esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Fractures and Layers
#### 4.6.2 Fractures of Hydration Forms  
Fractures in the detached rock layer cut it at right and oblique angles or parallel to its top or base. They represent both extension and shear types occurring with the opening between fracture walls and without any space-separating walls. The landform surface is dominated mainly by transverse fractures parallel to the MLE direction, as well as longitudinal, equally frequent ones perpendicular to this direction (Figures 3b and 7c,e,f). Irregular fractures (oblique to the MLE direction, Figure 3b) occur in a smaller amount than the abovementioned ones, but they can still be found in 34 of the 74 investigated forms. Spherical (Figures 3b and 5a,c) and radial fractures (form no. 22, Figure S2) are by far the least common. Spherical fractures are associated with the effects of spheroidal weathering of anhydrite blocks subjected to hydration [14], described elsewhere as \"cannonball\" structures [106].  
The presence of joints cutting the forms and continuing outside of them representing the above-described sets A-E are characteristic of 28 of the 74 examined forms. Such joints are excellently visible in the exemplary form in EF (Figures 11a and S8).  
The surface density of fractures in the detached rock layer (recorded in 69 forms; five forms were without visible fractures) ranges from 0.04 to 3.38 m/m\({}^{2}\) (Table 2, Figures 13 and 14b). The areas of the quarry with the large impact of the master joints are characterized by a higher F\({}_{\rm D}\) value compared to other areas (Figure 13). For all the hydration landforms, regardless of whether they are connected with master joints or not, the density of fractures is only slightly larger for the tepee-shaped forms than for the dome-shaped ones (Figure 13). The highest average F\({}_{\rm D}\) is recorded in the SW part of the EF influenced by the master joint system, where one form is characterized by a plentiful amount of fractures and the largest recorded F\({}_{\rm D}\) of 3.38 m/m\({}^{2}\) (no. 67, Figures 4, 13 and S8; Table S1).  
For 48 hydration landforms with measured thickness of detached layer, the density of fractures is variable, both very large and modest (Figure 14a). The lower \(\rm F_{D}\) values are usually connected to greater thickness. For a thickness lower than or equal to 20 cm, the surface density can attain as much as almost 3 m/m\({}^{2}\), but if the thickness is higher than 20 cm, then this density does not overpass 1.66 m/m\({}^{2}\). Additionally, the relationship between \(\rm F_{D}\) and the length of forms indicates that the smaller forms attain a higher fracture density (Figure 14b).
# Untargeted 1H-NMR Urine Metabolomic Analysis of Preterm Infants with Neonatal Sepsis
## 3 Results
### Metabolic Profile Alterations of Pretems with EOS
#### 3.1.2 EOS Preterms versus NICU Control Preterms  
\({}^{1}\)H NMR urine metabolic profile of the first day of life from twenty-three (n = 23) preterm infants with EOS was compared to that of thirteen (n = 13) preterms hospitalized in NICU without any sign or symptom of sepsis or other infection. This comparison was conducted complementary to the group of preterms without the need of hospitalization, to examine and reduce the effect of NICU hospitalization on newborns urinary metabolism. Multivariate analysis of EOS and NICU control preterms displayed less distinctive classification (Supplementary Materials Figure S3a) regarding the comparison with healthy non-NICU preterms (Supplementary Materials Figure S1a). The PLS-DA model (Figure 3a) separated the two groups, but the low value of the R\({}^{2}\) = 0.491 and the negative Q\({}^{2}\) indicate an overfitted model with low predictability. The buckets responsible for this group separation, as resulting from the PCA loadings plot (Supplementary Materials Figure S3b) and from VIP scores, are related to gluconate, threonine/lactate and 7.40 ppm chemicals shifts of unknown peaks (Figure 3b). Gluconate seems to be present in higher concentration on EOS group, but some buckets related to gluconate (4.09 ppm, 4.15 ppm) have higher VIP score in the control group. This may be justified by the shift of gluconate peaks in different spectra or the presence of sugars, such as glucose and lactose. The pattern of 7.40, 1.40 ppm had higher intensity for control neonates, and may be related to different medical treatment and nutrition. In accordance with prior analysis, EOS neonates differ from controls at the 3.25 ppm spectral region, where mainly betaine is located, overlapping myo-inositol and taurine.  
Figure 1: Multivariate analysis of NMR data belonging to neonates diagnosed with EOS (pink circles) and healthy neonates without need for NICU hospitalization (blue circles). **(a)** PLS-DA scores plot of the EOS and healthy non-NICU preterms. **(b)** VIP scores and metabolites related to buckets with different concentration among the two groups. **(c)** Box plots of normalized concentration for the discriminant metabolites.
## 2 Materials and Methods
### Methodology
#### 2.3.2 Calculation Principles of TOPSIS  
TOPSIS is an evaluation method that combines the impact of multiple indicators [35]. Based on the distance between the measured value and the ideal solution, i.e., closeness, alternatives are evaluated and ranked: The better the alternative, the smaller the deviation from the ideal solution. Thus, closeness is used to determine the SRE classification threshold of the TBM construction section. The following steps can be used to classify SRE [36].  
(1) The initial evaluation index matrix is constructed. If there are \(m\) samples and \(n\) evaluation indexes, the initial evaluation index matrix can be expressed as  
\[R=(x_{ij})_{mn} \tag{4}\]  
where \(x_{ij}\) is the sample value, and \(R\) is the initial evaluation index matrix.  
(2) The normalized evaluation index matrix is constructed. In order to eliminate the dimensional variability of different indicators, the initial evaluation index matrix needs to be normalized:  
Benefit-type indicators:  
\[r_{ij}=\frac{x_{ij}-~{}min(x_{ij})}{max(x_{ij})~{}-~{}min(x_{ij})} \tag{5}\]  
Cost-type indicators:  
\[r_{ij}=\frac{max(x_{ij})~{}-~{}x_{ij}}{max(x_{ij})~{}-~{}min(x_{ij})} \tag{6}\]  
The final normalized matrix can be expressed as  
\[V=(v_{ij})_{mn}=\begin{bmatrix}r_{11}&r_{12}&\cdots&r_{1n}\\ r_{21}&r_{22}&\cdots&r_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ r_{m1}&r_{m2}&\cdots&r_{mn}\\ \end{bmatrix} \tag{7}\]  
(3) The positive and negative ideal solutions of the normalized decision matrix are determined:  
\[\left\{\begin{array}{l}V^{+}{=}~{}[max(v_{ij})]=[v_{1}^{+},v_{2}^{+},\cdots,v_{n}^{+}]\\ V^{-}{=}~{}[min(v_{ij})]=[v_{1}^{-},v_{2}^{-},\cdots,v_{n}^{-}]\\ \end{array}\right. \tag{8}\]where \(V^{+}\) and \(V^{-}\) are the positive and negative ideal solutions, respectively.  
(4) the closeness coefficient is calculated:  
\[T= \frac{D_{i-}}{D_{i-}+D_{i+}} \tag{9}\]  
\[\left\{\begin{array}{l}D_{i+}=\sqrt{\sum\limits_{j=1}^{n}{\left({v_{\,ij}-\,\,v _{j}^{+}}\right)^{2}}}\\ D_{i-}=\sqrt{\sum\limits_{j=1}^{n}{\left({v_{\,ij}-\,\,v_{j}^{-}}\right)^{2}} }\end{array}\right. \tag{10}\]  
where \(T\) is the closeness, \(D_{i+}\) is the distance between the scheme and the positive ideal solution, and \(D_{i-}\) is the distance between the scheme and the negative ideal solution.  
(5) SRE classification is then determined. According to Steps (1)-(4), the closeness between the sample parameters and the excavatability classification boundary is calculated to achieve SRE classification.
## 3 Mapping of Land Cover and Physiological or Structural Variables
### Physiological Variables
#### 3.2.1 Leaf Area Structure
Recently, reliable LAI and LAI profiles measurements have been produced from space-borne LiDAR data by the Global Ecosystem Dynamics Investigation (GEDI) at a higher resolution of 25 m [108].
# Energy Efficiency of a Decode-and-Forward Multiple-Relay Network with Rate Adaptive LDPC Codes +
## 1 Introduction
It is noted that we extend the work in the conference version of this paper [21], where a single relay was assumed, by considering a more general scenario with multiple relays, which is more realistic in IoT networks. Moreover, the system under consideration in this paper provides a comprehensive framework for optimal power allocation in a multiple relay network when variable code rates are applied according to the channel conditions. In addition, we tackle the transmit power allocation issue in such a way that it guarantees the provision of quality-of-service (QoS) in terms of lower bit error rate (BER) and maximal energy efficiency along with reduced latency. The major contributions of this work are as follows:  
1. We design and analyze decoupled code rate adaptive LDPC-coded cooperative networks, where during one end-to-end transmission interval different coding rates may be used on both hops. This procedure entails the possible need for a temporary buffering of data at the involved relay stations in case the number of bits that may be forwarded with the adapted code rate on a certain hop is smaller than the number of bits received from the previous node. The achievable end-to-end performance is investigated with rigorous Monte Carlo simulations;
2. A new algorithm has been designed and developed that selects a certain code rate according to the channel conditions, with the following characteristics:- Only the instantaneous CSI is required for code rate adaptation;  
- The algorithm works in a distributed manner and gives individual estimate of both hops;  
- The amount of feedback overhead is kept minimal because by design the algorithm only sends a few bits as CSI to the respective source node;  
3. Optimized power allocation is considered under the constraint of the network's maximal energy efficiency and is compared with equal power allocation and with a heuristic based approach. The remainder of this paper has been organized as follows. In Section 2, we present the system model of the wireless system under consideration along with some fundamental assumptions. In Section 3, different power allocation schemes have been discussed in certain detail. Towards the end of the paper, in Section 4, we discuss the results of different power allocation techniques through rigorous Monte Carlo simulations.
## 4 Experimental Validation on Public Datasets
### Health Indicator Construction  
The processed time-domain and frequency-domain signal features were calculated and subsequently input into the Health Indicator Construction Module (MH-GAM). Deep learning models possess numerous parameters, and improper parameter settings can result in slow convergence and diminished accuracy. Consequently, this paper delineates the parameters for each module, with the input and output sizes presented in Table 5.  
In this paper, the moving average method was employed to smooth all fused health indicators, with a sliding window size of 20. The smoothed health indicators demonstrate strong monotonicity and reduced volatility. To illustrate the superiority of the health indicators developed in this study, they are compared with those constructed by the DRSN (Deep Residual Shrinkage Network) method and the root mean square (RMS) value as a health indicator. As shown in Figure 12, the health indicators  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Layer** & **Parameter** & **Output Size** \\ \hline \multirow{2}{*}{Gated Recurrent Unit} & Number of layers: 3; Input & \\  & dimension: 16; Hidden state size: 64 & \\ \hline \multirow{2}{*}{Self-Attention Mechanism} & Input dimension: 128; Number of & \\  & attention heads: 8 & \\ \hline \multirow{2}{*}{Relu} & / & 1024 \(\times\) 128 \\ \hline \multirow{2}{*}{Dropout} & 0.2 & 1024 \(\times\) 128 \\ \hline \multirow{2}{*}{Full Connection} & / & 1024 \(\times\) 128 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter settings for the Health Indicator Construction Module.  
To quantitatively compare the performance of the three health indicators--correlation, monotonicity, and robustness--a comparative analysis was conducted, with the results presented in Figure 15. The average values of the health indicators constructed using the method proposed in this paper showed increases of 11% and 331% in correlation, 27% and 540% in monotonicity, and 10.1% and 12.2% in robustness, respectively, compared to the average values of the DRSN and RMS health indicators. The data indicate that utilizing the root mean square (RMS) as a health indicator for characterizing the degradation performance of bearings is less effective. In contrast, the health indicators developed by the proposed method demonstrate superior overall performance.
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 1 Introduction  
### Background  
With rapid development of microelectronic technology and wireless communications, Wireless Sensor Networks (WSNs) have drawn much attention in recent years [1]. WSNs are usually comprised of distributed sensor nodes to support many potential applications, including home networking, monitoring and health care, etc. In such scenarios, sensor nodes are typically powered by energy-limited batteries, which supply sensing, data processing and communication components [2]. In practice, the energy consumption of a sensor node is dominated by the communication components [3]. It has been shown that sensor node lifetime has a strong dependence on battery lifetime [1; 2; 3]. The malfunctioning of a few main nodes might lead to paralysis of the entire network. Hence, minimizing the sensor energy consumption is of great importance.
## 3 Related Work
### Using Multiple Connections for Streaming  
Some works have investigated multiple connections in HTTP streaming. [PERSON] et al. [21] quantitatively investigate the SVC based HAS live streaming for Live TV in high delay networks. For a client with limited buffer capacity, they propose a cursor-based SVC client heuristic. A fixed number of parallel connections and pipeline download of segment layers are suggested to overcome end-to-end delay problems. [PERSON] et al. [22] implement a MultiTCP receiver driven system with multiple TCP connections in an application layer. The system provides resilience against short-term bandwidth fluctuations. Their system in congested periods can control and achieve the desired sending rate. [PERSON] et al. [23] propose a multi-path DASH (MP-DASH) streaming framework taking into account the preferences of the client network interface by strategically scheduling a chunk delivery to satisfy client preferences, such as preferring WiFi over the cellular connection. Instead of improving the quality of video streaming with MPTCP, it reduces overall streaming costs. [PERSON] et al. [24] proposes a model to improve bandwidth allocation and incentive mechanisms for layered video streaming in peer-to-peer (P2P) networks using an auction game model. The purpose is to make sure that neighbouring peers first have a minimum quality level stream, and then the remaining bandwidth is allocated to the higher layers. This optimization results in efficient use of the bandwidth and improved video quality.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 2 Related Work  
External and internal events play a crucial role in the reliability of data transmissions, and their study has grown increasingly appealing in recent years. Table 1 summarizes recent studies on noise reduction, considering both internal and external events.
## 5 Experimental Setup Development
### Dielectric Breakdown Analysis of the Fabricated FGIL Models
#### 5.1.2 Lightning Impulse Discharge Test  
Concerning the impulse withstand characteristics of the developed models, lightning impulse voltage tests were conducted for SF\({}_{6}\)- and air-filled FGIL models with simple and SCF-coated stranded conductors. After creating vacuum in the FGIL models, moisture-free air was injected at different gas pressures from 1 to 2.5 bar, and lightning impulse discharge voltage was noted for both GIL models. Followed by air, similar lightning impulse discharge tests were performed regarding SF\({}_{6}\) gas for both FGIL models at different gas pressures from 1 to 2.5 bar. Figure 21 represents the test results of SF\({}_{6}\)- and air-filled simple and film-coated stranded-conductor-based GIL models under different gas pressures. Critical analysis of Figure 21 reveals that the impulse discharge voltage increased with increasing gas pressure in all cases. However, the GIL model with a film-coated stranded conductor had relatively higher discharge voltages regarding air as well as sulfur hexafluoride respectively in comparison to the GIL model with a simple stranded conductor. Further, owing to its higher dielectric strength, SF\({}_{6}\)-filled GIL models achieved the required BIL value for 11 kV beyond 2 bar pressure. Figure 22 shows the  
Figure 20: Power frequency disruptive discharge investigations regarding air and SF\({}_{6}\) for different FGIL models.  
recorded waveforms of lightning impulse discharge tests regarding air and SF\({}_{6}\) gases at a pressure of 2.38 bar.
# Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures
## 2 Theoretical Background
### Spectral Entropy  
Spectral entropy (SE), like PE, is also related to Shannon entropy. The idea was proposed by [PERSON] et al. [58] and later extended and applied by [PERSON] et al. [59] in a study on electroencephalogram signals. The previous study revealed that SE is a convenient indicator for quantifying the power spectral distribution and measuring the flatness of a frequency spectrum [60]. The basis for SE is the Shannon entropy of the power spectral density (PSD) of the signal. The PSD is normalized to satisfy [59]  
\[\int_{f=0}^{f_{s}/2}\mathrm{P}(f)df=1, \tag{10}\]  
where \(\mathrm{P}(f)\) is the power value of the harmonic \(f\), and \(f_{s}\) is the sampling frequency. The normalized power of the analyzed signal is the ratio of the power of each harmonic of the analyzed signal to the signal's total power. The previous expression can be better understood using two signals: a pure sine wave and the sum of two sine waves at a different frequency. For a pure sine wave, Equation (10) is \(\mathrm{P}(f_{p})=1\) (\(f_{p}\) frequency of the sine wave). As SE is the Shannon entropy of the PSD of the signal, for a pure sine wave, the value SE is  
\[\mathrm{SE}=-1\cdot\log_{2}(1)=0. \tag{11}\]  
Taking as an example the signal \(s=A\sin(f_{1}t)+A\sin(f_{2}t)\), where \(f_{1}\) and \(f_{2}\) are different frequencies and therefore both sine waves give same power contribution, Equation (10) results in \(\mathrm{P}(f_{1})=\mathrm{P}(f_{2})=\frac{1}{2}\). Then, the SE value is  
\[\mathrm{SE}=-2\cdot(1/2)\log_{2}(1/2)=1. \tag{12}\]  
The first signal has a single frequency component, and thus the PSD is concentrated only in that frequency. Because the signal is completely regular in time, and therefore totally predictable, it has the smallest entropy. In contrast, the second signal, with two frequency components, has greater irregularity in time in comparison to the first signal, and thus its SE is higher. Then, SE for a signal over the entire frequency range is [59]  
\[\mathrm{SE}=-\sum_{f=0}^{f_{s}/2}\mathrm{P}(f)\log_{2}\mathrm{P}(f), \tag{13}\]  
where \(\mathrm{P}(f)\) is the normalized PSD of the signal [61], and \(f_{\mathrm{s}}\) the sampling frequency.
## 1 Introduction
The proposed scheme works by initially splitting the original image into non-overlapping blocks, applying the SVD transform to each of them and subsequently embedding a watermark into the singular vectors. Each watermark value is embedded by modifying a set of singular vector angles, i.e., angles formed by the right singular vectors of each block. The main contribution of this work can be identified in:  
* the use of the angles formed by the singular vectors to embed the watermark while maintaining the property of orthonormality. To our knowledge, there are no watermarking schemes, based on singular vectors, that respect this property;
* an increased security of the watermarking process based on the SVD transform, due to the use of singular vectors for the watermark insertion rather than singular values. Indeed, many of the existing SVD-based algorithms embed the watermark into the singular values of the image, which implies a high robustness against common image processing operations and geometric attacks but, on the other hand, a complete vulnerability towards attacks based on singular value substitution, as explained for example in [15, 16]. The remainder of the paper is organized as follows: Section 2 presents detailed information on the SVD transform, while Section 3 introduces the most relevant works related to digital watermarking based on SVD. Section 4 is completely focused on the description of the novel scheme, detailing both the embedding and detection procedures, whose main properties are subsequently examined in Section 5. In Section 6, experimental results are shown to prove the effectiveness of the proposed solution. Finally, in Section 7 we conclude discussing open problems and possible improvements of our watermarking algorithm.
# Improving 2-5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
## 2 Materials and Methods
### Metrics and Algorithms
#### 2.3.1 Traditional Phase Estimation  
Recall that the QPE algorithm estimates the phase of the eigenvalue of a unitary operator (i.e., the \(\theta\in[0,1)\) in the characteristic equation \(\hat{U}|u\rangle=e^{2\pi i\theta}\left|u\right\rangle\)). The traditional method of estimation only approximates the phase to a precision limited by the number of qubits in the quantum computer. This is performed by taking the most occurring output (in the numerical basis ranging from 0 to \(2^{n-1}\), where \(n\) is the number of qubits in the first register) and dividing it by \(2^{n}\). Thus, the traditional estimator for the phase based on a given dataset is the mode of the dataset divided by \(2^{n}\). This is the traditional method since it can be shown that the mode of the dataset occurs with probability at least \(4/\pi^{2}\approx 0.405\), and that this is the best approximation of \(\theta\). As an example, if the most common sequence of bits in a 4-qubit first register was 0110, the estimation for \(\theta\) would be \(10/16=0.625\). This traditional method leaves information about the phase behind in the resulting distribution since values of \(\theta\) in the range (9.5/16, 10.5/16) will all produce distinct distributions, all with mode 0110. That is, given \(n\) bits used in the first register, the traditional, mode estimator can only estimate \(\theta\) to one part in \(2^{-n}\) bits of accuracy. Note that, due to the periodic nature of the complex exponential, \(\theta\) near 1 can cause the mode to occur at 0 instead of \(2^{n}-1\). For large \(n\), this method can produce a very accurate result, but large numbers of highly accurate qubits are not available on today's computers. We are seeking to compare how well the traditional method compares to machine learning methods to predict the phase of a unitary operator. Efforts to use machine learning to estimate the phase given the output of the algorithm would be considered successful if they achieved a level of precision greater than that afforded by the traditional and trivial method for the given number of qubits.  
The model performance in traditional estimation was calculated on the entire dataset, as opposed to using the test/holdout datasets that the other algorithms used. This is due to the traditional method relying on a calculation based on the features for each row, instead of an algorithm that learns and generalizes on the training data.