esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### HPO-KNN Outlier Detection  
The K-Nearest Neighbors algorithm (KNN) is a method for outlier detection that calculates distances between different samples [49]. The core idea of KNN outlier detection is that outlier points refer to sample points that are far away from most normal points. To put it simply, outlier points must be far away from most sample points.  
The calculation formula for distances between different samples is:  
\[d=\sqrt{\sum_{i=1}^{n}\left(x_{i}-y_{i}\right)^{2}} \tag{5}\]  
The meaning of the expression is that in n-dimensional space, there are a(\(x_{1}\), \(x_{2}\),..., \(x_{n}\)) and b(\(y_{1}\), \(y_{2}\),..., \(y_{n}\)), and the KNN distance value between them is the value calculated by the formula.  
The calculation formula for outlier detection algorithm is:  
\[od=\frac{1}{n}\sum_{i=1}^{n}d_{i} \tag{6}\]  
where \(d_{i}\) is the distance between the current node and i-th node.  
The KNN outlier detection algorithm is shown in Figure 6.  
On the left side of the figure, the average distance of the three neighbors is calculated as \((3+4+3)/3=3.33\). Conversely, on the right side, the average distance of the three neighbors is \((7+9+5)/3=7\). Clearly, the second point is more anomalous compared to the first point.  
In order to improve the accuracy of prediction, the KNN is used to process data outliers. However, when processing data outliers based on the KNN, the important parameters of the KNN are artificial and random, which brings great uncertainty to the acquisition of the optimal parameter values. In this study, the HPO algorithm is utilized to intelligently optimize the n_neighbors and contamination parameters of the KNN. The SUR is used as the control experimental model. The fitness function is set to the MAE between the predicted value and the observed value. The n_neighbors and contamination values corresponding to the minimum MAE are chosen as the optimal parameters for this experiment.  
The detailed steps of the HPO-KNN are as follows:  
1. Collect historical photovoltaic power data and perform corresponding preprocessing.
2. Parameter initialization: Initialize the parameters of the HPO algorithm, including the number of search agents N and the maximum number of iterations T, and set the upper and lower boundaries of the HPO algorithm and mapping them to the upper and lower bounds of the KNN parameters n_neighbors and contamination.
3. Obtain the initial optimal fitness value through SRU training and prediction.
4. Adjust the positions of the hunters and prey according to the rules of the HPO, simultaneously updating the fitness values of members whose positions have been adjusted.  
Figure 6: KNN outlier detection.  
Obtain the best solution to the problem and output the optimal parameters for the KNN. Use these optimal parameters for outlier detection.
## 2 Materials and Methods
### Set-Up of Experiments  
#### 2.4.1 Dataset Preparation  
The initial stage of the experiment involved dataset preparation, specifically targeting the Galaxies Zoo dataset [53]. Prior to training, dataset refinement was necessary to transform continuous target variables into discrete labels. Threshold comparisons facilitated this transformation, resulting in the delineation of five distinct classes.  
Since certain selected ML algorithms were incompatible with direct image input, the ResNet-50 CNN, pre-trained on extensive image datasets, functioned as the feature extractor. Consequently, each image yielded a 2048-sized feature vector post-extraction.  
Subsequently, dataset balancing was undertaken. To ensure an unbiased training process, balancing involved identifying the least populated class to set a consistent threshold. Excess samples from remaining classes were systematically removed to equalize sample counts across all categories, thereby forming a uniformly balanced dataset suitable for precise modeling. The importance of dataset balancing lies in preventing model bias toward dominant classes, thus ensuring accurate, representative results across all classifications.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Category** & **Count** & **Percentage** \\ \hline Smooth & 25,000 & 40.6\% \\ Features/Disk & 32,000 & 52.0\% \\ Star/Artifact & 458 & 0.7\% \\ Edge-On & 5000 & 8.1\% \\ Bar & 2000 & 3.2\% \\ Odd & 1500 & 2.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Approximate class distribution in the training set.  
Figure 1 portrays the preparatory procedure used to transform a large resource into equalized subsets containing the same quantity of samples. An images branch was included for Torch-driven experimentation, followed by a features subset that was processed by Scikit-based pipelines. On the other hand, Figure 2 presents the 2048-dimensional embeddings generated by ResNet-50 are visualized using t-SNE. Five compact, non-overlapping clusters emerge, each corresponding to one of the balanced morphological classes. The regular, almost circular shapes of the clusters and the clear inter-cluster margins indicate that the extracted features already contain strong class-specific information, making the subsequent classification task linearly separable to a large extent. The small sprinkling of mis-placed points mainly occurs at cluster boundaries, reflecting images with ambiguous visual cues rather than weakness of the embedding.
## 2 Materials and Methods
### Electron Beam Radiolysis Process
#### 2.1.2 Radiolytic Process Kinetics  
In diluted aqueous solutions, practically all irradiated energy is absorbed and contributes to water radiolysis, without affecting the solution's radioactivity. In addition to the radicals previously mentioned, also other, less reactive, species such as H\({}_{2}\), H\({}_{2}\)O\({}_{2}\) and H\({}_{3}\)O\({}^{+}\) can be formed. In pure solutions, the reaction rates of OH-radicals with most organic species are approximately \(10^{9}\) times higher than O\({}_{3}\) reaction rates [19]. As with traditional AOPs, in complex solution matrixes, naturally present compounds may function as radical scavengers (e.g., O\({}_{2}\), HCO\({}_{3}\)\({}^{-}\), CO\({}_{2}\)\({}^{-}\), Cl\({}^{-}\), NO\({}_{2}\)\({}^{-}\), NO\({}_{3}\)\({}^{-}\), DOC), decreasing overall process efficiency or requiring a higher energy input.  
Kinetic models can be applied to monocomponent solutions with appropriate individual reaction constants for the preliminary estimation of radiolytic degradation processes. Decomposition yields will change as function of initial concentration, pH, and other operating conditions. However, pollutants decomposition kinetics in complex solutions may significantly vary from theoretical ones as result of interfering phenomena (radical scavenging, components cross-interferences, matrix effects). From a practical aspect, such effects could only be determined experimentally, as normally occurs for application of conventional AOPs in current applications.  
Table 4 summarizes the role and efficiency of the main radicals generated by water radiolysis in the degradation of CECs and known hazardous pollutants. It is interesting to see how the combination effects of the different radical species generated by irradiation may enhance the overall treatment effectiveness.  
Figure 1: Modern EB apparatus: schematics (**left**) and view (**right**).
# Valorisation of Star Wastewater by Anaerobic Fermentation
## 1 Introduction  
Starch is an industrial commodity produced for the food, packaging and pharmaceutical markets with a growing production volume of 150 million tonnes in 2020 and set to surpass 165 million tonnes in 2026 [1]. In the EU the annual production volume stands at 11 million tonnes of starch and starch derivatives (which is an increase of approximately 3 million tonnes from 2004), extracted from around 25 million tonnes of raw materials. Of this annual output, 9 million tonnes are utilised domestically, the majority of it being starch sweeteners [2].  
The main feedstocks in the EU production market are corn, wheat and potatoes; however, a wide range of feedstocks is applied worldwide: cassava, rice, rye, barley, oat, milo, sweet potatoes and sago palms [3]. One of the challenges in industrial starch production is the accumulation of large amounts of high strength wastewaters. Depending on feedstocks and technologies, different kinds of wastewater can occur at different steps of the process: (1) washing of the feedstocks (e.g., potatoes), (2) steeping of the feedstocks (corn), (3) starch refinement (e.g., corn gluten water), (4) starch saccharification (e.g., glucose wastewater); (5) modification of the starch [3, 4, 5]. Some of these processes are thermal (evaporation, drying), where condensates accumulate. In general, starch production processes have been optimised throughout the decades so that wastewaters are often re-used internally to reduce their amounts. Anaerobic fermentation, especially of the wastewaters containing higher COD concentrations, is of interest.  
Anaerobic biotechnological processes such as biogas, biohydrogen, lactic acid and acetone-butanol-ethanol (ABE) fermentations can be interesting approaches to utilise starch wastewaters. It is often not necessary to provide sterile conditions, which reduces costs and efforts decisively. The variety of the product range is attractive and can range from energy carriers to bulk chemicals.  
In the scientific literature, an overview of the different anaerobic valorisation possibilities of starch wastewater is lacking. Recent works mainly focus on only biohydrogen production from starch wastewater [4]; others on only biogas production [3,6]. There are also some papers on a combined production in a two-step process [7]. Other authors give an overview of biohydrogen production from different wastewaters [8] but do not focus on starch wastewaters. The wastewater treatment perspective is intrinsic in other publications [5,9] and not the product range. However, from an industrial perspective, it is of interest to give a comprehensive comparison of the existing different anaerobic valorisation options for the different types of starch wastewater. This review aims at filling this gap.
# Review: A Survey on Objective Evaluation of Image Sharpness
## 2 Evaluation Methods and Analysis
### Spatial Domain-Based Methods  
Early work [24; 25; 26] on the sharpness evaluation of no-reference images was mainly performed in the spatial domain. The spatial domain evaluation functions use the characteristics of the image in the spatial domain to distinguish between blurred and clear images [27]. This type of evaluation function generally evaluates images directly by calculating the relationship between image pixels and their neighbors. We mainly divide spatial domain methods into gradient grayscale and edge detection evaluation methods.
# Dual-Mic Speech Enhancement Based on TF-GSC with Leakage Suppression and Signal Recovery
## 3 Proposed TF-GSC with Leakage Suppression and Signal Recovery  
To alleviate the problems caused by steering error, this paper proposes a leakage suppression module to suppress the leaked desired signal in the output of the BM and the signal recovery module to restore the attenuated desired signal in the FBF output, which is shown in Figure 1 in red. The postfiltering and the feedback from it are also adopted for both the conventional and the proposed TF-GSC, which is omitted from the figure.
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 5 Performance Evaluation  
This section presents the performance analysis of the proposed algorithm. First, the simulation configuration is described. Then, we compare DDS with OAPF [16], OGP [19], and two extended algorithms of DASF [25].
## 2 Proposed Method
### Construction of DT-Based Flood Control Operation Rules  
#### 2.2.1 Generation of Reservoir Flood Control Operation Dataset  
The main factors affecting reservoir discharge include flood occurrence time, rainfall, net rainfall, reservoir water level, rate of inflow, and volume of inflow. In regions with an uneven distribution of rainfall throughout the year, the rainfall volume and intensity in the flood season are usually high, resulting in large discharges, whereas there is little rain in the non-flood season, resulting in small discharges. Therefore, the occurrence of flooding is related to reservoir discharge. The water level reflects the water stored by the reservoir; the higher the water level, the greater the discharge required during flooding to ensure the safety of the dam. The total rainfall, net rainfall, rate of inflow, and volume of inflow reflect the amount and intensity of inflow into the reservoir and are directly proportional to required discharge; that is, the larger these parameters, the greater the required discharge, and vice versa. This analysis indicates that a reservoir flood control operation dataset includes many attributes, among which the discharge in each period is the decision attribute, whereas the others are conditional attributes.
# A Cross-Layer, Anomaly-Based IDS for WSN and MANET
## 7 Related Work  
In this section, we present a brief review about the use of decision trees, random forest, and linear regression in IDS design. The lack of a standard dataset in the field of IDS design for MANETs makes an accurate comparison process between different systems difficult. This situation is not present in legacy networks, where datasets such as the KDD99 have been used extensively. One of the earliest efforts in the field of IDS design in MANET using C4.5 decision trees is the work of [PERSON] and [PERSON] [20]. They used a set of statistical features which can be derived from routing tables and then applied C4.5 to detect normal versus abnormal behavior. [PERSON] et al. [21] proposed a \"cross-feature analysis\" method to capture the inter-feature correlation patterns in normal traffic. An intrusion detection and adaptive response mechanism (IDAR) was proposed by [PERSON] and [PERSON] [22]. All nodes in the network operate as one of the three roles of manager node (MN), cluster heads (CH), and cluster nodes (CNs). A decision tree in the MN with the calculated confidence on attack detected (COA) and network performance degradation (NPD) values are used to identify the intrusion response action. A distributed combined authentication and intrusion detection with data fusion in MANET was proposed by [PERSON] et al. [23], who used a decision tree to organize the attack signatures into a tree structure. [PERSON] et al. [24] used Linear Regression and Variance Analysis (LRVA),  
Figure 8: The AMoF for different nodes and the fitted slope for those nodes for scenario NS15P3 (\(Tr=100\) s, \(Ts=5\) s): (**a**) The AMoF for different NUTs; (**b**) The fitted slope, and its confidence for different NUTs.  
a multi-step-ahead prediction where the fake parameters broadcasted in the network were detected, with the malicious/misbehaving nodes identified.
## 3 Test Results
### Skeleton Curve and Ductility Coefficient  
By connecting the peak point of each hysteretic loop, the skeleton curves of the joints were obtained and plotted in Figure 8. It can be seen that the skeleton curve development trend of each joint was basically similar. The strengthening method of grouting concrete in the chord improved the stiffness and bearing capacity of the joints. Compared with the K-CC joint, the ultimate capacity of K-CCC1 and K-CCC2 increased by 71.6% and 67.5%, and the ultimate capacity of K-RCC1 and K-RCC2 was 73.6% and 71.8% higher than that of the corresponding K-RC joint. As far as the section form of the chord was concerned, the bearing capacity of the joint with the circular chord was slightly smaller than that of the joint with the square chord, but no more than 4%, under the same conditions.  
Ductility is an important mechanical property index of structure or component. The displacement ductility ratio \(\mu\) was introduced to assess the deformation capacity of the joints. The ductility coefficient \(\mu\) was defined as \(\delta_{\text{u}}/\delta_{y}\), \(\delta_{\text{u}}\) and \(\delta_{y}\) and were the ultimate displacements and the yield displacement of the joint appearing in the skeleton curve. The ductility coefficients of the joints are shown in Table 3. It can be seen that the yield displacement of K-CCC1 and K-CCC2 were both about 2.2 times that of the K-CC joint, and the yield displacement of K-RCC1 and K-RCC2 were both about 2.1 times that of the K-RC joint. The ultimate displacement of the strengthened joints was obviously increased because the chord was filled with concrete. On the whole, the ductility coefficients of the unstrengthened joints were higher than those of strengthened joints, which showed that the ductility of the joint was weakened to a certain extent, although the bearing capacity of the joint was effectively improved by concrete filling in the chord. The ductility of the strengthened joint with fly ash concrete filled in the chord was better than that of the reinforced joint with ordinary concrete filled in the chord.  
Figure 8: Skeleton curve of _P_-\(\delta\). (**a**) Joints with a circular chord and brace; (**b**) joints with a square chord and circular brace.
# Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory
###### Abstract  
In the manufacturing industry, the process capability index (Cpk) measures the level and capability required to improve the processes. However, the Cpk is not enough to represent the process capability and performance of the manufacturing processes. In other words, considering that the smart manufacturing environment can accommodate the big data collected from various facilities, we need to understand the state of the process by comprehensively considering diverse factors contained in the manufacturing. In this paper, a two-stage method is proposed to analyze the process quality performance (PQP) and predict future process quality. First, we propose the PQP as a new measure for representing process capability and performance, which is defined by a composite statistical process analysis of such factors as manufacturing cycle time analysis, process trajectory of abnormal detection, statistical process control analysis, and process capability control analysis. Second, PQP analysis results are used to predict and estimate the stability of the production process using a long short-term memory (LSTM) neural network, which is a deep learning algorithm-based method. The present work compares the LSTM prediction model with the random forest, autoregressive integrated moving average, and artificial neural network models to convincingly demonstrate the effectiveness of our proposed approach. Notably, the LSTM model achieved higher accuracy than the other models.  
long short-term memory; smart manufacturing statistical process analysis; process capability index; process quality performance
# Phenolic Compounds and Antioxidant Activity in Edible Flower Species from Oxaxa
## 3 Results
### Phenolic Compounds and Antioxidant Activity in Inflorescences of D. americana  
The composition of cauchepil inflorescences showed high variability and different response patterns depending on the origin of the samples. For example, in terms of polyphenol content, the samples differed significantly and frequently within the same locality or municipality, such as in the cases of Santos Reyes, Sola de Vega, and the municipality of Santa Cruz Xitla; the same pattern was observed for flavonoid content and antioxidant activity. However, for the concentration of polyphenols, the samples from Santo Reyes and Sola de Vega 2 and the three samples from Santa Cruz Xitla stand out; this is also the case for catechin-equivalent flavonoids and antioxidant activity evaluated using the FRAP and DPPH methods. In terms of the concentration of quercetin-equivalent flavonoids, Agua Fria Sola de Vega 2 and Santo Reyes Sola de Vega 2 stand out. In contrast, lower contents of polyphenols and flavonoids and antioxidant activity were frequently observed in samples from San Felipe Zapotilan, San Pedro El Alto, San Andres Paztlan, Agua Fria Sola de Vega 1, and Santos Reyes Sola de Vega 1. The variability in the polyphenol contents ranged from 38.7 to 59.6 mg GAE g\({}^{-1}\), the concentration of catechin equivalents ranged from 18.8 to 38.1 mg CE g\({}^{-1}\), and the concentration of quercetin equivalents ranged from 11.2 to 24.9 mg QE g\({}^{-1}\). The antioxidant activities evaluated using DPPH and FRAP ranged from 185.7 to 299.6 and from 290.4 to 508.7 umol TE g\({}^{-1}\), respectively (Table 5).  
According to the results of Pearson's correlation analysis, significant and positive correlations were detected between polyphenol and flavonoid contents and antioxidant activity according to the DPPH (0.36 \(\leq\) r \(\leq\) 0.84; \(p\) < 0.01) and FRAP (0.44 \(\leq\) r \(\leq\) 0.94; \(p\) < 0.01) methods. In these cases, the contents of polyphenol and flavonoid equivalents of catechin showed the highest correlation values.
# Performance Analysis of Dual-Hop Hybrid RF-UOWC NOMA Systems
## 4 Ergodic Capacity Analysis
### Ergodic Capacity \(EC_{x_{1}}\)  
The CDF \(F_{\gamma_{a}}(\gamma)\) is given as  
\[\begin{split} F_{\gamma_{a}}(\gamma)&=1-Pr(\gamma_{R} ^{1}>\gamma,\gamma_{D1}^{1}>\gamma,\gamma_{D2}^{1}>\gamma)\\ &\overset{(d)}{=}1-Pr(|h_{w}|^{2}>\frac{\gamma}{\rho_{s}d^{-v}( a_{1}-a_{2}\gamma)})Pr(|h_{1}|^{2}>\\ &\frac{\gamma}{\rho_{R}(a_{1}-a_{2}\gamma)})Pr(|h_{2}|^{2}>\frac{ \gamma}{\rho_{R}(a_{1}-a_{2}\gamma)}),\end{split} \tag{18}\]  
where \((d)\) stems from the independence of the channels gain and \(0<\gamma<\frac{a_{1}}{a_{2}}\). Then  
\[\begin{split} EC_{x_{1}}&=\frac{1}{2\ell n2}\int \limits_{\gamma=0}^{a_{1}/a2}\frac{1}{1+\gamma}(1-F_{|h_{w}|^{2}}(\frac{\gamma }{\rho_{s}d^{-v}(a_{1}-a_{2}\gamma)}))\\ &\times(1-F_{|h_{1}|^{2}}(\frac{\gamma}{\rho_{R}(a_{1}-a_{2} \gamma)}))(1-F_{|h_{2}|^{2}}(\frac{\gamma}{\rho_{R}(a_{1}-a_{2}\gamma)}))d \gamma,\end{split} \tag{19}\]  
then applying variable transformation of \(\tau_{a}=\gamma/(a_{1}-a_{2}\gamma)\) and using the exponential distribution CDF in (3) and the tight approximated EGG CDF at high SNR in (11), we can write  
\[EC_{x_{1}}=\frac{1}{2\ell n2}\int\limits_{\gamma=0}^{\infty}\frac{e^{-\phi_{1} \tau_{a}}\left(1-\phi_{2}\tau_{a}\frac{1}{\gamma}-\phi_{3}\tau_{a}\frac{\phi}{ \gamma}\right)^{2}}{(1+\tau_{a})(1+a_{2}\tau_{a})}d\tau_{a}, \tag{20}\]  
where \(\phi_{1}=1/\rho_{s}d^{-v}\), \(\phi_{2}=(w/\lambda)(1/\rho_{R}\mu_{r})^{\frac{1}{r}}\), and \(\phi_{3}=((1-w)/\Gamma(a+1))(1/b^{r}\rho_{R}\mu_{r})^{\frac{\gamma}{r}}\). Using binomial expansion  
\[EC_{x_{1}}=\frac{1}{2\ell n2}\Big{(}I_{1}-2\phi_{2}I_{2}-2\phi_{3}I_{3}+\phi_{2 }^{2}I_{4}+2\phi_{2}\phi_{3}I_{5}+\phi_{3}^{2}I_{6}\Big{)}, \tag{21}\]  
where  
\[\begin{split} I_{K}&=\int\limits_{\gamma=0}^{ \infty}\frac{\tau_{a}^{X_{K}}e^{-\phi_{1}\tau_{a}}d\tau_{a}}{(1+\tau_{a})(1+a_{ 2}\tau_{a})}\\ &=\frac{1}{a_{1}}\int\limits_{\gamma=0}^{\infty}\frac{\tau_{a}^{ X_{K}}e^{-\phi_{1}\tau_{a}}d\tau_{a}}{(1+\tau_{a})}-\frac{1}{a_{1}}\int\limits_{ \gamma=0}^{\infty}\frac{\tau_{a}^{X_{K}}e^{-\phi_{1}\tau_{a}}d\tau_{a}}{((1/a_ {2})+\tau_{a})},\end{split} \tag{22}\]  
where \(K\in[1,6]\) and \(X_{K}\) is the \(K^{th}\) element in the vector \(X=[0,(1/r),(ac/r),(2/r),\\ ((1+ac)/r),(2 ac/r)]\). Utilizing [23] (Equation 3.383.10), \(I_{K}\) can be expressed as  
\[I_{K}=\frac{1}{a_{1}}\Gamma(X_{K}+1)[(e^{\phi_{1}}\Gamma(-X_{K},\phi_{1}))-(( \frac{1}{a_{2}})^{\tau_{K}}e^{\frac{\phi_{1}}{\tau_{2}}\Gamma(-X_{K},\frac{\phi _{1}}{a_{2}}))})]. \tag{23}\]  
Substituting (23) into (21), a closed-form expression of \(EC_{x_{1}}\) is obtained.