esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Rise Social Housing in Hot-Humid Climates: Towards an 'Airhouse' Standard for Comfort
###### Abstract  
The pressure to provide social housing in a fast and economic way, as well as outdated regulations, constrain the design of these buildings, having serious implications for the comfort of occupants and the environment. This becomes more critical in hot-humid climates, such as Malaysia, with uniformly high temperature and humidity and low wind speeds. In its capital, Kuala Lumpur, an extensive program of construction for high-rise social housing is being carried out, however, shortly after the flats are occupied, or as soon as they can afford it, the residents fit wall mounted air conditioning units. This research started by looking at Malay vernacular architecture and the traditional strategies for ventilation and cooling. After a review of current building regulations and green tools employed in the country, two campaigns of fieldwork were carried out to assess the actual indoor and outdoor thermal and air quality conditions in the buildings, which were found to be inadequate for both the local regulations and international recommendations. The fieldwork also allowed the identification of the critical design issues to address. A ventilation and filtering ceiling system has been identified as one of the possible solutions for the current situation and has been tested through physical and computer models. The system improves comfort by reducing the air temperature, humidity, and amount of airborne particles and gases, as well as constantly providing an adequate airflow rate. It is the first attempt to develop what we have named the 'airhouse' standard for tropical countries.  
high-rise; social housing; hot-humid climate; comfort; thermal comfort; indoor air quality; dynamic insulation; 'airhouse' standard; Malaysia +
Footnote †: journal: Applied Sciences
# Attention-Guided Wireless Channel Modeling and Generating
## 4 Experimental Simulation Analysis  
Extensive simulation experiments were performed to validate the effectiveness of the proposed algorithm, with detailed simulation parameters provided in Table 1. In our experiments, the training process was conducted on an NVIDIA GeForce RTX 4060 GPU. Each experiment involved 200 training epochs, with a total training time of approximately 16 min. To ensure the accuracy and representativeness of the proposed channel model, the simulation parameters chosen in this study must align with the parameters used in the actual communication system from which the dataset is derived. Specifically, the parameters are selected based on the settings from real-world communication environments. This alignment ensures that the generated channel model accurately reflects the behavior of the actual system during the simulation process. By maintaining consistency between  
Figure 4: Structure of SE-GAN.  
the simulation parameters and the real-world system, the experimental results are better aligned with practical applications, thus enhancing the validity and reliability of the model. We numerically assessed the consistency and variability between real channel data and generated channel data using similarity and diversity metrics. The dataset employed in this study is derived from a MIMO channel environment configured with 32 transmit antennas and 4 receive antennas. It consists of 4000 distinct samples, each representing the complete channel state information for a single channel realization, making it well suited for channel estimation and modeling in MIMO systems. Specifically, the dataset is provided in a MATLAB (R2018b) file format, encompassing all the channel information matrices. The data of each sample is expressed as a complex number, and the amplitude and phase information can be obtained by the real and imaginary parts of the complex number. Each sample comprises 4096 data points, structured into a 4 \(\times\) 32 matrix that captures the channel gains between the 4 receive antennas and the 32 transmit antennas. To comprehensively account for the delay spread effects in MIMO systems, each sample also incorporates 32 delay spread components. Specifically, the data dimensions are arranged in the order of 4 \(\times\) 32 \(\times\) 32, enabling effective characterization of the spatial and temporal properties of the channel.
# HH-NIDS: Heterogeneous Hardware-Based Network Intrusion Detection Framework for IoT Security
###### Abstract  
This study proposes a heterogeneous hardware-based framework for network intrusion detection using lightweight artificial neural network models. With the increase in the volume of exchanged data, IoT networks' security has become a crucial issue. Anomaly-based intrusion detection systems (IDS) using machine learning have recently gained increased popularity due to their generation's ability to detect unseen attacks. However, the deployment of anomaly-based AI-assisted IDS for IoT devices is computationally expensive. A high-performance and ultra-low power consumption anomaly-based IDS framework is proposed and evaluated in this paper. The framework has achieved the highest accuracy of 98.57% and 99.66% on the UNSW-NB15 and IoT-23 datasets, respectively. The inference engine on the MAX78000 EVKIT AI-microcontroller is 11.3 times faster than the Intel Core i7-9750H 2.6 GHz and 21.3 times faster than NVIDIA GeForce GTX 1650 graphics cards, when the power drawn was 18 mW. In addition, the pipelined design on the PYNQ-Z2 SoC FPGA board with the Xilinx Zynq xc72020-lclg400c device is optimised to run at the on-chip frequency (100 MHz), which shows a speedup of 53.5 times compared to the MAX78000 EVKIT.  
network security; artificial neural Networks; hardware accelerators; low power; high-performance; microcontrollers; CPU; GPU; FPGA +
Footnote †: journal:  
0022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 5 Simulations
### Preprocessing Evaluations  
Compared to MRSE, one extra operation of CIMS is to do the PCA. Figure 5 shows the time of doing the PCA in CIMS. Frankly speaking, PCA requires quite a lof of time for the computation, and the time increased as the size of the corpus increased. However, PCA is only performed in the initial phase for document index generation. No further computations are required for the online search. It would not affect the user experience for the system.  
Figure 6 shows the time for doing the encryption of the document indexes. Recall that the encryption operations include splitting the index to two vectors and multiplying the encryption matrices to blur the data. From the results, the required time increases with the length of the index, i.e., the \(r\) values, and the number of documents. Again, the encryption computation only occurs in the initial phase before outsourcing the documents to the cloud. Although the time increases with the size of the corpus, it would not affect the search time that users will experience online.  
The time for trapdoor generation is shown in Figure 7. In the simulations, two words are randomly selected from the dictionary to generate the query vector, which is then projected to the new space to generate the condensed query vector. The condensed query vector is then processed to generate the trapdoor. Note that the length of the condensed query vector is the same as the condensed document index, which is equal to \(r\). From the figure, the trapdoor generation time increases with the length of the condensed query  
Figure 5: Time of doing PCA.  
Figure 6: Time for doing the encryption.  
vector. The number of documents in the corpus would not affect the query vector length, but the search time.  
Figure 8 illustrates the effects on different numbers of keywords in a query. The number of documents in the corpus is 2225. Since the length of the query vector is controlled by the value of \(r\), when \(r\) increases, the searching time also increases. The CIMS scheme allows the data user to propose multiple keywords in a query. However, the search time is not affected by the number of keywords in the query, since all the keywords are included in one query vector.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.17 Tough  
TOUGH is basically a series of programs that can be used to simulate flows in fissured and porous media. TOUGH2 is a basic program for multiphase flows and heat transport. The flow takes into account the transitions between liquid and vapour states and various forces that influence the fluid movement (gravity, pressure, and viscosity). Several modules from the Tough software family can be used for the reactive transport of hydrocarbons, T2 VOC, TMVOC, and TOUGHREACT. The first two modules are designed for three-phase flows (water, air, and VOCs) in heterogeneous porous media. The last module is more suitable for reactive transport studies where chemical interactions between solutes need to be considered. These chemical transformations include kinetic processes such as precipitation or dissolution reactions. There are many papers dealing with the transport of hydrocarbons using TOUGH or one of its modules. Many of these have been published in TOUGH conference proceedings and articles, for example Hodges et al. [735], Fagerlund and Niemi [736], Falta [737], Fagerlund et al. [738], Yang et al. [739], and Zhou et al. [740]. The latter combined two models, HYDRUS-1D and TOUGH, to simulate the influence of the freeze-thaw process in the soil on the water flow and the redistribution of NAPL (toluene in this study) with the water fluctuations.
## 4 The Analytical Methods  
In this study, Equation (2) serves as the base model for setting up and analyzing both non-spatial and spatial econometric models to examine the relationship between air pollution and economic factors. Specifically, the models consider P to be the population density, A to be the gross regional and provincial product per capita (GPP) and industrial density, and T to be the energy consumption per capita.  
The data for this study were obtained from various sources, as shown in Table 4, including satellite data (MERRA-2), the Industrial Census Survey of Thailand (NSO), and the Gross Regional and Provincial Product (NESDC). The dataset covers the years 2012, 2017, and 2022 and consists of the panel data for each province in each of those years in Thailand.  
As previously noted, a bivariate Moran scatter plot builds on the traditional Moran scatter plot by extending it to two variables. While the original plot compares a variable with its own spatial lag, the bivariate version compares one variable on the \(x\)-axis with the spatial lag of a different variable on the \(y\)-axis. Essentially, this approach measures how the value of one variable in a specific location is spatially correlated with the values of another variable in neighboring areas [48].  
\[I_{i}^{B}=cx_{i}{\sum_{j}}w_{ij}y_{i} \tag{3}\]  
where \(c\) is a constant and \(w_{ij}\) are the elements of the spatial weight matrix. As with its global counterpart, this statistic needs to be interpreted with caution since it ignores the in situ correlation between the two variables (\(x_{i}\) and \(y_{i}\)).
## 6 Functional Properties and Applications of Nanocomposites
### Polymer-nanocomposite-photoresists
\begin{table}
\begin{tabular}{|p{113.8 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline
**Material** & **Initial specific capacity [mAh/g]** & \# cycles/capacity retention & \# cycles/capacity retention & \# cycles/capacity retention & **Ref.** \\ \hline Pure graphite (C) & 300 & 10/100 \% & 30/99.1 \% & 50/97.8 \% & [228] \\ \hline Bulk SnO\({}_{2}\) & 652 & 10/63 \% & 30/49.8 \% & 50/31.7 \% & [228] \\ \hline
4 wt \% SnO\({}_{2}\) in C & 342 & 10/99.6 \% & 30/96.6 \% & 50/88 \% & [228] \\ \hline
9.8 wt \% SnO\({}_{2}\) C & 384 & 10/99.4 \% & 30/96.3 \% & 50/88.3 \% & [228] \\ \hline
16.5 wt \% SnO\({}_{2}\) in C & 428 & 10/99.2 \% & 30/90.1 \% & 50/72.4 \% & [228] \\ \hline
14.2 wt \% SnO\({}_{2}\) in C & 465 & 40/90 \% & 60/80 \% & & [229] \\ \hline
14.9 wt \% SnO\({}_{2}\) in C & 472 & 40/89 \% & 60/75 \% & & [229] \\ \hline
14.4 wt \% SnO\({}_{2}\) in C & 460 & 40/74 \% & 60/56 \% & & [229] \\ \hline TiO\({}_{2}\) & n.a. & 10/67.5 \% & & & [56] \\ \hline TiO\({}_{2}\)/C (87/13) & 122 & 10/96.7 \% & & & [56] \\ \hline SnO\({}_{2}\)/PPy (81.75/18.25) & 562 & 20/70 \% & & & [230] \\ \hline SnO\({}_{2}\) & 570 & 20/40 \% & & & [230] \\ \hline SnO\({}_{2}\)/graphite & 633 & 30/57 \% & & & [57] \\ \hline SnO\({}_{2}\) at C & 667 & 18/55 \% & 30/55 \% & 40/55 \% & [59] \\ \hline TNHCs & 831 & 10/\(>\)96 \% & \(>\)100/66.2 \% & & [65] \\ \hline SnO\({}_{2}\)/C (75/25) & 993 & 50/62 \% & 200/62 \% & & [231] \\ \hline SnO\({}_{2}\)/graphene (40/60) & 765 & 100/66.9 \% & & & [232] \\ \hline \end{tabular} Abbreviations: TNHCs: Tin nanoparticles encapsulated elastic hollow carbon spheres.  
\end{table} 
Table 6: Overview on performance of new nanocomposite anode materials for Li-ion battery.
## 2 Methods
### Monitoring
#### 2.12.3. Privacy and Regulatory Compliance  
The JAVIS system is fully compliant with the Data 3 Laws in South Korea, which collectively govern data privacy, utilization, and protection. These laws, comprising the _Personal Information Protection Act (PIPA)_, the _Act on Promotion of Information and Communications Network Utilization and Information Protection (Information and Communications Network Act)_, and the _Credit Information Use and Protection Act_, serve as the Korean equivalent of the Health Insurance Portability and Accountability Act (HIPAA) in the United States. Together, they establish comprehensive guidelines for safeguarding sensitive personal and health-related data, emphasizing principles such as data minimization, secure processing, and accountability.  
To ensure strict compliance with these laws, the JAVIS system operates exclusively within a secure, closed internal network environment. This architecture minimizes exposure to external threats and unauthorized access while providing multiple layers of protection, including encryption, firewalls, intrusion detection systems, and role-based access control (RBAC).  
The system adheres to the following principles of privacy and security:  
* **Data minimization and protection**: Sensitive information is processed and stored in compliance with the Data 3 Laws at secure, PIPA-compliant domestic facilities. Encryption and secure access controls ensure data remains protected against breaches and misuse.
* **Pseudonymization and anonymization**: all data received through JAVIS is already anonymized due to the network's security, allowing the secure utilization of data for purposes such as statistical analysis, scientific research, and service optimization without compromising individual privacy.
* **Monitoring and accountability**: The system maintains comprehensive audit logs and monitoring mechanisms, enabling real-time detection of unauthorized access attempts. Regular vulnerability assessments, penetration tests, and compliance audits are conducted to ensure the system remains robust and secure.  
By aligning with the Data 3 Laws, the JAVIS system embodies a _privacy-by-design_ approach. Its secure infrastructure, advanced safeguards, and adherence to regulatory standards ensure the safe and lawful processing of sensitive information. This makes JAVIS a model for secure, regulatory-compliant software systems in the healthcare and data industries.
## 3 Results  
Growth and Morphology of G. lucidum Cultured with Different Concentrations of Na\({}_{2}\)SeO\({}_{3}\)  
The influence of different concentrations of Na\({}_{2}\)SeO\({}_{3}\) on mycelial growth was examined. Visible indicators were noted with Na\({}_{2}\)SeO\({}_{3}\) concentrations >100 ppm, specifically with the mycelia color turning from white to bright red on days 8, 5, 5, and 4 with the addition of 100, 150, 200, and 500 ppm Na\({}_{2}\)SeO\({}_{3}\), respectively. The red color indicated the formation of elemental Se in the mycelia. Moreover, it also gave off an unpleasant odor accompanied by the mycelial color change. No distinct differences in color, odor, and shape were observed between the mycelia cultured with 50 ppm Na\({}_{2}\)SeO\({}_{3}\) and with no Na\({}_{2}\)SeO\({}_{3}\) (Figure 1a).  
The biomass yield of _G. lucidum_ varied considerably in response to different levels of Se. As shown in Figure 1b, as compared with no Na\({}_{2}\)SeO\({}_{3}\) addition, mycelia growth changed from being promoted to inhibited with an increase in Na\({}_{2}\)SeO\({}_{3}\) concentration. Higher Se (IV) doses resulted in a considerable reduction in biomass compared to that with low Se content. Specifically, 50 ppm Na\({}_{2}\)SeO\({}_{3}\) slightly promoted growth in the whole process, leading to the highest increment of 27.5% in biomass on the 5 th day, whereas in the 100 ppm and 150 ppm Na\({}_{2}\)SeO\({}_{3}\) groups, biomass initially increased, followed by a decrease, with the growth turning point occurring on the 5 th and 7 th days. An obvious reduction in growth was measured for _G. lucidum_ in 200 ppm and 500 ppm Na\({}_{2}\)SeO\({}_{3}\) groups, with much lower biomass, especially since the 4 th day, which was also in agreement with the residual sugar consumption, with basically no consumption since the 4 th day (Figure 1c). It could be clearly seen that the biomass declined at a rapid rate in the 100-500 ppm groups from the 4 th to 7 th day; pronounced cell damage occurred in a short time with excessive Se.  
These results indicated that a low level of Na\({}_{2}\)SeO\({}_{3}\) could be efficiently assimilated by _G. lucidum_ mycelia, but there was saturation and even a suppressive role with a high Se level. Therefore, according to the growth response to selenite, 50 ppm and 200 ppm were chosen as the promoting and inhibiting Se concentrations to compare the _G. lucidum_ growth and bioactive metabolites changing over the period of notable change (4 th-6 th day) in the liquid culture process.
## 3 Results and Discussion
### Droplet Impact on a Stationary Micropillar Surface
In the Cassie state, due to the low kinetic energy of the droplet, the kinetic energy is insufficient to overcome the surface adhesion, although the micro-pillar surface is hydrophobic, preventing the droplet from bouncing off the surface. In the partial penetration state, as the impact velocity increases, the contact time first decreases and then increases. This trend is also observed in the study by [PERSON] and [PERSON] [12], wherethe contact time varies parabolically with velocity. This is primarily because, at lower velocities, the retraction behavior is slower, leading to longer contact times. As the velocity increases, the retraction accelerates, and the contact time reaches a minimum. As the velocity continues to increase, considering the relatively high micro-pillar height in this study, when the droplet penetration depth becomes too large, viscous dissipation increases, and the distance the droplet needs to travel to retract from the surface becomes longer, resulting in an increase in contact time again. In the Wenzel state, within the Weber number range of this study, the high micro-pillar height leads to excessive energy dissipation, causing the droplet to eventually become trapped within the micro-pillar array (as shown in Figure 5c). In this state, the contact time tends to infinity, similar to the Cassie state. Figure 5: Dynamic behavior of a droplet impacting on a stationary surface with a micro-pillar structure. (**a**) Cassie state, \(W\!e_{n}=0.04\); (**b**) partial penetration state, \(W\!e_{n}=3\); (**c**) Wenzel state, \(W\!e_{n}=9\). Figure 6: Variations in different parameters with the impact velocity on a stationary micro-pillar surface. (**a**) Dimensionless penetration depth; (**b**) dimensionless contact time; (**c**) dimensionless maximum spreading diameter; (**d**) velocity restitution coefficient. The blue area is the Cassie state, the green area is the partial penetration state, and the red area is the Wenzel state. The dimensionless maximum spreading diameter during droplet impact on a stationary wall is defined as \(D_{0}^{*}=D_{max}/D_{0}\), where \(D_{max}\) represents the maximum diameter of the droplet during impact. The dimensionless maximum spreading diameter for droplet impact on a stationary microstructured wall satisfies the universal formula proposed by [PERSON] [55], that is,  
\[D_{0}^{*}=(aWe_{n})^{b}, \tag{11}\]  
where the parameters \(a\) and \(b\) take different values depending on the surface conditions. As shown in Figure 7, referring to this form, a dimensionless maximum spreading diameter formula for droplet impact on a stationary micro-pillar surface is fitted based on the data obtained in this study, which is given as follows: \(D_{0}^{*}=(0.47 We_{n})^{0.15}\). The \(R^{2}\) value of this fitting formula is 0.97, indicating that the formula provides a good description of the trend of \(D_{0}^{*}\) with respect to \(We_{n}\).
## 3 Results  
### Water Table Level and Soil Temperature  
The average (\(\pm\)std) water table level (WTL) for the entire measurement period increased in the series Ryan--Tall ryam--Ridge--Open bog--E.hollow--S.hollow and amounted to 45 \(\pm\) 24--43 \(\pm\) 25--33\(\pm\) 10--22 \(\pm\) 15--14 \(\pm\) 17--6 \(\pm\) 7 cm below the soil surface. In all of the studied plots (except Ridge), the highest WTL was observed on 31 May-1 June (spring field campaign); by 24-26 June, WTL dropped by 5-20 cm, and by 27-28 July, it reached its minimum for three field campaigns, falling by another 2-40 cm (Figure 2).  
A slightly different WTL dynamic was at the Ridge, where the WTL was similar at the first and second field campaigns (FC) and at the Open bog, where it was identical in the second and third FC. The highest WTL amplitude for the considered months (40-50 cm) is typical for the Tall ryam and Ryam located on the outskirts of the bog area and the most drained ones; a smaller amplitude of WTL fluctuations was noted for Open bog, E.hollow  
Figure 2: Spatial means of water table level, cm (blue columns; bars—min and max values) and soil surface temperature, \({}^{\circ}\)C (red lines; bars—min and max values) at investigated sites during measurements of carbon dioxide fluxes.  
and Ridge (15-20 cm), and the minimum was noted on the most humid S. hollow (up to 10 cm).  
The average soil surface temperature (T\({}_{0}\)) differed insignificantly among the considered plots, from 14 \(\pm\) 6 (Ridge) to 20 \(\pm\) 2 \({}^{\circ}\)C (S.hollow). The dynamics of T\({}_{0}\) in all plots (except for S.hollow) were identical: a minimum (up to 4 \(\pm\) 1 \({}^{\circ}\)C in Ryam) on 31 May-1 June, a maximum on 24-26 June (up to 20 \(\pm\) 1 \({}^{\circ}\)C in E.hollow) and some decline by 27-28 July. On S.hollow, the maximum soil surface temperature (22 \(\pm\) 1 \({}^{\circ}\)C) was observed in spring, with a gradual decrease towards June and July to 18 \(\pm\) 1 \({}^{\circ}\)C. The average soil temperature at a depth of 15 cm (T\({}_{15}\)) varied within close limits, from 16 \(\pm\) 5 (Ridge) to 20 \(\pm\) 6 \({}^{\circ}\)C (S.hollow). The pH of the bogs varied from 3.2 in Tall ryam to 4.1 in S.hollow; EC from 30 in E.Hollow to 100 \(\mu\)S/cm in Ryam. Thus, within the framework of our observations, we were able to cover a wide range of the hydrological and temperature soil conditions typical of these habitats.