esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Energy Efficiency
### Hybrid Precoding for the Partially Connected Architectures
#### 4.2.1 Analog Precoder Design  
Based on the structural properties of the analog precoder for the partially connected architectures, we can initialize the analog precoding matrix by using the phased-zero-forcing (PZF) precoding proposed in [34]. The analog precoding matrix uses a phase-shifting network to map the signals from the RF chain to the transmit antenna. Therefore, each phase shifter in the network should satisfy the unit-mode constraint, which is translated into a constraint on each entry of the analog precoding matrix. For the \(l\)th satellite, \(\left|\big{[}\mathbf{F}_{I}^{\mathbf{A}}\big{]}_{i,j}\right|=1,\forall i, \forall j=\lceil i/Ng\rceil\). Initialize element \(\big{[}\mathbf{F}_{I}^{\mathbf{A}}\big{]}_{i,j}=e^{j\varphi_{i,j}}\), where \(\varphi_{i,j}\) denotes the phase of the \((i,j)\) element in \(\mathbf{F}_{I}^{\mathbf{A}}\). In practical implementations, the phase of each entry is often quantized due to the practical constraints of variable phase shifters. Therefore, the performance of the PZF precoding scheme needs to be investigated in this realistic scenario, i.e., the phase of the entries of \(\mathbf{F}_{l}^{\mathbf{A}}\) is quantized to B-bit precision. Each phase is quantized to its nearest neighbor according to the closest Euclidean distance. Thus, the phase of each term of \(\mathbf{F}_{l}^{\mathbf{A}}\) is written as \(\hat{\phi}=2\pi\hat{n}/2^{B}\), where  
\[\hat{n}=\arg\min_{n\in\left\{0,1,\ldots,2^{B}-1\right\}}\bigg{|}\varphi-\frac{ 2\pi n}{2^{B}}\bigg{|}. \tag{33}\]  
Therefore, the analog precoder design can be expressed as  
\[\begin{array}{l}\min\limits_{\mathbf{F}_{l}^{\mathbf{A}}}\sum\limits_{l=1}^ {L}\left\|\mathbf{F}_{l}-\mathbf{F}_{l}^{\mathbf{A}}\mathbf{F}_{l}^{\mathbf{D }}\right\|_{F}^{2}\\ \text{s.t.}\ \mathbf{F}_{l}^{\mathbf{A}}\in\mathcal{A}_{p},\forall l\in\{1,2, \cdots,L\},\end{array} \tag{34}\]  
where \(\mathcal{A}_{p}\) denotes a set of block matrices after being quantized, with an \(N_{g}\) dimension non-zero vector in each block.  
As shown in [33], there exists a closed-form expression for \(\mathbf{F}_{l}^{\mathbf{A}}\), which can be written as  
\[\arg\Big{\{}\left(\mathbf{F}_{l}^{\mathbf{A}}\right)_{n,m}\Big{\}}=\arg\Big{\{} \left(\mathbf{F}\right)_{n,:}\left(\mathbf{F}_{l}^{\mathbf{D}}\right)_{m,:}^{ H}\Big{\}},\ 1\leq n\leq N,m=\left\lceil n\frac{N_{BF}}{N}\right\rceil. \tag{35}\]
## 2 Materials and Methods
### Instrument and Experimental Conditions  
GC-MS/MS analyses were carried out with a Shimadzu Nexis GC-2030 (Shimadzu Corporation, Kyoto, Japan), equipped with an AOC-6000 Plus auto-sampler (Shimadzu Corporation, Kyoto, Japan) and coupled with a triple quadrupole mass spectrometer equipped with an electronic ionization source (70 eV, 280 \({}^{\circ}\)C) (GCMS-TQ8040 NX (Shimadzu Corporation, Kyoto, Japan)). The GC-MS/MS analyses were performed according to the method and parameters presented below. Sample extracts were injected using an AOC-6000 Plus auto-sampler (Shimadzu Corporation, Kyoto, Japan), with a 10 \(\mu\)L syringe, into a multi-mode injector equipped with an ultra-inert dimpled inlet liner (2 mm ID) from Shimadzu.  
The injector temperature was set at 250 \({}^{\circ}\)C. Splitless-mode injection for 1 min was applied with an injection volume of 1 \(\mu\)L. Separation was performed on an SH-I-5 MS (Shimadzu Corporation, Kyoto, Japan) fused silica capillary column of 30 m \(\times\) 0.25 mm, at 0.25 \(\mu\)m of the film thickness (Shimadzu). Helium (purity 99.9995%) was used as the carrier gas at a constant-pressure flow rate of 1.4 mL/min. The column oven temperature program was as follows: the initial temperature was set at 105 \({}^{\circ}\)C and maintained for 3 min; then, it was increased to 130 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min, increased to 200 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C/min, increased to 280 \({}^{\circ}\)C at a rate of 8 \({}^{\circ}\)C/min, and held for 10 min. The total program time was 43 min. The mass spectrometer was operated with an electron impact (EI) source using the multiple reaction monitoring (MRM) mode. In Table S1, a characterization of the ions used for qualitative analysis of the pesticides is presented. The ion source and transfer line temperatures were set at 225 \({}^{\circ}\)C and 280 \({}^{\circ}\)C, respectively. Each target compound was monitored in the multiple reaction monitoring (MRM) mode used for both quantification and identification purposes. The software LabSolutions GCMS (version 4.20) was used for instrument control, and data acquisition and processing. The identification of target compounds relied on comparing the retention times (tR) of chromatographic peaks for the estimation and qualification of ions with the peaks of reference standards. Each standard was injected twice at the beginning and end of the validation sequence. Table S1 presents the operational parameters of the tandem mass spectrometer MS/MS, with a specific emphasis on the most intense transitions in multiple reaction monitoring (MRM), as well as collision energies. Additionally, the Kovats retention index (IR) and retention time (tR) are provided for each of the analyzed compounds.
## 2 Experimental Section
### Basic Mix Proportion Design  
This experiment is based on the tightest packing theory for basic mix proportion design. Due to the advantage of considering particle size distribution in the modified Andreasen and Andersen model (MAAM) among many UHPC mix design models based on the tightest packing theory, which is more accurate and effective [19; 20], this method is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **CaO/\%** & **SiO2/\%** & **Al2O3/\%** & **Fe2O3/\%** & **Fe/\%** & **MgO/\%** \\ \hline Cement & 67.12 & 23.45 & 3.46 & 3.23 & / & 2.74 \\ Silica Fume & 1.11 & 93.77 & 2.08 & 0.98 & / & 2.06 \\ Steel slag & 60.21 & 2.32 & 13.57 & 7.42 & 8.82 & 7.66 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main chemical components of cementitious materials.  
Figure 1: Sieving curves of raw materials.  
Figure 2: SEM image of cementitious materials.  
used to calculate and analyse the basic mix proportion of steel slag micro powder UHPC. The calculation formula is shown in Equation (1).  
\[\mathrm{P(D)}=\frac{\mathrm{D^{q}-D_{min}^{q}}}{\mathrm{D_{max}^{q}-D_{min}^{q}}} \times 100\% \tag{1}\]  
where \(\mathrm{P(D)}\) is the cumulative percentage of sieve residue with particle size less than D, %; D is the particle size, \(\mathrm{\mu m}\); \(\mathrm{D_{max}}\), \(\mathrm{D_{min}}\) are maximum and minimum particle size in the distribution system, respectively,\(\mathrm{\mu m}\); and \(q\) is the modulus of particle size distribution, taken as 0.24 in this study.  
Using Python software to model and analyse Equation (1), based on the particle distribution curves and MAAM target curves of different raw materials in Figure 1, the least squares method was used to calculate the raw material ratio. By adjusting the proportion of each material to make the stacking curve as close as possible to the target curve, the basic proportion of steel slag micro powder UHPC was calculated as shown in Table 2, p10, and the fixed water-cement ratio was 0.18.  
Steel slag micro powder is an ultrafine powder processed from steel slag through magnetic separation and ball milling technology. It contains various oxides and minerals and can form stable and high-strength granular materials. It also has a particle size and specific surface area similar to quartz powder. Using the above mix ratio to replace some quartz powder with steel slag micro powder can reduce the production cost of UHPC to a certain extent and achieve waste utilization, effectively solving the resource utilization problem of solid waste from steel manufacturers. UHPC can be mixed with coarse aggregate without affecting its performance, which can further reduce the cost. In view of the greater brittleness of UHPC and the improvement in its toughness by adding a certain amount of steel fibre, the following tests prove that a reasonable amount of steel slag micro powder Ecotype UHPC can be prepared.
## 3 Representative Elementary Volume  
The concept of a representative elementary volume (REV) is a prime consideration in adopting a continuous-media methodology. It is of great importance in understanding the behavior of fractured rock masses. The definition of an REV is inherently related to scale effects, and an REV can be defined for various physical parameters. For example, [PERSON] [28] defined the REV using the concept of porosity in rock masses according to the applicability of the theory of porous media (Figure 2). Using REV in continuum-based modeling also generates new generations of numerical simulation strategies, e.g., [PERSON] et al. [29; 30]. According to [PERSON] [31], rock mass strength will reach a constant value when the size of individual rock pieces is sufficiently small relative to the size of the problem being considered. [PERSON] and [PERSON] [22] captured this phenomenon regarding the mechanical behavior of fractured pillars subjected to compressive loading, as shown in Figure 3. Similarly, it is reasonable to assume that a rock mass rating (e.g., RQD, RMR, Q or GSI) calculated for different problem scales would change, reaching a constant value with increasing problem scale (for a single geotechnical domain). As shown in Figure 4, as the problem scale increases, so does the sampling area used to calculate rock mass parameters. If a parameter (e.g., RQD) were to be measured concerning a relatively short core run or scanline, it would likely show large variations similar to those shown in Figure 2 for small-scale porosity.  
Figure 3: _Cont._  
Figure 2: Variation in porosity as a function of the representative elementary volume [28].
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 2 Review of Early Fault Diagnosis Approaches
### Empirical Wavelet Transform  
Empirical wavelet transform (EWT) is a novel adaptive decomposition method proposed by [PERSON] (2013) in 2013. EWT not only has the merits of wavelet transform and EMD but also overcomes the drawbacks of the wavelet and EMD. The EWT algorithm adopts adaptive wavelets, which are able to extract the AM-FM components of a signal. The key idea of EWT is that such constituent amplitude-modulated and frequency-modulated (AM-FM) components have a compact supporting Fourier spectrum.  
The signal can be reconstructed by inverse empirical wavelet transform as follows:  
\[x(t)=W(0,t)*\phi_{1}(t)+\sum_{n=1}^{N}W(n,t)*\psi_{n}(t) \tag{3}\]  
where \(*\) denotes the convolution operator. The empirical modes are expressed in Equations (4) and (5):  
\[x_{0}(t)=W(0,t)*\phi_{1}(t) \tag{4}\]  
\[x_{k}(t)=W(k,t)*\psi_{k}(t) \tag{5}\]  
The detailed coefficients of the inner products with the empirical wavelets and approximation coefficients by the inner product with the scaling function respectively are written in Equations (6) and (7):  
\[W(n,t)=\int x(\tau)\psi_{n}(\tau-t)d\tau \tag{6}\]  
\[W(0,t)=\int x(\tau)\phi_{1}(\tau-t)d\tau \tag{7}\]  
Like WT, EWT separates the empirical mode from low frequency to high frequency, however, the bandwidth is not dyadic.
## 6 Prototype Implementation and Evaluation  
This section describes the testbed integration performed as part of the FP7 EXALTED project (Expanding LTE for Devices) [31] to demonstrate the capability to disseminate eHealth specific data on the next-generation Internet from a vehicular setting. The underlying network communication protocols used were relying exclusively on IPv6 as the next generation of Internet protocols. The application layer protocols included, but were not limited to, HTTP and HTTPS.
###### Abstract
On the other hand, to use it as a PUF, the MRAM needs to be written only once (during the enrollment phase). The data that get written to PUF addresses depends on the PV which cannot be replicated by a malicious entity. It should be noted that PUF can be morphed to TRNG if written multiple times as proposed for TRNG. _To the best of our knowledge, this is the first experimental demonstration of a PUF and TRNG using commercial MRAM chip._ A work-in-progress version of this work has been published in [24] where the methodology is discussed and initial data were presented. However, this work explains the design and results in detail. In summary, we make the following contributions:* We characterize the MRAM bit-to-bit write latency under voltage and temperature variations. * We characterize the MRAM response under multiple write disturbs which can be useful for TRNG. * We propose a write PUF (wPUF) by biasing the MRAM with a write latency with 50% switching probability. The proposed PUF exhibits excellent stability and uniqueness. * We show that 75% of the bits could be unresponsive to a challenge and propose techniques to convert them into useful bits avoiding expensive row and columns masking. * We propose a TRNG by exploiting random MRAM responses under multiple write disturbs. * We benchmark the proposed PUF and TRNG with existing designs. The rest of the paper is organized as follows: Section 2 provides the background of MRAM technology, details of the experimental setup and analysis of the MRAM responses to write latency and number of writes. Sections 3 and 4 describe the proposed PUF and TRNG. Section 5 presents discussion and Section 6 draws conclusions.
## 5 Materials and Methods
### Datasets
#### 5.1.1 Use Case Points  
The original UCP framework proposed by [PERSON] [56] consists of seven steps. First, a calculation of the unadjusted actor weighting (UAW) is performed by classifying the actors into three levels of complexity and assigning a weight for each actor based on its complexity as notated in Equation (10).  
\[UAW=\sum_{i=1}^{3}W_{i}*A_{i} \tag{10}\]  
where \(W_{i}\) is weight factor classified as simple for 1, the average for 2, and complex actor for 3. \(A_{i}\) is a number of actors in the use case diagrams based on the same classification as \(W_{i}\). Second, the unadjusted use case weighting (UUCW) is calculated by classifying the use case into three levels of complexity and assigning a weight for each actor based on its complexity level as formulated in Equation (11).  
\[\text{UUCW}=\sum_{i=1}^{3}W_{i}*UC_{i} \tag{11}\]  
where \(W_{i}\) is a weight factor which classified as simple (5), average (10), and complex (15) use case, respectively. \(UC_{i}\) is number of transactions counted in use-case specification diagrams based on the same classification as \(W_{i}\). Alongside the original weight level, Table 2 presents the complexity weight level derived by [57,58].  
Third, the unadjusted use case points (UUCP) are calculated as notated in Equation (12). UAW in Equation (10) is used alongside the UUCW in Equation (11) to obtain UUCP.  
\[\text{UUCP}=\text{UAW}+\text{UUCW} \tag{12}\]  
Fourth, technical complexity factors (TCF) are calculated. TCF is formulated in Equation (13) by grading (\(G_{i}\)) the thirteen weight factors (\(W_{i}\)) using a score of 0 to 5.  
\[\text{TCF}=0.6+\left(0.01*\sum_{i=1}^{13}W_{i}*G_{i}\right) \tag{13}\]  
Fifth, the environmental complexity factors (ECF) are calculated as notated in Equation (14) by grading (\(G_{i}\)) the eight factors (\(W_{i}\)) using a score of 0 to 5.  
\[\text{ECF}=1.4+\left(-0.03*\sum_{i=1}^{8}W_{i}*G_{i}\right) \tag{14}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Use Case Transactions** & **Original Weight Level** & **Modified Weight Level** \\ \hline
1–2 & 5 & 5.00 \\
3 & 5 & 6.45 \\
4 & 10 & 7.50 \\
5 & 10 & 8.55 \\
6 & 10 & 10.00 \\
7 & 10 & 11.40 \\
8 & 15 & 12.50 \\
9 & 15 & 13.60 \\ \textgreater{}10 & 15 & 15.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The original and modified use case complexity weight level.  
Sixth, the use case points (UCP) are calculated as formulated in Equation (15). UCP is obtained by multiply UUCP in Equation (12) by TCF in Equation (13) and ECF in Equation (14).  
\[\text{UCP}=\text{UUCP}*\text{TCF}*\text{ECF} \tag{15}\]  
Seventh, the estimated effort is calculated as formulated in Equation (16). Estimated effort is obtained by multiply the UCP in Equation (15) with Productivity Factors (PF).  
\[\text{Effort}=\text{UCP}*\text{PF} \tag{16}\]  
PF is the productivity factor, and it can be set equal to 20 person-hours/UCP, 8.2 person-hours/UCP [59; 60], or using the learning productivity ratio as proposed by [61].
# De Novo Transcriptome Identifies Olfactory Genes in _Diachassimomorpha Ionigicaudata_ (Ashmead)
## 3 Results
### Olfactory Genes  
We next identified genes encoding the olfactory gene from five families, including OBP, CSP, OR, GR, IR, and SNMP. In the Ashmead transcriptome, we found 43 transcripts encoding OBPs (Table 2, Table S2) by similarity and 35 of them were predicted to have complete ORFs by TransDecoder. The Ashmead OBPs were categorized into four sub-families: OBP-56, -69, -72, and -83 (Table S2). Multiple sequence alignment (Figure S1) showed these OBPs have 6 conserved cysteine residues (Cys) and SignalP predicted that 26 of these OBPs have the signal peptides located in the first 23 amino acids (aa) and 2 located in the first 37 aa. We identified that 69 transcripts had the potential of encoding CSPs in the Ashmead transcriptome by aligning the likely proteins to the known CSPs (Table 2, Table S2) and 63 of them had intact ORFs. SignalP identified 58 CSPs with the signal peptides in the first 28 aa while transmembrane domains were found in 13 CSPs. Surprisingly, 689 Ashmead transcripts had the capacity of encoding ORs and 115 transcripts were found to encode OR-13 (Table 2, Table S2). We also identified nine transcripts encoding OR coreceptors (OR-co). Out of the 347 OR transcripts that had intact ORFs, 276 were predicted to have transmembrane domains (Table 2). In the Ashmead transcriptome, there were 26 transcripts encoding IRs, including 3 IR-21, 18 IR-25, 4 IR-68, and 2 IR-93 (Table 2). Two thirds of the IR transcripts that had intact ORFs were predicted to have transmembrane domains. In addition, we identified 60 GR and 14 SNMP transcripts (Table 2). GR-28 and SNMP-1 were the largest group, which corresponded 19 and 11 transcripts, respectively. Further, we found 55 transcripts that had either the 7 tm chemosensory receptor (PF02949) or GOBP (PF01395) PFAM domain in their protein sequences (Table 2, Table S2).
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 2 Model Description
### NGSO Satellite System Model  
#### 2.3.1 Constellation  
The Walker constellation is the most common constellation configuration used by most NGSO constellation systems, where the position of one of the satellites is known so that the position of the other satellites can be deduced. The Walker constellation is a set circular orbits proposed by [PERSON], which can be expressed by four parameters: orbital inclination (\(i\)), number of constellation satellites (\(N\)), number of orbital planes (\(P\)), and phase factor (\(F\)). The position states of all satellites in the configuration of [PERSON]'s constellation can be determined using the following expressions:  
\[\Omega_{m,n}=\frac{2\pi}{P}(m-1)+\Omega_{1,1}, \tag{8}\]  
\[M_{m,n}=\frac{2\pi}{P}(n-1)+\frac{2\pi}{N}F(m-1)+M_{1,1}, \tag{9}\]  
where \(\Omega_{1,1}\) and \(M_{1,1}\) are the right ascension at the ascending node (RAAN) and mean anomaly of the reference satellites, respectively. The right ascension at the ascending node of the \(n\)th satellite in the \(m\)th plane is denoted as \(\Omega_{m,n}\), and its mean anomaly is denoted as \(M_{m,n}\). According to the height of orbit \(h\), the orbital velocity is \(\sqrt{\frac{GM}{R_{e}+h}}\), where the gravitational constant \(G=6.67408\times 10^{-11}\,\mathrm{m^{3}kg^{-1}s^{-2}}\), the mass of Earth \(M=5.9722\times 10^{24}\) kg and the radius of Earth \(R=6.5478\times 10^{6}\) m.  
Figure 2: The definitions of the elevation and azimuth angles of NGSO satellite.
## 2 System Model
### Transmitter  
The information bits are mapped into symbols using the Gray code. If we denote the order of digital phase modulation by \(M\), then one symbol contains \(\log_{2}M\) bits. The symbols are then written to the interleaver and read out from it. The interleaver is used in order to make fading values over the channel to be mutually uncorrelated over the adjacent symbols. Also, it is assumed that the values the thermal noise during the duration of one symbol are uncorrelated with the values of this noise during the duration of the next symbol, which also applies to the influence of the phase noise. In other words, by using interleaving at the transmitter and complementary de-interleaving in the receiver, the signal is transmitted over a memoryless channel. The interleaver output symbols are fed to the MPSK modulator. Over the duration of one information symbol, the modulator outputs  
Figure 1: System model.  
a signal of the following form: \(s_{i}=A\,\mathrm{e}^{i\phi_{n}}\), where the signal amplitude \(A\) is constant and the modulated phase has a value from the set \(\phi_{n}\in\{0,2\pi/M,\cdots 2\pi(M-1)/M\}\), where \(M\) is the number of phase levels [2].
# Improved Calculation Method for Siphon Drainage with Extended Horizontal Sections
## 4 Experimental Setup and Scheme  
The siphon drainage experimental setup, as shown in Figure 9, was used to validate the proposed calculation method. The experimental setup is a full-scale model and the pipe is made of polyurethane (PU). Thirty-six experiments were performed, and the schemes are given in Table 1, where \(D\) represents the diameter of siphon pipe, \(L_{hori}\) represents the horizontal section length of siphon pipe, and \(H\) represents the siphon lift. The flow velocity was measured by weighting the water collected in the water storage tank for a period. It should be noted that a single-phase flowmeter does not work well in these experiments since the flow pattern is multi-phase.  
Figure 9: Diagram of the experimental setup.
## 3 Application Examples  
The performance of the bioreactor was tested by growing two plant species in sterile hydroponics and collecting root exudates; by comparing growths rate of hairy roots generated from two plant species; and by collecting and analyzing hairy root exudates.
# Data Analysis for Information Discovery
## 8 Experiments and Results
### Room Occupancy Estimation Dataset  
8.2.1. Clustering and Silhouettes Using Raw Data  
The original dataset is composed of 20 numerical features. Applying PCA to this dataset, losing less than 2% of the total variation in the data, produced a dataset with two dimensions.  
The following Tables 7-9 show the obtained results in terms of silhouettes varying the number of clusters from 10 to 50 (see columns). Each row represents the percentage of points with a silhouette greater than a given threshold, that is 0.7, 0.
## 5 Evaluation  
In this section, a series of experiments is conducted to evaluate the mutation-based ICS time-series anomaly generation proposed in this paper for the ICS dataset. To our best knowledge, there is no prior research on the generation of anomalous data for time-series. Therefore, instead of comparing performance with other studies, the plausibility of the generated anomalies is assessed, and binary classification is performed using them. From this, two research questions (RQs) are defined as follows:  
* RQ1: Are the synthetic ICS time-series anomalies visually and in the embedding space similar to real data?
* RQ2: Does a binary classifier trained on the synthetic ICS time-series anomalies perform better than a one-class classifier?