esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In manufacturing, it is important for a prognostic tool to consider both sensor data and information about previous and future workloads [10]. The objective of this study is to explore the potential benefits and challenges of Industry 4.0 and its impact on maintenance strategies, specifically focusing on the integration of prognostics and health management (PHM) into the manufacturing process. In particular, this work aims to: (i) propose the integration of machine workload information, previously not considered in other studies, into a well-established PHM algorithm; (ii) formulate a methodology for integrating machine workload information into any given PHM algorithm in order to explore the potential benefits and challenges of Industry 4.0 on maintenance strategies and to contribute to the development of more efficient and effective maintenance strategies in the industry. The relevance of this work to practitioners lies in the potential for improved decision-making in maintenance strategies for Industry 4.0 production systems. By incorporating machine workload information into a well-established prognostic model, practitioners can benefit from more accurate forecasting of failure probability, leading to more efficient utilisation of production resources and lower maintenance costs. Additionally, the proposed approach can be applied to various monitored machines and equipment without any specific constraints, making it a versatile solution for practitioners in various industrial contexts. Overall, this research aims to provide practitioners with a practical and effective solution for improving the autonomy of CPSs in accepting or declining scheduled jobs according to their forecasted health state, ultimately leading to improved production efficiency and cost savings. The proposed prognostic approach consists of four different phases. The first stage involves acquiring signals from the sensors of the monitored machine. In the second step, a degradation assessment model is developed using the logistic regression tool. In the third phase, a feed-forward neural network (FNN) is used to build the prognostic model. Finally, in the final step, a set of auxiliary artificial networks (ANNs) are added to the model to integrate workload information into the data. This model, due to the versatility of the analytical tools employed, can be applied to various monitored machines and equipment without any specific constraints or hypotheses on their behaviour or application context. The remainder of this paper is organised as follows. In Section 2, the related literature on prognostics and the digital industry is discussed. Section 3 presents the context and formal description of the prognostic model, while in Section 4, a case study is considered and all computational results are presented. Finally, Section 5 concludes the work and discusses further research.
## 3 Model and Assumptions
### Assumptions  
In this paragraph, we list all of the assumptions we require in our scheme. As noted earlier, the deployment points are pre-determined before the actual deployment and can be easily inferred from the group IDs by the sensor nodes in the network. Hence, nodes are incapable of claiming an incorrect deployment point, even if they are compromised (assuming of course that the adversary is not capable of generating new \"Sybil\" identities, which is a tacit assumption in prior work). In addition to keys that are pre-loaded to enable pairwise key establishment as described earlier, every sensor node is also equipped with keys to generate digital signatures. We use an identity-based public key scheme popularized by several replica detection schemes in the literature [10, 14, 15, 16, 18] and that has been shown to be feasible in the current generation of sensor motes [28, 29, 30]. Lastly, we assume that an attack-resistant location estimation protocol has been implemented in the network using one of the schemes outlined in the literature [31, 32, 33]. This permits a sensor node to discover its actual location coordinates after deployment. This assumption is quite reasonable and has been used by existing node replica detection schemes in the literature [10, 18].  
A final assumption we require is that nodes under the adversary's control (both compromised nodes and their replicas) follow the procedures expected by \(\mathbf{Discard}\). This has also been tacitly assumed by existing schemes outlined earlier.
## 3 Proposed MPTC with Multi-Objective Ranking
### File Formats for Graphics  
Stator current \(i_{s}\) and stator flux \(\psi_{s}\) at moment \(k\) are taken as initial variables in MPTC for the stator flux and torque to track their corresponding reference values. A physical prediction model is used to predict stator flux \(\psi_{s}\) and electromagnetic torque \(T_{e}\) at moment \(k+1\). The following cost function is defined to minimize the error between the estimated stator flux and torque and the reference value:  
\[g=\left|T^{ref}-\overset{\cdot\cdot\cdot\cdot k+1}{T}\right|+k_{\psi}\left| \psi_{s}^{ref}\right|-\left|\overset{\cdot\cdot\cdot k+1}{\psi_{s}}\right| \tag{13}\]  
where \(\overset{\cdot\cdot k+1}{T}\) and \(\overset{\cdot\cdot k+1}{\psi_{s}}\) are calculated by Equations (7) and (8). Torque reference \(T^{ref}\) is generated by the external PI speed controller, and \(\psi_{s}^{ref}\) is the stator flux reference. \(k_{\psi}\) is the weight factor of the stator flux, which should be adjusted frequently:  
\[k_{\psi}=\lambda\frac{T_{n}}{\psi_{sn}} \tag{14}\]  
where \(T_{n}\) and \(\psi_{sn}\) are the nominal values of torque and stator flux linkage, respectively. \(\lambda\) can be adjusted to track different targets. \(k_{\psi}\) is determined using a genetic algorithm and is typically debugged offline. It significantly affects the performance of the controller.  
A certain time delay exists between the control voltage calculated from the previous sample and the actual voltage by considering the limited time available for the control algorithm to execute at each step during hardware processing. Requisite compensation is necessary to mitigate the impact of delay on MPTC performance. A model-based prediction method is used to reduce the impact of the delay in the work. Stator current \(i_{s}^{k}\) and stator's magnetic flux linkage \(\psi_{s}^{k}\) at the \(k_{\text{th}}\) instant arestimated and predicted for \(i_{s}^{k+1}\) and \(\psi_{s}^{k+1}\) according to (5). The variables at the \(\left(k+1\right)_{\text{th}}\) moment is used as initial conditions for the instant \(\left(k+2\right)\)th prediction to obtain the final values of \(\psi_{s}^{k+2}\) and \(T_{e}^{k+2}\). The minimized cost function is now defined by  
\[g=\left|T^{ref}-\overset{\cdot\cdot k+2}{T}\right|+k_{\psi}\left|\left|\psi_{ s}^{ref}\right|-\left|\overset{\cdot\cdot k+2}{\psi_{s}}\right|\right| \tag{15}\]  
Then, the stator voltage vector that minimizes (13) is selected to be output at the next sampling time:  
\[\mathrm{v}_{\text{opt}}=\arg\underset{\{v_{0},\ldots,v_{3}\}}{min}g\left(v_{ s}^{k+1}\right) \tag{16}\]  
where \(v_{opt}\) is the optimal output of the stator voltage vector at the next sampling time.
# Evaluation of Glottal Inverse Filtering Techniques on
###### Abstract  
Current articulatory-based three-dimensional source-filter models, which allow the production of vowels and diphtongs, still present very limited expressiveness. Glottal inverse filtering (GIF) techniques can become instrumental to identify specific characteristics of both the glottal source signal and the vocal tract transfer function to resemble expressive speech. Several GIF methods have been proposed in the literature; however, their comparison becomes difficult due to the lack of common and exhaustive experimental settings. In this work, first, a two-phase analysis methodology for the comparison of GIF techniques based on a reference dataset is introduced. Next, state-of-the-art GIF techniques based on iterative adaptive inverse filtering (IAIF) and quasi closed phase (QCP) approaches are thoroughly evaluated on OPENGLOT, an open database specifically designed to evaluate GIF, computing well-established GIF error measures after extending male vowels with their female counterparts. The results show that GIF methods obtain better results on male vowels. The QCP-based techniques significantly outperform IAIF-based methods for almost all error metrics and scenarios and are, at the same time, more stable across sex, phonation type, F0, and vowels. The IAIF variants improve the original technique for most error metrics on male vowels, while QCP with spectral tilt compensation achieves a lower spectral tilt error for male vowels than the original QCP.  
Performance evaluation; glottal inverse filtering; glottal source; phonation types; speech analysis; OPENGLOT
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 3 Related Work
[53] used multipath routing to improve resilience against security attacks. [PERSON] et al. [54] considered regional challenges and implemented their work on a different type of network, Sprint. [PERSON] et al. [55] implemented a system that works in a somewhat similar way to ours; however, they did not test it under external challenges. To the best of our knowledge, our proposed MPResiSDN system is the first to address utilizing SDN and multipath routing to improve network resilience against external challenges. We implemented our scheme on a modeled smart city environment and applied external challenges dynamically while the SDN controller was running. Then, we compared the scheme performance under the applied challenges in terms of throughput, goodput, overhead, and delay to its performance under normal operating conditions.
## 4 Engineering Applications and Verification
### Risk Evaluation of Water Intrusion  
#### 4.2.1 Evaluation Index Analysis  
According to geological and hydrogeological conditions, six sections in the Qiyueshan Tunnel were selected for evaluation in this paper (Table 7).
# Privacy Policy Analysis of Banks and Mobile Money Services in the Middle East
###### Abstract  
Privacy compliance of the Middle East's financial sector has been relatively unexplored. This paper evaluates the privacy compliance and readability of privacy statements for top banks and mobile money services in the Middle East. Our analysis shows that, overall, Middle Eastern banks have better privacy policy availability and language distribution, and are more privacy compliant compared to mobile money services. However, both the banks and mobile money services need to improve (1) compliance with the principles of _children/adolescent's data protection_, _ accountability and enforcement_, and _data minimization/retention_, and (2) privacy statement texts to be comprehensible for a reader with \(\sim\)8 years of education or less.  
banks; FDIC; GSMA; middle east; mobile money; privacy compliance; privacy policy +
Footnote †: journal: _Future Internet_  
Global System for Mobile Communications (GSMA) [12] and Federal Deposit Insurance Corporation (FDIC) [13], respectively, using a methodology similar to [PERSON] et al. [9]. This is because there is no such standard for the Middle East and only a few of the Middle Eastern countries currently have their own data protection laws [14]. We investigate the following research questions (RQs):  
**RQ1**.: How compliant are the privacy policies of Middle Eastern banks and mobile money services with the GSMA and FDIC privacy principles?
**RQ2**.: How readable are the privacy policies of Middle Eastern banks and mobile money services?  
To answer these research questions, we performed a manual analysis on a privacy policy dataset of top 25 banks and 30 mobile money services from the Middle East. Our results show that, overall, Middle Eastern banks have better privacy policy availability and language distribution, and are more privacy compliant compared to mobile money services. However, both the banks and mobile money services need to improve (1) compliance to the principles of _children/adolescent's data protection, accountability and enforcement_, and _data minimization/retention_, and (2) privacy statement texts to be comprehensible for a reader with about eight years of education or less.  
The rest of the paper is organized as follows: Section 2 discusses the background for this work. Section 3 discusses the existing literature relevant to this topic. Section 4 describes our methodology in detail, and Section 5 shows the results of our analysis. In Section 6, we discuss our findings along with their practical implications, and opportunities for future work. Section 7 concludes the paper with main takeaways.
## 3 Results
(**b**) The sharpness of the laser modes as a function of the width of pump area. The length of pump area is 10 mm and the pump energy density is about 0.54 mJ/cm\({}^{2}\). value of the sharpness \(S\) approaches 1, the laser mode is very good and sharp. When the value of the sharpness \(S\) approaches 0, the emission spectrum is smooth. Figure 6b shows the sharpness of laser modes as a function of the width of pump area. As we can see in Figure 6b, the sharpness of laser modes decreases with the increase of the width of pump area. When the width of pump area exceeds 6 mm, the sharpness \(S\) is close to 0. For comparison, the emission spectrum of the NPDDNLC sample is studied when the pump area is a circular spot. In this process, the cylindrical lens shown in Figure 1 is replaced by a convex lens with the focal length of 10 cm, which can focus the pump light to be a circular spot. The diameter of pump spot can be tuned by changing the distance between the sample and the convex lens. Figure 7a shows the emission spectrum of the NPDDNLC sample at different pump energies, when the diameter (D) of pump area is 10 mm. Unlike the emission spectrum shown in Figure 2a, the discrete sharp peaks cannot be observed clearly on the top of the emission spectrum shown in Figure 7a when the pump energy density increases from 0.098 mJ/cm\({}^{2}\) to 0.543 mJ/cm\({}^{2}\). The emission spectrum is more like an ASE spectrum. The inset of the Figure 7a shows the peak intensity of the corresponding emission spectrum as a function of the pump energy density. The peak intensity of the emission spectrum increases near linearly with the increase of the pump energy density. There is no obvious lasing threshold as shown in the inset of the Figure 7a. This further proves that the emission spectrum is more like an ASE spectrum. Figure 7b shows the emission spectrum of the NPDDNLC sample as a function of the diameter of pump area, when the pump energy density is 0.57 mJ/cm\({}^{2}\). The inset of the Figure 7b shows the peak intensity of the corresponding emission spectrum as a function of the diameter of pump area. The intensity of emission spectrum decreases with the diameter of pump area decreases from 10 mm to less than 1 mm. However, the obvious discrete sharp peaks are not observed during this process. From what has been discussed above, we may safely draw the conclusion that the random lasing modes and the lasing threshold can be controlled by the external pumping setup. The transition between the coherent random laser and the amplified spontaneous emission can be operated by changing the shape of the pump area. In addition, the stripe-shape of the pump area is more effective than the circle-shape of the pump area for the occurrence of coherent random lasers.
## 3 Results and Discussion
### Polyphenols and Carotenoids Content
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**N-** & **Quercetin-3-** & **Kaempfer-3-** & **Luteolin** & **Quercetin** & **Apigenin** & **Kaempfer** & **Sum of** **Flavonoids** \\
**Dose/Variety** & **O-Rutinoside** & **O-Glucoside** & & & & & \\ \hline
60 kg N/ha & 4.85 \(\pm\) 1.57 AB & 4.07 \(\pm\) 1.39 A & 2.62 \(\pm\) 0.31 A & 2.40 \(\pm\) 0.64 A & 13.80 \(\pm\) 3.56 A & 11.09 \(\pm\) 1.57 B & 38.82 \(\pm\) 3.54 A \\
90 kg N/ha & 5.25 \(\pm\) 1.95 B & 4.72 \(\pm\) 2.55 B & 2.65 \(\pm\) 0.50 A & 3.46 \(\pm\) 1.18 B & 17.50 \(\pm\) 4.01 B & 8.94 \(\pm\) 3.44 A & 42.52 \(\pm\) 8.02 AB \\
120 kg N/ha & 3.90 \(\pm\) 1.21 A & 6.88 \(\pm\) 0.42 C & 2.97 \(\pm\) 0.68 A & 3.80 \(\pm\) 1.21 B & 17.18 \(\pm\) 3.87 B & 10.59 \(\pm\) 0.48 B & 45.32 \(\pm\) 4.05 B \\
150 kg N/ha & 4.17 \(\pm\) 1.38 A & 7.61 \(\pm\) 0.99 C & 2.66 \(\pm\) 0.48 A & 3.84 \(\pm\) 0.77 B & 18.22 \(\pm\) 3.02 B & 10.27 \(\pm\) 1.09 B & 46.78 \(\pm\) 4.35 B \\ \hline Zoom & 3.87 \(\pm\) 0.95 a & 6.46 \(\pm\) 0.75 bc & 2.90 \(\pm\) 0.61 a & 2.45 \(\pm\) 0.41 a & 19.04 \(\pm\) 5.13 b & 11.10 \(\pm\) 1.75 b & 45.82 \(\pm\) 5.38 bc \\ Hobbit & 3.98 \(\pm\) 1.39 a & 6.55 \(\pm\) 2.27 c & 2.80 \(\pm\) 0.42 a & 3.76 \(\pm\) 1.30 bc & 18.74 \(\pm\) 1.41 b & 10.65 \(\pm\) 0.70 b & 46.48 \(\pm\) 5.25 c \\ Galation & 4.91 \(\pm\) 1.59 ab & 4.64 \(\pm\) 2.76 a & 2.32 \(\pm\) 0.50 a & 3.12 \(\pm\) 0.90 ab & 16.18 \(\pm\) 2.18 b & 8.45 \(\pm\) 3.16 a & 39.62 \(\pm\) 6.50 a \\ Sandra & 5.41 \(\pm\) 1.92 b & 5.63 \(\pm\) 1.85 b & 2.89 \(\pm\) 0.30 a & 4.18 \(\pm\) 0.89 c & 12.72 \(\pm\) 1.92 a & 10.69 \(\pm\) 0.38 b & 41.52 \(\pm\) 4.16 ab \\ \hline \multicolumn{7}{c}{\(p\)-value} \\ \hline N-dose (N) & 0.0066 & \(<\)0.0001 & ns & 0.0008 & 0.0010 & 0.0008 & 0.0005 \\ Variety (V) & 0.0018 & \(<\)0.0001 & 0.0255 & 0.0002 & \(<\)0.0001 & \(<\)0.0001 & 0.0033 \\ \multicolumn{7}{c}{\(\mathrm{N}\times\mathrm{V}\) interaction} \\ \hline  & & & & & \\ \end{tabular} Data are presented as means \(\pm\) standard deviations (SD); within columns and factors (N-dose/variety), mean values followed by different letters (A–C for N-dose; a–C for varieties) are significantly different at \(p<\) 0.05; ns–not statistically significant.  
\end{table} 
Table 4: Flavonoid concentrations (mg/100 g) in the grain of four barley varieties grown in different conditions of nitrogen fertilization (N-dose) in the controlled field trial in Poland.
## 2 Mechanical Analysis and Experimental Setup of Gas-Insulated Switchgear (GIS)  
In this paper, we study the GIS mechanical fault-diagnosis technology based on vibration information. The experiment uses a 110 kV three-phase common box GIS true test platform, a vibration signal acquisition system consisting of an acceleration sensor YD-81D, a charge-amplifier DHF-7-3, and a data acquisition card EM9118B. The acquisition system has a measuring range of \(\pm\)0.5 g (g = 9.8 m/s\({}^{2}\)), sensitivity of 10 V/g, natural frequency of 40 kHz, and frequency response of 10 kHz. The sampling rate is 10 kHz and the sampling period is 1 s, with the experimental measurement platform shown in Figure 1a. Figure 1b shows the fault simulation involved in this paper and experimental data recording results are shown in Table 1. GIS vibration data were measured under three current levels (500, 1000, 1500 A) and three classes (normal case, isolation switch fault, looseness of flange screw). In a current level, a class included 200 data samples. Therefore, the sample sizes of all data are 1800 (\(200\times 3\times 3\)).  
(1) Simulating the GIS isolation switch fault according to the dynamic loop resistance measurement method. Under normal conditions, the GIS dynamic loop resistance is 401 u\(\Omega\). Dynamic loop resistance is measured by continuously adjusting the position of the isolating switch to reach at 828 u\(\Omega\) in order to simulate isolation switch failure.  
(2) The loosening of some bolts of the GIS connecting flange to simulate GIS flange failure.  
The vibration data under different working conditions were measured 200 times in the case of the GIS power supply at 500 A/1000 A/1500 A current excitations which formed the data set of the research process in this paper.  
Figure 1: _Cont._  
Using the hammer method to measure the vibration information of the GIS system, we analyzed the natural frequency characteristics of the system, tapping the point B in Figure 1a, and measured the time domain map and the Fourier spectrum of the vibration signal at point A, as shown in Figure 2.  
It can be seen from Figure 2 that under the impact excitation, the GIS vibration of test point A is horn-shaped decay over time, and the system is characterized by a damping system. At the same time, the Fourier spectrum of the signal shows that the natural frequency of the system is mainly concentrated within 1-1.8 kHz; it presents an excitation mode with multiple frequency points.
## 4 The 3D Point Cloud Reconstruction
### The 3D Space Point Cloud Densification  
In order to align the data of the visual frame and the lidar frame, we only collect point cloud data at the visual key frame, which causes data loss in the adjacent two frames \(k\) and \(k+1\).  
In order to obtain a dense point cloud map, this paper utilizes the local features of point clouds at two adjacent frames to fill the point cloud information between two frames. As shown in Figure 3, we obtain the pose matrixes \(T_{k}\) and \(T_{k+1}\) at the \(k\)th and \(k+1\)th frames, respectively. The pose matrix includes a rotation matrix \(R\) and a translation vector \(t\). In order to provide the pose for each moment between two frames, we adopt different interpolation strategies to interpolate \(R\) and \(t\). For the rotation matrix \(R\), the pose is often expressed by the quaternion, so we use nonlinear spherical interpolation (Equation (13)), while for the translation vector \(t\), we use linear interpolation (Equation (14)). By combining the pose of each moment with the lidar data of the world coordinate system \((X_{k}^{i(W)},Y_{k}^{i(W)},Z_{k}^{i(W)})\), the loss of 3D point cloud data can be filled:  
\[\left(\begin{array}{c}X_{l}^{i(W)}\\ Y_{l}^{i(W)}\\ Z_{l}^{i(W)}\end{array}\right)=R_{k}^{l}\left(\begin{array}{c}X_{k}^{i(W)}\\ Y_{k}^{i(W)}\\ Z_{k}^{i(W)}\end{array}\right)+t_{k}^{l} \tag{12}\]  
\[R_{k}^{l}=\frac{\sin\left(\theta_{k}-\theta_{k}^{l}\right)R_{k}+\sin(\theta_{ k}^{l})R_{k+1}}{\sin\theta_{k}} \tag{13}\]  
\[t_{k}^{l}=\frac{(n-l)t_{k}+lt_{k+1}}{n} \tag{14}\]  
where \(R_{k}\) represents the rotation matrix of the system at the initial moment of the \(k\)th key frame; \(t_{k}\) is the translation vector of the system at the initial moment of the \(k\)th key frame; \(n\) is the total number of point cloud rings to be filled between two key frames; and \(l\) indicates the sequence of point cloud rings between two key frames. \(R_{k}^{l}\) is obtained by using nonlinear spherical interpolation, and \(t_{k}^{l}\) is obtained by using linear quadratic interpolation.  
The sparse point cloud at the key frame is densified between frames to form the initial point cloud map.