green tablets) and 2.6% (red tablets). Although the content of fiber is low, the impact in terms of the physiological benefits and in the food industry could be important. According to [PERSON] et al., the fiber in the cactus pear fruit is characterized by a high insoluble dietary fiber content and the presence of mucilages with high molecular weight which interact in an entangled network, able to hydrate and increase the viscosity of the liquid phase in the food system [55]. Therefore, the physicochemical properties of these samples could be affected in the presence of water.  
For the LHC and WHC parameters presented, the GM formulation had the highest values, in contrast with the RL formulation, which had the lowest values for both parameters (Table 1). The lipid and water holding capacity may be attributed to the high amount of insoluble fiber in the cactus pear residue, which is capable of trapping oil and water particles in its structure [56; 57]. The structure of the microcrystalline cellulose fiber favors retention [44], and this could explain the faster disintegration of these tablets compared to the lactose formulations (Table 1). Other studies on fibers from fruit by-products have reported good water and lipid retention. For example, a fiber supplement from lemon peel presented 5 g/g and 7 g/g WRC and LRC, respectively, while pineapple peel presented 11 g/g and 6 g/g WRC and LRC, respectively [20; 58]. However, these properties have not been reported in other dietary supplements in solid dosage forms, though they are physiologically important, since WHC leads to the hydration of the intestinal content for soft and easy-to-pass stools, and LHC has clinical significance in certain disorders of colonic function, obesity, and lipid blood concentrations [59].
## 2 Mathematical Model of Aerostatic Bearing
### Engineering Calculation Method
The expression of the inflow gas mass flow in each region can be obtained by integrating Equation (1) in the direction of gas film thickness and bringing it into Equation (2):  
\[m_{i1}=-\frac{\pi Rh_{i}^{3}\rho_{a}}{6\mu nP_{a}}p\frac{dP}{dx} \tag{6}\]  
By separating variables and integrating \(x\) and \(P\), we can obtain:  
\[P^{2}-P_{0i}^{2}=-\frac{12\mu nP_{a}}{\pi Rh_{i}^{3}\rho_{a}}m_{i1}\cdot x(0 \leq x\leq l) \tag{7}\]  
Inserting the boundary condition when \(x=l\), \(P=P_{a}\), we can obtain:  
\[{P_{a}}^{2}-P_{0i}^{2}=-\frac{12\mu nP_{a}}{\pi Rh_{i}^{3}\rho_{a}}m_{i1}l \tag{8}\]  
It can be seen from the continuity equation that \(m_{i1}=m_{i2}\). We can bring \(m_{i2}\) into Equation (8) and simplify it according to the gas state equation to obtain:  
\[P_{0i}^{2}-{P_{a}}^{2}=\frac{12\mu n}{\pi Rh_{i}^{3}}lA\phi P_{s}\sqrt{\frac{2 P_{a}}{\rho_{a}}}\cdot\psi_{i} \tag{9}\]  
The projection of the carrying capacity \(F_{i}\) provided by the air film in the \(i\)th region on the vertical direction can be obtained by:  
\[F_{i}=2\left[RP_{0i}(L-2l)\sin\frac{\pi}{n}+\int_{0}^{l}2 RP\sin\frac{\pi}{n}dx \right]\cos\alpha_{i} \tag{10}\]  
Thus, the total carrying capacity \(W\) can be obtained by superimposing the carrying capacity provided by the gas film of each region:  
\[W=\sum_{i=1}^{n}F_{i}=2R\sin\frac{\pi}{n}{\sum_{i=1}^{n}\left[P_{0i}(L-2l)+ \int_{0}^{l}Pdx\right]\cos\alpha_{i}} \tag{11}\]  
The expression of pressure \(P\) can be obtained from Equations (7) and (8). Then, we can bring it into Equation (11):  
\[W=2R\sin\frac{\pi}{n}{\sum_{i=1}^{n}P_{0i}}\left[(L-2l)+\frac{4}{3}l\frac{1- \left(\frac{P_{a}}{P_{0i}}\right)^{3}}{1-\left(\frac{P_{a}}{P_{0i}}\right)^{2 }}\right]\cos\alpha_{i} \tag{12}\]  
Figure 2: Sketch of gas film in the circumferential direction. The orifice outlet pressure of the \(i\)th region \(P_{0i}\) can be obtained by solving the nonlinear Equation (9) numerically. Then, the gas consumption of each region can be obtained using Equation (13):  
\[Q=AP_{s}\phi\sqrt{\frac{2\rho_{s}}{P_{s}}}\sum_{i=1}^{n}\psi_{i} \tag{13}\]
# A Comparative Analysis of the TDCGAN Model for Data Balancing and Intrusion Detection
## 2 Materials and Methods  
### Datasets  
In this study, we utilize four distinct datasets to assess and compare the efficacy of the TDCGAN model against other chosen models in the context of data balancing and intrusion detection tasks. The datasets are CIC-IDS2017, CSE-CIC-IDS2018, KDD-cup 99, and BOT-IOT.  
The CIC-IDS2017 dataset is a well-known resource in cybersecurity, commonly used for assessing the performance of intrusion detection systems. It contains network traffic data gathered from a variety of scenarios, including both normal traffic and several attack types, such as denial-of-service (DoS), distributed denial-of-service (DDoS), brute-force attacks, and botnet attacks. The dataset provides a realistic representation of network traffic in both benign and malicious environments, making it valuable for training and testing intrusion detection models. Researchers often use the CIC-IDS2017 dataset to benchmark the performance of their intrusion detection algorithms due to its diversity and comprehensiveness [12].  
The CSE-CIC-IDS2018 dataset offers labeled network traffic flows, providing detailed information such as timestamps, source and destination IP addresses, ports, protocols, and attack types. This facilitates in-depth analysis and model training. The dataset includes seven distinct attack scenarios: Brute-force, Heartbleed, Botnet, DoS, DDoS, Web attacks, and internal network infiltration [12].  
KDD-cup 99 was developed for the KDD Cup 1999 competition, which focused on creating effective techniques for detecting unauthorized network access. The dataset features a substantial amount of network traffic data gathered from a simulated military network environment. It encompasses a range of network attacks, including DoS attacks, probing attacks, and user-to-root attacks, alongside normal traffic [13].  
The BOT-IoT dataset is a comprehensive dataset designed specifically for research in the domain of Internet of Things (IoT) security. It comprises network traffic data collected from a realistic IoT environment, including various types of IoT devices such as IP cameras, smart thermostats, and wearable fitness trackers. The dataset covers both benign traffic and malicious activities, including botnet attacks targeting IoT devices [14].
# A Noncontact Method for Calibrating the Angle and Position of the Composite Module Array
## 1 Introduction
Therefore, the use of a high-precision, noncontact calibration method has extensive applicability. In view of these problems, this paper proposes a self-developed angle sensor based on the principle of optical self-collimation to achieve noncontact calibration. The method uses an angle sensor to scan the surface of the composite module to obtain relevant information, and proposes the concept of feature points according to the geometric characteristics of the rotating paraboloid. Finally, a mathematical model is established to actualize the calibration of the angle and position. The influence of array placement error on calibration is also analyzed. A series of experiments showed that the method has high repeatability and reliability. The principle of this method is simple--it can solve the benchmark problem in practical application, offer a new idea for noncontact measurement, and provide a reference method for freeform surface array calibration.
## 2 Literature Review
### Feature Selection
Finally, Lasso Regression, DAE, and PCA stand out in the embedded category, incorporating feature selection directly into the model training process and adjusting variable coefficients to maximize model performance. As one can observe, there is a concern regarding feature selection aimed at verifying the identification of the most relevant characteristics, simplifying complexity, and enhancing the computational efficiency of the model. This process accelerates the model training process and improves its generalization ability, reducing the risk of overfitting. Industries can identify which characteristics of the collected data are most pertinent to the problem under study, enabling them to prioritize the deployment of sensors that capture these specific features, thus ensuring that the collected data are as informative as possible for machine learning models.
# Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment
## 6 Conclusions  
In the time-varying indoor environment, the complex multipath effect will lead to a fingerprint similarity problem, and the environmental changes will cause variations in signal distribution, which may degrade the accuracy of device-free localization (DFL). To address these issues, a device-free localization method is proposed based on the Multilayer Online Sequence Extreme Learning Machine (ML-OSELM). To be specific, a kind of discriminable fingerprint is first designed using the parameters of ML-OSELM. To adapt to environmental changes, an adaptive DFL model is built based on the online sequence extreme learning machine (OS-ELM), which uses weight-based fingerprints as inputs. Experimental results illustrate the high adaptability of the proposed method to the environment, which obtains meter-scale localization accuracy. The proposed approach is used to update the model periodically, being incapable of detecting changes of the environment in a timely manner. In the future work, it is advised that the online DFL model can be studied based on modeling the dynamic changes of the environment to improve the practical applicability of indoor localization.  
Conceptualization, J.X. and Q.C.; Methodology, X.C. and Q.C.; software, J.X. and X.C.; validation, J.X. and Q.C.; formal analysis, Q.C.; investigation, W.X. and Q.C.; resources, W.X. and X.C.; data curation, J.X. and X.C.; writing--original draft preparation, J.X. and X.C.; writing--review and editing, W.X. and Q.C.; supervision, X.C. and W.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundations of China (NSFC) under grant 62173032, the Foshan Science and Technology Innovation Special Project under grant BK22 BF005, the Regional Joint Fund of the Guangdong Basic and Applied Basic Research Fund under grant 2022A1515140109, and the Natural Science Foundation of Shandong Province under grant ZR202212040125.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## Analysis of Results
### FED Results by Using the Micronized Water System  
The number of potential users exposed at risk of fatalities and/or injuries in the event of a fire in the investigated tunnel was evaluated by means of the Fractional Effective Dose (FED) concept, for which an escaping user is considered to be incapacitated and unable to reach a safe place when the FED due to the exposure of toxic gas concentration (FEDtoxic gases) and/or due to the heat and radiant heat flux (FEDheat) is more than 1 (the analytic expressions both of the FEDtoxic gases and FEDheat are reported in [PERSON] et al. (2016)).  
Table 1 shows the number of potential fatalities computed by using the aforementioned FEDtoxic gases (given that FEDheat was less than 1) for each longitudinal position within the tunnel (145, 280, 420, 570, and 710 m from portal A) assumed for the burning vehicle that is characterized by HRRmax of 8, 15, and 30 MW, respectively. From Table 1 one may note that the number of potential fatalities is significantly reduced when the micronized water system is activated. In fact, the number of potential fatalities might be between 0 and 24 against the range of 0-67 without the mentioned water system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Distance from** **Portal A** **[m]**} & \multirow{3}{*}{**Fire Scenarios**} & \multicolumn{3}{c}{**Number of Potential Fatalities Computed by Using**} \\  & & & **the FDS + Evac** \\ \cline{3-5}  & & **HRRmax** & & **With a** & **With a Micronized Water System.** \\  & & **[MW]** & **Micronized** & **Fire Growth Curves Controlled at:** \\ \cline{3-5}  & & **Water System** & **8 MW** & **6 MW** & **4 MW** \\ \hline \hline \multirow{3}{*}{145} & 8 & 0 & - & 0 & 0 \\  & 15 & 2 & 0 & 0 & 0 \\  & 30 & 10 & 4 & 2 & 0 \\  & 8 & 2 & - & 0 & 0 \\
270 & 15 & 7 & 3 & 0 & 0 \\  & 30 & 28 & 8 & 4 & 2 \\  & 8 & 8 & - & 4 & 0 \\
420 & 15 & 16 & 8 & 6 & 3 \\  & 30 & 50 & 17 & 12 & 9 \\  & 8 & 12 & - & 6 & 3 \\
570 & 15 & 25 & 11 & 7 & 5 \\  & 30 & 59 & 21 & 15 & 12 \\  & 8 & 14 & - & 7 & 5 \\
710 & 15 & 36 & 13 & 9 & 7 \\  & 30 & 67 & 24 & 21 & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of potential fatalities computed by using the FDS + Evac with and without a micronized water system.
## 1 Introduction
For example, Reference [17] revealed that people can use multiple apertures technique at both the transmitter and receiver sides to greatly enhance the transmission data rate; Reference [18] proposed a novel optimally weighted non-coherent paradigm to combat the inter-symbol interference and then minimize the BER of a strong scattering plus time-varying channel response ultraviolet communication system; Reference [19] surveyed the co-deployment of a hybrid RF/optical or optical/optical wireless system in improving the individual system performance in terms of throughput, reliability, and energy efficiency. In this paper, based on the former work on APD noise construction [13], and on the receiver signal modeling about the input-dependent and thermal noises in [15], we establish a new APD receiver signal model suitable for UWOC to thoroughly study the channel capacity of UWOC system with APD detector. Moreover, based on this new system model, the closed-form expressions of capacity lower bounds in APD receiving scenario are derived in detail, which mathematically disclose the relationship between the capacity bound and the pointing error angle. Furthermore, with maximizing the capacity bound as an optimization goal with respect to the tilting receiver angle, we prove that this is in fact a simple convex optimization problem and can easily achieve the corresponding optimum solution. Theoretical analysis and simulation results both verify the effects of tilting the receiver plane on improving the system capacity. The remainder of the paper is organized as follows: a system model suitable for describing the signal transmission in clear ocean water type UWOC with APD receiver and channel path loss together with pointing error is established in Section 2. In Section 3, based on the system model presented above, theoretical closed-form expressions of the capacity lower bounds for UWOC with APD are derived in detail under some necessary constraints. In Section 4, an optimization problem is formulated and solved to improve the channel capacity of UWOC with APD by appropriately tilting the receiver's plane. Section 5 demonstrates a series of numerical simulations to verify our mathematical optimization problem; and finally, in Section 6, we conclude the paper with some concise remarks.
## 5 Factor Affecting the Properties of GPC
### Curing Temperature  
Curing temperature results in high early strength but causes a porous geopolymer mix, which reduces the strength at later age. The reduction in strength at later ages and at higher temperatures is due to early and fast evaporation of water, which not only disturbs the geopolymerization process and hinders the dissolution of Silica (SiO\({}_{2}\)) and Alumina (Al\({}_{2}\)O\({}_{3}\)), but also increases porosity [129].  
[PERSON] et al. [134] studied the effect of temperature curing (80 \({}^{\circ}\)C) and ambient curing (25 \({}^{\circ}\)C) of a fly ash/slag aluminosilicates mix on the strength development. It was found that temperature cured samples showed higher compressive strength than the samples cured at room temperature after 7 days due to heat release during both alkali activation and temperature curing process. At 28 days, the samples cured at room temperature possessed higher compressive strength than samples temperature cured at 80 \({}^{\circ}\)C due to formation of CSH or CASH. According to the authors, this behavior is expected to be due to the inhomogeneity of microstructure and the increase of cumulative pore volume of GPC at high temperature curing. The authors also investigated the microstructure of both temperature cured and ambient cured samples and found that the temperature cured samples had rough microstructures with voids distributed throughout the surface (Figure 26a). In contrast, the ambient cured samples exhibited smooth, compact, and soft microstructure with low void content which could be responsible for their superior strength at 28 days, as shown in Figure 26b.  
[PERSON] et al. [2] also did not recommend heat curing for alkali-activated slag concrete because the formation of C-S-H is affected by an increase of temperature as the rise in dry curing temperature leads to more water evaporation. Likewise, [PERSON] et al. [50] investigated that with an increase in curing temperature of 10 \({}^{\circ}\)C from 60 \({}^{\circ}\)C to 70 \({}^{\circ}\)C, the compressive strength of SCGC increases, but beyond 70 \({}^{\circ}\)C, the strength decreases. Unlikely, it was found that for FA-based geopolymer concrete curing temperature accelerates the polymerization and significantly affects the mechanical strength as presented in Figure 27[162; 164; 165]. This might be due to different composition of fly ash and slag aluminosilicates.  
Figure 26: SEM analysis. (**a**) Temperature cured sample; (**b**) ambient temperature cured samples [134].
## 3 Experimental Results
### Permeability Evolution  
The permeability evolution of coal samples with time is shown in Figure 7.  
Figure 7: Permeability evolution with time of coal samples. (**a**) Coal sample M1; (**b**) coal sample M2; (**c**) coal sample M3. Note: Point A is the unloading axial stress point; Point B is the unloading confining pressure point; Point C is the loading confining pressure point; Point D is the loading axial stress point; Point E is the secondary damage point.  
The table could be obtained b0y the statistics of the test data of each coal sample, as shown in Table 2.  
As shown in Figure 7 and Table 2, the peak stress of coal samples M1, M2, and M3 was 14 MPa, 21 MPa, and 23 MPa, and the corresponding permeability of the peak stress point was 0.84 mD, 0.72 mD, and 0.64 mD. The permeability evolution trend of M2 and M3 was almost the same, but was different from M1; this was because M1 had no secondary loading confining pressure stage. In the secondary damage period, the rate of permeability evolution was significantly higher than in other conditions. The maximum value and the minimum value of permeability decreased with the growth of confining pressure, such as at Point E, the permeability of M1, M2, and M3 was 1.15 mD, 0.72 mD, and 0.63 mD, which showed a decreasing trend.  
As shown in Figure 8, when the axial stress was unloaded, the permeability of coal samples increased, and the permeability decreased with the growth of confining pressure. When the axial stress was unloaded to 1 MPa, the permeability of M1, M2, and M3 was 1.06 mD, 0.89 mD, and 0.80 mD. When the confining pressure was unloaded, the permeability increased; when confining pressure was unloaded to 1 MPa, the permeability of M1, M2, and M3 was 1.14 mD, 1.12 mD, and 1.03 mD. It can be seen that the permeability of M1 was higher than that of M2 and M3 under the same confining pressure. When the confining pressure was loaded, the permeability decreased; when the confining pressure was loaded to 2 MPa, the permeability of M2 and M3 was 0.95 mD and 1.1 mD. When the coal samples were loaded to the secondary damage, the permeability first decreased, then increased, and the permeability still decreased with the increase of confining pressure. From the above analysis, we could know under any condition that the permeability was M1 > M2 > M3, and this was due to that fact that as the confining pressure increased, the micropores in the coal body were compacted, resulting in a decrease in porosity. The permeability evolution with axial stress and confining pressure in the unloading process showed an exponential relation; however, in the loading confining pressure process, the relationship was linear.  
Figure 8: Comparison of permeability evolution under different parameters. (**a**) Unloading axial stress; (**b**) unloading confining pressure; (**c**) loading confining pressure; (**d**) loading to the secondary damage.