esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Force Analysis of Casing Damaged Wells  
### Basic Theory  
While there is general consensus on the mechanism of casing damage among researchers, variations exist in research methodologies. A large number of scholars have explored the law of casing damage caused by mudstone creep under non-uniform in-situ stress conditions. The research on the external squeezing force caused by mudstone creep on the casing is mainly based on two methods: an experimental method and a theoretical calculation method. The research methods for the stress of casing damage are the finite element method and fracture mechanics method. In addition, previous studies have shown that during water injection development, the formation of pore pressure increases, and the injected water enters the mudstone layer, causing changes in the local stress field. The increase in horizontal in-situ stress on the wellbore creates a non-uniform in-situ stress field, which will induce casing deformation or damage. The key to casing damage, whether caused by geological factors or the combination of geological and engineering factors, is that as long as the external force exceeds the bearing capacity of the casing, casing damage will occur. Therefore, from a mechanical perspective, the underlying causes of casing damage are considered, and the basic connection between casing damage and the casing damage mechanism is established as follows:  
\[\sigma_{\text{cMax}}\geq\gamma\sigma_{\text{o}} \tag{1}\]  
In the equation, \(\sigma_{\text{cMax}}\) is the maximum stress that the casing can withstand; \(\gamma\) is the allowable safety factor for the casing design, ranging from 1.5 to 4.0; \(\sigma_{\text{o}}\) is the stress generated by an external load on the casing, which is obtained from a filed measurement.  
The stress generated by external loads on the casing is the focus of research, including the changes in external loads caused by mudstone creep, the collapse strength of the casing, and the tensile shrinkage of the casing. It boils down to studying the stress state of casing under non-uniform in-situ stress conditions and predicting and diagnosing the possibility  
Figure 4: Service life of G155 block casing damaged wells.  
of casing damage based on the magnitude of in-situ stress in a certain formation or well section. There are three mutually perpendicular principal stresses in the strata, namely the vertical in-situ stress caused by the self-weight of rock and the two-horizontal principal in-situ stresses. In general, the three in-situ stresses are not equal.
## 2 Materials and Methods  
### Motor Design and Configurations  
The lab-scale hybrid motor used during the test campaigns is composed of four main components: (1) a catalyst bed containing catalytic material, where the HTP is decomposed producing high-temperature O\({}_{2}\) and H\({}_{2}\)O vapor; (2) a swirl injector imposing a rotation to the oxidizer flow along the motor axis; (3) a combustion chamber where the reactions between the oxidizer and the fuel grain (i.e., HDPE and paraffin-based fuels) occur; and (4) a nozzle holder encasing the graphite nozzle insert.  
The catalyst bed and the combustion chamber metal case are connected by a clamp-type joint with a graphite braid providing gas sealing. The combustion chamber structural case is made of steel and consists of two flanges and a hollow cylinder between them. A set of eight M16 rods close the flanges against the cylinder.  
The vortex injection plate is placed inside the inlet flange of the motor case. The combustion chamber parts (e.g., fuel grain, post chamber and thermal protection, TP) are bonded together using a silicone filler. These assembled consumables form the so-called motor cartridge, which can be easily integrated and removed from the cylindrical metal case. The nozzle holder is screwed to the outlet flange with a silicon O-ring, ensuring the sealing from the combustion gases.  
The aft part of the catalyst assembly is connected to the fluidic line once the motor is placed on the test bench (Figure 1).  
The catalyst bed, nozzle and metal flanges were kept unchanged across the campaign, while the swirl number and the combustion chamber configuration varied. In the combustion chamber, this was done either by changing the fuel type (i.e., F5, F8 and HDPE), or the fuel length and initial port diameter, or by adding a post-combustion chamber. These changes were necessary to ensure an optimal oxidizer-to-fuel (\(O/F\)) ratio and combustion efficiency, which could decrease when using a low \(SN_{g}\).  
The oxidizer injection \(SN_{g}\) was altered by changing the vortex injector cup. This metal part is installed on the flange between the catalyst bed and the combustion chamber. For an axial oxidizer injection, a metal ring is assembled in place of the swirl injector.  
Figure 2 shows the vortex injector position and the motor configurations with and without the addition of the post chamber and with different grain initial port diameters.
## 5 Discussion
### Analysis of Misclassification Reasons  
Our experimental results indicate that although MS-RRBR exhibits excellent performance in recognizing certain actions, it remains inadequate in classifying \"jumping\", \"self-biting\", and \"object rejection\". The underlying reasons can be summarized as follows:  
First, the limited size of the dataset directly affects the ability of UniformV2 to recognize these actions. Previous research has shown that data imbalance can severely impact models' performance in low-frequency categories [46]. In our dataset, the representativeness of the aforementioned actions was relatively low, leading to insufficient learning of these behaviors' diversity and characteristics during training, and ultimately influencing the initial classification accuracy of MS-RRBR.  
Second, a high degree of similarity among certain actions also contributes to misclassification. For example, \"jumping\" may overlap with other rapid movements in terms of frequency and visual features, and this action also exhibits low recognition accuracy in the study by [PERSON] et al. [9]. Such close similarity can cause confusion in model processing, particularly when capturing temporal features, making it difficult to distinguish subtle inter-class differences.  
Lastly, the complex and composite nature of certain actions significantly influences recognition outcomes. For instance, \"object rejection\" is often accompanied by other bodily movements such as \"arm flapping\" or \"head shaking\". Particularly in dynamic scenarios, MS-RRBR may fail to clearly discern the intention and context of a single action, thereby affecting its recognition performance.
## 5 Results and Comparison
The selected hyperparameters are listed in Table 13.  
ROC curve and performance evaluation: The receiver operating characteristic (ROC) curve in Figure 4 provides insights into the model's classification performance. The x-axis represents the false-positive rate (FPR), while the y-axis represents the true.positive rate (TPR). A high TPR and low FPR indicate strong model performance. The closer the ROC curve is to the top-left corner, the better the model's accuracy. \begin{table}
\begin{tabular}{c c} \hline
**Hyperparameters** & **Values** \\ \hline Activation function & ReLU, sigmoid \\ Number of epochs & 20 \\ Units & 64-128-256-1 \\ Optimizer & Adam \\ Loss & Binary cross-entropy \\ Hidden layer & 3 \\ Accuracy & 99.999\% \\ Recall & 99.998\% \\ Precision & 99.999\% \\ F1 score & 99.999\% \\ \hline \end{tabular}
\end{table} 
Table 13: Hyperparameters of MLP. \begin{table}
\begin{tabular}{l c c c c} \hline
**Model** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline GRU & 0.94826 & 0.95276 & 0.94655 & 0.95276 \\ Decision Tree & 0.99576 & 0.995514 & 0.995573 & 0.995514 \\ Naïve Bayes & 0.860146 & 0.927441 & 0.892527 & 0.927441 \\ Logistic Regression & 0.998691 & 0.998694 & 0.998691 & 0.998694 \\ CART & 0.999863 & 0.999863 & 0.999863 & 0.999863 \\ Random Forest & 0.995927 & 0.995717 & 0.995769 & 0.995717 \\ RNN & 0.86015 & 0.92744 & 0.89253 & 0.92744 \\ CNN & 0.86015 & 0.92744 & 0.89253 & 0.92744 \\
**MLP** & **0.999961** & **0.998081** & **0.999020** & **0.999985** \\ LSTM & 0.95643 & 0.95763 & 0.95075 & 0.95763 \\ CNN–LSTM & 0.94848 & 0.95319 & 0.94746 & 0.95319 \\ LSTM–CNN & 0.91513 & 0.92289 & 0.91328 & 0.92289 \\ \hline \end{tabular}
\end{table} 
Table 12: Results of accuracy, precision, and F1 score parameters. Recent studies show that the use of traditional methods for attack detection is decreasing and AI-based approaches are becoming more common. Hybrid models are increasingly being developed to minimize false positives. However, in this study, such hybrid approaches showed lower performance. This is thought to be due to the unique feature structure of the dataset. As seen in Table 1, a comprehensive literature review revealed that this study achieved the highest accuracy on the WUSTL-2021-IIoT dataset using a wide range of models. These results show the importance of choosing the right classification model according to dataset complexity.
# FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning
## 3 Proposed Approach  
The overall architecture of our proposed FedQAS system is shown in Figure 2. The main modules are private data pipeline, federated learning settings, question answering, and incremental learning. The private data pipeline module allows local users (clients) to process and prepare their data locally to be used by federated learning methods. The federated learning module enables multiple private clients to form an alliance to collaboratively train machine learning/deep learning models and send parameters to the server for global model generation (aggregation of local models). Afterward, the system allows the client to add new data locally and train the model incrementally through a defined number of rounds to improve the performance using incremental learning techniques. Finally, participating clients can use the global model for question answering system. The system is implemented using the FEDn federated learning framework [2] and a web interface using Flask ([[https://flask.palletsprojects.com/](https://flask.palletsprojects.com/)]([https://flask.palletsprojects.com/](https://flask.palletsprojects.com/)), accessed on 24 January 2022). FEDn provides a highly scalable federated learning run-time, and Flask is used to develop interactive and user-friendly interfaces for the different processes in the workflow. The list of available datasets related to question answering used for the demo is placed in the local data sources. Moreover, the developed system is scalable, flexible, and can be expanded with new clients/data sets on-demand (without the need to re-train the federated model).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **Answer Type** & **Domain-Specific** \\ \hline MCTest [8] & Multiple choice & Children’s stories \\ CNN/Daily Mail [21] & Spans & News \\ Children’s book [22] & Spans & Children’s stories \\ MS MARCO [23] & Free-form text & Web Search \\ NewsQA [24] & Spans & News \\ SearchQA [25] & Spans & Jeopardy \\ TriviaQA [26] & Spans & Trivia \\ SQuAD [27] & Spans & Wikipedia \\ SQuAD 2.0 [28] & Spans, Unanswerable & Wikipedia \\ CoQA [29] & Free-form text, & News, Reddit \\  & & Wikipedia \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of some existing MRC datasets.
## 3 Understanding Biofilms
### Role of Biofilm Formation in Disease Pathogenesis  
Biofilms significantly contribute to persistent infections as they attach to organic (tissues and teeth) and inorganic surfaces (such as medical implants and catheters) [31; 32]. Once formed, biofilms are exceedingly challenging to eliminate, resulting in long-lasting and recurring infections. Biofilms are responsible for approximately 80% of difficult-to-treat, long-lasting infections, which presents a significant challenge in clinical treatment [7]. Infections that persist, such as equine recurrent uveitis, often involve biofilm production, which can impede treatment efforts [9].  
Figure 2: Role of different biomolecules in the life cycle stages of biofilm.  
Biofilm-associated organisms exhibit markedly increased antibiotic resistance compared to their planktonic counterparts. This enhanced resistance can be attributed to factors such as restricted drug penetration, scarcity of nutrients, and adaptive stress responses [10]. Moreover, pathogens gain multiple advantages from producing biofilms, including protection against the host's immune system, resistance to antibiotics, and the recurrence of infections [33]. Biofilms can evade the host's immune system through the EPS matrix, a physical obstacle for immune cells. Additionally, in some cases, biofilms can even incite immunological responses that result in damage to the surrounding tissues [34].  
Notable examples of diseases linked to biofilms include chronic wound infections, where biofilms hinder the healing process; dental plaque causing tooth decay and gum disease; persistent lung infections in individuals with cystic fibrosis; and infections associated with medical implants such as catheters and prosthetic joints [35; 36; 37; 38]. For instance, managing lung infections in cystic fibrosis is notably tricky because of the biofilm development pattern shown by pathogenic microorganisms. In contrast to planktonic bacteria, biofilms have a notable capacity to endure very high levels of antimicrobials, substantially reducing treatment effectiveness. Bacterial biofilms in the lungs of individuals with cystic fibrosis include clusters of microorganisms encased in EPS, all immersed in the dense sputum typical of the condition. This matrix promotes bacterial resistance and obstructs antibiotic penetration, contributing to chronic lung infections' persistence [39; 40]. The pathogenic effects of different types of biofilms at different sites are described in Figure 3.
## 4 Simulation Results and Dissections
### Commercial Load
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{\
\begin{tabular}{c} **Position** \\ **(Size (kW))** \\ \end{tabular} } & **PV Energy** & **E\({}_{\text{PV to grid}}\)** & **Charging** & **Discharging** & **P\({}_{\text{loss}}\)** \\  & & **(Size (kW))** & **(kWh)** & **(kWh)** & **Energy** & **Energy** & **(kW)** \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Commercial \\ Load \\ \end{tabular} } & Without & & & & & & & \\  & PV and BES & - & - & - & - & - & 2173.851 \\  & 1 & PV & 61 (3832.2) & 28,843 & 17,155 & - & - & 825.1585 \\  & BES & 61 (2064.9) & - & - & 11,688 & 8936.1 & - \\  & 2 & PV & 61 (3644.3) & 61 (27,429) & 61 (16,253) & - & - & 709.9147 \\  & 2 & 17 (1089.2) & 17 (8197.6) & 17 (4853.2) & 17 (8544.5) & 17 (8544.5) & 1 \\  & & 61 (1945.8) & - & - & 61 (11,175) & 61 (8544.5) & 1 \\  & & 61 (3517.3) & 61 (26473) & 61 (15685) & 17 (3344.4) & 17 (2557.1) & \\  & 3 & PV & 18 (783.5) & 18 (5897.1) & 18 (3483.5) & - & - & 688.1289 \\  & & 11 (1065.6) & 11 (8020.3) & 11 (4777.6) & 16 (10788) & 61 (8248.5) & \\  & & 61 (1878.5) & & - & 18 (2413.6) & 18 (1845.4) & \\  & BES & 18 (420.22) & - & - & 18 (2413.6) & 18 (1845.4) & \\  & & 11 (564.685) & & & 11 (3242.7) & 11 (2479.4) & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The obtained results with and without installing PV with BES in commercial system loads. Figure 29: PV output during the day by installing one PV with BES in commercial system load. Figure 31: PV output during the day by installing two PV with BES in commercial system load. Figure 32: BES output during the day by installing two PV with BES in commercial system load.
## 2 State of the Art
This methodology takes the advantages of local and global information so navigation is highly benefited. Finally, the whole system was successfully validated in a commercial vineyard. In Reference [20], the authors proposed a novel field-of-view machine system to control a small agricultural robot that navigates between rows in cornfields. The robot was guided using a camera with pitch and yaw motion control in the prototype, and it moved to accommodate the near, far, and lateral fields of view mode. The approach we propose differs from others since satellite imagery and a camera placed in the autonomous robot (see References [21; 22]) are combined for advanced navigation. GPS sensor and satellite imagery are used (1) to acquire global knowledge of the groves where the robots should navigate and (2) to autonomously generate a first path planning. This first path planning contains the route where each robotic system should navigate. Moreover, the local vision system at the autonomous robot, that is composed by a colour Charge Coupled Device (CCD) camera with auto iris control, is used to add real-time information and to allow the robot to navigate centred in the path (see Reference [21]). Although the first path planning already introduces a route to navigate centred in the paths, local vision provides more precise information. We can benefit from combining global and local information; we can establish a first path planning from remote imagery which can be updated by local imagery captured in real time.
## 7 Simulation Results
### Performance Evaluation in a MANET Scenario
#### 7.2.2 Utility Function Values  
In this section, we will compute the gain of our game-theoretical routing scheme. Let us define \(U_{G_{i}}\) as the utility function for player \(i\) when the game-theoretical scheme is used and \(U_{NG_{i}}\) as the utility function  
Figure 14: Gain in terms of packet losses for I and P video frames using the game-theoretical scheme with respect to not using it.  
when it is not used. Both utility function values will be computed using Equation (17). \(G_{i}\) is the gain obtained for player \(i\) by using the game-theoretical scheme with respect to not using it, \(0\leq G_{i}\leq 1\).  
\[G_{i}=\frac{U_{G_{i}}-U_{NG_{i}}}{U_{G_{i}}}=\frac{U_{p=p_{i}^{*}}-U_{p=1}}{U_{p= p_{i}^{*}}} \tag{53}\]  
Using Equation (17) in Equation (53) we obtain:  
\[G_{i}=\frac{U_{G_{i}}-U_{NG_{i}}}{U_{G_{i}}}=\frac{\left(\frac{n_{n_{i,k}}-n_{ i,l};p_{1}^{*}}{n_{n_{i,l}};p_{1}^{*}}\right)\cdot\phi_{b,i}\cdot\frac{(p_{1}^{*}) ^{2}}{J_{b,i}}+\left(\frac{n_{r,m_{i,i}}-n_{s,i}\cdot(1-p_{1}^{*})}{n_{n_{s,i}} \cdot(1-p_{1}^{*})}\right)\cdot\phi_{m,i}\cdot\frac{(1-p_{1}^{*})^{2}}{J_{m,i} }-\left(\frac{n_{n,i}-n_{s,i}\cdot 1}{n_{s,i}\cdot 1}\right)\cdot\phi_{b,i} \cdot\frac{1}{J_{b,i}}}{\left(\frac{n_{n,k}-n_{s,i}\cdot p_{1}^{*}}{n_{s,i} \cdot p_{1}^{*}}\right)\cdot\phi_{b,i}\cdot\frac{(p_{1}^{*})^{2}}{J_{b,i}}+ \left(\frac{n_{r,m_{i,i}}-n_{s,i}\cdot(1-p_{1}^{*})}{n_{s,i}\cdot(1-p_{1}^{*} )}\right)\cdot\phi_{m,i}\cdot\frac{(1-p_{1}^{*})^{2}}{J_{m,i}}} \tag{54}\]
# Parasitic-Based Active Gate Driver Improving the Turn-On Process of 1.7 kV SiC Power MOSFET
## 1 Introduction
The proposed solution can be used in mostly all power SiC MOSFETs, affecting instant losses depletion without strengthening EMI. Modulation of the gate current is acquired by application of the multi-level gate voltage method. This approach allows having a constant value of the gate resistance, which optimizes the layout of the gate driver for the MV SiC MOSFET. The model of the proposed AGD was designed without any complex circuits such as CPLD as in [15] and is based on low-cost, high-speed, off-the-shelf integrated circuits. The main assumption was to improve the turn-on process with the proposed method since turn-on is usually the cause of much higher power losses in comparison to turn-off in the case of MV SiC MOSFETs in most hard switching applications [17]. The proposed AGD operates independently from the maximum drain current value in a range of operational conditions, which will be also discussed later in the paper. Thus, it can be used in highly efficient MV high-power electronic applications. The paper is organized as follows. The Section 2 of this paper provides a theoretical introduction of the methods used in the developed system. Then, the concept of the driver is verified in simulations in the Section 3. The experimental results and comparison of the AGD with CGD are shown in Section 4. Finally, the paper is concluded in Section 5.
## 3 Results and Discussion  
### Material Synthesis and Characterization  
Ca\({}_{2}\)Fe\({}_{2}\)O\({}_{5}\) was synthesized by the solid state synthesis technique. The crystal structures of Ca\({}_{2}\)Fe\({}_{2}\)O\({}_{5}\) had been previously reported [3]. XRD, XPS, and SEM were utilized to elucidate the structural properties of the material further. Figure 1 shows the powder XRD data of Ca\({}_{2}\)Fe\({}_{2}\)O\({}_{5}\). The crystal structure of the material was confirmed by Rietveld refinement using the data obtained from its powder X-ray diffraction. Our Rietveld refinement result was consistent with the previously reported structures (brownmillerite) with the _Pnma_ space group. Figure 1 shows the alternating arrangement of tetrahedra and octahedra with orthorhombic unit cells. The tetrahedra were oriented in the same direction in the same layer and in the opposite directions in the alternating layers, represented by different colors. The red tetrahedra represent an orientation toward the right, and the blue tetrahedra represent an orientation toward the left. Table 1 lists the refined structural parameters of Ca\({}_{2}\)Fe\({}_{2}\)O\({}_{5}\).  
Figure 1: XRD data of Ca\({}_{2}\)Fe\({}_{2}\)O\({}_{5}\) with Rietveld refinement. Crosses and vertical pink tick marks represent experimental data and Bragg peak positions, respectively. The solid red line is the _Pnma_ model, and the lower blue line is the plot difference.  
The scanning electron microscopy image shows close contact between the crystallites of Ca\({}_{2}\)Fe\({}_{2}\)O\({}_{5}\), as shown in Figure 2. As seen in the SEM image, the material was not porous. The non-porosity was also examined gravimetrically. The theoretical calculation was conducted based on the method described in the literature [25]. The experimental density of Ca\({}_{2}\)Fe\({}_{2}\)O\({}_{5}\) was observed to be 98% of the theoretical density. This also illustrates the nonporous nature of the bulk material. Though the crystallites were different in size, the big crystallites dominated the smaller ones. Generally, the 2P\({}_{3/2}\) peaks for trivalent Fe were observed at \(\sim\) 710-711 eV [26; 27]. Along with this peak, a satellite peak was observed at about 8 eV higher than the 2P\({}_{3/2}\) peak to confirm the presence of trivalent Fe [26; 27]. The XPS data for Ca\({}_{2}\)Fe\({}_{2}\)O\({}_{5}\) (Figure 3) demonstrate the presence of the 2P\({}_{3/2}\) peak observed at \(\sim\)710 eV, which was followed by a satellite peak observed at \(\sim\)718 eV, confirming the trivalent state of Fe. The XPS data were further supported by iodometric titrations. The titration showed an oxygen stoichiometry of 5.0 mol per formula unit (i.e., Ca\({}_{2}\)Fe\({}_{2}\)O\({}_{5}\)). The matching data of titration and XPS confirmed the oxidation state of Fe to be 3+.