esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation of Polyphenols by High-Performance Liquid Chromatography (HPLC) in Formulated Aggregates  
Concentrations of individual polyphenols in tart cherry/dairy protein aggregates were determined using an Agilent HPLC system 1260 Infinity II (Agilent Technology, Santa Clara, CA, USA). The system consisted of a quaternary pump, a vial sampler and a column (Poroshell 120 EC C-18; 4.6 \(\times\) 100 mm, 2.7 um). Mobile phase A was 0.1% orthophosphoric acid, and mobile phas B was 100% methanol. The gradient used was previously published in the study by [PERSON] et al. [31]. Before injection into the system, extracts were filtered through PTFE filters (pore size 0.2 um). The flow rate was set at 1 mL/min and the injected volume was 5 um. Each extract was injected two times. UV/Vis spectra were recorded in the range from 190 to 600 nm. Peaks were identified by comparing the UV-Vis spectra of peaks in extracts and retention times. The calibration curves were constructed for all standards of polyphenols (cyanidin-3-rutinoside, neochlorogenic acid, chlorogenic acid, \(p\)-coumaric acid, rutin, quercetin and (\(-\))-epicatechin) and calibration curve linearity was confirmed by r\({}^{2}\) > 0.99. Cyanidin-3-glucosyl-rutinoside was identified according to the literature [32] and quantified using the cyanidin-3-rutinoside curve. The concentration of \(p\)-coumaric acid derivate was calculated using the calibration curve constructed for \(p\)-coumaric acid. Concentrations of identified polyphenols were expressed as mg of polyphenol per kg of tart cherry/protein aggregates (mg/kg).
## 2 Dynamics Model of a Robot
\(m\) is the mass of the ROV, \(I\) is the moment of inertia, and \(X_{\dot{u}},Y_{\dot{v}},Z_{\dot{w}}\) represent the hydrodynamic forces induced by the added mass in the \(x\), \(y\), and \(z\) directions, respectively, with unit acceleration along the \(\dot{u}\), \(\dot{v}\), and \(\dot{w}\) axes. \(N_{\dot{r}}\) represents the additional inertial force generated by the unit angular acceleration \(\dot{r}\) in the direction of the \(z\)-axis. \[C_{A}(v)= \begin{bmatrix}0&0&0&Y_{\dot{v}}v\\ 0&0&0&-X_{\dot{u}}u\\ 0&0&0&0\\ -Y_{\dot{v}}v&X_{\dot{u}}u&0&0\end{bmatrix} \qquad\qquad C_{RB}(v)= \begin{bmatrix} 0&0&0&-mv\\ 0&0&0&mu\\ 0&0&0&0\\ mv&-mu&0&0\end{bmatrix}  \tag{6}\]  
\[C_{0}(v)=C_{RB}+C_{A}= \begin{bmatrix}0&0&0&-(m-Y_{\dot{v}})v\\ 0&0&0&(m-X_{\dot{u}})u\\ 0&0&0&0\\ (m-Y_{\dot{v}})v&-(m-X_{\dot{u}})u&0&0\end{bmatrix} \]  
In Formula (6), \(C_{0}(v)\in R^{4\times 4}\), \(C_{0}(v)=C_{RB}+C_{A}\), where \(C_{RB}\) represents the matrix encompassing the rigid body Coriolis force and centripetal force, while \(C_{A}\) denotes the matrix accounting for the Coriolis force and centripetal force resulting from the added mass of the inertial fluid dynamics [25]. \[D_{0}(v)=- \begin{bmatrix}X_{u}+X_{u|u|}|u|&0&0&0\\ 0&Y_{v}+Y_{v|v|}|v|&0&0\\ 0&0&Z_{w}+Z_{w|w|}|w|&0\\ 0&0&0&N_{r}+N_{r|r|}|r|\end{bmatrix}  \tag{7}\]  
In the Formula (7), \(D_{0}(V)\in R^{4\times 4}\) denotes the damping matrix, which arises from the effects of viscous fluid dynamics on the robot. The symbols \(X_{u},Y_{v},Z_{w},N_{r}\) and \(X_{u|u|},Y_{v|v|},Z_{w|w|},N_{r|r|}\) correspondingly denote the primary and secondary hydrodynamic damping coefficients that emerge during the motion of the underwater robot [25]. \[g(\eta)= \begin{bmatrix}0&0&-(W-B)&0\end{bmatrix} ^{T} \tag{8}\]  
In Formula (8), \(g(\eta)\) is represented as the restoring force and moment caused by gravity and buoyancy [25]. \(W\) is the gravity of the underwater robot, and \(B\) is the buoyancy. In the physical structure design of the ROV, buoyancy is equal to gravity, so it can be further expressed as: \(g(\eta)= \begin{bmatrix}0&0&0&0\end{bmatrix} ^{T}\). In Formula (3), \(\tau\in R^{4\times 4}\) represents the thrust generated by the propeller, expressed as \(\tau= \begin{bmatrix}F_{x},F_{y},F_{Z},N_{Z}\end{bmatrix} ^{T}\), where \(F_{x},F_{y},F_{Z}\) are the thrusts generated by the propeller along the three coordinate axes, and \(N_{Z}\) are the moments generated by the propeller around the coordinate axes. In Formula (3), \(\Delta f\in R^{4\times 4}\) represents external disturbances such as ocean currents in the working environment, and \(\tau_{\Delta}\in R^{4\times 4}\) represents the uncertainty of dynamic lumped parameters, where \(\tau_{\Delta}=-\Delta M\dot{v}-\Delta C(v)v-\Delta D(v)v\). For the subsequent design of LADRC, we unify the model parameter uncertainty terms \((\Delta M,\Delta C(V),\Delta D(V))\) and external disturbances as total disturbances.
## 4 Experimental Results  
In our experiment, nine standard gray level images including (a) Tank, (b) Bridge, (c) Elaine, (d) Lena, (e) Peppers, (f) Wine, (g) Goldhill, (h) Bird, and (i) Baboon, as shown in Figure 18, are applied, each of them is sized as \(512\times 512\). VQ compression is executed with a codebook of length 256, and all images are divided into blocks of size \(4\times 4\). Images of different features, smooth or complex, are included. The platform for implementation are Intel Core i5-9400F CPU at 2.90 GHz and an 8 GB RAM personal computer with Windows 10 Professional operating system. The output of three processing steps for data hider are investigated, which are the visual quality of VQ images, the effectiveness of side-match evaluation, and the compression performance of SOC technique.
## 2 Materials and Methods
### Methodology  
We evaluated the effectiveness of LR (a statistical analysis technique) and ensemble models (namely, RF and XGBoost) for co-seismic landslide susceptibility modeling. The methodological flow of this study is depicted in Figure 9, while the entire procedural framework can be systematically categorized into three distinct phases as follows.  
Figure 8: Anthropogenic factors: (**a**) distance to road, (**b**) LULC, and (**c**) NDVI.  
1. Data collection: The preliminary phase encompasses the thorough acquisition of landslide inventory data. This compiled dataset is subsequently divided into two subsets: one designated for model training and the other for result validation. This initial phase entails the collection of diverse data types, including digital elevation models (DEMs), precipitation, slope, aspect, curvature, proximity to fault lines, road networks, water streams, elevation, geological features, land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), peak ground acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), and earthquake-induced landslide inventory data. These datasets are processed to derive landslide conditioning factors (LCFs) utilizing ArcGIS pro and the Python (Ver. 3.11.11) programming environment.
2. Model application: The subsequent phase involves the aggregation of landslide causative factors (CFs). These factors are thereafter subjected to analysis through three widely recognized supervised ML models: RF, LR, and XGBoost. The analytical process is conducted using the training dataset. The data are partitioned into a training set (80%) and a testing set (20%). The algorithms undergo training with the training set to discern the most significant factors via feature importance analysis.  
Figure 9: Workflow for predicting earthquake-induced landslide susceptibility using ML.  
Result validation and prediction: The final phase involves accuracy assessments of the models using performance metrics like the performance evaluation matrix and Area Under the Curve (AUC) values. The results from the ML models are combined to generate a co-seismic landslide susceptibility map.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 6 Existing Methods and Proposed Solution
### Proposed Solution
The pseudocode is shown in Algorithm 1 below:  
```
1:I/P:Downlink\(\delta_{eff}\) reported by the UAVs
2:O/P:Power Control Commands to optimise received \(\delta\)
3:Initialise states, time, replay buffer \(\mathcal{D}\) and actions until convergence or termination until \(t\geq T\)
4:Increment t
5:Current state observed is \(s_{t}\)
6:\(\epsilon:=\max(\epsilon\cdot d,\epsilon_{min})\)
7:Sample \(r\,r\leq\epsilon\)
8:\(a_{t}\in\mathcal{A}\) is randomly selected
9:\(a_{t}=\arg\max_{a^{{}^{\prime}}}Q_{\pi}(s_{t},a^{{}^{\prime}};\varphi_{t})\)
10:Calculate \(\delta^{l}_{eff}[t]\) and \(w_{s^{\prime},a^{{}^{\prime}}}[t;q]\)\(\delta^{l}_{eff}[t]<\delta_{min}\)
11:\(w_{s^{\prime},a}[t;q]:=w_{min}\)
12:Terminate episode
13:Next state \(s^{\prime}\) is observed
14:Save \(i[t]\triangleq(s_{t},a_{t},w_{s^{\prime},a^{{}^{\prime}}},s^{{}^{\prime}})\) in \(\mathcal{D}\)
15:Minibatch experience sample from \(\mathcal{D}\): \(i_{j}\triangleq(s_{t},a_{t},w_{j},s_{j+1})\)
16:\(\gamma_{i}:=w_{j}+y_{max^{\prime}}Q_{\pi}(s_{j+1},a^{{}^{\prime}};\varphi_{t})\) is set
17:Stochastic Gradient Descent is performed on \((y_{i}-Q_{\pi}(s_{j},a_{j};\varphi_{t})^{2})\), and determine \(\varphi^{*}\)
18:Update \(\varphi_{t}:=\varphi^{*}\) in the DQN and calculate loss \(L_{t}\)
19:\(s_{t}:=s^{{}^{\prime}}\)\(\delta^{l}_{eff}[t]\geq\delta_{target}\)
20:\(w_{s^{\prime},a}[t;q]:=w_{s,s^{{}^{\prime}},a}[t;q]+w_{max}\)
21:end
```  
**Algorithm 1** Deep Q-Learning Algorithm for UAV interference mitigation  
DQL is an efficient solution to the problem of interference as it does not require channel CSI (unlike traditional link adaptation techniques) to calculate SINR. Additionally, it also reduces the requirement for UAVs to provide feedback to the gNB. Moreover, DQL also decreases the need for UAVs to send feedback to the gNB. Currently, as per the methods documented in existing works, UAVs need to report their CSI, leading to an increase in overhead and complexity due to the large amount of information (in the form of CSI) being transmitted in systems. The proposed DQL algorithm offers a more streamlined communication mechanism between UAVs and gNBs. The UAVs are only required to transmit their SINR and coordinates, leaving the agent in charge of power control. This stands in contrast to conventional practices in the industry, which usually necessitate the transmission of specific commands for power control to both the associated and interfering gNBs. The associated gNB need only transmit power control commands to the UAV, thus reducing the amount of transmitted information and simplifying the overall communication process. Table 5 summarises the symbols used.
# A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
## 2 Theoretical Background
### Anomaly Detection  
Anomaly is a general term which is defined as recordings or transactions that differ from the common behavior of the data transactions. Identifying the abnormal behavior of the data is called anomaly detection. As mentioned in [10], anomaly detection is widely used in many areas such as credit card fraud, insurance fraud, application fraud, and intrusion detection. In these studies, there may be contextual anomaly behaviors as well as collective anomaly behaviors on the data sets [11]. The literature provides some studies employing analytical techniques such as machine learning and deep learning for analyzing anomalous and respective data patterns [12].
# A Multi-Robot Formation Platform based on an Indoor Global Positioning System
## 2 Indoor Multi-Robot Formation Platform
### Multi-Robot Communication and Monitoring System  
For a reliable execution of coordination tasks by multi-robot, communication between robots is a key issue, and in order to observe the experimental process, the in-process data also should be recorded. In this paper, the TCP/IP wireless communication infrastructure is selected to support the communication among robots and the data collection in the process of experiments.  
As a low-power and highly integrated Wi-Fi module, ESP8266 incorporates a firmware and provides a simple means of wirelessly communicating. The module has a complete and self-contained Wi-Fi network capability that can be either alone or as a slave to other host MCUs [31]. So, it is available to support communication in our multi-robot formation platform.  
Figure 3 illustrates the network architecture showing the data collection terminal and a number of mobile robots. Wi-Fi module ESP8266 mounted on each robot can communicate with the microcontroller via interruptions using the UART at the speed of 38,400 bps. Besides, the module ESP8266 is connected with PC as a wireless data collection terminal through USB-TLL.  
Figure 3: Multi-robot wireless network architecture.  
In order to communicate with each other, the ESP8266 module needs to be connected to the same Wi-Fi network. Under the same network, the communication among robots will be established when one of the robots is considered as TCP servers and others join the servers as clients. The point-to-point connection between the server robot and the client robot can avoid information loss, and extend the number of server robots according to task requirements. Further details about this application in leader-follower formation will be discussed in next part.  
The ESP8266 supports the TCP/IP protocol and fully complies with the 802.11 b/g/n WLAN MAC protocol. The master host can achieve full duplex data transmission of up to four slaves. The clock frequency in master mode is up to 80 MHz, and the clock frequency in slave mode is up to 20 MHz.  
As for the computer screen, shown in Figure 4, the real-time monitoring system has been developed in LabVIEW to record the pose information and monitor each robot trajectory. The ESP8266 module connects with PC through USB-TTL, so the PC can get the information collected by the ESP8266 module.  
The real-time monitoring system reads the information in the multi-robot task process using the function of LabVIEW's serial port read, and through the image drawing VI, robot trajectory is recorded. To facilitate data analysis and processing, all strings in the buffer are sorted according to the title of each data frame and record in the table.  
In the multi-robot formation platform, the communication method is distributed, and the distributed architecture is originally designed for scalability, although we only use several robots to form a team. Because of the distributed architecture and its communication methods, there is not much limit on the number of follower robots.
## 3 Experimental Results
### The Used Regression Datasets  
The descriptions for these datasets are provided below:  
1. **Abalone** dataset [93].
2. **Airfoil** dataset, which is a dataset derived from NASA [94].
3. **BK** dataset [95], which contains data from various basketball games.
4. **BL** dataset, which contains data from an electricity experiment.
5. **Baseball** dataset, which is used to predict the average income of baseball players.
6. **Concrete** dataset [96].
7. **Dec** dataset, which contains measurements of the price of electricity.
8. **FY,** which is used to measure the longevity of fruit flies.
9. **HO** dataset, which appeared in the STALIB repository.
10. **Housing** dataset, which originated in [97].
11. **Laser** dataset, which contains data from physics experiments
12. **LW** dataset, which contains measurements of low weight babies.
13. **MORTGAGE** dataset, which is related to some economic measurements from the USA.
14. **MUNDIAL,** which appeared in the STALIB repository.
15. **PL** dataset, which is included in the STALIB repository.
16. **QUAKE** dataset, which contains data about earthquakes.
17. **REALSTATE,** which is included in the STALIB repository.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DATASET** & **INPUTS** & **CLASSES** \\ \hline HABERMAN & 3 & 2 \\ \hline HAYES ROTH & 5 & 3 \\ \hline HEART & 13 & 2 \\ \hline HEART ATTACK & 13 & 2 \\ \hline HOUSEVOTES & 16 & 2 \\ \hline LIVERDISORDER & 6 & 2 \\ \hline IONOSPHERE & 34 & 2 \\ \hline MAMMOGRAPHIC & 5 & 2 \\ \hline PARKINSONS & 22 & 2 \\ \hline PIMA & 8 & 2 \\ \hline POPFAILURES & 18 & 2 \\ \hline REGIONS2 & 18 & 5 \\ \hline SAHEART & 9 & 2 \\ \hline SEGMENT & 19 & 7 \\ \hline SPIRAL & 2 & 2 \\ \hline STUDENT & 5 & 4 \\ \hline TRANSFUSION & 4 & 2 \\ \hline WDBC & 30 & 2 \\ \hline WINE & 13 & 3 \\ \hline Z\_F\_S & 21 & 3 \\ \hline Z\_O\_N\_F\_S & 21 & 5 \\ \hline ZO\_NF\_S & 21 & 3 \\ \hline ZONF\_S & 21 & 2 \\ \hline ZOO & 16 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._* **SN** dataset, which contains measurements from an experiment related to trellising and pruning.
* **Treasury** dataset, which deals with some factors of the USA economy.
* **VE** dataset, which is included in the STALIB repository.
* **TZ** dataset, which is included in the STALIB repository.  
The number of inputs for each dataset is provided in Table 2.
# Optimal Scheduling of Hydro-PV-Wind Hybrid System Considering CHP and BESS Coordination
## 1 Introduction
[PERSON] [36] proposed an optimization method for sizing and scheduling BESS and the smart inverter (SI) of a photovoltaic (PV) system to ensure the PV system owner's investment returns and to assist the distribution system operator (DSO) in adjusting the voltages. [PERSON] [37] proposed an intelligent control strategy for a grid connected hybrid energy generation system consisting of photovoltaic (PV) panels, fuel cell (FC) stack, and BESS. [PERSON] [38] put forward the integration of RES considering the installation of a battery energy storage system (BESS) into an isolated power grid to keep the costs down. However, to our knowledge, the concept of coupling all the above energy together has not been proposed in the existing literature. In this paper, we coupled the photovoltaic modules (PV), a wind turbine (WT), battery energy storage modules (BESS), electric vehicle chargers (EV), CHP, and a hydroelectric power plant as a portfolio, in an attempt to reduce the imbalance and ultimately minimize the cost of the portfolio considering risk factor. Based on the perspective of the concept, a renewable-based hybrid energy system is proposed in the paper, and an optimal scheduling model of this system to minimize the cost of operation and risk is put forward considering multiple uncertainties, which include renewable resource volatility, the load demand, and different energy service providers' coefficients of risk aversion. To handle this complex optimization problem, a method combining the Latin hypercube sampling, scene reduction, and piecewise linearization is proposed. A large number of samples were generated with the Latin hypercube sampling method according to the uncertainties, including the renewable resources availability, the load demand, and the risk aversion coefficients, and the generated samples were reduced with the scene reduction method. Additionally, the piecewise linearization method was applied to convert nonlinear constraints into linear to obtain the best results of each scene. In summary, the main contributions of this paper can be listed as below:  
1. Propose a concept of a renewable-based hybrid energy system along with a corresponding mathematical model which can be used to simulate and optimize its performance. 2. Introduce an optimal optimization model which focuses on minimizing the operating cost of energy service providers considering the environment as much as possible. 3. Investigate the distinction of uncertainty variables volatility on the energy exchange with the power grid. 4. Research the impact of different risk aversion coefficients on the operation of energy service providers. The remainder of this paper is organized as follows. Section 2 formulates the mathematical model for the operation of the hybrid power system. In Section 3, the solution method is described in detail. In Section 4, numerical simulations of the proposed model are applied to a test system on real-life data, and discussions are provided accordingly. Finally, the conclusions of the paper are drawn in Section 5.
## 4 Experimental Results and Analysis
### Torsional Moment versus Twist Response  
In this study, cyclic load testing with eccentric lateral forces was conducted to consider the effects of torsion on reinforced concrete columns during actual earthquakes. Based on the experimental results, the torsional moment and twist angles were calculated, and the relationship between them was used to analyze the seismic performance of the RSL system. The twist and torsional moment were determined using Equations (3) and (4), respectively. Specifically, to calculate the twist, Linear Variable Differential Transformers (LVDTs) were installed at the top of the columns, as shown in Figure 13, and the twist was determined by the difference in displacement measured by the LVDTs. \[\mathrm{M_{i}}=\mathrm{P}\times\ell\times\mathrm{cos}\theta_{i} \tag{3}\]  
\[\theta_{i}=\mathrm{tan}^{-1}\left(\frac{\Delta_{2}-\Delta_{1}}{\mathrm{d}}\right) \tag{4}\]  
Here, \(M_{i}\) is the torsional moment generated in the specimen at the i-th drift ratio, \(\mathrm{P}\) is the maximum load at the i-th drift ratio, \(\ell\) is the eccentric distance, \(\theta_{i}\) is the twist of the column cross section, \(\mathrm{d}\) represents the distance between the LVDTs, and \(\Delta_{1,2}\) denotes the displacement value measured by the LVDTs. Figure 12: Load–displacement curves: (**a**) 10 mm sliding slot length and (**b**) 5 mm sliding slot length. Figure 14 shows the torsional moment-twist envelopes for each specimen. As NE-5 and NE-10 had no eccentric distance and, thus, no torsion, the torsional moments and twist angles for specimens E65-5, E130-5, and E130-10, which were subjected to eccentric lateral loads, were analyzed. The torsional moments and twist angles at key points for these specimens are summarized in Table 4. For all specimens, the twist angles and torsional moments were calculated from the first cycle at each drift ratio. At all drift ratios, the torsional moments and twist angles for E130-5 and E130-10, which had a larger eccentric distance, were significantly higher than those for E65-5. This is attributed to the greater torsion induced in the specimens as the eccentric distance increases. Comparing the torsional moments and twist angles of E65-5 and E130-5 at the point of major crack initiation, the torsional moment and twist angle for E130-5 were approximately 1.7 and 2 times higher than those for E65-5 at the initial crack, 1.5 and 1.1 times higher at the shear crack, and 1.7 and 1.2 times higher at the maximum values, respectively. After the initial crack, both specimens showed reduced stiffness due to crack initiation, leading to a slight decrease in the difference attributed to the eccentric distance. Subsequent reduction occurred in the increase of twist angle with load increase as the RSL system began to resist torsion. The reductions in torsional moment from maximum to the end of the experiment for E65-5 and E130-5 were 80% and 70%, respectively, indicating more frequent brittle failure due to increased torsion with larger eccentric distances.