esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries
### System Model  
Figure 1 illustrates the system architecture of the proposed scheme. The system model for smart farming comprises three entities: the central server (CS), user, and IoT sensors.  
* Central server: As a trusted entity, the CS possesses sufficient computational and storage capabilities. The user and IoT sensor must be registered with the CS before mutual authentication. After the mutual authentication, the CS can store the sensor data and provide insights or decision support information to the user using an AI model. Deep learning models, like a CNN and LSTM, can analyze sensor data to detect crop diseases and pests and use this information to predict yields and harvest timing.  
Figure 1: System model for the proposed scheme.  
Users: The user refers to farmers or farm managers with mobile devices to access smart farming systems. After the authentication, the user can directly view the sensor data and receive the AI analysis results from the CS.
* IoT sensors: The IoT sensors deployed in smart farms have limited resources. They collect numerous real-time data, including temperature, humidity, soil condition, rain volume, and pest presence.
* Gateway: The gateway exchanges data between three other entities, acting as a communication relay.
## 3 Results and Discussion
### 3.1. Asphalt Binder Performance Analysis
#### 3.1.3 Low-Temperature Properties of Asphalt Binders  
The SSEA stress relaxation curves with time at 10 \({}^{\circ}\)C and \(-\)10 \({}^{\circ}\)C are given in Figures 11 and 12.  
From Figure 11, it can be seen that the SSEA with different asphalt contents has rapid stress relaxation in the first 10 s. The stress relaxation rate gradually decreases in the second 50 s, and finally, the curve levels off. The initial stress values of SSEA 1 to SSEA-5 are 0.439 MPa, 0.331 MPa, 0.223 MPa, 0.208 MPa, 0.193 MPa, and the residual stress values after 60 s were 0.021 MPa, 0.016 MPa, 0.010 MPa, 0.010 MPa, 0.009 MPa, respectively. The  
Figure 10: Tensile test results of SSEA (20 \({}^{\circ}\)C).  
stress residual rates are 4.83%, 4.72%, 4.48%, 4.66%, and 4.87%, respectively, which shows that the stress of SSEA decreases rapidly in the time of the 60 s.  
From Figure 12, it can be seen that SSEA with different asphalt contents has rapid stress relaxation in the first 100 s, and the stress relaxation rate gradually decreases in the second 500 s, and, finally, the curve levels off. The initial stress values of SSEA-1 to SSEA-5 are 2.669 MPa, 2.267 MPa, 1.914 MPa, 1.572 MPa, and 1.231 MPa, respectively, and the residual stress values after 10 min are 1.173 MPa, 0.775 MPa, 0.423 MPa, 0.286 MPa, and 0.149 MPa, respectively. The stress residual rates are 44.12%, 34.21%, 22.08%, 18.19%, and 12.14%, respectively. It can be seen that with an increase in asphalt content, the initial stress  
Figure 11: Stress relaxation curves of SSEA at 10 \({}^{\circ}\)C.  
Figure 12: Stress relaxation curves of SSEA at \(-\)10 \({}^{\circ}\)C.  
value, residual stress value, and stress residual rate of SSEA all tend to decrease, and that the stress relaxation of SSEA-5 is the best and the stress relaxation of SSEA-1 is the worst. Compared with 10 \({}^{\circ}\)C, the initial stress value, residual stress value, and stress residual rate of SSEA at \(-\)10 \({}^{\circ}\)C are higher, and the stress relaxation capacity decreases faster with decreasing temperature.  
With the increase in asphalt content, the initial stress value and the residual stress of SSEA both show a decreasing trend, SSEA-5 has the best stress relaxation, and SSEA-1 has the worst stress relaxation. This is attributed to the higher asphalt content in SSEA-5, which provides more flexible components, allowing the material to absorb and dissipate stress effectively at low temperatures. However, this also results in lower initial stress values. On the other hand, SSEA-1, with its higher epoxy resin content, has greater rigidity, which leads to higher stress accumulation and poorer relaxation capability. These results align with the findings of [PERSON] (2018), who demonstrated that high epoxy resin content limits low-temperature flexibility, whereas the incorporation of SBR/SBS modifiers compensates for this deficiency to some extent.
## 3 Self-Adaptive-Coefficient Double-Power Sliding Mode Control Algorithm  
The control block diagram of the self-adaptive-coefficient double-power sliding mode control method is shown in Figure 3. The design is mainly divided into three parts. Part (I): the dynamic model of the lower limb rehabilitation exoskeleton robot shown in Equation (1) is regarded as an estimated dynamic model. The estimated control torque \(\overline{\mathbf{u}}\) is obtained to reduce the nonlinear and strong coupling influences on the robot. Part (II): based on a double-power reaching law, we designed a compensation term \(\mathbf{\tau}_{c}\), the sum of the compensation term \(\mathbf{\tau}_{c}\), and the estimated control torque \(\overline{\mathbf{u}}\) is used as the actual control torque \(\mathbf{\tau}\) of the robot to reduce the effects of model error and environmental interference. Part (III): a self-adaptive coefficient \(\mathbf{\Lambda}\) was designed for the compensation term \(\mathbf{\sigma}_{c}\), and the self-adaptive-coefficient double-power compensation term (SACDPT) \(\mathbf{\Lambda}\mathbf{\tau}_{c}\) adjusts adaptively, improving the robust performance of the robot trajectory tracking control.
# Surface Defect Detection of Rolled Steel Based on Lightweight Model
## 2 Related Works
### The Deep-Learning-Based Methods  
Deep learning can automatically extract features by means of convolutional neural networks, and it has a broad scope and high adaptability. [PERSON] et al. [28] introduced an enhanced Mask R-CNN technique that uses the k-means II clustering algorithm to improve the production of anchor boxes, eliminates the mask branch, and achieves a detection rate of 5.9 frames per second. On the basis of YOLOv3, [PERSON] et al. [29] adjusted the a prioriframe settings using the weighted K-means method and added a large-scale detection layer, resulting in an 11% increase in detection accuracy. Based on the Resnet50 and MobilenetV3 models, [PERSON] et al. [30] created an enhanced lightweight neural network model using the knowledge distillation technique. On average, it took 24 milliseconds per image for the mobile terminal to detect steel surface flaws. Deep learning has been applied extensively in the industrial sector, and the trend to realize online detection of industrial production is to investigate a more precise learning framework, reduce the complexity of the learning model, and enhance the real-time performance of the defect detection system.
# Transformative Technology for FLASH Radiation Therapy
## 4 Fixed-Field Gradient Accelerators for FLASH-RT
### Non-scaling FFGA for FLASH
#### 4.2.4 Source, LEBT, and RFQ, and Injector  
The ion source and beam capture system comprise an electron-cyclotron-resonance (ECR) source coupled to a radio-frequency quadrupole linac (RFQ) through a conventional low-energy beam transport (LEBT) section, as shown in Figure 9. The ion ECR source has one of the highest ionization efficiencies for gaseous elements. An RFQ linac, which uses electrical RF focusing, can capture, auto-bunch, and efficiently accelerate DC (constant current) ion beams directly from the source, achieving energies of several MeV, efficiently replacing complex and lengthy pre-injector elements. The LEBT will consist of an Einzel and solenoid lens system (beam chopping may be required to match RF frequencies between RFQ and injector). In addition, a mixture of ion species can be ionized in ECRs to produce a mixed or 'cocktail' of ion beams which potentially can be accelerated in the proposed accelerator system--including all therapeutic ions plus protons in the form of H\({}_{2}\)+--thus combining imaging and therapeutic beams for real-time dosimetry.  
Figure 7: Outer dimensions for a variable energy 330 MeV/nucleon therapeutic ring **(left)** and a dual ring system of 430 MeV/nucleon **(right)** compared with the Heidelberg ion therapy facility. On the right, the FLASH-capable, CW, and variable-energy 430-MeV/nucleon ion accelerator nested system is compared to equivalent-energy, low-duty-cycle Heidelberg ion therapy synchrotron. Inner ring racetrack is 250 MeV/nucleon and can provide independent beam delivery.  
An advanced, small-footprint, heavy ion injector iso-cyclotron has been developed for the injector. This novel normal-conducting, separated-sector injector has an optimized strong-focusing field gradient designed to efficiently accelerate light ions with a charge-to-mass near \(\frac{1}{2}\) (namely, protons in the form of H\({}_{2}\)\({}^{+}\), D\({}^{+}\), He\({}^{2+}\), Li\({}^{3+}\), B\({}^{5+}\), C\({}^{6+}\), N\({}^{7+}\), O\({}^{8+}\), Ne\({}^{10+}\), S\({}^{16+}\), and Ca\({}^{20+}\)) scalable up to 70-100 MeV/u [51; 52]. Dual, high-gradient, 0.2 MV cavities with a tuning range of \(\pm\)1% in frequency can accelerate any ion species with this charge to a mass ratio on the 8 th harmonic (\(\sim\)45 MHz) with a large turn-to-turn, almost centimeter-level separation--an enabling compact and low-loss extraction technology that eliminates the charge-changing foils (used for injection in ion synchrotrons and extraction in H\({}^{-}\) cyclotrons). Low (percent level or less) extraction losses are projected to be compared with the 20-60% (or even higher) losses of proton-therapy CW cyclotrons at extraction.
###### Abstract  
Numerical simulations are performed to determine the angular dependence of the Mle scattering cross-section intensities of pure water droplets and pollutants such as contaminated water droplets and black carbon as a function of the wavelength of the incident laser light, complex refractive index, and size of the scatterer. Our results show distinct scattering features when varying the various scattering parameters, thereby allowing the identification of the scattering particle with specific application to the identification of atmospheric pollutants including black carbon. Regardless of the type of scatterer, the scattering intensity is nearly uniform with a slight preference for forward scattering when the size of the particle is within 20% of the incident laser's wavelength. The scattering patterns start to exhibit distinguishable features when the size parameter equals 1.77, corresponding to an incident laser wavelength of 0.355 um and a particle radius of 0.1 um. The patterns then become increasingly unique as the size parameter increases. Based on these calculations, we propose a time-gated lidar scheme consisting of multiple detectors that can rotate through a telescopic angle and be placed equidistantly around the scattering particles to collect the backscattered light and a commercially available Q-switched laser system emitting at tunable laser wavelengths. By using a pulsed laser with 10-ns pulse duration, our scheme could distinguish scattering centers that are at least 3 m apart. Our scheme called Mle Scattering Time-gated multi-Static LIDAR (MISTS-LIDAR) would be capable of identifying the type of atmospheric pollutant and mapping its location with a spatial resolution of a few meters.  
Mle scattering LIDAR; particulate matter; black carbon; atmospheric monitoring of pollutants +
Footnote †: journal: Physics Letters B  
article
# Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry
## 2 Literature Review
### Artificial Neural Network  
Since demand forecasting is a topic widely studied by academia and industry, the application of traditional techniques combined with artificial intelligence (AI) has been considered. Some of the related AI techniques are artificial neural networks, fuzzy logic, genetic algorithms, among many others.  
The classical methods implementation is commonly derived from the certainty generated in the predictions process. The work developed by [28], proposes the prediction methods integration with application in psychology, statistics and administrative sciences. On the other hand, [29] include integration collection techniques in times series and judgements of experts and [30] propose a forecasting methodology integrated by expert judgements and quality components, combining qualitative and quantitative forecasts at the same level or simultaneous action and establishing an entry to this type of model.  
Artificial intelligence techniques have acquired great relevance in sales projection. Artificial intelligence techniques applied in the forecast of supply chain demand through multilayer neural networks and evaluation of learning algorithms have been one of the most popular applications. For example, in the multilayer perceptron artificial neural network presented by [31], the forecasted demand is made up of historical data and applies a backpropagation learning (BPN) rule, which shows its effectiveness.  
In addition, artificial neural networks have been implemented in the prediction of demand by [32], in their work traditional methodologies are developed with a focus on the historical data of the textile industry. In addition, the research conducted by [33] makes the comparison of traditional mathematical techniques and statistics with the results of the artificial neural network and [34] propose an approach for comparison in evolutionary artificial neural networks by integrating genetic algorithms.  
Access to the use of new technologies also allows the development of new methods for forecasting demand. These are fed through the study of qualitative variables that are weighted to understand the environmental and quantitative factors that are commonly known as historical data, billing, or sales. For example, the work of [32] studies the integration of statistical techniques and the opinions of experts. Its objective is to unite different tools to improve your certainty.  
Research made by [35] replicates an adaptive neuro fuzzy inference system (ANFIS) implementing the method to develop a forecast approach that integrates quantitative and qualitative variables. The experts' data are processed by hybrid artificial neural networks with fuzzy logic integrated.  
The new techniques allow to integrate different tools, from qualitative and quantitative variables such as mathematical models that allow to generate projections with greater certainty in organizations and in their supply chains.
## 3 GPS SDR Design for CRPA Using General-Purpose Parallel Processors
### GPU-Based Parallel Correlator
#### 3.2.3 Carrier and Code Wipeoff  
After the data copy and synthesis in Figure 5, the carrier wipeoff is performed. Since shared memory space is not sufficient to store sine and cosine values in addition to data samples and C/A code values, the necessary sine and cosine values are directly accessed from global memory. Sine and cosine values are not numerically calculated in our implementation to reduce computational burden. Instead, lookup tables are stored in global memory. These lookup tables have sine and cosine values within a possible carrier frequency range at baseband (\(-10\) kHz to \(10\) kHz) with a \(10\) Hz resolution. The reason to use the limited shared memory to store C/A code values rather than sine and cosine values is that each C/A code value is accessed three times-once for an early, a prompt, and a late replica while sine and cosine values need to be accessed once. Thus, it is more beneficial in terms of reducing total global memory access to use shared memory for C/A code values rather than sine and cosine values.  
Once the carrier wipeoff is completed, the code wipeoff is performed (Figure 5). At this stage, the C/A code values and the inphase and quadrature samples after the carrier wipeoff are already stored in shared memory. Thus, their access latency is roughly 100 times lower than global memory access if there are no shared memory bank conflicts (Section 3.2.2 of [43]). The GTX 480 GPU has 32 shared memory banks (Table 3). Since consecutive 32-bit words are assigned to consecutive banks, consecutive data samples (32-bit floating-point numbers in our SDR) are assigned to consecutive banks. If different threads access different banks simultaneously, bank conflicts do not occur and shared memory accesses can be parallelized. By appropriate thread indexing for linear addressing (consecutive threads access consecutive 32-bit data samples as in Figure F-2 of [36]), we remove shared memory bank conflicts. Now the code wipeoff operation can be done very efficiently without further global memory access or bank-conflicted shared memory access.
# Modeling and Assessment of GPS/BDS Combined Precise Point Positioning
## 3 Data Processing
### Position Differences between Traditional and New Model  
Assessing the differences between the position estimates can directly illustrate to what extent the traditional and simplified models agree in their positioning results. In the following we compare the GPS/BDS combined static PPP results between the traditional and simplified models.  
For each of the 11 IGS MGEX stations, we computed GPS/BDS combined static PPP position differences between the two models over 1 month. The biggest position difference is less than 1 mm and most points have position differences of less than 0.1 mm. Figure 6 shows the magnitude distribution of these coordinate differences in the North, East, and Up component. The mean biases are \(-0.6\), \(-2.5\) and 0.4 \(\upmu\)m and RMS are 1.1, 3.9, and 1.9 \(\upmu\)m for each coordinate component. In addition, about 91.5% in the North, 96.9% in the East, and 98.7% in the Up component of all deviations are within twice the standard deviations. These results verify the same position estimates of these two models.  
Figure 4: Magnitude distribution of 3D RMS of coordinate differences between epoch-wise kinematic PPP and IGS daily solutions. Subplot presents the GPS/BDS combined, GPS-only and BDS-only solutions from left to right, respectively. The texts of each subplot shows the median and mean 3D RMS.  
Figure 5: Epoch-wise kinematic PPP coordinate bias of GPS/BDS combined, GPS-only and BDS-only solutions in the North, East and Up component. Station: JFNG, DOY 028, 2014. Bottom-right subplot shows the number of satellites tracked at each epoch.
# Economic Routing of Electric Vehicles using Dynamic Pricing in Consideration of System Voltage
## 1 Introduction  
Recently, there have been rising global efforts towards reducing greenhouse gas emissions. There are various regulations on carbon dioxide, which is the main cause of greenhouse gas emissions, and there is a lot of interest in technologies with lower carbon dioxide footprints. As a result, environment-friendly technologies and products such as electric vehicles (EVs) and renewable energy are attracting attention as countermeasures against rising global greenhouse gases. So far, most of the research related to EV routes has dealt with the convenience to electric vehicle users. In addition, the main research focus has been on finding a route that enhances a user's convenience by considering a Markov chain using a probabilistic model [1, 2]. Research on route setting mainly consists of a probabilistic model of the user's pattern that is optimized considering various cost aspects according to the model [3]. Optimal charging of an EV on the user side is an efficient method because it sets the optimal route for a user's purpose. However, this method has the disadvantage that as the number of EVs increases, various problems can arise in the power system.  
As EV infrastructure continues to grow, there are more active data on the operations of charging stations [4, 5, 6]. In [4], an operating method is introduced that plans to charge in a highway environment and minimize total travel time. In [5], an operation that minimizes costs is suggested, including the cost of a power supply to EVs and the uncertainty of renewable energy. In [6], a charging strategy between charging stations (CSs) is proposed to reduce EV charging waiting time by considering traffic information and the statuses of CSs.  
The purpose of this paper is to improve the issue of system voltage without compromising the convenience to the users, which is proposed by setting the route as the minimum charging cost for the EV while necessarily keeping the system stable and meeting the charging demands of electric vehicles. A control strategy for CSs with dynamic pricing is proposed in which the system operator shares information on charging schedules and pricing information between the charging stations and EVs and measures the voltage regulation of the system through the shared information. In response to voltage regulation, a price signal is sent to the charging station as dynamic pricing. Then, the EV resets the route to reflect the charging price information of the CS. In Section 2, the proposed routing algorithm for EVs is presented using [PERSON]'s algorithm. A control scheme for the CS and operator is presented in Section 3, and the simulation results for the control strategy are presented in Section 4. The control method proposed in this paper is verified using MATLAB.
## 4 Graph Conversion from Dependency Tree
### Graph Construction
#### 4.1.1 Creating Nodes  
As discussed in Section 3, the proposed graph is defined by three types of nodes as follows:  
* Entities: Entities refer to the objects that physically occupy space in a scene, e.g., \"car\", \"person\", \"dog\".
* Actions: Actions refer to the various events or activities connected to the entities, e.g., \"drive\", \"stand\", \"play\";
* Attributes: Attributes generally refer to the various properties that further define the entities or actions. Attribute nodes are generally connected to either entity or action nodes via an \"attr\" edge, e.g., \"red\", \"tall\", \"two\".  
All these nodes are represented using tuples \(\langle id,token,lemma,pos\_tags\rangle\). Nodes can be created in three different nodes:  
* Primary nodes: These nodes are created by only observing the POS tags of the words. The node is defined typically by the actual token and lemma.
* Secondary nodes: These nodes are created by observing a combination of POS tags along with other factors such as the lemma or the named entity recognition tags. In these cases, the lemma of the created node is assigned a generic keyword.
* Tertiary nodes: These nodes are created when edges are created by reading the dependency relations. It may happen that an incoming dependency is coming from a word for which no primary or secondary node exists.  
Figure 4: Generating graphs for the generated captions as shown in Figure 3. **Left**: dependency parse tree generated using Stanford dependency parser. **Right**: generated Graph using the proposed approach.