esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### FMLSP Restores Gut Microbial Composition in Mice  
We also noticed alterations in the gut microbiota's composition at the genus level (Figure 6A). We found that FMLSP intervention significantly increased the abundance of beneficial bacteria such as _Akkermansia_, _Lachospiraceae_NK4A136_group_, _Alloprecrotella_, and _Lactobacillus_ and markedly decreased the abundance of harmful bacteria such as _Rikenellaceae_RC9_gut_group_ in T2 DM mice (Figure 6B). The results suggest that FMLSP intervention increases the beneficial microorganisms that protect the body, decreases the presence of harmful microorganisms, and restores the balance of the gut microbiota.  
According to Figure 6C,D from the LDA effect size (LEfSe), for the NC, T2 DM, Met, FMLSPL, and FMLSPH treatment groups, 5, 9, 12, 1, and 1 bacterial clades showed statistically significant differences, respectively. Some of the bacterial taxa with large differences in abundance belonged to _Oscillospiraceae_ and _Lachospiraceae_. Meanwhile, the relationship between gut microbiota and physiological markers of diabetes mellitus is examined in Figure 7. Three beneficial bacteria, _Akkermansia_, _Alloprecrotella_, and _Lactobacillus_, showed a significantly negative correlation with TC, TG, LDL-C, ALT, AST, insulin, and GSP and a significantly positive correlation with HDL-C, liver glycogen, HK, PK, CAT, GSH-PX, and SOD. However, _Lachospiraceae_NK4A136_group_ only showed a significantly negative correlation with the content of MDA. _Rikenellaceae_RC9_gut_group_, and _Enterrohudus_, two harmful bacteria, were the opposite of beneficial bacteria. Consistent with previous experimental data, it indicates that FMLSP can ameliorate symptoms of IR, disorders of glucolipid metabolism, liver injury, and oxidative stress by regulating the abundance of gut microbiota. In summary, mice with diabetes have an imbalanced gut flora. Through the promotion of gut metabolism, FMLSP may mitigate the effects of diabetes on the body.  
Figure 5: Effect of FMLSP on the taxonomic diversity of gut microflora in T2 DM mice (**A**) OUT; (**B**) Chao1 index; (**C**) Shannon index; (**D**) PCoA; (**E**) abundance at the phylum level of gut microbiota; (**F**) _Frimicute_ richness; and (**G**) _Bacteroidota_ richness. (*) \(p<0.05\) and (**), \(p<0.01\) vs. T2 DM group.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Comparison Dimension** & **Characteristics of Existing Studies** & **Innovative Points of This Article** & **Contributions and Strengths** \\ \hline \multirow{3}{*}{Environmental factors coverage} & Focus more on single pollutants or short-term environmental impacts [13] & Long-term performance decay under chemical, dry-wet and freeze-thaw cycles is systematically studied & Provide lifecycle prediction models in complex environments to fill the long-term dynamic evaluation gap \\ \hline \multirow{3}{*}{Material improvement measures} & Optimized mainly for traditional bentonite or single modified materials [14] & Comprehensive evaluation of a wide range of new materials & Propose material selection and proportioning guidelines for efficient cut-off wall design in heavy metal-polluted sites \\ \hline \multirow{3}{*}{In situ assessment techniques} & Reliance on single-principle evaluation methods, not systematic [15] & ERT, CFTU, and dynamic stress modeling are integrated for multi-parameter detection & Improve leakage location accuracy and support in situ dynamic monitoring \\ \hline \multirow{3}{*}{Dynamic performance modeling} & Prediction of service life based on static parameters or empirical formulas [16] & Combining multiple models to predict long-term performance & Transcend traditional model limitations, reduce prediction errors, and apply to complex pollution \\ \hline \multirow{3}{*}{Integrated pollution adaptation} & The effects of combined heavy metal pollution and dynamic concentration shifts are often overlooked [17] & Analyze ion threshold effects and material compatibility for multi-heavy metal pollution & Offer theoretical basis for cut-off wall design in high-pollutant sites \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the innovativeness of this study with the recent related literature. effects on the operational performance of SB cutoff walls. Section 4 quantitatively explores the permeability degradation mechanisms in soil-bentonite vertical containment systems under environmental stressors. Section 5 evaluates state-of-the-art in situ assessment techniques for SB vertical cut-off walls. Section 6 describes potential future technological developments in soil-bentonite vertical cut-off wall monitoring. The concluding section synthesizes research findings and proposes future research directions. The flowchart is shown in Figure 2. This systematic investigation contributes to enhanced understanding of performance evolution in vertical cut-off wall systems and provides methodological support for their lifecycle management.
# Dynamic Energy Management Strategy of a Solar-and-Energy Storage-Integrated Smart Charging Station
## 5 Simulation Inputs and Results
### Simulation Results
#### 5.2.4 Operational Income of EVCS  
This study simulated the situations for the entire months of July 2022 (summer) and January 2023 (non-summer) and derived the operational levels and cost scenarios as indicated in Table 3. A comparison is made between the conditions with and without the integration of the EMS (and photovoltaic ESS). Furthermore, the scenarios under different contract capacity assumptions with the implementation of the EMS were also assessed.  
Based on the simulated operational outcomes, the overall revenue of the charging station (including revenue from charging and solar energy feed-in) is calculated, and the net operational income after deducting the total costs is shown in Table 4.  
Based on the comprehensive analysis of simulation results and revenue evaluation (see Table 5), the optimal configuration for maximizing the net operational income of the EVCS involves the implementation of EMS, solar, and energy storage systems with the setting of a contracted capacity at 300 kW. This study confirms that the adoption of Smart EMS contributes significantly to the operational net income of the charging station by 1.25 times.  
In the above simulation of economic performance and cost considerations, not all costs and long-term dynamic updates and corrections are fully included. In this regard, there is still room for improvement in modeling the lifespan and costs of the system, which needs to be expanded and realized. However, what is clearly observable is that the integration of solar systems and energy storage systems with charging stations has a significantly positive impact on economic efficiency.
# Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments
## 6 Multi-Operator Game for Channel Selection
### Best Response Algorithm for Computing the NE
In Proceedings of the 2019 XVI International Symposium \"Problems of Redundancy in Information and Control Systems\" (REDUNDANCY), Moscow, Russia, 21-25 October 2019; pp. 199-204. * [PERSON] et al. (2017) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Exploring: Extending the performance of lora by suitable spreading factor allocations. In Proceedings of the 2017 IEEE 13 th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Rome, Italy, 9-11 October 2017; pp. 1-8. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A novel rate and channel control scheme based on data extraction rate for lora networks. In Proceedings of the 2019 IEEE Wireless Communications and Networking Conference (WCNC), Marrakesh, Morocco, 15-18 April 2019; pp. 1-6. * [PERSON] and [PERSON] (2018) [PERSON]; [PERSON]; [PERSON] A scheduling algorithm for improving scalability of lorawan. In Proceedings of the 2018 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea, 17-19 October 2018; pp. 1383-1388. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Performance evaluation of machine learning based channel selection algorithm implemented on iot sensor devices in coexisting iot networks. In Proceedings of the 2020 IEEE 17 th Annual Consumer Communications Networking Conference (CCNC), Las Vegas, NV, USA, 10-13 January 2020; pp. 1-5. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Improving reliability and scalability of lorawans through lightweight scheduling. _IEEE Internet Things J._**2018**, \(5\), 1830-1842. [CrossRef]
* [PERSON] Charging and rate control for elastic traffic. _Eur. Trans. Telecommun._**1997**, \(8\), 33-37. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Energy-efficient joint scheduling and power control in multi-cell wireless networks _IEEE J. Sel. Areas Commun._**2016**, _34_, 3409-3426. [CrossRef]
* [PERSON] and [PERSON] (2013) [PERSON]; [PERSON] Learning equilibria in games by stochastic distributed algorithms. In _Computer and Information Sciences III_; Springer: Berlin, Germany, 2013; pp. 31-38. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON] Modeling the energy performance of lorawan. _Sensors_**2017**, _17_, 2364. [CrossRef]
## 2 Materials and Methods
### Tailpipe Measurements
#### 3.1.2 CO\({}_{2}\) Concentrations Comparison  
Figure 5 presents the differences between the CO\({}_{2}\) analyzers measuring at the tailpipe of the vehicle. As all of the analyzers had similar specifications, their mean value was considered as the reference value. For the SI vehicle, the measured absolute CO\({}_{2}\) concentrations were within 11% and 13%, while for the CI vehicle, they were between 3% and 7%. The differences between the instruments were within approximately 1% for the SI vehicle, and approximately 2% for the CI vehicle. The smaller uncertainty at the 11-13% range was probably due to the measured range, which was close to the calibrated concentration (around 14%). There was no particular trend of any of the PEMS underestimating, overestimating, or having linearity issues. There was an indication that the tailpipe laboratory analyzer was generally measuring slightly lower than the PEMS.  
There was one point that had higher differences, the cold start with the SI vehicle (PEMS #1 measured 4% higher than the mean), as shown in Figure 6, which presents the real time data of the three analyzers for the two vehicles. The agreement was very good, with the exception of the cold start. PEMS #1 measured a 'wet' concentration (heated NDIR, i.e., without using a drier to remove the water content), while PEMS #2, PEMS #3, and the laboratory equipment measured on a dry basis and used a correction factor to report in the 'wet' basis. This correction factor was based on the RDE regulation based on the fuel composition and the measured CO and CO\({}_{2}\) concentrations (approximately 0.89 for the SI and 0.95 for the CI vehicle). The observed cold start differences were probably due to the condensation and evaporation of water during the cold start, which the dry-wet corrections do not take into account accurately. Nevertheless, the effect of this over (or under) estimation for the whole WLTC was small (see larger symbols in Figure 5, all within 2.5%).  
The results of Figure 5 are in agreement with the specifications of the analyzers of 2%, which however refers to comparisons with reference to the calibration gas in a cylinder. Similar differences between the analyzers (2% for CO\({}_{2}\)) were also found by another study [10].  
Figure 4: Examples of real time comparison of systems measuring exhaust flow. **Left panel:** SI vehicle.  
Figure 5: Differences of CO\({}_{2}\) analyzers. **Left panel:** SI vehicle. **Right panel:** CI vehicle. Each point is the mean value of three repetitions. The dashed lines show the 2% limit when compared with the calibration span gas.
## 4 A Study on the Application of Nine Bridges in the Proposed Prioritization Model
### Prioritizing Maintenance of Data-Based Bridges  
The proposed maintenance prioritization model was applied to bridges using data collected from open sources, and the results were derived. The calculation process for deriving the maintenance prioritization results is shown in Figure 5, and the results of applying the data-based bridge maintenance prioritization model are illustrated in Figures 6-8.  
Figure 5: Example entered into a Microsoft Excel spreadsheet for bridge maintenance and Bridge Health Index (BHI) score calculation.  
Figure 6: Distribution chart based on Bridge Maintenance Priority (BMP) score results.  
To evaluate the maintenance priorities of the bridges, Bridge A achieved the highest score of 0.2626414, indicating the most urgent need for maintenance. In contrast, Bridge D received a score of 0.1187838, representing the lowest priority for maintenance. Bridge A obtained the highest scores in both the Bridge Maintenance Priority (BMP) score segment (0.1246014) and the Priority Index (PI) score segment (0.13804). Additionally, it recorded the highest scores in the risk and criticality sections of the BMP score, with values of 0.341 and 0.63, respectively. The comparison score section also showed a score of 0.58, the second highest among all bridges. These evaluation results are expected to have a significant impact on bridge safety and maintenance, allowing for the efficient allocation of resources for bridge management. Furthermore, utilizing this scoring system is crucial for being able to make prompt and informed decisions when developing maintenance plans.
# Design of a Compact Microstrip Decoupled Array
## 2 Materials and Methods
### Decoupling Feed Network
Figure 12: Equivalent circuit schematics. (**a**) Equivalent circuit of the transmission line; (**b**) equivalent schematic of the decoupling network. The transmission (ABCD) matrix presented in Figure 11 is  
\[T_{1}= \begin{bmatrix}\cos\theta&jZ\sin\theta\\ jY\sin\theta&\cos\theta\end{bmatrix}  \tag{9}\]  
\[T_{2}= \begin{bmatrix}1&0\\ jY_{s}\tan\theta_{s}&1\end{bmatrix} T_{1} \begin{bmatrix}1&0\\ jY_{s}\tan\theta_{s}&1\end{bmatrix} T_{1} \begin{bmatrix}1&0\\ jY_{s}\tan\theta_{s}&1\end{bmatrix}  \tag{10}\]  
For \(T_{1}=T_{2}\), the impedance of the transmission line and the open branch can be calculated as  
\[Z_{m}=\frac{Z\sin\theta}{\sin\theta_{m}} \tag{11}\]  
\[Z_{s}=\frac{Z\sin\theta\tan\theta_{s}}{\cos\theta_{m}-\cos\theta} \tag{12}\]  
Considering the fabrication accuracy, the equivalent microstrip line cannot be fabricated and used if its width is less than 0.2 mm. Fixing the \(\theta_{s}\) to 20\({}^{\circ}\), the results of the final optimization parameters are recorded in Table 2, and the final feeding network model is shown in Figure 13.  
As shown in Figure 14, the power fed to the network at 2.45 GHz has equal error in the two ports, at 0.1 dB, and the port isolation is greater than 25 dB. We combine the two parts and match the external ports. The antenna model of the final design is shown in Figure 2. Figure 15 shows the changes in the \(S\) parameters of the antenna before and after decoupling. The reduction in \(S_{21}\) by about 30 dB after antenna decoupling indicates that the designed decoupling network can isolate the two outer ports well. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{4}{c}{**Main Line**} & \multicolumn{4}{c}{**Equivalent Unit**} \\ \hline \(\theta_{m}\) (\({}^{\circ}\)) & \(Z_{m}\) (\(\Omega\)) & \(W_{m}\) (mm) & \(L_{m}\) (mm) & \(\theta_{s}\) (\({}^{\circ}\)) & \(Z_{s}\)/\(2\) (\(\Omega\)) & \(W_{s}\) (mm) & \(L_{s}\) (mm) \\ \hline
22.5 & 130.6 & 0.24 & 5.5 & 20 & 42 & 2.58 & 4.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Equivalent model size. Figure 13: The structure of the miniaturization feed network.
## 2 Theory
### The Mean Shift Algorithm
Thus, we analyze the second term, to define:  
\[f_{n,K}=\frac{C_{h}}{2}\sum_{i=1}^{n_{k}}w_{i}k\left(\left|\left|\frac{y-x_{i }}{h}\right|\right|^{2}\right) \tag{21}\]  
which is very similar with the definition of the kernel density function. The mean shift vector also can be calculated, pointing towards the center point y of the target's actual position from the candidate point \(y_{0}\), satisfying:  
\[m_{h,G}(y)=y-y_{0}=\frac{\sum_{i=1}^{n_{k}}x_{i}w_{i}g\left(\left|\left|\frac {y-x_{i}}{h}\right|\right|^{2}\right)}{\sum_{i=1}^{n_{k}}w_{i}g\left(\left| \left|\frac{y-x_{i}}{h}\right|\right|^{2}\right)}-y_{0} \tag{22}\]  
Supposing that the distribution of target's model is \(\hat{q}_{u}\) u = 1,...,m, the estimated position in the current frame is \(y_{0}\), and the error allowed is \(\epsilon\), then the mean shift algorithm can be implement by the following steps, as is shown in Figure 6:1. Assume that the target's initial position in the current frame is the central point \(\mathrm{y}_{0}\) of the target in last frame. Firstly, compute the probability density estimation \(\hat{p}_{u}\big{(}\mathrm{y}_{0}\big{)}\) of the candidate target in current image frame using the same method as Equation (15). Then, utilize Equation (17) to calculate the Bhattacharyya coefficient \(\rho(\mathrm{y}_{0})\);
2. Compute the weight of each bin \(\{\mathrm{w}_{\mathrm{i}}\}_{\mathrm{i=1,..n}}\) with Equation (20);
3. Update the center point \(\mathrm{y}\) of target region;
4. Calculate the Bhattacharyya coefficient \(\rho(\mathrm{y})\);
5. If \(\rho(\mathrm{y})>\rho(\mathrm{y}_{0})\), the center of the search region transfers to point \(\mathrm{y}\);
6. If \(\mathrm{\mid y-y_{0}\mid<\varepsilon}\), the point \(\mathrm{y}\) is considered as the center point of the target region in the current frame, else skip to step 1.
# Transformer: An OS-Supported Reconfigurable Hybrid Memory Architecture
## 3 Design and Implementation
### Page Hotness Monitoring
#### 3.1.1 Page Access Counting  
Most modern operating systems use virtual addresses to manage memory. A _page table entry_ (PTE) is exploited to record the virtual-to-physical address mapping for each page. Due to the vast range of virtual address space, multi-level page tables are used to reduce the storage overhead of PTEs in modern OS. In the newest version of the 64-bit Linux kernel, the number of page table levels reaches four even five. The page size is usually 4 KB, and the lowest 12-bit address is the offset in a page because the address is aligned to 4 KB. As shown in Figure 3, the lowest 12 bits of the last level page table record the status of a page. Among them, the _access bit_ (red 'A') is used by the kernel to judge whether this page should be reclaimed. _Translation Lookside Buffer_ (TLB) only caches pages whose _access bit_ is set to 1. When the _access bit_ is cleared, the entry in the TLB of this page would be flushed. The _access bit_ can be modified by hardware and cleared by OS. Upon a page fault, the CPU needs to perform page table walking to implement virtual-to-physical address translation. Meanwhile, the hardware modifies the _access bit_ of this page.  
Transformer uses a kernel thread in the background to monitor the page access for each process. For each monitored process, the kernel thread scans page tables and checks the _access bit_ in page table entries periodically. If the _access bit_ has been modified, meaning this page has been accessed recently, we obtain one access count and record it. Then, the kernel thread clears the _access bit_ immediately. In the next period of scanning, the kernel thread will check the _access bit_ again to determine whether this page has been accessed between these two adjacent periods. The kernel thread keeps sampling the _access bit_ till the monitored process is finished. The number of page accesses counted by the kernel thread is not the actual access counts. However, it is unnecessary to track all access counts accurately in our architecture. Instead, it only needs to figure out the top-K hot pages according to the relative access frequency, which can be determined by programmers. In our experiments, we select the top 20% of frequently accessed pages as hot pages, like the previous work [11]. Therefore, the statistical results can reflect the page hotness of the monitored process.
## 6 Conclusions  
In order to improve the wireless transmission performance of a WSN in an underground coal mine, a WSN based on time-frequency coded cooperative MC-CDMA for underground coal mines is proposed:  
1. Wireless sensor nodes with MC-CDMA wireless transmission can make full use of the open space, time and frequency resources of the underground coal mine to improve the wireless transmission performance.
2. Wireless sensor nodes with time-frequency coded cooperative wireless transmission can obtain coding gain and spatial diversity gain which will significantly improve the wireless transmission performance of sensors with bad channel conditions.  
Figure 8: Influence of the number of source sensors to the average BER. \(K=4\), \(N_{c}=128\); \(K=8\), \(N_{c}=256\); \(K=16\), \(N_{c}=512\).  
Figure 7: Influence of different power allocation scheme to the average BER. \(K=8\), \(N_{c}=256\).  
3. The MAI among sensors of the time-frequency coded cooperative MC-CDMA WSN can be reduced effectively by adopting the D-PSO algorithm with particle swarm optimization which will also significantly improve the wireless transmission performance of the proposed WSN.  
This work was supported by the Natural Science Foundation of China under Grant (51274018, 51474015) and the National Science & Technology Pillar Program (2013 BAK06B03).