esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In conclusion, an all-in-one collimating splitter base on metafibers is proposed. Compared to the traditional configuration consisting of discrete optical components that are bulky and heavy, the metafiber design enjoys more compactness, which is beneficial for practical applications, like face identification. By synthesizing the phase of a collimating lens and a Dammann grating and integrating the final metasurface with a single-mode fiber, the Gaussian incidence can be collimated and split into multiple beams with high uniformity with only a single lenslet. Compared to the global optimization method, the phase synthesis design with excellent optical performance consumes less computing resources and is convenient for large-scale design on the fiber array platform. A 3 \(\times\) 3 metasurface collimating splitter with a large FOV of 62\({}^{\circ}\)\(\times\) 62\({}^{\circ}\) is designed and simulated using the angular spectrum propagation theory and FDTD. The uniformity reaches 1.29%, characterized by the standard deviation. Combining the deflection effect of the Dammann grating and the Fourier transformation of the collimating lens, the proposed metasurface can enlarge the scale of the spot array by N \(\times\) N times by simply integrating it with the N \(\times\) N fiber array. The compact design can also be transplanted to a VCSEL platform for higher integration. The proposed design, with high compactness and flexibility, shows great potential in the applications of face identification and motion sensing.  
Conceptualization, N.H.; Data curation, N.H.; Formal analysis, N.H.; Funding acquisition, T.G., Y.J. and S.H.; Investigation, N.H.; Methodology, N.H.; Project administration, T.G.; Resources, N.H.; Software, N.H.; Supervision, T.G., Y.J. and S.H.; Validation, N.H.; Visualization, N.H.; Writing--original draft, N.H.; Writing--review and editing, N.H., T.G. and Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (62105284, 61875174, 91833303, and 11621101), the National Key Research and Development Program of China (2017 YFA0205700), the Fundamental Research Funds for the Central Universities, and the Ningbo Science and Technology Project (2018B10093).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 7: Metasurface collimating splitter based on a fiber array. (**a**) Schematic for the large-scale spot array generation by the metasurface collimating splitter based on a fiber array, combining both the effect of Fourier transformation and the diffraction of the Dammann grating component. (**b**) Far-field intensity of the metasurface collimating splitter based on a 5 \(\times\) 5 fiber array using the angular spectrum propagation theory.
## 2 Materials and Methods  
The design of this study was an observational cross-sectional study with samples stratified according to tobacco exposure. The convenience sample consisted of 66 consecutive patients of the Dental Clinic, Clinical Hospital Center Rijeka, Croatia, who came for an examination at one of the six departments: Prosthodonics, Orthodontics, Oral medicine, Oral surgery, Periodontology, or Restorative dentistry with Endodontics. Three groups were formed (each \(N=22\)): (I) subjects smoking classic cigarettes (S), (II) users of IQOS, and (III) subjects who have never smoked either classic cigarettes or used the IQOS system, that is, with non-smoking status (CTRL). According to periodontal examination, subjects were further classified into subgroups with periodontitis and without periodontitis. First, the IQOS group was formed, as they were least numerous, and those subjects were matched by age and gender with non-smokers and cigarette smokers. Samples were collected between 1 June 2022 and 1 June 2023.  
The sample size was calculated based on previous research for differences in clinical periodontal status [33; 34]. If the probing depth is expected to be 4.5 mm in smokers, 4.1 in IQOS, and 4.5 in non-smokers with a standard deviation of 0.4 in each group, considering the Bonferroni correction for multiple comparisons and drop-outs, 22 subjects are needed in each group to detect differences among groups. This calculation was made with a test power of 0.8 and a significance level of 0.05, using an online sample size calculator (v. 1.061; University of Vienna, Vienna, Austria) [35]. Power analysis also showed that, for the effect f = 0.4 with a test power of 0.8 and a significance level of 0.05, the minimum sample size for research was 66 respondents (22 per group). The calculation was conducted using the G*Power 3.1.9.4 software (Heinrich-Heine-Universitat Dusseldorf, Dusseldorf, Germany).  
The research question was formulated according to the PECO (Population, Exposure, Control or Comparison, Outcome) strategy [36]:  
P (Population): Adult smokers;  
E (Exposure): Aerosol from IQOS and tobacco cigarette smoke;  
C (Comparison): Non-smokers;  
O (Outcome): Microbial composition of subgingival plaque and periodontitis.  
The clinical question in \"PECO\" format was the following: Is there a significant difference in microbial composition of subgingival dental plaque in IQOS users, when compared to classic cigarettes users and non-smoking subjects with or without periodontal disease?
## SmartPipes: Smart Wireless Sensor Networks for Leak Detection in Water Pipelines  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 1 and [PERSON] 2**  
1 School of Civil Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2 TT, UK; E-Mails: [EMAIL_ADDRESS] (A.M.S.); [EMAIL_ADDRESS] (D.N.C.)  
2 School of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2 TT, UK; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +44-0-121-414-4182; Fax: +44-0-121-414-3675.  
_Received: 18 December 2013; in revised form: 7 February 2014 / Accepted: 10 February 2014 / Published: 20 February 2014_  
**Abstract:** Asset monitoring, specifically infrastructure monitoring such as water distribution pipelines, is becoming increasingly critical for utility owners who face new challenges due to an aging network. In the UK alone, during the period of 2009-2010, approximately 3281 mega litres (\(10^{6}\)) of water were wasted due to failure or leaks in water pipelines. Various techniques can be used for the monitoring of water distribution networks. This paper presents the design, development and testing of a smart wireless sensor network for leak detection in water pipelines, based on the measurement of relative indirect pressure changes in plastic pipes. Power consumption of the sensor nodes is minimised to 2.2 \(\mu\)W based on one measurement every 6 h in order to prolong the lifetime of the network and increase the sensor nodes' compatibility with current levels of power available by energy harvesting methods and long life batteries. A novel pressure sensing method is investigated for its performance and capabilities by both laboratory and field trials. The sensors were capable of measuring pressure changes due to leaks. These pressure profiles can also be used to locate the leaks.  
**Keywords:** pipe monitoring; leak detection; wireless sensor network; ultra-low power consumption; laboratory trials; field trials
## 2 Proposed Fault Diagnosis Method
### Health Condition Classification Based on the SSDAE
#### 2.4.2 Stacked Sparse Denoising Autoencoder  
Deep learning produces excellent performance by optimizing network structure and training methods. Therefore, to recognize fault states correctly in complex working conditions for bearing fault diagnosis, it is essential to construct a deep network with a reasonable structure and excellent training methods. [PERSON] [29] proposed a deep learning network constructed by a stacked autoencoder that is composed of several shallow AEs. The training idea is that, through an unsupervised self-learning method, each AE is trained sequentially so that the whole network is trained. The hidden layer parameters of each AE are then stacked to construct a deep structure.  
Based on the above analysis, a stacking training strategy is simpler than directly training large-scale deep network architecture, and the network training more easily converges. However, it is worth studying which features are more conducive to health condition recognition and how extracted features can be more easily classified. Therefore, in our research, to better manage the high-dimensional feature vector and extract sparse and robust features, a hybrid autoencoder, which combines SAE and DAE, is proposed.  
Figure 5 depicts the construction and training process of the proposed SSDAE model; Figure 5a,b are the shallow SAE and DAE, respectively. In the training process, \(H_{i}\) represents the hidden layer feature, \(W_{e}^{i}\) is the coding weight from the input layer to the hidden layer; \(W_{d}^{i}\) is the decoding weight from the hidden layer to the output layer; and \(i\) represents the training group. Firstly, the SAE is unsupervised trained, and the trained hidden layer parameters \(H_{1}\) are used as the input of the DAE, which is also unsupervised trained. Finally, based on the stacked training parameters of the two models, a hybrid autoencoder is constructed, as shown in Figure 5c. \(W_{e}^{1}\) and \(W_{e}^{2}\) are the coding weights of the first and second layers, respectively; and \(W_{d}^{1}\)\(W_{d}^{2}\) are the decoding weights of the third and fourth layers, respectively. As shown in Figure 5d, taking the two trained hidden layers and adding a Softmax classifier, the supervised learning SSDAE model is constructed. Thus, by establishing a  
reasonable model arrangement order and stacking form, input features can be optimally selected and fault recognition performance can be further improved.
# Stability Analysis of Jinchuan Hydropower Station Hydraulic Tunnels during Excavation and Unloading
## 3 Study of the MS Activity of the Surrounding Rock
### Spatial Evolution Characteristics of MS Events  
As micro-fractures inside the surrounding rock are characterized by MS events, the evolution of the micro-fractures is characterized by the spatial concentration of MS events and the dynamic migration of the concentration zone. In order to ensure the safety and efficiency of the site's construction, it is necessary to delineate the damage zone and determine the potential for surrounding rock deformation by studying the spatial concentration and dynamic migration of MS events.  
Figure 5 shows the spatial distribution and density of MS events. In the spatial distribution diagram, the filled circles indicate MS events; the size of the filled circles indicates the energy level of the MS events; the color of the filled circles indicates the moment magnitude of the MS events. In the density cloud diagram, different colors indicate different densities of MS events. The spatial distribution law of MS events can be observed by an analysis of Figure 5. In particular, according to the top view, the MS events were distributed in the surrounding rock on both sides of the spillway tunnel, mainly concentrated in three zones: stakes \(0+340\) m, \(0+400\) m to \(0+450\) m, and \(0+500\) m to \(0+540\) m. With the excavation and unloading of the spillway tunnel, micro-fractures were concentrated in the area of poor geological structure and the area disturbed by strong unloading under the influence of stress adjustment, which is consistent with the law presented in the spatial distribution diagram and density cloud diagram of MS events in Figure 5.  
Figure 4: Temporal distribution of the moment magnitude of MS events.  
One of the most crucial elements of micro-fractures which can be used to assess the degree of damage to the surrounding rock is the energy release of MS events [41]. The continuous release of internal elastic energy from the surrounding rock frequently coincided with the occurrence of micro-fractures, which are reflections of rock damage. This resulted in the deterioration of the mechanical performance of the surrounding rock, which in turn led to a worsening of the damage to the surrounding rock. Figure 6 shows the density of energy of micro-fractures that occurred during the excavation and unloading of the spillway tunnel. It is clear that the energy from the surrounding rock in the spillway tunnel was mainly concentrated in three zones, namely, stakes 0 + 300 m to 0 + 340 m, 0 + 420 m to 0 + 450 m, and 0 + 500 m to 0 + 580 m, which were dominated by MS events with higher levels of energy and larger moment magnitude scales. A comparison of Figures 5 and 6 reveals that the spatial distribution of energy and MS events was roughly the same. As a result, the two zones with the highest energy densities sustained the most serious damage and were at risk of becoming unstable, and thus, it is important to reinforce the support for these zones.  
Figure 5: Spatial distribution and density of MS events (2 August to 16 December 2020).
# Practical Guide to Ultrasound Beam Forming: Beam Pattern and Image Reconstruction Analysis
## 1 Introduction
From Equation (9), we can deduce that for a circular aperture with radius R, we can write the pressure field as:  
\[p(X,Y,t)|_{Y=0}\propto\sin c\left(\frac{RXf_{0}}{c_{0}2L}\right) \tag{10}\]  
From Equation (10), we can deduce that the ultrasound beam size is influenced by the aperture size and transmitted frequency, and that it changes over depth. The beam can be defined as the area where the pressure amplitude is above a specific value, which is normally considered in relation to the maximum pressure generated (e.g., the \(-20\) dB beam). A larger aperture and higher frequencies mean a smaller beam. Moreover, the beam generally widens with increasing depths. The beam size defines the spatial resolution in the lateral and elevation direction. The smaller the beam, the higher the spatial resolution. On the other hand, the smaller the beam, the smaller the volume that can be insonified with a single transmission, and more transmission events are thus required to cover a given volume. In the next section, the basic differences between linear and phased array beam forming will be introduced. Subsequently, multi-line acquisition (MLA), multi-line transmission (MLT), plane and diverging wave imaging, synthetic aperture, and more recent beam-forming modalities will be described. To summarize the analysis, a table is presented where the peculiarities of each modality are highlighted.
# Time-Dependent Alternative Route Planning: Theory and Practice +
###### Abstract  
We consider the problem of computing a set of meaningful alternative origin-to-destination routes, in real-world road network instances whose arcs are accompanied by travel-time _functions_ rather than fixed costs. In this time-dependent alternative route scenario, we present a novel query algorithm, called _Time-Dependent Alternative Graph_ (TDAG), that exploits the outcome of a time-consuming preprocessing phase to create a manageable amount of travel-time metadata, in order to provide answers for arbitrary alternative-routes queries, in only a few milliseconds for continental-size instances. The resulting set of alternative routes is aggregated in the form of a time-dependent alternative graph, which is characterized by the minimum route overlap, small stretch factor, small size, and low complexity. To our knowledge, this is the first work that deals with the time-dependent setting in the framework of alternative routes. The preprocessed metadata prescribe the minimum travel-time informations between a small set of \"landmark\" nodes and all other nodes in the graph. The TDAG query algorithm carries out the work in two distinct phases: initially, a _collection phase_ constructs candidate alternative routes; consequently, a _pruning phase_ cautiously discards uninteresting or low-quality routes from the candidate set. Our experimental evaluation on real-world, time-dependent road networks demonstrates that TDAG performed much better (by one or two orders of magnitude) than the existing baseline approaches.  
time-dependent shortest path; alternative routes; travel-time oracle; plateau and penalty methods +
Footnote †: This paper is an extended version of our paper published in the Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS), Pisa, Italy, 7-8 September 2020.
## 5 Results and Discussions
### User Data Rate (UDR)
When changing the physical characteristics of this scenario, it is notorious that the UDR distributions for all signal combining schemes are different compared to the mining roadway scenario. The differences include the shape and location of the lobes and maximum UDRs reached in the environment, and mainly the drastic decrease in maximum UDRs for all techniques. This is due to the increase in distances between \(T_{x}\)s and the ADR in \(R_{x}\), which causes the VLC channel to degrade and the SINR to decrease. By comparing Figure 11a-c, the technique that achieves the highest data rate continues to be the MRC (maximum value of 120 Mbps and minimum value of 20 Mbps). In contrast, the technique that has the worst performance is the EGC (maximum value of 50 Mbps and minimum value of 10 Mbps). Figure 12 compares UDR distributions reached by the pyramidal ADR in the mine working face scenario. Comparing it with the hemi-dodecahedron ADR in this scenario, it presents similar characteristics in the UDR distribution; however, its performance is lower when reaching the maximum data rate. By comparing Figure 12a-c, the technique that achieves the highest data rate continues to be the MRC (a maximum value of 110 Mbps and a minimum value of 10 Mbps). In contrast, the technique that has the worst performance is the EGC (a maximum value of 45 Mbps and a minimum value of 5 Mbps). Figure 11: UDR distribution of the hemi-dodecahedron ADR in the mine working face scenario: (**a**) MRC, (**b**) EGC, and (**c**) SBC schemes. Figure 10: UDR distribution of the pyramidal ADR in the mining roadway scenario: (**a**) MRC scheme, (**b**) EGC scheme, and (**c**) SBC scheme.
## 2 TBD System Setup
### Target Dynamic Model  
The target state is represented by a five-dimensional state vector \(\textbf{x}_{k}\!=\!\!\left[x_{k}\,\dot{x}_{k}\,y_{k}\,\dot{y}_{k}\,I_{k}\right]^ {T}\), where \((x_{k},y_{k})\), \((\dot{x}_{k},\dot{y}_{k})\) and \(I_{k}\) represent the position and velocity in the \(x\) and \(y\) dimensions and the intensity of the target at time \(k\), respectively. \(k\) is the time index. A constant-velocity (CV) model is widely used as an example.  
The discrete-time system dynamics of this model is of the form:  
\[\textbf{x}_{k}=\textbf{F}\textbf{x}_{k-1}+\textbf{w}_{k} \tag{1}\]  
where \(\textbf{w}_{k}\) is the zero mean Gaussian noise process with covariance \(\textbf{Q}_{cv}\) and \(\textbf{F}_{cv}\) is the process transition matrix, which are defined by:\[\mathbf{F}_{C^{\prime}}=\begin{bmatrix}1&T&0&0&0\\ 0&1&0&0&0\\ 0&0&1&T&0\\ 0&0&0&1&0\\ 0&0&0&0&1\end{bmatrix} \tag{2}\]  
\[\mathbf{Q}_{C^{\prime}}=\begin{bmatrix}q_{1}{T_{C^{\prime}}}^{3}/3&q_{1}{T_{C^{ \prime}}}^{2}/2&0&0&0\\ q_{1}{T_{C^{\prime}}}^{2}/2&q_{1}{T_{C^{\prime}}}&0&0&0\\ 0&0&q_{1}{T_{C^{\prime}}}^{3}/3&q_{1}{T_{C^{\prime}}}^{2}/2&0\\ 0&0&q_{1}{T_{C^{\prime}}}^{2}/2&q_{1}{T_{C^{\prime}}}&0\\ 0&0&0&0&q_{2}{T_{C^{\prime}}}\end{bmatrix} \tag{3}\]  
where \(T_{C^{\prime}}\) is the sampling period, \(q_{1}\)and \(q_{2}\) are the power spectral density of the acceleration noise and the noise in the rate of change of target return intensity, respectively.  
The presence of a target in the data is denoted by a target existence variable \(E_{k}\), a target existing in the data, \(E_{k}=1\), otherwise \(E_{k}=0\). The probability of target existence is modeled as a two-state Markov chain, the probability transition matrix of which is defined by:  
\[\Pi=\begin{bmatrix}1-P_{d}&P_{b}\\ P_{d}&1-P_{b}\end{bmatrix} \tag{4}\]  
where \(P_{b}\) and \(P_{d}\) denote the probability of target birth and death, respectively.
## 4 Analysis of the Test Results  
The test was carried out at high temperatures in summer. The temperature of the environment without direct sunlight on the day of the test and the temperature changes of the track slab, the CA mortar layer and the track bed are shown in Figure 6a, the vertical temperature gradient changes of the track slab, the CA mortar layer and the track bed are shown in Figure 6b.  
It can be seen from the curve in Figure 6a that during the test, the ambient temperature under the condition of no direct sunlight first increased and then decreased in a sinusoidal form. The highest ambient temperature was observed at 17:00 (8 h after the initial conditions were recorded), reaching 36.2 \({}^{\circ}\)C, while the lowest ambient temperature, 32.6 \({}^{\circ}\)C, was observed at 7:00 (22 h after the initial conditions were recorded), thus showing a temperature difference of 3.6 \({}^{\circ}\)C. The temperature variation of each structural layer of track system is similar to that of CRTS-II slab ballastless track on simply supported box girder bridge in reference [17]. The temperature in the track system layers was higher than the ambient temperature, and the temperature change and trend of the temperature change were essentially consistent with the ambient temperature with a certain lag. Due to the track slab is fully exposed in the atmosphere, the temperature was higher at the track slab top surface than the ambient temperature and other structural layers, with the shortest lag time. The temperature decreased in each structure layer with an increasing vertical depth of the ballastless track system structure, and the trend and the lag time of the temperature change gradually. Due to the poor thermal conductivity of concrete, the temperature change in the track system structural layer was approximately 1 \({}^{\circ}\)C smaller than that of the ambient temperature. This shows that the structure had good thermal insulation properties.  
In order to further understand the temperature change of each structural layer, the vertical temperature gradient changes of the track slab, the CA mortar layer and the base slab are compared and analyzed, as shown in Figure 6b. It can be seen from three temperature gradient time history curves that during the test, the change trend of the temperature gradient of the CA mortar