esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions and Future Perspectives  
This study has successfully examined the capability of extracted bagasse (_Sacharum__officnarum_) to be used as a natural coagulant in replacing the use of the current chemical coagulants. Bagasse shows promising coagulation performance with turbidity removal exceeding 90%. Bagasse is comprised of high polysaccharide content, making it the main active coagulant agent. The study also showed that bagasse with high polysaccharide content requires a lower coagulant dosage. The coagulation mechanism is interparticle bridging and not charge neutralization.  
From this finding, future perspectives should include the application of extracted bagasse as a natural coagulant in the removal of various other pollutants such as colour, biochemical oxygen demand (BOD) and heavy metals from other sources of wastewater such as palm oil mill effluent (POME). Besides, the coagulation process relies heavily on the operating conditions and the operating conditions should be optimized for optimum removal of pollutants.  
Figure 6: Turbidity removal using extracted bagasse as a natural coagulant with different coagulant dosages at a constant of pH 6.  
Figure 7: Turbidity removal using extracted bagasse as a natural coagulant on different pH at constant dosage of 50 mg/L.  
**Author Contributions:** Data curation, M.B.B.; Writing--original draft, M.B.B.; Conceptualization, N.S.Z., A.S. and R.B.; Funding acquisition, N.S.Z.; Project administration, N.S.Z.; Writing--review and editing, N.S.Z., A.K., N.H., A.S. and R.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Universiti Teknologi Malaysia (UTM) (QJ130000.2451.09G79).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors thank the Universitas Nahldlattul Ulama Surabaya for realizing the current work. The authors also thank [PERSON], [PERSON], and [PERSON], [PERSON] for their technical support during the study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Quantum Approximation for Wireless Scheduling
## 2 Preliminaries
### Quantum Approximate Optimization Algorithm (QAOA)  
QAOA is one of the well-known NISQ optimization algorithms to combat combinatorial problems [5; 6; 7; 8]. QAOA formulates \(H_{P}\) (i.e., problem Hamiltonian) and \(H_{M}\) (i.e., mixing Hamiltonian) from the objective function \(f(y)\); and then generates the parameterized states \(\ket{\gamma,\beta}\) by alternately applying the \(H_{P}\) and \(H_{M}\) on initial state \(\ket{s}\). Here, \(f(y)\), \(H_{P}\ket{y}\), \(H_{M}\), and \(\ket{\gamma,\beta}\) are defined as follows:  
\[f(y) \triangleq f(y_{1},y_{2},...,y_{n}), \tag{8}\] \[H_{P}\ket{y} \triangleq f(y)\ket{y},\] (9) \[H_{M} \triangleq \sum_{k=1}^{n}X_{k},\] (10) \[\ket{\gamma,\beta} \triangleq e^{-i\beta_{P}H_{M}}e^{-i\gamma_{P}H_{P}}\cdots e^{-i\beta_{2} H_{M}}e^{-i\gamma_{2}H_{P}}e^{-i\beta_{1}H_{M}}e^{-i\gamma_{1}H_{P}}\ket{s}, \tag{11}\]  
where \(n\in\mathbb{Z}^{+}\), \(p\in\mathbb{Z}^{+}\), and \(X_{k}\) is the Pauli-\(X\) operator applying on the \(k\)th qubit; \(\gamma\) and \(\beta\) are learnable parameters that tune the result. Note that \(H_{P}\) encodes \(f(y)\) in Equation (9), operating diagonally in the \(n\)-qubit quantum computational basis states [20].  
In QAOA, through iterative measurement of \(\ket{\gamma,\beta}\), the expectation value of \(H_{P}\) should be taken; then, eventually, the samples of \(f(y)\) should be computed as follows [5]:  
\[\left\langle f(y)\right\rangle_{\gamma,\beta}=\ \left\langle\gamma,\beta|H_{P}| \gamma,\beta\right\rangle. \tag{12}\]  
The optimal values of the parameters \(\gamma\) and \(\beta\) can be obtained via classical numerical optimization methods such as gradient descent [21; 22]. Therefore, the solution can be computed from Equation (12) via the parameters obtained. Thus, QAOA is a hybrid quantum-classical optimization algorithm in which proper Hamiltonian design and the discovery of good parameters in a classical optimization loop are key [20; 23; 24].
## 3 Results and Discussion
### Dried Juice and Pomace
#### 3.3.8 Content of Betalin Pigments in Dried Juice and Pomace  
The content of betanin pigments in the spray-dried juices (DASDJC) was in the range of 418-496 mg/100 g d.m., and the content of vulgaxanthins was 292-400 mg/100 g d.m. (Table 2). In the case of freeze-dried samples (FDP), the betanin content varied from 542 to 619 mg/100 g d.m, and vulgaxanthins ranged from 241 to 285 mg/100 g d.m. The degradation of pigments was observed in all of the dried samples regardless of the method used, unlike in the raw materials. However, considering that the freeze-dried pomace, from which some betalains were removed during processing along with the juice, it can be stated that the freeze-drying process itself caused smaller losses of these compounds. Moreover, regardless of the method, the drying process caused greater losses of red pigments than yellow ones. It can be observed that in the spray-dried samples, both betanin and vulgaxanthin pigments were the highest in the variants that were not pretreated with a PEF (DASDJC0) and when the highest amount of pulses was applied (DASDJC2). Concerning the freeze-dried samples, it was noted that PEF pretreatment did not influence the betalain content. This study does not support previous research in this area. In fact, [PERSON] et al. [52], who freeze-dried beetroots pretreated with a PEF, noted that the samples that were treated with a PEF showed a higher pigment yield, which was the effect of increased permeability of the cells.
# An Overview of Bioactive Flavonoids from _Citrus_ Fruits
## 5 _Citrus_ Flavonoid Extraction Techniques
### Non-Conventional Extraction Techniques
#### 5.2.2 Supercritical Fluid Extraction (SFE)  
Supercritical extraction is a modern technique that uses gases that have exceeded their critical pressure and temperature, resulting in a fluid with qualities in between a gas and a liquid [60]. Supercritical CO\({}_{2}\) extraction (using CO\({}_{2}\) as a solvent, mainly due to its adaptability, availability, and low cost), is a preferred approach for extracting numerous active compounds. Despite the fact that any gas can be employed as a supercritical fluid [61] because flavonoids are polar molecules, SFE requires the presence of a cosolvent, such as ethanol or methanol [62]. A study was carried out to extract nobiletin and tangeritin from _C. depressa_ var Hayata. The authors tested both methanol and ethanol as solvents. Under the conditions reported in their paper, [PERSON] et al. [36] found that SFE provides a higher amount of flavonoids (+7%) than UAE.
###### Abstract  
Glaucoma is a common eye disease that damages the optic nerve and leads to loss of vision. The disease shows few symptoms in the early stages, making its identification a complex task. To overcome the challenges associated with this task, this study aimed to tackle the localization and segmentation of the optic disc, as well as the classification of glaucoma. For the optic disc segmentation, we propose a novel metaheuristic approach called Grey Wolf Optimization (GWO). Two different approaches are used for glaucoma classification: a one-stage approach, in which the whole image without cropping is used for classification, and a two-stage approach. In the two-stage approach, the optic disc region is detected using the You Only Look Once (YOLO) detection algorithm. Once the optic disc region of interest (ROI) is identified, glaucoma classification is performed using pre-trained convolutional neural networks (CNNs) and vision transformation techniques. In addition, both the one-stage and the two-stage approaches are applied in combination with the pre-trained CNN using the Random Forest algorithm. In segmentation, GWO achieved an average sensitivity of 96.04%, a specificity of 99.58%, an accuracy of 99.39%, a DICE coefficient of 94.15%, and a Jaccard index of 90.4% on the Drishti-GS dataset. For classification, the proposed method achieved remarkable results with a high-test accuracy of 100% and 88.18% for hold-out validation and three-fold cross-validation for the Drishti-GS dataset, and 96.15% and 93.84% for ORIGA with hold-out and five-fold cross-validation, respectively. Comparing the results with previous studies, the proposed CNN model outperforms them. In addition, the use of the Swin transformer shows its effectiveness in classifying glaucoma in different subsets of the data.  
optic disc segmentation; glaucoma disease; human retinal images; pre-trained CNNs; grey wolf optimization; YOLO; swin transformer Article
# Identification of Ratholes in Desert Steppe Based on UAV Hyperspectral Remote Sensing
## 6 Conclusions  
Grassland ecosystems are an important component of Earth's terrestrial ecosystem, which are tasked with the significant mission of regulating global climate change and precipitation. The need to dynamically monitor grassland degradation and rodent damage is urgent. Currently, the existing vegetation index methods cannot meet the monitoring requirements for desert steppe rodent information and are not effective in extracting rodent hole characteristics in desert steppes. This study collected spectral data of rodent holes in desert steppes using a low-altitude unmanned aerial vehicle hyperspectral remote sensing platform. Based on PCA, the rodent burrow images were extracted, and the threshold interval for the holes was calculated as [1.126505, 2.195495], while the threshold interval for non-burrow areas was [\(-\)0.019260, 0.631748]. After validation, the overall classification accuracy reached 97%, and the kappa coefficient reached 0.896. The successful application of this method effectively overcomes the difficulty in identifying rodent holes in desert steppes and improves the identification accuracy. In field surveys of grassland rodent infestation, this method can save labor costs, solve accuracy problems in the identification of fine features based on satellite remote sensing, and, at the same time, enrich the technical means of geospatial data surveys based on low-altitude remote sensing platform application. This method provides a technical means for future research on rodent holes in desert steppes and can provide assistance in monitoring desert steppe degradation, preventing and controlling grassland rodent pests, and preventing plague.  
Conceptualization, J.D. and X.G.; methodology, Y.B. and X.G.; software, X.G.; formal analysis, X.G.; investigation, X.G.; data curation, X.G.; writing--original draft preparation, X.G.; writing--review and editing, J.D. and Y.B.; supervision, J.D. and Y.B.; project administration, J.D.; funding acquisition, J.D. and Y.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, the Key Projects of Higher Education Research in Inner Mongolia Autonomous Region, Natural Science Foundation of Inner Mongolia Autonomous Region Joint Fund Project and Scientific Research Projects of Higher Education Institutions in Inner Mongolia Autonomous Region, grant number No. 31660137, NJZZ23037, 2023 LHMS06010 and NJZY21518. The APC was funded by No. 31660137.  
Not applicable.  
The authors declare no conflict of interest.
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 4 Security Scheme
### Registration Phase  
In the registration phase, the legitimate nodes, being the nodes with security material generated by the TTP, contact the NMM to construct a common shared secret key. To do so, the \(N_{i}\) first selects a random value \(R_{1}\) at timestamp \(T_{1}\) and computes the following two values \(M_{1}\) and \(M_{2}\).  
\[M_{1} = R_{1}\oplus H(A_{i})\] \[M_{2} = H(H(ID_{NMM}\|H(x))\|H(A_{i})\|R_{1}\|T_{1})\]  
The message \((B_{i},M_{1},M_{2},T_{1})\) is sent to the NMM.  
Upon arrival, the NMM verifies whether the current timestamp \(T_{2}\) is sufficiently close to \(T_{1}\). If so, the NMM derives the random value \(R_{1}\) using its stored secret \(H(y)\).  
\[A_{i} = H(y)\oplus B_{i}\] \[R_{1} = M_{1}\oplus H(A_{i})\]  
To check the integrity of \(R_{1}\), the NMM will substitute the parameters \(R_{1},T_{1},H(A_{i})\) together with the secret key material \(H(ID_{NMM}\|H(x))\) into the hash value \(H(H(ID_{NMM}\|H(x))\|H(A_{i})\|R_{1}\|T_{1})\). If the result is equal to the received \(M_{2}\), the NMM continues the process. Otherwise, the communication is interrupted. Next, the NMM chooses a second random value \(R_{2}\) and computes  
\[k_{i} = H(R_{1}\|R_{2}\|T_{1}\|T_{2}\|B_{i}\|ID_{NMM})\] \[M_{3} = R_{2}\oplus B_{i}\oplus H(A_{i})\] \[M_{4} = H(k_{i}\|H(ID_{NMM}\|H(x))\|T_{1}\|T_{2})\]  
Please note that \(k_{i}\) represents a common shared secret key between \(N_{i}\) and the NMM. The message \((T_{2},M_{3},M_{4})\) is sent from the NMM to the node \(N_{i}\).  
After receiving this message, the node first verifies the freshness of the communication, i.e., if the current timestamp \(T_{3}\) is not much larger than \(T_{2}\). If so, it continues and derives \(R_{2}\) from \(M_{3}\). Using this value, the key \(k_{i}\) can be computed and its integrity verified with the parameter \(M_{4}\).  
As a result, both \(N_{i}\) and the NMM possess a mutual authenticated shared key, which is used to communicate the interests and capabilities of \(N_{i}\) to the NMM.  
A summary of the registration phase can be found in Figure 2.
## 5 Experiment
### Experiment Setting
#### 5.1.1 Dataset Preprocessing  
In our experiments, we used the Telecom Italia dataset [41], which contains Call, Internet, and SMS data collected in Milan from 1 November 2013 to 1 January 2014. Following [14], we aggregated the data from 10-minute intervals into 1-hour increments. We designated the last 7 days of data as the test set and used the remaining data for training. Additionally, we applied Min-Max normalization to scale the values between 0 and 1, ensuring smoother model training.  
Originally, the Telecom Italia dataset is structured as a 100 \(\times\) 100 grid of regions. However, previous methods have used different optimal cell sizes for training and predicting network traffic. For example, STDenseNet [14] uses a 20 \(\times\) 20 grid size, whereas HSTNet [15] uses a 100 \(\times\) 100 grid data. Therefore, to ensure a fair comparison, we conducted experiments with both 20 \(\times\) 20 and 100 \(\times\) 100 grid sizes and evaluated the prediction performance accordingly. Also, to ensure consistency with the experimental settings of [15], we added the in-channel and out-channel to form a single channel in 100 \(\times\) 100 grid experiments, unlike the 20 \(\times\) 20 grid experiment.  
Additionally, we also used the dataset collected in Trentino [41, 47]. While the Milan dataset records data in city area, the Trentino dataset handles broader area, so Trentino dataset has sparser data distribution than Milan one. By using this, we can evaluate robustness of our method when used in environments with limited data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Configuration** \\ \hline epoch & 100 \\ learning rate & 0.01 \\ LR decay scale & 0.1 \\ LR decay step & 50, 75 epochs \\ optimizer & Adam \\ \hline dense block & 3 \\ input data set (h/day) & 3/3 \\ \hline dataset & Milan, Trentino \\ Model & STDenseNet, HSTNet, MVSTGN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of experiments’ setup.
## 3 Numerical Calculation and Discussion of Results  
### Analysis of the Convergence and Validity of the Model  
The purpose of this section is to verify the convergence and validity of the model. According to [PERSON] et al.'s research [35], achieving convergence using the Gram-Schmidt orthogonal polynomial and artificial spring methods necessitates a high error truncation. To overcome this issue, a boundary function is used to determine the boundary conditions and reduce the value of \(N\). In this paper, the convergence of the vibrational shape function is evaluated by calculating the error of the first \(N\) terms of the vibrational shape function. The results are presented in Figure 3. The computational results demonstrate that the natural frequency and modal loss factor gradually converge as the truncation number \(N\) increases. Specifically, when \(N=10\), the errors of both the natural frequency and the modal loss factor are less than \(10^{-5}\), meeting the accuracy requirements of this paper. Hence, in subsequent work, a truncation number of \(N=10\) will be employed. The error of the natural frequency and modal loss factor is calculated in the following equation:  
\[e_{f}=\left|f_{m,k}-f_{m,k-1}\right|,e_{q}=\left|\eta_{m,k}-\eta_{m,k-1}\right| \tag{47}\]  
This paper employs \(F\)-\(F\) boundary conditions and sets \(k_{i}=0\) (\(i=1\), \(2\), \(3\), \(4\)) to ensure effective validation. The proposed method is verified by comparing the calculated parameters \(R=0.0635\) m, \(L\)= 0.502 m, \(h=0.0635\) m, \(E=2.1\)\(\times\)\(10^{11}\) Pa, and \(\mu=0.28\) with the results obtained by [PERSON] et al. [36] and the finite element method (FEM), respectively. The natural frequencies of the MRCS are obtained by substituting the calculated parameters into Equation (34), and the comparative verification results are presented in Table 1. The errors produced by all three methods are less than 5%, indicating that the proposed model is valid.  
Furthermore, the proposed method has a low computational cost as only the integration results need to be calculated in advance, and the integral calculation is separable. Hence, it can significantly reduce computational resource consumption and prevent the waste of resources. This saves much computation time, especially in the case of the high term number \(N\).
## 3 5G RCS Message Filtering Models and Methods
### The CNN Model for RCS Message Filtering
#### 3.3.2 2D Convolution for Relevant Features Extraction  
The convolution operation is applied to the RCS message matrix \(M\in\mathbb{R}^{m\times d}\) and kernel \(F\), \(F\in\mathbb{R}^{d\times k}\), where \(k\) is the kernel region. In this case, the CNN model has four convolutional layers, in which the first convolutional layer has 16 filters, the second one has 32 filters, the third one contains 64 filters and the fourth one contains 128 filters. For each convolutional layer, different square filter kernel sizes are chosen, that is, \(2\times 2\), \(3\times 3\), \(4\times 4\) and \(5\times 5\). For each CNN, filters are involved to yield a matrix of relevant features. In this case, since the RCS service has just started, the current RCS messages usually have a few cards filled with contents and other cards remain blank. To emphasize the features on the matrix edge, a padding value, represented by \(p\), is applied to expend the matrix \(M\) and the yielded feature matrix for each CNN. The stride value, represented by \(s\), is also specified with 1.  
Therefore, in the process of convolution operation, the kernel region first slides over the matrix \(M\), strides one line for each step and produces the feature matrix with size \(16\times 15\), calculated by the following formula when \(padding=1, kernelsize=2\) and \(stride=1\):  
\[out=((in+2\times p-k)/s+1). \tag{2}\]  
Then, the features go through an activation function. Rectified linear unit (ReLU) is applied.  
\[f_{ReLU}(x)=max(0,x). \tag{3}\]It returns the input value if it is positive and returns 0 for negative inputs.
## 1 Introduction
In this paper, we experimentally demonstrate optically delaying an RF-LFM pulse by modulating the LFM pulse on optical carriers from a Kerr comb and sending the modulated carriers through a concatenation of off-the-shelf linearly chirped FBG (LC-FBG) and chirped-and-sampled FBG (CS-FBG). When we measure the PSL of a delayed LFM pulse with 1 GHz bandwidth at a 10 GHz center frequency, the results show a PSL of >34.4 dB, only a few dBs worse than the ideal. We present these results along with a complete analysis of the system characteristics and compare the performance with a system using a bank of discrete laser sources instead of the comb, with PSL values better than 41 dB. The rest of the paper is organized as follows. Section 2 presents the concept. Section 3 presents the experimental setup. Section 4 presents the characterization of the optical delay system. Section 5 presents the experimental results for measuring the PSL after delaying the signal. Finally, Section 6 presents the conclusion.
# NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
## 3 Data Services
### Data Subsetting, Format Conversion and Other Data Services  
Spatial data subsetting services (e.g., Table 4) are necessary for many satellite- or model-based datasets to reduce the data download volume, speed up the data download process and increase the server performance, especially because most datasets are global and spatial resolution for some datasets can be high. For example, each IMERG half-hourly dataset contains over 340,000 files, as of this writing, with the 0.1 deg. \(\times\) 0.1 deg. spatial resolution available from June 2000 onwards. It can be time consuming to download all the original archived files due to the large volume (over 3 TB). Spatial subsetting services are especially helpful to considerably reduce the data download volume, thus time. Likewise, many GPM Level-2 datasets are quite large in file size such as the GPM dual-frequency radar dataset (over 8 TB for a single dataset collection).  
The data subsetting services (Table 4) at GES DISC contain different subsetting options, depending on dataset level. Three download methods are currently available: original files, OPeNDAPand the GES DISC subsetter. The option for downloading original files allows downloading files straight from the dataset archive and no subsetting or any change to the original files is available. The second option is to use OPeNDAP for parameter and spatial subsetting options and the output format is NetCDF (Network Common Data Form) or ASCII. The last subsetting option has three choices for spatial subsetting: a single point, a circular area and a rectangular latitude-longitude box. The output format is HDF5 for Level-2 and NetCDF for Level-3. For Level-3 datasets, the last available option provides conversion to over 30 different grid structures (e.g., GPCP, TMPA) with four interpolation methods (bilinear, bicubic, distance-weighted averaging, and nearest neighbor).  
Other data services listed in Table 4 include online archive, Giovanni, OPeNDAP, GrADS Data Server (GDS), OGC Web map services, and data rods. Original archived data can be directly downloaded from HTTPS. OPeNDAP is quite popular in research and application communities and many tools can access data through this protocol such as Panoply. GDS is popular in the meteorological community and the data can be imported in Panoply as well. OGC Web Map Services provide convenience (e.g., WMS, WCS) for users in the GIS community. For users who want long time series data, data rod services [49] can be very helpful to avoid downloading a large amount of data. For example, there are over 340,000 half-hourly files for IMERG Final Run and downloading them for creating time series can be a difficult job. Data rods for IMERG are currently under development.