esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results  
1. Settings  
Before conducting the experiments on the ARFM model, we discuss two key issues that the model must address.  
Q1. Does our model have smaller loss values than other models when tested in various scenarios?  
Q2. Is our model more accurate than the others when tested in a variety of situations?  
(1) Dataset  
When selecting the dataset, we considered the type and size of the dataset. In terms of type, we chose a dataset based on user data and a dataset based on objects. The user dataset is the data extracted by [PERSON] from the 1994 census database [36]. Each dataset records a person's age, work, and other data; income can be predicted using this dataset. The MovieLens dataset [37] was selected as the object-based dataset, and the 1 M and 10 M datasets were selected according to their sizes to achieve multiclassification of movie grades.  
(2) Input data processing  
The processing of input data is relatively simple. First, we remove useless or invalid data from the dataset. This step ensures that we obtain the correct encoding sequence when we use one-hot for the category features. Next, one-hot coding is conducted for category features, including the user's job type, gender, residence, education level, and movie classification, rating, director, leading actor, and other features. In this step, coding sequences with different sparsity degrees were obtained. In particular, we clipped features of more than 100 categories to ensure that the model operation would not get stuck due to a large number of inputs. Finally, we integrate the processed one-HOT encoding and the original numerical features into the ARFM input \(data_{input}\).  
(3) Evaluation methodology  
The evaluation of our model primarily focused on the two aforementioned questions, considering the loss size and accuracy rate. The dataset was divided into training and test sets, according to a certain scale. The model was trained on the training set and was evaluated using the test set. For the size of the loss, the loss function we choose is the log loss; that is,  
\[J(\theta)=-\frac{1}{m}\sum_{i=1}^{m}[y_{i}logh_{\theta}(x_{i})+(1-y_{i})\log( 1-  
(3) Figure 11 shows the performance results for dataset 3:  
(4) Conclusions:  
In this section, we compare the performance of the loss values for the different datasets for each model. It is evident from the figure that the loss value of the ARFM model is excellent overall, second only to the performance of the wide and deep models in Datasets 1 and 2, and the gap narrows, finally reaching the optimal value in Dataset 3. This shows that the ARFM model performs well on all types of datasets, and is more inclined to deal with large datasets.  
Figure 11: Loss performance of each model on dataset 3.  
Figure 10: Loss performance of each model on dataset 2.
## 5 Conclusions
### Nomenclature  
_Indices and sets_  
\(i\) & Index of bus  
\(l\) & Index of line  
\(t\) & Index of time step  
\(\varphi_{i}\) & Set of network buses  
\(\varphi_{l}\) & Set of distribution lines  
\(\varphi_{t}\) & Set of time steps  
_Variables_  
\(IL^{re*}\), \(IL^{re-}\) & Positive and negative terms of real part of current flow through the line in per-unit (p.u.)  
\(IL^{im+}\), \(IL^{im-}\) & Positive and negative components of imaginary part of current flow through the line (p.u.)  
\(IG^{re}\) & Real part of current generation by DGs (p.u.)  
\(IG^{im}\) & Reactive part of current generation by DGs (p.u.)  
\(ID^{re}\) & Real part of the consumption current (p.u.)  
\(ID^{im}\) & Imaginary part of the consumption current (p.u.)  
\(PG\) & Active power generation by DGs (p.u.)  
\(QG\) & Reactive power generation by DGs (p.u.)  
\(PD\) & Consumption active power (p.u.)  
\(QD\) & Consumption reactive power (p.u.)  
\(V^{re}\) & Real part of the voltage (p.u.)  
\(V^{im}\) & Imaginary part of the voltage (p.u.)  
\(V\) & Voltage magnitude (p.u.)  
\(w^{re}\) & An auxiliary variable in equation of real part of voltage drop (p.u.)  
\(w^{im}\) & An auxiliary variable in equation of imaginary part of voltage drop (p.u.)  
\(y^{*}\), \(y^{-}\) & Binary variables for determining the status of lines and current flow through them  
\(\Delta I^{re}\) & Real part of deviation of line current (p.u.)  
_Constants_  
\(C_{p}\) & Coincidence factor  
\(N_{ch}\) & Maximum number of switching  
\(R\) & Resistance of line (p.u.)  
\(X\) & Reactance of line (p.u.)  
\(P^{0}\), \(P^{1}\), \(P^{2}\) & Coefficients of active load curve  
\(Q^{0}\), \(Q^{1}\), \(Q^{2}\) & Coefficients of reactive load curve  
\(IL^{max}\) & Maximum capacity of line (p.u.)  
\(V^{max}\) & Maximum magnitude of voltage (p.u.)  
\(V^{min}\) & Minimum magnitude of the voltage (p.u.)  
\(N_{bus}\) & Number of buses  
\(PG^{max}\) & Maximum active power of DGs (p.u.)  
\(QG^{max}\) & Maximum reactive power of DGs (p.u.)  
\(P^{min}\) & Minimum power factor  
\(\overline{w}^{re}\) & Maximum value of \(w^{re}\) (p.u.)  
\(\overline{w}^{in}\) & Minimum value of \(w^{in}\) (p.u.)
## 2 Geopolymer Concrete (GPC)
### Geopolymerization Process  
Geopolymers are alkali-activated cement manufactured by polymerizing aluminosilicates with alkaline activators [21]. The polymerization process involves the quick reaction of silica and alumina under an alkaline environment that results in a three-dimensional polymeric aluminosilicate network [89]. Generally, the dissolution of silica and alumina species takes place in alkaline solution followed by an orientation of these species to convert into a three-dimensional chain of silica aluminate polymeric structure [90]. Depending on the composition of aluminosilicates, various geopolymer types are obtained. As far as geopolymer OPC concrete (geopolymer concrete with the inclusion of OPC) is concerned, it develops its mechanical properties based on both calcium silicate hydrate (CSH) bonding and polymerization process [91].  
The research studies on GPC and alkali activation emerged in the 1950s. Glukhovsky model [92] has widely been used for alkali activation of aluminosilicates materials [93]. Later on, researchers have conducted experimental investigations and extended the [PERSON]'s theory on the geopolymerization process involved in GPC [94]. The research of GPC has shown an increasing trend in recent years, as shown in Figure 7. The data presented are from the year 2001 onwards.  
In GPC, when aluminosilicates fully replace the cement, the polymerization process is involved in the activation of aluminosilicates [95]. The geopolymerization process is an exothermic process that involved the dissolution of aluminosilicate into aluminosilicate oxides under highly alkaline conditions. These oxides are taken up into aqueous phase in which they are dissolved by high pH solution and led to the formation of gel as oligomers species consisting of polymeric bonds of Si-O-Si and Si-O-Al or both that then form the geopolymeric framework. This 3-D framework hardened into the final polymeric structure by the bonding of filler materials and un-reacted solid particles [96; 97]. [PERSON] et al. [98] found that when aluminosilicates completely replace cement in geopolymer concrete, the poly-condensation of silica and alumina species occurred rather than the formation of C-S-H gel, as in the case of OPC. The process of geopolymerization is elaborated in Figure 8.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  
Figure 8: Geopolymerization process [98].  
Figure 7: Research trend of GPC modified from [9]; Elsevier, 2018.
## 3 Methods
### Emotion Detection Based on Voice Features
#### 3.2.3 Model Architecture
Compilation of the model--the loss function is defined (in this case, _binary cross-entropy_), which measures how well the model makes its predictions in relation to the real values. Then the optimizer is defined (in this case, _Adam_), which adjusts the parameters (weights) of the model to minimize the loss function. * Training the model: The data are divided into training and validation sets. The training set is used to train the model, while the validation set is used to monitor the model's performance and prevent overfitting. The model is trained over 150 epochs (complete iterations through the training set), and batches of 64 training examples are used in each iteration. * Prediction: Using the trained model, predictions are made on the labeled data in the testing set, which comprises 20% of the total dataset. * Validation and evaluation: The performance of the model is evaluated in terms of _precision_, _recall_, _F1 score_, and overall _accuracy_. Curves showing accuracy during training and testing for different epochs and the loss value are presented. The evaluation metrics are defined below:
* _Precision_ represents the ratio of instances predicted as positive that are correctly positive, which is calculated as follows: \[Precision=\ \frac{TP}{TP+FP}\] (1) where _TP_ (true positives) is the number of correctly identified positive cases and _FP_ (false positives) is the number of negative cases incorrectly identified as positive. * _Recall_ is the ratio of correctly identified negative instances. This metric complements _sensitivity_ and is important for evaluating how well the algorithm avoids false alarms, which is calculated as follows: \[Recall=\frac{TP}{TP+FN}\] (2) where _FN_ (false negatives) is the number of positive cases incorrectly identified as negative. * _F1 Score_ is the harmonic mean of _precision_ and _sensitivity_, calculated as follows: \[F1\ Score=2*\frac{Precision*Recall}{Precision+Recall}\] (3)
* _Accuracy_ is a general measure of performance that numerically represents the ratio of correct predictions to the total predictions made, which is calculated as follows: \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (4) where _TN_ (true negatives) is the number of correctly identified negative cases.
# A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities
## 3 System Functionalities
### Localization  
There are already several systems that can detect invading drones through a radar. These radars could be the first device to identify the invading drone, as they work autonomously. However, there must be a complementarity/cooperation of both systems (localization and anti-UAV techniques). To determine the exact location of the UAV, the implemented system integrates three sensors and a receiver.  
Figure 5 illustrates how the estimation of the position of the intruder drone is carried out. The LIDAR Lite v3 [22] is responsible for measuring the distance to the UAV, which is marked as d) in the image. We can project a right triangle between the system and the drone, as is visible in Figure 5. Through the 3D accelerometer MPU6050 [23], which is used for tilt/pitch measurements, it is possible to determine the angle p). With the angle p) and the distance d); using simple trigonometry, it is possible to determine the distance between the drone and the system in relation to the earth plane. Finally, using the e-compass LSM303D [24], which is responsible for measuring the system orientation, m), we can obtain the direction of the drone. By combining the information obtained from this set of sensors with the known location of the system itself, it is then possible to determine the location of the UAV [21]. Note that the system position is estimated through the GPS signal receiver [25].
## 2 Data and Materials  
### Inventory Data  
To develop and evaluate our co-registration method, we used inventory plots from the latest BWI (2011-2012) within the area of Hunsruck-Hochwald National Park located in Rhineland-Palatinate, Germany (Figure 1). The plot centers are arranged in a regular 2 km \(\times\) 2 km grid. The plot centers were measured with differential GPS and projected into the ETRS89/UTM coordinate system.  
In the BWI, a tree is recorded as a sample tree according to the angle count sampling technique [32]. As the BWI uses an angle count factor of 4, a tree is hence recorded if its distance to the plot center is less than 25 times its DBH. A further selection criterion for a tree to be recorded is a DBH of at least 7 cm. For each sampled tree, its species and DBH are recorded, whereas the tree height is measured only for a subset of the sample. The remaining tree heights are estimated using regression models based on the DBHs. Finally stand-specific taper functions [33; 34] are applied. The tree location is determined by measuring the distance to the plot center and the corresponding azimuth angle.  
As a result of this sampling technique, each plot is characterized by an individual maximum radius (determined by the most distant tree) as well as the maximum limiting circle (maximum distance where the strongest tree would still have been selected). The angle count sampling technique realizes the sample inclusion probability of a tree being proportional to its diameter. Therefore, this technique prefers in particular the selection of diameter strong (usually tall) trees.  
We focused on plots with at least two recorded trees of at least 4 m height, which resulted in 133 plots (65 plots surveyed 2011 and 76 surveyed 2012). These plots comprise 1015 trees in total, consisting of 43.3% Norway spruce (_Picea abies_), 37.6% European beech (_Fagus silvatica_), 4.8% sessile oak (_Quercus petraea_), 4.4% Douglas fir (_Pseudotsuga menziesii_), 4.1% European larch (_Larix decidua_), 3.3% European white birch (_Betula pendula_), and 2.4% others. Detailed information on the investigated plots is summarized in Table 1. As an indicator for the GNSS satellite constellation, the horizontal dilution of precision (HDOP, c.f. [36]) is specified.
## 2 Materials and Methods
### Preprocessing Method of DLLME/HS-SPME  
Four types of seafood such as dried squid, instant squid larvae, instant kelp, and instant small yellow croaker were selected as test samples. Two grams of each sample was mixed with 10 mL of deionized water in a centrifuge tube. In order to extract maltol and ethyl maltol sufficiently from a sample, the mixture was homogenized (6000 rpm) for 3 min with a high-speed homogenizer (XHF-DY, Scientific, 84 Ningbo, China), and then treated with ultrasound for 10 min (SB-800 DT, Ningbo, China). After ultrasonic treatment, the sample aqueous solution was centrifuged (4000 rpm) for 5 min. The supernatant was collected, and then further treated using the DLLME technique.  
Food matrix components (such as protein and fatty) interact with organic solvent in DLLME to cause an emulsifying phenomenon, which reduces the volume of the separated organic phase [31]. In DLLME, high preconcentration factor and sufficient volume of precipitated phase must be ensured for further analysis after centrifugation. Generally, the volume of dispersant and extractant are in the ranges of 50-3000 \(\mu\)L and 15-2000 \(\mu\)L, respectively [31]. In this study, extractant (500 \(\mu\)L) and dispersant (1.5 mL) were mixed, and then injected into the sample supernatant (2 mL). After centrifugation (6000 rpm) for 3 min, 200 \(\mu\)L of the lower organic phase mixed with 15 \(\mu\)L of cyclohexanone standard (50 mg/L) was transferred into 20 mL headspace vials and further evaporated under vacuum conditions to eliminate the organic solvent. The dried sample was incubated at a constant temperature, and then further extracted by head-space SPME (DVB/CAR/PDMS, 50/30 m). In this study, the optimal conditions for the DLLME treatment were selected by comparing the effects of different extractants (dichloromethane, chloroform, and carbon disulfide), dispersants (methanol, acetonitrile, and acetone), extractant-to-dispersant volume ratios (1:4, 2:5, and 1:2), and ratios of water sample volume to total volume of dispersant and extractant (2:3, 4:5, and 1:1) on the extraction effect of flavor enhancers. The optimal conditions for the HS-SPME treatment were selected by comparing the results of different incubation temperatures (40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C), incubation times (10 min, 15 min, 20 min, and 25 min), and extraction times (10 min, 20 min, 30 min, and 40 min) on flavor enhancer extraction.
# Tightly-Coupled Data Compression for Efficient Face Alignment
## 3 Tightly-Coupled Data Compression Algorithm
### Compressed Model Data Storage Arrangement  
The final compressed model consists of the stacked version of all \(D\) steps of regressors \(\mathbf{r}_{d}\). Each regressor \(\mathbf{r}_{d}\) consists of three parts: \(Q\) quantized values for the projection matrix; the quantized projection matrix \(\mathbf{AQ}_{d}\) that corresponds to \(\mathbf{A}_{d}\); the bias term \(\mathbf{b}_{d}\).  
The quantized values are stored with single-precision floating point numbers. There are \(Q\) single-precision floating point numbers for each regression step.  
The quantized projection matrix \(\mathbf{AQ}_{d}\) has the same dimension as that of \(\mathbf{A}_{d}\). Each element in \(\mathbf{AQ}_{d}\) is an index to the quantized value, which was described above. Since there are \(Q\) different quantized values, each element in \(\mathbf{AQ}_{d}\) only consists of \(\log_{2}Q\) bits that usually consume much fewer bits than 32-bit floating point numbers. Through \(\mathbf{AQ}_{d}\), the corresponding quantized value can be fetched according to the index, and the approximate projection matrix can be reconstructed. In such a way, the data compression purpose can be achieved. Throughout this paper, we chose \(Q=64\), which is justified in the experimental results section.  
The bias term \(\mathbf{b}_{d}\) is stored directly as a floating point number since its size is relatively small, as stated before. Figure 6 illustrates the diagram of the compressed data storage arrangement.  
Figure 5: The flow chart of the proposed tightly-coupled training algorithm.
## 1 Introduction
In this paper, a rapid-erection backstepping tracking controller for the electrohydraulic lifting mechanism of the launcher system with asymptotic stability is developed. To deal with the work, firstly, the dynamic equations and state-space forms of the electrohydraulic lifting mechanism of the launcher system are modeled. Based on the state-space system function, a nonlinear robust controller is constructed to achieve the improvement of the system control performance, in which a nonlinear feedback term is employed to remove the effects of uncertainties and disturbances on tracking performance. In the meanwhile, based on the Lyapunov theory analysis, the asymptotic tracking stability of the closed-loop system can be assured. In the end, the simulation example of an actual electrohydraulic lifting mechanism of the launcher system is carried out to validate the effectiveness with the proposed controller. The main contributions of this article include the following aspects. (1) A rapid-erection backstepping robust control with asymptotic tracking performance is developed for the electrohydraulic lifting mechanism of launcher systems subject to mechanism nonlinearities, friction nonlinearities and time-variant disturbances. (2) A continuous nonlinear feedback term is adopted to strengthen the robustness of the system and realize the improvement of the control accuracy. (3) The control merits of the presented control method are tested by corresponding simulation results when compared to the existing controllers containing feedback linearization control in [11], robust control and proportional-integral control. The arrangement of this paper is provided as follows: System modeling of the electrohydraulic lifting mechanism of the launcher system can be seen in Section 2. The controller design and its theoretical analysis are provided in Section 3. Simulation results are found from Section 4. The simulation results uncover the validity of the developed control method. The conclusion is made in Section 5.
# Hybrid Wind-Redox Flow Battery System for Decarbonizing Off-Grid Mining Operations
## 5 Validation of the Global Model  
The global model integrates the four sub-models presented earlier, providing a comprehensive simulation of the Raglan energy system. This model accounts for both existing infrastructure and newly proposed installations, ensuring a realistic representation of energy production, storage, and consumption at the site.  
To guarantee accuracy and computational efficiency, the MATLAB-based program has been thoroughly tested for  
* Error-free execution;
* Optimized calculation times;
* Seamless operation with a single command.  
The MATLAB simulation results are automatically exported to an Excel file using the \"writetable\" function to facilitate further analysis and generate structured data tables.  
Its predictions were compared against actual operating data from the Raglan mining site to validate the model's accuracy. The results are presented in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **Modeled Value** & **Real Value** & **Relative Error (\%)** \\ \hline \hline \begin{tabular}{c} Diesel consumed \\ by generators (L) \\ \end{tabular} & 37,979,558 & 37,141,356 & 2.26 \\ \hline \begin{tabular}{c} Diesel consumed \\ by boilers (L) \\ \end{tabular} & 111,815 & 111,182 & 0.57 \\ \hline \
\begin{tabular}{c} Total diesel \\ consumed by the \\ 25 kV network (L) \\ \end{tabular} & 38,091,374 & 37,252,539 & 2.25 \\ \hline
\begin{tabular}{c} Wind power \\ production (MWh) \\ \end{tabular} & 16,286 & 16,871 & 3.59 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between actual plant data and the present work.  
Figure 13: Thermal part management.  
The low relative errors observed in all key parameters confirm the accuracy and reliability of the simulation. The results demonstrate that the model provides a realistic approximation of the Raglan mine's energy system, making it a valuable tool for assessing new renewable energy integration and optimizing system performance.