esa-sceva/satcom-chunk-collection huggingface dataset:
# Overview on Different Types of Solar Cells: An Update
## 5 Thin-Film Solar Cells
### Quantum Confinement Effect in Thin-Film Materials  
Several researchers have reported quantum confinement effects in the obtained nanostructured materials. The chemical precipitation method has been used to produce cadmium sulfide and Gd-doped CdS films [260]. Based on the Raman spectra analysis, 1 LO,2 LO and 3 LO peaks have been moved to lower wavenumbers in Gd-doped CdS films (296, 591, and 896 cm\({}^{-1}\)) when compared with CdS films (301, 598, and 902 cm\({}^{-1}\)) because a smaller ionic radius could be observed in Gd (0.94 A) than in cadmium (0.96 A). In addition, they pointed out that the absorption edge had been shifted to a longer wavelength (due to the quantum confinement effect), when compared with CdS based on the optical absorption spectra. Zinc selenide films have been synthesized on glass substrates using the e-beam evaporation method [261]. Experimental results confirmed that the blue shift of the band gap occurred (about 0.16 eV in the thinner films). The band gap increased from 2.62 eV, 2.67 eV to 2.78 eV when the film thickness was reduced from 47 nm, 12.3 nm to 7.9 nm according to optical absorption spectroscopy analysis. The presence of a strong quantum-confinement effect has been reported based on theoretical calculation using the potential morphing method. [PERSON] and co-workers [262] described the synthesis of SnS films via the ultrasonic irradiation method. The experimental findings showed that the absorption edge had been moved towards the lower wavelength side (blue shift). The particle size and the direct band gap were found to be 4 nm and 1.74 eV (blue shifted when compared with the bulk band gap), respectively. The chemical deposition (spin-coating method) of SnSe (tin selenide) films exhibited size-dependent electronic band gap values [263]. The particle size (10.13 nm to 14.32 nm) and film thickness (1 \(\upmu\)m to 4 \(\upmu\)m) increased when the band gap was reduced (1.92 eV to 1.51 eV). It was noted that the obtained band gap was higher when compared with bulk value (1.1 eV) because of the quantum confinement effects.
[PERSON] and co-workers [264] highlighted that the wavelengths of light affect the performance of solar cells. The specification of the solar module was reported (maximum system voltage = 8V, short circuit current-238 Ma, open circuit voltage = 6.5V, dimensions = 180 mm \(\times\) 120 mm, weight = 3 kg, temperature co-efficient = 25 \({}^{\circ}\)C). Based on Table 7, red color produced the highest power-conversion efficiency when compared with the other colors.
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 2 Resilience in Smart Grids
### Resilience and QoS in ICT Networks  
ICT networks traditionally rely on QoS metrics to define SLAs [76]. These metrics consisting of delay, jitter, bandwidth, packet loss, bit error rate, and traffic load are performance measures that do not give a comprehensive view of network state. Therefore, other complementary metrics are adopted in SLAs in order to better quantify the system state, namely availability metrics.  
In the initial introduction of Quality of Resilience (QoR) in [77], QoS is divided into short-term quality parameters referred to by availability, and long-term quality parameters grouped under QoR. In other words, resilience is considered as an aspect of QoS, as latency or packet loss. However, QoR is presented in [78] as a concept-treating quality at different levels of the Open Systems Interconnection model (OSI-model), including the network level which corresponds to traditional QoS. Figure 1 shows how QoR extends QoS to include other types of quality: Quality of Experience (QoE), Quality of Delivery (QoD), and Quality of Protection (QoP). This is done by considering the additional metrics from each level. QoR is used as a transverse evaluation for all aforementioned qualities. This is done by considering the metrics that describe different resilience stages. From a high-level perspective, we can say that QoR is a shift from client-centric evaluation, conducted using QoS, toward a more general framework that includes the system potential in terms of resources, organizational processes, and humans.  
Once again, in both cases above, the need for resilience stems from harsh large-scale events imposing consideration of stress in the system and recovery strategies. Then, despite the slight divergence in terminology that is still the case today, the two concepts go beyond the traditional QoS evaluation, to capture both requirements of customers and enhancement strategies of operators. Nevertheless, the idea that resilience takes in QoS is gaining more attention [75], suggesting that a system cannot be resilient if it does not offer acceptable QoS, but providing acceptable QoS is not the only requirement for a network to be resilient.  
Figure 1: OSI-based classification of quality in relation with resilience [78].
## 3 Results
### Doping of HA Nanoparticles
#### 3.3.6 Boron-Doped Nano-Hydroxyapatite (B-Nano-HA)  
Recent advancements in enhancing the osteoinductive properties of hydroxyapatite (HA) involve the incorporation of boron (B), a trace element crucial for human health and bone metabolism [57]. Boron-doped nano-hydroxyapatite (B-nano-HA) has shown promising potential in promoting bone formation and regeneration. Studies reveal that boron enhances mesenchymal stem cell proliferation and osteogenic differentiation, as evidenced by increased alkaline phosphatase activity--a marker of osteoblast differentiation--compared to standard nano-HA or boric acid alone [58].  
[PERSON] et al. demonstrated that boron-infused nano-HA improved mesenchymal stem cell activity, with higher doses promoting cell proliferation, although effects on osteoblasts varied. Similarly, research by [PERSON] et al. showed that encapsulated B-nano-HA stimulated the proliferation and differentiation of MC3T3-E1 pre-osteoblastic cells in vitro. In their study, boron-doped HA was synthesized using microwave-assisted biomimetic precipitation and encapsulated within poly(butylene adipate-co-terephthalate) fibers at a 5% (\(w/w\)) ratio, significantly improving scaffold osteogenic activity [57].  
In vivo studies also highlight the potential of boron-infused nano-HA in bone tissue engineering. [PERSON] et al. evaluated the impact of injectable boron-containing nano-HA composites combined with hyaluron in osteoporoptic rabbit femurs. Although the increase in bone mineralization and new bone formation was not statistically significant compared to the nano-HA group, the findings suggest that boron composites may aid in preventing fractures in osteoporoptic femurs by enhancing bone mineral density over time [59].  
B-nano-HA offers a versatile material for scaffolds and injectable systems in orthopedic applications, combining osteogenic stimulation with tissue engineering advancements. While further research is required to optimize its efficacy, boron-doped composites show considerable potential for preventing fractures and supporting bone regeneration in patients with osteoporosis.
# Unraveling the Complexity of Plant Trichomes: Models, Mechanisms, and Bioengineering Strategies
## 3 Non-Glandular Trichomes: A Model System for Studying Cell Differentiation  
### Structure and Morphology Characteristics of NGTs  
Non-glandular trichomes have a relatively simple structure, with most being unicellular in nature. Arabidopsis has typical unicellular, non-glandular trichomes with two  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plant Species** & **Trichome Types** & **Trichome Morphology** & **Major Biological Function(s)** \\ \hline _Arabidopsis thaliana_ & NGT & unicellular branched & Physical barrier defense \\ _Gosspimur spp._ & NGT & unicellular unbranched & Cellulose storage (commercial fiber) \\ _Glycine max_ & NGT & unicellular unbranched & Enhanced photosynthetic efficiency \\ _Brassica junca_ & NGT & unicellular unbranched & Defense-related responses \\ _Cannabis sativa_ & GST & Multicellular: capitate, bulbous & Secretion of cannabinoids (THC/CBD) \\ _Solanum Lyopersicum_ & GST & Multicellular: digitate, pelitate & Secretion of acyl sugars, insect repellence \\ _Artemisia annua_ & GST & Multicellular: pelitate & Artemisinin secretion (antimalarial) \\ _Schizonepeta tenuifolia_ & GST & Multicellular: pelitate, capitate & Secretion of menthol-rich essential oils \\ _Lenicera japonica_ & GST & Multicellular: pelitate, capitate & (anthbacterial, insecticidal) \\ _Ocimum basilicum_ & GST & Multicellular: bulbous, capitate & Secretion of chlorogenic acid (anticoxidant, medicinal) \\ _Philiprea talidia_ & GST & Multicellular: pelitate & Secretion of etiepondi/linalool (antibacterial, spice) \\ _Cucumis sativus_ & GST & Multicellular: pelitate & Secretion of triterpenoids (stress responses) \\ _Thymus vulgaris_ & GST & Multicellular: conical & Secretion of cucurbications (anti-herbivory) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of trichome types, morphology, and biological functions in representative plant species.  
or three branches [20]. The development of non-glandular trichomes in Arabidopsis has been extensively studied, making it a key system for understanding epidermal cell fate determination. A trichome's development typically involves initiation followed by four rounds of endoreplication and branching. Natural resources such as cotton fiber, which is composed of unique unicellular trichomes that arise from the seed coat epidermis, are vital for the textile industry [21]. There are four distinct stages in their development: (1) initiation, (2) elongation (primary cell wall synthesis), (3) secondary cell wall synthesis, and (4) maturation.
## 4 Conclusions and Future Work  
In this paper, we proposed the sentiment-feature-enhanced deep neural network for text sentiment classification tasks by integrating sentiment linguistic knowledge into the deep neural network via a sentiment attention mechanism. We implemented the novel sentiment attention mechanism by combining a traditional sentiment lexicon with an attention mechanism to learn sentiment-feature-enhanced word representation, bridging the knowledge gap between conventional sentiment linguistic knowledge and the deep learning method. In addition, we also designed a deep neural network to effectively combine local features within the sentence captured by the convolutional neural network and the sequence information across a sentence generated by a bidirectional GRU  
Figure 5: Comparison of initialized attention and ultimate attention. The attention score calculated by Equation (6) is used for the color-coding. The color depth indicates the importance degree of a word to the sentiment polarity of a sentence.  
network, which can further improve the quality of text representation for achieving greater promotion of text sentiment classification tasks. Extensive experiments were conducted on real-world datasets with a binary-sentiment-label and a multi-sentiment-label. The experimental results showed that the proposed SDNN significantly outperformed the state-of-the-art methods for text sentiment classification tasks.  
In the future, we will try to use this model to analyze the actual emotions of specific users, such as sadness, happiness or depression, which can lay a good foundation for providing better specific users' experience in terms of the marketing service industry. Furthermore, it can be seen from experimental results that the proposed model still has much room for improvement on the dataset with fine-grained sentiment labels. Therefore, the reinforcement learning method to build structured text representation without explicit linguistic structure annotations will be tried in our experiment to see if it will achieve better performance.  
[PERSON]. ([PERSON]), [PERSON], and [PERSON] conceived and designed the study. [PERSON]. ([PERSON]), [PERSON], and [PERSON] ([PERSON]) performed the experiments. [PERSON] provided the data. [PERSON]. ([PERSON]) and [PERSON] analyzed the data. [PERSON]. ([PERSON]) and [PERSON] ([PERSON]) proofed the algorithm. [PERSON]. ([PERSON]) wrote the paper. All authors read and approved the paper.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61373148), the Science Foundation of the Ministry of Education of China (No. 14Y[C860042), and the Shandong Provincial Social Science Planning Project (No. 18 CXWJ01/18 BJY04/17 CHLJ33/17 CHLJ30/17 CHLJ18).  
The authors declare no conflict of interest.
## 2 The Smart Angel Project
### Smart Angel and Urban Mobility
#### 2.5.1 The Serious Games  
Having in mind the group of teenager users, Smart Angel has developed three Serious Games in a visually immersive virtual environment with the aim of supporting the development of some basic skills and abilities useful for urban mobility.  
The first serious game aims at exercising road crossing and awareness of street dangers [18]. The second presents the words \"left\", \"right\", and \"straight\", as well as the concepts linked to them, in order to be able to understand and follow simple road directions. The last game stimulates the development and consolidation of the spatial perspective taking skill. Two different versions of this game have been developed: one in a desktop based virtual reality [19] and the second one in a visually immersive virtual world [20].  
Since many people with intellectual disabilities have difficulties in generalizing the concepts they have learned in a specific environment to a different one, in the Smart Angel project we have decided to implement all the Serious Games in an immersive virtual environment, which looks like the real world. Virtual environments are supposed to be more effective than other approaches with respect to the acquisition of several abilities. This is because the brain recognizes the virtual world as real and this facilitates the transfer of the learned skills to the real world [21], that is the ability to apply acquired knowledge or skills in different situations or environments.  
All the games are developed to be played by the target users in the presence of a tutor. The tutors pay attention to limiting the play sessions to less than 10 minutes each, alternating them with small breaks in order not to tire the users too much and maximize learning performance. Nevertheless, all the games can be used independently by a single player. The first tests are now carried out at the day care centre, but it is foreseen that games will also be used at home with the help of caregivers or family members.
## 4 Experimental Results
### Bearing Fault Diagnosis Accuracy in Noise Environments  
We also evaluated the accuracy of our proposed bearing fault diagnosis method in noisy environments, which is common in industrial working conditions. In those cases, the vibration signals of compound faults are collected under shaft speed variation and a low level of signal-to-noise ratio (SNR). The vibration signal samples from three datasets above (datasets 5, 6, and 7) have three white noise levels added (SNR = 0, 5, and 10 dB). The SNR is defined as the ratio of the power of a signal (meaningful input) to the power of background noise:  
\[SNR_{dB}=10\log_{10}\left(\frac{P_{signal}}{P_{noise}}\right) \tag{6}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Computational Cost** \\ \hline STFT & 218,000 (MAC) \\ \hline VGG16 & 15.52 (GMAC) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computational costs for Short-Time Fourier Transform (STFT) and VGG16.  
Figure 9: Confusion matrices showing classification results of the proposed method for (a) dataset 5, (b) dataset 6, and (c) dataset 7.  
Table 6 provides the accuracy of our proposed bearing fault diagnosis method in noisy environments. It can be observed that noise degrades the accuracy of the proposed method, but it still provides high fault diagnosis accuracy. When the SNR values are positive, the average accuracy is higher than 98.6% when the crack size is 12 mm. The worst average accuracy is 85.3% when the crack size is 3 mm and SNR is 0.
## 3 Results
### Low Concentration Effect  
Results from this research indicate that the reliability of the pXRF analysis decreased at low concentrations for some elements, thus resulting in an overestimation, as in the case of As, Cu, and Fe. The results of the comparison considering \"lab-prepared\" samples from the two mining sites between confirmatory analyses (ICP-MS/DMA-80) and errors that are represented with a percent \"site-specific accuracy\" (pXRF analyte concentration/reference concentration \(\times\) 100) are presented in Figure 2.  
The field-based MDL was variable considering different elements. Regarding the pXRF analysis, acceptable results were obtained at concentrations of Fe > 10,000 mg/kg, As > 100 mg/kg, and Cu > 30 mg/kg, respectively. Below such MDLs, site-specific recoveries showed a non-linear increase, which started approximately from 120-130%, reaching almost 500% at very low concentrations. Method detection limits (MDLs) and overestimation observed in this research were consistent with previous studies reporting similar MDLs [11]. Especially for such elements, MDLs were found to be several orders of magnitude higher than the LOD indicated by the instrument documentation (LOD: Fe = 10 mg/kg, As = 1 mg/kg, Cu = 3 mg/kg, with a measurement time of 120 s/beam, against 20 s/beam of this study). The discrepancy is due to differences in counting time and to disparities in the developer's definition of MDLs [11]. The concentration effect for Cu and Fe was mostly found in the Pb-Zn samples most likely due to a lower geogenic abundance of these two elements with respect to the other investigated site. Indeed, the geological matrices of the Cu-Sb site are constituted by higher background levels of Cu and Fe, and subsequently, such elements can be better determined by pXRF. The main reasons for the higher background in the Cu-Sb site are (1) dominance of silicate minerals in the host rock, which are in general characterised by higher concentrations in various elements, including Fe, with respect to carbonates (dolomite, calcite), which are the major mineralogical constituent of wall rocks and gangue in the Pb-Zn site, and (2) the natural enrichment, as in the case of Cu, related to the high and disseminated occurrence of Cu-bearing tetrahedrite minerals and possibly to the presence of a halo, which is the product of diffusion of trace elements related to the ore deposit during
###### Abstract  
The Western Gulf of Corinth (WGoC) exhibits significant seismicity patterns, combining intense microseismic background activity with both seismic swarms and short-lived aftershock sequences. Herein, we present a catalogue of -9000 events, derived by manual analysis and double-difference relocation, for the seismicity of the WGoC during 2013-2014. The high spatial resolution of the hypocentral distribution permitted the delineation of the activated structures and their relation to major mapped faults on the surface. The spatiotemporal analysis of seismicity revealed a 32-km-long earthquake migration pattern, related to pore-pressure diffusion, triggering moderate mainshock-aftershock sequences, as fluids propagated eastwards in the course of \(\sim\)15 months. The anisotropic properties of the upper crust were examined through automatic shear-wave splitting (SWS) analysis, with over 2000 SWS measurements at local stations. An average fast shear-wave polarization direction of N98.8\({}^{\circ}\)E \(\pm\) 2.8\({}^{\circ}\) was determined, consistent with the direction of the maximum horizontal regional stress. Temporal variations of normalized time-delays between fast and slow shear-waves imply alterations in the level of stress or microcrack fluid saturation during the long-lasting pore-pressure diffusion episode, particularly before major events. The present study provides novel insights regarding seismicity patterns, active fault structures, anisotropic properties of the upper crust and triggering mechanisms of seismicity in the WGoC.  
earthquakes; relocation; seismicity migration; shear-wave splitting; upper crust anisotropy; fluids diffusion; seismic swarms; Western Gulf of Corinth; Greece +
Footnote †: journal: Applied Sciences
## 2 Basic Theories
### MVO-VMD-MPE-WTD Flowchart  
In summary, this paper proposes a combined denoising method based on the optimization of VMD using MVO, coupled with MPE and WTD, referred to as MVO-VMD-MPE-WTD. Compared to traditional single denoising methods such as VMD decomposition or WTD, the proposed combined method offers significant advantages. Traditional VMD decomposes the signal by setting decomposition parameters, which may not fully account for the optimization of multiple objectives. In contrast, the MVO algorithm optimizes the decomposition parameters of VMD to enable the extracted modes to more accurately represent the characteristic patterns of the original signal. Meanwhile, MPE can quantify the entropy values of each mode to assess signal complexity and randomness, to identify which modal components may contain more noise. This further improves the ability to distinguish between noise and signal characteristics.  
WTD, as an effective tool for processing non-stationary signals, when combined with VMD and MPE, allows for more refined multiscale signal processing in the time-frequency domain, thereby enhancing denoising performance. Specifically, the MVO-VMD-MPE-WTD method demonstrates stronger adaptability and noise reduction capability, effectively removing noise while retaining more useful signal characteristics. The flowchart of the combined denoising process is shown in Figure 1.  
Figure 1: Flowchart of MVO-VMD-MPE-WTD.
# Magnetooptics in Cylindrical Structures
## 1 Introduction
The nonreciprocal propagation is illustrated on waveguides with yttrium iron garnet (Y\({}_{3}\)Fe\({}_{3}\)O\({}_{12}\)) core and lower refractive index cladding formed by gallium substituted yttrium iron garnet (Y\({}_{3}\)Fe\({}_{5-x}\)Ga\({}_{x}\)O\({}_{12}\)) at the optical communication wavelength of 1.55 \(\upmu\)m (Section 4). The conclusions are briefly summarized in Section 5.
## 2 Materials and Methods
### Image Data Acquisition and Processing
#### 2.4.1 Processing of Landsat 8 OLI Collection 2 Level-1  
Landsat 8 OLI Collection 2 Level-1 images required processing, including radiometric calibration and atmospheric correction, implemented in ENVI 5.6, with the FLAASH atmospheric correction [19]. The output images were converted from WGS 84/UTM zone 50N (EPSG: 32650) coordinate system to WGS 84 (EPSG: 4326). QGIS 3.34.0 software was used to extract spectral values from the processed images for the sample points. Finally, the soil reflectance for Landsat 8 OLI Collection 2 Level-1 was calculated by multiplying the extracted spectral values by scale factor 0.0001 (Table 3).