esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Optimal Allocation of Water Resources
#### 2.2.2 Statistical Downscaling Model  
Regional climate change significantly impacts the water cycle processes in irrigation areas, making the study of future climate change crucial for agricultural production. General circulation models (GCMs) are essential tools for predicting future climate change. However, the low resolution of these models limits their effectiveness in studying regional climate change. Downscaling technology addresses this issue effectively and is widely used in forecasting regional meteorological factors. The Statistical Downscaling Model (SDSM) utilizes multiple regression principles and a weather generator to establish an empirical statistical relationship between large-scale predictors and regional (or station) prediction variables, thereby enabling climate prediction. Precipitation and reference evapotranspiration are critical factors in determining irrigation water demand and influencing irrigation water diversion and return flow [29; 30]. Previous studies have demonstrated that changes in precipitation, and reference evapotranspiration can be accurately predicted using the SDSM model. This study, therefore, focuses on predicting and analyzing these two meteorological factors based on the SDSM model. The SDSM model divides the prediction of daily precipitation and reference evapotranspiration into three steps. First, reference evapotranspiration is calculated using the Penman-Monteith formula based on observed meteorological data. Second, the statistical relationship between prediction variables and predictors is established, and the model parameters are determined. Third, estimated data from GCMs (HadCM3 under the B2 scenario) are used as input for the SDSM model to simulate changes in precipitation and reference evapotranspiration.
# Optically Pumped Magnetometer Measuring Fatigue-Induced Damage in Steel
## 1 Introduction
The technological development of OPMs has seen great progress in recent years, from their modest beginnings as large lamp pumped sensors [15], to the first miniaturized sensors in a laboratory [16] and the first commercial miniaturized sensors [17]. Based on these developments, several startup companies have started to sell OPMs. The most prominent application case of these commercial OPMs is magnetoencephalography (MEG) [18]. However, the exceptional characteristics of these sensors has led to work with either commercial or self-made sensors in fields, such as zero-field NMR spectroscopy [19], electro-chemical battery characterization [20], and materials testing [21]. The current OPM research in the field of materials testing has mainly concentrated on non-destructive measurement of metallic specimens [21]. In this work, we adopt the OPM technology for materials testing of ferromagnetic specimens under mechanical fatigue loading. This paper is divided into three parts. First, we describe the mechanical and magnetic subsystems of the experimental apparatus. Then, we describe the relevant physical observables of the setup. Finally, we present the measurement results with this novel setup using a ferritin steel specimen.
## 5 System Implementation
### Voice Interaction  
At the beginning of the voice interaction, the virtual teacher system will start its speech recognition technology, as shown in Figure 4; first, it converts the student's voice input into text, so that the system can understand the student's question or request, and the system will quickly recognize and accurately understand this instruction. This is followed by the natural language processing phase of the virtual teacher system. The system not only understands the student's voice input, but also analyzes and processes the input based on its content. After the comprehension and processing phase, the virtual teacher system will respond through speech or text. If the student is using voice input, the system answers the question in voice form, which makes the communication more natural and vivid. In addition, if students prefer written feedback, the system can also present the solution in the form of text, so that students can consult and understand it more easily.  
Iflytek provides the application programming interface for intelligent voice development technology. In this paper, the IFlytek API will be used as the technical support for speech recognition and speech synthesis, the appID, apiSecret, and apiKey of the obtained application will be used as the verification conditions, and the connection with the Iflytek platform will be established through the play() function. Its API is directly embedded in the overall system. When users use this system to ask voice questions, they can directly access the IFlytek platform through the network, call its speech recognition API, and return the converted text information to the system. After the system query obtains the correct answer dialog, the IFlytek platform is accessed through the network again, and its speech synthesis API is called to convert the text information of the answer dialog into audio information. By calling the iflytek API, the construction process of the virtual teacher answering system is greatly simplified and the response speed of the overall voice technology of the system is improved.  
Once the user's speech has been successfully converted to text, these data need to be sent to the ChatGPT model to generate a text response. With the aid of the application programming interface provided by OpenAI, it is easy to realize the communication with ChatGPT. In Unreal Engine, network request and data processing functions can be written using the Set Open AiApi Key blueprint system.
# Characteristic Analysis and Experiment of Adaptive Fiber Optic Current Sensor Technology
## 6 Experiment  
In the experiment, the two optical paths pass through the magneto-optical glass and the optical fiber respectively as Figure 11 shows, then enter the labview signal processing system through the photodetector. The light source of fiber optic current sensor is an LPS-PM1310-FC (THORLABS, Shanghai, China) 1310 nm laser diode with an extinction ratio of 28.2 dB driven by a compact laser diode controller. The wavelength of magneto-optic current sensor's light source is 850 nm. The sensing fiber is made of low-birefringence fiber (Spun LoBi Fiber). The MR-1 magneto-optical glass in the solenoid structure is diamagnetic, which is independent of temperature with higher sensitivity and stability. The diameter of magneto-optical glass is 4.5 mm, the length of it is 40 mm as Figure 12 shows.  
Figure 11: Experimental platform: 1. Compact laser diode controller and laser diode; 2. Light source; 3. Polarizer; 4. Magneto-optical glass sensor; 5. Sensing optical fiber (Between two splints); 6. Input alternative current; 7. Analyzer; 8. Photodetector.  
Figure 12: Magneto-optical glass sensor: 1. Collimator; 2. Polarizer; 3. Magneto-optical glass; 4. Analyzer; 5. Collimator.  
In the experiment, 1.5 A AC is energized by Agilent AC power, the frequency of alternative current is 50 Hz, the number of turns of the coil was 300, and the equivalent current was 450 A. The fiber signal is disturbed by temperature drifts, and the signal fluctuates as Figure 13a shows, but the output signal of the magneto-optical glass is still a stable sine wave as Figure 13b shows, and the final signal output as Figure 13c shows is obtained after processing by Labview. The final current output RMS value obtained after signal processing is 448.8 A, the accuracy of the adaptive method is 0.267%.  
When decreasing the input current to 0.3 A AC which is equivalent to RMS 90 A, the result shows the output RMS value is 89.74 A, the accuracy of it is 0.289%. Figure 14 shows the result of the test of 0.3 A. The experiment verifies the effectiveness of the signal processing system.  
Figure 13: (**a**) fiber signal; (**b**) magneto-optical glass signal; (**c**) final output signal.
## 4 Experimental Results
### Evaluation Metrics  
The confusion matrix and ROC (Receiver Operating Characteristic) curve are considered evaluation criteria for anomaly detection. Importantly, TP denotes the number of normal samples correctly detected, while FN denotes the number of samples incorrectly detected as abnormal. TN denotes the number of correctly detected abnormal samples, while FP denotes the number of samples that were incorrectly detected as normal. Incidentally, the AUROC value is the area size below the ROC curve. It is worth mentioning that _accuracy_, _F1 score_, _recall_, and _precision_ can be calculated from the confusion matrix, as shown below.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{12}\] \[F1 Score=\frac{2\times Precion\times Recall}{Precision+Recall}\] (13) \[Recall=\frac{TP}{TP+FN}\] (14) \[Precision=\frac{TP}{TP+FP} \tag{15}\]
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 3 Materials and Methods
### Model Architectures
#### 3.3.2 Deep Learning Models
The BiLSTM and BiGRU model architecture is illustrated in \[i_{t}=\sigma(v_{i}I_{t}+w_{i}L_{t-1}+a_{i}) \tag{2}\]3 The approach begins by converting a character sequence into an integer index using a character encoder, where each index corresponds to a specific vocabulary character. The input layer is shaped according to the training dataset. The embedding layer transforms input tokens into dense vectors of a predetermined size, set to 128 in this study. The BiGRU or BiLSTM layer then processes the sequence bidirectionally, capturing dependencies from past and future tokens. If the number of layers exceeds one, the return-sequence parameter is set to True. The dimensionality of the bidirectional layer is fixed at 256. To mitigate overfitting, a dropout layer with a rate of 0.2 is employed. A fully connected (dense) layer follows, mapping to the number of output classes--14 in this case. The Softmax function is used as the activation function, and the final prediction corresponds to the category with the highest probability in the Softmax output.
# The Effect of Hydrolysis on the Antioxidant Activity of Olive Mill Waste
## 3 Results
### Antioxidant Activity
#### 3.2.3 Hydrogen Peroxide and Superoxide Radical Scavenging  
Figure 8 shows that macerated extracts had the highest concentration of hydrogen peroxide scavenging activity, followed by acid and alkaline hydrolysed extracts. For maceration extraction, the highest scavenging activity was found in the Maltese cultivar 'Bidni' (1.81 \(\pm\) 0.41), whilst the lowest H2O2 scavenging activity was found in Cerasuola (0.19 \(\pm\) 0.04). The highest scavenging activity for acid hydrolysis was found in the cultivar of Picholine (1.02 \(\pm\) 0.02), whilst the lowest was found in the Maltese cultivar 'Malti' (0.18 \(\pm\) 0.04). Lastly, for alkaline hydrolysis, the highest scavenging activity was found in Bella di Spagna (1.33 \(\pm\) 0.08) whilst Leccino had the lowest concentration of 0.043 \(\pm\) 0.01. All concentrations were expressed in mg AAE/g (ascorbic acid equivalent).  
Percentage of _Superoxide Radical Scavenging_ against concentration graphs can be found in Supplementary Materials Figure S7 for alkaline-derived extracts, Figure S8 for acid-derived extracts and Figure S9 for methanol-derived extracts. IC\({}_{508}\) are quoted in Table 3. Results show that alkaline hydrolysis had the highest antioxidant activity; in fact, it had the  
Figure 8: Mean values for hydrogen peroxide scavenging activity of olive mill waste extracts using three different solvent extraction methods—methanol, acid and alkaline (black, red and green bars, respectively) with error bars showing \(\pm\) 1 SD for three independent trails.  
Figure 7: Mean of ferric reducing antioxidant activity of olive mill waste extracts using three different solvent extraction methods—methanol, acid and alkaline (black, red and green bars, respectively) with error bars showing \(\pm\) 1 SD for three independent trails.  
lowest IC\({}_{50}\) of 0.28 \(\pm\) 0.35, found in the cultivar of Carolea (17 November 2021), whilst the highest IC\({}_{50}\) was found in the mixed cultivar (2 October 2021) with an IC\({}_{50}\) of 23.86 \(\pm\) 4.26. Macerated extracts also had a relatively low IC\({}_{50}\) of 2.46 \(\pm\) 1.77 in the cultivar of 'Bidni' (29 September 2021). The highest IC\({}_{50}\) was also found in the 'Bidni' cultivar, but in this case, the sample was collected in November (26 October 2021), when the olive fruit tends to be more mature. For this cultivar, the IC\({}_{50}\) was 44.79 \(\pm\) 9.10. Lastly, acid hydrolysed extracts had the lowest IC\({}_{50}\) in the cultivar of Carolea (7 October 2021) with a value of 3.36 \(\pm\) 0.92, whereas the highest IC\({}_{50}\) was found in 'Bidni' (26 October 2021) with a value of 36.68 \(\pm\) 2.12. The concentrations were expressed in mg/mL.
# Water Temperature and Salinity Measurement Using Frequency Comb
However, the refractive index of the auxiliary prism is necessary and the accurate measurement of the angle value is required, which makes the measurement system complicated, and the measurement uncertainty is only at the order of \(10^{-4}\). Besides, Michelson interferometers can also be used in temperature and salinity measurements [26; 27]. However, the geometry of the water container needs to be accurately measured, otherwise the thermal expansion of the water container itself can produce considerable measurement uncertainty. An adaptive measuring device [28] can be used to compensate the thermal expansion of the water container, and the measurement uncertainty can be better than \(10^{-5}\). However, the existing reports are based on the fringe counting principle of a continuous wave (Cw) laser source to measure the optical path difference, which requires continuous movement of the mirror, and the procedure cannot be interrupted. An optical frequency comb as a measuring light source can solve this problem. Optical frequency combs have been used to measure the refractive index of air [29], the thickness and refractive index of glass [30], etc. However, refractive index measurements for water have rarely been reported. In recent years, since the frequency combs were invented, scientists all over the world have been searching for its application field. The underwater application is a brand-new area for frequency combs, which has rarely been reported on before. Recently, our group has realized underwater distance measurement by frequency comb in both time domain and frequency domain [31]. The experiment results show a difference of 100 \(\upmu\)m in the range of 8 m. In this paper, we adopt the frequency comb to realize the detection of water temperature and salinity. The experiments are carried out in a water tank. We adopt a simple Michelson interferometer, in which two measurement arms are designed. A pair of cross-correlation patterns can be obtained to detect the distance between the water arm and air arm, which can be converted to the temperature and salinity by empirical formulas. In particular, our configuration can eliminate the effects of water tank thermal expansion. Meanwhile, a salinity sensor and temperature sensor are involved, whose results can be used to evaluate our measurement results. The experimental results show that our method can precisely measure temperature and salinity.
## 3 Visual-Based System
### Visual Recognition and Object Tracking  
AUVs equipped with cameras are described as underwater vehicles that can visualize objects. AUVs that can perform image feature extraction are referred to as having visuality functions. After calculating visual correspondence between images and the AUV, Bigeye Barracuda can perform visual recognition.  
To develop immediate visual-control functions, a local calculus is used in the image to effectively reduce computational burden. The Huygens principle posits that every particle of a medium situated on a wavefront acts as a new wave source from which a new wave originates in a straight line centered on the light source [24]. According to this principle, the image of the point source diffuses in an approximately circular contour. Therefore, a least-squares circular fitting algorithm [25] is performed on the binary image on the basis of image feature extraction. Assume that \(\mathbf{u}=(u_{1},\dots,u_{n})^{T}\) is a vector with \(n\) unknowns and \(f(\mathbf{u})\) is considered as a nonlinear system with \(m\) equations. If \(m>n\), the minimum value can be obtained as below:  
\[\sum_{i=1}^{m}f_{i}(\mathbf{u})^{2}=min \tag{1}\]  
Figure 5: Procedure for (**a**) capturing video from the AUV front camera, (**b**) RGB-to-HSI color space conversion, (**c**) histogram equalization and binarization, (**d**) morphological dilation and erosion, and (**e**) obtaining object profile features, and (**f**) approximation and circle-fitting algorithms.  
As shown in Figure 5f, by calculating the center of the fitting circle of the target object (light-source point) on the AUV visual coordinate plane and the area of the approximate circle, the relative relationship and distance between the target and the AUV can be estimated [26]. The visual coordinate and area can then be defined as object-tracking control parameters (i.e., the orientations and distance), as shown in Figure 6.  
Figure 6 depicts the target as a circle. The coordinate value and circle area value O(x, y, size) of the target on the visual coordinate plane is calculated to define the relative horizontal and vertical relationship between the AUV and the target and the relative distance between the AUV and the target.  
Figure 6a presents the benchmark visual image when the relative distance of the target matches the requirements of forward speed and heading angle set to zero. Compared with the image in Figure 6a, that of Figure 6b can be defined as an operation strategy of turning left and down when the target is in the lower-left corner of the AUV. The visual image displayed in Figure 6c can be identified as an operation strategy of backward motion when the size of the target is larger than that in Figure 6a.
## 3 Results
### Validating Yield Retrievals Based on Leaf N Estimates Derived from LAI  
We repeated the approach in Experiment 1 across all trial plots for both seasons (i.e., including those without destructive sampling and determination of N status). This time, however, we tested the reliability of yield predictions generated from assimilating plot-level growing season LAI and meteorology time-series only. This experiment, thus, was to determine if yield variation could be identified from these simple growing season observations. Our results showed that the assimilation process was able to resolve the variability in N treatment effects on yield across the range of applications (Experiment 1; Figure 7) and for both years using a generic model calibration that was adjusted for each plot using local data only to estimate the two N dilution parameters.  
Figure 5: Bar plots of the DALEC-Crop ensemble mean yield estimates for the validation trial plots derived from the multi-year model calibrations (green) compared to the observations (grey) covering the range of N treatments for the 2016–2017 and 2017–2018 growing seasons. Error bars represent the standard deviation of the distribution of modelled yield estimates and measurements.  
For the first growing season the mean NRMSE of yield prediction was 12% and for the second season it was 13%. This model performance for yield was, therefore, comparable to the results in the model calibration stage, during which leaf N information was provided (Figure 5).  
Figure 6: Experiment 1: Evaluating DALEC-Crop for the multi-temporal retrieval of leaf nitrogen per area (LNA) compared to LNA estimated from the 5 destructively sampled plots. Including a time-series comparison of model estimates (triangles) on dates corresponding to destructive LNA observations (dots) (left panel), and the linear-fit between all modelled and measured LNA values (solid black line), along with 1:1 line (grey dashed line) (right panel). Note: measured-model plot x-axis uncertainty error bars represent the difference between the high and low values from two replicated destructive samples, whereas the y-axis uncertainty is based on the standard deviation of the model estimates.  
Figure 7: Experiment 1: Evaluating DALEC-Crop for the retrieval of wheat yield when constraining the model based on LAI observations. Experiment applied using the multi-year model calibration applied at all trial plots for the 2016–2017 (green) and 2017–2018 (blue) growing seasons. Error bars represent the standard deviation of the distribution of modelled yield values for each treatment. Note: NRMSE is calculated from independent yield data.
# Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data
## 2 Materials and Methods  
In this section, the used dataset is carefully described to allow the comprehension of the assumptions associated with this choice. Furthermore, the different forecasting approaches are theoretically presented, as well as the description of the training/validation/test subset split and the inputs' selection phase.
# RMRRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection
## 3 Robust Matrix Factorization with Robust Adaptive Structure Learning
### Algorithm Description  
The pseudo code of RMFRASL is listed in Algorithm 1, and a flowchart of the proposed method is shown Figure 2. Moreover, the termination condition of the algorithm is that the change in objective function values between two successive iterations is less than a threshold or has reached the predefined maximum number of iterations in our work.  
```
Input: The matrix of sample dataset \(Z=[z_{1};z_{2};\ldots;z_{n}]\in R^{n\times d}\), parameters \(\alpha>0\), \(\beta>0\), \(\lambda>0\)
1:Initialization: matrices \(S_{0}\), \(A_{0}\), and \(W_{0}\) are initial nonnegative matrices, \(t=0\)
2:Calculate matrices \(U_{t}\), and \(C_{t}\) according to Equations (16) and (29),
3:Repeat
4: Update \(S_{t}\) according to Equation (20)
5: Update \(A_{t}\) according to Equation (26)
6: Update \(W_{t}\) according to Equation (33)
7: Update \(U_{t}\) and \(C_{t}\) according to Equations (16) and (29)
8: Update \(t=t+1\)
9:Until the objective function is convergent.
```  
**Output:** Index matrix \(S\), coefficient matrix \(A\), and weight matrix \(W\)  
Figure 2: The flowchart of the proposed RMFRASL method.