esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Review: Slant Links Rain Fade Models
### Physical Models  
#### 4.3.1 Bryant Model  
The Bryant model [37] comprises rainfall rate, Earth station height above sea level, and elevation angle at the Earth station. Equation (40) determines the rain attenuation, whereas Equations (35)-(39) are used to determine the required parameter values that are used to determine rain attenuation.  
\[H_{R} =4.5+0.0005R^{1.65}\;[\text{km}] \tag{35}\] \[D_{m} =170R^{-1.2}\;[\text{km}]\] (36) \[k_{n} =e^{0.007R}\] (37) \[\tilde{\xi} =\left\{\begin{array}{cc}(1/\sqrt{2})e^{\sin\theta}&\theta\leq 5 5^{\circ}\\ 1.1\tan\theta&\theta>55^{\circ}\end{array}\right. \tag{38}\]\[L_{S}=\left\{\begin{aligned} \frac{H_{R}-H_{S}}{\sin\theta}\ [\text{km}]& \theta\geq 5^{\circ}\\ \frac{2(H_{R}-H_{S})}{\sqrt{\sin^{2}\theta+2(H_{R}-H_{S})/R_{E}+ \sin\theta}}\ [\text{km}]&\theta<5^{\circ}\end{aligned}\right. \tag{39}\] \[A=1.57D_{m}k_{n}\gamma\frac{L_{S}}{\xi L_{G}+D},\text{ for }3\text{ mm/h} \tag{40}\]  
where \(R_{e}\) is the equivalent radius of the Earth, \(L_{G}\) is the projection of the slant path in the horizontal direction, and \(D\) is the single-cell diameter. The model has been validated using the ITU-R databank. The rain attenuation determination procedure by this model is presented in Algorithm 15.  
```
1 Compute the rain height \(H_{R}\gets 4.5+0.0005R^{1.65}\ [\text{km}]\)
2 Rain cell diameter \(D\gets 170R^{-1.2}\ [\text{km}]\)
3 Slant path length \(L_{G}\gets H_{R}/\tan\theta\)
4 Multiple cells (rain dependent) \(k_{n}\gets e^{0.007\cdot R}\)
5 if\(\theta\leq 55^{\circ}\)then
6\(\xi\leftarrow\frac{1}{\sqrt{2}}\exp(\sin\theta)\)
7 else
8\(\xi\gets 1.1\tan\theta\)
9 if\(\theta\geq 5^{\circ}\)then
10\(L_{s}\leftarrow\frac{H_{R}-H_{S}}{\sin\theta}\ [\text{km}]\)
11 else
12\(L_{s}=\frac{2(H_{R}-H_{S})}{\left[\sin^{2}\theta+2(H_{R}-H_{S})/R_{e}\right]^{1 /2}+\sin\theta}\ [\text{km}]\ /*\ R_{e}\):Effective radius earth */ // Invalid for the rain rate \(R\
mid 3\) mm/h
13 if\(R\
mid 3\)mm/h
14 Stop
15 else
16\(A_{p}\gets 1.57D_{m}k_{n}\gamma\frac{L_{s}}{\xi L+D}\)
17 Return \(A_{p}\)
```  
**Algorithm 15**Bryant Model [37]  
Advantages  
The model proposes rainfall rate distribution in two classes based on a breakpoint (a fixed rainfall rate), where rainfall rates below and above this breakpoint is uniform and non-uniformly distributed.  
Limitations  
For available rainfall data, it uses full rainfall rate distribution (e.g., effective rain cell) and argues a fixed breakpoint rainfall rate that is almost independent of the local climate. However, precise conclusions have not been made.
# Calculation of Trusses System in MATLAB--Multibody
## 2 Example of Analytical Design Based on In-Plane Deformation Conditions
The deflection vector \(\delta_{\text{C}}\) of node C derived from the first Castigliano theorem can be calculated using the following equation:  
\[\delta_{\text{C}}=\frac{l}{E\cdot S} \begin{bmatrix}\sum F_{i}\cdot\frac{\partial F _{i}}{\partial F_{i}}\\ \sum F_{i}\cdot\frac{\partial F_{i}}{\partial F_{i}}\end{bmatrix}  \tag{53}\]  
Each component of the force \(F_{i}\) is expressed as a function of the component of the external force vector \(\mathbf{F_{\text{eC}}}\) as follows:  
\[\delta_{\text{C}}=\frac{l}{E\cdot S} \begin{bmatrix}F_{\text{eC}x}+N_{\text{AB} }\frac{\partial N_{\text{AB}}}{\partial F_{\text{eC}x}}+\sqrt{2}\cdot F_{AC} \cdot\frac{\partial F_{\text{eC}x}}{\partial F_{\text{eC}x}}+F_{\text{BC}} \cdot\frac{\partial F_{\text{BC}}}{\partial F_{\text{C}x}}+\sqrt{2}\cdot F_{ BD}\cdot\frac{\partial F_{\text{BD}}}{\partial F_{\text{C}x}}+F_{\text{CD}} \cdot\frac{\partial F_{\text{CD}}}{\partial F_{\text{C}x}}\\ F_{\text{C}y}+N_{\text{AB}}\cdot\frac{\partial N_{\text{AB}}}{\partial F_{ \text{C}y}}+\sqrt{2}\cdot F_{AC}\cdot\frac{\partial F_{\text{AC}}}{\partial F _{\text{C}y}}+F_{\text{BC}}\cdot\frac{\partial F_{\text{BC}}^{\text{BC}}}{ \partial F_{\text{C}y}}+\sqrt{2}\cdot F_{BD}\cdot\frac{\partial F_{\text{BD}}}{ \partial F_{\text{C}y}}+F_{\text{CD}}\cdot\frac{\partial F_{\text{CD}}}{ \partial F_{\text{C}y}}\end{bmatrix}  \tag{54}\]  
Consider again the system of the given Equations (5)-(8) with the additional vector of the external force \(\mathbf{F_{\text{eC}}}\):  
\[N_{\text{AB}}-\frac{\sqrt{2}\,F_{\text{BD}}}{2}=F_{Bx} \tag{55}\]  
\[F_{\text{BC}}+\frac{\sqrt{2}\,F_{\text{BD}}}{2}=F_{By} \tag{56}\]  
\[-F_{\text{CD}}-F_{\text{eC}x}-\frac{\sqrt{2}\,F_{\text{AC}}}{2}=0 \tag{57}\]  
\[-F_{\text{BC}}-F_{\text{eC}y}-\frac{\sqrt{2}\,F_{\text{AC}}}{2}=0 \tag{58}\]  
And its transcription into matrix entry for automated computer calculation is as follows:  
\[ \begin{bmatrix}1&0&0&-\frac{\sqrt{2}}{2}&0&0&0\\ 0&0&1&\frac{\sqrt{2}}{2}&0&0&0\\ 0&-\frac{\sqrt{2}}{2}&0&0&-1&-1&0\\ 0&-\frac{\sqrt{2}}{2}&-1&0&0&0&-1\end{bmatrix} \cdot \begin{bmatrix}N_{\text{AB} }\\ F_{\text{AC}}\\ F_{\text{BC}}\\ F_{\text{BD}}\\ F_{\text{CD}}\\ F_{\text{eC}x}\\ F_{\text{eC}y}\end{bmatrix} = \begin{bmatrix}F_{\text{Bx}}\\ F_{\text{By}}\\ 0\\ 0\end{bmatrix}
## 5 Simulations and Performance Evaluations
### Different Data Transmission Rates between MEC-BS and MEC-NBS  
As shown in Figure 10, we measured the total time consumption by the system in completing all tasks under the 3 schemes with different data transmission rates between MEC-BS and MEC-NBS. In simulations, we increase the \(s_{k}^{\text{B2 NBS}}\) and \(s_{k}^{\text{NBS2 BS}},\forall k\in\mathcal{K}\) from 10 to 40 MB/s, while other settings are listed in Table 1, except fixed values \(M=60\), \(R=6\), \(K=6\), \(\phi_{k}=2.4\times 10^{6}\) MBPS, \(s_{k}^{\text{C2 NBS}}=80\) MB/s, \(s_{k}^{\text{NBS2C}}=30\) MB/s, \(\forall k\in\mathcal{K}\).  
Similar to the data transmission rates between MEC-NBS and C, \(s_{k}^{\text{BS2 NBS}}\) and \(s_{k}^{\text{NBS2 BS}}\), \(\forall k\in\mathcal{K}\) impacts the time consumptions of transmission processes in further offloading and data caching. The overall trend of the curves of OCMEC and OMEC are decreasing. Similarly, the curve of NMEC is still unchanged because further offloading and data caching are disabled.  
Figure 10: \(f\) gained by the 3 schemes with the increase of \(s_{k}^{\text{B2 NBS}}\) and \(s_{k}^{\text{NBS2 BS}}\), the data transmission rates between MEC-BS and MEC-NBS, \(k=1,2,\ldots,K\).  
Figure 9: \(f\) gained by the 3 schemes with the increase of \(s_{k}^{\text{NBS2C}}\), the data transmission rates between MEC-NBS and C, \(k=1,2,\ldots,K\).
# Tropical Cyclone Winds from WindSat, AMSR2, and SMAP: Comparison with the HWRF Model
###### Abstract  
A new data set of tropical cyclone winds (TC-winds') through rain as observed by the WindSat and AMSR2 microwave radiometers has been developed by making use of a linear combination of C- and X-band frequency channels. These winds, along with tropical cyclone winds from the SMAP L-band radiometer, are compared with the Hurricane Weather Research and Forecasting (HWRF) model. Due to differences in spatial scales between the satellites and the high-resolution HWRF model, resampling must be performed on the model winds before comparisons are done. Various ways of spatial resampling are discussed in detail, and an optimal method is determined. Additionally, resampled model winds must be temporally interpolated to the time of the satellite before direct comparisons are made. This interpolation can occasionally result in un-physical 2D wind fields, especially for fast-moving storms. To assist users with this problem, a methodology for handling un-physical wind features is detailed. Results of overall comparisons between the satellites and HWRF for 19 storms between 2017 and 2020 displayed consistent storm features, with overall average biases less than 1 m/s and standard deviations below 4 m/s for all tropical cyclone winds between 10 and 60 m/s. Differences were seen when the comparisons were performed separately for the Atlantic and Pacific basins, with biases and standard deviations between the satellites and HWRF showing better agreement in the Atlantic. The impact of rain on the satellite wind retrievals is discussed, and no systematic bias was seen between the three sensors, despite the fact that they use different frequency channels in their tropical cyclone winds-through-rain retrieval algorithms.  
microwave radiometers; tropical cyclones; winds +
Footnote †: journal: Remote Sensing Sensing  
1
## 2 Methodology and Materials
### Data Splitting  
After the preprocessing step, the dataset is divided into the training and testing group. In this step, three different experiments are performed according to how the data are divided and processed. In the first experiment, 70% of the dataset is allocated for training, whereas the remaining 30% of the dataset is allocated for testing. In the second experiment, 80% of the dataset is allocated for training, while the remaining 20% of dataset is allocated for testing. The 70/30 split was chosen for initial evaluations to balance training and testing data, while the 80/20 split was used to provide more data for training the DNN model, aiming to improve its generalization ability. These ratios are commonly used in machine learning research and were selected based on the dataset size and model requirements. These two experiments are used in modeling the DNN and evaluating its performance. In addition, in the third experiment, experiments are carried out using 10-fold cross-validation. The third case is used for modeling traditional machine learning techniques and evaluating their performance. Since the training phase of the DNN takes a very long time, the third data splitting technique is performed only on traditional machine learning algorithms.
## 2 Research Method
### Satellite Images of TRMM  
The TRMM is the meteorological satellite launched by National Aeronautics and Space Agency, NASA, and National Space Development Agency, NASDA, with the aim of quantitative measuring of precipitation in tropical and subtropical areas [6]. Being approximately 350 km above sea level, this satellite has five primary sensors, each measuring some kind of rainfall-dependent parameters. This satellite voyages several times each day around the globe to capture the required information. The repetition of data acquisition and transmission of the satellite varies from area to area and depends on the latitudes. According to the research, the information obtained from TRMM 3B42 and 3B43 are typically used to inspect the rainfall values of via TRMM satellite. The main difference between these two byproducts is their temporal scale. The TRMM 3B42 encompasses daily and hourly data and 3B43 includes monthly data of precipitation. The latter is a combination of satellite estimation data of TRMM 3B42 and observational data [6]. In this study, TRMM 3B43 monthly precipitation data was used.
## 3 Fecal VOC in Gastrointestinal DISEASES
### Colorectal Cancer  
Colorectal cancer (CRC) is one of the most prevalent malignancies in the industrialized world and is an important cause of cancer-related mortality [41; 42]. Between 70% and 90% of CRC originates from adenomatous polyps, and early detection and removal of these precancerous adenomas significantly decrease CRC incidence and mortality [43]. Therefore, current CRC screening aims for early detection of CRC, and those adenomas with a high risk of malignant transformation (advanced adenomas) [44]. In screening tests, individuals with a positive fecal immunological test (FIT) are referred for colonoscopy (gold standard). However, performance of this test is suboptimal, with sensitivity for CRC and advanced adenomas of 66-87% and 27-38%, respectively, depending on cut-off value used [45; 46; 47; 48]. In addition, screening with FIT results in a substantial number of false positive tests, and as a consequence, unneeded colonoscopies. Because of these limitations, an unmet need exists for a more sensitive and specific test to select only high risk individuals for colonoscopy. VOCs originating from breath, feces, blood and urine are increasingly considered as potential noninvasive biomarkers for CRC and advanced adenomas. Up to now, two studies have focused on the analysis of feces for this purpose.  
In 2014, fecal VOC patterns of 40 CRC patients, 60 patients with advanced adenomas (AA) and 57 healthy controls were analyzed by means of an eNose [49]. They found that both patients with CRC and AA could be discriminated from controls (Table 3). In a second study, fecal VOC profiles of patients with a positive fecal occult blood test (FOBT) were assessed by means of SIFT-MS [50]. By colonoscopy, patients were classified into risk groups (e.g., low risk and high risk) based on their histopathology. Of 62 FOBT positive patients 31 were classified as high risk, meaning they were diagnosed with AA or CRC, while the remaining 31 patients were classified as low risk. This study demonstrated that classification based on VOC profiles was possible with good sensitivity and specificity (Table 3).  
The results of these studies emphasize the potential of fecal VOCs as non-invasive biomarkers for the detection of colonic neoplasia, in particular of precancerous adenomas. Whether this could lead to earlier cancer detection and consequently to lower (unnecessary) endoscopy rates and a higher curation chance should be studied in larger validation settings, comparing the diagnostic accuracy of the currently used FIT screening with VOC patterns. Furthermore, many studies focused on the identification of disease specific VOCs, however, limited studies report quantitative data which might be used in the determination of cut-off values for example CRC.
# CoSiWiNeT: A Clock Synchronization Algorithm for Wide Area IIoT Network
## 1 Introduction
Moreover, the management of PTP in a giant network such as WAN is challenging due to the complexity of the network structure, the difference of regional environments, the numerous and dispersed nodes, and limited fiber coverage. A software-based network time protocol (NTP) [4; 5] is another network packet-based clock synchronization technique that uses software time-stamps to compute delays. NTP is easy to implement, deploy, and cost-effective compared to  
Figure 1: Typical WAN in IIoT. GPS and PTP. NTP is a promising clock synchronization solution for WAN, but it lacks in accuracy required for emerging remote monitoring and control applications. In order to address these challenges, this paper proposes a software-based, scalable, yet accurate and precise clock synchronization algorithm for WAN called CoSiWiNeT based on Random sample consensus (RANSAC) algorithm to effectively deal with outliers in noisy offset data and predict correct clock offsets. For the evaluation of CoSiWiNeT, data measurements in a real WAN connecting two cities separated by 107 km were conducted. The results demonstrated that CoSiWiNeT outperforms NTP and state-of-the-art methods by achieving around 40% improved performance. The contributions of this paper are as follows:  
1. A scalable and precise clock synchronization algorithm based on RANSAC for heterogeneous WAN that provides improved synchronization over a degrading network condition has been proposed. 2. state-of-the-art methods typically use simulated network data or data from controlled environments, e.g., laboratories. The proposed algorithm is evaluated by means of simulation based on the data from real WAN with different degrees of network qualities--from good to degrading networks. 3. The algorithms' performance was benchmarked against widely used in-practice time synchronization protocols such as NTP as well as state-of-the-art methods based on Kalman filter (KF) [6; 7] and windowed least square (LS) [8] available in the literature. The proposed algorithm's greatly improved performance with methods from practice and literature strengthens the new algorithm's positioning. The paper is organized as follows. First, related work is presented in Section 2. Section 3 describes the measurements from WAN and characterizes them. Section 4 introduces the CoSiWiNeT algorithm, and Section 5 evaluates its performance based on the measured network data. The conclusions have been provided at the end.
# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 4 Challenges
### Changes in User's Information  
The mobile identity is tied to the SIM number. We frequently hear that people keep changing their SIM number, also having multiple SIM cards and shared SIMs. This is quite troublesome in maintaining the integrity of the identity, especially when recycling mobile numbers is also widespread and still growing. The challenge is to make information change convenient at the consumer end. The harder it becomes, the lesser public involvement in registering for mobile identity. This limits the expansion potential of the mobile identity system.  
To overcome this issue, the user identity cannot be tied to a mobile number or known as a Mobile Station International Subscriber Directory Number (MSISDN). It may be identified using the MSISDN account number, which is only known by the customer and the service provider. The account number may act as the physical identity card number and will be permanently assigned to the user. So, the mobile identity system, instead of using an MSISDN number, should register the user using the user's service provider account number. In this case, changing or having multiple SIMs should not be an issue.
# Comparison of Heat Transfer Enhancement Techniques in Latent Heat Storage
## 3 Results and Discussion
### Composites
#### 3.4.1 Metal Foam Based Composites  
Across the metal foams (MF) considered as TCE options for LHES systems, the most widely studied are aluminum [21; 22], copper [23; 24; 25], and nickel [26] foams.  
For instance, [PERSON] et al. [22] considered aluminum foams of different porosities (N.2, N.3) on a shell and tube arrangement and achieved reductions in discharge time of up to 63% and a proportional increase in its heat transfer performance when compared to the case with only PCM (N.1). However, only a marginal increase in performance is observed with decreasing metal foam porosity (FP) (and consequently PCM volume fraction) as shown in Figure 4. An ultimate difference of around 15% additional PCM volume fraction is sacrificed to accommodate the lower porosity foam but little to no effect is shown in terms of heat transfer enhancement. Additionally, the energy density of the system is heavily affected, decreasing from 112 kWh/m\({}^{3}\) in the case of pure PCM (N.1), to 108 kWh/m\({}^{3}\) for the 95% porosity foam (N.2) and finally 94 kWh/m\({}^{3}\) in the case of the 77% porosity foam (N.3). It becomes clear that the energy density trade-off when accomodating the higher porosity foam (N.2) is worth it in terms of performance, but, once a sufficient highly conductive network is created, there is no substantial enhancement in increasing the amount of metal in the unit.  
[PERSON] et al. [21] also studied the effect of aluminum foams with slightly different porosities and number of pores per inch (PPI) achieving in the best case a reduction of around 90% on the solidification time. In a similar way, [PERSON] et al. [23] used copper foams of increasing PPI and attained a reduction in charging time of around 27%. Similarly, [PERSON] et al. [24] used copper and nickel foams of different amounts of PPI to improve PCM conductivity. From the measurements performed by the authors, a great improvement is evident for all cases when compared to the original 0.305 W/m\(\cdot\)K. For instance, the copper foams embedded in the PCM produced conductivities of 5 W/m\(\cdot\)K and 16 W/m\(\cdot\)K for 97% and 88.9% porosity samples, respectively.  
The latter examples, although worth mentioning, are not shown in this study since their experimental setup was not conceived as LHES units working with a heat transfer fluid and thus it was not possible to accurately calculate the proposed KPIs without inaccurate assumptions. Similarly, a major share of the considered references pertaining the use of metallic foams concerned effective thermal conductivity measurements, and experimental setups focused towards electronic heat management strategies. In order to compare in terms of the NHTPC methodology, either more  
Figure 4: Calculated NHTTPC vs. \(\Phi_{PCM}\) for the references concerning carbon based structures and metal foams as TCE in LHES unit configurations.  
experimental work or validated models that place these materials in a LHES unit configuration are necessary to further study and evaluate their potential.
# Monitoring Forest Phenology in a Changing World
## 4 Monitoring Challenges to Be Addressed
### Upscaling from Monitoring Sites to Biomes  
Translating high resolution spatial data into landscape or biome level phenological metrics is needed to take the step from describing site-specific phenology to understanding the ecological impact of phenology at landscape and biome scales. At the simplest level, upscaling happens via the aggregation of point location observations to generate weighted means of phenological occurrences over larger areas, e.g., an average phenological metric across 1 ha [142; 159; 213]. Other methods have been proposed such as using Bayesian Hierarchical models that incorporate structure and model the uncertainties in aggregated metrics to predict larger scale phenological trends. For example, modelling a single taxa across multiple sites or multiple taxa at individual sites [214; 215]. However, many of the previously proposed methods have been for studies in temperate biomes with relatively low diversity. A more significant challenge lies in upscaling phenological metrics in highly diverse environments such as tropical forest systems. Fully automated hierarchical monitoring systems and the construction of validation chains may help with this. In-person observations or fixed camera imagery can be used to calibrate high spatial resolution/intermediate spatial scale UAV imagery, and in turn this UAV data can be used to calibrate medium or low spatial resolution/large spatial scale satellite imagery. This framework could be adapted to the upscaling of temporal phenological patterns as well. By exploiting hierarchies of temporal monitoring, observations made at high temporal resolution can be used to understand the proportion of events that might be missed by low resolution methods. Together, spatial and temporal upscaling provide a framework to simultaneously exploit the outputs from multiple monitoring methods to provide the information needed to more accurately quantify phenology across space and time.
# Experimental Validation of GNSS Interferometric Radio Occultation
## 4 Data Processing, Analysis and Discussion
### Conventional RO Processing
#### 4.2.1 Clean Replica with Closed Loop  
The collected raw samples are organized by consecutive 1-sec files. For each file, the signal processing is started with a _blind_ search of both C/A-code delay and carrier frequency. Once the delay-Doppler cell containing the signal has been detected, the code phase and carrier Doppler are tracked with conventional DLL (Delay Locking Loop) and PLL (Phase Locking Loop) [12] to generate error signals that keep the replica and received codes aligned and also keep the local carrier tuned to the correct frequency.  
The Doppler estimated with the phase tracking loop is presented in Figure 5 together with the predicted one. It can be seen that the estimated Doppler agrees well with its prediction. However, the closed-loop Doppler observations become noisy for the low elevation (below \(5^{\circ}\) of elevation) where the degradation of signal to noise ratio (SNR) makes the PLL loose lock.  
Figure 4: Elevation of PRN24 **(a)** and its predicted Doppler frequency (**b**). Only the receiver and transmitter geometry has been used to obtain the predicted Doppler, no atmospheric effects have been considered.