esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Verification of Comprehensive Evaluation Model for Honing Processing Quality
### Effect of CBN Grain Size on Honing Quality
#### 5.4.3 The Relationship Between Grain Size and Processing Efficiency  
As shown in Figure 10, with the reduction in grain mesh size of the CBN honing wheel, cutting performance and honing processing efficiency are significantly improved. On the one hand, the size of abrasive particles increases with the decrease of grain mesh size. At the same time, the surface width of the abrasive particles and the nominal width of the abrasive chip cutting layer in front of the tool is increased, resulting in more material being cut off from the workpiece surface per unit time, which improves the honing efficiency. On the other hand, when the grain mesh size is small, the contact area between the abrasive particle and the binder is larger, and the height of the abrasive edge is also larger, which makes the chip space and lubricating cooling effect of the honing wheel better, thus enhancing the ability to resist external intrusion. The abrasive particles are less likely to fall off, and the low wear rate of the honing wheel improves the life and processing efficiency of the honing wheel.  
Figure 9: Relationship between abrasive grain size and surface roughness with honing gear.  
In addition, it is found that the honing state of clogging and surface passivation is likely easy to occur when the honing wheel is machined with large grain mesh size CBN. This phenomenon leads to a decrease in honing efficiency because the large grain mesh size will cause the pore volume and the nominal cross-sectional area of the debris of the honing wheel to be reduced. In addition, the number of effective abrasive cutting edges and abrasive chips increases. Therefore, in the range of small cut times, a part of the grinding chips is taken away by the cutting fluid, and the other part is retained in the pore volume. This results in a lower actual cutting depth of abrasive particles and a lower effective material removal rate per unit of time compared to the theoretical material removal rate.  
With the increase in honing times, the probability of abrasive cutting-edge wear increases. The effective cutting depth of the 100\({}^{\#}\) honing wheel is greater than that of the 540\({}^{\#}\) honing wheel. The chip fusion in the pore of the honing wheel increases with the increase in temperature. After a certain number of honing cycles, the amount of abrasive plugging increases with the increase of the grain mesh size of the CBN honing wheel. Therefore, the honing wheel with a large grain mesh size should be selected in semi-fine honing and fine honing. On the contrary, the honing wheel with a small grain mesh size has a large penetration depth of abrasive particles, high honing temperature, and large material removal. At the same time, there are more grinding chips and fused caking materials blocked in the gap between abrasive particles, so the mesh number of small particles is more suitable for coarse honing.
# SIW Cavity-Backed Antenna Array Based on Double Slots for mmWave Communications
## 1 Introduction
The proposed antenna represents a low-cost design with enhanced radiation performance for radiofrequency (RF) front-end hardware in future communications systems at millimeter-wave bands.
Future Internet_**2013**, \(5\), 168-189; doi:10.3390/fi5020168  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**QoS Self-Provisioning and Interference Management for Co-Channel Deployed 3G Femtocells**  
**[PERSON] 1, [PERSON] 1, [PERSON] 2,* and [PERSON] 1,2**  
\({}^{1}\) Nokia Siemens Networks, Research Center Aalborg, Niels Jernes Vej 10, 9220 Aalborg, Denmark; E-Mails: [EMAIL_ADDRESS] (T.K.); [EMAIL_ADDRESS] (P.O.); [EMAIL_ADDRESS] (K.P.)  
\({}^{2}\) Radio Access Technology, Department of Electronic Systems, Aalborg University, Niels Jernes Vej 12 A6-1, 9220 Aalborg, Denmark  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +45-9940-8645.  
_Received: 21 February 2013; in revised form: 7 March 2013 / Accepted: 22 April 2013 / Published: 2 May 2013_  
**Abstract:** A highly efficient self-provisioning interference management scheme is derived for 3G Home Node-Bs (HNB). The proposed scheme comprises self-adjustment of the HNB transmission parameters to meet the targeted QoS (quality of service) requirements in terms of downlink and uplink guaranteed minimum throughput and coverage. This objective is achieved by means of an autonomous HNB solution, where the transmit power of pilot and data are adjusted separately, while also controlling the uplink interference pollution towards the macro-layer. The proposed scheme is evaluated by means of extensive system level simulations and the results show significant performance improvements in terms of user throughput outage probability, power efficiency, femtocell coverage, and impact on macro-layer performance as compared to prior art baseline techniques. The paper is concluded by also showing corresponding measurements from live 3G high-speed packet access (HSPA) HNB field-trials, confirming the validity of major simulation results and assumptions.  
**Keywords:** 3G and/or WCDMA; femtocell and/or home NodeB; QoS provisioning; interference management; network listen mode
# Advances in IoMT for Healthcare Systems
### Smart Hospitals and Mobile Health Services  
The continued availability of healthcare services and the sustainability of medical facilities, even under extreme climatic conditions and natural disasters such as earthquakes, hurricanes or floods, is among the main goals of modern healthcare systems [9; 10]. The installation of smart hospitals and mobile health services can control the downtime of medical services and provide better care to victims with their mobile health services and always-available infrastructure. In this regard, emergency services require upgrading via IoMT devices and mobile health service integration for virtual consultation and immediate treatment in disaster situations. Additionally, traditional hospitals need to be upgraded by supporting IoT technologies to improve medical responses and better decision-making during emergencies. The use of 5G infrastructure and Wi-Fi 6 technology can enable faster and more reliable communication between IoMT devices, supporting high-speed bandwidth requirements and real-time medical applications.
## 4 TinyOS
### Programming Model  
Earlier versions of TinyOS did not provide any multithreading support, with application development strictly following the event driven programming model. TinyOS version 2.1 provides support for multithreading and these TinyOS threads are called TOS Threads. In [6], the authors pointed out the problem that, given the motes' resource constraints, an event-based OS permits greater concurrency. However, preemptive threads offer an intuitive programming paradigm. The TOS threading package provides the ease of a threading programming model coupled with the efficiency of an event driven kernel. TOS threads are backward compatible with existing TinyOS code. TOS threads use a cooperative threading approach, _i.e._, TOS threads rely on applications to explicitly yield the processor. This adds an additional burden on the programmer to explicitly manage the concurrency. Application level threads in TinyOS can preempt other application level threads but they cannot preempt tasks and interrupt handlers. A high priority kernel thread is dedicated to running the TinyOS scheduler. For communication between the application threads and the kernel, TinyOS 2.1 provides message passing. When an application program makes a system call, it does not directly execute the code. Rather it posts a message to the kernel thread by posting a task. Afterwards, the kernel thread preempts the running thread and executes the system call. This mechanism ensures that only the kernel directly executes TinyOS code. System calls like _Create_, _Destroy_, _Pause_, _Resume and Join_ are provided by the TOS threading library.  
Figure 2: TinyOS Architecture.  
TOS threads dynamically allocate Thread Control Blocks (TCB) with space for a fixed size stack that does not grow over time. TOS Threads context switches and system calls introduce an overhead of less than 0.92% [3].  
Earlier versions of TinyOS imposed atomicity by disabling the interrupts, _i.e._, telling the hardware to delay handing the external events until after the application completed an atomic operation. This scheme works well on uniprocessor systems. Critical section can occur in the user level threads and the designer of the OS does not want the user to disable the interrupts due to system performance and usability issues. This problem is circumvented in TinyOS version 2.1. It provides synchronization support with the help of condition variables and mutexes. These synchronization primitives are implemented with the help of special hardware instructions e.g., test & set instruction.
## 4 Review and Status of Cryogenic Laser Technology
### Cryogenic CW Lasers  
In this section of the manuscript we will review the progress that has been made in the development of CW, nanosecond, picosecond, and femtosecond cryogenic laser sources in the modern era. We begin by first looking at the evolution of Yb:YAG, Yb:YLF, and Yb:CaF\({}_{2}\) CW cryogenic lasers; Yb:YAG and Yb:YLF materials have been used for the highest power CW demonstrations to date. Figure 44 shows the CW output power obtained from Yb:YAG cryogenically-cooled lasers operating near 80 K. The corresponding data and references are shown in Table 7[126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139]. The first CW Yb:YAG demonstration was by Lacovara[1], who reported 40 W output power. The earliest demonstration of CW power in excess of 100 W is attributed to [109], whom reported a cryogenic Yb:YLF laser with output power of 110 W. This was followed by two early high power CW Yb:YAG cryogenic laser demonstrations at 165 W[126] and 300 W[127] by the MIT cryogenic laser group. CW cryogenic Yb:YAG lasers with CW output power \(\geq\)400 W have been reported by a number of groups, including 455 W[3], 400 W[129], 550 W[130], 500 W[135], and 494 W[138]. The largest CW power for a Yb:YAG cryogenic laser reported to date, 963 W, was for a Yb:YAG cryogenic oscillator driven double-passed Yb:YAG preamplifier followed by a Yb:YAG power amplifier demonstrated by our group[132]. We include Yb:CaF\({}_{2}\) here due to a recent high power demonstration with a quantum-defect of only 1.11%[134].  
For Yb:YLF, the highest average power reported to date is 180 W by the MIT cryogenic laser group[108]. We note that almost all of the CW laser sources included in Figure 44 had near-diffraction-limited beam-quality with M\({}^{2}\) values typically <1.3. The one exception is [129], which reported multi-transverse-mode operation.  
Figure 43: Absorption cross-section as a function of wavelength for a hydrothermally-grown Dy\({}^{3+}\):Lu\({}_{2}\)O\({}_{3}\) crystal at 80 K (blue) and 298 K (green).
# A Bimodal Lognormal Distribution Model for the Prediction of COVID-19 Deaths
## 5 Results  
Result presented in this section have been obtained by fitting the available data between 31 December 2019 and 30 September 2020. Then, extrapolations have been computed up to 31 December 2020.  
Table 3 shows the results obtained for the two countries with the largest number of actual COVID-19 deaths in each continent. In the next subsections, plots are presented and commented for a few representative countries, as well as for the six continents and the World. The complete results and plots (for all of the 211 locations of the OWID dataset, as well as the continental and World summaries) are available in the Supplementary Materials.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Continent**} & \multirow{2}{*}{**Country**} & \(A\) & \(M_{1}\) & \(\sigma_{\tt 11}\) & \(M_{2}\) & \(\sigma_{\tt 12}\) & \(\alpha\) & Actual & Predicted \\  & & **(No.)** & **(Days)** & **(Days)** & **(Days)** & **(Days)** & **(-)** & **Deaths [1]** & Deaths [2]** \\ \hline \multirow{2}{*}{Africa} & South Africa & 19,585.9 & 214.7 & 55.8 & 217.5 & 13.9 & 0.730 & 16,667 & 19,289 \\  & Egypt & 7105.3 & 181.3 & 15.9 & 184.0 & 97.0 & 0.368 & 5914 & 6694 \\ \hline \multirow{2}{*}{Asia} & India & 187,322.1 & 235.3 & 86.7 & 276.9 & 48.3 & 0.658 & 97,497 & 167,051 \\  & Iran & 28,584.6 & 93.3 & 19.8 & 211.6 & 48.7 & 0.238 & 25,986 & 28,394 \\ \hline \multirow{2}{*}{Europe} & United Kingdom & 46,279.2 & 111.3 & 19.0 & 205.3 & 166.9 & 0.823 & 42,072 & 44,095 \\  & Italy & 39,462.5 & 96.9 & 20.3 & 199.1 & 224.4 & 0.836 & 35,875 & 37,280 \\ \hline North & United States & 253,254.2 & 115.0 & 18.5 & 221.7 & 69.2 & 0.396 & 205,998 & 244,290 \\ America & Mexico & 98,272.7 & 166.2 & 10.9 & 203.7 & 66.9 & 0.031 & 77,163 & 94,706 \\ \hline \multirow{2}{*}{Oceania} & Australia & 970.2 & 110.4 & 187.5 & 237.0 & 17.6 & 0.232 & 882 & 934 \\  & Guam & 228.0 & 278.1 & 0.2 & 305.2 & 48.7 & 0.000 & 47 & 195 \\ \hline South & Brazil & 166,675.9 & 150.2 & 29.1 & 223.4 & 50.5 & 0.333 & 142,921 & 164,969 \\ America & Peru & 35,635.6 & 185.0 & 61.5 & 218.9 & 15.2 & 0.701 & 32,396 & 35,184 \\ \hline \hline \end{tabular}  
* On 30 September 2020. 2 On 31 December 2020.  
\end{table}
Table 3: Results for the two countries with largest number of actual deaths in each continent.  
Figure 3: Extrapolation error vs. extrapolation interval length.
# GNSS Real-Time Precise Point Positioning in Arctic Northeast Passage
## 2 Materials and Methods
### GNSS Real-Time PPP Model
#### 2.2.2 Multi-GNSS Ionospheric-Free (IF) DF RT-PPP Model  
The ionosphere is a dissipative medium; thus, the ionospheric errors of GNSS signals are frequency dependent, which can be expressed as:  
\[I_{r,j}^{sys,s}=k_{j}I_{r,1}^{sys,s},k_{j}=\frac{f_{1}^{sys}}{f_{j}^{sys}} \tag{7}\]  
Therefore, the linear combination (LC) of GNSS observations at different frequencies can effectively eliminate the influence of the first-order ionosphere errors,  
\[\begin{cases}L_{r,IF}^{sys,s}=\alpha L_{r,1}^{sys,s}+\beta L_{r,2}^{sys,s}\\ P_{r,IF}^{sys,s}=\alpha L_{r,1}^{sys,s}+\beta L_{r,2}^{sys,s}\end{cases},\alpha= \frac{f_{1}^{sys^{2}}}{f_{1}^{sys^{2}}-f_{2}^{sys^{2}}},\beta=-\frac{f_{2}^{ sys^{2}}}{f_{1}^{sys^{2}}-f_{2}^{sys^{2}}} \tag{8}\]  
Then, the undifferentiated IF-LC RT-PPP model can be expressed [22]:  
\[\begin{cases}L_{r,IF}^{sys,s}=\rho_{r}^{sys,s}+c\left(dt_{r}^{sys}-dt^{sys,s} \right)+A_{IF}^{sys}\left(b_{r,IF}^{sys}-b_{IF}^{sys,s}\right)+A_{IF}^{sys}N_{r,IF}^{sys,s}+T_{r}^{sys,s}+\varepsilon_{r,IF}^{sys,s}\\ P_{r,IF}^{sys,s}=\rho_{r}^{sys,s}+c\left(dt_{r}^{sys}-dt^{sys,s}\right)+d_{r, IF}^{sys}-d_{IF}^{sys,s}+T_{r}^{sys,s}+\varepsilon_{r,IF}^{sys,s}\end{cases} \tag{9}\]  
To ensure the calculation efficiency, as well as the product accuracy, IGS and some of the ACs use the IF-LC model in real-time precise ephemeris and clock calculation. As a result, the receiver and the satellite code biases \(d_{r,IF}\) and \(d_{IF}^{sys,s}\) will be absorbed by the receiver and satellite clock, respectively. Meanwhile, the phase delays \(b_{r,IF}^{sys}\) and \(b_{IF}^{sys,s}\) will be absorbed by phase ambiguities. Therefore, Equation (9) can be rewritten as:  
\[\begin{cases}L_{r,IF}^{sys,s}=\rho_{r}^{sys,s}+c\left(dt_{r}^{sys}-dt^{sys,s} \right)+A_{IF}^{sys}N_{r,IF}^{sys,s}+T_{r}^{sys,s}+\varepsilon_{r,IF}^{sys,s}\\ P_{r,IF}^{sys,s}=\rho_{r}^{sys,s}+c\left(dt_{r}^{sys}-dt^{sys,s}\right)+T_{r}^{ sys,s}+e_{r,IF}^{sys,s}\end{cases} \tag{10}\]  
with  
\[\begin{cases}cd_{r}^{sys}=cd_{r}^{sys}+d_{r,IF}^{sys}\\ cdI_{r}^{sys,s}=cd_{r}^{sys,s}+d_{IF}^{sys,s}\\ N_{r,IF}^{sys,s}=N_{r,IF}^{sys,s}+\left(b_{r,IF}^{sys}-b_{IF}^{sys,s}\right)+ \frac{1}{\lambda_{IF}^{sys}}\left(dt_{r}^{sys}-dt^{sys,s}\right)\end{cases} \tag{11}\]
## 5 Evaluation
### Performance Evaluation  
We applied the default experimental parameter set \(\{C=144\), \(A=51.84,\sigma=0\}\) for evaluation to check the feasibility of our solutions. Figure 15 illustrates fingerprint shifts in the testing area. RSSI values at the same location can differ by up to 52.1 dB, which indicates that the fingerprints are sensitive to codeword change, thereby demonstrating the feasibility of using the DeepMIMO for our evaluation.  
To visualize the location-estimation errors, we used bar charts: each bar's middle line is the mean squared error (MSE), and its height equals two times the standard deviation (Var) over the mean squared error of all concerned testing points. Most errors are within this interval, which indicates the prediction stability of each method. The numerical results are also provided in tables.  
For the experimental parameter set \(\{C=144,A=51.84,\sigma=0\}\), the results of the oracle/baseline cases and our solution are shown in Figure 16. One may notice that the location-estimation errors of our solution are much closer to that of the oracle case rather than the baseline case, which illustrates the representations extracted by our method accommodate different codewords. Therefore, we can conclude that the feature extractor in our system can obtain codeword-independent representations of fingerprints. Then, compared with the RV version, the desired CV version of our method obtained smaller errors, which supports our analysis in Section 4.6. The detailed numerical results presented in Table 2 show that we achieved centimeter-level accuracy.  
Figure 16: The location-estimation errors of both LoS and NLoS areas for the oracle/baseline cases, and the CV/RV versions of our solution when \(\{C=144,A=51.84,\sigma=0\}\). Given the squared error set \(e\), for each bar, the middle line is \(mean(e)\), i.e., MSE, and its height ranges from MSE - \(std(e)\) to MSE + \(std(e)\).  
Figure 15: Fingerprint shifts (dB) in the testing area for three frequencies. The height means the maximum RSSI shift at that position when \(\{C=144,A=51.84,\sigma=0\}\).
# Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes
###### Abstract  
Node localization is an essential requirement in the increasing prevalence of wireless sensor networks applications. As the most commonly used localization algorithm, the DV_Distance algorithm is more sensitive to ranging error, and also has lower localization accuracy. Therefore, this paper proposes a novel difference DV_Distance localization algorithm using correction coefficients of unknown nodes. Taking account of the fact that correction coefficients of unknown nodes should be different, the proposed method has employed the correction model based on unknown nodes. Some correction coefficients for different direction anchor nodes can be indirectly calculated using the known difference of actual Euclidean distance and corresponding accumulated hop distance between anchor nodes, and then the weighting factors for the correction coefficients of different direction anchor nodes are also computed according to their actual contribution degree, so as to make sure that the corrected distances from unknown nodes to anchor nodes, modified by the final correction coefficient, are closer to the actual distances. At last, the positions of unknown nodes can be calculated using multilateral distance measurement. The simulation results demonstrate that the proposed approach is a localization algorithm with easier implementation, and it not only has better performance on localization accuracy than existing DV_Distance localization algorithm, but also improves the localization stability under the same experimental conditions.  
Keywords:wireless sensor network; node localization; DV_Distance; unknown node; difference; distance correction +
Footnote †: journal: Sensors
# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
## 4 Relationships Necessary for the Application of the Method
### Solar EV Charging
#### 4.4.4 Relationship 4: Production-to-Consumption Ratio versus and Self-Production Rate  
This section explores the relationship between the Production-to-Consumption ratio (\(PTC\)) and the \(SPR\), given a particular charging strategy chosen among the three described in Section 4.4.2. In order to vary the \(PTC\), as the total energy consumption of the cars is independent of the charging strategy, we have modified the value of the PV production. Fifteen different sizes of the PV plant have been considered for this study: \(S_{peak}\) = [0, 1, 25, 50, 75, 100, 125, 150, 175, 200, 300, 700, 1000, 5000, 12,000] kWp.  
First, we perform the following 17 simulations with the horizon spanning more than a 6-year period:  
* One simulation of the Plug and Charge strategy;
* One simulation of the Mean Power strategy;
* One simulation Smart charging strategy for each of the 15 different values of \(S_{peak}\).  
Daily \(PTC\) and \(SPR\) for each of these simulations and for each value of PV plant size are then calculated. Figure 11 illustrates the values of daily \(PTC\) along with daily \(SPR\), computed for a peak power value of 100 kWp, using more than 6 years of data. This figure distinguishes between the different charging strategies: in yellow, the results with smart charging, in red with Mean Power and in blue with Plug and Charge. We observe that the \(SPR\) is the highest for the smart charging strategy and is the lowest for the Plug and Charge strategy. We also observe that very high values of \(SPR\) are obtained with the SC strategy as soon as the \(PTC\) is higher than 1.  
In addition, we have computed the \(PTC\) and \(SPR\) over the entire 6-year long period. Each point of Figure 12 has \(PTC\) as x-coordinates and \(SPR\) as y-coordinates. For the purpose of simplicity, only the \(PTC\) values obtained with the 10 lowest PV peak power values {0, 1, 25, 50, 75, 100, 125, 150, 175, 200} are displayed. The colors of the points distinguish different strategies similar to those in Figure 11.  
Figure 11: Daily \(SPR\) and Daily \(PTC\) for every day for 6 years of the experimentation.  
We observe that, for a given value of _PTC_, the self-production increases with the SC strategy compared with the MP strategy. In the same way, the _SPR_ increases with the MP strategy compared to the PC strategy. For example, for _PTC_ = 1, the _SPR_ is equal to 56% for the SC strategy, is equal to 48% for the MP strategy and is equal to 38% for the PC strategy. We can also observe that in order to obtain an _SPR_ of 70%, the _PTC_ has to be equal to 1.5 with the SC strategy, to 2.5 with the MP and to 4.1 with the PC strategy.  
Notice that the results presented in Figure 12 allow us to simplistically aggregate our dataset and chosen strategies into the computational procedure described in Section 3.4.2. Such a choice is discussed in Section 5.2. The following subsection exemplifies the importance of these results in a specific use-case.