esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Spawn Multi-Mobile Agent Itinerary Planning (SMIP) Approach
### Spawn Mobile Agent (SMA) Algorithm in SMIP  
Once the partitioning of the network is done, the sink node determine the number of the MAs, as well as their itineraries for each partition. In the previous MIP approaches, each MA starts its migration from the sink and returns back to the sink with the accumulated data after completing the gathering process. This means that the sink is the start and end point of each MA itinerary. In this article, a spawn multi-mobile agent itinerary planning (SMIP) approach is proposed. The spawn mobile agent (SMA) algorithm is based on one of the characteristics of the agent, named _agent spawning_[26], employed in the proposed SMIP. Agent spawning is the ability to create a new agent that has different capacities and capabilities that are contrary to the original agent. The spawning agent's task is to handle a part of the tasks at hand of the original agent. Therefore, in this work, we adopt this characteristic (agent spawning) in order to distribute the data gathering task among the MAs such that some MAs have different assigned tasks from others. The sink node assigns one main MA to each partition. The main task of the main MA is to collect the data from the source nodes, and it also has the ability to spawn the new MA (SMA) to do a different task at a certain point. Here, the task of the SMA is only to carry the accumulated data of the main MA back to the sink. As a result of this spawning action, two types of itineraries are defined: main MA itinerary and SMA itinerary.  
It should be noted that the SMA is an entity built inside the main MA packet. The packet structure of the main MA is described in Figure 2. The main MA packet is defined as an entity of six attributes: MA ID, MA itinerary, data payload, SMA code, SMA itinerary, and data aggregation code. The description of these attributes is as follows:  
* MA ID: is the identification number of each MA dispatched by the sink node.
* MA Itinerary: contains the itinerary information (source nodes' visited order list) assigned by the sink node when dispatched.
* Data payload: MA's data buffer which carries the aggregation data results.
* SMA code: is the code of the spawning carried by MA.
* SMA Packet: includes the SMA ID, the itinerary information of the SMA (visited sensor nodes) to get back to the sink node, and SMA payload data. Note that the MA could carry more than one SMA.
* Data aggregation code: is the implementation of the data aggregation algorithm.  
Figure 2: Main MA packet structure. SMA: spawn mobile agent.
# Analysis of Hydrological Characteristics of Blue Nile Basin, Nishe Watershed
## 3 Results and Discussion
### Model Validation  
The validation was carried out to assess the model's efficiency using the calibrated components to simulate the watershed's hydrological parameters. Validation was also conducted using monthly flow data for 2000-2008. According to the performance evaluation requirements, good agreement between the simulations and observations was also shown in the validation. The observed and simulated streamflow were plotted to compare the correlation of stream flows of the watershed during calibration and validation period as shown in Figures 5-8 using different SWAT algorithms. The statistical values of objective functions were depicted at Table 4. Therefore, depending on the results, it is reasonable that the model is applicable specifically for Nashe catchment and the Blue Nile Basin in general.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Objective Function** & **Methods** & **Calibration** & **Validation** \\ \hline \multirow{4}{*}{R\({}^{2}\)} & Sufi-2 & 0.88 & 0.85 \\  & Glue & 0.87 & 0.85 \\  & Parasol & 0.87 & 0.84 \\  & PSO & 0.88 & 0.87 \\ \hline \multirow{4}{*}{NSE} & Sufi-2 & 0.78 & 0.80 \\  & Glue & 0.80 & 0.71 \\  & Parasol & 0.82 & 0.72 \\  & PSO & 0.73 & 0.77 \\ \hline \multirow{4}{*}{PBIAS} & Sufi-2 & \(-2.70\) & 1.60 \\  & Glue & \(-8.70\) & 5.70 \\  & Parasol & 10.00 & 8.00 \\  & PSO & 15.00 & 13.30 \\ \hline \multirow{4}{*}{P-factor} & Sufi-2 & 0.69 & 0.64 \\  & Glue & 0.37 & 0.26 \\ \cline{1-1}  & Parasol & 0.22 & 0.15 \\ \cline{1-1}  & PSO & 0.51 & 0.47 \\ \hline \multirow{4}{*}{r-factor} & Sufi-2 & 1.10 & 1.00 \\ \cline{1-1}  & Glue & 0.86 & 0.78 \\ \cline{1-1}  & Parasol & 0.37 & 0.40 \\ \cline{1-1}  & PSO & 0.69 & 0.76 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation performance results of streamflow calibration and validation.  
Figure 5: Monthly simulated and observed streamflow for calibration and validation using SUFI-2.  
Figure 6: Monthly simulated and observed streamflow for calibration and validation using GLUE.  
Figure 7: Monthly observed and simulated stream flow using Parasol.
## 5 Analysis of Open Source Data Availability  
This section explores the open data sources of cities and countries involved in our case study. First, the Eurostat dataset ([[https://ec.europa.eu/eurostat/web/main/home](https://ec.europa.eu/eurostat/web/main/home)]([https://ec.europa.eu/eurostat/web/main/home](https://ec.europa.eu/eurostat/web/main/home)), accessed on 27 September 2024) is examined to look for listed KPIs for the considered cities and countries. Some cities have their own open data portal with some KPIs listed in it. However, these portals do not follow a unified structure, and most of them are written in  
Figure 2: The alignment of the KPI monitoring process with the UP2030 activities. the country's mother tongue (with no option to change the language). On the contrary, the Eurostat database provides a source of data that is unified in its representation. The explored KPIs are listed in Table 1. In the Eurostat dataset, most KPIs can be retrieved in different units depending on the KPI and specific needs, and some KPIs are derived by combining several recorded values in the dataset--each KPI requires basic variables for its calculation (Section 4). For example, low-carbon emission passenger vehicles include electric, hybrid, fuel cell, and plug-in vehicles. Therefore, the total number of low-carbon emission vehicles is calculated by summing these categories. The dataset is explored to assess the availability of KPIs for the cities. KPIs in Table 1 are categorized into four dimensions related to urban planning and design aspects:  
* Economy dimension: This dimension is divided into three categories: (i) transportation category, which includes public transport network convenience (PTN), the opportunity for active mobility (OAM), and low-carbon emission passenger vehicles (LCE); (ii) waste category, which includes municipal waste rate (MWR) and municipal solid waste recycling rate (MWRe); and (iii) employment category, which includes unemployment rate (UER). * Environment dimension: This dimension includes GHG emissions (GHGes), renewable energy consumption (REC), and noise exposure (NE). * Social and culture dimension, which includes the Gini coefficient (GINI) and the violent crime rate (VCR). * Governance dimension: This dimension is represented by a corruption perception index (CI) in the selected KPIs list. The availability of recorded KPI values is represented by the number of recorded years and the continuity of the records. Tables 2 and 3, as well as Figure 3 record the availability of these KPIs in the Eurostat dataset for cities, their countries, and capitals, respectively. It is seen that not all KPIs are tracked for all countries and cities in the dataset. The majority of the cities
## 1 Introduction
The influence of various parameters was investigated, including porosity, porous material type, corrugated core thickness, structural damping coefficient, angle of incidence, and external Mach number. A study was conducted to analyze the performance of structural sound insulation with and without porous media. The research results indicated that the sound insulation performance of this structure was poor in the frequency range of 100-1000 Hz. [PERSON] et al. [18], combining theoretical analysis and finite element simulation, investigated the influence of sound-absorbing material thickness, material properties, and arrangement on the vibrational acoustic radiation performance of single-layer cylindrical shells with ribs. The results indicated that altering the properties and arrangement of sound-absorbing materials effectively enhances the structural sound-absorbing performance, thereby achieving vibration and noise reduction. However, the scope of this article's study was limited to single-layer cylindrical shell structures. In summary, both domestic and international scholars typically employ methods such as optimizing shell structure parameters, introducing micro-perforated structures, or applying porous materials as sound-absorbing materials to achieve vibration and noise reduction in cylindrical shell structures. However, in practical engineering scenarios, each of these methods has its shortcomings, such as poor low-frequency performance and limited effective frequency ranges. Furthermore, there is currently a paucity of research on the vibration and acoustic radiation performance of micro-perforated laminated cylindrical shells. Micro-perforated structures possess the capability to reduce vibration propagation, enhance acoustic performance, and reduce noise radiation [19], making them a valuable contribution to improving the vibration and noise reduction performance of laminated cylindrical shells. Simultaneously, the coupling of multiple sound absorption modules to meet specific requirements is a common approach in the field of acoustics, providing insights into the design of vibration and noise reduction in laminated cylindrical shells. Therefore, this paper combines two noise reduction measures: perforating the inner shell and applying sound-absorbing foam on the outer shell. The research focuses on micro-perforated ribbed sandwich cylindrical shell structures and specifically investigates their vibration and acoustic radiation performance in the frequency range of 1-1000 Hz under free conditions. The paper reveals the influence of different sandwich shell structures, micro-perforation parameters (porosity, aperture), sound-absorbing foam materials, and installation methods. This study is beneficial for enhancing vibration reduction and noise reduction performance in areas such as automobiles, aircraft, buildings, and industrial equipment [10], providing a valuable reference for the design and performance optimization of double-layer cylindrical shell structures.
# Flexible Fashion Product Retrieval Using Multimodality-Based Deep Learning
## 4 System Implementation and Experimental Analysis
### Qualitative Results of Case Studies
Pseudo-SQL-based queries were employed to determine how well the proposed deep learning method can interpret complicated queries and to thereby investigate the scalability and flexibility of the method (Figure 11). The first query--\"SELECT * FROM men\"--is similar in meaning to the query using the single keyword, \"men\", shown in Figure 8, although the format is completely different. The search results for this pseudo-SQL query are consistent with the results for the single keyword query shown in Figure 8. The results with the highest probability mainly include men's clothes, whereas those with the lowest probability are women's clothes. For the second query--\"SELECT * FROM men WHERE red\"--the search focused on red apparel for men. The results with the highest probability were appropriate, and those with the lowest probability included women's clothing without the red color. For the last query--\"SELECT * FROM men where blue AND shirt\"--the results with the highest probability were blue men's shirts, as intended, and the 10 images with the lowest probability included women's clothes without the blue color. Figure 10: Fashion product search results for category-based queries. Figure 9: Fashion product search results for queries with Boolean expressions. We compared and analyzed the results for the three aforementioned types of queries. As shown in Figure 12, when the queries were designed to find black pants for men, they retrieved similar or the same images. The 10 results with the highest probability included men's black trousers, as intended. The 10 results with the lowest probability mainly included women's clothing. Figure 11: Search results obtained using pseudo-SQL queries. Figure 12: Search results for three different types of queries with similar or the same meaning. We also examined the result for a ternary Boolean operation. Previously, we did not use a Boolean operation with three query terms. A third term was arbitrarily added to evaluate the flexibility of the proposed approach. The results for the query \"men AND pants AND black\" were good, indicating that the proposed deep-learning network works correctly and flexibly even for user queries not included in the training. We also investigated the fashion product retrieval results for queries that were not used during the training, such as the ternary Boolean query shown in Figure 6:0 We randomly mixed two query forms or nonexistent conditions (e.g., women's leggings in men's clothing). As shown in Figure 13, the query \"men \(\rightarrow\) coat AND shirt\" is a mixture of Boolean and category queries. Nevertheless, the search worked well. In particular, the query \"men \(\rightarrow\) leggings\" is unusual, as there is no such fashion product for men. According to the characteristics of leggings, the query results included pants, and skirts and dresses had the lowest probability.
# Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides
## 4 Results  
### Elimination of Noise  
As mentioned above, the DEM has a significant effect on susceptibility assessment through three indices, namely elevation, topography, and slope. The changes in the DEM from 1992 to 2000 and from 2000 to 2009 are obtained to explore the impact of the temporal variation of DEM data on susceptibility assessment. Meanwhile, the average values and standard deviation of the elevation differences are obtained from the topography changes revealed by the DEM data. The confidence probability is considered as 95% in this study. The corresponding standard scores and confidence intervals are, therefore, calculated (Table 3). After the noise is eliminated, the optimized map of topography changes from 1992 to 2000 and from 2000 to 2009 are obtained (Figure 7).  
Figure 7: Topography changes revealed by the DEM data: (**a**) from 1992 to 2000; (**b**) from 2000 to 2009.
## 3 System-Level Configuration
### System-Level Simulations
Therefore, if possible, the antennas of BS should be placed as high as possible so that they are above the height of the high clutter density characteristic of indoor factory scenarios. The comparison of Figure 16 with Figure 13 indicates that the aggregate throughput of each sector of the factory is half of the throughput of the stadium. This is explained by the number of BSs in the factory being half of the number in the stadium. Therefore, if we need to increase the aggregate throughput of the factory scenario, we have two option: (1) we can increase the number of BSs, or (2) we can use carrier aggregation. Indeed, 5G NR allows for the aggregation of 16 carriers, each one with 400 MHz. At 100 GHz, it is possible to have available a total bandwidth of 6.4 GHz. The first solution involves densification of the network, and it is not possible to achieve energy efficiency with this solution. To be energy-efficient and achieve high sector throughput, we need to aggregate carriers and place RIS surfaces distributed around the BSs of the scenario.
## 3 Results and Discussion
### Passenger Infection Risk Assessment  
This study employed the dose-response model [58; 59; 60] to calculate the exposure risk of passengers traveling with an infected passenger and compiled the number of passengers with different infection risks, as shown in Table 3. The statistical results revealed that the infection risk of passengers is closely linked to the initial conditions of the respiratory droplets released by the infected passenger, particularly the initial droplet size. From Formula (12), it is evident that as the initial droplet size increases, the quantity of virus particles carried by the droplets also increases. Consequently, the infection risk generated when an equal quantity of droplets is trapped by passengers' bodies is higher. For the same passenger, achieving an infection risk of 0.05 requires capturing 1400 droplets (10 \(\upmu\)m), 12 droplets (50 \(\upmu\)m), and 2 droplets (100 \(\upmu\)m), respectively.  
Due to the initial droplet size being 50 \(\upmu\)m in both case 2 and case 6, the infection risk for passengers in these two scenarios is significantly higher than those in the rest of the scenarios. In case 6, the low \(RH\) exacerbates the spread of droplets within the carriage, leading to more droplets being trapped by passengers' bodies. The number of passengers with an infection risk exceeding 0.5 is twice the number in case 2. In scenarios with an initial droplet size of 10 \(\upmu\)m (case 1, case 4, case 5, case 7), the infection risk for passengers is generally lower and does not exceed 0.1. Besides the significantly smaller quantity of virus particles carried by smaller droplets than those of larger sizes (50 \(\upmu\)m), smaller droplets shrink in volume after evaporation. In general, smaller particles with enhanced diffusion and movement capabilities lead to a larger diffusion area within the carriage. Droplets disperse more widely and are captured by more passengers' bodies. Therefore, in these scenarios, only a few passengers standing around the infected passenger would have an infection risk exceeding 0.01. In case 7, although the total quantity of droplets trapped by all passengers' bodies exceeds 50% of the total droplet quantity (see Figure 11c), due to the relatively dispersed distribution of droplets on passengers' bodies, the results show that only two fellow passengers have infection risks exceeding 0.05.
## 7 Bioprocess for Xanthophylls Production by Microalgae
### Factors Determining Xanthophylls Production
#### 7.2.1 Light  
Light availability constitutes the most effective controlling factor for the production of numerous xanthophylls [119]. Light intensity and photoperiods affect the growth of cells, biomass and production of several high-value metabolites in many microalgae species. Higher light intensity caused a threefold increase in _Haematococcus pluvialis_ ASX content. Besides, ASX and lutein contents are also changed under a high intensity of light [148]. _Muriellosis_ sp. lutein content reached the maximum at 460 umol photons/m\({}^{2}\)/s. Maximum lutein productivity (3.6 mg/L/day) was obtained under high light intensity with _Desmodesmus_ sp. [149] The lutein synthesis and accumulation were studied in _Chorella sorokiniana_[119] and _Scenedesmus_ sp. [78] because they both have high growth rates and high lutein production ability. Light stress was applied concomitantly with other stressors such as nitrogen [150], salinity [151] and temperature [152]. In addition, [PERSON] et al. (2018) demonstrated that of nitrogen lack and light stress together enhanced the ASX accumulation in _Haematococcus pluvialis_, which reached 1.85% of the cell dry weight [153]. Light stress can stimulate the expression of the lycopene beta-cyclase gene [154], which is the key enzyme for carotenoid accumulation in microalgae. Moreover, [PERSON] et al. (2008) also showed that high light intensity can regulate the activities of phytoene synthase and phytoene desaturase [155].
# PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography
## 1 Introduction  
### Background  
Pneumonia is a serious lower respiratory infection marked by inflammation of the lung parenchyma and alveoli. It remains a leading cause of mortality, particularly among children under five and older adults, with 740,180 deaths reported globally in children under five in 2019 alone [1, 2, 3]. Timely and accurate diagnosis is essential for improving outcomes, especially in under-resourced settings [4, 5]. While diagnostic tools include clinical exams, lab tests, and imaging, chest radiography (CXR) is the most widely used modality due to its accessibility and non-invasive nature [6].  
Despite its ubiquity, interpreting CXR images presents challenges: overlapping anatomical features, subtle opacities, and radiographic similarity to other thoracic conditions can hinder accurate assessment--especially in regions with limited access to experienced radiologists or advanced imaging tools [7; 8; 9]. Recent developments in deep learning (DL), particularly convolutional neural networks (CNNs), have demonstrated strong potential in automating image-based diagnostics, including pneumonia detection [10; 11; 12; 13; 14]. DL-based systems can extract features directly from raw images, reducing subjectivity and potentially enhancing diagnostic accuracy and throughput.  
This study investigates and compares the performance of a diverse set of DL architectures--including CNNs, Transformer-based models, and state space (Mamba) models--for pneumonia detection in CXR images. By leveraging a large-scale, curated dataset and evaluating all models under identical conditions, we aim to provide a comprehensive analysis of their diagnostic potential, generalizability, and suitability for deployment in clinical workflows.
# Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation
## 3 The Analysis of Density Evolution and Gaussian Approximation of the TDMP Algorithm  
### The Decoding Procedure of the TDMP Algorithm  
Before discussing density evolution and Gaussian approximation of the TDMP algorithm, firstly, we simply review the decoding procedure of the TDMP algorithm [26; 27]. Check matrix H with \(m\) rows and \(n\) columns is composed of \(c\) check sub-matrices in series, and each sub-matrix has \(k\) rows,then \(m=c\times k\). Meanwhile, each sub-matrix can be regarded as a horizontal layer, and the main idea of the TDMP algorithm is to decode each layer successively.  
In iteration decoding, we first update the check messages in the first layer, and then update the variable messages in this layer immediately. After that, we use the updated variable messages to calculate the prior messages which will be sent to the next layer for updating the check messages until the last layer. Then the updated prior messages are the outputs of iteration decoding. The TDMP algorithm directly uses the updated information of the previous layer when updating the current layer information, thereby speeding up the convergence speed of the decoding. The message passing scheme of the TDMP algorithm is shown in Figure 1.  
Decoding the convergence speed can be increased by utilizing the prior messages of the previous layer directly to update the check messages of the current layer. Meanwhile, the essence of layered decoding is optimizing the procedure of message passing but not changing the implementation of the check messages update in each layer. The BP algorithm can also use layered decoding, and the analysis of density evolution and Gaussian approximation of the TDMP algorithm is based on the BP algorithm. Thus, the procedure of the check messages update in each layer of the TDMP algorithm is the same as that of the BP algorithm which is introduced in Section 2. Therefore, in the next section, the major work is to illustrate the variable messages updated in each layer.