esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis
## 3 Methodology
### Architecture and Learning Scheme
#### 3.3.2 Learning Scheme
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **MAE (Syn) \(\downarrow\)** & **MRE (Syn) \(\downarrow\)** & **Acc. (Syn) \(\uparrow\)** & **MAE (flat) \(\downarrow\)** & **MRE (flat) \(\downarrow\)** & **Acc. (Nat) \(\uparrow\)** \\ \hline SC-VAE-B (_u_) & 0.10 & 0.81\% & 67.9\% & **0.48** & **5.12\%** & **61.8\%** \\ \hline \multicolumn{8}{c}{BF (bright-field microscopy)} \\ \hline EfficientNet (ss) & 6.502 & 67.1\% & 4.5\% & 1.13 & 17.2\% & 33.9\% \\ BiT (ss) & N/A & N/A & N/A & 1.79 & 22.45\% & 38.7\% \\ Twin-VAE (ss) & 0.48 & 4.27\% & 60.1\% & 0.68 & 7.6\% & 53.2\% \\ Transfer & 0.40 & 3.87\% & 66.6\% & 0.52 & 5.47\% & 60.7\% \\ Twin-VAE (ss) & & & & & & \\ Dual Transfer & 0.35 & 3.73\% & 66.8\% & **0.51** & **5.43\%** & **60.8\%** \\ Twin-VAE (_u_) & 1.92 & 39.0\% & 2.0\% & 2.39 & 32.0\% & 32.0\% \\ C-VAE (_u_) & 0.67 & 5.72\% & 50.8\% & 1.96 & 21.8\% & 26.3\% \\ S-VAE (_u_) & **0.33** & **3.66 \%** & **67.3\%** & 2.09 & 34.2\% & 18.6\% \\ SC-VAE (_u_) & 0.41 & 3.88\% & 62.5\% & 0.60 & 7.1\% & 56.6\% \\ SC-VAE-B (_u_) & 0.39 & 3.77\% & 62.6\% & 0.56 & 6.51\% & 58.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._training procedure described above. This cost is applied for natural, synthetic, and both types of translated data and is defined as follows:  
\[KLD_{loss}(x,t)=C_{\mathcal{D}_{\text{KL}}}^{n,t\to s}\cdot\mathcal{D}_{ \text{KL}}(x_{n,t\to s})+C_{\mathcal{D}_{\text{KL}}}^{s,s\to n}\cdot \mathcal{D}_{\text{KL}}(x_{s,s\to n}) \tag{4}\]  
All coefficient factors have to be chosen mindfully, balancing the main target of punishing incorrect cell counts on natural data and relaxing the importance of details in visual reconstruction, but not undervaluing the KLD at the same time. Doing so can make the training procedure unstable, while applying very large regularization costs hinders the learning process and slows it down. To minimize the number of hyperparameters that have to be optimized by hand, the weighting factors for the \(\mathcal{D}_{\text{KL}}\) losses have been grouped and a Bayesian optimization [45] in the form of a Gaussian process regressor [46] was used to quickly find baseline values for the most important hyperparameters, such as the learning rate and the loss weight factors. We combine these losses to form our overall SCVAE\({}_{\text{loss}}(x,l,t)\), use the coefficients of the different terms to balance the impacts between natural, synthetic, and both types of translated images, and handle input images with missing cell counts by fixing \(C_{\text{Reg}}^{n,l}=C_{\text{Reg}}^{n\to s,l}=0\):  
\[\text{SCVAE}_{\text{loss}}(x,l,t)=REC_{loss}(x,t)+REC\text{-}T_{loss}(x,t)+ REG_{loss}(x,l,t)+KLD_{loss}(x,t) \tag{5}\]
## 2 Theoretical Background of the Equivalent Stress
### Time-Dependent Stress Analysis Based on the Linear Viscoelastic Approach  
First, as mentioned, the mechanical properties of materials such a modulus, strength, and Poisson's ratio exhibit characteristics of elastic, solid, and viscous fluid behavior [1; 2; 16]. Therefore, because steels have mechanical properties that are a function of time, then a linear viscoelastic analysis is necessary. The linear viscoelastic approach to determine the stress depending on time is based on the Maxwell rheological model [1; 2] given by  
\[\varepsilon_{(t=i)}=\frac{\sigma}{E}+\frac{\sigma t}{\eta} \tag{7}\]  
where \(\eta\) is the coefficient of tensile viscosity, \(E\) is the elastic modulus, and \(\varepsilon\) is the generated strain. Thus, by substituting the given stress, the elastic modulus depending on time is given as  
\[E_{(t=i)}\ =\frac{\sigma}{\varepsilon}=\frac{1}{\frac{1}{E}+\frac{t}{\eta}} \tag{8}\]  
Since by using the elementary theory of bending beams, the generated strains can be seen as \(\varepsilon_{(t=i)}=\frac{\Delta}{dx}\), and the infinitesimally curvature can be analyzed as \(d\theta=dx/\rho\)[17; 18; 19], then from this analysis and by using Equation (7), the generated curvature can be seen as  
\[\frac{d\theta}{dx}=\frac{1}{\rho}=\left[\frac{\sigma}{E}+\frac{\sigma t}{\eta}\right] \tag{9}\]  
Consequently, the basic deflection curve equation of a beam is given as  
\[\frac{d^{2}y}{dx^{2}}=\frac{M}{EI} \tag{10}\]  
where, \(M\) is the bending moment and \(I\) is the inertia moment. Thus, from Equations (9) and (10), the cross-sectional area of the beam can be analyzed as  
\[\frac{1}{\rho}=\frac{M}{EI} \tag{11}\]  
By substituting both analyses we have  
\[\frac{M}{EI}=\left[\frac{\sigma}{E}+\frac{\sigma t}{\eta}\right] \tag{12}\]Consequently, the stress depending on time can be estimated as  
\[\sigma_{t=i}=\frac{My}{I+\frac{tl}{\eta}E_{t}} \tag{13}\]  
Finally, by integrating Equation (10), the time dependence bending deflection function can be analyzed as  
\[f(\delta)=E_{t}Iv^{\prime\prime}=M \tag{14}\]  
where \(f(\delta)\) is the maximum deflection curve. As a summary, Equation (8) represents the time-dependent elastic modulus function; Equation (13) represents the required stress depending on time, and; Equation (14) represents the time-dependent deflection curve.  
In congruence with the [PERSON] criterion, the stress \(\sigma_{1}\) value is the parameter that is affected through time by the cyclical stress application. Here, it is modeled by using the stress normal distribution \(N_{s}(\mu_{s},\sigma_{dev})\) with mean \(\mu_{s}\) and standard deviation \(\sigma_{dev}\). Although the analysis can be performed based on the initial applied and final required stress values that correspond to any desired time \(t=i\) value, here the analysis is performed at \(10^{6}\) cycles to failure \((N_{f})\).  
Now, based on the material's strength properties, let us formulate the corresponding strength distribution as follows.
# Photon-In/Photon-Out X-ray Free-Electron Laser Studies of Radiolysis
## 2 Methods
### Chemical Stage Methods  
Many studies have been published on examination of the long-term radiation effects and date from original experiments in the Curie laboratories [51]. These experiments examine the production of final products with no direct determination of the mechanism. Reactions leading to the observed products can be inferred by using competition kinetics. For instance, solutions are commonly saturated with N\({}_{2}\)O to convert the hydrated electron to the OH radical. A decrease in product yield would infer there was a hydrated electron intermediate while a doubling of the yield indicates the OH radical is important. Solutes that trap free radicals can be used to probe their role in the radiolysis process. Many clever uses of competition kinetics have been invoked over the decades to infer the reactions of intermediates occurring in the chemical stage.  
The sophistication for probing the chemical stage has advanced as technology has developed. The most significant advance for direct observation of the transient species was the development of pulse radiolysis in the 1960s. These early instruments were electron accelerator based with resolution on the microsecond time scale [52; 53; 54]. They used thermal-based electron cathodes to make pulses of sufficient intensity that the transients could be examined using optical detection techniques. Many of these early studies focused on reactions of the hydrated electron because of its relatively large extinction coefficient [2; 3; 4]. Inherent jitter in the timing of the cathode pulse with detectors limited these instruments to the nanosecond time scale. Pulse radiolysis on the picosecond time domain came with the adoption of laser driven cathodes in which optical detectors could be inherently correlated to pulse formation [55; 56]. These latter techniques greatly increased knowledge on the reactions of transients in the chemical stage. Examination of processes occurring in the chemical stage is now limited by appropriate detectors. However, present pulse radiolysis techniques are still insufficient to examine initial water decomposition. Probing the physico-chemical stage using pulse radiolysis is again limited to indirect techniques. For instance, the concentration of solutes with high reactivity to the precursor to the hydrated electron can be varied while observing the picosecond yield of the hydrated electron to give an indication of the underlying kinetics.
# A New Low-Cost Compact Antenna for the 2.45 and 5.8 GHz ISM Bands
## 3 Parameters Study and Simulation Results  
The antenna's reflection coefficient at the four steps is shown in Figure 3. It should be noted that the frequency bands obtained after the simulation of the antenna corresponding to the initial structure (step 1) are not adapted to ISM frequencies. At design step 1, the antenna has two desired operating frequencies, but with a significant frequency offset, namely, 3.1 GHz and 6.8 GHz (not visible on the curve, as this latter frequency is not of interest here), and a reflection coefficient greater than \(-10\) dB (\(-6.2\) dB) at 3.1 GHz. In step 2, the antenna has two frequency bands very close to the two ISM bands, i.e., 2.41 GHz and 6 GHz. At these frequency bands, the reflection coefficient is less than \(-10\) dB. Step 3 of the design resulted in the two desired ISM frequency bands. Finally, in step 4, an improvement in antenna impedance matching and bandwidth around the two ISM center frequencies is observed.  
Table 2 shows the antenna performance parameters at optimization steps from 2 to 4. From the first to the last optimization step, there is a 77% and 30% increase in bandwidth at frequencies of 2.45 GHz and 5.8 GHz, respectively.
## 4 Measured Results and Discussion
### Measured Results  
The simulated and measured reflection coefficients are shown in Figure 11. We simulated the proposed antenna through the HFSS software. There are two peaks in the reflection coefficient curve with the central frequencies of 27 GHz and 30.2 GHz, which are derived from the driven layer and parasitic layer, respectively. It can be observed that the simulated and measured results agree well with each other at low frequencies, but the measured results are greater than the simulated one in the range of 29 GHz to 30 GHz, and the resonant point near 30 GHz moves to the high-frequency band, which may be due to the influence of mutual coupling between parasitic patches in the measured field. In the frequency range of 25 GHz to 32 GHz, the reflection coefficient of the proposed antenna array \(|\)S11\(|\)\(<\)\(-\)10 dB, and the relative bandwidth is 24.5%.  
Figure 11: Simulated and measured results of reflection coefficient.  
Figure 10: The fabricated antenna array.
# Joint Embedding of Semantic and Statistical Features for Effective Code Search
## 3 Approach
### Joint Embedding of Semantic and Statistical Features
#### 3.2.1 Code Embedding Network
It contains the last cell state and new information generated based on the current input and the last hidden state information: \(c_{t}=i_{t}u_{t}+f_{t}c_{t-1}\); \(u_{t}=tanh(W_{x_{c}}x_{t}+W_{h_{t}}h_{t-1}+W_{c_{c}}c_{t-1}+b_{c})\). Output gate. Used to decide which information is output: \(o_{t}=\sigma(W_{x_{o}}x_{t}+W_{h_{o}}h_{t-1}+W_{c_{c}}c_{t}+b_{o})\). Figure 3: Code embedded in the network. Finally, the current hidden state of the output is obtained by multiplying the current cell state by the weight of the output gate: \[h_{t}=o_{t}tanh(c_{t}).\] (3)
* Attention layer: Denote the vector set of the output of the LSTM layer as \(H:[h_{1},h_{2},\dots,h_{T}]\). We calculate the normalized weight of each hidden state \(h_{i}\) according to Formula (3). These individual hidden state vectors are then weighted and summed to obtain a vector representation of the API invocation. Tokens collection embedding. The embedding method focuses on embedding layer and attention layer. * Embedding layer: Like the embedding layer embedded in the method name, this layer converts the token collection into a real matrix and passes it to the next layer. * Attention layer: Like the attention layer embedded in the method name, the weight of each word vector \(e_{i}\) is calculated according to Formula (3). These individual word vectors are then weighted and summed to obtain a vector representation of the set of tokens. Feature fusion. The vectors of these three features are fused into a final code representation vector through the fully connected layer. The fully connected layer is a multi-layer perceptron, which can convert the feature vector into the output of the specified dimension. The specific calculation formula for fusing the three code feature vectors is as follows:  
\[E_{c}=tanh(W_{c}[E_{M},E_{A},E_{TS}]), \tag{4}\]  
where \([E_{M},E_{A},E_{TS}]\) represents the simple concatenation of the vectors of these three features and \(W_{C}\) is the matrix of trainable parameters in the MLP. The output vector \(E_{C}\) represents the final embedding of the code segment. In this section, we extracted three code features based on semantic analysis and modeling of the source code and we constructed a code embedding network based on multi-feature modeling, which is used to represent the code fragment as a fixed dimension that can reflect a code semantics vector. Figure 4: API invocation embedding.
## 2 Results and Discussion
### Oxidation of Different Substrates  
CeNR was further tested as catalyst in the oxidation of cyclohexene and cyclohexanol, at 55 \({}^{\circ}\)C, during 24 h (Table 3). For both substrates, when H\({}_{2}\)O\({}_{2}\) was used as oxidant, conversions were negligible, similar to that observed for PhEt. These results contrast with those reported for mesoporous Ce-SBA-15 in the oxidation of cyclohexene and cyclohexanol with H\({}_{2}\)O\({}_{2}\), under similar conditions to those used in the present work: 40% cyclohexene conversion was reached after 10 h, at 50 \({}^{\circ}\)C, forming the epoxide as the main product (other products included the 1,2-diol, -en-1-ol and -en-1-one) and, on the other hand, the reaction of cyclohexanol with H\({}_{2}\)O\({}_{2}\) gave a maximum of ca. 15% conversion after 7 h, at 60 \({}^{\circ}\)C [22]. The competitive 'non-productive' decomposition of H\({}_{2}\)O\({}_{2}\) is probably less important for the referred supported ceria catalyst than for CeNR. Differences in catalyst surface polarity may lead to different competitive adsorption effects, which may partly explain the different catalytic performances of CeNR and Ce-SBA-15 using H\({}_{2}\)O\({}_{2}\) as oxidant.  
The reaction of cyclohexene with t-BuOOH in the presence of CeNR gives 22% conversion after 24 h and the main product is 1-(_tert_-butyl-peroxy)-2-cyclohexene (86% selectivity): by-products include 2-cyclohexen-1-one and minor amounts of 2-cyclohexen-1-ol (when no catalyst is added, no reaction is observed). The products selectivity for cyclohexene somewhat parallels that obtained for PhEt, in that for both substrates the major product is a _tert_-butyl-peroxy (alkyl or aryl) compound, and saturated C-H bonds are preferentially activated over the unsaturated ones. The 1-(_tert_-butyl-peroxy)-2-cyclohexene product has been identified in the oxidation of cyclohexene with _t_-BuOOH _via_ radical reactions in the presence of free and alumina-supported transition metal complexes bearing Schiff base ligands [23; 24; 25].  
The reaction of cyclohexanol with _t_-BuOOH gives cyclohexanone as the only product in 33% yield, after 24 h, at 55 \({}^{\circ}\)C (when no catalyst is added, cyclohexanone yield is 11%), Table 3. Based on these results the choice of a solvent for the catalytic oxidation reactions should preferably not include alcohols. For all substrates, CeNR600 gave negligible conversions, similar to that observed for PhEt.  
Figure 5: **(A)** - Ethylbenzene oxidation with t-BuOOH, at 70 \({}^{\circ}\)C, using acetonitrile as solvent, in the presence of CeNR **(O)**, CeNR calcined at 280 \({}^{\circ}\)C (\(\bigtimes\)),CeNR pre-treated with H\({}_{2}\)O\({}_{2}\) (\(\square\)) or _t_-BuOOH (\(\bigtriangleup\)). **(B)** - Dependence of selectivity to PhEtOOT (solid lines) and acetophenone (dashed lines) on conversion for the reactions conditions indicated in (A) - the same symbols were used for each reaction.
# Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors
## 5 Conclusions  
In this paper, we presented a low-cost and lightweight system for travel data collection, sensing and travel mode detection based on smartphone sensors. For collecting the different sensors data, we developed a prototype application. Compared with traditional classification model, we proposed a deep learning-based method that can automatically and accurately detect different travel mode. Five representative travel modes (walk, bike, bus, car, and metro) are detected using the proposed model. In the experimental analysis, we investigated the classification performance of the CNN under different hyperparameters and combinations of sensors. We also discussed the impact of different sensors combinations on classification accuracy for each type of travel mode. The experimental results show that the precision, recall and F value of the proposed method can reach more than 97%. In future research, we will focus on more other types of travel mode recognition methods, such as the currently popular travel modes such as shared electric vehicles.  
Conceptualization, L.G. and W.M.; methodology, L.S. and J.H.; software, L.Z. and W.Y.; validation, [PERSON] and J.P.; formal analysis, [PERSON]; investigation, [PERSON] and L.S.; resources, [PERSON] and W.M.; data curation, W.M.; writing--original draft preparation, [PERSON] and J.H.; writing--review and editing, [PERSON] and J.H.; visualization, L.G. and W.M.; supervision, L.G. and W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Project Funded by China Postdoctoral Science Foundation (2021 TQ0369, 2019M653009), National Natural Science Foundation of China (42001324, 41701519 and 42001404, 62102456), Shenzhen Scientific Research and Development Funding Program (JCYJ20180305125058727, JCYJ20170412105839839), Nature Science Foundation of Shenzhen University (2019094), and Key R&D Program of Ningxia Autonomous Region: Ecological environment monitoring and platform development of ecological barrier protection system for Helan Mountain (2022 CMG02014).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Actual** & **Predicted** & **Bike** & **Bus** & **Car** & **Metro** & **Walk** \\ \hline
**Bike** & 2163 & 18 & 6 & 0 & 14 \\
**Bus** & 21 & 2189 & 6 & 2 & 11 \\
**Car** & 0 & 1 & 2101 & 3 & 0 \\
**Metro** & 0 & 11 & 6 & 2139 & 2 \\
**Walk** & 45 & 5 & 8 & 3 & 2130 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The confusion matrix of the proposed method.
## 3 Experiments and Discussion  
The overall accuracy (OA), average accuracy (AA), and kappa coefficient (kappa) were employed as the evaluation indices of the classification performance. The AA is equal to the sum of the number of correctly classified samples divided by the total number of samples. AA represents the average value of each accuracy for each category. The kappa coefficient is a reference metric that enables the calculation of overall consistency and classification consistency.
# An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
## 3 Model  
### Problem Description  
The scheduling of earth observation satellite is an NP-hard problem [5]; therefore, the EOSIS problem is also an NP-hard problem. Basically, satellites need to orbit around the earth and take pictures when they pass by a requested ground target. We call such a time interval when a requested target is visible to a satellite as a visible time window (VTW). Then, satellites download the image data to the ground when they pass by a ground station. Similarly, we define the time interval when a satellite is visible to a ground station as a download window (DW). The problem is defined by the following components:  
1. a set of satellite resources \(\mathcal{A}\). Each satellite \(a\) is associated with an onboard memory capacity \(m_{a}\), an imaging data rate \(p_{a}\), a transmission data rate \(b_{a}\) and a set of download time windows (DW) \(\mathcal{O}_{a}\).
2. a set of user observation request \(\mathcal{R}\). Each request \(r\) is characterized by an observation profit \(p_{r}\), a minimal observation duration \(d_{r}\), and a set of visible time window (VTW) \(\mathcal{V}_{t}\).  
The EOSIS problem consists of selecting a subset of user requests and arranging observations within their available VTWs. The objective is to maximize observation profit without violating any constraint.
## 9 Security and Privacy Risk Controls
### Software Integration Testing  
Software integration testing is a level of software testing where individual units are combined and tested as a group. Integration tests help to identify whether independently developed units of software work correctly when they are connected together. Integration testing can adopt different approaches, such as: Black Box Testing, White Box Testing and Gray Box Testing methods. During software integration testing, the developer needs to conduct two key tests:  
* Security and privacy requirements testing--to validate the security and privacy requirements identified during the risk assessment are implemented properly by conducting functional, performance and scalability testing.
* Threat and vulnerabilities mitigating testing--to validate the effectiveness of the implemented controls against the identified threats and vulnerabilities. The following steps should be conducted at the software integration testing stage:
* Perform integration testing by conducting functional, unit-test, black-box, white box and gray box testing. Organizations can use one or a combination of multiple testing approaches to conduct the integration testing based on the QA resource expertise and availability.
* If an integration test fails, then check whether it failed due to a security risk control  
* If no, then take appropriate measures to fix the failure case and conduct the software integration test again.
* If yes, then review based on considerations presented in Section 9.3 in order to identify the reason for failure and take appropriate measures to address the failure case and conduct the software integration test again.
## 5 Conclusions  
The validated MCID platform allows the fast implementation of a personalized MC dosimetry, based on patient imaging data, avoiding complex and time-consuming manual coding to the user. The system could be easily integrated in the clinical practice, considering the total computational time of the MC simulation (\(\sim\) 5 h), suitable with clinical routine. For liver with homogenous density, the comparison between absorbed dose assessed from MC simulations and from the MIRD approach at the organ and voxel level showed a very good agreement. For smaller regions, PVEs from imaging deeply influence the final absorbed dose evaluation, therefore a careful analysis and PVEs corrections are required. Finally, patient heterogeneities should be considered in the treatment planning in order to obtain accurate dosimetric estimations.  
Conceptualization, M.P., R.F., A.M.; methodology, M.P., R.F.; software, A.V.G., A.M., E.F., N.L.; validation, E.F., M.P., A.V.G.; formal analysis, A.M., E.F., M.P., R.F., A.V.G.; investigation, A.M., M.P.; resources, M.P., A.V.G., N.L.; data curation, A.M.; writing--original draft preparation, A.M., M.P., M.C.; writing--review and editing, all authors; visualization, all authors; supervision, M.P.; project administration, M.P.; funding acquisition, M.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Union for International Cancer Control, grant number UICC-TF/17/376845.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank [PERSON] for the useful comments and suggestions on the manuscript.  
The authors declare no conflict of interest.