esa-sceva/satcom-chunk-collection huggingface dataset:
# Chemical Characterization of Human Body Odor Headspace Components
## 3 Results  
### Whole-Body Sampling  
To monitor the feasibility of the SPME-GC/MS method for human odor volatile detection, a group of ten (10) individuals were sampled in the human chamber. Across the ten (10) participants sampled, a total of 105 compounds were detected, with 22 compounds having a frequency of 50% or higher. These compounds of high frequency were used to build Figure 2 and Table 1. Each individual emitted a distinctive odor profile, with varying compounds and abundance ratios. The compounds with the highest frequency included decanal, hexadecane, tetradecane, and 5-Hepten-2-one, 6-methyl-, which were present in all ten (100%) odor profiles. Nonanal was the next most frequent, with a 90% frequency. Aldehyde compounds such as nonanal, decanal, and octanal have all been previously identified in the literature as emanations from either hand and/or axillary human samples [21; 22; 25]. Aliphatics such as hexadecane and tetradecane have also been reported in the literature from human biospecimen (axillary/hand) odor profiles [21; 22; 25]. The compound octanal, 2-phenylmethylene has not been observed in the literature of human odor volatiles and is unique to this study. As previously mentioned, Table 2 depicts the compounds that had a frequency of occurrence of 50% or greater within the chamber-collected samples. This allows for a direct comparison of whole-body volatiles in relation to volatile presence in individual  
Figure 2: Stacked bar graph depicting the whole-human odor of male and female subjects collected via the human chamber. Only compounds with a frequency of 50% or higher are included in this figure. biospecimens. As can be seen in the table, many compounds detected within the present study have been previously reported in other human scent studies. These compounds include benzyl alcohol; decanal; nonanal; and 5-Hepten-2-one, 6-methyl-. Given that these compounds have been cited in previous studies and were observed in the present study via the chamber, it can be said that these compounds are not only emitted through individual bios Specimens (hand, axillary, breath), but also represent a full whole-body emission origin. Compounds that have been cited in non-human scent related studies, such as benzene, (1-butylhexyl)-, benzene, (1-ethyloctyl)-, and other similar derivatives, are not included in the table, given that they have been reported to be VOCs released from microplastics [36; 37]. Similarly, o-cymene and copaene were also omitted, as they have been cited to be food-related VOCs [6]. Additionally, ethanol, 2-(2-ethoxyoxy)- and similar derivatives were also not included in the table, as glycol ethers have previously been detected in pharmaceutical and cosmetic products [38]. Figure 3 portrays the functional group distribution of VOCs across the different sample types (chamber, hand, axillary, breath). Primary odor compounds--those present in all four (4) locations of the chamber, all triplicate (hand) samples, and all duplicate (axillary/breath) samples--were used to represent a snapshot of functional group distribution. Among all
## 4 Experiments and Discussion  
### Experimental Equipment  
An experimental study was performed to validate the effect of surface roughness on back-surface micro-crack. Figure 10 shows a schematic diagram of the experimental setup, which mainly consisted of an ultrasonic scanner driver with ultrasonic transducer, and signal processing system, as well as several test samples. The ultrasonic scanner driver was used to move the transducer in two directions. The transducer was a broadband, 8 mm \(\times\) 8 mm piezoelectric wafer. The nominal center frequency of the transducer was 5 MHz. The focal length of the transducer was 25 mm and it was oriented obliquely with respect to the sample's front surface. The transducer was at a stand-off distance of 20 mm from the front surface. The incident angle was set at 17\({}^{\circ}\).  
Figure 10: Schematic diagram of the experimental setup.  
Figure 9: **(a)** Normalized mean signal amplitude of the same crack reduces as front-surface roughness \(\mathbf{R_{sm}}\) increases. (**b**) Normalized mean signal amplitude of the same crack reduces as back-surface roughness \(\mathbf{R_{sm}}\) increases.  
The incident longitudinal wave generated by the transducer traveled through water and entered the plate with a smooth or rough surface. The pulse-echo generated by interaction of the transmitted shear wave with micro-crack on back surface was received by the same transducer and fed into a digital oscilloscope in the form of a time-race (A-scan). For each micro-crack, ten measurements were conducted and averaged to improve the accuracy of measurement while the other parts of the experimental setup remained unchanged.  
A series of mild steel plates with 9 mm thickness were provided as test samples, and the experimental parameters of the test samples are shown in Table 1 and Figure 11. One side of each test sample with different surface roughness \(R_{a}\) and \(R_{sm}\) was machined by vertical CNC milling and CNC turning, respectively. The opposite sides of all test samples were ground to make their surfaces smooth. To validate surface variations of the test samples, a Comprehensive Measurement System (Form Talysurf PG1830, Taylor Hobson Limited, Leicester, UK) was used to measure roughness profiles across the surfaces of each sample. There was good agreement with the set roughness values. Different rectangular notches on the rough and smooth surfaces of each sample were machined by a laser cutting machine. The notches were used as reference defects for studying the ultrasonic detection capacity of micro-cracks. The notches were at the bottom of the surface texture.
# Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area
## 1 Introduction
In this work, we focus on the performance of the LoRa network that is used to monitor and control the IoT devices. The optimization of the deployment of the LoRa end nodes and the gateway requires carrying out radio planning tasks. Such tasks are essential for scenarios such as the one described for this paper (i.e., forestry vegetation area), which are especially challenging in terms of radio propagation due to numerical obstacles that impact the electromagnetic waves. LoRaWAN applications take advantage of the wide segment of the ISM band assigned in the 868 MHz region, where it is possible to use multiple concurrent channels to sustain a great number of end nodes [15]. Several studies concerning the aspects of propagation and performance of LoRa modulation in the 868 MHz band are found in the literature [16; 17], but only few works [18; 19] have performed an experimental analysis in the 433 MHz band, which is also available in Europe. This paper aims at evaluating the performance of LoRa transmission technology in a real-life experiment when devices are deployed in a light-dense and very-dense forest vegetation environments. We investigate how the quality of reception depends on the main LoRa configuration parameters in the 433 MHz and 868 MHz frequency bands. In addition, we provide a theoretical coverage study from an accurate radio planning tool [20], which uses accurate terrain elevation data and the ITM Longley-Rice propagation model. In this paper, our key contributions are:  
* A comprehensive state-of-the-art section is provided on the LPWAN technologies used for smart agriculture applications and wildlife monitoring network. * The design and implementation of a prototype device used for the experimental tests are described in detail. * The radio planning analysis, which evaluates the performance of LoRa network operating in the 433 MHz and 868 MHz aimed at wildlife monitoring in a forest vegetation, is presented, together with experimental results. * Comparison of real-life RSSI values collected from the experimental test to values generated using an RF planning tool. The rest of this paper is organized as follows: Section 2 introduces the state-of-the-art and the related works. The overall system architecture is described in Section 3. In Section 4, we present the experimental setup, whereas experimental results and discussion are presented in Section 5. Lastly, Section 6 concludes the paper with some final remarks.
# Design of a Noise Mitigation System Using Lightweight Graded Micro-Porous Material
## 2 Establish Predictive Methods to Calculate Flow Properties of Polydisperse Particles
### Micro-Porous Hollow Bubble (\(\upmu\)-PHB) Composite Material
#### 2.2.1 \(\upmu\)-PHB Specimens for Impedance Test
The processed specimens of 25 mm thickness were used for porosity measurement; the porosity was measured as a function of soak time using the water saturation method [44]. The procedure of porosity measurement is described in detail in reference [25]. The measured porosities of the uPHB composite materials were 0.447, 0.478, and 0.602 for small, medium, and large particle sizes, respectively. The data are also listed in Table 3. Further, the measured mean bulk densities were 0.34 g/cc, 0.44 g/cc, and 0.35 g/cc for the small, medium, and large particle-size uPHB composite materials, respectively. Figure 9 illustrates the SEM imaging of the medium-sized uPHB composite material. A Scanning Electron Microscope (SEM), as a sophisticated scientific instrument, enables the investigation of the microstructure of various materials and specimens with remarkable detail and clarity. In the current work, we used a Hitachi SU8000, a high-resolution field-emission SEM, to capture the morphology of the uPHB composite material. Since the material is of an insulating or non-conductive kind, the specimen was coated with a thin layer of gold (a conductive material) to prevent charging effects during electron beam irradiation, and then the microscopy was conducted. This is an advanced and powerful SEM; however, it has certain limitations, like still requiring a coating on non-conductive materials, its complexity of operation, high-voltage imaging leading to electron beam penetration in soft materials, cost, etc. From Figure 9, the image reveals near-spherical particles characterized by surface roughness, which can enhance sound energy dissipation through friction, and complex packing, improving tortuosity. Also, we can see that the binder amount is sufficient to create point-to-point contact between the bubbles (a good bonding strength) and not alter the pore properties by retaining the same porosities as loose hollow microbubbles. These features of the hollow microbubbles can help to develop a noise-mitigating micro-porous material system with both sound absorption and transmission loss. Similar microstructure features were noticed in large and small size uPHB composite materials. Figure 8: (a) Loose hollow microbubbles before processing, (b) processed uPHB composite materials, (c) magnified images of the uPHB composite materials with different particle sizes.
## 7 Conclusions  
The presented research, which is an extension of [54], studied the use of machine learning in sentiment analysis tasks, particularly in situations where the text is given in a language with a relatively minimal set of linguistic analysis gazetteers and modules, such as the POS tagger, syntactic shallow parsers, etc. Modern Greek has its place to this category of under-resourced languages, and the present work collected real-world sentiment data, obtained from Web 2.0 platforms, and followed an idea of using machine translation of Greek tokens as a feature generation step. More specifically, instead of utilizing a hand-crafted polarity lexicon for Greek, which would have insufficient impact on the accuracy of the predictions due to the noisy social media linguistic style, we proposed a method that considers the translation of each Greek token as an additional input feature. Even though this process may appear to bring additional effort and complexity to the majority of classification algorithms, the use of gradient boosting machines, a robust ensemble method that can handle sparsity in high-dimensional data, appeared to be valuable for the task at hand, outperforming a family of well-known methods for sentiment analysis. Even when confronted with other state of the art classifiers, such as deep neural networks (from the family of recurrent neural networks), the proposed method demonstrated superior performance. Moreover, since sentiment data in real cases present a high level of class imbalance between the positive and the negative label, we applied sophisticated sampling methods for not allowing the bias of the classifiers towards the majority class. Yet again, GBM was found to be the superior solution in terms of precision and recall per each class label. In the future, we are oriented towards using semantic features, such as topic models, to further improve the sentiment analysis models.  
[PERSON]. conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON]. analyzed the data; [PERSON] and [PERSON] wrote the paper. Authorship must be limited to those who have contributed substantially to the work reported.
## 4 Results and Discussion
The duration of the external calibration mode is less than 10 min; the actual measurements should, therefore, take less than 60 min. This duration value is further restricted to the pointing stability holding time, as shown in [1]. Assuming that the cold-sky observation time is 10 min, the single-antenna brightness temperature observation results are given in Figure 10a under the above three selection conditions. The brightness temperature stability is 0.0096 K during the period, and the area is shown by the red dot in Figure 8b. During the above simulated time period, the cold-sky observation time that meets the brightness temperature stability of the antenna is  
Figure 8: **(a)** The simulated cold-sky observation results from 04:24:00.000 on 15 January 2021 to 08:24:00.000 on 19 January 2021. **(b)** The result of the area selected by the criteria of weighted land fraction <0.03%. less than 0.01 K/10 min, and the other two criteria at the same time can achieve a maximum duration of 13 min in the open ocean of the Pacific Ocean, where the latitude is between 58 degrees and 15 degrees south and the longitude is between 138 degrees and 152 degrees west. In addition, the antenna brightness temperature stability is 0.0084 K.  
In order to evaluate the effect of the strategy mentioned above on the cold-sky calibration of an L-band synthetic aperture radiometer on board, the properties of the materials of the synthetic aperture radiometer antenna horn or reflector, such as the coefficient of thermal expansion and thermal model, should be utilized to evaluate antenna deformation [18; 19]. Antenna thermal deformation on board directly translates to deviations in the antenna patterns and, finally, to noise in the system. The eclipse season of the orbit at an altitude of \(\sim\)600 km occurs when the beta angles are under certain values, which is when the worst-case thermal environment happens and thereby should not be used for external calibration. When the candidate time and locations are selected by the first two conditions shown in Section 3, the beta angles of these times and locations should be examined to determine whether they meet the requirements of thermal stability and the antenna pattern error or not, as shown in Figure 10b. The synthetic aperture radiometer system performance evaluation of cold-sky calibration is given in Figure 11, based on the statistics of the error in the retrieved brightness temperature maps, while there are some unavoidable antenna normalization pattern errors between the in-flight and on-ground measurements due to the space environment. Figure 9: The bias of the brightness temperature of the antenna between the Earth ellipsoid model and the simplified spherical model.
## 3 Materials and Methods
### Adaptive Ensemble Attention for Autonomous Driving  
Attention mechanisms are crucial in autonomous driving, enhancing the segmentation of complex scenes by directing the model's focus toward the most pertinent features and regions within an image. Self-attention effectively facilitates the comprehension of global dependencies in a scene. This enables the model to associate distant features, such as aligning lane markings throughout the image or identifying spatial relationships between a pedestrian on one side of the road and a vehicle on the opposite side. The global context is essential for precise segmentation in situations with multiple objects, including intersections or high-traffic zones.  
Spatial attention, then again, improves division by focusing on key spatial areas, guaranteeing that basic regions like street limits, vehicles, and walkers are given more significance. By powerfully weighting spatial locales, spatial attention checks that less significant parts, like the sky or building's backgrounds, are deprioritized. This is particularly helpful in distinguishing objects in crowded environments or cluttered ones, where focusing on unambiguous regions can essentially further develop segmentation precision.  
Channel attention supplements these mechanisms by accentuating the main component maps created by the network. By recalculating feature channels, it guarantees that basic details, for example, street surfaces, vehicle edges, or traffic signal elements, are enhanced while unessential elements are stifled. This particular accentuation upgrades the general component portrayal, prompting more exact division, even in testing conditions like poor lighting or unfriendly environments. This adaptive ensemble approach enables the model to generalize across diverse driving environments, from urban traffic to rural roads, handling challenges like occlusions, varying lighting, and weather conditions. By utilizing the advantages of each attention mechanism and dynamically adjusting their contributions, the Adaptive Ensemble Attention GAN provides robust and precise semantic segmentation, a critical capability for the ADAS in real-world autonomous driving applications.  
In open highway scenarios, self-attention may be predominant in capturing global dependencies such as lane alignment. In contrast, in densely populated urban environments, spatial attention may prioritize critical areas such as pedestrians or parked vehicles. Channel attention prioritizes the most relevant feature maps consistently across various scenarios. Equations (5)-(7) present the mathematical formulation for the AEA [30].  
\[F=[Di.Dl,\ Pi,Pl] \tag{5}\]  
\[AW=[Dai,Dal,\ Pai,Pal] \tag{6}\]  
\[EA=(Di*Dai+Dl*Dal+Pi*Pai+Pl+Pal) \tag{7}\]Here, _Di, Dl_ stand for MobileNetv3's intermediate and final feature maps, _Dai_, and _Dal_ for corresponding attention. _Pi,  Pl_ stands for EfficientNetB7's intermediate and final feature map, _Pai,  Pal_ corresponding attention is the entire features.
## 2 Lean and Green Overview
### Industries Application of Lean and Green  
#### 2.1.1 Automotive Industry  
Ref. [39] conducted a case study in the automotive industry to examine the proposed conceptual framework for assessing green and lean supply chains; the findings revealed high scores in these companies, which are attributed to the effective combination of green and lean implementation. This highlights the importance of applying lean and green in the automotive industry. Furthermore, ref. [40] explored the significance of lean-green practices in supply chain management for sustainable performance in the automotive industry; the outcome provided a theoretical and empirical roadmap for decision-makers seeking to enhance sustainable performance in the automotive industry. Also, ref. [41] studied four original equipment manufacturers and two first-tier suppliers in the automotive industry, examining their operations strategies, lean practices, and green practices. The result showed that companies that introduced lean and green practices changed decision areas and competitive operations priorities while adopting various operations strategies to improve their sustainable competitive advantage. In addition, major car manufacturers worldwide are implementing environmental practices, such as green building certification, decoupling, and recycling designs for manufacturing and non-manufacturing facilities [42]. For example, the Toyota Group currently enhances its eco-design by developing new technologies to decrease engine emissions and create clean-energy vehicles [43]. The company also implements green initiatives to reduce environmental impact, including renewable energy source purchases, employee training, and recycling programs [42]. Moreover, it is important to state that the authors of [39; 40; 41; 42] highlighted tools and practices such as JIT; set-up time reduction (STR); smaller lot sizes (SLZ); green purchasing (GP); environmental management systems (EMSs); reverse logistics (RLs); reduce, reuse, and recycle (3R), environmental value stream mapping (EVSM), and green value stream mapping (GVSM) among others used in lean-green integration.  
In SSA, the prevalence of local production and assembly plants for these major automotive manufacturers has ensured that the impacts of lean and green implementation in their processes are visible through affordable product prices and upskilling local employees [44; 45].
## 3 Results and Discussion
#### 3.2.1 Signal Amplitude  
Similarly, for the purpose of analyzing the influences of F-T cycles on the splitting fracture damage, the variation between signal amplitude and load level versus time during splitting were obtained for the SBS-modified asphalt mixtures with basalt fiber under various F-T cycles. As shown in Figure 8a, the entire loading process and AE signal amplitude can be divided into three stages according to the combination of the intensity of the AE signal amplitude and the load level. During the first stage, the lower AE signal amplitude values distributed at about 40 dB with fewer signals were generated and recorded during the splitting loading, while the load level was below 0.2. During the initial stage under the splitting loading, several initial internal microcracks were preliminarily formed. During the second stage, the load level was between 0.2 and 0.8, a significant increase in the AE signal amplitude began to appear and the values were larger than those at the first stage. The abrupt changes of the AE signal amplitude indicates that the internal damage of the asphalt mixture formed rapidly, and the microcracks developed and expanded during this stage. As the splitting loading continued, the AE signal reached the third stage. The AE signal amplitude values reached the maximum value and slowly declined. This is because macroscopic cracks occurred when the damage accumulated to a certain degree, and eventually the asphalt mixture specimen was destroyed.  
Figure 8: _Cont._In general, the AE signal amplitude and load level have similar changes under various repeated F-T cycles. A comparison of the AE signal amplitudes of various F-T cycles showed that the density of the AE signal amplitude gradually became slightly smaller with increasing F-T cycles. The first stage was gradually extended, and the third stage was gradually shortened. The loading time presented a gradual decreasing trend with F-T cycles. This variation trend means that asphalt mixtures processed with more F-T cycles began preliminary damage in a short load time. Thus, it is evident that the F-T cycle has a visible adverse influence on the splitting strength of SBS-modified asphalt mixture with basalt fiber, which makes asphalt mixtures more likely to cause early failure under the splitting load.