esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design of Mobile Robot Person-Following System
### Human Detection and Tracking  
Many algorithms for the localization of objects in the scene have been developed in recent years. These algorithms, which include Faster R-CNN (regions with CNN) [26], the SSD [22], and YOLO [27], have high detection accuracy and efficiency in using 2D bounding boxes to detect objects. The algorithms predict the most likely class for each region, generate bounding boxes around possible objects, and discard regions with low probability scores. However, they require pre-trained datasets. The SSD achieves a better balance between speed and accuracy compared to its counterparts. It is faster than Faster R-CNN and more accurate than YOLO [22]. We implemented the comparison in terms of the speed for the three algorithms on our workstation using the CPU. Faster R-CNN, YOLO, and the SSD achieved real-time performance at 1.40, 2.50, and 25.03 fps using the CUP, respectively. The performance may change if a GPU is available.  
With our objective of obtaining a high frame rate, we ultimately adopted the SSD detector to distinguish people from other objects using the MobileNets database [7]. The details of the SSD are beyond the scope of this paper, so we only briefly describe the SSD here. The SSD is a detector based on a fully convolutional neural network consisting of different layers that produce feature maps with multiple scales and aspect ratios. A non-maximum suppression method is then implemented to detect objects of different sizes using  
Figure 3: Mobile robot mounted with the necessary sensors.  
bounding boxes and to generate the confidence scores as an output, as shown in Figure 2a. The SSD requires only a sequence of \(300\times 300\) 2D color images as the input to a single feed-forward process. The vertices of the bounding boxes and the centers of these boxes relative to the whole image resolution are given in the formats of \((u_{max},\,u_{min},\,v_{max},\,v_{min})\) and \(c_{u}=((u_{max}-u_{min})/2)\), \(c_{v}=((v_{max}-v_{min})/2)\), respectively.
# Enhancing Robustness of Per-Packet Load-Balancing for Fat-Tree
## 4 Simulation Results
### Long-lived flows in an 8-ary fat-tree  
To validate the functionality of LBSP for long-lived flows, we chose to use a similar test scenario to the one used in [6] considering the limit of our simulation environment. We generated a small number of 100 MB flows from hosts on ToR switches in two specific pods transmitting to hosts on another specific ToR switch in a different pod. We simulated four flows with size 100 MB repeatedly for about 20 s. The source and destination hosts were selected as depicted in Figure 3. For the source-destination pairs <S1, D1>, two flows were generated. For each of the other source destination pairs <S2, D2> and <S3, D3>, one flow was generated. This scenario is not advantageous to LBSP because <S1,D1> and <S2,D2> were selected so that, under the link failure, the three flows from S1 and S3 take the same paths. Figure 6b shows the 99.9 th percentile of flow completion times for the original packet scatter scheme and LBSP. When there is no failure in the network, both schemes perform almost equal. When a failure occurs at a link between a ToR switch and an aggregation switch, as shown in Figure 3, LBSP performs better than the original packet scatter scheme despite the disadvantageous scenario. For the original packet scatter scheme, the flow from S3 suffers the greatest degradation of performance. The reason is that the packets from S3 arrive at D3 out of order due to different queuing delays. In LBSP, however, the flows seldom experience packet reordering. When an aggregation switch in the source pod of the three flows fails, the gap between two schemes becomes greater than that shown in Figure 6b.
## 2 Data Details
### Independent Variables  
The independent variables used in this paper are consistent with what other researchers have suggested and used in the present context [10; 21; 22]. The _continuous independent variables_ (on the natural logarithm scale) include:  
* Electricity demand (or load);
* Natural gas prices;
* Baseload nuclear power generation;
* Wind generation;
* Solar generation.  
Coal prices are excluded, since coal is an inframarginal resource. That is, coal plants have operating costs which are typically lower than the market clearing price; hence, operating coal plants are insensitive to how market prices change. Natural gas generation is also excluded, since it is well-known that this data is highly collinear with load levels.  
The dataset starts on 1 January 2015 and ends on 31 December 2018, and the data may be accessed at [[http://www.ercot.com/mktinfo](http://www.ercot.com/mktinfo)]([http://www.ercot.com/mktinfo](http://www.ercot.com/mktinfo)). (URL accessed on 12 December 2022.)  
Natural gas prices for Henry Hub (located in Erath, Louisiana) were obtained from the DOE/EIA.1. As [PERSON] et al. (2021) note, Henry Hub prices replace local natural gas prices, since they bypass any endogeneity issue that local natural gas prices might induce [10]. Moreover, Henry Hub prices are highly correlated with local natural gas prices (r > 0.95). Daily natural gas prices are assumed constant across all 15 min intervals within each day.  
Following [PERSON] et al. (2020), data for 2019 and early 2020 are omitted [23]. In 2019, the Public Utility Commission of Texas required the ERCOT to increase revenue to generators by shifting the Operating Reserves Demand Curve. [PERSON] et al. (2021) noted that \"the subsequent impact on market prices is very difficult to model\" [10].  
We also cannot incorporate the influence, if any, of climate change, on renewable energy and its subsequent impact on electricity pricing in Texas. We do not have the data, nor the modeling capabilities, to conduct such an analysis.
## 2 Materials and Methods
### Homogenate-Antisolvent Precipitation Procedure  
The schematic diagram of the HAP apparatus is presented in Figure 2. Briefly, the solution of raw spiramycin dissolved in DMSO was filtrated through a membrane (0.45 \(\upmu\)m) for removing the possible particulate impurities. Then all of the spiramycin solution (5-60 mg/mL) prepared was pumped into an aqueous solution (antisolvent) by an injection pump (P230p, Dalian Elite Analytical Instruments Co., Ltd., Dalian, China) at a certain flow rate (10-70 mL/h), with a volume ratio of antisolvent to solvent of 1-11 mL/mL. After stirring the solution using a homogenate rotor at a certain speed (100-15,000 rpm), and bathing under a certain temperature (\(-12\) to 25 \({}^{\circ}\)C) for a certain time (2-120 min), a milk-like suspension was formed. Then the suspension was centrifuged at 5000 rpm for 5 min and then washed by water, the process of which was repeated three times in order to remove the DMSO organic solvent completely. The precipitate was lyophilized at \(-50\) \({}^{\circ}\)C for 48 h, and the spiramycin microparticles were obtained.  
Many parameters have affect the mean particle size (MPS) of spiramycin microparticles, including precipitation temperature, precipitation time, spiramycin concentration, drip  
Figure 2: Schematic drawing of homogenate-antisolvent precipitation apparatus to prepare the micronized spiramycin.  
adding into the antisolvent, volume ratio of the antisolvent to the solvent, shear rate, type of solvent and antisolvent, and so on.  
The parameter optimization process for spiramycin micromization was realized using RSM experiment which performed with three3 factors and three levels. Based on the results of preliminary experiments and single factor experiments, spiramycin concentration (\(X_{1}\)), the volume ratio of antisolvent to solvent (\(X_{2}\)), and the dripping speed of the solvent adding into the antisolvent (\(X_{3}\)) were selected and analyzed using a BBD with Design Expert 8.0 software (Stat-Ease, Minneapolis, MN, USA). The boundaries for the three factors were 10-30 mg/mL for \(X_{1}\), 3-7 mL/mL for \(X_{2}\), and 40-60 mL/h for \(X_{3}\). We used the same solvent (DMSO) and antisolvent (pure water), precipitation temperature of 4.6 \({}^{\circ}\)C, precipitation time of 10 min, and shear rate of 5000 rpm for all HAP experiments. The 17 combinations of different factors for the BBD are presented in Table 1, with the MPS of micronized spiramycin as the response. Each experiment was performed in triplicate. The full second-order polynomial equation, listed as follows, was used as the RSM model, which used for regression analysis:  
\[Y=\beta_{0}+\sum_{i=1}^{3}\beta_{i}X_{i}+\sum_{i=1}^{3}\beta_{ii}X_{i}^{2}+\sum _{i=1}^{2}\sum_{j=i+1}^{3}\beta_{ij}X_{i}X_{j}, \tag{1}\]  
where \(Y\) is the predicted response; \(\beta_{0}\), \(\beta_{i}\), \(\beta_{ii}\), and \(\beta_{ij}\) are the regression coefficients of variables for intercept, linear, quadratic, and interaction terms, respectively; and \(X_{i}\) and \(X_{j}\) are the coded independent variables.
## 4 Materials and Methods  
### Chronic Leg Wound Bacterial Strains Collection  
The tested strains were isolated at the Dermatovenerology Department of the Central Military University Emergency Hospital \"Carol Davila\" in Bucharest, between October 2014 and September 2015, from 103 hospitalized patients with skin ulcers (secondary to chronic venous insufficiency, arterial insufficiency, type 1 and 2 diabetes, necrotizing vasculitis, Kaposi's disease, squamous-cell carcinoma, bone necrosis). The study protocol complied with the ethical prerogatives of the 1975 Helsinki Declaration and the standards of Good Clinical Practice (GCP). The Dermatovenerology Department obtained the approval of the Ethics Commission of the Central Military University Emergency Hospital \"Carol Davila\" from Bucharest. The strains were isolated from wound secretion. Harvesting was performed before any antibiotic therapy or within the first 24 h using exudate swabs. The cotton swab was collected from the most representative area of the ulcer, either the purulent or exudative area. In the absence of representative areas, it was harvested from the edge of the ulcer. One sample was taken from each ulcer if there were multiple ulcers. Two exudate swabs were used for this study: an exudate swab was used to make a direct smear of the pathological product \"at the patient's bed\", while the second swab was used to transfer the pathological product (pp) in the storage and transport environment. Containers with sterile culture medium (thioglycolate broth) were used to store and transport the pathological product. To obtain isolated colonies, sowing was practiced in the \"open pentagon\" of the solid-blood agar medium distributed in a Petri dish by depleting the inoculum with the bacteriological loop. After sowing, the Petri dish was incubated at 35-37 \({}^{\circ}\)C, for 18-24 h. The representative isolated colonies were further purified and identified by conventional biochemical tests, Vitek-2 and MALDI-TOF.  
Qualitative Screening of the Spectrum of Sensitivity of Different Microbial Strains to Antiseptic Substances by the Adapted Version of the Diffusion Method  
With the help of sterile forceps, sterilized filter-paper disks were distributed in solid-blood agar medium distributed in Petri dishes previously seeded with the bacterial inoculum of the test strain. Using the micropipette, 10 \(\mu\)L of pharmaceutical antiseptic solution of silver nitrate 1% diluted 1/6000 in distilled water were distributed on the filter-paper disk. The plates were left at room temperature for 20-30 min to ensure the diffusion of the substance. Then the plates were incubated for 16-18 h at 37 \({}^{\circ}\)C, with the lid down. The reading of the results involved observing and measuring the area of inhibition of microbial growth around the discs impregnated with the compound.
## 2 Applications and Background of Uwsn  
### Applications of Uwsn  
Now a days underwater communication has become very important and is being employed in many practical applications like pollution monitoring, seismic monitoring, remote control in offshore oil industry, monitoring marine life, collect scientific data from different sea level, detecting natural disasters and warn beforehand, discovering natural resources, monitoring underwater pipelines, national security monitoring, and so forth.
###### Abstract  
Vegetation slope protection plays an important role in improving the slope stability and protecting the environment. In this study, the mechanical properties of root-soil composites in different growth periods and their effects on slope stability were investigated. First, the shear strength of undisturbed root-soil composites associated with _Cymodon dactylan_ (C.d), _Magnolia multiiflora_ (M.m) and grass and shrub mixture (G.s) were measured by large direct shear tests. Then, the effects of plant reinforcement in different growth periods on slope stability were analyzed using ABAQUS. The results show that the shear strength of the root-soil composites were significantly higher than that of the unreinforced soil. The root-soil composites met the Mohr-Coulomb criterion. The shear strength of the three root-soil composites increased first and then decreased during the vegetation growth period. The calculation results show that the factor of safety (FS) of the bare slope was 1.482. The FS values of the C.d, M.m, and G.s slopes were 1.601, 1.658 and 1.715, which increased by 8%, 11.9% and 15.7% compared to the bare soil slopes. Therefore, vegetation could significantly improve slope stability, especially the grass-shrub mixture model. This could provide a rational basis for designing and constructing plant slope protection.  
vegetation slope protection; root-soil composite; large direct shear test; factor of safety +
Footnote †: journal: Applied sciences
# A Vision of 6 th Generation of Fixed Networks (F6G): Challenges and Proposed Directions
## 4 Optical Transmission: Key Challenges and Proposed Directions
### Capacity Increase Employing a Greater Number of Channels
#### 4.3.4 Combining the Benefits of UWB and SDM  
In this section, we demonstrate how a multi-PB/s TRx block can be formed using UWB- and SDM-based systems along with the use of oDAC as it was described in Section 3. The oDAC, as a building block (BB), can be aggregated using several forms of multiplexing, namely spectral (WDM), spatial (SDM), polarization, and quadrature multiplexing, as depicted in Figure 29. The oDAC will be the key component of the transceivers, which will enable the aggregation of higher-capacity lanes in terms of spectral efficiency. TRxs incorporating single oDACs may suffice to serve low-to-moderate capacity 6G photonics interconnecting fiber links without additional degradation using DD. To achieve higher data rates for the highest UWB capacity, spectral and/or spatial multiplexing can be applied to the outputs of an array of unipolar or bipolar PAM oDAC outputs.  
With the programmable oDAC, we can obtain a key building block to spur higher-level flexible multiplexing of lanes, combining UWB WDM and SDM to scale the overall transmission capacity up to Pb/s, as depicted in Figure 29. UWB/SDM optical links are then aggregated from per-wavelength BBs of up to 2 Tb/s. The envisioned optical link using compact oDAC arrays would support \(L\) spectral lanes and \(N\) spatial lanes over a single SDM fiber. Having \(N\geq 20\) and \(L\approx 100\) (over the S, C, and/or L bands), thus  
\begin{table}
\begin{tabular}{c c c} \hline  & **Ultra-Wideband (UWB)** & **Space Division Multiplexing Based on Fiber Bundles (Bu-MF)** \\ \hline Cost & Significantly lower costs than SDM in cases of limited fiber availability & Lower component associated costs when mainly C-band is exploited \\ \hline Connectivity and reach & Increased connectivity and flexibility as each band can be exploited for different transmission length & Higher transparent length as C-band shows the best physical layer performance and the fibers/components have optimized performance \\ \hline Upgradeability & Very high considering a pay as you grow policy & Very high considering a pay as you grow policy \\ \hline Diversity & Diversity in data rate per channel and reach can be attained by tailoring each band to specific requests (e.g., lower bands to shorter links and higher bands to longer ones) & All channels are considered “premium”, so diversification in terms of data rate per channel and reach cannot be considered \\ \hline Commercialization & Mainly C and L bands & Well established commercially available technology in C-band \\ \hline Spatially integrated network elements & Only when UWB amplifiers are used, e.g., Raman, SOA > 100 nm, C + L EDFA & Possible, e.g., shared amplification pumps among the various fibers and lasers at the transceiver sides \\ \hline Best solution for & terrestrial networks & access/DCI, submarine networks \\ \hline \end{tabular}
\end{table}
Table 6: Qualitative Comparison of UWB and C-band SDM systems.
# Investigation of Guidewire Deformation in Blood Vessels Based on an SQP Algorithm
## Materials and Methods  
### Modeling  
For a more realistic simulation of the deformation of a guidewire in a blood vessel, proper modeling of the guidewire is very important. Elastic object simulation based on a physical model has always been a key research topic in the field of computer graphics. In the past few decades, simulation methods for one-dimensional, two-dimensional, and three-dimensional elastic objects have been proposed. One-dimensional elastic objects are also called linear elastic objects [21], and the study of their deformation simulation plays a critical role in many important research fields (such as engineering and microbiology). The simulation of one-dimensional elastic objects is often used for the simulation of the deformation of guidewires in a simulation training system for interventional surgery. At present, the simulation algorithms for linear elastic objects are mostly based on [PERSON]'s three laws, [PERSON]'s law, [PERSON]'s elastic theory, and the principle of minimum energy [22].  
The guidewire shown in Figure 1 was experimentally measured to have a diameter of 0.78 mm, a Young's modulus of 3.56 GPa, and a moment of inertia of 1.816 \(\times\) 10\({}^{-14}\) m\({}^{4}\). In this paper, the guidewire is regarded as a rigid light rod articulated model, and the guidewire is divided into n-section incompressible and inflexible equal-length light rods articulated to each other, and the adjacent light rods can only be relatively deflected at the joint node. As is shown in Figure 2, a plane rectangular coordinate system is established with the starting contact point A as the origin, and B as the end contact point. Of course, there may be other points that are in contact with the vessel wall between A and B. \(\Delta\theta\) represents the relative deflection angle between two adjacent light rods, and \(\theta\) represents the deflection angle of one light rod.  
In this paper, the deformation of the guidewire in the blood vessel is studied according to the principle of minimum energy. The advancement of the guidewire in the blood vessel can be seen as a quasi-static process. Therefore, taking the guidewire as the research object, in order to minimize the elastic potential energy of the guidewire, all the light rods between the contact points of the guidewire need to constantly adjust their pose to optimal states. We will construct a mathematical model based on the principle of minimum energy below, and use a sequential quadratic programming (SQP) algorithm to solve the mathematical model, and transform the deformation problem of the guidewire into a nonlinear programming problem under multi-constraint conditions.  
Figure 2: The discrete model of the guidewire.
# Initial Structure Design and Optimization of an Automotive Remote Head-Up Display
## 2 Basic Theory and Design Objective  
### Design Principle  
The light path diagram of the HUD structure includes five main parts: source image, reflectors M\({}_{1}\) and M\({}_{2}\), windshield, and eyebox. As shown in Figure 1, light is emitted from the source image at a certain divergence angle and enters the human eye after being reflected by reflectors M\({}_{1}\) and M\({}_{2}\) and the windshield. Finally, it converges at a certain distance to form a virtual image, which enables drivers to see driving information and the road at the same time.
## 3 Results and Discussion
### Acute Toxicity Assays
#### 3.3.1 Growth Inhibition
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ionic Liquid** & **M\({}_{\mathbf{W}}\) [g mol\({}^{-1}\)]** & \multicolumn{2}{c}{**Growth inhibition**} & \multicolumn{2}{c}{**Max. PSII quantum yield**} \\  & & _T. obliquus_ & _R. subcapitata_ & _T. obliquus_ & _R. subcapitata_ \\  & & **EC\({}_{50}\)\(\pm\) SD** & **EC\({}_{50}\)\(\pm\) SD** & **EC\({}_{50}\)\(\pm\) SD** & **EC\({}_{50}\)\(\pm\) SD** \\ \hline
[P\({}_{66614}\)][HNA] & 671.03 & 1.76 \(\pm\) 0.17 & 0.47 \(\pm\) 0.01 & 1.81 \(\pm\) 0.03 & 0.52 \(\pm\) 0.05 \\
[P\({}_{1888}\)][HNA] & 572.86 & 2.61 \(\pm\) 0.06 & 0.39 \(\pm\) 0.05 & 1.86 \(\pm\) 0.06 & 0.13 \(\pm\) 0.03 \\
[N\({}_{1888}\)][HNA] & 555.88 & 2.68 \(\pm\) 0.44 & 0.28 \(\pm\) 0.01 & 2.04 \(\pm\) 0.22 & 0.24 \(\pm\) 0.06 \\
[N\({}_{1888}\)][C\({}_{6}\)SAc]\({}^{-}\) & 543.98 & 0.93 \(\pm\) 0.16 & 0.05 \(\pm\) 0.01 & n/a & n/a \\
[P\({}_{1888}\)][C\({}_{6}\)SAc]\({}^{-}\) & 560.95 & 8.96 \(\pm\) 0.43 & 0.04 \(\pm\) 0.01 & n/a & n/a \\
[N\({}_{1888}\)][C\({}^{-}\)] & 404.16 & 0.30 \(\pm\) 0.03 & 0.07 \(\pm\) 0.01 & n/a & n/a \\
[P\({}_{66614}\)][Cl]\({}^{-}\) & 519.31 & 0.39 \(\pm\) 0.02 & 0.10 \(\pm\) 0.01 & n/a & n/a \\ \hline \hline \end{tabular}  
\({}^{*}\) [PERSON] et al. [13]; n/a = not available  
\end{table} 
Table 4: Influence of the ILs on growth and chlorophyll fluorescence after 72 h (EC\({}_{50}\) values \(\pm\) SD) in acute toxicity assays.