esa-sceva/satcom-chunk-collection huggingface dataset:
# The Analysis of Experimental Deployment of IGLUNA 2019 Trans-Ice Longwave System
## 7 Conclusions  
For the first edition of the IGLUNA program a means of direct communication between an ice-cave-hidden lunar habitat and the surface was proposed. This system, operating on longwave of 270 kHz with possible subcarriers (in the broadcasting part of the spectrum with magnetic quarter-wavelength loop antennas on high altitude), was set up and operated in June 2019 on the Klein Matterhorn glacier in Switzerland. Basic measured and calculated parameters of the antenna system were presented; a formula describing the obtained field intensity versus distance has been proposed. The parameters of the surrounding medium have also been measured and calculated. The system showed a possibility of radio communication on large distances, as well as a possibility of remote longwave sensing of the nearby rock and ice layers [4], similar to ground penetrating radars [33; 34; 35]-applicable also on the IGLUNA program's targeted celestial body, where the attenuation of the signal is expected to be even lower due to less lossy environment than on humid Earth [36].  
Conceptualization, investigation, writing: T.A.M.; supervision: J.M. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Foundation of the Development of Radiocommunication and Multimedia Technology (2019).  
The author would like to thank the IGLUNA 2019 Staff and IGLUNA P14 GLACiER Team members from Warsaw University of Technology, Poland, for their support, encouragement and help during the development, transportation, setting up, and operation of the experiment during the field campaign; the Foundation for the Support of Radiocommunication and Multimedia Technology Development for financially supporting the attendance to the field campaign; the Babice Translantatic Radio Station Culture Park Association for providing access to professional radio transmitter components for the tests of the developed communication system, and the members of the Facebook groups 'VLF-ULF-ELF' and 'Earth probes used as antenna', without whom the world-ranged signal monitoring would not have been possible.  
The authors declare no conflict of interest.  
Figure 16: The value of the channel capacity as a function of the bandwidth for the IGLUNA 2019 longwave system.
# Bar-Wave Calibration of Acoustic Emission Sensors
## 3 Results and Discussion
### AE Sensor Response
#### 3.2.11 PAC R15a Sensor  
This sensor is a newer design of R15 sensor discussed in Section 3.2.10 above, a nominally 12.7 mm diameter, 150 kHz sensor for routine AE applications. Results of bar-wave calibration tests are given in Figure 19. The displacement receiving sensitivities are shown in blue curves and velocity sensitivities are in red as in Figure 17. Solid lines are for the bar-wave data, while dashed curves are for the sensitivities to normally incident waves. As was the case for R15 above, the blue curve has the maximum of 16.7 dB at 156 kHz and the sensitivity deceases as frequency increases above 300 kHz. The blue dashed curve (normally incident wave sensitivity) has a flatter response. From 100 to 1200 kHz (except 600-800 kHz), the sensitivity is mostly within \(\pm\)5 dB. This can indeed be used for broadband detection. The velocity responses (red curves) reflect the displacement responses as expected. The solid green curve is the velocity calibration data provided by the manufacturer (and was converted to V/m/s scale by adding 143.4 dB [4]). From its frequency dependence, this was for normally incident wave sensitivity. As noted previously [4], such calibration data is usually overstated by 15 dB and the present result confirmed the same behavior.  
The bar-wave responses are different from those of R15 sensor, except for a dip at 460 kHz. Aperture effects are difficult to interpret here, perhaps resulting from a more complex sensing element design.  
Figure 18: Receiving sensitivity of PAC R15 (BS90). The displacement receiving sensitivities to bar waves are shown in solid blue curve for the present work and in red curve from the old data [8]. (reproduced with permission from [8]. Copyright AE Group, 2010). Green curve is the displacement receiving sensitivity to normally incident waves [4]. The velocity sensitivity corresponding to the solid blue curve is given in dotted blue curve.
# Can ISO GPS and ASME Torancing Systems Define the Same Functional Requirements?
## 3 Conclusions
The novelty of this paper can be found in providing detailed information regarding how use the ISO GPS system indicators to express limits for a part's geometry variation that were initially expressed by symbols given in ASME Y14.5. This paper focused on tools given in recently published ISO GPS standards. Table 1 shows that there are differences in syntax and semantics in geometrical tolerance indications. The differences in semantics are more dangerous because the same syntax (the same graphical representation) establishes another geometrical requirement that may be less or even not at all related to a part function. Currently, no computer tools are available to transfer specifications made according to the ASME Y14.5 standard to specifications made according to the ISO GPS system's rules. Education at universities and vocational trainings should cover tolerancing and knowledge in geometry assurance to a greater extent [2; 30]. At present, practice-oriented factory training courses [31] can help the industry to have highly skilled personnel to perform a manual transformation of geometrical tolerances specified according to the ASME Y14.5 standard to geometrical tolerances specified according to the ISO GPS system. This is the first step, but a future possibility for such transformation shall be built-in commercial CAD systems. The discussed cases of conversion from ASME Y14.5 specifications to the ISO GPS system do not cover all possible tasks that may face designers and production and quality staff in the industry, however, the most critical issues were considered. The paper's aim was to highlight that in both systems there are a number of different default interpretations, as well as the fact that the meaning of the same symbols may be different always or only in certain cases.
## 3 Results  
### Experiment 1  
Mu rhythm suppression in each brain area due to UI was examined by comparing the power values in the mu band in the resting state and during the operation, by one sample \(t\)-test (Figure 4). As a result, RC in the TUI/CAN condition showed a significant difference (\(t\) (14) = \(-\)2.46; \(p\) = 0.028). In contrast, no brain area showed a significant difference in the TUI/OBJECT condition or the GUI condition. Based on the data from the resting state and the data from the operation, mu rhythm suppression was determined for LC and RC in different conditions (TUI/OBJECT condition, TUI/CAN condition, and GUI condition; Figure 4).  
Figure 4: Mu rhythm suppression (LC and RC) in different conditions in Experiment 1. Asterisk (\({}^{*}p<0.05\)) means the significant mu rhythm suppression from baseline to observation of each actions. Daggers (\(+\)\(p<0.10\)) mean the tendency in comparison with other conditions at RC.  
Next, the main effect of conditions (TUI/OBJECT condition, TUI/CAN condition, and GUI condition) and brain areas (LC and RC), and their interactions were tested by two-way ANOVA. The ANOVA results confirmed an interaction between condition and brain area (\(F\)(2, 28) = 4.245; \(p\) = 0.025; \(\eta\)2\(p\) = 0.233), and a main effect of condition in RC (\(F\)(2, 28) = 4.234; \(p\) = 0.025; \(\eta\)2\(p\) = 0.232). By \(t\)-test, there was a tendency that the TUI/CAN condition resulted in a higher suppression in comparison with other two conditions (\(t\) (14) = 2.50; \(p\) = 0.077 in TUI/CAN vs. TUI/OBJECT, \(t\) (14) = 2.02; \(p\) = 0.077 in TUI/CAN vs. GUI).
## 2 Materials and Methods
### Simulation Methodology of the Faults  
Specific software modules were created to simulate the behavior of the 6 DO concentration sensor errors. These are integrated into the dynamic WWTP simulator. For each error type, the error signal produced by the sensor is given to the proportional-integral oxygen controller as the measured oxygen process variable. The DO control loop adjusts the airflow accordingly, returning back the controlled DO to the desired reference of 2 mg O\({}_{2}\)/L without offset. For each failure, the scenario was run as follows. The simulation was carried out without any fault for 139 days. Then, each fault was implemented starting from the 140 th day of the simulations and lasted for a period of 28 days. The process variables taken into consideration for the fault identification were considered from the first day of faulty operation, i.e., day 140 [47]. The same type period of measurements for 28 days was considered for collecting data that describe the normal operation.  
The DO sensor uses a galvanic measurement technique. The considered scenarios for the faults were generated, such as to imitate the real DO sensor's steady state and dynamic behavior.  
For the bias type of fault, the DO sensor signal was considered to have a bias of +1.5 mg O\({}_{2}\)/L added to the actual DO process value. It simulates a constant difference between the true DO value and the DO signal produced by the defective air blast assembly of the sensor.  
To mimic the drift, a constant value of 0.05 mg O\({}_{2}\)/L was integrated in time, and it supplemented the DO genuine value. This fault simulates a defect in the electronic circuits of the transmitter, produced by a time-increasing parasite electric capacitance.  
The sensor's wrong gain type of fault was considered by an incorrect gain factor of 1.4, which consists of an incorrect relationship between the actual DO process value and the sensor output. This fault type is potentially generated by a wrong calibration of the sensor or by calibration sudden change due to internal membrane deterioration. The first order filter with a time constant of 0.3 days was used to smoothly introduce the faulty gain over time for passing from normal to fault-affected operation.  
The defective measured process variable for the case of loss of accuracy type of fault was generated by adding to the DO true value a random signal value from the interval \(-\)2.5, 2.5. Such sensor fault can originate from the partial dislocation of the anode of the cathode electrodes of the sensor. Each random sample value had a duration of 0.1 days.  
For the fixed value type of fault, the constant of 2.2 mg O\({}_{2}\)/L was used. This sensor defect can be produced due to the leak of the sensor filling solution.  
For the complete failure minimum and maximum types of faults, the two very low and very high values of 0.1 mg O\({}_{2}\)/L and 6 mg O\({}_{2}\)/L were considered. They correspond to the practical circumstances when the electrical supply or parts of the transmitter electronic circuits are malfunctioning or due to the defective self-cleaning assembly, which leads to sensor clogging.
## 3 Experimental Results and Analysis
### Effect of Vertical Pressure on Heat Transfer
The results show that when the temperature of the storage structure wall is affected by the weather, the heat of the grain pile is transferred to the boundary of the grain pile via conduction and natural convection. Moreover, as the depth of grain pile increases, the vertical pressure increases, and the porosity of the grain pile decreases. As the porosity of the grain pile decreases, the contact area between the grain particles increases and the heat conduction rate accelerates. The temperature difference between the grain pile and low-temperature boundary wall tends to attain equilibrium faster. Figure 3: Average temperature variation at the middle plane under different pressures.
## 5 Simulations and Discussions
### Real-Time Ability  
The mean and standard deviation of the optimization time of the previously mentioned six controllers are shown in Figure 11. It can be seen that the mean of optimization time falls in the range of \([5,25]\) ms. Besides, the standard deviation of the optimization time is 0.0124 ms, indicating that the optimization time is relatively constant. Therefore, the proposed MPC scheme can be used in real-time applications.  
Figure 11: Comparison of the optimization time.  
Figure 10: Simulations’ comparison with/without feedback corrections.
# Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey
## 5 Reliable Routing  
According to the Reliable Routing scheme, protocols are classified in two subcategories, depending on whether they support QoS or whether they avoid redundant data and tolerate path failures with the use of multiple paths. These subcategories are QoS based and Multipath based.
## 2 Dynamic System Model
### Application of Boundary Conditions  
To realize the dynamic behavior of the welding tool, motion conditions were applied such that the tool could move along the welding line of the base material, as shown in Figure 3. As basic settings, the rotation velocity and progress velocity of the welding tool were set to 2000 RPM and 600 mm/min, respectively. The load of the welding tool is transferred to the base material through vertical descent, and the transferred load increases as the vertical descent displacement increases. The total length of the welding line was 663.45 mm, as shown in Figure 4, and 0-1 s was set as the settling time to stabilize the analysis. After increasing the velocity for 1 s, dynamic behavior was implemented along the welding line at a constant velocity starting from 2 s. Accordingly, the total physical time required for welding the entire line was calculated as 67.85 s.  
The welding tool was divided into a shoulder and a pin, and the size of the welding tool was modeled by referring to the prototype tool fabricated for testing. To confirm the structural deflection of the base material, in this study, the lower surface of the shoulder and the upper surface of the base material were set in contact in the structural/dynamic analysis. Accordingly, the vertical downward displacement of the shoulder was forcibly applied to facilitate the calculation of the reaction force generated from the base material. According to the Hertzian contact law, for the elements in contact between bodies, the stiffness need not exceed 100,000 N/m if the Young's modulus is lower than that of steel [27]. As summarized in Table 1, the Young's modulus of each body used in this study was lower than that of steel, and the range of stiffness and damping coefficient was calculated as summarized in Table 2. If the coefficient is too high, the analysis time increases greatly. By contrast, if it is too low, appropriate contact between the bodies is not ensured. The appropriate range of the coefficient was calculated by referring to the Hertzian contact law. The standard of the calculated range is the range obtained through case runs depending on the variable, such that the correct dynamic motion is achieved.  
Figure 3: Friction stir welding analysis model.
# A Novel Localization Technique Using Luminous Flux
## 1 Introduction
This approach enables us to decouple the position propagation aspects for error estimation. Hence, we will investigate the PDoP attributes with our positioning model. Thus, this paper advocates that the PDoP approach be used to modify light sources and their positions randomly. The potential contributions of this work can be summarized as follows. * The configurations of the LED lattice are devised by using the Lambertian model for individual LEDs. * According to our knowledge, no specific research has been performed using PDoP attributes prior to visible light-based indoor localization. * We analyze PDoP with respect to channel impulse response time, considering both direct and multipath reflections, which has not been done before. * Novel positioning algorithms are used to compute PDoP on each LED, along with a comparison of root mean square error (RMS) based on the simulated RSS values, the RMS errors, and the SSE errors are computed with two different LED lattices to estimate the positioning error. The structure of this paper is as follows. Section 2 describes the system model (including the VLC principle) and the indoor channel models. Section 3 outlines the power measurement steps performed within the system. The adaptive collaboration positioning algorithm is discussed in Section 4. Experiments, results, and discussion are presented in Section 5. Finally, the concluding interpretations are given in Section 6.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 4 Discussion
### Future Directions
#### 4.3.2 Real-Time World Testing and Deployment  
Numerous technologies have only been evaluated in simulation environments so far; therefore, in the future, they should be tested in real-world conditions. For example, several VLC technology experiments in the discussed literature were only carried out in simulation environments (i.e., [112; 113; 115; 116; 117; 118]). In the future, they should be tested in real environments under variable conditions, such as windows with ambient light, different wall colours, and reflective materials.  
Furthermore, in [131], the experiments were carried out using a smartphone only in LoS conditions; therefore, challenges associated with multipath propagation were not sufficiently tested. In particular, different techniques for filtering out the noise and eliminating multipath effects for acoustic-based systems should be tested in real-world conditions in the future. Furthermore, throughout the literature, IMU-related tracking algorithms mostly assume a normal walking pace in implementing PDR, and no other paces from the real world are tested. In fact, the experiments are commonly carried out with only a single participant within a controlled environment; therefore, future directions for real-world testing can include multiple participants in large open crowded environments.
## 4 Near-Surface Defect Detection and Identification
### Evaluation Indexes  
This paper utilizes various evaluation metrics such as accuracy, loss, recall, precision, F1-score, Floating Point Operations (FLOPs), parameters, and model size to assess the efficacy of the model. Accuracy measures the proportion of accurately predicted samples among all the samples. The recall evaluates the proportion of positively predicted samples out of all the samples. On the other hand, precision reflects the proportion of accurately predicted real samples among all accurately predicted samples. The F1-score considers both precision and recall to find a balance between the two. FLOPs is a crucial index used to assess the computational complexity held by the model. The smaller the FLOPs, the simpler the model's calculations. The parameters signify the total number of parameters in the model and is used to assess the size of the model.  
Table 2 showcases the confusion matrix for a classification problem, where true positive (TP) denotes the correct identification of a positive sample, true negative (TN) reflects the correct identification of a negative sample, false positive (FP) indicates the negative sample being falsely identified as positive, and false negative (FN) implies the inaccurate identification of a positive sample as negative.  
The calculation formula for each index is as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\times 100\%, \tag{6}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{7}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{8}\]  
\[F1-score=2\frac{Precision\cdot Recall}{Precision+Recall}\times 100\%. \tag{9}\]  
The FLOPs calculation formula for models in ordinary convolution layer, DSC layer, and fully connected layer are as follows:  
\[FLOPs_{ordinary}=2H_{out}W_{out}K_{th}K_{w}C_{in}C_{out}, \tag{10}\]  
\[FLOPs_{DSC}=2H_{out}W_{out}C_{in}(K_{th}K_{w}+C_{out}), \tag{11}\]  
\[FLOPs_{FC}=2C_{in}C_{out}, \tag{12}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**True Class**} & \multicolumn{2}{c}{**Predicted Class**} \\ \cline{2-3}  & **Positive** & **Negative** \\ \hline
**Positive** & TP & FN \\
**Negative** & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
where \(H_{out}\) represents the height of the output feature maps, \(W_{out}\) represents the width of the output feature maps, \(K_{h}\) represents the height of the kernel size, \(K_{w}\) represents the width of the kernel size, \(C_{in}\) represents the number of input channels, and \(C_{out}\) represents the number of output channels.
## 3 Proposed System
### Detailed Operation of System
#### 3.4.1 Preparation (\(k\))  
With security parameter \(k\) as the input, generate the generator, \(g\), as well as \(G_{0}\), which is a prime order, \(p\).  
The bilinear map is \(\hat{e}:G_{0}\times G_{0}\to G_{1}\), and the system attribute group is \(\Omega=(a_{1},a_{2},\cdots a_{n})\) (where \(n\) is an integer).  
\(a_{j}\in\Omega\) selects a random element, \(t_{j}\in Z_{p}^{*}\).  
Use Equation (1) to generate \(y\), public key (\(pk\)), and master key (\(mk\)).  
\[\begin{split} y=\hat{e}(g,g)^{a}(\alpha\in\ RZ_{p}^{*},T_{j}=g^{t _{j}}(1\leq j\leq n))\\ pk=(\hat{e},g,y,T_{j}(1\leq j\leq n))\\ mk=(\alpha,t_{j}(1\leq j\leq n))\end{split} \tag{1}\]
# Development of a Submillimetric GNSS-Based Distance Meter for Length Metrology +
###### Abstract  
Absolute distance determination in the open air with an uncertainty of a few tenths of a millimetre is increasingly required in many applications that involve high precision geodetic metrology. No matter the technique used to measure, the resulting distances must be proven consistent with the unit of length (SI-metre) as realized in the outdoor facilities traditionally used in length metrology, which are also known as calibration baselines of reference. The current calibration baselines of reference have distances in the range of 10 to 1000 m, but at present there is no solution on the market to provide distances with submillimetric precision in that range. Consequently, new techniques such as multi-wave interferometry, two-wave laser telemeters or laser trackers are being developed. A possible alternative to those sophisticated and expensive techniques is the use of widely used Global Navigation Satellite Systems (GNSS) in order to provide a GNSS-Based Distance Meter (GBDM). The use of a GBDM as a potential technique for length metrology has been thoroughly analysed in several European research projects by using the state-of-the-art geodetic software, such as Bernese 5.2, but no definite conclusions have been drawn and some metrological questions are considered still open. In this paper, we describe a dedicated approach to build up a submillimetric GBDM able to be applied in the current calibration baselines of reference, as well as possible methods to cope with the multipath error of the GNSS signals which is the major limitation for the practical uptaking of the technique in metrology. The accuracy of the proposed approach has been tested following the length metrology standards in four experiments carried out in the Universitat Politecnica de Valencia (UPV). The results demonstrate that the proposed GBDM can provide an accuracy of a few tenths of a millimetre in the current calibration baselines of reference.  
Global Navigation Satellite Systems (GNSS); length; metrology; multipath +
Footnote †: This paper is an extended version of our paper published in [PERSON]; [PERSON]; [PERSON] Submillimetric GNSS Distance Determination for Metrological Purposes. In Proceedings of the 5 th International Colloquium on Scientific and Fundamental Aspects of GNSS/Gallico, Braunschweig, Germany, 27-29 October 2015.