esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Results
### Model Error Susceptibility to Input Variables  
An ideal predictive model should exhibit no discernible correlation or pattern between its input design variables and the resulting model error, as indicated in reference [28]. As per reference [29], correlation coefficients can be interpreted as follows: coefficients from 0.4 to 0.6 demonstrate moderate correlations, those from 0.2 to 0.4 indicate weak correlations, and coefficients ranging from 0 to 0.2 signify very weak correlations. In order to delve deeper into the predictive capabilities of the proposed equation, it is crucial to evaluate the model error trend in relation to the primary design variable, as illustrated in Figure 10. Upon examining the figure referred to, it becomes evident that the MSP-based strength model boasts a high level of accuracy, with no significant trends observed in connection with the design parameters. This suggests that the model is effective in maintaining a low correlation between input design variables and model error, adhering to the principles of an ideal predictive model.
## 3 Performed Full-Scale/Model Test Comparisons
### Cooling Towers  
[PERSON] [47] compared the wind pressure fluctuations from a model test and in situ measurements on a cooling tower. Using the model, wind pressures were measured using two microphones with a Reynolds number (_Re_) = 5.04 \(\times\) 10\({}^{5}\), and in situ wind pressure values were obtained at _Re_ = 3.48 \(-\) 5.22 \(\times\) 10\({}^{7}\). It was found that when certain conditions were preserved, the agreement between the two experiments was very good.  
In 2009, a full-scale measurement campaign for wind effects on a 167-meter-high cooling tower was undertaken by the authors at Peng-cheng electric power station in Xu-zhou, China [48]. To the south of the tower, there was an adjacent cooling tower of the same size as this one for taking measurements, and there was an industrial complex to its west. However, to its north and east, there was no large building interfering with the wind effects. During its construction, 36 transducers were evenly installed around the tower's throat section at 130 m high. The whole full-scale measurement campaign lasted from 2010 to 2015 on the basis of 2-3 iterations of intensive tests per year. Each time, the occurrence of the strong wind scenario was predicted based on a local meteorological center's weather forecast. Equipment was set up before the arrival of the strong winds, and simultaneous 24-h recordings of wind and wind-induced pressures were then conducted, which usually continued for 1 to 2 weeks. Comparing the full-scale measurement data with the corresponding model test data, [PERSON] et al. [49] found that the agreement in terms of mean wind pressure distribution between the two experiments was good overall, but that in terms of dynamic wind effects, it was not satisfactory. In addition, [PERSON] et al. [50] found that the model test conducted at the cooling tower's design stage was conservative in use since comparing the results to the full-scale measurement results showed that unfavorable wind pressure spectra and coherence were likely to be measured in the wind tunnels. The Peng-cheng cooling tower full-scale measurement campaign will be revisited in Section 5.
# Boat-to-Grid Electrical Energy Storage Potentials around the
###### Abstract  
With the dynamic growth in both the global hunger for electrical energy and the share of variable renewable energy sources in the energy mix, distribution networks are facing new challenges where conventional solutions may not be the best ones. The increase in load in distribution grids is routinely countered by network development and expansion, in a great part to supply for on-peak load demand, which could also be done by utilizing supplementary technologies to lessen the need for or defer such expansion. Vehicle-to-grid technology could efficiently contribute to handling this issue, as electric vehicles can potentially function as storage capacities to mitigate the fluctuations of power generation. The battery energy storage systems of hybrid or completely electric watercraft, which are becoming increasingly popular, are no exception, either. These vehicles represent a considerable potential to create more complex vehicle-to-grid solutions for countries with significant inland or seaport networks, for example, Hungary, with the largest lake of Central Europe. Since there is only deficient information on the topic, the main goal of this study was to explore the energy storage capacities of small electric boats in the context of Lake Balaton, Hungary. By this example, the paper presents the potential utilization of Europe's significant network of sea and inland recreational ports for the purpose of energy storage. Similarly to other European countries, Hungary's energy strategy for 2030 also includes the promotion of virtual production integration, local energy communities and micro-grid solutions. At the beginning of 2021, the small electric boats in the saling marinas of Lake Balaton represented a cumulative energy storage capacity of 4.8 MWh, which may reach even 15.6 MWh by 2030, by the promotion of micro-grid solutions. The innovative novelty of this study is that it regards small fully electric boats not just as vehicles, but also explores their energy storage potentials. The novel goal of these examinations is to explore the volume of the energy storage potentials of the small fully electric boats around Lake Balaton, the knowledge of which may facilitate the creation of new types of flexibility services related to energy storage.  
solar energy; grid integration; energy storage; vehicle-to-grid; Hungary +
Footnote †: journal: Applied Sciences
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 2 The Concept of Watermarking  
In general, the watermarking technique involves embedding and extraction phases [31] as illustrated in Figure 2. The embedding process allows users to include watermarks within their digital content (multimedia objects) for a variety of reasons such as authentication, copyright protection, etc. After that, the secret key is added to the embedding process in order to insert the secret data into the multimedia object by using a number of relevant transformations or spatial techniques [32]. Moreover, another technique can be used along with the secret key for improving data authentication such as encryption, hashing and encoding [33]. The watermarked object is defined as the output, which is produced from the embedding algorithm where the same secret key is used to embed the watermark that is used again for extracting the data from the watermarked object.  
The extracted watermark may or may not resemble the pristine watermark due to the encountered attack. Therefore, there are three types of watermarking procedures, which are used to measure the properties of these procedures. These types include: non-blind, blind and semi-blind procedures. In the non-blind procedure, the pristine object and secret key are essential in determining the extracted watermark, while in the blind procedure the pristine object does not necessarily determine the extracted watermark. In the semi-blind procedure, the secret key is only by means applied for extracting the data [34].  
The two phases of watermarks are discussed in detail as follows.  
Figure 2: The watermarking Technique.
## 3 Results and Discussion
### Effective Moisture Diffusion  
Table 3 outlays the values of effective diffusion coefficient \(D\) as well as the coefficient of determination, root means square error, and mean absolute percentage error acquired from individual fittings at each drying condition. The statistics confirmed the capability of the short time equation for \(1\leq X^{*}\leq 0.2\) to predict closely the experimental data at a high accuracy of \(R^{2}\geq 0.941\), \(RMSE\leq 5.595\times 10^{-2}\) and \(MAPE\leq 10.8\%\). From the inspection of Table 3, values of \(D\) increased with the increase of \(T\), \(v\) and decrease of \(RH\). The values of \(D\) varied from \(2.474\times 10^{-12}\) to \(3.921\times 10^{-11}\) for \(T\) from \(10\) to \(50~{}^{\circ}\)C, \(7.843\times 10^{-11}\) to \(9.822\times 10^{-12}\) for \(RH\) from \(20\) to \(60\%\) and \(8.963\times 10^{-12}\) to \(1.063\times 10^{-11}\) for \(v\) from \(0.15\) to \(1.00\) ms\({}^{-1}\). This can be ascribed to the higher energy of molecules at high \(T\), low \(RH\)  
Figure 6: Drying constant \(k\) as affected by (**a**) the drying air temperature \(T\), (**b**) relative humidity \(RH\), and (**c**) airflow velocity \(v\). Solid lines represent Page model fitting, dashed lines show extrapolation beyond the dataset used for fitting. (**d**) Predicted values of drying constant \(k_{pml}\) vs. observed \(k_{obs}\).  
and high \(v\), which in turn increases the mobility of molecules, resulting in a faster rate of moisture transfer via diffusion. The derived values of effective diffusion coefficient \(D\) are in the similar range with the findings of [PERSON], [PERSON] and [PERSON] [29] and [PERSON] and [PERSON] [30] particularly at \(T\geq 30\)\({}^{\circ}\)C.  
Moreover, the observed values were slightly lower compared to the values reported by [PERSON], [PERSON] and [PERSON] [33] at the same range of drying temperatures which can be attributed to differences in wheat varieties and systems used for drying. However, the effective diffusion coefficient subjected to temperatures lower than 30 \({}^{\circ}\)C, as well as various sets of \(RH\) and \(v\), has not been studied to date. In an analogy with Equation (25), an analytical model with embodied drying air conditions \(D=f(T,RH,v)\) was established for describing the effective moisture diffusion coefficients (Equation 26).  
\[\begin{array}{l}k=2.077\times 10^{-12}\times e^{0.073\times T}\times RH^{-0.156 }\times v^{0.066}\\ R^{2}=0.997,\ RMSE=5.828\times 10^{-13},\ MAPE=6.8\%\end{array} \tag{26}\]  
The insertion of Equation (26) in Equation (21) yielded a generalized model based on short time diffusive solution able to depict the drying behavior of wheat cv. 'Pionier' at an accuracy of \(R^{2}=0.988\), \(RMSE=4.239\times 10^{-2}\) and \(MAPE=7.7\%\).
## 1 Introduction
Moreover, the convolutional cross-substitution attention module merges convolution and Transformer architectures, enhancing the capability to extract local-global information while reducing attention mechanism computational complexity. The main contributions of this study are as follows. 1. We present a novel dual-branch multi-granularity convolutional cross-substitution Transformer (DMCCT) model, which innovatively combines the strengths of both Transformers and CNNs. This integration facilitates the efficient extraction of local and global spatial and spectral features from hyperspectral images. The DMCCT model effectively addresses the limitations of existing approaches in fusing CNNs and Transformers, offering new insights and methodologies for overcoming challenges in hyperspectral image classification. 2. The design of the multi-granularity embedding module in this research enhances the model's capability to extract global information by leveraging multi-scale and multi-level spectral and spatial data, thereby effectively mitigating the Transformer's shortcomings in capturing multi-granularity local features. The developed convolutional cross-substitution Transformer module enables seamless integration of Transformer and CNN architectures, fostering long-range dependencies among local features. This advancement significantly enhances the model's capacity to extract both local and global information across spatial and spectral dimensions while concurrently reducing the computational complexity of the attention mechanism, thereby improving overall model performance and effectiveness. 3. We validate the proposed method comprehensively using three publicly available datasets and perform detailed comparative analyses with existing state-of-the-art methods. The experimental results indicate a substantial improvement in classification accuracy, affirming the effectiveness and superiority of the proposed model and methodologies. This research represents a valuable contribution to the field of hyperspectral image classification and serves as a significant reference for future endeavors in this area. The remaining sections of this paper are structured as follows. Section 2 provides a brief overview of CNNs and Transformers. Section 3 elaborates on the proposed method in detail. Section 4 describes the experimental results and presents a comparative analysis of the results. Section 5 discusses and analyzes the model's impact factors. Finally, Section 6 gives some conclusions about this paper.
## 4 Discussion
According to a comprehensive review of studies, implants placed with GBR techniques had a mean 95.5% rate of survival for follow-ups of 5-74 months [22]. The survival rate of the implants inserted in augmented sites, irrespective of the surgical technique, ranged from 91.7% to 100%, while survival rates for non-grafted sites ranged between 93.2% and 100% at follow-up periods of 1-5 years [23]. A review of studies with 1-3 years follow-up, found non-significant changes of peri-implant soft tissues clinical parameters and marginal bone levels, when lateral bone augmentation techniques are used [24]. The implant survival rate for the alveolar bone submitted to lateral ridge augmentation was 97% to 100% at 6-12 months of follow-up [25]. At 24 months follow-up the probing depth and the peri-implant marginal bone loss are higher than control group. This result is correlated with higher resorption rate of the augmented alveolar area when compared with the implants placed in naturally healed alveolar bone. However, S-GBR is an augmentation technique with very low rate of complications [7]. The only complication could be the exposure of the osteosynthesis screw, managed by taking out the screw without any need of anesthesia. An interesting research issue would be the comparison of the results obtained with S-GBR technique and immediate implantation when used in combination with different graft materials. Further studies will be published by our research group to report the influence of the graft material on the success and survival rate of dental implants placed in mandibular alveolar bone. S-GBR technique is an easy method to regenerate the bone around implants and can be used in the daily work by every practitioner. The big advantage of this method is the low complication rate and the protection offered by the osteosynthesis screws to the augmented side against the compression forces in the oral cavity. The comparison and interpretation of the results with those reported by literature must be done with caution as various factors impact the long-term success of the implant-prosthetic restorations [26; 27; 28; 29]. Literature data is scarce in studies investigating the results of the implant-prosthetic treatments in simultaneous approach (augmentation technique combined with immediate implantation). Also, the success of the dental implants following immediate implantation in mandibular sites reconstructed with S-GBR technique, was not yet investigated. Further studies can highlight the benefits and limits of this augmentation technique and can establish specific clinical situations when this technique can be recommended. The limitations of this study are related to insufficient follow-up which is not enough to establish long-term survival rate of the implants, low sample size, and possible subjective bias of the investigators.
# The Quanta Image Sensor: Every Photon Counts
## 6 Low-Power and High-Speed Readout Circuits
### Readout Circuits for Multi-Bit QIS  
Conceptually, once input-referred read noise is low enough to count a single photoelectron reliably, counting multiple photoelectrons with the same photodetector and readout structure and a low-bit-depth ADC also becomes practical, allowing implementation of a multi-bit QIS. The ADC digital value is the number of photoelectrons in the jet.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Process** & **X-FAB**, **0.18 μm, 6M1P (Non-Standard Implants)** \\ \hline VDD & 1.3 V (Analog and Digital), 1.8 V (Array), 3 V (I/O pads) \\ Pixel type & 3T-APS \\ Pixel pitch & 3.6 μm \\ Photo-detector & Partially pinned photodiode \\ Conversion gain & 119 μV/e\(-\) \\ Array & 1376 (H) \(\times\) 768 (V) \\ Column noise & 2 e\(-\) \\ Field rate & 1000 fps \\ ADC sampling rate & 768 KSa/s \\ ADC resolution & 1 bit (VLSB = 1 mV) \\ Output data rate & 32 (output pins) \(\times\) 33 Mb/s = 1 Gb/s \\ Package & PGA with 256 pins \\ \hline \multirow{4}{*}{Power} & Pixel array & 8.6 mW \\ \cline{1-1}  & ADCs & 2.6 mW \\ \cline{1-1}  & Addressing & 3.8 mW \\ \cline{1-1}  & 1/O pads & 5 mW \\ \cline{1-1}  & Total & 20 mW \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the 1 Mpixel binary image sensor.  
Increasing the bit depth of a jet from single-bit to \(n\) bits allows the field readout rate to be reduced while maintaining constant flux capacity. Thus, while ADC energy per readout is increased by increasing the jet bit depth, the power dissipation increase is mitigated or negated by the reduced field readout rate. The multi-bit QIS approach also addresses the column limited bandwidth issue, where in single-bit QIS imager, since the integration time is shorter than the integration time in multi-bit QIS, imaging throughput is limited. We have explored several variations of multi-bit QIS architectures, including single-slope, cyclic, and successive approximation ADCs implemented in 180 nm CIS process [14]. Results are promising and will be reported in a future publication.
## 3 Monte Carlo Simulation
### Example with Breaking Strength Data  
We used the empirical datasets of [PERSON] et al. (2017) about the breaking strengths of jute fiber at gauge lengths of 10 and 20 mm. The diameters of jute fibers were measured with an XSP-8 CA digital biological microscope (Shanghai Optical Instrument Factory, Shanghai, China). For the sake of simplicity, we considered the fibers to be perfectly cylindrical. We tested 20 fiber samples at four gauge lengths (5, 10, 15, and 20 mm). The ultimate cells constrained by the environment break when increasing the tensile strength applied on the jute filament, until the length of the ultimate cell reaches a minimum load, which can no longer buildup to its breaking strength measured in megapascal (MPa), and is given as follows:  
Dataset 1 (10 millimeters) MPa: 693.73, 704.66, 323.83, 778.17, 123.06, 637.66, 383.43, 151.48, 108.94, 50.16, 671.49, 183.16, 257.44, 727.23, 291.27, 101.15, 376.42, 163.40, 141.38, 700.74, 262.90, 353.24, 422.11, 43.93, 590.48, 212.13, 303.90, 506.60, 530.55, 177.25;  
Dataset 2 (20 millimeters) MPa: 71.46, 419.02, 284.64, 585.57, 456.60, 113.85, 187.85, 688.16, 662.66, 45.58, 578.62, 756.70, 594.29, 166.49, 99.72, 707.36, 765.14, 187.13, 145.96, 350.70, 547.44, 116.99, 375.81, 581.60, 119.86, 48.01, 200.16, 36.75, 244.53, 83.55.  
We fitted an exponentiated moment-based exponential distribution for each dataset. We used the Kolmogorov-Smirnov test as a goodness-of-fit test of the exponentiated moment-based exponential  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vanuatu Island (Group)** & **Area (Hectare)** & **Loss of Forest (Hectare)** \\ \hline Torres Islands & 11.52 & 45.8 \\ Banks Islands & 75.359 & 56.8 \\ Santo & 423.897 & 1114.4 \\ Maewo & 30.39 & 217 \\ Aoba & 40.566 & 210.4 \\ Pentecost & 49.49 & 249 \\ Malakula & 206.756 & 293.4 \\ Ambrym & 73.246 & 447.4 \\ Epi & 53.324 & 190.3 \\ Efate & 97.004 & 302.5 \\ Erromango & 88.874 & 666 \\ Tanna & 56.668 & 811.6 \\ Aneityum & 17.21 & 73 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics of forest loss derived from satellite data analysis of 1990–2000.  
distribution. The Kolmogorov-Smirnov distance between the empirical and the fitted distribution was 0.11 with a \(p\)-value = 0.89 for the first dataset and 0.15 with a \(p\)-value = 0.47 for the second dataset, indicating satisfactory fits.  
We found \(\hat{\alpha}_{1}\) = 0.77 and \(\hat{\alpha}_{2}\) = 0.76, \(\hat{R}_{1,3}\) = 0.75 and \(\hat{R}_{2,4}\) = 0.60. The 95% confidence interval for \(R_{1,3}\) was [0.6551, 0.8449] and for \(R_{2,4}\) it was [0.4634, 0.7366].
# Seismic Hazard Evaluation and Strain Dynamics in the
## 4 Discussion and Findings  
To evaluate the strain accumulation along the SFZ--one of the key tectonic features in Western Anatolia--and analyze the region's seismic hazard, seismic parameter values were estimated firstly in this study. For this purpose, all recorded earthquakes that occurred in the SFZ and its surrounding regions between 1900 and 2024 were first converted to a common moment magnitude (Mw). Using a comprehensive catalog (>4500 events), the \(a\) and \(b\) parameters of the Gutenberg-Richter (G-R) law were estimated through the nonlinear least squares method and earthquake recurrence intervals were calculated.
## 3 Dependence of Geometric Placement of Antenna Array Elements in Regards to Radio Link Improvements
### Effects of Circular Antenna Arrays over Doppler Power Spectrum and Coherence Time Metrics
Besides, very convenient coherence time values were obtained, ranging from the order of almost one second to hundreds of milliseconds; that is, values that far exceed the coherence time reported by the IEEE model, and by other models. Figure 7: Simulation and comparison of the DPS by Gans model at different carrier frequencies, including the gain of the CA-21 and CA-47. The numerical values of the coherence times are shown in Table 4, which shows the coherence time values of the IEEE model, corresponding to the four carrier frequencies considered. From Table 4, we can conclude the following aspects: the smaller the beam width generated by the antenna array, the greater the coherence time. The higher the carrier frequency, the shorter the coherence time. The coherence time from a beamwidth and a carrier frequency remain constant in the user's mobility condition within the interfering ring. The variation in one or even two orders of magnitude in the coherence time in the cases of \(\mu_{r}=1^{\circ}\), and \(\mu_{r}=90^{\circ}\), is because, in this last condition, the radio signal experiences multipaths, increasing with it the RMS delay spread at the receiver, and as a result, a considerable decrease in coherence time. Even in the most unfavorable condition  
Figure 8: Simulations and comparison of the coherence time at different carrier frequencies, using the VaMod and IEEE model. Taking into account the two pointing angles cases: (**a**) when it is small, and (**b**) when it is not small. The CT of the CA of the 21 and 47 elements is shown. of the orthogonal movement of the mobile concerning the source, the coherence time values are higher than those obtained by the IEEE model. Finally, in Table 5, we make a comparison of our work and results with other similar and cited studies. This comparison is realized in terms of important contributions in relation to a typical wireless communications environment. Thus, the principal contributions of this work can be shown.