esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Future Research Directions
### Human Factors  
The unstructured environment of a live construction site is a major challenge for robots [268]. Robots can perform well in structured and straightforward tasks but need human reasoning in complex tasks that occur in an unstructured environment [268]. Human-robot collaboration (HRC) combines the strengths of humans and robots [269]. Shared autonomy has been studied [80], which involves humans carrying out higher-level reasoning, such as deciding where to take images [270], while the robot manages low-level tasks such as avoiding obstacles and maintaining a safe distance from the wall. The development of shared autonomy workflows for different types of robots in different application domains for inspection and monitoring of the built environment can contribute to the mutual adaptation of human-robot teams.  
Other domains have investigated various modes of human-robot interactions (HRI) [271]. Construction noise, lighting conditions, and the diverse technical literacy of construction workers necessitate a closer examination of HRI in the context of construction. Future studies can investigate the effect of different HRI modes on the performance of human-robot teams during the inspection and monitoring of the built environment.  
Human-robot collaboration can also improve the autonomy of the robot in the long run by making the robot learn gradually from the human. Reinforcement Learning (RL) can be used to develop human-in-the-loop methodologies in which robots learn to identify defect indicators from human inspectors through observations. Future work should develop an HRI model to understand human behavior in response to external stimuli in human-robot collaboration tasks and train the robot to learn from it for increased autonomy in future tasks in similar situations.  
Recent growth in the use of automation and robotics in construction has necessitated reskilling of the construction workforce for the adoption of technology safely and efficiently [272]. Studies have been conducted on training the construction workforce in usingrobots through VR [272]. VR provides a safe and cost-effective medium to train construction personnel. Future research can identify training needs to develop continuing education programs for the construction industry to assist with adopting robotic technologies in the inspection and monitoring of the built environment through the use of VR or other mediums. Incorporating robotics in construction education should be explored to prepare future experts with the required competencies. Future research should survey robotics experts to identify the knowledge, skills, and abilities required for future construction workers and professionals in conducting inspection and monitoring of the built environment to efficiently use robotics in the architecture, engineering, construction, and operation (AECO) industry.
## 1 Introduction
It should be noticed that in these works [11; 12; 13; 14; 15; 16; 17] Landsat imagery was used, which means that the best achievable detection period is 16 days (but due to the atmospheric conditions the period is usually worse). Additionally, with the exception of [15], more than one observation of previous data is needed for the detection. Usually, the change detection relies on the slope of the time-series and consecutive variations in vegetation indices. These applications showed the importance of the temporal and spatial resources for disturbances detection. The purpose of this work is to identify when maintenance operations occur in the FBs. Although there is not an evaluation of the quality of the operation, only complete operation in the FB are to be detected. Relatively to the common change detections methods the goals were:  
* Use of Sentinel-2 data instead of Landsat imagery, due to its increased frequency and spatial resolution;
* Identify only a specific kind of operation efficiently, dealing with the phenology and different types of vegetation;
* Use of common vegetation indices and other indices;
* Reduce the previous data used, identifying the maintenance as soon as possible, allowing a classification whenever a new observation is made, as in [15]. To achieve these objectives and based on current literature, several requirements were defined as follows:  
* Object-based classification--since an FB is a well-defined area, it will be defined as an object. This approach can--better capture its spatial characteristics;
* Temporal dynamics--the use of time-series allows the determination of the temporal dynamics, which is essential in change detection methods;
* Machine learning--the use of artificial intelligence techniques to enhance the change detection classification. There are two main stages: the data extraction, and the maintenance operations detection with an artificial neural network (ANN). In the first stage, the time-series and the datasets are created. It includes the geolocation correction of the observations, calculation of the mean values of the reflectance bands within the FB, followed by the application of a noise reduction filter and finally, computation of the spectral indices. The second stage includes a preliminary training step for feature selection, ANN training and error estimation, and a classification step to identify the months where a maintenance operation was executed. The structure of this paper is as follows: Section 2 describes the study area, the data used for the implementation, and presents the dataset and the detection methodology; Section 3 show the results of the intermediary steps for designing the ANN and the detection results followed by their discussion in Section 4. Finally, the conclusions are in Section 5.
# Study on the Load-Bearing Behavior of Underground Steel Structures with Pitting Corrosion Damage
## 2 Random Pitting Damage Model  
The structures of steel structures that have been in service for a long time in harsh underground environments are complex, and the causes and forms of damage are also diverse. Due to the random nature of corrosion in the actual underground environment, the size, depth, and location of the corroded area are all random. Some scholars have studied the growth shape of corrosion pits of metals in salt solutions and found that the pits are mostly hemispherical and gradually become semi-ellipsoidal in the later stage of development [20; 21]. Moreover, hemispherical pits themselves have the characteristic of being shallow near the edge and deep at the center. In addition, when two or more pits intersect, a large pit is naturally formed. Therefore, based on the hemispherical pit model with a radius of 4 mm, a pitting corrosion model with random position is generated in this paper, allowing pitting pits to intersect. The randomly distributed pitting corrosion model established is shown in Figure 1. This figure utilized simulation methods to model the impact of natural corrosion pits on the load-bearing performance of steel structures. It presented the intricate characteristics of randomly distributed pitting corrosion and interwoven local areas, accurately replicating the corrosion morphology under actual working conditions.  
The main effect of pitting damage on the steel structure is that stress concentrations form easily near pits, resulting in sharp increase in stress. Under certain large or even conventional working loads, the material near the pit may enter a state of yield prematurely. Under the action of cyclic load, plastic accumulation will occur. Plastic accumulation to a certain extent will reduce the bearing capacity of the steel component.
###### Abstract  
Multi-load wireless power transfer systems generally require the configuration of multiple transmitting coils. Using traditional single-output inverters will increase the number of inverters, leading to increased system costs and complex structures. Therefore, this paper proposes a single-input multi-output inverter that can drive multiple transmitting coils simultaneously. Compared with traditional single-output inverters and existing multi-output inverters, the proposed inverter utilizes a topology improvement design and an efficient expansion method. By adding only one inductor, one capacitor, and a small number of power switches, it can generate multiple controllable and stable outputs while ensuring output gain, which is expected to simplify the system structure and improve system performance. This paper first introduces the topology evolution process and operating principle of the proposed inverter. Secondly, a mathematical model is established to analyze its operating characteristics, and its parameter design is carried out. Meanwhile, a comparison with existing multi-output inverters is conducted. Then, the resonant compensation networks are analyzed and selected to match the requirements of different loads. Finally, a simulation model of the proposed inverter is constructed, and an experimental setup is set up. The feasibility and superiority of the proposed inverter are verified through simulation and experiments.  
wireless power transfer; inverter; multi-output; multi-load; resonant compensation Article
## 3 Method Architecture
### Semantic Encoding  
The feature encoding involved in this article is divided into three parts, namely text semantic encoding, POS feature encoding, and pinyin feature encoding. In the semantic encoding stage, given a text sequence \(X=[x_{1},x_{2},\ldots,x_{i},\ldots,x_{n}]\), \(x_{i}\) represents the character with index \(i\) in the text sequence, and n represents the length of the text sequence, as shown in Formula (1):  
\[h_{x}^{i}=BERT(x_{i}) \tag{1}\]  
Here, \(BERT()\) is the encoding function of text semantics. The BERT pre-trained model is employed in the embedding layer of the model to generate dynamic word vectors for text sequences. This paper chooses BERT as the semantic encoder of text sequences, mainly based on the following reasons: First, compared to traditional unidirectional language models, BERT captures contextual semantic information more effectively and demonstrates superior semantic understanding capabilities. Second, despite having fewer parameters, the BERT model can generate high-quality semantic representations and quickly adapt to downstream tasks through fine-tuning after pre-training. Based on BERT's bidirectional encoding mechanism, the Transformer structure, and pre-training results on a large-scale corpus, this paper can achieve high-quality text semantic encoding while maintaining a low computational cost. Therefore, BERT is chosen as the semantic encoder for this model.
# Removal of Phosphate from Aqueous Solution by
## 2 Materials and Methods
### Characterization of Zeolite-Biochar Composites and Phosphate Determination  
At a mass ratio of adsorbent to deionized water of 1:10 (g/mL), electrical conductivity and pH were measured using a conductivity meter (DDS-307, Shanghai Leici Instrument Co., Ltd., Shanghai, China) and a pH meter (pHS-3E, Shanghai Leici Instrument Co., Ltd.). X-ray diffraction (XRD) analysis was carried out on a D/MAX2000 diffractometer (Rigaku Co., Tokyo, Japan). SSA was determined by the BET method with a surface area analyzer (ASAP 2460, Micrometrics, Norcross, GA, USA). SEM and TEM with an energy dispersive spectrometer analyzer (JSM-IT 300, Japan electronics Co., Ltd., Tokyo, Japan; Tecnai G\({}^{2}\) F20 S-Twin, FEI Inc., Valley City, ND, USA) were used to describe the surface morphology and elemental composition of the materials. An FTIR spectrometer was used to characterize the functional groups on the adsorbent surface in the spectrum region of 400 to 4000 cm\({}^{-1}\) (Nicolet iS 5, Thermo Scientific, Waltham, MA, USA). An elemental analyzer (Elementar Varioel Cube, Hanau, Germany) was used for elemental analysis. The Malvern Zetasizer Nano ZSE (Malvern Instruments, Malvern, UK) was used to measure zeta potentials. The phosphate concentration in the filtrate was measured based on the colorimetric molybdate blue method ([PERSON] and [PERSON] 1962).
## 5 Development of an Evaluation System
### Determining Transmission Power  
In water, the packet arrival rate is affected by the frequency used and noises present. A preliminary experiment using simulation has been conducted to determine the necessary transmission power.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Set Value** \\ \hline Underwater depth (D) & 10 m \\ Temperature (T) & 25 \({}^{\circ}\)C \\ Salt concentration (S) & 34.17 \% \\ pH & 8.1 \\ Underwear sound velocity & 1532.912 m/s \\ Field size & 200 \(\times\) 200 \(\times\) 10 m\({}^{3}\) \\ Diffusion factor & 1 \\ Sipping activity & 0 \\ Wind speed & 5 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Undersea conditions.  
Figure 10: Influences of various noises at different frequencies.  
In this simulation, which modeled an underwater environment, a source node sent a packet to a node at a certain distance.  
The transmission power is adjusted in such a way that no packets travel beyond 20 m, the communication radius, r, assumed in the present research. Figure 11 shows how the packet arrival rate decreases with an increase in the inter-node distance. The figure shows that a small portion of packets arrives at the 21-m point in the case where the transmission power is 0.72 mW but no packets travel beyond 20 m in the case where the transmission power is 0.7 mW. Thus, we have decided to use transmission power of 0.7 mW.
# Marine Bioactives and Their Application in the Food Industry: A Review
## 2 Marine Sources of Bioactive Molecules
### Marine Algae
#### 2.2.3 Other Biomolecules from Marine Algae
To illustrate, red algae contain B vitamins (B1, B2, and B12), while green algae encompass vitamins E and C. Vitamin C is found in all macroalgae, with red algae being notably employed to combat scurvy stemming from vitamin C deficiency. Seaweed can exceed daily vitamin requirements, particularly for vitamins A, B12, B2, and C [90]. Beyond pigments, phenolics, and vitamins, marine algae are also renowned for their richness in essential minerals (e.g., magnesium, sodium, calcium, potassium, and phosphorus) and trace minerals (iodine, zinc, and manganese). Their mineral presence is attributed to their ability to accumulate inorganic matter from seawater [51]. The mineral composition of marine algae is contingent upon the species and can vary due to factors like environment, geography, seasonality, and physiology. In particular, marine algae can contain mineral levels 10 to 100 times higher than those in terrestrial plants. Indeed, the mineral content of specific algal species (e.g., red and brown algae) can extend to approximately 40% when measured on a dry weight basis, whereas spinach, known for its remarkable mineral content, has been documented to possess only 20% dry weight mineral content [88]. As a result, selected marine algae are broadly incorporated into dietary supplements to meet mineral and trace element requirements. Furthermore, owing to the balanced ratio of sodium and potassium, mainly evident in green algae like _Ultra rigida_, these algae might contribute positively to preventing hypertension [55]. Therefore, marine algae or their products exhibit an array of health benefits, encompassing the prevention of various ailments such as symptoms of osteoporosis, hypocalcemia, goiter, creinism, and mental disorders [85]. Consequently, it is crucial to make concerted efforts to expand their inclusion in innovative food formulations and to rigorously assess the purported health benefits tied to these novel products.
# Robust Text-to-Cypher Using Combination of BERT, GraphSAGE, and Transformer (CoBGT) Model
## 6 Conclusions  
In this paper, we propose a model for text-to-Cypher task. In this model, we use the BERT model to extract the key value (key-value extraction module) and GraphSAGE to exploit the relation-properties of the database (relation-properties extraction module). Then, these features were fed to the Transformer to generate the Cypher query (Cypher prediction module). Moreover, we provide a small text-to-Cypher dataset for evaluation and comparison on the text-to-Cypher task. The simulation results show that our proposed model achieves higher performance compared with the seq2 seq models (T5 and GPT2). Specifically, our proposed model achieved 87.1% EM and outperformed 39.04% for T5 and 35.81% for GPT2. In future work, we plan to create a larger dataset and improve our model to achieve better performance.  
Conceptualization, Q.-B.-H.T. and S.-T.C.; methodology, Q.-B.-H.T. and S.-T.C.; software, Q.-B.-H.T. and A.A.W.; validation, Q.-B.-H.T. and A.A.W.; investigation, Q.-B.-H.T., A.A.W. and S.-T.C.; writing--original draft preparation, Q.-B.-H.T.; writing--review and editing, Q.-B.-H.T. and S.-T.C.; supervision, S.-T.C.; project administration, S.-T.C.; funding acquisition, S.-T.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (2024-0-00071) supervised by the IITP (Institute of Information & communications Technology Planning & Evaluation).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
## 2 Methods
### Satellite Architecture  
Because of the large range of possible QKD satellite designs, for the purposes of this analysis we assume that the hypothetical satellite targeted by the ground-based laser is very similar to _Micius_, i.e., it has the same QKD protocol and implementation, similar satellite bus design, and is in a circular orbit at an altitude of 500 km. Table 1 lists key aspects of the design of _Micius_ that are used to inform our analysis.
## 4 Experimental Results
### Dataset  
We evaluated our system using the publicly available KITTI Odometry dataset [31]. The KITTI dataset is a widely used public dataset for evaluating visual SLAM algorithms in outdoor car driving scenarios. It encompasses diverse environments, including urban, rural, and highway scenes, captured by a vehicle equipped with multiple sensors such as several stereo cameras, GPS, lidar, and Inertial Measurement Unit (IMU). This paper utilized the visual odometry data from the KITTI dataset, which comprises extensive outdoor images of large-scale scenes. There are moving vehicles and pedestrians in some images, as well as various degrees of occlusion, and changes in light and weather conditions.  
The KITTI visual odometry dataset consists of 22 image sequences, with 11 of them (KITTI 00-KITTI 10) accompanied by ground truth trajectories, allowing for the evaluation of SLAM systems' trajectory estimation. The image data include calibration files, color images from the left and right cameras, as well as grayscale images from the left and right cameras. In this paper, four sequences, namely KITTI 00, KITTI 05, KITTI 06, and KITTI 07, which containing loop closures, were selected for testing purposes. Only the sequence of left camera grayscale images was used to evaluate the monocular direct method visual SLAM system. The specific parameters associated with the four sequences (KITTI 00, KITTI 05, KITTI 06, and KITTI 07) are presented in Table 1.  
To evaluate the performance of the loop closure detection method, we manually created a list of real loop closures, with each time interval in the list having a matched time interval.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Number of Images** & **Image Size (Height \(\times\) Width)** \\ \hline KITTI 00 & 4541 & \(376\times 1241\) \\ KITTI 05 & 2761 & \(376\times 1241\) \\ KITTI 06 & 1101 & \(376\times 1241\) \\ KITTI 07 & 1101 & \(376\times 1241\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of KITTI visual odometry dataset.
## 2 Materials and Methods
### Bioinformatics and Statistical Analysis  
Sequences were searched and analyzed using NCBI BLAST, Chromas2.6 and ClustalX2.1 to confirm specific infection.  
V3-V4 16S amplicon sequencing data was quality filtered using the FLASH method [27]. All sequence analyses were provided in the Quantitative Insights Into Microbial Ecology (QIIME1.9.1) [28] according to the QIIME instructions with some modification. Further error correction was performed using USEARCH with de novo models, and the remaining sequences were clustered into operational taxonomic units (OTUs) using UCLUST [29] with a cutoff of 97% similarity. Taxonomy was assigned to the representative sequence of each OTU using the Ribosomal Database Project (RDP) classifier [30] with QIIME and aligned using the Greengenes13.8 reference database. Two alpha diversity measurements including Chao1 and Shannon diversity index were calculated from OTU counts to evaluate the biodiversity of the bacterial population in samples at the genus level. A Newick formatted tree was obtained by utilizing the QIIME package, and subsequently using Unweighted Pair Group Method with Arithmetic mean (UPGMA) clustering to generate a distance matrix that was visualized by principal coordinate analysis (PCoA). The relative abundance of taxonomy was processed using STAMP2.1.3 [31] for statistical analysis.  
Data were analyzed by independent-samples t-test using the SPSS21 (IBM, Armonk, New York, USA) between control (negative samples) and treatments (positive samples). Differences were considered as significant at \(p<0.05\).
## 8 Experimental Scenarios and Results  
This paper considers a few scenarios that effectively test the feasibility of the proposed ANN-based mode detection method with the developed MA layers. In order to compare the performances of the proposed method against existing methods, two existing methods are also carried on in addition to the proposed one. The two existing techniques are the conventional GPS data logger (DL) method and smartphone (SP) method. The DL approach uses a conventional GPS data logger that is capable of directly measuring its current location and speed. Indirectly-estimated acceleration values are produced from the speed values. In the case of the SP approach, the GPS location values are fixed quicker with the aid of signals from cell phone towers with the smartphones' embedded AGPS chips. In addition, directly measured acceleration values from the accelerometers are collected in forms of absolute magnitude of 3D acceleration vectors. Finally, another measurement from a magnetometer further aids the mode detection.  
The newly-proposed method involves using the smartphone approach with a mode availability GIS layers, namely the MAGIS method. The MAGIS requires pre-processing of a city's road networks filtered for available mode routes information. However, once such GIS layers are built, the database can be easily maintained in the form of a live GIS database and can be easily updated as the routes for each mode can be modified in real-time in the database.  
Figure 5: Artificial neural network layout for universal mode detection with data logger (DL), smartphone (SP), and smartphone with mode availability GIS (MAGIS).