esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of Computer-Aided Engineering in the Design of Ground-Improvement Technologies
## 3 Outline of Middle-Pressure Jet-Grouting Method  
This method is an innovative technology that uses jets with pressure values less than 20 MPa [18; 19; 20], but the equivalent or higher quality of soil-improved body formation can be achieved compared to the traditionally used high-pressure jet-grouting method. [PERSON] et al. [18] proposed the first middle-pressure jet-grouting method, containing a single-pipe rod with a jet nozzle, as shown in Figure 1, in which the ground is loosened by spraying a horizontal water jet while the rod is penetrating the ground. The rod is then pulled up and the cement slurry starts spraying at middle pressure in a counter-clockwise direction, producing a maximum 1 m diameter columnar soil-improved body. [PERSON] et al. [19] proposed the second method that uses a special mixing blade with an escape prevention plate at the end, attached to the rod for facilitating the mixing of soil and cement slurry mechanically that has been sprayed at middle pressure.  
This ground-improvement method allows for the selection of one or both of the methods introduced by [PERSON] et al. [18; 19]. Reduction in the surrounding soil displacement and promoting the soil discharge will help in the formation of a maximum-diameter column  
Figure 2: Calculation algorithm flowchart for MPS.  
nar soil-improved body [30; 31]. Thus, a sample confirmation test for a full-length columnar soil-improved body was conducted after a full-scale experiment and construction. The quality of construction was then verified by unconfined compression tests and others, but it was not analytically verified. Therefore, the authors attempted to analytically verify it for the middle-pressure jet-grouting method by applying the MPS-CAE method.
# A Binocular Vision-Based 3D Sampling Moire Method for Complex Shape Measurement
## 3 Digital Simulation Experiment and Discussions
### Precision of 3D Shape Measurement  
Firstly, 3D digital image correlation (3D-DIC) was used for comparison with the results of the 3D sampling moir\(\acute{\text{e}}\) to verify its accuracy. For random speckle patterns, the 3D-DIC method can be used to calculate displacement by tracking the location of subsets. Three dimensional DIC has been widely used for 3D measurement with high reliability [28,29]. For a virtual plane, its upper and lower parts are projected onto fringe and speckle patterns, respectively (shown in Figure 5a). The fringe spacing is 10 pixels, and the diameter of a single speckle spot is 6-9 pixels. In the simulation experiment, the plane was translated 1, 2, 3, and 4 mm along the \(z\)-axis. Three-dimensional SM and 3D-DIC were used, respectively, to calculate the plane shape after translation. The calculation results are shown in Figure 5b. Further error analysis (Figure 5c) showed that at different translation positions, the measurement errors of 3D-SM and 3D-DIC were steady and at the same order of magnitude, which proves the reliability of 3D-SM.
# Selective Laser-Melted Alloy 625: Optimization of Stress-Relieving and Aging Treatments
## 3 Results and Discussion
### As-Built and Stress-Relived Conditions
[33] observed significantly higher corrosion resistance in the as-built condition (0.72 mm/year) compared to that of the conventional wrought and soft-annealed material (1.50 mm/year). They attributed this difference to the very fine dendritic structure of \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Plane**} & **As-Built** & **Stress-Relieved (875 \({}^{\circ}\)C 45 min)** \\  & **HV30** & **HV30** \\ \hline XY & 312 & 314 \\ \hline YZ & 306 & 307 \\ \hline ZX & 310 & 311 \\ \hline Average & 309 & 311 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Vickers hardness HV30 of the SLMed alloy 625 in both the as-built and stress-relieved conditions for each plane. The standard deviation values range from 1 to 3 HV30. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**As-Built**} & \multicolumn{4}{c}{**Stress-Relieved (875 \({}^{\circ}\)C 45 min)**} \\ \cline{2-10}  & **YS** & **UTS** & **YS/UTS** & **A\%** & **Z\%** & **YS** & **UTS** & **A\%** & **Z\%** \\ \hline This work & 732 & 1041 & 0.70 & 31.0 & 38 & 742 & 1078 & 0.69 & 28.0 & 30 \\ \hline Minimum ASTM B446 & 414 & 827 & — & 30.0 & — & 414 & 827 & — & 30.0 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Room-temperature mechanical properties of the SLMed alloy 625 in both the as-built and stress-relieved conditions. Tensile specimens were machined along the Y direction of the SLMed bars (longitudinal direction). \(\text{Z}\%\) is the percentage reduction in area. The standard deviation values range from 1 to 2% for A% and Z% and from 2 to 10 MPa for YS and UTS. the selective laser-melted alloy. In the stress-relieved condition, the corrosion rate was equal to 7.07 mm/year. As also reported in the literature [17; 18; 25; 34; 45], such a dramatic loss compared to the excellent behavior of the as-built material is mainly determined by the fast precipitation of carbides and the \(\delta\) phase starting from the interdendritic regions. Consequently, this result confirms that the stress-relieving treatment at the temperature (875 \({}^{\circ}\)C) recommended for the conventional alloy is not feasible in the selective laser-melted material because of the significant loss in corrosion resistance induced by its faster precipitation response.
# No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
## 3 Experimental Results and Analysis
### Comparison to the State of the Art
To further demonstrate the effectiveness of the proposed method, a cross-database test was also carried out, where the examined algorithms were trained on the large KonIQ-10k [4] and tested on the smaller CLIVE [50]. The results of this cross-database test are \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**SPAQ [5]**} & \multicolumn{3}{c}{**TID2013 [51]**} \\ \hline
**Method** & **PLCC** & **SROCC** & **KROCC** & **PLCC** & **SROCC** & **KROCC** \\ \hline BLIINDER [66] & 0.872 & 0.869 & 0.683 & 0.834 & 0.816 & 0.720 \\ DeepRN [67] & 0.870 & 0.850 & 0.676 & 0.745 & 0.636 & 0.560 \\ BLIINDS-II [14] & 0.676 & 0.675 & 0.486 & 0.558 & 0.513 & 0.339 \\ BMPRI [68] & 0.739 & 0.734 & 0.506 & 0.701 & 0.588 & 0.427 \\ BRISQUE [15] & 0.726 & 0.720 & 0.518 & 0.478 & 0.427 & 0.278 \\ CurveletQA [11] & 0.793 & 0.774 & 0.503 & 0.553 & 0.505 & 0.359 \\ DIVINE [9] & 0.774 & 0.756 & 0.514 & 0.692 & 0.599 & 0.431 \\ ENQPA [69] & 0.813 & 0.804 & 0.603 & 0.604 & 0.555 & 0.397 \\ GRAD-LOC-TC [70] & 0.786 & 0.782 & 0.572 & 0.671 & 0.627 & 0.470 \\ MultiGAP- & 0.909 & 0.903 & **0.693** & 0.710 & 0.433 & 0.302 \\ NRIQA [36] & & & & & & \\ MSDET-IQA [10] & 0.900 & 0.894 & 0.692 & 0.727 & 0.448 & 0.311 \\ NBIQA [71] & 0.802 & 0.793 & 0.539 & 0.723 & 0.628 & 0.427 \\ PIQE [72] & 0.211 & 0.156 & 0.091 & 0.464 & 0.365 & 0.257 \\ OG-IQA [73] & 0.726 & 0.724 & 0.594 & 0.564 & 0.452 & 0.321 \\ SSEQ [74] & 0.745 & 0.742 & 0.549 & 0.618 & 0.520 & 0.375 \\ UNIQUE [75] & 0.907 & 0.906 & 0.687 & 0.812 & 0.826 & 0.578 \\ \hline CONTROLQUE [76] & 0.919 & 0.914 & - & 0.857 & 0.843 & - \\ DB-CNN [77] & 0.915 & 0.914 & - & 0.865 & 0.816 & - \\ DeepFL-IQA [78] & - & - & - & **0.876** & **0.858** & - \\ Korcepti2 [79] & - & - & - & - & - & - \\ MLSP [78,80] & - & - & - & - & - & - \\ PaQ-2-PQ [46] & - & - & - & - & - & - \\ PQR [81] & - & - & - & 0.798 & 0.740 & - \\ WSP [43] & - & - & - & - & - & - \\ \hline DF-CNN-IQA & **0.921** & **0.915** & **0.693** & 0.743 & 0.709 & 0.496 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of _DF-CNN-IQA_ to the state of the art on SPAQ [5] and TID2013 [51]. Median PLCC, SROCC and KROCC values were measured over 100 random train–validation–test splits. The best results are typed in **bold**, and the second-best results are underlined. We denoted by ‘\(\cdot\)’ if the data were not available. summarized in Table 8. From the presented results, it can be seen that the proposed method is able to outperform all the other considered algorithms by a large margin. This means that the proposed method possesses a rather strong generalization capability.
## 2 Related Work
\[L_{LOS} =\left\{7.2+7.1\cdot\log\left(\frac{h_{r}h_{r}}{\lambda}\right) \right\}\cdot\log(d)+28.3\cdot\log\left(1+\frac{d}{d_{b}}\right) \tag{6}\] \[-1.2\cdot\log(f)-19.6\cdot\log(W_{s})+65.9\] \[L_{NLOS} =\left\{47.6+6.6\cdot\log\left(\frac{h_{r}h_{r}}{\lambda}\right) \right\}\cdot\log(d)+\left\{89.1-33\cdot\log\left(\frac{d_{1}}{\lambda} \right)\right\}\] (7) \[\cdot\log\left(1+\frac{d}{d_{b}}\right)+19.9\cdot\log(f)-11.3 \cdot\log(W_{1}\cdot W_{2}+2.8\]  
The authors in [8] focus on the development of an adaptive algorithm to determine the condition of LOS between two vehicles, depending on the their position in the scenario's streets. Some characteristics of the transmitted signal may determine if the nodes can directly communicate with each other or not. To this aim, three different cases were described:  
* Vehicles on the same street: For two vehicles on the same street, there is an LOS between them, since no buildings interfere with the signal's path. * Vehicles on different streets: If a couple of vehicles are located on different streets, it is necessary to check if there is an open area allowing communication between them (LOS). This involves identifying whether existing buildings completely interfere with the wireless signals. Success in communication, however, also depends on the distance between nodes and on the attenuation scheme used. * Vehicles near junctions: Although there is no LOS between two vehicles, some electromagnetic phenomena of signals may help to obtain a successful communication. If the vehicles are on different streets, but near the corner where the streets meet, reflection, refraction or diffraction of signals over solid obstacles might sometimes produce such a positive effect. Some empirical results show that only vehicles close enough (\(<20\) m) to junctions are able to communicate with each other under NLOS conditions. The following flowchart (Figure 3) shows the conditions used to determine if a packet is successfully received using the proposed model in [8]. As illustrated, the computation to determine if two vehicles are in the LOS is only done after two discarding steps. These steps are based both on the reception probability of packets and on the transmission range of nodes. This model tries to reduce the number of times that the LOS operations have to be done, since they are computationally expensive.
###### Abstract  
In an RGB-LED-based optical camera communication system, it is an essential goal to have better performance in the data rate and BER. However, in a higher symbol rate, due to the conventional sampling algorithm, the deterioration of transmission performance brought by the inter-symbol interference and inter-channel interference is significant. Innovatively, in this paper, the sub-image obtained by a captured frame of received video is encoded by a channel-attention-Net-based encoder to generate a descriptor without existing sampling methods. Moreover, we propose an LSTM-based equalizer to decode the descriptor and mitigate transmission performance deterioration. Utilizing the long-short-term memory of an LSTM unit, an equalizer not only can reduce bit error rates but also increase the data rate. The experimental results show that at a symbol rate of 46 kbaud/s, a record-high data rate at 44.03 kbit/s is achieved under random data transmission while still meeting the pre-forward error correction requirement.  
Keywords:optical camera communication; rolling shutter effect; visible light communication; machine learning; machine vision +
Footnote †: journal: Electronics
## 3 OFDM RadCom Signal Model  
The transmitted OFDM signal  
\[x_{o}[n]=\sum_{r=0}^{M-1}\sum_{q=0}^{N_{c}-1}a_{q,r}e^{j\frac{2\pi n(n-nN_{o})}{N_{ c}}} \tag{18}\]  
where \(N_{o}=N_{c}+N_{cp}\) is the OFDM symbol length. \(N_{cp}\) denotes the CP length needed to avoid ISIs. The received OFDM signal can be expressed as  
\[y_{o}[n]=x_{o}[n]\ \otimes h[n]+z[n], \tag{19}\]  
where \(z[n]\) denotes additive Gaussian noise with variance \(\sigma_{z}^{2}\).  
Using the propagation channel model as in (5), knowing that \(T_{s}=\frac{1}{N_{c}\Delta f}\), (19) becomes  
\[y_{o}[n]=\sum_{r=0}^{M-1}\sum_{j=0}^{L_{\rm{el}}-1}\sum_{q=0}^{N_ {c}-1}h_{j}a_{q,r}e^{j\frac{2\pi n}{N_{c}}}e^{-j2\pi q\Delta f\tau_{j}}\] \[e^{-\frac{j2\pi np_{j}n}{N_{c}}}e^{-\frac{j2\pi np_{j}n}{N_{c}}}e^ {\frac{j2\pi f_{j}n}{N_{c}\Delta f}}+z[n]\]  
The signal model for each OFDM symbol can be simplified to  
\[y_{o}[n,r]=\sum_{j=0}^{L_{\rm{el}}-1}\sum_{q=0}^{N_{c}-1}h_{j}a_ {q,r}e^{j\frac{2\pi n}{N_{c}}}e^{-j2\pi q\Delta f\tau_{j}}\] \[e^{-\frac{j2\pi np_{j}n}{N_{c}}}e^{-\frac{j2\pi np_{j}n}{N_{c}}} e^{\frac{2\pi f_{j}n}{N_{c}\Delta f}}e^{\frac{j2\pi f_{j}n}{N_{c}\Delta f}}+z[n]\]  
1. Term \(e^{j\frac{2\pi n}{N_{c}}}\) represents OFDM subcarriers and can be modeled as a DFT.
2. Second term \(e^{-j2\pi q\Delta f\tau_{j}}\) represents the delay effect on the OFDM symbol. For the communication part, this effect is compensated for through equalization. In the radar part, this term is used for range estimation.
3. Term \(e^{\frac{j2\pi f_{j}n}{N_{c}\Delta f}}\) describes the Doppler shift that may lead to ICI.
4. Term \(e^{\frac{j2\pi f_{j}n}{N_{c}\Delta f}}\) is the Doppler shift effect on each OFDM symbol. It is used for velocity estimation for radar.
## 4 Conclusions  
The potency of the anti-sagging properties of fly ash was investigated using ilmenite and barite as weighting agents, and the results were compared. However, the experimental results obtained for barite and fly ash concentrations at different temperatures were developed into empirical models using RSM at different wellbore inclinations. The results of the regression equation depicted a high confidence level, as the R\({}^{2}\) tended to unity. The empirical equations were presented in pictorial form using contour and response surface plots. To determine the reliability of the models in predicting the SF of the drilling fluid system as applicable to the oil and well industry, their accuracy was validated by calculating the percentage error to be less than 3% when compared with the experimental results. The following findings were obtained in this research:  
* The fluid sample formulated without fly ash was discovered to exhibit a higher SF and VSST above the acceptable API recommended value of 0.530 and 1 ppg, respectively. The addition of 4 lb/bbl of fly ash in the barite-densified fluid was effective in reducing the SF and VSST by 3.4% and 63.9%, respectively. However, it was slightly less effective in the ilmenite-densified drilling fluid by reducing the SF and VSST by 2.4% and 63.1%, respectively, which was earlier reported in [3]. * The static and dynamic anti-sagging properties of fly ash were more potent in barite-densified drilling fluid than in ilmenite-densified drilling fluid for all temperatures, angles of inclination, and concentrations (3 and 4 lb/bbl) of fly ash investigated. * It was unveiled from the elemental composition, as revealed by the XRF analysis result of ilmenite, that it predominantly contains 54.72% iron (Fe), which may be responsible for its higher density as compared with barite, which was made up of 77.5% barium, a less dense element. The D\({}_{50}\) PSD of ilmenite (22 um) is larger than that of barite (6.5 um); the larger particle size of ilmenite, in addition to its density, may be responsible for faster sagging of ilmenite, and thus less potency of fly ash in ilmenite-densified fluid. * An increase in the quantity of fly ash added to the barite-densified fluid caused a corresponding increase in the critical angle; 0, 3, and 4 lb/bbl of fly ash resulted in critical angles of 62\({}^{\circ}\), 63\({}^{\circ}\), and 65\({}^{\circ}\), respectively. * Optimization of the variables through the 3D surface response plots shows an inverse relationship between the ratio of fly ash and temperature. Increasing the ratio of fly ash and decreasing the temperature improves the SF of the WBF. * The contour plots of 45\({}^{\circ}\), 60\({}^{\circ}\), and 70\({}^{\circ}\) well deviations show that a high quantity of fly ash ratio may be required to mitigate sagging due to the observed fewer interactions between the variables (fly ash and temperature at wellbore angles), which resulted in fewer slope contour lines. However, this further validates the huge sagging challenges encountered at these well inclinations.
### Applications of the K-Identification Problem for IoT  
The use of PSC for MC systems, whose objective is based on _recognition_ of specific event, is studied in [20; 48]. In the vision of IoT, the identities of the _things_ are often required to be verified for each other. This identification task is needed in order to make sure that the _things_ can address and reliably communicate with themselves. Consequently, the identification capacity [49] is the primary relevant quantitative metric in such systems, and the TR capacity [17] may not be the primary performance measure. In particular, for event-recognition, alarm-prompt or object-finding problems, where the receiver aims to recognize the occurrence of a specific event, determine an alarm, or realize the presence of an object, with respect to a set, in terms of a _reliable_ Yes / No final decision, the so-called K-identification capacity [26] is the appropriate metric. For the K-identification problems, the receiver is focused on a subset of size \(K\) of the message set, \(\mathcal{M}\), which is known as the goal message set. The recipient chooses a message at random, and confirms if it is part of the specified goal message set. The error requirements imposed on the associated K-identification codes guarantee that each _inclusion test_ is reliable for every arbitrary choice of the goal message set.  
In the context of IoT, specific instances of the K-identification problem may be found in the detection of damaged cells in a memory disk drive, where, e.g., a failure detector wants to know whether or not the corrupted cell is present in a group of cells; in lottery prize events, where, e.g., a person aims to determine whether a winner is among their favorite teams or where people seek to know if a specific lottery number is among their collection of numbers; in smart traffic management, where, e.g., one may be interested in finding to which group/set of streets a goal location belongs to. Additionally, K-identification might be used in health monitoring within the context of smart medical and health-care systems. For example, in a remote surgery [50], where the _inclusion_ of a particular cancer or illness inside a goal group of K-cancers/diseases may be the communication goal. Finally, the K-identification problem may find applications in the _generalized identification with decoding_ problem [26] in various IoT applications. Such a problem is an extension of the K-identification, wherein when the receiver identifies that the message belongs to set \(\mathsf{K}\), and it also identifies the message itself.
###### Abstract
The spike attention module dynamically emphasizes critical spatial-temporal features, whereas the MDRF module efficiently captures extensive temporal dependencies through dilated convolutions and multi-scale kernels. Additionally, SpikeCAN employs carefully designed residual connections to alleviate spike-vanishing issues, thereby ensuring robust training and enhanced accuracy. We comprehensively evaluate the proposed SpikeCAN model using two prominent benchmark datasets in the bearing fault diagnosis domain: the Case Western Reserve University (CWRU) dataset [29] and the Society for Machinery Failure Prevention Technology (MFPT) dataset [30]. Our results demonstrate outstanding accuracy, with SpikeCAN achieving 99.86% accuracy on the four-class classification task of the CWRU dataset using five-fold cross-validation and 99.88% accuracy under a conventional 70:30 train-test split averaged over five random seeds. Additionally, SpikeCAN achieves a notable 96.31% accuracy on the challenging fifteen-class MFPT dataset, surpassing existing spike-driven approaches and establishing a new state-of-the-art benchmark. Overall, our findings underscore the potential of SpikeCAN to significantly advance energy-efficient, real-time bearing fault diagnosis, highlighting its practical applicability in industrial settings. The remainder of this paper is structured as follows. Section 2 provides a review of related work and discusses the motivation behind the proposed approach. Section 3 introduces the SpikeCAN model, detailing its architecture and key mechanisms. In Section 4, we describe the experimental setup and datasets used in this study. Section 5 presents the evaluation methods along with comprehensive results and analyses. Section 6 outlines the methodology employed to calculate energy consumption. Section 7 reports on detailed ablation studies to validate the effectiveness of individual components and the model's robustness to variations in training sample size. Finally, Section 8 provides conclusions and discussions along with potential future research directions.