esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminary Design Study  
As presented in the flow chart in Figure 5, the preliminary design of the MIMPS-G500 mN propulsion system started by assessing the thermodynamic, thermochemical, and performance characteristics of the selected EIL green monopropellants. Rocket Propulsion Analysis (RPA) academic version was used in propellants assessment for propellants with precise known formulation, such as LMP-103s and FLP-106, the inputs for the analysis tool were the chemical formulae of constituents, molecular weights, standard heat of formation, and weight percent of the formula constituents. Predefined values given to the analysis tool for the monopropellant engine were 500 mN thrust value, moreover, iterations of simulations were made between 1-2 MPa combustion pressures. The nozzle expansion ratio was also iterated between 50 to 100:1. Further, thermodynamic properties of the simulated propulsion system were extracted, such as the chamber temperature, specific heats, and specific heat ratio at the thruster different regions. Theoretical (ideal) performance as well as estimated delivered performance were assessed, namely the effective exhaust velocity and the weight-specific impulse at vacuum condition. Other proprietary propellants such as ASCENT (formerly AF-M315E) and HNP225, with unknown precise formulation weight fractions, were not possible to be simulated in the analysis tool, thus it was relied on the published literature by propulsion system developers and manufacturers to acquire the above-mentioned data.  
Figure 6: MIMPS-G propulsion system schematic diagram (including the optional auxiliary vapor propulsion in multimode architecture).  
Micro electric pump and microvalves were chosen COTS parts based on the operation pressures, propellant mass flow rate, size constraints, and electric power constraints. As mentioned before, the thruster considered in the preliminary design is a commercial model by Busek company that is the 0.5 N green propellant thruster [43; 44; 45]. After laying out the main propulsion system components, see Figure A2 (Appendix A), the propellant tank was structurally sized and verified for operation pressures, temperatures, material compatibility, and design modularity and expandability. The tank will use a Propellant Management Device (PMD) consisting of vanes and a sponge on the outlet with light-weight compatible materials to the green nonpropellants. The structural design of the tank considered a titanium wetted inner structure reinforced by carbon fiber composites on the outside to ensure long-term propellant material compatibility [46]. The tank design dedicated a rough 10% and 5% of the volume for the PMD and usage, respectively.
## 4 Experiments and Results
### Evaluation Metrics  
To extensively evaluate the performance of the proposed G-UNETR++, we used various metrics, including the dice similarity coefficient (DSC), volumetric overlap error (VOE), relative absolute volume difference (RAVD), average symmetric surface distance (ASSD), and maximum symmetric surface distance (MSSD).  
DSC is commonly used to measure the overlapping similarity between the predicted output (B) and the ground truth (A). The range of DSC is from 0 to 1, with a higher value indicating a more accurate prediction. DSC is defined as follows:  
\[\text{DSC}=2\frac{|\text{A}\cap\text{B}|}{|\text{A}|+|\text{B}|} \tag{8}\]  
Another metric based on overlap is the Jaccard index that measures the degree of agreement between the predicted output and the ground truth as the ratio of the intersection over the union between the predicted output and the ground truth. VOE is the corresponding error measure of the Jaccard index, defined as follows:  
\[\text{VOE}=1-\frac{|\text{A}\cap\text{B}|}{|\text{A}\cup\text{B}|} \tag{9}\]  
Figure 6: Comparison of liver segmentation results in green before (**left**) and after (**right**) post-processing.  
RAVD is the relative coefficient of the non-overlapping parts between the predicted output and the ground truth, ranging from 0 to 1, with a smaller value indicating a more accurate prediction. RAVD is defined as follows:  
\[\text{RAVD}=\left|\frac{|\text{B}|-|\text{A}|}{|\text{A}|}\right| \tag{10}\]  
ASSD measures the average distance between the predicted output and the ground truth, with 0 mm indicating a perfect segmentation. The shortest distance of a voxel \(x\) to the set of surface voxels \(S(\text{A})\) of A is defined as follows:  
\[d(x,S(\text{A}))=\underset{y\in S(A)}{min}\ \|x-y\| \tag{11}\]  
where \(\|x-y\|\) denotes the Euclidean distance between \(x\) and \(y\). ASSD is defined as follows:  
\[\text{ASSD}=\frac{1}{|S(\text{A})|+|S(\text{B})|}\times\left(\sum_{x\in S( \text{B})}d(x,\ S(\text{A}))+\sum_{y\in S(\text{A})}d(y,S(\text{B}))\right) \tag{12}\]  
MSSD, also known as HD, is computed by the maximum distance between the surface voxels of the predicted output and the ground truth, with 0 mm indicating a perfect segmentation. MSSD is defined as follows:  
\[\text{MSSD}=max\bigg{(}\underset{x\in S(\text{B})}{max}\ d(x,\ S(\text{A})), \underset{y\in S(\text{A})}{max}\ d(y,\ S(\text{B}))\bigg{)} \tag{13}\]  
Other metrics including accuracy, precision, recall, and F1-score were used to further evaluate the proposed model. Their calculation formulas are defined as follows [46]:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{14}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{15}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{16}\]  
\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{17}\]  
where TP, TN, FP, and FN represent the confusion matrix's true positive, true negative, false positive, and false negative values.
# Evaluation of the Large-Length Indoor Standard Installation Based on Single Point Scanning
## 4 Results and Analysis
### Comparative Analysis of Multiple Measurement Results
Since there was drifting of the laser tracker, the maximum drift introduced by the reading time difference was not more than 0.2 \(\mathrm{\SIUnitSymbolMicro m}\). Assuming a uniform distribution, then, \(\mathrm{u_{B2}(s_{2})=\frac{0.2}{\sqrt{3}}\approx 0.12\)\(\mathrm{\SIUnitSymbolMicro m}\). The digital resolution of the laser tracker was 1 \(\mathrm{\SIUnitSymbolMicro m}\), the half width of the interval was 0.5 \(\mathrm{\SIUnitSymbolMicro m}\), and \(k=\sqrt{3}\) by assuming a uniform distribution. Thus, the uncertainty component was \(\mathrm{u_{B3}(s_{3})=0.29\)\(\mathrm{\SIUnitSymbolMicro m}\). Moreover, the environmental factors included temperature, humidity, and air pressure. The measurement error of the average optical temperature of the laser interferometer was 0.1 \(\mathrm{\SIUnitSymbolMicro C}\), the measurement error of the optical air pressure was 11 Pa, and the measurement error of the partial air steam was 40 Pa. Thus, \(\mathrm{u_{B3}(s_{5})\approx 0.3\)\(\mathrm{\SIUnitSymbolMicro m}\) based on the assumption of uniform distributions. The optical center error refers to the deviation of the optical center and the mechanical center of the reflector within the effective acceptance angle. The uncertainty component \(\mathrm{u_{B5}(s_{5})=0.5\)\(\mathrm{\SIUnitSymbolMicro m}\) was introduced by the optical center error. An equal interval (0.1 s) was used for data collection of the laser tracker. The notebook time was maintained by the crystal oscillator when there was no internet, and the uncertainty component introduced by the collection time was \(\mathrm{u_{2}(t)=5\)\(\mathrm{\SIUnitSymbolMicro s}\). For the uncertainty components of the measurement repeatability and the resolution of the laser tracker, the maximum values were taken. The synthetic uncertainty and the expanded uncertainty are as follows:  
\[\mathrm{u_{1}(s)=\sqrt{\mathrm{u_{A}(s_{1})}^{2}+\mathrm{u_{B1}(s_{1})}^{2}+ \mathrm{u_{B2}(s_{2})}^{2}+\mathrm{u_{B3}(s_{3})}^{2}+\mathrm{u_{B4}(s_{4})}^ {2}}\approx 11.49\)\(\mathrm{\SIUnitSymbolMicro m}\)  
\[\mathrm{t=0.1\ s,\ s=0.015\ m,\ and\ thus,\ u_{c}(v)=\sqrt{\frac{1}{t^{2}}u_{1}^{2}( s_{i})+\frac{s^{2}}{t^{4}}u_{2}^{2}(t_{i})\ \approx\ 115.1\ \mathrm{\SIUnitSymbolMicro m}/s}\]  
The expanded uncertainty of the local speed of the laser tracker method is \(U\approx 0.23\)\(\mathrm{\SIUnitSymbolMicro m}\)/s, (\(k\) = 2).
## 2 Materials and Methods
Original RGB image (**a**) is converted to HSV colour space to increase differences between background and object pixels (**b**). Colour saturation layer is very useful for selecting object pixels and creates a mask (**c**). Detected mask is adjusted to a rectangle or a polygon if necessary (**d**). Outcome of the automatic image labelling process is a mask saved as a png image, or a region of interest defined with a rectangle in an xml file or a polygon in a json format. * Perform data augmentation, overlapping objects on images that contain only the background. This step is represented with function \(f_{3}\) in Figure 2. Several objects can be synthetized with the same background to perform data augmentation. * Save the masks defined by the silhouette. * A bounding box of the mask defines a rectangle. Rectangles are saved in an xml file. * Perform polygon approximation to obtain the polygon vertex of an object silhouette to be saved in json format. * Masks are saved in a png image. With this algorithm, image labelling is completed quickly and the results are saved in several formats that can immediately be used in the training process. This technique is suitable for projects that need to resolve a new problem without previous images and work. For example, many scientific or industrial quality and process control applications, need computer vision systems to make decisions. Additionally, chemical and biological researching applications need to detect elements in a microscope image to define the results of their experiments. To train these systems, a deep neural network model could be a means of solving existing problems that remain unresolved by 2D computer vision algorithms, such as the semantic segmentation of objects, even with adjoining or overlapping objects or the recognition of heterogeneous textures (e.g., plants). In these applications, it is easy to generate a set of images with controlled backgrounds. Moreover, if it is necessary to add new images to an existing labelled image set, this controlled background technique dramatically reduces the labelling time. In both cases, to capture new images, it must be considered that controlled backgrounds are conducive to accurate object labelling. This will reduce the object labelling time noticeably and ensures accuracy of the labelling process. This algorithm is tested in GPU Nvidia Geforce Titan XP. Figure 2: Proposed fast object detection algorithm with data augmentation. Images with controlled background are labelled as is shown in Figure 1. Detected objects are overlapped with a texturized background to create a standard image taken under uncontrolled conditions. Moreover, several objects can be overlapped with the same background: \(f_{1}\) represents an image subtraction to obtain the object mask and \(f_{2}\) is a pixel selection from original image using a mask detected with \(f_{1}\). With \(f_{3}\) selected pixels are synthesized with real background images to improve the training capabilities of the image set.
## 4 Experimental Results
### Comparison of Segmentation Methods
The grayscale values of image backgrounds change greatly everywhere, so it is obviously impossible to select a certain global thresholding to separate all the objects from the backgrounds. However, the local thresholding method needs to select appropriate sub-blocks according to the size of the objects. In addition, it should not only ensure that the complete object is in the same sub-block but also ensure that there must be objects in each sub-block, otherwise there are many problems such as false alarms and incomplete object recognition across sub-blocks. For a star image with a large number of objects, it \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multirow{2}{*}{**Scenes**} & \multicolumn{2}{c}{**Global Methods**} & \multicolumn{2}{c}{**Local Methods**} & \multicolumn{1}{c}{**Our**} \\ \cline{3-6}  & & **T1** & **T2** & **T1** & **T2** & **Method** \\ \hline Number of & S4 & 6 & 3 & 27 & 13 & 47 \\ recognized & S5 & 1 & 3 & 34 & 21 & 40 \\ objects & S6 & 5 & 1 & 30 & 11 & 40 \\ \hline Number of & S4 & Heavy & 0 & 8 & 0 & 0 \\ false & S5 & Heavy & Slight & 15 & 0 & 0 \\ alarms & S6 & Heavy & 0 & 4 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Segmentation results of different star images with different methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenes**} & \multicolumn{2}{c}{**The Number of**} & \multirow{2}{*}{**Recognition**} & \multicolumn{2}{c}{**The Number of**} & \multirow{2}{*}{**False Alarms**} & \multirow{2}{*}{**False Alarm**} & \multirow{2}{*}{**Algorithm**} \\  & \multicolumn{1}{c}{**Recognized Objects**} & & & & & \\ \cline{2-7}  & \(m\) & \(\sigma\) & & \(m\) & \(\sigma\) & **Rate** & \\ \hline S5 & 39.02 & 1.08 & 97.6\% & 0.17 & 0.47 & 0.43\% & 0.29 \\ S6 & 39.16 & 1.05 & 97.9\% & 0.19 & 0.51 & 0.48\% & 0.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recognition results of simulation star images with 2000 frames each (\(m\): mean, \(\sigma\): standard deviation). is difficult to satisfy the two situations. Our proposed correlation segmentation method solves the problems well and overcomes the limitation of gray segmentation.
## 5 Surface Roughness Estimation
### Surface Roughness Classification  
There are 2040 acquired samples in the experiment, where the surface roughness \(Ra\) is estimated by a mobile roughness measuring instrument. Values of the surface roughness \(Ra\) are randomly distributed between 0.244 and 7.376. The mean surface roughness \(Ra\) is 3.727276 and the standard deviation is 2.217645. As a state-of-art approach in pattern recognition problems [40], CNN can make proper classifications on the labeled dataset.  
In practical applications, balanced datasets have been empirically shown to outperform imbalanced datasets [39]. In this research, oversampling which duplicating instances of under-represented classes until a balanced dataset is created is used to lessen the impact of imbalanced datasets. Ten roughness conditions are considered: [\(-\)Inf, 0.957], [0.957, 1.67], [1.67, 2.38], [2.38, 3.1], [3.81, 4.52], [4.52, 5.24], [5.24, 5.95], [5.95, 6.66] and [6.66, \(\infty\)). The description of the surface roughness condition is listed in Table 3. After adjustment, the total sample numbers is 3700.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Information** \\ \hline Exposure time & 150 ms 10 \(\upmu\)s \\ Gamma & 0 \\ Goal & 49 \\ Gain & 1.375\(\times\) \\ Saturation & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Capture parameters.  
Figure 12: Structure sketch of the image acquisition system.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
###### Abstract  
Visible light communication (VLC) is an emerging mode of wireless communication that supports both illumination and communication. One essential function of VLC systems is the dimming control, which requires a sensitive receiver for low-light conditions. The use of an array of single-photon avalanche diodes (SPADs) is one promising approach to enhancing receivers' sensitivity in a VLC system. However, because of the non-linear effects brought on by the SPAD dead time, an increase in the brightness of the light might degrade its performance. In this paper, an adaptive SPAD receiver is proposed for VLC systems to ensure reliable operation under various dimming levels. In the proposed receiver, a variable optical attenuator (VOA) is used to adaptively control the SPAD's incident photon rate according to the instantaneous received optical power so that SPAD operates in its optimal conditions. The application of the proposed receiver in systems with various modulation schemes is investigated. When binary on-off keying (OOK) modulation is employed due to its good power efficiency, two dimming control methods of the IEEE 802.15.7 standard based on analogue and digital dimming are considered. We also investigate the application of the proposed receiver in the spectral efficient VLC systems with multi-carrier modulation schemes, i.e., direct current (DCO) and asymmetrically clipped optical (ACO) orthogonal frequency division multiplexing (OFDM). Through extensive numerical results, it is demonstrated that the suggested adaptive receiver outperforms the conventional PIN FD and SPAD array receivers in terms of bit error rate (BER) and achievable data rate.  
visible light communication (VLC); orthogonal frequency division multiplexing (OFDM); light fidelity; dimming control; single-photon avalanche diode (SPAD) Article
## 2 Methodology
### Functional Requirements  
Given the application to be developed, the key elements identified for the realization of the DT model of the infrastructure are:  
1. Analysis and forecasting of the power consumption of the main equipment that make up the case study: 1. monitoring of electrical consumers using smart plugs to analyze instantaneous and average power (W), status (on-off) and time of use (min);* monitoring the consumption of electric actuators (door and window movement) in terms of instantaneous power (W), status (on-off) and time of use (min);
* monitoring of general electrical consumption of utilities, lighting, air conditioning;
* Indoor Environmental Quality (IEQ) monitoring and forecasting:
* analysis and prediction of air quality using dedicated sensors (tVOC, PM2.5, PM10, temperature, relative humidity, CO\({}_{2}\)) to obtain complex data (IAQ and CO\({}_{2}\) limit UNI EN ISO 16000 [36]);
* analysis and prediction of thermal comfort using dedicated sensors (temperature, relative humidity) to obtain complex data (PMV, PPD);
* analysis and prediction of lighting comfort using dedicated sensors (luminosity);
* control of window opening (open/closed);
* Analysis of presence through dedicated sensors (presence). Possibility of counting people (number) using computer vision with dedicated cameras;
* Interoperability of communication protocols (MQTT, HTTP, etc.) and use of common standards (Wi-Fi, Ethernet, Zigbee, etc.) with the possibility of external access to data [37];
* Data management on dedicated infrastructure;
* Data visualization through aggregated metrics.  
These functional requirements identify all the monitorable quantities within the case study and can be translated into system requirements.
## 4 Lifetime Improvement Mechanisms in Routing
### Multi-Sink Mechanisms
After that, the deputies that receive these beacons from the relative sink node rebroadcast them to the other neighboring nodes in the network. When the beacon is received by node \(N_{i}\), it firstly updates its neighbor table and deputy table. After that, it begins to broadcast the beaconing message periodically. (3) _Packet transferring:_ it is the last phase of routing stage. When the node \(N_{i}\) completes the updating process by refreshing the neighbor and deputy tables, it is able to generate and forward data packets to deputy nodes if it finds more than one valid entry at the mentioned tables. In order to balance the loads among all deputies, two methods are used in MSLBR: (a) in a round-robin mechanism, the destination of a new packet can be set to one of all deputy nodes in the network; (b) according to the forward factor of neighbors, a node can relay the packets to the next hop. Since the kernel of MSLBR protocol is to route a packet to a deputy randomly, it can efficiently distribute the traffic load on most of sensor nodes and extend the network lifetime [7]. The test-bed experiments [7] show that MSLBR protocol has on average 7.1% and 14.4% longer lifetime compared to the primary based routing (PBR) and energy level based routing (ELBR) algorithms. However, the time cost for updating routing table and finding the match deputy for a packet in MSLBR is a bit high. Therefore, the data transmission delay is increased rather more than in the two other approaches.
# Detecting the Extremely Small Angle of an Axicon by Phase-Shifting Digital Holography
## 1 Introduction  
As proposed by [PERSON] [1], Bessel beams have been receiving much attention because of their interesting properties. For example, Bessel beams have long diffraction-free distance and can reconstruct in the presence of an obstacle (self-reconstructing) [2; 3; 4; 5]. Such properties make Bessel beams attractive for a broad range of applications, such as optical tweezers and particle trapping [6; 7; 8; 9]. Bessel beams are also particularly involved in the applications in strong-field science, laser machining, biomedical imaging, and confocal microscopy [10; 11; 12; 13; 14; 15; 16]. Given the specific properties and important applications, the generation of Bessel beams is of considerable importance.  
Several techniques such as diffractive optical element, spatial light modulator (SLM), and axicon, have been used to generate Bessel beams [17; 18; 19; 20; 21]. Among the techniques, the most common method to generate a Bessel beam is by using an axicon because of the low energy loss and high beam quality. The diffraction-free distance of a Bessel beam can be adjusted by changing the base angle of the axicon. In general, the smaller the base angle of the axicon is, the longer the diffraction-free distance of the generated Bessel beam will be [22]. Therefore, the production of axicons with a small base angle is important. However, as the base angle of an axicon decreases, its measurement becomes increasingly challenging. Thus, a method that can accurately measure the base angle of an axicon is necessary.  
Some methods have been proposed to measure the base angle of an axicon. The coordinate measuring machine (CMM) method uses a contact probe to detect the base angle of an axicon. However, similar to other contact measurement methods, CMM may cause surface damage to the axicon, and the process is time-consuming. Computer-generated holograms (CGHs) were applied for the measurement of the axicon [23]. However, CGHs need to be precisely designed. A method based on the chromatic dispersion optical path was also proposed [24]. Although this method is easy to regulate, its angle deviation is relatively high. [PERSON] et al. measured an axicon with a base angle of 20\({}^{\circ}\) by using an auto-collimation optical path [25]. Here, we applied the phase-shifting digital holographytechnique [26; 27] to measure the base angle of axicons. Results show that this method can precisely detect the extremely small base angle of an axicon.