esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Design of a Miniaturized Airborne Push-Broom Spectrometer
## 2 System Specification
### Optical System Parameters
#### 2.2.4 Dispersion Width of the Slit  
A wide dispersion width facilitates an increase in spectral resolution, but the dispersion of energy also reduces the SNR. Although a narrow dispersion width is advantageous for improving the SNR, the positioning accuracy of the secondary spectral filter is required to be much higher. Therefore, the dispersion width of the slit depends on the specifications of the sensor, spectral range, spectral resolution, and SNR.  
From Table 1, the spectral resolution of the spectrometer is 15 nm, and the spectral range is 400-1000 nm, which could be divided into 40 channels. For spectral detection, one channel could be extended to 10 rows of pixels, that is, the slit image is expanded to 400 pixels. The dispersion width of the slit (\(d\)) could be calculated by:  
\[d=N\times c\times p \tag{3}\]  
where \(N\) is the number of spectral channels and \(c\) is the number of pixel rows in each channel. According to Table 2, \(d\) is 2.6 mm.
## 2 Photonic Crystal Cavities for Opto-Mechanical Interaction  
### Photonic Crystal Cavities  
Optical cavities are capable of confining light based on the RI difference between photonic resonator and its dielectric surroundings. Light circulating inside the photonic cavities can result in an increased effective optical path distance to enhance sensing performance. PhC cavities consist of functional periodic dielectric structures with a periodic variation in the dielectric constant. The periodic dielectric structure gives rise to photonic pass and stop bands, which strongly affects the propagation of electromagnetic waves. Light with frequencies propagating through the periodic structures are called modes. Generally, PhC cavities are established by introducing defects into the cavities, including point or line defect in the periodic structure to create a microcavity or waveguide. From the view of refractive index distribution, PhC cavities can be broadly divided into three types: one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) structures [37; 38]. Their typical configurations are shown in Figure 1. One-dimensional PhC cavities (see Figure 1a), also called nanobeam cavities, are composed of two symmetrical PhC Bragg mirrors and taper air holes. These two Bragg mirrors can give rise to an F-P-like resonator, which makes Q-factors of 1D PhC cavities over \(10^{6}\)[39]. Inducing defects inside a planar photonic crystal gives rise to 2D PhC cavities. The line defect in a 2D PhC (see Figure 1b) is called a waveguide and allows the propagation of certain frequencies within the bandgap. Currently reported 2D PhC cavities based on silicon-on-insulator (SOI) material can have Q-factors reaching up to \(10^{6}\)[40]. In 3D PhCs, a complete photonic bandgap can be obtained by modulating RI distribution in total three directions. By carefully placing a defect at the lattice center (see Figure 1c), cavity modes can be achieved [41]. Considering the existing nanofabrication technologies with planar lithography, 1D and 2D cavities can be easily achieved owing to their planar structure. In contrast, 3D cavities are difficult to be fabricated in practice. Therefore, we mainly focus on the recent research works based on 1D and 2D PhC cavities in this review.
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 1 Introduction
Our contributions can be summarized as follows:  
* Rather than a pixel-wise classification problem, we treat the semantic segmentation problem as an image-to-image translation problem through regression using the DTS layer, and construct segmentation maps using the higher-resolution image reconstruction approach of the super-resolution task;
* We propose DTS-Net, a deep model that uses Xception architecture as a feature extractor for high-accuracy critical applications, as well as a small lightweight model, DTS-Net-Lite, for high-speed critical applications that uses MobileNetV2 architecture as a feature extractor;
* We reduce the typical decoding stage complexity for segmentation mask construction to that of the DTS image construction layer and show that this layer can construct segmentation masks with far lower computational cost and much higher precision than conventional CNN-based decoding architectures. * We propose a new segmentation improvement technique namely nearest label filtration (NLF) to improve the segmentation by correcting the wrong predicted pixels by DTS-layer in the segmentation mask. The proposed method achieves higher accuracy and speed than the recent semantic segmentation methods. Further, learning of highly detailed feature maps is possible as we depend on CNN architecture in addition to the DTS layer; this allows the construction of higher-resolution prediction maps. In addition, we explore the joint semantic segmentation and depth estimation task and achieve promising results. A preview of our results is shown in Figure 1. The remainder of this paper is structured as follows. Section 2 summarizes related work and Section 3 presents the proposed method and architectures. Sections 4 and 5 discuss the training and test datasets and present the results with comparison to state-of-the-art (SOTA) methods, respectively. Section 6 discusses future work and Section 7 presents conclusions.
## 4 Results and Discussion
This is because when the fiber separation decreases, the occupied pixels in the RS pattern also decrease, as illustrated in Table 3. When the fiber separation is 0.5 cm, a total data rate of 6 kbit/s (i.e., two fibers case), satisfying the pre-FEC BER of 3.8 \(\times\) 10\({}^{-3}\), can be achieved. At this fiber separation of 0.5 cm, the occupied number of pixels between the two fibers is \(\sim\)20 pixels. As our CMOS image sensor has a 1920 \(\times\) 1440 pixel resolution, it can be estimated that 96 fibers can be captured in the image sensor. Hence, an aggregated data rate \(>\) 200 kbit/s (i.e., 96 fibers \(\times\) 3 kbit/s) could be possible.
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 8 Challenges of IoT, Blockchain and the Integration
### Scalability  
Scalability is another significant challenge to IoT as well as the blockchain network. IoT devices are easier to operate in small numbers in a local setting. However, the real-world requirements may demand the use of thousands of sensors (or actuators), which demands high-level experts to deploy and address the scalability concerns. Sensors collect very granular data values related to the environment of their deployments. It is crucial to consider the expansibility of computing power, data storage, and bandwidth, among others [78].  
Concerning blockchain, regular (miner) nodes in the network require higher bandwidth, storage, and computational power. Specifically, computational power becomes more important if the blockchain network employs PoW as a consensus mechanism. Since IoT devices have minimal computational and storage capability, such nodes do not help use a blockchain network regular nodes [16]. Efforts are in progress to develop blockchain and IoT network protocols for efficient transaction validations [77]. Techniques such as off-chain transaction processing and network (or transaction or state) sharding (Sharding is the practice of segmenting an extensive network into subnets or large databases into smaller databases.) [79] techniques have been proposed and are under testing to improve the throughput of a blockchain network.
## 3 System Model and Methodology
It involves setting up simulations that mimic real-world user mobility and interaction patterns, allowing for data collection on how the strategies perform across various network scenarios. The third and final phase focuses on analyzing the collected data to assess the performance of the privacy-aware forwarding strategies. This phase thoroughly examines the strategies' impact on privacy preservation and network efficiency. This final phase ensures that the methodology addresses the theoretical aspects of privacy in OMSNs and stands up to practical scrutiny.
## 2 The Experimental Setup  
### PE Pipe Parameters  
A water supply pipe made with black PE80 material was chosen as the experimental object. Since the mechanical properties of PE materials with medium and high density vary greatly with different processing techniques, a material tensile test was conducted. The parameters of the PE material and pipe are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(L\) (m) & \(D\) (mm) & \(\delta\) (mm) & MRS (MPa) & \(\rho_{P}\) (kg:m\({}^{-3}\)) & \(E_{P}\) (MPa) & \(\mu\) & \(\sigma_{b}\) (MPa) & \(\xi\%\) & \(\alpha_{s}\) \\ \hline
4.8 & 314.9 & 18.4 & 8 & 936 & 834.9 & 0.38 & 31.6 & 116 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the polyethylene material and pipe.  
In Table 1, \(L\) is the total length of the pipe, \(D\) is the outside diameter of the pipe, \(\delta\) is the wall thickness, \(\rho_{p}\) is the density, \(E_{p}\) is the young modulus of the PE material, \(\mu\) is the Poisson ratio, \(\delta_{b}\) is the ultimate strength, \(\xi\%\) is the elongation, and \(\alpha_{s}\) is the relative stiffness coefficient of the pipe and the soil. When \(\alpha_{s}\) is less than 1, it means that the pipe is a flexible pipeline. The definition of \(\alpha_{s}\) is as follows:  
\[\alpha_{s}=(E_{p}\delta^{3})/(E_{d}r_{0}^{3}) \tag{1}\]  
where \(r_{0}\) is the average radius of the pipe. \(E_{d}\) is the soil deformation modulus, and here, it is 8 MPa.
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 4 Case Study: NDN Architecture for Ground IoT Network
### Name-Based Push/Pull Mechanism  
Although the existing NDN architecture, particularly for mobile networks, provides inefficient consumer-centric support for repossessing data, to retrieve data from a respective producer, the NDN consumer node must broadcast an \"Interest\" packet in the whole network. As the consumer must wait to get the \"Interest\" satisfied from a producer data response, it causes a long delay between nodes communication. In contrast, in a wildfire situation, nodes are scattered due to the wildfire. Therefore, we need a robust and efficient communication mechanism to incorporate a large volume of data within no time [21]. However, the traditional NDN pull-based communication mechanism fails to provide such a rapid response [61]. Therefore, a unique competitive push-based critical data forwarding mechanism is proposed to handle emergency situations [62] that allows nodes with critical content to broadcast a unique beacon message to their one-hop neighbor's distance. In reply to that beacon message, the neighbors make short-term entries in their PIT data structure, and hence, in this way, the upcoming critical content chunks are cached instead of being discarded due to unsolicited chunks treatment. The existing NDN architecture is modified to manage PIT content systematically and to enable pushing of content where essential [21; 61]. The application permits the integration of the preeminent forwarding and routing strategies to suit a disaster situation. However, the traditional IP infrastructure cannot deal with disastrous situations, especially in the case of network partition or fragmentation. This is mainly because of the Transmission Control Protocol (TCP/IP) protocol on end-to-end communication [21; 37; 61; 62].
## 4 Numerical Results and Analysis
### Performance of CPolSK-Modulated System  
Figure 5 shows the BER of the GSM-beam-based CPolSK-modulated optical communication system as a function of the mean-receiving SNR in the downlink for different source coherence parameters. For reference, the BER curve of a fully coherent light source in an OOK-modulated optical communication system is also shown (solid black line).  
As shown in Figure 5, an increase in the average receiving SNR leads to a decrease in the BER for both the CPolSK and OOK systems. In downlink, when the source coherence parameter is one (i.e., the transmitting beam is fully coherent), the CPolSK system achieves optimal performance. When the source coherence parameter was set to two, the BER of the CPolSK system slightly outperformed that of the OOK-modulated system with fully coherent light. This is because, in downlink, the influence of atmospheric turbulence is relatively weak, making the reduction in the received optical intensity due to the GSM beam more significant than the scintillation suppression effect.  
At a system BER of \(1\times 10^{-9}\), the OOK modulated system requires an average receiving SNR of 14 dB, whereas the CPolSK system using fully coherent light achieves the same BER at 12.49 dB, demonstrating a 1.51 dB reduction in the required SNR. The coherent beam achieves a lower BER at the same transmit power; this is because the partially coherent beam has a reduced mean intensity at the receiver, offsetting its lower scintillation. This indicates that using fully coherent light enhances the performance of the CPolSK-modulated system in the downlink under weak turbulence conditions.
## 3 Results and Discussion
### Ammonia Emission Rate
Internal PM\({}_{2.5}\) concentrations were also measured during the ventilation-off experiment and were found to initially rise from approximately 3-10 \(\mu\)g m\({}^{-3}\) under ventilated conditions to 18 \(\mu\)g m\({}^{-3}\) when the ventilation system was disengaged, before decreasing again to \(\sim\)5 \(\mu\)g m\({}^{-3}\) during the experiment. At the end of the experiment, when the ventilation system was re-engaged, PM\({}_{2.5}\) concentrations peaked at 65 \(\mu\)g m\({}^{-3}\). It was expected that the high concentrations of NH\({}_{3}\) and SO\({}_{2}\) during the ventilation-off experiment would lead to secondary inorganic particulate formation and higher PM\({}_{2.5}\) concentrations. The presence of PM\({}_{2.5}\) concentration peaks at the beginning and end of the experiment, but not during it when NH\({}_{3}\) and SO\({}_{2}\) concentrations were highest, suggests that the processes affecting PM\({}_{2.5}\) formation were more complex than the co-existence of precursor gases alone. This could be a productive area for future research.
# Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0
## 2 Theoretical Background
### Use of Sensors  
Sensors are sources of information for the control system (computer, brain) and technical devices, which measure certain physical and technical quantities and convert them into a signal that is remotely transmitted and further processed. These are various global positioning system (GPS) sensors, cameras, and microphones, forming the digital nervous system. These devices acquire information on position, distance, motion, speed, displacement, temperature, drought, humidity, sound, vibration, gases, chemicals, flow, strength, load, pressure, level, electricity, acceleration, tilt, light, etc. The use of sensors in smart factories has many facets, as discussed by many authors [6; 7; 8]. To make full use of the sensors, the availability of efficient and affordable sensor networks (such as radio frequency identification, RFID) is a prerequisite [9]. Based on this, intelligent objects and devices are created, enabling real-time communication between computers, work resources, and application systems. Together, this technological development provides the basis for the introduction of new production processes and business models in smart factories [10]. As they are able to retrieve and process data, they can check certain tasks and communicate with people through an interface [6]. The importance of sensors for Industry 4.0 is also illustrated by the fact that, as mentioned by Reference [7], by 2020, nearly 20.8 billion devices will be connected and RFID will be fully utilized. Such a shift will have an impact on most industrial sectors and, in particular, manufacturing industries. RFID technology is used to identify various objects in warehouses, production halls, logistics companies, distribution centers, retail outlets, and disposal/recycling stages [11]. Analysis of monitored activities will be used for fault detection and predictive maintenance [12]. Based on the information gathered and also usingmachine-to-machine (M2M) communication, the resources will be remotely controlled to improve industrial processes [13].
## 2 Numerical Study: Physical Understanding of Wave Propagation Mechanism
### Numerical Parameters
#### 2.1.2 Initial and Boundary Conditions  
The density-based solver (with absolute velocity formulation) was used because of its higher accuracy in terms of shock resolution compared to the pressure-based solver. The density-based solver automatically includes the viscous dissipation terms in the energy equation. Since one deals with compressible flow, the energy equation was activated. Air was used as the fluid with ideal gas properties, and other properties were kept constant. A pressure-inlet was defined at the entrance of the leak and was set at atmospheric conditions. In order to reproduce the real initial conditions in the best way, the numerical pressure inside the duct was initialized at 20 kPa and the leak at atmospheric conditions at 101.3 kPa. To finish, a zero (no-slip) velocity and adiabatic condition were applied on all the walls.
## 4 Discussion  
Investigating dynamic processes with high spatial and temporal resolution is a challenging task. Therefore, suitable imaging techniques and data acquisition protocols are necessary. We showed that OCT and intravalt microscopy are promising tools for matching the effort of appropriated imaging techniques for the visualization of lung tissue movement during uninterrupted artificial ventilation in rats. A high-speed video camera and an electrically tunable focus lens controlled by the depth information obtained from fast scanning OCT cross sections were used to keep the lung surface in the focal plane during axial movement. This, allowed acquisition of video sequences over the whole ventilation cycle. The use of an external trigger signal from the animal ventilator in combination with a continuous OCT data acquisition mode enabled to employ a conventional OCT system (line scan rate 12 kHz) for the investigation of fast dynamic processes in vivo without the need of specialized high-speed OCT systems. Some limitations still remain beside these advantages. The continuous scanning mode enables the visualization of tissue movement over one ventilation cycle by combining images that were measured over several cycles. While this scheme works well during plateau phases (end of inspiration, end of expiration), jitter and other disturbances (from the tissue response on artificial ventilation) arising during phases of fast tissue movement result in small misalignments during the reconstruction of each individual 3D stack. This can be seen during the fast tissue movements in inspiration and expiration. Further work will deal with these disturbances on the one hand by improving the handshaking between the animal ventilator and the OCT system and on the other hand by developing a post-processing algorithm for the automatic alignment of adjacent cross sections. Especially, the first point has a major impact on the jitter of the OCT data shown in this paper. As OCT data capture and ventilation are not synchronized, images with a shift of up to one frame period (29 ms) are combined to one volume. This problem will be avoided in future measurements by implementing bidirectional handshaking. For this purpose, the OCT system will also give a trigger signal to the ventilator when an image is still acquired, and the ventilator will wait up to 29 ms before triggering the OCT system. This additional time of 29 ms will influence the breathing rate only marginally, but will help prevent the jitter in the rearranged 3D OCT stacks. Furthermore, we will combine other imaging techniques such as confocal fluorescence microscopy to gather functional information of elastic fibers surrounding the alveoli during artificial uninterrupted ventilation. Finally, new segmentation algorithms will be implemented to handle the mass of image data, and automatically extract alveolar volume changes and tissue characteristics from the 3D OCT stacks.
## Appendix A Basic Theory of Particle Swarm Method  
The idea of Particle Swarm Method (PSO) [31] originated from the study of bird flock foraging behavior. This enables the group to collectively share information and find the optimum destination.  
The most important iterative parameters are as follows:  
* Position (\(x_{i}\)): The values of the design variables for each particle.
* Velocity (\(v_{i}\)): Moving distance and direction for each particle.
* Fitness (\(f_{i}\)): Magnitude of the objective function.
* Pfitness (\(f_{pi}\)): The fitness where the historical best was found for each particle.
* Gfitness (\(f_{g}\)): The fitness where the historical best was found within the whole swarm.
* Pbest (\(p_{i,pbest}\)): The position where the Pffitness is for each particle.
* Gbest (\(p_{gbest}\)): The position where the Gfitness is within the whole swarm.  
The problem defining parameters are as follows:  
* Population (\(N_{p}\)): Count of the particles. A smaller population size may result in falling into the local optimum, while a larger population size can improve convergence and find the global optimum solution faster.
* Iteration Count (\(T_{\text{max}}\)): Maximum number of generations, serving as the ending criterion.
* Inertia Weight (\(\omega\)): The influence of the previous generation's velocity on the current generation's velocity, which was introduced by [PERSON] [32]. A larger value of \(\omega\) enhances the particle's ability to explore new regions and conduct global optimization searches but weakens its ability to conduct local optimization searches.
* Learning Factor (\(c\)): \(c_{1}\) is the particle learning factor. \(c_{1}\) represents the weight of the particle's next action based on its own experience, indicating its attraction to its PBest. \(c_{2}\) is the swarm learning factor. \(c_{2}\) represents that based on the others' experience, indicating its attraction to the Gbest.  
The \(v_{i}\) and \(x_{i}\) are updated in the new generation for each particle as  
\[v_{i}^{(k+1)}=\omega v_{i}^{(k)}+c_{1}r_{1}\Big{(}p_{i,pbest}^{(k)}-x_{i}^{(k )}\Big{)}+c_{2}r_{2}\Big{(}p_{gbest}^{(k)}-x_{i}^{(k)}\Big{)} \tag{1}\]  
\[x_{i}^{(k+1)}=x_{i}^{(k)}+v_{i}^{(k+1)} \tag{2}\]  
where \(r_{1}\) and \(r_{2}\) are random numbers from 0 to 1 to increase the randomness of the search. The update of particle's velocity and position is illustrated in Figure 1.
# Ground-Borne Vibration Due to Construction Works with Respect to Brownfield Areas
## 2 Materials and Methods
### Measuring Instrumentation  
For the experimental seismic measurement, seismic instrumentation produced by the Czech company Vistec was used. Specifically, the seismic station Gaia2T and the sensor ViGeo2 were selected. Gaia2T is a three-channel seismic system with a dynamic range of 138 dBp-p. It can be used to run both continuous and triggered data recording. A GPS module is used for time synchronization, and data recording is performed on a Compact Flash disk. ViGeo2 is a compact, active, short-period, three-component, velocity seismometer for field and stationary use. Three mechanical oscillating systems (piezoelectric seismometers) are included, with a frequency of 2 Hz and a frequency range of 2 to 200 Hz (Figure 2).  
Figure 1: Part of the revitalized brownfield “Karolina”.  
Figure 2: Frequency range of the ViGeo2 sensor.  
For the processing of seismic data, the Seismic Wave Interpretation Program \"SWIP\" was used, which is supplied by Vistec Praha as standard for seismic instrumentation. In this program, the seismic signal can be processed in both the amplitude and the frequency range [38].  
In recent years, modern progressive technologies (such as Micro-Electro-Mechanical Systems (MEMS) or fiber-optic technologies) are also used to measure vibration on structures or on rock mass in civil engineering applications [18; 39].