esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials: System Description and Experimental Set-Up
### Test Programme  
The radiant solution described has been tested in refrigeration mode under the following conditions:  
* Both the ambient temperature and the average radiant temperature were not controlled. Only thermal excitation was carried out using a 4 kW fan coil and 1.2 kW radiators to simulate internal gains (conditions similar to the summer season). The temperature of the water in the tank was controlled. The heat pump cooled the water in the tank to the lowest possible temperature to obtain large temperature gradients between the average radiant temperature and the ambient temperature concerning the average temperature of the water that circulated through the radiant solution.  
Figure 4: Test cell—position of sensors: air temperature (**left**) and surface temperature (**right**).  
Figure 5: Test cell–position of water temperature sensors into the panel.  
* The adjustable variable was the flow. Each mode described below was tested under a range of flow rates between 2 \(\frac{1}{\text{min}}\) and 8 \(\frac{1}{\text{min}}\). These values corresponded to the total water flow. The different modes of operation had in common the prior conditioning of the space by preparing the test cell. For the tests carried out under cooling conditions, during the night, the cell was conditioned by using a 4 kW fan coil and two radiators arranged in the central area of each circuit (see Figure 6). This guaranteed a high interior temperature in the early morning hours, thus obtaining the natural conditions of the cooling regime. In turn, the water in the tank was cooled to the lowest possible temperature. Therefore, it was possible to simulate the summer conditions within the test cell. It was necessary to check that the water was below 19 \({}^{\circ}\)C and that the average radiant temperature and ambient temperature were 6 \({}^{\circ}\)C higher than the water tank's temperature. Once the test cell and the water in the tank were prepared, the following operating modes were carried out: mode 1--the fan coil was turned off, the oil radiators arranged in the central area of each circuit remained on, and the pump heat performance was measured. Under these conditions, the radiant solution started to work and was tested for an extended period (half of the experimental test period). Once mode 1 ended, mode 2 started. The heat pump was switched off to analyse the free evolution of the tank without external thermal support.  
Finally, unlike in cooling mode, the heat pump was used to heat the water in the tank to a specific temperature in heating mode. Therefore, it was unnecessary to carry out the conditioning of the previous space in this operating mode since it was possible to guarantee a high-temperature gradient. In addition, the adjustable variable in this regime, as in cooling mode, was the water flow.
# Path Tracking of Agricultural Vehicles Based on 4 WIS-4 WID Structure and Fuzzy Control
## 3 Results and Discussion
### Analysis and Discussion
#### 3.2.4 Influence of Mechanical Structure on the Quality of Path Tracking  
As seen in Figure 12, the convergence trajectory of the prototype vehicle from the initial state to near the origin point in the TPP model is a tortuous spiral due to mechanical constraints, and the convergence process is relatively long. Compared with TPP, the convergence from the starting point to near the origin is much shorter in the FCP model because FCP takes full advantage of the 4 WIS-4 WID structure. The steering center is flexibly distributed around the vehicle's body to achieve simultaneous correction of the position and direction, which can also be seen in Figure 12.
## 2 Requirements of 5G-Based V2X Use Cases
### QoS Requirements  
#### 2.1.1 Throughput Requirements  
One of the most fundamental requirements of any use case is the throughput or data rate. The 3 GPP defines data rate requirements for the different V2X scenarios on a per-user level. These recommended data rate values are achievable, but there are quite a few fully operational use cases or measurement campaigns where requirements such as the data rate are comprehensively examined. The main challenge would be the use case behavior in crowded areas and how the overall capacity and data rate requirements can be reduced in such transportation scenarios [19; 28; 29].
# The Onset Threshold of Cybersickness in Constant and Accelerating Optical Flow
## 2 Background and Related Work
### Cybersickness Induced by Scene Movements along Different Axes  
Previous studies tried to find the most influencing rotational or translational movement causing CS-related symptoms, but most failed to find a significant difference in the severity of symptoms.  
[PERSON] and [PERSON] (2016) summarized the characteristics of CS caused by each rotational axis [14]. Whereas pitch motion generates visual inputs similar to seasickness, roll motion is relatively uncommon in daily life and work. Humans may not fully develop the sensory system for managing roll motion, and thus may feel less severe motion sickness for roll axis than others. Yaw is the most natural rotation for a human turn, so that it may be most susceptible to visual-vestibular cue mismatch. [PERSON] et al. (2009) reported that there was no difference in the severity of CS between the pitch and roll (\(p\) = 0.69) in the VE [17]. [PERSON] and [PERSON] (2008) investigated the effect of the sinusoidal roll and pitch oscillation on the level of CS, but found no difference between the roll and pitch [15]. They discovered that roll and pitch increase CS proportional to the motion magnitude. [PERSON] and [PERSON] (2001) also reported that the oscillation in the VE significantly increased CS in roll, pitch, and yaw without significant differences among them [12]. [PERSON] and [PERSON] (1991) reported that the pitch axis motion causes visually induced MS most seriously, followed by the roll and yaw motion [36]. [PERSON] (2006) investigated the effect of translational motion on CS and found that navigation on all three axes increased the level of CS (\(p\) < 0.01) [13]. The CS level was lower in the surge condition (\(p\) < 0.05) than the lateral and vertical conditions. However, their following work failed to show the dominant axis inducing visually induced MS [16].  
The result of previous studies made no consensus yet. They attempted to find differences in the severity level of CS measured by SSQ or similar ratings. However, there were chances that CS occurs in a different order, but the severity of symptoms was not different because the exposure time was too long or short. In this paper, we aim to find whether there is a dominant rotational or translational axis that causes the onset of CS in terms of threshold detection.
# Issues and Solutions in Deep Learning-Enabled Recommendation Systems within the E-Commerce Field
## 2 Background
### Similarity Concept in the Recommendation System  
One of the main concepts used in the recommendation system is similarity. This consists of finding products that are comparable to prior products that the user has acquired or that are similar to each other (nearest neighbor). For example, on Amazon, we may recommend to the target user the products that other similar users have already purchased. Using the evaluations and preferences of all users, the similarity of these persons to the target user can be calculated. Thus, it will be possible to make recommendations to the target user for products that have not yet been purchased, according to their similarities with other users, and the ratings of these items. The success or accuracy of the method used in the recommendation system is dependent on the precision of the calculation of similarity in the prediction. The calculation of similarity between items and users is one of the important steps of the collaborative filtering algorithm and in selecting people identical to the target user. The similarity of elements can be determined by binary search and prediction methods, as well as vectorization models. Classification, clustering, and regression algorithms are most commonly used in similarity learning [16; 17; 18; 19; 20; 21].
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 3 SatScope Model Framework
### Network Simulator
#### 3.3.5 Traffic Generator  
SatScope has developed a measurement data-driven approach for generating network traffic, serving as the default algorithm to emulate real-world network traffic characteristics. Based on real-time network measurements [30], the mean values \(\mu\) of the uplink and downlink rates and the 95 th percentile \(P\)\(P\)\({}_{95}\) are determined. The average traffic volume \(R_{\mu}\) and standard deviation \(R_{\sigma}\), which follow a normal distribution, can be calculated using Formulas (4a) and (4b):  
\[R_{\mu}=\mu, \tag{4a}\] \[R_{\sigma}\approx\frac{P_{95}-\mu}{1.645}. \tag{4b}\]  
Based on the user distribution in Section 3.2.3, we generate a specified number of traffic events at time \(t\). To simplify the model, we assume the traffic model follows Markov chain properties, which means the number of traffic events at time \(t+1\) depends solely on that at time \(t\). We introduce \(\gamma\) to adjust the number of traffic events inherited from time \(t\) to \(t+1\), and parameter \(\delta\) to regulate the ratio of new traffic events to inherited ones at \(t+1\). With these parameters, we constructed a process for generating continuous-discrete traffic.  
SatScope employs a data-driven approach to model both the space and ground segments, establishing dynamic satellite-to-ground links that integrate these components seamlessly. Researchers can configure various satellite network topologies, routing algorithms, and load-balancing schemes to quantitatively evaluate the performance of different satellite network optimization algorithms. Moreover, SatScope offers a range of interfaces that facilitate the assessment of networking strategies' effectiveness across satellite-to-ground networks of varying scales.
## 3 CC-VC-UKF
### Space-Time Joint Cleaning Based on CC-VC-UKF Algorithm  
#### 4.2.1 Filtering Based on Reference Time Series  
(1) The reference time series corresponding to FTU and TTU measurement data is obtained by K-means algorithm clustering. Taking FTU as an example, the state equation is fitted according to the reference time series. The specific process of fitting is as follows:  
First, the measurement value of the equipment is set as the ordinate value. The measurement time interval of the measurement equipment is every 5 min, with 288 measurement time points in one day, and the unit of the abscissa is set to 1. Thus, the trigonometric function relationship between measurement data values and measurement time points can be fitted, as follows:  
\[x_{k}^{a}=408.6+117.1\sin(0.02274k+0.145\pi) \tag{22}\]  
where \(x_{k}^{a}\) represents the FTU measurement reference time series data value at time \(k\).  
(2) After mathematical derivation of the trigonometric function relationship, the measured data relationship at two adjacent moments can be obtained as the fitted state equation, as follows:  
\[x_{k}^{a}=f\big{(}x_{k-1}^{a}\big{)}+w_{k}^{a} \tag{23}\]  
where \(x_{k}^{a}\) represents the FTU measurement reference time series data value at time \(k\), \(f\Big{(}x_{k-1}^{a}\big{)}\) represents the conversion relationship between the variables at time \(k\), and time \(k-1\), \(w_{k}^{a}\) represents the deviation between the FTU reference time series value at time \(k\) and the reference time series value predicted from time \(k-1\). Then, the FTU and TTU measurement data are filtered based on the CC-VC-UKF algorithm.
# Generative Music with Partitioned Quantum Cellular Automata
## 3 Partitioned Quantum Cellular Automata  
Quantum cellular automata (QCA), as its name suggests, are CA for quantum computers. Actually, they were introduced as an alternative paradigm for quantum computation and were shown to be universal: QCA can function as a quantum Turing machine. Please, refer to [35] for a theoretical discussion about QCA and universal computation.  
In QCA, the cells are implemented as qubits. However, implementing a QCA with currently available quantum hardware is not trivial. The difficulty lies in updating all cells, or qubits, at the same cycle. For instance, if we attempt to implement a quantum version of any classical CA algorithm, we would need to store a copy of the original state of a qubit to update the others. However, this is not allowed with a quantum computer because of the non-cloning principle mentioned earlier [32].  
A number of approaches have been proposed to get around the difficulty mentioned above [35]. One of them is referred to as partitioned quantum cellular automata (PQCA) [17; 36].  
This section introduces the basics of PQCA. For a rigorous theoretical discussion please refer to [17; 36], in particular, to learn more about their nonclassical behaviour. Here, we focus on the practical aspects of implementing PQCA.  
To recapitulate, a CA at a certain time \(t\) is characterized by two properties: its current state and an update step that establishes the values of the cells at time \(t+1\). Thus, we need to apply an update circuit to all qubits of the automaton simultaneously at each time step.  
In PQCA, we first define one or more _partition_ schemes to split an arrangement of cells into tessellating _supercells_. Then, a _global update circuit_ is built from _update frames_. These update frames are defined using the partitions and _local circuits_.
## 2 Digital Image Correlation Technique
### Implementation of DIC for Strain Measurement in This Study  
Based on the principle of DIC described above, this study combines the use of a universal testing machine, automation control system, charge-coupled device camera, and computer system for the characterization and prediction of the failure pattern of sandstone materials. Figure 2 shows the implementation procedure of the study. For each experiment cycle, five steps are essentially executed. Step 1 is to prepare specimens in specific dimensions and set up equipment systems. To ensure test effects, specimens can be artificially treated, e.g., painted to create scattered speckles. The equipment systems used for compression testing, image recording, and storing are set up. Step 2 is loading specimens. Equipment positions are adjusted to enable the studied specimens to be placed at the center of the universal testing machine for accurate experiment processes. Step 3 records pre-deformation images and stores them on the computer as reference images. Step 4 operates the testing machine and  
Figure 1: Working principle of digital image correlation.  
obtains post-deformation images at different time points. Step 5 is to conduct DIC analysis to calculate numerical displacement and strain information based on the process in Figure 1. Dynamic strain-time curves are plotted based on the received information. Specific equipment and operation environment information is as follows. A Basler SCA1600 14 FM CCD black-white camera was used for image collection. It is equipped with a Sony ICX274 CCD sensor and can supply up to 14 frames per second with a 2 MP resolution and \(4.4\times 4.4\) um pixel size. The adopted universal testing machine has a loading capacity of 0-600 kN. A regular HP-Z600 workstation was used for image storage and processing. VIC-2D software from Correlated Solutions was installed on the workstation for DIC analysis. All the experiments were performed in fall with a dry bulb temperature around 24 \({}^{\circ}\)C and a relative humidity about 55% in Qingdao City, China.
# FluoC Spec 2--An Automated Field Spectroscopy System to Monitor Canopy Solar-Induced Fluorescence
## 2 Materials and Methods
### Calibrations and SIF Retrieval
In the field, both the calibrated spectrometer and FluoSpec 2 measure a spectralon panel at the same time. The calibration factor of FluoSpec 2 is then calculated as the ratio between the radiance measured by the reference spectrometer (Rad in Equation (4)) and the DN from FluoSpec 2 (Raw-Dark in Equation (4)). Since the spectral resolution of QEpro is high, there is an obvious spectral shift between spectrometers, which results in a wiggle-like artefact in the O\({}_{2}\)A band in the calibration factor spectrum (Figure 3A). The true calibration factor spectrum is a smooth curve with no obvious wiggles. To deal with this issue, a moving window was applied to the reference radiance and the DN from FluoSpec 2. The key assumption here is that even though the original radiance spectra (particularly near 765 nm) have obvious structures, the calibration factor curve actually is smooth. Thus, we can use the smoothed spectra from FluoSpec 2 and the reference spectrometer as inputs to Equation (4) to calculate the calibration factor. We estimated the uncertainty of this approach using two spectrometers which were both calibrated using HELIOS in the lab. We then measured a spectralon panel using one of them as a reference, and the other one as a spectrometer to be calibrated. We tested various sizes of the moving window (0.1 nm, 0.2 nm, 0.5 nm, 1 nm, 2 nm, 5 nm, and 10 nm) against the truth from the HELIOS calibration. Another important note about the spectrometers is their nonlinearity. Nonlinearity refers to the issue that the response of the spectrometer to radiance is not linear across the entire dynamic range (in the case of QEpro, 0 to 200,000). The sensitivity of the detector, \(\alpha\), is defined as the change in DN (\(\Delta DN\)) resulting from a change in signal at the detector, \(\Delta I\):  
\[\alpha=\frac{\Delta DN}{\Delta I}, \tag{5}\]  
For a perfectly linear system, \(\alpha\) is constant, irrespective of the light intensity. However, most spectrometers are nonlinear to a certain degree, which results in varied levels of sensitivity at different light intensities. QEpro is close to linear when the DN is around 100,000. However, if a strong absorption feature is needed in SIF retrieval, such as the O\({}_{2}\)A band, the range of DN from the inside to the outside of the absorption can be from 10,000 to 100,000. Thus, correction for nonlinearity is necessary. The nonlinearity of this system was characterized by the manufacturer and a set of nonlinearity correction factors is included. It is recommended that the raw data is always corrected for nonlinearity:  
\[DN_{cor}=D+(S-D)/(C_{0}+C_{1}(S-D)+C_{2}(S-D)^{2}+\ldots+C_{7}(S-D)^{7}), \tag{6}\]  
where D is the mean values of the dummy pixels, and S is the signal. We used both the Spectral Fitting Method (SFM) and Singular Vector Decomposition (SVD) to retrieve SIF at 760 nm [46]. Given the high spectral resolution of this spectrometer, SIF can also be retrieved with only Fraunhofer lines with the SVD approach [12; 44].
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Benchmarking
#### 2.1.2 Evaluation Metrics  
A range of metrics may be used in the comparison of predicted and actual fruit loads. Most yield estimation studies employ the use of the parameters of Root Mean Square Error (RMSE), Relative RMSE (RRMSE), Mean Squared Error (MSE), Coefficient of determination (R\({}^{2}\)), or Mean Absolute Error (MAE), with a few studies employing variants of these parameters such as Mean Absolute Percentage Error (MAPE), Lin's Concordance Correlation Coefficient (CCC), Simple Average Ensemble (SAE), Reference Change Values (RCV), and Matthews Correlation Coefficient (MCC) [1], as defined in Table 2. Useful terms for the characterization of population variability is the coefficient of error (CE), defined as the standard error of mean divided by the mean and coefficient of variation (CV), defined as the standard deviation divided by the mean.  
While these evaluation metrics are all distinct, generally, a report will only include two or three of the metrics. Importantly, reports should include more than a correlation coefficient, which confounds information on predictive error with sample standard deviation. It is recommended that all reports include a metric related to the absolute deviation from observed values (e.g., MSE, RMSE or MAPE).
# Fusion Information Multi-View Classification Method for Remote Sensing Cloud Detection
## 4 Experiment
### Visual Performance  
We compare the proposed method with several advanced cloud detection methods. Cloud-Net is a cloud detection method based on deep learning and is widely used as the baseline of cloud detection experiments. Hierarchical fusion convolutional neural network (HFCNN) [19] is a network detection method with multi-level feature extraction, and HFCNN is a super-pixel-level judgment method. Furthermore, we added the SVM method and the MCPK method. In the SVM method, the extracted view A data and view B data are used as one-dimensional features for training. In MCPK, the data of view A and view B are used. Through these two experiments, we can explore the effectiveness of multi-view learning and privileged information addition.  
Typical cloud-containing image blocks are selected from GF-1_WHU and Cloud-38 to display the detection results. These images have a variety of cloud coverage and backgrounds. The comparison results with the proposed method are shown in Figures 4 and 5. In the figures, from left to right, are original image, ground truth, Cloud-net, HFCNN, SVM, MCPK, and our method. From the visual results, on the whole, the proposed method is closer to ground truth. There are several aspects worth noting. In Figure 4, the test object in the third line is an image with highlighted features. Cloud-Net has a certain misjudgment, and SVM method judges a large area into a cloud. Compared with HFCNN, the proposed method is more rigorous for thin cloud detection. SVM methods often lack large-scale cloud areas, and MCPK will misjudge to a certain extent in continuous clouds.  
Figure 3: Performance of different super pixel classification levels on parameter optimization dataset.
## 4 Results and Discussion
### Limitations and Future Research Directions  
Despite the promising results, several limitations and challenges deserve careful consideration. For example, groundwater in dry regions may exhibit different physicochemical characteristics than surface water in temperate zones. Future studies should validate these architectures across diverse water quality datasets representing multi-regional and contamination scenarios.  
The interpretability challenges encountered with the TAPE architecture highlight ongoing limitations in translating complex deep learning models into actionable water quality management insights. Developing more transparent autoencoder-based approaches could enhance the utility of these models in regulatory and public health contexts where decision justifications are critical.  
Additionally, while the current study focused on binary potability classification, future work could explore multi-class approaches that differentiate between various treatment requirements (e.g., no treatment, conventional treatment, and advanced treatment) or specific contamination types, providing more nuanced guidance for water resource management. The study did not account for temporal dynamics in water quality, which are critical for detecting seasonal variations or event-driven contamination (e.g., heavy rainfall or industrial spills).