esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Technical Indicators
#### Deep Models  
Deep models, or artificial neural networks, consist of an input, output, and hidden layer. Of these three, the hidden layer is the most varied, with architectures differing in depth and layer make-up. Below, we describe some of the most fundamental layers and modules used in deep models.  
* **Fully Connected Layer (FCL)**: Fully connected neurons, comprising of linear regression with an added non-linearity [30], are stacked to build an FCL. FCLs can be used to approximate any continuous function [31], explaining why, with increasing computational power, they are frequently used in state-of-the-art DAM predictors.
* **Convolutional Layer (CONV)**: Locally connected neural networks, or convolutional neural networks (CNNs) [32], are used for feature mapping/extraction. The primary module used by these networks, CONV, works by sliding equally sized filters with trainable parameters across input data producing 2D activation maps. While FCLs tune the parameters of every neuron, CONVs implement parameter sharing to remain computationally feasible. For a single CONV filter of size (NxM), _NxMxdepth_ parameters are trained. The inclusion of the depth parameter is a consequence of CONV's fully connected architecture across the final depth dimension. Overall, CONVs have proved adept at identifying features in images [33] and time series [34], making them potentially very powerful modules for technical analysis.
* **Residual Module**: A residual module, ResNet [35], adds the inputs from one module to the outputs of another module. It thus creates a direct identity mapping in a network between module inputs and outputs, combating both the vanishing gradient problem and the degradation problem, which otherwise impede the training of deep networks.  
We explore the performance of TIs with each of the above-described layers and modules. Specifically, we use neural networks with: two FCLs (**2 NN**); a single CONV (**CNN**); two CONVs (**2 CNN**); two CONVs and a single FCL (**2 CNN_NN**); seven CONV residual modules (**ResNet**).
# Research on Compression Behavior of Square Thin-Walled CFST Columns with Steel-Bar Stiffeners
## 3 Test Results
### Ultimate Load
So when these steel bars were used outside of the steel tube as specimen C5, they could not give sufficient support to the steel tube to resist local buckling, although the total area of the column section was bigger. When the steel bars were used inside of the steel tube as specimen C3, the core concrete inside the tube could provide good confinement to the steel bars. Thus, the stiffness of the bars could be increased and better support could be provided to the steel tube to resist local buckling. Therefore, the bearing capacity of C3 was higher than that of C5. Meanwhile, the actual bearing capacity of C6 with 16 mm diameter steel bars outside was higher than that of C4 with the same diameter bars inside the tube. This condition was different to that of C3 and C5, which can be explained as follows. When the 16 mm diameter steel bars were used as stiffeners, the cross-section area ratio of steel to column increased from 3.96% to 5.91% (Table 1). This 1.95% increase of the steel cross-section area ratio could lead to sufficient stiffness increase in the steel tube, and provide better support to the steel tube to resist local buckling regardless of being inside or outside the tube. Thus, when they were put outside the tube, the total area of column section to bear the load was larger than that of the column with the steel bars put inside the tube. Therefore, the bearing capacity of C6 was higher than C4. From the above analysis, it can be seen that steel-bar stiffeners can effectively improve the bearing capacity of square thin-walled CFST columns. In addition, compared with the traditional way of increasing wall thickness, the steel-bar stiffener has a better effect on improving the bearing capacity of the column. Taking C4 as an example, when four 16 mm steel bars were used, the increase of steel content was equivalent to increasing the wall thickness of the steel pipe from 2 mm to 3 mm. However, it is calculated that, when the steel-pipe wall thickness is 3 mm, the increase of the bearing capacity of the columns is about 15% [25] relative to a wall thickness of 2 mm, while the increase is about 50% after steel-bar stiffeners are used.
## 5 Simulations Setup and Results
### Simulation Results
#### 5.2.1 Computation Time of Auditing versus the Number of Challenged Data Blocks  
In this experiment, we measure the computation time of auditing in seconds versus the number of challenged blocks (please refer to Figure 2). We proceed from measurement to measurement by increasing the number of challenged blocks by 500. Our results show that at 4500 challenged blocks, the computation time on auditing for LAPP is about 2.25 s. For the other methods, it varies between 2.9 and 3.25 s.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### DR and Load Management in Operation
#### 3.2.3 Customer Participation in DR and Its Operational Challenges  
Customer participation in DR contributes to grid stability, especially with the increasing integration of RESs. It offers a two-fold benefit: helping to balance the electricity supply and demand and providing economic incentives to consumers. The effectiveness of these tools in promoting customer participation in DR is evident in their ability to manage reverse power flow, as shown in the real-life DR campaign discussed in [59].  
Challenges in customer DR participation include engagement and awareness issues [60], technological integration complexities, unpredictability of response [63], data security concerns, and ensuring equitable access. Overcoming these necessitates collaboration among utilities, policymakers, and technology providers, with the development of risk-based tools [62] and innovative engagement strategies being crucial. Addressing these challenges is essential for leveraging DR's full potential for a sustainable grid.
# Performance of Differential-Phase OFDM Systems over Selective Multipath Fading Channels
## 4 Power Delay Profiles  
Multipath radio channels are typically characterized by their power delay profile, which is a measure of how the relative power of the channel is distributed versus the excess propagation delay. One of the most common power delay profiles is the decaying exponential profile. In this channel, the transmitted signal will propagate through different paths [21]. Signals from those paths will arrive at the receiver at different time intervals. The decaying exponential profile is defined as  
\[\rho(\tau) = e^{-\tau/\tau_{rms}} \tag{38}\]  
where \(\tau_{rms}\) is the root mean squared (RMS) delay spread of the power delay profile defined by  
\[\tau_{rms}=\sqrt{\overline{\tau^{2}}-\overline{\tau}^{2}} \tag{39}\]  
and \(\overline{\tau}\) is the mean excess delay of the power delay profile given by  
\[\overline{\tau}=\frac{\sum_{p=1}^{P}\rho(\tau_{p})\,\tau_{p}}{ \sum_{p=1}^{P}\rho(\tau_{p})} \tag{40}\]  
and \(\overline{\tau^{2}}\) is defined by  
\[\overline{\tau^{2}}=\frac{\sum_{p=1}^{P}\rho(\tau_{p})\,\tau_{p} ^{2}}{\sum_{p=1}^{P}\rho(\tau_{p})} \tag{41}\]  
The delay \(\tau_{p}\) is measured with respect to the first detectable signal arriving at the receiver at \(\tau_{1}\) from the shortest path. Equations (40) and (41) do not rely on the absolute power level of \(\rho(\tau)\) but only on the relative amplitudes of the multipath components within \(\rho(\tau)\)[13]. It is important to understand the delay profiles in order to design an OFDM system with a proper guard period. A practical solution worthy of consideration is to make the guard period \(T_{g}\) large enough such that the effect of ICI and ISI is very small. One typical design method is to make \(T_{g}\) large such that the number of paths whose average power is almost 90% of the total average power will not be affected by interference.
# _Ab Initio_ Study of the Mechanism of Proton Migration in Perovskite LaScO\({}_{3}\)
## 4 Discussion
The activation energy for proton diffusion depends on the degree of the lattice deformation, which in turn is determined by the elastic properties [24]. Proton transfer is facilitated by cooperative processes such as lattice vibrations. In perovskites of the ABO\({}_{3}\) type, the level of proton conductivity depends on the nature of the atoms in this compound. Typically, the proton conductivity increases as the elements A and B become less negative. The proton conductivity in perovskite can be increased by increasing the number of oxygen vacancies, i.e., by doping. In particular, the formation of oxygen vacancies is facilitated by the substitution of Sr for La in the LaScO\({}_{3}\) perovskite. However, this way of increasing the proton conductivity is associated with fundamental limitations. First, the formation of an oxygen vacancy is associated with the breaking of the oxygen-metal (O-M) bond, which requires a certain amount of energy. Second, the formation of an electrically neutral vacancy is associated with the reduction of neighboring cations [25], and, therefore, with changes in the chemical energy. Third, at an excessive number of oxygen vacancies, the stability of the compound containing them can be violated [26]. Thus, only a limited number of oxygen vacancies can be created in a perovskite of the ABO\({}_{3}\) type. The perovskites based on rare earth elements demonstrate nontrivial electrical properties, such as metal-insulator transition, high-temperature superconductivity, etc. [27]. The existence of the tunable bandwidth of the materials is explained by the variations in localized and delocalized electron patterns. The technology of high-performance solar cells production requires uniform large-area perovskite films obtained by the crystallization process [28]. It should be noted that the main problems of high-efficiency solar cells are the high production cost and decrease in their efficiency with an increase in the size of the solar module [29; 30]. Polymer conductors have their own criteria for proton conductivity. Acidic hydrophilic groups often provide high proton conductivity of a compound (proton polymer conductor) with a supramolecular network [31]. For the effective work of the proton polymer conductors, it is necessary to achieve their significant acidity, i.e., high degree of protonation of hydrophilic groups. The acidity index is the main factor in the synthesis of the polymer conductor with good proton-conducting ability.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 6 Validation of Use Cases
### KPIs and Measurement Methodology
#### 6.1.4 Service Instantiation Time (SIT)  
The time required for the provisioning and deployment of an NS over a given slice. This operation includes three main actions, namely:  
* Set up of the networking in OpenStack required to connect each VNF included in the NS with the Monitoring component;
* Computation of the VNFs allocation (i.e., VNF-to-compute-chunk mapping) according to the algorithm employed by the Resource Placement component;* Deployment and configuration of the NS instance through OSM as NFVO.  
In essence, the SIT is measured from the instant when the instantiation request of an NS is sent to the Slice Manager, until the moment when the service instantiation is completed, i.e., when all the virtual components that are entailed in the service descriptor are active and running. Since in the proposed framework OSM is used as NFVO, the indication of a successful deployment is triggered when the service instance in OSM appears as running (operational status) and configured (configuration status).  
As with the SDT, to compute this KPI, a custom Python script was used to automate the service instantiation over a given slice by sending the required REST API calls to the Slice Manager. For this test, all the required descriptors are available in the platform Catalogue and onboarded to OSM. Likewise, all the required images are already available in OpenStack, therefore only the instantiation time is considered without including the descriptors creation and onboarding processes.
# A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network
## 2 Literature Review
### Discussion  
Although many approaches were proposed for state recognition of complex equipment in the above literature, they had some common disadvantages as follows. Firstly, the proposed methods need a large number of simulation data to train the algorithms, while it is difficult to gain real-time and accurate data due to the complicated working conditions. Secondly, in several approaches, an expert knowledge base is needed. However, the existing knowledge base is usually incomplete and could only describe part of the working conditions for the complex equipment. Finally, different judgments may be available for a certain working conditions and have limitations in some working conditions.  
In order to tackle the above problems, this paper proposes a novel multi-sensor information fusion method for state recognition through integration of fuzzy logic theory and a probabilistic neural network. The simulation example and industry application are provided and the proposed approach is proven feasible.
## 3 Results and Discussion
### Effect of HPP on RS Content
#### 3.2.2 Effect of Cooling  
Research conducted thus far has primarily focused on assessing the impact of cooling following the application of various techniques such as cooking [64], autoclaving [25], and extrusion [65]. However, there is no research evaluating the impact of cooling after prior HPP treatment. The results shown in Figure 7 reveal an increase in RS content for lentil, chickpeas, and beans after the cooling treatment (24 h, 4 \({}^{\circ}\)C). After applying all the pressures and pressurization time, we showed the average cooling effect on the tested samples. This increase was found to be statistically significant (\(p<0.05\)). [PERSON] et al. demonstrated that multiple heating and cooling cycles significantly increased RS content, likely attributed to retrogradation [64]. Starch retrogradation is the process that occurs when chains of gelatinized starch begin to reassociate into ordered structures. In the initial stages, two or more chains linking to each other initiate the formation of increasingly extensive ordered regions. In the cooling process of previously gelatinized starch, low energy levels cause further hydrogen bonding. As the starch chains bond further, the gel structure is tighter, and the water-holding capacity decreases until it reaches crystallinity [66]. Commonly studied storage temperatures for starch retrogradation are 4, 25, or 30 \({}^{\circ}\)C or temperature cycling between 4 and 30 \({}^{\circ}\)C. It is worth noting that storing starchy products at 4 \({}^{\circ}\)C resulted in more rapid crystallization of amylopectin than at 25 or 30 \({}^{\circ}\)C [67; 68]. In our study, samples were refrigerated for 24 h to ensure microbiological safety and facilitate practical application. The findings demonstrated that the cooling process (24 h, 4 \({}^{\circ}\)C) of previously pressurized samples in each product led to a significant increase in the content of RS, as illustrated in Figure 7. This increase occurred irrespective of pressurization time and pressure values and is possibly a consequence of the retrogradation process of starch that underwent gelatinization due to HPP.
## 3 Experimental Results and Discussion
### Small-Signal and Large-Signal Characterizations  
Figure 12a,b presents the S-parameters observed under biased currents of \(I_{D1}\) = 16 mA and \(I_{D2}\) = 21 mA and supply voltages of \(V_{D1}\) = \(V_{D2}\) = 28 V. The split CoB wire-bond assembly PA demonstrated a small-signal gain of 21.7 dB and a 3 dB bandwidth ranging from 2.85 to 4.48 GHz, resulting in a 44.5% fractional bandwidth. Similarly, the stack CoB wire-bond assembly PA achieved a small-signal gain of 21.6 dB and a 3 dB bandwidth ranging from 2.84 to 4.47 GHz, leading to a 44.6% fractional bandwidth. Analytical evaluations indicated favorable agreement between the simulated and measured results. These findings suggest that the designed PAs perform well and exhibit good agreement between the simulated performance and the measured performance.  
Figure 11: The assembly chip photographs of (**a**) split CoB wire-bond assembly design; (**b**) stack CoB wire-bond assembly design.  
The large-signal power performances were evaluated using pulsed RF measurements conducted with PXA-N5247B to avoid excessive temperature rise and potential damage to the GaN chips. The measurements involved a pulse width of 100 \(\upmu\)s and a duty cycle of 10%. Figure 13a,b presents the power performances at 3.4 GHz. The split CoB wire-bond assembly PA exhibited a \(P_{sat}\) value of 40.3 dBm (10.7 W), an \(OP_{1 dB}\) value of 36.4 dBm, and a peak PAE of 39.3%. The stack CoB wire-bond assembly PA exhibited a \(P_{sat}\) value of 40 dBm (10 W), an \(OP_{1 dB}\) value of 36.4 dBm, and a peak PAE of 35.6%.  
Figure 13a,b illustrates plots showing power versus frequency, both measured and simulated, for the designed PAs. For the split CoB wire-bond assembly PA, the measured \(P_{sat}\) value exceeded 37.4 dBm (equivalent to 5.5 W) within the frequency range of 2.7 to 4.5 GHz, and the peak power-added efficiency (PAE) was above 27.5%. Additionally, with a \(P_{sat}\) value of 40.3 dBm, the 3 dB power bandwidth covered the range from 2.7 to 4.5 GHz, resulting in a 42% power fractional bandwidth (PFBW). On the other hand, for the stack CoB wire-bond assembly PA, the measured \(P_{sat}\) value surpassed 37.9 dBm (6.2 W) within the 2.7 to 4.5 GHz frequency range, and the peak PAE was higher than 22%. The \(P_{sat}\) value of 40 dBm indicated a 3 dB power bandwidth spanning from 2.7 to 4.5 GHz, providing a 50% PFBW. These measured power frequency responses of the designed PAs are well-suited for 5  
Figure 12: The S-parameters of the proposed quasi-MMIC PAs: (**a**) split CoB wire-bond assembly design; (**b**) stack CoB wire-bond assembly design.  
Figure 13: The measured and simulated power performance at 3.4 GHz: (**a**) split CoB wire-bond assembly design; (**b**) stack CoB wire-bond assembly design.
# Perception of Risks and Usefulness of Smart Video Surveillance Systems
## 1 Introduction  
In the past, the number of video cameras in public places increased due to reasons such as detecting dangers and security breaches in crowds [1; 2; 3]. Especially terror attacks, such as those that occurred in Hanau (Germany) and Metz (France) in 2020 [4], pose a huge threat to the safety and security of people. Therefore, it is important to be able to assess risky situations at large events directly when they occur [2]. However, this also brings challenges with it, as the amount of data is continuously increasing and a manual analysis of the image material requires a lot of effort.  
In a study about crime anxiety, 70% of 3023 people mentioned that they perceived the duty of state authorities to take action against crime [5]. Among other steps, they therefore supported conventional video surveillance. For this reason, a smart solution, i.e., driven by machine learning methods and other algorithms, for the evaluation of the data is necessary [2]. Such technically advanced systems might confront people with their fear of artificial intelligence. Therefore, such algorithms should respect the acceptance and privacy of the population [6]. The results of a study on video surveillance showed that 40% of about 1000 European civilians perceived video surveillance as an invasion of their privacy [7].  
When developing smart monitoring systems, the factors influencing acceptance such as usefulness, risks [6] and privacy [8] should be determined. In this way, the acceptance of these systems can be achieved by adapting it to the fears and doubts of those affected andnot vice versa. Possible ways to address this issue could be to censor, pixelate or to ensure an increased level of data-privacy by omitting information that allows the identification of people [9; 10; 11].  
In the presented study, we focus on the third solution. It can be seen as an exemplary system for smart video surveillance that does not analyze faces [2; 12; 13]. Such solutions are typically designed to detect and evaluate abnormalities and salience. The focus is on movement and motion patterns by looking at the position of the body and its limbs. For this purpose, the algorithm of this system creates a digital skeleton of the human body. This type of smart video surveillance is examined in this paper with regard to its acceptance. For this reason, the Technology Acceptance Model for Video Surveillance (TAM-VS) proposed by [PERSON] and [PERSON] [6; 14] was mainly applied in the presented study. In the TAM-VS, the perceived usefulness and the risk of misuse were identified as factors influencing the acceptance (see Section 1.3).
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 4 City-Wide Deployments
### Deployment of the Neutral Host Platform
#### 4.4.1 Automated Deployment  
An automated approach was followed to efficiently deploy the platform components in the city-wide pilots. Deployment tasks were divided into two categories, namely Day 0 and Day 1 configurations.  
* _Day 0 Configurations:_ The tasks automated in this group were related to the creation of VMs for each of the platform components. To this end, we used Terraform [53], a cloud-agnostic management tool that provides a flexible way to define the computing and networking requirements of platform components as a blueprint that can be deployed at any moment.
* _Day 1 Configurations:_ Once the VMs are instantiated on the cloud infrastructure, the following task to address is related to the code installation and configuration. This was accomplished using Ansible [54], which has proven to be very efficient to configure, deploy, and orchestrate the code of each platform component.  
The aforementioned automated deployment approach allows us to properly replicate the deployment across multiple instances of the platform. In this way, the platform deployment was efficiently conducted saving efforts and time by significantly reducing the probability of error-prone operations.