esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results: Threat Analysis
### Threat 2: Intentional or Accidental Misconduct by Users
While refusing payment after receiving the asset would be a critical issue, such a scenario cannot occur in the proposed framework due to its sequential execution model. Consider a case where a user \(u\) attempts to sell the digital asset \(D_{K}\) to \(v\) as in (9), by submitting \(tx_{1}\) to transfer \(D_{K}\), and \(v\) subsequently refuses to initiate the payment transaction \(TX_{4}\) as described in (13). In this case, the smart contract \(sc_{P}\) detects the missing payment step during execution. As a result, the intermediate effects of \(tx_{1}\), including those from \(tx_{2}\) in (11) and \(tx_{3}\) in (12), are rolled back, keeping the asset \(D_{K}\) in \(u\)'s ownership. Thus, \(u\) suffers only the loss of the transaction execution cost for \(tx_{1}\), while \(v\) incurs no penalty. To discourage such opportunistic refusal, the framework introduces a deposit mechanism. Each participant is required to stake a small refundable security deposit when initiating asset-related transactions. If a user fails to complete a payment obligation after committing to a trade, the deposit can be partially or fully redirected to compensate the affected party. This mechanism aligns user incentives and prevents dishonest behavior such as strategic payment refusal. * **Smart Contract Confinement:** All user-created transactions must only interact with predefined and registered smart contract interfaces. This restriction ensures standardized behavior, prevents arbitrary operations, and enables the platform to detect, reject, and recover from any invalid or unexpected transactions in a verifiable and controlled manner. * **Deterrence Against Malicious Behavior:** Users who attempt to submit deliberately invalid transactions, such as flooding the system or triggering executions that are bound to fail, gain no advantage in the proposed framework. Every transaction is subject to validity checks by predefined smart contracts before execution, and any invalid transaction is immediately rejected without altering the system state. In addition, users must pay the execution fee regardless of whether the transaction succeeds, which makes repeated abuse financially unviable and discourages malicious attempts.
## 3 Design of the Interferometer
It can be seen from the interference measurement optical bench that the optical path resolved by detector PD1 is  
\[\mathrm{L}_{\mathrm{PD1}}=\mathrm{L1}+\mathrm{L2}+\mathrm{L3}-\mathrm{L2}- \mathrm{L4}-\mathrm{L6}=\mathrm{L1}+\mathrm{L3}-\mathrm{L4}-\mathrm{L6} \tag{14}\]  
The optical path resolved by detector PD2 is  
\[\mathrm{L}_{\mathrm{PD2}}=\mathrm{L1}+\mathrm{L3}+\mathrm{L5}-\mathrm{L4}- \mathrm{L5}-\mathrm{L6}=\mathrm{L1}+\mathrm{L3}-\mathrm{L4}-\mathrm{L6} \tag{15}\]  
Figure 5: Layout diagram of the interferometer: (**a**) the modulation bench; (**b**) the optical bench (AOM: An acousto-optic modulator. LP: A linear polarizer. BS: A beam splitter. M: A reference mirror. SM: A steering mirror to be measured. PD1: A photodetector in the measurement interferometer. PD2: A photodetector in the reference interferometer. The red and green lines represent two laser beams with a certain frequency difference, while the yellow line represents the interfered light obtained after combining these two laser beams). The differential optical path is  
\[\Delta\mathrm{L}=\mathrm{L}_{\mathrm{PD1}}-\mathrm{L}_{\mathrm{PD2}}=0 \tag{16}\]  
The result obtained after subtraction is 0. In theory, equal-arm-length interferometry can be achieved, which will not change due to the length of a single optical path and can eliminate laser frequency noise. However, due to the manufacturing errors of optical components and the challenge of precisely aligning the light beam combination, there will still exist a slight discrepancy in arm lengths, resulting in partial optical path noise. However, the resulting difference in arm lengths due to this reason is relatively small, insufficient to bring about significant changes to the measured optical path difference. On an optical quartz substrate with a diameter of 280 mm, we fixed some optical components, such as BSs, a reference mirror, a steering mirror [21], and photodetectors (CETC, GD4542-20 MHZ-12K), on the substrate through UV-glue bonding. The fiber couplings, photodetectors, and the steering mirror were all combined and bonded to the optical substrate together with some specially designed mechanical structural parts, as shown in \[\overset{\rightarrow}{E}_{2}\left(\overset{\rightarrow}{r}, \mathbf{t}\right) =\overset{\rightarrow}{A}_{2}\left(\overset{\rightarrow}{r} \right)\cdot\cos[2\pi(f_{0}+f_{2})\mathbf{t}+\varphi_{2}] \tag{10}\]0 The laser source used a semiconductor laser (Coherent, Mephisto S) with a nominal wavelength of 1064 nm. To generate the 200 kHz heterodyne frequency, we used two AOMs (CETC, SGTF150-1064-1P) in two paths, shifting the two laser frequencies by 150.1 MHz and 149.9 MHz, respectively. To extract the phase from the heterodyne signal, we used a commercial phase meter (Liquid Instruments, Moku: Pro) and adopted the Phase-Locked Loop (PLL) algorithm with a sampling frequency of 37 Hz. To ensure interference visibility, all fibers in the system were polarization-maintaining fibers (PMFs).
# A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks
###### Abstract  
Long Term Evolution networks, which are cellular networks, are subject to many impairments due to the nature of the transmission channel used, i.e. the air. Intercell interference is the main impairment faced by Long Term Evolution networks as it uses frequency reuse one scheme, where the whole bandwidth is used in each cell. In this paper, we propose a full dynamic intercell interference coordination scheme with no bandwidth partitioning for downlink Long Term Evolution networks. We use a reinforcement learning approach. The proposed scheme is a joint resource allocation and power allocation scheme and its purpose is to minimize intercell interference in Long Term Evolution networks. Performances of proposed scheme shows quality of service improvement in terms of SINR, packet loss and delay compared to other algorithms.  
intercell interference coordination; resource allocation; power allocation; reinforcement learning; genetic algorithm; optimization +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 19; doi:10.3390/f11010019
## 2 Technical Overview
### Radiofrequency Electromagnetic Fields (RF-EMF)  
High frequency EMFs are part of the electromagnetic spectrum between the low frequency and the optical part of the spectrum. As this part of the spectrum is used for broadcasting and telecommunication, it is termed radio frequency (RF). The RF spectrum is defined in the frequency range between 9 kHz and 300 GHz. In this review, we will focus on the effect of frequencies close to 900 MHz. EMFs in this frequency range may have a continuous sinusoidal waveform, but more often they have a complex amplitude distribution over time. For example, for broadcast or telecommunication purposes they are modulated or pulsed. There are many sources of RF that we can encounter during everyday life. Among them, the most common (as a function of their emission frequency) are: (i) TV and PC monitors (3-30 kHz); (ii) amplitude modulated radio emitters (30 kHz-3 MHz); (iii) induction heaters for industry (0.3-3 MHz); (iv) thermo-shielding, diathermia chirurgic systems (3-30 MHz); (v) mobile telephones; (vi) microwave ovens, diathermia chirurgic systems (0.3-3 GHz); (vii) RADAR, satellite communications systems, microwave communications systems (3-30 GHz); and (viii) solar radiations (30-300 GHz).  
The RF fields with frequency over 10 GHz are highly absorbed by the skin and a very small part of the energy can reach the internal organs of the human body. The dosimeter unity for these fields is the field intensity measured as power density (W/m\({}^{2}\)). Powers over 1000 W/m\({}^{2}\) are necessary to produce harmful effects, such as skin burns or ocular cataracts with fields of frequency over 10 GHz. Such powers should only be found near powerful radars, but the presence of humans on those planes are prohibited by law. RF fields with frequencies in the range from 1 MHz to 10 GHz can penetrate exposed organic tissues and produce heating by absorption of the waves. The field's penetration depth is a function of the field frequency, being bigger for the lower frequencies. The energy absorbed by the tissues and organs is measured in this case as a specific absorption coefficient in units of power per tissue mass (W/kg), which is the dosimeter unit in this case. To produce any harmful effect on human health, a specific absorption coefficient of 4 W/kg should be reached.
## 2 PFC Converter Total Harmonic Distortion (THD) and Power Factor (PF)
To clearly explain the relationship between the PF and THD in the PFC boost converters for a 5-degree phase shift between supply voltage and current, to achieve a PF of more than 99%, which is usually required in the specifications of telecom power applications [28], THD must be less than 11.20%.
# IoT-Enabled Smart Cities: Evolution and Outlook
## 3 Key Technologies Developed
### Artificial Intelligence and Big Data
This can be used to take actions to improve the situation, e.g., adapt the traffic light scheduling to improve the traffic flow and reduce traffic congestion. Machine learning approaches can be classified into four different categories [86]:* **Supervised Learning** tries to learn how to map a set of input values to an output value. This requires labelled training data, i.e., data for which this mapping is given. After training, the resulting model is able to predict the output for new input data. For example, based on the movement characteristics of a user as extracted from a smart phone, the means of transport can be determined. Typical supervised learning techniques are Support Vector Machine (SVM), K-nearest Neighbour (KNN), Random Forest, Linear Regression (LR) and Decision Trees (DT). * **Unsupervised Learning** tries to find clusters with closely related input values. For example, identifying tourists showing similar behaviour can be used for targeted advertising. In some cases, noise and outliers can also be found in a dataset. k-means and DBSCAN are typical examples of unsupervised learning techniques. * **Reinforcement Learning** is an approach where a system can adapt its own behaviour in different ways, evaluating the results achieved. An improved result leads to a higher reward and the goal is to maximize the cumulative results. For example, reinforcement learning can be used to adapt traffic signal control, e.g., with the goal of minimizing queue length. Examples of reinforcement learning techniques are Q-learning and SARSA. * **Deep Learning** represents a family of machine learning methods, where \"_deep_\" refers to the use of artificial neural networks with _multiple_ layers. Deep learning is particularly suitable for applications with high data dimensionality and can cope with high volume and velocity of data. On the other hand, deep learning requires a large amount of training data. Typical deep learning techniques are Convolutional Neural Networks (CNN), Restricted Boltzman Machine and Deep Belief Networks. To enable humans to make sense of the processing results, to grasp the insights gained and to take appropriate decisions as a result, data visualization is of paramount importance. The goal is to reveal patterns, show trends and find correlations in the underlying data. For example, line graphs are used to show how values like temperature and humidity change over time; Pie charts are useful to illustrate proportions, e.g., the age distribution of people frequenting a certain place. Heat maps can be used to show hotspots, e.g., areas specifically affected by bad air quality.
# Pore Water Pressure Prediction Based on Machine Learning Methods--Application to an Earth Dam Case
## 2 Materials and Methods
### Optimization Methods  
The ANNs require an optimizer to adjust the weights and biases based on the loss between the target and observed values. Stochastic gradient descent (SGD) is utilized to determine optimal weights to minimize prediction errors. This approach reduces the computational burden in high-dimensional optimization problems, resulting in faster iterations but at the cost of a lower convergence rate. Adam optimization serves as an extension of SGD and offers a more efficient alternative to traditional classical stochastic gradient descent for updating network weights. It adjusts the learning rate using squared gradients and incorporates momentum by utilizing a moving average of the gradient, similar to SGD with momentum. The Adam optimizer is a popular choice in machine learning. The Adam optimizer was selected in this study due to its applicability to various problem types, including those with sparse or noisy gradients. Its straightforward fine-tuning feature allows for quick and effective results.
## 3 Experimental Research
### Step 4: GR Calculations
#### 3.4.5 Earth Resistance of Square and Rectangular Meshes  
The calculation of the GR of the rectangular mesh with 6 RR configuration of Figure 1 is calculated through the Schwarz equations. The total resistance (\(R_{g}\)) for a uniform soil containing conductors and rods is calculated using Equation (2). While \(R_{1}\), \(R_{2}\) and \(R_{m}\) are defined with Equations (14), (15) and (16), respectively. The coefficients \(k_{1}\) and \(k_{2}\) are obtained from Figures 2 and 3, respectively.  
\[R_{1}=\frac{\rho}{\pi L_{c}}\bigg{[}\ln\left(\frac{2L_{c}}{a^{\prime}}\right)+ \frac{k_{1}\times L_{c}}{\sqrt{A}}-k_{2}\bigg{]} \tag{14}\]  
\[R_{2}=\frac{\rho}{2\pi n_{R}L_{r}}\bigg{[}\ln\left(\frac{4L_{r}}{b}\right)-1+ \frac{2k_{1}\times L_{r}}{\sqrt{A}}\big{(}\sqrt{n_{R}}-1\big{)}^{2}\bigg{]} \tag{15}\]  
\[R_{m}=\frac{\rho}{\pi L_{c}}\bigg{[}\ln\left(\frac{2L_{c}}{L_{r}}\right)+ \frac{k_{1}\times L_{c}}{\sqrt{A}}-k_{2}+1\bigg{]} \tag{16}\]  
Equation (16) can also be written as Equation (17).  
\[R_{m}=R_{1}-\frac{\rho}{\pi L_{c}}\bigg{[}\ln\left(\frac{L_{r}}{\sqrt{h\times 2 a}}\right)-1\bigg{]} \tag{17}\]  
where:  
\(\rho\): ground resistivity, [\(\Omega\cdot\)m]  
\(L_{r}\): rod length, [m]  
\(b\): rod radius, [m]  
\(R_{1}\): resistance to earth of the conductor, [\(\Omega\)]  
\(R_{2}\): earth resistance of the rods, [\(\Omega\)]  
\(R_{m}\): mutual resistance between mesh and rods, [\(\Omega\)]  
\(d\): separation distance between two rods, \(d>L_{r}\), [m]  
\(h\): conductor burial depth, [m]  
\(a\): conductor radius, [m]  
\(a^{\prime}\): is \(a\) for the conductor on the surface of the earth, [m] or  
\(a^{\prime}\): is \(\sqrt{a\cdot 2h}\) for conductors buried at a depth \(h\), [m]\(k_{1}\) and \(k_{2}\): coefficients [see Figures 2 and 3, respectively]  
\(L_{c}\): total length of the conductors connected to the mesh, [m]  
\(A\): is the area covered by the conductors, [m\({}^{2}\)]  
\(n_{R}\): number of rods located in the area \(A\)
# Deep Learning for Short-Term Load Forecasting--Industrial Consumer Case Study
## 2 Literature Review
Day-ahead forecasting of hourly large city load based on deep learning is studied by [16] with a novel flexible architecture that integrates multiple input features processed using different types of neural network components according to their specific characteristics. The authors have implemented multiple parallel CNN components with different filter sizes to introduce parallel structure into the DNN model instead of stacking DNN layers. The proposed architecture (MAPE: 1.405%) outperformed the CNN-LSTM (MAPE: 1.475%) and the DNN (MAPE: 1.665%). Another approach based on RNN and CNN is proposed by [17], consisting of convolutional layers and bidirectional LSTM and GRU recurrent layers to predict the next hour utility load. The results of experiments on two datasets (0.67% and 0.36% MAPE) demonstrate that the proposed model outperforms the conventional GRU and LSTM models. In this article, we found that the deep GRU network performs better than the combined GRU + LSTM network. A comprehensive comparison performed by authors in [18] concludes that RNNs require more resources than traditional models, but perform better. We reached similar findings in our article, the GRU unit is simpler than the LSTM unit, as well as faster in computations. The article presents that overall the LSTM performs better than GRU, which contradicts the results for short-term load forecasting presented in our article. The authors in [19] compare different variations of the LSTM algorithm and conclude that the longer the historical data available for training, the better the load forecasting accuracy would be. For building loads, the day-ahead forecasting errors show up to 45% improvement using RNNs (LSTM, LSTM with attention, BiLSTM, BiLSTM with attention) in comparison with other states of the art forecasting techniques.
## 3 Results and Discussion
### Volatile Compound Profile of Hop Extracts Obtained under Different SFE-CO\({}_{2}\) Conditions
Recent aroma profile analysis of _Ella_ hop essential oil also indicated the presence of various esters at a total of \(\sim\)10% amount [26], which is comparable to the 10 MPa-derived SFE-CO\({}_{2}\) sample. In agreement with the latter research [26], methyl-4-decenoate (5.5-11.1%) was the most major of the identified esters, followed by the pentyl 2-methylpropanoate (1.4-2.8%), methyl nonanoate (0.9-2.8%) and methyl octanoate (1.2-1.3%). Relatively high amounts (3-7%) of methyl-4-decenoate and pentyl 2-methylpropanoate were also characteristic of the essences obtained using hydrodistillation or SFE-CO\({}_{2}\) from _Galaxy_, _Topaz_, _Vic Secret_, _Super Pride_, _Hallertau Tradition_, _Saphir_, _Spalter Select_ and _Tetmanager_ hops [26; 45]. The content of other volatiles in the headspace was rather low (Table 5; Supplementary Materials, Table S4): alcohols (up to 2.3%), ketones (up to 1.3%), fatty acids (up to 1.0%) and aldehydes (up to 0.3%). 2-Undecanol (1.5-1.9%) with fresh, waxy and cloth notes was the most abundant compound within this group of volatiles, previously identified in Portuguese hops' essential oils [24]. Fruity and waxy ketones 2-undecanone and 2-tridecanone comprised \(\sim\)0.6% across different samples tested and were the predominant ketone fraction compounds for other hop varieties as well [26; 45]. Besides organoleptic features, several identified major volatiles, particularly mono- and sesquiterpenes, are also known for their specific medicinal properties [5]. For example, direct radical scavenger myrcene shows antinociceptive and antimutagenic properties and acts protectively towards the inflammation and oxidation-induced brain, heart and skin tissue damages, including UVB-induced photoaging. \(\beta\)-Pinene exhibits antidepressant, sedative, supraspinal antinociceptive and antiproliferative activities and exerts antiviral properties against herpes simplex virus. Anticancer, anti-allergic and corticoid drug dexamethasone-like anti-inflammatory activities have been reported for humulene and its derivatives, while sedative, antidepressant, anticonvulsant and neuroprotective actions were additionally ascribed to linalool [5].
# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
## 1 Introduction
The key to this advancement is the use of spatial redundancy features (including phase coherence and frequency variability), which can reflect subtle color changes in the face and other features suggested in previous works. A predefined filter bank decomposes the pulse signal into frequency bands, and machine learning is used to select the signals from bands that reflect dominant heart activity. The approach was validated with an open-access dataset, demonstrating its potential for improved pulse signal detection. We also aimed to assess the relative importance of these features. The main contributions of this work are as follows:  
* Proposed a novel machine learning approach to isolate pulse signal components with dominant cardiac activity to refine pulse signal;
* Evaluated the importance of features demonstrating their effectiveness in improving pulse signal detection accuracy.
## 2 Spatial Audio Recording Techniques for Soundscape Design
### Recording Techniques
#### 2.1.2 Microphone Array Recording
Theoretical foundations of spherical array processing are discussed in-depth in [35] As shown in Figure 2, examples of circular or spherical array include B&K [36], Nokia OZO [37], MTB [38], Eigenmike microphone from MHAcoustics [39], and the VisiSonics [40] microphone array. An illustration of various microphone array geometries can be found in [41]. Figure 2: Examples of spherical microphone array (from left to right) from Nokia OZO with 8 microphones, Dysonics Randomic with 8 channels, Eigenmike with 32 channels, B&K with 36 channels, and VisiSonics with 64 channels. For interactive sound reproduction applications, such as virtual reality, the listening positions might be changing from time to time. In this case, the way of using moving array or static array might be different. Under this circumstance, the moving array should be used to follow the main sound event [42]. The static array can also be used with advanced signal processing applied so that the reproduced sound adapts to sound event or user movements [29].
## 2 Materials and Methods
### Vaccine Design and Production  
The sequence of the Spike protein gene was extracted on 4 April 2020 from the official GISAID reference sequence WIV04 ([[https://www.gisaid.org/](https://www.gisaid.org/)]([https://www.gisaid.org/](https://www.gisaid.org/)), accessed on 4 April 2020) and modified to obtain the D614G, K986P and V987P variant named D614G-PP. A cloning cassette flanked by _Stu_I/_Sat_I restriction sites was synthesized by Genscript (NJ, USA) including a Kozak consensus sequence (GCCACCATG), the codon optimized Spike (D614G-PP), 589 bp of the woodchuck hepatitis virus posttranscriptional regulatory element (WPRE) [26] and 222 bp of the SV40 virus late polyadenylation signal. A codon optimization was performed with the Vector Builder software ([[https://en.vectorbuilder.com/tool/codon-optimization.html](https://en.vectorbuilder.com/tool/codon-optimization.html)]([https://en.vectorbuilder.com/tool/codon-optimization.html](https://en.vectorbuilder.com/tool/codon-optimization.html)), accessed on 23 September 2021). The synthesized 4,650 bp fragment was cloned into the pShuttle-Pr2-Luc vector digested with _Stu_I/_Sat_I to exchange the luciferase ORF by the designed Spike cassette, downstream of Pr2. The sequence of the resulting plasmid pS-Spike(D614G)-PP was confirmed by sequencing (Macrogen, Seoul, Korea). To construct the non-replicating adenoviruses, the plasmid pS-Spike(D614G)-PP was linearized with _Pmel_ and co-transformed with E1/E3 (pCoroVaxG.5) or E1 (pCoroVaxG.3) deleted adenoviral backbone vectors in electrocompetent Bj5183 bacteria. The identity of the plasmids was confirmed by sequencing. The recombinant DNAs were linearized with _Pac_I and transfected into 911 cells. The viruses were propagated in HEK-293 cells in CellSTACK(r) cell culture chambers (Corning, Corning, NY, USA),purified by double CsCl density gradient centrifugation and stored in 10% glycerol in single-use aliquots at \(-80\)\({}^{\circ}\)C.