esa-sceva/satcom-chunk-collection huggingface dataset:
# On Evaluating IoT Data Trust via Machine Learning
## 3 Synthesis of Trust-Labeled Data  
In practice, acquiring large amounts of data with trust labels, specifically, data that are explicitly labeled as trustworthy or untrustworthy, is challenging. Collecting untrustworthy data that accurately reflect the types of data encountered in the real world can be particularly challenging. In many cases, data collection efforts focus on obtaining trustworthy and high-quality data for analysis and decision-making. As a result, untrustworthy data, which may include data with errors, noise, biases, or malicious manipulations, are less readily available or intentionally avoided.  
The scarcity of trust-labeled data severely limits the utility of supervised ML algorithms for evaluating trust in IoT data. This has prompted many researchers to resort to using unsupervised clustering algorithms and labeling the trustworthiness of IoT data based on the inferred cluster assignments. However, in the absence of ground-truth labels, there is no assurance of the accuracy of the labels produced via clustering nor is there any reliable way to verify their veracity. Here, we propose a novel method to synthesize realistic untrustworthy IoT sensor data using real-world data collected through IoT sensor networks. The synthesized data can reasonably be considered untrustworthy as they do not originate from any real sensor measurement and are designed to deviate from the known trustworthy data in meaningful ways that can be practically expected. In other words, our synthetic untrustworthy data intentionally embody deviations or errors, which can represent those that may occur in the real world. As a result, they do not accurately reflect the complexities and nuances of real data and, hence, are untrustworthy.  
To generate a trust-labeled dataset, we utilize the Intel Lab dataset that contains temperature readings from 54 sensors distributed around the Intel Berkeley Research Lab [18] (see Figure 1). We label the data of the original dataset as trustworthy except for conspicuous outliers, which have values that are three or more standard deviations away from their respective means. We propose a new algorithm, called random walk infilling (RWI), to synthesize untrustworthy data by altering sections of the original trustworthy time-series data. For comparison, we also consider an existing time-series data synthesis method proposed in [10], which we refer to as Drift.  
Figure 1: Intel Lab layout with sensor locations.  
Similar to previous studies, e.g., [3; 11], we only consider sensor data with continuous values. While acknowledging that analog signals acquired by IoT sensors are typically quantized in practice, we assume a reasonably high quantization resolution, such as 8 or 16 bits. Investigating the trustworthiness evaluation of binary or discrete IoT sensor data using ML presents a potential avenue for future research.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 3 Polyaniline-Based Electrodes  
The conductive states of polyaniline (PANI), which were first highlighted by [PERSON] et al. are of research importance because of their redox behavior, simple doping/dedoping conditions, good environmental stability, facile synthesis, and lower synthetic costs compared to their counterparts [94; 95]. PANI is particularly useful for analyte detection in a matrix of immobilized biomolecules, as it facilitates electron transfer processes in redox or enzymatic reactions. Therefore, PANI serves as a mediator for enzyme electrodes, as it undergoes redox cycling reactions and facilitates the direct coupling of enzyme active site electrons to the electrode's surface. The size- and shape-dependent properties of PANI-based nanomaterials are crucial for fabricating high-performance sensors because of their unique charge-transport properties such as their doping and oxidation levels and the length of conjugation. These traits are strongly influenced by the shapes of the materials, whether they are nanofibers, nanowires, nanorods, nanotubes, or nanoparticles [96]. Extensive research is still needed to establish sustainable synthetic routes that enable researchers to adjust the morphology of the resulting PANI nanomaterials, thereby making them effective biosensor electrodes.
## 5 Case Studies
### Validation with Person Trip Survey Data
#### 5.1.3 Comparisons and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Guomao**} & \multicolumn{3}{c}{**Shenzhen University**} & \multicolumn{3}{c}{**Daxin**} & \multicolumn{3}{c}{**Airport East**} \\ \cline{3-13}  & **Suters** & **Static** & **Dynamic** & **Survey** & **Static** & **Dynamic** & **Survey** & **Static** & **Dynamic** & **Survey** & **Static** & **Dynamic** \\  & **Data** & **Model** & **Model** & **Data** & **Model** & **Model** & **Data** & **Model** & **Model** & **Model** & **Data** & **Model** & **Model** \\ \hline \multirow{3}{*}{7:00–9:00} & Volumes & 291 & 1464 & 1448 & 338 & 1549 & 1538 & 318 & 1528 & 1528 & 278 & 1032 & 1026 \\  & Average & 10.87 & 10.54 & 10.49 & 11.03 & 9.11 & 8.96 & 13.31 & 10.09 & 10.09 & 10.76 & 8.78 & 8.66 \\  & Variance & 22.14 & 19.84 & 18.52 & 58.55 & 24.99 & 22.64 & 38.47 & 29.17 & 29.17 & 15.63 & 12.78 & 10.29 \\  & U test & - & 0.27 & 0.28 & - & 0.25 & 0.26 & - & 0.18 & 0.18 & - & 0.17 & 0.19 \\ \hline \multirow{3}{*}{18:00–20:00} & Volumes & 290 & 857 & 875 & 287 & 823 & 838 & 250 & 858 & 851 & 223 & 792 & 784 \\  & Average & 14.17 & 10.25 & 10.56 & 15.76 & 10.52 & 10.92 & 16.27 & 11.43 & 11.28 & 12.03 & 8.72 & 8.31 \\ \cline{1-1}  & Variance & 48.57 & 33.14 & 39.68 & 65.21 & 32.22 & 40.15 & 37.35 & 31.08 & 31.01 & 21.94 & 33.22 & 25.53 \\ \cline{1-1}  & U test & - & 0.16 & 0.29 & - & 0.17 & 0.27 & - & 0.16 & 0.17 & - & 0.19 & 0.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical comparisons the different methods. Figure 8: Comparison of two metro-to-bus transfer identification methods at four stations at 7:00–9:00 on 21–22 March. (**a**) Guomao station. (**b**) Shenzhen university station. (**c**) Daxin station. (**d**) Airport east station. on 21-22 March in this study is much smaller than the traditional method because the transfer time thresholds identified in this study are less than 30 min. On the contrary, the means of transfer time in this study at 18:00-20:00 on 23-24 March are greater and the thresholds of the previous method are greater than 30 min. Hence, the dynamic station-specific recognition method can effectively identify the actual interchange behavior of the majority of bus passengers and remove the few transfer trips which have other purposes, such as shopping or business holding near the metro or bus station.
# Retrofitting Masonry Walls against Out-Of-Plane Loading with Timber Based Panels
## 2 Material and Method
### Materials  
Four components, which are brick unit, mortar, timber panel and connections, were used for the experiments in this study. Specifically, engineering class B solid fired clay bricks with UK standard size 215 \(\times\) 102.5 \(\times\) 65 mm were used to construct all test specimens.  
Type N (general purpose) mortar mix with a ratio of 1:1:6 (Type II Cement: aerial lime: sand) by volume was used to construct the specimens with 10 mm-thick nominal mortar joint.  
An 18 mm-thick OSB type-3, which is a load-bearing engineered wood-based panel for use in humid conditions, was selected for this study. The OSB is manufactured from strands of wood bonded together with a synthetic resin.  
Two types of connection systems were selected for this study. The selected connections were made of A4 (1.4401 or 316) stainless steel. The connections are classified as connection type 1 and type 2. Type 1 is an adhesive anchor connection system which is a combination of FIS V 360 S injection mortar and FIS A4 anchor rod. Type 2 is a mechanical connection system that is a combination of a frame-fixing SXS plastic plug made of high-quality nylon and FUS A4 anchor rod.  
The details of the properties of the materials including the tests used to determine them are presented under the materials characterisation test in Section 3.1; full details can be found in [14; 15].
## 2 Calculation Method for Surface Settlement
### Surface Settlement Induced by Groundwater Level Drawdown (\(u_{3}\))  
Groundwater loss during tunnel construction leads to a decline in the groundwater level, which induces surface settlement due to soil over-consolidation. The settlement calculation model is illustrated in Figure 4.  
For the over-consolidation settlement in a single-layer soil, the settlement ds of a unit element is determined by taking dz\(\in\)(t\({}_{0}\),t\({}_{0}\) + t\({}_{1}\)) as follows:  
\[ds=\varepsilon dz=\frac{\Delta P_{01}}{E_{s1}}dz \tag{26}\]  
where ds is the settlement of a unit element; \(\Delta P_{01}\) is the change in effective stress in the first soil layer caused by groundwater level changes, \(\Delta P_{01}=\gamma_{w}(z-t_{0})\); E\({}_{s1}\) is the compression modulus of the i-th soil layer, \(E_{s1}=\frac{1-\mu_{1}-\mu_{1}}{E_{01}(1-\mu_{1})}\).  
The settlement in the first soil layer is obtained through integration:  
\[s_{1}=\int_{t_{0}}^{t_{0}+t_{1}}\frac{\gamma_{w}(z-t_{0})(1-\mu_{1}-\mu_{1}^{ 2})}{E_{01}(1-\mu_{1})}dz=\frac{\gamma_{w}(1-\mu_{1}-\mu_{1}^{2}){t_{1}}^{2}}{ 2E_{01}(1-\mu_{1})} \tag{27}\]  
where \(\mu_{1}\) is the Poisson's ratio of the first soil layer; \(E_{01}\) is the elastic modulus of the first soil layer; and \(\gamma_{w}\) is the unit weight of water.  
Similarly, the settlements of the i-th and \(n\)-th soil layers can be calculated.  
\[s_{i}=\frac{\gamma_{w}(1-\mu_{i}-\mu_{i}^{2})\big{[}(t_{1}+\cdots+t_{i})^{2}-( t_{1}+\cdots+t_{i-1})^{2}\big{]}}{2E_{0i}(1-\mu_{i})} \tag{28}\]  
\[s_{n}=\frac{\gamma_{w}(1-\mu_{i}-\mu_{i}^{2})(\Delta ht_{n}+\frac{\mu_{n}^{2} }{2})}{E_{0i}(1-\mu_{i})} \tag{29}\]  
where \(\Delta ht\) is the height of the groundwater level drawdown,\(\mu_{i}\) is the Poisson's ratio of the i-th soil layer; \(E_{0i}\) is the elastic modulus of the i-th soil layer; and \(t_{i}\) is the thickness of the i-th soil layer.  
Figure 4: Over-consolidation settlement calculation model.  
For the i-th soil layer, the constant term bi is expressed as:  
\[b_{i}=\frac{\gamma_{w}(1-\mu_{i}-{\mu_{i}}^{2})}{E_{0i}(1-\mu_{i})} \tag{30}\]  
The total over-consolidation settlement for n soil layers is obtained by summing the over-consolidation settlements of each individual soil layer.  
\[u_{3}=\sum_{i=1}^{n}s_{i}=b_{n}(\Delta ht_{n}+\frac{{t_{n}}^{2}}{2})+\frac{b_{ 1}{t_{1}}^{2}}{2}+\frac{1}{2}{\sum_{i=2}^{n-1}b_{i}}\Big{[}\big{(}t_{1}+\cdots+ t_{i}\big{)}^{2}-(t_{1}+\cdots+t_{i-1})^{2}\Big{]} \tag{31}\]  
where \(b_{i}=\frac{\gamma_{w}(1-\mu_{i}-\mu_{i})^{2}}{E_{0i}(1-\mu_{i})}\), and \(n\) is the number of soil layers. If the groundwater level is located at the ground surface and the drawdown only affects a single soil layer (\(n=1\)), only the first term of the formula is considered. If the groundwater level is below the ground surface and the drawdown only impacts a single soil layer (\(n=2\)), only the first term of the formula is considered. If the groundwater level is below the ground surface and the drawdown influences multiple soil layers (\(n>2\)), all three terms of the formula are taken into account.
## 5 Numerical Test and Results
### Case 3: Voltage Regulation with OLTC and ACs  
To solve the delay problem of the OLTC, the ACs will provide voltage regulation services during the delay time. At 11:10, the distribution network encounters the over-voltage problem. The ACs and the OLTC both participate in voltage regulation. The ACs in node 18 increase 58.46 kW operating power for over-voltage regulation. The four groups of the ACs increase 0 kW (Group 1: 0 ACs), 26.31 kW (Group 2: 50 ACs), 29.57 kW (Group 3: 47 ACs) and 25.58 kW (Group 4: 3 ACs) of operating power, respectively. The voltage of node 18 decreases to 1.099 p.u. After 1 min, the action of the OLTC finishes, the ACs exit voltage regulation, and the voltage of node 18 decease to 1.091 p.u. At 18:40, the distribution network encounters the under-voltage problem. The ACs and the OLTC both participate in voltage regulation. The ACs in node 18 decrease 37.31 kW operating power for under-voltage regulation. The four groups of the ACs decrease 0 kW (Group 1: 0 ACs), 0.24 kW (Group 2: 5 ACs), 13.81 kW (Group 3: 49 ACs) and 23.26 kW (Group 4: 46 ACs) of operating power, respectively. The voltage of node 18 increases to 0.900 p.u. After 1 min, the action of the OLTC finishes, and when the ACs exit voltage regulation, the voltage of node 18 increases to 0.911 p.u.. The nodal voltage curves are shown in Figure 8.
## 2 Working Principle of Electromagnetic-Ring Balancing Actuator
### Basic Structure of the Balancing Actuator
#### 2.2.2 Magnetic Circuit Analysis  
The electromagnetic ring actuator proposed in this study is an axisymmetric structure, so one quarter structure is used to design the magnetic circuit in Figure 3. The coil case, center stator and inside and outside tooth rings are all magnetic conductive materials. When the coil is powered on, the coil case is instantly magnetized and a magnetic field is generated. The direction of the magnetic field is determined by the coil voltage. When the coil voltage is alternately changed between positive and negative, the direction of the magnetic field is alternately changed clockwise and counterclockwise. Through the air gap \(\delta_{1}\) and \(\delta_{2}\) between the rotatory part and stationary part, the magnetic field is transferred to the rotatory part, the inside and outside tooth rings are correspondingly magnetized and an N-pole and S-pole are produced on top of the teeth of the two tooth rings. Permanent magnets are mounted on the counterweight disc. According to the principle of \"same-sex exclusion, opposite-sex attraction\" of magnets, the counterweight disc is driven to step rotate based on the magnetic force between the magnetized tooth rings and permanent magnets.  
Figure 1: Working principle of the balancing system.
# A Power Control Algorithm Based on Chicken Game Theory in Multi-Hop Networks
## 1 Introduction  
In the middle and late 20 th century, mobile networks played a vital role in modern life. Similar to other technologies, wireless communication systems were first used by national governments, emergency services, and especially for military purposes. With the development of internet networks, an increase in both the data transmitting rate and the system capacity was needed in order to meet the increase in demand. Taking peak data rates as an example, there has been an increase in demand in IMT-2000 (International Mobile Telecom-2000) from 2 Mbps to more than 1 Gbps. Satisfying these demands is going to be the most important task in future 5G and even 6G wireless communication systems.  
In order to meet the above requirements, certain problems need to be addressed. As well as data rate increases, there will soon also be greater peak power requirements. Consequently, the conception of multi-hop virtual cellular networks was proposed [1]. If a virtual cellular network (VCN) works well, mobile ports will receive their own wireless signals via individual cells, and then, the signals will access core networks with the help of a central terminal.  
Power control plays a vital role in multi-hop networks in that it can efficiently use and manage wireless resources. Some power control algorithms have been proposed in recent years. In multi-hop networks, there is no base station, and communication between hops relies on a hopping scheme. Atthe same time, there is a great dynamic change of topological structure in the network, meaning that each hop should use a strategy that will allow them to gain maximum capacity.  
[PERSON] and [PERSON] [2; 3; 4] researched throughput based on power control in multi-hop networks. Other previous studies [5; 6; 7; 8; 9; 10] have investigated capacity based on the influence of interference on hops. Recently, scientists have discovered that game theory may be used to optimize the power control of multi-hop networks [11; 12]. In this paper, a new power control system based on chicken game theory [13] is introduced, and a computer simulation [14; 15] proves that the method is more efficient than others.
# Proxy Signature-Based Management Model of Sharing Energy Storage in Blockchain Environment
## 1 Introduction
In a localized consortium blockchain, a P2P electricity trading pricing model among prosumers with game theory is proposed, where the profit of sellers increases by 12.61% while the utility sacrifice of buyers decreases by 4.36% [22]. The P2P energy trading method based on blockchains is focused on the existing works and projects [17]. However, in order to reduce transaction and management costs, some small-scale owners may delegate a professional operator to trade their energy on the blockchain platform. Due to the lack of flexibility of the current blockchains [17], it is difficult to transfer the rights of nodes on the blockchain safely and reliably, and to be consistent with the off-line management model. In a blockchain, the information security and ownership of nodes could be guaranteed by the digital signature mechanism based on asymmetric encryption [23; 24]. The digital signature produced by a public-key system stands for the digital signature power of nodes on the blockchain, which could provide a proof of user identity and have the same legal effect as the traditional handwritten signature. The existing digital signature schemes (e.g., RSA [25], ElGamal [26] and elliptic curve cryptography (ECC) [27]) can be applied in blockchains. Besides, a special digital signature scheme, a proxy signature, is proposed in [28]. By the proxy signature, a node called the original signer can delegate the digital signature power to another node called the proxy signer, and then the proxy signer can issue information and sign contracts on behalf of the original signer. The aim of this work is to select a management model for distributed SES resources in the blockchain environment and design a digital signature mechanism to make the digital signature power of nodes transfer flexibly on blockchains. The basic concept and characteristics of SES are described and several typical management models are analyzed in terms of benefit, cost, resources, and so on. Moreover, a blockchain framework and its transaction process of SES are presented. Furthermore, a proxy signature mechanism based on the ElGamal algorithm is designed for the SES blockchain considering the requirement of the principal-agent management model. The security, identification and distinguishability of the proxy signature mechanism will be analyzed and verified in the following sections.
## 4 Discussion
The parameters A3, A4, and D2 are the left area, right area, and the depth of the second absorption band. Additionally, the parameters A5, A6, and D3 are the left area, right area, and the depth of the third absorption band. The results from Table 6 show that the parameter values of A4, A5, D2, and D3 of T2 experienced a significant decline when compared with T1. In addition, the normalized widths of the second and third absorption bands (W1, W2) calculated with Equations (6) and (7), shown in Table 7, reveal that there was a significant increase in parameter values of W1 and W2 of T2. For example, in comparison with T1, W1 of T2 increased by 17.50% and 11.16% at DAH 14d and DAH 21d (2016), respectively, and W2 increased by 7.87%, 11.76%, and 30.77% at DAH 14d, DAH 21d, and DAH 28d, respectively. In general, the depth and area of the second and third absorption band of T2 significantly reduced, but the width increased. The difference in absorption band shape between T1 and T2 is shown in Figure 7. In fact, the second and third absorption bands (the average wavelength was in the range of 874-1257 nm) are mainly affected by water vapor absorption [62; 63; 64]. Therefore, an increasing diffuse radiation fraction could also affect the water vapor absorption process of rice. \[W1=\frac{(A3+A4)}{D2} \tag{6}\]  
\[W2=\frac{(A5+A6)}{D3} \tag{7}\] \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Treatments**} & \multicolumn{3}{c}{**2016**} & \multicolumn{3}{c}{**2017**} \\ \cline{3-8}  & & **DAH 14d** & **DAH 21d** & **DAH 28d** & **DAH 14d** & **DAH 21d** & **DAH 28d** \\ \hline \multirow{2}{*}{W1} & T1 & 43.94 & 43.72 & 47.08 & 42.73 & 36.18 & 39.19 \\  & T2 & 51.63 & 48.60 & 45.23 & 43.09 & 37.81 & 39.62 \\ \multirow{2}{*}{W2} & T1 & 82.38 & 86.00 & 78.19 & 74.50 & 71.46 & 65.32 \\  & T2 & 88.86 & 96.11 & 102.25 & 77.20 & 73.08 & 75.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Normalized widths of the second and third absorption bands in T1 and T2. It is shown that an increase in diffuse radiation fraction had a positive effect on rice growth, including an increase in LAI, population photosynthesis capacity, and yield [13; 17; 18; 19; 43], which is also the reason for the changes in canopy spectra and red-edge parameters in this experiment.
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 2 The Construction of RC-NB-LDPC Codes
### QC-LDPC Codes and Superposition Construction  
The check matrix \(H\) corresponding to a class of quasi-cyclic LDPC codes can be expressed by  
\[H=\left[\begin{array}{cccccc}I(P_{11})&I(P_{12})&.&.&.&I(P_{1n})\\ I(P_{21})&I(P_{22})&.&.&.&I(P_{2n})\\.&.&&&.\\.&.&.&.&.\\.&.&&&.\\ I(P_{m1})&I(P_{m2})&.&.&.&I(P_{mn})\\ \end{array}\right] \tag{1}\]  
where \(I\big{(}P_{ij}\big{)}(1\leq i\leq m,1\leq j\leq n)\) is a \(b\times b\)-dimensional circulant matrix, and is referred to as the circulant permutation matrix [35]. This matrix can be obtained by rotating each row vector of a \(b\times b\)-dimensional unit matrix \(P_{ij}\) element to the right, and \(P_{ij}\in\{0,1,2,\cdots,b-1,\infty\}\). Herein, \(P_{ij}=0\) means that \(I\big{(}P_{ij}\big{)}\) is a unit matrix, and \(P_{ij}=\infty\) means that \(I\big{(}P_{ij}\big{)}\) is a zero matrix. For any \(i,j\), replacing the circulant permutation matrix \(I\big{(}P_{ij}\big{)}\) in the check matrix \(H\) by value \(P_{ij}\), one can obtain a new matrix \(P\), referred to as the circulation coefficient-matrix [36], which is shown by  
\[P=\left[\begin{array}{cccccc}P_{11}&P_{12}&.&.&.&P_{1n}\\ P_{12}&P_{22}&.&.&.&P_{2n}\\.&.&&&.\\.&.&.&.&.&.\\.&.&&&.\\ P_{m1}&P_{m2}&.&.&.&P_{mn}\\ \end{array}\right] \tag{2}\]  
\(H_{ij}=0\) when \(P_{ij}=\infty\) and \(H_{ij}=1\); otherwise, for any \(i,j\), one can obtain the corresponding base matrix \(H_{b}\), which is given by \[H_{b}=\left[\begin{array}{cccccc}H_{11}&H_{12}&.&.&.&.&H_{1n}\\ H_{12}&H_{22}&.&.&.&H_{2n}\\.&.&&&&.\\.&.&.&.&.&.\\.&.&&&.\\ H_{m1}&H_{m2}&.&.&.&H_{mn}\end{array}\right] \tag{3}\]  
where \(H_{ij}\in\{0.1\},(1\leq i\leq m,1\leq j\leq n)\).  
Thus, if we obtain a small \(m\times n\) base matrix \(H_{b}\) and a set of \(b\times b\) circulant permutation matrices, we can construct an \(m\times n\) array \(H\) of \(b\times b\) matrices by replacing each 1-entry in \(H_{b}\) by a circulant permutation matrix and each 0-entry by a zero matrix. This replacement of entries in \(H_{b}\) by the circulant permutation matrices and/or zero matrices expands the matrix \(H_{b}\) into a \(mb\times nb\) sparse matrix \(H\).