esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The Optimal Control
\begin{array}{rcl}\zeta_{5,i}=A_{2}+\zeta_{1,i+1}\rho_{1}+\zeta_{5,i+1}\left( 2-\rho_{1}-\gamma_{3}-d\right)+\zeta_{6,i+1}\gamma_{3}\\ &&\\ \Delta\zeta_{6,i}=-\frac{\partial H}{\partial I_{i}^{d}}=-\left(A_{3}+\zeta_{ 4,i+1}T_{i}+\zeta_{6,i+1}\left(1-T_{i}-\mu_{2}-d\right)+\zeta_{7,i+1}\mu_{2} \right)\end{array} \]  
then  
\[ \begin{array}{rcl}\zeta_{6,i}=A_{3}+\zeta_{4,i+1}T_{i}+\zeta_{6,i+1}\left(2 -T_{i}-\mu_{2}-d\right)+\zeta_{7,i+1}\mu_{2}\\ &&\\ \Delta\zeta_{7,i}=-\frac{\partial H}{\partial D_{i}}=-\zeta_{7,i+1}\end{array} \]  
then  
\[ \begin{array}{rcl}\zeta_{7,i}=2\zeta_{7,i+1}\end{array} \]  
with transversality conditions  
\[ \begin{array}{rcl}\zeta_{1,N}=0,\ \zeta_{2,N}=A_{1},\ \zeta_{3,N}=0,\ \zeta_{4,N}=0,\\ \zeta_{5,N}=A_{2},\ \zeta_{6,N}=A_{3},\ \mbox{and}\ \zeta_{7,N}=0.\end{array} \]  
To obtain the optimality conditions, we take the variation with respect to control \(u_{i}^{*}\), \(\varphi_{i}^{*}\), \(T_{i}^{*}\) and set it equal to zero  
\[ \begin{array}{rcl}\frac{\partial H}{\partial u_{i}}&=&\tau_{1}u_{i}+\zeta_ {1,i+1}\beta_{1}\frac{S_{i}I_{i}}{N_{i}}-\zeta_{2,i+1}\beta_{1}\frac{S_{i}I_{i} }{N_{i}}=0,\\ \frac{\partial H}{\partial v_{i}}&=&\tau_{2}v_{i}+\zeta_{1,i+1}\beta_{2}\frac{S_ {i}E_{i}}{N_{i}}-\zeta_{2,i+1}\beta_{2}\frac{S_{i}E_{i}}{N_{i}}=0,\\ \frac{\partial H}{\partial T_{i}}&=&\tau_{3}T_{i}+\zeta_{4,i+1}I_{i}^{d}-\zeta_ {6,i+1}I_{i}^{d}=0,\end{array} \]Then we obtain the optimal control  
\[{u_{i}}^{*} = \beta_{1}\frac{S_{i}I_{i}}{\tau_{1}N_{i}}\left(\zeta_{2,i+1}-\zeta_{ 1,i+1}\right);\] \[{v_{i}}^{*} = \beta_{2}\frac{S_{i}E_{i}}{\tau_{2}N_{i}}\left(\zeta_{2,i+1}-\zeta_ {1,i+1}\right);\] \[{T_{i}}^{*} = \frac{I_{i}^{q}}{\tau_{3}}\left(\zeta_{6,i+1}-\zeta_{4,i+1}\right);\]  
By the bounds in \(\mathcal{U}_{dd}\), it is easy to obtain \({u_{i}}^{*}\), \({v_{i}}^{*}\), \({T_{i}^{*}}\) in the following form  
\[{u_{i}}^{*} = \min\{\max\{\beta_{1}\frac{S_{i}I_{i}}{\tau_{1}N_{i}}\left(\zeta_ {2,i+1}-\zeta_{1,i+1}\right),u_{min}\},u_{max}\},\] \[{v_{i}}^{*} = \min\{\max\{\beta_{2}\frac{S_{i}E_{i}}{\tau_{2}N_{i}}\left(\zeta_ {2,i+1}-\zeta_{1,i+1}\right),v_{min}\},v_{max}\},\] \[{T_{i}}^{*} = \min\{\max\{\frac{I_{i}^{q}}{\tau_{3}}\left(\zeta_{6,i+1}-\zeta_ {4,i+1}\right),T_{min}\},T_{max}\},\]  
for \(i=0,\ldots,N-1\).
## 3 Results
### Blastocystis  
Subjects negative for common enteric pathogens (xTAG Luminex panel), EV, PeV, AdV (real-time PCR), _Blastocystis_, and _Entamoeba_ spp. (PCR) were assumed as the control group (Figure S3A_C). The other groups contained positive individuals who tested positive for one or more of these microorganisms. Only five individuals aged 5 to 10 years (4F/1M, 7.8%) were found to have multiple infections and exclusively reported frequent abdominal pains (Table S4A). The beta diversity analysis showed a significant lower abundance of the genus _Ruminococcaceae_ UCG-002 in negative (0.0116 [0-0.082]) versus positive individuals (0.065 [0-0.8272]) (\(p<0.01\)). The opposite trend for the genus _Faecalibacterium_ does not reach significance.  
No difference was found between positive versus negative subjects for the different age groups (Figure 4).  
### Blastocystis  
To shed light on a possible implication of single or co-detection _Blastocystis_ on gut microbiota, the control group (Group 1) was compared with 42 positive _Blastocystis_ patients further divided as follows: 6 subjects positive for _Blastocystis_ only (Group 2); 12 subjects positive for _Blastocystis_ and _Entamoeba_ spp. only (Group 3); 5 subjects positive for _Blastocystis_, viruses, and bacteria (Group 4a); 6 subjects positive for _Blastocystis_ and _Entamoeba_ spp., viruses and bacteria (Group 4b); 13 positive subjects for _Blastocystis_ and _Entamoeba_ spp. and _G. duodenalis_ and other pathogens (Group 5b). Alpha diversity indices increased when _Entamoeba_ spp. co-infections were present ( Table S4B).  
As for bacterial composition, significant differences were observed between Group 1 versus Groups 3, 4a, 4b, and 5b but not versus subjects positive for _Blastocystis_ only (Group 2). In detail, at the genus level, Group 1 showed: (i) a lower median of _Alloprecotella_ (\(p<0.01\)), _Rikenellaceae_ RG, _Succinivibrio_ (\(p<0.05\)), and _Ruminococcaceae_ UCG-010 (\(p<0.01\)) versus Group 3, (ii) a higher median of _Faecalibacterium_ (\(p<0.05\)) versus Group 4a; (iii) a  
Figure 4: The UniFrac distances between enteric pathogen positive versus enteric pathogen negative subjects is displayed by age groups, using principal coordinate analysis (PCoA). The infant group tends to separate out according to Axis 1 (7.6%) and Axis 2 component (7%). The differences of the infant group from other age groups as shown in Figure 1, are also confirmed for comparisons of enteric pathogen negative versus enteric pathogen positive subjects.  
lower median of _Ruminococcaceae_ UCG-002 and _Ruminococcaceae_ UCG-010 versus Group 4b; (iv) a higher median of _Bacteroides_ (\(p<0.05\)) and _Lachnoclostridium_ (\(p<0.05\)) and a lower median of _Ruminococcaceae_ UCG-010 (\(p<0.05\)) compared with Group 5b. Finally, Group 2 showed an increase of _Alloprevotella_ and _Prerotella_ 2 (\(p<0.05\)) versus Group 3 (Figure 5).
## 5 Data Offloading Solution
### Task Offloading Decision
The objective function: the optimization is based on the following equation:  
\[J(\theta)=-E_{\pi\theta}[Q_{\pi}(s_{t},a_{t})\log\pi_{\theta}(s_{t},a_{t})] \tag{18}\]  
where \(Q_{\pi}(s_{t},a_{t})\) indicates the value acquired when taking action \(a_{t}\) under state \(s_{t}\). We obtain the following:  
\[Q_{\pi}(s_{t},a_{t})= \begin{cases}r_{t}+\gamma Q_{\pi}(s_{t+1},a_{t+1})&s_{t} \textit{is terminal state}\\ r_{t}&otherwise\end{cases}  \tag{19}\]  
where \(\gamma\) is the attenuation factor, and \(r_{t}\) is the reward received by taking the action at state \(S_{t}\). Algorithm 1 summarizes the task performer selection process based on DRL. ```
1. Input:iteration number, step, batch-size, the action set \(A\), attenuation factor \(\gamma\)
2. Output: \(\text{A}_{t}=(a_{t1},a_{t2},\cdots,a_{tM})\)
3. for episode from 1 to iteration number
4. Initialize \(s_{0}\) and then obtain the feature vector \(\phi\) (\(s_{0}\));
5. for \(t\) from 1 to step
6. chose action \(a_{t}\) according to constrain (17);
7. take action \(a_{t}\), obtain reward value and alter \(st\) by \(st\)\(\leftarrow\)\(s_{t+1}\);
8. store \(\{\phi\) (\(s_{t}\));\(a_{t}\);\(s_{t}\); \(\phi\) (\(s_{t+1}\));\(\}\) in memory;
9. if \(s_{t+1}\) is the terminal state
10. end the iteration;
11. else
12. continue;
13. end for
14. sample batch-size of transitions randomly for training;
15.update the objective function according to the gradient descent;
16. end for
```  
**Algorithm 1**Task Performance Allocation Based on DRL.
## 4 Calculation Results and Experimental Validation
### Sensitivity Calculation Results Based on Optimized Central Gradient Method  
Sensitivity calculations were made using the optimal design parameters as the center of optimization, with the vertical stiffness \(K_{V}\) and the horizontal stiffness \(K_{H}\) of the isolation bearing serving as the sensitivity evaluation functions; the calculation results based on the orthogonal experimental design table are presented in Table 1.  
Using the orthogonal design method [30], the sensitivity of the influencing factors is assessed by calculating the statistical parameters \(K_{ij}\) and the range \(R_{j}\). A quantitative comparison of the effects of four-dimensional parameters on stiffness is conducted, resulting in a range analysis of each parameter, as shown in Table 2.  
From Tables 1 and 2, it can be observed that among the four design parameters of the three-dimensional seismic isolation bearing, based on the analysis of the parameter range, the sensitivity to vertical stiffness ranks from high to low as follows: \(l>h_{1}>r>h_{2}\). In contrast, their sensitivity to horizontal stiffness ranks from high to low as follows: \(l>h_{2}>r>h_{1}\). Therefore, if adjustments to the vertical and horizontal stiffness of the isolation bearing are required, it is advisable to first finely tune the parameter value of \(l\), followed by a broader adjustment of \(r\), \(h_{1}\), and \(h_{2}\).
###### Abstract  
Nonlinear amplification is typically done on velocity signals from low-strain pile integrity tests to enhance weak echoes and superimpose any peak reflections. This conventional method may sometimes fail to untangle the hidden information within the signal that is obscured by the presence of noise. In this study, a pile defect identification system based on the conventional nonlinear amplification method and the wavelet packet transform (WPT) was proposed to easily detect the presence of any geometric or material defects by identifying feature parameters. Diagnostic rules, which have been lacking in the literature, were presented to serve as a guide in interpreting decomposed signals and in analyzing various characteristics of peak waveforms that are associated with certain types of defects. In this study, the finite element method was used to simulate the impact echo test of nine cases of defective piles. To verify the proposed scheme, six data sets of the nine cases of defective piles were made, in which a total of 54 piles were analyzed. The results of the study showed that the identification method based on WPT could detect defects 87.04% of the time compared to the conventional method, which only detected defects 64.81% of the time.  
diagnostic rule; pile defects; impact echo method; wavelet packet transform; nonlinear amplification +
Footnote †: journal: Applied  
Article
## 5 Experiments and Results
### Parsing and Extracting Features  
In the next step, basic pre-processing was done to perform tokenization, part-of-speech (POS) tagging, named entity recognition (NER), lemmatization, and dependency relation. The Stanford CoreNLP tool was used for pre-processing. Each tweet from the dataset is processed through the Stanford CoreNLP tool. As a result, 22,932 rows were generated with all essential features discussed in Section 4.1.  
In the next step, the feature words for each category were extracted using grammar rules. Since there is no automatic way to validate the correctness of the rule-based features, we manually validated the correctness of extracted feature words with the help of a domain expert. The comparative results of manually extracted feature words by the language expert and automatically extracted feature words by the rule-based module are shown in Table 7.  
From Table 7, it is evident that automatically extracting the feature words using linguistic grammar rules achieved a significant F-score for each feature category. There is a slight difference in feature extraction compared to features extracted by the language expert, except for Feature-13, which corresponds to location of a disaster event. The reason is that location can be expressed in many ways, such as country, state, region, city, street etc. and more complex rules/understanding is required to map a location precisely. In future, we intend to define more complex rules for extracting location features; however, in this research, we only focus on reasonable feasibility of adapting linguistic rules for automatic feature extraction to classify eyewitness messages.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Feature \#** & **Feature Description** & **Grammar-Rule** & **Comments** \\ \hline
11 & “Short tweet-length” & NER\textless{}’URL’ and TweetWordsCount & The number of words-count remains an open research area. \\
12 & “Caution and advice for others” & POS = “VB’ and IDX \textless{} HeadIDX and DepRel = ‘cop’ and OnTarget (POS =?]”) & Feature was dropped by [PERSON]’s approach. \\
13 & “Mention of disaster locations” & LocationType (NER) or WORD is in (‘east’, ‘west’, ‘north’, ‘south’) & Feature was dropped by [PERSON]’s approach. \\
14 & New Feature “No URL” & IF (URL Found in content) THEN “NO” ELSE “YES” & URL in the text means that the information is not firsthand. \\ \hline \end{tabular}
\end{table}
Table 6: _Cont_.  
Table 8 shows only ten features by dropping those not considered in [PERSON]'s approach. Table 9 shows the results generated using all thirteen features identified by [PERSON]'s approach, whereas Table 10 shows the results by including a new feature (No URL).  
It is evident from the results that if we compare the eyewitness category of an earthquake event type, we can see a significant performance shift from an F-Score of 0.79 (for 10-Features) see Table 8, 0.82 (for all 13-Features) see Table 9, to 0.91 (with the addition of new-Feature) see Table 10.
# On the Virtual Cell Transmission in Ultra Dense Networks
## 1 Introduction
### Related Works  
Despite the appealing features of UDN, the continuing increase of cell density brings new challenges [7]. In a typical UDN, many low power remote base station antennas (RBAs) are irregularly (usually randomly) distributed over a given area and connected to a central processor; consequently, a dense and irregular network topology is introduced. Since the interference environment of a random topology network is rather harsh, it is necessary to design the transmission schemes, as well as the resource allocation subtly. We can generally put the existing UDN transmission schemes into three categories.
# Metalens Antenna Based on an Ultrathin Dual-Polarized Double-Layer Huygens' Metasurface
## 3 Design and Discussion of a Dual-Polarized Metalens Antenna
Figure 7c illustrates the radiation gain of the metalens antenna under \(y\)-polarized spherical wave excitation, as well as the gain of the feed source. It can be obviously seen that the metalens antenna significantly enhances the radiation gain of the horn antenna,achieving a maximum gain of 30.4 dBi at 25 GHz and a half-power bandwidth ranging from 23.9 GHz to 26 GHz, which corresponds to a relative bandwidth of approximately 8.4%. Figure 7d presents the aperture efficiency, which shows that the maximum aperture efficiency of 47.8% is achieved at 25 GHz. Furthermore, compared with conventional metasurfaces or Huygens' metasurfaces, whose thickness typically exceeds one-tenth of the wavelength, the proposed metalens antenna features a dielectric substrate with a thickness of only one-fifteenth of the wavelength at the design frequency of 25 GHz. More comparisons with the performance of previously published metalens antennas are presented in Table 1. It can be seen that the proposed metalens achieves outstanding performance while maintaining a thin thickness, which is  
Figure 5: Design of the metalens antenna. (**a**) Theoretical discretized phase distribution. (**b**) Actual phase distribution. (**c**) Absolute phase error between the theoretical and actual distribution. (**d**) Actual transmission amplitude distribution after meta-atom mapping of the metalens. Figure 6: (**a**) Configuration of a metalens antenna, in which the metalens is designed by the proposed Huygens’ meta-atom. (**b**) \(E_{y}\) field distribution under \(y\)-polarized excitation at 25 GHz, demonstrating the directional radiation. The white solid line indicates the position of the metalens, and the black solid lines sketch the profile of the horn antenna. favorable for device integration and miniaturization. This makes it highly valuable and promising for applications in the K-band.
# Detection of Mineralization Zones Using Aeromagnetic Data
## 3 Methodology
### Regional-Residual Separation  
The spectral analysis technique is used to isolate the regional and residual components from the RTP map (Radial Average Power Spectrum Technique). The use of this approach on the RTP map is to separate the regional and residual maps [21]. The Fast Fourier transformation is used in this procedure.
# Enhancing Methyl Orange Degradation with Laser-Generated ZnO and Ce-Doped ZnO Nanoparticles
## 2 Materials and Methods
### Removal Efficiency of Degradation  
The degradation efficacy refers to the catalyst's performance in degrading MO dye. Thus, the percentage degradation efficiency was evaluated by employing the equation given below:  
\[\text{Degradation Efficiency}\ (\%)=\frac{C_{o}-C_{t}}{C_{o}}\times 100\% \tag{1}\]  
where \(C_{o}\) is the concentration of aqueous MO dye without photocatalyst (20 ppm), and \(C_{t}\) is the concentration of MO dye with photocatalyst through the reaction under light exposure for any time \(t\) (min) [17].
###### Abstract  
Employing floating treatment wetlands (FTWs) and constructed wetlands (CWs) is one of the most eco-friendly strategies for the bioremediation of water contaminants. Here, the efficiency of FTWs and CWs was compared for the degradation of phenanthrene-contaminated water for the first time. The FTWs and CWs were established by vegetated _Phragmites australis_ in phenanthrene (1000 mg L\({}^{-1}\))-contaminated water. Both wetlands were augmented with a bacterial consortium of four bacterial strains: _Burkholderia phytofrimans_ PsJN, _Pseudomonas anguilseptic_ TIR153, _Arthobacter oxydans_ TIRH49, and _Achromobacter xylosoxidans_ ITS170. Overall, the wetlands removed 91-93% of the phenanthrene whilst the augmentation of the bacterial strains had a synergistic effect. In comparison, the CWs showed a better treatment efficiency, with a 93% reduction in phenanthrene, a 91.7% reduction in the chemical oxygen demand, an 89% reduction in the biochemical oxygen demand, and a 100% reduction in toxicity. The inoculated bacteria were found growing in the shoots, roots, and water of both wetlands, but were comparatively better adapted to the CWs when compared with the FTWs. Similarly, the plants vegetated in the CWs exhibited better growth than that observed in the FTWs. This study revealed that the FTWs and CWs vegetated with _P. australis_ both had promising potential for the cost-effective bioremediation of phenanthrene-contaminated water.  
wellands; bacterial consortium; bioremediation; resource recovery; sustainability Article
# Resource Indexing and Querying in Large Connected Environments
## 4 Preliminaries & Assumptions
### Global and Local Index  
After having detailed all the connected environment components considered in this work, we here formally define the structure of the global resource index of the environment (cf. Definition 7), as well as the structure of the local index stored on each sensing device (cf. Definition 8). It should be noted that a zone containing at least one device is called a covered zone, while a zone containing no devices is called an uncovered zone.  
**Definition 7** (Global Index).: \[gi:(D,Z,bRule)\quad where:\] (7)  
* \(D\) _Devices representing the index rows._
* \(Z\) _Zones representing the index columns._
* \(bRule\) _represents a binary association rule that establishes a link between a designated zone and a defined set of devices. Its related algorithm follows several specific patterns according to the requirements (detailed in the upcoming sections)._  
**Example 7**.: _The global index generated by the monitoring pc in our motivating scenario, (composed of 4 covered zones, 2 uncovered zones, and 4 devices), is_  
\[gi_{1}=\begin{bmatrix}z_{1}&z_{2}&z_{3}&z_{4}&z_{5}&z_{6}\\ d_{1}&1&0&0&0&0&0\\ d_{2}&0&1&1&0&0&0\\ d_{3}&0&0&1&1&1&0\\ d_{4}&1&1&0&1&0&1\end{bmatrix}.\]  
**Definition 8** (Local Index).: _A local index \(li\) of a device \(d_{i}\) is a \(3\)-dimensional structure, defined as follows:_  
\[li_{d_{i}}:(D^{\prime},Z^{\prime},f)\quad where: \tag{8}\]  
* \(D^{\prime}\subseteq D\) _is a set of devices that are visible by the device_ \(d_{i}\)__
* \(Z^{\prime}\subseteq Z\) _is a set of covered and uncovered zones that are known by_ \(d_{i}\)__
* \(f\) _is a function that associates (_1_) the current device_ \(d_{i}\) _to its corresponding covered zone (using the symbol \"*\"), (_2_) each of the other covered zones in_ \(Z^{\prime}\) _to one accessible device in_ \(D^{\prime}\)_, and (_3_) each uncovered zone in_ \(Z^{\prime}\) _to the symbol_ \(\square\) _(allowing_ \(d_{i}\) _to be aware of the zone uncoveredness)._  
**Example 8**.: _In this example, we show below the content of the local index \(li_{d_{3}}\) that covers \(z_{3}\) in our motivating scenario. If a query is received by \(d_{3}\) and is requesting information from \(z_{3}\), the device will respond with its measured temperature. In addition, it (indirectly) covers the zone \(z_{4}\) with \(d_{4}\). For example, if \(d_{3}\) receives a request intended for \(z_{4}\), it will forward the request to \(d_{4}\). \(z_{5}\) is marked as an uncovered zone using the following symbol \(\square\). If a query requests information from \(z_{5}\), the device will respond by noting that it is an uncovered zone._  
\[li_{d_{3}}=\left[\begin{array}{ccc}z_{3}&z_{4}&z_{5}\\ *&d_{4}&\square\end{array}\right]\]
# A Survey of Candidate Waveforms for beyond 5G Systems
## 3 Candidate Waveforms
### Filtered Orthogonal Frequency Division Multiplexing  
The F-OFDM waveform is still one of the most promising waveform candidates for 5G and beyond. It is based on OFDM, allowing to share some of the properties with OFDM-based designs, such as MIMO friendliness, PAPR reduction techniques based on discrete Fourier transform (DFT) precoding, and low complexity channel equalization in the receiver, since it relies on a CP and does not require any interference cancellation algorithm [44]. However, F-OFDM relies on sub-band based splitting and filtering, allowing the co-existence of different time-frequency granularities, where independent OFDM systems can co-exist in the assigned bandwidth [45]. This way, this system allows inter-sub-band asynchronous transmission to optimize the communication based on the different channel conditions and enabling diverse applications by adopting different numerologies(sub-carrier spacing, CP length, transmission time interval) in different sub-bands. Also, global synchronization is no longer a requirement.  
In Figure 2 it is presented the basic transmitter and receiver architecture of F-OFDM. In each sub-band, a filter is applied in order to obtain lower OOB emission and to suppress the inter-sub-band interference. This is made of the expanse of losing of the time domain orthogonality between consecutive OFDM symbols in each sub-band [45], inducing inter sub-band interference (ITSBI). In general, the sub-bands do not overlap with each other and a guard interval is employed between sub-bands to mitigate ITSBI. One of the main drawbacks F-OFDM, compared with OFDM, is its relatively high complexity due to the filtering operation [9]. Furthermore, by supporting asynchronous transmission with flexible sub-band numerology, inner-sub-band interference (INSBI) may arise along with the aforementioned ITSBI. These two interference classes depend on many factors, such as the timing offset between the different interfering users, the guard band length and the filters employed in each sub-band [51].
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 4 Model Details
### Segmentation Network  
Mainstream semantic segmentation networks include U-Net [34], SegNet [35], DeepLab [36], PSPNet [37], and HRNet [31]. This study selects HRNet for extreme low-light image enhancement due to the following advantages: (1) Multi-Scale Feature Retention: HRNet preserves multi-resolution features, enhancing feature extraction in low-light conditions and improving segmentation accuracy. (2) Cross-Branch Feature Fusion: Its multi-branch parallel structure and cross-branch feature aggregation enhance representation capability and generalization in semantic segmentation. (3) High Computational Efficiency: HRNet maintains high precision while enabling efficient inference, reducing the computational cost, and accelerating low-light image restoration. We integrate a pre-trained HRNet into the low-light image enhancement network to perform semantic segmentation on the enhanced image \(I_{\rm{out}}\), generating a segmentation map (segmap) and extracting multi-scale segmentation features to further assist enhancement.