esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To improve energy efficiency and maintain the stability of the power grid, time-of-use (TOU) electricity tariffs have been widely used around the world, which bring both opportunities and challenges to the energy-efficient scheduling problems. Single machine scheduling problems under TOU electricity tariffs are of great significance both in theory and practice. Although methods based on discrete-time or continuous-time models have been put forward for addressing this problem, they are deficient in solution quality or time complexity, especially when dealing with large-size instances. To address large-scale problems more efficiently, a new greedy insertion heuristic algorithm with a multi-stage filtering mechanism including coarse granularity and fine granularity filtering is developed in this paper. Based on the concentration and diffusion strategy, the algorithm can quickly filter out many impossible positions in the coarse granularity filtering stage, and then, each job can find its optimal position in a relatively large space in the fine granularity filtering stage. To show the effectiveness and computational process of the proposed algorithm, a real case study is provided. Furthermore, two sets of contrast experiments are conducted, aiming to demonstrate the good application of the algorithm. The experiments indicate that the small-size instances can be solved within 0.02 s using our algorithm, and the accuracy is further improved. For the large-size instances, the computation speed of our algorithm is improved greatly compared with the classic greedy insertion heuristic algorithm.  
energy-conscious single machine scheduling; time-of-use electricity tariffs; greedy insertion heuristic; coarse granularity and fine granularity filtering mechanism; concentration and diffusion strategy +
Footnote †: journal:  
_Article_  
A New Greedy Insertion Heuristic Algorithm with a Multi-Stage Filtering Mechanism for Energy-Efficient Single Machine Scheduling Problems
## 3 Results and Discussion
### Huanglongbing Effects on the Volatile Profile of \"Ray Ruby\" Grapefruit Peel Oil  
A total of 75 VOCs were detected in 'Ray Ruby' grapefruit peel oil samples over the two harvest seasons (Table 2 and Figure S1). The compounds included 16 sesquiterpenes, 15 alcohols (2 aliphatics + 13 terpene alcohols), 13 monoterpens, 8 aliphatic aldehydes, 7 esters, 5 terpene aldehydes, 5 terpene oxides, 4 ketones, and 2 acids (Table 2). Monoterpene hydrocarbons were the predominant chemical class in all samples regardless of the year and treatment, with the content being 96.68% and 96.81% in HLB+ and 97.11% and 98.20% in HLB\(-\) peel oil samples in 2020 and 2021, respectively (Table 2). D-limonene is the main volatile in citrus fruit and is an important contributor to the citrus flavor [31]. CA and PCA analysis showed that D-limonene was associated with HLB\(-\) samples (higher concentrations in HLB\(-\) samples, Figures 3 and 4). However, there were no significant differences (Table 2). The results agreed with reports on orange peel oils [18; 21]. The other abundant compounds in 'Ray Ruby' grapefruit peel oil samples were \(\beta\)-myrcene, \(\alpha\)-pinene, (E)- \(\beta\)-aryophyllene, and decanal (Table 2). \(\beta\)-Myrcene and \(\alpha\)-pinene, major monoterpens next to D-limonene, were found in significantly higher contents in HLB\(-\) samples in 2020 and 2021 (Table 2). This differed from most reports that showed HLB did not significantly affect monoterpene content in orange peel oils [18; 21]. These volatiles have been found in high concentrations in citrus peel oils and have a large contribution to the peel oil aroma [32]. \(\beta\)-Myrcene is reported to have a green and mossy odor and is a much stronger odorant than D-limonene [33]. \(\beta\)-Myrcene is a versatile terpene used in different applications, mainly in the perfume and fragrance industries [34]. \(\alpha\)-Pinene presents a pine tree resin aroma that can make a positive background contribution to the aroma of the sweet orange [33]. Figure 2: Fruit appearance of ‘Ray Ruby’ grapefruit of healthy (HLB\(-\)) and HLB-affected (HLB+) fruits (2021). \begin{table}
\begin{tabular}{c l c c c c c c c c c c} \hline
**Peak No.
# Self-Sustainable Biomedical Devices Powered by RF Energy: A Review
## 2 RF Energy Harvesting Principles
### Antenna Design and Characteristics for RF Energy Harvesting  
The receiving antenna plays a pivotal role in the RFEH system. The antenna is responsible for capturing the received RF signal from the extended sources and significantly impacts the conversion efficiency. The optimization of an antenna to resonate can be at single or multiple frequency bands. A high gain receiving antenna design is required to meet the requirement of high-power input for standard applications such as recharging the mobile phone battery remotely, waking up sensors in a sleep mood, and energizing the wireless sensor network [59]. The antennas need to supply sufficient power for the applications from limited ambient signals. The desirable characteristics in the antenna design for RFEH are high gain, wide bandwidth, miniaturization while upholding the efficiency, and circular polarization for minimum mismatch loss. In biomedical applications, several other aspects need to be considered in designing wearable and implantable antennas, such as flexibility, stretchability, lightweight, and safety. Therefore, this subsection discusses the design characterization and specification of wearable and implantable antennas that are used in the RFEH system.
## 1 Introduction
### The Study Variables and Proposed Hypotheses
#### 1.2.3 Online Smartphone Purchase Intention  
Online purchase intention reflects the likelihood that a consumer will opt to purchase a smartphone via e-commerce platforms. This variable is influenced by factors such as previous experience, income, and brand of smartphone owned.  
According to the study conducted by [PERSON] [13] through the Technology Acceptance Model (TAM), the intention to use technology is determined by perceived usefulness and ease of use. [PERSON] [15] conducted a systematic review of consumer behavior studies and identified an increased interest in green behavior and food purchase intention. Also, [PERSON] et al. [17] revealed that perceived behavioral control influences online purchase intention.  
In the context of this study, smartphone online purchase intention is analyzed concerning the changes brought about by the COVID-19 pandemic and emerging e-commerce trends. The COVID-19 pandemic has accelerated the digitization of purchases, and recent studies [12] indicate a possible persistence of this behavior.  
**H3.**: _Consumer intentions to purchase smartphones online are increasing. The dependent variable is online purchase intention and the independent variables include previous experience, income, and current smartphone brand._
# Closed Form Solutions for Thermal Buckling of Functionally Graded Rectangular Thin Plates
###### Abstract  
This work concerns the critical buckling temperature of functionally graded rectangular thin plates; the properties of functionally graded material vary continuously in accordance with the power law of thickness \(z\). Closed form solutions for the critical thermal parameter are obtained for the plate with the following boundary condition combinations: simply supported, clamped and guided edges, under uniform, linear and nonlinear temperature fields via the separation-of-variable method. Furthermore, a new method is proposed to determine the critical buckling temperature from the critical thermal parameter. The present results coincide well with those in the literature, verifying the correctness of the present method. The influences of the length-thickness ratio, length-width ratio, power law index and initial temperature on critical buckling temperature are investigated.  
functionally graded materials; thermal buckling; rectangular thin plates; critical temperature; closed form solution +
Footnote †: journal: Applied Sciences
## 2 Study Areas and Data
### Data  
Combined with the topographic and geomorphic characteristics, 70 Sentinel-1A descending orbit data were obtained for SBAS-InSAR processing (data from the website [[https://search.asf.alaska.edu](https://search.asf.alaska.edu)]([https://search.asf.alaska.edu](https://search.asf.alaska.edu)), accessed on 4 December 2024), which could be used to obtain the surface deformation information of the study area. The Sentinel-1A satellite is an Earth observation satellite jointly developed by the European Commission and the European Space Agency as part of the Copernicus program. It was launched in April 2014 and is characterized by all-weather and day-and-night capabilities. The satellite operates in the C-band in a near-polar sun-synchronous orbit with an altitude of about 700 km and a revisit period of 12 days. It has four operational modes and four polarization options. The satellite collects deformation data by emitting microwave signals from its SAR sensor to surface objects and receiving the reflected signals. The observation data are then transmitted to the ground station via a data link. After preliminary processing, such as noise removal, image stitching, and integration, usable SAR data are generated.  
In this study, we selected SAR data from 4 February 2018 to 21 May 2023 for surface deformation monitoring. The data coverage is shown in Figure 2. Since the transmission line in the study area is relatively long, the data span two frames. In order to ensure high coherence of the data, the data had a temporal interval of 24 days per acquisition, using the interferometric wide swath (IW) slant-distance single look complex (SLC) mode, with a spatial resolution of 5 \(\times\) 20 m and an incidence angle of 33.91\({}^{\circ}\). The basic information of the data is shown in Table 1. In addition, the Shuttle Radar Topography Mission (SRTM) elevation DEM was used as external DEM data in this study to simulate and eliminate terrain phases in interferometric measurement and improve measurement accuracy. The SRTM is a mission implemented by NASA and NGA. It utilizes radar equipment aboard the Space Shuttle to measure surface elevation data using radar waves. Typically, there are two types of data: 1 arc-second (with a resolution of approximately 30 m) and 3 arc-seconds (with a resolution of approximately 90 m). In this study, we used the 30 m DEM data.  
Figure 2: The coverage of the Sentinel-1A data in the study area.
# Gaze Estimation via Strip Pooling and Multi-Criss-Cross Attention Networks
## 3 Proposed SPMCCA-Net Models
### Improved ResNet-SMC  
The ResNet-SMC backbone network is based on ResNet-50 [29]. The bottleneck blocks are the constituent structure of ResNet-50, which consists of three convolutional layers. The structure uses a \(1\times 1\) convolutional kernel to reduce the number of input channels, then a \(3\times 3\) convolutional kernel to learn the features, and finally a \(1\times 1\) convolutional kernel to increase the number of output channels. This structure reduces the computational cost without losing accuracy. In this paper, the strip pooling module is added to the bottleneck block to create a feature-enhanced bottleneck block with greater feature representation capability. The purpose of this module is to improve the receptive field of the deep neural network so that it can better capture the global information in the image. It extracts the global information by dividing the input feature map into multiple stripes and then performing a pooling operation on each stripe. This modification improves the perceptual field of the network, enabling it to capture long-distance dependencies between the eyes and reduce sight-independent information. In the backbone network, a multi-criss-cross attention module has also been added, which improves the ability of the network to capture long-distance dependencies between the eyes as well as contextual information. The network can aggregate both global and local contextual information within two modules with good capability to distinguish and recognize different feature information from face pictures, thus preventing redundant face information from interfering with the eye regions. Figure 2 shows the structure of ResNet-SMC.  
Figure 1: SPMCCA-Net model architecture.
# Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts
## 2 Preliminaries
### Logical Rule and Forward Chaining  
In this paper, we refer to a logical rule as a Horn clause (a subset statements of first order logic) rule, represented in the form of an implication \(p_{1}\wedge p_{2}\wedge\cdots\wedge p_{n}\Rightarrow q\), where \(p_{i}(i=1,\cdots,n)\) and \(q\) are positive atoms. The left side of the implication \"\(\Rightarrow\)\" is known as the premise, i.e., a conjunction of several atoms, and the right side the conclusion, which contains a single atom. Given a KG \(\mathcal{K}=\{\mathcal{E},\mathcal{R},\mathcal{T}\}\), an atom can be \((x,\text{city\_of},y)\), where \(x,y\) are variables from \(\mathcal{E}\) and \"city_of\" is a concrete relation. An example of a logical rule over \(\mathcal{K}\) can be:  
\[\forall x,y:(x,\text{child\_of},y)\Rightarrow(y,\text{parent\_of},x), \tag{8}\]  
which means that if two entities are linked by relation \"child_of\", then they should also be linked by relation \"parent_of\".  
When we propositionalize a logical rule by instantiating all variables in the rule with concrete entities in \(\mathcal{E}\), we get a grounding of the rule (or a ground rule). For instance, one grounding of the above example rule can be: \((\text{[PERSON]},\text{child\_of},\text{Mom})\;\Rightarrow\;(\text{Mom},\text{parent\_of}, \text{Jane})\). Apparently, propositionalizing rules to get all groundings is costly, since the entity vocabulary \(\mathcal{E}\) is large. Nevertheless, many groundings are meaningless and unnecessary, such as \((\text{USA},\text{child\_of},\text{Mom})\;\Rightarrow\;(\text{Mom},\text{parent\_of}, \text{USA})\). Therefore, in practice, we take as valid groundings only those whose premise triples are observed in or derived from \(\mathcal{K}\), while the conclusion triples are not observed in \(\mathcal{K}\).  
The key role of logical rules is that we can perform reasoning through them over a given KG to derive new facts. The process of reasoning starts from the premises of a rule to reach a certain conclusion. That means if some facts in KG are the instances of the premises in a rule, the conclusion instance then can be derived. There are several methods to perform the reasoning. Forward chaining is one of the main methods, which typically works in the way of three phase cycles, also named the match-select-act cycle. More precisely, in one cycle, forward chaining firstly matches the current observed or derived facts in KG against the premises of all known rules to find out the satisfied rules. Then, it selects one rule via some strategies from these satisfied rules. Lastly, it derives the conclusion of the selected rule and puts it into KG as a new fact if it is not in KG. The cycle is repeated until no more new facts are derived.  
Notably, during the process of forward chaining, when we derive a new fact from a rule, we also get its grounding, whose conclusion is this new fact. Therefore, we can obtain plenty of corresponding groundings of rules after performing forward chaining inference.
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 3 Results
### Experimental Settings
#### 3.2.2 Data Augmentation  
On the one hand, due to the high resolution of remote sensing images, they cannot be directly input into deep learning neural networks for training. On the other hand, deep learning requires a large dataset for training to achieve better results, and the number of remote sensing cage images is small. Therefore, we perform data augmentation operations on the training set. Take building a training set of \(512\times 512\) pixels as an example. First, the original high-resolution image and the label image are simultaneously randomly cropped to a size of \(512\times 512\) pixels. Then, perform data augmentation operations on the cropped image, such as random rotation transformation, blur transformation, flip transformation, and adding Gaussian noise. Finally, the augmented data is extended to the training set. The random rotation and flip transformations model the difference in the cage's position. Blur transforms and Gaussian noise better simulates various environments. Finally, by looping, the desired number of different training samples can be obtained. As shown in Figure 7, it shows the effect of performing data augmentation.
## 3 The Gut-Brain Axis (GBA) and Gut Microbiome
### Enteroendocrine Communication Pathway  
Enteroendocrine cells (EECs) are situated in the GI tract. They constitute a large endocrine organ and exhibit a regulatory function on the GBA. EECs exhibit transporters and specific G protein-coupled receptors (GPCRs) that permit the EECs to identify alterations in the gut lumen nutrients, gut microbiota, and metabolites [27; 28].  
After neural or mechanical stimulation, there is a release of gut molecules through the basolateral membrane into the outer environment, where these peptides trigger vagal afferent neurons [5]. Many gut hormones, such as GLP-1, are associated with the activity of GBA. GLP-1 ameliorates memory, learning, and motor activity and displays potential neuroprotective function in NDDs such as AD and PD. Gut microbiota regulates the release of GLP-1 [29].  
Bacterial metabolites trigger EECs to produce neuropeptides that pass into the blood circulation and affect the ENS. The consequent stimulation of the immune system provokes the release of cytokines or the stimulation of the vagal nerve. This process affects neurotransmission and neurogenesis and can be involved in neuroinflammation [3; 5].
## 3 Problem Formulation
### Parameter Definitions  
Suppose the set of UAVs is \(UAV=\{U_{1},U_{2},\ldots,U_{i},\ldots,U_{n}\}\), the initial coordinates of the UAVs are \(P=(\alpha,\gamma)\), and the flying speed of the UAVs is \(V\). Each UAV has sufficient power to complete its assigned sequence of pest and disease monitoring tasks  
Figure 1: Schematic diagram of task assignment.  
and return to the agricultural command center. The set of tasks that all UAVs need to perform is \(Task=\{Task_{1},Task_{2},\ldots,Task_{j},\ldots,Task_{m}\}\), the position coordinates that each task is abstracted into are \(Q_{m}=(o,q)\), and the execution times are represented by \(Time=\{Time_{1},Time_{2},\ldots,Time_{m}\}\). The sequence of tasks performed by UAV \(U_{i}\) can be denoted as \(Seq^{i}=\left\{M_{1}^{i},M_{2}^{i},\ldots,M_{N_{i}}^{i}\right\}\), and \(N_{i}\) represents the number of tasks assigned to UAV \(U_{i}\). Define \(C_{ij}\) as a decision variable that indicates the allocation relationship between UAV \(U_{i}\) and task \(Task_{j}\). When task \(Task_{j}\) is assigned to UAV \(U_{i}\), \(C_{ij}=1\); otherwise, \(C_{ij}=0\).
# Whole Organ Engineering: Approaches, Challenges, and Future Directions
## 5 Recent Developments and Accomplishments
### Liver
[PERSON] and colleagues found that the decellularized livers maintained about 50% and 40% of the hepatocyte growth factor (HGF) and basic fibroblast growth factor (bFGF) respectively, contained in native livers [42]. HGF and bFGF contribute to stimulation of epithelial cell proliferation and migration, angiogenesis, and wound healing in the liver, which contributed to the >90% cell engraftment efficiency observed after recellularization with rat hepatocytes, with engrafted cells showing hepatic metabolic activity [43; 103; 104]. Other liver decellularization methods have leveraged the native ECM to improve patient-specific cell sourcing and reendothelialization of vascular networks in engineered whole livers [41; 42; 105]. [PERSON] et al. showed that the mechanical and physiological signals of decellularized liver ECM resulted in improved hepatic differentiation of human iPSCs when compared to the same protocol with Matrigel, improving prospects for patient-derived iPSCs to be used in patient-specific engineered livers [41]. Improved reendothelialization of decellularized livers was shown to result from the conjugation of the REDV cell binding domain of endothelial cells to an elastin-like peptide (ELP) by [PERSON] et al. [42]. A different approach to reendothelialization of decellularized whole livers by [PERSON] et al. used porcine umbilical cord-derived endothelial cells, resulting in maintenance of an intact vascular network after an extensive 72 h of ex vivo perfusion without thrombus formation, which can easily form when perfusing an organ for that long [103]. These studies represent successful, ongoing efforts to engineer functional vasculature within decellularized whole livers in order to recellularize them with functional hepatocytes and generate clinically applicable engineered livers.
## 3 Experimental Analysis
### Displacement Analysis
#### 3.2.2 Shield Ring Centerline Displacement  
The relative displacement of the shield ring centerline was measured twice, using point clouds \(P_{0}\) and \(P_{i}\). The absolute deviation was calculated by comparing the displacement values between these two point clouds. Since both measurements were referenced to the tunnel design point cloud, the absolute deviation provided a valid metric for assessing the displacement between the two point clouds.  
Table 1 compares centerline displacement values measured by our method, SILSS, providing insight into the accuracy and reliability of our approach. For most rings, the displacements were recorded closely, with absolute deviations below 0.1 mm in several cases (e.g., rings 1, 3, 4, and 12), suggesting that SILSS can achieve high precision in detecting small shifts. However, for certain rings (e.g., rings 5, 9, 11, and 14), larger deviations were observed. This analysis demonstrates that SILSS is a reliable method for tunnel deformation monitoring, with the absolute deviation values generally remaining within acceptable limits.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Ring ID** & \(P_{0}\) **(mm)** & \(P_{i}\) **(mm)** & **Absolute Deviation (mm)** \\ \hline
1 & 0.594 & 0.614 & 0.020 \\
2 & 0.744 & 0.701 & 0.043 \\
3 & 0.642 & 0.655 & 0.012 \\
4 & 0.590 & 0.603 & 0.012 \\
5 & 0.129 & 0.  
Overall, the results indicate that SILSS is a promising tool for tunnel displacement monitoring, with deviations that generally remain within acceptable ranges for practical applications.
# Review of Traffic Assignment and Future Challenges
## 3 Traffic Assignment: Theoretical Background
### Dynamic Traffic Assignment (DTA)
#### 3.2.1 Time-Dependent User Equilibrium
Conversely, the greater the variation in costs experienced by vehicles connecting the same \(o\)-\(d\) pair, the higher the value of \(Rgap\) becomes. In practice, achieving identical travel times for a given \(o\)-\(d\) pair, especially when measuring travel times based on a microscopic model, is challenging. Hence, the solution of \(DTA\) is deemed valid when the relative gap is below an accepted threshold. At that point, it is considered that the network in question has reached an equilibrium state. In addition to the relative gap, other criteria can be taken into account, particularly when travel demand is high. Microscopic simulation can reveal situations of waiting for access to the network or of deadlock where no vehicle can move [80; 81; 82], as shown in Figure 3A. The percentage of vehicles served is also an interesting indicator.