esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Transient Engine Modelling
#### 5.2.2 Inertia Testing of GT1548 Turbocharger  
With the transient simulation set up, all that is required is the inertia of the turbines. As there is no data available for the GT1548, a simple experiment was conducted to determine the mass moment of inertia of the original turbocharger about its axis of rotation, and then, it was scaled up to the matched engine model turbocharger diameters.  
The moment of inertia of the turbine was measured by suspending it freely so it could rotate about the shaft axis with strings on two sides of the shaft, approximately 180\({}^{\circ}\) apart. The shaft was then turned 180\({}^{\circ}\). The turbine was then allowed to oscillate around the axis of rotation until the angular displacement was relatively small. The time to complete 10 and 5 oscillations was then recorded, and this was repeated 10 times to get an average time period. This was repeated with the compressor wheel attached. The moment of inertia could then be obtained from Equation (17), if k the torsion coefficient of the string used is known [22].  
\[I=\ k\frac{t_{p}^{2}}{4\pi^{2}} \tag{17}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Engine Speed (rpm)** & **Number of Cycles** \\ \hline
1000 & 25 \\
2000 & 50 \\
3000 & 75 \\
4000 & 100 \\
5000 & 125 \\
6000 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Number of cycles required to complete a 3-s transient simulation for each engine speed.  
Figure 23: Transient WAVE simulation component setup.  
If the torsion coefficient is not known, then before being able to obtain the moment of inertia, the string torsion coefficient must be calculated. This is done by repeating the above experiment for a disk of which the inertia is known, and therefore, the torsion coefficient of the string can be obtained using the previous equation. In this specific case, the torsion coefficient of the string was calculated to be 0.0002266. With both inertias now obtained, the calculated compressor wheel moment of inertia is given in Table 8.  
Once the moment of inertia of the turbocharger has been obtained, it requires scaling up to the matched engine condition. As the turbine and compressor have been scaled differently, their inertia needs to be scaled up separately according to the scale factor. The final results are shown in Table 9.  
This method has obtained a realistic value of the moment of inertia of the original turbine scaled up, which correlates well with the axial turbine of the same diameter. Finally, the inertia has been implemented into the transient model with the shaft model switch from dimensionless balance parameter to inertia to provide the actual transient responses.
## 3 Results and Discussion
### Adsorption Mechanism  
To elucidate the possible bonding mechanism between the resin and the REEs, the residue of the highest point in the adsorption batch test was characterized using SEM-EDX and ATR-FTIR, as shown in Figure 5.  
The SEM-EDX and ATR-FTIR results suggest that yttrium forms a complex with CMPO, while chlorine acts as a counter ion stabilizing the complex of the ligand and REEs. The complex formation of Ce with DGA and CMPO is also supported by the literature. The equilibrium reaction for metal ion extraction using DGA is shown in Equation (5)  
\[\text{Reactants}\rightleftharpoons\text{Products} \tag{5}\]  
\[\text{M}^{n+}{}_{(\text{aq})}+\text{nA}^{-}{}_{(\text{aq})}+\text{yl}{}_{( \text{org})}\rightleftharpoons\text{M}{}(\text{A}){}_{(\text{n})}\cdot\text{yl}{}_{( \text{org})}{}^{-}/(\text{M}{}_{(\text{hyd})}\cdot\text{L}{}_{(\text{y})}{}^{ (n+1)}\cdot\text{nA}^{-}{}_{\text{org}}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **adsorbent Resin** & **pH** & **Element** & **qmax (mg/g)** \\ \hline \multirow{6}{*}{This study} & \multirow{6}{*}{TK221} & \(-\)0.264—0.325 & Nd & 45.2 \\  & & \(-\)0.230—0.327 & Ce & 43.1 \\  & & \(-\)0.274—0.313 & Er & 35.1 \\  & & \(-\)0.116—0.202 & Y & 15.6 \\  & & \(-\)0.116-0.102 & Fe & 12.3 \\ \hline \multirow{2}{*}{[20]} & TODGA-modified macroporous & - & Zr & 20.0 \\  & silica-polymer-based & - & Sc & 8.16 \\ \hline \multirow{2}{*}{[29]} & \multirow{2}{*}{TEHDGA impregnated resin} & - & La & 1.31 \\  & & - & Nd & 0.53 \\  & & - & Y & 1.45 \\  & & - & Er & 1.3 \\ \hline \multirow{2}{*}{[51]} & \multirow{2}{*}{Functionalized Cr-MIL-101} & 2-6 & La & 37.4 \\  & & 2–6 & Ce & 49.0 \\ \cline{1-1}  & & 2–6 & Nd & 70.9 \\ \cline{1-1}  & & 2–6 & Sm & 72.7 \\ \cline{1-1}  & & 2–6 & Gd & 90.0 \\ \hline \hline \end{tabular} Note: “-” means “not evaluated”.  
\end{table}
Table 6: Comparison of Nd, Ce, Er, Y, and Fe adsorption capacity of TK221 with previous works.  
Furthermore, it is observed that CMPO and DGA have similar behavior in the extraction of Am(III) [52]. ATR-FTIR analysis of the loaded resin shows that the peaks of C=O and P=O have shifted, indicating that they have coordinated with REEs after the adsorption of Y and Ce. The IR peak at 3279 cm\({}^{-1}\) was attributed to the O-H stretching of water, while the peak at 2365 cm\({}^{-1}\) was assigned to the Cl\({}^{-}\). Overall, these results suggest that the bonding mechanism between the resin and the REEs involved the coordination of C=O and P=O groups with the REEs, as well as the formation of complexes with CMPO and counter ions such as Cl\({}^{-}\).  
Figure 5: (**a**) Elemental mapping of after adsorption of TK221 resin loaded with yttrium, (**b**) Elemental Mapping of after adsorption of TK221 resin loaded with cerium, (**c**) ATR-FTIR analysis of TK221 of blank (red), loaded with yttrium (green), and loaded with cerium (blue).
# A Workflow for Creating Gastric Computational Models from SPARC Scaffolds
## 1 Introduction
The SPARC program has facilitated the collection and development of multi-species data and knowledge including anatomically based 3D (three-dimensional) scaffolds of organs (e.g., heart, lung, stomach, intestine, colon) from various species (e.g., mouse, rat, sheep, pig, human) ([[https://sparc.science/data](https://sparc.science/data)]([https://sparc.science/data](https://sparc.science/data))). These scaffolds are in-silico 3D organ models representing the species-specific intricacies of anatomical details of the organs and can be an ideal basis for performing computational studies. Currently, most of these scaffolds, including the rat stomach, lack precision in their structural details. These structural details play a critical role in in-silico investigations and are necessary to perform more realistic simulations of physiological functions. However, it is feasible to integrate various tissue structures with these scaffolds, such as muscle fiber orientations, vascular geometry, neural pathways, and spatial distributions of genomic data. Linking the computer-based scaffolds to function at a sub-organ level is a way in which their validity may be tested, and they could be further refined. Validated virtual scaffolds could then possibly be used to manufacture 3D-printed scaffolds for surgical planning, regenerative medicine and education [24; 25; 26]. In this study, a workflow was developed to create computational models of the electrical events in the rat stomach using the SPARC scaffolds [27; 28]. We have also incorporated additional anatomical features that have been determined as part of the SPARC program [14]. Three computational models with varying anatomical details were developed and used in whole-organ slow wave simulations. The impact of anatomical details on slow waves was investigated by comparing spatiotemporal profiles of cellular activation in the three models. The workflow and the baseline models developed in this study will serve as a platform for future investigations on the impact of ICC degradation in functional GI disorders and explore the efficacy of electrical therapy protocols.
# Tamed Warping Network for High-Resolution Semantic Video Segmentation
## 5 Experiments
### Comparison with Other Methods  
We compare TWNet with other SOTAs in Table 9. Since different models are evaluated on different GPUs, it is informative to provide an estimate of how other models would perform on our GPU. Following the work of [19], we include the \"FPS norm\" value based on the GPU types of previous methods (GPU Benchmark: www.techpowerup.com/gpu-specs (accessed on 5 September 2023)). The scaling factors are 1.0 for 1080 Ti, 0.61 for Titan X Maxwell, 1.03 for TitanX Pascal, 1.12 for Titan XP, 0.44 for Tesla K40, 0.79 for Tesla K80, and 1.28 for 2080 Ti. All of our models run on the platform with CUDA 9.2, cuDNN 7.3, and TensorFlow 1.12, and we use the timeline tool in TensorFlow to measure the speed.  
The results demonstrate that TWNet achieves the highest inference speed with comparable accuracy at a resolution of \(1024\times 2048\). The accuracy of TWNet decreases more slightly than other video-based methods.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Name** & **Head1** & **Head2** & **Head3** \\ \hline Per-frame & ✓ & ✓ & ✓ \\ TWNet-Layer1 & ✓ & ✓ & \\ TWNet-Layer2 & ✓ & & \\ TWNet-Layer3 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: The required head layers for different versions of TWNet. head \(n\): the \(n\)th head layer of the encoder. ✓ indicates the specific layer is required.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Backbone** & **Head1** & **Head2** & **Head3** \\ \hline MobileNetV1 & _conv2d\_3_ & _conv2d\_5_ & _conv2d\_11_ \\ MobileNetV2 & _layer\_4_ & _layer\_7_ & _layer\_14_ \\ ResNet-18 & _conv2\_2_ & _conv3\_2_ & _conv4\_2_ \\ \hline \hline \end{tabular}
\end{table}
Table 7: Head layers for different backbones. We quote the notations from the TensorFlow Slim package.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Backbone** & **Warp** & **mIoU \(\uparrow\)** & **FPS \(\uparrow\)** & **Speed-Up (\(\times\))** \\ \hline \multirow{4}{*}{MobileNetV1} & None & **73.6** & 35.5 & - \\  & Layer1 & 71.6 & 61.8 & 1.74 \\  & Layer2 & 69.5 & 84.9 & 2.39 \\  & Layer3 & 63.2 & 119.7 & 3.37 \\ \hline \multirow{4}{*}{MobileNetV2} & None & 73.2 & 32.3 & - \\  & Layer1 & 71.3 & 59.6 & 1.85 \\  & Layer2 & 69.4 & 82.5 & 2.55 \\  & Layer3 & 62.4 & 115.8 & 3.59 \\ \hline \multirow{4}{*}{ResNet-18} & None & 71.6 & 36.9 & - \\  & Layer1 & 69.4 & 63.6 & 1.72 \\ \cline{1-1}  & Layer2 & 67.7 & 86.8 & 2.35 \\ \cline{1-1}  & Layer3 & 61.9 & **120.4** & 3.26 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of TWNet based on different backbone networks. Warp: the layer where feature warping is performed. “None” means no feature warping. \(\uparrow\): higher is better. \(\times\) indicates the speed-up times. The best results are shown in bold.
# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 3 State of the Art Anomaly Detection for Spacecraft Telemetry Data
### Artificial Intelligence Methods
#### 3.2.4 Computational Efficiency  
Although performance should always be a key priority, computational efficiency has significant interest for real-time spacecraft applications. Table 3 reports the average training/inference time in seconds per channel/sample for some proposed models on the SMAP/MSL datasets. For example, TranAD [31] provides the best performance in training time consumption. This is because TranAD uses positional encoding to push the whole sequence as an input in place of using local windows. Transfer learning used within the LSTM method [22] saves more training time compared to FCN and ResNet [8] architectures. From our analysis, the computational time required depends highly on the deployment setting and computational resources. All these variables make it difficult to fairly benchmark computational efficiency, although it is clear that the TranAD method has an edge, as also evidenced by the results we obtain for TranAD on a different dataset. Figure 2: Spacecraft anomaly detection taxonomy. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method(s)** & **Dataset(s)** & **Anomaly Type(s)** & **Metric(s)** & **Benefits(s) and Limits(s)** \\ \hline \multirow{4}{*}{Thresholding [9, 24]} & \multirow{4}{*}{- ESA XMM-Newton - Military communication satellite} & \multirow{4}{*}{Sequential anomaly} & \multirow{4}{*}{- Precision - Recall - - \(F_{1}\) score - \(F_{2}\) score - Graphic presentations} & \multirow{4}{*}{- Top-based systems with a several numbers of sensors - Requires engineering effort - Requires amount of tuning} \\  & & & & + Simple to use \\  & & & & + Easy to understand \\  & & & & - Not sufficient for the complex \\  & - & & & - Special \\  & - & & - \(F_{1}\) score - Graphic presentations & - \\ \hline \multirow{4}{*}{SVM [26, 37, 38], OCSVM [39], ICSVM [39], ICSVM [41], ICSVM [40], ICSVM [42], ICSVM [43], ICSVM [44] - ESA OPS-SAT - Collective & \multirow{4}{*}{- Point - Contextual - Collective - \(F_{1}\) score - \(F_{2}\) score - \(F_{3}\) score - \(F_{1}\) score - Time} & \multirow{4}{*}{- Top-based systems with large datasets and high-dimensional spaces.} \\ \cline{1-1} \cline{5-5}  & & & - Accuracy & + High accuracy \\ \cline{1-1} \cline{5-5}  & - ESA OPS-SAT - AADAPT-Lite EPS & \multirow{4}{*}{—} & \multirow{4}{*}{- Precision - \(F_{
## 1 Introduction
### Novel Approach toward Digitalization
Then, a SCADA system (Supervisory Control And Data Acquisition) is implemented over the entire machine to assure data interconnection capabilities. The proposed system presents sound advantages when compared to the previous manual procedures, namely: (i) high accuracy due to new automatic positioning system; (ii) no need for highly skilled operators to process measurements; (iii) measurements are much easier to derive, and digital results are quickly delivered and stored. Finally, the structure will be made mostly out of normalized equipment, allowing lower prices and flexibility to scale up the machine when needed.
## 3 Leakage Abuse Attack
### Attacks on Prior Works
#### 3.3.2 [PERSON] et al. Scheme  
[PERSON] et al. [14] proposed a non-SGX-based Type-II backward private scheme called Aura. Aura requires the client to revoke the encryption key after each search. The search result does not need to be re-encrypted because the previous search result is cached.  
When the client adds a keyword and document identifier ind to the database, the client retrieves the most recently used encryption key _msk_, and computes a ciphertext with ind and a tag \(t=F_{K_{t}}(w,\text{ind})\), where \(F\) is a pseudo-random function and \(K_{t}\) is secret key for \(t\). Then, the computed ciphertext is inserted into the database \(\text{EDB}_{add}\). For deletion, the client inserts tag \(t\) corresponding to the deleting entry \((w,\text{ind})\) into the deletion list \(D\).  
For searching on keyword \(w\), the client retrieves the number of searches on keyword \(w\), denoted as \(i\), the current secret key \(sk\), and the deletion list \(D\). Then, the client computes the revoked secret key \(sk_{R}\) and query token \(tkn\), sends them to the server, and refreshes _msk_. The server retrieves encrypted indices matching \(w\) and decrypts the non-deleted indices with \(sk_{R}\). The non-deleted indices are added to a list NewInd; the deleted tags are added to a list DellInd. Then, the server retrieves indices stored in \(\text{EDB}_{cache}[tkn]\) and excludes entries that are in DellInd, where \(\text{EDB}_{cache}\) stores the previous search result. Finally, NewInd together with non-deleted indices stored in cache \(\text{EDB}_{cache}[tkn]\) are returned to the client.  
**Extraction Scenario.** As explained above, during the search protocol, the server accesses \(\text{EDB}_{cache}[tkn]\) for retrieving cached search results. By comparing the accessed \(\text{EDB}_{cache}[tkn]\), the adversary can learn which previous search query is on the same keyword \(w\). The timestamps of addition update queries on \(w\) (i.e., \(TimeDB(w)\)) that occurred between two search queries on \(w\) are revealed to the adversary. Note that remaining update queries between two search queries can be possibly deletion updates on \(w\). During the execution of the second search query, the adversary can obtain additional information on how many deletion updates on \(w\) occurred by observing the number of entries excluded from \(\text{EDB}_{cache}\). Since the entries stored in \(\text{EDB}_{cache}\) are in the form of \((\text{ind},t)\), the deleted indices are also revealed while excluding deleted entries from the cache.  
Because the insertion timestamps of each \((w,\text{ind})\) are revealed, the adversary, similar to the case of [PERSON] et al.'s scheme [11], is able to extract \(DelHist(w)\) using the timestamps of possible deletion updates on \(w\) and deleted ind. However, the attack against Aura has a lower success rate than that of SGX-SE. The reason is that Aura does not follow the traditional definition of Type-II backward privacy and follows a somewhat different definition for Type-II backward privacy.
## 2 Materials and Methods
### SVD Denoising Algorithm  
Singular value decomposition (SVD) is a classical noise-reduction method, which is widely used in vibration signal processing [29].  
Assuming the signal \(X=[x(1),x(2),\cdots x(n+m-1)]\), the Hankel matrix can be constructed as follows [37]:  
\[H=\begin{pmatrix}x(1)&x(2)&\cdots&x(n)\\ x(2)&x(3)&\cdots&x(n+1)\\ \vdots&\vdots&&\vdots\\ x(m)&x(m+1)&\cdots&x(n+m-1)\end{pmatrix} \tag{8}\]  
where \(H\) is the matrix of \(m\times n\), \(m\geq n\).  
The singular value decomposition of \(H\) can be obtained. For any \(m\times n\) orders real matrix \(H\), there must be an orthogonal matrix \(U\in R_{m\times m}\) and an orthogonal matrix \(V\in R_{n\times n}\) to meet Equation (9).  
\[H=UDV^{T} \tag{9}\]  
where \(U\in R_{m\times n}\); \(V\in R_{n\times n}\); \(D=(diag(\sigma_{1},\sigma_{2},\cdots,\sigma_{p}),0)\) or its transpose; \(0\) denotes the zero matrix; \(\sigma_{i}\) represents the singular value obtained by decomposition, and satisfies \(\sigma_{i}\geq\sigma_{i+1}\).  
Assuming the optimal singular value order is \(r\), the denoised signal \(X=[x(1),x(2),\cdots x(n+m-1)]\) can be obtained by preserving the \(r\) order singular value of the singular value matrix \(D\) and reconstructing the matrix. The key problem is to determine the optimal order of singular value. If selected order is too low, the valid signal may be mistaken as noise, resulting in the loss of the valid signal. If selected order is too high, there will be a lot of residual noises, which affects the de-noising effect. The optimal order should preserve the valid signal in the maximal degree and filter out most of the noise.  
A singular entropy increment is applied to identify the optimal order in this paper [28]; the order when the singular entropy increment curve tends to be steady is chosen for the optimal singular value order \(r\), which ensures that the signal features are preserved and the unfavorable noise is filtered effectively.
## 3 Experiment and Results
### Microsphere Measurement
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Group** & **Angle (\({}^{\circ}\))** & **Diameter (\(\upmu\)m)** & **Roundness (\(\upmu\)m)** \\ \hline \multirow{8}{*}{**Before Eliminating Yaw**} & **1** & 0 & 303.581 & 0.092 \\  & **2** & 30 & 303.685 & 0.103 \\  & **3** & 60 & 303.678 & 0.102 \\  & **4** & 90 & 303.681 & 0.095 \\  & **5** & 120 & 303.565 & 0.086 \\  & **6** & 150 & 303.675 & 0.122 \\  & **Diameter (\(\upmu\)m)** & & 303.645 \\  & **Sphericity (\(\upmu\)m)** & & 0.151 \\  & **Group** & **Angle (\({}^{\circ}\))** & **Diameter (\(\upmu\)m)** & **Roundness (\(\upmu\)m)** \\  & **1** & 0 & 303.578 & 0.075 \\  & **2** & 30 & 303.688 & 0.091 \\  & **3** & 60 & 303.672 & 0.087 \\
**After Eliminating Yaw** & **4** & 90 & 303.685 & 0.085 \\  & **5** & 120 & 303.565 & 0.065 \\  & **6** & 150 & 303.677 & 0.096 \\  & **Diameter (\(\upmu\)m)** & & 303.643 & \\  & **Sphericity (\(\upmu\)m)** & & 0.132 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Measurement results of circle dimensions in different angles. The residual yaw errors of the measured six cross-sectional profiles are fluctuated within \(-\)31.9-26.5 nm, and the standard deviation of the residual yaw is 12.9 nm. The alignment error, the repeatability, the linearity of micro stage-3 and the residual yaw error are the mainly factors that affect the accuracy of the diameter and the roundness. The combined error of the four errors is approximate 22.1 nm. In the process of the measurement of a cross-sectional circle, the repeatability and the residual yaw error are influenced by each other, thus the actual measurement error is less than the mentioned combined error. For the measurement result after eliminating the yaw error, the standard uncertainty of the diameter of a cross-sectional circle is about 8.3 nm, which is defined as \(u_{1}\) and calculated based on the transitive relation of the least-squares theory. The standard deviation of the diameters of the six cross-sectional profiles is about 23.1 nm and defined as \(u_{2}\). The standard uncertainty of the measured diameter of the microsphere can be calculated by combing \(u_{1}\) and \(u_{2}\), which is about 24.6 nm. Here, the chosen coverage factor \(k\) is 2, then the expanded uncertainty of the diameter is 49.2 nm. Therefore, the measured diameter of the microsphere is \(303.643\pm 0.049\) um, and the sphericity is 0.132 um.
## 2 Materials and Methods
### Quantification of Folate Content  
Folate was quantified using the HPLC as cited by [PERSON] et al. [24]. The HPLC system comprised a pump model Jasco PU- 2089 (Jasco, Easton, MD, USA), a fluorescence detector (FLD) Jasco FP-2020 Plus (Jasco, Easton, MD, USA) set at wavelengths of 290 nm excitation and 360 nm emission, and an autosampler Jasco AS-4050 (Jasco, Easton, MD, USA). The analytical column was a Luna C18, 250 \(\times\) 4.6, 5 \(\upmu\)m (Phenomenex, Torrance, CA, USA), protected by a Phenomenex 4.0 \(\times\) 3.0 mm C18 ODS guard column (Phenomenex, Torrance, CA, USA). Quantification of folate content was determined through a calibration curve prepared by running standard concentrations of 5-methyl-tetrahydrofuranoidic acid (5-CH\({}_{3}\)-H\({}_{4}\) folate). The gradient program consisted of two mobile phases: A (30 mM phosphate buffer with 2.3 pH) and B (Acetonitrile). It started with 94%A for 8 min, then decreased to 75% A for 27 min, and increased again to 94% A for 15 min.  
Results are expressed as \(\upmu\)g 5-CH\({}_{3}\)-H\({}_{4}\) folate per 1 kg of fresh weight of strawberries (\(\upmu\)g 5-CH\({}_{3}\)-H\({}_{4}\)folate/kg FW).