esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### HWE Analysis of the Thrombic Variants  
Among the 13 thrombophilic genes, we have selected only three genes in this study, and each SNP was selected from the _FVL_ (G1691A), _PT_/_FII_ (G20210A) and _MTHFR_ (C677T) genes. To inspect the impact of each selected SNP from the genes on FI, we performed HWE analysis in both the controls and cases. All details are explained in Table 3. In this study, all controls in the G1691A (\(\chi^{2}\) = 0.002; \(p\) = 0.95), G2021A (\(\chi^{2}\) = 0.01; \(p\) = 0.91) and C677T (\(\chi^{2}\) = 0.03; \(p\) = 0.84) SNPs were found to be consistent (\(p\) > 0.05). Unfortunately, none of the infertile women were found to be under a significant association in the HWE analysis (\(p\) < 0.05) for the G1691A (\(\chi^{2}\) = 9.77; \(p\) = 0.001), G2021A (\(\chi^{2}\) = 4.78; \(p\) = 0.02) or C677T (\(\chi^{2}\) = 3.93; \(p\) = 0.04) SNPs.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Women’s Data** & **Infertile Cases (_n_ = 100)** & **Fertile Controls (_n_ = 100)** & _p-Value_ \\ \hline Age (years) & 30.79 \(\pm\) 5.36 & 31.39 \(\pm\) 6.70 & 0.485 \\ \hline Height (cm) & 157.61 \(\pm\) 5.04 & 159.02 \(\pm\) 6.88 & 0.099 \\ \hline Weight (kg) & 73.88 \(\pm\) 11.27 & 77.56 \(\pm\) 11.86 & 0.02 \\ \hline BMI (kg/m\({}^{2}\)) & 29.41 \(\pm\) 4.43 & 30.68 \(\pm\) 4.53 & 0.04 \\ \hline FSH (IU/mL) & 7.33 \(\pm\) 0.77 & 6.08 \(\pm\) 2.43 & 0.001 \\ \hline LH (IU/mL) & 5.57 \(\pm\) 0.46 & 6.95 \(\pm\) 2.36 & \textless{}0.0001 \\ \hline LH/FSH ratio & 0.77 \(\pm\) 0.10 & 1.37 \(\pm\) 0.86 & \textless{}0.0001 \\ \hline TSH (IU/mL) & 2.50 \(\pm\) 0.31 & 2.16 \(\pm\) 0.31 & 0.01 \\ \hline Infertility & 100 (100\%) & 00 (00\%) & \textless{}0.0001 \\ \hline Family history of FI & 53 (53\%) & 00 (00\%) & \textless{}0.0001 \\ \hline BMI = Body mass index; FSH = Follicle-stimulating hormone; LH = Luteinizing hormone; TSH-Thyroid-stimulating hormone. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Clinical details adopted from infertile and non-infertile Saudi women.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{3}{c}{**Fertile Women (_n_ = 100)**} & \multicolumn{3}{c}{**Infertile Women (_n_ = 100)**} \\ \cline{2-6}  & **G1691A** & **G20210A** & **C677T** & **G1691A** & **G20210A** & **C677T** \\ \hline HWE analysis & 0.01 & 0.01 & 0.15 & 0.03 & 0.04 & 0.16 \\ \hline \(\chi^{2}\) & 0.002 & 0.01 & 0.03 & 9.77 & 4.78 & 3.93 \\ \hline \(p\)-Value & 0.95 & 0.91 & 0.84 & 0.001 & 0.02 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 3: HWE analysis between the SNPs of infertile and fertile women.
## 7 Results and Discussion
### Results  
The proposed schemes are evaluated and compared with FBCFP, TEEN and LEACH in terms of throughput, packet delivery ratio, residual energy, average end-to-end delay and network lifetime at different mobility speeds. The TEEN and LEACH protocols are  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Area of sensor deployment & \(100\times 100\) m \\ Location of base-station & \((50,50)\) m \\ Total number of nodes & \(100\) \\ Number of normal nodes & \(50\) \\ Femtocells (Intermediate nodes) & \(30\) \\ Picocells (Advance nodes) & \(20\) \\ Initial energy of normal nodes & \(0.5\) J \\ Initial energy of intermediate nodes & \(0.7\) J \\ Initial energy of advance nodes & \(1\) J \\ Mobility of normal nodes & \(1\)-\(5\) m/s \\ \(E_{\mathit{T}a}\) & \(50\times 10^{-10}\) \\ \(E_{\mathit{Ra}}\) & \(50\times 10^{-10}\) \\ Maximum rounds (rmax) & \(3000\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
selected for comparison because these are the standard routing protocols and many more protocols are created based on these protocols, such as FBCFP [38].
## 4 Review: Slant Links Rain Fade Models
### Fade Slope Model
#### 4.4.3 ITU-R P.1623  
The ITU-R fade slope model [98] is valid for the frequency range 10-30 GHz and applicable with elevation angles of 10-50\({}^{\circ}\). ITU-R P.1623 defines the rate of change of fade by Equation (59). The distribution of the fade slope depends on the attenuation signal strength, the smaller time interval, and the specifications of moving-average filters. To compute the fade slope, wepass the attenuation time series through a low-pass filter (LPF). To calculate \(F(f_{B},\Delta t)\):  
\[F(f_{B},\Delta t)=\sqrt{\frac{2\pi^{2}}{\left(1/f_{B}^{2.3}+\left(2\Delta t \right)^{2.3}\right)^{1/2.3}}} \tag{59}\]  
We then use \(F(f_{B},\Delta t)\) to determine \(\sigma_{\zeta}\):  
\[\sigma_{\zeta}=s\cdot F(f_{B},\Delta t)\cdot A\bigg{(}\frac{\mathrm{dB}}{ \mathrm{s}}\bigg{)} \tag{60}\]  
where \(s\) is the constant that depends on elevation angle and climate. We calculate the slope:  
\[p(\zeta\mid A)=\frac{2}{\pi\sigma_{\zeta}\bigg{(}1+\left(\frac{\zeta}{\sigma_ {\zeta}}\right)^{2}\bigg{)}}\quad\left(\frac{\mathrm{dB}}{\mathrm{s}}\right)^ {-1} \tag{61}\]  
The fade attenuation determination procedure by this model is presented in Algorithm 21.  
Advantages  
The ITU-R P.1623 model provides the fundamental idea for the fade slope model. Many other models have been developed using the ITU-R P.1623 model.
# In-Situ Nanoindentation Surface Topography of Lead-Free Piezoelectric Thin Films
## 2 Materials and Methods
### Nanindentation Tests  
The Hysitron Ti980 from Bruker (Billerica, MA, USA), equipped with a diamond Berkovich tip, was used to perform indentation tests. The tip area function was calibrated using a fused quartz reference sample, employing the Oliver and Pharr method [26]. The same methodology was employed for the processing of the experimental curves and the determination of the mechanical properties. Equations (1) and (2) are used to, respectively, determine the hardness \(H\) and the reduced Young's modulus \(E_{r}\) as defined in [26].  
\[H=\frac{F_{max}}{A_{c}}, \tag{1}\]  
where \(H\) is the hardness, \(F_{max}\) the maximum force applied, and \(A_{c}\) the projected contact area.  
\[\frac{1}{E_{r}}=\frac{1-\
u_{s}^{2}}{E_{s}}+\frac{1-\
u_{i}^{2}}{E_{i}}, \tag{2}\]  
where \(E_{r}\) is the reduced [PERSON]'s modulus, \(E\) is the [PERSON]'s modulus, and \(\
u\) is the [PERSON]'s ratio; subscripts \(s\) and \(i\) refer to sample and indenter, respectively.  
Only the reduced modulus is reported here, since the [PERSON]'s ratio of the BNT films is not reported elsewhere.  
The analysis makes use of more than 400 reliable tests. The tests were conducted using the ultra-fast mapping mode (XPM mode) with arrays of 10 \(\times\) 10 indents spaced by 1 \(\upmu\)m. The arrays are spaced by 50 \(\upmu\)M. The lateral move speed of the tip during the fast mapping was set to 10 \(\upmu\)M/s, and the tip was maintained in contact with the surface at a setpoint of 1 \(\upmu\)N. The maximum load applied was 250 \(\upmu\)N, reached in 0.2 s. This was followed by a hold of 0.2 s, then an unloading of 0.2 s.
## 1 Introduction
### Paper Contributions and Outline  
In this paper, we propose a novel model where the IoT-SPs are the owners of the WSNs, which analyzes not only the competition between Internet of Things-Service Providers (IoT-SPs), but also models how the sensing data is obtained and the related costs,. Both IoT-SPs compete to provide WSN-data-based services to final users. The WSN-data is gathered by each IoT-SP through a Network Operator (OP).In this work, we study the feasibility of the model from a positive-profit point of view for all the actors. The model is analyzed as two games with two stages each one using game theory. The first game studies the competition between WSNs in order to upload the sensing data to their IoT-SPs. The behavior of WSNs is modeled using a delay-sensitive utility function and an equilibrium is found using population games. The second game studies the IoT-SPs price competition. The game is analyzed using backward induction and a Nash equilibrium is found. Our model has the peculiarity that both games are connected, specifically the IoT-SPs-Users game is influenced by the Sinks-OP game solution. We also provide detailed mathematical procedures and graphic representations, that demonstrate the economic viability of the model for all the actors involved if the number of potential customers is high enough and their data/price ratio requirements is bounded. In addition, it also opens the possibility of improving the OP network efficiency as well as IoT-SPs profits by offering services with different data-quality requirements.  
One real-life application of the proposed model is a real-time route planning service. A car manufacturer, acting as a service provider, installs location-tracking sensors in every car in order to offer a real-time route optimization service. The real-time location data is collected through an OP. The final users decide to subscribe or not based on the quality of the proposed routes and the price charged for the service. Finally, the quality of the route service is related with both, the amount of data collected and the data accuracy, which is related with the data delay.  
The rest of this paper is organized as follows: in Section 2, we describe in detail the model with the actors, the utility of each actor and the pricing scheme. In Section 3, the two games of the model are described and the subscription and pricing strategies are solved. Section 4 shows and discuss the results and Section 5 draws the conclusions of the work.
## 1 Introduction
Digital elevation and terrain models of the Vila Nova de Gaia coast in Northern Portugal, extracted from aerial photographic surveys carried out between November 2008 and February 2019, were analysed to quantify changes in shoreline position and sediment budgets for the study area as a whole and distinct beach segments. Changes were analysed at seasonal, yearly, five-yearly and decadal time scales. The observed dynamics were subsequently related to relevant parameters, including beach type/geology, the presence of a groyne and a breakwater, and dominant wave and wind directions and intensities.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Challenges in C-RAN
#### 2.6.1 Heterogeneous C-RAN  
H-CRAN is a novel paradigm that combines a heterogeneous cellular network architecture with the cloud infrastructure. In a heterogeneous network, various classes of low power nodes (LPNs) such as pico base stations, femto base stations, and small cell base stations are distributed throughout the network. The LPN is one of the key components for increasing the cellular network capacity in dense areas with a high traffic demand. It can be combined with a high-power node (HPN) such as a macro or micro base station to form a heterogeneous network [7]. HPNs are generally employed for increasing network coverage and controlling network signals to prevent unnecessary handovers in small cells. The full advantages of both heterogeneous network and C-RAN have been utilized in an H-CRAN, by incorporating HPNs into CRAN, in which multiple heterogeneous networks can be converged to provide seamless coverage [36]. This results in improved spectral and energy efficiencies as well as increased data rates. In [23], the authors presented a few similarities and dissimilarities between C-RAN and H-CRAN. According to this study, both architectures have a large number of RRHs connected to the BBU pool to achieve a high cooperative gain and to increase the energy efficiency. Moreover, the functional splits of both the architectures include higher layer functions that perform in a BBU and radio functions that perform in RRHs. However, an H-CRAN has simplified capacity and time delay in fronthaul links. The requirements of the fronthaul are alleviated with the participation of HPNs, as control signaling, and system broadcasting data are transmitted to users through HPNs [36]. Moreover, in H-CRAN, an RRH can be turned off for improving the energy efficiency during low traffic demand, whereas the BBU pool is responsible for managing the RRHs that are in the sleeping mode. The authors in [37] studied subchannel allocation and power optimization in NOMA-based Heterogeneous networks, considering energy harvesting and cross-tier interference mitigation. Energy harvesting is utilized due to the limited available energy at the base station nodes. They considered simultaneous wireless information and power transfer for achieving the efficient utilization of energy resources and ensure the QoS simultaneously.
## 6 Evaluation and Experimental Results
### Energy Model
The energy communication cost of one HIP packet, we calculated using the following formula:  
\[\text{HipEnergyComm (mJ)}=\left[\left(\text{BUSY}_{-}\text{TX}\times 14\text{ mA}\right)+\left(\text{RX}_{-}\text{ON}\times 12\text{ mA}\right)\right]\times 4.8\text{ V}, \tag{2}\]where BUSY_TX is the transmission time and the RX_ON is the listening period [40] and is about 0.000208 s. For calculating the transmission time (BUSY_TX), we use the following formula:  
\[\text{BUSY\_TX(s)}=PacketSize\ (\text{bytes})\times\frac{8}{250\times 1000}. \tag{3}\]  
To study in detail the efficiency of our work in terms of energy consumption during the communication of the HIP DEX packets, we conducted the necessary experiments to measure the efficiency of our scheme proposed previously in [9] and the same experiments are conducted with the new compression scheme proposed in this paper. We, firstly, evaluate only the energy consumption when exchanging the handshake packets (4 packets). Then, we execute a full HIP DEX process within the eight corresponding packets (handshake packets + signaling packets = 6 packets). Finally, we evaluate our proposal of optimization discussed in Section 4 (only six exchanged packets: four handshake packets + two signaling packets). Figures 12-14 show the obtained results before and after the proposed HIP DEX header was combined with 6 LoWPAN compression. We can observe, from Figure 10, that the percentage gain in the energy communication cost of the four handshake packets on one Hop communication architecture is about 6.95%, 6.2% on four Hops and 5.4% after the proposed HIP DEX header compression on eight Hops. Figure 12: Energy communication costs of HIP DEX handshake (4 packets). (**a**): Calculation done according to results obtained in [9]. (**b**): Results obtained with LC-DEX. Figure 13: Energy communication costs of HIP DEX handshake packets + signalling packets before optimization (8 packets). (**a**): Calculation done according to results obtained in [9]. (**b**): Results obtained with LC-DEX. Regarding the energy communication cost of the handshake including the eight packets from [9] and during the multi-hop transmission architecture, we remark that the compression of HIP DEX header gives an improvement of about 8% on one Hop communication architecture, 3% on four Hops, and about 6% on eight Hops (see Figure 13). As presented in Figure 14, the percentage gain in energy communication cost of the handshake process within six exchanged packets and with the adoption of our solution in [9], after the proposed HIP DEX packets' number minimization explained in Section 4, is about 8.5% on 1 Hop architecture, 18.2% on four Hop architecture and, 6.3% on eight Hop architecture.
# Going Deeper into OSNR Estimation with CNN
## 2 Principle of Proposed Scheme  
### OSNR Measurement  
The optical signal-to-noise ratio (OSNR) represents the transmission performance of optical links and has a direct relationship with BER [19; 31]. A sufficiently high OSNR is an essential requirement to maintain communication within acceptable error limits. Thus, OSNR measurement can practically promote automatic fault detection and diagnosis, as well as in-service characterization of signal quality [32].  
OSNR can be defined as the logarithmic ratio of the average optical signal power to average optical noise power over a specific spectral bandwidth measured at the input of an optical receiver photodiode [5].  
\[\text{OSNR}=10\log\frac{P_{sig}}{P_{noise}}=10\log\frac{P_{sig+noise}(B_{0})- P_{noise}(B_{0})}{P_{noise}(B_{0})}, \tag{1}\]  
where \(P_{sig}\) and \(P_{noise}\) are the signal and noise power bound by the signal spectral bandwidth \(B_{0}\). In practice, signal power \(P_{sig}\), which is difficult to be measured directly due to being obscured by noise (where the same goes for noises at signal wavelength), is calculated by the difference between total power \(P_{sig+noise}\) and noise power bound by the signal bandwidth \(B_{0}\). A noise equivalent bandwidth (NEB) filter with a bandwidth of \(B_{m}\) was applied to a signal skirt to estimate the power of noise in \(B_{0}\) by interpolating the noise power measured outside the bandwidth, see (3). The instrument, the optical spectrum analyzer (OSA), calculates OSNR as (2), where the specific spectral bandwidth \(B_{r}\) for signal and noise measurement is referred to as OSA's resolution bandwidth (RBW) [5].  
\[\text{OSNR}=10\log\frac{P_{sig}}{P_{noise}}+10\log\frac{B_{m}}{B_{r}} \tag{2}\]  
\[P_{noise}=\frac{P_{noise}(\lambda_{1})+P_{noise}(\lambda_{2})}{2} \tag{3}\]  
Given the definition of the OSNR and OSA measurement method, separation of the noise and signal is the key point of the algorithm. Besides, OSNR describes the relationship between the second moment of two parts of the received optical signal, signal, and noise. Consequently, the trained neural network should learn how to extract the signal part from noise as well as a mapping from some characteristics of the second moment to the ultimate OSNR value. The Ref. [33] reveals a convolutional layer which has the potential of extraction while the mapping process can be realized in a fully connected network.
## 3 Principle and Processing Flow of Atmospheric Correction Algorithm
### Atmospheric Parameter Retrieval  
The basic principle of atmospheric parameter retrieval is based on Equation (1), where various methods are employed to subtract surface reflectance information to derive the atmospheric aerosol optical depth (AOD) and column water vapor (CUV). Since the presence of clouds significantly affects sensor measurements, cloud detection is a critical preprocessing step to ensure accurate parameter retrieval.  
Cloud detection is performed using the PSAC L1 product, applying a combination of techniques such as single-band reflectance thresholds, multi-band ratios, the normalized dust index, the normalized snow cover index, and the 1.38 \(\upmu\)m cirrus band. These methods provide robust identification of cloud pixels under varying atmospheric conditions.  
For non-cloud pixels, the normalized I, Q, and U values of four bands at 0.443 \(\upmu\)m, 0.555 \(\upmu\)m, 0.670 \(\upmu\)m, and 0.865 \(\upmu\)m are used to calculate the reflectance and polarized reflectance at the sensor's aperture. Then, by constructing a surface reflectance ratio database and subtracting the surface information, AOD retrieval is achieved. The CWV is retrieved using the normalized radiance values \(I\) from the water vapor absorption band at 0.91 \(\upmu\)m and the adjacent non-water vapor absorption band at 0.865 \(\upmu\)m. This process enables the simultaneous retrieval of atmospheric parameters, resulting in the Level-2 (L2) product from the atmospheric correction instrument. For detailed algorithms, refer to [16].  
The PSAC L2 product is stored in HDF files on a scene-by-scene basis. It includes not only atmospheric parameters, such as cloud detection results, aerosol optical depth, and column water vapor, but also auxiliary information, including latitude and longitude, land-ocean classification, and altitude. These data products provide valuable input for atmospheric correction.
## 7 Synchrosqueezed CWT Scalogram Fault Prediction System
### Synchrosqueezed Wavelet Transform  
The aim of the synchrosqueezed transform is to retrieve the time-varying amplitudes and the instantaneous frequencies in the signal. Therefore, synchrosqueezed transform is applied to improve the readability of the scalogram due to the elimination of the interference factors.  
In Figure 7, the same colour scale bar applies. The locations for high value pulses are significantly concentrated. The readability for pulse counting is improved as the pulse in the scalogram is easier to identify.
## 3 Healthcare
### Assistive Technologies for the Visually Impaired
#### 3.2.3 Challenges and Future Directions  
Despite being efficient enough, the proposed real-time object detection application is far from perfect. It is a local system that is trained to identify only 82 objects. To have a larger number of objects, the machine-learning model needs a significant amount of processing power, thus the next step is to move the detection algorithm to a powerful, cloud-based server. However, the challenge then is the connectivity between the smartphone and the sever, especially since the network delay significantly increases [55; 58].  
The delay affecting the live video affects the performance of the framework and thus makes it harder for a visually impaired person to use it. Thus, for this use case to be efficiently implemented, it requires the network to handle a high-resolution video in a timely manner, which is especially challenging since the streamed video requires a significant amount of data to be transmitted in high quality. Additionally, to have the framework work efficiently, it should work in real time, thus we require the network to have very low latency. Lastly, as the goal is to have the users be able to use the system independently, it requires the network coverage to be large enough to provide the service in most areas.