esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evaluation of the Effect of Uncertainty Characteristics on Flexibility
### Evaluation Procedure for the Effect of NLFE Characteristics on Flexibility  
To define the effect-evaluation procedure, the following thing was first considered: the time interval when the largest \(RSP_{t}\) occurs was chosen because it is the riskiest time in terms of flexibility. This assumption helps enhance analyzability while considering that the entire time interval may be time-consuming in analyzing the result. Moreover, this study does not focus on evaluating the flexibility related to the load-generation balance for the whole period but rather on analyzing the effect on flexibility according to the changes in the NLFE characteristics. If the entire period is considered, the effect of the NLFE characteristics is weakened during the evaluation of flexibility.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PDF Type** & \multicolumn{2}{c}{**Equations with Origin at the Mean**} & \multicolumn{1}{c}{**Type Decision**} \\ \hline I & \(\phi^{*}(z^{*})\) = \(y_{0}\big{(}1+z^{*}/A_{1}\big{)}^{m_{1}}\big{(}1-z^{*}/A_{2}\big{)}^{m_{2}}\) & \(-\infty<\gamma<0\) \\ IV & \(\phi^{*}(z^{*})\) = & & \\  & \(y_{0}\big{(}1+(z^{*}/a  
In addition to the NLFE characteristics, the FNL is included as a parameter because it can affect the online state of generating units through the generation schedule and thus the flexibility. The FNL also changes the extent to which the NLFE characteristics affect the flexibility because the NLFE is expressed as the % value of the FNL. Thus, three parameters were considered: skewness, kurtosis, and FNL. The generation schedule and RSP can be varied using these parameters. Its influence on flexibility is the RSP result. The scenarios for NLFE characteristics and FNL are listed in Table 3.  
Figure 2: Example of Pearson distribution of NLFE: (**a**) effect of change in skewness, (**b**) effect of change in kurtosis.  
The impact of skewness and kurtosis on flexibility may vary with FNL because the generation schedule can be changed with the FNL. Thus, the FNL is arbitrarily divided into three classes--high, medium, and low. The basic case of the FNL was set to the medium class. The values of skewness and kurtosis are given as a particular range; in particular, the sign of skewness needs to be considered. Each parameter in the scenarios can be selected based on the given system. Empirical approaches can be used to determine the class and level of the FNL. To analyze the individual effect for every FNL, the following condition is considered: the skewness (kurtosis) is fixed, and the kurtosis (skewness) is changed. The influential factor can be determined through the flexibility evaluation for each scenario. The evaluation procedure is summarized in Figure 3.
## 6 Outlier Detection
#### Importance of the Input Parameters  
To determine which inputs had the greatest impact on \(C_{p}\), we used the relevancy factor (\(r\)), where \(r\) is in the range of \([-1,1]\). The \(r\) values are calculated using Equation (32) [84]:  
\[r=\frac{\sum_{i=1}^{n}\left(X_{k,i}-\overline{X_{k}}\right)\left(Y_{i}- \overline{Y}\right)}{\sqrt{\sum_{i=1}^{n}\left(X_{k,i}-\overline{X_{k}} \right)^{2}}\sum_{i=1}^{n}\left(Y_{i}-\overline{Y}\right)^{2}}, \tag{32}\]  
where \(X_{k,i}\) and \(Y_{i}\) are the \(i\)th input and output, respectively; \(\overline{Y}\) and \(\overline{X_{k}}\) are the average value of output and \(k\)th input, respectively; \(n\) denotes the total number of data points. As shown in Figure 7, \(C_{p}\) was directly related to \(T\), \(M_{w}\), and \(T_{c}\), and had an inverse relation with \(\omega\) and \(x\). \(M_{w}\) and \(T_{c}\) had the highest and \(\omega\) had the lowest impact on \(C_{p}\) (with \(r\) equal to 0.451).  
Figure 6: The detection of outlying data for the different models: (**a**) ANFIS, (**b**) MLP–ANN, (**c**) RBF–ANN, and (**d**) SGB.
## 5 AI-Empowered Communication with Perceptual Constraint
### AI-Empowered Semantic Communication
#### 5.2.1 Theoretical Foundation and Practical Motivation  
In the RDP framework, common and private randomness have been shown to play crucial roles in improving perceptual fidelity while maintaining efficient communication. Theoretically, common randomness (shared between the transmitter and receiver) and private randomness (available only to one node) provide additional degrees of freedom that can be leveraged to optimize the trade-off between rate, distortion, and perception. In practical systems, this can be implemented through several methods.  
Shared Random Seeds in End-to-End Training  
In learning-based communication systems, common randomness can be implemented by sharing random seeds between the transmitter and receiver during the training phase. For example, in systems using VAEs or GANs, the encoder and decoder can be trained with a shared random seed that initializes their respective random number generators. This ensures that both ends of the communication system can generate correlated random variations, which can be used to improve perceptual quality.  
Pilot Signals and Synchronization  
In wireless communication systems, common randomness can be established through pilot signals transmitted from the base station to user equipment. These pilot signals can  
Figure 3: (**a**) Separate source and channel coding (SSCC) in current communication system. (**b**) Joint source–channel coding (JSCC) scheme as in [13]. (**c**) The dashed lines link SSCC and JSCC to form a hybrid communication scheme, as in [105].  
be used to synchronize random number generators at both ends of the communication link. For instance, in massive MIMO systems, the base station can transmit reference signals that user equipment uses to align its random seed with the base station's generator.  
Implementation of Private Randomness  
Both transmitters and receivers can incorporate local random number generators that do not require coordination with the other end of the communication system. These generators can be based on physical phenomena (such as thermal noise) or algorithmic approaches (such as pseudorandom number generators with locally maintained seeds).  
In the following, we identify three use cases where generative models can be deployed on user equipment (UE), base stations (BSs), and the core network (CN), as illustrated in Figure 4.
# Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy
## 2 Materials and Methods
### Chemometric Analysis
#### 2.3.4 Polynomial Fitting Model  
Polynomial fitting is a kind of function approximation method, which fits out the relationship between the independent variable \(x\) and the dependent variable \(y\) through mathematical calculation principles [28]. This data analysis method does not require the approximate function to be the same as the true value at each node; it only requires that the approximate function can reflect the basic trend of the given data point and a sense of \"infinite approximation\" as much as possible. The sum of squares for error (\(SSE\)) was used to indicate the stability of the model as follows.  
\[SSE=\sum_{i=0}^{m}\left[p(x_{i})-y_{i}\right]^{2} \tag{1}\]  
where \(p(x_{i})\) was the predicted moisture content, and \(y_{i}\) was the true moisture content. \(I=1\), \(2\dots\ m\).
## 2 Basic Information on the Presented Case Studies
### Remediation Works in a Former Coking Plant Concerning Pollution with Petroleum Compounds  
The soil used for the research comes from the area of a former coking plant, which is currently undergoing the process of remediation and reclamation. It is an area of approx. 5 ha, which has been polluted mainly with petroleum compounds, but also cyanides, heavy metals and metalloids (including arsenic, barium, zinc, cobalt, copper, nickel and lead). The remediation project assumes ex situ treatment, using bioremediation, phytoremediation, as well as the incorporation of reactive barriers. As a result of the entire implementation, a green recreational space is to be created in the area of the coking plant, with small architectural objects and an educational path covering the secured and restored remains of the plant, which operated at the beginning of the 20 th century.  
Due to poor geotechnical conditions, the foundations of the new facilities were based on soil improved by means of deep soil mixing (DSM) using a hydraulic binder (cement) with additives chosen to reduce the negative impact of contamination. The initial amount of C6-C12 hydrocarbons (gasoline fraction) exceeded by 38 times the acceptable content, equal to 1 mg/kg. The soil was mixed with CEM I 42.5R cement grout with a density of 1.7 kg/dm\({}^{3}\) and a corresponding water/cement ration equal to W/C = 1.4. The applied amount of cement provided approx. 240 kg of cement for a resulting 1 m\({}^{3}\) of the composite. As the average weight of the cubic sample was 6.48 kg and its density was 1920 kg/m\({}^{3}\), this means that the amount of cement in the composite reached almost 12.5%.  
Activated carbon (charcoal) was added to half of the sample series. Activated carbon is a highly porous substance with a highly developed specific surface, which is responsible for its remarkable ability to adsorb chemical compounds from gases and liquids. It is made of wood, hard coal, lignite, peat and other materials. It takes the form of fibers, non-woven fabrics, spheres or a dusty or granular form. It is used, among other things, for the purification of industrial waters, sewage and drinking water from oil-derivative pollutants. In this study, 1.5% of the total weight of the mixture was used.
## 1 Introduction  
Major limb amputation is typically framed as a debilitating consequence of failed limb salvage that results in poor health-related quality of life (QoL) amongst patients [1]. However, the standard method of lower extremity amputation continues to be utilized, even though it often fails to meet patients' expectations of modern medicine. Studies demonstrate that patients with standard lower limb amputations often struggle with chronic neuropathic pain [2, 3]. Residual and phantom limb pain (RLP and PLP) have been shown to negatively impact an amputee's quality of life (QoL) [2, 4, 5, 6]. Consequently, QoL is an important outcome when evaluating the efficacy of novel methods of amputation that aim to mitigate post-operative chronic pain.  
For centuries, the standard surgical approach of major limb amputation has not been subject to significant innovation. In the last two decades, however, several neuroma prevention and amelioration procedures have emerged, such as regenerative peripheral nerve interface (RPNI) construction and targeted muscle reinnervation (TMR). RPNI and TMR have been shown to mitigate RLP/PLP in the setting of lower extremity amputation [7, 8, 9].  
While utilizing these neuroma prevention methods at the time of amputation may alleviate the negative impacts of phantom and residual limb pain on QoL, QoL could also be disrupted by other factors such as loss of proprioception, which may result in functional limitations [5].  
In recognition of this fact, we have developed the agonist-antagonist myoneural interface (AMI). The AMI is a surgical construct that involves the biomechanical coaptation of muscles at the time of amputation that have an agonist-antagonist relationship in the uninjured state. Volitional activation of the agonist muscle results in its contraction and simultaneous stretch of its paired antagonist; this, in turn, stimulates native biomechanical receptors in the constituent muscles that transmit joint position sense information to the brain. The intended consequence of this transmission is the preservation or restoration of limb proprioception through the recapitulation of native neural loops between the peripheral and central nervous systems [10; 11; 12; 13; 14; 15; 16; 17].  
Since developing the AMI, we have incorporated its design into modified amputation procedures in both the lower and upper extremities. Our goal in exploring these modified amputation procedures has been to not only provide patients with minimized pain but also improve functionality through restoring proprioception. It has been our hope that the combination of reduced pain and improved functionality may offer patients undergoing these procedures significantly enhanced QoL. The purpose of this investigation was to test this hypothesis in our modified lower extremity amputation patient cohort.
# Experimental Investigation into Failure Modes of Low-Yield-Point Steel Plate Shear Walls
## 2 Horizontal Cyclic Loading Test  
### Test Scheme  
#### 2.1.1 Design of Specim 1  
Because low-yield-point steel belongs to a new type of structural material and the current market demand is not extensive, only a few thickness specifications of steel plate can be selected and purchased. Therefore, the shear infill panels used for the loading experiment of the LYP steel plate shear wall in this paper were adopted as 8 mm thickness with a yield strength of 100 MPa. The rest of the members in the specimen were made of Q235 steel with a yield strength of 235 MPa. The ultimate bearing capacity was limited to 800 kN in the design of the specimen, taking into account the loading equipment. Therefore, the section size of each member in the reduced scale specimens of the LYP SPSW structure designed according to the shear bearing capacity of the infill panel was determined as shown in Table 1.  
In order to ensure that the superstructure of specimen 1 withstood the horizontal load as a whole, this specimen was rigidly connected at the bottom by high-strength bolts to a 60 mm thick steel plate, which was rigidly connected to the laboratory floor using anchor bolts to provide rigid restraint support for this specimen. The frame beam with a section size of H150 \(\times\) 150 \(\times\) 7 \(\times\) 10 mm was located at the upper part of specimen 1, whereas frame columns with a section size of H125 \(\times\) 125 \(\times\) 6.5 \(\times\) 9 mm were at the left and right sides. The loading connection device was designed at one end of the frame beam and was connected to the loading end of the horizontal loading actuator MTS so as to implement the horizontal load on the specimen. The shear infill panel, frame beam, and frame columns in the specimen were connected by welding with the basic form of the weld section being a fillet weld and the distribution of the weld being a perimeter weld. The design details for the drawing of specimen 1 are shown in Figures 1-4.  
For the specimen 1 under the horizontal low cyclic load, the seismic performance of the LYP SPSW in this specimen was obtained by utilizing the reaction wall and the horizontal loading actuator MTS in the structural engineering laboratory, and the top beam was connected to the actuator through the loading connection device on this specimen. A set of brackets was designed at the front and rear of the shear infill panel to control the apparent out-of-plane torsional deformation of the shear infill panel during the loading process and to guarantee that the specimen remained in the horizontal direction when loaded with horizontal load and to ensure the accuracy of the test data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Member** & **Specimen 1** \\ \hline Frame column & H125 \(\times\) 125 \(\times\) 6.5 \(\times\) 9 \\ Frame beam & H150 \(\times\) 150 \(\times\) 7 \(\times\) 10 \\ Infill panel & \(-\)650 \(\times\) 650 \(\times\) 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The section size of two specimens (units: mm).  
Figure 1: Elevation of specimen 1 (units: mm).
## 2 Materials and Methods
### Preparation of Nanocrystalline First Transition-Metal (Ti, Mn, Co, Ni and Zn) Oxide Nanofibers  
The organometallic saline solution was added to the uniform and transparent PVP ethanol solution (14 mL, 0.3 g/mL), and the homogeneous liquid was obtained by ultrasonic treatment. For the electrospinning process, the solution was poured into the syringe with a needle of 1 mm diameter at the tip and connected to a high voltage power supply. The needle tip to the collector drum distance was fixed at 15 cm. The nanofibers were spun with a flow rate of 3 mL/h at 17-18 kV, and the obtained nanofibers were dried in an oven for 4 h at 60 \({}^{\circ}\)C. Finally, the as-spun nanofibers were put into a ceramic crucible and calcined in a muffle furnace with a heating rate of 10 \({}^{\circ}\)C/min, where the end temperature was 500 \({}^{\circ}\)C. After many experimental screenings (such as solution viscosity, flow rate, electric field intensity and work distance), the detailed parameters are shown in Table 1. It should be pointed out that the amount of acetate is determined by its solubility in PVP/ethanol solution, and a certain amount of water is necessary. In addition, butyl titanate is a common raw material for the preparation of nano-TiO\({}_{2}\), but it is easy to decompose in water, so glacial acetic acid is often added to prevent its hydrolysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Nanofibers** & **PVP/Ethanol (0.3 g/mL)** & **Organometallic Salt (g)** & **H\({}_{2}\)O (mL)** & **Acetic Acid (mL)** \\ \hline TiO\({}_{2}\) & 14 & 3.0 & - & 2 \\ Mn\({}_{2}\)O\({}_{3}\) & 14 & 2.0 & 2 & - \\ Co\({}_{3}\)O\({}_{4}\) & 14 & 2.0 & 2 & - \\ NiO & 14 & 1.3 & 2 & - \\ ZnO & 14 & 1.5 & 2 & - \\ \hline \end{tabular}
\end{table}
Table 1: The detailed parameters of the preparation of nanocrystalline first transition-metal (Ti, Mn, Co, Ni and Zn) oxide nanofibers.  
Figure 1: Schematic illustration of the fabrication of nanocrystalline metal oxide nanofibers by electrospinning and controlled calcination.
# Efficient Machine Learning-Based Prediction of Solar Irradiance Using Multi-Site Data
###### Abstract  
Photovoltaic panels have become a promising solution for generating renewable energy and reducing our reliance on fossil fuels by capturing solar energy and converting it into electricity. The effectiveness of this conversion depends on several factors, such as the quality of the solar panels and the amount of solar radiation received in a specific region. This makes accurate solar irradiance forecasting essential for planning and managing efficient solar power systems. This study examines the application of machine learning (ML) models for accurately predicting global horizontal irradiance (GHI) using a three-year dataset from six distinct photovoltaic stations: NELHA, ULL, HSU, RaZON+, UNLV, and NWTC. The primary aim is to identify optimal shared features for GHI prediction across multiple sites using a 30 min time shift based on autocorrelation analysis. Key features identified for accurate GHI prediction include direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and solar panel temperatures. The predictions were performed using tree-based algorithms and ensemble learners, achieving \(R^{2}\) values exceeding 95% at most stations, with NWTC reaching 99%. Gradient Boosting Regression (GBR) performed best at NELHA, NWTC, and RaZON, while Multi-Layer Perceptron (MLP) excelled at ULL and UNLV. CatBoost was optimal for HSU. The impact of time-shifting values on performance was also examined, revealing that larger shifts led to performance deterioration, though MLP performed well under these conditions. The study further proposes a stacking ensemble approach to enhance model generalizability, integrating the strengths of various models for more robust GHI prediction.  
global horizontal irradiance; solar energy; machine learning; explainable AI +
Footnote †: journal: _Future Internet_
## 3 Accuracy and Robustness Simulation of the Improved Modular Vector Method
### Analysis of Anti-Random-Error Interference Ability  
Table 2 provides information regarding the initial exploration values, exploration step size, and termination step size for the improved modular vector method. To assess its resilience against random errors, the expected subsidence values for observation points on the working surface are adjusted based on the original expected results. Artificially introduced mean square errors of \(\pm\)10 mm and \(\pm\)20 mm, and the subsidence values with these errors, are employed for parameter inversion.  
Upon reviewing the inversion results (Figure 4), it becomes apparent that as the error in the subsidence value increases, the relative errors of the parameters inverted by the improved modular vector method exhibit a general upward trend. When the random error remains below 20 mm, the relative errors for the subsidence coefficient \((q)\), the main influence angle tangent value \((tg\beta)\), the propagation angle of mining influence \((\theta)\), and the sum of inflection point offsets \((S)\) all remain below 2%. During data collection, the observation results are inevitably influenced by various factors. The probability integral method, leveraging the improved modular vector method for parameter inversion, proves to be effective in robustly handling the cumulative impact of these factors within a defined range.
## 2 Review of Related Scientific Works
### Time Series Anomaly Detection
For example, the connectivity-based outlier factor (COF) aims to capture better clusters where the data points are distributed in a linear manner [54], while influenced outlierness (INFLO) attempts to better discriminate points nearby two clusters with different densities [55]. Finally, to account for the different feature importance, [56] developed an alternative anomaly detection algorithm, using a weighted kNN. Model-based techniques can be divided into (i) models that learn and predict whether the value is anomalous and (ii) models that compare the potential outlier with expected values drawn from a generative model or data distribution. Since model-based techniques require labeled data, active learning can be utilized to minimize the labeling effort [57]. Among the models of the first group, we find the SVM-based models, such as the one-class support vector machine (OC-SVM), which was introduced by [58], and later enhanced by many authors [59; 60]. Since the regular SVM algorithm can provide poor generalization on an imbalanced dataset, the authors suggested representing the anomalous classes with the high dimensional space origin and mapping anomalous instances close to it. Other SVM variants used for anomaly detection include support vector data description (SVDD) [61], and SVM-SVDD [62]. A different intuition is followed in the Isolation Forest. This tree-based model is based on the principle that the fewer instances of anomalies generate a smaller number of partitions, and thus are likely to have short paths in the tree structure [63]. Other models reported in the literature involve the use of random forests [64], gradient boosted machines [65], artificial neural networks [66], or voting ensembles [67]. Models from the second group have multiple configurations, varying the generative methods and outlier detection criteria. Some examples are the use of ARIMA models to predict future time series values and mark incoming readings as anomalies if they exceed a certain threshold when compared with the forecast [68; 69]. Other approaches fused statistical methods and ANNs [70], or used ANNs alone [31; 71]. Anomaly detection algorithms can identify anomalous forecasts in the context of a particular time series. Based on the algorithm type, insights can be gained on why the point forecast is considered anomalous. Additional insights can be obtained through XAI to understand which features were most influential to such forecast and provide counterfactual examples, highlighting value changes to those features that would produce a better outcome.