esa-sceva/satcom-chunk-collection huggingface dataset:
# Importance of Event Binary Features in Stock Price Prediction
## 2 Related Works  
### Recent Stock Price Prediction Studies Using Various Prediction Models  
In this section, recent studies of stock price prediction using machine learning or deep learning are introduced. Stock price prediction has been attempted by many people and has received a lot of attention. In addition, the study of stock price prediction has been conducted using a wide variety of techniques. The first related study [8] used various machine learning models. Backpropagation, the long short-term memory model (LSTM), support vector machine (SVM), and other neural network models were used to predict stock price volatility with up to 68% accuracy.  
The second related study [9] used stock chart images with the convolutional neural network (CNN), which showed a great amount of image sector deep learning. To predict the S&P (Standard & Poor's) index, nine technical indicators were selected, and the performance was verified by comparing the proposed CNN model with the artificial neural network (ANN) and SVM models. The study concludes that it is appropriate to build a stock prediction model with CNN and argues that it is useful to convert time series data into graphs.  
The fourth and fifth related studies [10; 11] investigated the prediction of stock prices by data mining in media such as social networks or news. The authors argues that the technique used to analyze emotions in text in social media is efficient for stock price prediction. They argue that social media data is suitable for prediction because it not only implies people's thoughts, but also accumulates in real time.  
All the recent stock prediction studies [8; 9; 10; 11] mentioned above have tested the possibility of stock price prediction using various methods. These studies [8; 9; 10; 11] were conducted primarily by applying different deep learning models to predict stock prices. However, most studies use well-known technical indicators for input features. Our research has tested different methods that have not yet been utilized. We used a basic model for deep learning, and only the input features were changed to verify the importance of well-tuned input features and to find the types of input features that are essential for predicting stock prices.
## 4 Co-Seismic Ground Deformation  
To study the co-seismic ground deformation on the epicentral area of the 27 September 2021 event, we combined space and ground-based geodetic techniques. Radar satellite data were used to produce differential interferograms to spatially study the co-seismic deformation. Moreover, geodetic data from continuous Global Navigation Satellite System (GNSS) stations operating on the broad affected area provided point-wise accurate 3D displacement vectors.
## 2 Related Work
### Digital Twin Technology in Healthcare and Smart Cities  
Digital Twin (DT) technology is making significant strides in healthcare and smart cities. Companies like Philips and GE Healthcare use DT technology to create virtual models of medical equipment, enabling predictive maintenance and improving reliability in critical care settings [26]. Researchers are also developing patient-specific Digital Twins to simulate disease progression and treatment outcomes, advancing personalized medicine [27].  
In smart cities, DT technology helps optimize urban environments [28] by simulating traffic flows and managing resources like water and energy. Singapore's Virtual Singapore project is a leading example, offering real-time insights for efficient urban planning [29].
# Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension
###### Abstract  
In the era of Industry 5.0, advanced technologies like artificial intelligence (AI), robotics, big data, and the Internet of Things (IoT) offer promising avenues for economic growth and solutions to societal challenges. Digital twin technology is important for real-time three-dimensional space reproduction in this transition, and unmanned aerial vehicles (UAVs) can support it. While recent studies have explored the potential applications of UAVs in nonterrestrial networks (NTNs), bandwidth limitations have restricted their utility. This paper addresses these constraints by integrating millimeter wave (mmWave) technology into UAV networks for high-definition video transmission. Specifically, we focus on coordinating intelligent reflective surfaces (IRSs) and UAV networks to extend coverage while maintaining virtual line-of-sight (LoS) conditions essential for mmWave communication. We present a novel approach for integrating IRS into Beyond 5G/6G networks to enhance high-speed communication coverage. Our proposed IRS selection method ensures optimal communication paths between UAVs and user equipment (UE). We perform numerical analysis in a realistically modeled 3D urban environment to validate our approach. Our results demonstrate significant improvements in the received SNR for multiple UEs upon the introduction of IRSs, and they confirm the feasibility of coverage extension in mmWave UAV networks.  
Industry 5.0; digital twin; millimeter wave; UAV cellular networks; IRS; coverage extension 2024242006. [[https://doi.org/10.3390/s24062006](https://doi.org/10.3390/s24062006)]([https://doi.org/10.3390/s24062006](https://doi.org/10.3390/s24062006))
## 4 Methods
### Preprocessing
#### 4.1.2 Spatial Filtering  
To reduce the influence of speckle on the change detection-based soil moisture estimation, a denoising filter was applied to the co-polarized band of each Sentinel-1 SAR scene. While improving the image quality, changes in backscattering signals between two consecutive images are becoming more related to changes in soil moisture, rather than due to the speckle effect. To preserve the edges of individual agricultural fields, a focal median filter with a 50 m radius circular kernel (according to the area of around 78 resolution cells) was applied to all scenes in this study. For each pixel, it calculates the median from its surrounding pixels values and rewrites it into the original pixel. While its fast performance is suitable for processing large time series, its better ability to preserve the edges of objects makes it more suitable for field scale soil moisture estimation compared to the focal mean filter [70].
## 3 Results
### Results of Study Quality Assessment  
Table 11 reports criterion-specific and global ratings from the study quality assessment. The included studies were assessed using the National Institutes of Health's Quality Assessment Tool for Controlled Intervention Studies, which includes 14 criteria. On average, the studies scored 10 out of 14, with scores ranging from 8 to 12, indicating moderate to high methodological quality. All studies strictly adhered to the pre-specified intervention protocols, addressed confounders by controlling baseline characteristics, and maintained consistent training plans with controlled intensity and volume. Additionally, outcomes were assessed using valid and reliable measures, and no additional training was arranged outside the study protocols. Twenty-one out of twenty-five studies used an adequate RCT design, while the remaining four were non-randomized field experiments. The overall dropout rate was within an acceptable range in most studies, with some exceptions. For instance, in some studies, the overall dropout rate at endpoint exceeded 20% of the number allocated to treatment, and the differential dropout rate between treatment groups also varied. Only one did not analyze the results based on its original random treatment allocation. Out of all the studies, only one utilized assessor blinding, and while all lacked double-blinding, some employed single-blinding of the subjects. The lack of double-blinding may introduce potential bias, although the use of single-blinding and rigorous adherence to intervention protocols helped mitigate this risk.
## 5 Conclusions  
For improving the adaptability of a tracked vehicle equipped with a symmetrical suspension system on complex hilly terrain, an MBD-DEM coupling model was built and verified. The dynamic characteristics of the tracked vehicle equipped with symmetrical suspensions under different conditions were studied, verifying its good adaptability to various working conditions. The following conclusions could be drawn:  
1. Based on the coupling method of MBD and DEM, a corresponding MBD-DEM coupling model was built and preliminarily verified by using a mathematical model of the Euler-Lagrange method.
2. The vertical acceleration of the vehicle body is lower than that of the symmetrical suspensions by about 35%, indicating that the symmetrical suspensions could decrease the vibration amplitude of the vertical body and improve the move stability of the vertical body. The acceleration amplitude of the vehicle body and the symmetrical suspensions increase approximately linearly with the increase in vehicle velocity.
3. When the tracked vehicle is under climbing conditions, the maximum pitch angles increase with the increase in slope angle, and the difference between the maximum pitch angles and the slope angle increases first and then decreases.
4. When the vehicle body encounters an obstacle, the vertical acceleration suddenly increases, followed by a violent vibration, and the intensity of the vibration increases with the increase in obstacle height.  
Though an MDB-DEM model was built and verified, the accuracy of this model should be improved in future studies. In addition, experiments to verify the dynamic behaviors of the tracked vehicle should also be conducted in future studies.  
[PERSON], Z.D. and X.C.: conducted the numerical simulation and wrote the manuscript. P.W.: assisted with data verification and analysis. [PERSON]: supervised the study. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Key Laboratory of Intelligent Manufacturing of Construction Machinery, IMCM2023 KF04 and IMCM202103; in part by the Scientific Research and Innovation Team Program of Sichuan University of Science and Technology, SUSE652A004; and in part by the Panzhihua Key Laboratory of Advanced Manufacturing Technology Open Fund Project, 2022 XJZD01.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# A Study of Emergency Aircraft Control During Landing
## 6 Control System
### Overall System Stability Characteristics  
The airliner chosen for the design of control system is a highly non-linear system with strictly limited thrust control capabilities. Performing theoretical stability analysis of such a system is a complex problem due to the presence of cross-couplings, which are problematic to model. The decoupled models of the aircraft's longitudinal and lateral dynamics proposed in this paper are only indirectly related through the module shown in Figure 3. Consequently, these models are unsuitable for a global stability assessment of the real aircraft control system, even for a single, stationary airframe configuration.  
An additional challenge in stability analysis arises from the use of anti-windup mechanism in both control channels. While global stability assessment is practically infeasible, the local stability of each control model considered separately can be inferred under several assumptions.  
Firstly, the aircraft model in the studied configurations exhibited longitudinal static stability with very low dynamic damping, resulting in phugoid oscillations that decay slowly over time (as presented in Figure A1a in Appendix B). Therefore, open-loop collective thrust control does not cause instability, even under maximum thrust inputs. The magnitude of the inputs, however, directly affects the amplitude of the induced phugoid oscillations. The eigenvalues for the open-loop longitudinal control are presented in Table A5a.  
Secondly, the linear-quadratic regulator algorithm yields a stable state-feedback control loop. The eigenvalues for the closed-loop longitudinal and lateral control systems, excluding the anti-windup mechanism, are shown in Table A5b,c.  
Lastly, the implemented anti-windup mechanism is intended to improve system stability under real operating conditions--in this case, during flight simulator trials. It limits the accumulation of the integration state, which would otherwise significantly degrade closed-loop performance, potentially leading to so-called bang-bang control effect and adverse oscillations in the system. Such phenomena may occur when the initial conditions of the system are considerably distant from the steady state. The anti-windup mechanism attempts to minimise the difference between saturated and unsaturated control signals by re-computing the integral action state, ensuring that the controller output remains at the saturation limit.
# A Maturity Model Proposal for Industrial Maintenance and Its Application to the Railway Sector
## 4 M3: Maintenance Maturity Model  
A maturity model is a set of characteristics, attributes or indicators that represent progression in each discipline. In this case, a maturity model for maintenance is presented. This type of model allows for establishing a series of guidelines and references for the integration of processes or practices in industries. A maturity model facilitates the determination of the current level of maturity of a given paradigm and provides guidelines on how to improve the current situation towards more advanced scenarios [46].  
There are three categories of maturity models: progression models, capability models and hybrid models [46]. Progression models are those that are specifically focused on defining attributes that assess maturity. Capability models define the capabilities required to reach the next maturity level. They measure the capabilities at each step of the progression. On the other hand, hybrid models are a mix of the two maturity models described above.  
Maintenance Maturity Model (M3) is a hybrid maturity model (see Figure 4). This type of maturity model reflects the capabilities acquired at the different transitions between levels, and at the same time defines the attributes needed to follow the progression. This type of model is a roadmap for acquiring maturity. M3, contrary to the literature proposals, does not focus on any concrete maintenance strategy and provides guidelines or implementation order to make a maturity assessment of the current maintenance. It also provides the necessary guidelines to evolve to more advanced strategies.  
Figure 4 shows that M3 is divided into four levels: preventive maintenance, condition-based maintenance, predictive maintenance and, finally, prescriptive maintenance. The characteristics or capabilities to be acquired at each of the levels are also divided into 3 areas: assets, status and maintenance. M3 is designed to acquire smarter maintenance strategies starting from the level on the left, in the right direction. At each level, guidelines described from top to bottom should be followed highlighting the new capabilities required.
# Variable Neighborhood Search for the Two-Echelon Electric Vehicle Routing Problem with Time Windows
## 5 Computational Experiments
### Generation of 2E-EVRP-TW Instances  
Due to a lack of available benchmark sets for the 2E-EVRP-TW, we generated new problem-specific instance sets by extending the benchmark sets provided in [12]. These instances were proposed for the electric vehicle routing problem with time windows and consist of 36 small and 56 large instances. Small instances are composed of 5, 10, or 15 customers with a varying number of charging stations (between 2 and 5), while the large ones include 100 customers and 21 charging stations. We have extended these instance sets following the methodology proposed in [26]. In particular, first, the number of satellites to be added to each instance was determined. Then, the locations of those satellites and the one of a single central warehouse was specified.  
Concerning the number of satellites, we decided to use one single satellite in the case of small instances with at most 10 customers, two satellites in the case of 15 customers, and eight satellites for the large instances. Note that the customers of each instance are scattered over the intersections of a 100 \(\times\) 100 grid. The location of the single central warehouse was determined for each instance to be outside this area, at \((50,150)\). The single satellite in the case of instances with at most 10 customers was placed at \((50,75)\), while the two satellites in the case of instances with 15 customers were places at \((50,25)\) and \((50,75)\). Finally, the  
Figure 6: An illustration of local search operators. (**a**) The _exchange(1,1)_ operator, (**b**) The _shift(1,0)_ operator, (**c**) The _relocation_ operator, (**d**) The _swap_ operator, (**e**) The _two-opt_ operator, (**f**) _CS_relocation_ operator, (**g**) _CS_reniscertion_ operator.  
eight satellites for all remaining instances were placed at \((25,25)\), \((25,50)\), \((25,75)\), \((50,25)\), \((50,75)\), \((75,25)\), \((75,50)\) and \((75,75)\). Figure 7 shows examples of all three cases.  
In addition, we updated the customers' time windows by adding the distance between the location of the central warehouse in the original instance set and the new central warehouse as an offset value. Lastly, we modified the capacities of the electric vehicles and large trucks, considering the fact that instances labeled with C2, R2, and RC2 are more capacity constrained as compared to those labeled C1, R1, and RC1. In particular, we have fixed the capacity ratio between large trucks and electric vehicles to \(4/0.5\) for instances of the first type, and to \(2/0.25\) for instances of the second type. All benchmark instances generated in this study and the executable of the proposed algorithm are available at [[https://github.com/manilakbay/2E-EVRP-TW](https://github.com/manilakbay/2E-EVRP-TW)]([https://github.com/manilakbay/2E-EVRP-TW](https://github.com/manilakbay/2E-EVRP-TW)), accessed on 12 January 2022.
# A Review of Requirements for Gamma Radiation Detection in Space Using CubeSats
## 2 Cubesat Specifications and Constraints
### Communication Subsystem  
The Communication Subsystem is used to establish communication between the satellite and the ground station. Early educational satellites primarily utilized amateur frequencies in the very high frequency (VHF) and ultra-high frequency (UHF) ranges with a maximum data rate of 9600 bps. Considering that a satellite operating in the low-Earth orbit (LEO) has a line of sight contact with the ground station roughly of the order of around 10 min, the total data that can be downlinked is less than 1 MB and should also cater to the requirements of obtaining satellite telemetry data.  
For missions requiring more capacity, commonly S-band transmitters offering data rates of 0.5-2 Mbps are normally used. Some advanced S-band transmitters can provide up to 8 Mbps. X-band transmitters are utilized in missions requiring very high data rates (>50 Mbps).  
However, power requirement can be prohibitively high for utilizing X-band transmitters in constrained systems like CubeSats. Higher frequencies such as Ku-band are also being utilized mainly on an experimental basis [19].
## 3 Numerical Simulation and Experimental Validation
The normalized FR measured via the microspeker driver and a closed-box microspeker module is shown in Figure 5a,b, respectively. The simulated results are also plotted in the figures for comparison. The degree of agreements between the normalized FR curves at most frequencies were satisfactory, thereby validating the theoretical analysis and numerical simulations. Relatively larger discrepancies exist at higher frequencies, and may have resulted from measurement errors.
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 2 Methodology
### Landslide State Clustering and Dataset Production  
The K-means algorithm is an unsupervised clustering algorithm [29]. Using Euclidean distance for evaluating data similarity, the method adds classification labels to the landslide data, adaptively classifies the landslide temporal data and produces a dataset for subsequent classification by the ResNet algorithm.  
Its basic idea is to initialize \(K\) cluster class centers and classify the samples of each cluster according to their distance to the cluster centroid at first. Then, iterate to achieve the minimum distance between the sample and the cluster center.  
The loss function is Equation (1).  
\[J(C)=\sum\limits_{k=1}^{K}\sum\limits_{x^{(i)}\in C_{k}}\
orm{x^{(i)}-\mu^{(k) }}_{2}^{2} \tag{1}\]  
where \(x^{(i)}\) denotes the data point, \(C_{k}\) denotes the dataset of class \(k\) and \(\mu^{(k)}=\frac{1}{|C_{k}|}\sum\limits_{x^{(i)}\in C_{k}}x^{(i)}\) is the center of cluster \(C_{k}\).  
The K-means clustering algorithm consists of the following steps:  
1. Initialize the centers of \(K\) clusters, \(\mu^{(1)},\mu^{(2)},\ldots,\mu^{(k)}\).
2. Calculate the distance from a data point to all cluster centers, and take the label of the closest one (Equation (2)) as the label of that data point. \[\underset{k}{\arg\min}\
orm{x^{(i)}-\mu^{(k)}}_{2}^{2}\] (2)
3. Update the \(K\) clustering center according to the results of step 2.  
\[\mu^{(k)}=\frac{1}{|C_{k}|}\sum_{x^{(i)}\in C_{k}}x^{(i)} \tag{3}\]  
Repeat steps 2 and 3 until convergence, i.e., the \(K\) clustering centers do not change.  
The K-means algorithm divides the data into \(K\) clusters, and the effect of the algorithm vary greatly with the K value, which is determined by the elbow method.  
The core metric of the elbow method is the sum of the squared errors (\(SSE\)).  
\[SSE=\sum_{i=1}^{k}\sum_{p\in C_{i}}|p-m_{i}|^{2} \tag{4}\]  
where \(C_{i}\) is the \(i\)-th cluster, \(p\) is the sample point in \(C_{i}\), \(m_{i}\) is the centroid of \(C_{i}\) (the mean of all samples in \(C_{i}\)) and \(SSE\) is the clustering error of all samples, which reflects the clustering effectiveness.  
As the number of categories increases, the decline in \(SSE\) slows down abruptly, then levels off as the value of \(k\) continues increasing. The elbow method selects the inflection point as the K.