esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Prediction Model of Annular Pressure Buildup for Offshore Wells
## 3 Wellbore Annulus Volume Model  
As shown in Figure 2, the cementing operation is carried out after the casing is run in the wellbore, but the cement does not return to the wellhead. In the process of displacement change, all parts of the medium at the first and second cementation planes are always tightly cemented. According to the cement return height outside the casing, the casing can be divided into a free section and a sealed section. The free section of the casing is subjected to internal and external liquid pressure. The sealing section of the casing is subjected to internal and external hydraulic pressure as well as cement ring pressure. The change in wellbore annulus volume takes into account the deformation of the free section of the casing and the deformation of the sealing section [29; 30; 31; 32]. After the cementing operation, due to the cementing effect of cement slurry, the casing, cement ring, and formation in the sealing section become a tightly connected elastic body, which forms the outer surface of  
Figure 1: Schematic of single sealed annular pressure buildup.  
the wellbore formation system and plays the role of sealing formation fluid and preventing gas channeling. Under the coupling of temperature and pressure, the medium in each part of the wellbore formation system will change. The elastomer composed of casing, cement ring, and formation in the sealing section will produce elastic deformation under the effect of thermal stress and inner annular pressure and expand outward in the radial direction of the wellbore.
## 2 Materials and Methods
### Sampling and Measurements  
Soil samples were collected after 5, 10, 15, 20 and 25 days of irrigation, i.e., the duration of monitoring period was 25 days, referring to the research method of [PERSON] et al. [17]. More specifically, in each plot, soil samples were collected at 0-5 cm, 5-10 cm, 10-15 cm, 15-20 cm, 20-30 cm, 30-40 cm, 40-50 cm, and 50-60 cm depth. The metal ring samplers with 52 mm high and 70 mm in diameter were used to collect undisturbed soils. These ring samplers were hampered into the designed profile after the upper soils were collected and removed. No rainfall events were observed during the entire sampling period.  
Undisturbed soils were randomly sampled to measure water content and bulk density based on oven drying method. Disturbed samples were collected with soil auger for salt determination. In addition, the macropores areas filled with sand were not included.  
Soil moisture storage was estimated following the procedure described by [PERSON] et al. [18]:  
\[W=10\sum_{i=1}^{n}\theta_{gi}\rho_{si}z_{i}/\rho_{w} \tag{1}\]  
where \(W\) is soil moisture storage, mm; \(\theta_{gi}\) is soil gravimetric water content of \(i\) soil layer, g g\({}^{-1}\); \(\rho_{si}\) is soil bulk density of \(i\) soil layer, g cm\({}^{-3}\); \(z_{i}\) is thickness of \(i\) soil layer, cm; \(\rho_{w}\) is density of water, g cm\({}^{-3}\).  
The air-dried soil samples were pulverized and then passed through a 2 mm mesh sieve, while visible roots, pebbles and aggregates larger than 2 mm were screened out. Soil electrical conductivity was measured in a soil: water suspension (1:5) using an EC meter (DDS-11A, Shang Hai Yoke Instrument Co., Ltd., Shanghai City, China), and the soil salt content was calculated by an empirical equation, as described by [PERSON] et al. [19]:  
\[Y=3.471 Ec+0.015\dots\]  
where \(Y\) is soil salt content, g kg\({}^{-1}\); \(Ec\) is conductivity of soil extract at 25 \({}^{\circ}\)C, mS cm\({}^{-1}\).  
Soil salt storage was described as be the mass per unit area:  
\[S=10{\sum_{i=1}^{n}y_{i}\rho_{si}z_{i}}, \tag{2}\]  
where \(S\) is soil salt storage, g m\({}^{-2}\); \(y_{i}\) is soil salt content of \(i\) soil layer, g kg\({}^{-1}\); \(\rho_{si}\) is soil bulk density of \(i\) soil layer, g cm\({}^{-3}\); \(z_{i}\) is thickness of \(i\) soil layer, cm.
# Real-Time Phase Bias Estimation for BeiDou Satellites Based on Consideration of Orbit Errors
## 2 Methods
### Effects of Orbit Error on FCB Estimation
Based on the results given in Table 1, we can neglect the GPS orbit error; we therefore assign much less weight to the BDS observations so that the precision of both the ZTD parameter and the GPS receiver clock will not be affected by BDS orbital errors. The slant orbital error will therefore be absorbed by the BDS receiver clock and the IF ambiguities; the former can be compensated by \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **3D Error (m)** & **Scale Factor** & **NL FCB (m)** & **NL FCB (Cycle)** \\ \hline GEO & 0.718 & 0.0835 & 0.0599 & 0.599 \\ IGSO & 0.193 & 0.0835 & 0.0161 & 0.161 \\ MEO & 0.160 & 0.1390 & 0.0222 & 0.222 \\ GPS & 0.039 & 0.1477 & 0.0057 & 0.057 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effects of orbit error on narrow-lane (NL) fractional cycle bias (FCB) estimation (RMS). estimation of the BDS receiver's NL FCB parameter. We can thus ensure that, based on Equation (5), the BDS orbit error can be retained along with the NL FCB estimates from the IF ambiguities.
# Combined kNN Classification and Hierarchical Similarity Hash for Fast Malware Detection
## 5 Experimental Results
### Performance Metric  
In this section, performance evaluation indicators are described, before presenting the experimental results. The indicators used in this study are accuracy, precision, recall (detection rate), F1 score, and false positive rate (FPR). The confusion matrix used to calculate these values is shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Specification** \\ \hline OS & Windows 10 Pro \\ CPU & Intel i7 2.2 GHz \\ RAM & 16 GB \\ GPU & GeForce RTX 2060 \\ Cuda & 8.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**-** & **Malware** & **Normal File** \\ \hline Predicted Malware & TP & FP \\ \hline Predicted Normal File & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion Matrix.  
TP indicates that a file has been correctly evaluated by the system to be malicious, and TN indicates that the system correctly determined that a benign file is normal. Furthermore, FP indicates that a normal file has been incorrectly judged by the system as malicious, and FN indicates that the system has incorrectly identified a malicious file to be normal. Each indicator is calculated as follows.  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{FP}+\text{FN}+\text{ TN})\]  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP})\]  
\[\text{Recall}\ (\text{Detection Rate})=\text{TP}/(\text{TP}+\text{FN})\]  
\[\text{F1 score}=2\times\text{Precision}\times\text{Recall}/(\text{Precision }+\text{Recall})\]  
\[\text{FPR}=\text{FP}/(\text{FP}+\text{TN})\]
## 4 Analysis and Simulation Verification
### Real-Time Simulation-Based Validation  
To further validate the effectiveness of the proposed parameter optimization method, a multi-converter grid-tied system model is constructed on the RT-LAB platform. Figure 20 shows the details of the RT-LAB platform. It contains a Canadian OPAL-RTLAB real-time simulation machine OP5700 (a Xilinx Virtex-7 FPGA with 32 Intel Xeon processing cores) and a host PC, which is utilized for the real-time emulation of a multi-converter grid-tied system. The waveforms are measured through the I/O ports of RT-LAB. The host PC and LeCroy oscilloscope save the real-time simulation-based results. The parameters of the grid and VSCs are the same as in Tables A1 and A2.  
Figure 21 illustrates the PCC voltage before and after modifying the stability-weakest parameter \(K_{\text{ip1}}\) of system A. Before optimization, system A is unstable as shown in Figure 21a. According to the previous theoretical analysis, the parameter optimization value of \(K_{\text{ip1}}\) is 4.6 \(\times\) 10\({}^{-4}\). After optimizing \(k_{\text{ip1}}\) from 2.175 \(\times\) 10\({}^{-3}\) to 4.600 \(\times\) 10\({}^{-4}\), system A is stable as shown in Figure 21b. Figure 22 illustrates the PCC voltage before and after optimization of the stability-weakest parameters \(K_{\text{ip2}}\) and \(K_{\text{ip3}}\) is system B. Figure 22a shows the PCC voltage waveform of system B before parameter optimization. It can be seen that system B oscillates and is unstable. According to the previous theoretical analysis, the parameter optimization values of \(K_{\text{ip2}}\) and \(K_{\text{ip3}}\) are 8.800 \(\times\) 10\({}^{-4}\). After optimizing \(k_{\text{ip2}}\) and \(k_{\text{ip3}}\) from 6.600 \(\times\) 10\({}^{-3}\) to 8.800 \(\times\) 10\({}^{-4}\), system B is stable as shown in Figure 22b. The real-time simulation-based results are in agreement with the theoretical analysis and simulation results in Figures 14a and 18. The real-time simulation-based results verify the correctness of the proposed method. The proposed parameter optimization method can effectively improve the stability of the multi-converter grid-connected system.  
Figure 20: Detailed implementation of the RT-LAB platform.
## 3 Uses of Cold Plasma in Food Industry
### Food Sterilization  
To ensure optimal food safety, it is critical to use reliable and consistent food sterilization techniques. Due to its capability to inactivate a wide variety of foodborne pathogens without affecting food quality, CP is a promising food sterilization technology. The most researched of the numerous potential mechanisms is the chemical interaction of cell membranes with radicals (O, OH...), excited or reactive molecules (O\({}_{2}\), O\({}_{3}\), NO...), and charged particles [17; 63; 64]. Reactive species, created by the breakdown of air such as O\({}_{3}\), atomic oxygen, superoxide, peroxides, and hydroxyl radicals, are critical in the destruction of microbes and viruses like Coronavirus SARS-CoV-2 [65; 66]. NO and NO\({}_{2}\) play roles in microorganism inactivation by degrading chemical components such as protein molecules, fats, and nucleic acids [67]. Moreover, [PERSON] and her collaborators have shown that plasma can also damage the DNA/RNA, restricting the SARS-CoV-2 for viral replication [66]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mycotoxin** & **Food Matrix** & **Plasma Type** & **Results** & **References** \\ \hline \multirow{3}{*}{Fumonisin B2 and ochratoxin A} & \multirow{3}{*}{Date palm fruits} & atmospheric pressure & Degradation of the & \\  & & argon cold plasma jet & after 6 min and 7.5 min plasma & \\  & & & treatments, respectively & \\ \hline Deoxynivalenol, zeazalenone, & \multirow{3}{*}{Rice extracts} & \multirow{3}{*}{dielectric barrier discharge} & The 60 s treatment extensively & \\  & & & degraded pure mycotoxins & \\ \hline \multirow{3}{*}{Aflatoxin} & \multirow{3}{*}{Nuts} & Corn & high-voltage plasma & 90\% degradation & [51] \\ \cline{2-4}  & & Hazelnuts & DBD & Mycotoxin reduced by 70\% & [52] \\ \cline{2-4}  & & & Reduces aflatoxin & \\ \cline{2-4}  & & & production (90\%) & \\ \cline{2-4}  & & plasma & Degradation of & \\ \cline{2-4}  & & & myctoxin up to 72\% & \\ \cline{2-4}  & & Hazelnuts, peanuts, and & & \\ \cline{2-4}  & & pistachio nuts & low-pressure cold plasma & 20 min air plasma treatment & \\ \cline{2-4}  & & & reduced 50\% of total aflat & \\ \hline \multirow{3}{*}{AF B1} & \multirow{3}{*}{Glass coverslip} & nitrogen gas plasma & \multirow{3}{*}{The concentration reduced to \(<\)1/10 th after 15 min} & \multirow{3}{*}{[56]} \\  & & generated by a static & & \\ \cline{2-4}  & & induction thyristor & & \\ \hline \multirow{3}{*}{DON, D3G T-2} & \multirow{3}{*}{Barley} & low-pressure & \multirow{3}{*}{50\% reduction} & \multirow{3}{*}{[57]} \\  & & microwave-generated & & \\ \cline{1-1}  & & plasma & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cold plasma experiments illustrating mycotoxin degradation.
###### Abstract  
The prediction of severe weather events such as hurricanes is always a challenging task in the history of climate research, and many deep learning models have been developed for predicting the severity of weather events. When a disastrous hurricane strikes a coastal region, it causes serious hazards to human life and habitats and also reflects a prodigious amount of economic losses. Therefore, it is necessary to build models to improve the prediction accuracy and to avoid such significant losses in all aspects. However, it is impractical to predict or monitor every storm formation in real time. Though various techniques exist for diagnosing the tropical cyclone intensity such as convolutional neural networks (CNN), convolutional auto-encoders, recurrent neural network (RNN), etc., there are some challenges involved in estimating the tropical cyclone intensity. This study emphasizes estimating the tropical cyclone intensity to identify the different categories of hurricanes and to perform post-disaster management. An improved deep convolutional neural network (CNN) model is used for predicting the weakest to strongest hurricanes with the intensity values using infrared satellite imagery data and wind speed data from HURDAT2 database. The model achieves a lower Root mean squared error (RMSE) value of 7.6 knots and a Mean squared error (MSE) value of 6.68 knots by adding the batch normalization and dropout layers in the CNN model. Further, it is crucial to predict and evaluate the post-disaster damage for implementing advance measures and planning for the resources. The fine-tuning of the pre-trained visual geometry group (VGG 19) model is accomplished to predict the extent of damage and to perform automatic annotation for the image using the satellite imagery data of Greater Houston. VGG 19 is also trained using video datasets for classifying various types of severe weather events and to annotate the weather event automatically. An accuracy of 98% is achieved for hurricane damage prediction and 97% accuracy for classifying severe weather events. The results proved that the proposed models for hurricane intensity estimation and its damage prediction enhances the learning ability, which can ultimately help scientists and meteorologists to comprehend the formation of storm events. Finally, the mitigation steps in reducing the hurricane risks are addressed.  
deep learning (DL); hurricanes; convolutional neural network (CNN); visual geometry group (VGG 19); data augmentation (DA) +
Footnote †: journal: Applied Sci.
## 2 Materials and Methods
### Machine Learning Model Development (Training and Evaluation)  
To validate the expert-defined profiles, we trained two supervised classifiers--decision tree (DT) and random forest (RF)--to predict the four functional profile categories from the test data. Sixteen input features were used: 8 normalized strength values (HAbd, KE, KF, AP for both limbs), 6 normalized YBT scores (ANT, PM, PL for both limbs), encoded FMS  
Figure 1: Rule-based logic for functional profile classification. **Legend**: _Functionally Weak:_ Athletes with low overall movement quality, defined as an FMS score in the bottom quartile (\(\leq\)25 th percentile), regardless of strength or balance performance. This profile reflects generalized functional limitations in mobility and stability tasks. _Strength-Deficient_: Athletes not classified as Functionally Weak but with substantially low strength (\(Z\leq-0.5\)) and normal balance (YBT \(Z>-0.5\)). Indicates a specific deficit in force production capacity. _Stability-Deficient:_ Athletes with normal FMS and strength but poor balance (YBT \(Z\leq-0.5\)) and preserved strength (Strength \(Z>-0.5\)). Reflects isolated deficits in dynamic stability. _No Clear Dysfunction:_ Athletes with all three domains (FMS, strength, balance) within normal limits (i.e., no score \(\leq-0.5\) SD). Represents a well-rounded functional profile with no major deficits.  
category (Low = 0, Medium = 1, High = 2), BMI, and age. Although tree-based models are scale-invariant, all features were z-standardized (based on the soccer cohort) to maintain interpretability and support external application. The target variable was the categorical deficit profile.  
Given the small sample size, model complexity was constrained to limit over-fitting. For DT, the hyperparameters included max depth (3-6) and minimum samples per split (2, 5, 10). For RF, grid search varied tree count (50, 100, 200), max depth (None, 10, 20), and minimum split (2, 5). Five-fold stratified cross-validation (CV) was used with macro F1-score as the selection criterion. The optimal DT had depth = 3; RF performed best with 100 trees, no depth cap, and minimum split = 5. Final models were retrained on the full soccer dataset.  
To limit bias from the unequal distribution of the four deficit-profile labels, both DT and RF were trained with inverse-frequency class weights (balanced\"), and performance was judged with macro-F1 and balanced accuracy--metrics that are insensitive to prevalence differences.  
We restricted modeling to decision tree and random forest classifiers for two practical reasons. (i) Interpretability--both produce human-readable rules and can be explained with SHAP, aligning with our goal of a coach-friendly tool. (ii) Small-sample robustness--higher-capacity models such as XGBoost, LightGBM, or non-linear SVMs typically over-fit when \(n<50\).
## 2 Content Sharing Graphs
### Sparse Content Sharing Graph (SCSG)
The _SCSG_ graph for a folder on a Windows XP laptop shown in Figure 1 uses the STAR connection topology. \begin{table}
\begin{tabular}{|c|c c c c c c c c c c c c c c c|} \hline
**Files ID** & **chunk1** & **chunk2** & **chunk3** & **chunk4** & **chunk5** & **chunk6** & **chunk7** & **chunk8** & **chunk9** & **chunk10** & **chunk11** & **chunk12** & **chunk13** & **chunk14** & **chunk15** & **File Size** \\ \hline
**1** & 2Z11 & AAAA & BBB & CCCC & DODOD & 1111 & 2222 & 3333 & 4444 & 5555 & 6666 & 7777 & 8888 & 9999 & **L111** & 60 \\ \hline
**2** & 2Z22 & AAAA & BBB & CCCC & DODOD & 1111 & 2222 & 3333 & 4444 & 5555 & 6666 & 7777 & 8888 & 9999 An alternative connection topology of an _SCSG_ graph for the set of 19 files in Table 1 is shown in Figure 3, where the \(n\) nodes sharing the same content are connected in a linked list fashion. We name this a CHAIN topology. These connection topologies are just two somewhat extreme examples of _SCSG_ graphs. Any combination of them satisfies our definition of a _SCSG_ graph. The deduplicated size of the 19 files in Table 1 is the sum of the file sizes minus the sum of edge weights (in either one of the _SCSG_ graphs above), that is \(756-604=152\) bytes.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 4 Preliminaries
### Secret Sharing  
In this paper, we mainly considered ring \(\mathbb{Z}_{2^{l}}\).  
A two-out-of-two additive secret sharing scheme [38] over \(\mathbb{Z}_{2^{l}}\), referred to as 2P-ASS, splits a ring element \(x\) into random shares \(\langle x\rangle_{0}^{l}\) and \(\langle x\rangle_{1}^{l}\) with the only constraint being that \(\langle x\rangle_{0}^{l}+\langle x\rangle_{1}^{l}\equiv x\bmod 2^{l}\).  
Three-out-of-three additive secret sharing, referred to as 3P-ASS, similarly splits \(x\in\mathbb{Z}_{\mathrm{I}}\) into random shares into random shares \([x]_{0}^{l}\), \([x]_{1}^{l}\), \([x]_{2}^{l}\), such that \([x]_{0}^{l}+[x]_{1}^{l}+[x]_{2}^{l}\equiv x\bmod 2^{l}\).  
A two-out-of-three replicated secret sharing scheme (RSS) [39; 40], denoted as \([\![\cdot]\!]\)-sharing, splits \(x\) into 3 random parts satisfying \(x_{0}+x_{1}+x_{2}\equiv x\bmod 2^{l}\). Each party \(P_{b}\), \(b\in\{0,1,2\}\), holds \(\big{[}x]_{b}^{2^{l}}=([x]_{b}^{2^{l}},[x]_{(b+1\bmod 3)}^{2^{l}})\).  
For simplicity, we omit the superscript \(L\) where the context makes it unambiguous.
# LR-MPIBS: A LoRa-Based Maritime Position-Indicating Beacon System
## 3 Results and Discussion  
The physical configuration of LR-MPIBS is illustrated in Figure 6. SMBS is shown in Figure 6a, with its upper and lower interface connected to the LoRa communication antenna and the backend data management center, respectively. PIT is shown in Figure 6b; with overall dimensions of 87 \(\times\) 60 \(\times\) 22 mm, it is worn on the operator's shoulder during usage.  
In order to verify the operational performance of LR-MPIBS, the following experiments are conducted in this paper: a falling water detection function test, positioning function cold start time test, wireless communication performance test, key positioning data recognition rate simulation, and endurance time test. The performance of LR-MPIBS is verified through comprehensive testing in Tangdao Bay.
# A Critical Review of Supersonic Flow Control for High-Speed Applications
## 4 Active Control Methods
### Soft Computing Methods
#### 4.3.3 Machine Learning  
A neural network was employed to examine the predicted floor pressure in a convergent nozzle with shallow cavity internal flows at various subsonic Mach numbers [164]. The authors used feedforward neural networks (NNs) to highlight the modeling problem. A mixed density network was trained using an artificial neural network (ANN) methodology using the updated database of the future flow profile. The ANN [165] was used to learn and train flow characteristics over a transport aircraft configuration to estimate the aerodynamic coefficients using different network sizes. The K-means technique was used to cluster this massive amount of data. According to the RSM, microjets are efficient when a favorable pressure gradient influences the flow. The effect of a lower area ratio and a longer duct length on the base pressure is minimal. Furthermore, the random forest strategy was applied, which belongs to this group and is employed in Bootstrap aggregation employment regression and classification research [166]. Heat maps are used to visualize the massive amount of experimental data generated. Six back-propagation neural network models (BPMs) based on input and output possibilities are built to forecast pressure in high-speed flows for the first time. The six BPMs with two hidden layers containing four neurons were determined to be the best suited for regression analysis. The very non-linear values of the base and wall pressure are correctly predicted by BPM 5 and BPM 6 [167]. Figure 12 depicts a broad schematic depiction of the back-propagation model (BPM).