esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Space-Variant PSF of OAP System
### Space-Variance Correction in the Angular Direction  
Usually the research on propagation and the focus of the light or electromagnetic field relies on the Fresnel diffraction integral formula, Kirchhoff formula, or Rayleigh-Sommerfeld diffraction integrals. To avoid these complex formulas, we derive the PSF of the parent parabolic by the Collins formula, which has proved to be consistent with the Fresnel diffraction integral formula for the numerical analyses of symmetric paraxial optical systems [20]. As mentioned in [21], the parabolic mirror has the same function of light field transformation as a lens. Therefore, we can derive the ideal imaging of the parabolic reflector using the mature method of lens. According to [20], the PSF of the parent parabolic can be written as follows:  
\[h(x_{2},y_{2};\xi_{2},\eta_{2})=\frac{k^{2}exp(jkl)}{4\pi^{2}d_{ 1}d_{2}}exp(jk\frac{x_{2}^{2}+y_{2}^{2}}{2d_{2}})exp[-jk\frac{A(\xi_{1}^{2}+ \eta_{1}^{2})}{2d_{1}}]\\ \cdot\int_{0}^{2\pi}\int_{0}^{\frac{D}{2}}r_{0}exp\frac{-jk}{d_{ 2}}[(x_{2}-A\xi_{1})r_{0}cos\theta_{0}+(y_{2}-A\eta_{1})r_{0}sin\theta_{0}]dr_ {0}d\theta_{0}. \tag{1}\]  
In Equation (1), \(u_{1}(\xi_{1},\eta_{1})\) is the object plane, \(u_{2}(x_{2},y_{2})\) is the image plane, and \(u_{0}(r_{0},\theta_{0})\) is the pupil plane. So it is obvious that the PSF is variant along with the coordinate parameter. Inspired by the symmetrical, circular characteristics of the blur, we transform the object plane and image plane from Cartesian-coordinates into polar-coordinates followed by Equation (2):  
\[r_{1}^{2}=\xi_{1}^{2}+\eta_{1}^{2}\quad\theta_{1}=tan(\eta_{1}/\xi_{1})\quad; \quad r_{2}^{2}=x_{2}^{2}+y_{2}^{2}\quad\theta_{2}=tan(y_{2}/x_{2}). \tag{2}\]  
Figure 2: Off-axis parabolic and its focal plane.  
Then, we derive the PSF under the polar-coordinate:  
\[h(r_{2},\theta_{2};r_{1},\theta_{1})=\frac{k^{2}exp(jkL)}{4\pi^{2}d_{1}d_{2}}exp( jk\frac{r_{2}^{2}}{2d_{2}})exp[-jk\frac{A(r_{1}^{2})}{2d_{1}}]\cdot\int_{0}^{2\pi} \int_{0}^{\frac{\theta}{2}}r_{0}exp[\frac{2k}{d_{2}}r_{0}r_{2}cos(\theta_{2}- \theta_{0})]dr_{0}d\theta_{0}. \tag{3}\]  
In particular, we take the integral about \(\theta\) into consideration to get Equation (4):  
\[\int_{0}^{2\pi}exp[Mcos(\theta_{2}-\theta_{0})]d\theta_{0}, \tag{4}\]  
where \(M=\frac{2 jkr_{0}r_{2}}{d_{2}}\), and if \(\theta_{2}-\theta_{0}=t\), then \(d\theta_{0}=-dt\). Equation (4) can be calculated as follows according to the properties of trigonometric functions:  
\[\begin{split}&\int_{\theta_{2}}^{\theta_{2}-2\pi}-exp[Mcos(t)]dt\\ &=\int_{\theta_{2}-2\pi}^{\theta_{2}}-exp[Mcos(t)]dt\\ &=\int_{0}^{2\pi}-exp[Mcos(t)]dt.\end{split} \tag{5}\]  
So, as we can see, the PSF is not related to \(\theta\) under the polar-coordinate. In other words, the PSF is invariant to \(\theta\) after the coordinate transformation. The PSF in polar-coordinates can be called the PCPSF model, which decouples the two-dimensional space-variant blur from the angular direction.
# A Distributed Trustable Framework for AI-Aided Anomaly Detection +
## 2 ML-Aided Anomaly Detection Review
### Attack Types in 6G Networks  
3 GPP has provided TR 33.926, discussing threats and critical assets that must be protected in the context of security assurance specifications [18]. These threats were classified into the following six categories:  
* Spoofing identity involves illegal access and the use of another user's authentication information, such as the username and password.
* Tampering with data focuses on malicious data modification, including data alteration as it flows between two computers over the Internet and unauthorized changes to persistent data residing in a database.
* Repudiation threats are related to users who deny performing an action while, at the same time, other parties are not able to prove otherwise. For example, in this case, a user can perform an illegal operation in a system that is unable to trace the prohibited operations.
* Information disclosure includes information exposure of individuals who should not have access to it. More specifically, unauthorized users might access a file that they were not granted access to, or an intruder might read data transmitted between two devices.
* Denial-of-service attacks resulting in instances where valid users are denied requested services, e.g., by rendering a Web server temporarily unavailable or unusable.
* Elevation of privilege, where an unprivileged user obtains privileged access and threatens to compromise or destroy the entire system. An illustrative example involvesan attacker that has penetrated through all system defenses, becoming part of the trusted system.  
In 6G infrastructures, these threat types can be proactively detected by capitalizing on data-driven AI/ML-based solutions towards minimizing the impact of threats and anomalies during the network's operation.
# Current Progress Regarding _Cordyceps militaris_, Its Metabolite Function, and Its Production
## 3 Cultivation of C. _militaris_
### Liquid Culture  
Liquid culture is the other option for _C. militaris_ cultivation. It is considered a better procedure for application in industry due to its shorter cultivation times, smaller space requirements, and higher productivities [163]. _Cordyceps militaris_ is allowed to grow in liquid as mycelia, without forming fruiting bodies, and total cultivation times can decrease from 60 to 15 days [164]. Bioactive compounds are released from mycelia and accumulate in the culture broth to simplify the extraction process [156]. Liquid culture can be carried out without the need for light/dark treatment and regulation of a specific relative humidity [158]. Therefore, liquid culture is considered a promising _C. militaris_ cultivation procedure for obtaining useful and potent substances for industrial-scale applications.  
In a recent study, the liquid culture of _C. militaris_ was divided into submerged fermentation and surface culture methods. Submerged culture continually provides shaking or agitation during fermentation in order to dissolve nutrients and oxygen in the liquid medium [159]. For surface culture, the system remains in a static condition after the seeding of the inoculum. In surface culture, a biofilm forms on the surface, and some mycelia precipitate to the bottom of the culture flask, while the byproducts are directly secreted and accumulate in the medium [165]. In terms of metabolite yields, higher production can be achieved than that obtained using submerged culture; however, the surface culture time is relatively longer. In addition, surface culture is not easy to perform on an industrial scale [156]. Submerged culture can be carried out in a fermentation tank, and the fermentation time is relatively short and controllable [166]. Consequently, the _C. militaris_ industry has mainly focused on developing the submerged culture method.
# Investigation of OFDM-Based HS-PON Using Front-End LiFiSystem for 5G Networks
## 2 Proposed Model
In the LiFi system, the direct current bias signal with a voltage of 4 V is integrated with the electrical signals from the QAM detector to drive a blue LED (450 nm). LEDs operate in linear regions with 65% quantum efficiency. The incoming LED input is modulated with pseudo-random bit sequences in non-return to a zero format and then forwarded to the LiFi communication channel by an MZM. We also consider interference from external light sources and channel noise. To capture the transmitted signal, an optical receiver is used. In addition, BER analyzers are used to analyze the received information in terms of BER and eye patterns [22]. For uplink data transmission, 4 \(\times\) 150 Gbps OFDM signals operating at 195.3-195.6 THz frequencies are generated at the ONU and detected at the OLT using OFDM demodulation. In the simulation process, a buffer selector is included to select the last iteration. Figure 3 illustrates the time switch section, which consists of seven time slot modules. Each time slot module incorporates two cascaded 2 \(\times\) 1 dynamic optical switches (dynamic Y select blocks) for transmission at a specific timer slot. In Figure 4, we introduce the proposed model's LOS propagation scenario. To isolate uplink and downlink transmission and reduce interference between the channels, two separate fiber links are used in this model. For high signal quality and coverage across large areas, LiFi communication systems use LOS transmission. Figure 3: Block diagram of time switch module for uplink data transmission in the proposed model.
# Joint Power Allocation and Link Selection for Multi-Carrier Buffer Aided Relay Network
## 1 Introduction
### Contributions  
In this work, our target is to maximize the average throughput in an OFDM-based dual-hop buffer aided relaying transmission. We seek power optimization and the transmission hop selection under the half-duplex one-way relay communication. Specifically, our contributions can be outlined as:  
* To maximize the end-to-end rate of the system, an optimization problem is being formulated under individual power constraint at source/relay nodes and the link selection constraint.  
* We consider joint power optimization over different sub-carriers at the source node, the optimal power loading over carriers at the relay node and the transmission link selection at a given time slot.
* Under the buffer aided transmission at the relay, we exploit the fact the end-to-end throughput is the sum of the rates received at the second hop while the sum of data transmitted cannot exceed the total received and propose a joint solution over all variables.
* With the DF relaying protocol, we propose an efficient decomposition structure where the optimal power allocation at each node is allocated through the water-filling strategy while the optimal link selection is obtained for the obtain power solution.
* Later, the problem of power allocation over the entire time slot for conventional relay transmission and the corresponding solution is presented. Further, a sub-optimal solution for BAR is also proposed.
* Finally, results are evaluated through extensive simulations.
# Effect of Processing Methods on Amino Acid and Fatty Acid Composition of _Parkia biglobosa_ Seeds
## 2 Materials and Methods
### Amino Acid Analysis  
The amino acid composition of the samples was determined by using the ion-exchange chromatography method [17] using a Technicon Sequential Multi-Sample Amino Acid Analyser (TSM) (Technicon Instruments Corporation, Dublin, Ireland). The reported amino acid values are the means of two determinations and are expressed as a percentage of the total protein. Tryptophan was not determined.
# The Use of Machine Learning for Comparative Analysis of
## 3 Results
### Prediction of Antioxidant Activity Using Machine Learning
#### 3.2.2 Testing Phase  
After model training, it is tested on the test data, including three instances in our case. The MSE evaluation metric is evaluated, and its best value (after trying multiple trainings) comes out as \"0.0041\". A plot is constructed between the actual and the predicted values of the target variable (Figure 8). Here, the purple dots and the blue line represent the actual target values of the test instances and the green cross (x) represents the predicted target values. The distance between the predicted points and the model fit line is actually the prediction error.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Parameters** & **Values** \\ \hline Optimize Hyperparameters & auto \\ Hyperparameter Optimization Options & struct (“Acquisition Function Name”, “expected- \\  & improvement-plus”, “Max Objective Evaluations”, 20) \\ Standardize & true \\ Activations & relu \\ Lambda & 0 \\ Iteration Limit & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The significant values considered for training the NNRM.  
Figure 8: Actual vs. predicted target value plot. Purple dots depict the actual target values of the test instances and green crosses represent the predicted target values.  
Figure 7: Best-observed and best-estimated objective function value plot against the number of iterations after the last function evaluation.  
Mean Squared Error (MSE) was used as an evaluation metric. It is a measure that tells how close a regression line is to a set of predicted points. It calculates the average squared distance between the actual and the predicted values. It is squared to avoid the negative values obtained from subtraction. So, the lower the MSE, the better the prediction.  
\[MSE = \frac{1}{n}\sum_{i\,=\,1}^{n}{(Actual_{i}-Predicted_{i})}^{2}, \tag{15}\]  
where \(n\) is the total number of test instances; _Actual_ is actual value of the target; and _Predicted_ depicts the predicted value of the target by the trained model.  
To compare the performance of the machine-learning model to standard statistical techniques, multiple linear regression (MLR) was also performed on the dataset. This was conducted in R 4.0.5. As with the NNRM method, the data was split into 80% and 20% training and validation sets (Figure 9).
# Numerical Quantification of Controllability in the Null Space for Redundant Manipulators
## 2 Method
### Null Space Quality about Dynamic Consistency  
The null space quality could apply to the null space projector for other consistencies. For the other consistencies like dynamic [2] and stiffness [23], the null space projector can be changed. Although the joint relationship and limitation are changed by the different null space projectors, the null space quality can be obtained with the same method. So the null space quality could evaluate each null space projector.  
The null space projector for dynamic consistency is commonly used to control the redundancy because it guarantees not to interfere with the primary task in both the kinematics level and the dynamics level. The dynamically consistent null space projector \(\bar{N}_{H}^{T}\) is expressed as follows:  
\[\bar{N}_{H}^{T}=I-J_{H}^{T}\bar{J}_{H}^{T} \tag{16}\]  
\[\bar{J}_{H}=A^{-1}J^{T}\Lambda\]  
\[=A^{-1}J^{T}(JA^{-1}J^{T})^{-1} \tag{17}\]  
\(\bar{J}_{H}\) is a generalized inverse to minimize the kinetic energy. \(A\) is the \(n\times n\) joint space kinetic energy matrix. By substituting the \(\bar{N}_{H}^{T}\) to (7) instead of \(N_{H}^{T}\), the dynamically consistent null projected Jacobian \(\bar{J}_{H}\) can be written as:  
\[\bar{J}_{N}^{T}=\bar{N}_{H}^{T}\bar{J}_{L}^{T} \tag{18}\]  
The null space quality and ellipsoid of \(\bar{J}_{N}^{T}\) can be obtained by the same methods in Sections 2.2 and 2.3.
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 2 Materials and Methods
### Heart Rate Extraction
#### 2.4.4 Reference Heart Rate  
The reference heart rate was extracted using the single-lead ECG measurements. The R peaks of each patient's ECG were detected using RDECO [24] and processed into inter-beat intervals (IBIs). These IBIs were also divided into segments of 60 s, and the heart rate of each segment was calculated by taking the mean of the IBIs.
# Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques
## 4 Experimental Results
### Performance Measures  
We used four measures to evaluate the classification performance of the 28 multi-label classifiers built in this study: accuracy, precision, recall, and F1 score.  
* **Accuracy** measures the percentage of correctly classified observations out of the total number of observations, using the following formula: \[\text{Accuracy}=\frac{TN+TP}{(TP+FN+FP+TN)}.\]
* **Precision** measures the percentage of the correctly classified observations as positives out of the total classified positive observations, using the following formula: \[\text{Precision}\left(P\right)=\frac{TP}{(TP+FP)}.\]
* **Recall** measures the percentage of observations classified correctly as positive, out of the total actual positive observations using the following formula: \[\text{Recall}\left(R\right)=\frac{TP}{(TP+FN)}.\]
* **F1 Score** is the arithmetic mean over the harmonic mean of precision and recall calculated using the following formula: \[\text{F1 score}=\frac{1}{n}\sum_{x}F1_{x}=\frac{1}{n}\sum_{x}\frac{2P_{x}R_{x} }{P_{x}+R_{x}}.\]  
Although we report the four described measures, the F1 score is more relevant to the characteristics of our data set, as it combines both precision and recall.
## 3 Results and Discussion
### Evaluation of MIP and NIP Adsorption Capacity  
The amount of SLX bound to the polymer was determined by batch binding experiments. To describe and determine the adsorption properties of the MIP obtained, the adsorption isotherm data were matched with two types of adsorption isotherm models, namely Langmuir and Freundlich. The Langmuir and Freundlich models are general adsorption isotherm models to describe the adsorption process. For fitting results, we see that the MIP adsorption process is more suitable for the Freundlich model than the Langmuir model; however, NIP follows the opposite pattern. Figure 4 shows the fitting adsorption of MIP and NIP to the adsorption isotherm model.  
As shown in Table 2, the suitability of the MIP adsorption to the Freundlich adsorption model showed an R\({}^{2}\) value above 0.99. NIP is more likely to fit the Langmuir isotherm model with a convincing degree of similarity (R\({}^{2}\) > 0.99). The difference in the fitting results can be explained by the difference in the adsorption mechanism. For the Freundlich isotherm, the MIP adsorption process involves both surface diffusion and intraparticle diffusion as a result of the interaction of SLX with polymer binding sites; however, NIP adsorption is mainly dependent on surface diffusion [54]. This is also supported by the value of the heterogeneity index (m), which showed that the MIPs had a more heterogeneous distribution of binding sites (near zero) than the NIPs.  
In the previous study with the bulk polymerization method, the adsorption capacity of the polymer was 0.12, 0.05, 0.28, and 0.19 mg/g for MIP1, MIP2, MIP3, and MIP4, respectively. In the present study, the adsorption capacities with the precipitation method were increased, namely 0.46, 0.38, 0.89, and 0.70 mg/g for MIP1, MIP2, MIP3, and MIP4, respectively. This means that the MIPs produced with the precipitation method have adsorption properties that are better than in the previous method [7].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Polymer**} & \multicolumn{3}{c}{**Langmuir**} & \multicolumn{3}{c}{**Freundlich**} \\ \cline{2-7}  & **R\({}^{2}\)** & **K\({}_{\text{L}}\) (L/mg)** & **Q\({}_{\text{m}}\) (mg/g)** & **R\({}^{2}\)** & **m** & **a (mg/g)** \\ \hline MIP 1 & 0.9954 & 0.1409 & 2.8216 & 0.9998 & 0.5691 & 0.4640 \\ NIP 1 & 0.8289 & 0.0317 & 2.4850 & 0.9918 & 0.8025 & 0.0944 \\ MIP 2 & 0.9882 & 0.0770 & 4.6232 & 0.9994 & 0.7555 & 0.3801 \\ NIP 2 & 0.8859 & 0.0226 & 3.0450 & 0.9948 & 0.8593 & 0.0781 \\ MIP 3 & 0.9997 & 0.2721 & 3.6751 & 0.9997 & 0.2721 & 0.8908 \\ NIP 3 & 0.9958 & 0.9573 & 1.0446 & 0.9958 & 0.9573 & 0.0865 \\ MIP 4 & 0.999 & 0.346 & 2.8901 & 0.999 & 0.346 & 0.7025 \\ NIP 4 & 0.991 & 0.9714 & 1.0294 & 0.991 & 0.9714 & 0.0902 \\ \hline \end{tabular}
\end{table}
Table 2: Isotherm adsorption of MIP and NIP.
# Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing
## 3 Voucher-Based Addressing
### Threat Model  
In the projected threat model for the local network, threat actors are only interested in stealthy on-path attacks resulting from neighbor spoofing exploitation. Modeled threat actors are not concerned with network disruptions or denial-of-service attacks; they would prefer to remain quiet and unseen. For the most part, the success of an on-path attack arbitrating and examining unicast messages is dependent upon the threat actor remaining undiscovered on the path between two victim nodes in the first place. This model assumes that no two Link-Layer Identifiers (LLIDs; see Table 1) within the target broadcast domain can be the same value or be spoofed in the network without obvious disruptions to network activity. It also simultaneously assumes an insecure link layer.  
For external networks, the threat model includes risks to the privacy of an interface communicating off-link. Nodes can be remotely tracked, targeted, and even exploited through their unique, global unicast addresses if these addresses are not sufficiently rotated. This can be performed by some global adversary omniscient of all node traffic, or by some target infrastructure repeatedly contacted over some extended duration. Furthermore, if an address generation mechanism incorporates link-layer information and does not obscure it in some way, then attacks can be launched against addresses based on what might be revealed from the link-layer information. Address assignment schemes which do not encourage or permit regular primary address rotations are subjected to these threats and can be a valuable attack vector for targeting victims.
## 1 Introduction
Chemical substitutions of Cr by Sn, such as in CuCr\({}_{2.x}\)Sn\({}_{x}\)S\({}_{4}\) (\(x\) = 0.1 and 0.2), resulted in \(p\)-type conduction with electrical conductivity (\(\sigma\)) values of approximately 1 \(\times\) 10\({}^{3}\) S/m at room temperature (RT), revealing that CuCrSnS\({}_{4}\) exhibited semiconducting behavior with a band gap of approximately 0.9 eV [23]. In the case replacing Cr with Hf, CuCr\({}_{2.x}\)Hf\({}_{x}\)Se\({}_{4}\) exhibited metallic conduction, and the \(\sigma\) value increased by two orders of magnitude from \(x\) = 0.06 to \(x\) = 0.14 [24]. From the crystallographic and chemical information obtained from a single crystal sample of CuCr\({}_{1.1}\)-Sn\({}_{0.9}\)S\({}_{2.3}\)Se\({}_{1.7}\) a rational synthesis of a family of compounds with a chemical formula of CuCr\({}_{2.x}\)Sn\({}_{x}\)S\({}_{2.3}\)Se\({}_{1.7}\) and CuCr\({}_{2.x}\)Sn\({}_{x}\)S\({}_{1.7}\)Se\({}_{2.3}\) (\(x\) = 0.4, 0.6, and 1.0) was carried out to study the effect of the chemical replacement of Cr by Sn and Se by S on the physical properties. The present work describes the solid-state synthesis, crystallographic and electrical properties, and density functional theory (DFT) calculations to determine the influence of the chemical substitution of tin and chalcogen on the structure and physical properties of these compounds.
## 4 Results and Discussion
### Comparative Analysis of Different Optimization Models on DO Prediction Performance
The results show that the PSO has the best optimization effect on the BPNN and the worst optimization effect on the SVR, indicating that the PSO has a large variability in optimizing different base models and its optimization performance is unstable. Comparing the GA and GWO algorithms, the optimization accuracy and stability of the GWO for the two groups of basis models are better than that of the GA, indicating that the GWO has better model optimization performance. Therefore, in this paper we choose the SVR as the base model and the GWO as the optimization algorithm, and further adopt the DE algorithm for model optimization that propose the DE-GWO-SVR hybrid optimization model. Its prediction correlation coefficient achieves the highest value of R\({}^{2}\) = 0.94, and the error indicators MSE, MAE, and RMSE all achieve the corresponding lowest values of MSE = 0.108, MAE = 0.2629, and RMSE = 0.3293, respectively, which improves the prediction accuracy by 17.02% compared with that of the SVR base model.