esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Evaluation Index System Construction  
In response to realistic problems concerning fresh agricultural produce supply chain platform coordination, the quantity of produce required by the supply chain service quality evaluation index gradually increases, the requirements are higher, and the content is more complex. The evaluation index system based on supply chain coordination begins with actual situations involving fresh agricultural products, which thus enable us to determine the appropriate evaluation indexes and scientific evaluation models.  
Among the existing studies, theoretical research into the service quality of fresh agricultural produce supply chain platforms is relatively scarce; in particular, research based on coordination in evaluations is even scarcer. Therefore, the construction of a set of scientific, reasonable, convenient evaluations, and an easy-to-operate index system, can extend and deepen the relevant theoretical research in the field of fresh agricultural produce supply chain platform service quality evaluations.  
However, supply chain service quality is difficult to observe, and it is not easy to achieve an objective evaluation; the evaluation indexes are numerous. The coordinated service quality evaluation index system of the fresh agricultural produce supply chain platform should reflect the service capabilities of the fresh agricultural produce supply chain platform on the one hand, and on the other hand, after simplification, it should objectively reflect the development level of the four dimensions of logistics, capital flow, information flow, and business flow in the coordinated fresh agricultural produce supply chain platform [39].
# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 2 Methodology
### Local Linear Regression (LLR)  
LLR is a non-parametric method that is generally employed to make a fast prediction with a high degree of accuracy. The LLR model is the most effective and reliable tool in regions of high data density in the input space, but it is not significantly effective if the datapoints are scarce and distant from the locality of the query point. The main prerequisite of the model performance is the selection of the number of nearest neighbours, \(k\), of the query point from the particular dataset to build a linear model and then solve the linear matrix.
## 4 Hybrid Models
### Weather  
In terms of weather forecasting, research by [PERSON] et al. [57] suggests using a combination of regression and machine learning models to predict wind energy production over one, two, and seven day time horizons. The forecast is based on weather data such as wind speed and direction, air temperature and pressure, and density at the height of the measurement node. The preliminary results of this study indicate that the combination of ARIMA with Random Forest algorithms (ARIMA-RF) as well as the combination of ARIMA with Bayesian Regression and Classification Trees (BCART) help to improve the forecasting accuracy compared with the classical forecasting algorithm of ARIMA.  
Figure 6: Flowchart of hybrid Arima and Machine Learning model for time series forecasting. This particular workflow is not the only one used in the scientific literature. Different combinations of the ARIMA and ML models have been proposed ([5; 44]), based on the nature of the forecasting problem and the modeling approach.
# A Novel Resource Allocation Scheme in NOMA-Based Cellular Network with D2D Communications
### Distance-Aware Link Selection Algorithm  
In this section, we design a link selection algorithm for accessing DUs to different CUs on different SCs in order to further optimize the system data rate, as shown in Figure 5. The algorithm also influences the power allocation for DUs.  
In our model, a DU always chooses a CU to be the signal transmitter (DT) of the D2D pair, as mentioned in Section 2. Thus, the spectrum resources are reused by DUs and CUs of the same D2D pairs on the same SC. As the number of DU increases, the number of the spectrum reused links becomes large, leading to a high computational complexity cost. Different from the user scheduling scheme in Section 3, we select the D2D links by a distance-aware link selection algorithm to relieve complexity stress.  
There are two principles in the algorithm. The first one is the distance between the DT and the BS cannot be too short. Based on the interference-limited area based scheme [47], BS has a limit circle  
Figure 5: Model of the distance-aware link selection algorithm. In the figure, all the devices are serviced in the same cell by the same base station (BS).  
where the users cannot choose D2D links with radius \(s_{d}\), as shown by \(S_{0}\) in Figure 5. Because the DT may impose severe interference on BS and close-by CUs in the limit circle \(S_{0}\), this principle can improve the QoS of users in the network.  
The other principle is about the distance between DUs. The purpose of the principle, on the other hand, is to limit the co-spectrum interference. In this case, if two D2D receivers (DRs) are close enough that they prepare to choose the same CU to be the DT, the link which can provide better data rate would be chosen. The other DR can choose a relatively long distance link on different SCs.  
The principles for choosing appropriate D2D links is illustrated in Figure 5 as follows:  
1. [label=(0)]
2. \(\text{CU}_{1}\) cannot be chosen as a DT by DR\({}_{1}\), because it is inside the limited area \(S_{0}\).
3. In area \(S_{2}\), DR\({}_{2}\) has two CUs to be selected to build a D2D link. But one of the CUs has already been chosen by DR\({}_{3}\) because the distance between the D2D pair is shorter. Thus, DR\({}_{2}\) can choose CU\({}_{2}\) to be the DT, in the same area \(S_{2}\).
4. When two areas have a communal area (e.g., \(S_{2}\) and \(S_{3}\)), the DRs may choose the same CU as the DT. In this case, we allocate them in different SCs to limit the co-spectrum interference.
## 3 Case Analysis
### Comparative Experimental Analysis and Data Reconstruction Effectiveness
The error distribution of the filter particles in method 3 is even better than that in method 2, indicating that the improved particle filter method can better achieve data reconstruction and reduce data fluctuations. Through the comparison of these four methods, method 4 further confirms that the filter particles under its combined method approach closer to real data. This validates that the combination of the improved particle filter and MissForest filling method offers  
Figure 9: Comparison chart of experimental method distributions. (**a**) Comparison of measurement methods at P1 monitoring point: (**b**) comparison of measurement methods at P3 monitoring point: (**c**) comparison of measurement methods at P4 monitoring point: (**d**) comparison of measurement methods at P9 monitoring point. superior performance in data reconstruction, as well as advantages in controlling data fluctuations and errors. As can be seen from the distribution diagram in Figure 11, method 4 determines the coefficient of determination R\({}^{2}\), the root mean square error RMSE, and the average absolute error MAE are the best compared with the other three methods. The R\({}^{2}\) maximum value is 0.861: the minimum RMSE is also achieved, with the lowest being 0.050: the minimum MAE is also reached, with the lowest values of 0.052, further confirming the superiority of method 4 (improved particle filter + MissForest filling method). This indicates that the data processed by method 4 has a higher fit degree and better performance; the smallest RMSE value also reflects smaller reconstruction errors, verifying that the detected values are closer to the actual measured values; the minimum MAE value, considering it unaffected by data scale, further validates the advantages of method 4 in data processing and comprehensively demonstrates that method 4 has the highest accuracy in seepage pressure data reconstruction. Figure 10: Residual distribution comparison plot of experimental methods. (**a**) Comparative analysis of error distributions for monitoring point P1; (**b**) comparative analysis of error distributions for monitoring point P3; (**c**) comparative analysis of error distributions for monitoring point P4; (**d**) comparative analysis of error distributions for monitoring point P9.
# Study of Marine Particles Using Submersible Digital Holographic Camera during the Arctic Expedition
## 1 Introduction  
Marine particles of different origins and nature (sedimentation particles, plankton, gas bubbles, oil droplets, etc.) are one of the most important objects of marine studies [1]. Their study and classification are critical in addressing various tasks: global climate, river-sea interaction, carbon cycle, global warming, biodiversity, and diagnostics of marine ecosystems [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12], and mathematical modeling of light propagation in seawater [13; 14; 15].  
Traditionally, marine particles (for example, plankton) are studied through sampling followed by their processing and analysis in the laboratory using a microscope [16; 17]. It is obvious that the plankton particles thus studied are removed from the habitat, which makes it impossible to accurately indicate their location in the studied medium. In addition, their state is damaged. Digital holography can be used to solve this problem in situ and in real time [18; 19]. In this case, information on suspended particles contained in the fixed seawater volume is recorded as a digital hologram on the CCD/CMOS matrix. The DHC (digital holographic camera) hardware and software complex that we developed for this purpose are described in the works [20; 21]. The digital hologram processing algorithmsto extract information on marine plankton are presented in the research by the authors of [22; 23; 24]. However, mesoplankton is one of the suspension components, and the DHC can be used to study other particles [25; 26; 27; 28; 29; 30].  
One of the tasks accomplished during the 82 nd Arctic Expedition of the Laboratory of Arctic Research of the V.I. II'ichov Pacific Oceanological Institute on board the Academician [PERSON] (AMK-82) from 28 September to 4 November 2020, was to assess the study possibilities and features of such particles.  
In addition, in the context of this study, the tasks of the expedition were as follows:  
* To determine the equipment's performance characteristics;
* To estimate the DHC measurement error regarding marine particles;
* To assess the validity of a classification.  
To address the above tasks, we manufactured the \"DHC-probe Arctic\", which includes the DHC and a set of additional sensors that measure the medium parameters (temperature, illumination, pressure, electrical conductivity, oxygen saturation, etc.).  
This paper describes the results of using the \"DHC-probe Arctic\" (hereinafter referred to as the \"probe\") to solve these problems. In addition, for the first time, we tried to use submersible holographic data for in situ point estimates of the size and concentration of gas bubbles in areas of massive methane release offshore the Eastern Arctic Seas, which were identified during 2000-2020 [31; 32; 33; 34].
# Effect of Adding Waste Polyethylene and GGBFS on the Engineering Properties of Cement Mortar
## 2 Materials and Methods  
### Materials Used for the Experiment  
The materials used in this study are presented in Figure 1, and their physical properties and chemical compositions are shown in Tables 1 and 2.  
1. The Portland Type I cement produced by the Taiwan Cement Corporation was used, and the properties of this cement conformed to ASTM C150; the specific gravity was 3.15, and the fineness was 3450 cm\({}^{2}\)/g.
2. Waste polyethylene (waste PE) in its original shape was a large spherical particle. After being broken down by the grinder, its shape was plastic cotton fiber, as shown in Figure 1b, with a specific gravity of 0.923 and a moisture content of 8.2%. The chemical composition of PE is (C\({}_{10}\)H\({}_{8}\)O)n. The Fourier transform infrared spectroscopy (FTIR) spectrum of waste PE is shown in Figure 2.  
3. Ground granulated blast furnace slag (GGBFS) was obtained from the CHC Resources Corporation, and the properties conformed to CNS12549, with a specific gravity of 2.9 and a fineness of 4000 cm\({}^{2}\)/g.
4. The fine aggregate was river sand from the Ligang River, and the specific gravity was tested according to ASTM C127. The specific gravity was 2.65, and the water absorption was 1.48%.
## 3 Results and Discussions
### Application Possibilities in Stationary Gas Turbine Equipment
The requirements for the tribological properties of materials increase multiple times during start-up of gas pumping stations and during transients under conditions of contact between the sealing surfaces. For example, guaranteed clearance between operating surfaces is ensured by pumping the sealing gas into the sealing gap. The result of this mechanism is a very stable, yet very thin contact surface between the stationary primary seal ring and the rotating seal rings. The two surfaces are held apart and do not possess any contact point between them under normal dynamic operating conditions. However, the reaction force of the gas flow that occurs when the rotor starts to rotate may not besufficient to form a gas layer between the rings. In this case, when the rotor speed increases, the friction of the working surfaces will cause a sharp increase in temperature in the contact area and may lead to the destruction of the rings. The reduction of the sliding friction coefficient when using the \"SiC-SiC\" friction pair removes the risk of rings destruction and can also significantly reduce abrasion of the contact surfaces when the shaft rotates at low speeds, during start-up and shutdown modes or during transient modes, in which there is no stable gas layer between rings of stator and rotor. Laser treatment is recommended for those face surfaces of the rings of stator and rotor, which are in contact in the static position. It is known that machining causes localized scraping of material particles, creating a relief consisting of protrusions and depressions of relatively irregular shapes with sharp edges as a result of brittle fracture. A higher surface finish can be achieved by mechanical polishing, but this does not reduce the coefficient of friction as effectively as laser processing does. From surface profile investigations, it can be clearly observed that the laser treatment results in the smoothing of the micro-relief on the polished surface of silicon-carbide ceramics. The typical surface profile is illustrated in Figure 5 (at the bottom). Such a modification of the surface micro-relief is what makes it possible to effectively reduce the coefficient of friction.
## 3 Results and Discussion
### Distribution Mode
When \(m_{2}=3.102+0.0925\)i, Mode A and Mode B are similar in scattering efficiency, probably because both components have similar refractive index imaginary parts, so the distribution of components is less important. Figure 4: Radiometric properties at various particle radii for particle aggregates of different minor components at \(f=25\%\) (square symbol: Mode A, with the minor component particles randomly positioned; circle symbol: Mode B; black symbol: minor component \(m_{2}=3.102+0.0925\)i; red symbol: minor component \(m_{2}=1.46+2.75\)i; blue symbol: minor component \(m_{2}=0.15+3.15\)i). (**a**) Extinction efficiency; (**b**) absorption efficiency; (**c**) scattering efficiency; (**d**) asymmetric parameter. Figure 4d shows the asymmetric parameter. A smaller asymmetry parameter indicates less forward scattering compared to backward scattering by the particle aggregate. When \(m_{2}=0.15+3.15\)i or \(m_{2}=1.46+2.75\)i, Mode A has lower asymmetric parameter values compared to Mode B, showing that the former mode has a stronger backward scattering. It should be remembered that Mode B increases the absorption of the entire particle aggregate. For forward scattering, the effective absorption optical length is less than that of backward scattering, so the increase in the absorption of the aggregate cause a greater reduction in backward scattering. This results in an increase in the relative proportion of forward scattering to total scattering, namely the asymmetry parameter. When \(m_{2}=3.102+0.0925\)i and the imaginary parts of the refractive indices of the minor component are close to those of the major component (which is zero), Mode A and Mode B have similar asymmetric parameter values, showing that the distribution mode of the component is much less important when the minor component is close in absorption to the major component. Figure 4a shows the extinction efficiency, which is the sum of the absorption efficiency and the scattering efficiency. As seen in Figure 4a-d, the radiometric properties of all particle aggregates are generally insensitive to the change in particle radius, except the absorption efficiency, scattering efficiency, and extinction efficiency of Mode B with \(m_{2}=3.102+0.0925\)i. When \(m_{2}=3.102+0.0925\)i, the minor component has a relatively long optical length due to the large real part of its refractive index. In Mode B, the minor component is evenly scattered into each particle of the aggregate, which increases the optical length of the aggregate; as the particle radius increases, the optical length of the aggregate increases relatively significantly, causing more absorption as the incoming radiation passes through the aggregate and hence the absorption efficiency increases with the particle radius. The increase in the absorption of radiation also causes less radiation to be scattered and therefore reduces the scattering efficiency of the aggregate as the particle radius increases, as evidenced by the line for Mode B with \(m_{2}=3.102+0.0925\)i in Figure 4c.
###### Abstract  
It is crucial to study the axial compression behavior of concrete-filled steel tubular (CFST) columns to ensure the safe operation of engineering structures. The restriction between steel tubular and core concrete in CFSTs is complex and the relationship between geometric and material properties and axial compression behavior is highly nonlinear. These challenges have prompted the use of soft computing methods to predict the ultimate bearing capacity (abbreviated as \(N_{u}\)) under axial compression. Taking the square CFST short column as an example, a mass of experimental data is obtained through axial compression tests. Combined with support vector machine (SVM) and particle swarm optimization (PSO), this paper presents a new method termed PSVM (SVM optimized by PSO) for \(N_{u}\) value prediction. The nonlinear relationship in \(N_{u}\) value prediction is efficiently represented by SVM, and PSO is used to select the model parameters of SVM. The experimental dataset is utilized to verify the reliability of the PSVM model, and the prediction performance of PSVM is compared with that of traditional design methods and other benchmark models. The proposed PSVM model provides a better prediction of the ultimate axial capacity of square CFST short columns. As such, PSVM is an efficient alternative method other than empirical and theoretical formulas.  
Keywords:square CFST short columns; ultimate axial capacity prediction; axial compression test; support vector machine; particle swarm optimization +
Footnote †: journal: Applied Soft Computing
# PEMFC RUL Prediction for Non-Stationary Time Series Based on Crossformer Model
## 4 Results and Discussion  
### Experiment  
**Datasets:** The dataset used in this experiment was collected from logistics vehicles equipped with Proton-Exchange Membrane Fuel Cells (PEMFCs) in F City, G0 Province, China. The data collection period spanned from 16 October 2019 to 6 March 2021, with a sampling frequency of 30 s. The primary data collected include the PEMFC current, voltage, hydrogen concentration, and battery voltage. The experimental setup and data acquisition process are illustrated in Figure 8, which provides a schematic overview of the technical aspects of the experiment.  
**Baselines:** the experiment selected five models for comparative analysis--LSTnet [7], TCN [9], LSTM-ATTN [10], Transformer [11], and Crossformer [12].  
**Setup:** The experiment adopts a ratio of 0.7:0.1:0.2 for dividing the dataset into a training set, validation set, and test set. The time step length for input data is set at 48, while the prediction time step lengths are variously set at 8, 16, 24, 32, 48, and 64. The experiment employs two evaluation metrics to assess model performance--mean squared error (MSE) and dynamic time warping (DTW).  
Figure 8: Data acquisition technology lab diagram.
## 1 Introduction and Background
### Climate Factors and Energy Demand  
Addressing the evaluation of operational scenarios for achieving complete energy self-sufficiency in a building requires consideration not only of its constructional, morphological, and typological characteristics, but also of the environmental factors to which it is subjected, depending on its geographical location. The latter specifically refers to climatic variables that directly influence the building's energy performance, such as air temperature, humidity, and solar radiation.  
While it is certainly reasonable to experiment with efficiency scenarios based on predetermined characteristics identified according to geographic zones, the ability to monitor these factors through dedicated sensor technology enables the attainment of more precise and forward-looking results specifically tailored to the case study [20]. Moreover, the descriptive capabilities offered by modern sensor technology, when applied during the preliminary study phase of the experiment, allow for a detailed mapping of the variables that most significantly impact the thermal response of building envelopes [21].  
A detailed understanding of the solar radiation affecting a building and the thermal transmittance of its envelope are essential parameters for the technological design of windows and vertical closures (both transparent and opaque) [22]. Additionally, knowledge of the solar energy available in a given environment allows for maximizing passive thermal gains through the use of specific materials [23].  
Optimal thermal conditions in poultry tunnels range between 18 and 24 \({}^{\circ}\)C, with relative humidity under 70% to prevent heat stress and respiratory issues [24]. Sound levels should remain below 65 dB, and appropriate natural lighting or 10-20 lux artificial lighting is recommended to ensure animal welfare [25].  
Building on previous research conducted by other authors for comparable purposes [26; 27; 28], this study identifies the key variables that contribute to accurately mapping the responsiveness of a building to the climatic conditions it is subjected to. These variables include air temperature, relative humidity, wind speed, and the thermal fluxes measurable at the roof and ground slab levels.  
As previously stated, if these characteristics are properly monitored, they can yield valuable results in two key areas. Firstly, they provide an initial research outcome in the form of precise climatic data and the responsiveness variables of the building envelope. Secondly, they enable the definition of tailored operational efficiency scenarios specific to the building under investigation, without relying on standardized data for geographic zones similar to the case study. This distinction allows for the evaluation of customized and precise scenarios, avoiding assessment errors in LCA analysis that could arise from the extensive use of predetermined characteristics [29].