esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Set  
This section describes the followed procedure to obtain the data set explored in this study, which is partially summarized in Section 2. The main objective of these combinations is to predict the phenological status of the olive. The original olive phenology data collection that motivated this study, which has already been explored in recent studies [17; 24], is the result of three years of monitoring (2008-2010) performed in Tuscan olive groves (Italy). _Frantoio_ cultivar olive trees, selected due to their closeness to regional agrometeorological stations, were observed during the distinct phases of the olive development course: from January to March with bi-weekly periodicity and every week in April. The biological status of each phenology phase was represented with the popular numeric BBCH scale (_Biologische Bundesanstalt_, _Bundesortenamt_, _und CHemische Industrie_[24; 25]). The explored phenology observations were synchronized with climatic and geographic history derived from the ERA5, ECMWF atmospheric reanalysis open access service, as detailed in the study [17]. The input features considered to predict the olive phenology are listed in Table 1, grouped in two sets: Original and Created features. The first ones are raw data extracted from indicated data sources within the Table 1, while created features accumulate the indicated quantities from the first day of January, until the day of observation, denoted by _Day of the Year (DOY)_. In order to create the efficiency feature combinations detailed in Table 2 the extra-tree regressor method [26] was selected based on its remarkable efficiency outcomes published in [17]. This method, characterized by its extremely randomized trees, was combined with 5-fold-cross validation splitting techniques to compute the results available in Table 2. Originally, all possible feature combinations were analyzed, but due to improvement objectives, this study collects only the most capable feature consolidations. Indeed, the combinations with an inferior RMSE of 0.65 were eliminated, to ensure better performance results than the ones obtained using standard feature elimination methods such as RFE and Hierarchical clustering [17]. In addition, the DOY is a mandatory feature in all combinations, as it determines the forecasting day and the limit of created features Table 1. Thus, it has been eliminated to focus on the relations between the optional features. The error outcomes of Table 2 demonstrate the similar efficiency of those combinations. Unfortunately, this extensive registry does not provide an insightful and definitive feature proposition despite the high accuracy results. This is why this study aims to propose a methodology to extract a representative feature grouping from Table 2.
## 4 Mathematical Validation  
While previous experiments have sufficiently demonstrated the feasibility of the method for detecting damage in steel box beams and orthotropic plates, its practical utility and robustness still face significant challenges. Therefore, this study established numerical models for steel box beams and orthotropic plates, and analyzed the vibration acceleration responses of steel box beams under sinusoidal excitations at different frequencies. Initially, the sinusoidal excitation at 10 Hz is used to validate the experimental observations and then further tested at the first two natural frequencies, \(f_{1}\) and \(f_{2}\), to demonstrate the effectiveness of the WYPE method in damage identification. Additionally, the robustness of the method is also tested under various white noise interference conditions, thereby verifying its feasibility in real-world applications.
# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 4 Discussion
### Future Research  
In this study, the precipitation measured by rain gauges were used to evaluate the performances of the original TMPA and IMERG data, as well as the downscaled results based on satellite-based precipitation products. However, due to the harsh environment and complex terrain, the rain gauges were sparsely and unevenly distributed over the TP. A majority of the gauges were located in north and east and at middle or low altitudes, while few gauges in the western part of the TP and at high elevations [51]. Thus, the assessment may not be representative and reasonable in the ungauged areas in western part of the TP and regions with high elevations. Though radar precipitation data is also widely used as 'ground truth' values to evaluate the quality of satellite-based precipitation data, radar networks are unsound over the TP. So far, there are only four working radar stations, shown in Figure 14. Additionally, other shortages, such as beam blockage and range degradation, cause radar precipitation merely useless over the TP. Therefore, more reliable ground reference data are highly needed for the TP.  
Above all, this study evaluated the performances of TMPA and IMERG, and compared the downscaled results based on TMPA and IMERG, respectively, at annual and monthly temporal scales. However, the performance of satellite-based precipitation products may vary under extreme rainfall conditions, occurring at meteorological scale (e.g., hourly scale) [52]. It is very important to obtain the precipitation estimates with both finer spatial resolutions and accuracies to capture the extreme rainfall events, which is meaningful in flood disaster prevention. Therefore, additional downscaling techniques should be developed especially for higher temporal scales (hourly or half-hourly), to improve our understanding of precipitation activities during extreme rainfall events.
# Time Management in Wireless Sensor Networks for Industrial Process Control
## 2 Materials and Methods
### Hardware  
We have selected several NUCLEO boards manufactured by STMicroelectronics (Geneva, Switzerland) built around the STM32L4 family of microcontrollers [13], and the ATZB-212B-XPRO extension board from Microchip (Chandler, United States), built around the AT86 RF212B transceiver, to implement and validate time management in the wireless network. These components are well known for the abundance of features they provide alongside their ease of operation and low power consumption. The wireless node acting as the master clock or the time source is comprised of a pair of development boards: the NUCLEO-L432 KC coupled to the ATZB-212B-XPRO, which runs the implementation of the physical and data link layers, and the NUCLEO-L476 RG, which runs the application layer as defined by IEEE 802.15.4. The two wireless nodes, which will be part of the control loop, are comprised of the NUCLEO-L152 RE development board coupled to an ATZB-212B-XPRO extension to maintain consistency. In terms of time keeping, the NUCLEO-L432 KC features a 32,768 Hz crystal from NDK (Saitama, Japan), part no. NX3215 SA-32.768K-STD-MUA-14, with a 20-ppm frequency tolerance, and the NUCLEO-L152 RE features a 32,768 Hz crystal from Abracon (Austin, TX, USA), part no. ABS25-32.768 KHz-6-T, with the same 20-ppm frequency tolerance. Each development board is connected to the PC using USB cables that provide power to the board, and also a virtual serial (COM) port to log messages placed throughout the code, verify that it performs as expected, and signal the user when the application reaches a certain state.  
A Logic 8 logic analyzer from Saleae (San Francisco, United States) [14] was used to monitor microcontroller pins used for debugging. We have used two pins on each board to generate a pulse when the group of timeslots starts and when a single timeslot starts. We could, therefore, measure the time drift between two wireless nodes.
# Automatic Medical Image Segmentation with Vision Transformer
## 3 Method
### Infection Segmentation Network
#### 3.2.3 Parallel Partial Decoder  
Many established medical segmentation frameworks utilize both high-level and low-level representations extracted by the encoder to segment organs/lesions [2; 19; 49; 50; 51]. However, as pointed out in [47], compared to high-level feature representations, low-level representations require substantially increased computational resources due to their larger spatial resolutions, while contributing minimally to performance improvements.  
We introduced a parallel partial decoder (PPD), which is beneficial for medical image segmentation by allowing efficient multi-scale feature utilization, information fusion, and balancing precision with efficiency while being robust to scale variations. The parallel branches in the PPD allow for the concurrent decoding of features from multiple scales of the encoder. This incorporates both fine local details from early layers and high-level global context from later layers. The fusion of multi-scale information enables a more comprehensive understanding of image structures, improving segmentation accuracy. The decoder can selectively aggregate multi-scale features to balance precision and efficiency. To prioritize precision for small structures, fine-grained features are emphasized. For scenarios where efficiency is prioritized over some precision sacrifice, coarse-scale features are utilized instead. This configurability allows catering the trade-off to diverse segmentation scenarios with varying precision needs and resource constraints. In essence, the PPD facilitates the flexible utilization of multi-scale encoder representations. By fusing features from layers of varying granularity, it balances segmentation precision and efficiency in an application-specific manner. The configurability enables optimized trade-offs for different precision requirements and computational budgets.  
This motivated a more efficient utilization of multi-scale encodings in our framework. Rather than naively fusing layers, we strategically applied low-level features to refine boundaries via our edge attention module. The high-level features focus solely on generating an initial coarse prediction to localize regions of interest, as shown in Figure 2.  
Our PPD aggregation and cascaded reverse attention streams then allow low-level cues to be exploited in a targeted manner. By deliberating directing model capacity based on scale utility, we balance precision and efficiency. Experiments validated that our structured approach prevents wasteful redundancy while improving contour accuracy. These optimizations help address deployment constraints concerning inference speed, memory footprint, and power consumption.  
Specifically, given an input medical image, we extracted two low-level features \(\{f_{j},i=1,2\}\) and three high-level features \(\{f_{j},i=3,4,5\}\) using Res2 Net convolutional blocks [52]. We then aggregated the high-level feature maps through the PPD \(p_{d}(\cdot)\)[53] to produce a coarse global localization map \(S_{g}=p_{d}\{f_{3},f_{4},f_{5}\}\).
# An Adaptive Back-Off Mechanism for Wireless Sensor Networks
## 2 Review of Related Work
### Critical Technologies of S-MAC Protocol
#### 2.1.3 Message Passing  
Related research has revealed that a bigger packet size brings with a smaller transmission success rate. For this reason, S-MAC fragments big packets into small fragments and transmits them in a burst. Every time a receiver receives a data fragment, it should send an ACK to the transmitter. If the transmitter fails to receive the ACK, it will extend the reserved transmission time to re-transmit the corresponding fragment immediately.
# Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset
## 2 Material and Methods
### Datasets
#### 2.1.3 University Hospitals of Geneva--Interstitial Lung Disease  
Motivated by the low availability of public collections of Interstitial Lung Disease (ILD) cases, the University Hospitals of Geneva Interstitial Lung Disease (HUG-ILD) database [21] was created to provide a public platform of interstitial lung disease (ILD)cases for the development and evaluation of CAD systems. This collection comprises the 13 most common histological diagnosis of ILD, including conditions such as ground-glass, emphysema, fibrosis, consolidations, reticulation, and micronodules. The HUG-ILD database provides 112 CT scans with their respective binary lung segmentation masks. The number of slices along the \(z\)-axis per scan varies between 14 and 60 and the spacing between slices is within the range of 10-15 mm. Each slice is a matrix with 512 \(\times\) 512 dimension. The slice spacing is of \(0.70\pm 0.10\) mm and the slice thickness is of \(1.00\pm 0.00\) mm.
# Nondestructive Monitoring Techniques for Crack Detection and Localization in RC Elements
## 8 FOS Strain Measurements
### AE vs. FOS Measurements  
Figure 12a,b compares the strain history (depicted by a blue line) measured close to the bottom surface by FOS #2, respectively at the horizontal locations of \(-1600\) and \(-1200\) mm, with the accumulated AE activity (red line) recorded by the nearest AE sensor, respectively AE4 and AE5.  
Figure 11: FOS strain measurements at different loading stages along the beam length: FOS #1 (**up**), FOS #2 (**down**).  
Figure 12: Cumulative AE activity (red) and FOS strain (blue) time histories for: (**a**) fiber optic n.9 at x = \(-1.6\) m and AE sensor n.4; (**b**) fiber optic n.9 at x = \(-1.2\) m and AE sensor n.5. The dashed lines identify the different loading stages.  
Figure 13a,b compares the strain history measured close to the top surface by FOS #1, at x = \(-\)1600 and \(-\)1200 mm, with the accumulated AE activity recorded by the nearest AE sensors, namely AE0 and AE1. Both Figures 12 and 13 illustrate noticeable similarity between AE and FOS graphs.
# A Review of Deep Learning Based Speech Synthesis
## 3 Statistical Parametric Speech Synthesis
### Vocoder-Based Speech Synthesis  
Speech synthesizer or vocoder is an important component of statistical parametric speech synthesis, which aims at synthesizing speech waveform based on the estimated acoustic feature parameters. Traditional methods usually use the HTS_engine [33] synthesizer since it is free and fast to synthesize speech. However, the synthesized speech usually sounds dull, thus making the quality not good. To improve the quality of the synthesized speech, STRAIGHT [34; 35] is proposed and used in various studies, making it easy to manipulate speech. Other methods such as phase vocoder [36], PSOLA [18] and sinusoidal model [37] are also proposed. Legacy-STRAIGHT [38] and TANDEM-STRAIGHT [38] were developed as algorithms to meet the requirements for high-quality speech synthesis. Although these methods can synthesize speech with good quality, the speed still cannot meet the real-world application scenarios. To address this problem, real-time methods remain a popular research topic. For example, the authors in [34] proposed the real-time STRAIGHT as a way to meet the demand for real-time processing. The authors in [38] proposed a high-quality speech synthesis system which used WORLD [39] to meet the requirements of not only high sound quality but also real-time processing.
## 6 Metal Atom Diffusion  
Metals have been previously used in Si and Ge as they can lead to the increase of the rate of crystallization leading to large grain materials (via the MILC process). Typical crystallization inducers are copper, nickel, and palladium with the Ge growth taking place on a thin film of the metal that initiates crystallization.  
In a recent study, [PERSON] et al. [64] employed hybrid density functional theory calculations to investigate the diffusion of a number of metals (Ag, Pt, Li, Pd, Au, and Cu) in Ge. In that investigation, [PERSON] et al. [64] considered all the possible diffusion mechanisms (for example, the dissociative or Frank-Turnbull mechanism, the kick-out mechanism, and the ring mechanism); however, the direct interstitial process, Pd\({}_{\rm i}\leftrightarrow\) Pd\({}_{\rm i}\), (refer to Figure 5) was the lowest energy way for a Pd atom to diffuse in the Ge lattice. The migration energy for this process is only 0.03 eV, which is one of the lowest energies ever calculated for mass transport in a crystalline material [64]. In this order of magnitude are the migration energies of helium in tungsten and the self-diffusion of silver in \(\alpha\)-AgI [64].  
The question is what is driving an oversized metallic atom such as palladium to diffuse through the crystalline germanium lattice so fast? In fact, the migration energy is far smaller compared to the \(n\)-type dopants which are considered to be relatively fast diffusing atoms in a semiconductor lattice [2; 36]. To facilitate comparison [PERSON] et al. [64] considered the diffusion with the direct interstitial process of another five metals (Li, Cu, Ag, Pt, and Au) in Ge (refer to Figure 6). These metals have low migration energies (0.14-0.54 eV) but still comparatively lower than that of Pd [64]. Considering that it is not an atomic size effect and in this case the focus was on doping. In essence, diffusion may be visualized as a sequence of bond breaking and forming steps. Typically, in this picture at the saddle point (highest energy step), the migrating atom has broken all (or most) of its bonds with its neighboring host atoms. What is distinctly different in the metals considered by [PERSON] et al. [64], is that they still have bonding  
Figure 4: Diffusion of the PV in the presence of (**a**) Sn and (**b**) Hf significantly increases the migration energy barrier. Reproduced by permission from the PCCP Owner Societies [54].  
Figure 5: The calculated, by [PERSON] et al. [64], minimum migration energy mechanism is the direct interstitial process Pd\({}_{\rm i}\leftrightarrow\) Pd\({}_{\rm i}\). Copyright 2015 Royal Society of Chemistry [64].  
states at the saddle point and in essence throughout the diffusion process. For Pd, these bonding states are more pronounced as compared to the other metal atoms, and this could be the key for its extremely low migration energy of diffusion [64]. At any rate these concepts should be investigated further as their applicability in energy materials where diffusion processes are important (for example solid-oxide fuel cells and batteries) could lead to significant improvements in device performance.
## 1 Introduction
SERS has become a powerful spectral analytical method since Raman signals can be enhanced up to \(10^{14}\)-\(10^{15}\) times when light, molecules, and metal nanoparticles interact, and even enable single cell detection [37]. SERS was used to study the structure of RNA bases adsorbed on the surface of silver nanowires [38]. Researchers also investigated the structural changes of different proteins based on the nanostructure SERS substrate [39]. Various SERS-active nanomaterials were synthesized for nucleic acids sensing applications [40]. Therefore, SERS is very promising in revealing the molecular mechanism involved in drug-induced cancer cell apoptosis. In this study, we demonstrated the effectiveness of cordycepin in inducing apoptosis in HeLa cells and investigated its apoptosis-inducing mechanism through SERS. Experimental groups treated with cordycepin for 12 h and 48 h were used to study morphological and biomechanical changes in the early apoptosis stage and late apoptosis stage respectively. Our results showed that cordycepin potentially induced HeLa cell apoptosis through restraining the translation of RNA encoding ribosomal proteins. Furthermore, control cells, early-stage apoptotic cells, and late-stage apoptotic cells were easily differentiated through the SERS-based principal component analysis method. To our knowledge, there has been no report on the effect of cordycepin-induced HeLa cell apoptosis from the point of view of spectroscopy.
## 3 Results and Discussion
### Optimization Process
However, high values of adjusted R\({}^{2}\) = 0.9328-0.9938 indicated a close agreement between experimental and predicted results. On the contrary, the values of R\({}^{2}\) = 0.7751 and adjusted R\({}^{2}\) = 0.5503 for the response of biscuits' texture suggest that a high proportion of variability cannot be explained by the model, because R\({}^{2}\) should be at least 0.80 for a good fit. All linear and quadratic parameters of the empirical models were highly significant (F = 19.73-4554.65, \(p\) = 0.00022-0.047) for the DPPH results of methanolic extracts obtained by conventional extraction and overall acceptability of the biscuits enriched with RPC, while the interaction between amounts of RPC and SAFA in functional biscuits produced a significant negative effect only on their odor (F = 66.85, \(p\) = 0.015). However, the quadratic parameter of the SAFA content and linear term of RPC of the models significantly influenced (F values ranged between 19.10 and 3221.88, \(p\) < 0.05) on flavor and purchase intent of the prepared biscuits. In addition, only two parameters (RPC and RPC\({}^{2}\)) had significant effects on color of the novel biscuits (F = 19.64-846.11, \(p\) = 0.047-0.0012). Both linear (RPC, SAFA) and quadratic (SAFA\({}^{2}\)) parameters of the models were highly significant (F = 27.44-1332.02 and \(p\) = 0.035-0.00075) for DPPH of methanolic extracts after UAE, whereas linear term of SAFA and interaction between independent variables (RPC \(\times\) SAFA) caused insignificant effects (F = 0.00026-10.98, \(p\) = 0.080-0.99) on the texture of studied samples. It is noteworthy that the variable with the largest positive effect on the antioxidant potential of the baked biscuits determined by DPPH assay was the linear term of RPC content (F = 1332.02-4554.65, \(p\) < 0.001). Nevertheless, this independent contributed variable the most negatively to all sensory characteristics of the fortified biscuits (F = 76.73-5902.97, \(p\) < 0.05).