esa-sceva/satcom-chunk-collection huggingface dataset:
# Crude Oil Price Forecasting Model Based on Neural Networks and Error Correction
## 3 Experiments
### Experimental Evaluation of the Proposed Model
#### 3.3.3 Comparison with Diverse Decomposition Methods  
To fully evaluate the effectiveness of decomposition methods, we compared the forecasting results of model 4 and those of the model employing different decomposition algorithms. In addition, we also compared EWT with WPD and VMD to prove the superiority of EWT. Table 7 visually shows the evaluation indicators of experimental models, Table 8 shows the improvement of the model 4 on forecasting performance based on different decomposition methods and Figure 6 describes the comparison of scatter points predicted by experimental models. Table 9 shows MSE1, MSE2, and MSE3, which are the prediction results of each predictor on the decomposed data before using the ensemble model. From these experimental results we can conclude:  
1. The prediction performance of the proposed model utilizing the decomposition algorithm is more excellent than that of the model without the algorithm. The forecasting performance of models using the algorithm is improved by more than 40%. This shows decomposition methods greatly reduce the high fluctuation of raw data and the prediction errors. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Indices** & **Series \#1** & **Series \#2** & **Series \#3** \\ \hline \multirow{3}{*}{Model 4 vs. TCN} & P\({}_{\text{MAE}}\) (\%) & 4.5966 & 10.4114 & 6.9076 \\  & P\({}_{\text{MAPE}}\) (\%) & 4.7445 & 7.6672 & 1.5768 \\  & P\({}_{\text{RMSE}}\) (\%) & 3.9734 & 18.3111 & 10.7682 \\ \hline \multirow{3}{*}{Model 4 vs. GRU} & P\({}_{\text{MAE}}\) (\%) & 7.3047 & 5.2671 & 12.6604 \\  & P\({}_{\text{MAPE}}\) (\%) & 6.9521 & 5.9787 & 14.9405 \\ \cline{1-1}  & P\({}_{\text{RMSE}}\) (\%) & 3.5529 & 7.0649 & 18.5929 \\ \hline \multirow{3}{*}{Model 4 vs. ESN} & P\({}_{\text{MAE}}\) (\%) & 11.8343 & 7.6726 & 4.9572 \\  & P\({}_{\text{MAPE}}\) (\%) & 13.2524 & 6.8820 & 1.1698 \\ \cline{1-1}  & P\({}_{\text{RMSE}}\) (\%) & 10.4427 & 7.0649 & 6.7997 \\ \hline \hline \end{tabular} Note. Model 4 = SARSA-TCN-GRU-ESN. This table shows the promoting percentages of evaluation indicators for the SARSA-based ensemble compared to different single prediction methods. Series #1, Series #2, and Series #3 refer to three WTI crude oil price datasets with different time spans.  
\end{table} 
Table 6: Promoting percentages for ensemble methods. Figure 5: Scatter of the prediction results for SARSA and different heuristic ensemble methods. * In all experiments, the prediction errors of EWT are the lowest among the three decomposition algorithms. Compared with two other decomposition algorithms, EWT could effectually decrease the nonlinearity for raw data and enhance forecasting performance. The main reason is that EWT could adaptively decompose the raw series into multiple subsequences, which enhances the ability of the ensemble method to analyze volatility characteristics.
## 6 Performance Evaluation and Discussion
### Simulation Results
#### 6.2.3. Energy Efficiency and Network Lifetime
Moreover, this also makes mean field based solutions a potential candidate for solving complex problems in ultra-dense networks. Figure 10: Target SINR impact on MTCDs network. Figure 9: Impact of device density on the average SINR.
# Nutritional Composition and Biological Properties of Sixteen Edible Mushroom Species
## 4 Sixteen Edible Mushrooms of Greece
### Agaricus blazei  
_Agaricus blazei_ (_A. blazei_), also known as sun mushroom, belongs to the family Agaraceae and is widely used both as an edible mushroom and for medicinal purposes due to its contents in bioactive compounds, such as phenolic compounds and polysaccharides [22; 23]. The chemical composition of dried powder of _Agaricus blazei_ has been studied and it has been found that it contains 379.24 kcal/100 g DM, 59.42 g/100 g DM carbohydrates, 31.29 g/100 g DM proteins, 1.82 g/100 g DM fat, and 7.47 g/100 g DM ash [22]. In the analyses of the University of Thessaly, it was found to contain 335 kcal/100 g DM, 48.4 g/100 g DM carbohydrates, 28.6 g/100 g DM proteins, 1.6 g/100 g DM fat, and 8.4 g/100 g DM ash. When it comes to the fatty acids distribution, 73.58% comes from PUFA, 24.39% from saturated fatty acids (SFA), and 2.03% from monounsaturated fatty acids (MUFA), while the main fatty acids are linoleic, palmitic, stearic, and oleic acids [22]. Regarding the phenolic compounds composition, _A. blazei_ has been shown to contain p-hydroxybenzoic acid, _trans_-p-couramic acid, and cinnamic acid [22]. Furthermore, several studies have been conducted to evaluate the medicinal and biological properties of _A. blazei_ and it has been found that this mushroom possesses antioxidant [22], anti-viral [24], anti-diabetic [25], immunomodulatory [26], and anti-cancer properties [27]. Regarding its hypoglycemic effects, _A. blazei_ has been shown to significantly suppress the increases in fasting plasma glucose and hemoglobin A1c levels, reduce superoxide production from leukocytes, as well as improve the mass of pancreatic \(\beta\)-cells in streptozotocin-induced diabetic rats [25]. Another in vivo study regarding _A. blazei_ immune responses in leukemia mice showed that _A. blazei_ promoted natural killer cell activity and phagocytosis by macrophage/monocytes and enhanced cytokines such as interleukin (IL)-1\(\beta\), IL-6, and interferon-\(\gamma\) levels [26]. Lastly, the health claims proposed for _Agaricus blazei_ dietary supplements are: \"Helps the natural defences contributing to a normal immune response,\" \"helps the body to resist biologic insults,\" \"supports the immune system,\" and \"is rich in beta glucans that contributes to the immune activity\" [28].
# 3D Volumetric Analysis and Anatomical Considerations for
###### Abstract  
The aim of this study is to evaluate anatomical considerations and assess the volume of the maxillary sinus bone graft. There were sixty-three patients (eighty-three sinuses) who had taken CT scans for implant surgery. Patients included those whose height of the residual alveolar bone was less than 5 mm. The position of posterior superior alveolar artery, the thickness of the maxillary sinus wall, and the volume of the maxillary sinus according to the amount of sinus floor elevation were measured. The mean vertical distance of posterior superior alveolar artery was 11.91 \(\pm\) 4.79 mm from 3.03 mm to 24.05 mm. The mean thickness of the lateral wall was 1.71 \(\pm\) 0.55 mm in the range of 0.74 mm to 3.93 mm. The volume of 3 mm, 5 mm, 7 mm, and 10 mm from the sinus floor was 0.173 \(\pm\) 0.11 cm\({}^{3}\), 0.526 \(\pm\) 0.25 cm\({}^{3}\), 1.068 \(\pm\) 0.43 cm\({}^{3}\), and 2.184 \(\pm\) 0.74 cm\({}^{3}\) on average, respectively. The knowledge of the posterior superior alveolar artery position, the lateral wall thickness, and the volume of the maxillary sinus can help the clinician for sinus bone graft.  
one beam computed tomography; maxilla; maxillary artery; maxillary sinus; sinus bone graft +
Footnote †: journal: Applied Sciences
###### Abstract  
Optical communication is becoming more prevalent in orbit due to the need for increased data throughput. Nanosatellites, which are satellites that typically weigh less than 10 kg, are also becoming more common due to lower launch costs that enable the rapid testing of technology in a space environment. Nanosatellites are cheaper to launch than their larger counterparts and may be a viable option for communicating beyond Earth's orbit, but have strict Size, Weight, and Power (SWaP) requirements. The Miniature Optical Communication Transceiver (MOCT) is a compact optical transceiver designed to provide modest data rates to SWaP constrained platforms, like nanosatellites. This paper will cover the optical amplifier characterization and simulated performance of the MOCT amplifier design that produces 1 kW peak power pulses and closes three optical links which include Low Earth Orbit (LEO) to Earth, LEO to LEO, and Moon to Earth. Additionally, a benchtop version of the amplifier design was constructed and was able to produce amplified pulses with 1.37 W peak power, including a 35.7% transmit optics loss, at a pump power of 500 mW. Finally, the modulator, seed laser, amplifier, receiver, and time-to-digital converter were all used together to measure the Bit Error Ratio (BER), which was 0.00257 for a received optical peak power of 176 nW.  
optical communication; laser; nanosatellite; CubeSat; EDFA; transceiver; PPM; BER
# Comparison of 2- and 3-Dimensional Cultured Periodontal Ligament Stem Cells; a Pilot Study
## 2 Materials and Methods
### Viability Analyses Using Cell Counting Kit-8 and LIVE/DEAD Cell Viability Assays  
The Cell Counting Kit-8 assay (CCK-8; Dojindo Molecular Technologies, Kumamoto, Japan) and LIVE/DEAD assay (LIVE/DEAD Viability/Cytotoxicity Kit for mammalian cells; Invitrogen, Carlsbad, CA, USA) were used to quantify the cell proliferation ability. The CCK-8 assay is based on the activation of 1-methoxy phenazinium methylsulfate by nicotinamide adenine dinucleotide phosphate (NADP) and nicotinamide adenine dinucleotide phosphate hydrogen (NADPH) produced by dehydrogenase in the metabolism of living cells. Water-soluble tetrazolium salt, 2- (2-methoxy-4-nitrophenyl), -3- (4-nitrophenyl), -5- (2,4-disulfophenyl), -2H tetrazolium, and monosodium salt (WST-8) were reduced to orange formazan, therefore, the amount of WST-8 formazan produced by the dehydrogenase in the cell is directly proportional to the number of viable cells, and the proliferation ability of the cells is quantitatively examined.  
The LIVE/DEAD assay quantifies living and dead cells by measuring the esterase activity and plasma membrane integrity of the cells. The non-fluorescent dye, calcein AM, converts to a fluorescent calcein that fluoresces in the presence of esterase in living cells, producing a uniform green fluorescence. Ethidium homodimer-1 enters dead cells through damaged membranes and binds with nucleic acids to produce a red fluorescence.  
First, the 2D- and 3D-cultured PDLSCs were seeded at a density of 1.0 \(\times\) 10\({}^{5}\) per well on a 6-well plate and subjected to the LIVE/DEAD assay on days 3, 5, and 7. The images were taken with a fluorescence microscope (IX71; Olympus, Tokyo, Japan). Meanwhile, the CCK-8 assay was performed on days 1, 3, 5, and 10, and the absorbance was measured at 450 nm using a Benchmark Plus Multiplate Spectrophotometer (Bio-Rad, Hercules, CA, USA).
# Modeling User Acceptance of In-Vehicle Applications for Safer Road Environment
## 3 Research Model and Hypotheses Development
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Research Questions** & **Hypothesis** \\ \hline Q1: What relationship exists between the PU and EU variables of the research model? & H1: User-perceived ease of use (EU) of in-vehicle applications positively affects perceived usefulness (PU) of the applications. \\ \hline Q2: What influences exist between PU and EU with the mediating variable (AT) in the research model? & H2: User-perceived usefulness (PU) of in-vehicle applications positively affects their attitude toward the applications (AT). \\ \hline Q3: How does user attitude (AT) impact the user intention to use the in-vehicle applications (BI)? & H4: User attitude (AT) toward the in-vehicle applications positively affects their intention to use the application (BI). \\ \hline Q4: What influences exist between PU and EU with the target variable (BI) in the research model? & H5: User-perceived usefulness (PU) of in-vehicle applications positively affects their intention to use the applications (BI). \\ \hline Q5: What is the impact of social influence (SI) on user-perceived usefulness (PU) and perceived ease of use (EU) of the in-vehicle applications? & H7: Social influence (SI) positively influences drivers’ perceived usefulness (PU) of the applications. \\ \hline Q6: How does trust (T) influence perceived usefulness (PU) and perceived ease of use (EU) of the in-vehicle applications among users? & H8: Social influence (SI) positively influences drivers’ perceived usefulness (PU) of the applications. \\ \hline Q7: How do system characteristics (SCs) influence perceived usefulness (PU) and perceived ease of use (EU) of the in-vehicle applications among users? & H9: Trust (T) positively influences drivers’ perceived usefulness (PU) of the applications. \\ \hline Q8: How do personal characteristics (PCs) influence trust (T), social influence (SI) and system characteristics (SCs) of the in-vehicle applications among users? & H11: System characteristics (SCs) positively influence users’ perceived usefulness (PU) of the in-vehicle applications. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Research questions and hypotheses.
## Appendix A Deviation in AEP Using Lavd Assuming a Symmetrical Wind Distribution  
It has been demonstrated that a symmetrical wind distribution can be used without additional errors if LAPD is used. If LAVD is used, this is also valid for wind farms wind PS, meaning that if the entire layout is rotated by 180\({}^{\circ}\) about its center, it will result in the same layout. This is, e.g., the case of rhomboidal-shape OWFs, such as HR1. For the sake of space, the demonstration is not included. Nevertheless, it will be seen that, in general, the error considering a symmetrical wind distribution is meaningless.  
Several tests have been conducted on various wind farms (see Figure 11) to evaluate the deviation resulting from this simplification:  
1. Horns Rev 1 (HR1), characterized by a uniform distribution in rows and columns, thus exhibiting PS.
2. Horns Rev 2 (HR2), featuring a uniform distribution without PS.
3. Anholt is an offshore wind farm with an irregular shape.  
Wind conditions at Anholt, HR1, and HR2 (assumed to be equal to HR1), as well as the power curve for the installed wind turbines, are provided in Tables 11 and 12.  
The resulting comparison is presented in Table 13. As can be seen, the differences are negligible; therefore, it is not practical to use the original expression (4). Instead, evaluating half of the turbine interactions reduces the computation time by half.
## 3 Results and Discussion
### Radiation Pattern  
Thus, to understand the radiation behavior of the antenna well, the far-field radiation patterns are shown in Figure 11 at the resonance frequency both for the E-plane and H-plane. During the simulations and measurements, one of the antenna ports was activated with a load of 50 ohms. The proposed antenna showed near-stable E-plane directional radiation patterns close to resonance. Also, the H-plane radiation pattern at 5.8 GHz was again bidirectional, with a distinctly recognizable and integrated pattern.  
These results show that the developed antenna provides acceptable radiation characteristics in the studied frequency bands to be used in practical systems or measurement setups, as shown in Figure 3c. From the stable H-plane pattern, the usefulness of the antenna in providing directional coverage as required in certain application environments is evident, while the E-plane patterns to different frequencies give a glimpse of its directional pattern at the higher frequencies.
# FLGQM: Robust Federated Learning Based on Geometric and Qualitative Metrics
## 3 Results and Discussion
### Experimental Setup
#### 3.1.2 Poisoning Attack  
In our experiment, for data poisoning attacks, we focus on the label flipping attack. Regarding local model poisoning attacks, we focus on the LIE attack, Fang attack, and AGRT attack.  
**Label flipping attack**: [37]: For each sample of each malicious client, we cyclically shift the corresponding label \(l\) to \((l+1)modY\), where \(Y\) represents the total number of labels (e.g., \(Y=10\) in the MNIST dataset) \(l\in 1,\ 2,\ldots,Y\).  
**LIE attack**: [9]: The Little Is Enough (LIE) attack adds a little noise on the model that is the average of all uploaded local models in the non-adversarial setting. The perturbation used in the attack is carefully chosen to be large enough to significantly impact the global model but small enough to avoid detection by the Byzantine Robust Aggregation algorithm.  
**Fang attack**: [8]: The malicious client crafts poisoned local model updates that are intentionally opposite to the benign updates, allowing them to bypass the defense mechanism of Krum.  
**AGRT attack**[13]: The goal of the aggregation algorithm tailored (AGRT) attack is to maximize the effect on the global model in an optimization problem during each round of FL, i.e., to find the best malicious update that is not easily detected by the defense mechanism, while also achieving the maximum attack effect. Formally, the attacker builds a malicious update using the following optimization problem.  
\[\begin{split}\underset{\gamma}{\text{arg min}}\|\
abla^{b}-A( \
abla^{m}_{i\in[m]}\cup)\
abla_{i\in[m+1,n]}\|_{2}\\ \
abla^{m}_{i\in[m]}=\
abla^{b}+\gamma\bigtriangledown^{p}; \
abla^{b}=FedAvg(\
abla_{i\in[n]})\end{split} \tag{11}\]  
where \(A\) is the defense method deployed by the server (e.g., Krum, etc.). \(\
abla_{i\in[n]}\) is the benign update known to the attacker. FedAvg is a benign aggregation method that averages the updates from all clients. \(\
abla^{p}\) is a perturbation variable. \(\gamma\) is a scaling factor and theAGRT attack, i.e., solving for the optimal \(\gamma\) value. \(\
abla_{i\in[m]}^{m}\) is then the final malicious update obtained.
## 3 Results and Discussion
### Binding Interactions of Fibrillar Tau with TauRP1-14 Peptide
#### 3.7.3 Surface Plasmon Resonance Studies  
To examine real-time binding interactions of neat Tau as well as Tau bound to TauRP (1-14) peptide with LRP1-expressed HEK 293 cells, we conducted Surface Plasmon Resonance (SPR). The results can be seen in Figure 10. As shown, the average changes in reflectivity over a span of 1000 s reveal that neat fibrillar tau had strong binding interactions with LRP1-expressed cells. This is expected, given that LRP1 has a high affinity for Tau and is involved in the internalization of Tau fibrils into cells. The average % change in reflectivity was found to be 65.9% and 61.4%, indicating binding at both concentrations (30 \(\upmu\)M and at 40 \(\upmu\)M). In the presence of the TauRP (1-14) peptide pre-attached to Tau fibrils, however, a dramatic change was seen, where the percent change in reflectivity was found to be significantly lower. Furthermore, the decrease in percent change in reflectivity was found to be proportional to the concentration of fibrillar Tau-bound-TauRP (1-14) (DNSDEENCES) being used as the analyte. These results are indicative that fibrillar Tau, when pre-associated with the TauRP (1-14) peptide sequence, reduced the binding interactions with LRP1-expressed HEK-293 cells, possibly due to the mitigation of binding interactions with LRP1 as a result of conformation change of fibrillar Tau. These results suggest that TauRP (1-14) can influence LRP1-mediated Tau binding. In addition, these data further confirm that the TauRP1-14 peptide is effective in binding to fibrillar tau, and that this complex is successful in reducing LRP1-mediated uptake.  
Figure 9: Comparison of far UV CD spectra recorded for Tau fibrils before and after association with varying concentrations of TauRP (1–14).
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 7 Trade-Off Analysis
### Optimising Trade-Offs  
Optimising the trade-offs among the linear detectability, spherical detectability, RCS, and Q-factor involves balancing the conflicting requirements of each parameter to meet specific application needs. For instance, larger array configurations with better linear detectability may be preferred in critical long-range linear detection applications, even if they sacrifice spherical detectability and Q-factor.  
Conversely, smaller array configurations offering enhanced spherical detectability may be more suitable in applications requiring robust performance across various angles despite potentially lower linear detectability and RCS.  
Furthermore, optimising the trade-offs may involve iterative design iterations and simulations to find the most suitable array configuration that meets the desired performance criteria within the application environment and budget constraints. Ultimately, understanding and navigating the trade-offs between linear detectability, spherical detectability, RCS, and the Q-factor is essential for the triumphant design and implementation of chipless RFID systems that are fine-tuned to meet specific application requirements as illustrated in Figure 26.
# Time-Division Color Holographic Projection in Large Size Using a Digital Micromirror Device +
## 2 Methods
### Multi-Random Phase Method  
The multi-random phase method [10] averages out the noise in the reconstructed image using rapidly switching multiple CGHs generated by adding different random phases to the original image. When the number of CGHs is \(N\), the mean square error of the reconstructed image decreases by \(\sqrt{\text{N}}\).
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Machine Learning Algorithms
#### 3.2.4 Support Vector Machine (SVM)  
SVM was evaluated for its effectiveness in handling both linear and non-linear relationships and maximizing the margin of separation between distinct classes in employee capability. In SVM regression, the goal is to find a function f(x) that estimates the target variable, which is referred to as \"Time Factor\" (Y), given specific input features X [49; 50]. The objective is to find a hyperplane that best fits the data while minimizing the margin of error. The SVM regression model was represented as  
\[f(x)=\langle w,x\rangle+b \tag{7}\]  
where \(f(x)\) is the predicted Time Factor, \(w\) is the weight vector, \(x\) is the input feature vector, and \(b\) is the bias term.  
The SVM regression algorithm aims to minimize the following optimization problem:  
\[min_{w,b}\frac{1}{2}\|w\|^{2}+C\sum_{i=1}^{n}(\xi_{i}+\xi_{i}^{*}) \tag{8}\]  
subject to the following constraints:  
\[\begin{split} Y_{i}-\langle w,x_{i}\rangle-b\leq e+\xi_{i}\\ \langle w,x_{i}\rangle+b-Y_{i}\leq e+\xi_{i}^{*}\\ \xi_{i},\ \xi_{i}^{*}\geq 0\end{split} \tag{9}\]  
where \(Y_{i}\) is the actual Time Factor for data point \(i\), \(e\) is the maximum permissible error (epsilon-tube), and \(\xi_{i}\) and \(\xi_{i}^{*}\) are slack variables, representing the amount by which the predicted Time Factor can deviate from the actual time factor.  
The objective function aims to minimize the norm of the weight vector \(\|w\|^{2}\) while allowing for errors that are bounded by \(e\) and penalized by the hyperparameter \(C\). The parameter \(C\) controls the trade-off between maximizing the margin and minimizing the error. A small \(C\) encourages a broader margin with more errors, while a large \(C\) encourages a narrower margin with fewer errors.  
The Support Vector Machine (SVM) algorithm was implemented using MATLAB's _fitrsvm_ function, configured to utilize a linear kernel by default. The slack variable (CCC), which controls the trade-off between maximizing the margin and minimizing classification error, was set to its default value of 1. This configuration ensures a straightforward comparison of linear relationships within the dataset. While this study focuses on linear SVMs, future research could explore non-linear kernels such as RBF or polynomial to better capture complex, non-linear relationships in workforce data.