esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Daytime HONO Emission Source from Asphal Surface to Urban Air
## 3 Results
### No\({}_{2}\) Dependency  
Figure 3 shows the results of the experiment conducted to elucidate the NO\({}_{2}\) contribution to HONO production over the asphalt surface. HONO formation on the asphalt surface was linearly dependent on NO\({}_{2}\) up to 300 ppbv. As shown in Figure 2, HONO formation was not observed in dark conditions even with relatively high NO\({}_{2}\) concentrations, which indicates that heterogeneous NO\({}_{2}\) conversion with H\({}_{2}\)O is not a source of HONO over the asphalt surface. This result is not similar or analogous with glass surface studies, in which photo-enhancement of the reaction was not observed [7; 19; 33]. However, it is in good agreement with the results of a light-induced HONO formation study on aerosol surfaces [28; 34; 35].  
Photochemical HONO formation has been mainly suggested as a reaction of adsorbed NO\({}_{2}\) on the photocatalytic surface, such as TiO\({}_{2}\) and humic acid [28; 33]. Another potential HONO formation reaction is the photolysis of excited NO\({}_{2}\)* originating from the HNO\({}_{3}\) complex with water. Using our experimental setup without NO\({}_{2}\), no HONO production was observed, indicating that HNO\({}_{3}\) photolysis does not play a significant role in HONO formation with an asphalt surface. This implies that the observed HONO production on the asphalt surface is mainly caused by the heterogeneous photoactivated ions of NO\({}_{2}\) by a similar reaction on the humic acid as proposed by [PERSON] et al. [28] (R4).  
\[NO_{2}(g)\quad\stackrel{{ photoactivated humic acids}}{{\to}} HONO(g)\] (R4)  
Figure 3: HONO production rates as functions of NO\({}_{2}\) concentrations. Mixing ratios of toluene and total ultraviolet (TUV) were fixed as 500 ppbv and 11.3 Wm\({}^{-2}\), respectively.
## 4 Results
### Analysis of Initial Fracturing, Fracture Propagation, and Oil Well Production Performance
#### 4.3.3 Residual Oil Distribution Pre-Refracturing  
Figure 13 illustrates the law of residual oil distribution's dynamic evolution. While the pressure around the wells gradually fell, the reservoir pressure was high at the start of production in 2013, and the residual oil was mostly confined in the space between the fracture seams. The residual oil between the fracture seams steadily decreased with further depletion (Figure 13b), but the space between the wells continued to be undrained. The pressure surrounding the fracture seams rose with the installation of steam throughput in 2018 (Figure 13c), hastening the recovery of the residual oil in the inter-seam region. Most of the residual oil in the inter-seam area had been extracted by 2024 (Figure 13d); nevertheless, because of the excessive seam spacing and the insufficient half-length of the original fractured seam, a sizeable number of untapped reserves remained in the inter-well area. The cumulative oil production of the YP 3 well is 11,900 tons, according to numerical analysis, and the average drop in oil saturation surrounding the well is 4.74%. These figures closely match the outcomes of multi-field coupled simulation and validate the possibility of the development of the residual oil between fractures. Therefore, it is necessary to optimize the design through fracturing: increase the fracture spacing to 50-70% of the initial fracture, increase the fracturing fluid volume, and ensure that the half-length of  
Figure 12: Pressure field distribution of YP 3 horizontal wells in different production periods. (**a**) 2014-01-01. (**b**) 2016-01-01. (**c**) 2018-01-01. (**d**) 2020-01-01. (**e**) 2022-01-01. (**f**) 2024-01-01.  
the new fracture exceeds that of the initial fracture by 20-30%, to efficiently utilize the remaining oil resources in the inter-well.
# Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks
###### Abstract  
The non-terrestrial network (NTN) is a network that uses radio frequency (RF) resources mounted on satellites and includes satellite-based communications networks, high altitude platform systems (HAPS), and air-to-ground networks. The fifth generation (5G) and NTN may be crucial in utilizing communication infrastructure to provide 5G services in the future, anytime and anywhere. Based on the outcome of the Rel-16 study, the 3 rd generation partnership project (3 GPP) decided to start a work item on an NTN in 5G new radio (NR) Rel-17, and the focus of the study was on mobility management procedures, due to the movements of NTN platforms; especially, low earth orbit (LEO) satellites. Handover enhancements were discussed to tackle the frequent handover due to the fast satellite movement. Therefore, the major problem of handover in LEO satellite systems was the signaling storm created by handing over all user equipment (UE) in a cell to a new cell because when the UE crosses the boundary between the neighboring cell of a satellite, an intra-satellite or cell handover occurs; thus, all users in a cell are expected to experience a change of cell due to handover every few seconds. In addition, UE location is not easy to define due to moving cell/beam situations. In this study, we propose machine learning-based solutions for handover decisions in non-terrestrial networks for cell handovers or intra-satellite handovers to reduce signaling storms during handovers where the handover requests will be executed by clustered users. First, the dataset was generated by the simulator that simulates communication between users and satellites. Second, we preprocessed the data, and also used the feature creation technique to create the distance feature using the Haversine formula, and then applied clustering and classification algorithms. The experimental results show that the distance between a user and its cell center is an important parameter for handover decisions in NTN, and the random forest outperforms all models with a higher accuracy of 99% along with a better F1-score of 0.9961.  
non-terrestrial network (NTN); conditional handover (CHO); machine learning (ML); clustering +
Footnote †: journal: Electronics
## 5 IETF ROLL Working Group
### Implementation and Evaluation  
#### 5.2.1 Implementation  
Already during the standardization process of the RPL protocol, the draft of the protocol was implemented in several platforms and simulators. An overview of the different implementations is presented in Table 5.  
In Table 6 an overview is given of the network simulators that implement the RPL protocol. In the table the TOSSIM simulator, standard for TinyOS, is not mentioned because TOSSIM requires micaz support, which is not available for TinyRPL. Several research papers such as [52] and [53] also implemented the RPL protocol into the WSNet simulator. In [53] this implementation is based on draft-ietf-roll-rpl-05.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **Language** & **Protocol version** & **Notes (extensions,.)** \\ \hline
**Cooja [54]** & C with limited libs & RFC 6550 & MSPsim (TinyOS + Contiki) \\ NS-3 [55] & C\({}^{++}\) and Python & draft-ietf-roll-rpl-19 & \\
**OMNET++/Castalia** & C\({}^{++}\) (wrapped & draft-ietf-roll-rpl-19 & \\
[56] & together with NED) & & \\ J-SIM [57] & Tcl/ Java & draft-ietf-roll-rpl-19 & EU-funded FP7 ICT-257245 \\  & & & \\  & & & VITRO project \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulators incorporating RPL.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **OS** & **Protocol Version** & **Notes (Extensions,.)** \\ \hline
**TinyRPL [47]** & TinyOS & draft-ietf-roll-rpl-17 & - uses BLIP 2.0 \\  & & & - only storing mode \\  & & & - only single RPLInstanceID \\  & & & - security options not supported \\  & & & - only telosb and epic platform support \\
**ContikiRPL [48]** & Contiki & RFC 6550 & by default enabled on Tmote sky platform \\
**OpenWSN [49]** & OpenWSN & RFC 6550 & \\
**Nano-RK [50]** & Nano-RK & draft-ietf-roll-rpl-07 & \\
**NanoOplus [51]** & NanoOplus & draft-ietf-roll-rpl-13 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Implementations incorporating RPL.  
Figure 12: The DAO Base Object.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 3 Fair Editions of Decision Trees
### Optimal Fair Decision Trees  
The authors of [11] proposed a Mixed Integer Optimization (MIO) framework for learning optimal Decision Trees that integrate fairness constraints while maintaining high accuracy and interpretability. They construct a flow-based graph representation of the Decision Tree and employ binary constraints to enforce fairness within the tree structure and prediction paths. Subsequently, they utilize global optimization techniques to ensure fairness at all decision levels, distinguishing their approach from heuristic-based models. The authors conduct extensive benchmarking on real-world datasets, comparing their approach against RegOCT and three fairness-aware machine learning models: Correlation Remover, Exponentiated-Gradient Reduction, and Randomized Threshold Optimizer. The proposed model is highly interpretable and achieves near-perfect fairness parity, whereas other models struggle to fully enforce fairness. This is accomplished with only a minor accuracy trade-off of up to 4.2% compared to more complex models.
## 3 Preliminary and Model
### Traffic Degradation Image Restoration Model
#### 3.4.5 Datasets  
The study selects fog--one of the most challenging weather conditions that significantly impacts vehicle detection--as the research subject for model validation. Notably, the entire process does not explicitly incorporate any fog-specific prior knowledge like physical characteristics. That means TDIRM is not merely a restoration model specialized for a single adverse condition type. For other interference types, the model can achieve comparable processing effects through similar fine-tuning approaches.  
Figure 7: Network structure of triple control embedding attention module (TCE).  
The training data consist of two components: The synthesized paired foggy images (854 \(\times\) 3 pairs) used for robustness evaluation in Section 3.1.2. The OTS synthetic image pairs (538 \(\times\) 3 pairs) from RESIDE, where each clear image corresponds to three levels of foggy images. These datasets are split into training and test sets at an 8:2 ratio. Additionally, to effectively restore degraded traffic images in real-world scenarios, the evaluation particularly focuses on comparing restoration results using 76 real dense fog traffic images from the DAWN. Unlike synthetic image pairs, these real-world images lack corresponding clear references.
# Bioactive Compounds from Herbal Medicine Targeting Multiple Myeloma
## 3 Flavonoids in Multiple Myeloma
### Luteolin  
Luteolin is a flavone found in carrots, peppers, cabbage, broocoli, onion leaves, thyme, parsley, mint, basil, celery, and artichokes [152].  
Structurally luteolin has a hydroxyl (-OH) group attached to the 5-, 7-, 3'-, and 4'-positions of the flavone backbone structure. The chemical structure of luteolin is shown in Figure 4[152]. The antioxidant activity of luteolin is ensured by the ortho-dihydroxy structure in the B-ring and the 2,3-double bond in conjugation with the 4-oxo function of the C ring [153].  
Luteolin has shown antioxidant, antimicrobial, anti-inflammatory, chemopreventure, chemotherapeutic, cardioprotective, antidiabetic, neuroprotective and anti-allergic activity [152].  
Luteolin has shown anticancer activity in many cancers, such as breast [154; 155; 156; 157], colon [158], prostate [159; 160], cervical [161] cancers, as well as glioblastoma [162], oral squamous cell carcinoma [163; 164], adenocarcinoma [165], renal carcinoma [166], gastric cancer [167; 168; 169], hepatocellular carcinoma [170], and pancreatic cancer [171].  
Luteolin inhibited the carcinogenesis progression through various pathways, such as kinase inhibition, induction of apoptotic cell death, cell cycle regulation, and decreased the transcription factors [153]. Apoptosis induction involves DNA damage, regulation of redox and protein kinases in inhibiting cancer cell proliferation [172].  
Regarding the anti-myeloma activity of luteolin, it has been shown to inhibit RPMI-8226 cell proliferation by apoptosis or autophagy, being interactive or promoting each other [173].
## 4 Power Control For Multiple Sink Cluster WSNs (MSCWSNs-PC)
### Created Clusters Communication with Sink Nodes  
For the sensor nodes, we assume that all nodes can transmit with enough power to reach the sink if needed, that the nodes can use power control to vary the amount of transmit power, and that each node has the computational power to support different MAC protocols and perform signal processing functions. It means that the created clusters allow energy-efficient communication from the sensors to the sinks, all sensor nodes communicate directly to the sink or they use multi-hop communication in non-sinks as cluster head condition.
## 2 Thermal Analysis and Design Improvement of the WCLL COB Segment TC Region
### The First Update of the TC Region Design
#### 2.2.1 The \"TC Region-Mod\" Geometric Configuration  
The TC region-mod geometric configuration (Figure 4) represents the update of the TC region reference geometric layout. It is obtained by introducing the following:  
* The v06b geometric layout [10] for the DWTs in all the slices composing the model.
* A further TC toroidal cooling channel, through the region where the highest temperatures were predicted in the reference configuration.
* 22 DWTs in the slice housing the TC too (namely 6 DTWs more than the reference configuration), properly re-arranged (along radial, toroidal and poloidal directions) in order to minimize their distances from the SPs, allowing the improvement of the cooling performance.
* A spatial re-arrangement (along radial, toroidal and poloidal directions) of the DWTs in the slice adjacent to the TC region in order to improve the SPsh cooling.  
Figure 3: TC reference configuration—temperature spatial distribution within Eurofer.  
Hence, a new 3D FEM model is set up, adopting a mesh with the same features of those described in the previous section. The same set of loads and boundary conditions is used to assess the thermal behavior of the \"TC region-mod\" configuration under steady-state nominal conditions.
# Integrated Fiber-FSO WDM Access System with Fiber Fault Protection
## 4 Conclusions  
In summary, we proposed a hybrid fiber-FSO WDM-PON architecture with self-protected operation against fiber breakpoint. Here, the C- and L-band wavelengths were applied to regard as the 10 Gb/s OOK fiber and FSO signals in the presented WDM-PON network. In the demonstration, eight C- and L-band WDM wavelengths were applied to act as the 10 Gb/s fiber and FSO signals for downstream (\(\lambda_{\text{LC}}\), \(\lambda_{\text{2C}}\),... and \(\lambda_{\text{NC}}\) and \(\lambda_{\text{1L}}\), \(\lambda_{\text{2L}}\),... and \(\lambda_{\text{NL}}\)) and upstream traffics (\(\lambda_{(N+1)C}\), \(\lambda_{(N+2)C}\),... and \(\lambda_{(2N)C}\) and \(\lambda_{(N+1)L}\), \(\lambda_{(N+2)L}\),... and \(\lambda_{(2N)L}\)), respectively. In the measurement, eight C-band fiber signals (\(\lambda_{\text{IC}}\) to \(\lambda_{\text{SC}}\)) and eight L-band FSO signals (\(\lambda_{\text{1L}}\) to \(\lambda_{\text{SL}}\)) were exploited, respectively. The measured power sensitivities of downstream and upstream fiber and FSO traffics were below \(-27.5\) and \(-26.5\) dBm and \(-21.5\) and \(-22\) dBm, after through 25 km SMF and 2 m wireless FSO transmissions, respectively. All the obtained power budgets of fiber and FSO signals would meet with the proposed WDM-PON network either in the normal state or under the self-healing protection at the FEC target. Therefore, the presented PON architecture not only supported the fiber access FSO signals, but also provided the fault protection for the WDM fiber access link. In addition, the proposed PON system was also easy to build and implement.  
Data curation, S.-Y.J., C.-H.H. and S.-E.H.; investigation, C.-H.Y.; supervision, W.-P.L. and C.-W.C.; writing--review and editing, C.-H.Y. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by Ministry of Science and Technology, Taiwan, under Grant MOST-110-2221-E-035-058-MY2.  
The authors declare no conflict of interest.
## 5 Engineering Applications of Lattice Structures Built with Polygon Hollow Shapes
### Applications in Medical Industries  
Lattice structures constructed out of polygonal hollow shapes are capable of mimicking the structure of bone [8]. These structures are adopted for bone implants due to the fact that they are more effective in integrating with natural bone tissue and encourage the regeneration and healing processes of tissues [8; 17]. Such structures are additionally used for building scaffolds for the replacement or repair of soft tissue, as well as providing structural support for the growth of cells and regeneration of tissues [16; 17; 20]. Moreover, the porous nature of polygon hollow structures facilitates precise control over the distribution of drugs as well. Different drugs or chemical compounds are incorporated into the design of lattices, and their release is controlled by the design of the structures. This provides a more targeted and carefully controlled delivery method for drugs [8; 16]. For procedures in medical imaging, polygon hollow structures are often used as phantoms, whereby they mimic the structure of human tissues, thus assisting in the calibration as well as validation of imaging devices, such as magnetic resonance imaging (MRI) and computed tomography (CT) scanners [66].  
Lattice-structured tools and devices are lightweight but rigid and are therefore ideal for surgical procedures [17]. They can be tailored by being manufactured hard in particular regions whilst maintaining flexibility in other regions, which allows for precise as well as reduced invasive operations [8; 16]. Lattice structures are implemented for generating complex three-dimensional configurations for cell cultures that mimic the natural structures of tissues or organs. This is particularly useful in the development of organ-on-a-chip systems or bioreactors for the testing of drugs and tissue engineering [67]. In addition, custom lattice-structured prostheses offer the advantage of conforming and therefore are fitted to a human's exact anatomy. The strength, weight, and flexibility of these structures can be optimised, resulting in more beneficial and functional prosthetic devices [67]. Dental implants like prostheses make use of lattice designs. They are capable of increasing osseointegration and the jaw-bone strength, improving the mechanical performance and life expectancy of dental implants [17; 67].  
It is deduced from this section of this paper that lattice structures built of polygon hollow shapes are highly adaptable and can be precisely tailored to meet specific engineering applications. Moreover, computer-aided design (CAD) software and contemporary manufacturing methods such as 3D printing have simplified the design and fabrication of these structures. Given their lightweight nature as well as their good strength-to-weight ratio, lattice structures are particularly advantageous for applications necessitating both structural integrity and weight reduction.
# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 2 Methods
### Vector Tracking Loops
#### 2.3.4 Ultra-Tight Combining
Additionally, incorporating neural networks for information fusion will be crucial for addressing issues like error model inaccuracies and KF divergence. These advancements are vital for the continued development and practical application of ultra-tight integration systems [113].
# Sustainability Enhancement and Evaluation of a Concrete Dam Using Recycling
## 4 Results and Discussion
### Construction Stage
#### 4.1.2 LCC Results of the Construction Stage  
The results of the LCC analysis reveal that the estimated economic expenses for the dam's construction phase are anticipated to reach approximately USD 0.195 million. In Figure 7, a graphical depiction illustrates the contributions of different construction materials and operations to these costs. Concrete emerges as the most significant factor, making an impactful contribution of USD 0.0959 million. Excavation follows closely as the second most substantial contributor at USD 0.0384 million, succeeded by oil at USD 0.0270 million.
# Improving Very High Plastic Clays with the Combined Effect of Sand, Line, and Polypropylene Fibers
## 4 Results  
### Optimizing Sand and Lime Contents  
To optimize additives content for clay improvement, the procedure followed in this investigation is shown in Figure 3. Accordingly, the first step was optimizing the sand content based on the peak UCS strength. Four sand contents were used (0, 10, 20, and 30%) while the results of the UCS are shown in Figure 4. As shown, the optimal sand content was 20%, where the strength of raw clay was increased from 361 kPa to 402 kPa. This behavior may be explained by the fact to that the sand contributes to the rigidity of the mixture, but further addition reduces the beneficial action of cohesion of clay on the UCS; as shown in Table 5, the plasticity index has a larger fall beyond the optimal point. These findings indicate a limited effect of sand on improving the strength of clay soils and confirm that increasing the strength of clay soil by reducing its plasticity is an insufficient practice that is valid only for soils with sand contents up to 20% (additional quantities of sand has an adverse effect on strength). This is also shown in the UCS-PI chart in Figure 5, where R\({}^{2}\) = 0.94. Rationally thinking, this loss in strength due to the reduction of cohesion (and thus, the reduction in bonding between soil particles) can be replaced by chemical (lime) or mechanical (PP fibers) strengthening.  
The second step was to determine the quantity of the alkali activator necessary to ensure the pozzolanic reactivity between clay minerals and Ca(OH)\({}_{2}\) that was considered to be the lime fixation point; further addition of lime does not bring more changes in plasticity, as reported in the literature [12]. The lime demand was determined according to the Eads-Grim test [25]. Accordingly, the quantity of lime required to complete pozzolanic hydration products is that which elevated the pH value to 12.4. As shown in Figure 6, the results indicate that the lime demand for raw clay is equal to 8.4%, while when the clay is mixed with 10% and 20% sand, the lime demand decreases to 6.5% and 3.4%, respectively. However, lime demand remained unchanged for further increases in sand content. Thus, this finding corroborates that 20% sand is the optimal quantity that should be added to this specific clay.  
Figure 2: Repeatability tests for fiber-reinforced clays (0.6% PP fiber content).  
Figure 4: Effect of sand addition on UCS.  
Figure 3: Flow chart of the procedure followed to optimize additives used.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Clay** & **Sand** & **Liquid Limit** & **Plastic Limit** & **Plasticity Index** \\ **(\%)** & **(\%)** & **\(\omega\)l (\%)** & **\(\omega\)p (\%)** & **PI (\%)** \\ \hline
100 & 0 & 84 & 32 & 52 \\
90 & 10 & 79 & 30 & 49 \\
80 & 20 & 64 & 20 & 44 \\
70 & 30 & 55 & 21 & 34 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Plasticity characteristics for clay-sand mixes.