esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Feature Selection Results  
The results of feature ranking through a voting strategy are shown in Table 7.  
Based on the results of feature selection and considering the literature studies on pavement roughness, this paper finally selects ESAL, TEMP, PRECIPITATION, FREEZE_THAW, FREEZE_INDEX and MAINTENANCE as input variables for the pavement roughness prediction model. Although CLOUD_COVER and GB_THICKNESS ranked higher than PRECIPITATION and MAINTENANCE, they were excluded from the model. GB_THICKNESS was excluded because its influence is closely related to the structural capacity already represented by ESAL, reducing the need for additional structural input variables. PRECIPITATION was chosen over CLOUD_COVER because it directly impacts moisture content within the pavement structure, while CLOUD_COVER is an indirect climate-related factor. MAINTENANCE was included despite its lower ranking, since it provides critical insights that are not captured by structural or environmental variables. The selected features aim to balance model interpretability and predictive performance, ensuring the inclusion of variables with the most direct physical relevance of IRI. The effect of feature selection in this study is shown in Table 8.  
As can be seen from Table 8, after feature selection, the model brings a significant optimization in time consumption and a small improvement in prediction performance, although the memory saving is not significant. This reflects the effectiveness of the feature selection method in this study.
## 3 A Quick Tour of the Supergraph Technique  
Before proceeding to our algorithms, we quickly review the _supergraph technique_ (also known as the \(X-e+Y\)_method_), which is frequently used in designing enumeration algorithms [32; 33; 34; 35; 36]. The crux is summarized below in Theorem 1.  
Let \(\mathcal{S}\subseteq 2^{U}\) be a collection of subsets of a finite set \(U\). Here, we assume that every pair of distinct sets in \(\mathcal{S}\) is incomparable with respect to set inclusion, that is, for \(X,Y\in\mathcal{S}\), both \(X\setminus Y\) and \(Y\setminus X\) are nonempty unless \(X=Y\). The basic idea of the supergraph technique is to define a strongly connected directed graph \(\mathcal{D}=(\mathcal{S},\mathcal{A})\) on \(\mathcal{S}\) by appropriately defining an arc set \(\mathcal{A}\). Given this directed graph, we can enumerate all sets in \(\mathcal{S}\) by solely traversing \(\mathcal{D}\) from an arbitrary \(S\in\mathcal{S}\). To this end, we need to define the directed graph \(\mathcal{D}\) so that it is strongly connected.  
We first observe that, under the above incomparability on \(\mathcal{S}\), for \(X,Y\in\mathcal{S}\), \(|X\setminus Y|=0\) if and only if \(X=Y\). We define a set of arcs of \(\mathcal{D}\) in such a way that for any two \(X,Y\in\mathcal{S}\) with \(X\
eq Y\), \(X\) has an outgoing arc to some \(Z\in\mathcal{S}\) such that \(|X\setminus Y|>|Z\setminus Y|\). The (out-)neighborhood of \(X\) defined in this way is denoted by \(\mathcal{N}^{+}(X)\). Using the above observation, this directed graph \(\mathcal{D}\) is strongly connected, as we can inductively show that there is a directed path from \(X\) to \(Y\) in \(\mathcal{D}\) via \(Z\in\mathcal{N}^{+}(X)\). This idea is formalized as follows:  
Theorem 3.1 (e.g., [16; 34; 35]): _Let \(\mathcal{S}\subseteq 2^{U}\) such that both \(X\setminus Y\) and \(Y\setminus X\) are non-empty unless \(X=Y\) for \(X,Y\in\mathcal{S}\). Suppose that for \(X,Y\in\mathcal{S}\) with \(X\
eq Y\), the neighborhood \(\mathcal{N}^{+}(X)\) of \(X\) contains a set \(Z\in\mathcal{S}\) such that \(|X\setminus Y|>|Z\setminus Y|\). Moreover, suppose that given \(X\in\mathcal{S}\), we can compute the neighborhood of \(X\) in (total) time \(T(n)\), where \(n=|U|\). Then, given an arbitrary initial set \(S\in\mathcal{S}\), we can enumerate all sets in \(\mathcal{S}\) in delay \(T(n)\cdot n^{O(1)}\)._  
Due to Theorem 1, it suffices to define such a polynomial-time computable neighborhood \(\mathcal{N}^{+}\colon\mathcal{S}\to 2^{\mathcal{S}}\).
## 4 Numerical Results
### Impact of the W on \(S_{A}\)*, \(S_{Ar}\)*, \(p\)*, and F(T)  
In this subsection, we investigate the impact of the length of the mandatory carbon period, \(W\), on the optimal stock capacity for new products, \(S_{A}\)*, reconditioned products, \(S_{Ar}\)*, returned products percentage, \(p\), and total profit, \(F(T)\). Thus, we vary the value of \(W\), and by using the optimization algorithm, we determine the corresponding \(S_{A}\)*, \(S_{Ar}\)*, \(p\)*, and \(F(T)\). The simulations' results are illustrated in Table 3. In order to improve the visualization of the obtained results in the analyzed part, we add the Figure 6 to the presented Table.  
As it is shown in Table 3, the larger the mandatory carbon period is, the more the total profit decreases, which is explained by lower production in a large period. Also, the optimal of the length of the period of carbon is given (the bold line). Indeed, when the mandatory carbon period is low, the limitation for producing either for new or reconditioned products is low, thus the production is less limited, and the number of satisfying products increases, and then the lost sales costs decreases (see Figure 6). Consequently, to hold the produced products, the optimal storage capacities for both types of products increase. Thus, the demands will be satisfied more, and of course, the profit increases. This study allows a company manager to propose optimal decisions on the manufacturing, reconditioning, stocking, and quantity of returned worn products when a mandatory carbon period is imposed. Indeed, the government imposes a mandatory carbon period when the pollution reaches a level that is considered harmful to humans. In this period, the manager has to respect a fixed limit of carbon emissions. Consequently, the production is limited, and the manager has to find the optimal planning of production, stocking, and returning of worn products.
# Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR
## 5 Conclusions  
Low-frequency information can reflect the basic trend of the formation and improve the accuracy of velocity analysis and deep imaging. Conventional acquisition technology shows that low-frequency information below 10 Hz in the data is seriously polluted by noise, and some low-frequency information will be lost during processing. The low-frequency expansion method based on CS can reasonably compensate the low-frequency components to improve the quality of the data. However, this method is greatly affected by noise, and can effectively extend the low-frequency information only when the SNR is  
Figure 14: Filtering profile with 1–3 Hz of actual data before and after low-frequency expansion. (**a**) Original data, (**b**) processed data by CS, (**c**) processed data by the proposed method.  
high. In order to improve the anti-interference ability of the method, well information is introduced in the construction of the objective function to constrain the inversion process of estimating the reflection coefficient. Low-frequency information of the original data is extended by extracting the low-frequency information from the reflection coefficient. Model and actual data processing results show that the proposed method, restricting the reflection coefficients inversion process by the logging data, can obtain the accurate reflection coefficients, improve the anti-noise interference ability of the inversion process, and improve the extension effect of the low-frequency information even if the noise energy is large. In this paper, it is ideal to select the point where \(\vec{\mathbf{R}}\) and \(\mathbf{G}\) are equal. In fact, the slope of the equal point needs to be considered, which is also the problem to be solved in the further.  
Conceptualization, M.S. and Z.L.; methodology, M.S.; validation, J.W. and Y.S.; writing, review and editing, M.S. and Y.L.; funding acquisition, Z.L.; supervision, M.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Funding (NSF) under grant 41974145 and the Major Scientific and Technological Projects of CNPC (ZD2019-183-003).  
Not applicable.  
The authors declare no conflict of interest.
# Best-Suited Communication Technology for Maritime Signaling Facilities: A Literature Review
## 4 Conclusions and Directions for Future Work
For instance, integrating machine learning for predictive system performance analysis under various maritime conditions could be beneficial. It is also suggested in this paper that the possibility of combining multiple technologies (such as Iridium, VDES, or 5G-NR networks) is explored through advanced multi-criteria analysis methods, which would allow for optimizing communication system selection based on specific maritime applications. Conceptualization, I.K. and I.G.M. ; methodology, I.G.M. and J.S. ; software; validation, J.S. ; data curation, I.K., I.G.M. and J.S. ; writing--original draft preparation, I.K. ; writing--review and editing, I.G.M. ; supervision, I.G.M. and J.S. ; project administration, I.K. ; funding acquisition, J.S. and I.K. All authors have read and agreed to the published version of the manuscript. This research was funded by Faculty of Maritime Studies, Split, Republic of Croatia. Not applicable. All the needed data are reported in the paper. [PERSON] was employed by the company Plovput d.o.o. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Chromatographic Separation of Rare Earth Elements as MGDA Complexes on Anion Exchange Resins
## 3 Results and Discussion
### Stability of the MGDA-REE Complexes  
MGDA-REE complexes are stable within a large pH range, and they have a negative net charge in alkaline conditions. This negative charge and consequent electrostatic interaction with the functional group of the SBA resin is the main sorption mechanism. As discussed earlier, the separation is based on the differences between the complex stabilities of individual REE-MGDA complexes. Based on the structure of the MGDA molecule, the -COO- located near the methyl group is the most sensitive to protonation. The methyl group acts as an electron density donor, which destabilizes the -COO- and results in protonation of the acid group. Alkyl substituents are known to be electron density donors and, thus, induce a higher pKa value in the adjacent -COOH group [41].
# Role of Ion-Exchange Resins in Hydrogen Reactions
## 3 Ion-Exchange Resins Application to Hydrogenation Reactions
### Other Related Reactions
(2017) used nonionic polymeric resins (that is crosslinked AmberliteTM XAD-4 and hyper-crosslinked Purolite\({}^{\text{\textregistered}}\) MN200 and MN100, the latter with a small fraction of tertiary amine functional groups) and basic ST-DVB IER (i.e., AmberliteTM IRA-910 and IRA-96, with dimethyl-ethanolammonium and tertiary amine functional groups, respectively) as supports for Pd catalysts aiming at the catalytic hydrodechlorination of 4-chlorophenol. A Langmuir-Hinshelwood kinetic model was developed, with surface reaction as the rate-determining step, which suggested an enhancing effect of adsorption on the catalytic reactivity. IRA910 was found to facilitate Cl\({}^{-}\) adsorption and promote the production of PdCl\({}_{3}\)\({}^{-}\) and PdCl\({}_{4}\)\({}^{2-}\) species, which are inactive for catalytic reduction [124]. Hydrogenolysis of glycerol, which involves chemical bond cleavage with the simultaneous addition of a hydrogen atom to the resulting molecular fragments [125], yields the production of 1,2- and 1,3-propanediol, together with ethylene glycol. [PERSON] et al. (2005), [PERSON] et al. (2006), and [PERSON] et al. (2013) studied this reaction system in connection to the use of IER. However, physical mixtures of carbon-supported metal catalysts (e.g., Ru/C, Pt/C, Pd/C, Rh/C) with IER were found to be much more selective towards 1,2-propanediol (which is the preferred target product) than tested metal-doped IER [126; 127; 128]. Furthermore, in the framework of the hydrogenolysis of glycerol, [PERSON] and coworkers combined Ru/C catalysts with thermal-resistant IERs, such as Amberlyst\({}^{\text{TM}}\) DT and Amberlyst\({}^{\text{TM}}\) 70 [129]. Regarding hydrodeoxygenation, a particular type of hydrogenolysis involving oxygen removal from oxygen-containing compounds (which is an interesting reaction system for the valorization of biomass waste streams, e.g., [130]), has been found to be effectively achieved by monometallic Cu/C and bimetallic Mg-Cu/C obtained from the weakly acidic, acrylic IER Diaion\({}^{\text{TM}}\) WK11, which was calculated after ion-exchanging it with the designated precursor salts in [PERSON] et al. (2021, 2022). Particularly relevant features of those works are the reported high metal loadings (>50 wt.%) and small metal particle sizes (<15 nm) [131; 132].
# Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations
## 3 Constellation Observation Optimal Scheduling
### Principles of Observation Task Scheduling
Among them, the priority function of the observation task is used to clarify that the task needs to be processed and is selected according to the task priority. The priority is selected first, and the subtask execution order is also arranged according to the size of the priority, from largest to smallest, with the large task executed first and the small task executed later. Determining the priority of the satellite platform selection dynamically identifies the priority of each current idle platform according to the platform resource priority function. From all the priority values obtained by all idle platforms, the highest priority is selected first and the subtask is executed, followed by the next highest priority value until the required resources are met for the task to be executed. At the same time, the status of all the satellite platforms assigned to the task is set to the working state until the end of the task processing, and then they are set to an idle state and can be reassigned to a new task. The algorithm flow chart is shown in Figure 8.
# Automated Vision-Based Crack Detection on Concrete Surfaces Using Deep Learning
## 3 Results and Discussion
### Classification Using the Trained Network  
The pre-trained DCNN was trained with the training set of 14,000 images using the AlexNet model, which obtained 99.9% accuracy during the training process. After training, the trained model was validated before the test. The validation image dataset consisted of 10% of the total image dataset, and had 2000 images. During the validation process, the trained model predicted the images into two classes: crack and no-crack. Sample images of predicted crack and no-crack classes in the validation images are shown in  
Figure 4: Training progress plot of loss (%) with different learning rates: (**a**) 0.0001; (**b**) 0.001.  
Figure 3: Training progress plot of accuracy (%) with different learning rates: (**a**) 0.0001; (**b**) 0.001.  
After the validation of the trained model, the test images were classified using the trained network. The test image dataset consists of 20% of the images from the original dataset, comprising 4000 crack and no-crack images. The predicted crack and no-crack classes of the test images are shown in Figure 7. In the test image dataset, there were 2000 images of each class (crack and no-crack). The confusion matrix of the test images is shown in Figure 8. The model trained in the test images accurately predicted 3998 from a total of 4000 images. Only two images were left unpredicted. A total of 1999 crack images and 1999 non-crack images were accurately predicted, representing a 99.99% accuracy. Considering the total test images, the prediction accuracy was 99.9% with a 0.1% loss. The computed performance metrics are shown in Table 6. The precision, recall, and F1 scores obtained from the confusion matrix were all 0.99. In addition, the accuracy of the prediction in the test images was 0.99.  
The accuracy of the trained model was further compared to the other pretrained models and is shown in Table 7. The GoogleNet, ResNet101, InceptionResNet2, and VGG19 DCNNs were compared to the trained AlexNet DCNN model. The AlexNet, GoogleNet, and VGG19 obtained accuracies of 0.99. In addition, the ResNet101 and InceptionResNetv2 models obtained accuracies of 0.9833 and 0.95, respectively. While the other DCNN models also obtained high accuracies, the AlexNet has fewer layers compared to other DCNNs, and can be trained in less time. The other DCNNs have more layers for feature extraction, which requires more time for training. Therefore, AlexNet was superior to other pretrained DCNNs for crack detection and classification.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 4 In-Band Sensor Network Requirements
### Sensor Network  
A critical factor in any \(\overline{\text{DVO}}\) deployment is the management of voltage at the end of the line, or in locations that have historically low voltage levels. Ubiquitous distribution of conventional sensors is not sufficient, however. Sensors in a robust \(\overline{\text{DVO}}\) implementation must be aware of their schematic location on the distribution grid in order to report the feeder, phase, and other important characteristics of their supply circuit. This \"grid location awareness\" aids in system optimization as the voltage range is managed for conservation.
# GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization
## 3 Sensing Matrix Optimization of Compression Acquisition
### Sensing Matrix Optimization
#### 3.2.2 Shrink Operator Update Target Matrix  
For a fixed sensing matrix \(\mathbf{\Theta}\), an optimal target matrix \(\mathbf{G}_{\text{t}}\) by using the shrinkage operation can be found.  
A shrinking operation [30] is applied to \(\mathbf{G}^{k}\), which is computed using \(\mathbf{\Theta}_{x}^{\text{T}}\mathbf{\Psi}^{\text{T}}\mathbf{\Phi}^{\text{T}}\mathbf{\Phi} \mathbf{\Psi}\mathbf{\Theta}_{x}\) with \(\mathbf{\Phi}=\mathbf{\Phi}^{k-1}\)  
\[\mathbf{G}_{\text{t}}^{k}(i,j)=\left\{\begin{array}{cc}1,&i=j\\ \mathbf{G}_{\text{t}}^{k-1}(i,j),&i\
eq j,\xi\geq\left|\mathbf{G}_{\text{t}}^ {k-1}(i,j)\right|\\ \xi\cdot\text{sign}(\mathbf{G}_{\text{t}}^{k-1}(i,j)),&\left|\mathbf{G}_{\text {t}}^{k-1}\right|\geq\xi\end{array}\right. \tag{24}\]  
where \(\text{sign}(\cdot)\) is the sign function, \(\xi\) is a threshold value that limits the searching range of target matrix \(\mathbf{G}_{\text{t}}\), and \(\xi>0\). Thus, we obtain a shrunk matrix for the next optimization iteration in this method.
# Secure On-Off Transmission in UAV Relay-Assisted mmWave Networks
## 2 System Model
### Relay Selection and Transmission Scheme  
Under the decode-and-forward relaying strategy, we adopt an opportunistic relay selection scheme with two phases. First, we select a set of UAV relays, which can successfully decode the messages received from \(S\). Specifically, for a potential UAV relay \(R_{x}\in\Phi_{U}\), if the channel capacity between \(S\) and \(R_{x}\) is greater than a threshold \(C_{th}\), the relay \(R_{x}\) can decode the \(S\)'s messages correctly [35; 36], and the decoding set is defined as \(\Phi=\left\{R_{x}\in\Phi_{U},C_{SR_{x}}>C_{th}\right\}\). After that, the UAV relay \(R_{0}\), which provides the lowest path loss to \(D\), is selected from \(\Phi\) to forward the \(S\)'s messages to \(D\). To this end, the selected relay offers the minimum length of LOS or NLOS link to \(D\).  
Based on the above system model, there are multiple ground eavesdroppers monitoring the transmission of \(R_{0}\to D\) link and attempting to interpret the confidential messages. In order to enhance physical-layer security, the on-off transmission scheme is adopted at \(R_{0}\), as done in [21; 22]. More specifically, based on the pilot signals from \(D\), \(R_{0}\) can obtain the channel capacity \(C_{R_{0}D}\) of the \(R_{0}\to D\) link. Then \(R_{0}\) performs data transmission only when \(C_{R_{0}D}\) is above the threshold \(\mu\). In order to highlight the effect of secrecy performance in the on-off transmission scheme, the conventional non-on-off transmission scheme is analyzed in this paper as well, in which the selected UAV relay \(R_{0}\) always transmits the signals after decoding the messages from \(S\).  
To further clarify the relay selection and on-off transmission scheme, we also depict the detailed scheduling period of these two processes as Figure 3. The relay selection process contains three parts:  
\(T_{1}\): \(S\) sends the pilot symbols to all UAV relays, and the UAV relays calculate the channel capacities of their links from \(S\), then \(\hat{\Phi}\) can be obtained.  
\(T_{2}\): The UAV relays in \(\hat{\Phi}\) transmit the pilot signals to \(D\), then \(D\) can find \(R_{0}\).  
\(T_{3}\): \(D\) responds the selection results to the UAV relays, and the selection process is completed.  
After that, the selected UAV relay \(R_{0}\) performs on-off transmission, which is divided into four time slots:  
\(T_{4}\): \(D\) sends the pilot signals to \(R_{0}\), and \(R_{0}\) calculated the channel capacity \(C_{R_{0}D}\).  
\(T_{5}\): If the \(C_{R_{0}D}>\mu\), \(R_{0}\) sends the indicator signals to \(S\). But when \(C_{R_{0}D}\leq\mu\), \(R_{0}\) keeps silent and waits the next pilot signals from \(D\).  
\(T_{6}\): After \(S\) receives the indicator signals, it transmits the date symbols to \(R_{0}\).  
\(T_{7}\): \(R_{0}\) forwards the messages to \(D\) by using decode-and-forward strategy.  
It should be mentioned that there are beam training phases before \(S\) and \(R_{0}\) transmit the data symbols [37; 38; 39], which is not our focus.