esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities
## 5 Experimental Results  
This section analyzes water distribution in three scenarios using EPANET and LR, focusing on base demand, flow optimization, and the relationship between demand and diameter. The number of inhabitants per node for each location is shown in Table 2, where the total number of nodes per location can also be seen. Those optimization criteria mentioned in the scenario description are highlighted.  
The simulation from the described WDN (Section 3) provided data for the algorithms in Section 4.2, which integrated linear regression techniques. The regression line follows \(y=mx+n\), where \(m\) indicates the slope (increase in base demand per unit diameter) and \(n\) denotes the Y-axis intersection (base demand when diameter = 0).
# RESEMBLE: A Real-Time Stack for Synchronized Mesh Mobile Bluetooth Low Energy Networks
## 4 Resemble Design
### How to Enable Unsynchronized Node Transmissions  
To enable TDMA-based communications, the nodes have to be synchronized, so that each node knows when it can transmit. However, to be synchronized, the nodes need to transmit messages without interfering with regular transmissions. In the case a node wants to join the network, its transmissions have to be regulated so that they do not interfere with the transmissions of the other nodes.  
To achieve this, in RESEMBLE, time is divided into time intervals, called superframes (Figure 5), which are, in turn, partitioned in timeslots. Each network node is statically assigned one or multiple timeslots to transmit a single message using the ADV event. In the remaining time of the superframe, the node remains in scanning state to receive the messages transmitted from the other nodes. With this approach, each node knows the exact time at which it can transmit a message and is also aware of the transmission times of the other nodes. One or multiple timeslots within the superframe are marked as shared timeslots (SH in Figure 5) and are to transmit non-real-time messages. The transmission within shared timeslots is regulated by a random backoff algorithm. Before starting the transmission, a node with an outgoing non-real-time message has to wait a random number of shared timeslots, thus decreasing the collision probability.  
The proposed approach works in two phases:  
1. Synchronization and provisioning.
2. Online phase. Nodes are synchronized; therefore, they can transmit.  
The phase in which a node joins the network is called provisioning. Here, the provisioning procedure is the one specified in the Mesh BLE standard [22] with no modifications. The specifications [22] define \"unprovisioned\" as a node that has not joined the network and \"provisioned\" as a node that joined the network. The provisioning phase is managed by a single node, called the Provisioner. During the synchronization and provisioning phase, a new node willing to join the network exchanges messages with the Provisioner node (i.e., the node that manages the joining phase of new nodes in the network) to run the provisioning procedure to join the network. In the proposed approach, the Bearer Layer transmits provisioning messages in the non-real-time channel. This way, no modifications are needed to the provisioning procedure, as the Bearer acts in a transparent way. As clock synchronization is required, in addition to the provisioning procedure, the clock synchronization procedure has to be executed in this phase. In our design, clock synchronization is  
Figure 5: Example of RESEMBLE superframe.  
achieved with a centralized approach, in which a node with timing master functionalities (for instance, the Provisioner) provides the reference clock for all network nodes. Note that clock synchronization is used during the online phase to schedule real-time transmissions. Once synchronized, the nodes enter the online state, in which they communicate on the real-time channel.
# Dynamic Measuring Method of Laser Beam Incident Angle for Laser Doppler Vibrometer
## 3 Static Measuring Method of Incident Angle
### Static Measuring Device and Results
The physical distance \(d\) of the laser spot moving from points A to A\({}^{\prime}\) on the coordinate paper is calculated using Equation (6). \[d=d_{\rm pix}/d_{\rm p} \tag{6}\]  
The angle \(\beta\) was obtained using repeated measurement and averaging to reduce systematic error. Throughout the testing process, a total of 10 groups of experiments were completed. The displacements \(l_{i}\) and \(d_{i}\) corresponding to the ten tests are shown in Table 1, \(i\) = 1, 2, 3,..., 10. The table also lists the LDV incident angle \(\beta_{i}\) corresponding to the ten measurement results. Figure 5: Image recognition results, (**a**) before moving; (**b**) after moving. According to the table, the mean value of the ten angle results of \(\beta_{i}\) is 1.91204\({}^{\circ}\), the standard deviation is 0.002455\({}^{\circ}\), and the variation coefficient is 0.1284%. It should be noted that image processing is also affected by many factors, such as the calculation of the spot center, the resolution of the camera image, etc. When multiple factors above are combined, the error of distance \(d\) may reach 0.1 mm (i.e., 100 um). To verify the impact of this error on the static measurement results, we modified the \(d_{i}\) values in Table 1 to \(d_{i}^{\prime}=d_{i}+0.1\) mm and \(d_{i}^{\prime\prime}=d_{i}-0.1\) mm, \(i=1\), 2, 3, \(\ldots\), 10. Subsequently, Equation (3) was used to calculate the results of \(\beta_{i}\) after considering the measurement accuracy error, and were recorded as \(\beta_{i}^{\prime}\) and \(\beta_{i}^{\prime\prime}\), respectively, \(i=1\), 2, 3, \(\ldots\), 10. The summarized measurement results are also shown in the table. The table shows that the average values of the angle static measurement results \(\beta_{i}\), \(\beta_{i}^{\prime}\), and \(\beta_{i}^{\prime\prime}\) are 1.9120\({}^{\circ}\), 1.9172\({}^{\circ}\), and 1.9055\({}^{\circ}\), respectively. The differences between the latter two values and the first value are 0.0052\({}^{\circ}\) and \(-\)0.0065\({}^{\circ}\), respectively. These results show that the measurement accuracy caused by factors such as camera image resolution and LDV focus position does not have a serious impact on the results of this experiment. Therefore, we believe that the experimental design of the angle static measurement meets the testing requirements. Since the standard deviation and variation coefficient of the measurement results meet the engineering requirements, it is considered here that the measuring value of the LDV laser beam incident angle in this state is \(\beta_{\rm CAL}=1.9120^{\circ}\).
# No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction
## 4 Experiments and Analysis  
### Database and Evaluation Metrics  
TID2013 and LIVE are the current mainstream databases for image quality evaluation. These databases provide the subjective score for each distorted image. The LIVE [23] database images are color images of different sizes, with 29 reference images, including five common types of distortion: additive white Gaussian noise (WN), Gaussian blur (GB), JPEG compression and JPEG2000 compression (JP2K) and fast-fading (FF). The image size of the TID2013 [24] database is 512 \(\times\) 384, including 24 distortion types, each of which has five different degrees. The summary of each database is tabulated in Table 1 [23; 24].  
The IQA algorithm performance depends on the correlation between the subjective score and the prediction score. If their correlation is high, it means that the performance of the algorithm is better. We used two standard measures, i.e., Spearman rank-order correlation coefficient (SRCC) and Pearson linear correlation coefficient (PLCC). The SRCC is defined as:  
\[\text{SRCC}=1-\frac{6\sum_{i=1}^{N}d_{i}^{2}}{N(N^{2}-1)} \tag{11}\]  
where \(d_{i}\) is the difference between the predicted score and ground-truth score of the \(i\)th image, and \(N\) is the number of images. The PLCC is defined as:  
\[\text{PLCC}=\frac{\sum_{i=1}^{N}(p_{i}-\overline{p})(s_{i}-\overline{s})}{ \sqrt{\sum_{i=1}^{N}(p_{i}-\overline{p})^{2}}\sqrt{\sum_{i=1}^{N}(s_{i}- \overline{s})^{2}}} \tag{12}\]  
where \(p_{i}\) is the predicted score of the \(i\)th image, and \(s_{i}\) is the ground-truth score of the \(i\)th image, \(\overline{p}\) and \(\overline{s}\) are the average of each.  
Figure 6: The framework of channel weight module.
# Electromyogram in Cigarette Smoking Activity Recognition
## 2 Wearable System and Dataset
### EMG Channel Selection  
A channel selection method was performed to determine the most relevant sEMG channels, as using many sEMG channels is computationally expensive for real-time applications. Furthermore, too many EMG recordings could lead the model to overfit the training instance due to irrelevance or redundancy. In the literature, several methods have been used to determine the most informative sEMG channels including Fisher-Markov selector [36], sequential forward selection (SFS) [37; 38], and direct channel selection [39]. In this study, a commonly used SFS-based channel selection algorithm was used. The SFS chooses the best single particular channel for classifier input, and then it adds another channel to it at a time to maximize the accuracy of the classifier in combination with chosen channels.
## 1 Introduction
At the ideal converter input is connected an equivalent circuit consisting of the generator (\(V_{g}\)) and a series resistor (\(R_{X}\)), which is aimed at concentrating all the losses of interest in the actual converter originated by the parasitic resistances of its components (active and passive). Of course, \(R_{X}\) can be added to the generator internal resistance. At the ideal converter output, a source (\(V_{X}\)) and the load are connected. \(V_{X}\) concentrates the losses in the actual converter due to the threshold voltage of its diode. Currents and voltages in the model are given by their mean values. In this paper, and according to the model shown in Figure 1, the values of \(R_{X}\), \(V_{X}\) and \(A_{VI}\) are calculated for each basic topology (i.e., boost, buck and buck-boost). From here, input resistance, voltage gain and actual efficiency for each of these three topologies are found. Finally, model quality is assessed. The present paper is structured as follows: Section 2 is devoted to setting the conditions for the developed model and its parameters. The parameter values for each basic topology are calculated in Sections 3-5: boost, buck and buck-boost respectively. Section 6 is devoted to comparing the results of the developed model regarding actual converters. In Section 7 the results are discussed, the fruit of which becomes clear with the excellent behavior of the model, practically like an actual converter. Finally, the paper ends with some conclusions. Figure 1: Common static model for DC/DC power converters basic topologies.
## 3 Application of APD to a Soft Clay Site  
### Test Site  
This research focuses on Leda clays--soft and sensitive deposits found in eastern Canada. To facilitate detailed geotechnical investigation, the National Research Council of Canada (NRCC) established the Canadian Geotechnical Research Site No. 1 at South Gloucester, Ontario [7]. Early geotechnical investigations, beginning around 1954, addressed issues such as excessive settlements and poor shallow foundation performance [8]. Several years later, [PERSON] [9] conducted a comprehensive geotechnical study on the site. Other significant studies on the characterisation of this test site include works by [10; 11].  
The initial soil stratification identified nine distinct soil layers. Subsequently, a simplified profile was established, which includes a 2-meter crust underlain by three soft clay layers extending to a depth of 18 m [7]. Figure 2 presents the results of CPTu sounding (CPT 12-03 from [7]), DMT sounding (extracted from [7]) in the form of the corrected first and second readings (\(P_{0}\) and \(P_{1}\), respectively), and various shear wave velocity (\(V_{s}\)) profiles (as reported in [7]). The delineated layers are highlighted in the figure. For the upper crustal layer, the maximum recorded correct cone resistance (\(q_{t}\)) was 6.5 MPa, and the maximum recorded sleeve friction (\(f_{s}\)) was 118 kPa. The scales for \(q_{t}\) and \(f_{s}\) in Figure 2a,b have been clipped to improve visibility and facilitate comparison within the soft Leda clay layers. Beneath the crustal layer, \(q_{t}\) generally increases with depth, ranging from 200 to 850 kPa. Similarly, the porewater pressure measurements (\(u_{2}\)) increase with depth below the crust, with values ranging between 150 and 650 kPa. The \(f_{s}\) measurements are near the lower limit of strain gauge resolution for CPT sleeve readings in commercial penetrometers, with values typically below 5 kPa [7].  
Several research groups have conducted \(V_{s}\) measurements at the site, primarily using the downhole test (DHT) mode via seismic piezocone tests (SCPTu) [12; 13]. The overall  
Figure 1: Example input format for methods and parameters (modified from [3]).  
trend of \(V_{s}\) at the site (Figure 2e) shows a slight decrease in the upper crustal region, reaching a minimum at a depth of approximately 4 m, where \(V_{s}\) values drop as low as 60 m/s. Beyond this depth, \(V_{s}\) steadily increases, reaching 230 m/s at around 22 m. The lowest \(V_{s}\) values at 4 m correspond to a zone with high natural water content [7].  
The soft clay layers at Gloucester are slightly overconsolidated, likely due to a combination of aging, secondary compression, minor erosion, and bonding from geochemical processes. The groundwater table is relatively shallow, located approximately 0.8 m below ground level [7].
# Thermal Investigations on Carbon Nanotubes by Spectroscopic Techniques
## 2 Materials and Methods
### Experimental Section  
The Fourier Transform InfraRed (FTIR) method allows the analysis of vibrational and rotational movements of molecules [53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63]. This method enables analysis of the 14,000-10 cm\({}^{-1}\) range of the electromagnetic spectrum, which contains the Near-IR (4000-14,000 cm\({}^{-1}\)), the Mid-IR (400-4000 cm\({}^{-1}\)), and the Far-IR (10-400 cm\({}^{-1}\)).  
Such a technique turns out to be a complementary technique to other methods such as Raman spectroscopy and other neutron-based spectroscopies [64; 65; 66; 67; 68; 69; 70]. In this experimental work, CNTs spectra were registered by means of an ATR-FTIR Vertex 70 V spectrometer (Bruker Optics, Ettlingen, Germany) equipped with a platinum diamond ATR, working in the Mid-IR range from 4000 to 400 cm\({}^{-1}\) and in the temperature range of 25-450 \({}^{\circ}\)C.  
The main characteristic of the employed VERTEX 70v spectrometer (Bruker Optics, Ettlingen, Germany) is the RockSolidTM interferometer (Bruker Optics, Ettlingen, Germany), equipped with gold-coated optics and a 30\({}^{\circ}\) angle of incidence in order to maximize efficiency and sensitivity and to minimize polarization effects.  
For each spectrum, in order to obtain a good signal-to-noise ratio, 64 repetitive scans were accumulated with an average resolution of 4 cm\({}^{-1}\), in the 400-4000 cm\({}^{-1}\) spectral range. As far as the Raman spectroscopy was concerned, Raman spectra were obtained by means of the Spectrometer BRAVO (Bruker Optics, Ettlingen, Germany); the laser source delivers two different excitation wavelengths, i.e., 785 nm and 1064 nm. Such a characteristic is especially important since, due to fluorescence, the analysis of raw materials by Raman spectroscopy is often difficult. The BRAVO spectrometer manages the Sequentially Shifted Excitation (SSETM) procedure, a patented fluorescence moderation process which allows a broad range of raw materials to be measured.  
The investigated spectral range was 300-3200 cm\({}^{-1}\) in the temperature range of 25-450 \({}^{\circ}\)C; spot size value was 10-15 \(\upmu\)m at a magnification value of 10\(\times\) lens.  
Concerning the interpretation of spectra and the data analysis, preprocessing of the data was performed by means of the Bruker OPUS software. The following processing steps were performed:  
- baseline treatment to decrease the differences between the spectra due to the shift of the baseline
- smoothing treatment to reduce instrument noise
- first derivative treatment to correct baseline shift
- second derivative to better distinguish the spectral characteristics
- spectra normalization to correct the path length dissimilarity and to diminish the disparities among single measurements.  
Then, the data were processed by means of Matlab environment.
# Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure +
## 4 Implementation
### KubeEdge Extension for NDN Device Management  
We extend KubeEdge Edgecore with an NDN Edge Management client. This client communicates with the ndn-autoconfig server running inside each edge node. Whenever a responder connects to a border edge node, if it is the first time he joins the emergency communication network, the edge client will ask the Cloudcore to register the device to the system as a Kubernetes custom resource. Otherwise, the edge client will tell the Cloudcore to enable or disable the device based on the responder connection status to the edge. Moreover, we design an NDN mapper for responder devices. After the device is registered at Cloudcore, it can use the mosquito publish/subscribe framework to get updates from the cloud or push information to the central management center. Commander can monitor and update responder devices information by interacting with the KubeEdge control plane at cloud node by using commands such as \"kubectl get device\" and \"kubectl edit device\". The extended KubeEdge architecture design is shown in Figure 11.  
Figure 12 shows a sequence diagram of how a new responder NDN device is registered to the management system, subscribes to get mission updates, and publishes event status to the cloud.  
Figure 10: NDN containerized router deployment file example.
## 2 Materials and Methods
### ABAC System Performance  
The performance of the ABAC system was also evaluated as part of this analysis. There is a significant correlation between the DO set point established by the ABAC system and the DO sensor reading in both System A and System B of 54% and 64%, respectively; System C shares DO set point and DO readings with System B (see Figures A5-A7, and Table A2, Appendix B). Additionally, there is a similar trend with ammonia mass loading in each system. This demonstrates that the ABAC system successfully reacts to changes in ammonia loading by adjusting the DO set point accordingly [3,4].  
The benefits of an ammonia-based aeration control system also have the potential to impact downstream chlorine dosage [12,13]. Chlorine dosage at RP-1 is based on a chlorine residual setpoint. The residual chlorine, or free chlorine, is the remaining chlorine after the supplied chlorine dosage is consumed through treatment. If the chlorine analyzer reading of free chlorine is lower than the desired setpoint, the pump output is increased by setting of a higher dosage. This change in operation is typically a reaction to higher ammonia loading to the chlorination plant during tertiary treatment. Additionally, free chlorine can react with ammonia to form chloramines. This disinfect byproduct  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter.** & **Value** & **Error** & **Unit** \\ \hline
2018 (normalized) & 17,927 & \(\pm\)390 & Avg. daily kWh \\ \hline
2019 (normalized) & 16,502 & \(\pm\)422 & Avg. daily kWh \\ \hline Energy Savings & 1425 & \(\pm\)30 & Avg. daily kWh \\ \hline Cost Savings \({}^{\ast}\) & \$149 & \(\pm\)53 & Daily avg. \\ \hline Cost Savings & \$54,474 & \(\pm\)51100 & Per year \\ \hline
2019 Actual Cost & \$630,633 & \(\pm\)515,400 & Per year \\ \hline Estimated Savings & 9\% & \(\pm\)1\% & Per year \\ \hline Capital Cost of ABAC Unit: & \$223,273 & \multicolumn{2}{c}{Equipment/Installation} \\ \hline Return of Investment: & 4 & \multicolumn{2}{c}{Years} \\ \hline \hline \end{tabular}  
* $0.10 per kWh was used for the cost analysis.  
\end{table}
Table 1: Energy consumption cost analysis for August–December 2019.  
Figure 8: Average daily energy consumption in kWh for normalized data.  
will reduce the concentration of free chlorine as it is not registered by the chlorine analyzer [14]. Prior to the use of ABAC, plant operations took multiple hand samples of the aeration treatment process to determine if ammonia treatment was compromised (i.e., ammonia break-through). This was done as a reaction to observing a higher chlorine dosage at the end of the process. The hand samples were often inconclusive as they failed to capture the ammonia spike when it occurred. With the ABAC units, plant operations staff is notified of real-time ammonia spikes and can mitigate chlorine overdose by reducing flows as well as through the automatic increase of aeration at the basins. Figure 11, Appendix B displays the improvement in chlorine dosage in accordance to ammonia loading since the installation of the ABAC units in August 2019.
## 3 Wave Velocity and Mass Variation of Unloaded Samples under Dry-Wet Cycle
### Mass  
The water absorption and dehydration characteristics of rocks are closely related to the pore characteristics. In the process of repeated water absorption and dehydration, the mass change can well reflect the progressive deterioration effect of the dry-wet cycle on unloaded rocks. Since the mass change is too small relative to the mass base of the sample, and the mass in the wet state has a large error in the measurement process, the mass in the dry state is used in this analysis. The variation curves of the sample with cycles under different confining pressures and unloading amount are drawn, as shown in Figure 7.  
It can be seen from Figure 7 that the variation trend of unloaded sandstone after the dry-wet cycle can be roughly divided into three stages with the increase of cycles, which are rapid decrease, slow decrease, and steady state. In stage I (the first eight cycles), the mass decreased rapidly, the mass of each sample decreased by 0.22 g on average, and the cumulative average reduction percentage accounted for 52.79% of that of all stages. In stage II (8-15 cycles), the mass of the samples is in a relatively slow decreasing trend, and the total change of the stage is less than that of stage I. The mass of each sample is reduced by 0.15 g on average, and the cumulative reduction percentage accounted for 36.00% of all stages. In stage III (15-20 cycles), all the samples changed in the range of 0.08 g, and the mass change was relatively stable.  
The mass of the sample decreases with the increase of cycles as a whole. On the one hand, the effect of water-rock interaction is manifested in the contact between particles and skeleton. The unloading effect first causes cracks to increase, and fine cracks appear at the contact of particles, which provides action channels for the later dry-wet cycle. That is, the pore of the sample continues to increase during the whole dry-wet cycle of the test, which is consistent with the wave velocity results. On the other hand, under the repeated lubrication and softening of water, mineral particles and soluble cements fall off, resulting in mass reduction [36]. There is little difference between the mass reduction rate of stage I and stage II, mainly because soluble substances and cements are dissolved and transported in water and are accumulate in closed pores in a short time without complete precipitation, resulting in a less significant dry-wet cycle effect of samples in stage I compared with stage II.