esa-sceva/satcom-chunk-collection huggingface dataset:
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 4 Experiments and Results
### Different Languages
#### 4.7.1 Speech  
We selected from the Librivox corpus several audiobooks in other languages than English, listed in Table 3. For each language, the text corresponding to approximately 1 min of audio was manually aligned by native speakers. We publicly released the audio and the manually aligned transcript for each language ([[https://ircam-anasynth.github.io/papers/2023/a-linear-memory-ctc-based-algorithm-for-text-to-voice-alignment-of-very-long-au-dio-recordings](https://ircam-anasynth.github.io/papers/2023/a-linear-memory-ctc-based-algorithm-for-text-to-voice-alignment-of-very-long-au-dio-recordings)]([https://ircam-anasynth.github.io/papers/2023/a-linear-memory-ctc-based-algorithm-for-text-to-voice-alignment-of-very-long-au-dio-recordings](https://ircam-anasynth.github.io/papers/2023/a-linear-memory-ctc-based-algorithm-for-text-to-voice-alignment-of-very-long-au-dio-recordings)) (accessed on 31 January 2023)).  
For Latin alphabet languages, diacritic characters are converted to plain characters with the python unidecode package ([[https://pypi.org/project/Unidecode](https://pypi.org/project/Unidecode)]([https://pypi.org/project/Unidecode](https://pypi.org/project/Unidecode)) (accessed on 31 January 2023)). For non-Latin alphabet languages, the text was first transliterated before the synchronisation: for Chinese, we used the python pinyin package ([[https://pypi.org/project/pinyin](https://pypi.org/project/pinyin)]([https://pypi.org/project/pinyin](https://pypi.org/project/pinyin)) (accessed on 31 January 2023)), for Greek, we used the python polyglot package ([[https://github.com/aboSamoor/polyglot](https://github.com/aboSamoor/polyglot)]([https://github.com/aboSamoor/polyglot](https://github.com/aboSamoor/polyglot)) (accessed on 31 January 2023)), and for Arabic, we used a human manual transliteration. We used our system to align the text to the audio, and summarized the results in Table 3.  
It appears that although the system was trained in English audio and transcripts only, it performs very well for other languages as well, including for languages that have a very distinct pronunciation, as compared to English. We interpret these results by the fact that the system encounters enough anchor characters (typically spaces, vowels, and consonants that are common to English) to align the full words correctly, even though it might not be able to recognize some particular phonemes perfectly. The variations in the MAAE between languages is in fact mainly related to the clear or unclear articulation of the words, as can be heard on the videos available on our companion website.
###### Abstract  
The dynamics of terrestrial vegetation have changed a lot due to climate change and direct human interference. Monitoring these changes and understanding the mechanisms driving them are important for better understanding and projecting the Earth system. Here, we assessed the dynamics of vegetation in a semi-arid region of Northwest China for the years from 2000 to 2019 through satellite remote sensing using Vegetation Index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and analyzed the interannual covariation between vegetation and three climatic factors--air temperature, precipitation, and vapor pressure deficit (VPD)--at nine meteorological stations. The main findings of this research are: (1) herbaceous land greened up much more than forests (2.85%/year vs. 1.26%/year) in this semi-arid region; (2) the magnitudes of green-up for croplands and grasslands were very similar, suggesting that agricultural practices, such as fertilization and irrigation, might have contributed little to vegetation green-up in this semi-arid region; and (3) the interannual dynamics of vegetation at high altitudes in this region correlate little with temperature, precipitation, or VPD, suggesting that factors other than temperature and moisture control the interannual vegetation dynamics there.  
vegetation dynamics; temperature; moisture; high altitude; agriculture 2020. _Remote Sens._**2021**, 13, 4246.
###### Abstract  
Monitoring and classifying cognitive workload in real time is vital for optimizing human-machine interactions and enhancing performance while ensuring safety, particularly in industrial scenarios. Considering this significance, the authors aim to formulate a cognitive workload monitoring system (CWMS) by leveraging the deep gated neural network (DGNN), a hybrid model integrating bi-directional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU) networks. In our experimental setup, each of the four virtual users is equipped with a Raspberry Pi Zero W module to ensure efficient data transmission, thereby enhancing the reliability and efficacy of the monitoring process. This seamless monitoring framework utilizes the constrained application protocol (CoAP) and the Things Board platform to evaluate cognitive workload in real time. The most popular EEG benchmark dataset, the STEW is utilized for workload classification in this study. We employ the short-time Fourier transformation (STFT) to extract frequency bands corresponding to users in both high and low cognitive workload modes. The proposed DGNN models achieve a perfect accuracy of 99.45%, outperforming every previous state-of-the-art model. We meticulously monitored critical parameters, including latency, classification processing time, and cognitive workload levels. This research demonstrates the importance of continuous monitoring for increasing productivity and safety in industries by introducing a novel method of real-time cognitive workload monitoring. The implementation codes for each experiment are documented and made available for reproducibility.  
human-machine interface; mental workload; cognitive workload monitoring system; deep gated neural network; electroencephalogram; brain-computer interface applications Article
# Radiation Hardness of Oxide Thin Films Prepared by Magnetron Sputtering Deposition
## 3 Experiment Results and Discussion
### Radiation Hardness of Magnetron-Sputtering-Deposited a-Al\({}_{2}\)O\({}_{3}\) and a-MgO
Surprisingly, the gamma-ray-irradiated sample shows almost complete disappearance of the \(\sim\)415 cm\({}^{-1}\) Mg-O stretching peak (peak position is shifted to 430 cm\({}^{-1}\), peak area decreases \(\sim\)15\(\times\), and FWHM is reduced by 50%). Apparently, this type of irradiation strongly disrupts Mg-O bonds, but currently, the underlying cause remains unknown to us. This finding is in stark contrast to the high-energy heavy ion irradiation which preserves short-range order, i.e., for which the Mg-O stretch remains FTIR-active. Hence, the important takeaway here is that high-energy heavy ion irradiation cannot always be used as a proxy to emulate gamma-ray irradiation-induced material changes [21], but should be carefully investigated when this approach is feasible for a given material. Figure 4: FTIR spectra of (**a**) high-energy heavy-ion-irradiated a-Al\({}_{2}\)O\({}_{3}\) and (**b**) gamma-ray-irradiated and annealed a-Al\({}_{2}\)O\({}_{3}\).
# On the Melting Thresholds of Semiconductors under Nanosecond Pulse Laser Irradiation
## 2 Model Description
Similarly, the choice of \(\Delta z\) should enable a good approximation of the laser intensity attenuation toward the material depth and the temperature gradient within the heat affected zone. A very good approximation was achieved with \(\Delta t\) values of 5-10 ps and \(\Delta z=1\) nm. The sample is considered to be semi-infinite. The system of the linear Equation (1) with discretization to the form (4) represents a tridiagonal matrix, which is solved by the [PERSON] algorithm [35]. Material heating to the melting point followed by the melting process leads to an accumulation of the internal energy at constant \(T=T_{\rm m}\), and its ratio to the enthalpy of melting can be interpreted as a molten fraction in a computational element. In the presented calculations, we apply the method of through calculation without explicit selection of the phase interface [32; 33]. According to this method, the melting process is smoothed over a symmetric interval of a width of a few Kelvins around the melting point. Melting starts at a slightly lower temperature than \(T_{\rm m}\), reaches the melting point at the fraction of molten material of 0.5, and ends at a slightly higher temperature than \(T_{\rm m}\). In the interval of melting,a \(\delta\) function is added to the heat capacity term to account for absorption/release of the fusion heat at the melting/solidification front:  
\[\delta(T)=\frac{L_{\text{m}}}{A\sqrt{\pi}}e^{-(\frac{T-T_{\text{m}}}{A})^{2}}, \tag{6}\]  
where \(A\) is the width of the delta function in Kelvin. As the internal energy rises upon laser light absorption, the physical parameters are gradually changing from the solid to the liquid phase proportionally to the fraction of molten material [36]:  
\[\gamma=\gamma^{\text{s}}(T)(1-\eta)+\eta\gamma^{\text{l}}(T), \tag{7}\]  
where \(\gamma_{\text{s}}\), \(\gamma_{\text{l}}\) represent a property of material in solid and liquid state, respectively, and \(\eta\) is the fraction of molten material. For the sake of simplicity, the change in density upon melting is not taken into account so that the value of the solid-state density is also kept for the liquid state. In the presented model, we interpret the fluence needed for the fraction of molten material to reach the interval from 0-3% as the melting threshold fluence, \(F_{\text{th}}\). According to the \(\delta\)-function approach (Equations (1) and (6)), this occurs at ~1-2 K bellow the tabulated melting point and thus the edge of beginning of melting is blurred. However, from analyzing our simulation data, it follows that the position of \(F_{\text{th}}\) within the interval of melting has a minor effect on its resulting value. Furthermore, taking into account the ambiguity of \(F_{\text{th}}\) reported in the literature, this aspect plays only a small role.
# Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes
## 1 Introduction
### Motivation  
The intelligent sensors in smart home networks collect and transmit sensitive data that can expose the privacy of homeowners if captured by adversaries. As such, strong security solutions are needed to protect the various elements of these networks, such as the communication channels and the data residing in sensors. Therefore, many asymmetric and symmetric key-based protocols have been deployed to offer the required levels of security. However, most of these schemes are susceptible to many attacks. Some of these schemes also require the execution of computationally intensive cryptographic operations [4] that are not ideal for most smart home devices. Other researchers have also presented security protocols based on objects such as smart cards to secure smart home networks. However, these schemes are vulnerable to smart card theft or smart card loss attacks. It is also inconvenient for users to carry these cards around for multiple authentications. Although blockchain-based techniques can address these issues, privacy protection for the access policy remains an open challenge in these techniques. Moreover, the public key infrastructure-based schemes are unsuitable for deployment in smart home devices due to their computationally intensive cryptographic operations and storage requirements. There is, therefore, a need for a truly lightweight authentication scheme that can address some of these challenges.
## 2 Sample Extraction
### Solid-Phase Extraction  
The popularity of solid-phase extraction (SPE) in analytical laboratories is based on its flexibility in application. It is available in a variety of solid and mobile phase combinations. It can be used for concentration and purification and also allows the enzymatic hydrolysis of bound VOCs during sample extraction. In SPE sample preparation, VOCs are retained based on three mechanisms: partition, adsorption, and electronic interactions [101; 102; 103]. These mechanisms apply to small apolar analytes, large apolar molecules, and polar and polarizable analytes, respectively. Due to the different retention principles, SPE has a high extraction efficiency for both polar and apolar organic analytes. Many studies have been published on applying SPE to beverage analysis. Most of them focus on the quantification of a certain group of VOCs [104; 105; 106], and the HS-SPE application was also used to obtain extracts with a composition closer to the orthonasal olfaction process [107]. An exciting variety of SPE uses small particles made of nanomaterials for liquid samples. Because of their small size, the nanomaterials have a fairly large surface area and a high surface/volume ratio. Consequently, the extraction time is saved. In a study, a very short exposure time of the sorbent phase to the sample (1 min) was applied to extract biogenic amines in wines [108]. The major drawback of using SPE for beverage VOC analysis is the cumbersome extraction procedure, which can be mitigated by automation. Another expected variety is molecular imprinting-based SPE. The solid-phase polymer preserves the stereochemical properties and synergy of functional groups of target compounds [109]. Increasing selectivity benefits the quantification of target compounds. Firstly, a larger number of samples can be applied to improve the analytical sensitivity. Secondly, less separation power is required, saving analytical time. Molecular imprinting-based SPE has been successfully applied to ester compounds in an aqueous solution [84]. A promising alternative for beverage VOC analysis is membrane-assisted SPME. In this protocol, a miniaturized membrane bag is filled with an organic solvent or solution and inserted into the sample to allow analytes to pass into the extracting phase. The membrane selectively allows only analytes to migrate, overcoming time-consuming procedures. Stirring is used in this step to speed up the mass transfer. The injection volume is finally sampled from the extracting solvent and analyzed with the instrument. In terms of its application in the analysis of VOC in fermented beverages, this method was successfully used for the quantitation of varietal terpen in white and red wines [110].
# Thermal Relaxation Spectra for Evaluating Luminescence Quantum Efficiency of CASN:Eu2+
## 3 Results and Discussion
### Thermal Relaxation Spectrum  
Using quasi-monochromatic light from a white-light lamp (Xe lamp, L11034, Hamamatsu Photonics, Shizuoka, Japan) as the excitation light, the PTES have been successfully obtained for CASN:Eu\({}^{2+}\) powders [4] in the air as shown in Figure 5a, where photoluminescence (PL) and PLE spectra are also plotted.  
Comparison of the PTES of CASN:Eu\({}^{2+}\) with its PLES shows characteristic feature reflecting a high fluorescence quantum efficiency that the relative intensity of the PTES against the PLES decreases toward the absorption edge. In Figure 5b, in fact, the spectral shape of the PTES is reasonably reproduced by the PLES transformed with Equation (7) in Section 4, assuming that the absorbed light energy less than 1.9 eV (the peak energy of the PL spectrum) is converted into luminescence by 80 to 100% quantum efficiency and that the absorbed light energy exceeding 1.9 eV is all thermally relaxed to yield the photothermal signal. A theoretical discussion on the method how to transform PLES to reproduce PTES is undertaken in Section 4.
## 3 Results and Discussion
### Cyclic Voltammetry of EB-Irradiated PLC Nanocomposite  
The electrochemical performance of the PLC nanocomposite was examined using cyclic voltammetry in a 1 M KCl solution in the specially made three-electrode system at room temperature in the applied potential window of \(-4\) to 4 V with a scan rate 300 mV/s, as illustrated in Figure 14a. The 120 kGy EB dose irradiated PLC nanocomposite at various scan rates of 0.1 V/s, 0.2 V/s, 0.3 V/s, 0.4 V/s, 0.5 V/s, and 0.6 V/s is shown in Figure 14b, with the scan direction indicated by arrows. The shapes of the CV curves are typically different from the rectangular shape due to the progress of the redox reaction, which indicates that the pseudocapacitive behavior of the sample leads to an increase in the charge mobility, hence revealing the energy storage characteristics of the PLC nanocomposite.  
Figure 13: (**a**) Arrhenius behavior plots with the reciprocal temperature; (**b**) variation in the conductivity against the EB dose at low (303 K) and high (393 K) temperatures.  
As the irradiation dose increased, the integrated area under the CV curves increased significantly with an increase in the scan rate within the applied potential range. The shapes of the curves remained the same even at a high scan rate of about 600 mV/s, which is an indication of the capacitance behavior of the irradiated PLC nanocomposite. It was also observed that the current and voltage ranges of the PLC nanocomposite were enhanced after irradiation, which can be attributed to the fast ion transportation and, consequently, the high electrical conductivity [16; 51]. The findings strongly indicate that the high-energy electron-beam-irradiated PLC nanocomposite is a viable material for supercapacitor applications and serves effectively as an electrode material for electrochemical applications.
## 2 Materials and Methods
### Downscaling SMAP TB  
In an effort that follows the SMAP mission's original plan to downscale radiometer TB retrievals using radar retrievals, we downscaled SMAP TBs using CYGNSS reflectivity values. SMAP's original active-passive algorithm includes cross-polarization data, which is not available from CYGNSS retrievals. We therefore used a slightly modified version of the baseline SMAP active-passive TB algorithm (Equation (3)) with no cross-polarization measurements [1]. This algorithm merges coarse-scale (33 km, gridded at 9 km) SMAP TBs with fine-scale (3 km) CYGNSS reflectivity values to create fine-scale (3 km) SMAP/CYGNSS TBs, as depicted in Figure 2.  
\[TB_{F}=TB_{C}+\{\beta_{C}\times T_{S,C}\}\times[\Gamma_{F}-\Gamma_{C}]. \tag{3}\]  
Here, \(TB_{F}\) is the downscaled, SMAP/CYGNSS fine-scale brightness temperature, and \(TB_{C}\) is the SMAP coarse-scale brightness temperature. \(\Gamma_{F}\) is fine-scale CYGNSS reflectivity and \(\Gamma_{C}\) is coarse-scale CYGNSS reflectivity, \(\Gamma_{F}\) and \(\Gamma_{C}\) are further discussed in Section 2.2.3. \(\beta_{C}\) is the coarse-scale, spatially varying slope of the linear regression of SMAP emissivity and CYGNSS reflectivity values. Emissivity is defined as: \(e=TB_{C}/T_{S,C}\), where \(T_{S,C}\) is coarse-scale effective soil temperature, or surface temperature.  
We used vertically polarized SMAP enhanced TBs, gridded at 9 km with a native resolution of approximately 33 km [19], for our \(TB_{C}\) values [20]. We chose to use vertically polarized SMAP TBs because the SMAP NSSM algorithm using vertically polarized TBs outperforms the other NSSM algorithms [19]. We used the GMAO GEOS-5 (Global Modeling and Assimilation Office, Goddard Earth Observing System version 5) effective soil temperature data provided with the SMAP enhanced product for our \(T_{S,C}\) values [20]. An approximate equilibrium in temperature between the air, near-surface soil, and vegetation at 6 am increases the accuracy of derived NSSM [1]. We therefore only used 6 am SMAP enhanced TBs (from descending orbits) in this study. We gridded 3 km \(TB_{F}\) using the EASE-Grid 2.0.  
The goal of downscaling SMAP TB is to represent the spatial heterogeneity of TB at a finer scale than possible using passive microwave data alone. 3 km SMAP/CYGNSS TB spatial heterogeneity is represented by spatial variations in \(TB_{F}\) (Equation (3)) and is influenced by two factors. (1) Large spatial variations in CYGNSS reflectivity will create larger \(\Delta\Gamma\) values (\(\Gamma_{F}-\Gamma_{C}\)), which will increase the spatial variations of \(TB_{F}\). CYGNSS reflectivity varies due to natural spatial and temporal fluctuations in soil moisture, surface roughness, and vegetation. (2) More negative \(\beta\) values will also increase the spatial variations of \(TB_{F}\). SMAP/CYGNSS \(\beta\) values are further discussed in Section 2.2.2.  
Before we used Equation (3) to calculate \(TB_{F}\), we first collocated SMAP TB and CYGNSS reflectivity data and calculated \(\beta\).
## 2 Literature Review  
WSNs exhibit low power interference, bandwidth transmission, memory size, and data storage. Following these WSN regulations (strong IT resources, power supplies, and temporary communication environments), a number of security measures (including infiltration detection methods) have been developed. However, these security measures do not work directly on the WSN environment for older wired/wireless networks. Despite all of the efforts made to find a way to infiltrate WSNs [9; 10; 11; 12; 13], methods of energy conservation (or energy recovery [14; 15; 16; 18] are intended to generate energy in the environment, and their effectiveness for sensory networks is still measured in practice, although this may be due to nodes being malicious, which has however not been thoroughly researched, and there is no doubt that wireless networks are unreliable due to device checks and off-network connections. Security outbreaks against WSNs are divided into active and passive attacks. In unmanned attacks, the square-sized attackers are often hidden from view and can be a communications network that collects data or destroys active network components. Serious attacks can be categorized using visual inspection, node anomalies, and damage and traffic testing. In intense attacks, the opponent actually marks the performance of the network attacked. This result can also lead to a targeted attack. For example, network services can be compromised or interrupted with effect of the attacks. Acute attacks are categorized as DOS, jamming, hole attacks (black hole, worm, sinkhole, etc.), floods, and Sybil attacks. Readers interested in vulnerable security attacks against WSNs can visit [19; 20; 21] for more information.  
The grounds of our research claim rely on a remarkable discovery of innovation on wireless power transmission by [PERSON] et al. [22], which was revealed in 2007 and has since gathered attention globally. In [22], [PERSON] et al. utilized constant frequency resonance for the transmission of energy efficiently, wasting comparatively less energy in extraneous objects.  
This article further presents a novel approach for detecting compromised nodes and permits the sensor network to stay operational forever by victimizing and optimizing the energy utilized from the detected compromised node in the legitimate sensor nodes within the network through unmanned aerial vehicles.
## 5 Results and Discussion
### DMD Convergence Criterion
#### 5.2.2 Convergence of Sampling Period
The Strouhal number associated with the pumping as mentioned in the existing literature which is obtained through spectral analysis, may not be associated with a unique frequency but may be associated with eddies with a winder-range of frequency band and some phase relationship between them, and the DMD was able to predict them. However, this makes the identification of spurious modes more difficult and as well as necessitates the establishment of a new rule of how to in DMD of dealing with a mode that may represent the same structure but consists of motions with varying frequencies and phase relations between them.