esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Error Score and ROC Comparisons  
The root mean squared error (RMSE) measures the difference between the predicted and the original value and denotes the error rate. Figure 13 shows the error scores for GCNN, OpGCN, and our proposed GCNN compared to different hidden units. Our model's RMSE score was the lowest compared to GCNN's and OpGCN's, with a score of 3.9, and performed best at HU_100.  
Figure 12: Testing loss with respect to different batch sizes.  
Figure 13: Root Mean Square Error (RMSE) against different numbers of hidden units.  
We compared the ROC curves of four different models with the curve for our proposed GCNN model, applied to our dataset. The ROC is an important tool when predicting the probability of the results achieved. The ROC is basically a graph plotted between the False Positive Rate (FPR) and the True Positive Rate (TPR). The FPR is usually plotted on the \(x\)-axis, and the TPR is plotted on the \(y\)-axis. A model is considered efficient when the values on the plot are gathered towards the top left of the plot and away from the 45-degree diagonal, i.e., when there are higher values on the \(y\)-axis. In other words, it means that the model produced more correctly predicted values than falsely predicted ones. As we can see from Figure 14, our proposed model is the farthest from the 45-degree diagonal of the ROC space and performed better than the existing models, with higher accuracy. The models used for comparison include Association Rule Mining [52], GCNN, DeepWalk, PMF, and OpGCN.
## 4 Experimental Results and Its Analysis in Real-Time Environment  
In this section, the experiments were evaluated and analyzed over a real-time dataset PIDD [14] with a python environment with six essential critical parameters such as glucose, insulin, pregnancy, blood pressure, age, and BMI. In order to evaluate the effectiveness of the proposed framework, the results were compared with the similar and recent existing methods such as CNN [50], Bi-LSTM [52], DNN [53], and a combination of CNN-LSTM [54], and CNN-Bi-LSTM for the classification of Type 2 diabetes over PIDD [14]. In this section, five metrics were used to measure the overall success of our proposed model: accuracy (\(A\)), as seen in Equation (10) [25]; Recall (\(R\)), as in Equation (11) [55]; sensitivity (\(SN\)), as in Equation (12) [41]; and specificity (\(SP\)), as in Equation (13) [25], where the sensitivity of a model determines its capacity to classify patients who currently have a disease correctly; whereas the specificity of a model determines its capacity to classify disease-free patients correctly. The precision of the model defines the number of patients accurately described by the model. However, the ratio of the number of patients properly classified by the model is called accuracy. Formulas are stated as follows:  
\[A=\frac{TP+TN}{\text{Total no of samples}} \tag{10}\]  
\[R=\frac{TP}{TP+FN} \tag{11}\]\[SN=\frac{TP}{TP+FN} \tag{12}\] \[SP=\frac{TN}{TN+FP} \tag{13}\]  
where true positive (_TP_) is defined as the number of positive patients who were scored as positive. True negative (_TN_) is the number of negative patients predicted to be negative. False-positive (_FP_) is the percentage of patients who are classified as positive but are actually negative. Negative (_FN_) is used to calculate the percentage of positive patients negatively observed.
# Heterogeneous Network Switching Strategy Based on Communication Blind Area Dwell Time
## 2 Methodology  
### Basic Theory of VLC  
Line of sight (LOS) and non-line of sight (NLOS) are the two main types of indoor optical communication links. The experiment results demonstrate that the received power of indoor visible direct link accounts for 93.03% of the total received power, and the received power of primary reflection accounts for 5.53% of the total received power [11]. In order to reduce the errors generated during the process of calculating the BER, we only adopt LOS in this paper.  
The light-emitting diode (LED) light source distribution obeys the Lambert distribution, and the specific Lambert model can be expressed as  
\[R(\theta)=\frac{m+1}{2\pi}\cos^{m}(\theta) \tag{1}\]where \(\theta\) indicates the radiation angle of the LED light source, and \(m\) is the number of radiation modes of the light source, which is related to the half-power angle of the light source.  
In the case of LOS, the channel direct current (DC) gain is calculated by  
\[H(0)=\left\{\begin{array}{l}\frac{m+1}{2\pi d^{2}}A\cos^{m}(\theta)T_{s}(\psi )g(\psi)\cos(\psi),0\leq\psi\leq\psi_{c}\\ 0,\psi>\psi_{c}\end{array}\right. \tag{2}\]  
where \(A\) is the sensing area, \(T_{S}(\psi)\) is the optical filter DC gain, \(\psi_{c}\) represents the half field of view of the receiver, and \(d\) represents the position from the light source. The \(g(\psi)\) is the gain of the concentrator, which can be presented in the following equation:  
\[g(\psi)=\left\{\begin{array}{l}\frac{\eta}{\sin^{2}\psi_{c}},0\leq\psi\leq \psi_{c}\\ 0,\psi>\psi_{c}\end{array}\right. \tag{3}\]  
The received power under the conditions of the LOS is [12; 13]:  
\[P_{R}=P_{s}H(0) \tag{4}\]  
where \(P_{S}\) represents the transmit power, and \(P_{R}\) represents the received power under the circumstance of LOS.  
In the channel of indoor visible light transmission, Gaussian white noise is employed. There are two main types of noise: scattered particle noise and thermal noise. The scattered particle noise can be expressed as:  
\[\delta_{shot}^{2}=2 qR\Big{(}P_{R(signal)}+P_{R(ISI)}\Big{)}B+2 qI_{bg}I_{2}B \tag{5}\]  
where \(q\) is the quantity of electric charge, \(P_{R(signal)}\) is the signal received power, \(B\) is the noise bandwidth, \(P_{R(ISI)}\) is the inter-code interference power, \(I_{bg}\) is the dark current, and \(I_{2}\) is the noise bandwidth factor. The thermal noise can be expressed as  
\[\delta_{thermal}^{2}=\frac{8\pi kT_{k}}{G}C_{pd}AI_{2}B^{2}+\frac{16\pi^{2} kT_{k}\Gamma}{g_{m}}C_{pd}^{2}A^{2}I_{3}B^{3} \tag{6}\]  
where \(k\) is the Boltzmann constant, \(T_{k}\) is the absolute temperature, \(C_{pd}\) is the fixed capacitance per unit area of the photodetector, \(G\) is the open-loop voltage gain, \(\Gamma\) is the channel noise coefficient, \(g_{m}\) is the transconductance coefficient, and \(I_{2}\), \(I_{3}\) are both noise bandwidth coefficients. The signal-to-noise ratio (\(SNR\)) can be expressed as [14]:  
\[SNR=\frac{R^{2}P_{R(signal)}^{2}}{\delta_{shot}^{2}+\delta_{thermal}^{2}} \tag{7}\]
# SDR-Based 28 GHz mmWave Channel Modeling of Railway Marshaling Yard
### Contribution and Organization of the Paper  
Though a significant amount of research has been carried out from the four categories reviewed above, to the authors' best knowledge, there is still a lack of evaluation of the 28 GHz mmWave channel characteristics for the railway marshaling yard scenario. However, accurate propagation modeling and parameter analysis are crucial for achieving better performance for the radio-communication system, from the perspective of both link level and system level. To fill the aforementioned research gaps, the novelty of this work is to investigate the railway marshaling yard mmWave propagation scenarios of 28 GHz mmWave and conduct a measurement campaign with the SDR channel sounding system, using self-developed software under the LabVIEW platform to derive channel parameters,based on which, the channel characteristic and influence of typical objects are analyzed. The data collection for this paper was conducted on a sunny morning, and meteorological effects, such as the potential impact of snow or rain, was not in the scope of this study.  
The rest of this paper is structured as follows. In Section 2, we will describe the railway marshaling yard propagation scenario, and three testing scenarios are designed. The software-defined radio-based channel modeling system as well as construction of the measurement environment will be presented in Section 3. Then, corresponding analysis results are summarized in Section 4. Finally, Section 5 will present the conclusions.
# Optical Analysis of the Impact Transmission in Steel Sheet Arrays with Bolted-Type Points
## 1 Introduction
Structural joints are highly important as their selection depends on many factors, such as structure, geometry, load conditions, fabrication process, and maintenance requirements [23]. NASA has tested structural joints with one bolt but a different set of rows. It developed an analytical methodology to adequately modulate the behavior of a structure that represents the join of a wing [24]. Additionally, bolts were studied under reverse fault loadings [25] for the design of truss joints analyzing the stress and strain of plates [26], evidencing the importance of joints in precast concrete structures [27], to determine experimentally and numerically the mixed-mode fracture properties of adhesive structures [28]. A review of measurement techniques for residual stress was written, in which the authors expose the importance of internal stresses as a critical aspect of manufacturing, monitoring, repair, and model validation in the development of new metallic coating and joining technologies [29]. The present article is focused on the importance of measuring how a wave travels along different matrix arrays of steel sheets joined by bolts and nuts. The implemented methodology was performed by calculating the movement caused to different steel sheet arrays as a consequence of an indirect impact. Digital image correlation helped to evaluate the displacement of 5 cm before and after each structural joint. The experimental result was studied by means of coding the first derivative of the displacement, followed by taking out the high-frequency values to see the main impact reaction to the structure. This procedure was repeated for the different arrays. To study it, two analytical models were proposed, in which the solutions had harmonic and damped waveforms.
## 3 Materials and Methods
### Extraction and Analysis of Lipophilic Compounds
#### 3.3.2 Extraction and Analysis of Fatty Acids Using GC-FID  
To determine the fatty acid composition, the extraction and analysis were performed as previously described [29]. Briefly, 10 mg of pigmented rice powder were mixed with 2.5 mL of a chloroform:methanol solution (2:1, _v/v_) and 0.1 mL of pentadecanoic acid (1 mg/mL in chloroform; used as an IS). The samples were vortexed and sonicated for 20 min. Then, 2.5 mL of sodium chloride solution (0.58%, _w/v_) was added, and the mixtures were centrifuged at 4 \({}^{\circ}\)C, 13,000\(\times\)\(g\) for 5 min. The bottom layer was collected into new tubes. The transferred samples were then evaporated in a centrifugal concentrator for 30 min. Thereafter, 0.18 mL of methanol, 0.1 mL of toluene, and 0.02 mL of 5 M sodium hydroxide in water were added, and the samples heated at 85 \({}^{\circ}\)C for 5 min, with shaking at 300 rpm. For the saponification and methylation, 0.3 mL of 14% (_v/v_) boron trifluoride (BF3) was added, and the samples incubated at 85 \({}^{\circ}\)C for 5 min, with shaking at 300 rpm. After cooling at 25 \({}^{\circ}\)C, 400 \(\mu\)L of distilled water and 800 \(\mu\)L of pentane were added, and the samples centrifuged at 750\(\times\)\(g\) for 15 min at 4 \({}^{\circ}\)C. The upper pentane layer was transferred into 2 mL tubes and concentrated in a rotary evaporator (CC-105, TOMY, Tokyo, Japan). Prior to the analysis, the concentrated samples were diluted in 100 \(\mu\)L of hexane, and then filtered through a 0.5 \(\mu\)m syringe filter. The samples were analyzed using Agilent 7890B GC-FID (Agilent, Santa Clara, CA, USA) equipped with Agilent G4513A auto sampler (Agilent, Santa Clara, CA, USA). For the analysis, 1 \(\mu\)L of the extracted sample was injected into the column at a 1:10 ratio split mode. The front inlet and detector temperatures were 250 \({}^{\circ}\)C. The GC was equipped with a DB-WAX column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\mu\)m, Agilent, Santa Clara, CA, USA), and nitrogen was used as a carrier gas, at a flow rate of 1.00 mL/min. The column oven temperature was maintained at 130 \({}^{\circ}\)C for 3 min, and was then increased at a rate of 20 \({}^{\circ}\)C/min until it reached 230 \({}^{\circ}\)C. The temperature was then increased to 250 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min, and finally maintained at 250 \({}^{\circ}\)C for 5 min. The oven post-run time was 5 min. The peak data were acquired using the ChemStation software (Agilent, Santa Clara, CA, USA) (Figure S5). Qualitative and quantitative analyses of FAME were done using standards and FAME mixture (C\({}_{8}\)-C\({}_{24}\)) (Tables S10 and S14).
## 2 Materials and Methods
### Remote Sensing Platforms  
#### 2.4.1 Spaceborne Landsat-8  
Landsat-8 is a satellite-based RS platform jointly managed by the United States Geological Service (USGS) and the National Aeronautics and Space Administration (NASA). A Landsat-8 satellite is equipped with an operational land imager (OLI) and a thermal infrared sensor (TIRS). These instruments capture images of Earth's surface, with the OLI providing data at a spatial pixel resolution of 30 m and the TIRS at 100 m, resampled to 30 m [40]. These images are acquired every 16 days. The OLI sensor captures shortwave multispectral data, while the TIRS camera records longwave infrared (LWIR) thermal radiation images. The study sites, LIRF and IIC, are strategically located in the overlapping region of Landsat-8 scenes with path/row designations of 33/32 and 34/32, respectively. As a result, the temporal resolution for data acquisition for Landsat-8, in this study, was once every eight days depending on sky cloudiness conditions near noon time.  
Landsat-8 follows a sun-synchronous orbit around Earth, orbiting at an altitude of 705 km, with equator crossings occurring at approximately 11:30 a.m. local time. The original radiometric resolution of Landsat-8 imagery is 12 bits, but this is enhanced to 16 bits after post-processing by USGS/NASA. To convert digital numbers (DN) into ground surface reflectance and nadir-looking temperature (Landsat-8 Level-2 imagery), linear calibration coefficients are provided in the metadata of the imagery file. Level-2 images undergo rigorous calibration procedures, eliminating the need for additional post-processing once the final surface reflectance and temperature images are accurately derived from the original DN values, following the methods outlined by [PERSON] et al. (2014) in 2014. Further details regarding atmospheric corrections applied to Landsat-8 imagery can be found in ([PERSON] et al., 2014). Table 3 presents the spectral characteristics of the Landsat-8 bands considered in this research. The pixels that overlapped with the measurement stations at each research site were considered to be representative for the estimation of maize ET\({}_{a}\) using the TSEB algorithm.
# End-to-End Light Field Image Compression with Multi-Domain Feature Learning
## 2 Related Works  
### Traditional Light Field Image Compression  
Traditional methods typically explore compression techniques suitable for light field images based on existing video codecs [22; 23; 24]. For example, [PERSON] et al. [22] arranged the light field SAIs into pseudo-video sequences and compressed sequences using the standard video codec. [PERSON] et al. [25] arranged all SAIs into two scan orders, i.e., the spiral order and the zigzag order, and encoded them with the video codec, thereby effectively reducing spatial redundancy in light field images. Different from the fixed arrangement order,Porto et al. [23] designed a context-adaptive coding scheme that calculates the sum of absolute differences (SADs) among SAIs, and then dynamically arranges SAIs according to the minimum SAD, thus effectively reducing inter-frame prediction residual. To further improve the prediction accuracy, [PERSON] et al. [24] divided the prediction unit (PU) into three different categories based on the content attributes, and designed a Gaussian process regression-based prediction method to improve compression efficiency. However, the effect becomes limited when the number of SAIs increases, making it difficult to further improve compression efficiency.  
The recent standard Joint Photographic Experts Group Pleno (JPEG Pleno) only encodes selected SAIs and uses disparity information at the decoder to reconstruct unselected SAIs, which could significantly reduce the encoding bitrate and provide a new direction. Inspired by JPEG Pleno, [PERSON] et al. [6] transmitted half of the SAIs and their corresponding disparity maps, which improves the reconstruction quality with the cost of more bit consumption. To further reduce bit consumption, [PERSON] et al. [7] proposed a low-bitrate compression method with geometry and content consistency that achieves low-bitrate light field image compression while ensuring reconstruction quality. However, existing video codecs are not tailored for the unique characteristics of light field images, making it challenging to attain optimal compression efficiency.
## 3 Results
### Spatial Resolution with the FLUKA Simulation
#### 3.5.2 Line Spread Function  
The results from the LSF method and the 1 mm thick copper plate are summarized in Table 2. The number of primaries was chosen such that the statistical uncertainties were smaller than 5%. The spatial resolution was estimated by fitting the LSF by a Gaussian function and measuring the FWHM of the central 6 mm diameter holes, the 6 mm hole at the top of the plate, the three 3 mm holes separated by 4 mm pitch and the 1.3 mm holes separated by 2.5 mm pitch (see Figure 16).  
The profiles obtained for the three 1.3 mm holes separated by 1 mm (distance calculated from edge to edge) are shown in Figure 17. The three holes are perfectly resolvable following the FWHM criterion; the calculated hole diameter is smaller than or of the order of the hole. This implies that the spatial resolution is better than the hole size. Similar results were obtained with 3 mm thick copper plate and 5 mm diameter holes, which showed a spatial resolution of 4.87 \(\pm\) 0.03 mm (FWHM).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**FWHM (mm)**} \\ \cline{2-4}
**Ø Hole** & **Hole 1** & **Hole 2** & **Hole 3** \\ \hline
6 mm & 5.70 \(\pm\) 0.20 & 5.45 \(\pm\) 0.11 & 5.26 \(\pm\) 0.12 \\
6 mm & 5.82 \(\pm\) 0.13 & - & - \\
3 mm & 2.96 \(\pm\) 0.17 & 2.99 \(\pm\) 0.24 & 2.82 \(\pm\) 0.16 \\
1.3 mm & 1.32 \(\pm\) 0.12 & 1.61 \(\pm\) 0.16 & 1.48 \(\pm\) 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculated FWHM of the 1D profiles for the different regions of interest depicted in Figure 16.  
Figure 16: Image of the simulated energy deposition density in the drift gap. The solid yellow lines represent the selected regions for data analysis.
## 3 Results and Discussion
### Rebar Corrosion Resistance Accelerated Test
With the available data, it is not possible to explain the scattering observed in the depassivation times of the concrete C specimens. However, it should be considered that corrosion is always a complex stochastic process, for which it is frequent to find a considerable scattering. Figure 12a,b show the evolutions of steel corrosion rate (\(I_{corr}\)) during the corrosion tests of the reinforced specimens of concretes C and C*, respectively. Figure 13 contains the evolutions of the mean values of \(I_{corr}\) for both types of concrete. The corrosion rate is expressed in electrochemical units, i.e., \(\mu\)A/cm\({}^{2}\). \(I_{corr}\) is a kinetic parameter related with the actual intensity of the corrosion process. The values accepted in the literature [44; 45] for its interpretation are shown in Table 9. The graphs contain three horizontal lines at 0.1, 0.5, and 1 \(\mu\)A/cm\({}^{2}\) which indicate the accepted limits separating the domains of negligible, low, intermediate, and high corrosion rate [44; 45], see Table 9.  
and C3 respectively, while the corresponding corrosion initiation times (rise in \(I_{corr}\) values) were at 180, 150, and 110 days, respectively. In the case of concrete C*, the deg passivation took place in the range from 150 to 170 days, and the corresponding corrosion initiation times spanned in the same range. The comparison of the \(I_{corr}\) mean values of both concretes in Figure 13 seems to show that the presence of the admixture could increase the corrosion initiation time from about 110 to 170 days, but this conclusion is not definitive due to the high scatter observed in the case of the concrete C specimens. The obtained results of electrochemical measurements seem to indicate that the presence of the admixture CCADM in concrete could reduce the scattering of the corrosion initiation period and could delay the corrosion initiation (from 110 to 170 days, according to Figure 13), in the experimental conditions of this work. This outcome is in fairly good agreement with findings of previous works [16; 19]. However, the small number of tested specimens (from a statistical point of view) does not allow for unequivocal acceptance of this conclusion. More experiments would be necessary, with a greater number of specimens and a longer duration of the tests, to be able to carefully evaluate the effect of the incorporation of the admixture on the corrosion processes of steel reinforcement in concrete.
# A New Prediction Model of Annular Pressure Buildup for Offshore Wells
## 2 Source and Formation Mechanism of Annular Pressure  
### Thermally Induced Annular Pressure  
At present, the sources that cause the increase in annular pressure between casing mainly include the following three categories:  
(1) Sustained Casing Pressure  
Sustained casing pressure refers to the pressure phenomenon that persists in the casing annulus after the wellhead is relieved through the blowout valve. According to the statistics of the U.S. Department of Mines, sustained casing pressure mainly exists in the jacket annulus, which is mainly caused by casing leakage, packer failure, and poor cementing quality. Once sustained casing pressure is created, it is usually necessary to replace the oil casing to eliminate that part of the annular pressure;  
(2) Operator-Imposed Pressure  
The annulus pressure rise caused by operation and construction mainly comes from special production processes, such as gas lifting, heavy oil thermal recovery, and annulus pressure monitoring and pressure test measures. Therefore, the management of this part of annulus pressure needs to select appropriate process technology and manage it in accordance with relevant company standards;  
(3) Thermally Induced Pressure  
Due to the relatively high temperature of the oil and gas extracted from the formation, the high-temperature laminar flow will continue to lose heat to the low-temperature medium around the wellbore during the upward migration along the wellbore, which causes the thermal expansion of the closed fluid medium in the casing and forms the trap pressure. This part of pressure is common in the development of deep-water, high-temperature oil and gas fields. Because pressure relief cannot be achieved through the wellhead, casing collapse accidents caused by annular thermal effect are prone to occur in the initial stage of production, threatening wellbore safety and integrity. In this paper, the annular pressure caused by the thermal effect is studied and analyzed.