esa-sceva/satcom-chunk-collection huggingface dataset:
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
## 2 Algorithm for the Automatic Extraction of the Reflection Period Based on Image Processing
### Step 2: Quadratic Self-Correlation Operation  
The quadratic self-correlation method repeats the self-correlation operation on the result of the self-correlation operation. When there are periodic components in the function, the extremum of the self-correlation function can well reflect this periodicity. The traditional self-correlation detection method is to convolute the input signal and the delayed input signal. Assuming the discrete signal is \(y(k)=s(k)+n(k)\quad k=1,2,\ldots,K\) and that the target signal is \(s(k)\), the noise signal is \(n(k)\), and \(K\) is the number of sampling points. Then the self-correlation function of \(y(k)\) is  
\[\begin{array}{c}\text{R}_{yy}(m)=\sum\limits_{k=0}^{K}y(k)y(k-m)\\ =\sum\limits_{k=0}^{+\infty}[s(k)+n(k)][s(k-m)+n(k-m)]\\ =\sum\limits_{k=0}^{K}s(k)s(k-m)+\sum\limits_{k=0}^{K}s(k)n(k-m)+\sum\limits_{ k=0}^{K}n(k)s(k-m)+\sum\limits_{k=0}^{K}n(k)n(k-m)\\ =\text{R}_{ss}(m)+\text{R}_{ss}(m)+\text{R}_{ms}(m)+\text{R}_{ms}(m)\end{array} \tag{1}\]  
where \(R_{ss}(m)\) represents the self-correlation result of the target signal and \(R_{nm}(m)\) is the self-correlation function of the noisy signal. \(R_{sn}(m)\) and \(R_{ns}(m)\) represent the correlation result of the target signal and noise, respectively. Due to the non-correlation of the signal and noise as well as noise and noise, the values of \(R_{sn}(m),R_{ns}(m),R_{nm}(m)\) are zero. By performing quadratic self-correlation operations on the signal after wavelet denoising, one enhances the periodicity. For signals with a low SNR, its periodicity can be obtained by applying multiple self-correlations.
###### Abstract  
Treating peat soil foundations around Dianchi Lake and Erhai Lake in Yunnan is a complex problem in practical engineering projects. Peat soil solely reinforced with ordinary cement (OPC) does not satisfy demand. This study aims to solidify soil to achieve better mechanical properties. The preparation of peat soil incorporates a humic acid (HA) reagent into cohesive soil, and cement and ultra-fine cement (UFC) are mixed by stirring to prepare cement soil samples. They are then immersed in fulvic acid (FA) solution to simulate cement soil in the actual environment. X-ray diffraction (XRD), mercury intrusion porosimetry (MIP), scanning electron microscopy (SEM), and pores and cracks analysis system (PCAS) tests are used to study the impact of the UFC on the microscopic pore structure of cement soil in a peat soil environment. The unconfined compressive strength (UCS) test is used for verification. The microscopic test results indicate that incorporating UFC enhances the specimen's micropore structure. The XRD test results show the presence of C-S-H, C-A-S-H, and C-A-H. SEM and PCAS tests show that the UFC proportion increases by between 0% and 10%, and the percentage reduction in the macropore volume is the largest, at 38.84%. When the UFC admixture is 30%, the cumulative reduction in the percentage of macropore volume reaches 71.55%. The MIP test results show that the cumulative volume greater than 10 um in pore size decreases from 7.68% to 0.17% with an increase in the UFC proportion. The UCS test results show that the maximum strength growth of cement soil is 12.99% when the UFC admixture is 0-10%. Incorporating UFC to form a compound curing agent solves the problem of the traditional reinforcement treatment of peat soil foundation being undesirable and decreases the amount of cement. This study provides practical guidance for reducing carbon emissions in actual projects.  
compound curing agent; pore structure; micro tests; macromechanical properties Article
## 8 Conclusions  
This paper has reviewed the various properties of cement mortar and concrete in which binder or fine aggregates have been replaced with MoT at different proportions. The properties of MoT and the performance of cement mortar and concrete mixed with MoT have been analyzed from the reported results. The following conclusions are drawn:  
1. The physical properties including density, microscopic structure and fitness module, and chemical composition of molybdenum tailings are similar with those of natural sand whilst the radionuclide assessment of molybdenum tailing meets the requirements for using as structural materials. These basic properties assessment of MoT indicates that MoT is suitable to be used as the replacement of cement and fine aggregates in mortar and concrete;
2. As aggregates in binder, the content ratio of ground super-fine MoT has remarkable effect on the compressive strength of the cement binder while as fine aggregates in cement mortar, the replacement ratio of MoT has little effect on the strength of the cement mortar. It indicates that the usage of MoT as a replacement of natural sand is a more feasible way than that of ground super-fine MoT in cement binder;
3. As the replacement of natural sand, the replacement ratio of MoT has subtle influence on the compressive strength of concrete. It is feasible to use MoT as fine aggregate in the preparation of structural concrete, in term of compressive strength;
4. When the amount of ground super-fine MoT replacing cement is less than 10%, it is beneficial to improve the compactness of the system; hence the freeze-thaw and carbonization resistance of the concrete are barely affected. On contrary, when the replacement ratio of MoT is more than 10%, the hydration product and alkalinity of cement in unit volume concrete decreases, and the freeze-thaw and carbonization resistance of the system decreases with the increase of the replacement ratio.  
Conceptualization, S.G.; investigation, X.C.; resources, X.C.; writing--original draft preparation, S.G.; writing--review and editing, S.Z.; supervision, S.Z.; project administration, S.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NO. 51908085), Natural Science Basic Research Program of Shaanox (No.2019)Q-145), Open fund of Shaanoxi Key Laboratory of safety and durability of concrete structures (XJKfJ201803), The Youth Innovation Team of Shaanoxi Universities, Shenzhen Starting Fund for High Level Talents and Xijing University Special Foundation (XJ17T07).  
The authors declare no conflict of interest.
## 3 Material and Method
### Performance Metrics  
Generally, for performance evaluation of the classification algorithm, Sensitivity (Recall), Specificity and Accuracy are used [7]. Performance metrics of this study and assistant measurements (FP, FN, True Positive (TP) and True Negative (TN)) are used to calculate the performance metrics and are given below.  
* _TP_--positive samples which are predicted accurately as a positive label.
* _FN_--positive samples which are predicted incorrectly as a negative label.
* _FP_--negative samples which are incorrectly predicted as a positive label.
* _TN_--negative samples which are correctly predicted as a negative label.
* _Accuracy_--performance evaluation of the classification algorithm.
* \[\mathrm{Accuracy=\frac{TP+TN}{TP+FP+TN+FN}}\] (10)
* _Sensitivity (Recall)_--indicates what proportion of classified classes as a positive label have positive class labels.  
\[\mathrm{Sensitivity=\frac{TP}{TP+FN}} \tag{11}\]
* _Precision_--indicates the proportion of classified classes as positive on all positive predicted labels.  
\[\mathrm{Precision=\frac{TP}{TP+FP}} \tag{12}\]
* _Specificity_--indicates what proportion of classified classes as a negative label have negative class labels.  
\[\mathrm{Specificity=\frac{TN}{TN+FP}} \tag{13}\]
## 5 Analyses
### Static Data  
This section presents the analyses of the static data demonstrating the benefits of using the knowledge of antenna geometry for attitude determination and array-aided positioning. Figure 3 shows the satellite visibility: the skylpot, the number of satellites and the position dilution of precision (PDOP) values. With a \(10^{\circ}\) elevation cut-off, we have a moderate GPS satellite geometry (PDOP \(<3\)) during the experiment.  
Table 2 summarizes the empirical instantaneous integer ambiguity resolution success rate for attitude determination, indicating the advantage of using MC-LAMBDA for the case of single-frequency processing. Similarly, Table 3 demonstrates the improved success rate performance of the proposed array-aided positioning with two antennas/receivers on the frame. Note that further improvement is possible by having more antennas/receivers.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \multirow{2}{*}{**Frequency (\(f\))**} & \multicolumn{3}{c}{**Code**} & \multicolumn{3}{c}{**Phase**} \\ \cline{2-7}  & \(\mathbf{\sigma_{f:\text{p}_{0}}}\) & \(\mathbf{a_{f:\text{p}_{0}}}\) & \(\mathbf{\theta_{f:\text{p}_{0}}}\) & \(\mathbf{\sigma_{f:\text{o}_{0}}}\) & \(\mathbf{a_{f:\text{o}_{0}}}\) & \(\epsilon_{f:\text{o}_{0}}\) \\  & **(cm)** & & **(deg)** & **(mm)** & & **(deg)** \\ \hline L1 and L2 & \(15\) & \(5\) & \(20\) & \(1\) & \(5\) & \(20\) \\ \hline \end{tabular}
\end{table}
Table 1: Elevation-dependent stochastic model parameters defined in Equation (6).  
Figure 3: GPS satellite visibility during the static experiment with a 10\({}^{\circ}\) elevation cut-off. (**a**) The number of satellites and position dilution of precision (PDOP); and (**b**) skylpot.  
Figure 4 depicts the scatter plot of the ambiguity-fixed attitude angles, while Figure 5 shows the plots (scatter plot of the horizontal components and time series of the down component) for the ambiguity-fixed baseline estimates. Tables 4 and 5 summarise the corresponding empirical standard deviations. Note that, once the ambiguities have been resolved, the precision of the attitude solution is driven by the high precision of the carrier phase observations [16]. That is, the accuracy of the unconstrained attitude solution (using the LAMBDA method) is comparable to that of the constrained solution (using the MC-LAMBDA method), provided that ambiguities are correctly fixed. However, the knowledge of the antenna geometry plays an important role by strengthening the underlying model and, hence, improving the ambiguity resolution (see Table 2). In the case of baseline estimation (Table 5), the proposed method yields slightly improved estimates, due to the integrated processing with an array of antennas.
# Transport Systems and Mobility for Smart Cities
## 2 Literature Review
### Digital Transformation, Citizens and Governance  
The development of advanced sensing [7] information and communications systems, creating an intelligent city network, is a prerequisite for and a crucial key element of smart cities, feeding an evaluation and modelling system, and able to control several parameters for more efficient management of the city. This system is obligatory to support the governance of a city in the control of the quality of life, as well as for the implementation of measures towards the reduction of environmental and health impacts. In this context,a smart grid will also allow cities to manage energy demands, protect the distribution network, save energy, and reduce costs.  
The digital transformation of cities will allow for the integration of intelligence in the physical environment through embedded electronic and digital systems in buildings and infrastructures. This will result in millions of small disposable devices (IoTs), each one able to capture a physical aspect of the environment and endowed with wireless communication capabilities [8]. This provides a rich analytical context, highlighting the need to store and process vast amounts of heterogeneous data flowing at different velocities. This data is defined as Big Data, which imposes significant difficulties on traditional data techniques and technologies.  
However, the question is: in this complex process, what is the role of citizens? All the achievements, at last, will be in place to benefit their quality of life. However, it is certain that citizens will only participate actively as data providers and end-users when they feel prepared through adequate digital literacy and involved in all processes. By defining their needs, and finally feeling confident in IoT, they will promote the potential of smart technology. That is why city governance, being an enormously complex process, with the multifaceted and multilevel ecosystem of various agencies and stakeholder groups (governments, companies, citizens), will assume an irreplaceable role, including the solution of conflicting interests. As a result, smart cities require a proper governance system to connect all forces at work, allowing for knowledge transfers that facilitate decision making to maximize their socioeconomic and environmental performance [9].
## 5 Case Study
### Experiment Environment  
To validate the dynamic error-propagation model proposed above, four base stations (as shown in Table 3), marked as BS1, BS2, BS3, and BS4, are set up in a 10 m \(\times\) 10 m area (humidity 67%, temperature 15 \({}^{\circ}\)C, pressure 100 kph in the current environment) as shown in Figure 11. The base station IDs, respectively, are BS1:00003109, BS2:0000313f, BS3:000030f2, and BS4:000030f. The coordinates of the base station are BS1 (120,300,220), BS2 (420,300,220), BS3 (120,540,220), and BS4 (420,540,220). The unit is cm, with the lower corner of the right front as the origin of coordinates O (0,0) in the signal coverage area,calibrating UWB-Tag position at nine static points (T1, T2,... T9). Coordinate information is provided in Table 4.  
Figure 10: The hardware topology of the UWB positioning system.  
Figure 9: The functional structure of a UWB positioning system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tag** & **Position** & **Tag** & **Position** & **Tag** & **Position** \\ \hline T1 & (155, 335) & T4 & (255, 335) & T7 & (355, 335) \\ T2 & (155, 375) & T5 & (255, 375) & T8 & (355, 375) \\ T3 & (155, 415) & T6 & (255, 415) & T9 & (355, 415) \\ \hline \hline \end{tabular}
\end{table}
Table 4: UWB-Tag layout in position calibration.  
Figure 11: UWB positioning of AGV on a production line in an intelligent manufacturing lab. Note, T1, T2,... T9 are the static points used for UWB-tag position calibration in this lab.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Type** & **Key Performance** & **Picutre** \\ \hline Base station & Woxu UA-220 & UWB 3.24 GHz\(-\)6.74 GHz Wifi 5.725 \\  & Woxu UA-220 & GHz\(-\)5.845 GF \\  & & \(-\)41.3 dBm/MHz, DC 12 V\(-\)48 V \\  & & POE IEEE 802.3 af/at, \(-\)20\(-\)65 \({}^{\circ}\)C \\ \hline UWB tag & Woxu UT-212 & UWB 6.24 GHz-6.74 GHz, 6.8 Mbps, \(-\)41.3 dBm/MHz \\ \hline Server computer & HP DL20 Gen10 & CPU: Intel Xeon [EMAIL_ADDRESS] GHz, RAM 32 GB, Disk: 4 TB \(\times\)2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hardware configuration in UWB positioning system.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 5 Results
### Employed Devices  
The devices used during the course of this project were microcontrollers made by Pycom. FiPy and LoPy4 are MicroPython-capable development boards that provide a wide variety of connectivity, notably LoRa. Both of these devices have an Xtensa dual-core 32-bit LX6 microprocessor, 4 MB of RAM, and 8 MB of external flash. This relatively powerful processor allows them to even support multi-threading. For LoRa communication, they use a Semtech SX1272 modem. To greatly improve transmission, an external antenna was connected to the devices. These devices were coupled with PyTorch and Pysense expansion boards. The expansion shields provide additional functionalities to the development boards, like GPS connectivity, allowing the device to retrieve its latitude and longitude coordinates and access built-in sensors like accelerometers, temperature, light, and many more.  
Both FiPy and LoPy4 can be powered by an external battery through a micro-B USB port. When powered up, the device immediately executes the boot.py file in its flash memory. This code helps the device connect to a network or save power or even disable specific antennas if they are not being used. After that, the device runs the code that is stored in main.py. Additionally, each device has a lib/ folder containing the libraries needed during execution.  
The LoRa settings used while performing the tests were the following:  
* Frequency: 868 MHz;
* Bandwidth: 125 kHz;
* Transmit power: 14 dBm.  
We also recorded the power consumption of the devices during certain actions and reported it in Table 1.  
On Pycom devices, different types of sleep exist:  
* _Regular sleep_: All microcontroller functions keep running;
* _Light sleep_: Wireless modems are switched off, but the CPU and RAM remain active. The LoRa modem has to be re-initialized after wake-up;
* _Deep sleep_: In addition to what light sleep does, it also powers off the main CPU and RAM, leaving only a low-power coprocessor and RTC timer running. After waking up, the node will start the execution again, just like if the device was completely powered off and then back on.
## 5 Results and Discussion
### Findings and Implications for the Research  
The major objective of this study is to suggest a WSN routing technique that is secure and energy-efficient. The proposed MHSEER protocol's primary goals are energy efficiency, stability, and reliable data transfer capability because of its scarce resources. The finding of the paper demonstrates that the throughput rate of the protocol is 95.81%, and the packet drop ratio, packet delay, energy consumption, and faulty pathways are 5.12%, 0.10 ms, 0.0102 mJ, and 6.51%, respectively. As a result, the suggested method reduces energy consumption, erroneous routes, and latency while also greatly extending the network lifetime.
# A Time-Varying Filter for Doppler Compensation Applied to Underwater Acoustic OFDM
## 2 A Low-Complexity Digital Filter to Model Mobility
### Acoustic Propagation in Mobile Conditions
Then, the time-varying channel impulse response expressed in Equation (1) becomes  
\[h(t,\tau)=\sum_{p}h_{p}(t)\delta\left(\tau-\tau_{0,p}+(1-\alpha_{p}(t))t\right). \tag{6}\]  
In this work, the acoustic propagation is modelled for relatively shallow environments, in which the sound velocity is assumed to be constant and equal to \(c_{w}=1500\) m/s. In reality, the sound speed profile can vary as a function of depth, and this can induce different Doppler scaling factors for each path. In this scenario, the proposed model must be augmented, since this would induce different time-scaling factors \(\alpha_{p}(t)\) for each path \(p\). It is also useful to represent the channel frequency spectrum, particularly for multicarrier transmission. In [7], the channel frequency response at time instant \(t\) is obtained by taking the  
Figure 1: Representation of the multipath propagation behaviour in a shallow water mobile deployment. Fourier transform of Equation (6) with respect to \(\tau\) such that its frequency response as a function of time \(t\) and frequency \(f\) is  
\[H(t,f)=\sum_{p}h_{p}(t)\mathrm{e}^{-j2\pi f\tau_{0,p}}\mathrm{e}^{j2\pi\frac{ \tau_{p}}{\tau_{exp}}ft}, \tag{7}\]  
where the first exponential term \(\mathrm{e}^{-j2\pi f\tau_{0,p}}\) indicates that there is a frequency dependent phase shift of \(-2\pi\tau_{0,p}f\) and the second term \(\mathrm{e}^{j2\pi\frac{\tau_{p}}{\tau_{exp}}ft}\) shows a frequency dependent frequency shift of \(\frac{\tau_{p}}{\tau_{exp}}f\) on the \(p\)th path due to the relative speed \(v_{p}\). When the bandwidth \(B\) of the signal is relatively small with respect to the carrier \(f_{c}\), the phase shift at time \(t\), \(2\pi\frac{\tau_{p}}{\tau_{exp}}ft\) can be assumed constant over all the frequencies within the bandwidth. However, if the narrowband condition is not respected, then the Doppler effect cannot be modelled as a constant frequency shift for all frequencies [1]. In this case, it is preferable to model the Doppler effect using a filter that allows time-variant time-scaling, as described in the next section.
## 2 Network Community Detection
### Popular Algorithms  
In this section, we give a brief list of the algorithms developed for network community detection purposes. The basic approach and the complexity of execution is also given briefly (Table 1) in this subsection. * Fast greedy algorithm: This algorithm was developed by [PERSON] _et al._[22; 23]. It is modularity based and uses a hierarchical agglomerative approach. It is called fast greedy, because it is significantly faster than older algorithms and uses a greedy method. * Walktrap algorithm: This algorithm by [PERSON] and [PERSON] [15] uses a hierarchical agglomerative method. Here, the distance between two nodes is defined in terms of a random walk process. The basic idea is that if two nodes are in the same community, the probability to get to a third node located in the same community through a random walk should not be very different. The distance is constructed by summing these differences over all nodes, with a correction for degree. * Eigenvector algorithm: This algorithm by [PERSON] [24] is modularity based, and it uses an optimization method inspired by graph partitioning techniques. It relies on the eigenvectors of a so-called modularity matrix, instead of the graph Laplacian traditionally used in graph partitioning. * Label propagation algorithm: This algorithm by [PERSON] _et al._[13] uses the concept of node neighborhood and the diffusion of information in the network to identify communities. Initially, each node is labeled with a unique value. Then, an iterative process takes place, where each node takes the label that is the most spread in its neighborhood. This process goes on until one of several conditions is met, for instance no label change. The resulting communities are defined by the last label values. * Spinglass algorithm: This algorithm by [PERSON] and [PERSON] [25] is an optimization method relying on an analogy between the statistical mechanics of complex networks and physical spinglass models. There are more algorithms developed to solve the network community detection problem; a complete list can be obtained in several survey articles [7, 12, 14]. Some interesting recent articles are [26, 27, 28, 29, 30, 31, 32]. A partial list of algorithms developed for network community detection purpose is tabulated in Table 1. The algorithms are categorized into three main groups as spectral (SP), graph traversal based (GT) and semi-definite programming based (SDP). The categories and complexities are also given in the Table 1.
## 3 Proposed Hardware Architecture
### Arithmetic Logic Unit  
The unit is responsible for performing addition, multiplication, and squaring operations, as shown in Figure 1. For addition operation, an adder circuit is used which includes m bitwise exclusive OR gates. Here, m represents the key length. For multiplication operation, the radix-4 multiplier circuit is used. In addition to multiplication, the radix-4 multiplier circuit also performs the reduction step \(X\times Y\) mod p. As a result, it eliminates the need for costly division operations. Another characteristic of the proposed design is the employment of a dedicated squarer unit. The motivation behind the dedicated squarer is to reduce the required number of clock cycles during inversion. It extends each input data value by appending a \"0\". As a result, it significantly reduces the total number of clock cycles (CCs) described in Table 2. Finally, the inverse operation is performed by utilizing the block Itoh-Tsujii method [20]. In the following, two salient features of the proposed ALU are described. First, description is performed for efficient data routing. Subsequently, the design of the radix-4 multiplier is described.