esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evolution of Wireless Communication Technology (1G-6G)
### Second Generation (2G) Technology  
To enable applications such as international voice calls, messaging, and access to information over a wireless network, which require a high data transfer rate, and to make communication more secure, the second-generation (2G) wireless technology was designed in the 1980s and introduced in 1991 under the Global System for Mobile (GSM) communication standards in Finland [25]. The analog system of 1G was replaced by a digital system enabling the encryption of voice calls and thus providing security. The GSM uses Time Division Multiple Access (TDMA) such that each network user is allocated the channel bandwidth based on time slots [26]. The GSM operates on a 900-1800 MHz frequency band except for in America where it operates in the 1900 MHz band. TDMA was later used by other digital standards such as the Digital AMPS (D-AMPS) in the United States and the Personal Digital Cellular (PDC) in Japan. As an alternative to TDMA, Code Division Multiple Access (CDMA) was introduced in the United States on the IS-95 standard [22] which allowed multiple network users to simultaneously transmit data based on assigned unique code sequences. In addition to international roaming voice calls, 2G supported conference calls, call hold facility, short message services (SMS), and multimedia message services (MMS) with a data rate up to 9.6 Kbps.  
The continuous evolution of the GSM technology led to the development of General Packet Radio Service (GPRS), referred to as 2.5 G, which implemented packet switching in addition to circuit switching. GPRS has provided additional services such as Wireless Application Protocol (WAP) access and internet communication such as e-mail and World Wide Web (WWW) access [27]. It provides data rates up to 115 Kbps [28]. GPRS further evolved to the Enhanced Data Rates for GSM evolution (EDGE), providing higher data rates. For instance, a 40 KB text file can be transferred in 2 s using EDGE compared to 6 s in GPRS. EDGE was deployed on GSM networks in 2003 by Cingular (now AT&T) in the United States. The peak data speed of 2G is 50 Kbps using GPRS and 1 Mbps using EDGE. However, 2G networks were not capable of handling video conferencing, navigation services, and other applications which require high data rates, leading to the 3G network generation.
## 3 Method
### Overall Framework  
We present a comprehensive framework, CL-SR, which integrates SMOTE-RSB with a MoCo-based contrastive learning setup. This framework addresses imbalances in image datasets in an effective manner. The process commences with the pre-training of a MoCo encoder utilizing a vast array of unlabeled images to capture generalizable features. MoCo is designed to employ a dynamic dictionary of features and a contrastive loss function, which encourages the model to align the features of positive pairs closely and to distance those of negative pairs, thereby enhancing the quality of unsupervised feature learning over large datasets. Subsequently, the aforementioned features are extracted from the training dataset by the pre-trained encoder, thereby transforming the data into a feature space that reveals greater detail and complexity. During the resampling phase, SMOTE-RSB is applied within the aforementioned feature space to produce synthetic instances for the minority class, thus enriching the dataset. SMOTE-RSB incorporates rough set theory to filter out synthetic samples that are of low quality or lack informative content. The effectiveness of this framework is evaluated in the final stage using a Multilayer Perceptron (MLP) [50] classifier on a balanced dataset obtained through the aforementioned feature extraction and oversampling phases. This provides a quantitative measure of the improvements in accuracy and fairness. The methodology, comprising stages and processes, is represented visually and detailed in figures and algorithms, offering a comprehensive approach to addressing the challenge of imbalanced image data. The methodology is depicted in Figure 3 and elaborated in Algorithm 1, offering a comprehensive overview of the proposed solution to the challenge of imbalanced image data.  
Figure 3: Illustration of our proposed method.  
```
1:Input: Imbalanced dataset I, Quantity of oversampling r, Number of nearest neighbors k; \(D_{s}=\left(x_{s}^{i},y_{s}^{i}\right)_{i=1}^{n}\) consists of n images \(x_{s}^{i}\) and label pairs
2:Output: Balanced dataset O
3:F = encoder(\(x_{s}^{i^{\prime}}\)), F is the pre-trained model, \(x_{s}^{i^{\prime}}\) is the augmentation of \(x_{s}^{i}\)
4:For batch i = 1,..., B
5:\(E(\xi_{s^{\prime}}^{i},l_{s}^{i})\ =F(x_{s}^{i},y_{s}^{i})\)
6:EndFor
7:For batch i = 1,..., B
8:\(T(\xi_{s^{\prime}}^{i},l_{s}^{i})\ =F(x_{t}^{i},y_{t}^{i})\)
9:EndFor
10:\(E^{\prime}(\xi_{s^{\prime}}^{i},l_{s}^{i})\) = oversampling(\(e_{s}^{i},l_{s}^{i}\))
11:MLP(\(E^{\prime},T\))
```  
**Algorithm 1** CL-SR.
# Efficient Computer-Generated Holography Based on Mixed Linear Convolutional Neural Networks
## 2 Optical Configuration for ECGH  
The optical setup for encoding the light wave-front with ECGH is conceptually presented in Figure 1. A beam from a 532 nm laser is collimated and expanded to obtain a plane wave, which irradiates on the SLM encoded by the computer. The encoded light wave-front is adjusted by the polarizer to focus the convex lens, and the encoded image can be displayed at the focal length. A polarizer is used to modulate the polarization angle needed by the reflective phase-modulated SLM.  
Figure 1: Optical setup of ECGH reconstruction.  
An 8-bit binary gray scale image is encoded and then loaded on the SLM by the computer. The SLM can modulate the phase by converting the pixel gray level [0, 255] into a phase value of [0, 2\(\pi\)] under a linear transformation.  
Through the optical realization setup shown in Figure 1, the corresponding principle of the computational holography, the real amplitude, and the normalized intensity of the target image are denoted as  
\[Y\left(\phi_{SLM}\right)=\left\lvert\mathit{FT}\left(\exp\left(\mathrm{i} \times\phi_{SLM}/255\times 2\pi\right)\right)\right\rvert, \tag{1}\]  
and  
\[I=\left\lvert\mathit{y}\left(\phi_{SLM}\right)/\mathit{y}\left(\phi_{SLM} \right)_{\mathrm{max}}\right\rvert^{2} \tag{2}\]  
where \(\phi_{SLM}\) is the encoded image loaded. \(\mathit{FT}\) is a two-dimensional fast Fourier transform with a normalization ability needed to realize gradient back-propagation in deep learning. \(i\) is an imaginary unit. \(I\) is the optical intensity image with normalization by the maximum value denoted as \(\mathit{y}(\phi_{SLM})_{\mathrm{max}}\). The results of calculating Equation (1) must be normalized to ensure that the calculated result conforms to the physical constraints.
## 2 Materials and Methods
### Antimicrobial Activity  
An agar well diffusion assay was employed to explore the antimicrobial properties of plant-based AuNPs against a group of potentially pathogenic samples including  
Figure 1: Scheme for green synthesis of AuNPs using the aqueous extract of \(Z\). _officiale_ rhizome.  
Escherichia coli ATCC8739 and _Pseudomonas aeruginosa_ ATCC9027 as Gram-negative bacteria, _Bacillus subtilis_ ATCC6633 and _Staphylococcus aureus_ ATCC6538 as Gram-positive bacteria, _Candida albicans_ ATCC10231 as unicellular fungi, and _Aspergillus brasiliensis_ ATCC1640 as multicellular fungi [43]. The nutrient agar, YEPD agar, and Czapek Dox agar media were used to subculture the bacterial strains and unicellular and multicellular fungi, respectively. Microorganisms were incubated at 35 \(\pm\) 2 \({}^{\circ}\)C for 24 h for bacteria and _C. albicans_ and at 25 \(\pm\) 2 \({}^{\circ}\)C for 72 h for _A. brasiliensis_. After overnight growth, single colonies from separate bacterial strains and unicellular fungi were picked up using a sterile swab and spread uniformly on a Muller-Hinton agar plate (Ready-prepared, Oxoid Ltd., Altrincham, UK), whereas approximately 50 \(\upmu\)L of spore suspension of _A. brasiliensis_ was spread over the surface of Czapek Dox agar media. Then, four wells (0.7 mm in diameter) were prepared in each plate before being filled with 100 \(\upmu\)L of AuNP solutions (200, 100, 50, 25, 12.5, 6.25, and 3.12 \(\upmu\)g mL\({}^{-1}\)). The DMSO (solvent system) was systematically used as a control. The loaded plates were incubated at 35 \(\pm\) 2 \({}^{\circ}\)C for 24 h for bacteria and _C. albicans_ and at 25 \(\pm\) 2 \({}^{\circ}\)C for 72 h for _A. brasiliensis_. The results were recorded as the diameter of clear zones (mm) formed around each well after the selected incubation period. The minimum inhibitory concentration (MIC) for each organism was detected as the lowest concentration of synthesized AuNPs that formed a clear zone. The experiments were performed in triplicate, and the data are represented as average values and standard deviation intervals.
# A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data
## 3 Methodology
### Exploring Analysis Results  
#### 3.3.1 Data Visualization by the \(t\)-SNE Algorithm  
Many algorithms have been developed for mapping data from a multidimensional space to a low-dimensional space. The Classical Multidimensional Scaling [35] or Principal Coordinates Analysis (PCoA) is a popular technique for performing such tasks. PCoA is a linear technique computing a series of eigenvalues and eigenvectors.  
In this work, we are interested in nonlinear mapping techniques possessing greater flexibility than linear ones. Amongst numerous nonlinear mapping techniques [36], we selected the \(t\)-SNE algorithm [37]. The \(t\)-SNE algorithm has shown an excellent performance on both real world and artificial data [37]. Ability to embed a new observation onto a beforehand generated map is an appealing property of the algorithm.  
The \(t\)-SNE represents similarities (proximities) of observations as conditional probabilities. The proximity of \(\mathbf{x}_{j}\) to \(\mathbf{x}_{i}\) in a high-dimensional space (in our case this space would be given by the six audio parameters augmented with the GFI parameter from the query data) is defined as the conditional probability \(p_{j|i}\) that \(\mathbf{x}_{j}\) is chosen by \(\mathbf{x}_{i}\) as its neighbour when neighbours are chosen in proportion to their probability density defined by a Gaussian centered on \(\mathbf{x}_{i}\)[37]:  
\[p_{j|i}=\frac{\exp(-||\mathbf{x}_{i}-\mathbf{x}_{j}||^{2}/2\sigma^{2})}{\sum _{k\
eq i}\exp(-||\mathbf{x}_{i}-\mathbf{x}_{k}||^{2}/2\sigma^{2})}, \tag{6}\]  
where \(p_{j|i}\) are set to zero and \(\sigma\) is a parameter selected experimentally, the width of the Gaussian centered on an observation. In dense regions, smaller values of \(\sigma\) are more appropriate.  
A similar conditional probability \(q_{j|i}\) is calculated in the low-dimensional space: \(\mathbf{y}_{i}\) and \(\mathbf{y}_{j}\), counterparts of \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\). In this work, the low-dimensional space is given by the first and the second \(t\)-SNE coordinates, a two-dimensional space used for data visualization. The joint probability \(q_{ji}=q_{ij}\) is defined as:  
\[q_{ij}=\frac{(1+||\mathbf{y}_{i}-\mathbf{y}_{j}||^{2})^{-1}}{\sum_{k\
eq l}(1 +||\mathbf{y}_{k}-\mathbf{y}_{l}||^{2})^{-1}}, \tag{7}\]  
where \(q_{i|i}\) are set to zero. The mapping sought is obtained by minimizing the Kullback-Leibler divergence between the joint probability distributions \(P\) and \(Q\).  
In Equations (6) and (7), a distance is computed to assess similarity between observations. However, in multidimensional spaces, the distance-based similarity of observations suffers from a too big influence of irrelevant noisy variables. Therefore, we use a decision tree and association rules designed for data classification, to assess the similarity of observations in this work.
## 4 Applications of Intelligent Acoustic Monitoring
### Smart Cities and Urban Monitoring
#### 4.2.1 Indoor Positioning  
In [108], [PERSON] et al. proposed a hybrid indoor positioning system (H-IPS) that integrated data from three sources: mobile device MEMS sensors, Wi-Fi fingerprinting, and acoustic measurements. Accurate user localisation in challenging indoor environments, particularly with obstacles present (NLOS, or non-line-of-sight), was the primary objective. The authors developed the DMDD (data and model dual-driven) model, which enhanced accuracy in various phone-holding modes by integrating a deep learning-based speed predictor with the traditional inertial navigation system (INS) approach. Additionally, they developed a drift estimator (ADE) and a technique for detecting NLOS errors for acoustic signals, and they enhanced the Wi-Fi fingerprinting algorithm using a dual-weight K-NN (DW-KNN) and accuracy index (WAI). A data fusion algorithm based on the Adaptive Unfiltered Kalman Filter (AUKF) combined all these components. Even under challenging circumstances with signal interference, the system produced excellent results in field tests, achieving an accuracy of about 2 m. The authors showed that stable and accurate positioning on the scale of entire buildings was possible by combining different data sources and error compensation techniques.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 4 City-Wide Deployments  
Following the architecture presented in Section 3, the proposed neutral host framework was deployed in the cities of Barcelona, Bristol, and Lucca. In this section, we describe the deployment of the proposed framework in each one of three considered city pilots. A logical view of the three infrastructure deployments is depicted in Figure 2.  
The general methodology followed for deploying the proposed neutral host framework consisted of the following sequential phases in every city pilot:  
1. Infrastructure Deployment: the conceived three-tier architecture, including a RAN tier, an edge tier, which can be further extended to be closer to end-users, and a core Data Center (DC) tier, is mapped into physical infrastructure resources consisting of radio components, edge/MEC servers, and DC servers;
2. Infrastructure Setup Validation: to verify the correct installation and performance of the deployed infrastructure in the three cities, a similar set of validation tests was conducted. The main objective of these tests was to verify performance and better profile configurations in the three pilot environments;
3. Platform Installation: deployed servers at edge and DC sites in every city provide computing resources to host the different components of the software platform of the neutral host framework. In general, each software module of the platform is installed as a Virtual Machine (VM) in the virtualized computing infrastructure and interconnected to allow the required interaction among them;
4. Platform Setup Validation: the validation of the deployed platform consisted of a set of functional tests aimed at verifying the correct integration of the various orchestration elements, as well as the execution of lifecycle management operations for infrastructure resources, slices, and network services.  
Next, we first detail the infrastructure components deployed per testbed. Then, we describe the platform implementation, which is a common factor for the three city pilots.  
Figure 2: Infrastructure design for (**a**) Barcelona, (**b**) Bristol and (**c**) Lucca.
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 2 Background
### Secondary Phenomena (Aftershocks and Beyond)
#### 2.2.1 Soil Liquefaction  
Liquefaction occurs when water-saturated, loose granular sediments lose strength and act as a liquid under seismic shaking. This phenomenon happens due to a sudden increase in pore water pressure within the sediment during an earthquake, weakening the grain-to-grain contacts and causing the sediment to behave fluidly [25; 26]. Consequently, heavy infrastructure may sink into the liquefied ground, while lighter structures might rise or float. The likelihood of liquefaction is influenced by several factors, including the grain size distribution, density, clay content, mineralogy, the depth of the groundwater table, and the characteristics of the earthquake's ground motion, such as its magnitude and duration. These last two factors, reflecting the seismic vibrations and the site's proximity to the earthquake source, are particularly crucial in assessing liquefaction risk. Risk levels for liquefaction are determined using hazard maps developed by the Geological Survey of Israel [27] (see Figure 1 at the top right). It is important to note that, unlike the guaranteed impact of seismic shaking in an earthquake, the potential for liquefaction under the above conditions remains uncertain, with no guarantee that it will occur.
## 3 Digital Pulse Compression
### Linear Phase Sampling and Accumulating Method for Fast Pulse Compression
This indicates that the spectral periodic extension is not centered at some distributed frequency points as uniformly-spaced sampling, but spreads over some bands, and the extended amplitudes have an average attenuation of \(\sqrt{M\cdot B_{s}\cdot T_{c}}\) compared to the major signal. This is different from uniformly-spaced sampling, in which, the amplitudes of the extension spectrum are the same as the major signal. Theoretically, for uniformly-spaced sampling, in order to eliminate the influence of the extended spectrum, the sampling rate must be larger than the bandwidth of the measured signals. However, for linear sampling in the algorithm, the sampling rate can be appropriately reduced for the measured signals with a relatively low dynamic range. To detect and compress the IF chirp signals in real applications, we need to follow the detailed algorithm flow given below:  
1. Quantifying the IF chirp signal \(s_{ifc}(t)\) Different from Nyquist sampling, in the presented fast pulse compression method, the higher sampling rate \(f_{s}\) will yield more suitable and precise orthogonal sampling points, whose phases satisfy (16) and (17), leading to higher amplitude accuracy of the output pulses. 2. Selecting two sets of mutually orthogonal sampling points Due to the sampling, the ideal orthogonal sampling points that satisfy (16) and (17) may not exist; therefore, adjacent points can be used as alternative orthogonal points. Thus, the phases of the nonideal orthogonal sampling points satisfy the following approximation formulas: \[ \begin{split} 2\pi\Big{(}f_{bpftop}\big{(}t_{n}^{1}\big{)}- \tfrac{1}{2}k\big{(}t_{n}^{1}\big{)}^{2}\Big{)}=2n\pi\pm p,\ n=1,2,3\cdots N\\ 2\pi\Big{(}f_{bpftop}\big{(}t_{n}^{2}\big{)}-\tfrac{1}{2}k\big{(} t_{n}^{2}\big{)}^{2}\Big{)}=2n\pi+\tfrac{\pi}{2}\pm p,\ n=1,2,3\cdots N\end{split} \] (31) where \(N\) is the total number of the selected sampling points, which depends on the sampling rate, the characteristic of the IF chirp signal and the compression time. The notation p denotes the approximation factor, which influences the amplitude accuracy of the output pulses. A higher sampling rate allows a smaller approximation factor to  
range, the number of the orthogonal sampling points can be properly reduced due to the linear sampling, which can further reduce the required number of calculations.
# Developments on Perovskite Solar Cells (PSCs): A Critical Review
## 4 Perovskite Solar Cell Device Architecture
From the IPCE curve, MAPbBr\({}_{3}\) (solid line) shows a photon to the current conversion ratio of 62-65% at wavelengths less than 550 nm. However, changes in behavior at higher wavelengths indicate that MAPbBr\({}_{3}\) is most active at lower wavelengths, whereas MAPbI\({}_{3}\) (dash line) shows a 45% conversion rate over a wide wavelength range of 450-750 nm. The good short circuit current of 10.8 mAcm\({}^{-2}\) in MAPbI\({}_{3}\) and the good open-circuit voltage of 0.94 V in MAPbBr\({}_{3}\) from the corresponding I-V curves are attributed to the long IPCE ratio in MAPbI\({}_{3}\) and good electron transfer from the active layer LUMO to the ETM conduction band in MAPbBr\({}_{3}\), respectively. This study paved a path for a multitude of ideas in varying the perovskite ABX\({}_{3}\) crystal structure, resulting in hybrid perovskite materials of mixed-halide, mixed-cation, all organic, all-inorganic, perovskite-light-active thin films. Figure 4: (**a,b**) represent simplified images of a conventional n-i-p PSC device architecture. (**c,d**) represent the inverted p-i-n PSC device architecture [40]. Hybrids of mixed-halide PSCs result in synergistic properties not obtained in single halide PSCs. [PERSON] (2013) compared the optical and PV properties of planar MAPbI\({}_{3}\) single halide and planar MAPbI\({}_{3-}\)Cl\({}_{x}\) mixed-halide PSCs prepared via a dip-coating solution of PbI\({}_{2}\) thin films into precursor solutions containing (single-halide) MAI plus (mixed-halide) MACI at a controlled concentration ratio (wt%), as shown in Figure 6a. It is observed that the PSC performance increases in mixed-halide perovskite systems (Table 1). In the time-resolved photoluminescence results reported in Figure 6b, the planar MAPbI\({}_{3}\) device exhibits a short photoexcitation lifetime, minimizing the charge carrier diffusion length in the relatively thick MAPbI\({}_{3}\) film. Remarkably, it is observed that the addition of Cl at small wt% into the MAPbI\({}_{3}\) composition increases the charge carrier lifetime, resulting in diffusion lengths that are a few nanometers greater than the MAPbI\({}_{3-}\)Cl\({}_{x}\) thickness. The corresponding I-V results are demonstrated in Figure 6c; the mixed-halide MAPbI\({}_{3-}\)Cl\({}_{x}\) PSC device reaches high short circuit current (J\({}_{\rm sc}\)) and efficiency (\(\eta\)) values of 22.9 mAcm\({}^{-2}\) and 15.41% in comparison to the low PV parameters in single-halide MAPbI\({}_{3}\). This improved PSC performance is suggested to be attributed to the small average crystal size and minimum grain boundaries within the mixed halide promoting higher diffusion lengths. However, the study indicates that further addition of the second halide component (Cl) slightly decreases the device short circuit current and open-circuit voltage, therefore demonstrating that optimization in the preparation method is required for optimum device performance [20].