esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Model Material for Dynamic Geotechnical Model Tests
## 4 Experimental Results and Sensitivity Analysis
### Multivariate Linear Fitting of Physico-Mechanical Parameters  
According to the results of the orthogonal test and multivariate linear regression model, four relationships can be established between the database for the seven parameters (density, tensile strength, compressive strength, cohesive force, internal friction angle, dynamic shear modulus and dynamic elastic modulus) and the four variables (cementing material/solid materials (A), gypsum cementing material (B), quartz sand (C) and ferric concentrate powder/(barite powder + ferric concentrate powder) (D). Multivariate linear fitting by SPSS software was used to determine the model of each parameter. The formula in Figures 4-10 expresses the corresponding relationship between seven physico-mechanical parameters and four groups of similar material ratios, and the value before the four groups of similar material ratios indicates the influence degree of this factor on the parameters. The red dashed line represents the test value in the orthogonal test, and the blue dashed line represents the value calculated by fitting the above formula. Since there were many variables in the multiple linear regression model, the modified determination coefficient (\(R_{a}^{2}\)) was used to reflect the fitting degree of the model.  
(1) Multivariate linear fitting of static parameters  
As shown in Figures 4-8, the values of density, tensile strength, compressive strength, cohesion and internal friction angle obtained by the fitting formula were relatively consistent with those obtained through the test, and the determination coefficients were all greater than 0.6, among which the maximum determination coefficient of compressive strength was 0.802. The fitting formula well reflected the corresponding relationship between static parameters and proportions of ingredients in the model materials.  
Figure 4: Comparison of fitted and experimental results for density.  
Figure 8: Comparison of fitted and experimental results for internal friction angle.  
Figure 6: Comparison of fitted and experimental results for compressive strength.  
Figure 7: Comparison of fitted and experimental results for cohesion.  
Multivariate linear fitting of dynamic parameters  
As shown in Figures 9 and 10, the values of the dynamic shear modulus and dynamic elastic modulus obtained by the fitting formula were consistent with those obtained by experiments, and their determination coefficients were greater than 0.75. Overall, the determination coefficients of the dynamic parameters were larger than those of the static parameters, and the fitting formula well reflected the corresponding relationship between the dynamic parameters and proportions of components in the model materials.
# Experimental Study on Pullout Behavior of Coir Geotextiles Based on Transparent Soil
## 3 Results and Discussion
### Influence of Pullout Rate  
Figure 13 shows the relationship curve between the pullout force and the pullout displacement at different pullout rates. As it can be seen from Figure 13, at a higher pullout rate, the pullout test curve had a better stress-strain response in the initial stage of the pullout. With the increase in the pullout rate, the corresponding peak value of the pullout force increased from 2.2 kN to 2.4 kN successively. The pullout rate was positively correlated with the peak value of the pullout force. After reaching the peak of the pullout force, the pullout test curve dropped faster at high pullout rates, because, when the pullout rate of the coir geotextile was small in the pullout process, the transparent soil particles had enough time to rearrange. The stress distribution was more uniform during the pullout process, and the pullout force of the coir geotextile was small. When the pullout rate increased, the transparent soil particles did not have enough time to rearrange, which may produce a stress concentration effect, resulting in additional stress at the interface of the reinforced soil. When the coir geotextile was pulled, dilatation was more likely to occur, resulting in greater friction, which increased the pullout force. At the same time, under greater friction, the joints of the coir geotextile were more likely to be damaged, resulting in stress reduction. Therefore, at a higher pullout rate, that is, 3 mm/min, the rate of decline was faster after the pullout force had reached its peak.  
Figure 14 shows the contour of particle displacement at different pullout rates. As it can be seen from Figure 14, when the pullout rate changed, it affected the microscopic movement behavior of the reinforced soil interface. When the pullout rate increased from 1 mm/min to 3 mm/min, the thickness of the interface shear band increased from 16 mm to 18 mm. This indicates that the pullout rate affected the meso-behavior of the interfacial motion of the reinforced soil. With the increase in the pullout rate, the driving effect of the coir geotextile on the soil particles also was enhanced.  
In addition to the three factors of normal stress, anchorage length, and pullout rate, there are other factors worth considering. [PERSON] et al. [38] found that the morphology of geosynthetics could lead to differences in their pullout behavior, and the morphology of the coir geotextile may also affect the pullout behaviour. In addition, the sensitivity of the measurement tool may introduce further uncertainty into the results. In the follow-up study, the author will take more strict and standardized measures to carry out the experiment, and the subsequent research will focus on considering influencing factors such as the morphology of the coir geotextile.  
Figure 13: The relationship curve between the pullout force and the pullout displacement at different pullout rates.
# PC5-Based Cellular-V2X Evolution and Deployment
## 3 C-V2X Standard and Application Scenarios
### LTE-V2x  
Completed in March 2017, phase 1 of 3 GPP Rel-14 provided the initial standards supporting V2V services and the additional V2X services leveraging the cellular infrastructure. The primary safety features of C-V2X under 3 GPP Rel-14 were delivered through cellular networks or sidelink communications via the PC5 interface. A new LTE (Long-Term Evolution)-V2X band 47 (with 10 and 20 MHz bandwidth) was introduced to support C-V2X by unlicensed 5.9 GHz spectrum. 3 GPP Rel-14 also introduced two new physical channels for PC5-based C-V2X communication, namely the Physical Sidelink Shared Channel (PSSCH) and Physical Sideline Control Channel (PSCCH). As the name discloses, PSSCH carries data, and PSCCH has control information for decoding the data channel in the physical access layer. To accelerate the LTE-V2X development, LTE-V2X uses the centralized scheduling mode (Mode 3), and decentralized scheduling mode (Mode 4) [11] of LTE-D2D (Device-to-Device), supporting sidelink communications via PC5 interface. The cellular network allocates resources under Mode 3. On the other hand, Mode 4 does not require cellular coverage; vehicles can autonomously select their radio resources using a sensing-based Semi-Persistent Scheduling (SPS) scheme supported by congestion control mechanisms. In June 2018, 3 GPP Rel-15 was completed. The enhanced V2X services such as platooning, extended sensors, advanced driving, and remote driving [12] were introduced as phase 2 of 3 GPP V2X, which has formed a stable and robust ecosystem around LTE-V2X:  
* **Platooning:** Vehicles dynamically form a platoon traveling together. All vehicles in the platoon exchange information that enables them to maintain a short distance safely.
* **Extended Sensors:** Exchange of raw or processed sensor data among vehicles, roadside units, devices of pedestrians, and V2X application servers, enhancing environment perception beyond what sensors can detect (e.g., via the exchange of live video).
* **Advanced Driving**: Enable semi-automated or fully-automated driving. Perception data obtained from local sensors and driving intention exchanged with vehicles in proximity to synchronize and coordinate.
* **Remote Driving**: Remote driver or V2X application operates remote vehicle (e.g., for disabled passenger, vehicle in dangerous environment, predictable route driving, etc.).
## 3 Results
### Effects of Body Tissue and Body Types  
Since the comb and meander antennas showed a better performance, alteration in the performance of these antennas in different parts of the body was investigated. The percentage of the alteration of the resonant frequencies and bandwidth for the position placement of the antennas in the human voxel in comparison with the results of the antennas' simulation in the three-layer phantom model is illustrated in Figure 5. Laura human voxel of CST was selected for this simulation. Figure 5 shows four different possible placements of the implantable antennas in the body including chest, neck, arm, and head. Due to different electrical properties and the nonhomogeneity of each part of the body, some changes in the performance of each antenna are expected. Figure 5,d demonstrate the box plots of the variations in the resonant frequency and bandwidth of the comb and meander antennas at the three bands, when placed in the chest, neck, arm, and hand. The minimum, first quartile, median, third quartile, and maximum of the variations are presented in these figures. Based on the results in the human voxel, the meander antenna has a more stable performance at 400 MHZ band and the comb antenna has a better performance stability in 2.4 GHz and 5.7 GHz bands due to the implant location in the body. Both antennas have acceptable alterations as the operational frequency bands are covered in all simulations even in the head placement, which has thinner skin and considerably more skull tissue. The results also show that the bandwidth at 400 MHz and 2.4 GHz bands became slightly narrower and slightly wider in the 5.7 GHz band in the voxel in comparison to the three-layer phantom model.  
To compare the effect of different human body types on the antennas' performance, an overweight body model with a greater body fat index was selected, and it is shown in Figure 6. The percentage of the performance variation in both resonant frequency and bandwidth of the meander antenna in the neck and hand of the human voxel are presented. Results show that the resonant frequency and bandwidth are reduced in an overweight voxel in 400 MHZ, but it does not follow an arranged variation pattern at 2.4 GHz and 5.7 GHz bands, although the operational frequency bands are still covered in the overweight voxel similar to the normal voxel.  
Figure 5: Performance comparison of the meandered and comb antennas for different placements of the antennas in the human voxel model and in the three-layer phantom model. (**a**) Meandered antenna’s resonant frequency alteration percentage at each frequency band (**b**) Meandered antenna’s bandwidth alteration percentage at each frequency band (**c**) Comb antenna’s resonant frequency alteration percentage at each frequency band (**d**) Comb antenna’s resonant frequency alteration percentage at each frequency band, and (**e**) Positions of antenna’s placement in the human voxel.  
Figure 6: _Cont_.
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 2 Related Work
### Camera-IMU Calibration
Unlike existing approaches, we consider the difference in precision between the two IMU sensors in a mobile phone and use a finer-grained error metric consisting of two terms, instead of a unified one (as in [50; 51]), to reflect the resulting effect. Moreover, we do not assume perfect accelerometer-magnetometer alignment during the assembly of the sensor hardware and thus are able to decouple the accelerometer-related error and the magnetometer-related error. The reasons that we do not adopt a method of the first category are as follows: (1) the dynamic features of the gyroscope and the moving camera are nonessential to our measuring problem, in which instantaneous sensor outputs are employed; and (2) a calibration method that is independent of the gyroscope is applicable to a wider range of devices.
## 3 Circuit Design
### Design of Output Matching Network (OMN)  
The design of the OMN needs to comprehensively consider the following five aspects [13]:  
1. Achieve conjugate matching and attain the maximum PAE while maintaining a relatively high power;
2. The power transmitted by the microstrip line and the components cannot exceed its power tolerance;
3. Network matching should be as simple as possible to reduce losses;
4. The standing wave coefficient of the output terminal should not be too large, which is convenient for interconnection with external systems;
5. The design of the DC feeding network is reasonable.  
A low-loss OMN circuit is very important for gaining a high PAE [8]. To minimize the insertion loss, the OMN circuit consists of a low-loss double-layer microstrip and a high-reliability MIM capacitor. The final topology of the proposed OMN is shown in Figure 7.  
The structure of the OMN is simple; the insertion loss is reduced as much as possible on the premise of satisfying conjugate matching. The insertion loss and return loss of the OMN are shown in Figure 8a. It is seen that the loss of the output matching circuit is 1.3-0.6 dB across the frequency range of 7-13 GHz, with a return loss of better than 12 dB, which means the developed amplifier has a high efficiency and high \(P_{out}\) performance. Figure 8b demonstrates the matching effect reflected by the output matching network. It can be seen that the points of actual load impedance are basically located in the optimum impedance region throughout a frequency from 7 to 13 GHz, that mean a good output matching is achieved.  
Figure 7: Circuit structure of the proposed OMN.
# Mobility-Multihoming Duality
## 6 Results of Evaluation  
ILNP successfully provided smooth handovers transitioning from single-homed to a multihomed load-shared state and back to single-homed as it moved across the different network PoAs.  
Summary results are shown in Figure 9 on page 26 and Figure 10 on page 27. These allow comparison between TCP and UDP data as general comaprison of packet flow behaviour.  
Typical runs from the results for the lab scenario are also presented. These show, respectively, the individual runs of measurements for TCP and UDP. These provide a more intuitive view of the operation of mobility and multihoming duality with load-sharing functions. They show how the throughput varied over time, how the share of throughput was distributed across the multiple paths/PoAs used, and how the change in connectivity was initiated by the use of LU messages. The example runs are from the scenario with no additional delay, i.e., the LAN scenario:  
* TCP at the CN in Figure 11 and MN in Figure 12 on page 28;
* UDP at the CN in Figure 13 and MN in Figure 14 on page 30.  
For TCP in Figure 11 (for the CN) and Figure 12 (for the MN) on page 28, we note the following:  
* In the top graph of each Figure, there was a smooth transition of traffic as additional paths/PoAs were added and removed (top-3 facets) at both the CN and MN, as well as the consistent aggregate throughput (lower facet).
* In the bottom graph, the progression of sequence numbers for TCP showed little interruption (e.g., loss) to the flow of traffic at both the CN and MN.
* The similarity of the figures between the CN (Figure 11) and MN (Figure 12) shows the consistency in operation for TCP over ILNP.  
For UDP, we see a similar pattern in the results for Figure 13 (for the CN), and Figure 14 (for the MN) on page 30: a smooth transition of traffic and no loss.  
We also present aggregated results in the subsections below from 20 runs for each of TCP and UDP, and for each of the three delay configurations (as in Table 7--0 ms, 20 ms, and 200 ms). The flows were bi-directional, and both were used in the aggregated results, which were labelled as a CN or a MN according to where the observations were made.  
Note that we present the results for the TCP and UDP together to show the consistent behaviour from ILNP at the network layer only. The results for TCP and UDP are not directly comparable, as TCP has complex characteristics and behaviour compared to UDP. This difference is also why we used different metrics, as discussed in Section 5.4.
# PPDC: A Privacy-Preserving Distinct Counting Scheme for Mobile Sensing
## 2 Problem Statements and Preliminaries
### System and Security Model  
_System model._ We consider the system model in this paper as follows: there is a group of users with mobile devices who are providing data to a sensing task publisher or an aggregator to do some sensing task. Assume that sensing data of each user is a set of data with various forms of elements, including images and location data and so on. The aggregator needs to find the number of distinct data of all users' data, a big dataset composed by plenty of sub datasets. When transmitting sensing data, all users would not reveal their original data to the aggregator. We discuss a general network model in mobile sensing, in which there is a direct communication channel between the aggregator and every mobile device user. That is to say, the aggregator and all users form a star network topology. The communication channels could be 3G/4G, wifi, or other kinds of channels that are supported by the mobile devices and the aggregator in practical applications. Besides, as for each user's device, it has the ability to do the hash operation and bitwise XOR encryption to its sensing data and transmit worked data to the aggregator. The whole process of data aggregation in our scheme PPDC is described in Figure 1.  
_Security model._ In this paper, we assume that it is a semi-honest model. All the aggregators and users observe the data transmission and collection process described above. However, they may attempt to derive extra information about other participators' private inputs during the whole execution, which they should not know. Therefore, the scheme is believed to be secure if it guarantees that every participator can learn no more information from the process than the information that this participator is entitled to know. For the users, they should not be able to get the values of data of each other without permission. While for the aggregator, except for the encrypted data from users and the calculating result of these aggregated data, no extra knowledge about users ought to be acquired or speculated from the data he/she aggregates.  
Figure 1: Overview of PPDC.
# Apparatus and Method of Defect Detection for Resin Films
## 3 Experiments and Results
### Microscope Method
#### 3.1.2 Evaluation of the CNN Model  
To verify the performance of the proposed CNN, in this section, we compared the inspection results of this model with those of other CNN networks. The [PERSON] et al. [22] proposed SURFnet, which was inspired by the VGG network [36] configurations and residual learning [37]. It has nine convolution layers and one fully connected layer. Each convolution layer contains a batch normalization and using the parametric rectified linear unit as the activation function. The LeNet-5 architecture consists of two pairs of convolution and average pooling layers, followed by a flatten layer and two fully connected layers. Both network epochs were set to 200 and the batch size was set to 32 during training.  
Table 1 depicts the performance of the defect class with various networks. We mainly focused on whether all defects were detected, evaluating the recall of the defect class. The results prove that LeNet-5 has high precision, and their recall is the lowest. This proves that superior performance can be obtained if the average pooling layer was removed. This is because extracted objects may be only approximately 1 pixel. Average pooling can cause loss of vital features. For SURFnet, although the precision is high (up to 1), the recall is not as high as the LeNet-5-adjusted network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **Precision** & **Recall** & **F1-Score** \\ \hline SURFnet & **1.00** & 0.84 & 0.91 \\ Lenet-5 & 0.93 & 0.81 & 0.86 \\ Lenet-5-adjusted & 0.91 & **0.91** & **0.91** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of classification with different CNN networks of the defect class.
## 4 Results  
The results of our proposed model are compiled separately for both segmentation as well as classification in Sections 4.1 and 4.2, respectively. Evaluation matrices used for evaluating the results are dice, mIOU, precision, recall, and accuracy. Python 3.10, Matplotlib 3.6.2, PyTorch 1.12.0, and Keras 2.11.0 are the primary tools and libraries used for experimentation. The Adam optimizer and CCE are used, and the entire framework is developed on a GPU with a 4 GB NVIDIA Tesla graphics card and 32 GB of RAM. The model was trained on 250 epochs with a fixed batch size of 50.
## 3 Results and Discussion  
### Experimental Results  
In Figure 3 are plotted the free vibration time records and their FFT spectra for cantilever length \(L_{i},~{}i=\overline{1,4}\) in the case of the cantilever beam reinforced with hemp fibers.  
In Figure 4, the free vibration time records and the FFT spectra for the cantilever length \(L_{i},~{}i=\overline{1,4}\) are plotted, in the case of the specimens reinforced with coconut fibers.  
In the case of the cantilever beam reinforced with hemp fibers, the following frequencies were obtained experimentally for the specified length: \(f_{1}=10.38\) Hz, \(f_{2}=6.96\) Hz, \(f_{3}=5.25\) Hz, and \(f_{4}=4.03\) Hz, and in the case of coconut fibers: \(f_{1}=7.08\) Hz, \(f_{2}=5.62\) Hz, \(f_{3}=4.27\) Hz, and \(f_{4}=3.42\) Hz.  
The results obtained by fitting the experimental data with analytical model \(f(L)=\frac{C}{L^{3/2}}\) are shown in Figure 5.  
Figure 4: (**a**) Free vibration time records for composite specimens with coconut fibers; (**b**) Amplitude spectra for composite specimens with coconut fibers.  
Figure 3: (**a**) Free vibration time records for composite specimens with hemp fibers; (**b**) Amplitude spectra for composite specimens with hemp fibers.  
From relation (6), the value of the Young's modulus for the composites with hemp fibers is \(E_{h}=1819\) MPa and in the case of those with coconut fibers, \(E_{c}=962\) MPa.  
From the recorded free vibrations of the two types of material, shown in Figures 3a and 4a, it can be observed that they exhibit viscoelastic behavior. This is highlighted by the fact that their time histories can be accurately described by the analytical solution of Equation (1), as illustrated comparatively for the samples with a length of 0.22 m in the following charts, in Figure 6.
###### Abstract  
This article proposes a novel method for analyzing residual stress and deformation in butt welding on 6 mm SUS304 stainless steel plates, using MSC_MARC, a commercial finite element method software, to find the best location for jig fixtures that will minimize welding deformation. Simulation and experimental studies show that a distance of 100 mm between the jig center and the welding bead center is best for inhibiting welding deformation when the jigs experience downward displacement at 0 mm on the steel plate; the total displacement is only about 1.1 mm in the case of a 300 \(\times\) 250 \(\times\) 6 mm SUS304 steel plate. In addition, a numerical model shows that four jigs with pitches of 200 mm can better reduce welding deformation than six jigs with pitches of 100 mm. The largest residual stress after welding occurs around the weld bead center, and the residual stress away from the welding bead center increases gradually when jigs have been applied on the steel plate to prevent deformation. The reaction force of the jigs on the steel plate has no further effect in reducing deformation. We conclude that commercially available jigs can inhibit deformation during the welding process.  
Keywords:finite element method; flat position welding; welding deformation; jig +
Footnote †: journal: Applied Sciences