esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation
### Simple Three-Layer Model
In contrast to conventional RTM imaging methods, CR-MRTM requires the extraction of seismic data from different sources for the same receiver point and their reorganization into a common-receiver gather. Seismic data are then excited at the source positions, while the seismic wavelet is propagated forward at the receiver positions to obtain the imaging results. Figure 7 shows the imaging results obtained using CR-MRTM. It is evident that, compared to the conventional RTM method shown in Figure 6, our proposed approach significantly improves the effective imaging range, as illustrated by the section within the blue solid line box in Figure 7. The magnified result for this area is displayed at the top of Figure 7. Additionally, our method effectively suppresses the artifacts present in conventional RTM imaging. Furthermore, because the numerical experiment includes PML layers around the velocity model, input seismic data do not contain surface multiple  
Figure 5: Shot gather in the three-layer model: (**a**) original data; (**b**) situation in the dashed red box. Figure 6: Migration result of conventional W-VSP RTM method. reflections. Therefore, it can be demonstrated that the proposed method effectively handles interbed multiples in seismic data. To further illustrate the impact of CR-MRTM on imaging quality, we extracted the imaging results of both CR-MRTM and conventional RTM methods at a depth of 1 km, as shown in Figure 8. The reference reflectivity, indicated by the solid green line, is calculated based on the velocity model and other relevant information. It is clearly evident that, compared to the conventional RTM method, the imaging results obtained using CR-MRTM are much more consistent with the reference reflectivity. Furthermore, we designed an evaluation criterion to assess the discrepancy between the imaging results and the reference reflectivity. Specifically, we referenced and adjusted the calculation method for the Peak Signal-to-Noise Ratio (PSNR) to ensure that the resulting values are not influenced by errors introduced during the wavefield propagation process. The signal-to-noise ratios at a 1 km depth for both CR-MRTM and conventional RTM methods are presented in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **SNR(db)** \\ \hline conventional RTM & 18.6 \\ CR-MRTM & 22.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Imaging quality of different RTM methods, evaluated based on the signal-to-noise ratio. Figure 8: Migration results at 1 km depth. Figure 7: Migration result of CR-MRTM.
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 2 Related Works
### Algorithms for Transmission Scheduling of Physiological Signals
#### 3.4.2 Method 2: WFQ with Round Robin (WFQ_RR)  
Weighted Fair Queueing (WFQ) is an extension of Fair Queueing (FQ), which shares the link's capacity in equal subparts, so that different queues are given a fair chance to be scheduled, and to balance the delay and jitter of each flow in general. Compared with PQ, WFQ allows schedulers to specify which fraction of the capacity will be given to each flow by adding a priority consideration in the calculation of packet scheduling. Each queue follows the First in First out (FIFO) principle and is sent according to the order of joining the queue. At the time of queueing, WFQ allocates the bandwidth of each flow according  
Figure 11: A typical example of priority queueing.  
to the weight of the flow, which ensures fairness between the flow of the same priority and expresses the weighting between the flows of different priorities. Last, polling each queue and the corresponding number of packets are sent from the queue according to the bandwidth calculation as follows:  
\[BW_{j}=\frac{w_{j}}{\sum_{i=0}^{N}w_{i}}R \tag{2}\]  
where \(N\) is the total number of queues, \(R\) is the bandwidth of link, and \(w_{i}\) is the weight of each queue, the queue of number \(j\) will achieve the corresponding bandwidth \(BW_{j}\) with the calculation. As shown in Figure 12, the physiological signals are placed in their corresponding queues according to their types, where the weight of each queue is 5, 3, and 2, respectively. The bandwidth is allocated after configuring the weight Equation (2) of each queue (i.e., the bandwidth of ECG, \(BW_{1}=5/10\)\(R\)), and then the packets in the same queue are discarded in order.
# Unbridled Integons: A Matter of Host Factors
## 4 Host Factors Influencing the Expression of the Integrase
### Integrase Expression and SOS Response
#### 4.1.3 SOS Response and Horizontal Gene Transfer
Indeed, the efficiency of integration of a cassette provided as exogenous DNA in a chitin containing medium approached that of a cassette provided by conjugation without over-expression of IntIA (approximately \(10^{-6}\)). Deletion of the endogenous integrase did not allow for the observation of a single recombination event, proving that the induction of the endogenous integrase through natural transformation is enough to perform efficient recruitment in this natural setting. Interestingly, apart from the integration of the incoming cassette, the induction of IntIA through SOS response also caused cassette rearrangements within the SCI at a high frequency [56]. Natural competence has often been viewed as a stress response by itself, allowing the concerned organism to efficiently acquire exogeneous DNA as a way to adapt quickly to a new environment [61]. Hence, the interplay between the induction of competence and of the expression of the integrase in _V. cholerae_ is highly illustrative of the adaptive potential of this species. In a context of stress, a cassette coming from the environment or from an integron-containing bacterium [62] can be efficiently be recruited within the SCI and possibly allow the host to escape the original stress. In line with this, at the multispecies population level of a given environment, [PERSON] and colleagues have found that cassette pools are rapidly transferred across species boundaries to generate an endemic population structure [63]. Finally, another major contributor of HGT is phage-mediated transduction. This pathway differs from conjugation and transformation because it can result from the injection of either dsDNA or ssDNA depending on the nature of the phage. However, the infection by a phage with a dsDNA genome can still lead to SOS induction through the disruption of the cell-cycle and the associated genotoxic stress. There is only one example of formally characterized horizontal transfer of integron cassette by generalized transduction [64]. In _Salmonella enterica_ serovar _typhimuriumutum_, some strains are resistant to multiple antibiotics due to the presence of one or several integrins within their genome. Co-transduction of integron cassettes conferring simultaneous resistance to chloramphenicol, ampicillin, and tetracycline originating from the strain DT104 could effectively be observed in several other strains of the same species. However, it is very unlikely that these events correspond to cassette recruitment but rather to allelic exchange by homologous recombination with the transduced DNA.
# Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Network
## 4 Algorithm
### DQN-Based Computation Offloading Algorithm
#### 4.2.3 DCOS  
The DQN-based dynamic computation offloading algorithm DCOS is proposed in Algorithm 3. Algorithm 3 includes two parts, reinforcement settings and Q-network updates. A detailed description of the reinforcement settings and Q-network update is given in Algorithms 1 and 2.  
```
0:\(\theta,\mathbb{M}\)
0:\(a_{i}\)
1:for episode = 1, N do
2: Observe \(s_{0}\)
3:\(\mathbb{M}=\emptyset\)
4: Initialize \(\mathbb{M}\)
5:for i = 1, t do
6: /*Action Selection*/
7: Select \(a_{i}\) according pre-defined policy
8: Observe \(s_{i+1}\)
9: Calculate \(r_{i}\) by Equation (16)
10: /*Call Algorithm 2*/
11:\(\theta=QNetworkUpdate(s_{i},a_{i},r_{i},s_{i+1},\mathbb{M})\)
12: Output \(a_{i}\)
13:endfor
14:endfor
```  
**Algorithm 3** DCOS  
In view of the computational complexity of Algorithm 3, the basic elements of the network model are defined in Section 3.1: the network contains \(n\) edge servers and types of services, and \(k\) task requests are issued in each time slot. The time complexity of Algorithm 3 mainly focuses on reinforcement learning settings and the Q-network update. As shown in Section 4.2.1, the main steps in reinforcement learning settings include obtaining the system status, action selection and reward calculation. For obtaining system, the time complexity is related to the size of the state, which is \(O(2n+3m)\). The time complexity of reward calculation is \(O(n+m+k)\). Regarding the choice of actions, there are two cases, according to the value of \(\phi\). Considering these two cases simultaneously, the time complexity is \(O(n)\). In the Q-network update, the time complexity is related to the size of the experience replay memory \(\mathbb{M}\), which depends on the size of the state space \(\mathbb{S}\) and action space \(\mathbb{A}\). Therefore, the time complexity of this step is approximately \(O(n^{2})\). All steps are executed sequentially, so Algorithm 3 has polynomial time complexity.
# Parallelization of the Bison Algorithm Applied to Data Classification
## 3 Proposed Approach
### Apache Spark  
Apache Spark is a popular open-source analytics engine used for large-scale data processing [19]. Spark uses parallelization, which is a computation technique used to divide a task into smaller, independent sub-tasks that can be executed simultaneously across multiple processors, or cores. Parallelization aims to increase computational speed by reducing the overall time required to complete a task. By distributing a workload across multiple cores, performing operations such as classification can be conducted in parallel to ease computational load.  
The main program that runs the user application is called the driver program, which defines the high-level logic of the application, including details about the execution of tasks. The cluster manager manages the cluster of machines, or nodes, and allocates resources to the Spark application.  
Worker nodes are the machines in the cluster that execute the tasks assigned by the driver program. Each worker node runs an executor that executes the individual tasks. Apache Spark leverages distributed computing by breaking down large data processing tasks into smaller ones that are executed in parallel across clusters of computers, or cores, (seen in Figure 1), enabling efficient handling of large amounts of data processing, improving speed and scalability [20].
## 3 Results and Discussion
### Foamed Concrete Microstructure  
Scanning electron microscopy (SEM) was used to examine the microstructural characteristics of the foamed concrete. Figure 6 shows the light microscopic images of the control mix (mix C) as well as those of the fly ash mix (mix C/FA), and Figure 7 shows the SEM images. Foam bubbles can be easily detected in the images, and a wall between the bubbles can also be observed. Most of the pores had diameters in the range of 50-500 \(\upmu\)m. In addition, the products of cement hydration grew in the voids. In the second mix (mix C/FA), non-reacted fly ash spheres appeared, which explains the low compressive strength of the fly ash foamed concrete. Similar to the control mix, the hydration products of the cement grew in the voids. The images show the homogenous distribution of foam and the cement matrix, without the agglomeration of cement particles or the collapse of foam bubbles. However, at a sample age beyond 28 days, non-reacted fly ash particles can be easily seen in the images. In addition, the thickness of the walls between foam bubbles became thicker due to the addition of fly ash. These observations explain the role of fly ash in increasing the density and reducing the strength of foamed concrete. The SEM images also indicate that, because of the high amount of foam in the mix, the bubbles were very close to each other, thus explaining the low compressive strength of ultra-lightweight foam concrete.  
Figure 5: Drying shrinkage of foamed concrete mixes at 28 days.  
Figure 6: Light microscopic images of foamed concrete: control mix, C (**left**), C/FA (**right**).  
Figure 7: Scanning electron microscopy (SEM) images of foamed concrete mixes: control mix, C (**left**) and fly ash mix, C/FA (**right**).
## 7 Conclusions  
This study systematically investigated the effects of dual microstructures on ball-end milling tools through orthogonal experiments (four factors, three levels) and finite element simulations. The key conclusions are as follows:  
1. Optimized Parameters: The optimal microstructure configuration (T\({}_{4}\): D = 50 \(\upmu\)m, H = 60 \(\upmu\)m, L\({}_{1}\) = 100 \(\upmu\)m, L\({}_{2}\) = 100 \(\upmu\)m) significantly improved tool performance. Compared to T\({}_{1}\), T\({}_{4}\) reduced milling forces by 19.5% (T\({}_{4}\) vs. T\({}_{1}\)), 14.3% (T\({}_{4}\) vs. T\({}_{3}\)), and 5% (T\({}_{4}\) vs. T\({}_{2}\)), while cutting temperatures decreased by 19.1%, 9.9%, and 6.7%, respectively.
2. Wear Resistance: T\({}_{4}\) exhibited the lowest wear volume, attributed to its dual microstructures' ability to trap chips, reduce friction, and enhance heat dissipation. The front cutting face microstructures minimized secondary wear via chip containment, while the back cutting face microstructures increased heat dissipation area and fluid dynamic pressure effects for accelerated thermal energy removal.
3. Surface Finish: T\({}_{4}\) achieved up to a 52.9% improvement in surface roughness (Ra) compared to T\({}_{1}\), demonstrating its capacity to mitigate subsurface deformation and material adhesion through reduced frictional heat and forces.
4. Mechanistic Insights: The synergistic effects of dual microstructures--front cutting face chip management and back cutting face thermal regulation--enabled T\({}_{4}\) to outperform single-structure tools, validating their potential for enhancing machining efficiency and tool longevity.  
Conceptualization, Q.L. and Q.G.; methodology, W.X.; software, Q.G.; validation, T.X., Y.J. and Q.L.; formal analysis, Q.L.; investigation, Q.L.; resources, Q.G.; data curation, T.X.; writing--original draft preparation, T.X.; writing--review and editing, Q.G.; visualization, Q.G.; supervision, Q.L.; project administration. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Data Acquisition System Based on the Bluesky Suite in the Shanghai Synchrotron Radiation Facility
###### Abstract  
The Shanghai Synchrotron Radiation Facility Phase-II Project is now under construction and it consists of 16 newly built advanced beamlines. The experiments carried out at these new beamlines tend to be characterized by high flux, multi-mode, ultra-fast frequency, in situ and dynamic loading. To meet these emerging experimental demands of SSRF Phase-II beamlines, the authors have developed a unified and extensible data acquisition system based on Bluesky suite. It has been applied at the BL07U Spatial-resolved and Spin-resolved ARPES and magnetism beamline. The experiment results show that with up to 5 channel signals, the system is capable of collecting total electron yield signals of experimental sample and standard sample, and incident photon flux signals simultaneously. It improves the user experimental efficiency and quality. In addition, thanks to Bluesky suite, all devices can be abstracted as motors or detectors and then represented by a Python object. Therefore, with the architecture, modules and interface developed for BL07U, the system can be extended to more beamlines efficiently and easily.  
data acquisition system; Bluesky suite; beamline control; EPICS; SSRF; BL07U
## 5 Model Setup  
The open source library was used in this study. Python (version 3.6.4, Anaconda) was used as the programming language. Numpy (version 1.14.2) and Pandas (version 0.22.0) libraries inside Python were used for data management to execute the forecasting model. The forecasting model using the LSTM was developed by using the Tensorflow (version 1.8.0) provided by Google as an open source library. The TensorFlow is composed of data flow graph structure and is used in various research fields of machine learning and deep learning. Moreover, the forecasting of water elevation was performed by using a computer with the following specifications: Intel Core i5-6600 CPU (Central Processing Unit), 8.00 GB of RAM (Random Access Memory), 256 GB of SSD (Solid State Drive), and an Intel(R) HD Graphics 530 video card.
# Performance Analysis of Latency-Aware Data Management in Industrial IoT Networks +
## 6 Large-Scale Simulations
### Validation of the Simulation Model and Simulation Settings  
We constructed, in simulation, instances similar to the one that was tested in the Euratech testbed. The results obtained are displayed with green color in Figure 6. It is clear that the results obtained by the simulation model are very similar to the results obtained during the real experiment, and therefore, we can extract reliable conclusions from the simulation environment.  
Figure 6(a) displays a typical network deployment of 500 nodes, with the corresponding wireless links and with the controller \(C\) lying on the far right edge of the network, depicted as a red circle.  
Figure 6: Experimental results in the IoT-LAB Euratech testbed. The validation of the simulation model is displayed in green.  
Figure 7b displays the locations of the final set \(P\) of proxies depicted as red circles after running ProxySelection+. The spatial display of Figure 7b shows that the final selection results in a balanced proxy selection, ensuring that even isolated nodes, which are located near sparse areas of the network, also have access to a proxy.  
In the simulations, we focus on showcasing different aspects of the data management and distribution process. We construct larger and different deployments and topologies than the ones of the Euratech testbed; we investigate different values of \(L_{\text{max}}\); we consider diverse percentages of requesting nodes; and we also measure the energy consumption. The deployment area \(\mathcal{A}\) is set to be circular, and the nodes are deployed uniformly at random. We construct networks of different numbers of nodes, inserting the additional nodes in the same network area and at the same time decreasing the communication range \(r_{u}\) appropriately, so as to maintain a single strongly-connected component at all times. An example of a generated network of 500 nodes is depicted in Figure 7a. In the following, we present results where \(l^{(h)}\) is measured as the mean of latencies. Figure 7c shows the value of \(\overline{L}\) obtained in the case of Figure 7a, qualitatively confirming the results shown in Figure 6a.
## 3 Experimental Analysis
### Reflection Power Spectrum
#### 3.2.2 Temporal Response of Reflection Power  
Figures 7 and 8 show the temporal response of \(P_{T}(t,k|\mathbf{y}_{o})\) at different frequencies for different source positions in Room-1 and Room-2, respectively. As evident from these figures, the reflection power decays due to surface absorption, and the decay trend is similar for all source positions. Since the damping constants of room surfaces are frequency-dependent, each frequency in Figures 7 and 8 decays at different rates. The lower frequencies like 70 Hz, 141 Hz, and 211 Hz have slower decay rates compared to the other frequencies. As we move from 281 Hz to 633 Hz in Figures 7 and 8, the decay rate stabilizes towards the higher frequencies. Furthermore, the decay of higher frequencies is nearly linear, whereas the lower frequencies (70 Hz to 211 Hz) exhibit a non-linear decay, especially in Room-2. This can be attributed to the highly non-uniform power distribution of the lower frequency resonant modes, which leads to the concentration of sound absorption to certain surfaces [42; 43]. In comparison, the high frequencies have more diffused distribution of reflection power, and hence the decay behavior is averaged over broader surface areas.  
Figure 5: Reflection power with frequency for different source positions in Room-1.  
Figure 6: Reflection power with frequency for different source positions in Room-2.
Other properties have been described for astaxanthin, such as beneficial effects in cardiovascular, immune, inflammatory, diabetes, carcinogenic, and neurodegenerative diseases, and as an antiaging and sun proofing agent [15; 37; 38; 39; 40; 41]. The commercial origin of astaxanthin is from either chemical synthesis or natural resources such as -formentative production (microalgae, yeast) and crustacean byproducts [36; 41]. Therefore, more than 95% of the global market refers to synthetically obtained astaxanthin, which presents lower production costs (around $1000/kg) than the biological alternative. Nowadays, the major manufacturers are DSM (The Netherlands), BASF (France), and NHU (China). However, its petrochemical origin limits the final use, which is boosting the natural sources of this carotenoid [14; 41]. Thus, astaxanthin from natural sources, including bacteria such as _Paracoccus caordinificaiens_, yeasts like _X. dendrohous_, or algae like _Haematococcus pluvialis_, is a realistic alternative to synthetic astaxanthin. Figure 1: Astaxanthin biosynthetic pathway in _X. dendrohous_. Initially, a molecule of dimethylallyl pyrophosphate (DMAPP) and three molecules of isopentenyl pyrophosphate (IPP) are combined to obtain geranylgeranyl pyrophosphate (GGPP) by means of the GGPP synthase. Secondly, two molecules of GGPP are coupled by the phytoene synthase (_crtYB_ gene) to reach phytoene. The phytoene desaturase(_crtl_ gene) introduces four double bonds in the molecule of phytoene to to obtain lycopene. Then, the lycopene cyclase (_crtYB_ gene) converts one of the \(\psi\) acyclic ends of lycopene as \(\beta\)-ring to form \(\gamma\)-carotene, and subsequently the other to form \(\beta\)-carotene. Xanthophylls bioconversion in _X. dendrohous_ from \(\beta\)-carotene and \(\gamma\)-carotene includes the addition of two 4-keto groups in the molecule of \(\beta\)-carotene by the ketolase (K, discontinuous line) activity, and the inclusion of two 3-hydroxy groups by the hydroxylase (H, continuous dotted line) activity. Both K and H activities are presented in a single enzyme (astaxanthin synthetase; CrtS) encoded by a single gene (_crtS_). The cytochrome P450 reductase encoded by the _crtR_ gene is a CrtS helper protein, providing with electrons for substrate oxygenation. The existence of a monocyclic pathway to DCD was also proposed [42]. Main carotenoids detected in _X. dendrohous_ broths are shown inside a rectangle in concordance with their natural colours. This figure has been based on [PERSON] and co-workers [43]. The aim of this review is to provide an up-to-date version of the the carotenoids biosynthetic pathway of _X. dendrohous_, together with the main biotechnology approaches and omics tools that have been applied to this yeast for the improvement of astaxanthin production.
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 5 Future Directions and Challenges
### Future Research Directions
In wireless multimedia sensor networks (WMSNs) for environment monitoring, for example, wirelessly interconnected sensor nodes are equipped with multimedia devices, such as cameras and microphones, and are capable of retrieving video and audio streams as well as scalar sensor data [75]. Energy is more crucial here because dealing with multimedia contents requires a higher bandwidth and improved data rate. A game theory-based routing protocol may suit the subtle energy-saving requirement of WMSNs.