esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Laser Linewidth on the Brillouin Shift Frequency Accuracy of BOTDR
## Appendix B  
Appendix B mainly describes how the laser linewidth was measured with the delay fiber, the length of which was much shorter than the coherence length of the measured laser. It is well known that self-heterodyne interferometer (DSHI) is a frequently-used technology to measure laser linewidth. The output power spectrum of the DSHI can be expressed by _S(f, \(\Delta\)f)_[31]:  
\[S(f,\Delta f)=S_{1}S_{2}\] (B1)  
\[S_{1}=\frac{P_{0}^{2}}{4\pi}\frac{\Delta f^{2}}{\Delta f^{2}+\left(f-f_{1} \right)^{2}}\] (B2)  
\[S_{2}=1-\exp(-2\pi\Delta f\tau_{d})[\cos[2\pi(f-f_{1})\tau_{d}]+\Delta f\frac{ \sin[2\pi(f-f_{1})\tau_{d}]}{f-f_{1}}]\] (B3)  
where \(f\) is the optical frequency, and \(f\)1 = 200 MHz is the frequency shift of AOM. \(\tau_{d}\) = _nL/c_ is the delay time caused by the delay fiber, \(L\) = 2950 m is the length of delay fiber, \(c\) = 3 \(\times\) 10\({}^{8}\) m/s is the speed of light in vacuum, \(n\) = 1.5 is the refractive index, and \(\Delta\)_f_ = 98 Hz is the laser linewidth. The normalized power spectra of _S(f, \(\Delta\)f)_, \(S\)1, and \(S\)2 obtained by numerical simulation are plotted in Figure A1a.  
It has been experimentally and theoretically proven that the value of the contrast difference between the second peaks and second troughs (CDSPST, denoted as \(\Delta S\)), as shown in Figure A1a, is related closely to the product of the measured laser linewidth and the length of delay fiber. The relationship curve is plotted in Figure A1b.  
Hence, the value of \(\Delta S\) can be used for calculating the measured laser linewidth once the length of the delay fiber is fixed. From Figure 8e, the obtained value of \(\Delta S\) was 25.95 dB, and the corresponding value of \(\Delta f\cdot L\) was 288921 Hz\(\cdot\)m, as shown in Figure A1b.  
Thus, the laser linewidth can be calculated according to the below expression:  
\[\Delta f=\frac{288921}{2950}\approx 98\;\mathrm{Hz}\]
# Evaluation of Novel Preformed Particle Gel System for Conformance Control in Mature Oil Reservoirs
###### Abstract  
To address challenges associated with excessive water production in mature oil reservoirs, this study introduces a carboxymethyl cellulose (CMC)-based material as a novel preformed particle gel (PPG) designed to plug excessive water pathways and redistribute the subsequent injected water toward unswept zones. Through microwave-assisted grafting copolymerization of CMC with acrylamide (AM), we successfully generated multi-sized dry particles within the range of 250-800 \(\upmu\)m. Comprehensive analyses, including Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM), have confirmed the chemical composition and morphology of the resulting carboxymethyl cellulose-grafted crosslinked polyacrylamide (CMC/PAMBA). Swelling kinetics and rheology tests were conducted to confirm the ability of this novel PPG system to perform at different reservoir conditions. The results of core flooding experiments showed that the CMC/PAMBA PPG is capable of plugging open fractures with a water breakthrough pressure gradient of up to 144 psi/ft. This preformed particle gel (PPG) system was designed specifically for application in Middle East reservoirs, which are distinguished by high salinity and elevated temperature levels. This PPG system is able to swell up to 10 times its original size in seawater and maintain a strength of about 1300 Pa at a temperature of 80 \({}^{\circ}\)C. Further optimization is conceivable to enhance injection efficiency and achieve superior plugging outcomes.  
carboxymethyl cellulose; conformance control; excessive water production; plugging efficiency; preformed particle gel
## 3 Concepts and Models
### Time Slice Modeling  
The concept of time slicing originated from the scheduling process of each process by the kernel in a time-sharing operating system [24]. In the scheduling process of the operating system, the kernel allocates some time slices to each process. Within one time slice, the kernel only runs one process. Due to the typically short length of time slices, users do not perceive the switching process of time slices. In the user's view, all processes operate together. In this way, a time-sharing operating system can achieve concurrent computing.  
In a near-Earth satellite-UAV collaborative IoT system, all nodes continuously operate at high speeds within their respective orbits or tracks, and the distances and relative positions between nodes constantly change. A common approach is to divide the entire operational cycle of a dynamic network into multiple time slots and perform separate calculations within each time slot [25]. Each time slot corresponds to a period of time with a specific network topology, and the topology structure during that period is referred to as a topology snapshot. This abstraction allows us to transform the dynamic satellite-UAV-integrated networks into a static, discrete form of a graph. This work uses the equal time interval slicing method to divide time slices and topology sampling on an interval basis [26]. Equal time interval division is shown in Figure 3.
# MEMS Vaporizing Liquid Microthruster: A Comprehensive Review
## 4 Instability Control into VLMs
### Flow Boiling Instabilities in Microchannels
#### 4.1.2 Parallel Channels Interaction during Flow Boiling Instabilities  
Parallel channel interaction accelerates the adverse bubble dynamics [32]. Figure 9 presents a simplified schematic of the bubble growth stages that originate instabilities in microchannels. Severe parallel channel instabilities start with bubble clogging followed by rapid bubble growth and the reverse flow of the vapor in the inlet section. Coupled with parallel channels interactions, upstream compressibility also triggers adverse bubble dynamics. As primary causes of dynamic instabilities in conventional devices, they result from the interaction of a boiling pressure drop with upstream compressibility, appearing due to external circuit or pump behavior. Inlet subcooling temperature \(\Delta T_{sub,in}=(T_{sat}-T_{in})\) influences the bubble growth dynamics. In particular, the occurrence of the two-phase instabilities in microchannels accelerates as larger inlet subcooling conditions and a higher degree of wall superheat \(\Delta T_{sup}=(T_{wall}-T_{sat})\) are experienced.  
In the presence of a pressure drop and flow temperature oscillations, another kind of flow boiling instability establishes, named oscillating flow. The entity of oscillations is affected by the inlet/outlet configuration of channels, the mass flux, the inlet subcooling condition, and the heat fluxes at walls. When the oscillating phenomenon involves the fluid density and an alternating appearance of the liquid flow, and two-phase vapor-liquid flow is experienced, the density wave oscillation instability occurs. In such kinds of flow boiling instabilities, the fluid density fluctuations couple with an alternating heat transfer process which affects the pressure drop along the microchannels. As a result, fluid waves of higher and lower density mixtures alternatively travel along the microchannels system.
## 3 Results and Discussion  
### Optimization of Mass Conditions  
The MS/MS condition was optimized on a Xevo(r) IQ-S by individual infusion of each compound. To obtain the most intensive response of precursor ions, ionization mode, cone gas flow, desolvation gas flow and temperature, source temperature, capillary voltage and cone voltage were manually optimized in steps. Negative mode with capillary voltage set at \(-\)2.8 kV was adopted for all the analytes; other parameters were recommended in the method section. After that, the collision energy (CE) for each compound was tuned up individually to generate the most intensive and stable product ions. Two product ions were selected and optimized for each compound in MRM mode, one for quantification and another for identification, as listed in Table 1.
# An Adaptive Scheduling Mechanism Optimized for V2N Communications over Future Cellular Networks
## 5 Performance Evaluation
However, a broad degradation in the performance of the PF scheduler in the DL state is observed in the case of 60 vehicles, which affects all the studied UCs. A common observation in all four scenarios is that the SOVANET scheduler outperforms the PF scheduler both in terms of throughput and delay. This is especially true for the UL case where SOVANET is more balanced considering the overall load for the UL and the DL. Obviously, overprovisioning for the critical services of each vehicle while  
Figure 6: HDM DL mean throughput and delay. Figure 7: BSM UL mean throughput and delay. under-provisioning for the non-critical services provides for the needed prioritization of services and results in much better performance. However, it is worth noting that in heavy load scenarios, there is still a performance degradation for both UL ToD and HDM UCs compared to their minimum requirements, making them difficult to support. This is true even for SOVANET, and although it outperforms the PF scheduler, it does not meet the strict requirements. For such cases, services developers may consider allowing for some adaptability of their applications as a remedy to under-performance in network loaded conditions.
## 4 Design and Simulation of the Conceptual Model for Capacity Planning of an OEM Plant
### Simulation of a Specific Model for the Case Study of an Automotive Plant
* Bodywork shop 2: a nominal capacity of 300 units per day. * Paint shop: a nominal capacity of 600 units per day. * Pre-assembly shop, assembly 1: a nominal capacity of 600 units per day. * Mechanical assembly shop, assembly 2: a nominal capacity of 600 units per day. * Final assembly shop, assembly 3: a nominal capacity of 600 units per day. * Final inspection shop: a nominal capacity of 600 units per day. As can be seen in Figure 3, the two car models use the same production capacity for the painting and assembly processes but use different capacities for the bodywork and press processes. Four demand scenarios were simulated. In all scenarios, there are two products or car models with the same platform. The first model is at a mature stage in its life cycle, and the second model is newly launched with demand depicted in Figure 4. Demand was created in Excel in order to use replication to have exactly the same demand in all of the models. This method of data generation allowed us to create customized demand patterns to be read by Vensim. According to many authors, the basic demand patterns are steady, seasonal, trend, and sporadic demand patterns [16]. These demand patterns were applied in combination as a basis for demand in the models to describe the behavior of the simulation models. Figure 3: Simulation production flow (own elaboration). For the four previously described scenarios and both simulation models, the following key performance indicators (KPIs) were calculated from the simulation:  
* Profits (million euros): the result of the multiplication of the number of produced cars by the margin that was provided for the type of produced car. * Total production (units): the cumulative sum of all car units produced over the 1000 simulated production days. * Capacity utilization (%): the cumulative utilization of available capacities over the 1000 simulated production days. * Maximum production capacity (units/day): both models were initialized with a demand of 600 units per day. This KPI shows the maximum capacity that was achieved during the simulated period. * Service level (%): the quantity of units delivered on time divided by the total number of delivered units. * Customer order lead time (days): the number of days between the placement of the order and the delivery of the product. * Operational savings (million euros): the savings due to optimization of working hours, shifts, and maintenance activities. * Investment value (million euros): the amount of the investment made to increase capacities. The value can be 30 million euros for an increase of up to 700 units per day or 60 million euros for an increase of up to 800 units per day. * Return on investment (million euros): the margin of the products that can be produced thanks to the investment minus the investment value.
## 2 Materials and Methods
### SPME-GC-MS Analysis of Volatile Compounds  
To characterize the volatile chemical profile of _A. domesticus_ powder, sampling was carried out using the SPME technique. A small amount of powder (\(\sim\)1 g) was placed in a 7 mL glass vial with a PTFE-coated silicone septum. With the aim of extracting the volatile compounds, a DVB/CAR/PDMS (divinylbenzene/carboux/polydimethylsiloxane) SPME device was used.  
The fiber was conditioned at 270 \({}^{\circ}\)C for 30 min. Sample equilibration was carried out at 40 \({}^{\circ}\)C for 20 min before sampling. The fiber was then exposed to the headspace of the sample for 30 min at 40 \({}^{\circ}\)C and inserted into the GC injector at 250 \({}^{\circ}\)C in splitless mode.  
The gas chromatographic analyses were carried out on a Clarus 500 model Perkin Elmer (Waltham, MA, USA) coupled with a mass spectrometer equipped with an FID detector and a Varian Factor Four VF-1 capillary column. The temperature of the oven was initially programmed at 60 \({}^{\circ}\)C, increased to 220 \({}^{\circ}\)C with a rate of 6\({}^{\circ}\)/min, and maintained for 15 min. A carrier helium flow of 1 mL/min was used. Mass acquisitions were carried out at 70 eV (EI) in scan mode, considering a 40-400 \(m/z\) range at 220 \({}^{\circ}\)C.  
The Wiley 2.2 and Nist 02 mass spectra libraries were used for volatile compound identification, together with the linear retention index (LRIs) calculation using alkane standards analyzed in the same conditions. The relative concentrations, expressed as percentage, were calculated using the FID signal peak areas. For each batch, analyses were carried out in triplicate.
## 2 Materials and Methods
### Data Analysis
#### 2.3.3 Statistics  
For all samples sets, normal distribution of data was verified using a normal probability plot and Shapiro-Wilk test at significance level 0.05. Analysis of variance (ANOVA) was used to determine the significance of exposure, treatment and/or board orientation on NDT and 4 PB properties. A dependent \(t\)-test was used to compare mean values of NDT properties before and after exposure of 'exposed' control or TM boards. An F-test and independent \(t\)-test were used to compare variation and mean values of NDT and 4 PB properties between boards tested 'directly' and 'exposed' or between control and TM boards. Data on checks in cross-sections were not normally distributed as was emphasized earlier by [PERSON] [46] for length and area of checks. Therefore, a Wilcoxon rank sum and Kruskal-Wallis test were used to compare mean values of the number and maximum depth of checks in boards' cross-sections between 'exposed' control and TM boards, and between groups (i.e., pit up, bark up or edge), respectively. To make samples sets tested 'directly' and 'after exposure' as comparable as possible in terms of number of specimens and distribution, a subset of 84 control and 84 TM boards was taken from the 100 control and 100 TM boards that were tested 'directly' (Figure 1). The selection aimed for a comparable distribution of \(E_{\text{a,res}}\) between sample sets, since \(E_{\text{a,res}}\) is the best single predictor of static bending properties of unmodified and TM timber of spruce [21, 22]. Mean and standard deviation values were used to describe the level and variation of NDT and 4 PB properties. For measures of checking, mean values were calculated. All calculations and statistics were done in Matlab(r).
###### Abstract  
Thrips can be attracted or repelled by volatiles from different host plant species. _Houttuynia cordata_ is a common plant species with a strong, offensive smell, and few pests have been detected on this plant. Here, the olfactory responses of _Franklinella occidentalis_ to _H. cordata_ volatiles were tested using electroantennography (EAG) and behavioral bioassays in different types of olfactometers, and the behavioral responses of _Orius similis_, a natural enemy of _F. occidentalis_, to the related main volatile compounds were also evaluated. Y-tube olfactometer bioassays showed that _F. occidentalis_ performed negative responses to _H. cordata_ volatiles. Decanal (47.21%), 1-decanol (11.02%), dodecanal (7.13%), \(\beta\)-myrcrene (5.12%), and decanoyl acetaldehyde (3.7%) were the more abundant components in the _H. cordata_ volatile profile in gas chromatography-mass spectrometry analysis. EAG recordings showed that the antennae of female thrips could perceive these five compounds at a wide range of concentrations. In six-arm olfactometer bioassays, _F. occidentalis_ exhibited negative responses to decanal, dodecanal, and decanoyl acetaldehyde at various doses but performed positive responses to 1-decanol and \(\beta\)-myrcrene at certain doses. Furthermore, decanal, dodecanal, and decanoyl acetaldehyde at all concentrations showed no significant influences on the behavioral responses of _O. similis_. According to the results above, _H. cordata_ can be a repellent plant species to _F. occidentalis_, and decanal, dodecanal, and decanoyl acetaldehyde show great potential for development as repellents for the control of _F. occidentalis_. In short, our results suggest that an integrated pest management system combining _H. cordata_-derived biopesticides with releases of the predator _O. similis_ could effectively control _F. occidentalis_.  
Franklinella occidentalis; Houttuynia cordata; olfactory response; EAG response; volatiles; integrated pest management
## 3 Agent Based Fuzzy T-S Multi-Model System  
It has been proved that the fuzzy T-S model can approximate any nonlinear systems at arbitrary accuracy in [17] and [20], At the same time, it has been proved that the second kind of fuzzy T-S model has the similar approximation capability with the first kind of fuzzy T-S model [21]. Here we use the linearized consequent of the fuzzy T-S model to express the dynamical equation of the agent. Combine formula (2) with (6), the agent based fuzzy model can be written as:  
\[\begin{array}{ll}R_{i}:&if\ x_{i}(t)\ is\ A_{i1},\ x_{i}(t-1)\ is\ A_{i2},\...\\ &x_{i}(t-n+1)\ is\ A_{in},\ u_{i}(k-t_{d})\ isA_{in+1},...,\\ &u_{i}(k-t_{d}-m)\ is\ A_{in+m+1}\\ &then\ x_{i}(t+1)=a_{i1}x_{i}(t)+a_{i2}x_{i}(t-1)+...+\\ &a_{in}x_{i}(t-n+1)+a_{in+1}u_{i}(t-t_{d})+...+\\ &a_{in+m+1}u_{i}(t-t_{d}-m)\end{array} \tag{8}\]  
where \(R_{i}\) is the rule of agent \(i\), and \(A_{k}(k=i1,...,in+m+1)\) is the fuzzy subset, \(a_{ik}\) is the consequent parameter of linear sub model.  
The center average defuzzifier is adopted to the agent based fuzzy model, and then the agent based fuzzy T-S multi-model will be described as:  
\[y=x(t+1)=\frac{\sum\limits_{i=1}^{N}\lambda_{i}(\varphi(k))x_{i}(t+1)}{\sum \limits_{i=1}^{N}\lambda_{i}(\varphi(k))} \tag{9}\]  
where \(y\) is the system action which affected by the agents that instead the different work conditions, \(N\) is the number of the agents, \(x_{i}(t+1)=f_{i}(x_{i}(t),u_{i}(t))\) is the dynamical equation of agent \(i\) and its linearized expression is like the output of (8), the expression of \(\lambda_{i}(\varphi(k))\) is corresponding to the membership value of agent \(i\) and it can be calculated from Gaussian bells function as (10).  
\[\lambda_{i}(\varphi(k))=\exp[-\frac{(\varphi(k)-\bar{c}_{i})^{T}(\varphi(k)- \bar{c}_{i})}{s_{i}^{2}}] \tag{10}\]  
where \(\varphi^{T}(k)=[-x_{i}(t),...,-x_{i}(t-n+1),u_{i}(k-t_{d}),...,u_{i}(k-t_{d}-m)]\) is \((n+m+1)\) dimensional row vector, \(n\) is the number of the agent states, \(m\) is the number of the past input states that relevant to current agent output, \(\bar{c}_{i}\in R^{n_{\varphi}}\) is the central variable of the Gaussian function, and \(s_{i}\) is used to determine the width of the Gaussian function. Moreover, \(s_{i}\) can be gotten in the following way  
\[s_{i}=k_{s}\frac{1}{n}\sum\limits_{j=1}^{n}|\bar{c}_{i}-\bar{c}_{ij}| \tag{11}\]  
where \(\bar{c}_{ij}\) is the \(j^{th}\) neighbor center of the current center \(\bar{c}_{i}\), and constant \(k_{s}\) is used to determine the impact between deferent agents. Usually, the variation range of \(s_{i}\) is not significant, and the value of \(s_{i}\) can be chosen offline with the prior knowledge.  
As analysed, in the modeling process, agents are \(h\)-consensus to the system output. A single agent is equivalent to a local network model. The cooperation among agents is accomplished by their membership value.
## 3 Proposed Converter and PTZ Panorama
### JPEG 360  
JPEG Systems Part 6 defines metadata and functionalities for a 360-degree image in JPEG file format [13]. All properties of the JPEG 360 descriptive metadata are shown in Table 5. The equirectangular projection (ERP) format is the default projection format for JPEG 360, as it is the most-used projection format. JPEG 360 defines ERP as a map from a unit sphere with longitude angle \(\phi\) (phi) and latitude angle \(\theta\) (theta), as shown in Figure 4. \(\phi\)min, \(\phi\)max, \(\theta\)min, and \(\theta\)max are constrained by the following four conditions:  
\[-360^{\circ}\leq\phi_{\text{min}}\leq\phi_{\text{max}}\leq 360^{\circ} \tag{1}\]  
\[\phi_{\text{max}}-\phi_{\text{min}}\leq 360^{\circ} \tag{2}\]  
\[-180^{\circ}\leq\theta_{\text{min}}\leq\theta_{\text{max}}\leq 180^{\circ} \tag{3}\]  
\[\theta_{\text{max}}-\theta_{\text{min}}\leq 180^{\circ} \tag{4}\]  
A smaller range than the full sphere can be defined by the above-generalized inequalities, and it also helps to shift the origin (0,0) of the image according to the angle values range. Additionally, JPEG 360 deals with the situation when the camera is not held upright, and the direction of gravity is shifted relative to the camera's coordinate systems. A vector from the center to the point (\(\phi\) gravity, \(\theta\) gravity) is used for the Earth's gravity, as shown in Figure 4.  
JPEG 360 defines the subregion of ERP as a viewport. The viewport is a limited region of the 360-degree image to be presented to the viewer. The colored area in Figure 4c represents an example of the viewport. JPEG 360's viewport information helps the viewer select the initial region in the ERP image for rendering first. Metadata properties 'ViewportPhi' and ViewportTheta' are coordinates of the center of the viewport. ViewportPhiFOV and ViewportThetaFOV are the width and height of the viewport, respectively. JPEG 360 also supports a viewport with edges not parallel to the edges of the ERP. ViewportRoll is the rotation angle between a line-of-constant Phi through the viewport center point and the center line of the viewport. JPEG 360 supports multiple viewports.  
The JPEG 360's metadata contain basic schema elements. The schema is expressed in XML as a subset of W3C RDF for serializing and storing the metadata and is structured using the XMP [9]. JPEG 360 defines a content type JUMBF super box with an XML box as a sub box for JPEG 360 metadata. The 16-byte-type UUID in JUMBF description box for the JPEG 360 Metadata XML box is specified as '0x785f34b7-564b-474c-b89f-1d99e0e3a8 dd',  
Figure 3: GPano properties: spherical (**left**) and cylindrical (**right**).  
and the default label is JPEG360 Metadata'. JPEG 360 also uses the APP11 marker for embedding the JPEG 360 Metadata box in JPEG-1 image files.