esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Positional Encoding and Embedding  
Positional encoding is a technique that allows our hybrid model to incorporate temporal position information from the input sequences. This is useful to capture the sequentiality of time series data, which can be easily lost in ATT. On the other hand, the embedding layer maps the original input features, which represent variables such as irradiance, temperature and wind speed in a higher dimensional space, allowing the model to capture complex patterns and interactions between variables.  
The embedding operation can be expressed as Equation (23).  
\[\text{Embedding\_Out}=W_{embed}\cdot Input+b_{embed} \tag{23}\]  
where \(Input\) represents the input feature vector, \(W_{embed}\) is the embedding matrix, and \(b_{embed}\) is the bias term.  
Consequently, we add positional encoding so that we can add unique information about the time steps in the input sequence, with each position in the input sequence thus having a distinct time position. The positional encoding vector \(PE\) of each time step is represented using sinusoidal functions (see Equations (24) and (25)).  
\[PE_{(pos,2i)}=\sin\left(\frac{pos}{10000^{\frac{2i}{model}}}\right) \tag{24}\]  
\[PE_{(pos,2i+1)}=\cos\left(\frac{pos}{10000^{\frac{2i}{model}}}\right) \tag{25}\]  
where \(pos\) is the position of the time step in the sequence, \(i\) represents the dimension, and \(d_{model}\) is the dimensionality of the embedding space. Including of positional encoding allows the temporal structure of the data to be maintained, which is essential for the model to understand the temporal positions of the data in time-dependent problems, e.g., solar irradiance prediction.
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 4 Submarine Amplifiers
### Multiband Amplification Technology  
Multiband (C + L) amplification schemes (presented in Figure 20) is a complementary way to achieve the SDM plans. More specifically C + L repeaters can increase the cable capacity for systems with a limited number of FPs. Furthermore, a multiband C + L design solution (compared to C-band only) seems a cost-effective solution, as the wet plant cost remains the same (compared to C-Band only). Significant progress and research on multiband technology has been made at present. In [26] authors presented two 70.46 Tb/s and 71.65 Tb/s transmissions over 7600 km and 6970 km respectively with a C + L band  
Figure 19: The Hainan to Hong Kong Express (H2 HE) cable system will be a milestone in technological innovation as the world’s first 16 fiber pair repeated system, incorporating the HMN fifth-generation space-division multiplexing (SDM) technology [3, 12].  
EDFA by using a multidimensional coded modulation format with hybrid probabilistic and geometric constellation shaping.  
A later experiment improved upon the previous record by employing a C + L transmission achieving a capacity of 74.38 Tb/s over a transoceanic distance of 6300 km, by using SMF and implemented hybrid Raman/EDF amplifiers, as was demonstrated in [31]. A novel demonstration of a dual C + L band transmission with an in-line dual-band 6-mode-EDFA achieving a total capacity of 266.1-Tbit/s over 90.4-km was proposed in [32].  
[PERSON], [PERSON] et al. in [33] addressed the different design challenges and applications of machine learning (ML) in modeling optical amplifiers. The authors concluded that the optimization criteria of neural networks (NNs) depend on its various applications.  
Therefore performing an NN architecture search is vital in determining the best-fit ML model of an optical amplifier. Therefore, they proposed consideration of the NNs' properties in an effort to simplify the network implementation and to achieve more accurate future predictions.  
Another experiment using the previous ML technique for optimization of a C + L band hybrid EDFA-Raman amplifier was presented in [34]. Finally, two experiments achieving 4.88-b/s/Hz and 10.3-b/s/Hz net-spectral efficiency by using C + L band EDFAs were demonstrated in [35, 36], respectively.
## 3 Conclusions  
Theoretical calculations performed at the DFT/6-311+G(d,p) level showed that all investigated pyrazol[4,3-_e_][1,2,4]triazine sulfonamides showed large similarity in structural and electronic parameters (torsion angles, dipole moments and net charge at the atoms), and they were characterized by similar reactivity and stability indexes. Therefore, it can be assumed that they should behave similarly under physiological conditions.  
The molecular docking of the most active compound, in the direction of inhibitory effects on mushroom tyrosinase and jack bean urease, revealed that the analyzed compounds had high affinities to the active sites of these enzymes and the Cu2+ and Ni2+ ions in the binding pockets of tyrosinase and urease, respectively, and they may play a key role in the mechanism of these enzymes' inhibition.  
All presented sulfonamides were obtained using a multistep procedure and appeared to be inactive against cancer cell lines. They also did not show kinase inhibitory potency toward Abl or CDK2/cyclin E.  
The compounds were characterized by a small affinity for plasma proteins, which could be advantageous in potential in vivo studies. Their lipophilicity may be connected with the large polarities of the molecules, confirmed by large values of dipole moments theoretically calculated for the investigated sulfonamides using the DFT method.  
The investigated pyrazolotriazines, being not active against the selected tumor cell lines, were sensitive to metabolic transformations with phase I enzymes, which led to the hydroxylation and dealkylation products, whereas phase II transformations did not occur. It cannot be excluded that the observed phase I metabolites would be responsible for the modification of the final activity of the studied compounds. Moreover, polar metabolites would not only be easier for distribution in the organism, but they would also interact easier with molecular targets, including the selected plasma proteins.
# A Collaborative UAV-WSN Network for Monitoring Large Areas
## 2 Materials and Methods  
To overcome the limits of large-scale WSN monitoring systems and to improve the global system, our solution uses a heterogeneous data collection architecture based on WSN-UAV collaboration. This heterogeneous architecture implies that the UAV will ensure the collection of WSNs data, providing a reliable channel for the transmission of information. Depending on each application and the specific data collection requirements, the UAV can behave as a communication relay to connect WSNs to the remote base station or simply record ground data collected for subsequent analysis.
# VidBlock: A Web3.0-Enabled Decentralized Blockchain Architecture for Live Video Streaming
## 3 System Design
### VidBlock Data Model
#### 3.2.1 Data Structure and Registries in VidBlock
This personal storage allows client nodes to save both local and remote streams that are exchanged with ViB creator nodes through connections facilitated by blockchain relay nodes. By maintaining user-controlled storage, VidBlock ensures that users retain autonomy over their viewing data, which is in line with the system's decentralized and privacy-focused architecture. * **Relay node personal storage:** Relay nodes in the VidBlock network are specialized nodes equipped with dedicated personal storage to aid in the P2P streaming process. When a ViB creator node registers a live stream, it submits its ID and the content's metadata hash value to a relay node. Relay nodes temporarily store this information to manage incoming connection requests from ViB consumer nodes. When a ViB consumer node selects a stream from a distributed storage system list, it sends a request to the relay node to connect with the respective creator node. Once a P2P connection is successfully established between client nodes, the relay node purges consumer-related information from its storage, maintaining system efficiency and safeguarding user privacy. The data structure and registry components in VidBlock form the foundation of its decentralized streaming service, balancing efficiency, security, and privacy. The smart contract layer manages core operations, including node authentication and relay connections, through registries like the node registry, node registry addresses, and relay registry. Figure 2: Overview of VidBlock’s data structures. Meanwhile, distributed and personal storage systems handle content storage and access, utilizing a combination of network-wide distributed storage and node-specific personal storage. Together, these elements uphold VidBlock's commitment to decentralized data integrity, user-controlled privacy, and a robust, reliable streaming experience.
## 2 Materials and Methods
### Study for New Calibration Curves  
The thicknesses of the piezoelectric material (\(d\)) and all the layers below it need to be set to obtain a calibration curve. Then, resonators with varying top electrode heights (\(h\)) are fabricated. The greater the mass of the top electrode, the lower the phase velocity (\(v_{p}\)). Therefore, variations in h translate into a resonant frequency drift (\(f_{r}\)).  
The calibration curves are determined by fixing the piezoelectric thickness (\(d\)) and varying the top electrode (\(h\)). The value of \(f_{r}\) is evaluated for all \(h\) in the study, and then the corresponding phase velocities are obtained using Equation (1):  
\[v_{p}=(2d)\langle f_{r}\rangle \tag{1}\]  
where \(d\) is the piezoelectric thickness and \(v_{p}\) is the acoustic phase velocity.  
The corresponding value of the phase velocity is taken from Equation (1) for each resonator with different top electrode heights (\(h\)). Finally, the calibration curve of the phase velocity is expressed as a function of the ratio \(h/d\).  
The systematic study can be performed with FEM simulations or with experimental data. It is underlined that the reuse of past information is a crucial advantage of the proposed approach.  
Figure 1 reports the calibration curves related to the commonest materials used in the MEMS industry (aluminum, titanium, copper, silver, molybdenum, and gold), obtained considering a 1 \(\upmu\)m thick aluminum nitride piezoelectric layer and 160 nm thick molybdenum bottom electrode (see Figure 1a). The values of the calibration curves are derived using FEM parametric sweeps varying the metal and are reported in Figure 1b.
# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 4 In-Field Interference Detection Modules
### Power Spectral Density (PSD) Monitoring  
A well-known pre-correlation technique consists in monitoring the digitalized samples at the FE output by means of a PSD evaluation. In this case, any interferer with power level exceeding the noise floor can be detected by comparing the PSD of the incoming signal with a pre-set threshold mask. This method can detect the appearance of a disturbance at a very early stage, warning the user in real-time, and this is the approach we use in NGeneApp. Together with a post-correlation technique to assess the actual impairment to the receiver operations, this method has the potential to provide a reliable real-time interference detection tool. This technique works on the raw samples produced by the digital FE, therefore NGeneApp is the suitable tool to access those samples through the USB connection with the FE; the same processing cannot be directly applied to the commercial chipsets, because they do not provide the intermediate frequency signal samples. In the crowd-sourcing perspective, the mobile network can be exploited to send detected interference information to a remote server, for mapping, monitoring and analysis purposes.  
The spectral estimation method implemented in NGeneApp is the normalized Welch periodogram, based on the average of a sequence of windowed Fast Fourier Transforms (FFTs) computed over 4096 points. A threshold mask mechanism is currently applied, to detect and roughly classify GNSS interference source, either CW or wideband. A calibration phase has been performed in laboratory in order to properly set the detection masks as a function of the detectable Interference-over-Signal power ratio (I/S). In fact, the threshold was set based on the _total energy of error (TE)_ between the computed PSD and the PSD evaluated in the interference-free environment. The TE was computed as:  
\[TE=\sum_{i=1}^{N}(C_{i}-R_{i})^{2}\]  
where \(N\) is number of frequency samples per spectrum, \(C_{i}\) and \(R_{i}\) are the PSD values computed at frequency point \(i\) of the current PSD and the reference PSD, respectively.  
When no interference is detected (\(TE\) under threshold), the computed PSDs are sent to the server with a rate of 1 Hz, while when a disturbance is noticed (\(TE\) above threshold), the transmission rate is increased to 5 Hz (selectable).
# Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks
## 4 Experiments and Analysis
### BrnoComSpeed Dataset  
The dataset used is adapted from the BrnoCompSpeed Dataset [23], which is a new benchmark dataset including 18 full-HD traffic monitoring videos. The traffic videos in this dataset were filmed in six different locations with three cameras (Panasonic HDC-SD90, Sony Handycam HDR-PJ410, Panasonic HC-X920) from different angles in each site. Each camera recorded an approximate a one hour-long video. The frame rate of the videos is 50 FPS, while the resolution is 1080p. The 20,865 vehicles recorded in the videos have been accurately annotated by LIDAR and verified by several reference GPS tracks. The videos in BrnoCompSpeed Dataset cover all typical traffic surveillance cameras angles and various traffic situations (small traffic flow in Session 3, large traffic flow in Sessions 5 and 6). Figure 6 is the sample images taken by six cameras in BrnoComSpeed Dataset. To show the environment clearly, we intentionally select the pictures with carless lanes.  
**Vehicle Speed Dataset**: We split each of the 18 original videos from the BrnoCompSpeed Dataset into multiple short videos \(t\) seconds long each. Each \(t\)-second video clip is used as a sample. We then calculate the average vehicle speed in each of the \(t\)-second videos.Subsequently, we label each short video with the calculated average vehicle speed. The consideration behind splitting original videos is the tradeoff of time information length. It is understood that 3D ConvNets' receptive field in time domain is limited, which means 3D ConvNets cannot process time information of a long length. On the other hand, too short videos are not sufficiently informative. On balance, we set \(t=10\) in building the VehSpeedDataset10. Table 2 is on dataset partition over training and test sets. We distribute 80% of the total 5332 short videos to training set and the remaining 20% to test set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Training Set** & **Test Set** & **Amount** \\ \hline VehSpeedDataset10 & 4266 & 1066 & 5332 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Illustration of the Vehicle Speed Dataset named VehSpeedDataset10.  
Figure 6: Data sample.
## 2 Materials and Methods
### World Map Creation Module  
It is common sense that a computer works better with discrete data, rather than continuous data. Knowing this, it is important to find a way to uniformly and coherently split the world into small areas, in order for us to be able to compare cell powers in mutual areas and check for interference, as illustrated in Figure 9.  
As explained in Section 2.5, Geohash covered our requirements; even though there is an increasing error the closer we go to the poles, this is not significant, since we only care that the area between different cells that are both in the exact same space and have the exact same size.  
This module generates a world map. A world map is a data structure containing pixels representing the area covered by our problem (area covered by all cells we are trying to plan). We compute each propagation grid mentioned in Section 2.9 of each cell participating in our problem, all of which were identified by the lists created in Section 2.10.  
The module outputs a Tab-Separated Values (TSV) file representing a map, where each line is identified by a Geohash, followed by pairs of cell/power. With this we can easily identify which cells are in a certain area and their corresponding powers. We tried other file types and structures, but upon some testing, we noticed that all the others would either take up more space and/or were too slow to realistically process.
# Frequency-Shifted Optical Feedback Measurement Technologies Using a Solid-State Microchip Laser
###### Abstract  
Since its first application toward displacement measurements in the early-1960s, laser feedback interferometry has become a fast-developing precision measurement modality with many kinds of lasers. By employing the frequency-shifted optical feedback, microchip laser feedback interferometry has been widely researched due to its advantages of high sensitivity, simple structure, and easy alignment. More recently, the laser confocal feedback tomography has been proposed, which combines the high sensitivity of laser frequency-shifted feedback effect and the axial positioning ability of confocal microscopy. In this paper, the principles of a laser frequency-shifted optical feedback interferometer and laser confocal feedback tomography are briefly introduced. Then we describe their applications in various kinds of metrology regarding displacement measurement, vibration measurement, physical quantities measurement, imaging, profilometry, microstructure measurement, and so on. Finally, the existing challenges and promising future directions are discussed.  
laser feedback; precision measurement; frequency-shifted; solid-state laser +
Footnote †: journal: Applied Physics Letters
## 3 Case Studies
### Loads System  
When simulating a DCMG, the loads applied play a crucial role, as they influence the behavior, performance and stability of the system. The loads define the energy demand of the MG. Without a detailed analysis of the loads, it would be impossible to properly size the storage systems or RES. To ensure that the microgrid can meet all energy demands, the sizing of each component must take into account the consumption profile of the loads.  
Through the E-REDES Open Data portal [26], which is a portal for free access and sharing of energy distribution network data throughout Portugal, with the aim of promoting the country's energy transition and enabling the possibility of developing smart city projects, the example of a load profile needed for this work was taken. Figure 9 shows the load diagram used in the model, which is made up of three days of active energy consumption, in the month of February, for the zip code 4605-318, which refers to the town of Real, Amarante.  
A single-pole 700V DC power distribution structure was considered, with single-phase AC loads and a non-linear DC load, distributed over three cabinets. With regard to distances from the transmission line, a distance of 100 m was defined from the connection point of the production, storage and AC grid interface components to the first cabinet and a distance of 500 m between cabinets.
## 3 Analysis and Handling Methods of Uncertain Factors
### Methods for Handling Uncertain Factors  
According to the generation mechanism of uncertain factors, most people use the theories of fuzzy numbers, interval numbers, or random numbers to deal with uncertain factors. When studying the uncertainty of the medical waste emergency disposal logistics network in this section, we consider setting the uncertain parameters in the model as fuzzy numbers. Commonly used fuzzy variables include equipotential fuzzy variables, triangular fuzzy variables, trapezoidal fuzzy variables, exponential distribution, and normal distribution fuzzy variables. Triangular fuzzy numbers have the advantages of being intuitive and easy to understand, and they can well express the fuzzy states of decision-makers in emergency situations when they are pessimistic, normal, and optimistic about the estimated values.  
For the triangular fuzzy number \(\overline{D}=(d_{1},d_{2},d_{3})\) and satisfies \(d_{1}\leqslant d_{2}\leqslant d_{3}\), the expression for \(\mu_{\overline{D}}(x)\) is:  
\[\mu_{\overline{D}}(x)=\left\{\begin{array}{ll}0,&x<d_{1}\\ \frac{x-d_{1}}{d_{3}-d_{2}},&d_{1}<x\leqslant d_{2}\\ \frac{d_{3}-x}{d_{3}-d_{2}},&d_{2}<x\leqslant d_{3}\\ 0,&x\geqslant d_{3}\end{array}\right. \tag{1}\]  
The left function satisfied when the membership function \(\mu\frac{L}{D}(x)=\frac{x-d_{1}}{d_{2}-d_{3}}\) is \(x\in(d_{1},d_{2})\), and the right function satisfied when the membership function \(\mu\frac{R}{D}(x)=\frac{d_{3}-x}{d_{3}-d_{2}}\) is \(x\in(d_{2},d_{3})\), as shown in Figure 1.  
To effectively avoid the influence of fuzzy uncertain parameters, the Fuzzy Chance Constrained Programming (FCCP) method should be adopted.  
This paper considers minimizing both the economic cost and the infectious risk of medical waste during emergency disposal. Therefore, for the minimization problem, using probability as a measure, \(Pos\{\cdot\}\) represents the probability of time occurrence in \(\{\cdot\}\), which is generally expressed as:  
\[\begin{array}{ll}&\min\overline{f}\\ s.t.&Pos\{f(x,\xi)\leq f\}\geq\alpha\\ &Pos\{g,(x,\xi)\leq 0,i=1,2,\cdots,q\}\geq\beta\end{array} \tag{2}\]  
Among them, \(\alpha\) and \(\beta\) are the confidence levels predetermined by the decision-maker, \(\alpha,\beta\in(0,1)\). \(i\) represent the \(i\)-th constraint condition in the planning model, \(x\) represents the decision variable, and \(\xi\) represents the fuzzy variable.