esa-sceva/satcom-chunk-collection huggingface dataset:
# Combating Pathogens Using Carbon-Fiber Ionizers (CFIs) for Air Purification: A Narrative Review
## 4 Discussion  
This paper explores the efficacy of carbon fiber ionization technology in controlling pathogens in indoor environments. It assesses this method's effectiveness against a spectrum of pathogens, considers operational limitations, and evaluates potential impacts on indoor air quality and human safety. By focusing on carbon fiber ionization, this research aims to contribute to developing safer and more efficient pathogen control solutions for indoor public and private settings. Interestingly, [PERSON] et al. [65] highlighted carbon fiber ionization as an emerging technology in the control of bioaerosols within animal agriculture settings, stressing the importance of managing ozone levels as excessive ozone can be harmful to both animals and humans. It was particularly noted for its potential to be integrated into mechanically driven ventilation systems, helping to prevent disease spread from facility to facility. Both bipolar and unipolar ionization have the ability to charge airborne particles, enhancing their removal from the air. However, bipolar ionization offers advantages in terms of more effective particle collision and agglomeration, potentially leading to more efficient air purification. Moreover, the effectiveness of unipolar ionization can be highly dependent on ambient conditions such as temperature and humidity, which can affect ion mobility and lifespan. Available studies increasingly support the potential benefits of air ionization based on the use of carbon fibers in reducing bacterial accumulation on surfaces and deactivating airborne bacteria, viruses, and fungi. However, since there are significantly varied approaches to proving this, potentially hindering a valid interpretation of the results, caution should be exercised in drawing conclusions from the studies.
## 3 Distributed Target Tracking Systems
### Estimation Refinement with ANN
Figure 7: The original RAI (**top left**); the updated RAI (**top right**); the refined sample area (**bottom left**); the final sample area for particle filtering (**bottom right**).
## 2 Materials and Methods
### Sampling and Data Acquisition
#### 2.5.2 EMI Data Acquisition  
The EM34-3 electrical conductivity meter is a two-man portable instrument with two dipole modes: the Horizontal Dipole (HD), in which the coil axes are arranged vertically and the potential survey depth is approximately 0.75 times the distance between the coils, and the Vertical Dipole (VD), in which the coil axes are arranged horizontally and the survey depth is approximately 1.5 times the distance between the coils. As mentioned above, the Tx-Rx coils may be arranged at intervals of 10 m, 20 m, or 40 m. Under ideal conditions, these intervals permit the investigation of depths of approximately 7.5 m, 15 m, and 30 m, respectively, in the HD mode, and 15 m, 30 m, or 60 m in the VD mode [51].  
The EM34-3 electrical conductivity meter used in this study was an Electromagnetic Frequency (EMF) system developed by Geonics Limited (Mississauga, ON, Canada) for applications in Electromagnetic Induction. This apparatus is used as an alternative for electrical surveys that employ galvanic techniques to measure apparent electrical resistivity. The use of the EM34-3 permitted the rapid mapping of the conductivity of the subsurface of the study terrain [51].  
The EM data were collected in March 2017. The apparent conductivity data were measured in mS.m\({}^{-1}\). At one of the study sites (Area 2), the ponds had already been excavated to a depth of 1-2 m. We evaluated the data collected by the Horizontal Dipole (HD) with the coils spaced at 10 m intervals, which surveys depths of up to approximately 7.5 m [51]. The EMI surveys were run in the same directions as the GPR profiles (Figure 3).
## 3 Dynamic Traffic Grooming Based on Virtualization-Plane-Aided Optimization
### EOSNV-ETL-PE Algorithm
Calculate the \(K\) alternative paths \(P^{V}\) of the request \(r\) from small to large according to the size of \(P_{assess}\). If not, construct a set of alternative paths for twin-layer networks. In the physical topology \(G^{5}\), \(K\) alternative paths \(P^{S}\) for \(r\) are calculated from small to significant based on \(P_{assess}\). Finally, construct an alternative path \(P^{V}\)-\(S\) for a twin-layer network. The construction method is to have the virtual topology at the front and the physical topology at the back and remove duplicate paths. The EOSNV-ETL-PE algorithm functions proposed in this section are shown in Algorithm 1. ```
Establish network topology \(G^{S}=(N^{S},E^{S})\), \(G^{V}=(N^{V},E^{V})\), initialize link resource matrix \(link\_Rmatrix\). for\(r\)\(R\)do  Structure \(P^{V}\) and \(P^{S}\) based on request \(r\). if\(P^{V}=\emptyset\)then \(P^{V}\_S=P^{S}\). else  Delete duplicate paths in \(P^{V}\) and \(P^{S}\), and construct \(P^{V}\_S\). endif for Each alternative path \(P_{i,r}\) of \(r\), \(P_{i,r}\epsilon P^{V}\_S\)do  Construct a path resource utilization matrix \(path\_Rmatrix\) along \(P_{i,r}\). for Traverse \(t_{sr}\) along \(P_{i,r}\)do  Search for idle resource blocks in the \(link\_Rmatrix\). if Find continuous time slots of size \(t_{sr}\)then  Set the pre-allocation identifier \(Flag=1\) for \(r\), and save \(P_{i,r}\) and time slots. else \(Flag=0\) endif if if\(Flag=0\&\&P^{V}=\emptyset\)then  reject \(r\). endif endfor if\(Flag=1\)then  Update \(link\_Rmatrix\) based on \(P_{i,r}\) and time slots. Update \(G^{V}\) based on \(link\_Rmatrix\). endif endfor
```  
**Algorithm 1** EOSNV-ETL-PE algorithm  
In the initial stage of request arrival, there are few links connected in \(G^{V}\), so the alternate path in \(P^{V}\_S\) is basically from \(P^{S}\), and the selected path still follows the \(K\) path in the physical topology. In this case, the alternate paths in \(P^{V}\_S\) consist of \(P_{V}\) and \(P_{S}\). The newly arrived requests are diverted to the virtual topology to ensure that requests can use as many allocated link resources as possible to improve network resource utilization.
# Improved Color Attenuation Prior for Single-Image Haze Removal
## 3 Proposed Algorithm
### Adaptive Constraints for Transmission Map
The image local average, as well as local standard deviation, can be derived from Equation (2) using its linearity, as follows:  
\[mean_{i,j\in\Omega(x,y)}[\mathbf{J}(i,j)]=\frac{mean_{i,j\in\Omega(x,y)}[\mathbf{I}( i,j)]-A}{\mathbf{t}(x,y)}+A, \tag{19}\]  
\[std_{i,j\in\Omega(x,y)}[\mathbf{J}(i,j)]=\frac{std_{i,j\in\Omega(x,y)}[\mathbf{I}(i,j) ]}{\mathbf{t}(x,y)} \tag{20}\]  
Substituting Equations (19) and (20) into Equation (18) results in the second adaptive constraint, as:  
\[\mathbf{t}(x,y)\geq 1-\frac{mean_{i,j\in\Omega(x,y)}[\mathbf{I}(i,j)]-f\times std_{i, j\in\Omega(x,y)}[\mathbf{I}(i,j)]}{A}. \tag{21}\]  
Combining the result with the first constraint yields the complete constraint on the transmission map, as:  
\[max(1-min_{c\in[R,G,B]}\left[\frac{\mathbf{F}(x,y)}{A^{c}}\right]1-\frac{mean_{i,j\in\Omega(x,y)}[\mathbf{I}(i,j)]-f\times std_{i,j\in\Omega(x,y)}[\mathbf{I}(i,j)]}{ A})\leq\mathbf{t}(x,y)\leq 1, \tag{22}\]  
which is preferred over the simple constraint \(0.05\leq\mathbf{t}\leq 1\) used in the original CAP due to the adaptive lower limit derived from the effort to prevent undershoots and reduce the number of black pixels.
# Phytotoxic Substances Involved in Teak Allelopathy and Agroforestry
## 3 Allelopathic Property of Teak
### Effects of Teak Leaves  
Teak leaves were used as much for the cultivation of turmeric (_Curcuma longa_ L.) for six months, and the treatments resulted in significantly lower yield of turmeric rhizome [15]. Powder (100 g/7.2 m\({}^{2}\)) of fallen teak leaves was applied on a field of wheat (_Triticum aestivum_ L.), and weed emergence on the field was monitored. The dominant weed species in the field were _Cyndon dactylon_ (L.) [PERSON], _Echinocchio colona_ ([PERSON] [PERSON], _Cyperus rotundus_ L., _Cyperus difformis_ L., _Amarantthus viridis_ L., _Chenopodium album_ L. and _Melilotus alba_ L. At 21 days after the powder application, a 45% reduction in weed population was observed. However, the treatment did not affect the growth of wheat [39]. Those observations indicate that some phytotoxic substances were released from teak leaves, and suppressed weed eminence and growth.
# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 5 Conclusions  
The paper conducts research on the DSTT and designs a DSTT mathematical model suitable for operation by an intelligent algorithm. We proposed a fitness function with the goal of achieving the highest-effectiveness evaluation value. Based on PSO and recent research on the DPSO, the DPSO is specifically proposed for the DSTT.  
The DPSO redefines particle-motion direction and particle-motion target based on the two-dimensional _Schedule_. Based on the characteristics of discrete problems, it proposes a probability-selection model to solve the specific operation of particle-position updates in discontinuous problems. It sets three parameters, namely, the inertia-retention factor, the particle-best factor and the global-best factor, to control the particle-position update to meet the idea of the DPSO, and adopts a random-perturbation operation. It avoids particle motion falling into the trap of local-optimal solutions. In parameter testing, the traversal method is used to determine the parameter group with the best calculation effect.  
Finally, this paper outlines comparison experiments. In comparison experiments with GRs and IGAs, the effectiveness of the algorithm is verified using the success rate. Computational accuracy is verified using the calculation of evaluation values, and efficiency is compared using algorithm iterations. The results show that the DPSO has significant advantages in these three aspects.  
The research results of this paper have certain reference significance for task scheduling and the COP in other industries.  
The next research direction is to conduct multi-objective optimization research based on the research results of this paper, while ensuring the transmission effect and increasing the utilization rate of the equipment.  
Conceptualization, W.Y. and X.W.; methodology, W.Y. and X.W.; software, X.W.; validation, X.W.; formal analysis, W.Y.; investigation, W.Y. and X.W.; data curation, X.W.; writing--original draft preparation, X.W.; writing--review and editing, W.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 6: Two-algorithm calculate-time comparison chart of 100 tests.
# Multi-Band Array Antenna Sharing a Common Aperture with Heterogeneous Array Elements
###### Abstract  
This paper proposes a multi-band array antenna that shares a common aperture with heterogenous array elements. The multi-band array antenna includes one printed dipole antenna for the S-band and 3 \(\times\) 3 array E-shaped patch antennas for the X-band. The current directions of the printed dipole and E-shaped antenna are orthogonal to each other, which properly diminishes the mutual coupling interference. To decrease the mutual coupling interference among the X-band components, we placed cavities using multiple via surrounding the X-band components. To check the validity of the proposed design, the unit-cell was expanded to a 12 \(\times\) 12 X-band array configuration, and then the beam steering properties were examined. The proposed antenna's average gains are 5.2 dBi in the S-band and 5.2 dBi in the X-band. The bore-sight gain of the extended array configuration on the ship mast is 35.6 dBi. The results confirm that the proposed design is suitable for MFR applications even in a shared aperture.  
shared-aperture array antenna; multi-function radar; multi-band array; E-shaped antenna; printed dipole antenna +
Footnote †: journal: Applied  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
20222129348. [[https://doi.org/10.3390/app12189348](https://doi.org/10.3390/app12189348)]([https://doi.org/10.3390/app12189348](https://doi.org/10.3390/app12189348))
## 4 Experiments and Results
### Comparative Results  
The quantitative results of this experiment are shown in Table 2. In this experiment, MaskRCNN [13] was set as the basic model. From the Table 2, it can be found that both CloudRCNN (auxiliary loss) and CloudRCNN (feature fusion) demonstrate better performance than the baseline MaskRCNN model in every category. This proves the effectiveness of the two CloudRCNNs we designed. Compared with MaskRCNN, the mIoU of CloudRCNN (auxiliary loss) improves by 15.24%, and that of CloudRCNN (feature fusion) improves by 12.77%. We chose two other state-of-the-art models for comparison experiments with our models, including PointRend [19] and SCNet [20]. For each subclass, CloudRCNN (auxiliary loss) achieved the best results for the Flower and Gravel categories, CloudRCNN (feature fusion) achieved the best results for the Sugar category, and SCNet achieved the best results for the Fish category. In terms of mIoU, CloudRCNN (auxiliary loss) achieves the best mIoU among all models listed in Table 2. The experimental results show that our approaches outperform the baseline model and other advanced models and achieve state-of-the-art results with this dataset.  
Figure 10 shows two sets of comparative results of MaskRCNN, PointRend, SCNet, CloudRCNN (auxiliary loss), and CloudRCNN (feature fusion). Two example satellite cloud images of Gravel and Sugar clouds are demonstrated. For the example of the Gravel-type cloud (Image 061 cda5), MaskRCNN generates a false-positive semantic segmentation mask, and both the auxiliary and feature fusion models provide correct predictions, which indicates that the additional supervision of the background in our design enables the model to distinguish the foreground and the background better. Both PointRend and SCNet do not output a mask for false positives. For the case of the Sugar-type cloud (Image  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Fish** & **Flower** & **Gravel** & **Sugar** & **mIoU** \\ \hline MaskRCNN & 0.2830 & 0.1991 & 0.2458 & 0.3171 & 0.2613 \\ \hline PointRend & 0.3728 & 0.2816 & 0.4123 & 0.4254 & 0.3746 \\ \hline SCNet & **0.3953** & 0.3177 & 0.4769 & 0.3984 & 0.4019 \\ \hline CloudRCNN (auxiliary loss) & 0.3813 & **0.3288** & **0.5246** & 0.4202 & **0.4137** \\ \hline CloudRCNN (feature fusion) & 0.3520 & 0.2769 & 0.4668 & **0.4601** & 0.3890 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative results of five models.  
Figure 9: MaskRCNN: two-stage object instance segmentation neural network.  
0 ad0a05), MaskRCNN and SCNet wrap more irrelevant background areas, and PointRend appears to have a large area of missed detection. At the same time, the auxiliary and feature fusion versions provide results closer to the ground truth results. This indicates that our spatial attention deconvolution block and parallel convolution block can sufficiently extract information from the target object region and collect cloud features from this information, thus distinguishing background classes and cloud classes to a greater extent and making the semantic segmentation results much more accurate.
## 3 Results and Discussion
### Validation of the Test Setup
#### 3.1.1 Impact of the Frequency of the Alternating Current
For the electrical resistivity in the vertical direction, the same behaviour is detected, with higher electrical resistivities of the single electrode measurements (violet column) compared to the full area measurements (green column). Here, the full area measurements were performed using a parallel connection of each of the two electrodes at a height level and the current flow to the electrodes on another height level, while the single electrode measurements were based on a vertical current flow from one electrode to the other on the same side of the specimen at another height level. The slightly higher resistivity in the vertical direction, which can be seen for the fibre reinforced concretes, is probably caused by the compaction of the concrete on the vibrating table, which leads to the preferred orientation of the fibres in the horizontal direction and thus, an increased conduction ability in the horizontal direction. When comparing the results of the same specimens at the age of 38 days (see Figure 7), it can be observed that the higher age of the concrete leads to a higher electrical resistivity, which is based on the development of the pore structure that gets finer through a higher degree of hydration. This effect is already described in several studies (see, e.g., [42; 43; 44; 45]). In addition, as a result of the storage conditions, the drying process of the concrete itself takes place, which results in a lower pore saturation and thus, also increases the electrical resistivity of the concrete (see, e.g., [31; 46; 47]). Since the aging effect seems to be almost  
Figure 5: Electrical resistivity in the horizontal direction depending on the frequency and fibre content at a specimen age of 9 days and an electrode array with angular distances of 90\({}^{\circ}\). constant for all fibre contents with an increase in resistivity of about 50%, it can be stated that the age or condition of the concrete must be identified to enable a prediction of the fibre content, while the fibre orientation, vertical and horizontal, can be estimated independent of the concrete.
# Building Energy Savings by Developing Complex Smart Windows and Their Controllers
###### Abstract  
The interest in zero-energy buildings has increased in Korea recently. Following the significant increases in cooling and lighting energy consumption in offices, various studies have been conducted to implement energy-saving measures. The purpose of this study is to reduce lighting and cooling energy consumption in the summer through the dimming control of a complex smart window system. To achieve this, the optimal dimming control algorithm has been derived and applied in simulations to analyze the energy consumption for lighting and cooling. A smart window incorporates suspended particle display glass that actively responds to changes in indoor and outdoor environments and controls light transmittance. It also includes a light-guiding glass that can actively control solar reflectance. Simulations of office buildings were conducted to develop optimal control algorithms and controllers based on solar radiation. Subsequently, we installed this complex smart window in a test room along with the developed control algorithm and controller, which responded to the amount of insolation and time. To ensure the accuracy of the experiment, we constructed separate test and reference rooms. The experimental results obtained under the same conditions showed a reduction of approximately 36.9% in cooling energy consumption in the test room compared with the reference room and a 54.5% reduction in lighting energy consumption. Furthermore, based on additional simulations and experiments, we confirmed that the application of complex smart window systems in office buildings could reduce considerably the energy consumption for cooling and lighting.  
complex smart window; smart skin; zero-energy buildings; suspended particle display; energy savings; lighting-guide glass; dimming control LED; optimal control
# Clustering for Lifetime Enhancement in Wireless Sensor Networks
## 2 Related Work
It selects the cluster head based on the lifetime of neighbors and the remaining energy of the nodes as the most essential parameters, and a second parameter considers the lifetime as a member, which takes into account the average energy consumed in transmitting packets from the nodes to their neighbors. Then, a fuzzy system combines them and gives a cost value for each linked node, allowing the optimal path to the base station to be selected. In 2024, [PERSON] presented fuzzy logic-based clustering and quantum annealing algorithm-based routing to enhance WSNs' lifespan [21]. They suggested using the residual energy, the number of neighbors, the distance to the BS, and the nodes' centrality emphasized in a fuzzy inference system to obtain suitable CHs. Also, they implemented an energy threshold to neglect nodes with insufficient levels of energy, which gives more chances to nodes that are highly preferred and have a long lifetime period. To address the challenge of selecting the optimal cluster head (CH), [PERSON] presented a new clustering model based on a novel hybrid algorithm combining the dragonfly and firefly algorithms. The model optimizes CH selection based on nodes' energy, delay, distance, and security [22]. Finally, [PERSON] and [PERSON] proposed a new Region-Based Cluster Head Selection (RBCHS) protocol [23]. It optimizes energy in heterogeneous sensor networks. RBCHS divides the network area into multiple zones. It uses static clustering to ensure area coverage. It employs a hybrid routing scheme for data transmission to the BS. RBCHS selects CHs based on their minimum distance to the BS, their residual energy (RE), and their number of neighboring nodes. Table 1 provides a comprehensive overview of existing approaches. The assumption that \"clustering improves network lifetime and conserves energy\" is always used to develop the most efficient routing protocols in WSN and IoT networks. Moreover, most of the parameters, such as node residual energy, number of neighbors, centrality, and distance to the BS, are often chosen to select CHs and control node lifetime.
# POI Recommendation Scheme Based on User Activity Patterns and Category Similarity
## 2 Related Research
### POI Recommendation Methodology Employing the Voronoi Diagram Tracking Pattern  
The Voronoi diagram is an algorithm utilized for spatial division in location-based services. A Voronoi diagram can be employed to draw a perpendicular bisector between all points to rapidly determine the point a particular location is closest to. Voronoi diagrams have been employed to generate personalized recommendations [1]. Figure 2 shows the process of developing a virtual tracking pattern using a Voronoi diagram. A tracking pattern is established by representing a location and the user's trajectory as a sequence of cells in the Voronoi diagram. This tracking pattern is used in POI recommendations to generate personalized recommendations. In a Voronoi diagram, each cell is created for a specific POI. By analyzing users' movement patterns at the cell level, the scheme identifies the next POIs frequently visited by users and recommends them accordingly. However, this scheme is also restrictive because it generates recommendations in which only the user's tracking pattern is considered without time and spatial characteristics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**User**} & & & **Item** & & \\  & \(\mathbf{Item_{1}}\) & \(\ldots\) & \(\mathbf{Item_{j}}\) & \(\ldots\) & \(\mathbf{Item_{m}}\) \\ \hline User\({}_{1}\) & r\({}_{11}\) & \(\ldots\) & r\({}_{1j}\) & \(\ldots\) & r\({}_{1m}\) \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ User\({}_{i}\) & r\({}_{i1}\) & \(\ldots\) & r\({}_{ij}\) & \(\ldots\) & r\({}_{im}\) \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ User\({}_{n}\) & r\({}_{n1}\) & \(\ldots\) & r\({}_{nj}\) & \(\ldots\) & r\({}_{nm}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: User information matrix.  
Figure 2: Virtual tracking pattern employing the Voronoi diagram. The Voronoi diagram marked in red represents the diagram corresponding to the time period when the user moved along the path marked in red.