esa-sceva/satcom-chunk-collection huggingface dataset:
# 6G Technology for Indoor Localization by Deep Learning with Attention Mechanism
###### Abstract  
This paper explores 6G technology for indoor positioning, focusing on accuracy and reliability using convolutional neural networks (CNN) with channel state information (CSI). Indoor positioning is critical for smart applications and the Internet of Things (IoT). 6G is expected to significantly enhance positioning performance through the use of higher frequency bands, such as terahertz frequencies with wider bandwidth. Preliminary results show that 6G-based systems are expected to achieve centimeter-level positioning accuracy due to the integration of advanced artificial intelligence algorithms and terahertz frequencies. In addition, this paper also investigates the impact of self-attention (SA) and channel attention (CA) mechanisms on indoor positioning systems. The combination of these attention mechanisms with conventional CNNs has been proposed to further improve the accuracy and robustness of localization systems. CNN with SA demonstrates a 50% reduction in RMSE compared to CNN by capturing spatial dependencies more effectively.  
indoor localization; indoor positioning; internet of things; channel state information; fingerprint; 6G technology; terahertz frequencies; self-attention; channel attention +
Footnote †: journal: applied sciences  
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# Development and Characterization of a DC-Driven Thermal Oscillator Using Acrylate-Based Composites
## 3 Device Fabrication  
The synthesis process for acrylate-based PTC composites is illustrated in Figure 2. The PTC material used in the thermal oscillator was realized by dispersing the graphite (the conductive filler) with a semi-crystalline acrylate copolymer (the matrix). The preparation of the PTC composite was as follows. A semi-crystalline acrylate copolymer was fabricated by polymerizing two acrylate monomers with different alkyl side-chain lengths (20 mol% butyl acrylate (BA) and 80 mol% octadecyl acrylate (OA)), which was dispersed with 1.0 wt% 2,2-bis(hydroxymethyl)propionic acid (DMPA). Then, the copolymer was dissolved in an additional 100 wt% tetrahydrofuran (THF). The solution was thoroughly stirred using a magnetic stirrer for 1 h. Polymerization was carried out via exposure to 365 nm UV light (UVL-28 EL series, 4 W) for 1 d. The synthesized semi-crystalline copolymer was  
Figure 1: Illustration of the proposed DC-driven oscillator device. (**a**) Schematic of the thermal oscillator. (**b**) Resistivity–temperature characteristic of positive temperature coefficient (PTC) materials. (**c**) The circuit diagram. (**d**) Illustration of the self-switching on–off phenomenon.  
mixed with graphite particles (2-3 \(\upmu\)m in diameter) at 25 wt% using a magnetic stirrer. The organic solvent was then removed under vacuum in a desiccator for 1 d. Figure 3 shows the SEM images of the prepared PTC composite dispersed with graphite particles.  
Figure 4 details the steps of the fabrication process of the proposed thermal oscillator. First, a photoresist (AZ 1518; MicroChem Co.) was spin coated at 4000 rpm on a glass wafer (Figure 4a) and patterned (Figure 4b,c). A Ti/Au layer was then deposited (Figure 4d) and patterned by a lift-off process (Figure 4e). The thicknesses of the titanium layer and the gold layer were 300 A and 3000 A, respectively. A polyimide film was laid around the interdigital electrode with a 55 \(\upmu\)m-thick, 3 mm-diameter punched circular hole (Figure 4f). Then, the PTC composite was preheated and spin coated onto the interdigital electrodes (Figure 4g). Note that, during the process of spin coating the PTC composite, the PTC composite was preheated on a hot plate at 60 \({}^{\circ}\)C, which was much higher than the \(T_{m}\), to ensure that the  
Figure 3: SEM images of the prepared acrylate PTC composite.  
Figure 2: Preparation of the PTC composite.  
composite was in a colloidal state and would therefore easily be filled into the polyimide circular hole. Finally, the PTC composite was encapsulated by using a polyimide tape as a top cover (Figure 4h).  
Figure 5 shows the pictures of the fabricated devices. Figure 5a shows the glass substrate patterned with the microheater, interdigital electrode, and the C-shaped heat sink. The resistance of the microheater is approximately 25 \(\Omega\). The distance between the center of the heater and the center of the interdigital electrodes is 4 mm. The pitch between each interdigital electrode is 0.25 mm. The assembled thermal oscillator is shown in Figure 5b.
# Exploring the Bioethanol Production Potential of _Miscanthus_ cultivars
###### Abstract  
_Miscanthus_ is a fast-growing perennial grass that attracts significant attention for its potential application as a feedstock for bioethanol production. This report explores the difference in the lignocellulosic composition of various _Miscanthus_ cultivars, including _Miscanthus_\(\times\)_giganteus_ cultivated at the same location (mainly Lincoln, UK). It also assesses the sugar release profiles and mineral composition profiles of five _Miscanthus_ cultivars harvested over a growing period from November 2018 to February 2019. The results showed that _Miscanthus_\(\times\)_giganteus_ contains approximately 45.5% cellulose, 29.2% hemicellulose and 23.8% lignin (dry weight, \(w/w\)). Other cultivars of _Miscanthus_ also contain high quantities of carbohydrates (cellulose 41.1-46.0%, hemicellulose 24.3-32.6% and lignin 21.4-24.9%). Pre-treatment of _Miscanthus_ using dilute acid followed by enzymatic hydrolysis released 63.7-80.2% of the theoretical glucose content. Fermentation of a hydrolysate of _Miscanthus_\(\times\)_giganteus_ using _Saccharomyces cerevisiae_ NCYC2592 produced 13.58 \(\pm\) 1.11 g/L of ethanol from 35.13 \(\pm\) 0.46 g/L of glucose, corresponding to a yield of 0.148 g/g dry weight _Miscanthus_ biomass. Scanning electron microscopy was used to study the morphology of raw and hydrolys _Miscanthus_ samples, which provided visual proof of _Miscanthus_ lignocellulosic degradation in these processes. The sugar release profile showed that a consequence of _Miscanthus_ plant growth is an increase in difficulty in releasing monosaccharides from the biomass. The potassium, magnesium, sodium, sulphur and phosphorus contents in various _Miscanthus_ cultivars were analysed. The results revealed that these elements were slowly lost from the plants during the latter part of the growing season, for a specific cultivar, until February 2019.  
Miscanthus \(\times\)_giganteus_; bioethanol; pre-treatment and saccharification; yeast fermentation; SEM
# Closing Connectivity Gap: An Overview of Mobile Coverage Solutions for Not-Spots in Rural Zones
## 2 The Rural Paradigm Shift
### Indoor Case  
Indoor-improving techniques outlined the strategies that enhance user experiences inside closed spaces. Therefore, there is more interference resulting from physical obstacles. This case requires evaluating the best estimation of indoor coverage provided, looking for optimal system planning. The feasibility in implementing algorithmic solutions based on UE location estimation appears to be challenging since their location accuracies depend on the integrated sensors in devices used by authors in [31].  
Satellite-based networks and other high-altitude platforms suffer excess losses because the slant path intersects several obstructions than compared to terrorists. Nevertheless, using repeaters at lower frequency bands--despite the bandwidth limitation--can fulfill the requirements demanded from users [10]. These devices are low-cost and readily available, hence boosting signal propagation and simultaneously enhancing indoor coverage. Figure 4 shows a potential indoor-improvement deployment for a satellite-based backhaul.  
In addition to satellite-based backhaul solutions, other approaches outperform the repeater's throughput, particularly for IoT devices and MTC at a glimpse, and Section 2.3 foresees the need reinforce this limitation. On one hand, the authors in [32; 33] analyzed the development of indoor coverage--focused in suburban and rural areas--by deploying LTE-M, NB-IoT, LoRa, and GPRS solutions to cover a greater zone. On the other, there is a reduction in indoor time dispersion by dynamical estimation of the channel parameters in full swing machine learning techniques [34].  
Regarding the first approach, [PERSON] et. al contrasted the technologies in a particular Northern Denmark region area by assessing their supported Maximum Coupling Loss [32; 33]. The results accomplished 99% of indoor devices coverage for both LTE-M and NB-IoT solutions as long as these devices experienced 10 dB additional loss at its maximum level.  
Figure 4: Indoor solution for a satellite-based network.  
In the case of deep indoor devices--namely basement or underground located--NB-IoT performed the best, with 8% of outage probability, followed by LoRa (13%) and GPRS (60%) considering 30 dB additional losses. Despite the outstanding outcomes to shed light on optimal indoor coverage, the authors suggest a throughput evaluation of pico-cells or Wi-Fi access points to enhance connectivity inside closed spaces [32].
## 2 Materials and Methods
### Evaluation Methods
#### 2.2.2 Fourier-Transform Infrared (FTIR) Spectroscopy  
The chemical structure of archaeological and recently cut wood samples was analyzed with ATR-FTIR spectroscopy (Perkin Elmer Frontier, Waltham, MA, USA) using a Bruker Invenio R spectrometer fitted with a diamond ATR unit. The ATR-FTIR spectra were recorded in the range of 4000 and 400 cm\({}^{-1}\) at room temperature, averaging 64 scans with a resolution of 4 cm\({}^{-1}\). Three measurements were performed for each sample, and the average spectra were analyzed. The lignin index (_LI_) was calculated by lignin intensity at 1505 cm\({}^{-1}\) and CH/CH\({}_{3}\) groups at 1375 cm\({}^{-1}\)[31] using Equation (1):  
\[LI=[\frac{I_{1505\text{ cm}^{-1}}}{I_{1375\text{ cm}^{-1}}}] \tag{1}\]
# Cross-Corpus Training Strategy for Speech Emotion Recognition Using Self-Supervised Representations
## 3 Materials: Experimental Setup
### Databases
#### 3.1.2 Special Case: IEMOCAP Database  
IEMOCAP was specifically designed to address some of the challenges associated with acted datasets and to provide a more realistic representation of emotions in speech. While it is still an acted dataset, the audio recordings in IEMOCAP were obtained through improvised conversations between two actors, aiming to capture more authentic and natural emotions compared to scripted readings. To ensure objectivity and a wider perspective, the audio data in IEMOCAP was labelled by three different individuals who were independent of the researchers. This reduces biases and provides diverse emotional annotations. IEMOCAP is highly used in previous work due to its authentic nature and rigorous labelling process. It is considered a valuable dataset for studying SER, enabling researchers to develop advanced algorithms and models for emotion analysis and human-computer interaction applications.
## 4 Experimental Setup  
Two different measurement campaigns were performed, respectively in the laboratory and in the field; in both cases the same ad hoc hardware infrastructure was set up for what concerns transmitting and receiving nodes. In particular, a custom made LoRaWAN end node and an off-the-shelf LG308 Dragino Gateway, featuring a 2 dBi \(\lambda/4\) antenna and connected to a 4G router for data forwarding to the LoRaWAN server, were exploited. The transmitter node, whose architecture is schematized in Figure 1, was based on an RFM95x LoRa transceiver by HopeRF featuring a \(\lambda/8\) antenna with 2 dBi gain, integrated with an ATtiny84 microcontroller by Atmel/Microchip. The microcontroller managed the timing and frequency of the transmissions, and collected a sample data from an LM35 temperature sensor: this value was inserted in the default payload for each transmitted packet. The whole transmitting node was powered with the regulated voltage at 3.3 V of two parallel 18650 Li-Ion batteries, with a total capacity of 6400 mAh. Since the node had to be placed under water, all the circuitry was placed inside an ABS IP68 plastic box with dimensions 24 cm \(\times\) 12 cm \(\times\) 8 cm: the antenna was fixed to the internal upper surface of the box, oriented perpendicularly with respect to the antenna of the reader.  
The transmitter implemented a Class A LoRaWAN End Node: however, to speed up the tests and since the transmission range was notably limited by the water attenuation, the 1% duty-cycle rule was not respected. In order to test the system performance for all the Spreading Factors (SF), an ad hoc firmware was implemented: its operation foresaw the transmission of 300 packets of fixed length for each SF, from 7 to 12, with a payload size of 11 B. The transmitted power was fixed to \(P_{t}=14\,\mathrm{dBm}\) while the bandwidth (BW), the transmitting frequency \(f\) and the coding rate (CR) were equal respectively to \(125\,\mathrm{kHz}\), \(868\,\mathrm{MHz}\) and \(4/5\).
# Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
## Appendix A Methods of Estimating Net Radiation for Canopies and the Ground  
Net radiation (\(\mathrm{R_{n}}\)) of a sunlit leaf and a shaded leaf in the overstory and understory canopies are key parameters in calculating ET of a single sunlit or a single shaded leaf in the overstory and understory canopies. In BEPS, they are calculated separately as [PERSON] et al. (2014):\[R^{*}_{sun\_o}=S^{*}_{dir\_o}+S^{*}_{diff\_1}+L^{*}_{sun\_o} \tag{1}\]  
\[R^{*}_{sl\_o}=S^{*}_{diff\_2}+L^{*}_{sl\_o} \tag{2}\]  
\[R^{*}_{sun\_u}=S^{*}_{dir\_u}+S^{*}_{diff\_3}+L^{*}_{sun\_u} \tag{3}\]  
\[R^{*}_{sl\_u}=S^{*}_{diff\_4}+L^{*}_{sl\_u} \tag{4}\]  
where \(S\) and \(L\) represent shortwave and longwave radiation, respectively. The superscript asterisk represents the R\({}_{n}\) constituent. The subscripts'sun' and'sh' represent the single sunlit leaf and the single shaded leaf, respectively and the subscripts 'o' and 'u' represent overstory and understory, respectively. The subscripts 'dir' and 'df' represent direct and diffuse constituents. \(S^{*}_{diff\_1}\) and \(S^{*}_{diff\_2}\) represent the net shortwave diffuse radiation on the sunlit and shaded representative leaves of the overstory canopy, respectively. \(S^{*}_{diff\_3}\) and \(S^{*}_{diff\_4}\) represent the net shortwave diffuse radiation on the sunlit and shaded single leaves of the understory, respectively. Methods of calculating shortwave irradiances are taken from [PERSON] et al. (1999). The diffuse (\(S_{diff}\)) and direct (\(S_{dir}\)) components of the incoming global solar radiation are calculated as:  
\[\frac{S_{diff}}{S_{g}}=\left\{ \begin{array}{ll}0.943+0.734r-4.9r^{2}+1.796r^ {3}+2.058r^{4}&r<0.8\\ 0.13&r\geq 0.8\end{array} \right. \tag{5}\]  
\[S_{dir}=S_{g}-S_{diff} \tag{6}\]  
where \(S_{g}\) is the global solar radiation (W m\({}^{-2}\)) and \(r\) is calculated as:  
\[r=\frac{S_{g}}{S_{0}cos\theta} \tag{7}\]  
where S\({}_{0}\) is the solar constant (1367 W m\({}^{-2}\)). The direct part of net shortwave irradiance of a sunlit leaf in the overstory and understory canopies and the direct part of net shortwave irradiance of the ground are estimated, respectively, as:  
\[S^{*}_{dir\_o}=(1-\alpha_{o})S_{dir}cos\alpha/cos\theta \tag{8}\]  
\[S^{*}_{dir\_u}=(1-\alpha_{u})S_{dir}cos\alpha/cos\theta \tag{9}\]  
\[S^{*}_{dir\_g}=\left(1-\alpha_{g}\right)S_{dir}e^{-0.5\Omega(LAI_{o}+LAI_{u}) /cos\theta} \tag{10}\]  
where \(\alpha_{o}\), \(\alpha_{u}\) and \(\alpha_{g}\) are overstory, understory, and the ground albedo, respectively. \(\alpha\) is the mean leaf-sun angle. Net shortwave diffuse irradiance on a sunlit leaf in the overstory is estimated as:  
\[S^{*}_{diff\_1}=(1-\alpha_{o})(\frac{S_{diff}-S_{diff\_under1}}{LAI_{o\_sun}} +C_{o\_sun}) \tag{11}\]  
where \(S_{diff\_under1}\) is the shortwave diffuse radiation under the upper layers of leaves (mostly sunlit) in the overstory canopy.
# An Architecture for a Tri-Programming Model-Based Parallel Hybrid Testing Tool
## 2 Background
### Programming Models
#### 2.1.3 Compute Unified Device Architecture (CUDA)  
The CUDA [23] programming model and parallel platform commonly uses GPUs to increase computing performance. As CUDA 2006 was first introduced by the NVIDIA(r) Corporation (Santa Clara, CA, USA), it was only compatible with NVIDIA(r) GPUs for a long time, thereby rendering it unportable. However, Fortran has since developed a CUDA programming model using the C/C++ programming languages for GPUs. Nevertheless, CUDA's latest 12.0 version, which was released on 8 December 2022, only enables NVIDIA(r) GPUs to take full advantage of its extensive parallelism. When programming in CUDA, the low-level nature of the language facilitates reading from any memory address. Thisprovides programmers with insights into the process of transferring memory from the host to the device, the storage of temporary data, thread mapping during kernel start-up, and the implementation of parallelism.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Window Length** & **500 ms** & **750 ms** & **1000 ms** & **2000 ms** \\ \hline PSD & \(59.8\pm 9.6\%\) & \(57.9\pm 8.4\%\) & \(54.3\pm 8.3\%\) & \(64.8\pm 7.6\%\) \\ \hline Hjorth parameters & \(67.9\pm 4.7\%\) & \(68.6\pm 3.1\%\) & \(68.9\pm 3.9\%\) & \(54.0\pm 12.6\%\) \\ \hline Correlation coefficients & \(68.8\pm 3.1\%\) & \(71.6\pm 3.9\%\) & \(68.5\pm 4.3\%\) & \(53.9\pm 8.3\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracies of motor imagery classification in the approach according to the search for informative frequencies and segments for different features and window lengths (intrasubject). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Window Length** & **500 ms** & **750 ms** & **1000 ms** & **2000 ms** \\ \hline PSD & \(38.55\%\) & \(35.29\%\) & \(34.64\%\) & \(64.7\%\) \\ \hline Hjorth parameters & \(66.08\%\) & \(67.77\%\) & \(68.01\%\) & \(35.48\%\) \\ \hline Correlation coefficients & \(68.07\%\) & \(68.01\%\) & \(67.65\%\) & \(35.72\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Motor imagery classification accuracies in the approach according to the search for informative frequencies and segments for different features and window lengths during additional classification for ROC curves creation (intrasubject). for the latter, 750 ms along with 1000 ms is superior to 500 ms. Moreover, according to the averaged intrasubject accuracy (Table 2), the window of 750 ms is definitely superior. It is noteworthy that the signals of each MI class are processed independently, but an improvement in accuracy is still observed when they are combined again, that is, the method is able to identify general patterns. Moreover, this also leads to the accuracy rate of the intersubject classification being comparable (Table 3) to that of the intrasubject classification (Table 2). Among its advantages, one can also note the resistance to noise,  
Figure 6: ROC curves obtained in the approach according to the search for informative frequencies and segments for different features (first column (graphs **A,D,G,J**)—coefficients of pairwise interchannel correlation, second column (graphs **B,E,H,K**)—_H_jorth parameters, third column (graphs **C,F,I,L**)—PSD) and window lengths (first row (graphs **A–C**)—500 ms, second row (graphs **D–F**)—750 ms, third row (graphs **G–I**)—1000 ms, fourth row (graphs **J–L**)—2000 ms). since, in the process of its operation, all pairwise combinations of background windows and the MI signal are used. The classification accuracies for the different features used and the approaches implemented are summarized in Tables 1 and 2. Scatter plots with axes being the LDA components obtained are shown in Figures 7 and 8 for the Hjorth parameters and cross-channel correlations, respectively. One can clearly see that the right-hand MI is almost completely discriminated from other MIs, which is more likely due to the most subjects being right-handed and is also reflected in the ROC scores shown in Figure 6.
## 3 Results and Discussion
### Optimization of Voltammetrical Parameters
#### 3.4.3 Influence of Accumulation Parameters  
Accumulation can effectively boost the response peak currents of target species, so the influence of accumulation potential and time were also investigated. The anodic peak currents of DA and UA sharply increased with the accumulation potentials shifting from \(-0.4\) V to \(-0.3\) V, then they gradually decreased with a further increase of the accumulation potential (Figure 7A). The highest anodic peak currents of DA and UA were achieved at \(-0.3\) V, so \(-0.3\) V was chosen as the optimal accumulation potential. As presented in Figure 7B, their anodic peak currents gradually enhanced during the first 150 s, then decreased with the prolonging of the accumulation time. Therefore, accumulation was performed at \(-0.3\) V for 150 s in the following experiments.
## 4 Discussion on the Mechanism of Damage
### Mechanism of Mesoscale Damage  
During wet-dry cycling, various microstructural changes promote the formation of potential microscale cracks. As cycling continues, these microscale cracks connect and merge with internal pores, leading to their progressive development [40] and ultimately culminating in the formation of visible mesoscale cracks. This also elucidates why the first wet-dry cycling exerted a substantial impact on the microstructure, yet did not give rise to numerous mesoscale cracks. From a mechanical perspective, mesoscale cracks belong to tensile cracks. The emergence and propagation of mesoscale cracks predominantly occur during the dehydration stage. In this process, The decrease in moisture content leads to an increase in soil matrix suction, resulting in the formation of tensile stress between particles. However, the specimen develops an internal water content gradient, as water loss from the surface exceeds that from the bottom [41]. Meanwhile, the extent of shrinkage among different mineral particles varies considerably, leading to uneven shrinkage of the soil, thereby inducing stress concentration (Figure 9(b-2)). When the tensile stress exceeds the soil's tensile strength, internal cracks emerge and progressively develop [42].  
After the second wet-dry cycle, the main crack begins to emerge. These cracks serve as the crack framework and primary seepage paths throughout the entire wet-dry cycling process, resulting in an accelerated rate of crack development. With the process of wet-dry cycling, the main crack will progressively widen and deepen, while numerous smaller cracks will emerge in the vicinity of the main crack. The presence of these smaller cracks reduces the overall average crack width, leading to the stabilization of the average crack width when \(N\) equals three, although the maximum crack width and crack rate continue to increase. However, following repeated wet-dry cycles, the cracks will progressively widen and coalesce into an interconnected network, thereby facilitating increased water escape pathways and reducing discrepancies in volumetric deformation, leading to a decrease in tensile stress. Concurrently, crack formation leads to the relaxation of tensile stress (Figure 9(b-3)). When the tensile stress falls below the tensile strength, the propagation of microscale cracks within the soil mass will halt. This explains the restriction of crack development in the later stages of wet-dry cycles.