esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In recent years, Branemark-type implants with a mechanized surface have been considered the gold standard in modern implantology, leading to a predictable behavior [1]. However, several studies demonstrated that the biocompatibility and cell viability could be improved [2] by modifying the roughness of the implant surface using different techniques [3]. Other authors have incorporated stem cells with the same objective [4]. Furthermore, biomaterials and stem cells have been frequently combined, representing a common strategy for bone tissue engineering applications [5].  
This roughness in the implant surface seems to be one of the essential factors in achieving adequate osseointegration [6, 7]. The increase in roughness has been shown to lead to an increase in the bone-to-implant contact percentage (BIC) [8]. With the creation of these new surfaces, the time needed to achieve osseointegration was reduced due to the improvement in osteoconduction and osteogenesis [9]. It was even postulated that the increase in roughness could prevent bone loss [10]. However, other authors expressed concern about the greater susceptibility of these rough surfaces to bacterial contamination [11]. Regardless of the degree of roughness, another factor to consider is that the implant surfaces undergo changes from their manufacture to their clinical use.  
One procedure proposed to improve the implant surface is based on non-thermal low-pressure argon plasma (NTLP-ArP) [12]. Some authors have already shown that NTLP-ArP is capable of enhancing the implant surface characteristics, and therefore, the earliest biological response without altering the original topography [13].  
One interesting strategy to be evaluated by the industry would be the development of mechanized surfaces to which we can confer high osseointegration capacity. The mechanized surface would receive a surface treatment that does not modify its roughness but significantly improves the osseointegration capacity. With these characteristics, we would obtain better behavior against peri-implant diseases by not having a rough surface. The possibilities of effective treatment and simple maintenance would be increased.  
Our null hypothesis was that NTLP-ArP does not represent an improvement in terms of BIC values compared to surfaces treated and not treated with resorbable blast media (RBM). The alternative hypothesis was the existence of differences between using or not using NTLP-ArP on both surfaces.
## 5 Result and Discussion
### Application for Data Simulation  
The data simulation used in this study were synthetic images and natural images. For synthetic images, original images were also set for the GT. To add more challenge in segmentation, we provided noise (Gaussian noise, salt-paper noise, and speckle noise) to each image. The synthetic images (SI01 and SI02) and natural images (NI01 and NI02) with their GT are visualized in Figure 1.  
The segmentation results of each method for SI01, SI02, NI01, and NI02 are shown in Figures 2-5. The methods run under two clusters for SI01 and NI01, while SI02 and NI02 run under three clusters.  
The accuracy of segmentation results was shown by the \(MCR\). As visualized in Figure 6, the minimum \(MCR\) was shown by the Nenomimo (FSSN-MM and MSNBurr-MM). For different types of noise, the Nenomimo gave a better performance in segmenting both the synthetic images and natural images. This indicated that the Nenomimo is more robust in handling noise than the GMM and the GGMM. Overall, for the simulation dataset, the Nenomimo segmented the images more precisely than the model-based normal distribution.  
Figure 1: Synthetic images: (**a**) SI01 and (**b**) SI01. Natural images: (**c**) NI01 and (**d**) NI02.  
Figure 2: Segmentation result of SI01 for different methods.  
Figure 4: Segmentation result of NI01 for different methods.  
Figure 3: Segmentation result of SI02 for different methods.  
The next application was for a real dataset. The MRI-based brain tumor segmentation was also done under the Nenomimo, the GMM, and the GGMM.
## 3 Case Study
Calculation of new hourly probability for modified end uses: Once \(P_{sum}(h)\) is obtained for each hour, the new probability \(P_{end\_use\_new}(h)\) is calculated by dividing \(P_{sum}(h)\) by the number of consumers C: \[P_{end\_use\_new}(h)\ \ \frac{P_{sum}(h)}{C}\] (22)  
Additionally, it is essential to verify that the following condition holds, ensuring that the total sum of the matrix \(P_{end\_use}\) remains constant:  
\[\sum_{h=1}^{24}\sum_{i=1}^{c}P_{end\_use}(h,i)=P_{total} \tag{23}\]  
Maintaining \(P_{total}\) constant is crucial to preserving the coherence of the model and adhering to the principle of conservation of the probabilistic phenomenon. 8. Imposing variability: To introduce variability in the probability for each hour h and each consumer \(i\), the value of \(P_{end\_use\_new}(h,i)\) will range between 90% and 110% of \(P_{end\_use\_new}(h)\), i.e., \[P_{end\_use\_new}(h,i)\in[0.9\cdot P_{end\_use\_new}(h),1.1\cdot P_{end\_use\_ new}(h)]\] (24)
9. Evaluation of the percentage error: The percentage error is calculated for each hour \(h\) of the day. The formula for the percentage error is given by \[Error(h)=\frac{E_{metered}(h)-E_{modelal}(h)}{E_{metered}(h)}\cdot 100\] (25) where \(E_{metered}(h)\) represents the measured consumption and \(E_{modelal}(h)\) represents the modeled consumption, calculated as \[E_{modelal}(h)=E_{total\_non\_mod}(h)+E_{pushing\ machine}(h)+E_{disthous \_new}(h)+E_{dryer}(h)\] (26)  
The described procedure, which is based on the difference between the real measured values (\(E_{meteral}(h)\)) and the consumption for which the previously assumed probabilities are not modified (\(E_{total\_non\_mod}(h)\)), allows for the calculation of new probability values for flexible end uses. In this way, it is possible to identify the hours in which the probability of using a specific end use is higher than in other hours by leveraging the difference between the profiles. The goal is to ensure that the modeled curve (\(E_{modelal}(h)\)) gets as close as possible to the curve of the real measured data (\(E_{metoral}(h)\)), minimizing the prediction error.
# A Novel and Adaptive Angle Diversity-Based Receiver for 6G Underground Mining VLC Systems
## 4 Results and Discussion
### Distribution of the User Data Rate  
The user data rate is a metric that allows us to validate the maximum and minimum data transfer values that are received by the ADRs. Figure 4 shows the UDR distribution for the proposed solution based on the adaptive orientation of PDs in the hemidodecahedral ADR comparing it with the ADRs with the ToA method and fixed PDs. We can clearly see that, as with the received power, the best system performance is achieved with the proposal presented in this article in terms of the UDR. As we can see in the Figure 4a, UDR values between 19.2 and 185.4 Mbps are obtained, having full coverage in the UM-VLC scenario. In Table 2, we can also verify the improvement percentage of our solution compared to state-of-the-art solutions. These results allow us to deduce and re-validate that the improvement in the system performance is due to the fact that, in addition to the quantity and distribution of PDs in the hemidodecahedral geometry, this ADR architecture is better used and optimized in conjunction with the adaptive orientation PDs methodology according to the better reception of the optical signal.  
Figure 4: Distribution of the user data rate in the UM-VLC scenario with (**a**) hemidodecahedral ADR with adaptive orientation PDs, (**b**) hemidodecahedral ADR with PDs fixed, (**c**) hemidodecahedral ADR with the ToA method, (**d**) pyramid ADR with the ToA method, and (**e**) pyramid ADR with fixed PDs.
## 2 Theoretical Framework
### Data and Methodological Approaches
#### 3.2.1 Problem-Solution Diagnosis  
First, _problem-solution diagnosis_ is used to identify the missions themselves, with the research objective of defining the status quo by exploring what challenges need to be overcome, and what possible solutions exist by which to do so. Here, 95 projects were analyzed regarding their sustainability performance, of which 55 were _public tender projects_ (in which the municipality is the landowner) and 40 were _non-public tender projects_ (in which a private entity is the landowner; referred to by the municipality as _transformation projects_). Those SUD projects that result in a public call-for-tender can be conceived as GPP projects and therefore used to study the mechanisms of GPP in practice.  
Available documentation from the most recent PLABERUM phase per project was analyzed since these contained the most recent agreements and updates on which and how sustainability targets were reached. Documents were collected manually, through direct request of involved personnel within the municipality. Depending on the PLABERUM phase a project was in, one or several documents were made available by the SustainableDistrict Development (Duurzame Gebiedsontwikkeling; DGO) team to the researchers. These documents included preliminary documents from the municipality as framework for tender or non-tender projects (principal directives, project directives, investment directives, urban plans, and plot passports) and documents delivered by the developing party as part of their tender (building reports, sustainability reports, energy performance coefficient (Energieprestatiecoefficient, EPC) reports, preliminary project designs, and definitive project designs). A list of indicators was developed together with the municipality and used to investigate the documents, resulting in concretization of six missions relevant to SUD at the municipality, including their goals and usage of any relevant voluntary standards or solutions, including in comparison to regulations (as previously noted, these six missions were reduced to five by combining those on circular economy and waste management). These indicators consisted of relevant variables per each mission (and sub-mission, where relevant), and methods for valuing these factors. The list of types of indicators identified per theme is presented in Table 11, Appendix 12. These indicators also include particular voluntary standards (_specifications_, Table 11), which were used to identify whether and to what extend the projects examined had incorporated factors relevant to each of the missions examined. Finally, it is important to note that despite long project durations intrinsic to SUD, progress often happens quickly and, as a result, the analysis of this papers reflects one snapshot in time.
## 5 Experimental Results and Discussion and Analysis
### General Object Detection Experiments  
In the validation set of MS COCO2017 dataset, DAPONet performs well in all the indexes. mAP50 and mAP50-95 of DAPONet achieved the highest value and far exceeded the other models, reaching 48.3% and 33.4%, respectively. Despite having 1.6 M parameters and 1.7 G FLOPs--slightly higher than some lightweight models--DAPONet delivers  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **mAP50** & **mAP 50-95** & **Params** & **FLOPs** & **Model Size** \\ \hline NanoDet-Plus-m-1.5x & & 29.9 & 1.75 & 2.44 & 4.7 \\ DPNet & & 29.6 & 1.04 & 2.5 & \\ PP-PicoDet-ShuffleNetV2 & 44.6 & 30.0 & 1.17 & **1.53** & \\ PP-PicoDet-S & 45.5 & 30.6 & **0.99** & 1.24 & \\ EfficientDet-D1 & & 32.6 & 6.1 & 6.6 & \\ YOLOv5n & 45.7 & 28.0 & 1.9 & 4.5 & 3.9 \\
**DAPONet** & **48.3** & **33.4** & 1.6 & 1.7 & **3.6** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results on MS COCO2017 dataset val.  
Figure 9: F1-Confidence Curve of DAPONet on SVRDD dataset.  
superior performance. Additionally, DAPONet's model size of just 3.6 MB is smaller than most models, enhancing its suitability for resource-limited environments like mobile devices and embedded systems. In contrast, models such as NanoDet-Plus-m-1.5x and DPNet, though having fewer parameters and FLOPs, cannot achieve the same detection accuracy as DAPONet. Meanwhile, EfficientDet-D1, although much larger than DAPONet in terms of FLOPs and model size, achieves a mAP50-95 of only 32.6%, indicating limited performance gains despite significantly higher computational demands.
## 2 Important Terms
### Chaotic Maps
#### 2.2.4 Gauss Map  
A chaotic map generates pseudorandom numbers employed in the encryption process [9]. The Gauss map, often known as the Gaussian map, is a non-linear iterative function with precise intervals that was developed by [PERSON]. For example, Equation (5) may produce pseudorandom numbers from a Gaussian map. \(\alpha\) and \(\beta\) are the controlling parameters that exhibit chaotic behaviors.  
\[x_{n+1}=e^{-\alpha\times x_{n}\times x_{n}}+\beta \tag{5}\]  
where \(\alpha\) and \(\beta\) are input parameters that substantially impact the Gaussian map outcomes, a Gaussian map will create a succession of random values. This sequence will be employed to generate random sequences of the same length. The bifurcation diagram of the Gauss map is shown in Figure 4.  
Figure 3: Axis representation of the Arnold map.
## 3 System Model and Description
### Binary Tree Generation  
Following two conditions must be true for transmission of data packet from a node.
* A sensor or relay node must be used for linear transmission
* There must be a cross node for binary tree.  
If the node is a simple sensor node it will unicast the signal. Otherwise, binary tree formation starts with a source node acting as a root and neighbor nodes as leaves. Each parent node has almost two child nodes. In the proposed protocols, the main characteristics used in the generation of a tree is cross node.
# The Role of HNO2 in the Generation of Plasma-Activated Water by Air Transient Spark Discharge
## 2 Materials and Methods
### Analysis of Water  
The pH of collected plasma-activated water (PAW) was measured by a calibrated pH-EC-TDS-TEMP pH multimeter (EZ-9908, unbranded, China), and the concentrations of \(\text{NO}_{2}{}^{-}\)(aq) and \(\text{H}_{2}\text{O}_{2(\text{aq})}\) formed in water were detected by the colorimetric methods using the Shimadzu UV-1800 UV/VIS absorption spectrophotometer. A Nitrate/Nitrite Colorimetric Assay Kit (# 780001, Cayman Chemicals, Ann Arbor, MI, USA) with ready-to-use Griess reagents was used for a quantitative analysis of \(\text{NO}_{2}{}^{-}\)(aq). The Griess assay method is the most commonly used for \(\text{NO}_{2}{}^{-}\)(aq) detection in plasma-activated solutions [51; 52; 53; 54; 55; 56; 57]. In the Griess diazotation reaction, \(\text{NO}_{2}{}^{-}\)(aq) reacts with sulfanilic acid under acidic conditions to form a diazonium ion, which couples to \(\alpha\)-naphthylamine to form a readily water-soluble, deep purple colored azo dye, with the absorption maximum at 540 nm. The specificity and accuracy of the Griess assay for the detection of \(\text{NO}_{2}{}^{-}\)(aq) in PAW was confirmed by ion chromatography [58].  
Measurement of \(\text{H}_{2}\text{O}_{2(\text{aq})}\) was performed by the titanium oxysulfate assay based on the reaction of \(\text{H}_{2}\text{O}_{2(\text{aq})}\) with the titanium (IV) ions under acidic conditions. The yellow-colored product of peritanic acid \(\text{H}_{2}\text{TiO}_{4}\) is formed with the absorption maximum at 407 nm [59]. The concentration of \(\text{H}_{2}\text{O}_{2(\text{aq})}\) is proportional to the absorbance according to Lambert-Beer's law (molar extinction coefficient \(\varepsilon\) = 6.89 \(\times\) 10\({}^{2}\) L mol\({}^{-1}\) cm\({}^{-1}\)). Because of the possible \(\text{H}_{2}\text{O}_{2(\text{aq})}\) decomposition by \(\text{NO}_{2}{}^{-}\)(aq) under acidic conditions, sodium azide (NaN\({}_{3}\), 60 mM) was added to the sample prior to mixing with the titanium oxysulfate reagent [19]. Sodium azide immediately reduces \(\text{NO}_{2}{}^{-}\)(aq) into molecular nitrogen and preserves the \(\text{H}_{2}\text{O}_{2(\text{aq})}\) concentration intact.
# Meta-Heuristic Optimization and Comparison for Battery Pack Thermal Systems Using Simulink
## 5 Conclusions  
In this research, we utilized a non-model-based battery pack thermal system from Simulink to develop and implement both the PSO and GA methods. Our goal was to devise an optimal thermal system. Key takeaways from our application to Simulink include:  
For both PSO and GA, the calculation time is critical when integrating into Simulink. We found that controlling the particle size yields better accuracy when aiming to achieve an objective function, especially in a scenario with fewer particles and iterations. In our findings, the PSO method slightly outperformed the GA approach. However, this superiority might be due to the specific constraints of our study. As more researchers contribute and share their methodologies, we anticipate even more refined and enhanced results.  
This integrative approach offers tangible benefits for engineers focusing on advancing thermal system design and optimization. Our results indicate that, in comparison to the base model in Simulink, there is a saving of 1% in the overall state of charge of a battery and a reduction in battery temperature by over 4 \({}^{\circ}\)C compared to the initial temperature.  
Upon integrating and verifying computational outcomes, we observed an enhancement in the SOC by applying both PSO and GA results to the sample EV--this was achieved without altering its driving performance or battery voltage. By efficiently modulating the pump's performance, we were able to consistently regulate the battery pack temperature to the desired level. Notably, by managing the pump's functionality, we discerned that the optimal point within the thermal system model of Simulink can be identified using meta-heuristic approaches.  
Conceptualization, D.Y.K., K.H.L. and M.-S.K.; methodology, J.H.M.; software, D.Y.K., K.H.L. and J.H.M.; validation, D.Y.K., M.-S.K. and J.H.M.; formal analysis, D.Y.K. and K.H.L.; investigation, M.-S.K.; resources, J.H.M.; data curation, M.-S.K.; writing--original draft preparation, D.Y.K.; writing--review and editing, J.H.M.; visualization, M.-S.K.; supervision, J.H.M.; project administration, J.H.M.; funding acquisition, M.-S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea (NRF) funded by the Korea Government (MSIT) (NRF-2021R1F1A1049282). Additionally, this research was supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-004).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
This study was completed with the specialized help and cooperation of MATH-WORKS KOREA.  
Author [PERSON] was employed by the company LG Display. 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.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Compute** & **Number of** & **Number of** & **Previous GPU-based GPS** \\  & **Capability** & **Multiprocessors** & **CUDA Cores** & **SDR Development** \\ \hline GeForce 8800 & & & & 8 channels, 40 Msps, \\ GTX & 1.0 & 16 & 128 & 8-bit resolution [32] \\ GeForce GTX 280 & 1.3 & 30 & 240 & 12 channels, 8 Msps, \\  & & & & 4-bit resolution [33] \\ GeForce GTX 285 & 1.3 & 30 & 240 & 150 channels, 5 Msps, \\ GeForce GTX 480 & 2.0 & 15 & 480 & Acquisition only [35] \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of GPUs which have been used for GPS SDRs in the literature. The specifications of GPUs are selected from Table A-1 of [36]. This paper is organized as follows: Section 2 presents our GPS SDR architecture and discusses the computational challenges in detail. Our SDR design with a real-time phase calibration feature considers a GPS reference station receiver under RFI. Since a CRPA array is more voluminous than a conventional patch antenna, GPS infrastructure receivers such as differential GPS (DGPS) reference station receivers are more likely to support CRPA than mobile users. Section 3 explains our implementation details using an Intel Core i7 950 CPU and an NVIDIA GTX 480 GPU. Although a modern GPU can easily run thousands of threads in parallel, each GPU thread is not as powerful as a CPU thread. Roughly speaking, a CPU is more powerful for serial processing and a GPU is more powerful for parallel processing. In our SDR design, computational loads are balanced between CPU and GPU by considering their strengths. After discussing anti-jamming performance of the GPS SDR for a four-element CRPA in Section 4, conclusions are given in Section 5.
## 1 Introduction
However, the low-grade ATP from the original mine contains many impurities and has a rough surface and a low specific surface area, resulting in substantially weakened physicochemical properties, hampering its large-scale exploitation. Owing to its large specific surface area and strong reactivity, nanoscale zero-valent iron (nZVI) is widely used for remediating water and soil pollution [21]. The nZVI reduction technology offers several advantages, including a rapid reaction, a simple process, suitability for small-scale and decentralized treatments, and an absence of the drawbacks associated with traditional methods. In addition, nZVI is an active, inexpensive, widely available, and environmentally friendly substance, making it a popular choice for remediating heavy metal pollution [22]. However, owing to its propensity for easy agglomeration and oxidation, which reduces its overall performance, nZVI cannot be used alone for pollutant removal [23]. Furthermore, ATP can be used as an excellent solid phase negative carrier owing to its good dispersion ability and mechanical properties [20]. Accordingly, nZVI loading onto ATP to prepare nZVI@ATP--a new pollutant remover--can substantially improve nZVI dispersion and increase the contact area of nZVI with pollutants [24], affording a new and efficient composite material for removing Ni-induced pollution in water. This study explores the Ni\({}^{2+}\) removal performance and mechanism of nZVI@ATP in water. A liquid-phase reduction method was used to synthesize nZVI@ATP composites with different iron-to-soil ratios, after which static adsorption of Ni\({}^{2+}\) was conducted to investigate the effects of the initial concentration, dosing amount, initial pH, and coexisting ions on the adsorption capacity of nZVI@ATP composites. The kinetic and intraparticle diffusion models were used to investigate the nZVI@ATP adsorption properties of Niin water. Furthermore, scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS), Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and other characterization methods were used in combination to uncover the mechanism of Ni\({}^{2+}\) adsorption by nZVI@ATP.