esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Monitoring of Indoor Air Quality with Internet of Things-Based E-Nose
## 2 Related Work  
Due to the fast advancement in IoT and sensor techniques, interest in air quality measurements continues to grow. In [23] an environmental monitoring system was propose that included the sensors Raspberry Pi and MICS-4514, which enabled the assessment both indoors and outdoors at a university campus of multiple environmental parameters such as temperature, humidity, light, noise level, CO, NO\({}_{2}\). A proposed system for detecting environmental contamination [24] used various environmental detectors to identify temperature, humidity, ambient light, gas sensors and PM. All data measured by the system using the sensors STM32f4 xx and Sharp GP2Y1010 for PM detection, and TGS5342 for CO, are stored in the internal storage and on an Internet server over the Wi-Fi network. Using the ATmega328 AVR controller, DHT22 temperature sensor, Sharp GP2Y1010 AU0F dust sensor and UVM-30A UV sensors, an Integrated Environmental Monitoring System (IEMS) [25] was proposed to detect the microenvironment. In another study [26], the Nano Environmental Monitoring System (nEMoS) was proposed using an IoT-based indoor environmental quality (IEQ) assessment system created using an Arduino Uno module and low-cost sensors such as DHT22. To determine the quality of packaged products, an IoT-based measuring system was intended, including low-cost pressure, temperature, humidity, gas sensors (BME680, DHT22 and MQ5), Arduino and XBee wireless module [27]. In the study, in which iAQ, an air quality surveillance system based on IoT architecture, was proposed for AAL [28], the wireless sensor nodes (WSN), Gateway and Android user interface provided environmental data such as temperature, humidity and CO\({}_{2}\) to users through the mobile application. In the IoT-based system [29] developed for real-time IAQ surveillance, multiple gases such as CO\({}_{2}\), NO\({}_{2}\), ethanol, methane and propane were detected using ESP8266 as the controller and sensors MICS-6814 and MICS-6814 as the detection unit. This system, based on open source technology, has a mobile phone application that transmits real-time notifications to users. The solution is based on the IoT concept and is entirely wireless in a study that presented an IAQ surveillance solution that can measure temperature, humidity, PM\({}_{10}\), CO\({}_{2}\) and light intensity in real-time [30]. The Arduino Uno emitter utilizes the ESP8266 controller and the \"ThingSpeak\" open-source IoT platform to record wireless data to enable wireless Internet access. In this study, a real-time surveillance system based on IoT architecture for PM surveillance was presented [31]. The iDust system was created using open source technologies and low-cost sensors. System measurement data, consisting of a WEMOS D1 Wi-Fi controller and PMS 5003 dust sensor, are transferred to users via an IoT-based implementation.
## 5 Discussion  
Many studies recognize the difficulty in controlling all factors in real-world driving conditions on specific street segments, which can influence FFS measurements. Therefore, the measured FFS values will be different for each site. In our opinion, in order to obtain reliable results, it is more important not so much the number of measurement points but their correspondence to free traffic conditions, as well as the availability of reference road conditions. Isolating any one factor, such as one-way traffic, requires, if possible, eliminating all other factors and taking measurements under identical conditions.  
Although in our work, when choosing experimental road sections, we tried to ensure that all unstudied factors remained identical, it is not possible to completely solve this problem. So, for example, for the main streets in the site 1 section, there were three lanes, while in the site 2 section, there were four lanes. In terms of local traffic streets, site 3, unlike site 4, had sidewalks on both sides of the road and slightly heavier pedestrian traffic. The question of how much these differences could introduce disturbances into the FFS measurement results can be the subject of a separate discussion.  
It should be noted that the characteristics of the adjacent territory are rarely considered as a factor influencing speed. The effect of the number of lanes on FFS appears to be significant only for one- and two-lane urban roads [7], which is typical for local streets. In our case, the differences concern main streets with three and four lanes. The work [15] notes a more significant FFS correlation for the position of the vehicle in the lane than for the number of lanes. At the same time, in our opinion, the position of the car on the lane is a consequence of the speed chosen by the driver and not its cause.  
It should also be noted that transport flows in different countries and regions differ both in composition and in the quality of vehicles. The use of data only for passenger cars in this study makes the results more universal since traffic flows reduced to a passenger car can be used when solving traffic modeling problems. But still, the generalizability of the results may vary depending on the geographical and cultural context, which requires taking this factor into account in the practical sphere of traffic management on the street network of a particular country or region.
## 3 Results
### Accuracy of ANN-PM Model under Dry Climates  
In arid areas, most of the precipitation is consumed in the process of ET, and inadequate water supply could substantially limit the growth of crops in these regions. Therefore, accurate estimation of ET plays an important role in the sustainable development of agriculture in arid areas. Hence, we evaluated the ANN-PM model to simulate ET on a daily scale over flux sites covering a wide range of climate dryness, measured using aridity index (AI). The R\({}^{2}\) between simulation and observation is used to measure the model performance. The variations in R\({}^{2}\) of each flux site in relation to site-scale AI are shown in Figure 7, where low AI values correspond to dry climates. The driest site is US-Twt, followed by US-Tw3, US-Tw2, and DE-Rus. The average R\({}^{2}\) of the 16 flux sites is 0.74, and the average R\({}^{2}\) of the driest four flux sites with an AI index lower than 0.5 (DE-Rus = 0.49, US-Tw2 = 0.42, US-Tw3 = 0.30, and US-Twt = 0.26) is 0.77. In terms of R\({}^{2}\), the performances of the ANN-PM model at the dry sites are reasonable and comparable to those at the wet sites (Figure 7).  
Figure 6: Scatter plots of the observed ET values measured from the flux tower and predicted ET values of the Medlyn-PM (**left**) and the ANN-PM model (**right**) in estimating cropland evapotranspiration. (**a,c,e**) are the scatter plots of the observed ET values measured from the flux tower and predicted ET values of the Medlyn-PM model in the training, validation, and test datasets, respectively. (**b,d,f**) are the scatter plots of the observed ET values measured from the flux tower and predicted ET values of the ANN–PM model in the training, validation, and test datasets, respectively.  
The ANN-PM model can capture the time-series changes of ET at the dry sites well (Figure 8, four sites with an AI index lower than 0.5). At the driest site, US-Twt, which is a paddy field site, ET predicted by the ANN-PM model agreed well with the observations, indicating that the model can reflect the influence of irrigation on cropland ET under dry conditions. Consequently, the ANN-PM model can simulate cropland ET across a wide range of gradients of climate dryness, showing great potential to estimate cropland ET accurately on a regional scale.  
Figure 8: Time-series diagrams of observed ET (**black line**) measured from the flux tower and simulated ET (**red line**) by the ANN-PM model.  
Figure 7: AI and R\({}^{2}\) values of each flux site. AI is aridity index and R\({}^{2}\) is the determination coefficients between simulation and observation.
# Graph Layer Security: Encrypting Information via Common Networked Physics
###### Abstract  
The proliferation of low-cost Internet of Things (IoT) devices has led to a race between wireless security and channel attacks. Traditional cryptography requires high computational power and is not suitable for low-power IoT scenarios. Whilst recently developed physical layer security (PLS) can exploit common wireless channel state information (CSI), its sensitivity to channel estimation makes them vulnerable to attacks. In this work, we exploit an alternative common physics shared between IoT transceivers: the monitored channel-irrelevant physical networked dynamics (e.g., water/oil/gas/electrical signal-flows). Leveraging this, we propose, for the first time, graph layer security (GLS), by exploiting the dependency in physical dynamics among network nodes for information encryption and decryption. A graph Fourier transform (GFT) operator is used to characterise such dependency into a graph-bandlimited subspace, which allows the generation of channel-irrelevant cipher keys by maximising the secrecy rate. We evaluate our GLS against designed active and passive attackers, using IEEE 39-Bus system. Results demonstrate that GLS is not reliant on wireless CSI, and can combat attackers that have partial networked dynamic knowledge (realistic access to full dynamic and critical nodes remains challenging). We believe this novel GLS has widespread applicability in secure health monitoring and for digital twins in adversarial radio environments.  
cyber-physical systems; wireless security; sensor network; infrastructure health monitoring; graph signal processing +
Footnote †: journal: Sensors
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 4 Simulation Scenario
### Applications  
For the scope of this paper, we choose train monitoring and video surveillance applications because they cover telemetry and closed-circuit television (CCTV) services, as reported by [15].
## 4 Non-Model-Based Ultrasound Envelope Statistics Parametric Imaging Techniques
### Ultrasound Kurtosis Imaging  
Kurtosis \(K\) is a parameter of the shape of a probability distribution and is defined as the normalized fourth moment of envelope amplitude \(r\)[56]:  
\[K=\frac{E[(r-E(r))^{4}]}{\left(E[(r-E(r))^{2}]\right)^{2}}. \tag{4}\]  
The kurtosis is a measure of the peakedness of the probability distribution. Kurtosis \(K\) = 3 represents that the probability distribution of the backscatter envelope is a Gaussian distribution. When \(K\) is larger and smaller than 3, the probability distribution of the backscatter envelope is leptokurtic (slender) and platykurtic (broad), respectively.  
[PERSON] et al. [21] investigated ultrasound kurtosis imaging for characterizing hepatic steatosis of 107 patients. The reference standard was an ultrasonographic scoring system [36]. The kurtosis decreased from 5.41 \(\pm\) 0.89 to 3.68 \(\pm\) 0.12 with increasing the score of fatty liver from 0 (normal) to 3 (severe), indicating that hepatic steatosis reduces the degree of peakedness of backscatter statistics. The AUC was 0.92, 0.90, and 0.82 for \(\geq\)mild, \(\geq\)moderate, and \(\geq\)severe, respectively. It was suggested that ultrasound kurtosis imaging may be useful in designing computer-aided diagnosis tools to assist physicians in early detection of hepatic steatosis.
## 4 Conclusions  
This study proposes a multi-interference source localization method based on target detection in GNSS receiver C/N\({}_{0}\) distribution maps, specifically addressing challenging scenarios, including the coexistence of multiple directional interferences, increased diversity and density of GNSS interference, and the presence of multiple low-power interference sources. Building upon established system and signal models suitable for GNSS interference source localization, we innovatively exploit the similarity between GNSS receiver navigation information and image grayscale information to construct C/N\({}_{0}\) distribution maps, thereby transforming the multi-interference source localization problem into a computer vision-based object detection task. To address the specific challenges in target detection and localization for our application scenario,  
Figure 19: Interference detection and localization performance under different interference transmission powers when the interference type is set to 6: (**a**) 0 dBm interference transmission power; (**b**) \(-5\) dBm interference transmission power; and (**c**) \(-15\) dBm interference transmission power.  
we developed the OSF-RCNN framework by enhancing the Faster R-CNN architecture. The proposed method integrates an Oriented RPN to regress the orientation angles of directional antennas, effectively addressing their rotational characteristics. The FPN is used to fuse multi-scale feature information, enhancing the method's adaptability to different types of interference sources and varying transmission powers. The SE mechanism is introduced at key positions within the network to enhance focus on weak interference targets, improving the detection accuracy of small targets. The paper presents the corresponding system and signal models, constructs training and testing datasets, and conducts simulation tests to evaluate the detection and localization performance of the proposed method. The results demonstrate that the method effectively detects and localizes GNSS interference sources across a range of challenging scenarios.  
Conceptualization, Q.C. and R.L.; Methodology, Q.C. and R.L.; Software, Q.Y. and X.H.; Validation, R.L. and Q.Y.; Formal analysis, R.L. and X.H.; Investigation, Q.Y., Y.X. and Y.Z.; Resources, Q.C. and Y.L.; Data curation, Y.X. and Y.Z.; Writing--original draft, R.L. and Q.C.; Writing--review and editing, Q.Y. and Q.C.; Project administration, Q.C. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Discussion  
### Volume Estimation of Retroperitoneal Sarcoma  
Due to the nonspecific presentation of their initial symptoms, RPSs are often seen during an initial evaluation on a CT scan as part of a general abdominal survey [31]. A contrast- enhanced CT allows for confirmation of the site and origin of the mass, and often the tissue composition [32], with the additional benefit of wide availability. An MRI is reserved for patients with an allergy to iodinated contrast agents or problem-solving when some finding is equivocal on a CT scan. It has been reported that different histotypes of RPS can differ widely in terms of both histologic features and clinical behavior; these different histopathologic characteristics may influence glucose metabolism and thus the 18F-FDG uptake in PET examination [33]. Moreover, the difference observed in the maximum standardized uptake value (\(\text{SUV}_{\text{max}}\)) of RPS might be explained by the distinctions in cellularity and necrosis percentage in distinctive histotypes [34]. This different behavior may influence segmentation results. For example, [PERSON] et al. [35] reported that the same tumor segmentation yields different results in MRI and PET scans: in an MRI scan,  
Figure 3: Comparison of segmentation performance in three different slices for (**a**) patient #005, and (**b**) patient #033. Manual (yellow), ENet (red), and ERFNet (green) segmentations are superimposed. In the first study, ENET and ERFNet obtained optimal DSCs (96.69% and 93%, respectively). In the second study, ERFNet showed poor DSC (30.55%) while ENET maintained similar performance compared to the first study (95%).  
necrosis is considered part of the tumor, but it is not visible on the PET scan, as the necrosis is no longer metabolically active.  
The size of the RPS is an important prognostic criterion included in TNM staging (AJCC). [PERSON] et al. [36] reported that size, as measured for the greatest tumor length, was significant in predicting an early relapse. Moreover, in [PERSON]'s proportional hazard model, the time-to-event analysis and large size and weight (together with other variables, such as higher age, male sex, incomplete resection, and high grade) become significant, predicting an early recurrence. There is also increasing evidence for the benefits of radiotherapy for RPS to improve local relapse-free survival. [PERSON] et al. [37] identified size as the only tumor-related variable associated with the use of neoadjuvant radiotherapy. However, all previous studies based their results on tumor size, as measured for the greatest tumor length. An RPS often has a highly irregular shape; therefore, tumors with the same axis length may have a different volume. Further studies are necessary to evaluate whether an overall volume greater than the maximum diameter changes the prognosis and therapy in the era of precision medicine.
# Retractable Wireless Charging Windings for Inspection Robots
## 3 Design and Simulation of Retractable Wireless Power Transfer Coils
### Integration with Mechatronic Design  
The mechatronic design selection can be analyzed based on the simulation results. Among all six conceptual designs proposed in Table 2, three candidates are proved to be practical in terms of the criteria presented in the conceptual design matrix table namely Design 4 (telescopic), Design 5 (umbrella), and Design 6 (scissor linkages).  
The simulation results show that the optimal selection on the WPT coil shape to transmit the highest power with high energy efficiency is either in Group 2 (4-sided coils) or Group 3 (other polygons) in Table 3 with 90% energy efficiency at the output of the WPT system. The magnetic coupling achieved with a circular coil (\(N=32\) edges) of the same area is slightly greater than that of square and rectangular coils. This is likely due to distortions in the field distribution near the corners of the latter shapes. With reasonable accuracy, these results suggest that the primary factor influencing magnetic coupling is the area enclosed by the inductor, while the coil's exact shape has only a minor effect. The results are simulated in an ideal environment with minimum power losses and disturbances.  
Nevertheless, the potential forming shape is another factor contributing to the mechatronic design selection. Design 6 is capable of forming four shapes (hexagon, pentagon, square, and triangle). Regarding mechatronic complexity, although the number of mechanical joints in the scissor linkage is considered numerous, it could be balanced with the minimum actuator required to move the whole body. Electrical DC motors could be installed to move the scissor linkage into the desired form. Among the four potential forming shapes stated for the scissor-linkage concept, the square coil is proven to be the best candidate in terms of transmitted power (181.33 W) with an energy efficiency of 90.67%, followed by pentagon, hexagon, and triangle. In summary, it can be concluded that the square coil is the best candidate for a retractable WPT.  
In this study, Scout Mini is chosen to provide insight into the practical implementation of the proportional relationship between the coil frame's size and the mobile robot's. The width and length of the square coil are set to be 1 m and positioned on the top of the mobile platform. Figure 9a,b depict the comparison between the fully extended and fully retracted positions of the Rx coil on a mid-sized inspection robot (Scout Mini). Addressing the coil management issue, the flexible coil is fixed with string points which are essentially wound up using the pull string retraction mechanism mounted at the center back of the coil structure. The maximum retracting position is measured at 450 mm \(\times\) 450 mm (4.94:1 compression ratio).  
Figure 9: **(a)** Fully expanded. **(b)** Retractable coil with pull string retraction on Scout Mini robot.
## 3 Discussion and Conclusions  
This special issue has presented relevant recent advances of speech and language processing. Regarding the languages considered by these investigations, the majority of works have presented language-independent contributions. However, it is worth mentioning that several works have been considered Iberian Languages specifically or for evaluation purposes. Among them, Spanish has been considered by five papers, including one of them being a South American variant. Moreover, Catalan, Basque, and Portuguese have been used in one paper each. Regarding non-Iberian languages, five papers have considered English and one the Italian language. During the last decade, Deep Learning has shifted the dominant techniques applied to many areas of machine learning, including speech and natural language processing. On one side, the increase of data and computation resources, along with advances in optimization, allows for building larger and more powerful models, which in many cases outperform recent state-of-the-art models. Furthermore, there has been a significant shift towards knowledge sharing, using not only traditional research papers but also through social media, blogs, and sharing of algorithms in the form of source code, data, and experiments. As a result, the number of publications in speech and language processing using deep learning techniques is more and more frequent in all the relevant journals and conferences. IberSPEECH has not been an exception, with more than 65% of the regular papers proposing Deep Learning based techniques to different problems. Deep Learning also plays a relevant role in this special issue, with 5 of 12 regular papers applying these techniques and most of the systems described in the Evaluation Challenge paper. However, the other seven papers show that there is still space for research in many relevant areas, as perception, signal processing, and application. Some of these research works suffer data sparsity which limits the direct application of the main trend deep-learning models. In other papers, the goal is understanding some of the fundamentals of perception and production. Finally, we want to highlight the variety of research topics covered by this special number, which, together with the quality of the selected works, demonstrate the wide scope and quality of the research groups dedicated to Iberian languages, which will meet again to share the last advances in the forthcoming IberSPEECH2020 conference. Conceptualization, F.A., A.B., and A.T.; writing--original draft preparation, F.A., A.B., and A.T.; writing--review and editing, F.A., A.B., and A.T. All authors have read and agreed to the published version of the manuscript. This special issue has been partially funded by the IberSPEECH2018 conference. The authors would like to acknowledge the support of the Technical Program Committee of IberSPEECH2018 on the selection of the conference papers invited to this Special Issue and the Spanish Thematic Network on Speech Technologies (RTTH). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: