esa-sceva/satcom-chunk-collection huggingface dataset:
# X-ray Spectroscopies of High Energy Density Matter Created with X-ray Free Electron Lasers
## 4 Isochoric Heating of Solid with X-ray
Using the collisional-radiative population kinetic calculations, which were consistent with the observed spectral emissions, the creation of solid density Al with an electron temperature over 100 eV and electron densities of \(5\times 10^{23}\) cm\({}^{-3}\) (-3\(\times\) of the ambient Al) was reported [16]. In contrast to the above edge pumping of K-electrons, the XFEL photons with energies below the K abosrption edge can be absorbed only by L-shell and free electrons, and no K-shell vacancy was expected. However, a rich array of K\(\alpha\) emissions (marked as resonant transitions in Figure 4) was also observed by tuning the LCLS pulse to photon energies below the K absorption edge [48]. This unexpected observation can be achieved via sequential multi-photon absorption and the inner-shell resonant processes driven by an intense X-ray pulse. Even though the cross-section is small, multiple X-ray photons in a single pulse are sequentially absorbed in the same atom and ionize L-electrons. Then, a photon resonantly excites a K-electron into an L-hole (1s-2p transition) and opens up a channel for K\(\alpha\) production. Charge states resonating with X-ray photons are both single (1s\({}^{1}\)2p\({}^{n}\)) and double (1s\({}^{0}\)2p\({}^{n}\), hallow ions) K-hole states, indicating exotic plasma conditions. Owing to this new X-ray-matter interaction channel, the sample can be heated to more than 50 eV with pumping below the K absorption edge [48]. Isochoric heating with intense FEL has also been applied to other elements. [PERSON] et al. reported the ultrafast heating of cryogenic hydrogen using FLASH EUV FEL pulses and studied the structural transition from dense cryogenic molecules to a nearly uncorrelated plasma-like structure [22]. [PERSON] and [PERSON] investigated the multi-step structural transitions of diamond (diamond\(\rightarrow\)graphite-like\(\rightarrow\)WDM) irradiated by SACLA XFEL pulses [13]. [PERSON] et al. used an LCLS XFEL pulse to heat a silver foil and probed the hydrodynamic expansion of the front and rear surfaces of the sample to investigate the uniformity of energy deposition [50]. [PERSON] et al., also used an LCLS pulse to heat aluminum foil and studied the transient optical properties in the XUV regime [51]. These isochoric heating experiments were compared with various simulations and models, showing that valuable benchmark systems to study different properties of matter under extreme conditions can be created.
# Detection of Logos of Moving Vehicles under Complex Lighting Conditions
## 2 Background
Since then, deep learning algorithms have become popular in the field of computer vision research and scholars have begun to use such algorithms for logo recognition, forming an important branch of computer vision. [PERSON] et al. [30] put forward a convolutional neural network (CNN) method to replace the traditional feature points; specifically, a convolutional neural network was used to extract the high-level semantic features of logos, and then a support vector machine (SVM) was used to classify such features. [PERSON] et al. [31] borrowed the GoogleNet network structure [32] and proposed a different network structure to achieve end-to-end logo recognition, which achieved good recognition results on the public dataset Flickr Logos-32. The detection of moving vehicles' logos is much more complicated than static logo detection. Firstly, as to the quality control of vehicle images captured by cameras in different weathers, the complex light has a great impact on the automatic detection of vehicle logos; and secondly, due to different forms of vehicle logos and different sizes and proportions of vehicles, the detection of vehicle logos becomes challenging. By improving the quality of data under complex lighting conditions and the You Only Look One-level Feature (YOLOF) [33] algorithm for logo detection, this study will greatly improve the speed and accuracy of logo recognition.
# Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data
## 2 Related Works
### Learning Analytics Data Privacy  
Privacy presents severe challenges for current developments and research in the field of ubiquitous [55] and multimodal [56] learning environments. The way students' assignment data are represented in such VLEs is influential to the performance of LA methods and algorithms [57]. For instance, extensions of supervised learning focused on weakly labeled data have been used to predict the impact of students' assignments on their learning [58]. One of the main objectives of the FAIR principles is to enhance these weakly labeled data by enriching metadata with a plurality of attributes. Nevertheless, intelligent computing techniques, such as machine learning, have many security and ethical implications [59] that can be discordant with fulfilling such principles. Consequently, when applied to the arena of technology-enhanced learning, FAIR principles may pose an advantage to humans' learning support as well as a risk on their privacy.  
The application of PbD techniques is paramount for LA and analytics research in educational institutions [60]. LA systems development should account for privacy at the time of design rather than addressing privacy concerns as an afterthought [61]. For instance, de-identification helps protect privacy by preventing the revelation of Personal Identifiable Information (PII) that can be used to identify an individual [21]. Besides, quasi-identifiers can also be used to break basic anonymization techniques used for LA [18]. Since current VLEs are built on data from cloud-based environments [7; 11], LA requires improved PPDP methods capable of operating on data mashups, such that privacy constraints do not impose a limitation on LA solutions [13]. PPDP solutions used for LA [18; 21] have not considered the actual mashup structure of current VLEs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Privacy-Preserving Data Mashup Protocol** & **Data Partitioning** & **Method for \(k\)-Anonymizing Quasi-Identifiers** \\ \hline [PERSON] and [PERSON] (2015) [32] & Horizontal & Any method (e.g., generalization or microaggregation) \\ [PERSON] and [PERSON] (2019) [33] & Horizontal & Generalization, although other methods can be easily incorporated \\ [PERSON], [PERSON] and [PERSON] (2020) [34] & Horizontal & Any method (e.g., generalization or microaggregation) \\ [PERSON], [PERSON], [PERSON] and [PERSON] (2009) [37] & Vertical & Generalization (top-down specialization) \\ [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] (2012) [52] & Vertical & Generalization (top-down specialization) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of privacy-preserving data mashup protocols that yield \(k\)-anonymized raw data.
## 4 Performance Evaluation
### Experiments
#### 4.1.2 Analysis on Queue Length and Offloading  
To better understand the behaviors with our techniques, we recorded the queue length and the number of offloaded workloads, as shown in Figure 4. The cases in the figure are explained in the previous section. This experiment was carried out with a pair of smartphones, with \(\times 10\) workloads. The performance of this experiment may not match the result in the previous section, because we added file writing code to analyze each processed workload. The added write process was a significant burden to the devices. Therefore, the perceived processing power of the device is relatively limited.  
From Case 1 to Case 2, the added technique, _buffered message method_, allows _HeaLow_ to offload much more, improving the overall performance. In Case 3, the average queue length stabilizes, having less deviation. With _buffer length management_ and _hazard state management_ techniques, the device manages the queue length to stick around to the desirable length, so the deviation for the queue length is minimized. In Cases 4 and 5, the queue length is shorter, and offloading seems to last shorter than other cases. _Delayed response_ method alleviates packet contention and allows the device to offload more than five times within the same period of time. In addition, while the recipient device awaits for the deadline of the offloaded workload, more offloaded workload is processed, causing a chunk of workloads to be returned at the same time. As a result, shortened offloading time helps with the overall workload management.  
With the result shown in Figure 4, we can see that the most important factor to consider in offloading is the network congestion. Therefore, the performance improves significantly with _delayed response_. In that sense, _hazard state management_ is an indirect method of considering the unpredictable network, by forcing the device to evenly access the network over a period of time instead of abruptly demanding network resource when needed.
## 2 Materials and Methods
### Hyperspectral Reflectance Imaging Acquisition and Correction  
Hyperspectral images of maize seeds were acquired by applying a visible/near infrared line-scan hyperspectral imaging system in reflectance mode. This system consists of a back-illuminated 1392 \(\times\) 1024 pixel charge-coupled device (CCD) camera (Pixelfly QE IC*285 AL, Cooke, Thurmaston, LE, UK), a spectrograph equipped with a 25 \(\upmu\)m fixed-size internal slit (1003A-10140 HyperspcTM VNIR C-Series, Headwall Photonics Inc., Fitchburg, MA, USA), a zoom lens (10004A-21226 Lens, F/1.4 FL23 mm, Standard Barrel, C-Mount., Schneider-Kreuzanch, Van Nuys, CA, USA), a single optic line-light powered by a 150 W DC light source (halogen lamp, EKE, 3250K, Techniquip, Norton Fitzwarren, TA, UK), an electric moving stage driven by a stepping motor (VT-80, PI miCos, Auburn, MA, USA), a closed black box, and a computer supported with data acquisition and control software (Hyperspec-M Rev A.2.1.6., Headwall Photonics Inc., Fitchburg, MA, USA, 2010). The line-scanning imaging system has an effective spectral region of 400-1000 nm and exhibits a 0.15 mm/pixel spatial resolution and a 0.64 nm/pixel spectral resolution [14].  
For image acquisition, 32 seeds were placed onto a 20 cm \(\times\) 20 cm black background board in four rows (Figure 2) for easier background segmentation and were automatically moved with a moving stage. When the seeds entered the view slot of the CCD camera, the image was acquired line-by-line constantly at 300 ms exposure time. A total of 812 scanning lines were obtained for each group of seed (32 seeds). After four spectral binning operations, the hyperspectral images were obtained within the spectral range of 400-1000 nm with 2.56 nm interval among contiguous wavelengths (a total of  
Figure 1: The appearance of maize seeds of 17 varieties. (1. BNA07, 2. SNN12, 3. Bositian8, 4. [PERSON], 5. [PERSON], 6. [PERSON], 7. [PERSON], 8. [PERSON], 9. [PERSON], 10. [PERSON], 11. [PERSON], 12. [PERSON], 13. [PERSON], 14. [PERSON], 15. [PERSON], 16. [PERSON] and 17. [PERSON]).  
233 wavelengths). Thus, a hypercube with a size of 1392 \(\times\) 812 \(\times\) 233 was generated, and the images were saved in a band-interleaved-by-line format. The entire acquisition was conducted in a closed black box to prevent any possible interference with external light sources.  
After every four hyperspectral images of seeds were collected, spectral calibration were performed for image correction of the light source variation effect using the following equation [17]:  
\[I=\frac{I_{O}-I_{B}}{I_{W}-I_{B}} \tag{1}\]  
where \(I\) is the relative reflectance intensity of each wavelength slice of hyperspectral imaging, \(I_{O}\) is the original reflectance intensity of the hyperspectral image, \(I_{B}\) is the intensity of the dark current acquired by covering the lens with its cap and turning off the light source, and \(I_{W}\) is the reflectance intensity of the standard white panel (_i.e._, Teflon). Thus, all the corrected images were used in the following analyses.
## 3 Control Methods Used in Agricultural Vehicle Path Tracking Control
### Orchard Agricultural Machinery  
In the case of labor shortage and rising labor costs, the development of autonomous orchard agricultural machinery is helpful in reducing the labor intensity of orchard farmers and improving the efficiency and quality of orchard operations. [PERSON] et al. [67] proposed a model-based control method for orchard autonomous agricultural vehicles, which calculates the velocity and wheel angle based on the vehicle kinematics model, thereby improving the path tracking accuracy. [PERSON] et al. [68] developed a navigation system suitable for curve path tracking in orchards, and the average deviation of path tracking during the experiment was 0.275 m. Based on RTK-BDS positioning technology, [PERSON] et al. [69] developed a straight-line path-tracking controller for the orchard sprayer. When the sprayer speed is 2 km/h, the maximum linear tracking deviation is 0.13 m, and the average tracking deviation is less than 0.03 m, which can effectively achieve accurate straight-line path tracking of the orchard. To ensure the high robustness and speed adaptability of the navigation controller, [PERSON] et al. [70] developed an orchard tractor path-tracking controller. Simulation test results showed that the controller can track the straight line, and when the tractor turns, the average lateral errors at 2.5 m/s and 5 m/s are 7 cm and 13 cm, respectively. [PERSON] et al. [71] designed a machine vision navigation system based on the central axis for autonomous orchard robots, and the fuzzy controller with heading and error as input can well track the medial axis.  
In the study on path tracking of orchard tractors, [PERSON] et al. [72] used the look-ahead distance fuzzy adaptive pure pursuit model to calculate the expected wheel angle. In addition, to cope with the soft road surface of the orchard and the vibration of the tractor itself, the feedforward control is added to the pure pursuit model control to minimize the sideslip. To achieve automatic driving of agricultural machinery in orchards, [PERSON] et al. [73] developed an autonomous orchard vehicle system using a two-dimensional laser, which utilizes fuzzy control to adjust the look-ahead distance of pure pursuit model dynamically. The structure of the orchard vehicle automatic platform is shown in Figure 10. Orchards have a high planting density, and low and narrow working passages, and small tracked tractors have the advantages of small size and strong terrain adaptability, which are widely used in orchards [74]. [PERSON] et al. [75] designed a path-tracking algorithm using a virtual radar model for small, tracked tractors. The algorithm describes the relative position between vehicles and paths with a virtual radar map and generates driving instructions through a deep neural network to control the tractor. Compared with the fuzzy control algorithm, this algorithm has higher accuracy and driving stability and has better adaptability to orchard working paths.  
Figure 10: Orchard vehicle automatic test platform.
## 1 Introduction
The majority of concrete pavement slabs, when subjected to a wheel load, can give an indication as to what the ultimate strengths and weaknesses are in relation to finding a suitable concrete pavement for aircraft. Although the strength of the pavement is not the most crucial factor for airport concrete pavement, when the airport pavement slab is subjected to wheel load, deflection and deformation will have a significant impact on the overall flatness of the pavement. Furthermore, the environment, temperature-wise, and the contact between the precast pavement and the base, will degrade the flatness of the airport pavement to variable degrees, thereby impacting the regular operation of the airport runway. Since the 1940s, precast pavements of highway airports have been widely utilized in Japan, the United States, Germany, and other European nations. In addition, scientists from both the United States and overseas have investigated the mechanical model [26; 27; 28], joint load transfer, and transportation construction of precast pavements. On the highway, experimental engineering sections have been constructed, and a vast amount of data has been collected [11; 29; 30]. In China, however, there is little research on the use of precast pavements on airport runways [21]. Currently, the majority of studies concentrate on the theoretical model, the small size of the interior, and are in the stage of joint material structure optimization design. Moreover, the application of prestressed concrete pavements for use as airport runways is being preliminarily explored [31]. Based on the examination of the aforementioned research background and the current state of research, it can be concluded that the mechanical properties of the precast pavement slab under a wheel load, and the connections between neighboring slabs, have emerged as urgently needed solutions to the current scientific difficulties encountered during the laying of airport precast pavement slabs. These problems can be successfully resolved by creating a full-scale model for numerical simulation. In this paper, the elastic layered Boussinesq calculation theory and ABAQUS finite element are utilized to develop an analysis model of an aircraft's wheel load operating on the pavement slab. The distribution law of stress, and the displacement of a B737-800 aircraft's wheel load on the various positions of the pavement slab is investigated. Then,the load transfer of pavement slabs is examined, noting the influence of adding a dowel bar between adjacent pavement slabs. Furthermore, the attenuation law of the stress displacement on the loaded slab in the transverse, and the longitudinal direction and the load transfer coefficient of the deflection between the adjacent slabs, are discussed. The conclusion can provide some suggestions for the design and laying of precast concrete pavement for airport runways.
# Continuous-Time Perfect Control Algorithm--A State Feedback Approach
## 3 (Non)unique Right Inverses  
The idea of nonsquare matrix right inverses is built upon the statement of finding such a matrix \(\mathbf{M}^{\mathrm{R}}\) that for a given matrix \(\mathbf{M}\), the following relation  
\[\mathbf{M}\mathbf{M}^{\mathrm{R}}=\mathbf{I}, \tag{2}\]  
holds. For nonsquare matrices, this can be accomplished using several frameworks, resulting in both unique and nonunique solutions [27; 28]. The most popular is the unique minimum-norm Moore-Penrose \(T\)-inverse as follows:  
\[\mathbf{M}_{0}^{\mathrm{R}}=\mathbf{M}^{\mathrm{T}}(\mathbf{M}\mathbf{M}^{ \mathrm{T}})^{-1}, \tag{3}\]  
where the full-rank matrix \(\mathbf{M}\) needs to have more columns than rows to attain the property called right-invertibility. On the other hand, there is also a wide range of nonunique inverses available during the perfect control design. For instance, the right \(\sigma\)-inverse can be given in the following form [29]  
\[\mathbf{M}_{\sigma}^{\mathrm{R}}=\beta^{\mathrm{T}}(\mathbf{M}\beta^{\mathrm{ T}})^{-1}, \tag{4}\]  
where \(\beta\)--being of the same sizes as \(\mathbf{M}\)--provides a set of the so-called degrees of freedom. Of course, the additional limitation is only that the matrix \((\mathbf{M}\beta^{\mathrm{T}})\) is expected to be a full rank. The application of right \(\sigma\)-inverse has already proven its usefulness during the perfect control design. For instance, in Refs. [19; 28; 30], it was shown that the proper degree-of-freedom selection enables to obtain, e.g., pole-free, robust, or minimum-energy perfect control behavior. However, the said advantages were obtained for discrete-time plants only;thus, let us now continue with the perfect control algorithm for continuous-time systems. Therefore, this control strategy, defined in terms of the state-feedback system, is proposed in the next section. Observe that the discussed new approach is strictly dedicated to any MIMO plant with \(n_{u}\geqslant n_{y}\). The predecessor provided by Ref. [12] has only employed the systems with right-invertible state-space-related matrices \(\mathbf{B}\). The additional improvement, in comparison to the algorithm given in Ref. [12], is the fact that there is no longer a need to switch between two operating-time-originated ranges.
# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
###### Abstract  
Demographic growth in urban areas means that modern cities face challenges in ensuring a steady supply of water and electricity, smart transport, livable space, better health services, and citizens' safety. Advances in sensing, communication, and digital technologies promise to mitigate these challenges. Hence, many smart cities have taken a new step in moving away from internal information technology (IT) infrastructure to utility-supplied IT delivered over the Internet. The benefit of this move is to manage the vast amounts of data generated by the various city systems, including water and electricity systems, the waste management system, transportation system, public space management systems, health and education systems, and many more. Furthermore, many smart city applications are time-sensitive and need to quickly analyze data to react promptly to the various events occurring in a city. The new and emerging paradigms of edge and fog computing promise to address big data storage and analysis in the field of smart cities. Here, we review existing service delivery models in smart cities and present our perspective on adopting these two emerging paradigms. We specifically describe the design of a fog-based data pipeline to address the issues of latency and network bandwidth required by time-sensitive smart city applications.  
Keywords:fog computing; cloud computing; smart cities; smart grids; smart transportation; smart healthcare; big data; data storage; data analytics; data pipeline; data processing +
Footnote †: journal:  
_Future Internet_2020, _12_, 190; doi:10.3390/612110190
## 3 System Implementation Based on LPA Synchronization Algorithm
### Multi-h CPM Receiver Implementation
#### 3.2.5 Timing Control Unit  
The symbol clock is generated using the principle of a numerically controlled oscillator without ROM, which is also similar to DDS. The clock rate is configured by the sum of updated timing error \(\hat{t}_{n}\) and the fixed clock phase increment \(v_{n}\). Then, the sum value is accumulated in a fixed word length (set to 32 in general). The highest bit of the accumulator output is the synchronized symbol clock \(t_{c}\). Thus, the synchronized timing pulse can be calculated by the following logic expression  
\[t_{p}=\{t_{c}(k)\oplus t_{c}(k-1)\}t_{c}(k), \tag{43}\]  
where \(t_{c}(k-1)\) is the symbol clock with a sampling interval \(T_{s}\) delay.