esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Search Strategy  
The search strategy was developed using Boolean operators to ensure comprehensive coverage of the relevant literature. Key concepts were combined to create a robust search string, which included terms related to reverse supply chains, solid waste, circular economy, governance, multi-criteria decision analysis (MCDA), and Artificial Intelligence (AI). The combined search string used was as follows:(\"urban solid waste\" OR \"municipal waste\" OR \"industrial waste\" OR \"agricultural waste\") AND (\"circular economy\" OR \"circularity\" OR \"closed loop\" OR \"recycling\" OR \"upcycling\" OR \"valorisation\") AND (\"multi-criteria decision analysis\" OR \"MCDA\" OR \"decision-making models\" OR \"optimization models\") AND (\"artificial intelligence\" OR \"machine learning\" OR \"AI integration\" OR \"predictive analytics\") AND (\"reverse supply chain governance\" OR \"reverse logistics\" OR \"stakeholder collaboration\") AND (\"goverance models\" OR \"decision-making governance\" OR \"management frameworks\") AND (\"biotechnology\" OR \"green chemistry\" OR \"waste-to-energy technologies\" OR \"recycling technologies\") AND (\"public policies\" OR \"private sector intervention\" OR \"community-based waste management\" OR \"socio-economic impacts\" OR \"sustainability policies\").  
These terms were chosen based on their relevance to the research topic of integrating MCDA and AI into models of governance for solid waste in reverse supply chains. They were combined using Boolean operators such as \"AND\" and \"OR\" to refine the search and ensure precision while maintaining broad coverage of related studies.  
The scope was designed to capture a comprehensive view of the literature on MCDA and AI, the circular economy, technological innovations, governance practices, mechanisms, and models aligning with sustainable practices for global solid waste management.
## 3 Findings
### Data Characteristics  
As a variety of data sources were used, the characteristics of the datasets thus collected have exhibited considerable differences in both the size of the data samples and in their format (i.e., structured/unstructured text, images, videos). Only four articles indicated a sizeable data collection, that is, these studies have extracted more than 10,000 data records (i.e., posts, tweets, etc.) from social media platforms and among these, three articles had collected obesity-related data records in the order of millions [23; 33; 39]. Two articles closely analyzed videos on YouTube to explore how fat people are portrayed in visual displays [21; 32]; further, one other article used images to analyze the effect of body ideals on fat individuals [29]. Finally, several other research articles examined the effect of obesity-related discussions and experiences of being exposed to stigmatizing content by interviewing and monitoring human participants [22; 25; 31; 34; 38; 40]. This variation in data characteristics across the 22 articles being reviewed calls for different analytical methods, diverse techniques and a wide array of tools/models for conducting subsequent analysis.
For example, the most abundant derivatives of sea buckthorn from hilly areas of the Romanian Eastern Carpathians were ethyl esters of 2-methylbutanoic acid, 3-methylbutanoic acid, hexanoic acid, octanoic acid and butanoic acid, as well as 3-methylbutyl 3-methylbutanoate, 3-methylbutuatoate and benzoic acid ethyl ester [10]. The volatile compounds extracted from the leaves of 14 _H. rhamnides_ populations from China were analysed by GC-MS and 44 compounds were detected, and the main compounds included tetracosane (10-40%), hexadecanoic acid (<0.1-11%), octadecatrienol (5-27%), tetracosene (3-11%), eicosanol (<0.1-13%) and others [11]. However, the threshold value of each aroma substance is different, and it is not enough to evaluate its contribution to the overall flavour only by its content. Combined with the flavour threshold, the odour active value (OAV) can be calculated to determine the components that contribute to the flavours of foods [12]. Furthermore, based on a metabolomics approach, screening for differential metabolites before and after treatment can be used to annotate relevant metabolic pathways via Kyoto Encyclopedia of Genes and Genomes (KEGG) database analysis, and further determine the mechanisms of VOC formation [13; 14; 15]. E-nose is an instrument used for the rapid detection of VOCs in food. The E-nose sensor converts the smells of odour substances into electrical signals, and the responses of multiple sensors constitute the response spectrum of the sensor array to a given smell. Different odours can be distinguished by their characteristic response spectrum [16]. At present, electronic nose is not widely used. It is mainly used for the difference analysis or authenticity identification of food from different sources or craft. [PERSON] et al. [17] found that there was a difference in the volatile substances among the different LAB-fermented sea buckthorn juice by E-nose; the juice fermented by _Lactobacillus plantarum_ LP56 (SBJLP) could be easily identified by sensors sensitive to aromatic substances. Its advantage is that it is fast and convenient, but its disadvantage is that the sensor response value cannot accurately reflect the content of a specific substance. Conversely, GC-MS metabolome analysis is good at qualitative and quantitative analysis of flavour compounds. Therefore, correlation analysis between key VOCs identified by GC-MS metabolomics and E-nose data is useful, as the changes in key VOCs can be quickly verified based on the corresponding values of specific sensors [18]. Here, we compared changes in the flavour of sea buckthorn DL among different stages of processing, analysed the flavour profiles of SW and its DL, identified differential metabolites and key metabolic pathways, and then established the relationships between E-nose data and key VOCs to provide a basis for flavour optimisation of SW.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 2 Theoretical Background of TD-NIRS  
The principles, concepts, and theoretical background of TD-NIRS are described in this section by showing the fundamental equation of light propagation, the radiative transfer equation (RTE), followed by its approximate equations with analytical solutions, numerical solving methods, and quantities featuring TD-NIRS. With an understanding of the theoretical background and the featuring quantities, it will be easier to reasonably interpret the results of calculations of light propagation in in vitro and in vivo experiments and the clinical applications of TD-NIRS. The theoretical background, particularly for TD-DOT, is comprehensively reviewed by [PERSON] [4].
###### Abstract  
The integration of building information modeling (BIM) and geographic information system (GIS) is attracting more attention than ever due to its potential benefits for both the architecture, engineering, and construction (AEC) domain and the geospatial industry. The main challenge in BIM and GIS integrated application comes from the fundamental data conversion, especially for the geometric information. BIM and GIS use different modeling paradigms to represent objects. The BIM dataset takes, for example, Industry Foundation Classes (IFC) that use solid models, such as boundary representation (B-Rep), swept solid, constructive solid geometry (CSG), and clipping, while the GIS dataset mainly uses surface models or B-Rep. The fundamental data conversion between BIM and GIS is the foundation of BIM and GIS integrated application. However, the efficiency of data conversion has been greatly impaired by the human intervention needed, especially for the conversion of the clipping geometry. The goal of this study is to automate the conversion of IFC clipping representation into the shapefile format. A process-level approach was developed with an algorithm for instantiating unbounded half spaces using B-Rep. Four IFC models were used to validate the proposed method. The results show that (1) the proposed approach can successfully automate the conversion of IFC clipping representation into the shapefile format; and (2) increasing boundary size has no effect on the file size of unbounded half spaces, but slightly increases the producing time of half spaces and processing time of building components. The efficiency of this study can be further improved by using an open-source package, instead of using the low-efficiency packages provided by ArcGIS.  
building information modeling (BIM); geographic information system (GIS); geometry transformation; shapefile; Industry Foundation Classes (IFC); 3D 2020 10 2009 10.3390/app10062009
# A Flame Detection Algorithm Based on Improved YOLOv7
## 6 Conclusions  
In this work, we introduce a flame detection algorithm based on the improved YOLOv7 network. First, we added the SimAM non-parameter attention to the MP-1 module of YOLOv7 in order to solve the problem of missing detection of YOLOv7. Next, the ConvNeXt-based CNeB module replaces the ordinary convolution in the ELAN-W module of YOLOv7, which solves the false detection of YOLOv5 and YOLOv7 and also improves the performance of the model. Additionally, in order to enhance the robustness and generalization of our proposed algorithm, a sufficient amount of self-built data covering various application scenes is created based on different public data sets. The experimental results show that our improved YOLOv7 model has higher accuracy in flame detection without adding too many parameters, and the situations of missing detection and false detection have been distinctly improved. Although its detection speed is slower than the original YOLOv7, it can basically satisfy the requirement of real-time detection. In future work, the network structure will be further studied to improve the detection speed and the effect of real-time detection.  
Methodology, G.Y.; Software, S.Z. and R.X.; Formal analysis, G.Y.; Writing--original draft, G.Y., J.G. and D.Z.; Writing--review & editing, D.Z.; Supervision, Z.X. and Q.W.; Project administration, Z.X. and Q.W.; Funding acquisition, Q.W. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China under Grant No. 62002211.  
Figure 8: Comparison of test results.
## 3 Materials and Methods
### GC \(\times\) GC-TOFMS Analysis  
The analyses were performed on a GC \(\times\) GC-TOFMS system consisting of an Agilent 8890 GC System (Shanghai, China) with a BenchTOF-Select detector (Markes International, Bridgend, UK). An automatic sampler injector was used (CTC Analysis AG autosampler PAL-System, SepSolve Analytical, Zwingen, Switzerland) and the data were acquired and analyzed with ChromSpace of Markes International. Chromatographic separation was achieved with the INSIGHT(tm) flow modulator (SepSolve Analytical), equipped with a loop with 50 \(\upmu\)L, a BPX5 column (20 m length \(\times\) 0.18 mm i.d. and 0.18 \(\upmu\)m film thickness, SGE GC column, Trajan, Australia) as the first dimension (1D), and a BPX50 column (5 m length \(\times\) 0.25 mm i.d. and 0.1 \(\upmu\)m film thickness, SepSolve Analytical, Australia) as the second dimension (2D). The modulation period (PM) used was 5s. The oven temperature program began at 40 \({}^{\circ}\)C hold for 3 min; then, the temperature was raised at 3 \({}^{\circ}\)C min\({}^{-1}\) up to 150 \({}^{\circ}\)C, then 4 \({}^{\circ}\)C min\({}^{-1}\) up to 260 \({}^{\circ}\)C, and held for 10 min. Helium was used as carrier gas with a flow of 0.5 mL min\({}^{-1}\) in the first column and 20 mL min\({}^{-1}\) in the second column. The MS transfer line and source temperatures were set at 270 \({}^{\circ}\)C. Spectra were matched using the NIST MS Search Program Version 2020. To determine the retention times and characteristic mass fragments, electron ionization (EI) at 70 eV mass spectra of the analytes were recorded at full scan, from 30 to 400 m/z, and a data acquisition frequency of 50 Hz. The linear retention index values were calculated through an analysis of the commercial hydrocarbon mixture (C\({}_{8}\)-C\({}_{20}\)) [Supelco, Bellefonte, PA, USA], using the same chromatographic conditions. A mixture of terpen called MegaMix #1 (Restek) were injected to help identify terpen. The volatile compounds were first identified by matching the mass spectra with the spectra of the reference compounds in the NIST mass spectral library, also taking into consideration the structure and molecular weight, and by comparing the calculated LRIs with those described in the literature. The relative amount of each compound was calculated as the percent ratio of the respective peak area relative to the total peak area and expressed as percentage (%).
# Microchannel-Embedded D-Shaped Photonic Crystal Fiber-Based Highly Sensitive Plasmonic Biosensor
## 4 The Effect of Structural Parameters
However, the wavelength sensitivities are unchanged due to the change in \(\mathrm{d_{s}}\), similar to the case of pitch change. The effect of air hole diameter \(\mathrm{d}\) is shown in Figure 9e,f, and that of air hole  
Figure 8: Effect of (**a**) air-hole diameter \(\mathrm{d_{1}}\), (**b**) air-hole diameter \(\mathrm{d_{s}}\), (**c**) outer-channel diameter \(\mathrm{d_{ch}}\), and (**d**) air-hole diameter \(\mathrm{d}\), and (**e**) pitch on maximum wavelength sensitivity and sensing range. diameter d\({}_{1}\) is shown in Figure 9g,h. As one can see from Figure 9e-h, the effect of the change in parameters d and d\({}_{1}\) is not significant because these parameters can hardly affect the leakage path. Therefore, peak loss and the resonant wavelength are almost the same, which leads to no noteworthy change in the wavelength and amplitude sensitivities. Different open channel-based PCF sensors are available where the plasmonic layer is deposited either on the curved surface or flat surface of D-shaped sensors [39]. In some cases, a microchannel is produced in PCF by a complex milling process [40]. To avoid the complex fabrication process of the open channel, a large air hole is inserted in the stack preform of PCF that has a hexagonal lattice structure. We compare the performance analysis of those previously reported PCFs with the proposed open channel-based PCF sensor in Table 2. The sensor performance is analyzed in terms of maximum wavelength sensitivity, amplitude sensitivity, wavelength resolution, and FOM. The proposed sensor not only shows better performance but also eliminates the complex fabrication process. The structural parameters considered in the model are compatible with the fabrication process of the sensor by using the stack-and-draw fiber drawing technique. The fabrication tolerance investigation shows that structural parameter variations within \(\pm\)10% of their optimized value barely affect the wavelength and amplitude sensitivities. It is noteworthy that a fiber can be fabricated for structural parameter variation within \(\pm\)1%. The robust performance with a high sensitivity enables the sensor for biomedical application in the sensing range of RI from 1.33 to 1.44. Figure 9: Investigation of the sensor’s structural parameters for analyte RI = 1.34. Effect of pitch \(\Lambda\): (**a**) loss spectra (**b**) amplitude sensitivity, and effect of air-hole diameter d\({}_{\mathrm{s}}\): (**c**) loss spectra, (**d**) amplitude sensitivity, and effect of air-hole diameter d: (**e**) loss spectra, (**f**) amplitude sensitivity, and effect of air-hole diameter d\({}_{1}\): (**g**) loss spectra and (**h**) amplitude sensitivity.
# Ex-Situ Evaluation of Commercial Polymer Membranes for Vanadium Redox Flow Batteries (VRFBs)
## 2 Materials and Methods
### Ex-Situ Characterizations
#### 2.2.2 Membrane Mechanical Strength Measurement  
Tensile strength of the selected membranes was measured before and after soaking membranes into V5+ solution for 10 days. The membranes after chemical stability testing were soaked into 2 M H\({}_{2}\)SO4 for one day before tensile strength measurements. The membranes were die-cut to a barbell shape using a standard ASTM D638-4 cutter. The tensile tests were conducted on a universal test machine (2000 R System, Test Resources) with a load sensitivity of 0.1 N at room temperature (RT, 21 \({}^{\circ}\)C) and in atmosphere (approximately 40% RH was controlled by the building air condition system and measured when testing). The strain rate used was 0.015 s\({}^{-1}\). Triplicate measurements were conducted for each membrane. Wet state membrane samples were used for AHA, AMV and CMV membranes, while dry state membrane samples were used for the rest of the membranes.  
The measured tensile strength of the membrane before the chemical stability test is treated as the initial membrane mechanical strength, while the tensile strength measured after the chemical stability test is used for evaluating membrane chemical stability based on the mechanical property changes.
## 4 Problem Formulation
### Grouping and Task Execution  
In a massive MTC scenario, the execution of each application consists of a set of tasks, which may be triggered at different times. Some specific devices must execute those tasks within a specific time frame called their time constraint, which is denoted as a column vector \(\tau=(\tau_{1},\tau_{2},...,\tau_{m})^{T}\). From an application perspective, it is essential to note that the urgency of an application is not dictated by the number of tasks it performs. For instance, consider application \(m\), a smart meter tasked with the daily reporting of electricity usage within 24 h, as depicted in Figure 2.  
Typically, the tasks associated with an application can be handled by a single device. However, specific applications may necessitate the activation of multiple devices/sensors to complete their tasks. Some devices can perform one task while others can perform multiple, which explains the possibility of an application triggering one or multiple devices to execute its task. For example, in Figure 3, consider a patient-reported outcome application measuring a patient's vitals via an eHealth system that engages various sensors, such as ECG, blood pressure (BP), and body temperature monitors, to gather comprehensive health data. Each device has exclusive access to the network while other group members are disconnected or in sleep mode. In environments where groups contain numerous devices, there is an inherent risk that some tasks may not meet their designated time constraints. These are referred to as _missed tasks_, as illustrated in Figure 3.  
Figure 2: The task execution in groups from an application perspective.  
These perspectives are critical as they help us to strategically integrate tasks with lower delay tolerance within some empty spots in the groups, optimizing the utilization of group resources.
## 3 Results
### Primary and Secondary Implant Stability
Age, implant diameter, implant length, healing time (in weeks), ISQ values at insertion, and ISQ values at exposure are grouped by implant region (maximally vs. mandibular) and implant system (Straumann (STR) vs. Thommen (SPI)) (A). The distribution of implant localization (B). In 121/582 (20.8%) implants, ISQ values at exposure were lower than the respective ISQ values at insertion. Furthermore, 36 implants showed a drop of \(\geq\)10 in ISQ values between insertion and exposure, from which 29 were STR implants and 7 were SPI implants. The increase in ISQ between insertion and exposure was significantly higher in maxillary implants (2.74 \(\pm\) 8.8) compared to mandibular implants (1.39 \(\pm\) 6.5) (\(p\) = 0.016), as assessed in the pairwise analysis. Finally, healing time (in weeks) was the only predictive factor associated with an increase in ISQ values between insertion and exposure measurements in the linear regression model (reg. coeff. : 0.11, 95% CI: 0.04-0.19, \(p\) = 0.003) (Figure 6).
## 4 Algorithm Design
### Establishment of the Continuous Neighbor Set  
If congestion occurs, we use the MOLM algorithm to resolve the congestion. Before that, we need to establish a continuous neighbor set, which includes nodes that can maintain continuous connections with both the predecessors and successors of the congested node. The process of establishing the continuous neighbor set can be described using Algorithm 2.  
Assuming the paths for Service 1 and Service 2 are ['1','2','3','6'] and ['4','5','3','7']. The traffic volumes for path 1 and path 2 are 20 and 15, respectively. Therefore, the threshold is equal to 20 \(\times\) 1.5 = 30. If both paths traverse satellite node 3 with cumulative traffic exceeding the threshold, network congestion occurs, with node 3 identified as the congested node. To alleviate this congestion, we intend to modify the path of Service 2. The predecessor and successor nodes for this congestion are nodes 5 and 7, respectively.  
Figure 2: Overview of the MOLM algorithm.  
```
Input:\(path1\),\(path2\) Output:\(Continuous\_Neighbor\_Set\) if congestion occurs on path1 and path2 then
2 The congested node is identified as \(S_{D}\);
3 Find the predecessor node (\(S_{A}\)) and successor node (\(S_{B}\)) of the congested node in path2 ;
4 Extract vectors from the adjacency matrix of satellite nodes at time \(t\), representing the neighbors of \(S_{A}\) and \(S_{B}\) at time \(t\);
5 Extract vectors from the adjacency matrix of satellite nodes at time \(t+\Delta t\) s, representing the neighbors of \(S_{A}\) and \(S_{B}\) at time \(t+\Delta t\) s ;
6 Let \(Q=a_{S_{A}}\cap b_{S_{A}}\) and \(R=a_{S_{B}}\cap b_{S_{B}}\), where \(Q\) and \(R\), respectively, represent the continuous neighbors of \(S_{A}\) and \(S_{B}\) in the next \(\Delta t\) seconds;
7 Let the \(Continuous\_Neighbor\_Set=Q\cap R\) ;
8 return\(Continuous\_Neighbor\_Set\);
```  
**Algorithm 2** Establishment of the continuous neighbor set  
To obtain the nodes that can maintain continuous connections with both node 5 and node 7, we need to know the nodes that are adjacent to nodes 5 and 7 at the current time as well as the nodes that will remain adjacent to nodes 5 and 7 after a certain period. Therefore, we require the adjacency matrix of the satellite nodes at the current time and after a certain period. Figure 2(a) represents the adjacency matrix of satellite nodes at the current time \(t\), while Figure 2(b) represents the adjacency matrix of satellite nodes at time \(t+\Delta t\) s.  
Therefore, the nodes adjacent to nodes 5 and 7 at the current time \(t\) can be represented as \(a_{5}=\{\)1,0,1,0,0,1,1\(\}\) and \(a_{7}=\{\)1,0,0,1,1,1,0\(\}\), and the nodes adjacent to nodes 5 and 7 at time \(t+\Delta t\) can be represented as \(b_{5}=\{\)1,0,1,1,0,1,0\(\}\) and \(b_{7}=\{\)1,1,0,1,0,1,0\(\}\). Let \(P=(a_{5}\cap b_{5})\cap(a_{7}\cap b_{7})=\{\)1,0,0,0,0,1,0\(\}\), where \(P\) represents the continuous neighbor set. The nodes in the continuous neighbor set are node 1 and node 6.