esa-sceva/satcom-chunk-collection huggingface dataset:
# Forest Volatile Organic Compounds and Their Effects on Human Health: A State-of-the-Art Review
## 3 Results
### Limonene
#### 3.5.3 Antiproliferative Activity  
D-Limonene has been studied as an antiproliferative agent, and in vitro experiments have suggested a series of possible mechanisms, such as the induction of phase II carcinogen-metabolizing enzymes (a); the reduction of cancer cell proliferation via prenyl-transferase inhibiting activity (possibly diminishing the addition of FPP to the tail of RAS proteins) (b); increased tumor cell apoptosis, autophagy (via the mitochondrial death pathway, the PI3k/Akt pathway, and the caspase-3 and -9 activity), and differentiation (c); growth suppression (through a reduction in cyclin-D1 and an increase in TGF-\(\beta\) signaling leading to a more benign phenotype) (d); decreased tumor-induced immunosuppression, tumor-promoting inflammation, angiogenesis, and metastasis (through a reduction in circulating VEGF and a blockage of the receptor VEGFR1) (e); improved genome stability (increased DNA damage repair and PARP cleavage) (f) [4; 17; 24; 110; 111]. In spite of the many mechanistic studies, there is a dearth of preclinical studies with proper animal models and of human clinical trials. The most relevant ones are summarized in Table 4 and mostly involve the oral administration of limonene.
## 2 State-of-the-Art  
### Sound Pressure Measurements  
A standard method for quantitative sound pressure measurements is the detection by condenser microphones [24]. This allows for point-like measurement. The extension to a measurement field of one, two or three dimensions is possible with a matrix arrangement of microphones. By using beam forming, a two-dimensional arrangement of microphones is sufficient for a three-dimensional reconstruction [25]. However, measurements with microphones are invasive and distort the sound pressure field [26]. Furthermore, microphones typically feature diameters of several millimeters and have a direction characteristic, limiting the spatial resolution to several millimeters and distorting measurements based on relative sound source position [27]. An alternative, noninvasive measurement principle is the measurement of the transfer function between two microphones. This function then describes the acoustic behavior between the two measurement points but offers no spatial resolution [28].  
A noninvasive approach based on laser interferometric vibrometry (LIV) can be used for sound pressure measurement. The LIV measures the integral, sound-induced refractive index variation along the optical path [29]. If the measurement is also performed from different angles, the reconstruction of the local sound field for a point in the measurement volume can be calculated by tomographic reconstruction algorithms. This principle can be extended to a measurement plane or volume by using a camera as detector. At high-speed camera-based laser interferometric vibrometers (CLIV), each camera pixel performs a simultaneous line integral measurement [30].
## 3 Experiments and Result
### Result  
#### 3.2.1 Doppler Envelope  
The experimental data is used to extract Doppler envelopes using both the traditional percentile-based method and the proposed method. For comparison, the conventional method extracts Doppler envelopes by selecting the 5%, 25%, 50%, 75%, and 95%, while the proposed method automatically extracts three Doppler envelopes corresponding to the Foot, Leg, and Spine. As shown in Figures 6 and 10c, the Doppler envelopes are extracted for the subjects using the existing method and the proposed method, respectively.  
To evaluate the effectiveness of the proposed Doppler envelope extraction method, we compared the speed values estimated using three methods: the conventional percentile-based method, the proposed contour-based method, and the ground truth. The average velocity of a Doppler envelope is defined as the mean of its peak values. Figure 12 presents the estimated speeds for each body part (Foot, Leg, Spine) across Subject 3.  
As shown in Figure 12, the proposed method consistently produced speed estimates that closely align with those of the ground truth across all body parts. While the conventional method requires manual selection of percentiles for each body part, the proposed method automatically determines the representative velocities without the need for manual  
Figure 11: (**a**) Experimental environment (**b**) motion capture sensor placement.  
Figure 12: Speed comparison of Subject 3 obtained using the conventional method, the proposed method, and the ground truth. (**a**) Spine, (**b**) Leg, (**c**) Foot.  
tuning. This not only simplifies the process but also improves consistency and robustness in speed estimation.
## 4 Conclusions  
The preparation of the synthetic ilmenite slag was successfully carried out using a molybdenum crucible placed inside an induction furnace. The slag samples were quenched in water. Most of the slags consisted of three phases, namely pseudobrookite, rutile, and silicate phases. The quantity of the pseudobrookite formed in the synthetic slag was less than that found in industrial slags, but for a few samples, it was similar to an industrial slag. The samples having a lesser amount of Ti\({}_{2}\)O\({}_{3}\) in the slag mixture resulted in a larger percentage of pseudobrookite in the final slag. The solidified titanium slag consisted of a microstructure that is similar to that observed in the industrial slags. The silicate phases were formed between the pseudobrookite grains, and the pseudobrookite grains had elongated shapes. The results suggest that the induction furnace with a controlled atmosphere can be used to prepare synthetic slags with the desired chemical composition. The synthetic slags can be helpful when preparing a calibration model, which requires samples with varying chemical compositions. For example, quantitative analysis of ilmenite slags with laser-induced breakdown spectroscopy (LIBS) requires such calibration.  
Conceptualization, A.K.G. and M.A.; Data curation, A.K.G.; Formal analysis, A.K.G.; Funding acquisition, M.H. and T.F.; Investigation, A.K.G. and M.A.; Methodology, J.P.; Project administration, M.H. and T.F.; Resources, P.M.; Software, A.K.G.; Supervision, M.A., P.M., and T.F.; Validation, M.A. and T.F.; Visualization, A.K.G. and M.A.; Writing--original draft, A.K.G.; Writing--review & editing, M.A. and J.P. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 713606.  
Not applicable.  
Data Availability StatementData is contained within the article and supplementary material.  
The authors are grateful for the financial support from the IAFuture doctoral programme--Imaging for the Future. Furthermore, the authors acknowledge the support of the Academy of Finland projects (Academy of Finland, No. 311934 and No. 326291) and Metso Outotec Research Center for their participation in the project.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 1 Introduction
The characteristics of uncertainty, fuzziness, and randomness are inevitable in practical decision-making. Furthermore, the specific numbers could not be used to describe the preference of DMs limited to individual cognition and uncertainty of expression [22]. Hence, various linguistic variables have been presented and applied, such as two-dimensional linguistic variables [23, 24], interval-valued triangular fuzzy numbers [25], intuitionistic fuzzy sets [26, 27], and linguistic distribution assessments [17]. However, the aforementioned methods fail to describe and quantify the randomness of linguistic evaluation variables. To handle this problem, a cloud model is introduced to achieve the following: (1) reveal the uncertain linguistic variables; (2) deal with the randomness and fuzziness of qualitative concepts [28, 29]; and (3) transform qualitative linguistic variables into quantitative ones. The application of the cloud model will be helpful to rationalize and correct the decision-making in the RRS selection. Therefore, due to its unique characteristics and wide applications, the TODIM approach is utilized and modified to tackle the ranking of RRSs for TSF failure. However, it is difficult for the traditional TODIM method to deal with the randomness and uncertainty of actual circumstances, due to its evaluation values represented by crisp numbers. Because the computational complexity of the dominance matrix increases exponentially, the traditional TODIM method presents lower efficiency for large-scale problems; furthermore, the evaluation accuracy of traditional TODIM is sensitive to the loss attenuation coefficient, indicator weight, and decision maker cognitive bias. TODIM has significant advantages in risk decision-making, especially in scenarios where decision makers' psychological preferences need to be considered. However, its computational complexity and parameter sensitivity may limit its application in simple or large-scale problems. In practical use, it is necessary to weigh the pros and cons based on specific problems, and, if necessary, it can be combined with other methods such as fuzzy sets and machine learning to improve robustness. To make up for this gap, the cloud model is integrated with the TODIM model in this paper. The improved cloud-TODIM is applied to make more reasonable and scientific RRS selections for TSF failure. The remainder of this paper is organized as follows: Section 2 introduces the fundamental definition of the cloud model and its calculation. Section 3 proposes the cloud-TODIM method and provides the framework of decision-making for risk reduction scheme selection. Section 4 gives an example to clearly explain the decision-making process. Section 5 provides a sensitivity analysis and a comparative analysis. Section 6 summarizes the conclusions.
## 2 Materials and Methods
### Method Validation
#### 2.4.2 Linearity  
In the unknown samples to be analyzed, the average expected DEHP concentration is 0.7 mg/L. To check the linearity, 12 DEHP standard solutions (4 concentrations \(\times\) 3 independent replicates) were measured, whose concentrations ranged from 40 to 200% of the expected [6]. Specifically, 3 independent replicates were measured for each of the concentrations: 0.3, 0.7, 1.1, and 1.5 mg/L DEHP in a 40% \(v/v\) ethyl alcohol matrix. The 12 standard solutions were measured directly on the liquid chromatograph skipping the extraction step.
# The Variation of Surface Shape in the Gas Jet Forming
## 4 Experiments
### The Gas Jet Forming Method  
The gas jet forming of the mirror blanks was accomplished by combining the formation of the desired surface shape by the gas jet and the UV-catalysed curing of the photosensitive resin. The forming process was divided into four stages, as shown in Figure 10.  
The experimental setup used in this research is shown in Figure 11. The experimental setup was adjusted for the nozzle axis position and the vertical height of the nozzle mouth from the epoxy resin surface through a precision three-axis moving platform with an accuracy of 0.01 mm.  
During the experiment, the air was injected into the coupled gas tank (2) via the air compressor (1). The compressed air was released from the tank and filtered through the oil-water separator (3) and the refrigeration dryer (4) to ensure that the gas used in the experiments was clean and dry. The gas pressure was regulated via the gas MFC (5) to ensure a stable air pressure of the gas supplied during the experiment. The flow rate of the gas jet through the nozzle during the experiment was controlled using a gas jet controller (6). The experimental gas was finally jetted through the precision electroplated nozzle (8) onto the surface of the liquid epoxy resin in a transparent glass mould (10). The transparent glass mould containing the liquid epoxy resin was clamped to a marble platform (11), and the spatial position of the nozzle during the experiment was controlled by a precision three-axis moving platform (9). The forming process of the optical surface was directly observed through the transparent glass mould and recorded with a high-speed camera. The mirror blanks formed by solidifying the epoxy resin were measured using a surface profiler (Taylor Hobson PG) on the resulting surface.  
Figure 10: Schematic diagram of the process of the gas jet forming.
## 1 Introduction
* When implementing logic operations with the SIMPLY scheme operating at 77 K, the DMTJ-based scheme assures energy savings of about 69%, as compared to its SMTJ-based counterpart operating at 77 K.
* The energy efficiency observed at cryogenic temperatures makes the DMTJ-based SIMPLY scheme a viable option for low-power LIM. The rest of this paper is organized as follows: Section 2 provides background on hybrid CMOS/MTJ circuits operating at cryogenic temperatures. Section 3 details the SIMPLY architecture. Section 4 presents the simulation results obtained at cryogenic temperatures. Finally, Section 5 concludes the paper.
## 3 Results and Discussion
### Mechanical Strength
These results highlight, as it was observed in the flexural strength test, the complexity of the relationships between the resin content and the target granulometry in the strength properties of the PPC samples. The compressive modulus of elasticity showed close patterns to those obtained for the flexural modulus, as follows: (i) in all of the cases, the use of target materials increased the compressive modulus of elasticity compared with the pure resin samples; (ii) the sand and LS combinations showed an indirect relationship between the compressive modulus of elasticity and the resin dosage; and (iii) the AF-40 combination showed the highest compressive modulus of elasticity of this target. In this test, the resin reached 1.40 GPa with a \(\sigma\) value of 0.321. The sand combinations reached 2.23 GPa with a \(\sigma\) value of 0.031 for the S-20, 2.16 GPa with a \(\sigma\) value of 0.013 for the S-25, and 2.00 GPa with a \(\sigma\) value of 0.077 for the S-30 combination. The AF combinations reached 1.94 GPa with a \(\sigma\) value of 0.074 for the AF-35, 2.10 GPa with a \(\sigma\) value of 0.361 for the AF-40, and 1.90 GPa with a \(\sigma\) value of 0.117 for the AF-45 combination. Finally, the highest compressive modulus of elasticity and the lowest uncertainties corresponded to the LS combinations, which obtained 2.86 GPa with a \(\sigma\) value of 0.022 for the LS-35, 2.80 GPa with a \(\sigma\) value of 0.020 for the LS-40, and 2.72 GPa with a \(\sigma\) value of 0.011 for the LS-45 combination.
# Cooperative Opportunistic Pressure Based Routing for Underwater Wireless Sensor Networks
## 6 Conclusions and Future Work  
In this paper, Co-Hydrocast, Improved Hydrocast and Co-Improved Hydrocast routing protocols are proposed for UWSNs. Major limitations of Hydrocast were lengthy recovery paths in sparse networks which increased the packet drop ratio and the energy consumption of the network. Energy wasted by every dropped packet is minimized by establishing small routing paths between the source and the destination to ensure a reliable delivery of the data packet. The deployment of nodes at strategic locations helped in selecting an efficient relay node resulting in less energy consumption even in the sparse networks which leads to high PDR. Co-operative opportunistic (Co-Hydrocast, Co-Improved Hydrocast) routing minimized the packet drop ratio by using MRC technique to enhance the PDR at the cost of high energy consumption and more propagation delay. Simulation results reveal that our proposed schemes outperformed the existing compared counterpart scheme in terms of energy consumption per packet and PDR. Improved Hydrocast has minimized the propagation delay and Co-operative schemes consumed more energy which is compensated with high throughput.  
As a future work, we have planned to investigate the impact of nodes mobility due to water currents and interference on the network lifetime. The earlier issue will be handled by incorporating a mobility model and for later issue some technique will be proposed. Then influence of both issues will be analyzed in terms of network lifespan, end to end delay and PDR.  
This project was full financially supported by the King Saud University, through Vice Deanship of Research Chairs.  
[PERSON], [PERSON] and [PERSON] proposed and implemented (validated) the main idea. [PERSON], [PERSON], [PERSON] and [PERSON] wrote, organized and refined rest of the manuscript.  
The authors declare no conflict of interest.
# Visual Tracking Using Wang-Landau Reinforcement Sampler
## 2 Related Work
### Tracking Methods Based on Reinforcement Learning  
For visual tracking, [PERSON] et al. [8] adopted a policy network and defined the actions to localize the target in a current frame. [PERSON] et al. [9] trained a \(Q\) function using the YouTube video dataset and defined the actions used to reinitialize a visual tracker and modify the appearance model of the tracker. However, these trackers could not run in realtime. Thus, [PERSON] et al. [10] enhanced the speed of reinforcement-learning-based visual trackers and maintained their accuracy. They defined actions to determine whether the tracker easily tracks the target. If it is easy, the method tracks the target using inexpensive features; otherwise, the method tracks the target using expensive deep features. [PERSON] et al. [11] ran their tracker at a real-time speed of 43 FPS. They presented lighter-weight deep neural networks and optimized deep neural architectures for matching and policy networks.  
In contrast to these methods, our method aims to improve reinforcement learning accuracy by incorporating the Wang-Landau sampling. Please note that [PERSON] et al. [11] did not improve conventional reinforcement learning algorithms but efficiently applied an existing REINFORCE [12] method to target the appearance-updating problem. In contrast, we improve conventional reinforcement learning algorithms (i.e., Q-learning) using Wang-Landau sampling. We enhance conventional Q-learning algorithms to balance the exploitation and exploration abilities of reinforcement learning. Moreover, we adaptively control the randomness of policy using statistics on the number of visits in a particular state.
# Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
## 7 Conclusions  
This paper has described the collection methodology of three large LPWAN datasets, as well as their suitability for fingerprint-based localization. With these datasets, we intend to provide the global research community with a benchmark tool to evaluate fingerprinting algorithms for LPWAN standards. In the next months, we will keep collecting LPWAN messages in the same areas, by implementing the methodology that was described in Section 3. Apart from increasing the size of the datasets, this allows us to analyze short-term and long-term fluctuations in RSS measurements, and research how we can cope with them. We also aim to improve the spatial spread of the rural Sigfox dataset by deploying more Sigfox hardware in the rural area. Furthermore, three additional datasets will be built: a rural LoRaWAN dataset, a rural NB-IoT dataset and an urban NB-IoT dataset.  
In Section 5, we have summarized the results of a basic \(k\)NN fingerprinting method which was explained in Section 4. These results show a relatively high mean error which ranges from 215 to 689 m.  
However, localization algorithms with an estimation error of this magnitude could still be suitable for some applications, e.g., a logistics company could classify if their assets are on their site or on a working site a few kilometers away. On the other hand, this first basic fingerprinting approach is only meant to serve as an example to demonstrate how our LPWAN datasets can be used. Many different optimizations such as different training set sizes, other pattern recognition algorithms, other distance functions, etc. can still be researched with our datasets. Similar to previous research on indoor WiFi fingerprinting [22], we will analyze the effect of other distance functions, data representations and thresholding techniques using our LPWAN datasets as input.  
Part of this work was funded by the MuSCLe-IoT (Multimodal Sub-Gigahertz Communication and Localization for Low-power IoT applications) project, co-funded by imec, a research institute founded by the Flemish Government, with project support from VLAIO (contract number HBC.2016.0660). The MuSCLe-IoT industry partners are Flash, Sensolus, Enigie M2M, and Aertssen. Part of this research was funded by the Flemish FWO SBO 5004017N IDEAL-IoT (Intelligent DEsne And Longge range IoT networks) project. Part of the equipment used in this work was funded by the Flemish Hercules program.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed hardware to transmit the LPWAN messages; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
## 4 Results
### Averaged Solutions
(**c**) Histogram of difference distribution. The \"high\" solutions primarily serve to exclude outlier epochs, resulting in a narrower range. At the epoch level, the \"high\" solution yields a near-zero mean error (\(-0.007\) m), with STD (0.020 m) and about half maximum error (0.086 m) comparable to those of the original dataset (0.161 m). For those averaged solutions, the difference between \"high\" and \"orig\" is not significant. Further improvements are observed with temporal averaging. As the averaging window increases, precision improves progressively: standard deviations and maximum deviations decrease with each increment. Notably, the 6 h averaged \"high\" solutions yield the most stable results, with an STD of 0.007 m and maximum errors consistently below 0.02 m. These findings indicate that the system supports flexible resolution settings depending on the monitoring application's precision and temporal sensitivity requirements.
## 3 Experimental Results
### Performance Evaluation of Object Detection under Varying Brightness Levels
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Filter Used for Depth** & \multicolumn{4}{c}{**Brightness Level**} \\ \cline{2-5}  & **Map Preprocessing** & **100\%** & **70\%** & **40\%** \\ \hline Depth Map & - & 83.49 & 83.49 & 83.49 \\ Depth Map + RGB & - & 91.99 & 91.32 & 85.60 \\ [PERSON] and [PERSON] [12] & Morphology dilation & 91.94 & 91.00 & 86.05 \\ [PERSON] [13] & Unsharp Mask & 92.13 & 90.72 & 85.76 \\ [PERSON] and [PERSON] [14] & Canny Edge & 92.43 & 91.09 & 85.08 \\ Proposed model & Gaussian–Sobel & 92.07 & 91.49 & 87.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of pedestrian detection performance based on average precision (AP) [%] between the proposed model and similar models under varying brightness levels. issue disappears altogether. Furthermore, Figure 6c,e show that the preprocessing of the depth map actually blurs the contours of some objects, resulting in non-detections. This demonstrates that the use of a sharpening filter can positively affect the shape of certain objects, while adversely affecting others, leading to blurred object contours. Nevertheless, the proposed model solved the problems of missed detection and false alarms of other models by making object contours clearer compared with other models and stably detected all pedestrians. Figure 5: Cont. Figure 5: Comparison of pedestrian detection performance of the proposed model and similar models at 100% brightness: (**a**) depth map; (**b**) RGB + depth map; (**c**) [PERSON] and [PERSON] [12]; (**d**) [PERSON] [13]; (**e**) [PERSON] and [PERSON] [14]; (**f**) proposed model. Figure 6: _Cont._