esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in High-Precision NO2 Measurement by Quantum Cascade Laser Absorption Spectroscopy
###### Abstract  
Nitrogen dioxide (NO2) is a major tropospheric air pollutant. Its concentration in the atmosphere is most frequently monitored indirectly by chemiluminescence detection or using direct light absorption in the visible range. Both techniques are subject to known biases from other trace gases (including water vapor), making accurate measurements at low concentration very challenging. Selective measurements of NO2 in the mid-infrared have been proposed as a promising alternative, but field deployments and comparisons with established techniques remain sparse. Here, we describe the development and validation of a quantum cascade laser-based spectrometer (QCLAS). It relies on a custom-made eigenstate multipass absorption cell and a recently developed low heat dissipation laser driving and a FPGA based data acquisition approach. We demonstrate a sub-pptv precision (1 \(\sigma\)) for NO2 after 150 s integration time. The instrument performance in terms of long-term stability, linearity and field operation capability was assessed in the laboratory and during a two-week inter-comparison campaign at a suburban air pollution monitoring station. Four NO2 instruments corresponding to three different detection techniques (chemiluminescence detection (CLD), cavity-attenuated phase shift (CAPS) spectroscopy and QCLAS) were deployed after calibrating them with three different referencing methods: gas-phase titration of NO, dynamic high-concentration cylinder dilution and permeation. These measurements show that QCLAS is an attractive alternative for high-precision NO2 monitoring. Used in dual-laser configuration, its capabilities can be extended to NO, thus allowing for unambiguous quantification of nitrogen oxides (NO2), which are of key importance in air quality assessments.  
air pollution; trace gas; nitrogen dioxide; laser spectroscopy; mid-infrared; quantum cascade laser; selective detection +
Footnote †: journal: Applied Sciences
## 5 Discussion and Conclusions
### The Potential of DDPM in Advancing Medical Imaging Techniques  
In conclusion, the DDPM holds significant promise in advancing medical imaging technology. With its unique combination of denoising capabilities and probabilistic modeling, DDPM addresses key challenges in biomedical image segmentation, contributing to improved image quality, uncertainty quantification, and adaptability across diverse imaging modalities. The integration of DDPM into deep learning architectures further enhances its potential, offering a robust and interpretable solution for accurate segmentation in complex medical scenarios.  
DDPM's ability to handle limited annotated data, provide transparency in predictions, and mitigate biases aligns with the ethical considerations essential in healthcare applications. As a catalyst for research in hybrid approaches, DDPM sets the stage for innovative solutions that go beyond traditional segmentation methods, advancing the accuracy and reliability of medical imaging technology.  
The probabilistic framework of DDPM not only contributes to noise reduction but also fosters a deeper understanding of segmentation results, promoting trust among healthcare professionals. As medical imaging continues to play a pivotal role in diagnosis and treatment, DDPM stands at the forefront of a new wave of technologies that have the potential to reshape the landscape of image analysis, paving the way for more efficient and reliable clinical decision-making.  
In the evolving field of medical imaging, DDPM represents a promising paradigm shift, offering a holistic solution that addresses the complexities of biomedical image segmentation. As research and development in this area progress, the potential of DDPM to enhance the accuracy, interpretability, and ethical considerations in medical imaging technology positions it as a key player in shaping the future of diagnostic and therapeutic approaches in healthcare.
# Targeted Lipid-Based Drug Delivery Systems for Lung Cancer Therapy
## 3 Drug Delivery Systems
### Microemulsions  
Microemulsions have been introduced by [PERSON] and [PERSON] [42] and they consist of oil, water and surfactant (Figure 1a). They have been majorly employed for topical delivery, but have also been used for lung cancer therapy [43]. They are classed as self-aggregated systems and in contrast to conventional emulsions, they are more stable thermodynamically. They also offer better permeability due to their lower viscosity. Their disadvantages include the usage of toxic organic solvents for their preparation and a complex manufacturing procedure involving high-cost equipment and long processes [44]. Some drugs which have  
Figure 1: (a)-1,(a)-2 Microemulsion consisting of water, oil and surfactant; (a)-1 is water in oil emulsion and (a)-2 is oil in water emulsion. (b) Liposome containing of phospholipids and an aqueous core. (c) Transfersome comprising phospholipids, surfactant and an aqueous core. (d) Niosome consisting of neutral lipids like cholesterol and surfactant. (e) Solid lipid nanoparticle comprising of solid lipids and surfactant. (f) Nanostructured lipid carrier structure, consisting of solid lipids, liquid lipids and surfactant.  
been entrapped within microemulsions for lung cancer therapy as shown in Table 1 are etoposide [45], gemcitabine [43] and simvastatin [46]. Pharmacokine studies have shown that a microemulsion containing paclitaxel exhibited a significantly higher area under the curve (AUC), a much smaller elimination rate constant (K10) and reduced circulation time in rats compared to the plain paclitaxel [47].
## 2 Mechanical Analysis Model for TSWs
### Calculation of Bending Moments, and Internal Forces in Thin Plates  
Based on the formula in Theory of Plates and Shells [23], the bending moment in the cross-section of a rectangular thin plate can be calculated as follows:  
\[\left\{\begin{array}{l}M_{y}=-D\left(\frac{2^{2}w}{\partial y^{2}}+\mu\frac{ \partial^{2}w}{\partial z^{2}}\right)\\ M_{z}=-D\left(\frac{2^{2}w}{\partial z^{2}}+\mu\frac{\partial^{2}w}{\partial y ^{2}}\right)\\ M_{yz}=-D(1-\mu)\frac{\partial^{2}w}{\partial y^{2}}\end{array}\right. \tag{18}\]  
In the thickness direction of the TSW, the maximum stress component occurs at the mid-thickness interface (\(x=\frac{W_{h}}{2}\)). Therefore, the maximum stress at each point along the TSW can be determined as follows:  
\[\left\{\begin{array}{l}\sigma_{y}=\frac{6M_{y}}{W^{2}}\\ \sigma_{z}=\frac{6M_{z}}{W^{2}_{y}}\\ \tau_{yz}=\frac{12M_{yz}}{W^{2}_{y}}\end{array}\right. \tag{19}\]  
Because the internal forces in the thin plate calculated using this method are per unit length, the length of the edge of the TSW in the corresponding direction must be divided to determine the stresses applied within the TSW. The effect of \(\sigma_{x}\) in the thin plate can be neglected. According to the basic theory of plane stress problems, the ultimate positive stress at each point within the TSW can be calculated by the following equation:  
\[\left\{\begin{array}{l}\sigma_{\max}=\frac{\sigma_{y}+\sigma_{z}}{2}+\sqrt{ \left(\frac{\sigma_{y}-\sigma_{z}}{2}\right)^{2}+\tau_{yz}^{2}}\\ \sigma_{\min}=\frac{\sigma_{y}+\sigma_{z}}{2}-\sqrt{\left(\frac{\sigma_{y}- \sigma_{z}}{2}\right)^{2}+\tau_{yz}^{2}}\end{array}\right. \tag{20}\]  
From the analysis of the mechanical model above, it can be seen that at the midpoint of the left and right boundaries and the midpoint of the lateral load on the top and bottom boundaries, the TSW experiences the \(\sigma_{\max}\) and \(\sigma_{\min}\) on both sides. This indicates that the failure of the TSW begins at these midpoint locations, both at the boundary and lateral load center.
# Fully Open-Source Meeting Minutes Generation Tool
## 3 Proposal of Meeting Minutes Generation System
### Information Correlation Function  
The _information correlation function_ compares and validates the text data from the recorded presentation video and the extracted information with the original text from the presentation document. This function workflow is visualized in Figure 3.  
First, it recognizes the text from each frame in the video using _OCR_. Since the video was resized, only the text in the title area is recognized and extracted. For the comparison, the original text is gathered from the title text of each slide in the presentation document using the _python-pptx_ package.  
Then, it compares the _OCR_ text with the original text using the _regex_ string matching to determine which slide index the _OCR_ text corresponds to. The slide index refers to the order of slides that appear in the presentation document. This process determines the identical strings that appear in both _OCR_ text and the original text.  
Finally, after the slide index is known, the extracted information is assigned to the similar segmentation index. This process ensures that the information extracted from the recorded presentation is accurately correlated with the corresponding slides, thus improving the overall accuracy and usefulness of the extracted information and avoiding confusion for the readers.
# On the Optimisation of Practical Wireless Indoor and Outdoor Microcells Subject to QoS Constraints
## 6 Conclusions  
In this paper, a BSCP algorithm has been proposed for cell planning in WIOMs to find the optimal number of cell sites subject to QoS requirements of received power at remote units. A significant resource can be saved with the BSCP in terms of the number of cell sites. The BSCP algorithm has also been shown to require a much lower computational complexity compared to the conventional ESCP approach, especially when the number of candidate locations for cell sites is large.  
In particular, the effectiveness of the BSCP has been validated through EDX SignalPro where both indoor and outdoor environments with different antenna configurations are taken into account. Specifically, one third of the resources can be saved when compared to the case of employing all available cell sites, which accordingly validates the effectiveness of the proposed BSCP algorithm in reducing the waste of resources caused by overlapping coverage areas. This indeed indicates a considerable saving in installation and maintenance costs as well as the energy consumption.  
The proposed approach for network optimisation in this work can be regarded as an applied method for the network design and planning in practice. For future work, a stochastic geometry approach will be investigated for modelling and analysing the WIOMs, which allows us to provide analytically tractable solution for the proposed cell optimisation problem. In addition, we will investigate the usage of different simulation tools, such as NS2 or NS3, to validate the effectiveness of the proposed BSCP algorithm in terms of both network coverage and capacity.  
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the simulation, collected and analysed data; [PERSON] designed the mathematical model, proposed the method, and summarised the data; [PERSON] and [PERSON] wrote the paper. Both authors have read and approved the final manuscript.  
The authors declare no conflict of interest.  
Figure 12: Heuristic site location for outdoor with MIMO antennas.
# Efficient Modulation Scheme for Intermediate Relay-Aided IoT Networks
## 1 Introduction  
The world is growing at a fast pace, and so is data. The agility and flexibility of big data applications are gradually shaping the Internet-of-things (IoT) [1]. These IoT devices feature unique identities and are accessible from anywhere in the world. The ubiquitous deployment of interconnected devices is expected to grow in the next decade. This exponential growth is broadly supported by the increasing number of mobile devices (e.g., smartphones and tablets), smart sensors serving different markets (e.g., autonomous transportation, industrial controls and wearables), wireless sensors and actuators networks [2; 3; 4; 5; 6; 7; 8]. Reliable exchange of data by IoT sensors and actuators is one of the biggest challenges faced when deploying an IoT system. Although this kind of device has evolved radically in the last years, battery life, computation and storage capacity remain limited [9]. This means that they are not suitable for running heavy applications; instead, it is necessary to resort to more powerful computing resources, likely owned by third parties.  
For instance, drones, also known as unmanned aerial vehicles (UAVs), can be employed in many of applications that are difficult or dangerous for humans (e.g., military, disaster or emergency missions) [10; 11; 12; 13]. The mode of operation of drones has progressively changed from the traditional remote control by humans to sophisticated autonomous control, able to operate without human intervention. However, as all the processing required to analyze the data and make decisions are performed onboard, flight autonomy decreases substantially. For instance, let us consider an extremely resource-intensive application; say, the search for missing persons in disaster zones using image processing [10]. With regard to the image processing module, as it has no such tight time constraints, it can be forwarded to the relay, which will be responsible for processing the collected data (e.g., video, images or heat sensor data) and transmitting it reliably to the destination nodes. This way, the required decoding errors are tremendously reduced, resulting in an increase in terms of the overall functionality [14].  
In order to recover the lost packet for improving reliability at the network layer, one of the most widely used techniques is hybrid automatic repeat request (HARQ) [15]. HARQ in conjunction with relay networks has attracted substantial research interest in recent years [16]. Because data symbols in practical transmission often utilize linear modulations of finite-size constellations (e.g., quadrature phase-shift keying (PSK), or quadrature quadrature amplitude modulation (QAM)), the performances of relay-aided HARQ systems can benefit from modulation diversity, in which each group of \(\log_{2}Q\) bits is mapped to a different constellation point across different links and in different transmissions to better exploit channel diversity.
## 3 Methods
### Air Contamination Calibration  
The helium concentration measured by the helium detector at a drilling site is not the that in the examined formation, because the mud gas will be contaminated with air during the degassing process. Usually, the helium concentration in a formation is much higher than in air (5.24 ppm, [7]), and air mixing will lead to a lower measurement value. Therefore, the test results need to be corrected to obtain the actual helium concentration. Since the concentration of methane in air is extremely low, only 1.92 ppm [39], which is negligible compared to that in underground gas, we hypothesized that all the measured methane comes from the underground; so, the helium concentration can be corrected accordingly. The correction formula is as follows:  
\[He_{r}=\frac{He_{t}-He_{a}\times\left(1-\frac{TG}{100}\right)}{TG}\times 10^{-2}\]  
In the formula, \(He_{r}\) is the original helium concentration in the examined formation, in %; \(He_{t}\) is the helium concentration measured in the mud gas, in ppm; \(He_{a}\) is the helium concentration in air, with a value of 5.24 ppm; and \(TG\) is the total hydrocarbon concentration measured in mud gas, in %. This formula is applicable when the predominant gas in the reservoir is composed of hydrocarbons.
# Knowledge-Graph-Based Integrated Line Loss Evaluation Management System
## 5 Construction of a Mapping of Management Cases for Comprehensive Line Loss Evaluation
### Research Discussion
Concurrently, it is recommended that customized development be adopted and that line loss management models be developed that are adapted to the local environment. This should be performed in accordance with the characteristics of power grids in different regions, taking into account the differences between urban and rural power grids, for example. It is also essential to consider the model's adaptability in different environments, such as urban and rural contexts. Urban power grids are characterized by intricate structural complexity and a high density of users, whereas rural power grids often encompass expansive areas with a dispersed population. It is essential that the model takes these differences into account. For instance, urban power grids require a greater focus on load management and the demand response, whereas rural power grids necessitate a stronger emphasis on infrastructure construction and maintenance. Additionally, the model should integrate environmental factors, such as climate, geography, and population distribution, in order to adapt to the specific conditions of different regions.
# Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
## 3 Experiments and Results
### Prediction Task  
In the preprocessing of the dataset, the traffic flow and weather data were recorded every five minutes. Our prediction task was to use the first five records to predict the sixth datum. That is, the traffic flow datum for the next five minutes was predicted based on the information from the first sixty minutes of data. Finally, our proposed lane-level traffic flow prediction algorithm was evaluated by comparing the real data with the predicted data.
## 3 TIR Observations of Ryugu in Cruise and Approach Phases  
During a 3.5-years long cruise phase before arrival at Ryugu, images of deep sky backgrounds were taken several times per year as the dark frames for calibration, and the degradation of TIR was checked by comparing the temporal changes of its bias levels and the standard deviations as well as the number of bad pixels [4; 23]. No substantial degradation has been found for five years after launch, so that the radiation tolerance of the TIR against the deep-space environment has been proven.  
Hayabusa2 passed by Earth for the gravity assist to change its trajectory en route to Ryugu on 3 December 2015. From 14 October 2015, the spacecraft changed its attitude to point Earth and the Moon to observe them with the remote sensing instruments until 21 December 2015. During this period, TIR has taken images of Earth and the Moon from the various distances, so that the dependency of distance on thermal radiation intensities has been investigated [23]. The critical distance above the threshold level for detecting Earth and the Moon were obtained, which was applied to the estimate of the critical distance (\(-1.5\times 10^{4}\) km) or the critical size of diameter (\(-0.06\) mrad) at 1 au from the Sun for detecting Ryugu by TIR before arrival [23].  
The first light event of TIR for the observation of Ryugu was conducted on 6 June 2018 at \(2.5\times 10^{3}\) km from the asteroid. Ryugu was successfully detected as was predicted [23]. A series of one-shot imaging of Ryugu by TIR continued almost once a day until arrival at the asteroid (see Figure 2, Table 2) and a relationship of thermal radiation intensity to distance from the asteroid was obtained, as well as to distance from the Sun and the solar phase angle (nearly Sun-Probe-Earth angle). It was important that Ryugu was the only bright spot detected in the thermal images after subtraction of dark frames, which is different from optical images where numerous bright spots other than the target body were found such as bright background stars, bad pixels, or by irradiation. In addition, thermal images by TIR are never saturated by overexposure for most planetary surfaces (\(<460\) K). These features have an advantage of using thermal images for the survey of orbiting satellites around Ryugu or possible ejection of materials from Ryugu if they were larger than the critical size of diameter [23]. Such satellites or ejection have not been detected through the asteroid proximity phase, although some ejected flows were observed on the asteroid Bennu by the OSIRIS-Rex mission [24].  
Figure 2: Thermal images of asteroid 162173 Ryugu taken by TIR during the Approach Phase (©)AXA).
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 9 Proof of Concept
### Effect of Varying Height on Data Collection  
We evaluate the effect of changing height on the system performance and results are shown in Figure 16b. The X-axis shows the height of the UAV from 1.2 m to 20 m. and the Y axis represents both time consumed in seconds and energy utilization in PJ (Peta Joules).  
By increasing the height of the UAV, we could not notice any effect on the data collection time but it may have a massive impact on the CH energy utilization.
## 4 The Proposed QoS-Aware Routing
### Energy-Aware Geographic Forwarding
The absence of a global routing scheme reduces the networks setup and updating costs, eliminates the need for storage of network-wide routing information at each node, and alleviates the possibility of incorrect information at the nodes as changes in system topology occur. What requires only is that the nodes must broadcast an upkeep packet including node identification, current location information, average queuing delays, and remaining energy periodically throughout its lifetime.
# Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector
## 4 Results and Discussion  
This study proposes a building damage detection method, which is referred to as SSD_CAE, using SSD with the pretraining and data augmentation strategies. We implemented the proposed method under the framework of Pytorch (version 0.3.1) (Facebook, Inc., Menlo Park, CA, USA) [31]. A GPU (GTX1080 8G, NVIDIA Corporation, Santa Clara, CA, USA) processor was used to accelerate the calculation.  
To validate the effectiveness of the proposed SSD_CAE method, the traditional SSD method was utilized as the baseline for comparison. Some metrics, such as recall, precision, mF1, and average precision (AP), are used to compare the performance. Precision and recall are popular measures for the classification task. However, the comprehensive performance is difficult to evaluate by evaluating precision and recall separately. Accordingly, mF1 was adopted for comparison. The F-measure is the weighted harmonic average of precision and recall as follows:  
\[\text{F}=\frac{\left(\alpha^{2}+1\right)*P*R}{\alpha^{2}(P+R)}, \tag{4}\]  
In general, \(\alpha\) is equal to unity:  
\[\text{F1}=\frac{2*P*R}{P+R}, \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimizer** & **Momentum** & **Weight Decay** & **Learning Rate** \\ \hline Stochastic gradient descent (SGD) & 0.9 & \(5\times 10^{-4}\) & \(1\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimizer and hyperparameter settings before training.  
Figure 8: (**a**) The “debris” label represents ruins and destroyed buildings; (**b**) The “mild” label represents damaged buildings.  
where mF1 is the mean of F1 for all classes.  
Average precision (AP) aims to evaluate the ability of the model to detect a class. A set of recall thresholds [0, 0.1, 0.2,..., 1] was provided. Thereafter, the value of each recall threshold ranged from small to large, while the maximum precision corresponding to the recall threshold is calculated. Subsequently, we calculated 11 precisions. AP is the average of these 11 precisions. The mAP is the mean of AP for all classes.