esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case I: Passive Eavesdropper
### BER Performances
#### 3.5.2 BER at the Eavesdropper User E  
At the eavesdropper user E, the BER's performances versus \(SNR_{E}\) are also calculated to evaluate how much user E can decode the message sent from user B. For the rest of this paper, we have decided to keep the same BER ranges (\(10^{0}\) to \(10^{-5}\)) without focusing on  
Figure 10: \(BER_{A}\) versus \(SNR_{A}\) in case of a passive eavesdropper: (a) Blind without feedback; (b) Blind feedback.  
Figure 9: \(MSE_{BE}\) versus \(SNR_{E}\) in case of passive eavesdropper.  
the useful ranges in order to allow for a visual comparison of the different schemes and especially the performance differences between legitimate user A and eavesdropper E. As shown in Figure 11, it is shown that the presence of a self-jamming signal from user A has a significant impact on the estimation and decoding processes of user E, regardless of the knowledge of the channel coding used for decoding. The best BER that user E can obtain is about \(BER_{E}=10^{-3}\) at \(SNR_{E}=30\) dB. Furthermore, when the self-jamming-to-noise ratio \(\rho_{AE}\) is greater than 15 dB, user E almost cannot decode the intended message from user B. It can be explained that user A can estimate the SI channel well and cancel the SI component because user A has its generated self-jamming signal \(\mathbf{x}_{A}\) as reference. Moreover, applying the blind feedback scheme also improves the channel estimation and decoding processes, although user A also has no knowledge about the reference signal from user B. In contrast, user E has no knowledge about the reference signal of user B and the self-jamming signal of user A, and there is no interference cancellation mechanism applied; instead, it uses only the SPA decoding scheme to decode the intended message.  
Therefore, user E cannot operate efficiently in the estimation and decoding processes. In summary, by applying the joint iterative estimation and decoding to the legitimate receiver, user A can significantly improve the secrecy reliability factor in FD wiretap transmission.
# Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review
## 2 The Development and Main Research Contents of Inversion Theory
### Development of Inversion Theory
#### 2.1.1 Establishment Phase  
The period from the early 1970s to the early 1980s saw the emergence of inverse theory and its computational modeling. In 1971, [PERSON] et al. [9] pioneered the inversion of elastic modulus and other elasticity parameters of media using the finite element method. During this stage, inversion analysis was applied to tunnel and underground space engineering, for linear elastic problems with simple boundary conditions and a single parameter [10]. Simultaneously, optimization calculation methods such as the simplex method [11], the atlas method, and the quadratic gradient method [12] were also gradually developed.
## 5 Experimental Analysis
### Preprocessing  
Because of providing images to a convolutional neural network, a heavy preprocessing of the pictures was skipped as CNN is a compelling network that can identify significant features from raw images. However, some preprocessing steps were required. First, all the images were auto-cropped as most of the images had a large area with only background pixels. Figure 5 illustrates the effect of auto-cropping. After that, all the images were converted to binary images as there were some grayscale images in some of the classes. Figure 6 illustrates the effect of binarization. As the proposed architectures take inputs of size \(28\times 28\times 1\), all the input pictures were reshaped to \(28\times 28\times 1\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Layer** & **Layer** & **Output** & **Layer** & **3 \(\times\) 3 Path** & **5 \(\times\) 5 Path** & **7 \(\times\) 7 Path** \\
**Number** & **Name** & **Shape** & **Connected to** & **Parameters** & **Parameters** & **Parameters** \\ \hline
1 & Input & \(28\times 28\times 1\) & - & 0 & 0 & 0 \\
2 & Conv2D & \(28\times 28\times 32\) & 1 & 320 & 832 & 1600 \\
3 & Conv2D & \(28\times 28\times 32\) & 2 & 9248 & 25,632 & 50,208 \\
4 & Max-Pooling2D & \(14\times 14\times 32\) & 3 & 0 & 0 & 0 \\
5 & Dropout & \(14\times 14\times 32\) & 4 & 0 & 0 & 0 \\
6 & Conv2D & \(14\times 14\times 64\) & 5 & 18,496 & 51,264 & 100,416 \\
7 & Batch Normalization & \(14\times 14\times 64\) & 6 & 256 & 256 & 256 \\
8 & Conv2D & \(14\times 14\times 64\) & 7 & 36,938 & 102,464 & 200,768 \\
9 & Batch Normalization & \(14\times 14\times 64\) & 8 & 256 & 256 & 256 \\
10 & Max-Pooling2D & \(7\times 7\times 64\) & 9 & 0 & 0 & 0 \\
11 & Dropout & \(7\times 7\times 64\) & 10 & 0 & 0 & 0 \\
12 & Conv2D & \(14\times 14\times 64\) & 5 & 18,496 & 51,264 & 100,416 \\
13 & Conv2D & \(14\times 14\times 64\) & 12 & 36,938 & 102,464 & 200,768 \\
14 & Max-Pooling2D & \(7\times 7\times 64\) & 13 & 0 & 0 & 0 \\
15 & Dropout & \(7\times 7\times 64\) & 14 & 0 & 0 & 0 \\
16 & Concatenate & \(7\times 7\times 128\) & \(11,15\) & 0 & 0 & 0 \\
17 & Conv2D & \(7\times 7\times 64\) & 16 & 73,792 & 204,864 & 401,472 \\
18 & Max-Pooling2D & \(3\times 3\times 64\) & 17 & 0 & 0 & 0 \\
19 & Dropout & \(3\times 3\times 64\) & 18 & 0 & 0 & 0 \\
20 & Flatten & 576 & 19 & 0 & 0 & 0 \\
21 & Fully Connected Layer & 256 & 20 & 147,712 & 147,712 & 147,712 \\
22 & Dropout & 256 & 21 & 0 & 0 & 0 \\
23 & SoftMax Output & 256 & 22 & 65,792 & 65,792 \\ \hline  & Total Parameters: 408,224 (\(3\times 3\)) + 752,800 (\(5\times 5\)) + 1,269,664 (\(7\times 7\)) = 2,430,688 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Layer description, output shape, and number of parameters for each layer of BengaliNet considering 256 classes.  
Figure 5: Example of auto-cropping on a sample image.
## 2 Materials and Methods
### Measuring Equipment
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Measuring Equipment** & **Properties** & **Notes** \\ \hline \multirow{6}{*}{Multi-channel data logger Onset (Burlington, MA, USA) Hobo H22-001} & Measuring inputs: 3 FlexSmart multi-channel & \\  & modules and up to 6 Smart Sensors; & Used to record temperatures and \\  & Outputs: 2; & electricity parameters \\  & Operating range: –40 to +60 \({}^{\circ}\)C; & \\  & Memory: 512K nonvolatile flash data storage. & \\ \hline \multirow{2}{*}{12-Bit temperature smart sensor} & Measuring range: –40 to 100 \({}^{\circ}\)C; & Measures air and freon temperature. \\  & Accuracy: \(\pm\)0.2 \({}^{\circ}\)C from 0\({}^{\circ}\) to 50 \({}^{\circ}\)C. & Connects to Hobo H22-001 \\ \hline \multirow{6}{*}{3-Phase AC kWh transducer sensor onset T-VER-8044-100} & Measuring range: 0 to 100 A; & \\  & Operating range: 0 to +60 \({}^{\circ}\)C, 0 to 95\% RH; & \\  & Input primary voltage: 480 Volts AC rms; & \\  & T-VER-8044-100 & Output: 4–20 mA; & Connects to Hobo H22-001 \\  & Accuracy: \(\pm\)1\% per ANSI (from 10 to 100\% of & \\  & CT rating). & \\ \hline \multirow{6}{*}{Pressure sensors Sanhua (Shaoxing, China) YCQC05L09} & Measuring range: 0 bar to 44.8 bar; & \\  & Operating range: –40 to +80 \({}^{\circ}\)C, max. working & \\ \cline{1-1}  & & pressure 75 bar; & Used with Arduino Mega 2560 \\ \cline{1-1}  & & Output: 4–20 mA; & \\ \cline{1-1}  & & Accuracy: \(\pm\)0.8\% FS. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Volume** & & & & & & & & & & & \\  & & & & & & & & & & & \\ \hline \multirow{2}{*}{V0} & 1800; & 1920; & 2100; & 2220; & 1800; & 1920; & 2100; & 2220; & 1800; & 1920; & 2100; & 2220; \\  & 30 & 30 & 30 & 50 & 50 & 50 & 50 & 70 & 70 & 70 & 70 \\ \hline \multirow{2}{*}{V1} & 1800; & 1920; & 2100; & 2220; & 1800; & 1920; & 2100; & 2220; & 1800; & 1920; & 2100; & 2220; \\  & 30 & 30 & 30 & 50 & 50 & 50 & 50 & 70 & 70 & 70 & 70 \\ \hline \multirow{2}{*}{V2} & 1800; & 1920; & 2100; & 2220; & 1800; & 1920; & 2100; & 2220; & 1800; & 1920; & 2100; & 2220; \\  & 30 & 30 & 30 & 30 & 50 & 50 & 50 & 50 & 70 & 70 & 70 & 70 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Combinations of heat pump control component parameters. to 1920 rpm, and again, the process is allowed to stabilize. This approach is applied to all combinations of certain expansion valve and compressor speed settings. Once all compressor speeds have been examined, the expansion valve permeability is increased, and the compressor speed is varied again from 1800 rpm to 2220 rpm. Meanwhile, when all combinations of compressor speed and expansion valve permeability have been explored, the system volume is increased from V0 to V1, and tests are repeated, varying the speed and the permeability. This process yields all operational results for the heat pump (48 data points). Parameters for further analysis of each operating combination are based on the average of the parameters measured during a steady-state period of 10 min.
## 5 Conclusions  
In this work, a hybrid framework for simulating building collapse and ruin scenarios using an FE method and physics engine is proposed, and the corresponding program FEM2 RBD is developed. Shaking table tests of a three-story reinforced concrete frame and a real-world complex library building are performed to simulate the ruin scenarios. The following conclusions can be drawn:  
* In the proposed hybrid framework, the FE method simulates structural behaviors during the small-deformation stage, and the physics engine simulates structural behaviors during the large-deformation stage. The proposed framework efficiently combines the advantages of the FE method and the physics engine.
* Using a shaking table test of a three-story reinforced concrete frame, the proposed hybrid simulation method is demonstrated to be more accurate than an approach based on the physics engine alone. The case study of a real-world complex library building shows the high-fidelity of the collapse simulation.
* The collision of structural components and the distribution of ruins after the collapse are considered in the proposed hybrid method. The proposed framework has great significance for simulating building collapse and ruin scenarios for post-earthquake rescue training.  
Note that the proposed framework is also applicable to other FE software and physics engines, besides the MSC.Marc software and the Bullet physics engine that were used in this work.  
Figure 14: Distribution of the library building ruins.  
Figure 13: Collapse process of the library building: (**a**) \(t=2.01\) s; (**b**) \(t=3.01\) s; (**c**) \(t=3.64\) s; (**d**) \(t=4.26\) s; (**e**) \(t=5.51\) s; (**f**) \(t=6.97\) s; (**g**) \(t=14.26\) s; (**h**) \(t=18.43\) s.  
**Author Contributions**: Conceptualization, X.L. and Z.Z.; data curation, X.L. and Z.Z.; formal analysis, Z.Z.; funding acquisition, X.L.; investigation, Y.T. and Z.Z.; methodology, Y.T. and Z.Z.; project administration, X.L.; resources, X.L.; software, Z.Z.; supervision, X.L.; validation, Z.Z.; visualization, Z.Z.; writing--original draft, Z.Z.; writing--review and editing, X.L., Y.T., Z.Y. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by BEIJING MUNICIPAL NATURAL SCIENCE FOUNDATION (grant number 8182025)  
**Acknowledgments**: The authors would like to thank [PERSON] and [PERSON] for providing the copyright permission.  
**Conflicts of Interest**: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3. Results and Discussion
### 3.1. Workability
This causes an increase in the plastic viscosity and yield stress of the concrete and reduces the slump flow. The elapsed ratio decreases because HPMC enhances the water retention and delays the setting time of concrete. Generally, regardless of the amount and combinations of HPMC, ST has an effect on increasing fluidity and reducing the elapsed ratio. WG has an effect on reducing fluidity and increasing the elapsed ratio. These results are similarly reflected in all combinations of VMAs. Additionally, ST increases fluidity more than GG. Because of this effect, the tendency of the slump flow increases slightly. Figure 2: Slump flow and elapsed ratio of fresh concrete mixes by combinations of VMAs. Plain: non-VMA, HL: HPMC 0.3% only, HH: HPMC 0.6% only, WG: welan gum only, GG: guar gum only, ST: starch only, HLW: HPMC (0.3%) + welan gum (0.1%), HHW: HPMC (0.6%) + welan gum (0.1%), HLWG: HPMC (0.3%) + welan gum (0.1%), HLWS: HPMC (0.3%) + welan gum (0.1%) + starch (0.1%), HHWG: HPMC (0.6%) + welan gum (0.1%) + guar gum (0.1%), HHWS: HPMC (0.6%) + welan gum (0.1%) + starch (0.1%).
## 5 IETF ROLL Working Group
### Research Challenges
#### 6.1.4 Observation of Resource  
The state of a CoAP resource can change over time. In draft-ietf-core-observe [89] a simple CoAP extension is defined that enables a server to inform interested clients about the state change. A client interested in observing a resource includes the observe option in its GET request to the server. Whenever there is a change of the resource state, the server sends a notification to the client. Also, in case the state of the resource does not change, but the time since the last notification exceeds the max-age value of the resource, a notification is sent. As such, observe offers the possibility for a client to have an up-to-date representation of the resource without the client having to constantly poll for changes. New resource states are transmitted from the server to the clients according to a best-effort approach. The observe protocol foresees mechanisms to ensure consistency between the state observed by each client and the actual resource state.
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 5 Experimental Results and Evaluation
### AIS Trajectory Reconstruction
#### 5.1.3 Results  
For the purposes of a more complete study, we experimented with our approach using different historical penalty factors (\(W_{p}\)) and compared their accuracy. Table 2 provides the results for the proposed approach, as well as the total rate of improvement over the straight-line interpolation, calculated as follows:  
\[Impr.=100*\frac{Sli-Ash}{Sli}\]  
where \(Sli\) is the mean FastDTW for the straight-line interpolation and \(Ash\) is the respective accuracy of the proposed method.
## 2 Results and Discussion
### Extraction Temperature  
The extraction temperature for the DVB/CAR/PDMS fiber was optimized using an extraction time of 20 min. Four different extraction temperatures of 40, 50, 60, and 70 \({}^{\circ}\)C were investigated. As shown in Figure 2A, the metabolite profiles obtained using extraction temperatures of 60 and 70 \({}^{\circ}\)C contained the highest number of metabolites, and elevated temperatures increased the number of detected metabolites by 121% and 124% respectively compared to an extraction temperature of 50 \({}^{\circ}\)C. However, the total peak area of the metabolite profiles obtained at the extraction temperature of 60 \({}^{\circ}\)C was twice that obtained using an extraction temperature of 70 \({}^{\circ}\)C. Further, the broadest cross-section of volatile and semi-volatile metabolites was detected using an extraction temperature of 60 \({}^{\circ}\)C (Figure 2B). A temperature greater than 60 \({}^{\circ}\)C dramatically decreased the quantitative extraction efficiency for volatile metabolites in alfalfa due to a decrease in the partition coefficients and decreased extraction by the SPME fiber [18]. The extraction temperature plays an important role in transferring analytes from the solution to the headspace and from the headspace onto the fiber by affecting the diffusion rate of analytes and vapor pressure of the analytes in the sample [18; 23]. An optimized extraction temperature of 60 \({}^{\circ}\)C was selected for the optimized SPME extraction method.
## 2 Materials and Methods  
### Study Area and Field Data  
This study is based on the experimental site of the National Meteorological Station in Henan Township, Wushen Banner, Ordos City, Inner Mongolia (108 deg43\({}^{\prime}\) E, 37 deg51\({}^{\prime}\) N), located in the northern part of the Baiyu Mountains in the Ordos Basin, at the center of the Mu Us Desert, with an altitude of 1210 m above sea level. It has a typical arid and semi-arid climate with low precipitation and strong evaporation. The average annual rainfall is about 340.3 mm, mainly concentrated in June to September; the average annual evaporation is 2634.2 mm; the annual sunshine hours are about 2750 to 3025 h, and the average multi-year temperature is 8.0 degC. The average annual relative humidity is 54%, and the average annual wind speed is 3.5 m s\({}^{-1}\); the vegetation is sparse and the underlying surface is relatively flat. Groundwater resources are relatively abundant but exhibit large spatial differences. The depth of shallow groundwater on beaches is about 0.5 to 1.5 m, while the depth of sandy groundwater can reach several tens of meters.  
Figure 1a shows the profile of the soil column with a depth of 1.2 m with the top parallel to the ground. The initial groundwater level is approximately 1 m below the surface. Sensors are installed at depths of 5 cm, 10 cm, 20 cm, 50 cm, and 100 cm to measure soil water content, temperature, and matric potential, while heat flux plates are installed at depths of 5 cm, 10 cm, and 20 cm to measure soil heat flux. These data are recorded in real-time by a data logger with a monitoring frequency of every 5 min. Meteorological data, including atmospheric pressure, relative humidity, precipitation, air temperature, wind speed, and net radiation, is recorded every hour as an average. Figure 1b illustrates the daily variations in precipitation, soil surface temperature, and net radiation from 1 July 2019-31 October 2019. It is evident that soil surface temperature and net radiation exhibit consistent trends, with the soil surface temperature decreasing as net radiation decreases. Based on these observed data, a coupled soil water-vapor-heat model is developed, and dynamic data for water and heat transfer is generated using the AMALGAM algorithm for parameter inversion analysis. To make the simulation results more realistic, random disturbances following a normal distribution with variances of 0.001, 0.1, 1.0, and 0.5 are added to the data of soil water content, temperature, matric potential, and heat flux, respectively. The period from 1 July 2019-30 September 2019, is the calibration period, while 1 October 2019-31 October 2019, is the validation period.
## 1 Introduction  
According to the International Parking Institute (IPI), the number of vehicles on the road will reach 2.5 billion in 2050 [1]. With this projected increase in the volume of vehicles, parking has become an emerging issue that affects not only drivers looking for parking spaces, but also city governments in their planning, particularly in urban areas where land resources are limited and constrained. It has been reported that about 30% of traffic backup in a typical downtown area is caused by drivers searching for parking spaces [2]. The expected increase in the number of vehicles likely implies more new drivers and drivers who are unskilled in parking, thus leading to more road congestion and increased waste of valuable manpower-hours and resources. In recent years, with the continuous advancement and development in computer and control technologies, automated parking has become feasible and various strategies have been proposed to help alleviate the unskilled parking problem [3, 4]. Advances in V2X technology have also led related researchers to develop a more robust system of automated parking, namely the Automated Valet Parking System [5].  
Compared with the automated parking system, the concept of automated valet parking system is based on V2X communication technology, which enables self-driving vehicles to interact and collaborate with an intelligent parking infrastructure during the entire parking space search process, from the entrance to the self-parking space [6]. Existing intelligent parking administration systems can detect the status of the parking spaces in real time through camera recognition, infrared sensing, and other technologies [7, 8]. According to SAE (Society of Automotive Engineers) classification for autonomous driving levels, the automated valet parking system is classified as L4 autonomous driving, that is, under certain scenarios, an equipped vehicle can complete all the driving tasks autonomously without the participation of a driver. Although self-driving cars are gaining popularity, related research and testing are far from the level of completion required to bring automated valet parking to fruition. Even if L4 level self-driving cars were to enter the market in the short term, there is likely going to be a long transition period during which both human-operated vehicles (HVs), i.e., with drivers, and autonomous vehicles (AVs), i.e., self-driving or driverless, co-exist, and augmented search strategies need to be developed for the AVs. Self-driving vehicles execute well-defined algorithms based on sensor information, while drivers make cognitive decisions based on perception and surveying of the surrounding environment. The objective of this paper is to investigate parking space allocation and route selection (i.e., path planning to the allocated space) for self-driving vehicles during such a transition period in which significant interactions between the two types of vehicles are expected.
## 4 Discussion  
In this work, we have evaluated the performance of the four modern combat helmet designs: ACH, ECH, Ops-Core, and Airframe. The ECH is the next generation combat helmet selected a few years ago to replace the ACH. In contrast, the Ops-Core and Airframe are two more recent helmets designed for the Special Operations Community [35]. A literature search summarized in Table 2 indicates a considerable body of work exists on the role of helmets under blast loading using computational approaches. Unlike the theoretical studies, there is a scarcity of published experimental work on the blast mitigation of the helmets [6; 13; 18; 36], and two published only very recently by [PERSON] group [37; 38]. Most of these studies used ACH in their design, and there are only a few studies that used other helmets (Table 2). The ECH was used in only one study, while no studies evaluated the blast performance of the Ops-Core and Airframe helmets.  
The Ops-Core and Airframe are high-cut helmets (Figure 1C,D, respectively), while the ACH and ECH are low-cut helmets (Figure 1E). Compared to the ACH/ECH, both high-cut helmets have parts covering the ears on both sides removed. It is a design feature implemented after it was discovered that the water entering ear cups at high speed might pose a safety hazard [39]. The high-cut helmets are designed to incorporate the earmuffs, either as communication devices and/or personal protective equipment (PPE). It seems that the back part of the Ops-Core and Airframe helmets extends further downwards, and they have smaller radii of curvature, and hence a different shape compared to the ECH. A unique feature of the Airframe helmet is a two-shell construction held together by four screws, and a gap between the two parts with the largest separation on the top of the helmet (Figure 1D). The differences in the shape of the helmet (and suspension system) are likely to result in performance variability against blast waves.  
Figure 7: Principal Component Analysis of the surface pressures on the bare headform and equipped with Ops-Core, Airframe, and ECH helmets. The data were recorded at 70 kPa (\(\sim\)10 psi) nominal shock wave intensity, and the scores plot illustrates the clustering in the space of two principal components, PC1 and PC2. The shaded areas surrounding the data points are 95% confidence ellipses.