esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Areas of Higher and Lower Air Pollution by PM\({}_{2.5}\)  
The three areas with the highest air pollution and the three with the lowest were determined using data from the Sixth State of the Environment Report 2021 by the Chilean Ministry of the Environment (Section: Air Quality, p. 245), following the methodology described in [13].  
The PM\({}_{2.5}\) data used in this study were obtained from the National Air Quality Monitoring Network. These stations measure hourly PM\({}_{2.5}\) concentrations using automated instruments approved by the U.S. EPA, including Beta Attenuation Monitors (BAM) and Tapered Element Oscillating Microbalance (TEOM) devices, as specified in Decree D.S. N\({}^{\circ}\)12/2011. The stations used are located in areas of high population density, typically near residential zones and schools, and are classified as having population representativeness, making them appropriate for assessing chronic exposure in school-age populations.  
According to Decree D.S. N\({}^{\circ}\)12/2011, the monitoring network processes the data as follows to determine annual PM\({}_{2.5}\) concentrations:  
* Hourly measurements are collected and used to calculate daily averages, which are valid if at least 18 h of data are available.
* From valid daily averages, monthly means are calculated, requiring at least 75% of valid daily averages in a month.
* Annual averages are computed using at least 9 valid months; if only 9 or 10 are available, missing months are filled with the highest values from the preceding 12 months to reach 11 months.
* Finally, a three-year arithmetic average is calculated and compared to the national standard of 20 \(\upmu\)g/m\({}^{3}\)[13].  
Based on this methodology, we identified the three most and least polluted areas in Chile (Figure 1) [13]:  
* Most polluted areas (40-45 \(\upmu\)g/m\({}^{3}\)): Coyahique, Padre Las Casas, and Osorno.
* Least polluted areas (<15 \(\upmu\)g/m\({}^{3}\)): Punta Arenas, Huasco, and Antofagasta.
## 3 Results and Discussion
### Identification and Quantification of VOCs  
Many VOCs were detected, but only eight were at a quantification limit. The compounds quantified were isoamyl acetate, butyric acid, isoamyl alcohol, 2-phenyl ethylacetate, hexanoic acid, 2-phenyl ethanol, octanoic acid and decanoic acid. Some of these compounds have been found to have growth inhibition effect on selected fungal pathogens [28; 29]. VOCs with inhibition activity, isoamyl acetate, 2-phenyl ethylacetate, isoamyl alcohol and 2-phenyl ethanol [28] were considered to be responsible for growth inhibition activity in this study. The yeasts used were able to produce these compounds at different concentrations (Table 3). Only _C. pynalidae_ produced isoamyl acetate below the detection limit. The findings of the current work were in agreement with the current literature reviewed. The exploration of GPE as fermentation medium for the production of biopreservation compounds showed that this growth medium actually allowed a production of isoamyl acetate, butyric acid, isoamyl alcohol, 2-phenyl ethylacetate, hexanoic acid, 2-phenyl ethanol, octanoic acid  
Figure 5: Growth inhibition assay plates showing the inhibition activity against _D. bruxellensis_ before (**a**) and after (**b**) optimization of biopreservation compounds production by _Candida pyralidae_ Y1117 (1), _Pichia kluyveri_ Y1125 (2) and _P. kluyveri_ Y1164 (3).  
and decanoic acid at a higher concentration than that reported by [PERSON] et al. [28] when synthetic refined media was used. \"What other suitable, high yield and cost effective substrates can be used for production of biopreservation compounds?\" is one of the questions raised in the current literature reviewed that can be answered through the findings of the research reported herein as diluted GPE was found to yield higher quantities of biopreservation compounds. The result obtained in this work further extends the possible use of wild yeasts as a source of biopreservation compounds while using agro-waste (grape pomace extracts) as fermentation medium.
# Distribution and Deposition of Cylindrical Nanoparticles in a Turbulent Pipe Flow
## 5 Results and Discussion
### Penetration Efficiency  
The penetration efficiency is inversely proportional to the particle deposition velocity. In normal circumstances, the particle deposition velocity is mainly affected by the gravitational force, inertial force of particle, Brownian diffusion, turbulent diffusion and particle shape. In this paper, the gravitational force, and nanoparticles' inertial forces were negligible. The particles follow the fluid motion, which includes the mean motion and turbulence fluctuation, and the particles near the wall deposit on the wall due to Brownian diffusion and turbulent diffusion, resulting in the reduction of particle penetration efficiency. Therefore, the effects of Brownian diffusion, turbulent diffusion and particle shape on the deposition velocity and penetration efficiency should be explored. For the cylindrical nanoparticles, the shape was reflected by the aspect ratio.  
In order to compare the numerical results of penetration efficiency with experimental ones, we calculated the penetration efficiency of cylindrical nanoparticles with aspect ratio \(\lambda=1\) and compared with the experimental result of spherical particles [27] as shown in Figure 10 where Sch is the Schmidt number and PE is the penetration efficiency. The experiment was performed in a tube of 1 m length and 4.8 mm inner diameter, and WOx particles with diameters from 3 to 17 nm were used. It can be seen that the penetration efficiency of particles with \(\lambda=1\) was lower than that of spherical particles, which indicates that the effect of particle shape was significant even for the aspect ratio close to unity.
## 2 Materials and Methods
### Data Sources  
The carbon cycle, water cycle, vegetation succession, and meteorological data for the Loess Plateau vegetation covered in this paper were obtained from the GEE self-contained data products. The GPP and ET data were derived from the PML_V2v0.1.7 data product ([[https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PML_V2_v017](https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PML_V2_v017)]([https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PML_V2_v017](https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PML_V2_v017)), accessed on 18 July 2021) to express ecosystem carbon uptake and water depletion rates [26]. The NDVI data were derived from the MOD13A2V6 product ([[https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1](https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1)]([https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1](https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1)), accessed on 18 July 2021) to express vegetation growth rates with an inter-data resolution of 500 m and a temporal resolution of 8 d. The land cover type data were derived from the GCL_FC30 product ([[http://data.casearth.cn](http://data.casearth.cn)]([http://data.casearth.cn](http://data.casearth.cn)), accessed on 18 July 2021) of the Institute of Space and Space Innovation, Chinese Academy of Sciences [8], which was derived from Landsat ground surface reflection data; a Chinese land cover product with a spatial resolution of 30 m was obtained using supervised classification. The meteorological data were obtained from the China Meteorological Science Data Center ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 18 July 2021). Fifty-two meteorological stations within the Loess Plateau involving the seven provinces of Shanxi, Qinghai, Ningxia, Inner Mongolia, Henan, Gansu, and Shaanxi were used, including precipitation and mean temperature data. The data used in this paper all range in time from 2000 to 2020, and the specific information is shown in Table 1.
## 4 Experimental Settings and Results
### CNN Optimization  
To test and find the best configuration of the used CNN in the feature extraction module of Bina, several settings of CNNs were tested on the Persian and English test set and reported in this section. The BLSTM used in the recognition module is fixed in this experiment (the best tested setting of deep BLSTM is utilized and is described in Section 4.4).  
The structure of the whole feature extraction module and average accuracy is mentioned in Table 9. The best performance belongs to a CNN with 16 3*3 filters in the convolution layer followed by a 3*3 max-pooling layer.  
In addition, a CNN with two convolution layers and two pooling layers is tested. The configuration of the CNN is as follow: the size of the first convolution layer is 3,3, with 32 filters and pooling layer of size 3,3. The second convolution layer is 3,3 with 16 number of filters and a pooing layer of size 2,2. The number of outputs is 16. The network did not train after 100,000 iterations, and its accuracy was 63.63% for three sizes and 11 fonts.
# An Improved Small Target Segmentation Model Based on Mask Dino
###### Abstract  
To address the issue of low segmentation accuracy for small objects in the Mask Dino segmentation method, we propose an improved small object segmentation model called FFMask Dino. Initially, we introduce scaled cosine attention and the log-cpb method into the Swin Transformer backbone network. Subsequently, by adjusting the network structure, we enhance the feature extraction process, which helps the model maintain generalization across different datasets and reduces the risk of overfitting. Lastly, we propose the FFPN module to optimize the pathways for feature fusion and transmission. The improved FFN reduces unnecessary computations, accelerates model inference speed, and integrates multi-scale feature details and high-level semantic information to complement object features, thereby enhancing model segmentation accuracy. Experimental results demonstrate that the improved segmentation model achieves a mean Intersection over Union (mIoU) of 42.15% on the ADE20K dataset for semantic segmentation tasks, representing a 0.96% increase compared to the Mask Dino method. On the CoCo dataset for instance segmentation tasks, with the Swin Transformer backbone, the Mask AP and Box AP are 47.10 and 52.60, respectively, showing improvements of 1% and 1.3% over the Mask Dino method. With the ResNet-50 backbone, the Mask AP and Box AP are 40.00 and 44.10, respectively, with improvements of 0.5% and 0.9% over the Mask Dino method. For the CoCo dataset's panoptic segmentation tasks, with the Swin Transformer backbone, the PQ is 54.95, showing a 0.4% increase over the Mask Dino method. With the ResNet-50 backbone, the PQ is 46.93, showing a 0.9% increase over the Mask Dino method. These results effectively demonstrate the improved accuracy and precision of Mask Dino in segmenting small objects across various segmentation tasks.  
deep learning; small targets; Mask Dino; Swin Transformer; multi-scale +
Footnote †: journal: Scientific Journal
## 3 Proposed Method
### Morphological Post-Processing  
After the first extraction of cropland, the semantic segmentation results will have localized irregular spots, such as \"salt and pepper\" noise. Its existence will reduce the precision of cropland and affect the visual effect, which can be regarded as the noise part that needs to be dealt with. Next, morphological post-processing is performed to improve the accuracy of the overall extraction result. Determining the location of the spots and selecting the method to filter out is a key part of the post-processing algorithm.  
Spots appear irregularly distributed in the semantic segmentation result, and the number cannot be predicted. First, we select the 8-connected domain method to traverse the connected area of the semantic segmentation result, as shown in Figure 6a, and treat the blocks with pixels less than \(\delta\) as broken spots (in this paper, \(\delta=50\)) for marking.  
Each class in the semantic segmentation result is processed separately. After traversing all categories of broken spots, let the set of broken spots be \(S\).  
Traverse the set \(S\), record 4-connected pixels at the edge of each spot, and count the categories of the pixels as shown in Figure 6b. Assign the value of the class with the largest number to the currently processed spots, \(S_{i}\). After processing, we can obtain the final cropland extraction result.
## 4 Design Procedure and Application Example
### Inductor Design  
Taking into account that \(i_{L2}\) (output current of the Zeta converter) provides the high-frequency components of the load current \(i_{o}\), the maximum derivative of \(i_{L2}\) must be enough to supply the maximum derivative of \(i_{o}\):  
\[\max\biggl{(}\biggl{|}\frac{di_{i2}}{dt}\biggr{|}\biggr{)}>\max \biggl{(}\biggl{|}\frac{di_{o}}{dt}\biggr{|}\biggr{)} \tag{45}\]  
Replacing the derivative magnitude of \(i_{L2}\) given in (6) into (45) leads to the following limit for \(L_{2}\):  
\[L_{2}<\frac{v_{b}}{\max\biggl{(}\bigl{|}\frac{di_{i}}{dt}\bigr{|} \biggr{)}} \tag{46}\]  
Using the values given in Table 2 in inequality (46) produces \(L_{2}<685.71\) uH. However, with such a limit value, the MOSFETs will not switch states during the load transient; hence, to improve the dynamic behavior of the converter, \(L_{2}\) is selected as half of the limit value, which enables the MOSFETs to produce at least two commutations during a current transient. Then, adjusting to a commercial value, \(L_{2}=330\) uH. Finally, \(L_{1}\) is set equal to \(L_{2}\) to balance the current ripple injected into the \(C_{1}\) capacitor; hence, \(L_{1}=330\) uH.
# Occlusion-Aware Worker Detection in Masonry Work: Performance Evaluation of YOLOv8 and SAMURAI
## 2 Methodology
### YOLOv8 and SAMURAI Architecture
#### 2.3.3 Differences in Detection Between YOLOv8 and SAMURAI  
This section presents the structural and operational differences between YOLOv8 and SAMURAI, focusing on their distinct detection methodologies. The primary difference between these models lies in how they process frames and track objects over time. YOLOv8 functions as a frame-based object detection model, where each image is analyzed independently without referencing previous frames. This approach involves extracting features directly from each frame using a convolutional neural network (CNN) and performing object localization and classification within that frame. As a result, YOLOv8 provides detection results based only on the current frame without incorporating temporal dependencies.  
On the other hand, SAMURAI utilizes a memory-based tracking mechanism that references object information from previous frames. By integrating memory attention, SAMURAI can refine detection results based on past observations, allowing for frame-to-frame consistency in tracking. Additionally, motion modeling is employed to estimate object movement trajectories, leveraging historical motion patterns to complement detection results.  
Table 1 summarizes the key differences between YOLOv8 and SAMURAI in object detection and data processing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **YOLOv8** & **SAMURAI** \\ \hline \multirow{2}{*}{Frame Processing} & Processes each frame & Incorporates past frame \\  & independently for inference & information into detection \\  & Detect objects & Uses memory-based tracking \\  & within isolated frames & to associate frames \\  & Extracts feature per & Integrates feature information \\  & frame using CNN & from prior frames \\  & Detecting objects based on & Adjust detection by \\  & current frame visibility & referencing past frames \\  & Performs object detection on a & Maintains object identity \\  & per-frame basis & across multiple frames \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of detection mechanisms between YOLOv8 and SAMURAI.  
Figure 4: SAMURAI architecture.
###### Abstract  
Preserving historical porous materials requires careful monitoring of surface humidity to mitigate deterioration processes like salt crystallization, mold growth, and material decay. While microclimate monitoring is a recognized preventive conservation tool, its role in detecting surface-specific moisture risks remains underexplored. This study evaluates the relationship between indoor microclimate fluctuations and surface moisture dynamics across 13 historical sites in Northern Italy (Lake Como, Valtellina, Valposchiavo), encompassing diverse masonry typologies and environmental conditions. High-resolution sensors recorded temperature and relative humidity for a minimum of 13 months, and eight indicators--including dew point depression, critical temperature-humidity zones, and damp effect indices--were analyzed to assess the moisture risks. The results demonstrate that multivariate microclimate data could effectively predict humidity accumulation. The key findings reveal the impact of seasonal ventilation, thermal inertia, and localized air stagnation on moisture distribution, with unheated alpine sites showing the highest condensation risk. The study highlights the need for integrated monitoring approaches, combining dew point analysis, mixing ratio stability, and buffering performance, to enable early risk detection and targeted conservation strategies. These insights bridge the gap between environmental monitoring and surface moisture diagnostics in porous heritage materials.  
surface moisture; porous masonry; microclimate monitoring Article
## 2 Routing Goals
### Energy Conservation  
The most complicated limitations in the design a WSN are those concerning the minimum energy consumption required to drive the circuits and possible micro-electromechanical devices. When miniaturizing the node, the energy of the power supply is the principal concern. Existing technology supplies batteries with about 1 J/mm\({}^{3}\) of energy, while capacitors are able to attain as much as 1 mJ/mm\({}^{3}\). If a node is considered to have a moderately short lifetime, for instance, a few months, a battery is a reasonable solution. However, for nodes which must be able to produce sensor readings for lengthy periods of time, a charging technique for the supply can be better. Presently, research groupsare researching the use of solar cells to feed capacitors with photocurrent from the light. Solar flux can supply power density of about 1 mW/mm\({}^{2}\). The energy efficiency of a solar cell ranges from 10 to 30 percent in contemporary technologies, offering 300 mW in full sunlight in the ideal case scenario for a 1-mm solar cell working at 1 V. Series-stacked solar cells should be used to provide proper voltages.  
Sensors can operate with 1 nJ per sample, and recent processors are able to do computations using less than 1 nJ per command. For wireless communications, the main alternative technologies are based on RF and optical transmission methods which each have their own benefits and disadvantages. RF has a problem because the nodes may present very restricted space for antennas, thus requiring very short-wavelengths (for example, high-frequency) transmission, which must then tolerate very high attenuation. Consequently, communication in those system is not at this time compatible with low-power functions. Recent RF transmission mechanisms use approximately 100 nJ per bit for a transmission distance of 10 to 100 m, making communication very costly in comparison with data acquisition and processing [39; 40; 41].  
In WSNs, where sensors are capable of sampling, processing and data transmission, the transactions among these tasks is an important issue in power handling. Sensor nodes can use up their limited supply of energy performing the mentioned parameters in a wireless environment. As such, energy-conserving forms of communication and computation are essential. Therefore, sensor node lifetime shows a strong dependence on battery lifetime. In a multi-hop WSN, each node plays a dual role as data sender and data router. The malfunctioning of some sensor nodes because of power failure can cause significant topological changes and might require rerouting packets and reorganizing the network. Thus, the key challenge in data routing is conserving the sensor energy, so as to maximize their lifetime, and much research on this topic is in progress [29; 39; 42; 43; 44].
# A Systematic Review of Responsible Artificial Intelligence Principles and Practice
## 7 Finding 5: Principles of Responsible AI
### Accountability  
Accountability ensures that the people or organizations who developed the AI system hold responsibility for actions and decisions made from it throughout the lifecycle [31]. The lack of accountability can lead to the misuse of AI to go against human values. Explainability, which provides explanations and reasons for the decisions made by AI systems, and transparency are major requirements to establish accountability within the system [31; 79]. Without accountability, people who design and develop AI systems will be careless and blame algorithms instead of taking responsibility for the outcomes [31]. For example, people who engage in developing an AI model resulting in bias for a certain group because of selected features should take responsibility without passing the blame to the algorithm [31]. This addresses the \"responsibility gap\" that emerges as AI systems become more autonomous [71; 72; 91; 92; 93]. An accountability flowchart is presented in [93], which maps the conceptual decision points that determine whether an AI responsibility gap exists. It demonstrates different answers to fundamental questions, such as whether responsibility requires control or whether AI can possess moral agency, and how that leads toward either accepting or resolving the responsibility gap.  
Governments develop standards and policies to provide pathways to the safe, secure and privacy-preserved development of AI systems. However, accountability ensures that organizations adhere to these standards and policies during the design and development process of the AI system [31; 79]. Key approaches include establishing role-level accountability through formal contracts to define responsibility boundaries [75], creating open standards for ethical AI deployment [94], and implementing comprehensive legal frameworks [80]. Accountability guides the development of AI systems to meet human values by enforcing considerate product design, dependable technical architecture, rigorous evaluation of potential impacts, and transparent disclosure of information regarding these aspects [60]. Auditability of an AI system is also considered as a part of the accountability, which validates the compliance of the system according to policy, industry standards or regulations through a reviewing, assessing and auditing process [79; 95]. Ref. [95] proposed an end-to-end algorithmic auditing framework to close the accountability gap in the development of AI systems that cover the entire AI lifecycle. This framework audits the engineering processes involved in designing and developing AI systems to ensure that the organization's AI principles and guidelines, along with ethical expectations, are met during the entire AI lifecycle [95]. Human oversight in critical decision-making processes [70; 80] and continuous monitoring for biases and unintended consequences [74; 80] further strengthen accountability mechanisms in AI systems.