esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermodynamic Analysis and Performance Evaluation of Microjet Engines in Gas Turbine Education
## 5 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**NE** & **PT** & **PJ** & **PE** & **h3t** & **ITt** & \(\Delta\)**hJ** & \(\Delta\)**hE** & **ITt.id** & \(\Delta\)**hJ.p.id** & \(\Delta\)**hE.id** & \(\Delta\)**hJ.id** \\ \cline{2-13}
**rpm** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** \\ \hline
34,951 & 0.571 & 0.231 & 0.802 & 916.009 & 12.476 & 5.049 & 17.525 & 14.853 & 5.699 & 20.552 & 5.714 \\
47,160 & 1.136 & 0.703 & 1.839 & 905.688 & 18.475 & 11.432 & 29.907 & 21.994 & 12.890 & 34.884 & 12.938 \\
55,320 & 1.776 & 1.236 & 3.012 & 935.891 & 24.456 & 17.026 & 41.482 & 29.114 & 19.177 & 48.291 & 19.268 \\
68,295 & 3.493 & 2.465 & 5.958 & 909.517 & 35.580 & 25.108 & 60.688 & 42.357 & 28.209 & 70.566 & 28.416 \\
79,369 & 5.781 & 4.111 & 9.892 & 892.662 & 46.420 & 33.012 & 79.432 & 55.262 & 36.994 & 92.256 & 37.361 \\
88,787 & 8.764 & 6.089 & 14.853 & 897.855 & 58.259 & 40.475 & 98.734 & 69.356 & 45.236 & 114.592 & 45.806 \\
97,786 & 11.913 & 8.801 & 20.714 & 925.544 & 69.068 & 51.025 & 120.093 & 82.224 & 56.912 & 139.136 & 57.747 \\
105,423 & 15.793 & 11.714 & 27.507 & 948.480 & 80.493 &The third series of data results are defined by the compressor and turbine total adiabatic efficiency \((\eta_{C,t})\), \((\eta_{T,t})\), compressor outlet total pressure \((p_{2,t})\) and specific actual enthalpy \((h_{2,t})\), turbine total expansion degree \((\delta_{T,t})\) and outlet pressure \((p_{4,t})\), gases mass flow \((G_{f})\), jet nozzle outlet gas velocity \((C_{5})\), Mach number \((M_{5})\), velocity coefficient \((\varphi_{f})\), and thermal efficiency \((\eta_{te})\). Tables 10  
To expand the volume of presented data without duplication and provide a clearer comparison of values, the following charts from Figures 4-15 depict specific data results derived from the tables presented in Tables 6-11. These charts offer a visual representation of key performance metrics and parameters, making it easier to compare and analyze the data across different engine models and operating conditions. As shown in Figure 4, for \((dl_{T.t})\) and \((dl_{T.tid})\), a random variation from an engine to another is observed between 46.5% to 66.6% in the case of \((dl_{T.t})\) and between 47.5% to 70.8% in case of \((dl_{T.tid})\). For \((dh_{I})\) and \((dh_{I.id})\), a random variation between 33.4% to 53.4% is also observed in the case of \((dh_{I})\) and between 29.2% to 52.3% in the case of \((dh_{I.id})\). As shown in Figure 5, for \((dl_{T.t})\) and \((dl_{T.tid})\), a decreasing variation from the idle regime to the maximum is observed, with some random values between 71.2% to 54.8% in the case of \((dl_{T.t})\), and between 72.3% to 56.5% in the case of \((dl_{T.tid})\). For \((dh_{I})\) and \((dh_{I.id})\), an increasing variation is observed with some random values between 28.8% to 45.2% in the case of \((dh_{I})\) and between 27.7% to 43.5% in the case of \((dh_{I.id})\).
## 4 Results and Discussion
### Land/Water Discrimination from Intensity-Based Features  
The Jenks break optimization was used to identify \(I_{thresh}\) and \(ICOV_{thresh}\) in order to label water points. Table 5 presents the threshold values of intensity (\(I_{thresh}\)) and \(ICOV\) (\(ICOV_{thresh}\)). The \(ID_{thresh}\) was selected as 0.7 to minimize the type I (\(FP\)) and II (\(FN\)) errors. The labeled LiDAR points from different channels based on \(ICOV\) and \(ID\) are shown in Figure 6, while the accuracy measures of the labeled points are provided in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Point Feature**} & \multirow{2}{*}{**Channels**} & \multicolumn{2}{c}{**Completeness (\%)**} & \multicolumn{2}{c}{**Correctness (\%)**} & \multirow{2}{*}{**Overall Accuracy (\%)**} \\ \cline{3-3} \cline{5-6}  & & **Land** & **Water** & **Land** & **Water** \\ \hline \multirow{2}{*}{\(HV\)} & C1C3 & 62.9 & **97.5** & **97.7** & 61.1 & 75.8 \\  & C2C3 & **67.3** & 96.8 & 97.3 & **63.9** & **78.3** \\ \hline \multirow{2}{*}{\(HSD\)} & C1C3 & 57.5 & **97.4** & **97.4** & 57.8 & 72.4 \\  & C2C3 & **59.8** & 97.0 & 97.1 & **59.0** & **73.7** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy measures of labeled point based on \(HV\) and \(HSD\). The best results are highlighted in bold.  
Figure 5: Labeled LiDAR points based on \(HV\) and \(HSD\).  
Generally, the accuracy measures using intensity-based features are relatively low. The intermediate part of the water body had high intensity variation due to different angles of incidence of the laser pulse with respect to the water surface. Therefore, this characteristic was used as the basis of _ICOV_, where higher _ICOV_ values represented water points. However, some water points in C3 exhibited low _ICOV_ and were misclassified as land. As a result, the completeness of the water class from C1 (93.5%) and C2 3.6%) was higher than from C3 (54.0%). On the other hand, the correctness of the water class from C1 (25.0 %) and C2 (24.6%) was lower than from C3 (58.0%). This is because some vegetation areas had high intensity variation in C1 and C2, so they exhibited high _ICOV_ and were misclassified as water points.  
For _ID_ feature, the water points were assumed to have the lowest intensity values in the scene. However, based on this assumption, the results revealed low discrimination rates. This is mainly because the intensity variations of the water points as a result of low altitude and looking angles of C1 and C2. Thus, most returns of the water area were recorded at normal incidence angle and, hence, caused intensity variations. On the other hand, the discrimination results demonstrated the highest completeness (88.7%) and correctness (52.3%) of water class in C3. This is due to the returns from the water surface and benthic layer that exhibited lower intensity values. The intermediate part of the water body was misclassified as land due to high intensity variation. In land area, points that had low intensity values (i.e., vegetation points) were incorrectly classified as water.
## 3 Results and Analysis
### Reservoir Physical Property Evolution
#### 3.2.2 Reservoir Temperature  
Figure 7 presents the evolution diagram of reservoir temperature (\(T\)) for the cases without and with heating within 1080 days. Initially (60 days), a low-temperature area in the TPL of the reservoir close to the wellbore can be observed in the case without heating. This is because there is a substantial Joule-Thomson effect due to the significant amount of highly saturated free gas entering the wellbore from the TPL, and the wellbore contact area is only around 9.4 m\({}^{2}\). With the warm bottom water entering the wellbore, the reservoir temperature around the wellbore gradually increases during the latter period (360 days to 1080 days). In the temperature field diagram (60 days) of the heating case, this phenomenon can also be observed. A heating power of 100 W/m is not enough to completely offset the Joule-Thomson cooling effect located in the TPL. Therefore, a corresponding low-temperature region is also observed here. However, as production progresses, the temperature of the reservoir around the wellbore gradually increases because of the weakening of the Joule-Thomson effect, sustained electrical heating, and bottom warm water entering the wellbore.  
Figure 6: Evolution diagram of pore pressure without and with heating within 1080 days.
## 3 Experiments and Analysis
### Comparisons with the Baseline Methods
It can be seen that the number of bird objects detected by our method is closest to the ground truth in both our captured dataset and the more densely bird-dense TIV [36] dataset. In order to further clearly show the detection effect of each algorithm, four white dashed boxes are specially marked in three images in Figure 7. Taking the first white dashed box in the first image as an example, the number of the detected birds of Mask R-CNN [18], DRU-Net [31], DNA-Net [32], YOLOv4 [24], and our method is 5, 1, 5, 4, and 6, respectively. It can be seen that all of these comparison methods miss a certain number of targets compared to the actual number of six targets, however, only the detection result of our method is completely correct. These four comparison methods have the problem of identifying multiple birds in dense areas as independent individuals. Compared to these methods, our algorithm has absolute advantages.
# Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm for MIMO Detection
## 4 Complexity Analysis  
As in many other applications, the use of deep learning approaches in wireless communication systems presents significant challenges in terms of computational complexity. The CG and LCG methods have complexities of \(\mathcal{O}\left(\mathrm{K}\left(8N_{t}^{2}+14N_{t}+8\right)\right)+2 KRDiv\) and \(\mathcal{O}\left(\mathrm{K}\left(4N_{t}^{2}+6N_{t}+8\right)\right)\), respectively, where \(\mathrm{K}\) is the number of layers, and \(RDiv\) is one real division, assuming that one complex multiplication is four real multiplications and one complex division is four real multiplications plus one real division [28]. Computational complexity of the DU-TCG algorithm, \(\mathcal{O}\left(\mathrm{K}\left(8N_{t}^{3}+12N_{t}^{2}+6N_{t}\right)\right)\), has a higher complexity than that of the CG and LCG. The proposed DU-TCG method exhibits a computational complexity of \(\mathcal{O}\left(N_{t}^{3}\right)\) per iteration, primarily due to the matrix inversion operations involved in each iteration. However, the increased detection complexity improves the detection performance of DU-TCG compared to that of CG and LCG.
# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
## 3 History-Based Prediction Approach
### Potential Benefits of the HBP Approach  
In a previous work the authors determined a reference number of TP messages that could be predicted using an HBP approach [12] considering simple and theoretical interaction scenarios. Such a research work shows that between 50% and 80% of the time, the TP messages to be predicted had already appeared in the past, and therefore they were potentially predictable. According to these preliminary results, the performance of the HBP approach does not depend on the mobility model used by the nodes or their speed, however, its prediction performance decreases, while increasing the node density in the network. These numbers establish the upper-bound limit achievable by this prediction approach.  
In order to extend our understanding about the potential impact of the HBP approach we conducted new simulations, but using a real predictor implemented over OLSR protocol. The simulations involved more complex mobile scenarios.
# An Uplink Channel Estimator Using a Dedicated Instruction Set for 5G Small Cells
## 3 Algorithm and Implementation of Channel Estimation
### Interpolation Estimation between Pilots
We worked with a company to determine the parameters of Wiener filter interpolation.
## 1 Introduction
Comparisons between simulation and experimental data showed a standard deviation of 0.4% and a maximum deviation of 1.3%. [PERSON] [9] established a six-coefficient model and compared experimental and simulation data, which indicated that compressor performance is mainly influenced by the pressure ratio. Deviations at low speeds were mainly affected by internal leakage and a lack of lubrication. They also found that the efficiency of compressor inverters with power ranging from 1.5 to 6.5 kw is about 95% to 98%, indicating that the effect of the inverter-controlled compressor rotation speed on performance is relatively small. [PERSON] [10] analyzed the flow, heat transfer, exhaust temperature, and shaft power of scroll compressors through experiments and simulations. Using an EES to simulate compression process losses, including heat transfer, volumetric ratio, power loss, and power coefficient, they found that about 5% to 13% of heat transfer losses (including leakage losses) affect the compressor's flow. Currently, the performance simulation models for compressors generally do not consider the impact of the oil circulation rate. During experimental processes, the OCR is either controlled within a very small range or its impact is ignored. Therefore, some scholars have analyzed the mechanism of oil circulation rates and their effects. [PERSON] [11] studied the hysteresis situation of the refrigerant-oil mixture within the system, and the results showed that a lower mass flow rate or smaller OCR leads to less oil content, which causeshysteresis in the system. [PERSON] [12] tested the system and compressor performance when the R290 refrigerant replaced the R407C refrigerant. As the results indicated, the COP value of the R290 refrigerant is about 9% higher, and compressors perform better with R290 at medium- and low-pressure ratios than that with R407C, but the opposite is true with R290 at high pressure ratios. They also found that when the OCR value is between 0.35% and 1.4%, the performance of R290 and R407C is not significantly affected. [PERSON] and others [13] studied the oil circulation rate of R290 variable frequency compressors at different compressor speeds, and the results showed that the oil circulation rate increases with compressor speed. At present, the application of R290 in electrical vehicle air conditioning systems is still being studied. This paper establishes a performance simulation model for variable frequency compressors to analyze the thermodynamic properties of R290. An experimental system is set up to compare the effects of factors such as the compressor speed, operating condition, oil and refrigerant ratio, and OCR on compressor performance. A comparative analysis of the simulation and experimental results is also conducted.
## 5 Performance Analysis
### Performance Analysis on Natural Disaster  
The results of the existing methods, namely, the Sendai Framework [27], technical approach [28], mathematical model [29], intelligent processing technology [30], hybrid cloud architecture [31], pragmatic approach [32], integrated approach [33], and GLMM approach [34] are discussed with disaster dataset collected from kaggle. The URL of the dataset is published as [68]. Totally, 500 sensor nodes are deployed in a disaster area to conduct the simulation.  
Figure 7 illustrates the Efficiency \(E\), as defined in Equation (2), to demonstrate the performance of eight existing methods analyzed using varying numbers of disaster data points, ranging from 100 to 1000. When considering 600 disaster data points, the \(E\) values  
Figure 5: Measurement analysis of packet delivery ratio.  
Figure 6: Overall performance analysis of packet delivery ratio.  
obtained for each method are 89.29%, 93.53%, 82.59%, 94.89%, 83.33%, 72.68%, 67.22%, and 64.12%, respectively. The Intelligent Processing Technology demonstrates the highest \(E\) among the methods evaluated. This superior performance can be attributed to its data-driven approach, which accounts for various risk factors contributing to fire accidents and other security issues in urban settings. Consequently, the \(E\) of Intelligent Processing Technology surpasses that of the other methods by 6%, 2%, 15%, 10%, 27%, 35%, and 44% when compared to the other approaches.  
Figure 8 demonstrates the overall performance results of \(E\) across eight different existing methods. Among these, the Intelligent Processing Technology exhibits the highest \(E\). The \(E\) values recorded for each method are 89.17%, 92.48%, 81.98%, 94.5%, 85.77%, 74.54%, 70.14%, and 65.73%, respectively. These results indicate that the Intelligent Processing Technology significantly outperforms the other methods, highlighting its effectiveness in enhancing \(E\) in comparison to the alternatives.  
Figure 8: Overall performance analysis of efficiency.  
Figure 7: Measurement analysis of efficiency.
## 4 Lunar Antenna Types
### Planar Antennas
#### 4.1.1 Patch Antennas
However, it is important to note that this antenna has the narrowest bandwidth within the group, measuring 0.5% at 2.3 GHz and 3% at 16 GHz. The directivity at the S band is measured as 1.6 dBi, while at the Ku band, it reaches 7.3 dBi [44,45].
# Security and Independence of Process Safety and Control Systems in the Petroleum Industry
## 4 Technological Trends, New ICT Systems, and Industrial Internet of Things (IIoT) Solutions
### Opc Uia  
Open Platform Communication Unified Architecture (OPC UA) [14] is a standard for industrial communication and information modelling that was first published in 2008 and has been increasingly adopted in recent years. OPC UA is, as the name implies, an open standard, and the purpose of the standard is to ensure the secure and platform-independent exchange of data at the field equipment level and between OT and IT.  
Finding good solutions for this is becoming increasingly relevant as more field data becomes available. The OPC UA has been adopted by several sectors and is often described as the protocol that can bring data from the field equipment to the office network and/or cloud. The process industry often represents this exchange of data using the \"ISA-95 reference architecture/Purdue model\". The OPC UA has also become more international as the IEC has issued a number of OPC UA standards.
## Results and Discussion
### Limitations  
The results in this study are based on the average values of L\({}_{\text{Aeq}}\) collected for a limited period. Continuous measurements over a longer period and over more than one week could better represent the environment that the employees experience. For the purpose of more accurate results, using a dosimeter held by the employee could improve the accuracy of actual noise exposure (in the mall or even after work, at the home). In this study, the overall sound-pressure level was measured; the use of different frequency bands (1/1-octave of 1/3-octave bands) in the analysis could lead to a better  
Figure 12: The relation between the reverberation time and loudness rating.  
Figure 13: The relation between L\({}_{\text{Aeq}}\) and the subjective loudness rating.  
understanding of the subjective ratings of the employees and why the different sound-pressure levels have different effects on them based on their working place.  
Due the relatively small size of the sample, the analysis based on demographics, like age, gender, educational level, and income, was not significant enough to draw conclusions from and was discarded in this paper. Moreover, other parameters that may influence the participants' answers need to be investigated in the questionnaire, for example, whether the people like their job or not.  
This study considered acoustic comfort alone; however, other studies suggest the investigation of other parameters in parallel, such as the humidity, temperature, and lighting [11; 19]; the social and behavioral characteristics of the users [13]; the type of activity in the small [10]; and the Rapid Speech Transmission Index for speech intelligibility evaluation [5].
# A Technical Survey on Delay Defects in Nanoscale Digital VLSI Circuits
## 6 Test Pattern Generations for Delay Faults  
### ATPG for SDD  
The TDF model-based ATPG is the most convenient method for detecting delay defects. It has several advantages in reducing pattern count and the run time for pattern generation. Also, the stuck-at fault based CAD tool can be reused with minor changes to generate the TDF ATPG. In [15], the path delay distribution for the TDF model-based patterns of the ISCAS'89 benchmark circuit proves that it detects faults mostly through shorter paths. Because of this, the SDDs escape the test. Modifying the TDF-based pattern generation will help detect the SDDs.  
The timing-aware (TA) TDF-based ATPG is very helpful in detecting the SDDs by propagating the fault effect through the longest path [28]. But the TA mode of pattern generation increases the run time, and the pattern count is higher than the traditional TDF patterns.  
An alternative way of detecting SDDs is the timing-unaware N-detect TDF-based ATPG [29; 30]. The N-detect TDF-based ATPG sensitizes a particular fault effect through N different paths. This increases the probability of detection of SDDs, as it sensitizes the fault effect through more than one path at the expense of pattern count. But still, it is proved to be more efficient than TA ATPG in terms of CPU run time, and SDD coverage [21] as shown in Figure 6. As the N-detect ATPG pattern generation suffers the disadvantage of high pattern count, some pattern selection and evaluation methods are suggested in the literature to mitigate these effects, which are discussed in the Section 6.2.
###### Abstract  
The received global navigation satellite system (GNSS) signal has a very low power due to traveling a very long distance and to the nature of the signal's propagation medium. Thus, GNSS signals are easily susceptible to signal interference. Signal interference can cause severe degradation or interruption in GNSS position, navigation, and timing (PNT) services which could be very critical, especially in safety-critical applications. The objective of this paper is to evaluate the impact of the presence of jamming signals on a high-end GNSS receiver and investigate the benefits of using a multi-constellation system under such circumstances. Several jamming signals are considered in this research, including narrowband and wideband signals that are located on GPS L1 or GLONASS L1 frequency bands. Quasi-real dynamic trajectories are generated using the \(\text{S}\text{S}\text{P}\text{i}\text{T}\text{M}\text{S}\text{S}\text{S}\text{S} \text{S}\text{S}\text{S}\text{S}\text{S}\text{S}\text{S}\text{S}\text{S} \text{S}\text{S}\text{S}\text{S  
Many real jamming incidents have been reported during the past decade. A real jamming incident that happened at Newark Airport in 2013 was reported in [7]. A worker wanted to hide from his company's fleet tracking system; therefore, he operated a small jamming device, but the effect went wider than that. The in-the-area ground-based augmentation system (GBAS), a system used to assist aircraft during take-off and landing, consequently suffered from casual failures for about two years until the occurrences were identified by the US Federal Communications Commission (FCC) as jamming incidents. On top of the losses the authorities suffered, the individual was also fined with the amount of about U.S. $32,000. A more recent incident, which immediately underwent a police investigation, is what happened in Hong Kong in October 2018 where more than 40 drones fell from the sky while performing a professional light show. The damage cost, consequently reported, was about U.S. $127,500. According to the same source [8], the Hong Kong Tourism Board's Executive Director, [PERSON], articulated that the case was not a computer hacking, as initially concluded by police authorities, rather it was due to a global positioning system (GPS) signal jammer.  
Although some receivers have anti-jamming techniques incorporated in their inner circuitry or using special antenna arrangements, jamming effects in many cases cannot be predicted; thus, it is essential to conduct studies about the behavior of GNSS receivers under real signal jamming situations. Some studies have investigated this issue before from different perspectives. Nonetheless, this paper adds a remarkable discussion to the literature. GPS only or GPS/Galileo receivers have been considered in the previous literature [9], whereas this study addresses GPS/GLONASS receivers. The paper first discusses the benefits of using multi-constellation receivers over GPS-only receivers under signal jamming conditions. Secondly, the paper also investigates the behavior and response of theses receivers to different types of jamming signals.