esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios
## 4 LSTM for Load Estimation and PPO for Task Offloading Strategy  
### Load Estimation of BSs via LSTM  
LSTM is a specialized type of recurrent neural network (RNN) designed to handle long sequences of data while addressing issues such as vanishing and exploding gradients. This architecture manages the flow of information through a gating mechanism, which allows it to learn and retain long-term dependencies effectively. The key component of the LSTM is its unique cell structure, which contains three gates: the forget gate, the input gate, and the output gate.  
The forget gate is responsible for deciding which information, now outdated or irrelevant, should be discarded from the cell state. This ensures that the network remains focused on the most pertinent data. The input gate determines which new information should be stored in the cell state, updating the internal state of the network accordingly.  
The output gate controls the next hidden state's value and decides what information should be passed onto the subsequent layers of the network, enabling the network to utilize this information for making predictions.  
This gating mechanism allows the LSTM to capture and leverage long-term dependencies in data, making it particularly suitable for time-series prediction tasks. Since the real-time load state of the BS cannot be directly accessed due to protocol conflicts or incompatibilities, the BS load must be estimated with offloading decisions being based on these estimates. Given that the BS server load exhibits periodicity and volatility depending on traffic flow, it is a practical and effective approach to incorporate LSTM for predicting BS load patterns.
## 2 System Design
### Adjustment Method  
The pseudo-ranges in the first term of Equation (11) are measured on each satellite, which means the information must be collected on the slave satellite for subsequent processing. In other words, the pseudo-ranges obtained from the master satellite must be packed into the data frames and transmitted to the slave satellite. Here we propose using the Proximity-1 Version-3 Space Link Protocol as defined by the Consultative Committee for Space Data Systems (CCSDS) for data frame design [27]. With a data overlay of 1000 bits/s and 200 bits per sub frame, the minimum clock adjustment time period would be 0.2 s. The processor on the slave satellite extrapolates the previous clock errors with a nonlinear LS method. The time to be adjusted is given by a third-order PLL, which is then converted into the voltage control signal. The on-board VC-OCXOs of the slave satellites change the frequencies at the fixed time period:\[\Delta t\left(t_{i}+t\right)=x\left(t_{i}\right)+\left(y\left(t_{i}\right)+f_{jb} \left(t_{i}\right)\right)t+\frac{1}{2}a_{2}\left(t_{i}+t\right)^{2}+\psi\left(t_ {i}+t\right),0\leq t<t_{i}-t_{i-1} \tag{25}\]  
where \(t_{i}\) is the \(i\)th clock adjustment instance, \(f_{fb}\) is the adjustment frequency converted by the voltage control signal.  
As shown in Figure 7, a series of phase delay elimination procedures are implemented before the slave clock starts detecting the phase deviation between two clocks. It is noted that different effects should be considered in different scenarios, due to the space condition, separation distance and relative velocity. Equation (24) determines whether it is necessary to compensate for the relative motion error. The delay elimination methods for each kind of delays are represented in Table 2.
## 5 MIMO Antenna Design  
The single-element antenna design described above is used to develop a four-element MIMO antenna so as to attain both the high channel capacity and data rates in the mmWave frequency. As depicted in Figure 8, the proposed MIMO antenna has four antennas placed orthogonally (perpendicular) to each other to achieve adequate isolation between them, as in ref. [34].  
Each antenna element has a separate ground plane with a separate set of protruding stubs on the edge of each ground plane. The dimensions of these two protruding stubs, St, and the height of the ground plane have been updated to 2.4 mm and 2.6 mm, respectively, as shown in Figure 6a. Also, the dimensions of the feedline have been updated for each individual MIMO element so as to fine-tune the antenna's performance in MIMO configurations. The optimized size for the proposed MIMO antenna structure is 16.2 mm \(\times\) 16.2 mm.  
In order to have adequate isolation in the MIMO configuration, the separation between the individual MIMO elements has been optimally increased. In addition, a 0.3 mm wide connecting line in the ground plane has been added between the individual MIMO elements so as to have a co-surfaced ground plane [35]. Figure 9 shows the fabricated prototype of the proposed antenna.  
Figure 8: MIMO antenna design. (**a**) Top view, (**b**) modified feedline.
# Using LSTM to Identify Help Needs in Primary School Scratch Students
## 2 Materials and Methods
### Research Context and Technological Choices
#### 2.1.3 Time-Series Based Models  
Time series data are samples taken over time from a real process. Many time series are temporal dependent and non-stationary [24], and constitute a discipline of data modeling where past values of a given variable are analyzed to predict the future values of this variable [9].  
One of the most popular methods for processing time series is Recurrent Neural Networks (RNNs) [25]. This method is widely used for Time Series Classification (TSC), for example in forecasting [26; 27], as RNNs are able to preserve information between neurons over the time line [28].  
There are different types of architecture in RNNs, but the most common and currently used are LSTM and Gated Recurrent Unit (GRU) [25].  
LSTM is a solution of the problem of vanishing gradient and gradient explosion in the training of RNNs. This architecture has two gates: an \"input gate\" and a \"forgetting gate\". The \"input gate\" is used to add information to the neurons, and the \"forgetting gate\" decides which information is important and should be stored and which information to forget.  
GRU is more general than LSTM and simplifies both the mathematical model and the parameter mechanism [25].  
When there is a limited amount of historical sequence data, problems such as gradient explosion or gradient disappearance may occur when processing s long-term sequence of samples. This fact causes instability in the model, making it unable to converge to the optimal result. LSTM, with a strong feedback mechanism solves the problem of the long-term dependency in the Recurrent Neural Network (RNN) [10].  
In education, LSTM has been implemented in different domains. For example, in [10,11], LSTM has been implemented together with a Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN) to increase their accuracy. In [28] a new framework based on the LSTM algorithm was proposed for performing personalized and adaptive learning, giving better results than the Deep Knowledge Tracing (DKT) method. And in [29], a method based on LSTM helps programming students by providing the next word following an incomplete program.  
When a student is carrying out an exercise, it is crucial to identify the moment when the student will need help, therefore time is a dimension to take into account. Because we have a limited dataset, that may cause the gradient vanish or gradient explosion problem, we will implement a LSTM architecture.  
RNNs have different configurations, as is described in [30]:  
* One-to-one (Figure 1): One input and one output are suitable for classification tasks, but not for time series models;
* One-to-many (Figure 2): This model converts an input into a sequence;
* Many-to-many (Figure 3): This model is a sequence to sequence generator;
* Many-to-one (Figure 4): This model is suitable for a prediction or classification from a sequence.  
In this study, the input is a sequence of data and the
# Correction of Field-Measured Wind Speed Affected by Deterministic Interference Factors
## 4 Maximum Reference Wind Pressure of Super Typhoon Mangkhut  
The basic wind pressure defined in the Load Code for the Design of Building Structures in China [24] is as follows: the mean 10 min maximum wind speed obtained by statistics in 50 years at the height of 10 m above the ground on the relatively open landform is the maximum basic wind speed, and the wind basic pressure is equal to 1/1600 of the square of the maximum basic wind speed. Then, the values of the actual reference wind pressure in Shenzhen during Super Typhoon Mangkhut are discussed according to the \(C_{\text{VI}}\) of these sites.  
[PERSON] et al. [25] (2009) proposed a typhoon boundary layer model applicable to the sea based on the measured results of the downcast anemometer and the theoretical typhoon boundary layer model of [PERSON] [26]. The model could be expressed as follows:  
\[\left\{\begin{array}{l}U(z)=\frac{u_{z}}{x}\left[\ln\left(\frac{z}{z_{o}} \right)-0.4\left(\frac{z}{H^{e}}\right)^{2}\right]\\ H^{*}=343.7+0.260/I\\ I=\sqrt{\left(f+\frac{2V}{r}\right)\left(f+\frac{V}{r}+\frac{\partial V}{ \partial r}\right)}\\ H=1.12\times H^{*}\end{array}\right. \tag{5}\]  
Figure 10: Variation of CVI with wind direction of PFAC.  
where \(V\) is the tangential mean gradient wind speed, which can be ignored in calculation. \(H^{*}\) is the boundary layer height parameter, which is inversely proportional to the inertia stability \(I\), and the value ranges from 300 m to 1200 m based on [PERSON]'s measured data. \(H\) is the boundary layer thickness, \(\kappa\) is the von Karman coefficient with a value of 0.4, and \(u_{*}\) is friction velocity. \(z_{0}\) is the aerodynamic rough length, which is equal to 0.002 m in an open water area and 0.2 m in flat landform of dense urban suburb [27]. \(f\) is the Coriolis force coefficient.  
Assuming that the boundary layer thickness is 1000 m, then the wind speed of NDLI at 65 m height is revised as 34.9 m/s under the direction of 84\({}^{\circ}\) according to the WTT results. Hence, the maximum 10 min mean wind speed at 10 m height of the station is deduced as 29.01 m/s based on the profile formula. The value of 29.01 m/s can be approximately considered the maximum 10 min mean wind speed at 10 m height in Shenzhen during the typhoon because NDLI is very close to urban areas. Consequently, the converted reference pressure is 0.526 kPa. The 576 m height wind speed of PAFC at a wind direction of 93\({}^{\circ}\) is corrected as 56.9 m/s according to the WTT results, and it can be regarded as the gradient wind speed in this area. The maximum 10 min mean wind speed at 10 m height is deduced as 28.42 m/s. Thus, the converted reference pressure is 0.505 kPa. The reference wind pressures converted from the measured wind speeds of the typhoon at the two sites are relatively close to each other. The results are higher than the basic wind pressure of 0.45 kPa in the 10-year return period stipulated in the Load Code for the Design of Building Structures of China [24] but lower than that of 0.75 kPa in the 50-year return period.
## 2 Materials and Methods
### Extraction of RNA and Real-Time Polymerase Chain Reaction (RT-qPCR)  
Isolation of total RNA from cortical cultures was performed using the MagMAX mirVana kit (Thermo Fisher Scientific, Waltham, MA, USA). The quality of the obtained RNA was determined using electrophoresis. NanoDrop 1000c spectrophotometer was used to determine the RNA concentration. Reverse transcription was performed using the RevertAid H Minus First Strand kit (Thermo Fisher Scientific). Amplification was performed using the DTlite Real-Time PCR System (DNA-Technology, Moscow, Russia) in a 25 uL mixture containing 5 uL of qPCR-HS SYBR (Evrogen, Moscow, Russia, Cat. #PK147L), 1 uL (0.2 uM) of the primer solution, 18 uL water (RNase-free) and 1 uL cDNA. The amplification process details are described in ref. [23]. The sequences of all primers used in real-time PCR are shown in Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Gene Name** & **Forward Primer 5’->3\({}^{\prime}\)** & **Reverse Primer 5’->3\({}^{\prime}\)** \\ \hline GAPDH & ACATGCGTCAGACACCATG & GCCAGTGACTTCCCCGT \\ SELENOT & TCTCCTAGTGGCGGCCTC & GTCTATATATTGGTGTAGGGAGG \\ SELENOM & AGCCTCCTGTGTGCCTCCGC & AGGTCAGCGGTCCGAAG \\ SELENOF & GGAAGAGACCAAATTTGAAACC & TATGCGTCCAACTTTTCCACTC \\ SELENOK & TTTACATCTCGAACG & CAGCCTCCTCCTCCTCG \\ SELENOS & TGGACAGCATTGCGCAAAG & GCCAGTCTCCCAG \\ SELENON & TGATCTGCTGCGCGCATG & TCAGAGACTTCAGGTGTCG \\ DIO2 & ACGTCTTCTCCTCGTCC & AAAAGGGTCAAGTGCGTGTGT \\ CHOP & GCTCTGATGTACCGAATG & TCTGGAAAAGGTGGGTGTGTGT \\ GADD34 & CTCCGAGAAGGTCACACTGTCC & GACGGGGAAGGTGTGTG \\ PUMA & CAGATATGCGCCCGAGAT & CCATTCCTGGGTGTGTCT \\ BIM & GGACGACCCAACCGCACAGT & GTAAGGGCAGAGGTCCCA \\ \hline \end{tabular}
\end{table}
Table 1: Primers for the synthesis of human mRNA fragments.
## 3 Results
### Model Performance Evaluation
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Characteristics** & **RF** & **DT** & **GBDT** & **AB** & **SVM** & **MLP** & **RR** & **VC** & **KNN** \\ \hline AUC & 0.80 & 0.75 & 0.79 & 0.79 & 0.74 & 0.78 & 0.71 & 0.72 & 0.75 \\ Accuracy (\%) & 0.80 & 0.77 & 0.79 & 0.79 & 0.79 & 0.78 & 0.63 & 0.65 & 0.78 \\ Sensitivity/Recall & 0.60 & 0.59 & 0.55 & 0.56 & 0.56 & 0.56 & 0.61 & 0.61 & 0.52 \\ Specificity & 0.94 & 0.90 & 0.97 & 0.96 & 0.96 & 0.94 & 0.64 & 0.68 & 0.96 \\ FPR & 0.06 & 0.10 & 0.03 & 0.04 & 0.04 & 0.06 & 0.36 & 0.32 & 0.04 \\ FNR & 0.40 & 0.41 & 0.45 & 0.44 & 0.44 & 0.44 & 0.39 & 0.39 & 0.48 \\ PPV & 0.88 & 0.81 & 0.93 & 0.92 & 0.90 & 0.87 & 0.56 & 0.58 & 0.91 \\ NPV & 0.76 & 0.75 & 0.75 & 0.75 & 0.75 & 0.75 & 0.69 & 0.71 & 0.73 \\ F\({}_{1}\) score & 0.72 & 0.68 & 0.69 & 0.70 & 0.69 & 0.68 & 0.58 & 0.60 & 0.66 \\ \hline \hline \end{tabular} Note: RF, Random Forest; DT, Decision Tree; GBDT, Gradient Boosting Decision Tree; AB, AdaBoost; SVM, Support Vector Machine; MLP, Multilayer Perceptron; RR, Ridge Regression; VC, Voting Classifier; KNN, K-Nearest Neighbor; AUC, area under the receiver operator curve; FPR, false positive rate; FNR, false negative rate; PPV, positive predictive value; NPV, negative predictive value.  
\end{table} 
Table 2: Performance evaluation of machine learning models.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 8 Research Challenges and Future Directions
### Classification of Research Challenges in Telemedicine
#### 8.1.7 Cost of Deployment and Legal Framework  
There are many benefits of employing telemedicine, including increased access to quality healthcare services and expert physicians, among others. These benefits are often out of reach, especially in the developing countries, due to the high cost of fully deploying communication infrastructures such as fiber optic links and satellite communications to remote areas. This has made it difficult to adequately meet the needs of remote communities. Even when deployed, the cost of services can be high, and this invariably translates to fewer services for the end users. Poor legal frameworks are another issue in this regard.
# Numerical Study on Transient Annular Pressure Caused by Hydration Heat during Well Cementing
## 3 Model Validation  
(1) Kinetics model of cement hydration  
In order to validate the kinetics model of cement hydration, we compared the calculated results of the model in this paper and the experiments conducted by [PERSON] [24]. Using the isothermal calorimeter, [PERSON] [24] measured the curve of cement hydration heat at 26.6 \({}^{\circ}\)C and 65.5 \({}^{\circ}\)C, respectively.  
Using the curve of cement hydration heat, the kinetics parameters for Equations (4), (6) and (8) can be acquired. As shown in Table 1, \(\alpha_{1}\) is the boundary between the NG and I processes, and \(\alpha 2\) is the boundary between the I and D processes. According to the hydration parameters at different temperatures, the activation energy of Equations (10)-(12) for the NG, I, and D processes can be acquired as 20,787.4 J/mol, 25,407.5 J/mol, and 40,436.3 J/mol.  
As shown in Figures 2 and 3, the comparison results show that the model in this paper can describe precisely the hydration process of cement at different temperatures.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Temperature** & **m** & **KND** & **KI** & **KD** & \(\alpha_{1}\) & \(\alpha_{2}\) \\ \hline
26.6 \({}^{\circ}\)C & 2.421 & 0.033 & 0.0111 & 0.0027 & 0.163 & 0.388 \\
65.5 \({}^{\circ}\)C & 2.556 & 0.086 & 0.0358 & 0.0174 & 0.307 & 0.559 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculation results of hydration parameters.  
Figure 2: Comparison between the calculated and experimental results of the hydration process at 26.6 \({}^{\circ}\)C.  
Thermal model of the wellbore  
In addition to the cement hydration experiment, [PERSON] [24] also conducted a field test measuring the transient temperature of cement in a wellbore during the cement hydration process. The cement slurry used in the field test is the same as the cement used in the experiment, with a density of 1.68 g/cm\({}^{3}\). The field test was conducted in a vertical well. The well had a total depth of 3932 m. A temperature recorder was set at a position close to the well bottom (3840 m) to record the transient temperature during well construction. The diameter of the wellbore hole was 215.9 mm. The casing had a diameter of 139.7 mm. As shown in Figure 4, the comparison results between the calculated results and the measured results show that the temperature prediction error is within 5.6%.  
Figure 4: Calculation results of transient temperature at a well bottom during well construction.  
Figure 3: Comparison between the calculated and experimental results of the hydration process at 65.5 \({}^{\circ}\)C.
###### Abstract
The 3D X-ray microscopy with high resolution and high contrast can effectively detect the defects of 3D packaging devices such as bridges, poor soldering, solder joint cracks, and so on [23]. In microelectronics applications, the extreme resolution is a key limiting factor in the application of X-ray CT technology [24]. A high-resolution 3D X-ray microscopy using a two-stage magnification structure was developed [25]. It is different from traditional CT systems, whose resolution depends on focus size and geometric magnification. First of all, the sample image is geometrically magnified, just like traditional CT systems. The X-rays are then converted to visible light on the detector's scintillation screen, and the image is then magnified further by the optical system [26]. The dependence on geometric amplification is greatly reduced due to the use of the second-stage optical amplification structure. This results in the significant advantage of providing sub-micron resolution over large working distances, resulting in high resolution for large samples (up to 100 mm in diameter) [27]. The previous researches independently studied the application of one of the techniques of magnetic field imaging and high-resolution three-dimensional X-ray in failure analysis [14; 28; 29]. Achieving high-resolution in a defect location at long working distances without decapsulation or cutting samples is a current research difficulty [30; 31]. In addition, many different branches are proving very difficult to isolate and complicate the identification of the circuit, and therefore, analyze [32]. In this paper, magnetic field imaging and high-resolution three-dimensional X-ray techniques were applied simultaneously to open localization on a 3D TSV package for the first time, which was used to verify the effectiveness of both of them. A two-probe RF method in magnetic field imaging was performed to resolve isolation of the defect difficulties resulting from many different branches of TSV daisy chains. Additionally, a linear decay method was used to target sub-micron resolution at a long working distance. Multiple partition scans by high-resolution 3D X-ray microscopy with a two-stage magnification structure were used to achieve sub-micron resolution too. This paper is organized as follows: Section 2 describes the test sample. Section 3 demonstrates defect localization on the TSV sample after thermal cycling test by magnetic field imaging and high-resolution three-dimensional X-ray technology. Section 4 verifies the open location by microscopic physical analysis. Finally, a conclusion is wrapped up in Section 5.
## 3 Monitoring Data Collection and Preparation
### Seismic Data  
Seismicity plays a major role in the stability of volcanic flanks. During the analyzed period, a total of 6537 seismic events were recorded around El Hierro Island by the Instituto Geografico Nacional network [58]. We used this data to calculate the PGAs (Peak Ground Accelerations) of these events. The Canary Islands does not have developed local ground-motion attenuation relationship and there are no accelerometer network data available. For that reason, we applied an attenuation relationship developed for the Hawaiian Islands (to some point, a similar volcanic archipelago) by [59] and suggested to be used on the Canary Islands by [60]:  
\[log_{10}PGA=0.518+0.387\ (M-6)-log_{10}r-0.00256r+0.3355 \tag{2}\]  
where \(PGA\) is measured in units of g, \(M\) is the magnitude, \(r=(d^{2}+11.292)^{1/2}\) and \(S\) is 0 for lava sites and 1 at ash sites. The distance parameter \(d\) is the closest distance from the recording site to the surface projection of the fault rupture area.  
For this analysis, we used the maximum recorded PGA for the particular day and also calculated the sums of daily PGAs (named PGA dose). The maximum PGAs and PGA doses were calculated for both HIE2 and HIE3 instruments considering their geographic location.
## 2 Materials and Methods
### Chlorophylll Quantification  
Chlorophyll was extracted and quantified according to a previous method [18]. The chlorophyll and anthocyanin extractions were made using leaves different from the ones used for the terpene quantifications. The leaf position (e.g., lighting) alters the chlorophyll contents. Therefore, to minimize the errors among the analyses, we carefully harvested and chose the second and third leaves with similar sizes for terpene or chlorophyll measurement. Briefly, weighed leaf samples were placed into 2 mL of pure N,N-dimethylformamide (DMF; FUJIFILM Wako Chemicals, Osaka, Japan) at 4 \({}^{\circ}\)C in darkness overnight. The absorbance values at 647 and 664 nm of the aliquot of the extracted solution were measured using a spectrophotometer (U-5100, Hitachi, Tokyo, Japan). DMF was used as the blank control. The total amount of chlorophyll was calculated based on the following equation [18]: Chl a + b = \(17.67*\mathrm{A_{647}}+7.12*\mathrm{A_{664}}\).
# Mapping Subtidal Marine Forests in the Mediterranean Sea Using Copernicus Contributing Mission
## 3 Results  
### Mapping Subtidal Marine Forests and Related Senscapes  
Hard Classification Products  
The mapping of 2637.85 hectares of marine habitats (Table 3) was based on PlanetLabs SuperDove satellite imagery [54]. Although the focus was on subtidal _Cystoseira_ s.l. forests, the other habitats present were also classified. The spatial distribution of the identified marine habitats is presented in Figures 9-11.  
Figure 8: The sites of the field data collection for the _Cystoseira_ spp. class.  
Figure 7: The location of the evaluation dataset.  
Figure 10: Zoomed map of the southern part of the region.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Areal Extent** \\ \hline _Cystoseira_ s.l. & 25.83 \\ Sand (shallow) & 336.92 \\ Sand (deep) & 370.99 \\ _Posidonia oceanica_ & 922.96 \\ Rocky bottoms (without _Cystoseira_) & 981.13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Areal extend of the habitats mapped using Copernicus Contribution Mission data.  
Figure 9: The distribution of the coastal marine habitats, as resulted from the analysis of the Planet-Labs SuperDove imagery and the Random Forest supervised classification.