esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 3 Overview  
In this section, we propose an adaptive routing algorithm based on the Relation Tree (AR-RT) in DTN. Besides, the summary of important used variables in the paper is listed in Table 1.  
All nodes in the network topology maintain a Relation Tree with themselves as the root node and update the Relation Tree in real time. The AR-RT algorithm is divided into two phases: the adaptive replication phase, and the sub-policy forwarding phase. Whenever a source node initiates a forwarding task, it first enters the adaptive replication phase, in which it replicates the most appropriate number of message copies based on its own cache occupancy. From the time the source node encounters the first relay node that is not the destination node, it randomly enters the sub-policy forwarding phase, in which the node carrying the message adopts a different forwarding strategy depending on whether the Relation Tree of the encountered relay node contains the destination node of the message, and all subsequent relay nodes adopt the same forwarding strategy until they encounter the destination node to complete the final forwarding of the message. The entire flowchart is shown in Figure 1.
## 6 Discussion
Even under the condition \(\varepsilon=-0.02\), it is superior to NoBI in terms of GDP and superior to BI40% in terms of investment. Therefore, \(\varepsilon=-0.1\) can be said to show the results of the extreme clarification of the respective effects. From this, it can be inferred from the present results that the implementation of BI may, to a certain extent, bring about social well-being; however, an excessive level of BI weighting may not bring about positive results. Although this is not an elaborate model and is intended only as a reference, BI20% would be about 30% of the median income in real terms. Although this is a middle-range model [54] and intended for reference purposes only, BI20% is approximately 30% of the median income in real terms. This figure is close to a quarter of GDP per capita, which is considered the appropriate level for BI [1; 27]. As work motivation in this model is directly related to labor supply, the average decrease in work motivation due to an appropriate level of BI, \(\varepsilon=-0.02\), is 5%. Even households with the largest decrease in work motivation over the 360 days modeled are only about 10%. This is not a significant departure from the 5-10% decrease in work motivation [7; 8; 30; 31] observed in previous empirical studies. This suggests that our results mirror previous studies regarding the relation between BI and work motivation. However, at the same time, the existing empirical studies have paid limited attention to the impact analyses of BI on the decision making of firms related to investment and production behaviors. These studies are limited to those that address the matter from a corporate perspective, noting that firms would be forced to raise wages due to decreased labor supply [31]. Conversely, macro-quantitative analyses [11] have taken multilayered feedback into account, leading to a decrease in capital due to a decrease in the amount of labor resulting from the implementation of BI. These studies have shown that implementing BI results in a decrease in GDP because BI leads to an increased decline in labor supply, and the decline in households' self-preservation awareness contributes to reduced savings, resulting in a decline in overall capital and a deterioration in productivity. These findings differ from those of our model, which attempts to compensate for the decreased labor force through capital investment. A noteworthy difference in the results is that when considering the impact of the multilayered interactions caused by BI, individual firms will also experience differences due to factors that allow them to adapt autonomously to change.
## 1 Background of Nanostructured Materials for Gas Sensors
### Configuration of Gas Sensors and Performance Indicators  
When a gas comes into contact with a sensor, the molecules adsorb onto the metal oxide surface, either donating or withdrawing electrons depending on whether the gas is reducing or oxidizing. Reducing gases, like hydrogen (H\({}_{2}\)) or carbon monoxide (CO), donate electrons, lowering the sensor's resistance, while oxidizing gases, such as oxygen (O\({}_{2}\)) or nitrogen dioxide (NO\({}_{2}\)), withdraw electrons, increasing the resistance. This change in resistance, which can be measured and correlated with gas concentration, forms the basis of the sensor's functionality [33].  
The main performance parameters of metal oxide-based gas sensors are sensitivity, selectivity, response time, recovery time, operating temperature, stability, repeatability, detection limit, linearity, and power consumption. Sensitivity refers to the sensor's ability to detect low concentrations of gas, while selectivity reflects its ability to distinguish between different gases. Response time measures how quickly the sensor reacts to gas exposure, and recovery time describes how fast it returns to baseline conditions once the gas is removed. Most sensors operate at elevated temperatures to accelerate reactions between the gas and the metal oxide surface, enhancing sensitivity and response time. Stability and repeatability are critical for consistent performance over time, ensuring the sensor provides reliable data without significant drift across multiple measurements. Detection limit refers to the lowest concentration of gas the sensor can detect, and linearity describes how the sensor's output signal relates to gas concentration, with a more linear response being preferable for easier data interpretation. Power consumption is another key factor, especially for portable or battery-powered devices, as sensors that operate at higher temperatures often require more energy.  
Enhancing the gas-sensing capabilities of these materials often involves modifying both surface properties and bulk characteristics. Such modifications can be achieved through techniques like nanoparticle deposition, surface coatings, or doping with foreign elements. These adjustments typically result in improved sensitivity compared to unmodified 1D nanostructures [34].
# Multi-Pass Stamping Forming a Concave Ring
## 3 Simulation Analysis of the Concave Ring Formed by Multi-Pass Bending
### Stress-Strain Field  
Figure 8a shows the stress distribution of the workpiece after the first pass. From Figure 8a, the area where the blank contacts the upper-1 has the highest stress. The effective stress in this area is less than 500 MPa. The effective stress at the bottom of the workpiece is almost negligible. Figure 8b is the stress distribution of the workpiece after the second pass. From Figure 8b, the highest stress is concentrated in the bending area of the workpiece. The highest effective stress value does not exceed 700 MPa.  
Figure 8: Stress field. (**a**) First pass; (**b**) second pass.  
Figure 7: Finite element model of multi-pass stamping of concave ring. (**a**) First pass; (**b**) Second pass.  
Figure 9a shows the strain distribution of the workpiece after the first pass. From Figure 9a, the effective plastic strain is concentrated in the bending area, which shows that the plastic deformation of the workpiece only occurs in this area. It also means that the design of the upper-1 is reasonable. Figure 9b is the effective plastic strain distribution of the workpiece after the second pass. From Figure 9b, the effective plastic strain is still concentrated in the bending area. After two passes of forming, there were no obvious defects such as cracks or breakage in the bending area.  
According to the analysis of the stress and strain field, the plastic deformation area was concentrated in the bending area. There was no obvious plastic deformation in other areas. This shows that a concave ring can be stamped by the above stamping die.
## 3 Results and Discussions
### Three Parallel Microchannels
According to Equation (3), moving from the bottom (h = 0 \(\mu\)m, solid lines) to the middle (h = 100 \(\mu\)m, dotted lines) of the device, the wall shear stress increased near the wall and decreased in the middle. This can affect cell adhesion, cell migration, and alignment. Furthermore, the flow rate and microchannel size (width) can affect cell adhesion, cell migration, and alignment. Additionally, cell migration within microfluidic devices is influenced by microchannel width [23; 41]. Figure 7i shows the generated mesh study of the three parallel microchannels and the micropillars, showing a total of 810,805 elements, which is adequate to guarantee a grid-independent solution. Figure 7: The fluid flow simulation of the three parallel microchannels separated by two rows of micropillars with (**a**) flow rates of 2 \(\mu\)L/min; (**b**) 6 \(\mu\)L/min and (**c**) 10 \(\mu\)L/min, which are plotted versus the width of the device using three different flow rates within the side microchannels. The wall shear stress simulation in the three parallel microchannels device with a height of 200 \(\upmu\)m and three different flow rates within the side microchannels at (**d**) 2 \(\mu\)L/min; (**e**) 6 \(\mu\)L/min and (**f**) 10 \(\mu\)L/min (simulated shear stress and velocity magnitude along red line); (**g**) the velocity profile; (**h**) wall shear stress profile at the bottom of the microchannels (h = 0 \(\upmu\)m, solid lines) and wall shear stress profile at the middle of the microchannels (h = 100 \(\upmu\)m, dotted lines); (**i**) generated mesh density study for three parallel microchannels separated by two rows of micropillars. Red circles highlight the velocity and shear stress fluctuations near the micropillar structures. It is important to highlight that the dimensions of the micropillars can also influence the width of the microchannels. According to Equation (4), the width of the channels is inversely proportional to the wall shear stress. Therefore, as the size of the micropillars increases, the width of the channels decreases and, consequently, the wall shear stress will increase.
# Scalable OneM2M IoT Open-Source Platform Evaluated in an SDN Optical Network Controller Scenario +
## 4 Results
Figure 7: Representation of \(R_{POST}\), varying the number of sending ROADM devices \(N\) and the sleeping time. decreased). Table 2 shows the 25 th percentile, the median and the 75 th percentile for the produced results. The results confirm the capability of the platform to configure the network elements for provisioning purposes, as all update messages correctly arrive at the OneM2M platform from the controller and are delivered to the interested ROADM agents. The experiment also shows that the network is very reactive in changing its working parameters after receiving updates from the controller (e.g., a connection link that cannot be reached anymore). The required time is small even when the number of nodes sending (\(N\)) and receiving (\(M\)) updates increases. Moreover, we compared the obtained results with those obtained in analogous works employing pure SDN control. It is possible to notice that the time required by the SDN controller to configure the data-plane with OpenFlow and NETCONF protocols takes around 100 ms to complete the configuration procedure (i.e., the configuration of 8 devices), considering the control-plane setup time reported in [5]. The results obtained in our study perfectly match with literature SDN-based solutions also when the number of controller devices grows. We evaluated the scalability of the solution also considering the CPU and memory usage in the Virtual Machine hosting the OneM2M platform and on the Virtual Machines where the ROADM agents run. As shown in Figure 9, the platform can handle a significant amount of messages: the mean CPU usage is around 15%, with maximum peaks of around 50% at the moment in which the burst of messages arrives. The RAM results to be mostly unused as it has 41.4% of space. Figure 10 shows that the Virtual Machines in which the agents run have a workload similar to the one dedicated for the OneM2M platform. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sleeping Time** & **25 th Percentile** & **50 th Percentile** & **75 th Percentile** \\ \hline
1 s & 69.5 & 108 & 162 \\
2 s & 57 & 91 & 120 \\
30 s & 48 & 75 & 97.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: 25 th percentile, median and 75 th percentile of results for the second experiment  
Figure 8: Representation of configuration time \(T_{C}\), varying the number of agents subscribed to configuration messages \(M\) and the sleeping time between status updates of the \(N\) nodes.
## 4 Measurement and Result Analysis  
The schematic of the measurement setup for characterizing the fabricated microring resonator is given in Figure 8. A tunable laser (Newport, with a wavelength range: 890-910 nm and 20 pm resolution) and a power meter were utilized to scan the spectrum of the microring resonator. Since the waveguide was much smaller than the standard fiber, there was a large mode mismatch loss between the polymer waveguide and fiber. In order to solve this problem, the light was launched and collected by tapered lensed fibers. The polarization controllers were applied so as to ensure the whole system operated in the TE mode.  
The measured spectrum of the fabricated microring resonator is shown in Figure 9a. The free spectral range (FSR) and the ER could be obtained as 335 pm and 11.6 dB, respectively. The single peak was fitted by a Lorentz curve and the FWHM (full width at half maximum) was obtained as 39 pm. The Q factor can be calculated through the equation,  
\[Q=\frac{\lambda_{m}}{\text{FWHM}} \tag{3}\]  
where \(\lambda_{m}\) is the resonance wavelength. Then the Q factor could be obtained as \(2.3\times 10^{4}\).
## 3 Experimental Results and Analysis
### Metric Definitions  
In order to assess the performance of our system concisely, for \(N\) datasets, with each dataset of length \(n\), we define the following metrics:  
* Translation error (TE) (m): \(\|\mathbf{r}_{s}(1:2)-\hat{\mathbf{r}}_{s}(1:2)\|\). The TE indicates the 2D spatial closeness of the average of the estimated positions in the stance phase to the ground truth over the whole trajectory.
* Absolute heading error (AHE) (\(\circ\)): \(\left|\psi_{s}^{g}-\hat{\psi}_{s}\right|\). The AHE indicates the absolute error between the average heading of each stance phase and the ground truth over the whole trajectory.
* Heading drift (HD) (\(\circ/minute\)): \(\left|\psi_{n}^{g}-\hat{\psi}_{n}\right|/\text{duration}\).
* Start-end error (SE) (m): \(\|\mathbf{r}_{n}(1:2)-\mathbf{r}_{1}(1:2)\|\). The 2D translation error between the start point and end point.
* Total traveled distance error (TTDE) (%): \(\|\mathbf{r}_{n}(1:2)-\mathbf{r}_{1}(1:2)\|/\text{traveled-distance}\). The final 2D SE of the total traveled distance.
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 3 Microcontroller Design  
Design of a sensor node microcontroller (called TNODE after the node's first version developed in the Tandem project) is based on the IPMS430 processor core, which is fully compatible with the Texas Instruments MSP430 processor architecture [45]. The IPMS430 is an asynchronous processor addressing up to 1 MB of memory space. The processor communicates with peripherals via a dedicated peripheral interface. Peripherals are designed as standard synchronous devices having implicit clock synchronisation on their interfaces to the processor. A dedicated clock provided to each of the peripherals is only generated when a peripheral is accessed by the processor. The global clock runs independently and it does not interfere with the generated clocks. The peripheral interrupts are chained with predefined priorities.
# Optimized Combination of Local Beams for Wireless Sensor Networks
###### Abstract  
This paper proposes an optimization algorithm to determine the optimal coherent combination candidates of distributed local beams in a wireless sensor network. The beams are generated from analog uniform linear arrays of nodes and headed toward the random directions due to the irregular surface where the nodes are mounted. Our algorithm is based on one of the meta-heuristic schemes (i.e., the single-objective simulated annealing) and designed to solve the objective of minimizing the average interference-to-noise ratio (INR) under the millimeter wave channel, which leads to the reduction of sidelobes. The simulation results show that synthesizing the beams on the given system can form a deterministic mainlobe with considerable and unpredictable sidelobes in undesired directions, and the proposed algorithm can decrease the average INR (i.e., the average improvement of 12.2 dB and 3.1 dB are observed in the directions of \(\frac{\pi}{6}\) and \(\frac{2\pi}{3}\), respectively) significantly without the severe loss of signal-to-noise ratio (SNR) in the desired direction.  
collaborative beamforming; analog uniform linear array; millimeter wave channel; simulated annealing +
Footnote †: journal:  
_Article_  
Optimized Combination of Local Beams for Wireless Sensor Networks
## 3 Performance Analysis
### Channel Fading  
Fading channel models in THz communication are models that account for the effect of obstacles and losses encountered during the travel of THz signals. These patterns represent various channel losses that result in a decrease in the transmitted signal strength or a change in the signal shape.  
They are commonly used elements for selecting different fading channels in the THz band and evaluating their effects on performance. The use of fading profiles, environmental conditions, and frequency-dependent fading criteria is important in improving the efficiency of THz communication and determining the most suitable channels. Based on these criteria, analyses and evaluations of different channels can be made. For this reason, Rayleigh, Rician, Nakagami-m, Weibull, and log-normal fading channels were determined to provide the best communication performance.
## 2 Optimal Design Consideration and Analytical Modeling
### COMSOL Simulation of the Microheater
In our simulation setup, applied voltage was set to 5 V, the temperature in the cavity region was estimated to be approximately 90 \({}^{\circ}\)C based on the designed structure. Additionally, it was confirmed that the microheater exhibited excellent response time, recovery time, long-term stability, and repeatability.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 2 Materials and Methods
### Search Strategy  
The present work includes the available research references for the past 5 years, from January 2019 until August 2024. Several keywords were used to import the most relevant articles, conference papers, book chapters, and studies, such as object tracking, localization, positioning, indoor object tracking, indoor localization, indoor positioning, indoor positioning system, real-time tracking system, real-time localization system, ultra-wideband, UWB tracking, multi-tag positioning, time of arrival (ToA), time of departure (ToD), time difference of arrival (TDoA), angle of arrival (AoA), phase difference of arrival (PDoA), two-way ranging (TWR), multilateration, trilateration, and triangulation. The search was initially started on the IEEE Explore and ScienceDirect databases and then extended to the other listed databases. More than 600 related papers were imported from IEEE, Elsevier (Scopus and ScienceDirect), WoS, PubMed, and the ACM Library for the mentioned period for eligibility. A group of the most relevant papers was selected to be discussed in the present work. We focused on several points that helped with the inclusion and exclusion criteria. The systematic review was carried out following the PRISMA protocol.  
Figure 1: UWB and other RF technologies’ frequencies and power spectral densities.
## 2 Materials and Methods
### Model Calibration
#### 2.5.2 Regular Pilot Point Placement
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Statistics**} & \multirow{2}{*}{**Non Calibrated**} & \multicolumn{3}{c}{**35 Points**} & \multicolumn{3}{c}{**60 Points**} \\ \cline{3-5}  & & **Regular** & **MHMDG** & **Regular** & **MHMDG** \\ \hline Mean r squared & 0.396 & 0.695 & 0.683 & 0.886 & 0.901 \\ RMS (m) & 0.69 & 0.38 & 0.34 & 0.37 & 0.32 \\ Normalized RMS (\%) & 14.65 & 8.035 & 7.2 & 7.78 & 6.78 \\ Max residual (m) & \(-1.6\) & \(-0.96\) & 0.9 & \(-1\) & 0.9 \\ Min residual (m) & 0.078 & 0.0014 & \(-0.0033\) & 0.011 & \(-0.0053\) \\ residual Mean (m) & \(-0.22\) & 0.0063 & 0.024 & \(-0.0067\) & 0.015 \\ Standard error of & & & & & \\ estimation (m) & 0.11 & 0.063 & 0.056 & 0.061 & 0.053 \\ K min & \(4.71\times 10^{-5}\) & \(4.6\times 10^{-7}\) & \(1.7643\times 10^{-7}\) & \(1.7643\times 10^{-7}\) & \(1.2867\times 10^{-7}\) \\ K max & \(4.716\times 10^{-5}\) & \(1.037081\) & \(2.445149\) & \(2.445149\) & \(0.2444386\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistic of the simulation over 35 parameters and 60 parameters in regular and MHMDG pilot point placement.
## Appendix A An Algorithm to (Semi-)Automatically Remove Outliers from OBR Strain Data
The algorithm to detect outliers works now in the way described below. \"Left/right\" means data points with smaller/larger x or length values. Load the (aforementioned) datasets. Search the second dataset for flat sections. Check for each point if it constitutes a flat section with either the next or previous points or the point to the left and right. If this is not the case...  
\(\ldots\) put the point into the list of possible outliers, otherwise...  
\(\ldots\) put this point into the list of possible correct data for this flat section. At least three, in the x-direction, equally-spaced, subsequent, as correct assumed data points constitute a flat section. The flat section ends if a data point is an outlier. The next flat section begins when the above checks return at least three new subsequent ones, as correct assumed data points. If just two subsequent data points pass the above check, these are taken out of the list of correct assumed data points and put into the list of possible outliers. All data points in flat sections are considered to be correct. Find the \"undefined\" sectors between flat sections. The last/first point of the flat section to the left/right defines the start/end of an undefined sector. Check for each supposed outlier in the undefined sections if it lies (within a given threshold) on the line between the two endpoints of the related undefined sector. If this is the case, transfer this point to the list of correct data points. Check for each remaining possible outlier if it appears also in the third dataset (as described above). If this is the case, transfer the data point to the list of correct data points. If data points in the undefined sectors (in Step 3 determined) are not in the list of correct data points, substitute their strain values by interpolating according to Assumption 4. * All remaining outliers are just transferred to the list of correct data points to have a consistent dataset (see the text below). The threshold in Step 4 is larger than the one for determining flat sections. It seems reasonable to increase the threshold because the higher rapid change in the strain values may also cause larger fluctuations in the data. The interpolation in Step 6 requires two flat sections to interpolate between. In the case that no interpolation can take place, Step 7 takes effect. This algorithm detects approximately 90% of all outliers in the strain data. Given the non-corrected dataset in Figure 6 (black dots), this leaves ca. fifteen to twenty outliers undetected. If each corrected dataset is plotted automatically and exported as a PNG-file, the user can in an easy way and within a short amount of time spot the remaining outliers and correct these manually. This visual inspection of the corrected data is also necessary to correct the above-mentioned artifacts.