esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Integrating 5G and Tactile Internet to Industrial Case Studies  
Although the production floor is the most obvious location where 5G can benefit manufacturing, there are other ways in which 5G can contribute to the manufacturingenvironment in a non-direct way. The IIoT is used to integrate the equipment resources in the implementation of smart factories. As a result, the manufacturing system has perception, interconnection, and data integration capabilities. Further to that, the internet of services consists of interconnection, collaboration, and execution. Consequently, to transform a modern factory into a smart factory, key technologies in all layers must be thoroughly investigated [64]. Next, Figure 14 depicts the smart factory as being divided into several layers with different network choices at each layer; each network has different demands and values various properties differently. The level of integration that 5G can bring to manufacturing will be determined by decisions made at each of these levels. Network slicing, a key new concept in 5G, will allow tenants to get different levels of connectivity from their service provider to accommodate different use cases. 5G will be an all-cloud architecture to achieve network slicing, which will necessitate the use of software defined networking (SDN) and network function virtualization (NFV) (see Figure 15).
## 2 Study Area and Datasets
### Datasets
#### 2.2.2 GFO Time-Variable Solutions  
We use the GFO RL06 mason data released by the Center for Space Research (CSR) at the University of Texas at Austin [48] from May 2018 to August 2022. The CSR mason solutions are estimated on a hexagonal geodesic grid and have a spatial sampling of about \(1^{\circ}\) in equatorial longitude and a monthly temporal resolution, limited by the band-limited nature; the \(1^{\circ}\) spatial sampling is likely not the spatial resolution of the GRACE solutions, and the spatial resolution is about 300 - 500 km [49]. The CSR mason solutions reflect changes in the TWS, and they are expressed in terms of the equivalent water height (EWH). During generation, the CSR mascons are corrected for glacial isostatic adjustment and leakage effects. Note that the low-degree changes (e.g., degree-one, C20, and C30) are resolved with independent measurements or models [50; 51].  
Figure 4: The amplitudes (**a**,**c**) and phases (**b**,**d**) of the annual terms in vertical displacements induced by NTAL (**a**,**b**) and NTOL (**c**,**d**).
# Examining Gender Bias of Convolutional Neural Networks via Facial Recognition
## 3 Methodology
### Convolutional Neural Networks Detailed
#### 3.1.2 Inception v2/Inception v3  
While Convolutional Neural Networks are still seen as a 'newer' technology in the field of image classification, it still has a varied history. Before the creation of the Inception architecture, stacking convolution layers was the order of the day. It was believed that this was the best and easiest way to attain high performance and increased accuracy; however, the Inception model tries a different approach. Instead of taking the quick and easy route, Inception was engineered, leading to a quick evolution of its models, including several different versions that are still widely used today [34].  
The hope of Inception was to solve issues with some images that would vary in size, depending on the salient parts of those images. The inherent variation caused problems with models that were not robust, leading to issues with both model performance and overall image recognition accuracy. CNNs needed to use the correct kernel size for convolutions compared to the images that they were working with. In example, a smaller kernel size is useful for dealing with image information that is local (say, a part of an image), whereas a larger kernel size was preferable when dealing with an image that was global (where the image information consisted of the entire image).  
Inception successfully solved this issue by having different filters with multiple sizes. Instead of utilizing deeper networks, the Inception architecture opted to go wider with its convolutions. While there are several different Inception models widely in use today (with even more still being developed and investigated), we decided to go with Inception version 2 and Inception version 3 with our research paper, due both to their performance, and to their very recent use in several research papers [34; 35; 36].  
Figure 3: AlexNet Neural Network, based on the original architecture.  
Inception version 2 improved upon the initial Inception architecture by making the wider convolutions more efficient, as well as attempting to alleviate potential bottlenecks which could lead to a loss of information. Inception version 3 introduced novel 7-by-7 width parallel convolutions, and also tweaked some of the auxiliary classifiers. Due in part to these changes, while both versions of Inception share various similarities, they are able to have image classification accuracy and performance that is sometimes widely varied [35]. An example of this architecture can be seen in Figure 4.
## 5 Biosensor Application of Electrodeposited Composite Nanostructures
### Composite Electrodes Containing Prussian Blue
The H\({}_{2}\)O\({}_{2}\) produced by glucose oxidase, which underwent a reversible redox reaction with PB, was detected amperometrically at 0.0 V (versus Ag/AgCl) and a linear range from 6.25 to 93.75 \(\mu\)M and a detection limit of 1.56 \(\mu\)M. The first step for the construction of the nanocomposite electrode was to electrodeposit PB and chitosan from an aqueous solution of 0.10 M of KCl, 0.01 M of HCl, 0.5 mM of K\({}_{3}\)[Fe(CN)\({}_{6}\)], 0.5 mM of FeCl\({}_{3}\) and 0.01% chitosan by cycling the potential from \(-\)0.1 to 0.45 V at 20 mV sec\({}^{-1}\) for 10 cycles to deposit the optimal thickness of the film on the gold disk electrode. Then, a layer of gold nanoparticles and chitosan was electrodeposited at \(-\)0.2 V from a solution of 0.1% HAuCl\({}_{4}\) and 0.025% chitosan. SEM revealed 50-nm sized nanocubes spread on the surface for the first layer, and after the second layer was deposited, Au nanoparticles that had dispersed within the chitosan were visible. The solution of both enzymes was drop cast on the surface, which was then followed by drying and cross-linking in glutaraldehyde. Figure 10 shows SEM images of the composite nanostructures,along with CV scans of bare gold, PB, and chitosan (CS) on gold, gold nanoparticles with PB and chitosan on gold, and also with glucose oxidase (\(\beta\)-G-GOD) recorded in the presence of [Fe(CN)\({}_{6}\)]\({}^{3-}\) and [Fe(CN)\({}_{6}\)]\({}^{4-}\), showing that the addition of PB and gold nanoparticles enhances electron transfer to and from the diffusing redox probe.
## 4 Speed Guidance Strategy Considering the Uncertainty of Leading Vehicles
### Speed Guidance Model for Mixed Traffic Flow
\begin{array}{l}\left(0,\,t_{rg}-h_{s}\right]\text{ green phase}\\ \left[t_{rg},t_{rg}+T_{green}-h_{s}\right]\text{ red phase}\end{array} \right. \tag{20}\]The maximum number of vehicles, \(N_{k-1}\), in the target platoon that can accelerate through the stop line is:  
\[N_{k-1}=\left\lfloor\frac{t_{r,g}-t_{f}-(k-2)\tau_{c}-\tau_{d}}{h_{s}}\right\rfloor \tag{21}\]  
* After guiding part of the platoon of vehicles to slow down  
When guiding part of the target platoon to slow down, guide the vehicles to slow down to the target speed \(v_{1j}^{*}\Big{(}v_{\min}<v_{1j}^{*}<v_{\max},j=1,2,3,\cdots Q\Big{)}\) to achieve the green light phase, which just begins when the vehicle starts at the stop line. Then, the time of some vehicles in the target platoon \(t_{ij}(i=k,\cdots,W_{n},j=1,2,3,\cdots Q)\) passing the stop line should meet the following constraints:  
\[t_{ij}=\tau_{c}+\frac{v_{1j}^{*}-v_{ij}}{d_{ij}^{*}}+\frac{L_{g}+x_{ij}-v_{ij} \tau_{c}-\frac{\binom{n}{v_{1j}}^{2}-\binom{n}{v_{1j}}^{2}}{2d_{ij}^{*}}}{ \tau_{1j}^{*}}+h_{s} \tag{22}\]  
\[t_{ij}\in\left\{ \begin{array}{l}\big{(}t_{r,g}+T_{red},\,t_{r,g}+T_{green} +T_{red}\big{)}\quad\text{Green phase}\\ \big{[}\tau_{r,g}+T_{green}+T_{red},\tau_{r,g}+2T_{green}+T_{red}\big{]}\quad \text{Red phase}\end{array} \right. \tag{23}\]  
* Slow down and stop guidance strategy  
When the target platoon enters the speed guidance area, the signal light is red or green. Because there are many vehicles queuing in front of the signal light, no vehicles in the platoon can idle at the parking line. Currently, to avoid emergency braking of the vehicle, the optimal reduction in speed of the first car \(b_{1j}(j=1,2,3,\cdots Q)\) is:  
\[b_{1j}=\left\{ \begin{array}{l}\big{(}v_{1j}\big{)}^{2}/2\big{(}L_{g}-v_{1j} \tau_{d}\big{)},\,\,\text{Front vehicle type is HDV}\\ \big{(}v_{1j}\big{)}^{2}/2\big{(}L_{g}-v_{1j}\tau_{c}\big{)},\,\,\text{Front vehicle type is CAV}\end{array} \right. \tag{24}\]  
The optimal reduction in speed of the rear car \(b_{ij}(i=2,3,\cdots W_{n},j=1,2,3,\cdots Q)\) in the platoon meets the following constraints:  
\[b_{ij}=\frac{\big{(}v_{1j}\big{)}^{2}}{2\big{(}L_{g}-x_{ij}-(i-1)x_{s}-v_{1j} \tau_{c}\big{)}} \tag{25}\]  
\[ \begin{cases}t_{r,g}<\tau_{c}-\frac{v_{1j}}{b_{ij}}+(i-1)h_{s}<t_{r,g}+T_{red }&\text{green phase}\\ t_{f}+h_{s}\leq\tau_{c}-\frac{v_{1j}}{b_{ij}}+(i-1)h_{s}<t_{r,g}+T_{red}+T_{ green}&\text{red phase}\\ \big{(}v_{ij}\big{)}^{2}/2x_{c}\leq b_{ij}\end{cases}  \tag{26}\]  
Then, the optimal speed reduction rate \(b_{ij}^{*}(j=1,2,3,\cdots Q)\) is:  
\[\left|b_{ij}^{*}\right|=\min\left[\left|b_{ij}\right|,a_{\max}\right] \tag{27}\]  
The min represents that the optimal speed reduction is the minimum value between the two.
## 4 Results and Discussion
### Land/Water Discrimination from Elevation-Based Features  
The _HV_ and _HSD_ features were calculated and Equations (7) and (8) were used to label water points. The _HV_ and _HSD_ of water points in C3 were higher than of water points in C1 or C2. Therefore, the water points were labeled using a combination of C1 with C3 or C2 with C3. The labeled points  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Classified as**} & \multicolumn{2}{c}{**Reference**} \\ \cline{2-3}  & **Water** & **Land** \\ \hline Water & True Positive (_TP_) & False Positive (_FP_) \\ Land & False Negative (_FN_) & True Negative (_TN_) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix elements.  
based on \(HV\) and \(HSD\) are shown in Figure 5. The completeness, correctness and overall accuracy of the labeled points are provided in Table 4.  
The discrimination based on \(HV\) and \(HSD\) features varies between the two used combinations. The reason of this is the objects (e.g., vegetation) that have double or more returns in C3, but have single returns in C1 or C2 were misclassified as water points. On the contrary, water areas that have single returns in C3 were incorrectly classified as land. As a result, the correctness of the combination of C1 with C3 is lower than that of C2 with C3 because the classification errors of land points as water points are much higher in the first combination. In general, the \(HV\) and \(HSD\) features could enhance discrimination capacity wherever benthic returns are present, which reflects the potential use of multi-channels that have various characteristics in land and water regions.
# Quantification of the Benefits for Power System of Resilience Boosting Measures
## 2 A Framework to Model Power System Resilience
### Resilience Definition From CIGRE C4.47  
CIGRE WG C4.47 associates the concept of resilience to the system's ability to limit the extent, severity and duration of system degradation following an event. As the criterion of application for this property mainly regards extreme events, the CIGRE WG C4.47 defines Power System Resilience as \"the ability to limit the extent, severity and duration of system degradation following an extreme event\".  
Several key measures can be used to achieve system resilience over different time frames (from short term to long term). The second part of the definition is dedicated to these key measures:  
\"Power system resilience is achieved through a set of **key actionable measures** to be **taken** before, during and after extreme events, such as:  
* anticipation,
* preparation,
* absorption,
* adaptation,
* rapid recovery, and
* sustainment of critical system operation including application of lessons learnt\".
## 4 Conclusions  
The composite adsorbents were prepared from kaolin (SK) and two fly ashes (FAs) differing in the spent fuel (biomass and lignite combustion). Mixtures of SK with FA in the ratio of 50:50 wt.% were prepared at 20 \({}^{\circ}\)C, 65 \({}^{\circ}\)C and 110 \({}^{\circ}\)C in an autoclave. The source material and composites were tested as the adsorbents of Pb\({}^{2+}\) as a typical cationic pollutant, and AsO\({}_{4}\)\({}^{3-}\) representing a toxic risk anion, from water systems.  
Pb\({}^{2+}\) adsorption on the source materials, except SK, and on both the composite adsorbents proceeded at a high adsorption yield (\(\approx\)99%) and a low adsorbent consumption. AsO\({}_{4}\)\({}^{3-}\) adsorption proceeded selectively only on both FAs at an order-of-magnitude lower adsorption capacity compared to Pb\({}^{2+}\); and an adsorption yield of >97%.  
The growing preparation temperature of the composite adsorbents did not affect Pb\({}^{2+}\) adsorption at all but reduced the efficiency of AsO\({}_{4}\)\({}^{3-}\) adsorption by up to 30%. In terms of the composite adsorbents, a higher preparation temperature (65 \({}^{\circ}\)C, 110 \({}^{\circ}\)C in an autoclave) did not significantly affect either the phase composition or the structural and surface properties.  
The adsorption potential of kaolin-FA composites was preferably observed in the adsorption of cations from contaminated waters. Although anions demonstrated a higher sorbability on individual FAs, the participation of kaolin in composite adsorbent could definitely bring some benefits, particularly in increasing homogeneity and thus the quality of the composite, compared to an individual FA. Except for a wider range of adsorption sites, which is promising for a decontamination of mixed contaminated systems, its low price can play a key role in technological applications.  
Conceptualisation, B.D.; adsorption experiments and analysis, B.D., E.B., M.A. and K.M.; methodology, B.D. and M.L.; S\({}_{\text{BET}}\) measurement and evaluation, M.L.; XRD measurement, D.K.; material preparation and characterisation, L.P.; writing--original draft preparation, B.D.; writing--review and editing, B.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the project No. TH79020001 \"ABTOMAT2022--Utilisation of Aluminium Bearing Raw Materials for the Production of Aluminium Metal, Other Metals and Compounds\" of the Technology Agency of the Czech Republic.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.  
Figure 8: Efficiency of Pb\({}^{2+}\) and AsO\({}_{4}\)\({}^{3-}\) adsorption on thermally modified composites.
## 2 Overview of Microreactors  
Microreactors are in the initial stages of their development. The commissioning of a nuclear reactor requires several years of meticulous planning and collaboration between reactor designers and regulatory bodies. For instance, the U.S. Department of Defense aims to have the first microreactor operational by the end of 2027 (Nuclear Energy Institute, 2018). The typical timeline from the submission of a license application to the start of commercial operations and energy production is projected to be around 7 years. Nevertheless, the unique nature of this technology could introduce challenges and risks that might extend the overall timeline to anywhere between 5 and 10 years [67; 68; 69; 70]. Designs that utilize light water cooling are relatively mature as they rely heavily on established technologies from conventional reactors [71; 72; 73; 74; 75; 76; 77; 78; 79; 80]. On the other hand, advanced designs, including those using liquid metal, molten salt, or high-temperature gas, will necessitate more extensive design and certification efforts, likely resulting in a longer development timeline [81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93]. Microreactors are emerging as innovative technologies with the potential to revolutionize energy production. These small and modular reactors can be used for a wide range of applications, from providing energy supply in remote areas to industrial applications. The advantages of microreactors include rapid deployment, portability, flexibility, and low operational costs. However, the widespread adoption and effective use of this technology require overcoming various technical, economic, and regulatory challenges. Research questions are critical to understanding these challenges and fully realizing the potential of microreactors. In this study, various research questions have been developed to examine different aspects of microreactors. These questions cover topics such as technical performance, economic feasibility, safety and licensing processes, fuel utilization, environmental impacts, and the role of blockchain technology in these processes. The research questions aim to determine the position of microreactors in the energy sector, understand the obstacles and opportunities, and guide the future development of this technology. This comprehensive set of questions will provide an in-depth understanding of the various dimensions of microreactors.
# Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
###### Abstract  
In data centers, cloud-based services are usually deployed among multiple virtual machines (VMs), and these VMs have data traffic dependencies on each other. However, traffic dependency between VMs has not been fully considered when the services running in the data center are expanded by creating additional VMs. If highly dependent VMs are placed in different physical machines (PMs), the data traffic increases in the underlying physical network of the data center. To reduce the amount of data traffic in the underlying network and improve the service performance, we propose a traffic-dependency-based strategy for VM placement in software-defined data center (SDDC). The traffic dependencies between the VMs are analyzed by principal component analysis, and highly dependent VMs are grouped by gravity-based clustering. Each group of highly dependent VMs is placed within an appropriate PM based on the Hungarian matching method. This strategy of dependency-based VM placement facilitates reducing data traffic volume of the data center, since the highly dependent VMs are placed within the same PM. The results of the performance evaluation in SDDC testbed indicate that the proposed VM placement method efficiently reduces the amount of data traffic in the underlying network and improves the data center performance.  
Keywords:scalable VM placement; software-defined data center; traffic dependency; dependency analysis +
Footnote †: journal: Applied Sciences
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 4 Expt. 2: Spectral Reallocation Preferences for Band-Limited Speech
### Results
#### 4.3.5 Energetic Masking  
Figure 7 depicts spectral regions that survive energetic masking for the conditions of Expt. 2. Unlike the case in Expt. 1, many spectral regions contain no speech information for most of the range of adjustment available to listeners. As in Expt. 1, preferences suggest a desire to ensure that speech information is audible in regions above 1 kHz, and especially in the 2-4 kHz range.  
Figure 6: [Colour online] Distribution of preferences (blue bars) and percentage of keywords identified correctly at each preference (points with error bars; points in red correspond to preferences chosen only once). Details as for Figure 3, except distributions, sum to 185 (37 listeners \(\times\) 5 trials).
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 8 Challenges and Limitations
[293] focused on short-term trajectory predictions using convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, emphasizing the importance of accurate trajectory predictions in complex scenarios such as pedestrian crossings. Yet, the authors could have discussed the challenges of scaling these models to handle the variability and unpredictability inherent in pedestrian behavior in diverse urban environments. [PERSON] et al. [294] provided an overview of the uncertainty challenges in autonomous driving, categorizing them into external environmental factors and internal system processing and suggest mitigation techniques to enhance AV safety. However, the effectiveness of these mitigation techniques in diverse and unpredictable real-world conditions remains to be thoroughly examined. Collectively, these studies underscore the importance of robust uncertainty management strategies in ensuring reliable and safe AV operations. To fully understand the applicability and effectiveness of these strategies, further research should delve into the specific limitations and challenges posed by real-world implementations. The integration of autonomous vehicles (AVs) brings significant benefits but also introduces critical data privacy and security concerns. While innovative solutions are being proposed, a more detailed analysis of their practical feasibility and limitations is needed. [PERSON] et al. [295] discussed the vulnerabilities of AVs to malicious attacks and proposed the use of Blockchain and AI to enhance security and privacy, highlighting the mutual benefits of these technologies. However, the scalability of Blockchain solutions and the computational demands of AI-driven security measures in real-time AV operations need further consideration. [PERSON] et al. [296] presented a privacy-preserving data-sharing mechanism called PDSM-FC, which ensures secure communication across AV platoons by converting ciphertexts, thereby protecting against various attacks. Nonetheless, the potential challenges in maintaining low latency and high reliability in data communication within large AV networks are not fully explored. An innovative approach by [PERSON] [297] addresses data security through secret image-sharing solutions, ensuring that personal and national security information is protected. Yet, the practicality of implementing such image-sharing techniques on a large scale, particularly in the face of evolving cyber threats,warrants a deeper investigation. In summary, while these studies provide promising approaches to data privacy and security in AV systems, a more comprehensive analysis of the practical challenges and limitations associated with these methods is essential to understand their potential for real-world applications.