esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
## 3 Intelligent Application of Deep Learning in Tunnel Engineering
### Urban Subway Tunnel  
Urban rail transit is an essential infrastructure for urban construction. Rapid industrial modernization and the high concentration of the urban population led to the increasing pressure of the urban environment. A series of \"urban diseases\" problems, such as traffic congestion and environmental pollution, have led to the acceleration of the development of urban underground spaces. After decades of construction since the opening of the first subway in Beijing in 1969, China's urban tunnels in operation came to 1710 km in 2010, and the number of kilometers in operation grew to 4712 km by 2017. Up to 2021, 244 urban rail transit lines were opened in 45 cities across the country, and the total operating mileage increased to 7978.19 km. The construction concept behind urban underground rail is gradually changing from \"social needs\" to \"smart city\", which will alleviate the negative impacts caused by traffic pressure while expanding this concept to become an indispensable component of the future intelligent urban transportation system, improving people's living standards.  
The Issue of urban subway tunnels is a crucial aspect of underground space development. Throughout the design, construction, and operation phases of urban subway tunnels, they are often subjected to various factors such as roadway traffic loads, pedestrian loads, train travel disturbances, and the loads from existing buildings above. Simultaneously, it is necessary to consider the influence of underground pipelines, underground piles, and  
Figure 7: Tunnel vault collapse and water inrush at the tunnel face.  
diaphrag walls of existing buildings. These factors differentiate the structural design and construction approach of subway tunnels from that of mountain tunnels. Most subway tunnels are constructed using shield machines, and the tunnel structure mainly consists of segments. This construction method inevitably leads to issues such as cracks and water seepage during both construction and operation. However, the most significant challenge in building urban tunnels lies in controlling strata stability during excavation and managing deformation within acceptable limits for surface settlement. It is indispensable to predict the maximum surface settlement that may occur during construction and operation. In light of this context, this subsection will summarize the intelligent application of the DL (deep learning) method in addressing challenges related to recognizing shield tunnel lining defects and predicting surface subsidence values.
# Finding the Best 3-OPT Move in Subcubic Time
## 2 Selections, Schemes and Moves
### The Number of True \(3\)-OPT Moves  
For generality, we state here a result which applies to \(K\)-OPT for any \(K\geq 2\).  
**Theorem 1**: _For each \(K=2,\ldots,\lfloor n/2\rfloor\) the number of complete \(K\)-OPT selections is_  
\[\binom{n-K+1}{K}-\binom{n-K-1}{K-2}\]  
**Proof.** Assume the indices of a selection are \(0\leq i_{1}<i_{2}<\ldots<i_{K}\leq\bar{n}\). Consider the tour and let: \(x_{1}\) be the number of nodes between node \(0\) (included) and node \(i_{1}\) (excluded); \(x_{t}\), for \(t=2,\ldots,K\), be the number of nodes between node \(i_{t-1}\) (excluded) and node \(i_{t}\) (excluded); \(x_{K+1}\) be the number of nodes between node \(i_{K}\) (excluded) and node \(\bar{n}\) (included). Then, there are as many complete selections in a \(K\)-OPT move as the nonnegative integer solutions of the equation  
\[x_{1}+\cdots+x_{K+1}=n-K,\]  
subject to the constraints that \(x_{1}+x_{K+1}\geq 1\), and \(x_{t}\geq 1\) for \(t=2,\ldots,K\). If we ignore the first constraint and replace \(x_{t}\) by \(y_{t}+1\) for \(t=2,\ldots,K\) we get the equation  
\[x_{1}+y_{2}+\cdots+y_{K}+x_{K+1}=n-2K+1,\]  
Figure 1: The pure reinsertion schemes of 3-OPT.  
in nonnegative integer variables, which, by basic combinatorics, has \(\binom{(n-2K+1)+K}{K}\) solutions. We then have to remove all solutions in which \(x_{1}+x_{K+1}=0\), i.e., the solutions of the equation \(y_{2}+\cdots+y_{K}=n-2K+1\), of which there are \(\binom{(n-2K+1)+K-2}{K-2}\).  
Corollary 1: _The number of true \(3\)-OPT moves is_  
\[\frac{2n^{3}-18n^{2}+40n}{3},\]  
_i.e., it is, asymptotically, \(\frac{2}{3}n^{3}\)._  
Proof: We know from Theorem 1 that there are \(\binom{n-2}{3}-(n-4)=\frac{n^{3}-9n^{2}+20n}{6}\) complete selections, and from Section 2.1 that there are 4 reinsertion schemes. By multiplying the two values we get the claim.  
In Table 1 we report the number of true moves for various values of \(n\), giving a striking example of why the exploration of the \(3\)-OPT neighborhood would be impractical unless some effective strategies were adopted.
## 4 Discussions  
The approach used in this study aims to strike a balance between reducing the calibration time and maintaining high classification accuracy, which is directly linked to the size of the training sample used to train the model. To achieve this goal, we propose a novel transfer learning approach. To evaluate the effectiveness of our method, we utilize two state-of-the-art P300 speller-based datasets. This is because P300-based event-related potentials are subject-dependent [39, 40] and are commonly used in neuromuscular patient studies [6, 41, 42]. Therefore, reducing calibration time is a crucial factor in these cases, and our approach offers practical implications for the development and application of P300 speller-based BCI systems.  
Table 3 is a critical component of our study as it demonstrates our achievements. Conventional techniques such as CNN-1, MCNN-1, MCNN-3 [34], ESVM [35], and DRBM [36] are used for comparison purposes. Note that their results were obtained using all 85 training characters, yet they exhibit lower character recognition accuracy than our proposed method. The MsCNN-TL-ESVM, PCA-EWSVM, PCA-EWSVM, and CM-CW-CNN-ESVM [20] methods, which are demonstrated in Table 3, employ 45 training characters out of 85 available characters for model training. However, their performance is hindered by a limited training dataset, which often leads to overfitting of the model. To mitigate this issue, the TL technique involves training a pre-existing model on many training datasets, which helps avoid over-fitting, followed by fine-tuning with a smaller training dataset. While MsCNN-TL-ESVM uses the fine-tuning-based TL approach in their analysis, our proposed TL method achieves higher performance with a smaller number of training samples (30 training characters) due to the effectiveness of EA in the TL approach.  
To further evaluate, we performed a statistical significance comparison based on a \(t\)-test between the character recognition performances of our proposed method and the MsCNN-TL-ESVM technique (results on 15 repetitions) for two subjects. The results reveal that our method achieves significantly improved performance compared with the MsCNN-TL-ESVM technique (\(p<0.001\)). This suggests that our proposed TL method can be more effective in character recognition tasks, even with a limited training dataset. Furthermore, our proposed method achieves the highest ITR for the BCI competition III dataset II at the first and fifth epochs compared with other methods as demonstrated in Figure 4, whereas the MsCNN-TL-ESVM method demonstrates a comparable ITR at the tenth and fifteenth epochs. This indicates that our proposed method attains a higher speed than MsCNN-TL-ESVM with fewer epochs.
## 4 Results and Discussion
### The Friction Coefficient Curves with Different Cone Angles  
The friction coefficient curves with different cone angles are given in Figure 11. In Figure 11a, the axial force is 600 N, and the cone height is 3 mm. The pressure of T3 and T4 are 1.39 MPa and 0.84 MPa, respectively. The pressure on the conical surface decreases when the cone angle increases. The friction coefficient of T4 is bigger than that of T3. During the constant speed period, the friction coefficient of T3 is more stable. In Figure 11b, the axial force is 600 N and the cone height is 4 mm. The pressure of T1 and T2 are 0.85 MPa and 0.61 MPa, respectively. During the constant speed phase, the friction coefficient of T1 is bigger than that of T2. The cone angle can affect the contact state. When the load is equal, the smaller cone angle can increase the friction coefficient.  
Figure 11a shows that the friction disk with the cone height h = 3 mm has stronger stick-slip phenomenon. When h = 3 mm, the contact area of the friction disk is small, the contact state is unstable, and COF fluctuates obviously. When h = 4 mm, the contact area of the friction disk increases, the stick-slip phenomenon weakens, and the fluctuation of COF decreases.  
Compared with Figure 11a,b, the characteristic of COF has an opposite role when h = 3 mm and h = 4 mm. The main reason is that when h = 3 mm, the contact area is small, the contact state is unstable, and the cone angle has great influence; when h = 4 mm, the contact area is larger, the contact state is more stable, and the influence of height is greater.
## 6 Experimental Results
### Experiment C  
The third experiment dealt with the inclusion of Dataset C, i.e., the translated dataset, without any Greek tokens. Table 6 tabulates the performance outcomes per classifier. The best setting was found to be achieved when considering the PCA transformation, retaining 40% of the variance of the initial Dataset C.  
A closer look at the results reveals that the translated dataset is slightly better than the original one (i.e., the Greek texts only), but with a minor improvement of about 0.5% to 0.9%. Furthermore, a comparison of the table above with the outcomes of the mixed Dataset B shows that the proposed methodology of augmenting the initial set with the English translations is the most beneficial in terms of precision and recall metrics. Therefore, one could claim that a simple translation of Greek into English without considering both languages results in lower classification performance. Additionally, even in this case, GBM performs noticeably better that any other classifier. Results were slightly worse when utilizing the FS process, with the case of keeping the top-500 features being approximately 1.5% to 2.7% lower than PCA.  
As with the previous cases, we tabulate the optimal hyper-parameters for the involved algorithms in the following Table 7.
## 4 Experimental Design and Data Collection
### Simulation Experiments for Gas Leakage Detection in UUT
#### 4.1.2 Overall Experimental Process Design  
With the development of thermal imaging technology, gas leakage infrared imaging detection technology has been widely used in the industry. This study uses an infrared gas imager produced by a Chinese manufacturer. Its main product parameters are shown in Table 1. Natural gas is composed of a variety of mixed gases, of which methane accounts for the vast majority, and there are also small amounts of ethane, propane, and butane. When it leaks, infrared rays with a wavelength of 3.3-3.4 \(\upmu\)m will be strongly absorbed by natural gas. By setting a filter at the corresponding wavelength, the change can be detected by an infrared imaging gas detector.  
Figure 5 shows the experimental platform that was built. The gas-leakage detection experimental device is mainly composed of gas, pipelines, and detection models. Natural gas is used as the gas source. A brass pressure-reducing valve controls the gas flow rate. A hose is used to connect the natural gas, the brass pressure-reducing valve, and the seamless-steel pipe with a diameter of 40 mm, and a hole is punched in the middle of the seamless-steel pipe to simulate the gas leakage position in the real scene. After opening the valve of the natural-gas storage tank, the gas flow of the entire system can be quantitatively controlled by the brass pressure-reducing valve. Brass has excellent hot-workability and cutting performance. The brass pressure-reducing valve can control the flow of the medium, thereby achieving control of the gas leakage rate. It is worth noting that the experimental environment simulates the temperature, humidity, air pressure, and other conditions in the UUT. Since the recognition and classification of leaks by the CRNN depends on the state of the gas column ejected from the leak port, in order to ensure that the imaging information has a certain generalization performance and fully considers the influence of the diffused gas, a 3 min video was recorded each time as the training data of the model, which can fully include the state of the diffused gas in the UUT. However, the detection basis of the  
\begin{table}
\begin{tabular}{c c} \hline
**Detector Data Indicator** & **Parameter** \\ \hline Detector resolution & 640 \(\times\) 512 \\ Infrared response band & 7.0–8.5 \(\upmu\)m or central wavelength 10.55 \(\upmu\)m \\ Pixel size & 12 \(\upmu\)m \\ Thermal sensitivity & 23 mK \\ Focal length & 19 mm \\ Temperature measurement resolution & 0.1 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 1: Infrared gas imager main parameters.  
CRNN is still based on the state of the gas column ejected from the leak. In addition, each time the gas flow rate is changed, it is necessary to wait for 15 s until the gas column is stable before starting to record.
## 3 Test Results and Discussions
### Effect of Chloride Environment
[PERSON] [44] also showed that the tensile strength of glass yarns immersed in saline solution could be decreased by 40%, compared to that of controlled glass yarns. Moreover, [PERSON] indicated that the surface treatment using the polyvinyl alcohol (PVA) seems to be unable to protect the fiber textile reinforcement from a chloride environment, based on Scanning Electron Microscope (SEM) analysis on the cross-section of treated fiber textiles. Note that those CE coupons slightly differ from the Com coupons in the carbon fiber amount and details, but the main difference is the chloride environment. When comparing three previous TRM coupons (Com series), they showed a significant change in strength and deformability (Figure 14). However, in the coupons of the CE series subjected to chloride environment, such a difference was not observed (Figure 12). This indicates that the improvement of bond behavior by means of surface treatment in the Com series almost disappeared in the CE series, because of the chloride environment.
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 4 Non-Orbiting Microgravity Facilities  
### Drop Towers  
Drop towers provide facilities for scientific investigations that require relatively short microgravity time. In most cases, experiments are performed in an evacuated chamber to eliminate the effects of drag and friction forces. Drop towers are the most flexible and adaptable platforms for scientific applications and offer the best microgravity levels--as low as \(10^{-6}\) g (Table 3, Figure 3a). The height of the tower limits the period of microgravity, which can vary from 2.2 s in the NASA (National Aeronautics and Space Administration) Glenn 2.2 Second Drop Tower, up to 9.5 s in the case of Drop Tower Catapult System in Bremen (Table 3) [58].  
Drop towers are used to conduct short-term technological investigations, test instrumentation before use in Space, and preparatory experiments for long-duration missions. In biology, drop towers have been used to perform experiments focused on membrane physiology and gravitaxis. These include the study of the influence of microgravity on the electrophysiology of biological membranes and the orientation behaviour of ciliates and fish.  
Common to other free-fall-based platforms, samples on drop towers undergo transient accelerations. In particular, during the loading and breaking phase, the sample can experience accelerations of up 50 g. Payloads must thus be designed to withstand such forces.  
Drop tower platforms have many advantages. They are medium-cost and have relatively easy-to-handle safety requirements. Multiple experiments can be performed within a few days and the time between experiment planning, development, and execution can be relatively short. Experiment hardware can also be changed at short notice and the setup can be adjusted or improved between drops. Drop towers also allow for real-time monitoring of experiments, as both data upload and download are available during the experiment.  
Many environmental parameters can be regulated during each experimental session, like temperature and pressure, and electrical power is supplied. Centrifuges have also been paired to drop towers to reach hypogravity condition. For example, centrifuge hasbeen used during drop experiments to determine the threshold values of gravisensitivity of organisms [59].
# Dual-Probe Near-Field Phaseless Antenna Measurement System on Board a UAV
## 2 System Description  
### Hardware Architecture  
A picture of the implemented UAV-based system for the on-site antenna measurement is shown in Figure 1. The main subsystems onboard the UAV are:  
* _Communications subsystem:_: Consisting of a wireless local area network (WLAN) operating in the 2.4-2.5 GHz and 5.7-5.8 GHz frequency bands. The radio transmitter and receiver for radiofrequency control (R/C) of the UAV operate at 433 MHz.
* _Control subsystem:_: Composed of the UAV controller, which gathers positioning data and radiofrequency measurements, and forwards them to the ground control station that processes geo-referred NF measurements.
* _Radiofrequency subsystem:_: Composed of two commercial monopole antennas working in the 4 to 7 GHz frequency band [21] acting as probe antennas for NF measurements, connected to a dual-channel radiofrequency power detector based on the ADL5519 chip [22]. To maximize the distance between the two measurement acquisition surfaces, these probe antennas are placed 80 cm away, as shown in Figure 1. The output of these channels is converted into a digital sequence and sent to the ground station (a laptop) using the WLAN.
* _Positioning and geo-referring subsystem:_: Composed of the GNSS-RTK unit onboard the UAV [23]. A laser rangefinder is also integrated to improve height positioning, although the GNSS-RTK unit is accurate enough to avoid the need for a laser rangefinder (it was mandatory for accurate height information in previous versions of the prototype [9; 12]). The positioning system is completed by the default positioning components typically included on board UAVs, namely: conventional GNSS receiver, barometer and inertial measurement unit (IMU).  
Figure 1: Picture of the implemented prototype, highlighting the placement of the two-probe antenna, the mounting frames for the probe antennas, and the distance between them.
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 4 Proposed Autonomous Scheduling Scheme for Convergecast
### Sliding Slofframe Mechanism  
The sliding slotframe mechanism, which is responsible for running schedules in Escalator, forms a communication link between the parent and child nodes using the timeslot schedule assigned to the convergecast slotframe. In the TSCH network, a node forms a communication link with another node using the current slot number calculation. However, the timeslot schedule of the convergecast slotframe generated by the timeslot allocation mechanism in Section 4.1 could not form links using the existing slot number calculation. Through the new slot number calculation provided by the sliding slotframe mechanism, the operation assigned to the timeslot of one node's convergecast slotframe could form a link with the operation of the other node.  
The TSCH node determines the operation of the current timeslot by calculating the current slot number for each slotframe using the Absolute Slot Number (ASN). ASN is the total number of timeslots that have elapsed since the start of the network. The current slot number \(ts_{S}\) at the slotframe \(S\) could be obtained with Equation (1) in TSCH [4].  
\[ts_{S}=ASN\bmod L(S), \tag{1}\]  
where \(L(S)\) is the size of slotframe \(S\).  
The current slot number calculation of Escalator adds the hop count of the node to Equation (1). The current slot number, \(ts_{S_{Conv}}(v_{i})\), at the convergecast slotframe \(S_{Conv}\) at a node \(v_{i}\) could be obtained as Equation (2).  
\[ts_{S_{Conv}}(v_{i})=(ASN+H_{v_{i}})\bmod L(S_{Conv}), \tag{2}\]  
where \(S_{Conv}\) is the convergecast slotframe, \(L(S_{Conv})\) is the size of \(S_{Conv}\) and \(H_{v_{i}}\) is the hop count of \(v_{i}\).That is, the timeslot schedule of the convergecast slotframe for a node with the sliding slotframe mechanism is shown as being left shifted by the hop count of the node.  
Figure 3 shows an example of applying the sliding slotframe mechanism to the schedule in Figure 2. The schedule for node \(v_{2}\) is left shifted by \(H_{v_{2}}=1\). The schedules for node \(v_{3}\) and \(v_{4}\) are left shifted by \(H_{v_{3}}=H_{v_{4}}=2\). These schedule shifts allow the TX and RX operations of the nodes in the example to be paired with each other, and communication links are formed between them.
## 3 Comparative Analysis of Different Binarization Methods
### Comparison of Different Binarization Methods  
As shown in Table 1, the comparative analysis of different binarization methods shows that the method of binarization through the gray level is simple to achieve without additional hyperparameters. However, due to its fixed threshold, this method cannot simultaneously detect the regions where the gray is smaller than the background and the gray is larger than the background. The setting of two threshold values at the same time has the same effect as the background difference binarization method. Background difference binarization uses color difference as input, as long as the area with a different color from the background can be detected. Its disadvantage is that it needs to know the background color in advance, and as for the area similar to the background color, the color difference is too insignificant to detect. The Sobel operator is extremely sensitive to texture, so even the region with a similar color to the background can be detected by the Sobel operator as long as there are color fluctuations in or near the region. Its disadvantage is also obvious. For a large area with the same color, the result obtained by the Sobel operator is zero; that is, it cannot be detected. Therefore, many unrecognized regions appear when the Sobel operator is used as the binarization method alone.  
Figure 10: Binarization based on the edge detection operator.
## 4 Results and Analysis
### Performance Comparision  
In this section, we discuss and compare the identified potential artifacts from the proposed and existing strategies as shown in Table 5. It is depicted that [7; 9; 12; 29] strategies have not targeted the network traffic analysis. Meanwhile, [PERSON] et al. [19] aimed the network forensic of WhatsApp and successfully identified the video/audio calls (initiating, duration, termination) activity with an involved IP address. However, the messaging, typing, accessing, or closings of apps artifacts were not explored. Furthermore, the above results were taken from WhatsApp version 2.11.x.x that was released on 5 March 2015 almost 6 years ago. Similarly, [PERSON] et al. [8] successfully identified the call send/receive  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
messages, etc. on various apps except for the Signal app. Recently, [PERSON] et al. [18] successfully identified the artifacts only on the IMO app. However, the proposed strategy successfully identified the app opening/closing, typing, media messages, calling with involved IP addresses from the encrypted network traffic for the Signal app.