esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applying the Methodology to the Example of Dabar Power Tunnel
### Quantitative Analysis
#### 3.3.2 Statistical Analysis
This leads to the conclusion that the majority of overbreaks must have been a consequence of unfavourable geological conditions. It can also be concluded from Figure 11b that geological overbreaks are regularly recorded for high RMR values (RMR > 60), for which they fall in the range of 0-2.5 m\({}^{2}\). 4. From the histogram shown in Figure 12a, it can be concluded that the frequency of occurrence of overbreaks in the range of 0 to 5.0 m\({}^{2}\) accounts for the largest proportion, and it constitutes almost 90% of the analysed data (among which, 0-2.5 m\({}^{2}\) constitutes 56% of the total data). In the case of registered geological overbreaks (Figure 12b), overbreaks of 0-5.0 m\({}^{2}\) make up 52% of the measured data. Figure 12: Histograms showing frequency of occurrence of overbreaks for (**a**) entire analysed length of tunnel and (**b**) stations in which geological overbreaks were identified by mapping. The interpretation of the statistical analysis clearly indicates that any overbreak larger than 2.5 m\({}^{2}\) can be treated as geological and occurs in the condition in which the benchmark RMR value is below 60. Consequently, overbreaks smaller than 2.5 m\({}^{2}\) must be analysed in more detail in order to distinguish between technological and geological overbreaks.
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 3 Design of _uw_-Cellular Network
### Protocol Design
#### 3.3.1 Physical Layer Protocol Design  
Multicarrier modulation in the form of orthogonal frequency division multiplexing (OFDM) has received a great deal of attention due to its promise for high data rate underwater acoustic communications. In this paper, we implement OFDM modulation and demodulation for underwater acoustic communication in the physical layer.  
The data frame of the OFDM modem we implemented consists of a trigger, a Preamble and several Data Blocks. The trigger is a Hyperbolic Frequency Modulated (HFM) signal, which is widely used with hydroacoustic signal detection. The Preamble is used for packet detection and synchronization. The data payload contains up to sixteen ZP-OFDM Data Blocks. The data frame structure is designed as shown in the Figure 3.
# Probability Density Estimation through Nonparametric Adaptive Partitioning and Stitching
## 3 Results and Discussions
### PDF Visual Comparison between NAPS and NMEM
Consequently, NAPS can effectively estimate deep into the tails of a PDF, albeit with some error. The error in the tails arises because no attempt is made to model the asymptotic behavior of the heavy tails beyond the last empirical observation. As a result, normalization cannot be precisely defined without modeling the heavy tails asymptotically using an extrapolation process. Although it is possible to obtain good estimates of power law tails within the framework of NAPS using a semi-parametric version of NMEM on half-open blocks, such modeling goes beyond the scope of this work. Our semi-parametric modeling of heavy tails in an automated and adaptive way will be published elsewhere. Nevertheless, NAPS yields estimates with an accuracy commensurate with the statistical resolution set by the total number of samples, \(N\), and offers a substantial advantage over NMEM.
# A Review of the Dynamics Progress of Bubble Collapse within Droplet and Droplet Splash
## 5 Splash Velocity
### Splash Mechanism
#### 5.4.2 Taylor Instability Mechanism  
Another explanation for droplet splash is the Taylor instability mechanism. Figure 17 shows a schematic of the Taylor instability mechanism of bubble collapse within droplets [93]. Firstly, the surface of a droplet is disturbed by the shock wave of bubble collapse. When the bubble oscillates, the surface of a droplet is accelerated, and the disturbance amplitude of the surface is increased. When the bubble wall and droplet splash are in contact with the disturbed surface, interfacial instability occurs [94]. Among them, \(n\) is the number of wave peaks on the droplet surface, \(R_{d}\) is the droplet radius, \(\eta\) is the disturbance amplitude of the surface, \(P_{d}\) is the pressure on the droplet surface, \(R_{b}\) is the bubble radius, and \(P_{R}\) is the pressure at the bubble boundary.  
Figure 16: Theoretical schematic of interface depression singularity [92].  
Figure 17: Schematic of the Taylor instability mechanism of bubble collapse within droplet [93].
## 5 Discussion  
The permeability values obtained from each sub-volume are presented in Figures 11 and 12. These figures illustrate the standard deviation of permeability derived from the LBM simulation method when compared to the laboratory-measured values.  
It can be observed from Figures 11 and 12 that the permeability values of the sub-volumes are relatively close to those of the laboratory values. This suggests that on average, the sub-volume measurements are in good agreement with the laboratory measurements, and the same can be observed in Table 6. The variations in sub-volume permeability indicate the natural variability in the rock samples, primarily attributed to the variation in pore size and distribution. These sub-volume measurements, while exhibiting some variability, are still within a reasonable range of the laboratory values.  
Porosity vs permeability plots are shown in Figure 13 for both the samples, showing variations for each sub-volume at different resolutions.  
Figure 11: Permeability values for each sub-volume of sample S3 at 22 \(\upmu\)m (**left**) and 8 \(\upmu\)m (**right**) resolutions. The error bars represent the deviation of permeability for each sub-volume from the laboratory-derived values.  
Figure 12: Permeability values for each sub-volume of sample V4 at 22 \(\upmu\)m (**left**) and 8 \(\upmu\)m (**right**) resolutions. The error bars represent the deviation of permeability for each sub-volume from the laboratory-derived values.  
An observation of Figure 13 reveals that the high-resolution data yield more precise and consistent permeability measurements. This phenomenon can be attributed to the fact that in LBM simulations, the voxel size determines the computational mesh. A higher resolution results in a finer mesh, thus providing a better resolution of the boundaries between different phases within the porous medium. Consequently, it accurately captures small-scale features and irregularities.  
At both resolutions, the porosity and permeability values closely align with the laboratory values. This consistency across sub-volumes implies that the rock sample exhibits relative uniformity in terms of its porosity and permeability characteristics.  
The results highlight that the distribution of the petrophysical properties of the Lithuanian saline aquifers is uniform, reducing the uncertainties associated with the storage capacity estimation. An analysis of parameters at the pore scale helps to understand the processes occurring in the porous media [42; 43; 44]. This also facilitates uniform CO\({}_{2}\) distribution and storage throughout the reservoir, maximizing storage efficiency. So, formations with well-sealed caprock layers can enhance the long-term security of CO\({}_{2}\) storage by minimizing the risk of leakage or migration through preferential pathways.
## 3 Results and Discussion
### Volatile Profile and Composition

Table 1: Content of volatile compounds (ug L\({}^{-1}\)), retention index and odor descriptors in the studied wines as affected by the type of vessel used for the aging step (\(\&\)1-aged in clay-_tinja_) with non-permeable inner coating \(B\): aged in day-_tinja_ without inner coating C: aged in oak barrels). The 4 most abundant volatile compounds found in each wine and their sensory descriptors were: _(i) wine A_: ethyl decanoate, ethyl octanoate, octanoic acid and phenethyl alcohol; _(ii) wine B_: ethyl octanoate, ethyl decanoate, phenethyl alcohol and diethyl butane-dioate; and _(iii) wine C_: ethyl octanoate, ethyl decanoate, phenethyl alcohol and diethyl butanedioate. Thus, the predominant volatile compounds in the studied wines were esters (ethyl decanoate and ethyl octanoate) and alcohols (phenethyl alcohol). These compounds have been previously reported as playing a key role in _Monastrell, Temparnillo, Cabernet Sauvignon_ and _Alicante Bouschet_ wines [19; 20; 21]. Vitispirane was found at low concentration but deserves attention because this noriso-prenoid could be formed from the degradation of carotenoid molecules during wine aging [15]. In this way, _wine A_ had no measurable content of this compound; the other two wines had low but measurable contents and the compound could be, together with others, responsible for the wine aroma uniqueness due to its low detection threshold, 101 ug L\({}^{-1}\)[22]. Further studies are needed with different aging times to establish the role of this compound in the aging process. The total content of volatile compounds increased from 4.97 mg L\({}^{-1}\) in _wine A_ (aged in clay-_tinaja_ with non-permeable inner coating allowing no contact between the wine and the clay and avoiding a significant micro-oxygenation of the wine) and 6.26 mg L\({}^{-1}\) in _wine C_ (aged in oak barrels). A positive relationship between the total content of volatile compounds and the complexity of the wine is expected and will be assayed through the study of the wine descriptive sensory profile.
# On the Association between Dental Implants, Osteoporosis and Bone Modulating Therapy
## 1 Introduction  
The main current treatment alternative for partial/complete edentulism is implant-supported prostheses (ISP) [1]. Low bone volume is still a major issue challenging the option of implant dentistry in a substantial number of individuals [2, 3]. In cases with compromised bone volume, the success rates are lower [4]. In such circumstances, guided bone regeneration, (GBR) using barrier membranes, is often used to increase bone volume [5, 6, 7]. Various studies suggested the application of stem cells both for therapeutic and preventive purposes in order to lower the percentage of failed osseointegrated implants. Stem cells also enhance GBR [8, 9, 10, 11, 12, 13, 14, 15]. Still, even after the introduction of these methods, between 3 and10% of implants fail ([PERSON] et al., 2005, [PERSON] et al., 2007) [16; 17]. Systemic factors, such as osteoporosis, may lead to early implant failures (EIF up to 12 months post-loading) [17].  
Osteoporosis is a disease affecting the bone causing fragility fractures. The worldwide prevalence of osteoporosis is high affecting 21.2% of women older than 50 years and 6.3% of men [18; 19]. Risk factors include postmenopausal state in women, age, smoking, drugs (mainly glucocorticoids) and a variety of systemic diseases.  
Treatment of osteoporosis is long term and may last a lifetime. Treatment aims to reduce the risk of fragility fractures. Various treatment alternatives exist, including fall risk reduction and supplementary vitamin D and calcium, but the main and effective treatment are drugs developed for osteoporosisis. Antiresorptive drugs affect the osteoclast cells in various mechanisms leading to fracture rate reduction are among the most popular osteoporosis treatments. Two main classes are used: bisphosphonates--analogues of pyrophosphate characterized by a P-C-P bond and denosumab--antibody against receptor activator of nuclear factor ligand. Besides osteoporosis, these drugs are also used in other bone diseases, such as Paget and malignant lesion of the bone [20; 21; 22; 23].  
Osteonecrosis of the jaw (ONJ) related to osteopororotic treatment has been reported in post-marketing observation of bisphosphonates [5] and in the clinical trials of RANK ligand antagonists and also romosozumab. There have been no reports on osteonecrosis of the jaw related to treatments with SERM or teriparatide.  
These drugs are among the bone modulating therapies (BMT) encountering the risk of osteonecrosis of the jaws (ONJ). An ONJ diagnosis consists of (1) exposed necrotic bone; (2) history > 8 weeks; and (3) no history of radiation therapy or metastatic disease to the jaw [18; 19].  
The aim of the present study was to assess the incidence of EIF and ONJ in osteopororotic vs. non-osteoporotic patients undergoing implant-supported oral rehabilitation. The secondary aim was to assess differences between osteopororotic patients using vs. not using BMT.
## 3 Results and Discussion
### Impact of Different Attention Mechanisms  
To validate the superiority of the proposed PHAM method, the GradCAM method [26] was utilized to generate visual heat maps, and various attention mechanisms were compared for their effectiveness in focusing on strawberry fruit target areas on the SBDS dataset. Figure 9(a) shows the original strawberry image. Figure 9(b) presents the results of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Models** & **Precision** & **Recall** & **mAP\({}_{0.5}\)** & **mAP\({}_{0.50.95}\)** \\ \hline \multirow{3}{*}{SBDS} & Baseline & 90.1 & 85.2 & 91.0 & 75.1 \\  & Improved & 92.1 & 86.5 & 92.7 & 76.3 \\  & Baseline & 91.9 & 87.9 & 93.7 & 79.1 \\  & Improved & 93.6 & 89.2 & 94.7 & 80.0 \\  & Baseline & 86.5 & 79.9 & 85.9 & 70.2 \\  & Improved & 88.4 & 81.4 & 87.6 & 71.4 \\  & Baseline & 80.8 & 77.0 & 82.0 & 60.9 \\  & Improved & 83.3 & 78.4 & 83.5 & 62.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The precision, recall, mAP\({}_{0.5}\), and mAP\({}_{0.50.95}\) obtained from the baseline model and the proposed model across the SBDS, Straw_Dl_Db1, Straw_Det, and Straw_Cul datasets.  
original model without any attention mechanism. Figure 10c and d display the heat maps by applying the squeeze and excitation (SE) [27] and CBAM [28] attention mechanisms, respectively. Figure 10e, f, and g indicate the features processed with the coordinate attention (CA) [29], ELA, and Transformer attention mechanisms, respectively, while Figure 10h illustrates the results generated using the PHAM.  
It can be observed that the YOLOv8n model demonstrates a weak focus on strawberries when no attention mechanism is embedded. Although the SE and CBAM mechanisms improved the attention to strawberries to some extent, the enhancement remained limited. In contrast, the CA, ELA, and Transformer attention mechanisms exhibited superior performances in enhancing the focus on strawberries, though the CA attention mechanism still fell short in focusing on smaller strawberries. The proposed PHAM method not only improves the heat map coverage of strawberry locations but also enhanced the focus on smaller strawberries, achieving more precise positioning. These results confirmed that the PHAM effectively integrated contextual information from the surrounding environment, focusing on more critical strawberry fruit pixel areas, thus significantly boosting the detection performance.
## 3 Results
### Diurnal Variations in Canopy Spectral Observations
#### 3.3.2 Diurnal Variations in Remote Sensing Observations Related to Vegetation Physiology
Although the sum of \(\Phi_{\rm D}\) and \(\Phi_{\rm F}\) remained relatively stable throughout the day, under severe water stress, the sum of \(\Phi_{\rm D}\) and \(\Phi_{\rm F}\) was notably smaller (DOYs 222 and 225). With respect to leaf physiology, vegetation water stress primarily affects N and P under high-PAR conditions at midday. To analyze the differences in the diurnal variation characteristics of leaf-scale vegetation energy allocation in the morning and midday under different drought intensities, we grouped all the observation data from the two plots according to mild and severe drought conditions. We calculated daily mean values for the morning (8:00-9:00) and midday (12:00-13:00) periods. Figure 11 clearly shows the differences in energy allocation within the optical system of sunlit leaves under different water stress levels during the morning and noon periods. In the morning, the energy distribution pattern showed minimal differences across the different water stress levels, typically characterized by a lower \(\Phi_{\rm N}\) value of approximately 0.2 and a higher \(\Phi_{\rm P}\) value of approximately 0.5. Notably, compared with those under Mild Drought, the values under Severe Drought indicated a significant increase in \(\Phi_{\rm N}\) and a significant decrease in \(\Phi_{\rm F}\) at noon. Moreover, \(\Phi_{\rm N}\) increased from approximately 0.5 under Mild Drought to approximately 0.7 under Severe Drought. Under Severe Drought, \(\Phi_{\rm F}\) also decreased in the morning. Overall, due to the higher light intensity at midday, \(\Phi_{\rm N}\) was greater than the morning value, whereas \(\Phi_{\rm F}\) was lower. Water stress further increased \(\Phi_{\rm N}\) and decreased \(\Phi_{\rm N}\) at midday. Therefore, water stress primarily affects vegetation energy allocation at midday. Figure 10: Quantum efficiencies of illuminated leaves in the irr1 plot for four distinct energy pathways, alongside diurnal variations in the PAR value and corresponding changes in the relative soil moisture (RSM) with increasing water stress.
# Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor
## 1 Introduction
### Contributions  
In this paper, we propose a novel tree-based regression model for hybrid VLC-RF channel estimation in GFDM-based wireless sensor networks. Our key contributions are summarized as follows:  
1. We propose a realistic hybrid communication model combining AWGN-affected VLC and Rayleigh-faded RF channels integrated with GFDM to simulate practical indoor wireless environments.
2. We introduce a decision tree-based regressor to estimate the nonlinear and composite characteristics of hybrid channels by feeding directly received signal vectors, optimizing the model for interpretability and low computational demand.  
Figure 1: Outline of the entire manuscript.  
We constructed a large-scale dataset consisting of 18,000 signal samples across 36 distinct SNR levels, ranging from \(-5\) dB to 30 dB, to train and test our model under diverse noise conditions.
* We benchmarked the proposed model against traditional estimators (MMSE and LMMSE) and modern ML approaches (random forest, support vector machines, and linear regression) using performance metrics, including the Bit Error Rate (BER), accuracy, precision, recall, F1-score, and inference time.
* We validated the model for real-time deployment by demonstrating that the proposed tree model achieved an accuracy of 90.83% with a BER of 0.0917 at 10 dB, as well as 97.63% with a BER of 0.0237 at 30 dB, while maintaining a practical inference time of approximately 45.53 s for the entire test dataset. In comparison, the random forest model achieved 91.01% accuracy with a BER of 0.0899 at 10 dB, as well as 97.88% accuracy with a BER of 0.0212 at 30 dB, requiring 140.09 s (three times longer than the proposed model) to complete the same task. Similarly, the SVM model achieved 91.34% accuracy with a BER of 0.0866 at 10 dB, as well as 96.77% accuracy with a BER of 0.0323 at 30 dB, but it took 189.35 s (over four times the inference time of the proposed model). This shows that our proposed tree model offers a competitive trade-off between accuracy and inference time, making it a better choice for time-sensitive and low-power WSN applications.
# Intermitt FOCV Using an I-V Curve Tracer for Minimizing Energy Loss
## 1 Introduction  
Batteryless Internet-of-Things (IoT) devices have been widely used for various wireless sensors by converting light [1], vibration [2; 3], and thermal gradient [4] into electric energy. Among these various sources, a photovoltaic (PV) cell that harvests electric energy from light is the most anticipated energy source [1; 5]. Since the performance and I-V characteristics of the PV cell vary depending on external environmental factors such as irradiance, temperature, and load impedance, maximum power point tracking (MPPT) methods are necessary for maximizing the power output of PV cells under various conditions [6]. Figure 1a shows an electrical model of a PV cell. Sweeping the voltage of the PV cell, \(V_{PV}\), the current, \(I_{PV}\), and the power, \(P_{PV}\) can be obtained as Figure 1b [7]. The MPP can be determined at the voltage \(V_{MPP}=\kappa V_{OC}\), where \(\alpha\) (\(0\leq\alpha<1\)) is linear factor.  
There have been many MPPT methods: single step algorithm including perturb & observe (P & O), binary-weighted step algorithm including successive approximation register (SAR) [8], combined algorithm based on adaptively binary-weighted step (ABWS) and monotonically decreased step (MDS) [8], and fractional open-circuit voltage (FOCV) [9; 10; 11; 12]. The P & O method is very popular method due to its flexibility and balance between complexity and accuracy. It can also dynamically detect the actual MPP with high tracking efficiency, although at the expense of oscillation around the MPP at the steady state, which causes a large amount of energy loss [13; 12; 14]. Therefore, the P & O technique is not appropriate for low power and low energy environments. SAR MPPT technique is based on the binary-weighted step (BWS) for fast tracking [13], but the most-significant-bit (MSB) operation runs first in the SAR MPPT, which aggravates the overall energy loss [6]. Although least-significant-bit (LSB) switching at the initial step in [8] reduces abrupt energyloss, a large amount of power consumption for calculating PV power makes this method improper for low energy environments. Furthermore, the three aforementioned methods cannot detect the global MPP (GMPP), so that they are not appropriate for PV cells with multiple MPPs.  
As depicted in Figure 1b, in an FOCV method, the MPP is determined at the fraction of the open-circuit voltage \(V_{OC}\) of a PV cell, \(\alpha\). For a simple structure and low power consumption, the FOCV methods adopt approximation-based techniques that are a more appropriate method for low energy environments [8]. However, if changes in environmental conditions are present, the mismatch of \(\alpha\) between the new and the previous values by the FOCV leads to a large amount of energy loss. In this paper, while taking advantage of FOCV methods, the GMPP is regularly scanned by using an I-V curve tracer, and the re-evaluated \(\alpha\) is set for MPPT without using an additional ad-hoc parameter.