esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Experiment 2  
The experimental results showed that the proposed system was superior to the conventional method in terms of recall when 7 days of samples were used as the detection target. Figure 3 shows the result of recall between the conventional method and the proposed method for each sample group. Our method improved recall by about 0.1 across all samples, indicating that weighted voting can reduce false negatives.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Group** & **A** & **B** & **C** & **D** & **E** & **F** & **G** \\ \hline Collection and Inspection Date & 10/30 & 10/31 & 11/01 & 11/05 & 11/06 & 11/07 & 11/08 \\ \hline Number of Samples & 250 & 250 & 250 & 98 & 249 & 250 & 250 \\ \hline \end{tabular}
\end{table}
Table 2: Malware samples used in experiment 2 (collected in 2023).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Group** & **A** & **B** & **C** & **D** & **E** & **F** & **G** \\ \hline Re-inspection Date & 11/21 & 11/22 & 11/23 & 11/27 & 11/28 & 11/29 & 11/30 \\ \hline \end{tabular}
\end{table}
Table 3: Malware samples used in experiment 3. The groups were the same as those used in experiment 2.  
Therefore, the proposed system detects malware more accurately than the uniformly weighted voting and is also effective for detecting unknown malware that has not yet been registered.
## 4 Experimental Results and Discussion
The change in droplet diameter at impact on a non-treated and treated surface is illustrated in Figure 8.  
After the impact of a droplet on the non-treated surface, the contact diameter increases to 48 \(\upmu\)m and after oscillation decreases to a stable shape and size with a diameter of 32 \(\upmu\)m. In the case of a treated surface, the droplet slowly expands to a diameter of 64 \(\upmu\)m and remains still. It should be noted that the decrease in the contact area of the droplet on the non-treated surface of polyethylene terephthalate can be predicted by considering surface energies as well as interfacial energy.
# Effect of Pore Size Distribution and Amination on Adsorption Capacities of Polymeric Adsorbents
## 4 Conclusions  
We have synthesized a series of mesoporous resins with different pore size distributions via suspension polymerization. These resins, which were suitable for the adsorption of macromolecules. The adsorption kinetics data were well-fitted by a pseudo-second-order rate equation, and the adsorption equilibrium data were described by a three-parameter multilayer adsorption model. The adsorption of small molecules reached equilibrium  
Figure 7: (**a**) Adsorption isotherms of phenol at 288 K; (**b**) Adsorption isotherms of phenol at 303 K; (**c**) Adsorption isotherms of phenol at 318 K; (**d**) Adsorption isotherms of ABS acid at 288 K; (**e**) Adsorption isotherms of ABS acid at 303 K; (**f**) Adsorption isotherms of ABS acid at 318 K; (**g**) Adsorption isotherms of RBB4 at 288 K; (**h**) Adsorption isotherms of RBB4 at 303 K; (**i**) Adsorption isotherms of RBB4 at 318 K.  
quicker than large molecules, and resins with smaller pores had a lower adsorption capacity for large molecules because of the pore blockage effect. NDA-1 and NDA-2 could efficiently adsorb phenol and ABS acid, while NDA-3 could efficiently adsorb RBB4. The resins were modified by amine groups. The presence of polar functional groups led to more efficient RBB4 adsorption, while the decreased specific surface areas led to decreased phenol adsorption capacities. Thus, a composite functional adsorbent resin with an improved ability to adsorb the macromolecule RBB4 was obtained. These mesoporous resins are promising candidates for application in removing organic contaminants from wastewater.  
The following are available online, Figure S1: Characterization of the adsorbates, Table S1: The reagents used of synthetic resins, Figure S2: N2 adsorption isotherms of NDA-1 (a), NDA-2 (b), NDA-3 (c) and NDA-4 (d) resins at 77K (logarithmic scale (p/p0)), Figure S3: The infrared spectra of the four resins, Table S2: The corresponding parameters of three-parameter multilayer adsorption model.  
Conceptualization, W.L. and Y.D.; methodology, W.L. and J.T.; validation, S.W.; formal analysis, Y.Z.; investigation, W.L.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z. and W.L.; visualization, Y.Z. and W.L.; supervision, L.B.; project administration, L.B. and S.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by The National Key Research and Development Program of China (2018 YFC1801606), National Natural Science Foundation of China (51808267) and Independent Research Project of Key Laboratory of Environmental Engineering of Jiangsu Province (ZZ2020002).  
Not applicable.  
The authors declare no conflict of interest.
# Real-Time 3D PET Image with Pseudoinverse Reconstruction
### Theory
#### 1.2.5 Axial Rebinning and Transaxial Reconstruction with the Pseudoinverse  
Following the way the SRM was modeled into two independent components, the reconstruction process can be split into two smaller problems: axial rebinning (PINV\({}_{\rm z}\)) and 2D reconstruction of the corresponding rebinned slices (2D-PINV).  
\[Y_{R\phi}^{T}(z,\rho\theta)=A_{z}^{\dagger}(z,z_{1}z_{2})\cdot Y^{T}(z_{1}z_{2 },\rho\theta);\ Y_{R\phi}(\rho\theta,z)=\left(Y_{R\phi}^{T}(z,\rho\theta) \right)^{T}, \tag{11}\]  
\(A_{z}^{\dagger}(z,z_{1}z_{2})\) was obtained as the pseudoinverse of \(A_{z}(zw,z_{1}z_{2})\), obtained from Equation (2) and then collapsing (summing over) in \(w\)t. On the other hand,  
\[X(xy,z)=A_{xy}^{\dagger}(xy,\rho\theta)\cdot Y_{R\phi}(\rho\theta,z), \tag{12}\]  
where \(A_{xy}^{\dagger}(xy,\rho\theta)\) is the pseudoinverse of \(A_{xy}(xy,\rho\theta)\), obtained from Equation (3).  
Since both processes are independent and only involve matrix products, the reconstruction can be performed in a single step in which the 3D data are multiplied by each matrix from the left and right  
\[X(xy,z)=A_{xy}^{\dagger}(xy,\rho\theta)\cdot Y(\rho\theta,z_{1}z_{2})\cdot A _{z}^{\dagger}(z_{1}z_{2},z), \tag{13}\]  
The workflow of the reconstruction process is represented schematically in Figure 3.  
\(A_{xy}^{\dagger}(x,y;\rho,\theta)\times Sinogram(\rho,\theta,z_{1}z_{2})\times A _{z}^{\dagger}(z_{1}z_{2};z)=Reconstructed\ Image\ (x,y,z)\)  
Figure 3: Workflow of the reconstruction process. 3D data are multiplied PINVs of the axial and transaxial SRMs in a single step.  
Figure 2: Singular values of a small matrix (solid line) and its reciprocals using different regularization methods. Tikhonov (\(k\) = 0.01) and Landweber (\(n\) = 100) regularization for equivalent iterations. Truncated singular values (TSVD) with a threshold of 0.1 and original singular values (SV) are also shown.
## 3 Protocol Specification
### Initial Route Establishment  
In ACO-based routing algorithms, the pheromone is initialised to a small uniform value for the start of the route discovery. The probability distributions of route selection will not be updated until the first artificial ant arrives at the sink and returns back. Hence, the distributions do not guide the artificial ants, which end up constructing (and reinforcing) paths of very bad quality. As discussed in Section 2, AFSA demonstrates exceptionable global search abilities in the early stage of route discovery and can obtain high quality satisfactory routes quickly. Furthermore, AFSA is not sensitive to the initial parameter values. Therefore, we use the positive qualities of AFSA to apply the preferred routes obtained by AFSA as the initial pheromone value of the following ACO routing algorithm.  
Initialise the state vector of the artificial fish swarm attached to each sensor node as \(X=(x_{1},x_{2},...,x_{n})\), \(n\in N\); \(N\) represents the number of sensor nodes; \(x_{i}(i=1,2,...,n)\) denotes the residual energy of current node. The sensory distance and the crowd factor are represented by _visual_ and \(\delta(0<\delta<1)\). In our network model, _visual_ represents the communication radius of sensor node, which means the nodes in _visual_ of node \(i\) are the neighbour nodes of node \(i\). \(N_{i}\) denotes the set of nodes within the _visual_ of node \(i\). Crowd factor \(\delta\) represents the congestion degree around a certain sensor node, which is used to avoid over crowding or collision with neighbouring regions. We also define a fitness function \(Y=f(x_{i})\), which stands for the average energy within the communication radius of sensor node \(i\).  
\[Y=f(x_{i})=\frac{1}{N}\sum_{j\in N_{i}}x_{j} \tag{1}\]  
All the AF search for the optimal route toward the sink node using three distinct properties, namely preying, swarming and following.
## 2 Materials and Methods
### Experimental Set-Up  
Mean arterial blood pressure (MAP) was continuously and non-invasively monitored by finger photo-plethysmography (CNAP System, CNSystems Medizintechnik, Graz, Austria), which also yielded the continuous monitoring of cardiac output (CO) and heart rate (HR). The finger cuff was applied to the third finger of the right hand and a calibration cuff was placed around the ipsilateral arm (Figure 1A).  
The non-invasive monitoring of muscle blood volume and oxygenation was performed using near-infrared spectroscopy (NIRS) (NIRO-200X, Hamamatsu Photonics K.K, Shizuoka, Japan). Two NIRS probes were employed, with an inter-optode distance of 3 cm. The first one was positioned over the flexor muscles of the left forearm and the second one over the right vastus lateralis muscle. The device measures changes in concentration of deoxygenated (HHb) and oxygenated (O2 Hb) hemoglobin + myoglobin, expressed in uM-cm, based on the standard Beer-Lambert (BL) methodology. The sum of these two terms yields the total hemoglobin (tHb) concentration, which is an index of blood volume change. Additionally, the NIRS device calculates the tissue hemoglobin index (THI), which reveals relative changes in blood volume, and the tissue oxygenation index (TOI), which represents the percentage of oxygen-saturated (hemoglobin + myoglobin) over total (hemoglobin + myoglobin). These latter parameters are based on the spatially resolved methodology, which focusses the measurement in depth (muscle tissue) so they are thus little affected by hemodynamic changes occurring in the more superficial tissue layers [13].  
The negative pressure to the lower limbs was delivered through a lower body negative pressure (LBNP) chamber (LBNP 1100, TECHNAVANCE, Austin, TX, USA) [17]. The  
Figure 1: Experimental setup including CNAP and NIRS devices along with the LBNP chamber (**A**). Experimental protocol (**B**): the sequence of the 90 s lasting stimuli was randomized, while the 5 min lasting stimulus always followed at the end.  
device, controlled by a web interface, was used to deliver negative step changes in pressure of different magnitudes and durations. The value of the actual pressure inside the chamber is returned through an analog output.  
All signals were digitally sampled (1401 micro, CED, Cambridge, UK) at a sampling frequency of 1024 Hz and stored on a PC for offline analysis (Spike2, CED, Cambridge, UK). Data acquisition was continuously performed throughout the whole experimental session.
# Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer
## 3 Results
### Individual Study Models  
Key results for the three Individual Study Models are given in Table 7.  
For Study 1, the most accurate model used CatBoost, which was associated with a mean accuracy of 77%. This is a modest improvement on the chance level of accuracy which was 71%, based on the degree of class imbalance (i.e., 71% good responders). For Studies 2 and 3, the models did not provide an improvement on chance level.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Sensitivity** & **F1** & **AUC** \\ \hline
**Voting** & 70\% & 70\% & 69\% & 69\% & 64\% \\
**classifier** & 69\% & 70\% & 69\% & 69\% & 64\% \\
**XGBoost** & 69\% & 70\% & 69\% & 69\% & 64\% \\
**Catboost** & 69\% & 70\% & 69\% & 69\% & 64\% \\
**Logistic** & 66\% & 68\% & 66\% & 67\% & 63\% \\
**Regression** & 64\% & 68\% & 64\% & 65\% & 63\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance Metrics (mean %s) for the Separate Study Validation Models (averages are given for sensitivity, precision and F1 scores, weighted by the frequency of class labels).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Gini** & \
\begin{tabular}{c} **Good** \\ **Responders** \\ **(_n_ = 1683_)** \\ \end{tabular} &
\begin{tabular}{c} **Bad** \\ **Responders** \\ **(_n_ = 1970_)** \\ \end{tabular} \\ \hline Previous prostatectomy & 0.19 & 70\% & 37\% \\ Previous radiotherapy & 0.17 & 20\% & 15\% \\ PSA at baseline (median, ng/mL) 1 & 0.14 & 0.2 & 3 \\ PSA prior to baseline (median, ng/mL) 1 & 0.09 & 10 & 17 \\ Metastatic node disease & 0.05 & 18\% & 11\% \\ Vitamins, not specified & 0.04 & 11\% & 4\% \\ Clinical stage (proportion of median category) & 0.04 & 4\% & 4\% \\ Diazepam & 0.03 & 0\% & 1\% \\ Enalapril & 0.02 & 3\% & 5\% \\ Anti-anginal vasodilators, organic nitrates & 0.02 & 4\% & 8\% \\ Glyceryl trinitrate & 0.02 & 2\% & 4\% \\ Vitamin K antagonists & 0.02 & 2\% & 3\% \\ Age (mean, years) & 0.02 & 65 & 68 \\ Aspartate aminotransferase levels (mean, U/L) & 0.02 & 19 & 21 \\ Alanine aminotransferase levels (mean, U/L) & 0.02 & 20 & 22 \\ Gleason score category (median) & 0.02 & 2 & 2 \\ Weight (mean, kg) & 0.01 & 83 & 81 \\ HMG COA reductase inhibitors & 0.01 & 9\% & 6\% \\ Aspirin & 0.01 & 20\% & 16\% \\ Propionic acid derivatives & 0.01 & 7\% & 4\% \\ Furosemide & 0.01 & 2\% & 3\% \\ Inhalational select beta2-adrenooreceptor agonists & 0.01 & 3\% & 4\% \\ Lisinopril & 0.01 & 4\% & 3\% \\ \hline \hline \end{tabular}  
* \({}^{1}\) median was used as a measure of central tendency rather than mean due the presence of extreme values.  
\end{table}
Table 5: Gini importances for each feature (based on XGBoost) and comparisons of these features (proportions and measures of central tendency) for good and bad responders, for the Three-Study Merged Dataset.
## Appendix A Proofs of the Two Main Theorems
### Proof of the General Theorem
#### a.1.1 Verifying the Conditions of Proposition 2
Further Simplifications of the Desired Result with Successive Sufficient Conditions  
We find another, simpler, sufficient condition for (A2) from Equation (A5). This part is unique to our proof and absent from [7]. It is here that we relate the formal conditions of Proposition 2 to the specific assumptions of our Theorem 1. We first remove the dependency on the terms \(\{\theta_{n}\}_{n}\) of the EM sequence:  
\[\big{\|}\hat{S}_{n}(\theta_{n})-\hat{S}(\theta_{n})\big{\|}\mathds{1}_{\theta_ {n}\in K}\leq\sup_{\theta\in K}\,\big{\|}\hat{S}_{n}(\theta)-\hat{S}(\theta) \big{\|}.\] (A7)From Equations (A5)-(A7) we get that:  
\[\forall\text{ compact }K\subset\Theta,\quad\sup_{\theta\in K}\ \left\|\tilde{S}_{n}( \theta)-\tilde{S}(\theta)\right\|\underset{n\to\infty}{\longrightarrow}0\,,\]  
is a sufficient condition to have both Equations (A1) and (A2). To show that the hypotheses of Theorem 1 imply this sufficient condition, we express it in integral form. Let \(S=\left\{S_{u}\right\}_{u=1,\ldots,q}\). We recall that \(\tilde{S}_{n}(\theta)=\left\{\int_{z}S_{u}(z)\hat{p}_{\theta,n}(z)dz\right\}_{u =1}^{q}\) and \(\tilde{S}(\theta)=\left\{\int_{z}S_{u}(z)p_{\theta}(z)dz\right\}_{u=1}^{q}\). Hence:  
\[\tilde{S}_{n}(\theta)-\tilde{S}(\theta)=\left\{\int_{z}S_{u}(z)(\hat{p}_{ \theta,n}(z)-p_{\theta}(z))dz\right\}_{u=1}^{q}.\]  
These \(q\) terms can be upper bounded by two different terms depending on the existence of the involved quantities:  
\[\int_{z}S_{u}(z)(\hat{p}_{\theta,n}(z)-p_{\theta}(z))dz\leq\left(\int_{z}S_{u }(z)^{2}dz\right)^{\frac{1}{2}}\left(\int_{z}(\tilde{p}_{\theta,n}(z)-p_{ \theta}(z))^{2}dz\right)^{\frac{1}{2}},\]  
and  
\[\int_{z}S_{u}(z)(\hat{p}_{\theta,n}(z)-p_{\theta}(z))dz\leq\left(\int_{z}S_{u }(z)^{2}p_{\theta}(z)dz\right)^{\frac{1}{2}}\left(\int_{z}\left(\frac{\tilde{p }_{\theta,n}(z)}{p_{\theta}(z)}-1\right)^{2}p_{\theta}(z)dz\right)^{\frac{1}{ 2}}.\]  
As a consequence, if \(\int_{z}S_{u}(z)^{2}dz\) exists, then it is sufficient to show have:  
\[\sup_{\theta\in K}\ \int_{z}(\tilde{p}_{\theta,n}(z)-p_{\theta}(z))^{2}dz \underset{n\to\infty}{\longrightarrow}0\,,\]  
and if \(\int_{z}S_{u}(z)^{2}p_{\theta}(z)dz\) exists, then it is sufficient to show have:  
\[\sup_{\theta\in K}\ \int_{z}\left(\frac{\tilde{p}_{\theta,n}(z)}{p_{\theta}(z)}-1 \right)^{2}p_{\theta}(z)dz\underset{n\to\infty}{\longrightarrow}0\,.\]  
Among the assumptions of Theorem 1 is one that states that for all compacts \(K\subseteq\Theta\), one of those scenarios has to be true. Hence our sufficient condition is met. Conclusions  
With the hypotheses of Theorem 1, we have  
\[\forall\text{ compact }K\subseteq\Theta,\quad\sup_{\theta\in K}\ \left\|\tilde{S}_{n}(\theta)-\tilde{S}(\theta)\right\| \underset{n\to\infty}{\longrightarrow}0\,,\]  
which is a sufficient condition to verify both Equations (A1) and (A2). With these two conditions, we can apply Proposition 2.
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 4 Results and Analysis
### Verification of Estimated Yield  
A total of 29 sampling points were selected to verify that the improved CASA model and the NPP-yield conversion model provided accurate estimates of winter wheat yield. Each pixel for the sampling points was averaged with the eight adjacent pixels to obtain the estimated yield for the given sampling point. Figure 9 shows the estimated yield as a function of the measured yield.  
The determination coefficient \(R^{2}\) = 0.56 and RMSE was 1.22 t ha\({}^{-1}\). This analysis gave an average absolute error of \(-0.57\) t ha\({}^{-1}\) and an average relative error of \(-6.01\%\), which satisfied the accuracy requirements for estimating regional winter wheat yield. Thus, the proposed method may have good application potential.  
Figure 8: Spatial distribution of estimated winter wheat yield for the 2008–2009 growing season.
## 3 Interactions of Antitoxins and Toxins with Cellular Components
### Interactions Involving Antitoxins
#### 3.2.3 Direct Interactions with Chaperones and ATP-Proteases  
Type II antiotoxins are degraded by ATP-dependent proteases. Antitoxin instability is likely due to the fact that most have unstructured N-terminal parts and fold only upon toxin binding [123]. While most antiotoxins are a substrate for Lon protease, some are degraded by ClpXP (Phd, PaaA) and ClpAP (MazE). Most antioxins have half-lives of \(\sim\)15-20 min, while toxins are stable. Note that the SymE type I toxin is degraded by Lon [38]. Conditional degradation has been shown for MqsA. This antitoxin is stable for up to 60 min in steady-state conditions. Upon oxidative stress, MqsA is very unstable, and its half-life is estimated at around 1.5 min [118]. Recently, the group of [PERSON] showed that Lon-dependent degradation of the RelB and YefM antioxins relies on Poly-P (poly-phosphate) [124]. RelB and YefM are stable in a _ppk-ppx_ mutant, unable to synthesize Poly-P.  
Recently, a novel type of type II TA system was identified in _M. tuberculosis_. This system has been quoted as TAC (toxin-antitoxin-chaperone) [125]. This system is composed of a HigA antitoxin, a HigB toxin and a SecB-like chaperone. In the absence of the chaperone, HigA aggregates and is rapidly degraded; thus, the chaperone is essential for antitoxin activity [125].
## 2 Nonlinear Distortion Due to SBS for SSB Signals  
Figure 1 shows the schematic mechanism of SBS induced nonlinear distortion for SSB signals. The generation of a SSB signal can be realized by standard in-phase and quadrature (IQ) modulation, optical filtering, or carrier-signal offset combination, which is adopted in this work. As depicted by Figure 1a,c, left single-sideband (LSB) and right single-sideband (RSB) SSB signals can be flexibly obtained. The SSB signal can be realized with or without guard band between the carrier and the signal. Improved optical detection can be obtained for SSB with guard band, as shown by Figure 1e,g.  
As an important nonlinear distortion, SBS can be easily induced due to its comparably low threshold. In conventional applications, the signals are modulated with a high data rate without significant carrier power left and, thus, lead to neglected SBS. However, for the KK receiver, the SSB signal is needed with a high carrier power in order to maintain a small detection error. Typical CSPR exceeds 10 dB, which makes the SBS issue no longer neglectable, as shown in Figure 1b,d. In particular, as for SSB signals with guard band, the carrier is in fact a standalone laser with narrow linewidth. The SBS threshold would be even lower, and significant nonlinear distortion due to SBS can be expected, as schematically shown in Figure 1f,h.  
Figure 1: SBS induced nonlinear distortion for SSB signals. **(a)** RSB without guard band; **(b)** RSB without guard band after SBS distortion; **(c)** LSB without guard band; **(d)** LSB without guard band after SBS distortion; **(e)** RSB with guard band; **(f)** RSB with guard band after SBS distortion; **(g)** LSB with guard band; **(h)** LSB with guard band after SBS distortion.
## 3 Results and Discussions
### Effect of Circle Groove Shape
Seal clearance has a significant influence on turbine performance. With an increase in seal clearance, the mixing of the main flow and leakage flow gets stronger and the loss gets larger. Furthermore, the relative leakage increases while the power and efficiency decrease. 3. Arranging rectangle, circle and V-shaped grooves on the seal cavity surface near the outlet of the seal gap can enhance the energy dissipation, reduce the relative leakage and improve the power and efficiency. Moreover, the configuration with the circle groove performs the best. The relative leakage of stator1 and rotor, power and efficiency under the design condition are \(6.34\times 10^{-3}\), \(8.53\times 10^{-3}\), \(3.463\) MW and \(86.61\%\) respectively. 4. For the geometric parameters in this paper, increasing the circle groove width can improve the aerodynamic performance. However, the groove depth has little effect on performance. The configuration where the groove width is \(50\%\) of the pitch of seal tooth achieves the best performance. The relative leakage of stator1 and rotor, power and efficiency under the design condition are \(6.04\times 10^{-3}\), \(8.09\times 10^{-3}\), \(3.467\) MW and \(86.86\%\) respectively. 5. Under the same working condition and structural parameters, the relative leakage of the rotor is always higher than that of stator1. With an increase in pressure ratio, the relative leakage increases firstly and then remains unchanged. The power increases while the efficiency increases firstly and then decreases, reaching the peak value under the design condition. Conceptualization, Q.D. and D.Z. ; methodology, Q.D. ; software, Q.D. ; validation, Q.D. ; formal analysis, D.Z. ; investigation, D.Z. ; resources, D.Z. ; writing--original draft preparation, Q.D. ; writing--review and editing, Q.D. ; visualization, D.Z. ; supervision, D.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by Research Program supported by the 111 project (B16038), China. The authors declare no conflict of interest.