esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Asymmetric Communication  
Typically, all nodes in a WSN transmit with the same power. Then, communications between the sink node and sensor nodes are symmetric in either direction (uplink or downlink), and both require multiple hops as shown in Figure 1a.  
As explained in Section 1, a sink node usually has a stable power supply compared with sensor nodes, which enables asymmetric communication [4] (Figure 1b). Here, a sink node with a stable power supply can transmit with a larger power and directly reach all sensor nodes. In contrast, sensor nodes transmit with a small amount of power and reach the sink node by multi-hop routes. In this way, asymmetric communication helps to reduce the power consumption at each sensor node in downlink transmissions. However, most transmissions in WSNs are uplink. Therefore, applying asymmetric communication alone will not largely contribute to the reduction of energy consumption.
# Speckle Noise Detection and Removal for Laser Speech Measurement Systems
## 3 Automatic Noise Detection and Removal  
In order to avoid unnecessary processing and the distortion that wholesale processing can cause, the proposed speckle noise removal system consists of two subsystems: a detector and an interpolator (cf. Figure 4). The detector locates the position of each noise sample and the interpolator replaces it.  
The speckle noise detector plays a crucial role because accurate positioning is essential for effective noise removal. Speckle noise has certain characteristics, such as a large amplitude, random appearance, agitation, unpredictability, and indefinite duration. In view of these characteristics, a focus on energy and distribution can form the basis of distinguishing between speckle noise and speech. However, as previously mentioned, when there are only few occurrences of speckle noise or the amplitude of the speech is large, the energy of the speckle noise will not stand out. Similarly, if the speckle noise lasts for a long time, its distribution will not seem to be abnormal. Thus, relying on any single parameter will not produce a good result. We have also seen how the character of the  
Figure 4: Automatic Detection and Removal of Speckle Noise.  
speech can itself challenge the detection accuracy. To solve this problem, we have assumed that the speech signal will be correlated while the noise signal will be uncorrelated. We can then begin by decorrelating the measured speech signal. Through decorrelation, the degraded audio signal can be transformed into an extracted signal and the influence of the speech signal and stationary background noise can be eliminated.  
For a decorrelated signal, we make full use of the characteristics of the average short-term energy, which is sensitive to long-duration and high-amplitude speckle noise, and the kurtosis, which is sensitive to short-duration abnormal click speckle noise. These two noise detection methods complement each other and can accurately find the location of the speckle noise in a decorrelated signal.  
After obtaining the position of the noise frames, we can then use a recursive LP model-based interpolator to replace the signals that are distorted by the speckle noise, one by one. Thus, we finally obtain an enhanced signal. The speckle noise removal process is shown in Figure 4 and the detailed steps involved in the process are given below.
# Quantum Stream Cipher Based on Holevo-Yuen Theory
## 3 Feature of Quantum Stream Cipher
### Basic Scheme  
As explained in the previous section, the quantum stream cipher is expected to accelerate advanced application in future communication systems. The reason for this is that this scheme can utilize ordinary optical communication devices and is compatible with existing communication systems. In its design, optical communication, quantum theory, and cryptography are effectively integrated. Therefore, it is also called \"Y-00 optical communication quantum cryptography\" in implementation studies. Pioneering research on practical experiment for this system has been reported by Northwestern University [8; 9], Tamagawa University [10], and Hitachi Ltd. [11]. Theories of system design for the basic system have been given by [PERSON] and others [12; 13; 14; 15].  
Let us explain the principle of Y-00 quantum stream cipher. First, the Y-00 protocol starts by specifying the signal system that use the transmission medium. The actual signal to be transmitted is selected in terms of amplitude or intensity, phase, quadrature amplitude, etc., having coherent state \(|\alpha\rangle\) in quantum optics. Then, the design is made accordingly. Depending on the type of signal to be used, it is called ISK:Y-00, PSK:Y-00, QAM:Y-00, etc.  
Figure 3: Comparison of product capabilities for two types of quantum cryptography services.  
Figure 2: Principle of operation of Y-00 quantum stream cipher. Classical signal means that they have distinguishability, and quantum signal means it is impossible to distinguish them precisely. Y-00 encryption is the function of converting a classical signal into a quantum signal. It is also called quantum modulation.  
Here, one communication base consisting of various binary signals is randomly selected for each data slot. Then, a binary data is transmitted by using the communication base selected. Thus, ultra-multi-valued signals appear to be transmitted on the channel. The eavesdropper has to receive the ultra-multi-valued signal, because they do not know which communication base was selected.
## 2 Theory and Calculation
### Standard Approach  
Five ANNs with a different number of neurons were also developed for each mechanical parameter of interest using the MATLAB(r) ANN toolbox; these networks were trained with 80% of the available data set and tested on the remaining 20%. This configuration was chosen to match the data subdivision performed by the five-fold CV. The sampling process was completely random and carried out just one time by the standard approach implemented in the MATLAB(r) ANN toolbox. If such an initial subdivision of the data set, with the following training and testing of the model, gives an unsatisfactory performance, it is suggested, on the basis of the experience of the researcher, that the whole procedure is repeated with a new sampling of the data set; however there is no automatic procedure aimed at optimizing model performance. In order to evaluate the performance of the standard approach with respect to the sample variability, in this study, random sampling was repeated 10 times to obtain 10 random subdivisions of the available data set.  
The k-fold cross validation involves partitioning the available data set into k non-overlapping groups of equal size that represent the partition's classes of the initial data set [78; 81]. Therefore, given that the performance of a model is evaluated on k test sets (which by definition cannot contain common elements), the average of test errors across k trials represents a more reliable estimation of the model's predictive capacity [78; 81]. Indeed, in the standard approach, the subdivision of the data set in the training and test subsets is randomly performed and by repeating this operation, the identified test sets can contain common elements, which leads to a noisy estimation of the predictive performance for small-size data sets [78; 81]. Furthermore, the MATLAB(r) ANN toolbox carries out the data sampling as a \"black box\" and consequently it does not allow the performed subdivision of the data set to be easily identified; thus, the variability of the training and test set is difficult to establish.  
Therefore, this study of the standard-ANN's aimed to verify the wide variability of the models' predictive capacity that could be caused by data sample variability.  
Standard ANN Structure  
The designed standard-ANNs are characterized by one hidden layer with a hyperbolic tangent transfer function, and one output layer with a linear transfer function; this architecture is set as the default in the MATLAB(r) ANN toolbox. The number of neurons in the hidden layer ranges from 6 to 14 by increments of 2 neurons; as a result, five different standard-ANNs were obtained for each mechanical parameter considered. The seven types of input data already mentioned above (Section 2.3.1) were considered. Moreover, all the hyperparameters fixed in the model selection procedure for k-fold analysis were set to the same values (Section 2.3). In the following, these ANNs are simply called \"standard\".
## 2 Literature and Background of UA-WSNs
This achieves a high delivery of the packets at the final station with a minimum cost of energy and improves the lifespan of the network. In [37], the authors established the network as ten small portions. Two MSs are considered to collect all the information sent by the nodes. If a movable sink is available in the coverage area of the node, it collects data directly. Otherwise, the destination is selected for the data delivery to the MS. The destination checks the erroneous bits in the data when it receives the data and makes a decision for the acceptance or rejection of the data. When an MS receives data, it moves forward in the direction of the other nodes to collect data in order to increase delivery. The algorithm improves the packet delivery ratio (PDR) and consumption of energy. An opportunistic routing with void avoidance using MSs is presented in [38]. The division of the network into small cubes makes the energy expenditure better. The MSs pick the data from the nodes and also from the void area nodes to prevent data failure. The nodes hold the data for a predefined time for better packet reception. The performance of the network in terms of energy cost, delay, and throughput are enhanced. A receiver-initiated MAC is presented in [39] by [PERSON]. The protocol consists of four phases using three-way handshaking. In the first phase, the receiver sends the RTR (request to receive) message. Then, the sender in the second phase responds to the receiver using the ATS (available to send) message for the data transmission. Then, the sender starts exchanging of the data in the third phase. In the final stage, the receiver acknowledges reception of the data. In [40], a MAC protocol is proposed, which addresses the hidden terminal problem, as well as the spatial-temporal uncertainty. Both the sender and receiver schedule data sending and reception. The node awakes when it has to send the data. Otherwise, the node sleeps to reduce the energy expenditure. An energy-efficient MAC protocol is proposed in [41] using both optical and acoustic waves for communication. First, it ensures that the idle state of the channel uses the acoustic waves. If the channel is not free, the node waits. Then, for the data exchanging with optical waves, the channel is detected using the optical waves. If the channel supports the optical waves transmission, then the data are delivered with the optical waves. Otherwise, the high priority data are delivered using acoustic waves to the next destination. A hybrid network that utilizes both the acoustic and optical waves is presented in [42]. The optical waves are used for data exchanging with a high data rate, while the acoustic waves are used for the information sharing between nodes.
## 6 Performance Evaluation
### Performance Comparison on Signal Reconstruction
Given the PRR per single transmission, the PRR with \(Ntrans\) times transmission is theoretically:  
\[P_{prr}^{Ntrans}=1-(1-P_{prr})^{Ntrans} \tag{22}\]  
Therefore, given the packet needed \(M_{need}\) for interpolation to achieve the same recovery accuracy, the corresponding \(P_{prr}^{Ntrans}\) can be obtained as below:  
\[P_{prr}^{Ntrans}=\frac{M_{need}}{N} \tag{23}\]  
Then, once the single transmission PRR \(P_{prr}\) is known, the transmission times needed are represented by:  
\[Ntrans=\frac{\log(1-P_{prr}^{Ntrans})}{\log(1-P_{prr})} \tag{24}\]  
Figure 16 shows that when the link quality is extremely low, both the CS method and ARQ interpolation method cannot recover the original signal accurately, since many data packets have not been successfully received. As the quality of links improves, the CS method can recover the signal when the packets received exceed the threshold, as Equation (3) shows, while the ARQ interpolation method's performance is linearly promoted with the PRRs increasing. When the PRR of single data transmission is 0.3, ARQ interpolation need 6.5-times transmission to achieve the same performance as CS-based method. Even when the PRR is 0.8, nearly 1.5-times transmissions are still needed to achieve the same reconstruction accuracy as our method. All in all, the CS-based method can enable almost all of the lossy links in the transitional region to provide high quality information transmission and also significantly reduces the energy cost and latency. Further, considering that noise usually corrupts the data sampling in a real scenario, the robustness comparison between our CS-based method and ARQ interpolation is of great significance. Figure 17  
Figure 16: Required transmission times achieving compressive sensing’s (CS) error. shows the signal reconstruction error comparison with noise corruption. We can see that noise increases the recovery error, and the CS method outperforms the spline-multiple transmission methods generally. This is because CS reconstruction algorithms can adopt the noise threshold to improve the recovery performance, while the traditional interpolation method does not have this function.
# Failure Behavior Attributed to Internal Erosion Caused by Conduit Cracks in Homogeneous Embarment
## 3 Results and Discussion
### Finite Element Analysis
#### 3.3.2 Hydraulic Gradient via Finite Element Analysis  
The initial stage of internal erosion depends on the relationship between the critical hydraulic gradient and the soil permeability coefficient. Moreover, the risk of piping increases as internal erosion continues [9; 12; 13; 21]. According to the literature regarding the risk zone for the critical hydraulic gradient, the hydraulic gradient for internal erosion is reported to be approximately one-fifth to one-third of the critical hydraulic gradient to ensure soil stability, and abrupt changes in strength occur at hydraulic gradients \(\geq\) 0.5 [22]. In this analysis, the critical hydraulic gradient calculated based on the risk zone was in the range of 0.5-1.0. In this range, it was assumed that the hydraulic gradient had already reached conditions under which erosion could occur and that the top conduit and downstream slope may demonstrate unstable conditions for piping.  
Figure 10 shows the changes in the hydraulic gradient (\(I_{\mathrm{e}}\)) over time. While the water level increased (\(t\) = 30 min), the conduit leakage did not reach the downstream side. While an HWL was maintained (\(t\) = 120 min), \(I_{\mathrm{e}}\) was distributed around the bottom part of the conduit, and the hydraulic slope was extended to the downstream slope (\(t\) = 155-180 min). Furthermore, tracking the failure position of the slope, wherein the hydraulic gradient was concentrated, led to the finding that a failed surface could be potentially formed at a height (h2) of 1.6-2.0 times the height (h1) above the conduit. Herein, the potential failure range owing to \(I_{\mathrm{e}}\) was mostly consistent with the failure location of the downstream slope (h2 was equal to 1.4-2.4 times h1) as suggested in the experiment.  
Figure 9: Variations in pore-water pressure: (**a**) below the conduit (P1, P3, and P5) and (**b**) above the conduit (P2, P4, and P6).  
Therefore, the analysis results were valid as data supporting the experimental results and provide useful information for tracking the mechanism of slope failure due to the erosion of conduit cracks. Such information can be used to reinforce weak areas at the top conduit and downstream slope by tracking the hydraulic gradient and the failure location. Based on the analysis and experimental results, the location of the vulnerable area chosen to suppress the erosion depth on the downstream slope was suggested to be in the range of 1.4-2.4 times the height (h2) of the top conduit (h1). In addition, as the leak of the conduit eventually reached the downstream slope and caused failure, this experiment established the slope and conduit that were located on the downstream side as a risk zone.
# Bell-X, An Opportunistic Time Synchronization Mechanism for Scheduled Wireless Sensor Networks
## 1 Introduction
Derived from the conclusions of said work, an improved and configurable synchronization and connection mechanism, named Bell-X, is presented in the following article, which allows for finding a trade-off between connection time and energy consumption, while achieving 100% connectivity success. Bell-X allows for modifying dynamically the transmission period of the Enhanced Beacons, needed for the synchronization process. This beacon transmission period will be periodically re-configured using different tuneable parameters that modify the performance of the mechanism, enabling different beacon transmission zones. Zones with a lower beacon transmission period allows for the speeding up of the synchronization and connection phases, while a higher transmission period compensates this performance in order to obtain a duty cycle smaller in average and to save energy by reducing advertising traffic. The rest of the article is structured, as follows. Section 2 introduces a brief description of the TSCH access method and the RPL dynamic routing protocol, in order to understand how the medium access, synchronization, and networking is achieved in these WSNs. Section 3 introduces related work addressing the problem of synchronization and connectivity in WSNs. Section 4 shows the problem statement and the proposed mechanism and advertising method. Section 5 describes the testbed, experiments, and simulations that were used to achieve the results analysed in Section 6. Closing the article, Section 7 offers conclusions and possible future improvements.
# Highly Sensitive Non-Enzymatic Detection of Glucose at MWCNT-CuBTC Composite Electrode
## 3 Results
### Electrical and Electrochemical Characterization of the MWCNT-CuBTC Composite Electrode  
The results, obtained by four-point probe (FPP) resistance measurements, for the electrical characterization of the MWCNT-CuBTC composite, in comparison with a simple MWCNT composite within an epoxy matrix, are presented in Table 1.  
It is obvious that CuBTC incorporation within the composite composition improved the electrical conductivity, which should have a positive influence on the electrochemical behavior of the composite electrode.  
The ability to obtain electrochemical information from voltammetry measurements requires precise knowledge of the oxidation-reduction current peak positions for the targeted compounds and the electroactive surface area [26; 27; 28; 29]. It is well known that potassium ferricyanide (K\({}_{3}\)[Fe(CN)\({}_{6}\)]) is used in the classical method for determining the electroactive area. This method assumes the recording of a cyclic voltammetry (CV) of 4 mM K\({}_{3}\)[Fe(CN)\({}_{6}\)] in 1 M KNO\({}_{3}\), supporting the electrolyte at the MWCNT-CuBTC composite electrode at different scan rates (the results are shown in the Supplementary File). This is in accordance with the Randles-Sevcik equation:  
\[I_{p}=2.69\times 10^{5}AD^{1/2}n^{3/2}v^{1/2}C, \tag{1}\]  
where \(A\) represents the area of the electrode (cm\({}^{2}\)), \(n\) is the number of electrons participating in the reaction (and is equal to 1), \(D\) is the diffusion coefficient of the molecule in the solution, \(C\) is the concentration of the probe molecule in the solution (and is 4 mM), and \(v\) is the scan rate (Vs\({}^{-1}\)). For each scan rate, the apparent diffusion coefficient of K\({}_{3}\)[Fe(CN)\({}_{6}\)] was determined, and an overall apparent diffusion coefficient of \(3.21\times 10^{-4}\) cm\({}^{2}\)s\({}^{-1}\) was considered for determining the electroactive surface area, by comparison with the theoretical diffusion coefficient value of \(6.7\times 10^{-6}\) cm\({}^{2}\)s\({}^{-1}\) found in the literature data [30]. The value of 1.357 cm\({}^{2}\) was determined for the electroactive surface area versus the electrode geometric area of 0.196 cm\({}^{2}\).
## 3 Results and Discussion
### Chronology of Bricks  
The mineralogical characterization and chronology in the present study infer the existence of two distinct groups, with pottery technology having different firing temperatures. One, characterized by a firing temperature below 650 \({}^{\circ}\)C, resembles pottery from the XI-XIV centuries CE on the South Bank of the River Brahmaputra, and the other, characterized by a firing temperature above 650 \({}^{\circ}\)C up to 830 \({}^{\circ}\)C, resembles pottery from the VII-X centuries CE (Table 5) on the North Bank of the River Brahmaputra. These two divisions could indicate two different civilizations with technologies that allowed them to fire the bricks in different oxidizing kiln atmospheric conditions. From the present investigation, it can be summarized that the first group of people lived during the VII-X centuries CE, were technologically enhanced, and their state boundaries might be limited to the Northern bank of Brahmaputra, as well as the foothills of Arunachal Pradesh (studied bricks were from Padum Pukhuri, Pratima Garh, and Rukmini Garh). The second group of people, who lived during the XI-XIV centuries CE, were technologically less enhanced than the first group, and their state boundaries might be confined to the Southern bank of the river Brahmaputra. [PERSON] [88] dated bricks from the Kamakhya Temple of Assam, India, suggesting two distinct age ranges peaked at 500 CE and 800 CE. He opened that some segments of the existing Kamakhya temple might have been reconstructed later in 800 CE, when brick production technology was more advanced than that of one of the earlier inhabitants. Hence, it can be assumed that the bricks found in the Northern bank of Brahmaputra, as well as the foothills of Arunachal Pradesh (Padum Pukhuri, Pratima Garh, and Rukmini Garh), along with those bricks from Kamakhya Temple dating to 800 CE, were probably manufactured by a similar group of people residing in that time.  
Figure 10: Luminescence signals of kaolinite. (**a**) TL glow curves and (**b**) corresponding growth of TL signal with dose; (**c**) OSL shine-down curves and (**d**) corresponding growth of OSL signal with dose; and (**e**) IRSL shine-down curves and (**f**) corresponding growth of IRSL signal with dose.  
It is relevant to remember that dating the bulk material of construction bricks can mislead from the correct age since the dated event corresponds to the last heating (>500 \({}^{\circ}\)C) of the material, generally related to brick production, which can be significantly different from that of the construction, also due to the practice of reusing old bricks. However, it is noteworthy that the cause of technological changes between the two groups might be a scope for future research.  
The use of production materials like calcareous and non-calcareous clay having low and high refractory nature for making bricks also infers that the bricks might be produced in different production sites or reflect the natural inhomogeneity of local clay deposits and the application of different manufacture processes in local workshops.