esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model Analysis
### Service Time Model
#### 3.3.2 Computation Cost  
After the SNs offload the computational task, the receiver BS processes the task-specific portion, \(A_{t_{i}}\), and forwards the other portion, \(A_{t_{j}}\), to the cooperative BS for processing. Hence, the processing time for computational task to be offloaded to BS i within \(T_{s}\) is calculated as shown in Equation (11):  
\[\mathcal{T}_{\text{i}}^{\text{P}}(\text{t})=\frac{N[V_{n(T_{s})}\times A_{t_{ j}}]}{C_{c}} \tag{11}\]  
where \(V_{n(T_{s})}\) represents the binary vector of the offloading decision from SNs to node \(b_{i}\), i \(\in\mathcal{B}\) within \(T_{s}\). In case there is no offloading of the computational task from an SN, the \(V_{n}(T_{s})\) is equal to zero, and there will be no processing of the task at node \(b_{i},\text{i}\in\mathcal{B}\).  
The processing time for a computational task to be offloaded to node \(b_{j},\text{j}\in\mathcal{B}\) is calculated as shown in Equation (12):  
\[\mathcal{T}_{\text{j}}^{\text{P}}(\text{t})=\frac{N[V_{n(T_{s})}\times V_{b(T _{s})}\times A_{t_{j}}]}{C_{c}} \tag{12}\]where \(C_{c}\) is the computing capability of the BS, and \(N\) represents the number of computation cycles required to operate the data. In case there is no offloading of the computational task from node \(b_{i},i\in\mathcal{B}\) to the node \(b_{j},j\in\mathcal{B}\), the \(V_{b(T_{s})}\) is equal to zero, and there will be no processing of the task at node \(b_{j},j\in\mathcal{B}\). Remarkably, a new computation task offloaded within this \(T_{s}\) can be processed only if the computing tasks offloaded within the previous \(T_{s}\) are completed. To offload the task to node \(b_{i},i\in\mathcal{B}\), the total computational time (queuing time and processing time), \(\mathcal{T}_{i}(t)\), is calculated as shown in Equation (13):  
\[\mathcal{T}_{i}(t)=\mathcal{T}_{i}^{q}(t)+\mathcal{T}_{i}^{p}(t)=max\big{\{} \mathcal{T}_{q}(t)(t-1)+2Y,0\big{\}}, \tag{13}\]  
where the value of Y is the time length of \(T_{s}\). To offload the task to node \(b_{j},j\in\mathcal{B}\), the total computational time is calculated as shown in Equation (14):  
\[\mathcal{T}_{j}(t)=\mathcal{T}_{j}^{q}(t)+\mathcal{T}_{j}^{p}(t)=max\big{\{} \mathcal{T}_{q}(t)(t-1)+Y,0\big{\}}, \tag{14}\]  
where \(\mathcal{T}_{i}^{q}(t)\) and \(\mathcal{T}_{j}^{q}(t)\) are the queuing time of the offloaded task at node \(b_{i},i\in\mathcal{B}\) and the node \(b_{j},j\in\mathcal{B}\) in the \(T_{s}\). The division of the computational tasks to be offloaded by the SNs is taken into account in the following manners. The computational task is divided into two sub-components which are independent of each other.
# A Method for Detecting Surface Defects in Railhead by Magnetic Flux Leakage
## 4 Experimental Results and Analysis
### Physical Experiment Results and Analysis
#### 4.2.3 Experiment of the Different Defects  
The dimensions of four different defects and their detection data in \(x\) and \(z\) components are shown in Figures 13 and 14, respectively. The distance between auxiliary sensors 1 and 2 to the main sensor is 3.4 mm and the distance between auxiliary sensors 3 and 4 to the main sensor is 2.2 mm.  
Figure 12: \(z\)-component results with different distance.  
main sensor is zero, and the signal of auxiliary sensor 3 is the minimum, and the signal of auxiliary sensor 4 is the maximum. It can be determined that this is a defect by the method in Section 3.
## 6 Nanofertilizers
### Cerium Oxide Nanoparticles (CeO\({}_{2}\) NPs)  
Cerium oxide nanoparticles (CeO\({}_{2}\) NPs) are one of the sought after nanomaterials needed in agriculture for crop improvement and nutritional effect [91]. Their impact on crops depends on the concentration applied, the soil composition and the plant species. When the concentration of the nanoparticles applied to the soil is in minute quantity, they bring about enhanced crop development and nutritional value. However, they have some detrimental effects in higher concentrations, which depends on the nature of the plants. There are some reports supporting the effect of the higher concentrations of CeO\({}_{2}\) NPs on plants. One is the case of enhanced growth of lettuce (_Lactuca sativa_ L.) when treated with 100 mg kg\({}^{-1}\) CeO\({}_{2}\) NPs, but the growth of the plant was hindered at a higher concentration of 1000 mg kg\({}^{-1}\) CeO\({}_{2}\) NPs [92]. A similar situation was reported in soybean plant by [PERSON] et al. [93]. They reported that CeO\({}_{2}\) NPs greatly improved the photosynthetic rate of soybean under high moisture contents of the soil, but not under limited soil water conditions. The simple mechanism is that, at low moisture content of the soil, the plant stomata are closed, resulting from drought, which hinders transpiration and the uptake of CeO\({}_{2}\) by the plant [91]. Other mechanisms that portray the uptake of CeO\({}_{2}\) NPs by plants explain that positively charged CeO\({}_{2}\) NPs are better adsorbed by plant roots than negatively charged CeO\({}_{2}\) NPs. This enhances the transport of Ce to other plant parts as the positive surface charge is easily dissolved and attracted to the negative surface charge in the plant rhizosphere. These results are correct within the ambits of laboratory or small-scale application, while there could be different results when applied to the field due to different reactions involved in the soil.
# Prediction of Damage Level of Slab-Column Joints under Blast Load
## 3 Defining a Joint Region under Blast Load  
In the slab-column connection under static load, the section at half the effective depth of the slab from the column surface is considered as the critical section [30]. The joints, which are D-regions (disturbed or discontinued region) with complex strain distributions, may exhibit behavior different from that under a static load in a high strain rate region, such as an explosion load. Therefore, in this study, prior to performing the parametric analysis in Table 2, a preliminary numerical analysis was conducted to define the area of the joint in the slab-column connection under the explosive load.  
The U.S. Department of Defense (DoD) suggested shock wave parameters, such as pressure and impulse, according to the scaled distance for single members [10]. According to this, it shows a specific pressure or impulse value for an arbitrary scaled distance regardless of the type of members, such as a column or a slab. Therefore, in this study, through the preliminary numerical analysis of the slab-column connection, the scaled distance, where the analysis results for the slab and the column are similar to each other and at the same time to the value suggested by the DoD, was considered as the point separating the joint region from the single-member region.  
Figure 2 shows the pressure and impulse according to the scaled distance. The graphs for slab and column are the results obtained through numerical analysis. Here, the pressure and impulse represent the maximum reflected values that occur in the entire region of the modeled slab and column, according to the location of TNT 30 kg. Figure 2 also includes the values proposed by DoD by using the ConWep model [10]. For both pressure and impulse, the analysis results of the column and the slab differed from each other when the scaled distance was small, but from about 0.1 m/kg\({}^{1/3}\) or more, the difference was markedly reduced and showed similar values. It should also be noted that from about 0.1 m/kg\({}^{1/3}\), both the column and the slab showed pressure and impulse similar to those of the ConWep model. As mentioned above, considering that the ConWep values of DoD are the results derived for a single member, the region with a scaled distance of 0.1 m/kg\({}^{1/3}\) or less from the surface of the slab and column can be considered as the joint region, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Analysis Conditions** & \multicolumn{2}{c}{**Value**} \\ \hline Number of elements & Solids & 392,000 ea (\(20\times 20\times 20\) mm) \\  & Beam & 10,648 ea (20 mm) \\ Time step & & 0.1 ms \\ Analysis of end time & & 2000 ms \\ Analysis of running time & & 6 h 45 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis conditions.  
Figure 3: Joint region (Z = 0.1 m/kg\({}^{1/3}\)).  
Figure 2: Defining joint region under blast load; (**a**) Pressure; (**b**) Impulse.
# An Efficient Opportunistic Routing Based on Prediction for Nautical Wireless Ad Hoc Networks
## 4 Performance Evaluation
### Performance Comparison with AODV and PASS  
In this section, we compare the WRSP routing scheme with AODV and PASS schemes under different node densities. The results and analysis are shown as follows.  
Figure 4a shows the comparison results for the packet loss ratio. As expected, the packet loss ratio of all schemes improves as the node density increases, since more nodes means more chances of forwarding, and thus a lower packet loss ratio. However, compared to the blind flooding strategies with redundant replications of AODV and PASS, the proposed routing scheme predicts the transmission quality between nodes and the relay advancement to jointly reduce the number of aborted messages for apparently qualified relay nodes. Thus, the WRSP has the lowest packet loss ratio.  
From Figure 4b, we can see that AODV and PASS have lower throughput than WRSP routing. This is because the WRSP makes decisions on relay node selection by combining transmission quality evaluation and the probabilistic prediction of a relay node forwarding packets to the destination node. In contrast to blind broadcasting to its neighbors, as in AODV and PASS when a link is unavailable between nodes, WRSP not only avoids the unnecessary packet requests of senders but also predicts when the relay advancement indicates the packet cannot be forwarded further. Therefore, the proposed scheme has higher throughput than other routing schemes.  
As shown in Figure 4c, the average latency of the three schemes improves as the node density increases, because the existence of more nodes implies more intermediate nodes for selection, and thus higher average latency. Since the proposed scheme always tries to find a good forwarder with respect to each hop, it checks the ability of the forwarder to reach the destination node; thus, the average latency is the highest. However, in addition to an 18.92% and 10.12% increase in latency, the proposed scheme achieved 32.0% and 21.14% lower packet loss ratios and 31.79% and 20.85% more throughput than AODV and PASS routing schemes, during data transmissions. We feel that tolerating a delay that improves packets delivery greatly is a good trade-off.  
Figure 3: (**a**) Packet loss ratio vs. 10 coefficient combinations. (**b**) Throughput vs. 10 coefficient combinations.
## 2 Materials and Methods
### Growth and bioluminescence Measurements  
Bacterial growth was determined by the optical density at 595 nm (OD595) using an iMARK(tm) Microplate Absorbance Reader (BioRad, Hercules, CA, USA). The light emission intensity (RLU) was measured with the following steps by ModulusTM Single Tube Multimode Reader (Turner Biosystems, Hercules, CA, USA). The two strains were cultured in solid agars for 3 days at 22 \(\pm\) 1 \({}^{\circ}\)C, then inoculated into 250 mL sterilized conical flasks containing 80 mL of liquid medium. After incubation for 12 h (22 \(\pm\) 1 \({}^{\circ}\)C, 150 rpm), the OD595 and luminescent intensity of T3 strain reached 0.85 \(\pm\) 0.05 and 6.5 \(\times\) 10\({}^{7}\)\(\pm\) 10\({}^{4}\) RLU, respectively; the OD595 and luminescent intensity of Q67 reached 1.25 \(\pm\) 0.05 and 2.7 \(\times\) 10\({}^{6}\)\(\pm\) 10\({}^{3}\) RLU, respectively. Before each light emission intensity test, 100 \(\mu\)L bacterial suspension and 900 \(\mu\)L diluent (3% NaCl for T3 and 0.4% NaCl for Q67) were added into a 1.5 mL EP tube. Every test was performed in triplicate.
## 5 Asynchronous Sample Encryption Validation and Context Flow Correlation
### Authenticity Validation of Samples Based on TLS Parameters  
TLS is the most mainstream cryptographic communication protocol at present. Because there may be much external link interference (such as advertising) in the current application, not all TLS samples captured, belong to the application. There is a possibility of embedding other applications or external TLS links, so sample authenticity validation is required. Therefore, we propose a TLS encrypted traffic sample authenticity validation method based on TLS handshake parameters. We use parameters such as Server Name Indication (SNI) for reverse validation. Although SNI, as an editable and nullable field, is not suitable for directly encrypted traffic classification, it can validate the authenticity of samples because it contains domain information. It can be used to filter obviously problematic flows. We obtained SNI and other fields from traffic and established a local database to verify it through preliminary research and collection.
## 2 Related Research
### Fiber-Optic
#### 2.2.1 Light Intensity Modulation (LIM)  
When a modulator (e.g., mirror) rotates at the end of an optic-fiber, the reflected light intensity received through a fiber-optic varies substantially and is thus detectable after the light intensity is converted to a voltage. This principle can be used as a force sensor. Although the sensor structure can be simple and its measurement process is cost-effective, the sensor output of the light intensity tends to be influenced by the bending and deflection of the path of the optic fiber. [PERSON] et al. presented a surgical tool equipped with fiber-optic force sensors and tested it in an in-vivo experiment [19], [PERSON] et al. presented a robust uniaxial force sensor using three optic fibers as a force sensing probe for cardiac surgery [20], and [PERSON] et al. presented a three-axis fiber-optic body sensor for a flexible manipulator [21].
## 6 Index Regression and Correction
### Correction: Error Update  
#### 6.2.1 Kalman Filter  
The filter is named after [PERSON] (19 May 1930-2 July 2016). In 1960, [PERSON] published his famous paper describing a recursive solution to the discrete data linear filtering problem. The Kalman filter has been widely applied in fault detection [31; 32; 33; 34; 35; 36; 37; 38]. [PERSON] et al. [39] showed that the bulk modulus and the viscous damping coefficient of a hydrostatic actuation system could be correctly estimated, in sequence, using EKF. [PERSON] et al. [40] showed the feasibility and advantage of applying an EKF-based algorithm that describes the development and application of a model-based scheme for detecting the early signs of coil failure in solenoid valves over other existing techniques. Additionally, the Kalman filter provides a prediction of the future system state, based on the past estimations. The Kalman filter algorithm consists of two stages: prediction and update. Note that the terms \"prediction\" and \"update\" are often called \"propagation\" and \"correction\" [41; 42; 43]. The predicted state estimate is evolved from the updated previous state estimate. These two terms form a loop with each other and make predictions by reflecting on previous prediction errors. The update term has an error value between the measured value and the predicted value. Therefore, it is unnecessary to hold the entire past data, which guarantees faster operation and makes it suitable for real-time time series prediction.  
A prediction method was developed to reduce errors by applying the prediction-update loop idea of the Kalman filter.
## 3 Results and Discussion
### Emission Characteristics
#### 3.3.3 NOx Emissions  
Figure 10 represents the NOx emissions at different idling conditions for diesel, palm oil, and diesel fuel blends. As shown in Figure 10, it can be seen that the NOx emissions have a significant decreasing trend for each test fuel with the increase in engine speed. At 1500 rpm, the NOx emissions of CPO0, CPO10, CPO30, and CPO50 are respectively reduced by 64.28%, 64.26%, 65.24%, and 62.68% compared with at 750 rpm. At 2250 rpm, the NOx emissions of CPO0, CPO10, CPO30, and CPO50 are respectively reduced by 77.21%, 78.69%, 79.83%, and 82.28% compared with at 750 rpm. The decrease of NOx emissions with the increase of engine speed occurs because increasing engine speed improves the volumetric efficiency and gas flow motion within the combustion cylinders, ensures better mixing of air and fuel, and shortens the ignition delay, which allows less time for NOx formation [19]. This is consistent with the research results from [PERSON] et al. [23]. They also pointed out that the main mechanism of NOx generation is related to the shortening of ignition delay, which leads to insufficient time for the chemical reaction of free oxygen and nitrogen in the cylinder. On the other hand, the NOx emissions of all blended fuels are lower than that of neat diesel at each engine speed. At 750 rpm, the NOx emissions of COP10, COP30, and COP50 are reduced by 2.48%, 7.64%, and 17.56% compared with CPO0. At 1500 rpm, the NOx emissions of COP10, COP30, and COP50 are reduced by 2.44%, 10.13%, and 13.88% compared with CPO0. At 2250 rpm, the NOx emissions of COP10, COP30, and COP50 are reduced by 8.82%, 18.24%, and 35.88% compared with CPO0. In general, because biodiesel is an oxygenated fuel with a high cetane number, the NOx emission of most diesel engines fueled with biodiesel is higher than that of neat diesel fuel [36]. However, high viscosity fuel leads to improper atomization and poor mixing, as well as a decrease in temperature in the cylinder, which ultimately reduces the generation of NOx. In this study, the high viscosity and low cetane number (see Table 1) of palm oil are the reasons for the reduction of NOx.  
Figure 9: HC emissions of all test fuels according to various engine speeds.
# Biometrics for Internet-of-Things Security: A Review
## 4 Biometric-Based Systems for IoT-Oriented Authentication
### Single-Modal Versus Multi-Modal Biometric Authentication Systems
#### 4.1.3 Comparison of Single-Modal and Multi-Modal Biometric Authentication Systems  
As far as real-world implementations are concerned, single-modal biometric authentication systems that operate on a single biometric trait have issues such as noise in the sensed data, intraclass variation and interclass similarity, which can affect recognition accuracy. In contrast, multi-modal biometric authentication systems often outperform their single-modal counterparts by fusing multiple sources of information through different fusion strategies, such as feature-level fusion, score-level fusion and decision-level fusion [66].  
Table 2 reports the performance in terms of EER or RA of many existing single-modal and multi-modal biometric authentication systems. Specifically, it can be seen from Table 2 that biometric traits with more discriminative features (e.g., iris) tend to achieve superior performance. For example, the EER of 0.2% for iris authentication [43] is better than the EER of 0.36% for finger-vein authentication [42]. In addition, multi-modal biometric authentication systems perform better than unimodal systems. For instance, in [46] the EER is 8.04% with fused data from face and voice, in contrast to the EER of 14.05% with features from only face and the EER of 43.76% with features from only voice.  
Despite the advantages in recognition accuracy offered by multi-modal biometrics, issues pertinent to IoT applications should be considered. For example, stronger user cooperation is required for a multi-modal biometric authentication system, which may lead to inconvenience to IoT users. Moreover, collection, storage, and processing of multiple sources of information increase the complexity of the overall system and consume more resources. Given that most IoT devices have limited resources (e.g., storage, computational capability, bandwidth and power) [67], the benefits and drawbacks of implementing a multi-modal biometric authentication system in the IoT should be given full consideration.  
It can be seen from Table 2 that the common hardware for testing the proposed single-modal or multi-modal biometric-based methods is Raspberry Pi, and about half of the methods are tested by using simulators instead of real IoT devices. Certain methods listed in Table 2 only describe how to implement the basic biometric authentication function on IoT devices (e.g., Raspberry Pi) but with no information about recognition accuracy or security; thus, no recognition performance of these methods can be included in Table 2. It also manifests that applying biometrics to the IoT is not yet popular or mature. There are various reasons for this, among which the main reasons may be the constraints of IoT devices (e.g., limited memory and computing power) and user acceptability.
## 4 Joint Inversion  
### Damped Least Squares Inversion  
In recent years, there have been many studies on the inversion methods of Rayleigh surface wave fundamental dispersion curves, among which the damped least squares algorithm has been widely used due to its fast operation speed and high inversion accuracy. In the paper, the damped least squares method is used to invert the fundamental and higher-mode dispersion curves of the surface wave extracted from background noise, and to calculate the shear wave velocity structure of the strata. The multi-order joint inversion by the damped least squares algorithm is similar to the fundamental inversion method. The difference is that the higher-mode mode data are added in the inversion process, and the partial derivatives of the fundamental mode and the higher-mode mode to the shear velocity need to be calculated separately when calculating the Jacobi matrix [40]. Due to the low sensitivity of the Rayleigh wave phase velocity to density and P-wave velocity, only two parameters, namely S-wave velocity and layer thickness, are calculated in the inversion of the theoretical model in the paper.  
The objective function of the damped least squares inversion [41] is defined as:  
\[\phi=\left\|\mathbf{J}\Delta\mathbf{x}-\Delta\mathbf{b}\right\|_{2}\mathbf{W} \left\|\mathbf{J}\Delta\mathbf{x}-\Delta\mathbf{b}\right\|_{2}+\alpha\left\| \Delta\mathbf{x}\right\|_{2}^{2} \tag{6}\]  
where \(\mathbf{x}=\left[\mathbf{v}_{\mathrm{s1}},\mathbf{v}_{\mathrm{s2}},\mathbf{ v}_{\mathrm{s3}},\ldots,\mathbf{v}_{\mathrm{sn}}\right]^{\mathrm{T}}\) is the n-dimensional length vector of shear wave velocity, \(\mathbf{b}=\left[\mathbf{b}_{1},\mathbf{b}_{2},\mathbf{b}_{3},\ldots,\mathbf{ b}_{\mathrm{m}}\right]^{\mathrm{T}}\) is the m-dimensional length vector of Rayleigh wave phase velocity, \(\Delta\mathbf{x}\) is the correction of shear wave velocity, \(\Delta\mathbf{b}\) is the difference between the initial value model response and the observed data, \(\mathbf{J}\) is the Jacobi matrix with m rows and n columns, \(\alpha\) is the damping factor, \(\mathbf{W}\) is the weighting matrix, \(\mathbf{W}=\mathbf{L}^{\mathrm{T}}\mathbf{L}\), and \(\mathbf{L}\) is the diagonal matrix, which represents the partial derivative of phase velocity with respect to shear wave velocity. According to the singular value decomposition method, the correction of shear wave velocity can be obtained as follows:  
\[\Delta\mathbf{x}=\mathbf{V}\left(\mathbf{\Lambda}^{2}+\alpha\mathbf{I}\right) ^{-1}\mathbf{\Lambda}\mathbf{U}^{\mathrm{T}}\mathbf{d} \tag{7}\]  
where \(\mathbf{I}\) is the identity matrix, \(\mathbf{d}=\mathbf{L}\mathbf{b}\), and \(\mathbf{d}\) is the phase velocity matrix. The damping factor \(\alpha\) controls the direction and convergence rate of the shear wave velocity correction, and the stability and convergence of the inversion process can be controlled by adjusting the \(\alpha\) value. According to Equation (7), without considering the number of damping factor changes, the appropriate S-wave velocity correction can be calculated once by singular value decomposition, and the inversion process can be realized.