esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Plasmon Polariton Triggered Generation of 1D-Low Spatial Frequency LIPSS on Fused Silica
## 2 Experimental Section  
An ultrashort pulsed laser (Pharos, Light Conversion Ltd., Vilnius, Lithuania) equipped with a micromachining system with a galvo scanner (RTA AR800 2G+, Newson, Dendermonde, Belgium) and an f-\(\Theta\)-lens mounted on an adjustable z-stage were used. The f-\(\Theta\)-lens having a focal length of 100 mm leads to a Gaussian beam diameter \(2^{\mathrm{\SIUnitSymbolMicro}}\omega_{0}\) of 31 \(\mathrm{\SIUnitSymbolMicro}\mathrm{\SIUnitSymbolMicro}\mathrm{m}\) (1/e\({}^{2}\)), with \(\omega_{0}\) the beam radius in the focal plane as measured with a high-resolution CCD camera (UI-1490 SE-M-GL, IDS). The ultrashort pulsed laser is specified by a wavelength of 1030 nm and a pulse width of 230 fs (FWHM) at a repetition rate of 50 kHz in our experiments. The polarization is controlled with a \(\lambda/2\)-plate, while the pulse energy E\({}_{p}\), and thereby, the applied fluence \(\Phi\), defined to be \(\Phi=\mathrm{E}_{p}/(\pi^{\mathrm{\SIUnitSymbolMicro}}\omega_{0}^{2})\), is adjusted by an external attenuator. Natural fused silica has been chosen as a prominent candidate of a dielectric material with a large band gap. Figure 1 shows the used experimental setup, including the scanning direction and polarization of the laser, as well as the orientation of the generated structures. The structures and their surface modifications are analyzed in detail with a Scanning Electron Microscope (SEM, Phenom ProX, Phenom-World, Eindhoven, The Netherlands), an Atomic Force Microscope (AFM, Dimension Icon, Bruker, Billerica, MA, USA) and a transmitted light microscope (DM6000 M, Leica, Wetzlar, Germany). For the interpretation of the experimental results, the established method of transferring captured microscope pictures into Fourier spectra is used [12]. The geometrical dimensions of LSFL are captured via SEM with subsequent image analysis via 2D Fourier transformation. Based on Equation (1), the two-dimensional discrete Fourier transform of the SEM image can be calculated by:  
\[F(u,v)=\sum_{x=0}^{X-1}\sum_{y=0}^{Y-1}f(x,y)*e^{-i2\pi\frac{uv}{X}}*e^{-i2\pi \frac{uv}{Y}} \tag{1}\]  
wherein \(u\) and \(v\) are spatial frequencies in the \(x\) and \(y\) directions, respectively, and \(F(u,v)\) is the two-dimensional spectrum of the microscope image \(f(x,y)\). \(X\) and \(Y\) represent the size of the image \(f(x,y)\).
## 3 Design of the Pressure Control Algorithm
### Design of the Adaptive Fuzzy PID Algorithm
#### 3.2.2 Control Variable Fuzzification and Membership Function  
As mentioned above, the parameters \(\Delta K_{p}\), \(\Delta K_{i}\), and \(\Delta K_{d}\) are outputs. First of all, through closed-loop operation or simulation, the dynamic characteristics of the system were observed, and the parameters were repeatedly debugged, according to the influence of every parameter on the system. The PID control parameters were determined until a satisfactory response occurred, which were set as the initial parameters, \(K_{p0}\), \(K_{i0}\), and \(K_{d0}\), of the adaptive fuzzy PID controller. For the PID controller, the control parameters are \(K_{p0}=0.5\), \(K_{i0}=0.6\), and \(K_{d0}=0.02\). The appropriate fuzzy domain was then selected, based on the parameters, and the parameters \(\Delta K_{p}\), \(\Delta K_{i}\), and \(\Delta K_{d}\) of the adaptive fuzzy PID control were determined by the fuzzy controller in real-time.  
Based on the fuzzy control theory, the input and output variables were fuzzified as follows:  
Figure 4: Pressure control block diagram of electro-hydraulic hitch with compensation link.  
Figure 5: Principle diagram of the adaptive fuzzy PID controller for pressure control.  
Pressure deviation e. The basic domain is: [-1.5 MPa,1.5 MPa], the quantization domain is: {-3, -2, -1,0,1,2,3} = {NB, NM, NS, ZO, PS, PM, PB}, and the quantization factor is: 3/1.5 = 2; Pressure deviation change rate ec. The basic domain is: [-15,15], the quantization domain is: {-3, -2, -1,0,1,2,3} = {NB, NM, NS, ZO, PS, PM, PB}, and the quantization factor is: 3/15 = 0.2; Proportional parameter \(\Delta K_{p}\). The basic domain is: [-0.5,0.5], the quantization domain is: {-1,2/3,1/3, 0,1/3, 2/3, 1} = {NB, NM, NS, ZO, PS, PM, PB}, and the quantization factor is: 1/0.5 = 2; Integration parameter \(\Delta K_{l}\). The basic domain is: [-1,1], the quantization domain is: {-1,2/3,1/3, 0,1/3, 2/3,1} = {NB, NM, NS, ZO, PS, PM, PB}, and the quantization factor is: 1/1 = 1; Differential parameter \(\Delta K_{d}\). The basic domain is: [-0.02,0.02], the quantization domain is: {-1,2/3,1/3, 0,1/3, 2/3,1} = {NB, NM, NS, ZO, PS, PM, PB}, and the quantization factor is: 1/0.02 = 50. The input and output variables are summarized in Table 1.  
With the simple operation and small memory consumption of triangular membership function, they were selected as the membership function of the input and output variables. Seven fuzzy subsets are selected as triangular membership functions, which are NB, NM, NS, ZO, PS, PM and PB. The degree of membership functions corresponding to the input and output variables are shown in Figure 6.
## 3 The CPN Model
### The CPN Formalism  
The CPN formalism is an extension of the basic PN formalism, which has been implemented to overcome two important challenges--(1) The typical PN formalism does not distinguish between the tokens, in other words all the tokens have the same meaning and they are used to represent a system's state. However, in most of the cases, a system's entities may have some common attributes but the values of these attributes differ from entity to entity, while these values are of high importance for the study of the system. and (2) The typical PN formalism does not bother about the actual _timing_ of events that may change the system's state, but only for the sequence of these events. This is a disadvantage, when researchers try to model systems which have inherited timing attributes. In the remaining of this subsection, we discuss the data and timing extensions of the PN formalism, which have resulted to the CPN. Whenever necessary, we will give definitions regarding other important elements of Petri Nets. Also, we will discuss another useful extension, the _guarding expressions_, which generate the conditions under which an event can occur _at a certain time_. The Colored Petri Nets, not only take the timing factor into account, but also they use the guards to define conditions under which an event can (or cannot) occur at a certain time.
## 4 Discussion  
The performance of the proposed Ge-rich SiGe polarization-insensitive building blocks could be evaluated in terms of the number of channels that each structure can cover. It is important to point out that, regardless of polarization insensitivity, another key parameter is the broadband operation of the presented devices. As an example, if we consider a channel width of 5 nm, which can contain a QCL laser line with a maximum width of 0.2 cm\({}^{-1}\), we would be able to allocate more than 300 channels in each MIDIR atmosphere transparency window with the optimized waveguide presented here (Table 1).  
Figure 5: **(a)** Evolution of \(\frac{3}{8}\)L\({}_{\upmu\text{m}}\) (multimode interference structure (MMI) beating length) for TE and TM polarization as function of wavelength and MMI width. **(b)** Schematic view of the optimized MMI. **(c)** Field-intensity profile for the optimized structure for TE polarization (top) and TM polarization (bottom). **(d)** MMI loss as a function of the wavelength for TE and TM polarizations.  
The polarization-independent wideband components reported in this work are a first step toward the development of integrated polarization-insensitive and broadband Ge-rich SiGe photonic circuits for MIDIR free-space communications. For instance, the polarization-insensitive wideband MMI, and by extension, the Mach Zehnder interferometer can be employed to implement building blocks such as Fourier-transform spectrometers [13], multiplexers and demultiplexers [52], switches, and more.  
In conclusion, the use of broadband components paves the way for the reduction of needed components and the number of building blocks in general, which consequently results in efficient and cost-effective ground-ground, satellite-ground, and satellite-satellite free-space communications in the MIDIR range.  
Data curation, V.V., J.M.R. Formal analysis, V.V., J.F., and A.B. Funding acquisition, W.N.Y., G.I. and D.M.-M. Investigation, V.V., J.M.R. and C.A.-R. Supervision, P.C., W.N.Y., and D.M.-M. Validation, V.V., L.V., X.L.R. Writing--original draft, V.V., J.M.R., and Q.L. Writing--review and editing, C.A.-R., G.I., and D.M.-M.  
The European Reasearch Council (ERC) under the European Union's Horizon 2020 research and innovation program (No. 639107-INsPIRE).  
The authors declare no conflict of interest.
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 5 Performance Analysis  
In this section, evaluation metrics used for performance analysis are presented. In link-level simulation, \(BLER\) and throughput are used. Furthermore, link margin, impossible communication time, and total communication available time are defined for link budget calculation and analysis.
# Hyperspectral Image Classification Using 3D Capsule-Net Based Architecture
## 3 Proposed Methodology
### Implementation Details  
Four verification and validation, indices are provided, comprising overall accuracy (OA), average accuracy (AA), kappa statistics (\(\kappa\)), as well as the prediction accuracy of every land-cover category, to objectively examine the effectiveness of the suggested technique and other techniques for comparing. Each indicator's higher value indicates a greater categorization effect.  
We examined a variety of variables essential for optimizing, such as the patch size of the input cube and the amount of kernels of the 3D convolution layers, that have an impact on the training phase and classification accuracy. These parameters were selected using various experiments. The optimization variables were the patch size of the input image, the number of filters of the convolution layer, its kernel size, capsule dimension, number of channels, the kernel size of PrimaryCaps, and its strides.
## 5 Discussion
The article explained that modern-day AI, especially DL, can be used in different ways to address disease spread, diagnosis, treatment, patient care, and drug discovery during pandemics. However, DL requires large datasets and powerful computing resources, which may not be readily available during a pandemic. The paper presented a scholarly study of the potential and challenges of using DTL and ED during pandemics. It reviewed recent research and drew a pipeline of DTL over EC as a future method to assist in the mitigation of any pandemic [15]. The evidence presented in a previous study demonstrated that the proposed approach was successfully implemented [2]. The approach involved the use of wearable AR/MR devices, which had advanced sensing technologies to determine individuals' surroundings and detect potential situations that may increase the risk of infection, such as crowded places, high body temperature, and contaminated objects. To securely share this information among individuals in the same space, the authors developed a privacy-aware platform called Secure Connected AR Platform (SCARP), which enabled the sharing of detected information without the need for personally identifiable digital IDs. The proposed approach aimed to reduce the risk of COVID-19 infection in public areas, including airports, shopping malls, and restaurants. Based on the evidence presented, it can be concluded that the proposed approach was successful in reducing the spread of COVID-19 in these public places [2]. Some future directions for implementing the proposed approach of using EC, DL, and the IoMT to improve efficiency and productivity in the pharmaceutical industry are as follows:  
* Collaboration between pharmaceutical companies, medical-service providers, and technology firms to develop an integrated system that leverages EC, DL, and the IoMT to enhance the vaccine-development process. * Research on and development of more advanced and specialized EC-enabled IoMT devices with high computational power, storage capacity, and communication capabilities to handle the vast amounts of data generated by IoMT devices. * Building a secure network infrastructure that can withstand cyber-attacks and maintain the privacy of sensitive medical information. * Continued advancements in mobile communication systems to support the increasing demand for online services and communication during emergencies. * Utilizing DL-based methods and other AI-enabled computing techniques to strengthen healthcare systems and support different types of IoMT device. * Bringing data processing closer to the edge of the network, reducing latency, and improving the speed of data processing, which is critical in emergency situations. * Increasing storage space for big data generated by IoMT devices, thus facilitating the handling of vast amounts of data.
## 6 Simulation Verification and Analysis
### Analysis of Fixed Damping Characteristics in TVSG Control  
To evaluate the influence of fixed damping characteristics on the system's transient stability, simulation experiments with varying \(D_{\omega}\) coefficients in TVSG control were performed. The results are presented in Figure 12, which shows that the absence of damping characteristics in the TVSG control leads to a power overshoot of 7.6 kW and an oscillation duration of 1.7 s during given power disturbances. Additionally, a steady-state deviation occurs due to PFR characteristics during grid frequency variations. When \(D_{\omega}\) increases to 10, the power overshoot during given power disturbances reduces to 1.9 kW, and the oscillation time shortens to 0.6 s. However, the steady-state deviation under grid frequency disturbances rises to 3.5 kW. Consistent with the output characteristics of TVSG shown in Equation (6) and the corresponding theoretical analysis, as \(D_{\omega}\) increases, the power overshoot in TVSG control diminishes, and the adjustment time shortens, yet the steady-state deviation of the system output is further exacerbated. The simulation results and analysis confirm that increasing \(D_{\omega}\) in TVSG control effectively suppresses transient oscillations and reduces power overshoot. However, this adjustment also leads to an increase in the steady-state deviation of the output power.
## 2 System Model  
### V-MISO and V-MIMO  
Assuming that each HAP has a single antenna, a user link is established between the HAP and the sensor device. The HAPs have inter platforms links (IPLs) with each other. There are backhaul links from the HAPs to the ground stations or the satellite backbone network. With the help of synchronous processing at the ground station, each HAP can obtain perfect timing and carrier synchronization. The multiple HAPs can then transmit signals to the receiver at the same frequency in the same time slot. As a result, the multiple HAPs comprise a virtual antenna array (VAA). The \(N_{H}\) HAPs are equivalent to a distributed antenna system with \(N_{H}\) antennas. Each HAP antenna can be considered to be a small antenna on the virtual platform, which consists of multiple platforms. The V-MISO and V-MIMO system models are then established by considering single antenna users and multiple antenna users respectively, as shown in Figure 1. In a true/co-located MIMO HAP network architecture, the multiple antennas are connected to a single transmitter/receiver node, e.g., [17; 23]. Due to the close distance between the antennas, it is difficult to solve the large scale shadow fading problem caused by blocking from large obstacles. However, in the V-MIMO HAP network, each antenna is far apart from each other, so an independent large-scale fading channel is available, and a better diversity gain is obtained to prevent shadow fading.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Notation** & **Description** & **Notation** & **Description** \\ \hline \(\mathrm{E}\left[\cdot\right]\) & The expectation operator & \(s_{ij}\) & The RF signal voltage for the \(j\)th HAP \\ \(N_{H}\) & The number of HAPs & \(S_{i}\) & The signal power per transmitter antenna \\ \(N_{r}\) & The number of receiving antennas & \(\alpha^{2}\) & The power of LOS component \\ \(\gamma\) & The SNR of receiver & \(\gamma_{th}\) & The threshold of SNR for sensor device \\ \(\left\|\,\cdot\,\right\|_{F}\) & The Frobenius norm operator & \(H_{LOS}\) & The channel matrix of shadowed LOS path \\ \(\left|\,\cdot\,\right|\) & The determinant operator & \(H_{NLOS}\) & The channel matrix of NLOS path \\ \(tr(\cdot)\) & The trace operator & \(2\sigma^{2}\) & The the power of NLOS component \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.
# Evaluating the Performance of Lightweight Ciphers in Constrained Environments--The Case of Saturnin
## 4 Conclusions  
In this note, a preliminary comparative study between Saturnin and AES, with respect to their performance in constrained environments, is carried out. The main outcome from the experiments performed is that Saturnin is significantly faster than AES as block cipher. This performance gain is prevalent in the case of the Saturnin-Short version of Saturnin. However, since Saturnin-Short cannot be used, for security purposes, in encrypting big messages, it is essential to establish a similar comparative study for the case of Saturnin CTR Cascade, which clearly necessitates more CPU resources than Saturnin-Short. It is expected though that, even in this case, Saturnin will still remain faster than AES. Most importantly, it would be of great interest to examine the new version of Saturnin that requires only one pass over the data.  
The above result is of importance, taking into account that Saturnin, despite its nice security features (including post-quantum security), will not be standardized by NIST due to its Encrypt-then-MAC nature, which is not as efficient as single-pass AEAD encryption schemes. By this study, it becomes evident that lightweight cryptography suffices to provide practical security solutions in constrained environments. Especially in scenarios which do not necessitate the usage of a standardized algorithm, there are many options for choosing the proper cipher on an ad hoc basis depending on the specific needs (i.e., low battery/energy, need for high speed, etc.). Therefore, the NIST competition, independently from what its final outcome will be, provides the opportunity to have a large pool of important lightweight ciphers.  
[PERSON] and [PERSON] have contributed in conceptualization, methodology and writing. All the experiments have been set up and performed by [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The author declares no conflict of interest.  
Figure 4: RTTs for 100 transactions, initiated by client’s requests, for 32-byte messages.
## 4 Systematic Review of the Research Pillars
### Digital Twins in the Built Environment  
#### 4.1.1 Applications of Digital Twins in Construction and Facility Management  
There are many advantages to utilizing Digital Twins (DTs) in construction activities, and they can greatly improve project results. Throughout the project lifecycle, Digital Twins (DTs) enhance decision-making, efficiency, and collaboration by integrating real-time data and simulation capabilities. By providing a shared virtual visualization of the project, DTs promote transparency and empower stakeholders to assess progress and make informed decisions [42]. To ensure that stakeholders are on the same track during the modular building and operation phases, they facilitate information management and interchange [43]. Building Digital Twins (BDTs) enhances productivity and efficiency in construction site management by automating data collection and knowledge extraction, which reduces redundancies. Additionally, lean construction techniques, which aim to minimize waste and optimize processes, rely on constant monitoring and feedback loops enabled by DTs [42; 44]. Digital Twins (DTs) enhance predictive capabilities by enabling proactive risk management and improving project outcome predictions through the simulation of multiple scenarios [32]. They use innovative technologies like AI and IoT, which could improve data safety and reliability while tackling prevalent issues in the construction industry [32]. Even though DTs have many advantages, issues regarding data integration and quality continue to be major obstacles to their broad use in the construction sector.
## 2 Methodology
### The State-of-the-Art Methods
#### 2.2.3 Selective Binary and Gaussian Filtering Regularized Level Set (Sbgfrls)  
The SBGFRLS [47] is an ACM based on level set representation with a region-based description. This model uses both global and local information. At the initialization step, a user-defined active contour is determined and it is continuously updated by a region-based signed pressure function (SPF) defined by (25). Unlike the C-V method, obtaining \(c_{1}\) and \(c_{2}\) using (20) and (21), SBGFRLS uses the \(H\) Heaviside function described in (22) only with \(\varepsilon=0\). The SPF function tunes the signs of the pressure force inside and outside the region of interest. For example, the contour shrinks and expands based on whether it is outside or inside the object. The SBGFRLS obtains the corresponding variation level set by:  
\[\frac{\partial\phi}{\partial t}=\text{SPF}(I(x))\cdot\alpha.| \
abla\phi|, \tag{10}\]  
where, to control the speed, a constant \(\alpha\) is used. The advantages of SBGFRLS are as follows:  
* Robust against noise because the image statistical information is used to stop the curve evolution on the desired boundaries;
* Good performance on images with weak edges or even without edges;
* Initial curve can be defined anywhere to extract the interior boundaries of the objects However, the disadvantages of the ACM with SBGFRLS include the following cases:
* Difficult to use ACM with SBGFRLS on different images because it needs to be tuned according to the image which is why it cannot be used on real-time video images;
* The slowness of the method caused by propagating the SPF function results in the boundary of the level set function only using \(|\
abla\phi|\) at the level set. Updating only the boundary of the level set is the main cause of the slowness.
# DFA-UNet: Efficient Railroad Image Segmentation
## 4 Experimental Data and Results
### Ablation Study  
In this section, we performed the DFA-Unet ablation experiments. We analyzed the number of parameters per model (Params), the number of floating point operations per second (Flops), and the number of frames per second (FPS) transmitted by the model. Table 2 shows that our model has fewer parameters. To justify our choice of 7 \(\times\) 7 convolutional kernels, we compared different convolutional kernel sizes, and the final result shows the effectiveness of our choice of 7 \(\times\) 7-sized convolutional kernels. The results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **mIoU** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline U-Net & 0.8414 & 0.9902 & 0.8706 & 0.8960 & 0.8777 \\ Attention UNet & 0.8635 & **0.9924** & 0.8919 & 0.9020 & 0.8928 \\ LeViTUNet & 0.8235 & 0.9905 & 0.8812 & 0.8711 & 0.8684 \\ ResUNet++ & 0.8450 & 0.9921 & 0.8940 & 0.8814 & 0.8813 \\ SwinUNet & 0.7534 & 0.98338 & 0.8480 & 0.8196 & 0.8214 \\ UNet+SEnet & 0.8466 & 0.9912 & 0.8878 & 0.88677 & 0.8795 \\ DFA-UNet & **0.8662** & **0.9924** & **0.9037** & **0.9057** & **0.8997** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of the evaluation on the dataset.  
Figure 5: Training loss on different models.  
Figure 6: Qualitative comparison results between DFA-Net and other models on rail segmentation datasets.