esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methodology  
We propose the automated system for predicting water quality using IoT technology which is shown in Figure 1. The system leverages the AWS (version 2) cloud infrastructure for its scalability, reliability, and wide range of services. Terraform (version 2) is employed to define and provision the infrastructure as code, ensuring consistency and reproducibility. The system comprises several key components: data collection, storage, preprocessing, prediction, and visualization. * Data collection: Sensors are deployed in the aquatic environment, including lakes, large rivers, and canals, to collect raw water quality data. We have limited our research to a few primary parameters that are crucial for calculating the water quality index. The parameters in question are pH, DO, COD, and NH3-N. For the sensors, we used the ammo::lyser eco sensor module from the sca::n manufacturer. The Zigbee module is employed to transmit the collected data to a central system via wireless communication networks. * Data preprocessing: To prepare raw data for ML, they are subjected to preprocessing. This entails the transformation and cleansing of and potential reductions in the data to enhance the efficacy of the model. Additionally, Telegrat (version 1.17) is essential in that it is capable of determining the intervals of the data. The sensors receive data every second, but there are instances in which we do not require every second of data. Consequently, we can establish the intervals we desire using Telegrat. * Water quality prediction: The water quality is predicted by feeding the preprocessed data into a built-in machine learning model. An ensemble model, which is more precise and resilient, was implemented in this investigation. The water quality was predicted by feeding the preprocessed dataset, consisting of 214 instances, into an ensemble of ML models. In this paper, we implemented a hard-voting ensemble approach to improve the prediction accuracy and robustness. The ensemble combined three distinct models: XGB, RF, and HGB. Each model independently predicted the water quality class, and at the end of the process, we decided by a majority vote on the models' outputs. This hard-voting approach ensured that the ensemble capitalized on the strengths of each model, resulting in more precise and accurate predictions than those made by individual models. * Data storage: InfluxDB, which is hosted on AWS, was employed for data storage. InfluxDB is specifically engineered to facilitate the management of time series data.
## 2 Background
### Power Transmission Recording  
DFRs are designed to continuously monitor and store power line transmissions. DFRs read the voltage and current waveforms at an event sampling rate, \(f_{s,e}\), which is typically greater than the continuous storage sampling rate, \(f_{s}\). DFRs store an event's \(f_{s,e}\)-sampled waveform for subsequent analysis. The three-phase voltage and current waveforms are downsampled to the storage sampling rate and recorded in an OSG file regardless of whether an event was detected within the twenty-four-hour monitored period. These OSG files typically require 20 Gb to 50 Gb of memory based on the number of monitored lines. Other datasets, such as Oak Ridge National Laboratory's Grid Event Signature Library, provide various fault types [17]. To our knowledge, no publicly available databases provide recordings that include the twenty-four hours preceding the detected fault. The authors hope that other power-quality researchers can use our open-source software to create long-term 24/7 public datasets for further power-quality research [4]. It is estimated that a year's worth of histograms, generated at one-hour intervals from two hundred deployed DFRs with an average of six lines (i.e., three voltage and three current), would require 12 Gb of storage space, thus providing a tractable means for creating a histogram-based library.  
Figure 2: Process overview for calculating power signal histograms from an OSG file storing power signals recorded by a digital fault recorder.  
Such a library would allow for new, timely preventative or corrective action methods that enhance power grid operations by predicting various event types.
## 4 Materials and Methods
### Oxidative Stress Biomarkers  
For enzyme extractions, leaf samples of \(H\). _portulacoides_ stored at \(-\)80 \({}^{\circ}\)C were used. Extractions were performed at 4 \({}^{\circ}\)C according to [PERSON] et al. [80] Frozen leaves were homogenized in 50 mM sodium phosphate buffer (pH 7.6) supplemented with 0.1 mM Na-EDTA in a ceramic mortar with a proportion of 500 mg (FW) to 8 mL respectively. The homogenate was centrifuged at 8890\(\times\) g, 20 min at 4 \({}^{\circ}\)C, and the supernatant was transferred to a test tube and used for the antioxidant enzyme analyses.  
The enzyme activity measurements of catalase (CAT, EC.1.11.6.), ascorbate peroxidase (APx, E.C. 1.11.11), guaicaol peroxidase (GPX, E.C. 1.11.7), and superoxide dismutase (SOD, E.C. 1.15.1.1) were performed in a dual-beam spectrophotometer (Shimadzu UV/VIS UV160I Spectrophotometer) using quartz cuvettes. Catalase activity assays were performed according to the method of [PERSON] et al. [81], by monitoring the H\({}_{2}\)O\({}_{2}\) consumption and consequent decrease in absorbance at 240 nm (molar absorbance coefficient of 39.4 mM\({}^{-1}\) cm\({}^{-1}\)). Ascorbate peroxidase was measured according to [PERSON] et al. [80], by observing the ascorbate oxidation and consequent absorbance reduction at 290 nm (molar extinction absorbance of 2.8 mM\({}^{-1}\) cm\({}^{-1}\)). Guaicaol peroxidase measurement was performed according to [PERSON] et al. [82], by monitoring guaicaol oxidation products formation and its increase in absorbance during 60 s at 470 nm (molar extinction absorbance of 26.6 mM\({}^{-1}\) cm\({}^{-1}\)). Superoxide dismutase total activity was assayed according to the method of [PERSON] and [PERSON] [83], by measuring the oxidation rate of pyrogallol monitored at 325 nm. For reference, the autoxidation of pyrogallol was read without added enzyme extract in a parallel sample. Protein quantification was performed according to the Bradford method [84].  
Quantification of peroxidation of membrane lipids was performed according to [PERSON] [85]. First, leaf samples were homogenized in a freshly prepared Thiobarbituric acid (TBA) solution (0.5% (\(w/v\)) TBA in 20% (\(w/v\)) Trichoroacetic acid), in a proportion of 100 mg FW to 2 mL of solution. The homogenate was incubated for 30 min at 95 \({}^{\circ}\)C, cooled on ice to stop the reaction and centrifuged at 4000\(\times\)\(g\), 5 min at 4 \({}^{\circ}\)C. The absorbance was read at 532 nm and 600 nm in a Shimadzu UV-1601 spectrophotometer. Malondialdehyde-hyde (MDA) concentration was calculated using absorbance coefficient, 155 mM\({}^{-1}\) cm\({}^{-1}\) applying the following equation:  
\[A_{532\ nm}-A_{600\ nm}=[MDA]mM\times eMDA\]
# Affine Formation Maneuver Control for Multi-Agent Based on Optimal Flight System
## 4 Formation Control with Virtual Leader
### Phase III  
The purpose of this part is to test the affine-based local trajectory planning algorithm in the pre-defined positions of obstacles to follow the desired path without collision. The positions of three obstacles are located at \([-10,-5,30,6]\), \([-10,-30,30,50]\), and \([-10,-40,30,6]\). The initial velocity of the virtual leader is set \(v^{l}=[40\text{ m/s},0\text{ m/s}]\) and it moves with constant velocity through all the paths with direction intervals \([-\pi/2,\pi/2]\). Meanwhile, based on the velocity of the virtual leader \(v^{l}=[45\text{ m/s},0\text{ m/s}]\), the velocities of agents in the formation are randomly generated with a standard normal distribution. The initial acceleration of the agents is set \([0\text{ m/s}^{2}]\). The formation starts at the random positions \([p_{1},p_{2},\ldots,p_{7}]\) to follow the desired path \(p_{d}\), planning the local trajectory. From Figure 9a, it is known that all the accelerations of agents have changed drastically and unpredictably, especially in the interval of \(t=200\text{ s--300 s}\), and would converge in the final. The main reason is that input of agents would increase or decrease suddenly when the forces among agents undergo changes. Figure 9b depicts the velocity error of the agents, indicating that the reference velocity is well tracked by both leaders and followers throughout the trajectory tracking process. The experimental results particularly demonstrate that the proposed algorithm enables the asymptotic tracking of the desired trajectory and remains effective even when the trajectory changes suddenly due to the formation steering local planning to avoid collisions during the motion process. Figure 9c illustrates that the minimum distance between agents consistently remains greater than 0, indicating successful collision avoidance for the formation and the maintenance of the desired configuration within the acceptable error range. Additionally, Figure 9d indicates that trajectory tracking is achieved satisfactorily.  
Figure 8: Simulation results of local trajectory planning: (**a**) initial state; (**b**) snapshot of the formation at avoiding collision; (**c**) velocity of agents; (**d**) velocity error of agents; (**e**) trajectory error of agents; (**f**) total velocity error of all agents.
# Improving Performance of a Resonant String-Based Pulsation Attenuator in Hydraulic Systems
## 4 Optimization of RSHPA
### Geometry Optimization
#### 4.1.3 Experiment  
To validate the proposed method, we designed and fabricated the test platform of RSHPA, as shown in Figure 10a. Figure 10b demonstrates the inner structure of the RSHPA. The schematic diagram of the test platform is shown in Figure 2 where the pressure sensors are mounted before and after the RSHPA, respectively. The accuracy of the pressure sensors is \(\pm\)0.25% and the maximum frequency that can be measured is 1 kHz. The pulsation source is generated by a plunger pump with 9 plungers. The rated pressure, flow and power of the pump are 25 MPa, 180 L/min and 93 kW, respectively. The length of upstream and downstream pipes is 2.11 m and 3.41 m, respectively. The diameter of the expansion chamber and pipes is 0.042 m and 0.15 m, respectively. The effective length of the expansion chamber and strings is both 0.35 m. The load in the test is a throttle valve, whose working flow is between the range of 10 L-200 L/min.  
Figure 11 demonstrates the comparison results between the theoretical curve and experiment data. It can be seen that when RSHPA only contains single string, _IL_ in 110 Hz and 220 Hz does not present expected attenuation strength. This is because the effective attenuation frequency band of the string is narrow and when there is some error in parameter values or fabrication, the string-fluid resonator cannot attenuate noise at the desired frequency. In contrast, the multi-string combination shows significant attenuation effect at the design frequency (220 Hz) and the other order response frequencies of the string. It proves that the multi-string combination can effectively broaden the attenuation band of the target frequency, improving the system tolerance for machining and value selection.  
Figure 10: Experiment platform for testing the RSHPA.  
Figure 9: Attenuation effects after extending the performance around 220 Hz.
## 1 Introduction
Under the action of hydrodynamic pressure, two limit pins are cut off, and the differential motion assembly is lifted to drive the inner pipe up over the valve cover. Then, the valve cover automatically closes under the action of gravity to maintain the in situ pressure. According to this working principle, the differential motion assembly plays a vital role in the process of IPP-Coring during onshore oil and gas exploration, and it directly determines the success rate of IPP-Coring. Unfortunately, in the field service process, due to the complex deep well environment characterized by high pressure and temperature, high flow rates, and solids content of the drilling fluid, the differential motion assembly is often faced with the failure of the seal of the ball and ball seat as well as sticking caused by surface erosion. In addition, in the case of large positive displacement pumps, the differential motion assembly may be lifted at the wrong time (the pin is cut off before the ball is dropped) due to the greater pressure drop, which contributes to the failure of IPP-Coring. Therefore, it is necessary to improve the hydraulic structure of the differential motion assembly to decrease the surface erosion and pressure drop to further increase its reliability and service life. To improve the hydraulic structure, it is necessary to have a deep insight into the flow characteristics of the drilling fluid in the differential motion assembly. Computational fluid dynamics (CFD) can effectively analyze the flow characteristics of downhole tools [33; 34; 35; 36; 37; 38]. However, there has been little research on flow characteristics in differential motion assemblies in the IPP-Coring systems used in deep oil and gas exploration. The effects of the internal size and structure of the differential motion assembly on the flow velocity and pressure distribution are unclear, and it is impossible to target the design of the hydraulic structure to reduce erosion and pressure drop. As a result, the operational reliability and service life of differential motion assemblies have been low, which would result in a low success rate of the IPP-Coring. To increase the operational reliability and service life of differential motion assemblies and increase the success rate of IPP-Coring, this study employed CFD with the standard k-\(\varepsilon\) turbulence model to study the characteristics of flows with different rates in a differential motion assembly during IPP-Coring. The effects of the diameter of the ball seat and ball seat outlet structure on the fluid status, velocity, and pressure distribution were analyzed. Further, the hydraulic structure of the differential motion assembly was improved, and field experiments demonstrated the erosion resistance of the improved structure.
# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 4 Results and Discussion
### Performance Statistics  
#### 4.2.1 POST Dataset  
Table 3 presents the performance of the best model obtained from the POST dataset for the validation set. On the other hand, Table 4 shows the best model performance of POST data for the test set. The highest performance in both tables was obtained by RF, followed by ELM, SVM, LR and MLP. As was expected, the test set experienced a low decrease in DICE and accuracy performance indicators, but an improvement for omission and commission errors.
# Analytical Performance Evaluation of Massive MIMO Techniques for SC-FDE Modulations
## 4 Receivers Design  
Since massive MIMO systems are expected to operate with a great number of antennas in the transmitter and the receiver, it is important that the receivers used in these systems have as few matrix inversions as possible. The necessary resources used in matrix inversion increase significantly with the number of entries in that matrix. The greater the number of \(R\) and \(T\), the higher the associated complexity.  
Throughout this section, different iterative receivers based on FDE are presented. In this type of receiver, the estimated signal at the \(i\)_th_ iteration for the \(kth\) subcarrier \(\widetilde{\mathbf{X}}_{k,p}^{(i)}\) is given by:  
\[\widetilde{\mathbf{X}}_{k,p}^{(i)}=\mathbf{F}_{k,p}^{T}\mathbf{Y}_{k}- \mathbf{B}_{k,p}^{(i)T}\overline{\mathbf{X}}_{k,p}^{(i-1)}, \tag{2}\]  
with \(\mathbf{F}_{k,p}^{T}=[F_{k,p}^{(1)},\cdots,F_{k,p}^{(R)}]^{T}\) denoting the feedforward coefficients and \(\mathbf{B}_{k,p}^{T}=[B_{k,p}^{(1)},\cdots,B_{k,p}^{(P)}]^{T}\) denoting the feedback coefficients, responsible to reduce the residual ISI after the first iteration. \(\overline{\mathbf{X}}_{k,p}^{(i-1)}\) represents the estimated signal for the previous iteration which is the Discrete Fourier Transform (DFT) of \(\overline{x}_{n,p}\). \(\overline{x}_{n,p}\) is selected according to a mapping rule, in this case, a Quadrature Phase-Shift Keying (QPSK) with Gray mapping, (i.e., \(x_{n,p}=\pm 1\pm j\)). The average values for \(\overline{x}_{n,p}\), according to [22], are given by:  
\[\overline{x}_{n,p}=\tanh\left(\frac{L_{n,p}^{Re}}{2}\right)+j\tanh\left(\frac {L_{n,p}^{Im}}{2}\right), \tag{3}\]  
where  
\[L_{n,p}^{Re}=\frac{2}{\sigma_{n,p}^{2}}Re\{\tilde{s}_{n,p}\}, \tag{4}\]  
\[L_{n,p}^{Im}=\frac{2}{\sigma_{n,p}^{2}}Im\{\tilde{s}_{n,p}\}, \tag{5}\]  
and  
\[\sigma_{n,p}^{2}=\frac{1}{2N}\sum_{n^{\prime}=0}^{N-1}\left|\tilde{s}_{n^{ \prime},p}-s_{n^{\prime},p}\right|^{2}\simeq\frac{1}{2N}\sum_{n^{\prime}=0}^{N -1}\left|\tilde{s}_{n^{\prime},p}-\tilde{s}_{n^{\prime},p}\right|^{2}. \tag{6}\]  
For the first iteration, and since there are no previous iterations, Equation (2) can be simplified to:  
\[\widetilde{\mathbf{X}}_{k,p}^{(i)}=\mathbf{F}_{k,p}^{T}\mathbf{Y}_{k}. \tag{7}\]  
Each FDE receiver has different equations for the \(\mathbf{F}_{k,p}\) and \(\mathbf{B}_{k,p}\) coefficients. Sections 4.1, 4.2 and 4.3 present the equations for three distinct receivers.
# ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles
## 5 Results and Analysis
### Introduction of Evaluation
Thus, in the results of Section 5.3, \(T_{\text{part}}\) and \(T_{\text{cont}}\) are used to demonstrate the improvement of ARNS in the curved road scenarios. \(T_{\text{part}}\) is expressed as an average value of the partition delay in each hop, and \(T_{\text{cont}}\) is expressed in the same way. **PDR** is expressed as a ratio of the number of successful broadcasting messages to the total number of simulations. Successful broadcasting means that no packet loss occurs during the entire broadcasting process. **Maximum hops \(N_{\text{maxhops}}\)** is expressed as the maximum number of hops that a message is broadcasted from Node S\({}_{0}\) to the terminations of RoI. **Broadcast coverage \(\gamma_{\text{cov}}\)** is expressed as a ratio of the length of the road covered by broadcasting to the length of the entire road.
## 2 Materials and Methods
### Architectures
#### 2.2.2 Localizer Network  
The localizer network finds the best point in the image for the center of the marker band in the selected region. In this network (Figure 3), the input is a region of the original image that was selected in the first network, albeit it is the ground truth region in the training procedure. These subimages are loaded with a higher resolution to match the needed size of 224 \(\times\) 224 pixels. The network's output is the X- and Y-coordinates of this subimage (i.e., between 0 and 223). Since the landmark location is in the local coordinate system of the subimage, it is then converted to the global coordinates of the original image. The architecture of this network is similar to the previous network apart from the last layer, which now has two FC layers with 9 and 2 neurons, respectively. Here, training is performed with 100 epochs per 5-fold split (with the lowest radial distance error as cost function), with the longest training occurring with 500 epochs. The network's output provides a prediction based on the last two neurons, which represent the X- and Y-coordinates of the landmark in the local coordinate system of the subimage.  
Figure 3: Schematic visualization of image segmentation in localizer network.  
Figure 2: Schematic visualization of image segmentation in region selection network.
## 2 System Description  
In the proposed system, there are two essential functions for UAV location awareness and sharing, which are the acquisition of UAV location using GNSS and information sharing via communication among the flying vehicles. According to the concept shown in Figure 1, the system can be set up in UAVs and manned flying vehicles to communicate with each other. Furthermore, there is a ground station that optionally works for monitoring the flying vehicles if this functionality is required. With this system, all the location information of the flying vehicles can be measured, shared, and managed.  
For the transmissions among UAVs, LPWA technology with 920 MHz is adopted in our developed system. Although there are some other possible candidates for V2V communications, such as Bluetooth or Wi-Fi in the 2.4 GHz band, these technologies work within a very short communication distance in some countries and may be affected by the existing wireless systems. Therefore, these technologies are not proper for communicating with manned aircraft. In this system, we use Frequency Shift Keying (FSK) modulation and LoRa (long-range) modulation with 920 MHz for transmissions. LoRa is widely used in LPWA technologies that can robustly transmit the shared information among UAVs in a very power-saving fashion.  
Based on the above V2V communication system, we develop an AFCCS that can be equipped in each UAV to expand its functionality. The AFCCS primarily consists of three key components: a wireless data collection and transmission device [21], a flight coordination control device developed based on Raspberry Pi (a series of small, low-cost, single-board computers designed and developed by the Raspberry Pi Foundation, a UK-based charity organization), and a flight controller. An illustration of the AFCCS device implementation is shown in Figure 2. In the following subsections, more detailed explanations about these components are provided.  
Figure 1: Concept of V2V communication system using Drone Mapper®.
## 3 Results and Discussion
### HC Emission  
Hydrocarbon (HC) emissions represent the quantification of hydrocarbons that have been efficiency burned during the process of combustion. Figure 12 illustrates the comparison of HC emissions for various hydrogen flow rates provided through induction and advanced fuel injection timing. Based on the experimental findings, the hydrocarbon flow amounts exhibited lower values of HC emissions capered to diesel and biodiesel blends. Therefore, a higher concentration of hydrogen results in a decrease in the emission of HC in the exhaust. Figure 12 depicts the HC emissions recorded in several engine configurations, namely, diesel (D100), DP20, DP20H5 (with 5% hydrogen), DP20H10 (with 10% hydrogen), and DP20H15 (with 15% hydrogen). Fuel blends emit less HC emissions due to the presence of hydrogen in the blends, which is owed to superior combustion characteristics of hydrogen [41; 42]. As the fuel injection timing increases, this leads to decreases in HC emissions by up to 23.5 b TDC.  
Figure 11: CO emissions with injection timing and hydrogen blends.  
Figure 12: HC emissions with injection timing and hydrogen blends.
# A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion
## 4 Experiment and Analysis  
To demonstrate the superiority of our method, we compared it with several advanced methods, including the dehazing-based method (Dong) [13], the illumination estimation-based model (LIME) [14], a multiscale Retinex with color restoration (MSRCR) [12], the multi-deviation fusion method (MF) [17], the naturalness-preserving enhancement method (NPE) [15], and the simultaneous reflection and illumination estimation method (SRIE) [16].  
Our method consists of three parts: the brightness transformation function (BTF) model, weights definition, and pyramid fusion. By using the brightness transformation function and appropriate adjustment parameters, two images with different brightnesses can be obtained. One image has a lower brightness and less detail but appears more natural. The other image is high in brightness and has excellent detail but also has some brightness distortion. We set the parameter \(a\) in the BTF model to two different values, 0.25 and 0.55, to obtain two images with different brightness. We have verified the rationality of the parameters as much as possible, and the current parameters are suitable for most images. We set \(n\) in the BTF model equal to four, which can deal with most cases satisfactorily. In the pyramid fusion part, we set the number of pyramid layers to seven.