esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis, Evaluation and Simulation of Railway
## 1 Introduction
### Storage Possibilities  
#### 1.3.1 On-Board Energy Storage Systems (OESS)  
In On-board Energy Storage Systems (OESS), the energy from the regenerative braking can be stored and used at any time when the train needs it, for example, if there are discontinuities in the power supply, such as on bridges, on level crossings, in tunnels or for last-mile applications, e.g., TRAXX AC Last Mile Locomotive built by Bombadier [21]. These systems can operate without catenary, and also, they allow reducing the power peaks of the locomotive when it has to accelerate after a stop.  
In OESS applications, it is usually to install supercapacitors and batteries because of its less weight and volume in comparison with flywheels. Supercapacitors are used when the train needs a high level of power in a short period of time, such as when it is necessary to accelerate after a stop, and batteries are more adequate for supplying energy during larger distances (a few kilometers).  
Some real applications of OESS have been studied in Reference [11]. Trams circulating in Lisbon and Nice have Ni-MH batteries that allow working in catenary free operation mode and contributing to reducing their emissions and visual impact, as well as increase energy efficiency. Lisbon trans use the Sitzas HES system, developed by Siemens [22], while Nice tram was developed by Alstom [23].  
In September of 2018, Bombadier transportation [21] presented a hybrid (electric-battery) operated train called \"Bombardier Talent 3\" planned to passenger services which have an autonomy of around 40 km in battery traction mode. This prototype is currently being tested in Germany, in the Alb-Lake Constance region.  
There are also implemented some ESS composed by supercapacitors, e.g., in Mannheim, Seville and Saragossa (Europe). Mannheim tram ESS is called \"Mitrac Energy Saver\", and was developed by Bombadier [21]. Seville and Saragossa trans (Spain) are developed by CAF, using a system called the RCA system (Rapid Charge Accumulator) [24].  
There is also a project in Great Britain, called Parry People Movers, which has developed a light tramway that works with a flywheel system, and it is currently operating in Stourbridge [25].
## 5 Discussion
This adaptability ensured that the model could accurately classify trunk postures across various farming activities while accounting for individual variations, thus contributing to more comprehensive ergonomic evaluations and interventions. It is worth noting that we also attempted to consider other recognized models, including YoloV5 [49], PoseNet [23] and OpenPose [22] in our study. However, Yolo required a substantial number of labeled images for effective training while the number of our dataset was small [50]. Given this limitation, MoveNet's posture estimation seemed a more feasible choice. Furthermore, while PoseNet specialized in whole-body posture estimation and OpenPose was quite comprehensive, they were computationally intensive for real-time applications. One of our key interests was the MoveNet's lightweight architecture, which enabled efficient real-time posture estimation. This lightweight nature was particularly valuable for our research involving dynamic agricultural scenarios, where the real-time analysis of workers' postures was crucial. While our research provided valuable insights into posture detection in the agricultural field, there were certain limitations that should be acknowledged. Firstly, the number of images used or collected for our dataset was limited. This could potentially impact the generalizability of our model to other work tasks and environments outside the specific plant nursery setting. Furthermore, we observed that there were fewer instances of the 'half bend' class in our dataset. This could be attributed to the nature of the work, where employees were primarily engaged in sustained bending (in 'full bend' class) and walking around (in 'neutral' class). In addition, it is important to acknowledge that our research focused on a specific context and may have certain limitations in terms of its applicability to different work tasks and environments. Further research is needed to validate the effectiveness and generalizability of the approach in diverse agricultural settings.
## 1 Introduction
Firstly, there is  
Figure 1: Masked face age estimation system for smart store customer relationship management. no large benchmark masked face dataset annotated with proper age labels. Secondly, the features of the mouth and nose regions are severely damaged so that effective features are greatly reduced. Finally, faces wearing masks are hard to detect. To handle the above challenges, we built a synthetic masked face image dataset derived from the largest face image dataset, IMDB-WIKI [6], using the dlib machine learning library. MTCNN [7] was incorporated to detect face areas, face key point detection, and align the face. Our demonstrated method took a face mask as a feature in addition to the full face and deployed deep convolutional neural network (CNN)-based models to address the problem of masked face age estimation. It is obvious that CNN-based methods have strong robustness to illumination and facial occlusion changes. Finally, we conducted experiments on the hierarchical approach and compared it with the singular approach. We also released source codes and data freely available in GitHub repository [[https://github.com/MahubSub/Masked_Face_Age](https://github.com/MahubSub/Masked_Face_Age)]([https://github.com/MahubSub/Masked_Face_Age](https://github.com/MahubSub/Masked_Face_Age)) (accessed on 16 May 2022) for keen researchers to studies further. Overall, the contribution of this study is summarized as follows:  
* We derived a synthetic face mask image dataset annotated with real age from the largest in-the-wild face image dataset, IMDB-WIKI, toward the estimation of real age amid the COVID-19 pandemic circumstances. * We somewhat mitigated the data sparsity problem from the existing in-the-wild benchmark dataset, IMDB-WIKI. * We proposed a hierarchical approach to estimate real ages in a group-specific manner, where the intermediate result was a group age generated by the classification model, and the final result was a real age generated by the regression-through-classification techniques generated by the group-specific models. * We compared our hierarchical approach results with a one-off model and empirically demonstrated that a hierarchical approach is marginally better than a singular approach. * Moreover, this research is perhaps the maiden attempt toward real age estimation from masked face images. The rest of the article is divided into the following sections: In Section 2, the literature related to age estimation is presented. In Section 3, we present our proposed masked age estimation approach, describing all the components of this research. Section 4 demonstrates the empirical results. The comparative discussion and conclusion of this research are presented in Sections 5 and 6.
# Blockchain's Grand Promise for the Real Estate Sector: A Systematic Review
## 4 Discussion
In indirect real estate investment and leasing, the current regulatory frameworks and the related uncertainties do not appear as significant barriers as in most other real estate applications [19; 90; 91]. Similarly, property transactions generally attract interest as an area for blockchain application (see., e.g., [106; 133; 137; 148; 155]). RQ2: What are the main blockchain pros and cons in the real estate sector? Figure 6's upper left side compiles the main blockchain pros in the real estate sector. Generally, blockchain could bring more trust and security to the real estate sector [19; 137; 74; 78; 85; 89; 96; 134; 177; 174; 175; 176; 177], which is often plagued with fraud and corrup  
Figure 6: Blockchain’s grand promise for the real estate sector: the most attractive real estate subsectors for blockchain, current real estate issues, and blockchain pros and cons. tion [74; 79; 106; 116; 176; 178]. Blockchain could bring efficiency and transparency by replacing traditional paper-based and manual processes that depend on many intermediaries [17; 106; 114; 137; 177; 176; 177]. Smart contracts powered by the blockchain could automate the processes and provide disintermediation, saving time and costs [17; 19; 41; 63; 88; 89; 94; 114; 158]. Blockchain could also help develop a more inclusive real estate sector by allowing the fractionalization of property ownership and investments, creating efficient secondary markets for blockchain-based real estate tokens [17; 122; 179]. Blockchain could also encourage financial inclusion by providing access to trustworthy, transparent, and tamper-resistant property registration systems to more people, which is lacking, especially in many developing countries [77; 78; 89; 137; 176; 177]. Figure 6\({}^{\prime}\)s upper right side represents the main blockchain cons in the real estate sector. Contrary to how blockchain has been generally described as a technology to replace intermediaries, the literature here recognized that blockchain in the real estate sector would still likely require intermediation and intermediaries [1; 14; 19; 32; 52; 63; 118]. Replacing the governmental centralized land administration registries seems unlikely, at least in the short term [32; 34; 41; 80; 118; 180]. Blockchain is also technically immature, and many technical challenges will need to be solved before the technology is scalable, secure, and interoperable enough to be implemented in a disruptive way in the real estate context [15; 51; 78; 96; 116]. Moreover, digital identity solutions need to be developed and tested [19; 32; 51; 56; 96; 181]. The unavailability of reliable real estate data is yet another significant implementation barrier [51; 78; 96; 116]. Moreover, implementing blockchain technology in the real estate sector would be complex and require considerable collaboration, training, and governmental buy-in [14; 15; 17; 54; 66; 96; 177]. Resistance and trust issues would be expected [27; 66; 88; 119; 133; 182].
## 5 Experimental Evaluation
### Simulation Results
#### 5.2.3 Adaptability of DMD-JD Algorithm to Multipath Channels
The addition of equalization modules can not only greatly improve the BER performance, but also enable the FTN signaling to have a better matching relationship between the data sets in different multipath channels. The significance is that when used in actual scenarios, even if the mobile terminal is in different multipath channel switching states, it can still have a better bit error rate guarantee. Figure 8: Compression factor \(\tau=0.7\), fixed signal-to-noise ratio (7 dB) for training, and test performance for the same multipath channel with different signal-to-noise ratios. The simulation content in Figures 9-11 are similar, the differenceies in the different channels represented by the test set. The performanceeoss is all within 0.7 dB. Figure 10: Perform training in different channel environments under the condition of matching signal-to-noise ratio, test with VA-60 km/h, performance under the condition of compression factor \(\tau=0.7\). Figure 9: Perform training in different channel environments under the condition of matching signal-to-noise ratio, test with TU-30 km/h, performance under the condition of compression factor \(\tau=0.7\).
# A Framework for Cloud Based E-Government from the Perspective of Developing Countries
## 4 Case Study
### Study Context  
The study context is Nepal as a developing country. Nepal is a landlocked country with vast geographical diversity and is characterized by a highly centralized government. Local administration is performed through numbers of administration districts. At present, it has 5 development regions, 14 zones and 75 districts, however it is going through a serious reform following constitutional assembly election in 2008 and 2013. E-government initiative in Nepal started in 2006, with funding from the Korea IT Industry Promotion Agency (KIPA). The government had announced the project called the electronic government master plan of Nepal, which has proposed 21 various electronic government projects with the estimated budget of 65 million US dollar. Later only 8 projects were selected for execution because of the insufficient fund. The Asian development bank granted support of 25 million US dollars and the Nepal government contributed only 6 million US dollars [24]. Studies on the effectiveness of these e-government projects in Nepal have found that the master plan has failed to achieve its objective [24; 25]. Several issues have been encountered such as availability of the technology, budget, human resources and organizational structure while implementing the projects. Beside this, e-government services those are in existence also have several issues like accessibility, language, poor quality of information, lack of help and support, lack of integration of the government departments and lack of scalability and availability of the services [26]. Recently, the public service communication server for the services has crashed as it failed to handle an excessive number of requests from users [26].
# A Test Method for Acoustic Emission Properties of Natural Cellulose Fiber-Reinforced Composites
## 2 Experiments
### Acoustic Vibration Test Device
#### 2.2.4 Acoustic Vibration Characteristics  
The most important acoustic characteristics for a specimen are the dynamic elastic modulus \(\mathrm{E_{d}}\), the specific dynamic modulus \(\mathrm{E_{d}}/\rho\), the acoustic radiation damping coefficient \(\mathrm{R}\), and the acoustic impedance \(Z\)[23], among which \(\mathrm{E_{d}}\) is calculated as follows:  
\[\mathrm{E_{d}}=\frac{48\pi^{2}\,\mathrm{f}\,^{2}_{n}\,\mathrm{L}^{4}\rho}{ \mathrm{h}^{2}\,\beta_{n}^{4}}\times 10^{-9} \tag{3}\]  
where L is the specimen length, h is the specimen thickness, v is the sound transmission speed of the specimen, \(\rho\) is the specimen density, and \(\mathrm{f_{n}}\) is the fundamental vibration frequency [20], \(\beta_{n}\) is the constant corresponding to the fundamental frequency \(\mathrm{f_{n}}\), and \(\beta_{n}=(n+1/2)\,\pi\), n = 1, 2, 3, n. In practice, \(\mathrm{f_{n}}\) is determined from the frequency spectrum recorded by the acoustic testing system.  
The specific dynamic modulus, \(\mathrm{E_{d}}/\rho\), characterizes the dynamic rigidity of the material and is related to the intrinsic properties of the material, namely, flexural modulus and specific density, and extrinsic properties, such as cross-sectional shape and dimensions of the material [23]. Acoustic radiation damping coefficient (R) represents the intensity of the acoustic energy radiated into the air and is expressed as follows:  
\[\mathrm{R=}\sqrt{\frac{\mathrm{E_{d}}}{\rho^{3}}} \tag{4}\]  
The acoustic impedance Z is expressed as follows:  
\[\mathrm{Z=}\rho\mathrm{v=}\sqrt{\rho\mathrm{E_{d}}} \tag{5}\]The lower the acoustic energy loss due to internal friction, the higher the acoustic energy transferred into the air, leading to louder and longer-lasting sound emitted by the specimen, resulting in a higher conversion efficiency of sound vibration energy. Acoustic impedance (Z) is also called characteristic impedance. From the perspective of sound vibration efficiency, materials with good acoustic performance should have a larger specific dynamic modulus, a larger acoustic radiation damping coefficient, and smaller acoustic impedance, which facilitates the efficient conversion of sound energy and the improvement in response speed [24].
## 5 Discussion  
Since BDS2 is a regional navigation constellation and has only 14 satellites, there is still a large gap in the accuracy of relative orbit of TH-2 based on BDS2 compared with that based on GPS. The results showed that the accuracy of the relative orbit of TH-2 based on BDS2 in the Asia-Pacific region could reach 2.89 mm, which was comparable to the existing simulation results. Research on the precise orbit determination of LEO satellite based on BDS obtained the same conclusion; the addition of GEO would dramatically degrade the accuracy of orbit. However, the results of this paper show that GEO can significantly improve the accuracy of the relative orbit of TH-2 satellite formations. This is because in the precise relative orbit determination process, the ephemeris error can be greatly weakened  
Figure 16: Daily value of GPS ADOP.  
Figure 15: GPS ambiguity dilution of precision (ADOP) on DOY 311 in 2019.  
by CDGNSS technology. The relative position between TH-2A and TH-2B satellites is only a few hundred meters; hence, the influence of GEOs' ephemeris errors on the precise relative orbit determination of TH-2 could be ignored.  
The BDS3 has been officially opened. Compared to BDS2, BDS3 has many advantages, such as 30 satellites, the ability to broadcast four civil signals, a more stable atomic clock, and inter-satellite links. The performance of relative orbit determination based on BDS3 is expected to be better than that based on BDS2. The effect of GEO satellites on the precise relative orbit determination of the LEO satellites with a several-hundred-kilometer separation and the improvement of the precise relative orbit determination by integrating BDS3 and GPS will be studied further in the future.
# Valorisation of Star Wastewater by Anaerobic Fermentation
## 4 Other Anaerobic Fermentations
### Lactic Acid Fermentation  
Lactic acid can be used as a chemical in the food and feed industry; in addition, it is the precursor of the biopolymer poly-lactic acid (PLA). The traditional method of biological production of lactic acid from wastewater involves a two-step process where the substrate requires energy-intensive pre-treatment. [PERSON] et al. showed that _Lactobacillus plantarum_ was capable of utilising synthetic cassava starch wastewater for the production of lactic acid in a process known as simultaneous saccharification and fermentation (SSF), which is a more efficient method, compared to the traditional two-step process. Lactic acid concentrations of 28.71 g L\({}^{-1}\) could be achieved under non-sterile conditions. At the same time, high purification rates could be achieved: COD removal of 98% and nitrogen (TKN) removal of 85% [30].  
Similar results could be achieved with potato starch wastewater and the use of fungi such as _Rhizopus oryzae_, as demonstrated by [PERSON] et al. with concentrations of lactic acid above 10 g L\({}^{-1}\) at various pH levels, as well as an increase in fungal biomass [31]. The use of fungi brings some advantages with regard to use in an industrial environment due to their robustness and high tolerances. To combat the inhibition effect that high concentrations of lactic acid can have on microbial activity, supplementing the media with CaCO\({}_{3}\) is possible [32]. Besides the examples presented, there are many bacterial species that have been proven to show the ability to produce lactic acid, including _Carnobacterium_, _Leuconstoc_, _Streptococcus_ and _Oenococcus_[33].
# Text Semantics-Driven Data Classification Storage Optimization
## 5 Experiments and Evaluation  
We begin by outlining the experimental environment and pertinent settings of the CloudSimDisk simulator. Subsequently, we delve into performance analyses focusing on storage energy consumption and operational costs. For performance comparison, we employ the AS-H and K-ear methods as baselines.  
**AS-H:** This method adopts the access frequency of data text as the classification basis. By analyzing the access peak, it selects the highest peak as the seasonal feature contained in the text. It considers data with current seasonal characteristics as hot data and data with other seasonal characteristics as cold data. All disks storing data operate in high-performance mode.  
**K-ear:** This approach utilizes K-means as the text classification method and implements classification storage based on seasonal features. It considers data containing current seasonal characteristics as hot data and data containing other seasonal characteristics as cold data. The high-performance disks store hot data. The low-performance disks store cold data.
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
## 3 Reconstruction Algorithm
### Convex-Optimization Algorithm  
The convex-optimization algorithm converts a nonconvex problem into a convex one to solve signal approximation. Suppose that \(J(x)\) is the convex cost that promotes sparsity. That is to say, the value of \(J(x)\) is small when signal \(x\) is in high sparsity. On the basis of Equation (5), the reconstruction of signal \(x\) without noise could be described as  
\[min\{J(x)\},\;subject\;to\;\;y=\Phi x. \tag{24}\]  
Similarly, when there is noise, the reconstruction process is as follows:  
\[min\{J(x)\},\;subject\;to\;H(\Phi x,y)\leq\epsilon, \tag{25}\]  
where \(H\) is the cost function to penalize the distance between \(\Phi x\) and \(y\). Equation (25) can be expressed as a form without constraints as follows:  
\[min\{J(x)+\lambda H(\Phi x,y)\}, \tag{26}\]  
where \(\lambda\) is a penalty factor. In a convex-optimization algorithm, function \(J\) is usually chosen by the \(l_{1}\)-norm of sparse signal \(x\) as \(J(x)=\parallel x\parallel_{1}\), and \(H\) is solved as follows:  
\[H(\Phi x,y)=\frac{1}{2}\parallel\Phi x-y\parallel_{2}^{2}, \tag{27}\]  
which is the \(l_{2}\)-norm of error of \(y\) and \(\Phi x\). The most common convex-optimization algorithm is basic pursuit (BP), which uses the \(l_{1}\)-norm to solve the optimization problem by using linear-programming methods [37].  
On the basis of the pulse dictionary, an adaptive BP algorithm was introduced for vibration-signal processing and the fault diagnosis of rolling bearings [38]. This approach established the functional model of the impulse dictionary by using the characteristics of bearing fault signals. Simulation results proved that the method fundamentally reduced dictionary redundancy. The BP algorithm with low redundancy could make full use of this advantage in the fault diagnosis of rolling bearings.  
Another algorithm is the focal underdetermined system solver (FOCUSS) algorithm [39] that uses the \(l_{p}\) norm (\(p\leq 1\)) to solve optimization problems.  
[PERSON] et al. presented an improved multimeasurement-vector focal underdetermined system solver, and applied it to synthesize-mode reconfigurable sparse arrays [40]. They used sparse-recovery theory to establish a multiple-measurement-vector collaborative sparse-recovery model for the purpose of synthesizing mode-reconfigurable sparse arrays [41; 42; 43]. In addition, there are the \(SL_{0}\) method, the gradient projection for sparse-reconstruction algorithms [44], and sparse reconstruction by separable approximation [45].
## 2 RPC-FST Model Based on ALE Method
### Establishing the Model
Moreover, (\(\dot{\epsilon}_{s}\)) = 1 s\({}^{-1}\) is used to make indexes dimensionless, where (\(\dot{\epsilon}\)), \(a\), and \(b\) are the constant coefficients for RPC. \(a\) and \(b\) are set as 0.291 and 0.310, respectively. It is worth noting that detonation products are transmitted in the air with high-frequency shock waves, and their physical process and dynamic response are exceedingly complex. To reveal this process accurately, the determination of air domain size is essential. According to TNT distance, TNT size, RPC-FST, and other model parameters in the practical project, the high-frequency shock wave shows a large amplitude, and the behavior of RPC-FST displacement is geometrically nonlinear. Therefore, the size of the air domain was chosen as 3000 mm \(\times\) 1000 mm \(\times\) 8000 mm. The non-reflection boundary keyword is used for air boundary conditions to prevent wave reflection from the boundary. The mesh size denotes 20 mm, and the termination time denotes 50 ms.