esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 1 Introduction  
Compressive sensing (CS) is a mathematical theory for simultaneous data acquisition and compression. For sparse images, or for images that can be transformed to a sparse domain, CS measures a collective sum of \(m\) random projections, where \(m\) is less than the total pixels in the image otherwise required for Nyquist-rate sampling [1, 2, 3]. We apply this technique to gravitational microlensing events. Gravitational microlensing is an astronomical phenomenon that occurs due to a precise alignment of an observed source star and a lensing mass. The lensing system warps space-time due to its gravitational field causing a deflection in the light reaching the observatory from the source star. This deflection in light causes a change in magnification of the brightness of the source star, which can be quantitatively measured. Using this technique, exoplanets can be detected [4]. In our study, we show the efficacy of using CS to obtain these photometric light curves depicting microlensed events and the effects of noise on the measured light curves. For our CS modelling, we assume our image of interest, \(x\), to be \(k\)-sparse and of dimension \(n\times n\). We define a \(k\)-sparse signal as one with \(k\) coefficients whose values are significantly higher than the rest of the coefficients [1]. We apply the CS measurement matrix, \(A\), of dimension \(m\times n\), onto the image \(x\). The CS measurements acquired due to this projection are obtained in the matrix, \(y\), and are of dimension \(m\times n\), where \(m\approx n\). Once \(y\) is obtained, we can solve for a sparse \(x\), given \(A\), using various reconstruction algorithms [5, 6].  
We solve for Equation (1) to determine \(x\) through the observation \(y\).  
\[y=Ax \tag{1}\]  
In this work, we use a greedy algorithm--orthogonal matching pursuit, provided by the _sklearn_ Python library.
# A Design of a Parcel Delivery System for Point to Point Delivery with IoT Technology
## 6 Discussion of Existing Issues
### Power Efficiency  
Since IoT devices are attached to delivery boxes, IoT devices must all be operational until delivery is completed after receiving delivery. To do so, battery management of the IoT device is essential. For this purpose, the LoRa module was used as Class A format. Class A format is not always on but is a very efficient method for battery management by storing data in the buffer when ACK comes from the server or when the value is entered from the sensor and the operation is performed every certain period of time. A typical method cannot store all data, but when uses the same method of increasing the size of the hardware serial buffer in ESP32, it can store 1 kb of the message, and it can store all data. Energy management and performance are very important elements of IoT device, and both aspects of IoT device performance and energy management can be grasped through this method.
## 1 Introduction
OFDM has found widespread application in fourth-generation (4G) wireless communication systems, renowned for its efficient utilization of frequency resources and high-speed data rate processing capabilities. However, its high PAPR, interference among subcarriers, and inefficient use of frequency resources render it unsuitable for 5G and even 6G wireless communication [38]. UFMC has the potential to provide better performance compared to other waveform technologies, especially in multi-user scenarios and in the presence of channel impairments [39]. By reducing inter-carrier interference (ICI) and relaxing inter-carrier synchronization requirements, UFMC ensures enhanced performance in synthetic aperture radar (SAR) and integrated communication channels. Moreover, its avoidance of CP minimizes inter-symbol interference (ISI) and ICI, promoting spectrum efficiency and preventing errors in imaging targets. Its low delay spread also positions it more favorably than filter bank multicarrier (FBMC) in high-repetition-rate radar modes. In the study of reference [40], an integrated waveform combining FBMC with radar jamming was proposed using FBMC chirp as the design waveform to minimize out-of-band signal leakage. However, the use of prototype filters in FBMC results in an increased signal duration. FBMC also faces challenges in scenarios involving burst communication and short-duration emergency communications, and it is poorly suited for MIMO systems. In addition, it falls short of simultaneously managing communication and jamming with targets. Therefore, FBMC is not the best multicarrier modulation method to combine with radar jamming. Reference [41] analyzed the performance of UFMC in JRCS and found that JRCS based on UFMC can improve overall performance in terms of spectrum efficiency and complexity. However, their advantages in radar jamming and communication integration design have not been fully explored.
## 2 The Principle of the Invariant Imbedding (IIM) T-Matrix Method  
In this Section, the principle of the Invariant Imbedding T-matrix method is introduced in the following way: firstly, the T-matrix is introduced, then, the basic equations to calculate the T-matrix are derived from the Helmholtz Volume Integral Equation in Section 2.2. To simplify the calculation equation of the T-matrix, the invariant imbedding technique to calculate the T-matrix is briefly introduced in Section 2.3. Further, the acceleration scheme based on the Lorenz-Mie theory is proposed in Section 2.4.
# Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
## 2 Related Work
The efficient feature extraction capability of CNN motivates us to exploit it for wind power forecasting. CNN successfully extracts the spatiotemporal correlations in wind power data [36; 37]. [PERSON] et al. proposed an ensemble CNN model [36]. Wind power time series is decomposed by DWT. Short-term wind power is predicted using ensemble CNN. In ensemble CNN, multiple CNNs are used for prediction of a data point. Prediction is performed by taking (weighted) vote of multiple predictions made by all the CNNs. In [37], an enhanced CNN is proposed for WPF. A new activation function, Scaled Exponential Linear Unit (SELU), is proposed. NWP inputs are used for short-term wind power forecasting. The afore mentioned CNN based prediction models perform reasonably well. However, the effect of using both decomposed and exogenous inputs simultaneously on the accuracy of prediction model still needs to be investigated. According to our limited knowledge, both the decomposed data and NWP are not simultaneously used as input for predicting wind power.
## 3 Results and Discussion
### Error Analysis On-Site  
As seen in Section 1 and according to Reference [51], the output power of a PV array greatly depends, among other parameters, on solar radiation; however, this variable has an intermittent nature and suffers from rapid fluctuations. In Reference [41,51], the above is considered and some parameters besides the solar radiation such as clear sky or weather data are added; nonetheless, for these cases, the solar radiation is either the estimated output or an estimated input, contrary to this paper where this variable is measured on-site.  
Each coefficient \(\theta_{k}\) from Equations (9) and (10) represents the characteristic magnitude of its respective input variable \(x_{k}\). Its quantity values prove the influence in the mathematical model approximation, establishing an existing relationship between the meteorological variables involved and (responsible for solar fluctuations) the power output.  
Figure 16: A close-up of the results from MCS.  
Figure 15: The results obtained from MCS by gradient descent.  
Both statistical models estimate favorably the real electric power for each tested day as we can observe through the values in Table 8. A great closeness can be confirmed between the estimation and the real data for each location, which shows that the implementation of a gradient descent optimization achieves a satisfactory result.
# Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations
## 6 Simulation and Analysis
### Twin Scenario Construction and Test Verification for \"Phase 3\"
The controller is primarily responsible for the network control of the low Earth orbit constellation. In addition, the controller in the physical system also deploys a collection and control module, which interacts with the collection agent modules deployed at the onboard routing, satellite terminals, and border gateways to collectively collect information on the device status, network situations, and business traffic in the low Earth orbit constellation network. The collected information can be stored in the database. The database information exchange between the virtual and physical systems is illustrated in Figure 16. Leveraging this information, the twin system can provide nearreal-time feedback on the network state from the physical system to the twin network, including the restoration of the network topology, node failures, and link anomalies, as shown in Figures 17-19, respectively. In the synchronized collection of satellite node or inter-satellite link anomalies within the virtual-to-real LEO satellite constellation twin validation environment, the re-convergence of inter-satellite routing occurs, impacting the services passing through the affected nodes or links, as depicted in Figures 20-23. Specifically, Figures 20 and 22 illustrate the scenario wherein the business transmission delays vary due to node destruction or inter-satellite link anomalies, resulting in the switching of inter-satellite paths. Figures 21 and 23 demonstrate that, on average, the convergence time for routing is 7.6 s in the case of node destruction and 7.48 s in the case of inter-satellite link anomalies, based on five test iterations. The protocol testing conducted within this environment indicates that, subsequent to the twin system, the closed-loop control and optimization of the physical system can be achieved. Ultimately, this approach enables physical visualization, network simulation, validation and inference, autonomous twin networking, and intelligent decision-making for the LEO satellite constellation network. Figure 19: Schematic diagram of the twin system restoring the physical system’s inter-satellite link anomaly. Figure 18: Schematic diagram of restoration of physical system node faults by twin system. Figure 21: Average convergence time diagram. Figure 22: End-to-end delay changes caused by abnormal inter-satellite links. Figure 20: End-to-end delay changes caused by node failures.
## 2 Methods
### Convergence Analysis  
To provide a comprehensive evaluation process, this study employs additional metrics to assess both the convergence of the optimization results achieved using the NSGA-II algorithm and the diversity of the solutions.  
* Hypervolume indicator: The hypervolume measure was calculated using the raw objective values, with a reference point established at [1000, 1000, 1000]. This reference point symbolizes an estimated worst-case solution in the objective space. By measuring the hypervolume, one can assess the extent to which the trade-off solutions dominate the objective space over time. This metric is well-regarded in MOO, serving to evaluate both the convergence and diversity of the obtained solutions [18].
* Spacing metric and diversity in objective values: The spacing metric was calculated to quantify the uniformity of the solutions along the Pareto front. Specifically, it was computed using the formula: \[S=\frac{\sum_{i=1}^{N-1}\Bigl{|}d_{i}-\overline{d}\Bigr{|}}{(N-1)\times \overline{d}},\] (1) where \(d_{i}\) is the distance between consecutive solutions along the Pareto front, \(\overline{d}\) is the average of these distances, and \(N\) is the number of solutions in the current generation. This metric provides an indication of how evenly the solutions are spread along the front [19]. The diversity measure was used to evaluate the distribution of solutions across the objective space. In this study, diversity was assessed using the Diversity Index (_DI_), which considersboth the extreme solutions and the spread among solutions in the objective space. The \(DI\) is computed as: \[DI=\frac{\sum_{i=1}^{N-1}\lvert f_{i}-f_{i+1}\rvert}{Range},\] (2) where \(f_{i}\) and \(f_{i+1}\) represent objective values of consecutive solutions, and \(Range\) is the difference between the minimum and maximum objective values. This measure helps to ensure that the Pareto front remains well-distributed and does not become overcrowded in specific regions [19].  
Notably, no normalization was applied to the objective values during the convergence analysis; instead, the raw values for each objective were utilized directly in the optimization metrics. This approach preserves the original scales of the objectives, ensuring that the analysis reflects true magnitudes and trade-offs without introducing artificial scaling [25]. Retaining the raw objective values provides a transparent view of how the solutions evolve over time and offers a more interpretable understanding of trade-offs between the objectives. This follows well-established guidelines in MOO, such as those described in evolutionary optimization techniques like the NSGA-II algorithm [26]. Using raw values ensures that the absolute differences in magnitude between the objectives are preserved, offering direct insights into the actual trade-offs in real-world scenarios. Understanding the convergence and diversity of solutions is essential for assessing the multi-objective optimization processes, particularly in complex scenarios such as factory layout planning.
# Rough Estimator Based Asynchronous Distributed Super Points Detection on High Speed Network Edge
## 3 Distributed Super Points Detection MODEL and Difficulty
### Solution of This Paper  
If only part of the cardinality estimation structure at the observation node is sufficient to detect the global super point, then there is no need to transfer all of them between the observation node and the global server, which can further reduce the communication overhead. Based on this idea, this paper proposes a low communication cost distributed super points detection algorithm: Rough Estimator based Asynchronous Distributing Algorithm (READ).  
In a distributed environment, it is necessary to recover the global candidate super points at the end of the time window according to the information recorded at all observation nodes. DCDS and VBFA have the function of recovering candidate super points. However, DCDS and VBFA have to use LE to recover candidate super points. Although LE has a high accuracy, it also occupies a high amount of memory, resulting in a large amount of communication between observation nodes and the global server.  
RE not only runs fast, but also occupies less memory. If RE is used to generate candidate super points, a small amount of memory can be used to generate global candidate super points. The global server collects LE related to candidate super points from all observation nodes for estimating the cardinalities of candidate super points, and then completes super points detection without transmitting all cardinality estimation structure. The next section will describe how READ works.
## 1 Introduction
### Our Contributions  
This paper proposes a novel and new approach to evaluate, accurately and efficiently, BER of DDM for standard modulations with zero-mean orthogonal artificial noise. It will be shown that, under certain conditions that are easily met in real scenarios, BER can be derived without the need for transmitting a stream of symbols. This approach also leads to an improvement to the existing DDM synthesis techniques by equating the BER response to the amount of artificial noise injected at particular observation angles. This approach removes the need to carry out an iterative or optimization process.  
This document is structured as follows: Section 2 reviews the orthogonal vector approach for generating DDM; Section 3 derives an expression for the average signal-to-noise ratio in a DDM transmission as a function of the observation angle. In Section 4, the average signal-to-noise ratio is used to evaluate BER efficiently for different modulation schemes and DDM configurations. Limitations of the approach are also discussed in Section 4. In Section 5, we propose a novel non-iterative BER-driven DDM synthesis by applying the concepts of previous sections. Finally, conclusions are summarized in Section 6.
## 2 Experimental Methodology
### Operating Conditions  
All the tests were performed at a fixed engine speed of 1400 rpm, reaching 6 bar of brake mean effective pressure (BMEP). For each piston geometry, three fuels were investigated: pure Diesel (0 Ox), a blend of 70% Diesel and 30% OMEx by mass (30 Ox),  
Figure 4: Typical KL distribution. The white circle indicates the area considered for calculating the ensemble average KL.  
and a mixture of 50% Diesel and 50% OME\({}_{\text{X}}\) by mass (50 Ox). The different percentages used in this study have shown good miscibility in previous studies [16]. Two different oxygen concentrations in the combustion chamber were tested for each fuel: 21% and 15%. The 15% of oxygen simulated the optical engine operating under EGR conditions. A total of 12 different operating points were investigated. The operating conditions selected for this work are summarized in Table 2. In addition, the main fuel properties studied are summarized in Table 3.  
An injection strategy consisting of 2 pilots, 1 main and 1 post-injection, was used. The main injection pulse was adjusted to keep the same energy released between fuels (@21% O\({}_{2}\)), compensating for the lower OMEx's lower heating value (LHV). This adjustment allows us to keep the same engine load for all cases. The dwell time between the end of the main injection and the start of the post-injection was kept constant by appropriately shifting the last pulse. Figure 6 shows the electric injection signals adopted for each fuel and the duration in \(\upmu\)s of the main injection.  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{2}{c}{**Operating Conditions**} \\ \hline Piston bowl & Conventional/Radial-lips \\ Engine speed [rpm] & 1400 \\ Injector & CDS \\ BMEP [bar] & 6 \\ Inj. Scheduling \& Rail Pressure & Multiple @ 1182 bar \\ Intake pressure [bar] & 1.34 \\ Exhaust pressure [bar] & 1.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Engine operating conditions.  
Figure 5: A sketch of the methodology used to represent KL evolution maps.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **EN 590 Diesel** & **OME\({}_{\text{X}}\)** \\ \hline Density [kg/m\({}^{3}\)] (T = 15 \({}^{\circ}\)C) & 829.20 & 1057.10 \\ Viscosity [mm\({}^{2}\)/s] (T = 40 \({}^{\circ}\)C) & 2.59 & 1.08 \\ Lubricity [\(\upmu\)m] & 386 & 320 \\ Cetane number [-] & 52.0 & 68.6 \\ Carbon content [\% m/m] & 86.2 & 44.2 \\ Hydrogen content [\% m/m] & 13.8 & 8.8 \\ Oxygen content [\% m/m] & 0 & 45 \\ Lower heating value [MJ/kg] & 41.92 & 19.21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main fuel properties used in this study.
# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
## 4 The CHaChA Clustering Approach
### Online Cluster Adaptation  
Following the initial cluster formation, further concepts are required to react to changes in the network structure and to make cluster adjustments at runtime. With the exception of functions for joining an existing cluster constellation, which are performed in phase 0, all mechanisms take effect after completion of the clustering phase sequence in phase 7.  
Figure 4: Intermediate phase results in an example network (mesh links between grid neighbors not shown for simplification; cluster channels of secondary interfaces highlighted by different colors). Reprinted from ref. [18].  
The detection of connectivity problems and topology changes relies on CHaChA protocol messages at the application layer on the one hand, and on link and path information at the 802.11s link layer on the other hand. Figure 5 shows an overview of all handled cases as a state diagram.
## 5 Conclusions  
In this study, the effects of different opening ratios and opening sizes on torsional vibration and heave were studied via experiments in a 1:70 segment model. Additionally, the effects of different vortex isolation plates on the intrinsic characteristics of VIVs were compared. The main conclusions are as follows:  
The effect of the opening ratio of the vortex isolation plate on the heaving performance in a twin-box girder is more significant at a negative AOA than at a positive AOA. Furthermore, a decrease in the opening ratio improves the performance under heaving.  
There is little correlation between the performance under torsional VIVs and the vortex isolation plate's opening ratio. However, when the opening ratio is 9.35%, the overall performance under VIVs improves. Additionally, it has been found that a vortex isolation plate with a large opening size can reduce the lock-in region of VIVs to a certain extent.  
During torsional vibrations, the pressure taps on the lower surface of the downstream section and near the middle slot exhibit significant discontinuities regarding the average pressure coefficients, fluctuating pressure coefficients, and frequency amplitudes. During heave, the downstream upper surface and the upstream section exhibit a significant spread in the average pressure coefficient. Additionally, noticeable discontinuities in the downstream fluctuating pressure coefficient near the central slot are observed.  
The heave in twin-box girders is closely linked to the characteristics of the upstream and downstream sections. On the other hand, torsional VIVs are closely linked to the characteristics of the downstream section.
# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 3 Experimental Results
### Setup  
The experimental setup employed a workstation equipped with an AMD Threadripper PRO 5995 WX processor, 2 NVIDIA GTX3090 Ti graphics cards, and 128 GB of memory. The workstation ran on the Ubuntu20.04 operating system and was installed with various software, including CUDA 11.8, Python 3.8, PyTorch 1.11.0 [24], and torchvision 0.12.0. The MMRate 0.3.2 [34] framework was used for the entire experimental process and result generation. Both the hardware and software versions were relatively new and exhibited strong compatibility, allowing for easy replication in various environments.  
During training, data augmentation techniques, such as horizontal and vertical flips, were applied, with a batch size of 2. The entire network was optimized using the stochastic gradient descent algorithm, with a momentum of 0.9 and a weight decay of 0.0001. The IoUthreshold for positive samples in the RPN was set to 0.7. The horizontal NMS threshold was set to 0.7, and the oriented NMS threshold was set to 0.1.  
For the HRSC2016 dataset, the longer side of the images was adjusted to 1280 pixels, while maintaining the aspect ratio. If the shorter side was larger than 1280 pixels, it was reduced to 1280 pixels as well. During training, the blank regions were filled with black, and the model was trained for 12 epochs. The initial learning rate was set to 0.01, which was divided by 10 at epoch 9 and epoch 11.  
For the DOTA dataset, the original images were cropped to a size of \(1024\times 1024\), with a stride of 824 pixels, resulting in overlapping patches by 200 pixels. RePaDet was trained for 12 epochs with an initial learning rate of 0.01, which was also divided by 10 at epoch 9 and 11. Figure 7 illustrates the loss curve of our model during training on the DOTA dataset, with 3200 iterations per epoch. A significant decrease in loss was observed at the 8 th epoch (25,600 iterations). To ensure a fair comparison with other models, multi-scale training was conducted by randomly cropping and rotating the dataset at three scales {0.5, 1.0, 1.5} for both training and testing.