esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis  
### Experiments Details  
The motorcycle was instrumented with two analogue sensors namely a potentiometer (range of linearity \(\pm\)50\({}^{\circ}\)) and a piezoelectric sensor (range \(\pm\)200 Nm and sensitivity \(-\)175 pC/Nm) to measure the steering angle and the steering torque respectively. An inertia measurement unit (IMU) (angular rate range \(\pm\)400\({}^{\circ}\)/s with an angle measurement accuracy of 0.2\({}^{\circ}\)) was used for measuring roll, pitch and yaw angles, corresponding velocities and accelerations. A GPS antenna was used for measuring longitudinal displacement, lateral displacement and speed of the motorcycle. The data was logged at a sample rate of 100 Hz [27].  
The sensors were mounted on the motorcycle for the experiments. The rotating shaft of the potentiometer was mounted on top of the handlebar, and its non-rotating body was fixed to the frame. The handlebar and front steering system were disintegrated, and two flanges were fixed to them such that the faces of the flanges are perpendicular to the steering axis. The piezoelectric sensor was mounted between these flanges under high preload. The IMU was mounted on top of the pillion seat, and the GPS antenna was kept on a metal plate fixed to the rear frame of the motorcycle at the highest point on the motorcycle.  
Experiments were conducted on a proving ground using the motorcycle instrumented with aforementioned sensors at speeds 3, 5 and 10 km/h. The motorcycle was ridden on the same dry asphalt road to inhibit the road variations in the results. The riders had instructed to follow a straight line marked on the proving ground. The experiments were conducted mainly to focus on balancing the motorcycle. Three professional riders, having riding experience of more than 15 years, were selected for the experiments to ensure the quality of results. Each rider had repeated the experiments two times to ensure the repeatability of the results.  
Figure 5: Regions of stability for different values of roll angle gain \(a\) and lead time \(\tau\) for closed-loop system of motorcycle.
# Smart Clothing Framework for Health Monitoring Applications
## 4 Challenges and Future Development
### 4.2. Durability, Service Life and Maintenance  
#### 4.2.1 High Failure Rate  
Intricate processing techniques for sensor development and embedding in clothing make smart clothing highly vulnerable to frequent failure, which raises the question of its reliability in use [7]. Manufacturers should seek alternative processing techniques to develop a robust product that a user can rely on.
# mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm
## 5 Discussion and Future Work  
### Discussion  
The experimental results show that mmSight performs better in near-field conditions compared to other algorithms. In the Fully-Sampled single-target experiment, we perform basic validation of mmSight, and the results show that it can achieve high-resolution imaging. In the Sparsely-Sampled single-target experiment, unlike the MIMO in most papers, this paper is under SISO. Comparing Matched Filter and Back Projection, mmSight effectively suppresses the ghost image, which can ensure the algorithm robustness. In the hidden target experiment, we verify the penetration ability of the millimeter wave and successfully achieve the target imaging inside the carton. In the same-distance multi-targets experiment, we verified the feasibility of multiple targets imaging, and then discussed the effect of the spacing distance on the generation of ghost image. The experiment verified the spatial sampling theory that the closer the spacing distance, the smaller the sampling interval required by the spatial sampling criterion, and the more likely to generate ghost image. In the multi-targets experiment at different distances, we spaced two targets 27 mm apart, and no other algorithms could achieve the same distance projection that mmSight did. Comparing the dataset used in the literature [31,32], we can see the advantages of mmSight in different distances multi-targets projection and low noise, but we can also see that the projection is limited to the spacing distance, and the larger the spacing distance, the worse the projection effect. For the index IE, the smaller the value, the higher the image quality. mmSight is lower than other algorithms by 0.3372 on average, which can prove that mmSight's imaging quality is better than that of other algorithms. For the PSF's sidelobe and noise, mmSight is also lower than other algorithms. In summary, mmSight is able to achieve robust, high-quality imaging.
## 2 Materials and Methodology
### Four-Point Bending Test  
Four-point load tests as outlined in ASTM D 198 [17] were performed on the panels using a Young universal testing machine outfitted with a 445 kN load cell with the crosshead speed of 0.001 mm/.min and two linear variable differential transducers (LVDTs). One LVDT was connected to the Young testing machine to measure the local displacement of load heads over a 0-150 mm range, and the other one was installed on the top center of the panel to measure the displacement of the top center of the panel relative to the load heads over a 0-75 mm range. The deflections from both LVDTs and loading data were recorded with LabView(r) Software (2020 Version) [18] on a dedicated personal computer. The total deflection was the sum of the two recorded deflections from LVDTs. The experimental setup is illustrated schematically in Figure 6 with the actual test set-up shown in Figure 7.  
Figure 6: Schematic representation of testing arrangement and LVDTs’ locations for DCLT bending specimens.  
The corresponding bending stiffness value from this test setup was acquired from Equation (7) ASTM D 198 [17]:  
\[E_{app}=\frac{23\times l^{3}}{108\times b\times h^{3}}\times\frac{P}{\Delta} \tag{7}\]  
where \(\mathrm{E_{app}}\) is the apparent bending stiffness, \(\mathrm{P}/\Delta\) is the slope of the linear range of the load-deformation curve below the proportional limit with \(\mathrm{P}\) and \(\Delta\) accordingly being the maximum load and deflection at the proportional limit, \(\mathrm{l}\) is the span length, and \(\mathrm{b}\) and \(\mathrm{h}\) are the width and the thickness of the specimen.
## 2 Mathematical Model
The Arrhenius law in Equation (6) was obtained by Arrhenius [40], \((\frac{\hat{T}}{T_{\infty}})^{n}\exp(\frac{-E}{\hat{c}\hat{T}})(\hat{C}-\hat{C} _{\infty})\) represents the modified form of Arrhenius function in which \(\xi=8.61\times 10^{-5}\) eV denotes the Boltzmann constant, \(n\) is the unit less exponent fitted rate constant typically lies in range \(-1<n<1\) and \(E\) represents the activation energy. The variation in viscosity and thermal conductivity due to internal friction between the stretching surface and fluid parietal have to be taken into account, it is appropriate to assumed the mathematical formulation of the thermal conductivity and viscosity depends on temperature was investigated in [37; 38; 39] together with useful similarity variables as follows:  
\[ \begin{split}\mu(\hat{T})&=\hat{\mu}(a+b_{1}(\hat{T }_{w}-\hat{T})),\quad k(\hat{T})=\hat{\mu}(a_{1}+b_{2}(\hat{T}-\hat{T}_{\infty} )),\\ \theta(\hat{c})&=\frac{\hat{T}-\hat{T}_{\infty}}{ \hat{T}_{w}-\hat{T}_{o}},\quad\phi(\hat{c})=\frac{C-C_{\infty}}{C_{w}-\hat{C} _{o}}\end{split}  \tag{10}\]  
Transform governing model into ODEs by using following similarity variable as fellows:  
\[ \begin{split}\hat{T}_{w}&=\hat{T}_{o}+n_{1}(x+c)^{ \frac{1-m}{2}},\quad\hat{T}_{\infty}=\hat{T}_{o}+n_{2}(x+c)^{\frac{1-m}{2}},\\ \hat{C}_{w}&=\hat{C}_{o}+n_{3}(x+c)^{\frac{1-m}{2}},\quad\hat{C}_{\infty}=\hat{C}_{o}+n_{4}(x+c)^{\frac{1-m}{2}}.\end{split}  \tag{11}\]  
Using Equation (11) we obtained  
\[ \begin{split}\hat{T}_{w}-\hat{T}&=(1-\theta)\hat{T }_{w}-\hat{T}_{o}-n_{2}(x+c)^{\frac{1-m}{2}}\\ \hat{C}_{w}-\hat{C}&=(1-\phi)\hat{C}_{w}-\hat{C}_{o }-n_{4}(x+c)^{\frac{1-m}{2}}\end{split}  \tag{12}\]  
From Equations (10) and (11) can easily obtained as follows  
\[ \begin{split} b_{1}(\hat{T}_{w}-\hat{T}_{o})&=b_{1} n_{1}(x+c)^{\frac{1-m}{2}},\quad b_{1}(\hat{T}_{\infty}-\hat{T}_{o})=b_{1}n_{2}(x+c )^{\frac{1-m}{2}}\\ b_{2}(\hat{C}_{w}-\hat{C}_{o})&=b_{3}n_{1}(x+c)^{ \frac{1-m}{2}},\quad b_{2}(\hat{C}_{\infty}-\hat{C}_{o})=b_{4}n_{2}(x+c)^{ \frac{1-m}{2}}\end{split}  \tag{13}\]  
Figure 1: Geometry of the problem. in the above expression \(\hat{T}_{o}\) and \(\hat{C}_{o}\) are reference temperature and concentration respectively.
## 3 Results and Discussion
### Bacteria Captured by PEI-MNPs  
In order to verify the affinity of PEI for bacteria, _L. monocytogenes_ was selected as the model bacteria to investigate the CE by different MNPs. As shown in Figure 3a, PEI-MNPs had a higher CE for _L. monocytogenes_ than MNPs (\(p<0.01\)), which was due to the strong electrostatic interaction between PEI-MNPs and _L. monocytogenes_ after MNPs were modified with PEI [34]. The number of _L. monocytogenes_ captured by MNPs or PEI-MNPs, and the remaining bacteria in the PBS suspension after magnetic separation were cultured in BHI agar plates (Figure 3b). The distribution of single colonies indicated the excellent capture ability of MNP-PEI for _L. monocytogenes_, as well as the healthy viability of the bacteria.  
Figure 2: Characterization of MNP and PEI-MNPs. (**a**) Photo of MNP and PEI-MNPs adsorption under the action of a magnet. (**b**) Zeta potential of PEI-MNPs at pH 4 to 10. (**c**) Magnetic hysteresis loops of MNP and PEI-MNPs measured at room temperature. (**d**) SEM image of PEI-MNPs bound to the surface of _Listeria_.
## 3 The Mechanical Contribution of Waviness and Unevenness  
### Shear Test  
#### 3.1.1 Joint Specimen Preparation and Test Schemes  
As no two natural rock joint specimens are truly identical, it is difficult to obtain a large number of specimens with the same morphology [74]. Therefore, when testing schemes require multiple specimens, joint specimens with natural surfaces are produced mainly by copying techniques, such as creating fractures by splitting, sawing flat joints with undulated or irregular surfaces, and casting natural or stylized joints with silicon or rubber molds. However, the mechanical properties of casting material [75] are different from rock material. Thus, it is difficult to reflect the mechanical properties and failure mechanisms of joint specimens. Three-dimensional engraving technology [76] can produce a large number of joint specimens with the same morphology using rock as the raw material, thus avoiding the use of casting materials and rock materials that have different mechanical properties. In this study, a 3D engraving machine (MK-6060) was used to prepare joint specimens. The engraving system is composed of a control system, spindle, operating floor, cooling water circulation system, and milling cutter, as shown in Figure 7. The control system is used to control the movement of the spindle. The precision is 0.1 mm, which meets the accuracy requirement. The raw material used in the test was red sandstone, which was collected from Dawu County, Hubei province, China. It is a medium coarse quartz sandstone with an average grain size of 0.25-0.5 mm. The mineral composition of this red sandstone was identified by XRD test (Figure 8), showing the main mineral composition to include feldspar (39.25%), quartz (43.14%), calcite (7.7%), kaolinite (3.17%), illite (1.98%), chlorite (3.76%), and mixed-layer illite/montormillonite (0.1%). Figure 9 shows the structure of the  
Figure 6: Decomposition results of the standard JRC profiles.  
2. Stretching the profile onto the surface using the stretch command in CAD software and exporting the surface in.igs format;
3. Importing the surface file into JD-Paint software and generating an engraving path file;
4. Fixing the standard sandstone on the operating floor, then turning on the engraving machine.  
In order to analyze the mechanical contribution of waviness and unevenness to the shear strength of the joint specimen, three kinds of joint specimens were prepared (the Standard JRC profiles 5\({}^{\pi}\)\(\sim\)10\({}^{\pi}\), the profile containing only waviness, and the profile with the flat surface). A total of 39 groups of joint specimens were prepared. Three normal stresses were applied in this shear test, and the test schemes are shown in Table 4.
# Towards the Internet of Smart Trains:
## 6 IoT-Enabled Services: From More Efficient Operations to New Business Models
### Smart Infrastructure
#### 6.2.4 Key Findings  
The railway industry can benefit from the WSN ability for carrying out easy deployments that can reach places where a wired solution may not be installed. Additionally, such deployments can harness the flexibility, scalability, and self-organizing capabilities of WSNs. Nevertheless, WSNs have to deal with several issues specific for railway environments, like communications reliability, the necessity for higher sampling rates for measuring fast changing dynamic signals (e.g., vibrations), fast transmission rates, the capacity of managing high-volumes of data, energy efficiency, energy harvesting and the possibility of managing heterogeneous sensor signals (data fusion).  
Regarding video surveillance systems, the existence of a real-time viewing mode, a record mode, and a search/playback mode allows security managers to avoid threats. These systems support video analytics, intelligent incident response, and emergency communications. Cameras can increase passenger safety and protect assets by integrating video surveillance systems across a network infrastructure. By integrating heterogeneous subsystems (environmental and intrusion detection sensors, positioning and identification systems) and potentially thousands of cameras, an overall view of the whole infrastructure (i.e., trains, tracks, depots, and stations) can be obtained by operators and management systems at the TCC or by the staff operating in the field, with video analytics and real-world maps identifying, locating, and recording threats.  
Train delay is one of the most relevant factors that affect the perceived quality, since it affects the capacity of the system, its punctuality, its reliability and even its safety. Moreover, the availability of precise train positioning is essential for setting routes, controlling the traffic, rescheduling, and for offering accurate information to the passengers and the maintenance operators. Thanks to the transmission of real-time positioning data to control centers, the systems embedded into the train can help to reduce congestion by optimizing the deployment of the equipment and managing the track capacity.
## 4 Calibration Method
### Transit Offset Error Calibration Method  
As shown in Figure 7, if the transit offset error exists in the laser tracker and we do not consider the installation eccentricity error of test rods after the standing axis rotates \(180^{\circ}\), the offset between the transit axis before and after the standing axis rotation is the transit offset error(\(t\)). However, the test rods are noncoaxial with the transit axis due to the installation eccentricity error of \(O_{1}O_{1}^{\prime}\) and \(O_{2}O_{2}^{\prime}\). Therefore, the transit offset(\(t\)) can be calculated as  
\[t=O_{1}O_{2}/2=\big{(}EF+O_{1}E-O_{2}F\big{)}/2=\big{(}EF+O_{1}O_{1}^{\prime} \cdot\ cos\theta_{1}-O_{2}O_{2}^{\prime}\cdot\ cos\theta_{2}\big{)}/2 \tag{7}\]  
where \(EF\) is the offset between the central axis of test rod A and B, \(O_{1}E\) is the \(X\) component of the installation eccentricity of test rod A, and \(O_{2}F\) is the \(X\) component of the installation eccentricity of test rod B. The \(X\) component of the installation eccentricity of test rod A and B (\(O_{1}E\) and \(O_{2}F\), respectively) can be measured based on the method mentioned in Section 4.1. The offset (\(EF\)) between the central axis of test rod A and B can be measured by the telecentric measurement system. The telecentric measurement system is established at position 1, as shown in Figure 4. The transmitter and the receiver are vertically placed, and the green LED light emitted from the transmitter is projected to test rod A; the image of test rod A will be recorded and displayed on the monitor. The initial position of the central axis of test rod A is recorded as \(E\). The standing axis rotates \(180^{\circ}\), and the green LED light emitted from the transmitter is projected to test rod B; the position of the central axis of test rod B is recorded as \(F\), as is shown in Figure 7. The offset (\(EF\)) between the central axis of test rod A and B can be obtained.
## 2 Methods
### Regional Atmospheric Enhancement Rapid Precise Positioning System
\[E\big{(}\Delta\varnothing_{r,i}^{s}\big{)}=H\cdot x_{r}+c\cdot(\overset{ \sim}{dt}_{r}-\overset{\sim}{dt}^{\sim}{s})+m_{r}^{s,g}\cdot T_{r}^{s,g}-u_{i} \cdot\overline{T}_{r,i}^{g,g}+\lambda_{i}\cdot\overset{\sim}{N}_{r,i}^{s,g} \tag{6}\] \[\overline{T}_{r,i}^{s,g}-\overset{\sim}{I}_{r_{1}r_{2}\ldots r_{n,i}}=w_{1},w_{1}\sim N(0,\sigma_{w_{1}}^{2})\] (7) \[\overline{T}_{r}^{s,g}-\overset{\sim}{T}_{r_{1}r_{2}\ldots r_{n }}=w_{T},w_{T}\sim N(0,\sigma_{w_{T}}^{2})\] (8) \[\sum_{UC}=diag(\sigma_{p}^{2},\sigma_{Q}^{2},\sigma_{w_{1}}^{2}, \sigma_{w_{T}}^{2}) \tag{9}\]  
where \(\overset{\sim}{I}_{r_{1}r_{2}\ldots r_{n,i}}^{s,g}\) and \(\overset{\sim}{T}_{r_{1}r_{2}\ldots r_{n}}^{s,g}\) are the ionospheric and tropospheric error corrections obtained via interpolation at the reference stations, respectively; \(w_{1}\) and \(w_{T}\) represent the difference between the correction number and the true atmospheric delay, both of which are zero mean white noise processes with variances of \(\sigma_{w_{1}}^{2}\) and \(\sigma_{w_{T}}^{2}\). The PPP model with atmospheric constraint equations can effectively weaken the influence of atmospheric errors and accelerate the convergence of ambiguity. On this basis, after correcting the phase deviation through OSB, the final fixed ambiguity is achieved.
## 4 Experimental Results
### Comparison of Learning Rates and Hidden Sizes  
The \(F1_{overall}\) values under the six parameter combinations of hidden size and learning rate in the validation set for the HSAN are presented in Figure 4. The value of 0.0001 represents the optimal learning rate, and the value of 300 represents the optimal hidden size and word embedding size according to \(F1_{Overall}\).
## 2 Materials and Methods
### Chromatographic Analysis
#### 2.6.2 Gas Chromatography  
To estimate content of different fatty acid (FA) residues in the _B. aggregatus_ BM5/15 biomass, tandem gas chromatography-mass spectrometry was applied. Chloroform fractions obtained as described in Section 2.5 were used for the analysis. The chloroform was evaporated in the Ar atmosphere at room temperature, and the lipid residue was dissolved in methanol. FA methyl esters were prepared by transesterification of the lipids by refluxing for 1.5 h in methanol containing 5% concentrated sulfuric acid at 70 \({}^{\circ}\)C [69]. Methyl esters were extracted with \(n\)-hexane and immediately used for gas chromatography. The FA methyl esters were separated and identified according to retention times of standards (Sigma, St. Louis, MO, USA) and by characteristic mass spectra obtained with an Agilent 7890 gas chromatograph equipped with a 30-m HP5 MS UI capillary column coupled with an Agilent 5970 mass-selective detector (Agilent, CA, USA). He at a flow rate of 1 mL min\({}^{-1}\) was used as a carrier gas. Relative FA content was calculated as the mass fraction of their total content in the sample (%-mass). The unsaturation index (UI) [70] was calculated as:  
\[UI=\sum_{i}UFA_{i}/\sum_{i}SFA_{i} \tag{2}\]  
where \(UFA_{i}\) is the mass fraction of the unsaturated FA \(i\) (mass.%), \(SFA_{i}\) is the mass fraction of the saturated FA \(i\) (mass.%) in the sample.
# Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems
## 6 Evaluation and Results
### Performance of the Online Parser
#### 6.3.2 Performance of Multi-Source Log Parsing  
In this set of experiments, we investigated the performance of our method on parsing multi-source log records. Specifically, we used the same log sets shown in Tables 4 and 5 to construct our heterogeneous and homologous multi-source log streams, respectively.  
**Online Parsing of Heterogeneous Logs**: We first evaluated our online parser's capability to adapt to five different structural patterns of the heterogeneous log steam as follows:  
1. _Full length, fixed order_: all log records from each source were fed sequentially;
2. _Short length, fixed order_: 400 log records from each source were fed sequentially in an iterative manner;
3. _Medium length, fixed order_: 5000 log records from each source were fed sequentially in an iterative manner;
4. _Medium length, random order_: 5000 log records from a random source were fed in an iterative manner;
5. _Fully random_: each log fed was from a random source.  
Figure 9 demonstrates the results of our online parser on streams containing heterogeneous log records from different numbers of sources. It is obvious that our online parser performs well on streams with all structural patterns except for the'short length, fixed order' one (i.e., the parsing accuracy is significantly downgraded on streams with six and seven sources when \(r\geq 0.8\) ). Comparing to results under other settings, we believe that the input stream structure has a significant impact on the performance of our online parser, and it is quite challenging to achieve accurate parsing of streams with continuously shifting log patterns (i.e., sets of log records with a relatively limited size from different sources). In this case, the learning rate of the online parser might require explicit fine-tuning to guarantee the parsing accuracy. However, since our online parser performs well (both in terms of accuracy and stability) in the 'fully random' scenario, potential structural modifications can be conducted on log streams in practice to guarantee the effectiveness of our solution.  
**Online Parsing of Homologous Logs**: Similar to that of our offline parser, we also evaluated the performance of our online parser on homologous multi-source log streams. The results are shown in Figure 10, which demonstrates that our online parser obviously  
Figure 9: Online parsing accuracy on heterogeneous multi-source datasets legend from left to right: (1) full length from each source; (2) fixed length (400) from every single source with fixed order; (3) fixed length (5000) from every single source with fixed order; (4) fixed length (5000) from every single source with random order; (5) fully random.  
outperforms Drain and NuLog (up to 75%/32% in terms of accuracy). Moreover, our online parser performs evenly with our offline parser.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Frequency** & **Equalizer** & **Modulation Format** & **Distance** & **BER** \\ \hline
[18] & 120 GHz & 2D-CNN & 16-QAM & 200 m & \(\leq\)3.8 \(\times\) 10\({}^{-3}\) \\
[19] & 125/135/145 GHz & Volterra equalizer & PDM-16-QAM & 4600 m & \(\leq\)2.4 \(\times\) 10\({}^{-2}\) \\
[20] & 110–170 GHz & LSTM + RF & QPSK & 20 km & \(<\)3.8 \(\times\) 10\({}^{-3}\) \\
[21] & 340 GHz & 2D-CNN & 16-QAM & 54.6 m & \(\leq\)10\({}^{-2}\) \\
[22] & 125 GHz & LSTM + RF & 16-QAM & 4.5 m & 5.64 \(\times\) 10\({}^{-4}\) \\
[23] & 286/299/312 GHz & MMA + Viterbi & 64-QAM & 30 m & \(<\)4.5 \(\times\) 10\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Terahertz signal transmission performance with various equalizers and modulations.