esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis Strategies for MHz XPCS at the European XFEL
## 3 Results
### Benchmark on Diffusing Samples  
The correction schemes presented so far can provide reasonable results, but, to certify that such results are correct, we must benchmark them with theoretical predictions. The \(g_{2}\) functions obtained performing only the dark subtraction, photonization, and the cross correlation are reported in Figure 6a. For comparison, the \(g_{2}\) functions obtained from the same data but with a pipeline that performs all the calibration steps except the \"photonization\" are shown in Figure 6b. The correlation functions at the lower \(q\)-values are almost identical, and they become more different for larger \(q\). Not only does the contrast in Figure 6b drop more abruptly, but the time-scale of the relaxation is also different. A more quantitative comparison can be done by performing an exponential fit to the data (black lines) and plotting the respective relaxation rates. In Figure 6c, \(\Gamma(q)\) obtained from these two different sets of functions are shown. The value of \(D_{0}=(8.1\pm 0.6)\) nm\({}^{2}\)/\(\mu\)s is obtained from a fit of \(\Gamma(q)\) restricted to the values at q lower than 0.113 nm\({}^{-1}\), where both pipelines provide the same results, highlighted by filled symbols in the figure. \(D_{0}\) can be also obtained from the the Stokes-Einstein relation for spherical particles such as \(D_{0}=k_{B}T/(6\pi\eta(T)R_{0})\), with \(k_{B}\) Boltzman's constant, \(T\) the temperature, and \(\eta(T)\) the solvent's viscosity. Considering the average radius \(R_{0}\) obtained from the fits of the \(I(q)\) (Figure 3), we find the same diffusion constant for temperatures \(T\sim 302\) k, as expected from the fluence and repetition rate used for the measurement [17,18]. In our case, the predictable behaviour of the simple diffusion is useful to have a clear view of the differences between the results of the two pipelines. Extrapolating the \(D_{0}q^{2}\) law to the whole probed \(q\)-range (black dashed line in Figure 6c), we can clearly see that the results from the \"photonized\" (blue data) patterns remain close to the expected result, while the other (red data) strongly deviate in a non-monotonic manner, indicating that the dynamics results are dominated by the artifacts that have been reported e.g., in Figure 4b.  
Figure 6: Practical example of the advantages offered by the “photonization” of the speckle patterns. In (**a**), the \(g_{2}(t)-1\) obtained from the photonized patterns are reported along with their respective exponential fits (black lines). For comparison, in (**b**), the \(g_{2}(t)-1\) from the same run, but without the photonization step are reported. In (**c**), the relaxation rates obtained with simple exponential fits to the data are shown. The data points used for the fit of \(D_{0}\) are marked as filled symbols, the black continuous line is the fitted function, while the black dash dotted line is the extrapolation to the whole q-range.
## 2 Materials and Methods
### Quantification of Nitrite Concentration in PAM  
Griess reagent was prepared with 5% phosphoric acid containing 0.1% NEDD-N-(1-naphthyl) ethylenediamine and 1% sulfanilamide. An equal volume of the previously prepared solution was added to 50 \(\mu\)L of PAM and following a period of 30 min incubation, absorbance was quantified at 540 nm in a multi-well plate spectrophotometer (Synergy HT, Biotek, Winooski, VT, USA). A nitrite reference curve was plotted using average absorbance values of nitrite concentration of serial two-fold dilutions. The blank solution was identical, except for the nitrite.
## 2 Related Work
### Visual Context-Based Visualization  
Several research works have been conducted to provide visualization methods concerning visual contexts for practical task assistance in different environments [1; 8; 9; 13]. Most smart AR glasses support see-through augmentation. However, regarding visual contexts, they can provide either direct or indirect augmentation on physical artifacts. Direct augmentation requires visual registration between physical and virtual objects. Although it can provide a more immersive feeling, its FOV is narrower than the indirect augmentation. Therefore, there is a trade-off between these augmentations concerning visual contexts. However, there is little research to analyze visual contexts in wearable AR for task assistance.  
Previous studies proposed methods for task assistance in wearable AR. [PERSON] et al. [9] proposed AR-Mentor, which is an AR system for assisting in maintenance and repair tasks. Their method combined AR smart glasses with six degrees of freedom (DOF) pose tracking and virtual personal assistance to instruct a novice in performing maintenance tasks. [PERSON] et al. [16] proposed HoloCollab, which is a system using AR smart glasses to create an augmented training experience for both trainer and trainee. Some other works have attempted to combine an optical see-through display with projector-based spatial augmented reality [17], and have discussed how a working system should be developed by integrating smart glasses with a question-answering module [18].  
The visualization methods were also compared and evaluated in various devices [19; 20], including tablets, head mounted displays (HMDs), and projectors. [PERSON] et al. [1] investigated what characteristics of eye-wearable technology impact user performance in machine maintenance by comparing eyewear-central and eyewear-peripheral visualizations depending on the position of the display. They found that the eyewear-central display resulted in faster task completion time than the eyewear-peripheral display. Besides, the influence of the graphical context on task performance was also evaluated [8; 13; 14]. [PERSON] et al. [13; 14] assessed the effects of registration error in a Lego block placement task and the effectiveness of graphical context for task assistance. Their work showed that adding contexts to a scene due to registration error could help a user perform given tasks more effectively by reducing the number of mistakes and task completion time. [PERSON] et al. [8] evaluated the effectiveness of an AR-based context-aware assembly support system using two different visual overlays. Their experimental results showed that the visual overlay that presented target status next to real objects outperformed the direct overlaid visualization on the actual objects.
# Multi-Agent Reinforcement Learning: A Review of Challenges and Applications
## 2 Background
### Multi-Agent Framework
#### 2.1.4 Dec-POMDP  
A decentralized partial-observable Markov decision process (Dec-POMDP) is defined by the tuple \(\langle I,S,\mathbf{A},T,R,\mathbf{\Omega},O,\gamma\rangle\), where  
* \(I\) is the set of \(n\) agents;
* \(S\) is the state space;
* \(\mathbf{A}=\times_{i}A^{(i)}\) is the joint action space;* \(\mathbf{\Omega}=\times_{i}\Omega^{(i)}\) is the observation space with \(\Omega^{(i)}\).  
The state \(S\) evolution is based on the transition probability \(T(s,\mathbf{a},s^{\prime}):S\times\mathbf{A}\rightarrow\delta(S)\). It indicates the probability that, given the joint action \(\mathbf{a}\) and the current state \(s\), the next state will be \(s^{\prime}\). At every time step, all agents receive an observation \(o^{(i)}\in\Omega^{(i)}\) given by the joint observation probability \(O(\mathbf{o},s^{\prime},\mathbf{a})=P(\mathbf{o}|s^{\prime},a)\). For each agent \(i\), we can define its local observation history at iteration \(t\) as \(\vec{o}_{t}^{\,(i)}=(o_{1}^{(i)},\dots,o_{t}^{(i)})\), where \(\vec{o}_{t}^{\,(i)}\in\vec{O}_{t}^{\,(i)}\). At every iteration, the team receives a joint reward \(r_{t}=R(s_{t},\mathbf{a_{t}})\) conditioned by the joint action and the current state. This is used to maximize the expected return over the horizon \(T\)\(V=\mathbb{E}[\sum_{t=0}^{T}\gamma^{t}r_{t}]\) where \(\gamma\) is the discount factor [11]. As in the Markov games framework, the objective is to maximize the expected return by selecting an optimal joint policy. However, in this case, the policy performs a mapping from local observations to actions; thus, we can write the local policy of agent \(i\) as \(\pi^{(i)}:\vec{O}_{t}^{\,(i)}\to A^{(i)}\).
## 4 Further Analysis and Discussion
### Results/Implications from Modeling/Comparisons
This should be an important contribution that this study makes to the methodological field of MADM. In addition to these implications drawn from the proposed flow or its application, there is another important interesting observation: Section 3.4 shows that the ideal solution vector using TOPSIS is identical to the criteria weight vector that is obtained using AHP in the initial stage of AHP-TOPSIS, i.e., \(A^{+}=C\!V\!V^{*}\). This should be discussed extensively. After scrutiny, we found that this is because of the normalization method that is used! In this study, the method which is used to normalize \(D\) as \(D^{\prime}\) is as follows:  
\[D^{\prime}\left[i,j\right]\gets D[i,j]/\max(D[1\!:\!N\!alts\!:\!j]),\, \text{for any of a TMTB criteria }j,j\in\{1\,\dots\,Ncrit\} \tag{29}\]  
\[D^{\prime}\left[i,j\right]\leftarrow\min(D[1\!:\!N\!alts\!:\!j])/D[i,j],\, \text{for any of a TLTB criteria }j,j\in\{1\,\dots\,Ncrit\} \tag{30}\]  
As can be seen, the above process also aligns the direction of optimization to TMTB. Therefore, in Table 1, each column of \(D^{\prime}\) has at least one \"1\"(s), which tag(s) the alternative(s) that perform(s) the best, in terms of the criteria for that column. When TOPSIS is used to calculate the _WiNDMa_, these 1s are multiplied by the counterpart element in \(C\!V\!V^{*}\), depending upon the criterion that is being considered. For example, in Table 1, following the normalization, for c1, only A7 performs the best for this criterion and is assigned a value of 1. This, when multiplied by the first element in \(C\!V\!V^{*}\) (=0.15568452), becomes exactly the value that is the assessed weight for c1, as shown in Table 4. Criterion by criterion, this ideal-identification process selects these elements from _WiNDMa_ to produce the ideal solution vector, \(A^{+}\). For this example, the [A7, c1] cell in Table 4, whose value is 0.15568452, is regarded as the ideal value for the c1 criterion, so it is assigned as the first element of \(A^{+}\). Therefore, the normalization method used to normalize the source decision matrix at the beginning is responsible for this phenomenon. This finding, although minor, is interesting because it poses a question for future research to determine whether this affects any part of the solution process for the hybrid AHP-TOPSIS approach.
## 3 Results and Discussion
### Application of the New Developed Method for Quantification of Inulin from Jerusalem articloke Root  
By using extracts from UAE, HPLC-ELSD analysis was carried out: (i) without using TFA-assisted hydrolysis (Figure 4a); and (ii) applying the newly developed method consisting of specific hydrolysis for pretreatment (Figure 4b). In Figure 4, it is obvious that TFA-assisted hydrolysis prior to HPLC-ELSD analysis allowed the complete hydrolysis of all the fructans contained in _Jerusalem articloke_ root to fructose (peak at 6.397 min in Figure 4b) or glucose (peak at 7.680 min in Figure 4b), thus improving total inulin identification and quantification compared to the method without TFA-assisted hydrolysis.  
Inulin content was determined using a standard curve to evaluate the UAE process. The effect of extraction temperature on inulin content in the extracts is presented in Figure 5. Inulin content was significantly increased from 13.3 to 22 mg/mL with rising temperature from 20 to 55 \({}^{\circ}\)C. However, further increase of extraction temperature did not lead to better inulin extraction. The same trend was previously observed when other authors evaluated UAE of inulin from _Jerusalem articloke_ tube powder. These authors observed the maximal inulin extraction yield at 77 \({}^{\circ}\)C [4]. This may be due to the modification of tissue structure at higher temperature, or a potential modification in the structure of the molecules with different degree of polymerization which constitute inulin complex.  
Figure 4: HPLC-ELSD for: _Jerusalem articloke_ root extract (**a**); and hydrolysate of _Jerusalem articloke_ root extract with developed method in this study (**b**).  
On the other hand, the effect of ultrasonic power is displayed in Figure 6. The benefit of ultrasonic treatment is noticeable when the inulin content of the extracts obtained with and without ultrasonic treatment was compared. Due to more intense cavitational effect, an increased inulin content was found in the extracts obtained when ultrasonic power was 120 W compared to 60 W, obtaining the optimal inulin content (25 mg/mL) at 120 W. However, when ultrasonic power was increased up to 150 W, the inulin content decreased. This fact can be attributed to changes in tissue structure under high ultrasonic power treatment and/or modification in the inulin-related compounds due to the cavitational effect generated by the ultrasonic waves under these conditions, making the identification of inulin difficult.
# Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure +
## 3 System Design
### NDN Deployment over KubeEdge Architecture  
We first demonstrate the general network architecture for a single region where one KubeEdge cluster is used to deploy NDN network. The architecture is shown in Figure 3.  
KubeEdge Cloudcore is the sole component on the cloud side. Cloudcore manages all edge nodes in the cluster and responder devices' information that connect to them. It is also responsible for deploying containerized NDN routers at the correct edge nodes based on their pre-defined yaml files.  
The edge side of the architecture contains edge nodes with KubeEdge Edgecore installed inside them. Each edge node runs one instance of NDN containerized router deployed from CloudCore. Border edge nodes have Wi-Fi access points to provide an NDN gateway for responders to connect to the NDN network. In case an edge node is damaged during the disaster, Edgecore binary can easily be installed into any network equipment (even resources constraint once) thanks to the lightweight size of the Edgecore. After that, given that Cloudcore information is pre-installed inside the replaced edge node by an emergency repair team member, this node can re-join the KubeEdge cluster and deploy the corresponding NDN routers based on the command from Cloudcore. The NDN network is recovered at this moment.  
For large scale disaster area which spreads over multiple regions, multiple KubeEdge clusters are needed to provide NDN network. Figure 4 shows our designed architecture for deploying the NDN network using KubeEdge over multiple regions.  
Figure 4: NDN over KubeEdge general network architecture in two regions.  
Figure 3: Named Data Networking (NDN) over KubeEdge general network architecture in single region.  
The NDN network in each region will connect to an NDN router deployed at Cloud-core node. This router will act as an NDN gateway for each region and connect to an IP gateway router. To connect the network between two regions, a high-speed, high-bandwidth TCP tunnel will be created between two IP gateway routers. This design is referred to as a real NDN deployment presented in [27].
# An Experimental Survey of Extended Resolution Effects for SAT Solvers on the Pigeonhole Principle
## 4 Analysis Procedure
### Prioritizing Extended Variables  
The extended variables are actually redundant from the perspective of logical equivalence; the satisfiability of a formula without extended variables does not change. In fact, the problem size expansion can deteriorate the solver's performance, and it is the case for extended variables (see the experimental results below). To reduce the redundancy of a SAT problem, state-of-the-art SAT solvers conduct preprocessing on the given instance. Preprocessing [26] of a CNF removes some variables and clauses to shrink the size of the problem instance while preserving the logical equivalence. Preprocessing comprises subsumption, variable elimination, and hyper resolution. Extended variables are also the target of preprocessing (i.e., variable elimination). Hence, we switch off the preprocessing in the experiments.  
Merely adding extended variables in the original formula does not affect the solvers since they do not know which variables are extended ones or not. The solvers should utilize these variables to speed up their efficiency. To this end, we introduce two methods to prioritize the extended variables as decision variables at the early phase of the search. The actual codes are in Appendix A.  
One is a simple way: extended variables are always selected prior to non-extended variables. For each decision, the solver chooses an unassigned extended variable with the highest VSIDS score. If there remain no unassigned extended variables, then a non-extended variable is selected. We call this method \"m1\". In practice, the method is executed in the \"chooseDecisionVariable\" function in Algorithm 1.  
The other way is shown in Algorithm 2. We denote this method \"m2\". This function is called after every restart, just after the \"restart(0)\" function in Algorithm 1. The constant R is greater than 1 in order to add larger VSIDS score than the usual (inc_score is the regular value for increasing VSIDS scores). It induces the extended variables being selected as decision variables at the early phase after a restart. We try a couple of R values in the following experiments and choose the best one.  
```
1:inc_score // incremental value for VSIDS scores
2:for each extended variable ev do
3: increaseVSIDSScores(ev, inc_score * R)
4:endfor
```  
**Algorithm 2** Pseudo code of \"m2\" method.
## 8 Use Cases and Applications
### Warehouses and Logistics
This coordinated approach involves multiple vehicles interacting and acting as mobile stations for the drones [171]. The synchronized routing of trucks and drones optimizes the delivery route, thereby aiming to visit each customer only once--either by drone or vehicle--minimizing delivery time, and reducing costs.
# Application of Microgel as a Sorbent for Bisphenol Analysis in Liquid Food Samples
## 2 Materials and Methods  
### Reagents  
Solvents and standards used were of analytical grade. Methanol and acetonitrile were from Merck (Darmstadt, Germany, [[https://www.merckgroup.com/en](https://www.merckgroup.com/en)]([https://www.merckgroup.com/en](https://www.merckgroup.com/en))). Bisphenol A (BPA, 99% analytical purity), bisphenol F (BPF, 99.7% analytical purity), bisphenol E (BPE, 98% analytical purity) and bisphenol B (BPB, 98% analytical purity) were purchased from Sigma-Aldrich (Steinheim, Germany, [[https://www.sigmaaldrich.com](https://www.sigmaaldrich.com)]([https://www.sigmaaldrich.com](https://www.sigmaaldrich.com))). Di(ethylene glycol) methyl ether methacrylate (MEO\({}_{2}\)MA), ethylene dimethacrylate (EGDMA), oligo(ethylene glycol) methyl ether methacrylate (OEGMA), acrylic acid (AA) and ammonium persulfate (APS) were obtained from Sigma-Aldrich (Steinheim, Germany, [[https://www.sigmaaldrich.com](https://www.sigmaaldrich.com)]([https://www.sigmaaldrich.com](https://www.sigmaaldrich.com))). Supelclean(tm) ENVI-18 SPE cartridges were purchased from Sigma-Aldrich (Steinheim, Germany, [[https://www.sigmaaldrich.com](https://www.sigmaaldrich.com)]([https://www.sigmaaldrich.com](https://www.sigmaaldrich.com))), and Affinip(tm) cartridges were purchased from Affinisep (Petit-Couronne, France, [[https://www.affinisep.com/](https://www.affinisep.com/)]([https://www.affinisep.com/](https://www.affinisep.com/))). Water was purified by using a Milli-Q Plus system from Merck Millipore (Millipore, Bedford, MA, USA, [[http://www.millipore.com/](http://www.millipore.com/)]([http://www.millipore.com/](http://www.millipore.com/))). Sample workup and preparation were exclusively performed with gloves and bisphenol-free laboratory ware. Stock solutions of standards were prepared at a concentration of 1 g\(\cdot\)L\({}^{-1}\) in water/methanol (90:10, 10 mL) and were stored in the refrigerator until use. Working solutions for calibration were developed as a standard mix by dilution of the stock solutions with water and were used immediately before analysis.
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 2 Research Status of Tunnel Lighting System
### Progress of Tunnel Lighting Strategies
#### 2.3.2 Existing Lighting Strategies on Luminance Level
There is no uniform lighting regulation in the nighttime for the area inside the tunnel, this regulation varies according to traffic flow, tunnel length or design speed as shown in Table 9.
# Automated Vision-Based Crack Detection on Concrete Surfaces Using Deep Learning
## 3 Results and Discussion
### Cross-Dataset Study of the Trained Network  
To validate the ability of the trained AlexNet model, a cross-dataset was tested using different images that were not used for training. The dataset consists of crack and no-crack  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline
0.99 & 0.99 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance metrics of the test results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline
1 & 0.99 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance metrics of the validation images.  
Figure 7: Classification and labelled images with the test data.  
images taken on bridge-decks, walls, and pavements. Examples of the cross-image dataset are shown in Figure 9.  
The trained AlexNet model was saved, and a cross-image dataset was tested using the trained model. The trained model predicted the images taken on bridge-decks with an accuracy of 84.5%. The trained model also predicted the images taken on pavements and walls with 89.3% and 81.9% accuracy, respectively. The loss obtained from the images taken on bridge-decks, pavements, and walls was 15.5%, 10.7%, and 18.2%, respectively. The confusion matrix of the cross-dataset test images is shown in Figure 10. To quantify the trained model, the precision, recall, accuracy, and F\({}_{1}\) scores were computed and are  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Pretrained DCNN** & **Accuracy** \\ \hline
1 & AlexNet & 0.99 \\
2 & GoogleNet & 0.99 \\
3 & ResNet101 & 0.9833 \\
4 & InceptionResNetV2 & 0.95 \\
5 & VGG19 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracies of pretrained DCNNs.  
Figure 8: Confusion matrix of test images.  
Figure 9: Examples of cross-dataset images: (**a**) bridge-decks; (**b**) pavements; (**c**) walls.  
presented in Table 8. For the bridge-deck images, the obtained precision, recall, and F\({}_{1}\) scores were 0.89, 0.91, and 0.90. The precision, recall, and F\({}_{1}\) scores obtained from the images taken on pavements were all 0.92. Similarly, the precision, recall, and F\({}_{1}\) scores obtained from the images taken on walls were 0.88, 0.82, and 0.85, respectively. In addition, the prediction accuracies for the three categories were 0.84, 0.89, and 0.81, respectively. The prediction accuracy obtained from the original dataset was 0.99, while the accuracy of the cross-dataset were decreased to 0.84, 0.89, and 0.81, respectively. These decreases in the prediction accuracy were due to the presence of a variety of obstructions including shadows, surface roughness, scaling, edges, holes, and background debris in the images [46]. These obstructions resulted in the loss of accurate prediction of the images.