esa-sceva/satcom-chunk-collection huggingface dataset:
# Design, Fabrication, and Control Algorithm of Self-Reconfigurable Modular Intelligent Vehicles
## 5 Experimental Validation  
### Hardware and Communication Setup  
The hardware architecture for reconfigurable modular vehicles is shown in Figure 16. The OpenMv monocular camera on the main control vehicle detects the position and attitude according to AprilTag on the submodular vehicle and sends the 6-DOF information of the three modular vehicles to the MCU. Then ESP8266 converts the acquired position information into the speed information required by the four vehicles, and sends it to ESP8266 on each submodular vehicle through the on-chip Wi-Fi module. After receiving the speed information, the ESP8266 module on the submodule vehicle sends it to the ATmega328p single-chip microcomputer through the serial port, and makes the closed-loop control according to the motor speed feedback signal to realize the motion control of each submodular vehicle. Theoretically, through this framework, more submodular vehicles can be added into the system to contribute a more complex self-reconfigurable system.  
Figure 16: Schematic diagram of a hardware architecture for reconfigurable modular vehicles.  
Figure 15: Reconfiguration methodology.  
The DRV8701E motor drive chip of the TI company is used as the motor drive scheme of the modular car, as shown in Figure 17. DRV8701E utilizes an H-bridge to drive a 12-24 V bidirectional brush motor. The DRV8701E chip is also equipped with an operational amplifier to realize current sampling and overcurrent protection. A schematic diagram and a PCB board of the motor drive circuit are shown in Figure 17.  
A GA12-N20 DC motor with an encoder is selected as the driving motor, its driving voltage is 12 V, the speed is 200 rmp, and the rated power is 3.6 W. Detailed parameters can be found in Table 1. The connection mechanism of a motor with a Mecanum wheel is shown in Figure 18.  
Three lithium battery packs connected in series are utilized as the power source, as shown in Figure 19. The voltage of each battery pack is 3.7-4.2 V, the maximum outage current is 3A, and the capacity of each pack is 2300 mAh. A protection board is utilized to balance the charge/discharge current of the three battery packs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Parameter** & **Item** & **Parameter** \\ \hline Voltage & DC 12 V & Rated speed & 200 rpm \\ Rated power & 3.6 W & Reduction ratio & 1:150 \\ Rated torque & 5 N-m & Rated current & 0.3 A \\ Stall torque & 20 N-m & Stall current & 0.3 A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of a DC motor.  
Figure 17: Hardware setup for reconfigurable modular vehicles.  
Figure 18: Connection mechanism of a DC motor with a Mecanum wheel.
## 7 Conclusions  
Using YSDs can effectively improve a bridge's seismic performance at key locations overall. The influence of the yielding strength on the seismic responses of a medium-span cable-stayed bridge is investigated through comprehensive parametric analysis, and reasonable layouts of YSDs are achieved according to two different design objectives by unequal yielding strength analysis and equal yielding strength analysis. Then, a more practical method to quickly determine the effective yielding steel damper parameter is proposed for engineering practice. Conclusions can be drawn as follows:  
1. The increase of the yielding strength can effectively reduce the RD at each location, but its efficiency decreases with the increase of the yielding strength. For current engineering practice, the seismic design capacity of the auxiliary pier can essentially be decreased for cost efficiency.
2. Equal yielding strength analysis is much simpler than unequal yielding strength analysis; however, it may not be the most appropriate result theoretically, but it is effective enough for engineering practices.
3. The practical design method using equal yielding strength analysis can greatly reduce the analysis cases and hence improve the efficiency of seismic analysis.  
It should be noted that the yielding strengths achieved in this study might not work for other cases. However, we provide a practical procedure to quickly achieve the proper yielding strength and layout of steel dampers (not only the shape used in the study but also other shapes) by simply running a few time history analysis cases instead of carrying out comprehensive parametric study, which might cause less theoretically accurate values but is time-efficient in engineering practice.  
Conceptualization, methodology and writing--review and editing, Y.X.; validation, analysis and writing--original draft preparation, Z.Z.; software and investigation, C.C.; investigation and data curation, S.Z.  
This research was funded by National Key Research and Development Plan, China, grant number 2017 YFC1500702 and the National Science Foundation of China, grant number 51878492.  
The authors declare no conflict of interest.
## 3 Results and Discussions
### Incident S Wave
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multirow{2}{*}{\(\theta_{1}\)(c)} & \multicolumn{5}{c}{\(E_{2}\) = 30 GPa, \(\
u_{2}\) = 0.25, \(\rho_{2}\) = 2.4 g/cm\({}^{3}\)} \\ \cline{2-9}  & & **Zero** & **Peak** & **\(f\) for Peak** & **Roll-off** & **1\%** & **\(f\) for 1\%** \\  & & **Frequency Gain** & **Value** & **Value** & **dB/dec** & **Bandwidth** & **Bandwidth** \\ \hline  & 0 & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\  & 15 & 2.90 (3.00) & 3.20 (3.29) & 3718 (3400) & 7.75 (8.26) & 2.93 (3.03) & 748 (723) \\  & 30 & 5.02 (5.20) & 5.55 (5.71) & 3718 (3399) & 7.79 (8.28) & 5.07 (5.25) & 748 (723) \\ _FS_ & 45 & 5.80 (6.00) & 6.40 (6.59) & 3718 (3399) & 7.81 (8.30) & 5.86 (6.07) & 748 (723) \\  & 60 & 5.02 (5.20) & 5.55 (5.71) & 3718 (3399) & 7.80 (8.28) & 5.07 (5.25) & 748 (723) \\  & 75 & 2.90 (3.00) & 3.20 (3.29) & 3718 (3400) & 7.79 (8.26) & 2.93 (3.03) & 747 (723) \\  & 90 & (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\ \hline  & 0 & 5.02 (5.20) & 5.58 (5.74) & 3801 (3476) & 7.19 (7.47) & 5.07 (5.26) & 749 (716) \\  & 15 & 4.35 (4.50) & 4.82 (4.96) & 3766 (3443) & 7.45 (7.81) & 4.39 (4.55) & 748 (719) \\  & 30 & 2.51 (2.60) & 2.76 (2.84) & 3635 (3323) & 8.42 (9.13) & 2.54 (2.63) & 747 (730) \\ _TS_ & 45 & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & 0 (0) \\  & 60 & 2.51 (2.60) & 2.79 (2.87) & 3801 (3476) & 7.17 (7.47) & 2.54 (2.63) & 749 (716) \\  & 75 & 4.35 (4.50) & 4.79 (4.93) & 3670 (3355) & 8.14 (8.77) & 4.39 (4.55) & 748 (727) \\  & 90 & 5.02 (5.20) & 5.51 (5.67) & 3635 (3323) & 8.40 (9.13) & 5.07 (5.25) & 747 (730) \\ \hline \end{tabular} Note: the characteristic values of the empty borehole model are inside the brackets, and the characteristic values of the two-ring model are outside the brackets.  
\end{table} 
Table 5: Characteristic values for incident S wave amplitude spectrum of _FS_ and _TS_ at different \(\theta_{1}\). Figure 8: Influence of the [PERSON]’s ratio of the expansive grout on theoretical amplitude spectrum of gauge combination \(TS\) for the incident S wave. Figure 7: Influence of the [PERSON]’s modulus of the expansive grout on theoretical amplitude spectrum of gauge combination \(TS\) for the incident S wave.
## 2 Materials and Methods
### Satellite-Based Vegetation Dynamic Data  
A total of four satellite-based vegetation products were used to identify the spatiotemporal variability of the QTP's vegetation dynamics. Three of them, including the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Leaf Area Index (LAI) datasets, were collected from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor and are freely available at [[https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)]([https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)) (accessed in 1 October 2022). As a new satellite product to reveal the photosynthesis of vegetation, solar-induced chlorophyll fluorescence (SIF) was used as the fourth candidate dataset to describe the vegetation dynamics of the QTP. The SIF dataset, produced by [PERSON] et al. [42], provides SIF observations with a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) every 4 days. It was constructed by considering surface reflectance data from the MODIS sensor and SIF data from Orbiting Carbon Observatory-2 (OCO-2) using a neural network method and showed a superior capacity to describe gross primary production (GPP) compared to other SIF datasets [43]. We only considered the all-sky daily average SIF dataset in our work, as it is reported to have a better performance in describing photosynthetically active radiation (PAR) on cloudy days and is more closely linked to GPP [44].  
All the vegetation products were interpolated on a grid of the ERA5-Land reanalysis data using the Nearest-Neighbor Interpolation method. All available 8-day LAI measurements included in the same month were binned into a monthly dataset using the Maximum-Value Composite (MVC) approach to avoid possible noise due to cloud. The 4-day SIF data were averaged into a monthly dataset, following [PERSON] et al. [45]. To guarantee reliable observations for the vegetation dynamics, only vegetation index values of \"good quality\" were retained in the following analysis by considering the quality flag affiliated with the MODIS, NDVI, and EVI products. As for LAI and SIF data, only values \(>\)0 were kept. We further screened out the NDVI and EVI values that corresponded to temperatures \(<\)0 \({}^{\circ}\)C to make sure that they were gleaned from the growing season. To reveal the interannual variability of the four vegetation products in the following analyses, SIF data were temporally averaged, while EVI, NDVI, and LAI data were temporally binned by the MVC method.
## 2 Methods
### Deep Dictionary Learning and Encoding Networks
#### 2.2.2 Dictionary Learning and Encoding  
Next, we proceed to the operations of dictionary learning and encoding. The number of dictionaries of the first order bits of each layer's dictionary is denoted as q, and p images are selected from them for training. r is the total number of datasets. Note that this process is random, and the number of dictionaries is denoted by \(D_{1}=r*q\). After that, we have the following formula:  
\[\min_{V_{i}}\left[\frac{1}{2}||y_{i}-V_{i}a_{i}||_{2}^{2}\right]s.t.||_{a_{i} ||_{1}<=\lambda} \tag{1}\]  
Next, each encoding layer performs a subtransformation, which is the specific operation we perform for encoding. Of course, this process takes place after learning V, the body structure of the encoding, and \(\gamma^{1}=\left[\gamma_{1}^{1},\gamma_{2}^{1},\ldots,\gamma_{1}^{1}\right] \in R^{D_{1}*l}\). In general, coding goes like this:  
\[\min_{\gamma_{1}^{1}}\left[\sum_{i=1}^{l}\frac{1}{2}\|y_{i}-V\gamma_{1}^{1}\|_ {2}^{2}+\beta\|\gamma_{i}^{1}\Theta_{i}^{1}\|_{1}\right]s.t.1^{T}\gamma_{i}^{1 }=1 \tag{2}\]  
After multiple dictionary learning and feature encoding, we can get a deeper dictionary learning and encoding framework:  
\[\min_{\gamma_{i}^{n}}\left[\frac{1}{2}\|d_{i}^{n-1}-D^{n}\gamma_{i}^{n}\|_{2} ^{2}+\beta\|\gamma_{i}^{n}\Theta_{i}^{n}\|_{1}\right]s.t.1^{T}\gamma_{i}^{1}=1 \tag{3}\]  
Through deep learning and coding, the input image can be clearly expressed, which is the operation of DDLCN for output features. In addition, what we can see is that DDLCN improves the separability and reduces the possibility of excessive error. In the equation above, \(\zeta_{i}^{n}\) is one of the basis vectors adopted in the feature representation, \(\gamma_{i}^{n}\) is the nth  
Figure 2: Framework of DDLCN.  
layer coding, \(d_{i}^{n-1}\in D^{n-1}\) is at the (n \(-\) 1) coding layer, and it is expressed by a vector. To remove constraints, let us do it at another level. The diagram is shown in Figure 3.  
As an example, let us look at a two-level framework of tasks, augmented with a transpose, which is augmented from the representation of the first level of code. This shows that we are able to obtain such a result that the feature representation can be finally realized. Here is the formula:  
\[\left[\gamma_{i}^{1}(v_{j}),\gamma_{i}^{1}(v_{j})\right]\left[\gamma_{j}^{2}(u _{1}),\gamma_{j}^{2}(u_{2}),\ldots\gamma_{j}^{2}(u_{s_{2}})\right]\right]^{T} \tag{4}\]  
By concatenating the codes, we can get a \(D_{1}\times(1+D_{2})\)-dimensional vector, which is the final encoded representation we want. Not only that, DDLCN has good performance in each task because it can perform well in iteration and can complete better feature representation [67; 68; 69; 70].
###### Abstract  
The synthesis of cupric oxide (CuO) films on cost-efficient, optical grade borosilicate-crown glass substrates (BK7) via chemical spray pyrolysis (CSP), either in pure form or with a low concentration of Al doping (below 1%), is presented and discussed. As a non-toxic \(p\)-type semiconductor, exhibiting monoclinic crystal structure and widely tuneable band gap (Eg), it is used in various applications. The optical properties, morphology and crystalline phases of CuO films are influenced by substrate temperature during thin film growth (annealing) and also by chemical doping very often introduced to modify grain boundary energy. The importance of our research subject is therefore perfectly justified and is essentially based on the fact that the potential fields of application are wide. Thus, herein we emphasize impact of the annealing stage and Al doping upon the structural, optical and electrical properties of the resulting product. Raman spectroscopy analysis confirms the presence of vibrational bands characteristic of a CuO phase, while X-ray diffraction (XRD) confirms the polycrystalline nature of the pure films. The thickness of the CuO films grown at 350 \({}^{\circ}\)C over three annealing intervals is proportional to the annealing time, while the crystallite phase in the films is proportional with the annealing temperature. Furthermore, XRD analysis of the Al-CuO films indicates the formation of a monoclinic-type structure (CuO phase) exhibiting a preferred orientation along the (002) plane, together with a significant grain size reduction from \(-\)88 to \(\sim\)45 nm as Al content increases. The transmittance spectra (between 400 and 800 nm) reveal a decrease in the transmittance from 48% to 15% with as the Al doping ratio increases. Additionally, the bandgap energy of the films is measured, modelled and discussed, using data from an ultraviolet-visible (UV-Vis) spectrophotometer. The calculated Eg is approximately 3.5 eV, which decreases with respect to the increasing annealing temperature, while the electrical resistivity varies from \(\sim\)19 to \(\sim\)4.6 kOhm.cm. Finally, perspectives and applications of CuO films are suggested, since the films are found to have a remarkable improvement in their structure and optical properties when doped with Al.  
copper oxide; spray pyrolysis; semiconducting oxide; thin film; annealing; doping; cost-efficient substrates; Raman spectroscopy +
Footnote †: journal: Applied Physics Letters  
1 Physico-Chemistry of Surfaces and Interfaces Research Laboratory (LRPCSI), Department of Physics, University 20 August 1955 of Sikkada, Road of El Haddeik LP 26, Sikkada 21000, Algeria;  
[PERSON]] (R.D.); [PERSON]RESS] (B.B.); [EMAIL_ADDRESS] (M.B.);  
[PERSON]ESS] (M.S.A.)
# Fall Testing of the Personal Airborne Equipment Backpack: Ground and Flight Testing
## 2 Materials and Methods  
The controlled criteria for functionality and reliability for the purpose of this project were defined as follows [11]:  
* retain a failure-free fixing of the backpack onto the parachute equipment during dropping from the aircraft, the parachute opening, and flight itself;
* failure-free disconnection of the backpack from the parachute equipment, stopping of backpack's fall by the rope, and the process of the parachute flight with the backpack on the rope;
* failure-free operation of the parachute equipment, the anchoring points of the backpack, and the rope by a visual check of the fall test.  
General procedures for mountain-climbing rope fall tests were considered in developing the method for the TL-98 backpack free-fall tests as well as for ropes used as a personal protection tool for preventing falls in [1; 2; 8; 9; 10]. However, these procedures had to be adapted to the test and to standard parachute operation as well to the test's repeatability.  
For the purpose of the fall tests, a 5 m long rope (of which 1 meter was used to tie the knots) with a 6 mm diameter and meeting EN 564 standards was used. The manufacturer guarantees the minimum tensile strength to be 7200 N. The type of rope for testing was designed by the backpack manufacturer, taking into account two basic rope requirements. The first was sufficient strength and the second was the smallest possible rope diameter to take up as little volume as possible after being placed in the backpack. Therefore, a rope meeting the EN 564 standard with a diameter of 6 mm was used in the tests. For example, dynamic ropes meeting the EN 892 standard would be more advantageous for the absorption of impact forces, but they are only produced with a larger diameter.
# Yeast Fermentation for Production of Neutral Distilled Spirits
## 2 Overview of Neutral Distilled Spirits Production  
### What Are \"Neutral Distilled Spirits\"?  
A neutral spirit (or neutral alcohol) may be defined as a fermentation-derived distilled spirit product with a minimum ethanol content of 95-96% _v/v_ that lacks distinctive aroma and taste. In the US, a neutral spirit is distilled at (or above) 96% alcohol by volume (ABV), whilst in the EU, the minimum alcoholic strength following distillation is 96% ABV [2].  
Regarding its organoleptic characteristics, a neutral spirit should have no discernible taste, other than the starting raw materials, which should be of agricultural origin. These raw materials are typically cereals such as wheat, resulting in products referred to as _neutral grain spirits_ (NGSs) or _grain neutral spirits_ (GNSs). Other raw materials can also be used, including grapes, potatoes, molasses (either from sugarcane or sugar beets), and cheese whey (see Section 2.3). A neutral spirit is generally the most common base alcohol for the production of gin, vodka, and other flavoured spirit beverages. The earlier production stages for gin and vodka are relatively similar [3; 4]. The majority of gin is produced through flavouring of a neutral spirit with botanicals such as juniper, coriander, angelica, and others. This is achieved either by compounding botanicals in neutral spirits (macceration) or by re-distillation in the presence of the botanicals (macceration prior to distillation or vapour infusion) [5; 6]. For vodka, the subsequent removal of congeners takes place post-distillation and typically by charcoal filtration [7; 8]. Therefore, yeast alcohol production kinetics and yields are much more important than congener biosynthesis for producing the base neutral spirit for gin and vodka, as compared to other distilled beverages [1].  
In the European Union, the chemical properties of a spirit (referred to as the ethyl alcohol of agricultural origin [2]) destined for vodka, gin, or other botanicaly flavoured spirits (for example, kummel and akvavit) production and the allowable levels of specific congeners are defined and summarised in Table 1.
## 3 Proposed Method
### DSSIM-Map Generator  
DSSIM-MAP is a map of structural dissimilarity, which measured how much the reference image x and the comparison image y are different over a certain window. Only with simple pixel comparison to measure dissimilarity, if the background and an object have similar colors, it is difficult to recognize the object as differences. Another difficulty is that cameras installed outdoors may suffer from shaking, and in this case, applying the color difference method may result in sensitive responses.  
To resolve these problems, our research applies DSSIM-MAP, which is based on SSIM-MAP. A SSIM-MAP is a measured result of structural similarity of two regions through considering the color average and color variance [21]. The general equation to calculate SSIM is as Equation (1).  
\[\text{SSIM}(x,y)=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_{xy}+C_{ 2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\sigma_{x}^{2}+ \sigma_{y}^{2}+C_{2}\right)} \tag{1}\]  
In Equation (1), \(\mu_{x}\) and \(\mu_{y}\) are the average of the x-area and the y-area, respectively. \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) represent the variance of pixel colors in those two areas, respectively. And \(\sigma_{xy}\) is the standard deviation of the color of those two areas. \(C_{1}\) and \(C_{2}\) are constants to stabilise the denominator. These values can be calculated using Equation (2):  
\[C_{i}=\left(K_{i}\times L\right)^{2} \tag{2}\]  
In Equation (2), the value of \(C_{1}\) is typically 0.01 and the value of \(C_{2}\) is 0.03. \(L\) is the datarange of pixel. In this research we used 255 for \(L\).  
The equation to calculate DSSIM is as Equation (3) [19; 20].  
\[\text{DSSIM}(x,y)=\frac{1-\text{SSIM}(x,y)}{2} \tag{3}\]As presented in the above equations, DSSIM has the opposite direction to SSIM and has a range of values from 0 to 1.0. A window size used to generate a DSSIM-MAP decides the size of the reference area to be compared. In our study, the window size was set to 7 pixels. The DSSIM-MAP Generator produces outputs through three steps: grayscaling, image reduction, and DSSIM calculation.  
First, since SSIM and DSSIM are indicators designed based on grayscale images, the average of each color channel in the target image is calculated and grayscaled. Second, the image size is reduced to decrease the amount of calculation required for DSSIM-MAP generation. This is necessary for real-time processing in embedded devices such as surveillance cameras without high-performance GPU. Third, a SSIM-MAP is generated and converted to a DSSIM-MAP. Additionally, morphology calculation is performed on DSSIM-MAP to remove noise and make an object distinct. The noise can be removed by performing opening and closing operations, and the object area can be set sufficiently by erosion or dilate operations.
# Multi-Objective Routing and Categorization of Urban Network Segments for Cyclists
## 2 Materials and Methods
### Formulation of the Objective Functions
#### 2.1.1 Safety-Related Criteria
Bus transit: Whether a cyclist prefers a route that avoids buses may vary depending on personal preferences, specific conditions, and the available infrastructure in each area. Due to their volume, buses can make the road environment more difficult for cyclists, especially if there are no bicycle lanes. Some cyclists may prefer to avoid routes where buses are present altogether, while others might find sharing the road with buses intimidating or stressful, especially if they are less experienced or have had negative interactions in the past. In such cases, they may prefer routes that minimize interactions with buses to improve safety and the overall driving experience. However, choosing a route that avoids buses may result in longer travel times for cyclists. This trade-off between bus avoidance and travel efficiency may vary depending on cyclist priorities [28]. \[F_{4}(c,y)=\min\sum_{ij\in R}\sum_{k\in K}c_{ijk}l_{ijk}y_{ijk} \tag{4}\]  
To model this, let variable \(c_{\text{i,j,k}}\in\ \{0,\ 1\}\) be equal to 1 if the link \(N_{\text{i,j,k}}\) is also used by buses and 0 if it is not. One can minimize the travel distance where a cyclist needs to travel in a road segment with bus traffic by introducing the objective function above. Absence of bicycle lanes: One of the factors considered of primary importance for the selection of routes is the availability of cycling infrastructure [30]. Each link in the network \(N_{\text{i,j,k}}\in N\) might have a bicycle lane, or not. If it has a bicycle lane, then we can introduce a parameter \(b_{\text{i,j,k}}\), where \(b_{\text{i,j,k}}=1\). If it does not have, then \(b_{\text{i,j,k}}=0\). The following objective function is introduced:  
\[F_{5}(y)=\max\sum_{ij\in R}\sum_{k\in K}b_{ijk}l_{ijk}y_{ijk} \tag{5}\]  
in order to maximize the travel distance on road segments with bicycle lanes.