esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Analysis for the Designed ISTN Protocol Stack
### Simulation Platform Setup  
The designed ISTN protocol stack should be evaluated in a simulation platform to ensure its performance. Currently, the majority of those simulations are often demonstrated in OMNeT++ [18], which serves as a global simulated platform that runs on C++ and Python. However, the OMNeT++ programming codes must be designed in the C programming language for the satellite hardware. Instead of OMNeT++, the simulated software is designed and operated in INTEL VxWorks, which is utilized as the on-satellite real-time embedded operating system. The INTEL VxWorks Simulator is a simulated hardware target for use as a prototyping and test-bed environment for VxWorks. The VxWorks Simulator allows users to develop, run, and test VxWorks applications on the host systems (i.e., personal computers), reducing the need for target hardware during development. For external applications needing to interact with a VxWorks target, the capabilities of a VxWorks Simulator instance are identical to those of a VxWorks system running on real-world target hardware. It is worth noting that the versions of the VxWorks Simulator's workbench and image kernel are 3.3 and 6.9 respectively, while the real-world target hardware is a 64-bit MIPS-based microprocessor with VxWorks version 6.8.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Monitoring and Control through Smart Metering
#### 4.2.2 Nobel  
In the \"NOBEL\" project, in which participants from four countries took place, the usage of smart meters has been examined for grid monitoring purposes. In particular, it has been examined the case where the smart meters communicate with each other over low power radio, thus forming a wireless sensor network, whereas wireless standards have also been implemented [79].
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 4 Multiple MPTCP Connections
#### Learning for Continuous Actions
* Convergence of the dual-averaging (DA) approach is challenging, since the output will always be an extreme point. Therefore, we suggest to soften the approach by using a regularized mapping:  
\[y_{n}\gets arg\ max_{\theta_{i}}\{(y_{i},\theta_{i})-h_{i}(\theta_{i})\}, \tag{34}\]  
where the regularizing function \(h:\mathcal{C}\rightarrow\mathbb{R}\) is a penalty function on a compact convex subset \(\mathcal{C}\) of a finite-dimensional norm if: (i) \(h\) is strictly convex; (ii) the limit of \(h(\theta)\) as \(\theta\) approaches \(B\) equals \(h(B)\) (i.e., \(h\) is continuous). Observe that these conditions are also comparable to the MPTCP conditions reported in [23]. Furthermore, the mapping induced by \(h\) can be interpreted as [30]:  
\[\theta_{i,n,m} = V(\lambda_{i,n,m})\approx V(W_{i,n,m}), \tag{28}\]0  
In what follows, we assume that each MPTCP subflow has its own individual \(h_{i}\) value and focus on the interplay between dual and primal parameters (i.e., the outputs \(\theta_{i}\) and the actions \(W_{i}\), respectively). More precisely, from an MPTCP source perspective, it can be viewed as an aggregate penalty function \(h(\theta)=\sum_{i}h_{i}(\theta_{i})\). As shown in Algorithm 1, the learning process can be observed as a multi-agent version of gradient descent with (lazy) projections. The mapping '\(V_{n}\circ\theta_{i}\)' is most often defined as smooth and the regularizedbest response in a game-theoretical context; see [30; 31] for detailed discussions ([PERSON] et al. [27] referred to \(V_{n}\) as a 'quantal response function' in finite-player games). ```
1:procedureInitialize
2:Step size (\(\gamma_{m}\)), initial windows (\(W_{i}\))
3:endprocedure
4:procedureUpdate
5:while\(n\leq N\)do
6:for each MPTCP subflow \(i\)do
7: Choose action: use Equation (17)
8: Estimate gradient: use Equation (19)
9: Take gradient step: use Equation (18)
10:endfor
11:endwhile
12:endprocedure
```  
**Algorithm 1** Abstract View of the Dual-Averaging Algorithm  
One main advantage of dual averaging in concave games is that this leads to _no regret_ as long as the steps of Algorithm 1 are correctly picked [32]. While certain, within DA, the average payoff of each subflow corresponds to the most effective reverse case (note that this ignores changes in the behavior of other subflows owing to an alteration in the action selected by one subflow). With relevant insights from these observations and our ongoing foundational works [33; 34], we plan to improve on this worst-case guarantee and obtain appropriate convergence results for the actual play series triggered by DA in future work.
# Applications of Mueller Matrix Polarimetry to Biological and Agricultural Diagnostics: A Review
## 2 Principles of Mueller Matrix Analysis
### Transmission Mueller Matrix Spectropolarimetry
Note that, in the study described above, spectropilipsometry means the same as spectropolarimetry, since the Mueller matrix spectra were measured for transmission, not reflection, as is customary when the term spectropilipsometry is used. The cited studies show that the elements of the MM have specific spectral dependences, which suggests that MM spectropolarimetry provides significantly more information about the investigated substance than conventional spectrophotometry without polarization measurements or MMP at one wavelength.
# A Survey on Memory Subsystems for Deep Neural Network Accelerators
## 9 Summary of the Paper
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **Throughput** & **Power Consumption** & **Footprint** & **Technology Node** & **Energy Efficiency** & **Specific Application** \\ \hline Neurocube [4] & 132.4 GOPS/s & 21.5 W & 1.077 & 15 nm & 38.82 GOPS/s/W & Image classification \\  & & & mm\({}^{2}\)/single core & & Image and voice recognition \\ TETRIS [5] & N/A & 6.94 W & 3.5 mm\({}^{2}\)/single core & 45 nm & N/A & Internet-of-Things \\  & & & & (IoT) \\ [PERSON] et al. [6] & N/A & N/A & N/A & 28 nm & N/A & Image classification \\ [PERSON] et al. [7] & N/A & N/A & N/A & 22 nm & N/A & Image and speech recognition \\  & & & & & Image classification \\ RAPIDNN [8] & 1904.6 & 3.88 mm\({}^{2}\)/single & 45 nm & 839.1 GOPS/s/W & and \\  & GOPS/s/mm\({}^{2}\) & & Tile & & Internet-of-Things \\  & & & & (IoT) \\ DRIS-3 [9] & N/A & N/A & N/A & N/A & Image classification \\ DeepTrain [11] & 1.9 TOPS/s & 4.9 W & 1.2 mm\({}^{2}\)/single & 15 nm & N/A & Image classification \\  & & & core & & \\ QUEST [12] & 1.96 TOPS/s & 2.083 W & 121.55 & 40 nm & 0.877 TOPS/s/W & Image classification \\ AtomLayer [21] & N/A & 4.8 W & 53.2 mm\({}^{2}\) & 28 nm & 682.5 GOPS/s/W & Image classification \\  & 1.229 & N/A & 0.0348 & 45 nm & N/A & Image classification \\ PRIME [22] & TOPS/s/mm\({}^{2}\) & & mm\({}^{2}\)/single core & & \\ GraphSAR [23] & N/A & N/A & N/A & N/A & Social network \\ Sparse NN [24] & N/A & N/A & N/A & 65 nm & N/A & Image recognition \\ FPSR [25] & 38.004 & N/A & 0.022 & 45 nm & N/A & Image classification \\  & TOPS/s/mm\({}^{2}\) & & mm\({}^{2}\)/single core & & \\ Pipelayer [26] & 1485 & N/A & 82.63 mm\({}^{2}\) & 28 nm & 142.9 GOPS/s/W & and games \\  & & & & & Image, video, and \\
3D-ReG [27] & N/A & 41.2 W & 243.55 mm\({}^{2}\) & 25 nm & N/A & sensor data \\  & & & & processing \\ RED [28] & N/A & N/A & N/A & 65 nm & N/A & Image processing \\ GraphR [29] & N/A & N/A & N/A & 32 nm & N/A & Graph analyzing \\ DeepNVM [20] & N/A & N/A & N/A & 16 nm & N/A & Data processing \\  & 302.3 to 2392.4 & 62.60 W & 30.64 mm\({}^{2}\) & 28 nm & 81.84 to 695.1 \\  & GOPS/s/mm\({}^{2}\) & & GPOS/s/W & Image classification \\ ParaPIM [14] & N/A & N/A & N/A & 45 nm & N/A & Classification tasks \\ CMP-PIM [15] & N/A & N/A & 1.7 mm\({}^{2}\) & 45 nm & N/A & Image recognition \\ [PERSON] et al. [17] & N/A & N/A & N/A & 25 nm & N/A & Classification tasks \\ Bit-Precision [18] & N/A & N/A & 3.408 mm\({}^{2}\) & 65 nm & 2.06 to 117.3 \\  & & & & TOPS/s/W & \multicolumn{2}{c}{processing} \\  & & & & & Internet-of-Things \\ NDPX [19] & N/A & N/A & 9.02 mm\({}^{2}\) & 12 nm & N/A & (IoT) and image classification \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ranking of the reviewed architectures in this work.
# A 6 DOF Virtual Environment Space Docking Operation with Human Supervision
## 2 Real and Virtual Setup Description
### Simplified Model  
The variation in the reflected mass for a single LEMA has been generated from real-life simulators and the data analyzed thoroughly in collaboration with QINETIC Space Kruibbeke, Belgium (a confidentiality agreement is in place). To illustrate the high LPV degree in this berthing and docking mechanism system, we introduce a linear model approximation for the extreme cases. Since the leg positions must be controlled, these models capturing the leg dynamics can be approximated in the form indicated bellow:  
\[P(s)=\frac{K(s+z_{0})(s+z_{1})(s+z_{1}^{*})}{(s+p_{0})(s+p_{1})(s+p_{2})(s+p_{ 2}^{*})} \tag{4}\]  
where \(K\) is the gain, \(z_{0}\) is a real zero, \(z_{1}^{*}\) denotes the complex comprised of \(z_{1}\), \(p_{0},p_{1}\) real poles and \(p_{2}^{*}\) denotes the complex comprised of \(p_{2}\). This form of the transfer function has LPV parameter dynamics; i.e., changes in the \(z_{1}\) and \(p_{1}\) values. In order to illustrate the variability of the system, two extreme cases have been considered. The assumed linear model for case 1 is  
\[P_{1}(s)=2500\frac{(s+625)(s+(1.95+13.4i))(s+(1.95-13.4i))}{(s+53)(s+0.1)(s+(29 4+334i))(s+(294-334i))} \tag{5}\]  
The other extreme (case 2) of LPV dynamics is expressed by the system transfer function:  
\[P_{2}(s)=2500\frac{(s+628)(s+(5\cdot 10^{-4}+0.2i))(s+(5\cdot 10^{-4}-0.2i))}{(s+53) (s+3\cdot 10^{-5})(s+(292+322i))(s+(292-322i))} \tag{6}\]  
In this form of the models, the variations in pole/zero locations are clearly visible.
## 4 Materials, Exergy Storage Tool, and Methodology
### Exergy Storage Assessment--Methodology
Thus, they potentially provide the more accurate and greater storage estimates when compared to using only the planned or permitted 24 and 14 cavern numbers for the lesser exploited EISB [45] and Wessex [46] basins, respectively. The latter two basins currently represent higher-risk target storage horizons, where in the case of the EIS, remoteness and its offshore location also increase CAPEX and OPEX costs of storage projects [45]. Additional efforts to derive realistic exergy storage estimates were also undertaken through filtering the storage outputs based upon cavern heights, with two sets of caverns assessed based on the experience of the maximum heights of gas storage caverns in the same salt beds, or those of proposed storage caverns in the Wessex Basin and the EIS. Firstly, caverns of 100 m and greater were selected, arising from the general sizes of UGS caverns developed or proposed in the same halite beds (refer S2, Table S1). Caverns smaller than \(\approx\)90 m in height are less economic to operate for gas storage purposes and are likely even more so for CAES due to the lower volumetric energy density (\(\approx\)2.4 kWh/m\({}^{3}\)) of air in comparison to natural gas (1100 kWh/m\({}^{3}\)) [23]. Importantly, those caverns in which diameters are much larger than cavern heights of a few tens of metres could be geomechanically less stable, with caverns thus requiring smaller diameters and thereby likely to also result in uneconomically small cavern volumes [47]. Secondly, very large (tall) caverns carry stability issues and operational limits for rapid cycle storage, and therefore, cavern heights were limited to 100-150 m. This is in part based upon the nature of halite beds in the Wessex Basin, where geophysical logs reveal that the insoluble content can comprise significant percentages of the Dorset Halite Member (DHM) [37] and are likely to significantly impact cavern volumes, stability, and location. This will likely limit areas of development to those with suitably clean halite for cavern construction. However, salt exploration boreholes for the construction of 14 gas storage caverns have proved a saliferous sequence 470 m thick in areas of the basin, with the main halite unit (referred to as 'S7') up to 140 m thick with a low insoluble content of \(\approx\)16.5% and in which it was assessed that caverns of 100 m in height could be constructed for the purposes of gas storage [46]. Elsewhere, the Winterborne Kingston Borehole in the NE of the basin proved halite beds to be 190 m thick [37; 48]. Thus, constraining cavern heights to between 100 and 150 m was thought to be realistic for the UK in general and the Wessex Basin in particular. As previously, a percentage of potentially available caverns and volumes, based upon filtering for 1% and the percentage of UGS caverns relative to the UGS cavern numbers, was also extracted for the two cavern height ranges.
# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 2 Methods
### Vector Tracking Loops
#### 2.3.1 Evolution of Vector Tracking Technique
Co-OP tracking combines a low-bandwidth scalar tracking loop with feedback from a navigation processor and uses a carrier phase discriminator (rather than a carrier frequency discriminator) to calibrate the navigation solution. Subsequently, the vector carrier phase-locked loop (VPLL) was further developed by [PERSON], [PERSON], and others [33; 39; 104]. The evolution of VT technology has not only improved GNSS receiver performance in various environments but also laid a solid foundation for future high-precision positioning applications. As research continues, VT  
Figure 6: Tight and deep combination methods for vector tracking. technology is expected to play an increasingly important role in future GNSS applications, especially in the area of carrier phase tracking.
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 4 Model Details
### Semantically Guided Multi-Color Space Loss
#### 4.5.1 Selection and Analysis of Color Spaces  
Color images consist of multiple channels, with commonly used color spaces including RGB, LAB, and LCH. The RGB color space has strong physical significance and is suitable for storage and display. However, its three channels (R, G, B) are highly correlated and sensitive to brightness, shadows, and noise, leading to color distortion in extreme low-light conditions. Processing color solely in the RGB space often fails to restore the original colors of objects accurately. In contrast, LCH and LAB color spaces provide color representations more aligned with human vision.  
The LCH color space, comprising hue (H), chroma (C), and lightness (L), reflects human color perception more directly. Since LCH separates color attributes, adjustments become more intuitive and flexible. This separation particularly benefits low-light environments by preserving color clarity and fidelity, ensuring more accurate color analysis and processing. The LAB color space decomposes color into lightness (L), a red-green channel (a), and a yellow-blue channel (b), where the a and b channels are independent of the L channel. This allows separate processing of color and brightness information, enabling more precise color recovery in low-light scenarios. To address the limitations of color processing solely in the RGB space, we introduce a multi-color space loss function that integrates RGB, LAB, and LCH color spaces.
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 5 Discussion and Future Opportunities
### 5G-Based Grid Edge Application  
5G technologies are evolving rapidly and provide great opportunities for the development of edge computing. They allow for low delay, large bandwidth, and high-capacity communication. These advantages can benefit the power industry by efficiently connecting a vast number of smart edge devices. Additionally, 5G helps to establish reliable and accurate real-time control in smart grids. 5G is vital for solving problems in traditional power grids and extending the scale of DERs.  
Although 5G brings opportunities to the traditional power industry, it also raises challenges. One of the most urgent problems to be solved is the rapid growth of data volume. The combination of 5G and edge computing is an ideal approach to balance the computational and data transmission burden. However, this also brings about a series of new challenges, such as data storage and security problems in edge devices. Furthermore, 5G-based edge computing can support faster fault detection, recovery, and real-time operations.
# Interaural Level Difference Optimization of Binaural Ambisonic Rendering
## 3 Objective Evaluation  
The effect of AIO has been evaluated both objectively and perceptually. Theoretically, perfect Ambsonic reproduction would produce binaural Ambsonic rendered HRIRs equivalent to non-Ambisonic rendered HRIRs. Objective evaluation therefore compared binaural Ambsonic rendered HRIRs, both without and with AIO, to a reference dataset of HRIRs. Evaluation metrics used were accuracy of rendered ILD, both over all directions on the sphere and over frequency, and spectral difference.  
The high-resolution reference HRIR dataset used was chosen as the \(2^{\circ}\) Gauss-Legendre quadrature of the Bernschutz Neumann KU 100 HRIR database [40], which features measurements of 89 elevations at 180 different azimuth values in \(2^{\circ}\) increments, totaling 16,020 measurements. The reference dataset is herein referred to as \(\mathbf{H}\). Figure 4 plots the vertices of an \(8^{\circ}\) Gauss-Legendre quadrature with 23 elevations at 45 different azimuth values in \(8^{\circ}\) increments totaling 1035 points (a lower resolution than the \(2^{\circ}\) Gauss-Legendre quadrature used in the evaluation to aid visibility). Shading is based on the solid angle (denoted in this paper as \(\Omega\)) of each point, which is calculated from the area of the sphere for which a single point subtends [42], such that \(\sum\Omega=1\). Figure 4 illustrates the clustering of points at the poles in Gauss-Legendre quadrature, and thus the need for solid-angle weighting in calculations when obtaining a single value over all locations on the sphere in this paper.  
Figure 3: Estimated horizontal plane ILDs of HRIRs and binaural Ambsonic rendering, without and with Ambisonic ILD optimisation (AIO) for Ambisonic orders \(\{M=1,M=2,...,M=5\}\) and \(M=36\)].  
For each of the measurement locations \(\{e_{1},e_{2},...,e_{16,020}\}\) of \(\mathbf{H}\), Ambisonic gains were encoded and decoded binaurally for Ambisonic orders \(\{M=1,M=2,...,M=5\}\) to create corresponding datasets of Ambisonic rendered HRIRs \(\mathbf{\hat{H}}\), both standard binaural Ambisonic rendering (\(\mathbf{\hat{H}}^{std}\)) and binaural Ambisonic rendering with AIO pre-processed HRIRs (\(\mathbf{\hat{H}}^{AIO}\)).
## 2 Literature Review
Privacy preservation in location systems has already been addressed in some works, although in different domains, such as [25], which proposes a location privacy method based on k-anonymity, and [26], which uses blockchain to achieve the desired behavior. Cumulative pressure sensors [27] for large areas have been proposed to present a rough estimate of the number of persons present in a designated area (effectively measuring/counting the occupancy of a room). This technology is only useful for counting the number of occupants in the observed are; it lacks all the other IPS properties. Google and Apple have jointly developed an exposure notification system ([[https://www.google.com/covid19/exposurenotifications/](https://www.google.com/covid19/exposurenotifications/)]([https://www.google.com/covid19/exposurenotifications/](https://www.google.com/covid19/exposurenotifications/)), accessed on 19 December 2021) based on a shared sense of responsibility to help the global community fight the pandemic by keeping track of contact. In the background, users' phones and surrounding phones share randomly generated privacy IDs via Bluetooth. Routinely, the application checks if some of the IDs that the phone has been exposed to have a \"compromised\" ID, the IDs of owners who have anonymously proclaimed to be infected. The exposure notification system does not monitor users' locations; Google, Apple and other users cannot see users' identities; and the data are only available to the public health authorities. This system does not address the same issues as the system proposed in this paper as the proposed system cannot be utilized as a substitute of the Google/Apples solution for the lack of a backward loop (the information of the infection case cannot be linked to the pseudo-anonymous identities used in our system). [PERSON] et al. [28] present a non-intrusive fall detection solution based on a smart floor, which this paper extends to an indoor location system. The system enables a non-intrusive (with no need for special applications based on wearable devices, smartphones or any other devices) indoor location system with additional privacy preserving properties such as anonymity and sensor location/network anonymity. We achieved this by using the smart floor, coupled with onion routing for source location anonymity and blockchain for the final sink personal pseudo-anonymity.
## 2 Materials and Methods
### Constraints  
The constraints ensure that the real restrictions of the production process are adhered to and that a smooth process is guaranteed. Each machine can only process one task at a time, which limits machine availability. The sequence of tasks must be adhered to by precedence conditions so that the production process is not interrupted. Furthermore, no two tasks may be performed simultaneously on the same machine, which is ensured by disjunctivity conditions. In addition, set-up times are taken into account, which record the time required to switch between different task types. The mathematical formulation of the constraints ensures that all tasks are correctly assigned and executed within the planning period.  
Each task must be completed within the total time \(Z\):  
\[s_{jm}+r_{jm}\cdot y_{jm}\leq Z,\quad\forall(j,m)\in T \tag{2}\]  
The earliest possible start time of a task must not be violated:  
\[l_{jm}-(1-y_{jm})\cdot BIG\leq s_{jm},\quad\forall(j,m)\in T \tag{3}\]Each task must be completed before its due time:  
\[s_{jm}+r_{jm}\leq q_{jm},\quad\forall(j,m)\in T \tag{4}\]  
Precedence constraints ensure that a subsequent task starts only after its predecessor is completed:  
\[s_{kn}+r_{kn}\cdot y_{kn}\leq s_{jm}+(1-y_{jm})\cdot BIG,\quad\forall(j,m,k,n)\in O \tag{5}\]  
A task may only be executed on one machine at a given time:  
\[\sum_{m\in M}y_{jm}=1,\quad\forall j\in J \tag{6}\]  
Disjunctive constraints prevent two tasks from being executed simultaneously on the same machine:  
\[s_{jm}+r_{jm}\cdot y_{jm}+w_{jkm}=s_{km},\quad\forall(j,k,m)\in D \tag{7}\]  
The total completion time \(Z\) is a non-negative real number:  
\[Z\geq 0 \tag{8}\]  
The formulations developed form the basis for the subsequent explanation of the optimisation model. The pseudocode, which describes the structured implementation of the model in practice, is presented below.
## 3 Results
### pH Optimization  
The pH is important when working with heavy metal adsorption as it directly affects the amount of ions that can be removed from the aqueous solution. A pH variation can change the surface of the adsorbent, influencing electrostatic interactions with the solution [11]. The experimental results for the pH studies are presented in Figure 6.  
Figure 5: Characterization of burnt pine wood by FTIR-ATR analysis.  
The percentage of adsorption increased with pH until about pH 5.0 for all the materials studied. The best pH to remove nickel ions was around 5.0, where chestnut shell presented the highest adsorption percentage of almost 75% followed by walnut shell. Burnt pine wood showed slightly lower adsorption, while pine wood attained just over 40% adsorption. With the increase of pH after pH 5.0, all the percentages of adsorption decreased significantly.