esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Architecture of an Optimal Storage Location Planning Based Consolidated Picking Methodology  
This research applies the clustering methods to develop an optimal storage location planning-based consolidated picking methodology for driving the smart manufacturing of wireless modules. Figure 1 displays the architecture of the proposed methodology, which is divided into three parts. The first part is the analysis of storage space demand; the second part is the analysis of the optimal storage location planning; the third part is the analysis of picking list consolidation strategies.  
In the first part, the manufacturing orders are generated from the input data of the customers' orders. The bills of materials (BOM) are used to calculate the quantities of the components at the lowest level of each order. The storage space demand of each component can be obtained by using the above information. After the storage space demand of each component is determined, the second part can perform the analysis of the optimal storage location planning. In this part, the average DoS values for each component and each component classification in the warehouse are analyzed using historical data. The average DoS value and the storage space demand for each component classification can be used to determine the storage location. Hence, the average DoS values serve as the priorities of storage location planning. A shorter average DoS value indicates higher pick-up frequency and the component classification must be placed closer to the exit; thus, the relative position of the component classification can be determined to complete the classification-based storage location planning. Subsequently, the detailed storage location planning for each component is performed. Using the same concept, the average DoS value of each component within the classification can be utilized to decide the detailed relative location of each component. Based on the optimal storage location planning, the third part is to assist the pickers by increasing the picking efficiency of components. This study uses the types and amounts of the components in each picking order as the feature variables and employs the hierarchical clustering method and the K-means clustering method to consolidate multiple picking lists to reduce picking time. After considering the maximum pick-up capacity for a picker, this study can find feasible picking-list consolidations. Finally,the performance of the feasible picking orders consolidations is compared to determine the optimal picking-list consolidation strategy.
## 2 System Description and Current Controller
### Integral-Resonant State Feedback Control
The detailed block diagram of the state feedback current controller augmented with the integral and resonant control terms is depicted in Figure 3, in which the closed-loop eigenvalues are obtained from:  
\[\det[z\mathbf{I}-\mathbf{A}_{e}+\mathbf{B}_{e}\mathbf{K}]=0\]  
where \(\mathbf{A}_{e}=\left[ \begin{array}{cc}\mathbf{A}_{d}&0_{6\times 10}\\ -\mathbf{B}_{au}\mathbf{C}_{d}&\mathbf{A}_{au}\end{array} \right]\), \(\mathbf{B}_{e}=\left[ \begin{array}{cc}\mathbf{B}_{d}\\ 0_{10\times 2}\end{array} \right]\), and \(\mathbf{K}=\left[ \begin{array}{cc}\mathbf{K}_{x}&\mathbf{K}_{C}\end{array} \right]\). The full-state feedback control gain is systematically determined through the LQR approach by minimizing the discrete quadratic cost function as follows [15]:  
\[J=\frac{1}{2}\sum_{k=0}^{\infty}\mathbf{x}_{e}^{T}(k)\mathbf{Q}\mathbf{x}_{e }(k)+\mathbf{u}^{T}(k)\mathbf{R}\mathbf{u}(k) \tag{16}\]  
where \(\mathbf{x}_{e}(k)=\left[ \begin{array}{cc}\mathbf{x}(k)&\mathbf{x}_{c}(k) \end{array} \right]^{T}\), \(\mathbf{Q}=\text{diag}\left[ \begin{array}{cc}\mathbf{Q}_{i}&\mathbf{Q}_{r6} &\mathbf{Q}_{r12}\end{array} \right]\) is a positive semi-definite matrix, and \(\mathbf{R}\) is a positive definite matrix. To determine a gain matrix which minimizes the quadratic cost function, the symmetric weighting matrices are chosen with the relative importance of the state variables and expenditure of energy by control input signals. Since the augmentation of the control terms into the inverter systems causes the increase of the number of feedback gains, the LQR approach produces a systematical method to find the optimal gains of the current controller and to avoid laborious process for proper gain selections [33; 34]. The numerical calculation to determine the gain matrix is accomplished offline with MATLAB. In this study, the weighting matrices \(\mathbf{Q}\) and \(\mathbf{R}\) are selected by an iterative selection process and verified by both the simulation and experimental results. Furthermore, to implement full-state feedback current controller, a full-state observer is employed in the stationary frame to estimate the system states without installing an extra sensing device [16].
## 1 Introduction
[PERSON] results showed that pollen was recognized as a possible pathway for _X. arboricola_ pv. _juglandis_[3]. Therefore, the survival of the bacterium _X. arboricola_ pv. _juglandis_ in catkin buds and catkins should be checked before the bacterium overwinters in blighted leaf and catkin buds. When water carries the bacteria to the leaves and stigmatic surfaces of the flowers, blight spreads from these reservoirs. When catkins open and stimulate flowers release pollen, the disease may also spread through the air. Contaminated pollen may be carried by wind and deposited on any young growing parts of trees [4]. Artificial pollination technologies have been developed to address various challenges in natural pollination, including the issue of contaminated pollen or contributing to possible disease outbreaks from contaminated pollen [11; 12]. Artificial pollination systems have been developed to supplement natural pollination and provide yield security even in poor pollination scenarios. These technologies include various methods for pollen collection and application, such as hand pollination, handheld and vehicle-mounted devices, unmanned aerial vehicles (UAVs), and robotic and autonomous pollinators. These systems have been found to perform adequately for certain crops, including kiwifruit, olive, date palm, walnut, and tomato [13; 14]. Moreover, artificial pollination technologies have seen significant advancements, with the integration of machine learning and artificial intelligence (AI) playing a crucial role in the development of innovative solutions for future farming. Especially, AI and machine learning are being integrated into pollination technologies to develop autonomous pollination systems [15; 16]. Based on the above, researchers are developing various drone-based pollination technologies, such as the use of tiny drones equipped with artificial intelligence to autonomously navigate and carry pollen between plants [17; 18]. In our view, the parameters are important and will be further analyzed in this review paper. Further, while pollen-mediated transmission of bacterial pathogens has been suggested for _Xanthomonas arboricola_ pv. _juglandis_, we will try to answer what are the advantages of pollination drones and how they can help to prevent or reduce the risk of walnut blight disease. In the end, we have designed a path-planning algorithm for a pollination robot that involves determining the disease inoculum and an optimal route for the robot to pollinate flowers efficiently.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 2 Synthesis Methods
### Chemical Methods
#### 2.2.1 Precipitation and Coprecipitation  
Precipitation and coprecipitation stand out as the prevailing techniques for the fabrication of MNPs consisting of magnetic metal oxide nuclei from corresponding salts [17; 18]. These MNPs possess a regulated size and magnetic properties, owing to the straightforward, adaptable, and less detrimental procedures employed for their synthesis [17; 18; 19; 20]. Moreover, these approaches are secure, economical, and necessitate a lower synthesis temperature compared to alternative conventional methods [21; 22]. Precipitation and coprecipitation are particularly suitable when there is a demand for the production of large quantities of nanocrystals [19].  
In a typical scenario, metal ions are employed to dissolve in a solvent and to form a solution containing these metal ions, that will be further used for MNP production [19]. A common illustration of MNPs synthesized through these methods is Fe\({}_{3}\)O\({}_{4}\) or \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) NPs, obtained from ferrous (Fe\({}^{2+}\)) and ferric (Fe\({}^{3+}\)) salts dissolved in an alkaline solution [17; 20]. The typical composition involves a 2:1 ratio of Fe\({}^{2+}\) and Fe\({}^{3+}\) salts, either at room temperature or an elevated one (80-85 \({}^{\circ}\)C) [20]. Once the reaction concludes, a precipitate of Fe\({}_{3}\)O\({}_{4}\) forms at reactor's base. It can be recovered through either centrifugation or magnetic isolation [20]. The expected pH range for the resulting precipitate is between 7.5 and 14 [20]. It's noteworthy that the synthesis of this MNPs type heavily relies on the sources of metal ions and factors such as the temperature, pH, and ionic strength of the reaction medium [17]. In fact, a majority of commercially available iron oxide nanoparticles (IONPs) are synthesized using the aforementioned methods [23; 24].  
Other researchers obtained manganese ferrite (MnFe\({}_{2}\)O\({}_{4}\)) NPs using ferric chloride (FeCl\({}_{3}\)) and manganese (II) chloride (MnCl\({}_{2}\)) as metal ions sources, and sodium hydroxide (NaOH) as a precipitant [25]. MgFe\({}_{2}\)SO\({}_{4}\) nanocrystals can be generated through the coprecipitation of Fe\({}^{3+}\) and Mg\({}^{2+}\) ions, facilitated by the addition of NaOH [26].
## 3 System and Channel Model Development
### Channel Model
The power of each of the multipath component can be calculated as:  
\[P_{l}(dB)=P_{0}(dB)-10 nlog(d_{l})-L_{r}+G_{t}\big{(}\theta_{d,l}\big{)}-G_{t} (0)+G_{r}\big{(}\theta_{a,l}\big{)}-G_{r}(0) \tag{8}\]  
where \(L_{r}\) is the path loss in dB. Assuming the phase of the multipath components, \(\gamma_{l}\), are uniformly distributed over the interval (0,2\(\pi\)) the complex amplitudes of the multipath components are calculated as \(\alpha_{l}=10^{(P_{l}-P_{0})/20}e^{i\gamma_{l}}\).
# A DFT-Based Running Time Prediction Algorithm for Web Queries
## 2 Background
### The WAND and BM-WAND Dynamic Pruning Techniques  
Ranking algorithms return the top-\(k\) documents for user queries. To quickly process large inverted indices, these algorithms use dynamic pruning techniques to allow efficient retrieval by not fully scoring all postings of the documents matching a query. In this paper, we focus on the WAND [17] and the BM-WAND [18] techniques because they present significant benefits by avoiding the scoring of documents that cannot make the top-\(k\) retrieved documents set [18].  
The WAND strategy processes each query by looking for query terms in the inverted index and retrieving each posting list. Documents retrieved from the intersection of the posting lists allow us to answer conjunctive queries (AND bag of word query), and documents retrieved at least from one posting list allow us to answer disjunctive queries (OR bag of word query). It is based on two levels. In the first level, some potential documents are selected as results using an approximate evaluation. Then, in the second level, those potential documents are fully evaluated (e.g., using scoring functions such as the BM25 or the vector model) to obtain their scores. A heap keeps the current top-\(k\) documents where in the root is located the document with least score. The root score provides a threshold value which is used to decide whether to evaluate the full score of the remaining documents in the posting lists associated with the query terms.  
The Block-Max WAND (BM-WAND) technique [18] uses compressed posting lists organized in blocks. Each block stores the upper bound of the scores (Block max) for the documents inside that block in uncompressed form, thus enabling to skip large parts of the posting lists by skipping blocks. This reduces the cost of the WAND but does not guarantee correctness because some relevant documents could be lost. This problem is solved in [18] with an algorithm that moves forward and backwards in the posting lists to ensure that no documents are missed. Independently, the same idea was presented in [19].
# Characteristics of Low-Frequency Acoustic Wave Propagation in Ice-Covered Shallow Water Environment
## 3 Model Validation  
In this part, the elastic normal mode [23] is utilized to demonstrate the correctness of the ice-fluid-seabed model. This method is different from other methods such as ray theory, which is advantageous for studying the sound propagation characteristics of low-frequency sound signals.  
Medium parameters about the ice-cover shallow water model proposed in this paper are shown in Table 1. For the model of elastic normal mode and elastic parabolic equation with the same parameters, the sound field characteristics are analyzed respectively. When the frequency of the sound source is 50 Hz, the comparison between the transmission loss of the elastic normal mode model and elastic parabolic equation model are shown in Figure 2.  
It can be seen from Figure 2 that two curves signifying the solutions of elastic parabolic solution and elastic normal mode coincide with each other. Moreover, under the same medium parameters, the solution is in agreement with that of [PERSON] [8]. Through these verifications, this model is correct, and the method of an elastic parabolic equation can be used in a polar sound field model reasonably.
# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 2 Model Development
### Coupled Spatiotemporal Attention Model Considering Driving Styles  
The architecture of the HTSA-LSTM model is illustrated in Figure 4. To predict vehicle trajectories within a road network, the model leverages temporal and spatial attention mechanisms to capture dependencies in both time and space. Additionally, it integrates a driving style analysis module to account for the influence of varying driving behaviors on vehicle movement. The road network surrounding the target vehicle is discretized into a 3 \(\times\) 13 grid, as shown in Figure 1. The grid's rows represent the left, current, and right lanes relative to the target vehicle, while the columns correspond to discrete grid cells of width L [m], determined by the vehicle's length and the driving environment. In alignment with related studies [28], L is set to 4.6 m (15 ft) during training with the NGSIM dataset. All vehicles within the 3 \(\times\) 13 grid, excluding the target vehicle, are considered neighboring vehicles, each occupying a unique grid cell.  
The input to the HTSA-LSTM model consists of the historical trajectories over T steps and the driving style information of all vehicles within the grid. The model outputs the predicted trajectory of the target vehicle for the next H steps. The temporal attention mechanism analyzes the influence of historical trajectories of both the target and neighboring vehicles, while the spatial attention mechanism captures the impact of the surrounding environment on the prediction. Integration module driving styles analysis is used to obtain driving habits.  
In summary, the HTSA-LSTM model effectively integrates temporal and spatial attention mechanisms, combining time-series analysis with spatial feature recognition. The temporal attention identifies critical relationships between past trajectories and the current state, while the spatial attention emphasizes key environmental features. By employing a dynamic spatiotemporal attention mechanism, the model captures evolving spatiotemporal dependencies and incorporates driving style variations, enhancing its understanding of the interaction between these factors for more accurate trajectory predictions.
# Model-Based Fault Analysis and Diagnosis of PEM Fuel Cell Control System
## 6 Conclusions  
In this paper, a study of the model-based fault analysis and diagnosis of PEM fuel cells was conducted. The PEM fuel cell system model, including sensors and actuators, was defined and faults in the system were analyzed. The analysis of possible faults in the system could be carried out with the idea of sorting out the related variables in the system. Dulmage-Mendelsohn decomposition was a powerful method of reordering variables with relativeness. Variables in overdetermined parts were candidates of faults that could be detected. Based on the information from the fault analysis, residuals from known variables were calculated via a bi-partite graph, and a FDI algorithm was developed. The algorithm could be made much simpler with the fault signature matrix by separating variables into relative sets. The algorithm was validated on a PEM fuel cell model for simulation by being implemented in a simulation environment. As a result, residuals could detect changes in the system and the FDI algorithm could determine the fault. Eight faults (except for hardware redundancy) could be diagnosed by the FDI algorithms with given sensors in the system.  
The model-based method of designing a FDI algorithm throughout the paper had proven its effectiveness by sorting out faults in the PEM fuel cell system. Although the machine-learning method can overcome the complexity of the system in diagnosing fault, it still has its limit in the analysis of the target system. This particular method had its significance especially in closely examining the PEM fuel cell system since it has a complicated relation of variables, such as flow rate, pressure, temperature, and many others. Furthermore, the algorithm can be easily modified in case there is a change of components in the system based on the information about the system studied. The methodology shown in the paper has its power in having a deeper look into how components of the system affect one another more than other methods of detecting fault.  
Figure 11: Fault indicator in case of air pressure sensor fault.  
**Author Contributions:** Formal analysis, B.K. and W.N.; project administration, B.K., W.N. and H.L.; resources, B.K. and W.N.; software, B.K. and W.N.; validation, B.K. and W.N.; writing--original draft preparation, B.K.; writing--review and editing, B.K. and H.L.; supervision, H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (20223030030010, Development and demonstration of optimized fuel-cell hybrid system technology for hydrogen bus). This paper is the result of research carried out by a research fund and technical support from HMC.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Photocatalytic Phenol Degradation by Silica-Modified Titanium Dioxide
## 2 Materials and Methods
### Photocatalytic Activity Test  
Photocatalytic activity test of the TiO\({}_{2}\) P25 Degussa and TiO\({}_{2}\)/SiO\({}_{2}\) composites was carried out on phenol and evaluated using reverse-phase high-performance liquid chromatography (HPLC, Jasco Co-2065 Plus, Tokyo, Japan), with a UV detector (Jasco UV-2075 Plus, Tokyo, Japan) and a C-18 column or octadecyl silica (ODS) as the stationary phase and a mixture of distilled water, methanol, and acetonitrile as the mobile phase. For the photocatalytic test of the TiO\({}_{2}\)/SiO\({}_{2}\) composites, phenol solutions were made with a concentration of 20 mg.L\({}^{-1}\) and a solution pH of 7.35. A total of 75 mg of this composite with a variation of TiO\({}_{2}\)/SiO\({}_{2}\) 1:1 was added to 50 mL of 20 mg.L\({}^{-1}\) phenol solution in the photoreactor. Furthermore, the phenol solution was stirred without irradiation for 60 min to achieve equilibrium. Subsequently, the solution was irradiated using a 300 W Xe lamp, UV ray with a wavelength \(<\)390 nm (PE300 BUV, Waltham, MA, United States) at a distance of 150 mm above the surface of the solution, with an irradiated area of 26 cm\({}^{2}\) (approximately 20 mW/cm\({}^{2}\)) for 120 min, while being stirred over a magnetic stirrer. A total of 0.5 mL was taken at 30-min intervals using a membrane syringe and the concentration was measured using the HPLC device. The same treatment was carried out for TiO\({}_{2}\)/SiO\({}_{2}\) 3:1 variation; TiO\({}_{2}\)/SiO\({}_{2}\) 7:1, and TiO\({}_{2}\) P25 Degussa [26].
# A GNN-Based False Data Detection Scheme for Smart Grids
## 1 Introduction
Of the physical network and the power network, the data are collected into the communication network through the powers of the IoT and the Internet. The communication network consists of intermediaries corresponding to energy production equipment, terminal devices for energy consumers, and corresponding communication networks. intermediaries are used to receive data from sensors and send control commands, as well as store basic data for each energy production device and monitor device status. Network terminals are used by power users and operators to retrieve and distribute network data, as well as manage power generation equipment or loads. Our research put forward an STGNN-based cross-domain anomaly detection model. The STGNN was utilized to process several information networks obtained from sensor measurements, with the intention of forecasting the state of the information network at future moments and simulating dynamic changes of the state of the power network. Subsequently, we use domain adaptation techniques to follow frontier studies [10; 11; 12] and extract potential representations of nodes in different domains using a shared encoder. In this way, the anomaly information from the communication systems and the physical transmission network is used to assist the power system in anomaly detection to tackle the costly issue of obtaining anomaly labels for power systems. The key contributions are summarized below:  
* The information graph was modeled by aggregating the structure and features of the network. The graph processed the communication layer dataset, converted the traffic data into graph structures, and used them as inputs for the model to ensure effective integration of the feature information. * A spatio-temporal graph neural network (STGNN) was developed to process several information networks obtained from sensor measurements to forecast the network state. * A loss function was designed to bridge the differences between different domains. The abnormal information from communication and transmission systems was utilized to analyze and detect anomaly information between real data and normal data. * The experiment revealed that the proposed scheme outperformed other comparative methods with respect to anomaly detection tasks and could accurately identify anomalous data in the early stages of data transmission. The remainder of this paper is organized in five additional sections. In the next section, we illustrate the relevant work. Section 3 summarizes the preliminaries. Section 4 proposes the GNN-based false data detection scheme. Section 5 presents the experimental evaluation. Significantly, the last section offers concluding remarks and prospective ideas.