esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Sparse matrix-vector (SpMV) multiplication is a vital building block for numerous scientific and engineering applications. This paper proposes SURAA (translates to _speed_ in arabic), a novel method for SpMV computations on graphics processing units (GPUs).The novelty lies in the way we group matrix rows into different segments, and adaptively schedule various segments to different types of kernels. The sparse matrix data structure is created by sorting the rows of the matrix on the basis of the nonzero elements per row (\(npr\)) and forming segments of equal size (containing approximately an equal number of nonzero elements per row) using the Freedman-Diaconis rule. The segments are assembled into three groups based on the mean \(npr\) of the segments. For each group, we use multiple kernels to execute the group segments on different streams. Hence, the number of threads to execute each segment is adaptively chosen. Dynamic Parallelism available in Nvidia GPUs is utilized to execute the group containing segments with the largest mean \(npr\), providing improved load balancing and coalesced memory access, and hence more efficient SpMV computations on GPUs. Therefore, SURAA minimizes the adverse effects of the \(npr\) variance by uniformly distributing the load using equal sized segments. We implement the SURAA method as a tool and compare its performance with the de facto best commercial (cuSPARSE) and open source (CUSP, MAGMA) tools using widely used benchmarks comprising 26 high \(npr\)_variance_ matrices from 13 diverse domains. SURAA outperforms the other tools by delivering 13.99x speedup on average. We believe that our approach provides a fundamental shift in addressing SpMV related challenges on GPUs including coalesced memory access, thread divergence, and load balancing, and is set to open new avenues for further improving SpMV performance in the future.  
sparse matrix-vector multiplication (SpMV); high performance computing (HPC); graphics processing units; general-purpose computing on graphics processing units (GPGPUs); iterative methods; data analysis; sparse matrix storage; load balancing; coalesced memory access; thread divergence; Freedman-Diaconis rule
## 4 ROLQ-TEE Scheme
### Detailed Construction
Additionally, the ROLQ-TEE scheme also implements the revocation of user query permissions, avoiding a situation wherein location data are abused by data users \(\mathcal{DU}_{\mu}\) after the query service expires. Compared with the scheme in reference [9], the ROLQ-TEE scheme achieves stronger privacy protection in terms of access control. Therefore, the ROLQ-TEE scheme also increases the computational overhead of key refresh and the communication overhead of key distribution accordingly. ### Memory Usage

From Table 3, we can see that, when the number of intersections \(n_{I}\) is one million, the occupancy rate of our scheme in the ciphertext is reduced by 88% compared with that in scheme [9]. However, when the number of candidate facilities \(s\) is 100, the memory usage of our scheme is higher than that of scheme [9]. This is because the facility location data transmitted in scheme [9] is in plain text, while our scheme is in ciphertext.
## 4 Simulation Results
### Eight Quadrature Amplitude Modulation (8 QAM)  
The 8 QAM coherent system is simulated for 30,000 bits and SNR values in the range of \(-6\) to 10 dB. An AWGN of variance = \(1/(10^{(\text{SNR}/10)})\) is added and then the bits are sent over a Gaussian link to the receiver. The received noisy signal then goes through the AdaBoost algorithm of \(T=25\) and \(K=8\), so as to separate the data into eight classes achieving the 8 QAM concept as shown in Figure 12.  
Figure 11: BER vs. SNR for noncoherent QPSK modulation over the Rayleigh fading channel.  
Figure 13 depicts both simulation and theoretical curves of BER vs. SNR of coherent 8 QAM. It is noted that at low SNRs, the BER increases, while it decreases at higher SNRs. At the beginning of the curve, the simulated result is slightly higher than the theoretical one. At the end the simulated curve, it slightly dips under the theoretical curve; other than that, the two curves match very well.
# Intelligent Prediction of Nitro Oxide Capture in Designable Ionic Liquids
## 4 Results and Discussion
### Trends and Comparisons of Different Models  
Further analyses were carried out to validate the performances of the suggested models. The trends in N2O solubility versus input variables are plotted in Figure 9 for each model in this study; two ionic liquids, [BMIM][Tf2N] and [(ETO)2 IM][Tf2N], were selected. As shown in Figure 9, in all the models in this study, N2O was more soluble in the two ionic liquids when the pressure was increased, but it was less soluble when the temperature was raised. Regarding the linear regression method, the data points and trends in model prediction exhibited obvious deviations with the experimental values. Meanwhile, when using the voting method, the prediction and experiment values were consistent in terms of both the data points and trends, with only a very tiny deviation. In the case of the TLFFNN method, the two trend curves of prediction and experiment were almost overlapping, which indicated the high reliability of the TLFFNN method.  
The TLFFNN model was the most superior model in this study; the values derived using it were highly consistent with the laboratory values. It could accurately predict N2O capture in various ionic liquids against different temperatures and pressures.  
Comparisons of previous research and our results are shown in Table 8. The MSE, RMSE, and R2 values of different models on test sets were compared. As seen in this table, the TLFFNN model had lower values of MSE and RMSE than other models from the recent literature, which means that it performed better in predicting N2O capture in various ionic liquids.  
Figure 8: Error histogram for N2O in [BMIM][Tf2N] via the TLFFNN method.  
Figure 9: The trend in N\({}_{2}\)O solubility against different temperatures and pressures. (a) N\({}_{2}\)O in [BMIM][Tf\({}_{2}\)N] via the linear regression method; (b) N\({}_{2}\)O in [(ETO)\({}_{2}\)IM][Tf\({}_{2}\)N] via the linear regression method; (c) N\({}_{2}\)O in [BMIM][Tf\({}_{2}\)N] via the voting method; (d) N\({}_{2}\)O in [(ETO)\({}_{2}\)IM][Tf\({}_{2}\)N] via the voting method; (e) N\({}_{2}\)O in [BMIM][Tf\({}_{2}\)N] via the TLFFNN method; and (f) N\({}_{2}\)O in [(ETO)\({}_{2}\)IM][Tf\({}_{2}\)N] via the TLFFNN method.
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 4 Virtual World Architecture Inspection
### Peer-To-Peer  
P2P virtual world aims at resolving the scalability issue of the classic C/S architecture [123], which is jointly promoted by P2P computing and virtual world research. In the P2P architecture (Figure 8b), services are collectively run by user devices that play the roles of both server and client. All user devices are connected to a P2P overlay network [124]. When a user is accessing a virtual world application, he/she is also providing services to others of the same application through his/her accessing device. Such a virtual world is supported by a reciprocal economy. A P2P virtual world can have an application provider who develops and distributes the application software to run on a P2P overlay network. Currently, P2P virtual worlds are still far from wide industry acceptance, partially due to their higher difficulty in system maintenance for developers [66].
## 5 Discussion  
### Efficiency of Pressure Grouting on Fractured Zone  
Experimental results demonstrated the superiority of pressure-grouted micropiles to gravitationally grouted ones. The benefits of using pressure grouting are magnified when the micropiles should be installed in ground with fractured zones. The pressure grouting in week zones plays the same role as fracture grouting to fill the gaps in the ground [29]. Fracture grouting is a technique to inject fluid grouting (e.g., cement slurry) with pressure into the ground to address fractures in the ground. As the pressure grouting is injected into the ground, grout lenses are generated horizontally in weak areas [30]. These grout lenses provide considerable support to the bearing capacity and pullout capacity of the micropile. This mechanism is illustrated in Figure 7. The pressure grouting infiltrates into the fractured zones, forming grout lenses in weak spots. The fractured rocks have a very low strength, but the fractured ground bonded with pressure grouting has a high strength, equivalent to that of intact rock. In addition, the bonding strength between soil and grout is improved. Thus, micropiles are an effective alternative when deep foundation systems cannot be used because of the presence of a hard layer, and there is uncertainty regarding the extent of the fractured zone. The pressure-grouted micropile not only ensures a better stress-strain relationship (Figure 5), but also overcomes the geological uncertainties (Table 4).
# Estimating Underwater Light Regime under Spatially Heterogeneous Sea Ice in the Arctic
## 3 Results
### Inelastic Scattering  
Based on in situ data, our results have pointed out that \(K_{Lu}\) is not a good proxy for \(K_{d}\) at longer wavelengths (Figures 11 and 12) because of the effect of Raman scattering. To validate this hypothesis, we used the HydroLight (Sequoia Scientific, Inc., Bellevue, WA, USA) radiative transfer numerical model to calculate theoretical downward irradiance and upward radiance and their associated vertical attenuation coefficients in an open water column in the presence of Raman scattering. The simulation was parametrised using IOPs measured during the field campaign (detailed information can be found in the supplementary section entitled Raman inelastic scattering included in Appendix A). The simulation was able to reproduce the observed decoupling between \(K_{d}\) and \(K_{Lu}\) observed at wavelengths \(\geq\)600 nm (Figure 13). These results are generally consistent with previous findings from radiative transfer simulations, which demonstrated the depth and spectral dependencies of diffuse attenuation coefficients as affected by Raman scattering [21; 22].
## 4 The Proposed Algorithm and Its Verification
#### Structure of the Algorithm  
* **Step 1** The signal is located in a register and the standard \(\sigma\) of the local Lipschitz (L) constant of its first seven samples is computed by means of the scalar product between two consecutive samples and Haar functions having two samples. Subsequently, one calculates the local Lipschitz constant considering the 7 th and 8 th sample correspondingly.
* **Step 2a** In case of the local Lipschitz constant being calculated considering the 7 th and 8 th sample of signal which is less than constant \(c_{1}\sigma\), the examined element of the sequence is not an outlier and the local Lipschitz constant is summed up with \(\sigma\).
* **Step 2b** In case of the local Lipschitz constant computed considering the 7 th and 8 th sample being bigger than constant \(c_{1}\sigma\), then the examined element of the sequence is an outlier and its local Lipschitz constant is not saved. The case of single outliers is reported in Figure 3.
* **Step 3** The local Lipschitz constant is saved and the next step of the sequence is taken under consideration.
* **Step 4** If the saved local Lipschitz constant value is bigger than \(c_{1}\sigma\), then the sign of the last two computed local Lipschitz constants is analyzed. In case of their not being opposite signs, multi-outliers appear (see Figure 6a).  
Assuming that they are opposite signs, the saved local Lipschitz constant value is checked. In the case that this value is not less than \(c_{2}\sigma\), it results in being a single inverse outlier, as it can seen in Figure 6b.  
Figure 6: (**a**) multiple outliers; (**b**) inverse outliers.  
**Remark 1**.: _With help of this algorithm, it is possible to accomplish soft, hard and very hard fault detection strategies simply by choosing parameters \(c_{1}\) and \(c_{2}\) together with the replacing term, if an outlier (fault) appears. For \(c_{1}=1\) and \(c_{2}=2\), together with_  
\[Replaced=Element(k-1)+\frac{(Outlier-Element(k-1))}{2},\]  
_a soft detection is obtained. For \(c_{1}=0.5\) and \(c_{2}=1\), together with_  
\[Replaced=Element(k-1)+\frac{(Outlier-Element(k-1))}{2},\]  
_a hard detection is obtained. For \(c_{1}=0.5\) and \(c_{2}=1\), together with_  
\[Replaced=Element(k-1),\]  
_a very hard detection is obtained._
## 3 Plane Wave Expansion from Finite Element Data
### Reference Case  
A typical meeting room (No. 4079 in Building 13 at the Highfield Campus of the University of Southampton) was selected as a reference case. It is an L-shaped room with a volume of approximately 88 m\({}^{3}\). FE simulations were conducted using the commercial package COMSOL v5.1 (COMSOL Inc., Stockholm, Sweden). The reader is referred to [21] for a detailed description of the simulation procedure, i.e., the characterization of the acoustic source, the geometric model of the enclosure, the boundary conditions and the measurements carried out to validate the predictions. Figure 3 shows a model of the enclosure identifying the location of the virtual microphone array.  
Two types of figures are presented to assess the performance of the inverse method. The first shows a comparison between the real parts of the target (numerical) and reconstructed acoustic pressures (Pa) over a cross-section of the domain (1.6 m). The second type of figure plots the absolute and phase components of the error between the target and reconstructed pressure fields, defined as:  
amplitude error:  
\[E_{pa}(\mathbf{x})=20\log_{10}\left(\frac{|\hat{p}(\mathbf{x})|}{|p(\mathbf{x}) |}\right). \tag{15}\]  
phase error:  
\[E_{pp}(\mathbf{x})=\angle\left(p(\mathbf{x})\hat{p}(\mathbf{x})^{*}\right), \tag{16}\]  
where \(\hat{p}(\mathbf{x})\) is the reconstructed pressure, \(p(\mathbf{x})\) is the target pressure, \((\cdot)^{*}\) indicates the complex conjugate operator and \(\angle\) represents the phase of a complex number. The amplitude error gives  
Figure 3: Model of the reference room model.  
These indicate that the area of accurate reconstruction depends on the frequency of the acoustic field, being more extensive at low frequencies. In terms of defining a radius within which the reconstruction is accurate, the acoustic errors show good agreement with Equation (14), represented by the circular arc in Figures 6 and 7, only for the higher frequency of 250 Hz. At the lower frequency, 63 Hz, the area is overestimated. This relates to the presence of the walls. At the lower frequency, the homogeneous Helmholtz equation is not satisfied within the radius predicted by Equation (14) since the walls of the room intrude into this region acting as reflective boundaries and playing a role similar to acoustic sources.
# Dynamic and Active THz Graphene Metamaterial Devices
## 3 Applications of Graphene Metamaterial
### Terahertz Photodetectors
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Detector Type** & **Frequency** & **NEP** & **Response Time** & **Ref.** \\ \hline Graphene & 1.8–4.2 THz & 80 pW/\(\sqrt{\mathrm{Hz}}\) & \textless{}30 ns & 2019 [104] \\ Graphene & 0.13 THz & 0.2 pW/\(\sqrt{\mathrm{Hz}}\) & - & 2021 [105] \\ Graphene & 3.4 THz & \(\leq 120\) pW/\(\sqrt{\mathrm{Hz}}\) & \textless{}10^{-1}\) ns & 2021 [106] \\ Graphene & 2.8 THz & \(<103\) pW/\(\sqrt{\mathrm{Hz}}\) & 5 ns & 2021 [121] \\ Graphene & 0.12 THz & 100 pW/\(\sqrt{\mathrm{Hz}}\) & - & 2018 [122] \\ Graphene & 0.1–0.4 THz & 34 pW/\(\sqrt{\mathrm{Hz}}\) & - & 2017 [123] \\ Graphene & 2 THz & 1700 pW/\(\sqrt{\mathrm{Hz}}\) & - & 2015 [124] \\ Graphene & 0.33 THz & 51 pW/\(\sqrt{\mathrm{Hz}}\) & - & 2017 [125] \\ BP & 3.4 THz & 7000 pW/\(\sqrt{\mathrm{Hz}}\) & - & 2019 [126] \\ BP & 0.29 THz & 138 pW/\(\sqrt{\mathrm{Hz}}\) & 800 ns & 2020 [127] \\ PtTe2 & 0.12 THz & \(<10\) pW/\(\sqrt{\mathrm{Hz}}\) & \(\approx\)1.7 \(\times\) 10\({}^{4}\) ns & 2019 [128] \\ PtTe2 & 0.3 THz & 57 pW/\(\sqrt{\mathrm{Hz}}\) & \(\approx\)10\({}^{3}\) ns & 2020 [129] \\ Bi2 Se3 & 0.3 THz & 0.36 pW/\(\sqrt{\mathrm{Hz}}\) & \(<\)6 \(\times\) 10\({}^{4}\) ns & 2018 [130] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of THz photodetectors. In addition to improving the performance parameters of the detector itself, an urgent challenge is how to apply it to the existing communication systems and achieve multi-platform compatibility without affecting the excellent performance of devices. [PERSON] et al. proposed an efficient optical detection method for monocrystalline and polycrystalline graphite, which was based on chemical vapor deposition (CVD) growth, with equivalent noise power \(<\) 1 nW/\(\sqrt{\text{Hz}}\) and response time \(\sim\)5 ns at room temperature [121]. Tuning in the entire THz range (0.1-10 THz) can be achieved by selecting the resonance of an on-chip patterned nanoantenna. CVD helps to relax geometric and technical constraints at terahertz frequencies, while retaining chip-level scalable CMOS technology compatibility. Other examples include a high-responsivity graphene photodetector integrated with a silicon microring resonator [131]. Under critical coupling, 90% light absorption was achieved in the SLG channel of \(\sim\)6 um along the Si waveguide, with a voltage responsivity of \(\sim\)90 V/W. At the same time, the receiver sensitivity guaranteed a bit-error-rate of only \(10^{-9}\). By introducing an ultra-thin and wide silicon-graphene hybrid plasmonic waveguide [132], a high-performance waveguide photodetector based on the radiant heat/photoconductivity effect was realized. When operating at 2 um, the photodetector's responsivity was -70 mA/W and the 3 dB bandwidth was \(>\)20 GHz. When operating at 1.55 um, the bandwidth of the photodetector was 40 GHz and the photodetector's responsivity was -0.4 A/W even with a low bias voltage of \(-\)0.3 V. All these measures pave the way for the future development of graphene-integrated devices.
# A Multi-Object Grasp Technique for Placement of Objects in Virtual Reality
## 4 User Study
### Conditions  
There were three conditions for the study: real world, Virtual Reality using single-object grasp and Virtual Reality using multi-object grasp.  
Real environmentIn the real environment, the users could employ both hands to collect and distribute the tokens. In this condition, the user can either pick objects in serial or in parallel, as they would do to complete daily tasks. We explained the users that they could only use their hands; actions such as using a T-shirt or a bag for holding the tokens were not allowed. The instructions for the trials were shown on a laptop with a 14'' screen, and were shown as they appear in Figure 3. Users reported no issues understanding or seeing them across the different scenarios.  
Virtual environment with single-object graspFor the virtual world, the users employed a VR headset (Oculus Quest 1) and two controllers. These controllers became the representation of the user's hands in the virtual environment and their actions were controlled with the buttons. Here, the single-object grasp technique was employed; they could grab only one object at a time in each hand. The instructions were shown on a virtual screen in the VR conditions; for different scenarios this screen was placed at different distances, but users reported no problem observing or understanding the instructions.  
Virtual environment with multi-object graspThis was identical to the previous condition, except, in this case, the user could grab multiple tokens in each hand and move them to the final location using the multi-object grasp technique described in Section 3.
## 2 State of the Art
### Acatech Industry 4.0 Maturity Index  
The goal of the \"Acatech STUDY\", conducted by the German Academy of Science and Engineering, is to successfully guide companies through their digital transformation. A key feature of the study is the Acatech Industry 4.0 Maturity Index (MI), which is used to indicate the current stage within the digital transformation process. The model consists of six successive maturity stages, each indicated by MI 1-6:  
* The first stage in the development path is _COMPUTERIZATION_ (MI 1). Compartization is the basis for digitalization. It is often used to perform repetitive tasks more efficiently and allows cheaper manufacturing to higher standards with higher precision. But manually operated machines without digital interfaces can still be found in factories;
* The second stage is _CONNECTIVITY_ (MI 2). The isolation of information technology in computerized machinery can be broken through connecting these components with each other. The internet protocol (IP) is widely used on the shop floor.
* The third stage is _VISIBILITY_ (MI 3). Installed sensors allow us to collect a vast amount of data from production machinery, logistics, etc. Processing these data with  
\begin{table}
\begin{tabular}{c c} \hline
**TRL** & **DEFINITION** \\ \hline
1 & Basic principles are observed \\
2 & Technology concept is formulated \\
3 & Experimental proofs of concept are provided \\
4 & Technology is validated in lab conditions \\
5 & Technology is validated in industrially relevant environment \\
6 & Technology is demonstrated in industrially relevant environment \\
7 & System prototype is demonstrated in operational environment \\
8 & System is complete and qualified \\
9 & Actual system is proven in operational environment \\ \hline \end{tabular}
\end{table}
Table 3: Technology Readiness Levels (TRLs).  
microchips and network technology such as the Internet of Things (IoT) allows the creation of a digital shadow (DS).
* The fourth stage is **TRANSPARENCY** (MI 4). Stage four is based on the previously created DS. The aim of transparency is to understand why something is happening by analyzing the data with engineering knowledge.
* The fifth stage is **PREDICTIVE CAPACITY** (MI 5). The fifth stage builds on the knowledge created in the transparency stage. Conducting various future scenario simulations and identifying the most likely outcomes allows us to project the DS into the future. Developments can be anticipated in advance, which ensures a robust operation.
* The sixth stage is **ADAPTABILITY** (MI 6). The five previous stages eventually enable the automation of actions and decision making in the sixth stage. Instead of executing them manually, they can be delegated to IT systems that can react to an evolving environment as fast as possible.
# Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT
## 4 Case Study
### Dataset Description  
The dataset utilized comprises raw seismic traces structured within a SEG-Y file. These traces encompass pressure measurements acquired via hydrophones mounted on permanent reservoir monitoring (PRM), strategically positioned across an irregular grid on the seafloor during an oil and gas reservoir monitoring survey. The Jubarte PRM (permanent reservoir monitoring) records 441 shot lines, spaced 25 m apart, with a shot interval of 25 m [26]. The files used for analysis come from 171 OBNs, each containing 5120 samples acquired at a 500 Hz sampling rate. The proposed strategy was applied to a single batch of 2000 traces while varying parameters of the compression algorithms.