esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Results and Discussion
### Example 5  
We solve a Hilbert linear problem with the following coefficient matrix:  
\[A_{ij}=\frac{1}{i+j-1},\ i,j=1,\ldots,n. \tag{58}\]  
In a practical application the problem is finding an \((n-1)\)-order polynomial function \(p(x)=a_{0}+a_{1}x+\ldots+a_{n-1}x^{n-1}\) to best match a continuous function \(f(x)\) in the interval of \(x\in[0,1]\):  
\[\min_{\deg(p)\leq n-1}\int_{0}^{1}|f(x)-p(x)|dx,\]  
which leads to a problem governed by Equation (1). **A** is the \(n\times n\) Hilbert matrix defined by Equation (58), **x** is composed of the \(n\) coefficients \(a_{0},a_{1},\ldots,a_{n-1}\) appearing in \(p(x)\), and  
\[\textbf{b}=\left[\begin{array}{c}\int_{0}^{1}f(x)dx\\ \int_{0}^{1}xf(x)dx\\ \vdots\\ \int_{0}^{1}x^{n-1}f(x)dx\end{array}\right]\]  
is uniquely determined by the function \(f(x)\).  
The Hilbert matrix is notoriously ill-conditioned, which can be seen from Table 18. The condition number of Hilbert matrix grows as \(e^{3.5n}\).  
Because the Hilbert matrix is seriously ill-conditioned, a loose convergence criterion with \(\varepsilon=10^{-3}\) is considered. We suppose that \(x_{j}=1,\ j=1,\ldots,n\) are the solutions. We first apply the Gauss-Seidel method to solve this problem. Table 19 lists NI, the maximum error (ME) = \(\max_{j=1,\ldots,n}|x_{j}-1|\), and the root-mean-square-error (RMSE) = \(\sqrt{\sum_{j=1}^{n}(x_{j}-1)^{2}/n}\) obtained by the Gauss-Seidel method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\(n\)** & **cond(A)** & **\(n\)** & **cond(A)** \\ \hline
3 & \(5.24\times 10^{2}\) & 7 & \(4.57\times 10^{8}\) \\
4 & \(1.55\times 10^{4}\) & 8 & \(1.53\times 10^{10}\) \\
5 & \(4.77\times 10^{5}\) & 9 & \(4.93\times 10^{11}\) \\
6 & \(1.50\times 10^{7}\) & 10 & \(1.60\times 10^{13}\) \\ \hline \hline \end{tabular}
\end{table}
Table 18: The condition numbers of Hilbert matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **HSS** & **MHSS** & **SBTS** & **GSOR** & **A1-AOR** \\ \hline NI & 65 & 54 & 31 & 22 & 106 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Example 4, NI obtained by different methods.  
Table 20 lists NI, ME and RMSE obtained by the SOR with \(w\) determined by Algorithm 3 at the first step. It is apparent that the SOR is superior than the Gauss-Seidel method.
## 4 Implementation and Benchmarking of L4S in FreeBSD
### Step 4: Kernel Integration of L4S-Based AQM  
To integrate our L4S AQM as a separate AQM scheduler within the ipfw module, we needed to propagate the changes throughout the FreeBSD system. This involved modifying the administrative utilities of the kernel to accommodate the new L4S algorithm and its distinct parameters. The process required a complete rebuild of the system using buildworld. Here is a succinct description of the steps we have undertaken:  
**Modification of the ipfw Module**  
* Implemented the L4S AQM scheduler within the ipfw module source code.
* Ensured that all necessary parameters and functionalities specific to the L4S algorithm were defined.  
**Propagation of Changes**
* Updated the relevant system files to reflect the addition of the new AQM scheduler.
* Made necessary adjustments to ensure compatibility and proper integration within the FreeBSD kernel.  
**Rebuilding the System**
* Executed the buildworld process to rebuild the FreeBSD world, incorporating the new AQM scheduler.
* This step recompiled all userland programs and utilities, ensuring they recognized and could utilize the new AQM parameters.
* Followed the buildkernel process to compile the updated kernel with the newly integrated L4S AQM.
* Installed the rebuilt kernel and rebooted the system to apply the changes.  
With these steps, we successfully integrated our L4S-based AQM into the FreeBSD kernel, ensuring that all administrative utilities were updated to accept and manage the new algorithm parameters.
## 4 Experiments
### Experiments on the Performance of Descriptors
#### 4.2.3 Comparison between Different Feature Vectors for Covariance Descriptor
Finally, Figure 6 shows that the performance of the covariance matrix descriptor formed by \(F_{7}\) drops slightly as noise increases from 0.1 mr to 0.5 mr, confirming that the feature vector used in this paper has good noise robustness and rotation and translation invariance.
###### Abstract
Using model testing and finite element back-analysis of saturated sand, some researchers have explored the disturbance induced by pile construction. By applying high-frequency imaging recording and digital image correlation (DIC) technology, they successfully evaluated soil displacement near the penetrating pile [22], and using field tests as an example, verified the pile construction on the excess pore pressure and deformation of the existing tunnel around and away from the pile through the superposition principle [23]. The acceleration generated by pile driving is a high-frequency vibration, and the peak ground acceleration caused by pile driving attenuation is unrelated to pile depth but falls fast with increasing distance from the pile [9]. The soil behind the piles would move away from the excavation pit if the earth pressure caused by the construction of the piles was not correctly regulated and was over its initial earth pressure [21]. The ground vibration and excess pore pressure caused by vibratory pile driving are smaller than those caused by impact pile driving. Low-frequency driving creates more vibration than high-frequency driving, but the response of ground motion is determined by the frequency, type, and interactions of produced stress waves [24]. In addition, some numerical models based on meshless methods (EFG, RKPM, and so on) have been developed for the vibration analyses of pile foundation [25]. However, during the construction of driven pile and PGP piles, both soil pressure and excess pore water pressure increase, which lead to a potential compression and harm to nearby structures. And the vibration and noise negatively impact workers and the public. For PGP piles, the cement slurry injection will reduce the disturbance. But the risk persists in sensitive areas like the subway and pipelines surrounding the construction site. The relevant research about PGP pile disturbance in deep soft soil areas has not been reported at present. For the coastal geological conditions, the construction disturbance and environmental impact of PGP piles and driven piles are qualitatively and quantitatively studied and compared in this paper. The disturbance effect evaluation factors, including earth pressure,lateral displacement, excess pore water pressure with depth and radial distance, and the vibration and noise responses of the surrounding soil, are tested during the construction of different piles. Based on these test results, the construction disturbance effects of the two types of pile are compared and analyzed, which provides an assessment of the scope of application of PGP piles in the project.
## 4 Simulation Results and Discussion
### MSE Performances: JISB_DSICED3_versus SB_DSICED3_W/OF and the Best Performance Scheme  
In this subsection, we also introduce, as a benchmark to characterise the optimality of the semi-blind scheme JISB_DSICED3 in terms of the MSE and for the performance comparison, a particular scheme called the Best Performance Scheme (BPS) corresponding to a lower bound (but not realistic in practice) using the proposed JISB_DSICED3 considering that all \(E^{\prime}=(1+\beta)E\) are known. Therefore, in this limit case, all the intended \(E\) symbols are also known and not only the pilots symbols for both channels' estimation and feedback. The MSE of the SI channel and the intended channel are respectively given by [26]:  
\[\text{MSE}_{SI}= |\textbf{h}_{AA}-\hat{\textbf{h}}_{AA}^{(i)}\ |^{2}, \tag{4}\] \[\text{MSE}_{BA}= |\textbf{h}_{BA}-\hat{\textbf{h}}_{BA}^{(i)}\ |^{2}. \tag{5}\]  
Figures 5 and 6 compare the MSEs of two schemes, such as JISB_DSICED3 and SB_DSICED3_W/OF, with the best performance scheme versus the SNR, \(p_{B}/\sigma_{w}^{2}\), for the SI channel and the intended channel, respectively. First of all, the results showed that the feedback scheme JISB_DSICED3 had a better performance when compared with the conventional scheme SB_DSICED3_W/OF, especially at a high SNR (\(SNR>5\) dB). Furthermore, the semi-blind feedback scheme JISB_DSICED3 showed a slightly better result at a low SNR (\(SNR<5\) dB), compared to the conventional scheme SB_DSICED3_W/OF due to the improvement after the feedback loops. The results also showed that the semi-blind scheme JISB_DSICED3 was nearly optimal as its performance was rather closed to  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Codeword length (\(N\)) & 64, 128, 256, 512, 1024 \\ Information length (\(K\)) & 32, 64, 128, 256, 512 \\ Code rate (\(R\)) & 1/3, 1/2, 2/3, 3/4, 5/6 \\ Number of transmission frames & 1,000,000 \\ Modulation scheme & QPSK (\(M=4\)) \\ SI channel taps & 3 \\ Intended channel taps & 4 \\ Forget factor \(\lambda\) & 0.999 \\ Index of joint iterations in JISB_DSICED3 scheme & \(i_{max}=4,j_{max}=1\) \\ Index of iteration of SPA decoding in SB_DSICED3_W/OF & \(j_{max}=20\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation specifications.  
that of the best performance scheme, i.e., assuming that the intended symbols are known at the receiver.
# Development on Solid Polymer Electrolytes for Electrochemical Devices
## 3 Basic Solid Polymer Electrolytes
\begin{table}
\begin{tabular}{c c c c} \hline
**Polymer** & **Functional Group(s)** & **Glass Transition** & **Reference** \\  & & **Temperature, \(T_{g}\) (\({}^{\circ}\)C)** & **Reference** \\ \hline Poly(ethylene oxide) (PEO) & Ether & \(-64\) & [19] \\ Polyacrylonitrile (PAN) & Nitrile & 125 & [19,20] \\ Poly(vinyl alcohol) (PVA) & Hydroxyl & 82 & [21] \\ Cellulose & Hydroxyl, Ether & 220 & [22] \\ Poly(methyl methacrylate) (PMMA) & Ester, Carbonyl & 105 & [19] \\ Polyethyl methacrylate (PEMA) & Ester, Carbonyl & 63 & [23] \\ Polyvinylidene fluoride (PVDF) & Diffluoromethylene & \(-40\) & [19] \\ Chitosan & Hydroxyl, Amine, Ether & 203 & [24] \\ Poly(vinylidene fluoride-hexafluoropropylene) & Dilfluoromethylene & \(-90\) & [19] \\ (PVDF-HFP) & Hydroxyl, Ether & 70 & [25] \\ \hline \end{tabular}
\end{table} 
Table 1: Typical polymers used in polymer electrolytes, along with their characteristics. \begin{table}
\begin{tabular}{c c c c c} \hline
**Salt** & **Cationic Radii** & **Anionic Radii** & **Lattice Energy** \\  & **(pm)** & **(pm)** & **(pm)** & **(kJ mol\({}^{-1}\))** \\ \hline Lithium bis(trifluoromenthanesulfonyl)imide ((LiN(CF\({}_{3}\)SO\({}_{2}\))\({}_{2}\)) or & & & \\ (LiTFSI) & 60 & 328 & 564 \\ Lithium trifluoromethane/sulfonate or lithium triflate (LiCF\({}_{3}\)SO\({}_{3}\)) & 60 & 268 & 730 \\ Lithium perchlorate (LiClO4) & 60 & 234 & 718 \\ Lithium tetrafluoroborate (LiBF4) & 60 & 227 & 735 \\ Lithium hexafluorophosphate (LiPF6) & 60 & 254 & 681 \\ Sodium iodide (NaI) & 102 & 216 & 674 \\ Sodium thiocyanate (NaSCN) & 102 & 213 & 680 \\ Ammonium iodide (NH4I) & 143 & 216 & 605 \\ Lithium iodide (LiI) & 60 & 216 & 762 \\ \hline \end{tabular}
\end{table} 
Table 2: Some popular salts used in polymer electrolytes, along with their properties [18; 26; 27].
# Performance of Different SLAM Algorithms for Indoor and Outdoor Mapping Applications
## 2 Materials and Methods  
In order to create a map with a moving LIDAR sensor, it is necessary to know the location of the LIDAR. However, a map is needed to know the LIDAR location, which creates a dilemma in robotics. SLAM solves this problem by simultaneously performing localization and creation of an unknown environment map [18]. With a mobile LIDAR sensor, both the mapping process and the accuracy of the map are contingent on the solution of the SLAM problem. In this study, open-source LOAM, A-LOAM, and HDL Graph SLAM algorithms were used to create indoor and outdoor environments maps.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 4 Discussion  
Both indoors and outdoors localization systems face numerous challenges. In this section, some of these challenges and future directions are discussed.
## 3 Results
### Decision Procedure Design
#### 3.2.1 Training Dataset  
The dataset used simulates a highly utilized industrial network corresponding to an electrical substation network [43] with control and management applications such as SCADA and Distribution Management System. In order to make the solution reasonably coherent, lower-layer industrial protocols such as GOOSE were not considered. The traffic was generated by a custom developed application [9], which in turn generated the target decisions (ALLOW, BLOCK, INSPECTION, FORWARD, QOS EF, QOS AF13, and QOS AF41). Data traffic was generated for the following scenarios: normal traffic (random TCP or UDP ports and action: ALLOW), DoS attack (number of packets per second for a single flow distinctly high and action: BLOCK), HTTP traffic (TCP destination port 80 and action: application layer INSPECTION), HTTPS traffic (TCP destination port 443 and action: FORWARD to selected ports) and three types of QoS (critical priority for TCP destination port 5060; high priority for TCP destination port 37; and low priority for source or destination ports 20, 21, 69 and 115). IP addresses and number of packets per second were randomly generated from defined intervals. The traffic map generated for neural network training consisted of 80,000 unique data patterns.  
The dataset was subdivided into a training set (70%), validation set (15%) and testing set (15%). The training set was used for neural network parameter adaptation during the training process, the validation set was used to identify the best network configuration during training and the testing set was used for final AI module evaluation.  
Note that inputs in the dataset were transformed using the depiction procedure described in Figure 5 and using the medium image shown in Figure 14.  
Figure 14: Optimized medium image.
# Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation
## 5 Experimental Setup and Evaluation
### Results and Analysis
#### 5.3.2 Runtime Performance Analysis
Figure 14 shows the runtime comparisons between our GPU-optimized technique in two operational modes and the default PyTorch's data-loading pipeline. The \(x\)-axis values indicate batch indices and the \(y\)-axis values show the time duration measured in seconds (s). The evaluation results are plotted on a logarithmic scale to help with visualization, but the actual value differences are much further apart. In this evaluation, we utilized one of the datasets from our earlier experiments, the dataset contains 500 features. We set the batch size to 500 and the sequence length to 1000 to be consistent with the other experiments. We took the average runtime for each batch of the data to be available in the GPU memory, ready to go through the training iteration. In all of the evaluated systems, the time duration to prepare and load each batch of data is consistent across all batches of data because of the static batch size and sequence length. However, only the \"Optimized in memory\" variant of our GPU-optimized sliding window approach has a distinct runtime difference between the first batch and the subsequent batches. This mode assumes that the raw data fit entirely within the available GPU memory. The entire dataset was loaded from the CPU into the GPU memory before the batch formation process. Since all of the batches were created when the raw data were already in the GPU memory, the largest runtime is in the data loading when creating the first batch. For the subsequent batches, the tensor data were already in the GPU memory resulting in significantly lower batching time. Figure 13: Runtime—increasing sequence length. (**a**) Runtime per batch; (**b**) runtime reduction percentage.
# Experimental Analysis of Pore and Permeability Characteristics of Coal by Low-Field NMR
## 4 Conclusions  
The pore structure and permeability of the different coal rank samples were analyzed by NMR, SEM, and MIP experiments. The conclusions are as follows.  
NMR can be used to evaluate porosity, the movable fluid porosity, and the fluid migration characteristics of coal reservoir. According to the equivalent pore size, the pores and fissures in coal reservoir were divided into adsorption-diffusion pores, micro-fissures, and fissures.  
The NMR results showed that the pore characteristics of the different ranks of coal are obvious difference by NMR, which indicated that the heterogeneity of the pore development of the coal reservoir was strong. The pores and fissures in the CZ coal samples were very well developed, and the micro-fissures were not developed. The pores, micro-fissures, and fissures in the WY coal samples were all more developed, but the total porosity was low, and the connectivity of micro-fissures was poor. The pores, micro-fissures, and fissures in the YJL coal samples were very well developed, the micro-fissures were particularly developed compared with other coal samples, and their connectivity was the best.  
An improved conversion method between the \(T_{2}\) and pore size was proposed based on the pore shape in the coal. The empirical equation between the movable water porosity and the porosity of nanopores, micro-fissures, and fissures was proposed, which can quickly and conveniently evaluate the migration characteristics of the reservoir fluid.  
The permeability of the coal reservoir is affected by many factors, such as porosity, pore structure, and connectivity [39]. The structure and connectivity heterogeneity of pores and fissures in the coal reservoir are significant, so it is impossible to establish a widely used relation expression between NMR porosity and gas permeability, which was confirmed by the results of multiple permeability calculation models.  
Conceptualization, X.J. and D.S.; Funding Acquisition, D.S. and Y.L.; Methodology, X.J.; Investigation, H.Z. and K.H.; and Validation, D.S.  
This research was funded by the National Science Foundation of China (41172141, 41602175). Coal seam gas Joint Foundation Shanxi (2016012001), and Henan Province basic and frontier technology research project (152300410099).  
The authors declare no conflicts of interest.
## Abbreviations  
\begin{tabular}{l l} IATA & International Air Transport Association \\ SAFs & Sustainable aviation fuels \\ FT-SPK & Fischer-Tropsch hydroprocessed synthesized paraffinic kerosine \\ ATAG & Air Transport Action Group \\ CAGR & Compound annual growth rate \\ HEFA SPK & Synthesized paraffinic kerosene hydroprocessed esters and fatty acids \\ SIP & Synthesized iso-paraffins \\ ATI-SPK & Alcohol-to-jet synthetic paraffinic kerosene \\ HC-HEFAs & Hydroprocessed hydrocarbons, esters, and fatty acids \\ APUs & Auxiliary power units \\ LCA & Life cycle analysis \\ RF & Radiative forcing \\ ERF & Effective radiative forcing \\ EI-CO\({}_{2}\) & CO\({}_{2}\) emission indices \\ EI-NO\({}_{x}\) & NO\({}_{x}\) emission indices \\ EI-CO & CO emission indices \\ EI-SO\({}_{2}\) & SO\({}_{2}\) emission indices \\ PM/EI-PM & Particulate matter/PM emission indices \\ GHGs/EI-GHGs & Greenhouse gases/GHGs emission index \\ UHC/EI-UHC & Unburnt hydrocarbons/UHC emission indices \\ WTWa & Well-to-wake \\ HRJ & Hydroprocessed renewable jet \\ NG & Natural gas \\ FER & Fossil energy ratio \\ HDCJ & Hydroprocessed depolymerized cellulosic jet \\ DSHC & Direct sugar to hydrocarbons \\ O\({}_{3}\) & Ozone \\ CH\({}_{4}\) & Methane \\ SWV & Stratospheric water vapor \\ RCP & Representative concentration pathways \\ MBJ & Medium-chain fatty acids to biolet \\ TTT & Turbine-inlet temperatures \\ CLD & Chemiluminescence detector \\ CDC & Colorless distributed combustion \\ SMD & Sauter mean diameter \\ FAME & Fatty acid methyl ester \\ AAFX & Alternative Aviation Fuel Experiment \\ BC & Black carbon \\ EGT & Exhaust gas temperature \\ FAEE & Fatty acids ethyl esters \\ LTO & Landing-take-off \\ BD & Biodiesel \\ nvPM & Non-volatile particulate matter \\ vPM & Volatile particulate matter \\ PM\({}_{2.5}\) & Particulate matter with a dry diameter less than 2.5 \(\upmu\)m \\ aCAE & Aerosol cloud albedo effect \\ FSC & Fuel sulfur content \\ ULSJ & Ultra-low sulfur jet fuel \\ RE & Radiative effect \\ GMD & Geometric mean diameter \\ GSD & Geometric standard deviation \\ VOCs & Volatile organic compounds \\ PAHs & Polycyclic aromatic hydrocarbons \\ MES & Main engine start \\ ECS & Environmental control system \\ \end{tabular}
# Tracked Wall-Climbing Robot for Calibration of Large Vertical Metal Tanks
## 3 Prototype and Field Tests
### External Measurement Test  
The working principle of the external ORLM was introduced in Section 1. As is shown in Figure 15, an external floating roof tank with a nominal volume of 50,000 m\({}^{3}\), located in Zhoushan City, China,  
Figure 14: Internal measurement progress with (a) trolley and (b) TWCR.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Ring Number**} & **Radial Deviation with** & **Radial Deviation with** & \multirow{2}{*}{**Difference (mm)**} \\  & **Trolley (mm)** & **TWCR (mm)** & \\ \hline
1 & 5.4 & 5.4 & 0.0 \\
2 & 1.3 & 1.3 & 0.0 \\
3 & 0.0 & 0.0 & 0.0 \\
4 & \(-\)2.2 & \(-\)1.1 & \(-\)1.1 \\
5 & \(-\)1.1 & \(-\)0.2 & \(-\)0.9 \\
6 & \(-\)1.5 & 0.3 & \(-\)1.8 \\
7 & 0.0 & 1.1 & \(-\)1.1 \\
8 & 0.2 & 1.6 & \(-\)1.4 \\
9 & 1.0 & 2.7 & \(-\)1.7 \\
10 & 2.0 & 3.8 & \(-\)1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Radial deviation with trolley and tracked wall-climbing robot (TWCR) (internally).  
was selected for the external experiment. On-site environmental conditions were wind speed 18 km/h, temperature 30 \({}^{\circ}\)C, and relative humidity 65%.  
The reference circumference was 188,762 mm, and there are 62 horizontal stations and 18 vertical stations. The offsets measured by the two methods are presented in Table 6. The radial deviation in the table is the average of the deviations of the 62 horizontal stations in the same ring (each ring has two vertical stations).  
It is noteworthy that the measurement performed by the manually-lifted trolley took 4 min per horizontal station, and the one performed by TWCR took 3 min per horizontal station.