esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Experimental Validation
Although the flow rate did not decrease as much as in the lower-flow-rate case, the transit time between the two sensors was much shorter since the flow rate increased Therefore, it is reasonable to increase the distance between the sensors to achieve better flow estimation when the flow rate increases. This is one of the motivations for the later section: the optimal temperature sensor location investigation. After validating the temperature and velocity quantities of interest for the low and high flow rates, the final step was to validate the flow rate estimated by the CCF method from the CFD simulations and compare it to the experimental estimation and the flow meter in the test facility. Figure 25 shows the CCF flow estimation based on the previous experimental results and the CFD simulation model. For the experimental data, a black \"x\" indicates the averaged CCF flow estimation value among five repetitions, with the error bar representing two standard deviations based on these repetitions. The CFD results are marked with a red \"o\". The overall CFD results are lower than the true flow rate. The main reason is that the CFD results measured the local flow rate near the wall, while the true flow rate is the average flow rate marked with a dashed line in Figures 21 and 23. The temperature was collected at the exact location as the endpoint of the two sensors near the wall in the experimental flow loop, which had a smaller local flow rate than the true flow rate. Additionally, Figure 25 shows that the CCF-estimated flow rate in the experiment was much lower than the true value. For the highest flow-rate case, even the two-standard-deviation bar does not reach the one-to-one line. As the main flow rate increased, the local turbulence became stronger. Comparing Figure 22 with Figure 24, the flow with a higher rate had less time to recover from being blocked by the injection pipe in the CFD simulations. It should be noted that the two sensor geometries were not modeled. Figure 22: Local flow-rate changes along the two sensor paths under a low flow rate. Therefore, in the experiment, both the injection pipe and sensors may have introduced extra local perturbations. This was not regarded as a good source for the correlated process signal because such local disturbances rapidly changed the local velocity distribution. They disappeared quickly and did not travel along the flow path to be detected by the second sensor. This only happened during high flow rates, mainly because during lower flow rates, such local perturbations did not dominate the correlated process signal, and the flow had more time to recover from being blocked. Potential future work will involve measuring the flow rate at the same locations as the two sensors instead of measuring the averaged mean flow rate. Tighter controls on the flow-rate measurements will be helpful, especially when the flow rate is high. Figure 24: Local flow-rate changes along the two sensor paths under a high flow rate. Figure 23: Velocity distribution under a high flow rate after water injection.
## 5 Simulations
### Transmission Delay  
The transmission delay of each algorithm is shown in Figure 8. Compared with the other three algorithms, SCR has the transmission delay decreased by some extent, which is shown in Table 4. Since SCR is cluster-based routing, and cluster members have strong social relationships with each other, it can reduce unnecessary data transmission, and thereby reduce the transmission delay.  
Figure 8: Transmission delay comparisons.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Algorithms** & **PRoPHETv2** & **DRAFT** & **BUBBLE** \\ \hline Infocom5 & 18.1 & 8.5 & 28.4 \\ Infocom6 & 8.5 & 3.7 & 27.3 \\ Cambridge & 4.2 & 3.4 & 10.5 \\ Intel & 9.5 & 3.5 & 14.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The PDR improvement of SCR compared with the other three algorithms (%).
# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 2 Techniques in Alzheimer's Disease Detection
### Deep Learning Models
#### 2.2.3 Recurrent Neural Networks (RNNs)  
Recurrent neural networks (RNNs) are a type of neural network designed to deal with sequential data by maintaining a hidden state that captures information about previous elements in the sequence. RNNs repeat connections, allowing information to persist, which makes them suitable for tasks where temporal dynamics are important. The equation for updating the hidden state in an RNN can be represented as [62]:  
\[h_{t}=f(W_{x}x_{t}+W_{h}h_{t-1}+b) \tag{4}\]  
where the hidden state at the time step is the input at the time step and are weight matrices, and \(b\) is the bias term. The function \(f\) is typically a non-linear activation function like tanh or ReLU. RNNs are particularly useful in scenarios where sequences of inputs can provide additional context, such as analyzing changes in brain scans over time. They can be used to analyze MRI scan sequences, helping to identify patterns or changes that indicate progression from MCI to AD. By tracking patient data over time, RNNs provide insights into the effectiveness of treatment or the progression of the disease [5].  
Table 3 provides an overview of previous articles and summarizes the models used and their limitations and advantages. The primary limitation in all models is \"inadequate explanation\", suggesting a need to improve the interpretability of AI models for AD detection. Despite this, each model offers distinct advantages, and each method addresses specific challenges but also highlights the ongoing need for more interpretable AI models.
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 4 Application of UAV-Aided Non-Terrestrial Networks in UAIS
### Connected and Automated Vehicles in UAV-Aided NTNs
#### 4.2.2 6G NTN-Enabled CAV Capabilities
Researchers [153] proposed a case study of a UAV-assisted network for CAVs V2X network utilizing random scattering by an IRS. Researchers [157] proposed a 5G-based IRS communication for secure V2X to enable applications such as advanced self-driving and extended sensing in the case of a UAV-V2X scenario. Open issues, such as the placement of UAV-IRS platforms, optimization of beamforming and IRS supported edge computing, are promising directions for further investigation [156; 157].
# Evaluating the Impact of Pre-Configured Uplink Resources in Narrowband IoT
## 6 Conclusions  
This paper examined the impact of PURs on NB-IoT energy consumption and compared it with those of EDT and the default RAP mechanism. We tested PURs using the LENA-NB framework in ns-3, and our tests indicated that PUR significantly reduces energy consumption and latency compared to the default RAP and EDT. By pre-allocating network resources, PURs reduced latency by 2.5-3.5 times and energy consumption by 2.1 times compared to the default RAP, while EDT reduced latency by 1.5-2 times and energy consumption by 1.6 times.  
Our research shows that using PURs and EDT can significantly improve the energy efficiency and overall performance of NB-IoT networks. Based on our simulation results, we strongly recommend that these optimization techniques be widely adopted in future IoT deployments to create more sustainable and efficient CIoT networks. However, we still need to verify our findings in real-world deployments, which we plan to perform once EDT and PURs are supported in commercial cellular networks. Additionally, we aim to explore the limitations and possibilities of using PURs for large-scale deployments. It will be crucial to assess the scalability of shared and dedicated PURs, and we plan to address this in our future work.  
Conceptualization, M.T.A.; methodology, M.T.A.; software, M.T.A.; validation, M.T.A., P.J., K.-J.G. and A.B.; formal analysis, M.T.A.; investigation, M.T.A.; resources, M.T.A.; data curation, M.T.A.; writing--original draft preparation, M.T.A.; writing--review and editing, M.T.A. and K.-J.G.; visualization, M.T.A.; supervision, K.-J.G., A.B., J.E., S.A., M.R. and C.W. All authors have read and agreed to the published version of the manuscript.  
This work was carried out within the Data-driven Latency-sensitive Mobile Services for a Digitalized Society (DRIVE) project, which is partly funded by the Knowledge Foundation of Sweden, and in the course of the projects 5 hine and Competence Center 5G.NRW, funded by means of the Federal State NRW by the Ministry for Economic Affairs, Industry, Climate Action and Energy (MWIKE) under the Funding ID 005-2108-0073 resp. 005-01903-0047./2.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 2 Background
### Pipeline Leak Detection  
It is generally accepted that the most economic, eco-friendly, and safest way to transport large amounts of gas and liquid products over long distances is through pipelines [27]. On the condition that a pipeline is maintained properly, its operational lifetime can stretch over several decades. The main factors that cause pipeline failures (leaks) include corrosion, natural hazards, and mechanical damage (commonly from an excavation) [28]. A pipeline leak has the potential to cause significant environmental and financial costs, depending on what product was being transported at that time [29].  
While preventing leaks is a principal goal in pipeline design, construction, and maintenance, the precautionary principle mandates that a pipeline operator also be prepared to detect and stop leaks if they occur. Many jurisdictions, including the US and Canada, will also require the operator to mitigate any environmental harm done at its own expense. Leak-detection systems are one technology employed for this purpose. They can detect and localize leaks as soon as they occur, which is a key step in stopping the leak and minimizing harm done. A broad range of leak-detection systems (LDSs) are utilized in energy pipeline industries that are recognized by the American Petroleum Institute (API). These LDSs are organized in three categories: non-continuous external LDS (periodic inspections for damage or leaks), continuous external LDS (condition monitoring via sensors), and continuous internal LDS (sensor streams are continuously compared with a computational model; also known as Computational Pipeline Monitoring, CPM [30]) [31].  
CPM systems obtain field measurements from the pipeline's supervisory control and acquisition (SCADA) system and use a computational algorithm to determine if a leak is present [30]. There are number of CPM algorithms in use, ranging from basic calculations and hydraulic models to artificial intelligence and machine learning techniques. Inferential sensing, in particular, has received considerable attention lately. The general concept is to estimate a complex system state (which is not directly observed) by combining readings from a number of simple sensors; machine learning has recently been a favored technique for estimating this state [32]. Applications to LDS have been reported in [33; 34].
## 3 Results
### Evaluation of MERRA-2 SSR
#### 3.1.2 At Station Level
The obtained results for the absolute and relative bias show that MERRA-2 generally overestimates SSR (both absolute fluxes and their anomalies) with respect to GEBA, underestimating it (green and blue colors in Figure 5) over some tropical, subtropical, polar and subpolar regions. Specifically, concerning SSR fluxes, MERRA-2 overestimates SSR in 998 stations and underestimates it in only 101 stations, resulting in an overall overestimation equal to 24.25 W/m\({}^{2}\). On the other hand, concerning the comparison against the much fewer (70) BSRN stations, MERRA-2 shows an overall overestimation of SSR fluxes by 10.31 W/m\({}^{2}\) (53 out of 70 overestimate). The average value of the absolute and relative % bias for all GEBA stations is 25.68 W/m\({}^{2}\) and 16.3%, respectively, with values ranging from \(-\)64.24 to 141.07 W/m\({}^{2}\) and from \(-\)20.3 to 103.9%, respectively. 75% of the GEBA stations have absolute and relative % bias values less than 33.21 W/m\({}^{2}\)/21.9%, respectively (Figure S5). Smaller biases are found against BSRN stations, namely an average absolute and relative % bias equal to 12.38 W/m\({}^{2}\) and 7.3%, respectively, with relative % and absolute bias values ranging from \(-\)15.8 to 35%, and from \(-\)36.32 to 54.97 W/m\({}^{2}\), respectively, and with 75% of the stations having relative % and absolute bias less than 13.7%/20.83 W/m\({}^{2}\), respectively. Focusing on China, where many stations and more similar studies to the present one exist, our results are in line with the study of [65], who evaluated both MERRA-2 and MERRA SSR fluxes against 2400 CMA stations from 1980 to 2014 and reported that MERRA-2 has a high mean bias over China, which is greater in its southern part, due to its incorrect assessment of cloud fraction and aerosol loadings. Moreover, previous studies [37; 47; 96; 100; 101] have reported that aerosols might be another important source of the large positive biases in China, where the rapid economic development produced large amounts of pollution. The results obtained by ([39], and references therein) further proved that the biases in the MERRA, Era-interim, JRA-55, NCEP-NCAR, NCEP/DOE and CFSR reanalysis cloud fraction result in SSR biases, and that both clouds and aerosol representation schemes should be improved in the reanalysis estimates. Figure 5: Global distribution of correlation coefficient (**i,ii-a**), bias (**i,ii-b**) and relative percent bias (**i,ii-c**) root mean squared error (**i,ii-d**), relative root mean squared error (**i,ii-e**), computed for the comparison between MERRA-2 and each GEBA (**(i)**, left column) and BSRN (**(ii)**, right column) station SSR fluxes, except for R that is computed using deseasonalized SSR anomalies.
## 4 Results and Discussions
### Influence of Working Parameters
#### 4.3.2 Transmission Flow Rate  
Figure 24 shows the velocity contours at different transmission flow rates (\(Q_{t}\)). It clearly shows that the variation of \(Q_{t}\) has no effect on the flow velocity distribution in the lower part of the collector; therefore, it has no effect on the ability to peel and lift the nodules from the ground. Figure 24 also shows that, as \(Q_{t}\) increases, the flow velocity in the conveying channel increases, thus allowing the nodules to be transported more smoothly and quickly. Most of the acquisition flow \(Q_{a}\), which is 160 m\({}^{3}\)/h, also entered the conveying channel and mixed with the transmission flow to co-transport the nodules. Thus, even if \(Q_{t}\) is only 40 m\({}^{3}\)/h, the total flow rate in the channel can still guarantee the smooth transport of the nodules. As a result, the collection efficiency (\(\eta\)) does not vary much at different \(Q_{t}\), and it was all above 95%, as shown by the black curve in Figure 25. The increase in \(Q_{t}\) will also inevitably make the power consumption (\(P\)) larger, as shown by the red curve in Figure 25. In addition, as \(Q_{t}\) increases, the percentage of \(Q_{t}\) compared to (\(Q_{a}+Q_{t}\)) keeps increasing, thus making the power curve shown in Figure 25 increase faster with increasing \(Q_{t}\). In particular, it was found that \(P\) is 21.21 kW at a \(Q_{t}\) of 80 m\({}^{3}\)/h, which is only a 9% increase from the 19.55 kW at a \(Q_{t}\) of 30 m\({}^{3}\)/h. Therefore, it is recommended that the transmission flow rate \(Q_{t}\) of the hydraulic collector should not exceed 80 m\({}^{3}\)/h so as to avoid the unnecessary waste of energy.  
Figure 24: Velocity contours of the hydraulic collector at different transmission flow rates (\(Q_{t}\)).  
Figure 23: Collection efficiency (\(\eta\)) and power (\(P\)) vs. acquisition flow rate (\(Q_{a}\)).
# Recent Developments and Emerging Trends in Paint Industry Wastewater Treatment Methods
## 3 Paint Industry Wastewater Treatment Methods
### Electrochemical Treatment
#### 3.3.3 Electroc coagulation  
Electrocoagulation uses an electric current for the removal of SS from paint industry effluents. The aim of electrocoagulation is the removal of particles from wastewater by destabilizing/neutralizing the repulsive forces that keep the particles suspended in water. When the repulsive forces are neutralized, the suspended particles will form larger particles that can settle down for easier separation from water [69]. The current creates an extreme temperature, concentrated in the liquid, coagulating its contents. Different electrode materials have been studied for this purpose, such as boron-doped diamond electrodes (BDDE) [28] or aluminum electrodes [63]. Better results, according to the COD removal, were obtained for BDDE using Al\({}_{2}\)(SO\({}_{4}\))\({}_{3}\) as a coagulant to treat water-based acrylic wastewater from the paint industry [28]. COD removal by using the BDDE has been studied in the pH range between 4 and 7 using Al\({}_{2}\)(SO\({}_{4}\))\({}_{3}\) as a coagulant agent. The COD removal was 85% with a smaller dose of coagulant (97% removal TS) and treated water was appropriate for disposal. In the case of aluminum electrodes, COD was reduced in total by 55% and the color of the wastewater by 56% after 60 min of electrolysis [63]. On the other hand, the application of BDDE increased turbidity with a higher coagulant dose required due to saturation of the solution [28; 70].  
A comparison of electrocoagulation with monopolar and bipolar wiring performance (aluminum electrodes) showed that monopolar connection is more efficient at reducing the COD value by 55%, (bipolar connection only 43%) with an applied voltage of 1.5 V during 1 h of electrolysis [63].  
A novel continuous electrocoagulation system has been developed and studied for the treatment of pain industry effluents. The system uses advanced electricity and flow control [65]. For both batch (i = 14.2 mA/cm\({}^{2}\)) and continuous (i = 14.4 mA/cm\({}^{2}\)) processesa higher current density than in a previous study [28] was used causing a slight increase in wastewater pH levels, the pH of the original wastewater was reported at 6.6 and it did not significantly change during the electrocoagulation process (7.15 after the batch process and 7.0 after the continuous process). Comparison of the two parallel analyses of both processes, similar results were obtained. The average efficiency of chemical oxygen demand removal was 68% in batch and 69.7% in the continuous system [65].  
The use of chemical coagulants/flocculants such as metal salts or polyelectrolytes is the main advantage of electrocoagulation over chemical coagulation/flocculation. The coagulants in electrocoagulation are generated in situ by the electrolytic oxidation of an appropriate anode material which results in much less sludge generation.
# Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability
###### Abstract  
In the development of autonomous driving technology, 5G-NR vehicle-to-everything (V2X) technology is a key technology that enhances safety and enables effective management of traffic information. Road-side units (RSUs) in 5G-NR V2X provide nearby vehicles with information and exchange traffic, and safety information with future autonomous vehicles, enhancing traffic safety and efficiency. This paper proposes a communication system for vehicle networks based on a 5G cellular network with RSUs consisting of the base station (BS) and user equipment (UE), and validates the system performance when providing services from different RSUs. The proposed approach maximizes the utilization of the entire network and ensures the reliability of V2I/V2N links between vehicles and each RSU. It also minimizes the shadowing area in the 5G-NR V2X environment, and maximizes the average throughput of vehicles through collaborative access between BS- and UE-type RSUs. The paper applies various resource management techniques, such as dynamic inter-cell interference coordination (ICIC), coordinated scheduling coordinated multi-point (CS-CoMP), cell range extension (CRE), and 3D beamforming, to achieve high reliability requirements. Simulation results demonstrate improved performance in outage probability, reduced shadowing area, and increased reliability through decreased interference and increased average throughput when collaborating with BS- and UE-type RSUs simultaneously.  
5G-NR-V2X; RSU; cooperative resource management; cell range expansion; 3D beamforming +
Footnote †: journal: Sensors  
**Citation:** [PERSON]; [PERSON] Resource Management for Collaborative  
SC-NR-V2X RSUs to Enhance V2I/N Link Reliability. _Sensors_**2023**, 23, 3989. [[https://doi.org/10.3390/s2308398](https://doi.org/10.3390/s2308398)]([https://doi.org/10.3390/s2308398](https://doi.org/10.3390/s2308398))
## 5 Experimental Results
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **Method** \\ \hline \multirow{3}{*}{Rotation} & 0.841 & 0.816 & 0.893 & 0.796 & 0.803 & 0.827 & A \\  & 0.863 & 0.814 & 0.905 & 0.826 & 0.841 & 0.897 & B \\  & 0.869 & 0.857 & 0.917 & 0.829 & 0.839 & 0.904 & C \\ \hline \multirow{3}{*}{Cropping} & 0.786 & 0.794 & 0.762 & 0.802 & 0.756 & 0.812 & A \\  & 0.794 & 0.831 & 0.754 & 0.827 & 0.794 & 0.808 & B \\  & 0.813 & 0.834 & 0.791 & 0.842 & 0.816 & 0.819 & C \\ \hline \multirow{3}{*}{Histogram Equalization} & 0.834 & 0.819 & 0.847 & 0.851 & 0.809 & 0.819 & A \\  & 0.847 & 0.837 & 0.855 & 0.869 & 0.814 & 0.810 & B \\  & 0.881 & 0.865 & 0.873 & 0.894 & 0.852 & 0.844 & C \\ \hline \multirow{3}{*}{Contrast Adjustment} & 0.871 & 0.854 & 0.883 & 0.901 & 0.872 & 0.821 & A \\  & 0.879 & 0.867 & 0.870 & 0.914 & 0.884 & 0.825 & B \\  & 0.891 & 0.893 & 0.879 & 0.914 & 0.895 & 0.853 & C \\ \hline \multirow{3}{*}{JPEG 25} & 0.902 & 0.913 & 0.894 & 0.921 & 0.908 & 0.917 & A \\  & 0.909 & 0.924 & 0.876 & 0.917 & 0.911 & 0.921 & B \\  & 0.918 & 0.929 & 0.901 & 0.930 & 0.908 & 0.927 & C \\ \hline \multirow{3}{*}{Gaussian Noise} & 0.875 & 0.881 & 0.867 & 0.855 & 0.902 & 0.873 & A \\  & 0.886 & 0.896 & 0.871 & 0.849 & 0.914 & 0.881 & B \\  & 0.881 & 0.905 & 0.889 & 0.873 & 0.913 & 0.897 & C \\ \hline \multirow{3}{*}{Resizing} & 0.924 & 0.917 & 0.931 & 0.922 & 0.919 & 0.925 & A \\  & 0.929 & 0.925 & 0.929 & 0.934 & 0.928 & 0.925 & B \\  & 0.946 & 0.955 & 0.953 & 0.951 & 0.937 & 0.942 & C \\ \hline Salt and & 0.857 & 0.882 & 0.849 & 0.893 & 0.901 & 0.839 & A \\ Pepper & 0.846 & 0.880 & 0.861 & 0.899 & 0.895 & 0.867 & B \\ Noise & 0.869 & 0.901 & 0.897 & 0.907 & 0.906 & 0.875 & C \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Similarity Ratio (SR) values after common attacks (A: DWT B: Block DWT C: PSF Block DWT). Figure 5: Similarity ratio values for extracted watermark from Lena image using FSF block water-marking.