esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Software-Defined Networking Data Centre Networks  
A typical DCN comprises a conglomeration of network elements that ensures the exchange of traffic between machines/serves and the Internet. These networks elements include servers that manage workloads and respond to different requests, switches that connect devices, routers that perform packet forwarding functions, gateways that serve as the junctions between the DCN and the Internet [1]. Despite the importance of DCNs, their architecture is still far from being optimal. Traditionally, DCNs use dedicated servers to run applications, resulting in poor server utilisation and high operational cost. To overcome this situation the emergence of server virtualisation technologies allows multiple virtual machines (VMs) to be allocated on a single physical machine. These technologies can provide performance isolation between collocated VMs to improve application performance and prevent interference attacks. However, server virtualisation itself is insufficient to address all limitations of scalability and managing the growing traffic in DCNs [1; 35].  
Motivated by the limitations aforementioned, there is an emerging trend towards the use of networking paradigms as SDN in DCNs, also known as SDDCN. An SDDCN combines virtualized compute, storage, and networking resources with a standardised platform for managing the entire integrated environment. Following [PERSON] et al. [1], the major foundations of an SDDCN are:  
* _Network virtualisation_--combines network resources by splitting the available bandwidth into independent channels that can be assigned or reassigned to a particular server or device in real-time.
* _Storage virtualisation_--pools the physically available storage capacity from multiple network devices. The storage virtualisation is managed from a central console.
* _Server virtualisation_--masks server resources from server users. The intention is to spare users from managing complicated server-resource details. It also increases resource sharing and utilisation while keeping the ability to expand capacity.  
Figure 1 shows a SDDCN with a conventional topology. In this SDDCN, the controller (or set of controllers) and the network applications running on it are responsible for handling the data plane. This plane includes a fat-tree topology that is composed by Top-of-Rack (ToR), Edge, Aggregation, and Core switches. ToR switch in the access layer provides connectivity to the servers mounted on every rack. Each aggregation switch in the aggregation layer (sometimes referred to as the distribution layer) forwards traffic from multiple access layer switches to the core layer. Every ToR switch is connected to multiple aggregation switches for redundancy. The core layer provides secure connectivity between aggregation switches and core routers connected to the Internet [6].  
Figure 1: SDDCN structure with a conventional topology.
## Gut Microbiota Imbalance is Related to Sporadic Colorectal Neoplasms. A Pilot Study
###### Abstract  
(1) Background: Colorectal cancer (CRC) development is sustained by multiple factors including the gut microbiota, as suggested by a growing body of evidence. Most CRCs have a sporadic (non-hereditary) onset and develop from sporadic colorectal adenomas/polyp (SCA/P). In the present study, we investigated the characteristic of anaerobic microorganisms in stool samples obtained from 20 patients with SCA/P and 20 subjects without evidence of proliferative lesions at colonoscopy (Controls). (2) Material and Methods: We designed this clinical trial using adaptive randomization by minimization. Selective culture media and Matrix Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) mass spectrometry techniques were used to identify the components of microbiota. The data obtained revealed a different variability of gut microbiota in stool samples of controls and SCA/P subjects. (3) Results: The most interesting difference was observed for _Bacteroides_ species, which represent the 50% of all bacterial species identified in the stool samples: two species, _Bacteroides stercoris_ and _Parabacteroides distasonis_, were found only in the feces from control group, whereas _Bacteroides fragilis_ and _Prerotella melaningenica_ species were presents only in SCA/P patients. Among Gram+ bacteria also, specific species were found in the two groups of feces: _Clostridium closridiforme_, _Propioniibacterium avium_ and _Pediococcus pentasacous_ were identified only in controls, while _Eubacterium limosum_, _Clostridium innocuum_ and _Corybeacterium xerosus_ were identified in SCA/P stool samples only. (4) Conclusions: Our findings suggest that, compared to control stool samples, a different intestinal microbiota is present in SCA/P stool samples, that may create a micro-environment predisposing for the development of proliferative phenomena. As a consequence, gut microbiota manipulation could be a future target for personalized treatments.  
Keywords: gut microbiota; dysbiosis; Bacteroides; probiotics; short-chain fatty acids (SCFAs), MALDI-TOF; intestinal polyps; colorectal cancer (CRC), personalized medicine; translational research
## 3 Results and Discussion  
### The Misting System Design  
Figure 2a-c are schematic diagrams representing the misting system's design. The system has worked according to the program we designed. The way the system works is as follows. The water reservoir (1) serves to hold water for misting. Two water filter units (2) ensure that the water is free from contamination with impurities that can clog the nozzles. When the misting system is active, the IoT module (7) will give a command to the pump (4) to drain water from the water reservoir (1) and transfer it to the misting nozzle (6). Thewater discharge and the amount/volume of water for misting are seen and recorded by the digital flow meter (3), whose data are connected to the IoT Module (7). After going through the digital flow meter (3), the water will pass through the pump (4) and then to the pressure regulator (5). The pressure regulator regulates the pump pressure; therefore, the water discharge is issued as programmed.  
The water that has passed the pressure regulator will flow to all (120 units) nozzles (6), which are scattered in the greenhouse. The water that comes out of the nozzles is in the form of water mist that can absorb heat in the greenhouse, allowing for changes in the temperature and humidity. Every hour, the digital thermometer-hygrometer (8) will measure the temperature and humidity and send the data to the IoT module. Electronic devices that are connected in parallel to the IoT module, namely, digital flow meters (3), water pumps (4), pressure regulators (5), and digital thermometer-hygrometers (8), will send data that can be viewed via a smartphone with eWeLink app. The eWeLink application is an application of the Sonoff WiFi TH16 Smart Switch (7) IoT system to manage and monitor data from each connected electronic device.
## 4 Discussions  
This study investigated the effects of CO\({}_{2}\)-CH\({}_{4}\) co-injection on CCS-EOR performance and mechanisms using the compositional 2D reservoir simulation. The 2D homogeneous plane model was designed in order to focus on the miscibility and sweep efficiency without gravity override during WAG simulations. We anticipate that the proposed compositional model can be extended to a three-dimensional (3D) heterogeneous model for real field applications. Adding CH\({}_{4}\) into the CO\({}_{2}\) stream may affect the vertical sweep efficiency, resulting in the change of the CCS-EOR performance because the density of CH\({}_{4}\) is much lighter than that of CO\({}_{2}\). Also, reservoirs are heterogeneous in reality. The effects of heterogeneity on the performance are under investigation as future works.  
Moreover, it is essential for the technically, economically, and environmentally sustainable development of CO\({}_{2}\)-EOR fields to analyze both oil recovery and carbon storage efficiencies in an integrated way. For an actual field application, the optimal CO\({}_{2}\)-CH\({}_{4}\) injection design should be developed with consideration for economic factors, such as the volatile oil price, CO\({}_{2}\) and CH\({}_{4}\) purchase and recycle prices, operating costs, and tax credits for CCS. [PERSON] et al. [46] focused on the sensitivity of economic parameters with various WAG ratios during CCS-EOR, but no study has been conducted in the CO\({}_{2}\)-CH\({}_{4}\) co-injection system to the best of our knowledge. Since the optimum operating conditions enhancing the performance of CCS-EOR depend on reservoir characteristics, it is necessary to develop a site-specific design based on economic analysis for the field application.  
Gas injection changes the stability of asphaltene precipitation and deposition, and formation damages by asphaltene deposition affect CCS-EOR performance. The effects of asphaltene deposition on CCS-EOR in the CO\({}_{2}\) WAG process have been investigated in [PERSON] et al. [19], while the effect of CH\({}_{4}\) addition has not been studied yet. We clarify that the CO\({}_{2}\)-CH\({}_{4}\) co-injection model considering asphaltene deposition is being developed as our on-going work.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 2 Problem Formulation
### Plain, Obstacles and Probability Scenarios  
Given the scenario above described, four different types of scenarios are considered:  
* Plain scenario: In the simplest scenario type, every cell is flyable, and all the discretization cells must be observed at least once to finish the mission (see Figure 2a).
* Probability distribution scenario: We assume prior knowledge about the possible location of targets to be found. According to this distribution, \(N_{t}=\frac{5}{2000}\cdot\frac{A_{t}}{N_{a}}\) targets are generated. The search is finished when all the targets have been observed. The probability distribution is generated using the midpoint displacement method, normally used as a terrain generator method. The initial distribution is generated randomly with a roughness \(r\in[0,\ 0.5]\) and its derivative \(r_{r}\in[0,\ 0.1]\). Seven iterations are applied. In Figure 2b, an example of this scenario type is shown.
* Obstacles scenario: Some of the search cells are occupied, and the agents cannot fly through them. The search is completed when all the discrete cells that are not inside the obstacles have been observed at least once. The map is generated using the Schelling segregation model, fixing the tolerance limit to 0.3 and the percentage of the population that look for new houses to 0.7. The percentage of non-flyable search cells is drawn from a normal distribution \(\mathcal{N}\left(0.75,\ 0.1^{2}\right)\), while the percentage of empty cells (cells that may be occupied by obstacles) is drawn from \(\mathcal{N}\left(0.25,\ 0.1^{2}\right)\). Once the equilibrium has been reached, the empty cells are transformed into flyable cells. It is checked that every flyable cell can be reached (i.e., there are no cells completely surrounded by obstacles). An example of the result of this procedure has been shown in Figure 2c.
* Probability distribution and obstacles scenario: Both the probability distribution of target locations and obstacles are considered; see Figure 2d.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Range of Values** \\ \hline \(A_{s}/N_{a}\) & Search area per agent & [2, 15]\(\times\) 103 m2 \\ \(N_{a}\) & Number of agents & [2, 30] \\ \(v_{n}\) & Nominal velocity of the agents & [2, 20] m/s \\ \(R_{f}\) & Radius of the sensor footprint & [5, 20] m \\ \(f_{A}\) & Aspect ratio of the search area & [0.25, 1] \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of parameters that define the scenario.  
Figure 1: Search area example. A search and a discretization cell have been highlighted. The agent, represented as a triangle, has moved in the diagonal and in the horizontal directions. The three visited search cells by the agent have been highlighted. The observed discretization cells have been marked in dark green. Note that the footprint area circumscribes the search cells.
## 4 Strain Results and Analysis
### Strain Analysis  
#### 4.2.1 The Test Data of the Peak Strain  
When a blast wave propagates into the soil, the stress amplitude and loading rate vary with the explosion center distance. According to the literature [32], when \(\overline{R}<6\) the blast wave in rock and soil is dominated by P- waves. When \(6\leq\overline{R}\leq 30\) the waveform starts to change, and the surface wave component gradually increases. When \(\overline{R}>30\) the blast wave is dominated by seismic waves. Therefore, the data (\(R=2.7\) m and \(R=3.75\) m) must be emphatically analyzed, because the scaled distance (6-11) is in the range of the waveform conversion partition. When \(R=2.7\) m, the peak tensile strain (PTS) and peak compressive strain (PCS) of each test point are shown in Table 3.  
It can be seen that, for Section 3 closest to the blast source, the hoop PCSs of test points 1 and 3 were generally greater than the PTS, and the axial PTS of test point 3 was larger than the PCS. At the top and bottom of the pipe (test points 2 and 4), the hoop PTS was generally greater than the PCS, and the axial PCS was generally greater than the PTS. When the scaled distance was small, the PHS was generally larger than the PAS at the same point. This was due to the high water content in the highly saturated clay, which was conducive to the propagation of explosive wave energy. The flexible PE pipe produced a greater hoop strain under the local impact. It should be pointed out that the PTS and PCS of the same test point were not always reached at the same time due to the dynamic effect of explosive wave propagation, and the interval was several milliseconds. The external surface of the pipe could be simplified as a biaxial stress state. In the linear elastic deformation range, the stress-strain relationship followed the generalized Hooke's law.
# Mueller Matrix-Based Approach for the Ex Vivo Detection of Riboflavin-Treated Transparent Biotissue
## 2 Materials and Methods
### Mueller Matrix Measurement Setup
The calibration relies on applying suitable values for the supply voltages of the LCRs for achieving \(\lambda/4\) and \(\lambda/2\) phase delays, respectively. All angles are aligned with respect to the first polarizer in the PSG which does not change its orientation during the whole process. The second polarizer is mounted in a motorized rotating holder and is then oriented perpendicular to the first one. Then, the first polarizer is manually set to 45\({}^{\circ}\) and the following steps are implemented: (i) the fast axes of all LCRs are calibrated to 0\({}^{\circ}\); (ii) a \(\lambda/2\) retardation is calibrated for all LCRs by the analysis of the relation between the applied supply voltage and the intensity at the sensor; and (iii) a \(\lambda/4\) retardation is calibrated accordingly for LCR2 and LCR3 by using a quarter-wave-plate. The procedure is based on intensity measurements with the imaging camera acting as the sensor. The displayed inhomogeneity of the Mueller matrix entries in Figure 2 can be partly due to changes in the required phase delay over the aperture of the LCRs [40]. However, measurements of the homogeneous samples such as air or polarizers (not displayed in this work) show homogeneity over the illuminated field-of-view. \begin{table}
\begin{tabular}{c c c c c} \hline  & **M[;1]** & **M[;2]** & **M[;3]** & **M[;4]** \\ \hline
**M[1,:]** & HH+HV+VH+VV & HH+HV-VH-VV & PH+PV-MH-MV & RH+RV-LH-LV \\ \hline
**M[2,:]** & HH-HV+VH-VV & HH-HV-VH+VV & PH-PV-MH+MV & RH-RV-LH+LV \\ \hline
**M[3,:]** & HP-HM+VP-VM & HP-HM-VP+VM & PP-PM-MP+MM & RP-RM-LP+LM \\ \hline
**M[4,:]** & HR-HL+VR-VL & HR-HL-VR+VL & PR-PL-MR+ML & RR-RL-LR+LL \\ \hline \end{tabular}
\end{table} 
Table 1: Calculation of the MM elements. The individual elements result from different polarization states of PSG and PSA (first and second letter on the right side of each matrix element equation). Symbols are as in (2). All MM images are normalized by the first matrix element M[1,1]. Figure 2: Result of a MM measurement in the transmission mode of a porcine cornea sample at a wavelength of 532 nm. The sample has a polarization-changing effect, as obvious from the non-zero entries of the different matrix elements, essentially all, which are normalized to the \(M(1,1)\) entry. However, the size of the effect is not homogeneously distributed over the surface of the cornea, which is evident from the individual entries and probably due to non-optimal sample fixation.
## 2 The \({}^{3}\)ColStar Satellite Mission
### Preliminary Design
#### 2.3.6 Payload  
The CubeSat's primary objective is to perform solar observations, specifically focusing on the measurement of solar wind particles. Solar wind is a stream of charged particles, primarily electrons and protons, flowing from the sun into the solar system. By measuring the composition, density, and energy spectrum of these solar wind particles, the mission aims to:  
* Enhance our understanding of the sun's activity and its impact on space weather.
* Collect valuable data for space weather forecasting, which is critical for safeguarding space assets and satellite operations.
* Improve our ability to predict and mitigate space weather-related disruptions to communication, navigation, and satellite systems.  
The instrument is composed by the optical system, a surface detector \"ADVACAM\", and by the main electronics that are currently an on-going project developed by Universidad Industrial de Santander (ASIC, memory, secondary computer, main computer). Finally, the payload is connected to the OBC in a bidirectional communication link that leads receive commands from the instruction set and sends data sets from the power link to the EPS subsystem (Figure 13).  
Figure 11: Schematic for \({}^{3}\)ColStar communication.  
Figure 12: Astrodev hellium features.  
The ADVACAM detector [58] will be used for monitoring cosmic weather. This technology provides timely warnings against increased solar activity, which can pose risks to the health of astronauts and disrupt the functionality of sensitive onboard electronics in satellites and spacecraft. The MiniPIX TPX3 SPACE particle [58] counting camera builds upon technology originally developed for basic particle physics research at the Large Hadron Collider (LHC) at CERN. This lightweight device, weighing just a few tens of grams, can distinguish the type of each individual particle, as well as its energy and direction.
## 4 Experimental Results and Analysis
### Optimize Experiment  
In addition to designing lighter network structures, quantitative methods can also be used to improve model recognition speed. Model quantization converts floating-point parameters in the model from FLOAT16 to INT8 type, which can reduce memory usage and improve inference speed, but this method can cause a decrease in accuracy. Table 4 shows the performance comparison before and after quantization. It can be seen from the table that the inference speed of the quantized model has been improved, but its detection accuracy has significantly decreased, with a loss of about 10%.
## 5 Conclusions  
This study attempts to explore the viability of DRL methods, particularly the DQN, to improve the urban traffic flow and reduce the environmental impacts. The DQN method is first trained on real-world traffic data and simulation environments, and it outperforms other DRL methods like PPO, DDQN and A2C in three scenarios with different level of complexity and traffic density.  
The findings of this study are as follows:  
* Fuel Consumption Reduction: In the most complex traffic conditions of Scenario 3, the DQN algorithm was found to consume up to 15% less fuel than other DRL methods.
* Emission Reduction: In all the scenarios, the DQN method was found to reduce the CO2 emissions by an average of 13% compared to the baseline methods.
* Improved Traffic Flow: The proposed method minimized stop-and-go movements and also optimized vehicle acceleration leading to a 22% decrease in the average time spent at traffic lights.  
While this study is promising, it does have limitations. The simulation-based approach might not have captured real-world complexity, including traffic incidents or differences in human behavior. Its reliance on accurate traffic data serves to further restrict its applicability in areas lacking access to these data. In addition, not being integrated with multi-modal transportation systems limits its capacity to serve as a full optimization solution.  
Future research could concentrate on the incorporation of real-time traffic data, investigations of full multi-modal systems as well as predictive capabilities that will allow the exploration of most likely traffic bottleneck scenarios.  
Conceptualization, M.K.; methodology, Y.Y.; software, Y.Y.; validation, M.K. and Y.Y.; formal analysis, M.K.; investigation, Y.Y.; resources, Y.Y.; data curation, Y.Y.; writing--original draft preparation, Y.Y.; writing--review and editing, Y.Y. and M.K.; visualization, Y.Y.; supervision, M.K.; project administration, M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.