esa-sceva/satcom-chunk-collection huggingface dataset:
# Transformative Technology for FLASH Radiation Therapy
## FLASH Studies with Laser-Driven Particle Sources Developed at Lawrence Berkeley National Laboratory
### Meeting the Criteria for FLASH-RT  
To ensure that both the periphery and exit dose rate were above the healthy-tissue-sparing threshold to minimize toxicity, we performed calculations assuming a minimum of four separate sources placed symmetrically around a water phantom volume approximating the average human torso's 16 cm radius [21; 129]. Each source provides 25 Gy/s at 1 m using a total beam current of 25 A; details are provided elsewhere [121]. This configuration achieved 50% beyond the required average healthy-tissue-sparing dose rate, or about 60 Gy/s throughout most of the volume. By contrast, a single source at the same level achieved the healthy-tissue-sparing dose rate >40 Gy/s nearest the source, but about 25% of the volume is below that dose rate, potentially inducing toxicity. Noting that the total beam current for LIAs typically exceeds 1 kA, the current can be used as a free parameter for an increased dose rate.  
Figure 11: Concept FLASH-RT system using a linear induction accelerator (LIA) providing four or more lines of sight. LIA is on axis with the patient. Blue components are the magnetic focusing elements that direct the electron beam to the patient. The active accelerator is 3.2 m. With the returning drift section, the overall system length is 3.5 m less the patient couch [121]. ([PERSON], et al., Sci. Rep. 2021, 11, 17104, [[https://doi.org/10.3389/fonc.2019.01563](https://doi.org/10.3389/fonc.2019.01563)]([https://doi.org/10.3389/fonc.2019.01563](https://doi.org/10.3389/fonc.2019.01563)) (accessed on 9 September 2022), reproduced with permission under the terms of the Creative Commons Attribution License (CC BY), [[http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)]([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/))) (accessed on 9 September 2022).  
The model assumed a single LIA to accelerate separate beamlets in an approximately 14 cm-diameter beam pipe. While four beams are shown, eight or more can be easily implemented in an actual system. Beam transport is managed through the accelerator with solenoid coils and integrated steering similar to the FXR geometry [125]. The added steering capability allows generating oblique rays for a closer approximation of multibeam conformal therapy. Based on the model, at 16 MeV, the system would be approximately 3.2 m long (Figure 11), delivering a uniform average dose rate of 60 Gy/s at a beam current of 25 A at 10 kHz PRF.
# Allocating Small Transporters to Large Jobs
## 7 Experiments
### Simulation Environment  
The simulations are done on a ThinkPad T490s with a processor Intel Core i7-8665U with 1.90 GHz. Algorithms are implemented under Windows 10 in C#, and we use the open-source library _Google-OR-Tools_ to solve mixed integer programs.  
For the sake of simplicity, we restrict our simulations to a limited case study. We evaluate the performance of the algorithms on 100 feasible instances with a limited transporter fleet, using the following scenarios:  
* 4 jobs, 20 transporters;
* 5 jobs, 25 transporters;
* 6 jobs, 30 transporters;
* 7 jobs, 35 transporters.  
We randomly generate 100 problem instances for each scenario. The number of jobs, number of transporters, as well as the job return times and the transporter filling times are all integers following an uniform distribution. The return time of every job is between 5 and 15, and the filling time of every transporter is between 1 and 5. Therefore, every job will need on average \(1+\lceil\frac{5+15}{1+5}\rceil=5\) transporters. Every job has the same saturated processing time of 180.  
We are interested in three criteria:  
1. The average computation time of the algorithm.
2. The number of infeasible instances, where the algorithm did not find an assignment.
3. The number of assigned transporters. Whenever the model is infeasible, we set the number of assigned transporters equal to the total number of transporters. Thus, we choose for this criterion to not penalize infeasible instances compared to a feasible assignment effectively using every transporter at our disposal.  
Each of these criteria is sought to be minimized: a good performing algorithm must have low values for these criteria. Note that for saturated algorithms, the completion time of every job is fixed; hence, it does not need to be evaluated.
# Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
## 3 LRI Ranging Phase
### Accuracy of Approximations  
In order to assess the accuracy of the formulas given in the two previous subsections, we have used an analytical model for the optical frequency \(\
u_{\text{R}}\) and the true inter-satellite distance, computed the optical phase \(\Phi_{\text{R}}\) and, retrieved from that, a simulated range \(\rho_{\text{TWR}}\). The models and numerical values are given in Appendix A and emulate conditions present in GRACE-FO. Based on Figure 2, one can state that the simplistic formula without frequency correction term (Equation (21)) yields a linearly drifting error of approx. \(68\,\mathrm{\SIUnitSymbolMicro m}\) per day (dashed red trace) if the laser frequency is linearly drifting by \(87\,\mathrm{kHz}\) per day (\(\
u_{d}/\
u_{0}=3.6\cdot 10^{-15}\,\mathrm{1/sec}\) with \(\
u_{0}=282\,\mathrm{THz}\)). An oscillating frequency with a fractional peak amplitude of \(\
u_{1}/\
u_{0}=4\cdot 10^{-12}\), which is approximately the modulation amplitudein the laser (and microwave) frequency from the proper time, yields errors in the order of \(1\,\mathrm{\SIUnitSymbolMicro m}\) at the \(1/\mathrm{rev}\) oscillation frequency (solid red trace).  
The \(\rho_{\mathrm{TWR,2 approx}}\) expression has no error when the frequency \(\
u_{\mathrm{R}}\) is drifting (dashed blue trace not visible, since zero is not present in a log-scale plot), and slightly lower errors than \(\rho_{\mathrm{TWR,1 corr}}\) when \(\
u_{\mathrm{R}}\) is oscillating (cf. solid blue vs. solid green trace). However, the accuracy of the \(\rho_{\mathrm{TWR,1 corr}}\) is already sufficient for GRACE-FO like applications, since the error is at the picometer level for a one-day-long time-series in the drifting and oscillating case.  
The error for \(\rho_{\mathrm{TWR,2 exact}}\) from Equation (28) cannot be shown in the log-scale figure because the equation is exact with zero error. The exact solution is our recommended way to transform the phase to range.
## 3 Results and Discussion
### Refined Analysis of Spatial Characteristics  
Beyond the rather qualitative description of reflection gratings on the basis of corresponding sectional gratings, imaging in sectional view allows refined analysis of spatial hologram characteristics as well. The parameter of interest is the reflection grating period, which ranges between 0.5 \(\upmu\)m and 130 nm, depending on the recording geometry (see Equation (1)). However, this is out of the range of light-microscopic resolution. Nevertheless, imaging in sectional view offers a possibility to determine \(\Lambda\) without the need to apply more complex methods. We propose an appropriate approach, which takes advantage of the fact, that the focus plane determines the sectional plane, as illustrated in Figure 2. This results, on the one hand, in a specific side effect of imaging in sectional view, according to which the stripes are moving along the sectional-grating vector if the focus plane is altered or adjusted [7]. On the other hand, a gradual tilt of the sample, which entails tuning of the slant angle \(\varphi\), results in an alternation of the sectional grating period \(\Lambda_{\mathrm{s}}\) according to Equation (2). In case of continuous tuning across the angular range, the sectional grating period is expected to describe the curve \(\Lambda_{\mathrm{s}}(\varphi)\), shown in Figure 5. Hereby \(\Lambda_{\mathrm{s}}\) converges to infinity if \(\varphi\) approaches zero.  
To determine the grating period of the corresponding reflection grating, the symmetry of the \(\Lambda_{\mathrm{s}}(\varphi)\)-curve can be utilized. Regardless of the actual tilt of the reflection grating in the polymer sample, the axis of symmetry, _i.e._, the case \(\varphi=0\) can be located by means of sampling the \(\Lambda_{\mathrm{s}}(\varphi)\)-curve for \(\varphi\to 0^{+}\) and \(\varphi\to 0^{-}\). Having located the symmetry axis, the absolute position of the grating is known, \(\varphi\) can be quantified and \(\Lambda\) can be calculated from Equation (2).  
Figure 4: Microscopic images (false-color display) of transmission grating (TG) and reflection grating (RG) without secondary gratings. Grating periods are \(\Lambda=7\)\(\upmu\)m (TG) and \(\Lambda_{\mathrm{s}}=32\)\(\upmu\)m (RG), respectively.
## 2 Spaceflight Environment and Ocular Risks
### Corneal Bullae Observations in Mice  
Animal studies also provide valuable insights into how microgravity impacts the cornea. The development of corneal bullae--fluid-filled blisters on the corneal surface--was observed in mice exposed to spaceflight conditions, highlighting the impact of microgravity on corneal integrity [21]. These bullae are believed to result from microgravity-induced oxidative stress and fluid shifts, which compromise corneal structure.  
Although corneal bullae have not been reported in human astronauts, the potential for similar effects exists, particularly during extended missions. A novel 3D model using corneal keratocytes embedded in bioprinted matrices revealed cellular responses to microgravity-induced oxidative stress, mirroring observations of corneal bullae in mouse studies [25]. The impact on the cornea's refractive properties underscores the importance of research into protective measures for ocular health during spaceflight [21; 26].
## 2 Material and Methods  
### Study Area  
The FPENC, where farming activities of crop and livestock production co-exist, is located in the semi-arid area of Northern China (\(103^{\circ}15^{\prime}\)-\(124^{\circ}37^{\prime}\) E, \(34^{\circ}48^{\prime}\)-\(48^{\circ}32^{\prime}\) N) (Figure 1). This region covers 9 provinces (autonomous regions), including Inner Mongolia, Heilongjiang, Jilin, Liaoning, Hebei, Shanxi, Shaanxi, Gansu, and Ningxia, encompassing 154 counties (banners, cities) with a total area of approximately 63.36 \(\times\) 10\({}^{4}\) km\({}^{2}\), equivalent to 6.6% of China's total land area and a population of about 70 million. The altitude increases from northeast to southwest, with the lowest altitude less than 200 m and the highest being close to 4500 m, and most areas are situated at elevations above 1000 m. The region is characterized by a typical temperate semi-arid continental monsoon climate, with the following main features: the average annual temperature ranges from 0 to 8 \({}^{\circ}\)C, and annual precipitation between 250 and 500 mm, centered from around 400 to 450 mm. Annual precipitation variability is reported to be 15% and 30%. Therefore, agro-meteorological factors were used to define the FPENC's boundaries in our study. We delineated the FPENC using 300 mm and 450 mm rainfall contours, incorporating county administrative divisions [34]. Over 70% of annual rainfall occurs between June and September (summer months often experience heavy rainstroms, while spring droughs are frequent). A \(\geq\)10 \({}^{\circ}\)C accumulated temperatures is 2000-3200 \({}^{\circ}\)C per day, sufficient for one crop per year (e.g., corn, potatoes). Winter extreme temperature lows of \(-\)20 to 30 \({}^{\circ}\)C (central Inner Mongolia can drop below \(-\)30 \({}^{\circ}\)C). The frost-free period was 120-180 days (longer in the east, shorter in the west). The aridity index (annual potential evaporation/precipitation) is 1.5-3.0. Annual evapotranspiration is 1800-2500 m, which is far exceeding precipitation, exacerbating drought risks. The soil type is mainly chestnut soil and brown soil, and the landform of the area is complex and diverse, with the Inner Mongolia Plateau as the main body, where grasslands, mountains, sandy lands, rivers, and lakes coexist. The main vegetation types are grasslands (GRAs) and cropland (CRO), which accounted for 74.6% and 22.1%, respectively. Additionally, savannas (SAVs), woody savannas (WSAs), closed shrulands (CSHs), deciduous broadleaf forests (DBFs), mixed forests (MFs), evergreen needleleaf forests (ENFs), deciduous needleleaf forests (DNFs), and open shrulands (OSHs) together account for 3.3% (Figure 1).
## 4 Antenna Array  
The proposed design is extended to build a linear antenna array of five elements. The electrical performance of such configuration plays a key role in the operation of the antenna system, since it shapes the antenna radiation pattern in elevation by means of beamforming and amplitude tapering. Thus, this design stage not only allows for studying the feasibility of the proposed solution, but it also paves the way for the implementation of the final planar array of \(5\times 8\) elements.  
According to the airborne SAR sensor requirements, a side-looking configuration of the antenna radiation pattern in elevation is desired. Due to the strong power echoes of the reflected signals in nadir, the projection of the aircraft flight path, a minimum of the antenna pattern in such angular direction, is required. In addition, a sidelobe level better than 20 dB is mandatory.  
Since SAR polarimetry applications make use of the polarization diversity in order to exploit the information of the scattered signals, the cross-polarization level of the antenna determines an important parameter of the system performance and its reduction becomes a relevant design aspect. Some examples to improve the cross-polarization by means of different rotation schemes are found in [19; 20; 21; 22]. However, these techniques are based on the composition of planar arrays out of \(2\times 2\) subarrays and using the image theory arrangement. In this work, a novel approach of a sequential 90\({}^{\circ}\) rotation of the antenna elements in linear array applications is presented.  
Next, three array configurations of five elements are built and measured. Each geometrical arrangement corresponds to a different rotation topology of the antenna elements: non-rotation, sequential rotation of 180\({}^{\circ}\) and 90\({}^{\circ}\). The performance of each configuration, in terms of co-polar and cross-polarization level, will be compared and discussed. An accurate analysis of the cross-polarization performance of the array leads to consider scenarios with antenna beam steering. Therefore, the scope of this study covers two cases for each configuration: broadside radiation and beamforming along with amplitude tapering, which can be considered as a potential SAR application.  
Figure 5: Simulated and measured radiation pattern of the single element @\(f_{0}=1.325\) GHz.
## 5 Posterior Conditional Probabilities for Gibbs Sampling
### Conditional Probability for a Bernoulli Distribution Parameter (\(\pi_{k}^{c}\))  
The model parameter \(\pi_{k}^{c}\) is associated with the \(k\)th atoms of the dictionary and the classifier to be used as prior for the Bernoulli distribution for the selection of these atoms for the representation of training examples and the corresponding labels belonging to class \(c\). The class-specific learning of the parameter induces discrimination in the dictionary and the classifier for associating labels with the dictionary and the classifier atoms. The factorized and standard forms of the conditional distribution of this parameter are  
\[\begin{split}& p(\pi_{k}^{c}|-)\propto\prod_{i\in I_{c}}\text{ Bernoulli}(z_{ik}^{c}|\pi_{k}^{c})\text{Beta}\bigg{(}\pi_{k}^{c}|\frac{a_{0}}{K}, \frac{b_{0}(K-1)}{K}\bigg{)},\text{ or}\\ & p(\pi_{k}^{c}|-)\propto\text{Beta}\bigg{(}\frac{a_{0}}{K}+ \sum_{i=1}^{|I_{c}|}z_{ik}^{c},\frac{b_{0}(K-1)}{K}+|I_{c}|-\sum_{i=1}^{|I_{c}| }z_{ik}^{c}\bigg{)}\end{split} \tag{16}\]  
As per analytical inference by [24], a dictionary atom \(\mathbf{\phi}_{k}\) can be pruned at each iteration of [PERSON] sampling according to whether \(\sum\limits_{c=1}^{C}\pi_{k}^{c}\to 0\) or not. Likewise, the classifier atom \(\mathbf{b}_{k}\) is also pruned.  
Conditional Distribution for Precision Parameter for a Representation Weight (\(\lambda_{s}^{c}\))  
A separate precision parameter for each class is learned to induce class-wise discrimination in the representation weights. The factorized form of the distribution is  
\[p(\lambda_{s}^{c}|-)\propto\prod_{i\in I_{c}}\mathcal{N}(\mathbf{s}_{i}^{c}| \mathbf{0},1/\lambda_{s}^{c}\mathbf{I}_{K})\text{Gam}(\lambda_{s}^{c}|c_{0},d_{0}) \tag{17}\]  
The expression is simplified as follows:  
\[\lambda_{s}^{c}\sim\text{Gam}\Bigg{(}\frac{|I_{c}|K}{2}+c_{0},\frac{1}{2}\sum _{i=1}^{|I_{c}|}\|s_{i}^{c}\|_{2}^{2}+d_{0}\Bigg{)} \tag{18}\]
# Research on the Impact of BDS-2/3 Receiver ISB on LEO Satellite POD
## 4 Results and Analysis
### GPS+BDS-2/3 Combined Orbit Solution
The orbit RMS differences of the GPS and BDS-3 combined POD solutions and GPS-based POD are smaller than that of GPS+BDS-based and GPS+BDS-2-based POD and are, respectively, 2.57 cm, 0.92 cm, 1.98 cm and 1.24 cm in the 3D, radial, along and cross directions. It is also important to note that the STD values of all the POD orbit RMS differences between the GPS+BDS-3-based orbits and GPS-based orbits are the smallest in the 3D, radial, along and cross directions.
## 7 Conclusions  
In this paper, we presented a comprehensive investigation of the \(\alpha\)-\(\mu\)/IGA composite fading model and applied this model to the MRC system. First, we studied the fundamental statistics of the univariate and bivariate \(\alpha\)-\(\mu\)/IGA fading models including the PDF, CDF, and MGF, and proposed a mixture \(\alpha\)-\(\mu\) model to approximate the \(\alpha\)-\(\mu\)/IGA distribution. Then, as per the derived statistical expressions, the exact and approximated expressions of the statistical properties of the sum of \(\alpha\)-\(\mu\)/IGA variates by utilizing the multivariate Fox's H-functions were obtained under the independent and the correlated fading environments. Third, some performance metrics of interest comprising the OP, ABEP/ ASEP, the average channel capacity, and the effective capacity were investigated in detail for the single-link and MRC systems, as well as their corresponding exact and approximated expressions, were derived in different channels conditions. Some asymptotic performance behaviors over \(\alpha\)-\(\mu\)/IGA fading were also provided in the high SNR regions. Finally, numerical analysis and Monte Carlo simulations were performed to prove the validity of the theoretical analysis under the different channel and system parameters. Our results will be helpful to improve the system reliability in the design and deployment of future communication applications, including 5G/6G wireless cellular systems, wearable communications, and vehicular wireless networks. Due to the simple and flexible form of the mixed \(\alpha\)-\(\mu\) model, we will apply it to other channel models and the complicated communication systems in future works, such as mmWave communications and Intelligent Reflecting Surface (IRS).  
Conceptualization, W.C. and G.W.; methodology, W.C. and T.M.; software, W.C. and T.M.; validation, W.C., T.M. and G.W.; investigation, T.M.; writing--original draft preparation, T.M. and W.C.; writing--review and editing, W.C. and G.W.; supervision, W.C.; project administration, W.C.; funding acquisition, G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Project Fund of the Key Laboratory of Mine Disaster Prevention and Control under grant number SMDPC202102.  
Not applicable.  
Figure 8: Effective capacity of various communication systems as a function of the average SNR (\(\overline{\gamma}\)) over different communication conditions with \(n=5\) and \(\mu=2\).  
**Acknowledgments:** The authors appreciate and acknowledge the financial support from the Open Project Fund of the Key Laboratory of Mine Disaster Prevention and Control (Grant No. SMDPC202102).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Survey Solutions for 3D Acquisition and Representation of Artificial and Natural Caves
## 2 Material and Methods
### D Survey of the Artificial Tunnel Segment  
The survey of the tunnel was done considering three approaches based on the use of active or passive instruments with different costs and characteristics. The first considered system is a terrestrial laser scanner (TLS). It represents the most accurate and expensive solution for the acquisition of a 3D model. Despite these systems allow to obtain products with high resolutions, their size and weight make them difficult to transport in many caves, where the accessibility and movement within them are limited. The second category of the considered instrument is portable LiDAR (PLS). PLS are characterized by an active sensor that works like the TLS, but the system has been designed to be more portable and easier to use. The limited cost and dimension make this category of instruments more adapted to the acquisition of environments characterized by problems of accessibility. The third considered solution is based on the use of a commercial photo camera and Structure from Motion (SfM) for the generation of a 3D point cloud. This is the cheapest solution that adopts a passive sensor. Table 1 shows the instruments exploited for this research work.  
Figure 2: (**a**) Gazzano cave cross-section from the traditional survey; (**b**) map of the cave with the polygonal survey; inset) Gazzano location map.  
We decide to use an artificial tunnel to test the considered methodologies because in several natural caves the transport and use of big instruments with heavy batteries can be very complex or even impossible. For this reason, we identified a segment of a tunnel that has favorable characteristics as the section that allows the use of cars for carrying instruments and its regular geometry that guarantees an easy comparison between results obtained by different instruments. The artificial test site also allows the temporary installation of a series of targets on the walls of the tunnel that has been fundamental for the alignment and comparison of different 3D models.
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
###### Abstract  
Cellulase and xylanase are in high demand for application in several industrial processes, consequently necessitating the bioprospecting and manipulation of microbes for novel and greater enzyme productivity. This study reports on the optimal conditions for cellulase and xylanase production by a marine bacterial isolate from Nahoon beach sediment, via the classical process of one variable per time. Furthermore, the inducing effect of mono- and polysaccharides on enzyme production was investigated. The 16S rDNA gene sequence analysis clearly assigned the isolate to the genus _Streptomyces_, and was deposited at the GenBank under the accession number KU171373. Cellulase and xylanase production was optimal at the following conditions: pH 6 and 8, incubation temperature of 40 and 35 \({}^{\circ}\)C, and agitation speed of 100 and 150 rpm, respectively. Under optimum conditions, 0.26 U/mL and 18.54 U/mL activities were attained at 60 and 48 h with specific productivity of 205 and 7417 U/g for cellulase and xylanase, respectively. Xylanase production was induced by the entire mono- and polysaccharides tested, while cellulase production was induced by some. The results from this study signify the resourcefulness of the _Streptomyces_ strain for production of cellulase and xylanase of industrial importance.  
cellularase; xylanase; optimization; saccharides; _Streptomyces_; activity; production +
Footnote †: journal: Applied and Environmental Microbiology Research Group, Department of Biochemistry and Microbiology, University of Fort Hare, Alice 5700, South Africa; [EMAIL_ADDRESS] (U.U.N.); [EMAIL_ADDRESS] (A.I.O.)