esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Flexible Method for Solving Multi-Expert Multi-Criterion Decision-Making Problems
## 4 Numerical Verification and Comparison
### Solution from the Induced LOWGA Operator Method [43]  
Based on the LOWGA operator, [PERSON] et al. [43] proposed a more general model for an aggregation operator, the induced LOWGA operator, which considers the weights associated with both operators and attributes. As in the LOWGA operator method, the induced LOWGA operator method requires that assessment information be certain information. Because the partial information provided by experts D2 and D4 was uncertain or incomplete, we considered only the complete information of experts D1 and D3.  
According to Equations (7) and (8), we used the fuzzy linguistic quantifier \"Most\" to get the weighting vector, as shown below.  
\[w_{j}=Q\left(\frac{j}{n}\right)-Q\left(\frac{j-1}{n}\right)\]  
\[w_{1}=Q\left(\frac{1}{6}\right)-Q\left(\frac{1-1}{6}\right)=0-0=0\]  
\[w_{2}=Q\left(\frac{2}{6}\right)-Q\left(\frac{2-1}{6}\right)=0.067-0=0.067\]  
\[w_{3}=Q\left(\frac{3}{6}\right)-Q\left(\frac{3-1}{6}\right)=0.4-0.067=0.333\]  
\[w_{4}=Q\left(\frac{4}{6}\right)-Q\left(\frac{4-1}{6}\right)=0.733-0.4=0.333\]  
\[w_{5}=Q\left(\frac{5}{6}\right)-Q\left(\frac{5-1}{6}\right)=1-0.733=0.267\]  
\[w_{6}=Q\left(\frac{6}{6}\right)-Q\left(\frac{6-1}{6}\right)=1-1=0\]  
[PERSON] et al. [43] utilized the \(z_{i}\) value to identify the ranking order of alternative tools. Based on Table 1, Table 2, Equation (4), and Equation (5), we calculated the \(z_{i}\) values.  
\[z_{1}=ILOWGA_{\overline{w},w}(s_{\alpha_{11}},s_{\alpha_{12}}, \ldots,s_{\alpha_{1n}})=ILOWGA_{\overline{w},w}(s_{4.5},\ s_{7},s_{4.5},s_{3},s_{3.5},s_{5.5})\]  
\[=\left(s_{6,720}\right)^{0}\otimes\left(s_{5,940}\right)^{0.067}\otimes\left(s _{5,400}\right)^{0.333}\otimes\left(s_{4.320}\right)^{0.333}\otimes\left(s_{2.940}\right)^{0.267}\otimes\left(s_{2.880}\right)^{0}\]  
\[=s_{(6,720}\right)^{0}\otimes\left(s_{5,940}\right)^{0.067}\otimes\left(s_{5, 400}\right)^{0.333}\otimes\left(s_{4.320}\right)^{0.333}\otimes\left(s_{2.940} \right)^{0.267}\otimes\left(s_{2.880}\right)^{0}\]  
\[=s_{(6,720}\right)^{0}\times 5.940^{0.067}\times 5.400^{0.333}\times 3.320^{0.3 33}\times 2.940^{0.267}\times 2.880^{0})\]  
\[=s_{4.289}\]  
The other values were also obtained using the induced LOWGA operator method.  
\(z_{2}=s_{5,628}\), \(z_{3}=s_{4.511}\), \(z_{4}=s_{4,698}\) and \(z_{5}=s_{5,921}\).  
According to induced LOWGA operator method operation, the ranking of alternatives was \(A_{5}>A_{2}>A_{4}>A_{3}>A_{1}\).
## 3 Results  
### Plant Morphology and Growth Characteristics  
To determine the effects of different light quality on basil growth, phenotypes of basil planted under red light (R), blue light (B), and white light (W) for five weeks were analyzed. Compared with the W group, the B group showed increased biomass, with greater plant height (about 1.12 times that of the W group) and leaf area, while the R group showed the smallest plant height and leaves. In terms of branching between stems, compared to W, the levels of branching under R were increased, while they decreased under B (Figure 1A). Interestingly, there was no significant difference in root length between the W and B groups, while the root length of the R group decreased significantly (Figure 1B). Consistent with the phenotype, the leaf and stem fresh weight were the highest in the B group (Figure 1C). The chlorophyll contents and carotenoid contents were both significantly higher in the B group but lower in the R group (Figure 1D).  
As shown in Figure 2, compared to the W group, the photosynthetic parameters--including the net photosynthetic rate (Pn), stomatal conductance (Gs), intercellular CO\({}_{2}\) concentration (Ci), and transpiration rate (Tr)--were much higher in the B group. In contrast, the red group showed lower photosynthetic capacity, except for a higher Ci value than that of the W group. Chlorophyll fluorescence analysis revealed that the maximum photochemical efficiency of photosystem II (Fv/Fm) was reduced to 0.64 in the R group, while the values for the B and W groups remained around 0.8. Meanwhile, compared to the W group, the actual photochemical efficiencies (\(\Phi\)PSII and \(\Phi\)PSI) were significantly increased and reduced in the B and R groups, respectively (Figure S1).  
Figure 1: Phenotypes of basil under blue and red light: **(A)** Shoot phenotype of the W, B, and R groups. **(B)** Root phenotype of the W, B, and R groups. **(C)** Fresh weight of leaves and stems. **(D)** Pigment content of plant leaves. W, white light; B, blue light; R, red light. Scale bar: 1 cm. The data are shown as the mean \(\pm\) SD (\(n\) = 3), and different letters indicate significant differences between the data at \(p\) < 0.05.
## 2 Methodology & Case Study
### The Low-Cost & Portable Monitoring System
#### 2.1.3 Low Power Consumption Design  
To design a cost-effective and easy-maintaining monitoring system, the power consumption of the CPU controller is also important., The 16-bit Ultra-Low-Power Microcontroller unit (MCU) (MSP430, Texas Instruments, Dallas, TX, USA, 1996), which includes extensive performance of 7 channel 12 bit Analog-to-Digital converters with synchronization, and 2 USART (Universal Asynchronous Receiver/Transmitter), pronounced I-two-C (I2C) modules, were integrated into the sensor unit. This MCU is ultra power-saving in standby mode with an electricity current intensity of 1.1 uA, and can operate at a low supply-voltage range from 1.8 to 3.6 Volt, thus it is very suitable to work in thefield where commercial power is unavailable. 4 Standard Power 7 Alkaline batteries can keep a single sensor unit continuously working for more than 1 year. By attaching an optional solar battery, which costs around 5 US dollar, the sensor unit can work semi-permanently without changing the batteries.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 5 USV Applications for Preventive Maintenance and Disaster Response
### Search and Rescue
#### 5.3.1 USVs for SAR
Among the relevant information collected, the ICARUS initiative has raised the importance of a 360\({}^{\circ}\) cameras and 3D rangefinders for SAR. Beyond ROAZ and the UCAPs, there were other USVs tested in REX 2014, such as the previously mentioned SWIFT, and ZARCO [208], a USV designed for assisting UUVs in rivers and estuaries.
## 3 Materials and Methods
### Materials
#### 3.1.2 Annotation Methodology  
Unlike traditional approaches that rely solely on machine translation, our methodology involves a multi-faceted and rigorous annotation process aimed at ensuring high-quality Tamil captions. The methodology consists of the following steps:  
1. **Two-Phase Translation:**  
* _Phase 1: Automatic Translation:_ Initial translation is performed using a machine translation-model Google Translator.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Domas** & **Words** \\ \hline Aesthetics & 5,595,316 \\ Commerce & 83,148 \\ Mass Media & 2,100,226 \\ Official Document & 12,768 \\ Science and Technology & 8,865,532 \\ Social Sciences & 2,253,912 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the LDC-IL Tamil Text Corpus.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **\# of Images** & **\# of Captions** \\ \hline Flickr8k & 8000 & 40,000 \\ Flickr30k & 31,783 & 158,915 \\ MSCOCO & 328,000 & 1,640,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset statistics.  
_Phase 2: Contextual Refinement:_ Linguistic experts in Tamil refine the machine-generated translations to ensure contextual accuracy.
2. **Annotation Scoring System:** Annotations are scored on the following scale: * _Score = 0_, if the translation is completely unrelated. * _Score = 1_, if the translation conveys the meaning but contains some grammatical errors. * _Score = 2_, if the translation accurately represents the meaning with good grammar. * _Score = 3_, if the translation not only accurately represents the meaning but also adds contextual richness specific to Tamil culture and language.
3. **Inter-Annotator Agreement:** [PERSON]'s Kappa (\(\kappa\)) metric is used to measure the level of agreement between multiple annotators. It is calculated using the formula: \[\kappa=\frac{p_{o}-p_{e}}{1-p_{e}}\] (1) where \(p_{o}\) is the observed agreement and \(p_{e}\) is the expected agreement.
4. **Annotation Review:** A secondary review process is implemented, where another set of linguistic experts review the initial annotations. In case of disagreement between the original annotator and the reviewer, a third expert is consulted.
5. **Contextual Testing:** A smaller subset of images that are culturally specific to the Tamil community is used for more rigorous annotation testing.
6. **Annotator Guidelines:** Annotators are provided with comprehensive guidelines that instruct them to look for contextual cues in images that may not be evident in the original English captions.
7. **Quality Assurance:** The finalized annotations are subjected to a quality assurance process that includes automated grammatical checks and random sampling for human review.  
This multi-layered annotation methodology ensures that the Tamil captions used for training and evaluating the DCAT model are of the highest quality, both linguistically and contextually. The annotation process has took nearly three months to complete due to the size of the dataset.
# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
## 5 Performance Evaluation and Results
### Impact of Traffic Dynamicity  
In this section, we examine the effect of traffic dynamicity on performance with varying traffic loads. The average packet generation rate varies from 1 to 10 packets per second to evaluate the performance of the proposed work with different traffic loads. The nodes deployed in the sensing region generate traffic while using poison function. We keep the other simulation parameters the same, as in Section 5.1.  
The network lifetime under OF0, MRHOF, SCA-OF, and our proposed COOF are shown in Figure 15a. All of the nodes in the network start with the equal battery power level. In this paper, the network lifetime is the time until the first node dies. We assume that when a node depletes 95% of initial energy, the node dies. In the proposed OF, each node consumes less energy than others. This indicates that the parent selection based on the combined metric is more energy-efficient than traditional RPL. As the traffic flows increases, network lifetime in all OFs decreases. However, as compared to others, the network lifetime under COOF is longer. On the other hand, the network lifetime of OF0 is the shortest among all others. One of the reasons is that it constructs the network topology based on hop counts, which creates the long-hops problem in the DODAG. Besides, the depth of the DODAG is higher than others, and the routing decision may spread to the bottleneck nodes.  
Similarly, we compare the end-to-end delay concerning the increasing traffic loads. It is defined as the average time interval from packet generation to packet delivery. As shown in Figure 15b, the delay in all OFs increases as the traffic load increases. However, we can see that the end-to-end delay in COOF is lower as compared to other OFs. It can be seen that, as the traffic load increases, OF0 and MRHOF delays both increase significantly when compared to COOF. The reason is that both OF0 and MRHOF select the parent based on a single metric, and these metrics are not aware of the node's queue status. On the other hand, when the traffic load is high, the delay of COOF decreases, because the QFI metric is aware of queue utilization. As a result, it can select the resource-efficient parent next  
Figure 15: The impact of the traffic load variation: (**a**) network lifetime with respect to different traffic flows, and (**b**) end-to-end delay in millisecond with respect to different traffic flows.  
node towards the root node that reduces the buffer overflow and packet loss; therefore, end-to-end delay decreases.
## 2 Materials and Methods  
First, a 1 \(\upmu\)m thick oxide layer was deposited as buried oxide. Subsequently, undoped poly-Si (100 nm thick) was deposited on a substrate to form the active region. Then, 5 \(\times\) 10\({}^{15}\) cm\({}^{-2}\) of phosphorus was implanted to form the source and drain region. After the active region had been created, a 50-nm thick HfO\({}_{2}\) with a high dielectric constant (\(\sim\)25) was deposited as the gate dielectric, and a 100-nm thick titanium nitride (TiN) was deposited as the gate electrode, with a work function of 4.9 eV. The equivalent oxide thickness (EOT) value of HfO\({}_{2}\) was 7.8 nm. Using high-k metal gate (HKMG) stacks can reduce the gate leakage and eliminate the poly-gate depletion [37; 38; 39]. A 250-nm thick tetra-ethyl-ortho-silicate (TEOS) oxide was deposited followed by chemical-mechanical polishing to planarize the top surface. Finally, an aluminum layer was deposited; this layer was defined as a FP by the mask.  
Figure 1 shows the 3-D structure of the FinTFTs with various extended wide drain lengths (\(L_{EX}\)). The channel length (\(L\)) and height (\(h\)) are 0.2 \(\upmu\)m and 100 nm, respectively. FinTFTs with different structures are simulated by varying each channel wire width (\(W_{0}\)) and fin number \(N_{f}\) (i.e., W40: \(W_{0}\) = 40 nm, \(N_{f}\) = 5 and W200: \(W_{0}\) = 200 nm, \(N_{f}\) = 3). The effective channel width \(W_{eff}\) is fixed at 1.2 \(\upmu\)m (=\((2h+W_{0})\times N_{f}\)) for comparison. The \(L_{EX}\) is from the drain region to the channel region and varies from 0 to 0.6 \(\upmu\)m. The length of the overlap between the FP and \(N^{+}\) region (\(L_{00T}\)) is 0.5 \(\upmu\)m, as shown in Figure 1e.  
Figure 1: 3-D structure of FinTFTs: (**a**) W40 with \(L_{EX}\) = 0 \(\upmu\)m; (**b**) W40 with \(L_{EX}\) = 0.6 \(\upmu\)m; (**c**) W200 with \(L_{EX}\) = 0 \(\upmu\)m; and (**d**) W200 with \(L_{EX}\) = 0.6 \(\upmu\)m; (**e**) Cross-section view of FinTFT.  
In this study, a Sentaurus technology computer aided design (TCAD) 3-D simulator was used. In the physic section of the device simulator, the mobility model, generation, and recombination models are included. For the mobility model, the \"Doping Dependence\", \"Enormal\", and \"High Field Saturation\" flags are used to consider mobility degradation due to impurity scattering, mobility degradation at the interfaces, and high electric field saturation, respectively. For generation and recombination models, \"SRH\", \"Auger\", \"Avalanche\", and \"Band 2 Band\" flags are used to model the Shockley-Read-Hall recombination, Auger recombination, avalanche generation (impact ionization), and band-to-band tunneling, respectively.
## 2 Data and Methods  
### Data  
#### 2.1.1 Study Field Geological Setting  
The study field is located about 200 km from Bintulu, in a water depth of 79.0 m, offshore in the Sarawak Basin of Malaysia (Figure 1a). Complex interaction and compressional among the continental Philippine tectonic plate to the Northeast, the oceanic Indian continental Australian tectonic plate to the South and Southwest and the oceanic Pacific tectonic plate to the East during the Cenozoic Era shaped the present-day tectonic of the studied field [12].  
Interpretation of seismic data indicates that a reservoir sand layer pinched out up-dip, and the effects of both structural and stratigraphic elements have contributed to the formation of a combination trap structure. The reservoir sand was deposited in the shallow marine innerritic environment (Figure 1b). It is an intervened area between reef complexes that have experienced differential compaction due to increased subsidence and deposition of a thicker elastic sequence.  
Figure 1: Study Field, (**a**) Field location, (**b**) Schematic diagram showing the major SC3 reservoir depositional model in study area, (**c**) 3D seismic survey grid showing three well locations and seismic time slice at 1000 ms two-way time depth, (**d**) Seismic survey map view showing the position of three available wells.
## 3 Results and Discussion
### Cytotoxicity  
In categorizing cytotoxicity of plant extracts, the US National Cancer Institute Guidelines consider extracts to have noteworthy in vitro anti-proliferative activity against cancer cells if 50% inhibitory concentration (IC\({}_{50}\)) value is less than 20 \(\upmu\)g/mL, extracts with IC\({}_{50}\) ranging from 20 \(\upmu\)g/mL to 50 \(\upmu\)g/mL are considered moderately toxic while those from 50 \(\upmu\)g/mL to 200 \(\upmu\)g/mL are less toxic and IC\({}_{50}\) above 200 \(\upmu\)g/mL are non-toxic [9]. The acetone crude extracts were more cytotoxic than water extracts and had substantial antiproliferative activity with IC\({}_{50}\) values ranging from 8 to 374 \(\upmu\)g/mL (Table 1). Chloroform fractions were relatively non-toxic to Vero cells with IC\({}_{50}\) values as high as 284 \(\upmu\)g/mL and also had moderate toxicity with IC\({}_{50}\) values of 33 \(\upmu\)g/mL on HepG2 cells. Acetone extracts had better antiproliferative activity compared to aqueous extracts and fractions. Liver and cervical cancer cells showed susceptibility against Hatfield and Walter Sisulu plant acetone extracts while breast cancer cells were susceptible against SANBI acetone extracts. However, SANBI acetone extracts were toxic against normal kidney cells, which questions their safety. These results also corroborated previous studies conducted on Vero cells [19]. The aqueous extracts had lower cytotoxic activity against normal cell lines tested. Similar findings were seen in other studies, which is encouraging, because traditional medicine made from plants is typically prepared as decoctions, infusions, and timctures made primarily from water [36]. However, it is important to note that all the aqueous extracts tested in the study were also not toxic to all the cancer cell lines tested in the study. The three isolated bioactive compounds were not toxic to the normal cells and the cancer cell lines tested in the study. Therefore, the isolated compounds from the non-polar fraction appear to not be those responsible for the low toxicity observed in both the acetone extracts and the chloroform fraction from which all the compounds were isolated.
# Optimizing Geospatial Data for ML/CV Applications:
## 5 Discussion
Adapting the technique to these features may require integrating spectral data, accommodating more complex geometries, and overcoming the computational overhead associated with non-linear or irregular shapes. Addressing these challenges will involve developing feature-specific adjustments to the pipeline and testing its scalability across diverse applications.
## 5 Discussion  
### Sentinel-Derived Predictors  
By comparing the results of the correlation analysis, the coefficients from GWR, and the attribute importance from RF, it was indicated that texture characteristics of Sentinel-1 had great potential for estimating AGB, which was also shown in previous studies [98; 99]. Additionally, it was a pioneering finding that the vegetation biophysical variables of Sentinel-2 were very helpful for AGB estimation using a local regression, which was found previously by non-parametric prediction [55]. The backscatter coefficient of Sentinel-1 and the vegetation indices of Sentinel-2 were useful and common predictors, as confirmed by other researchers [99; 100; 55; 101], but their roles were assisted and not apparent for forest AGB mapping in this study. This may have resulted from a mixture of forest types in the study area, while previous studies mainly aimed at a certain type of forest, or modeling by forests types. Besides, the texture characteristics and backscatter coefficients of Sentinel-1, and the multispectral bands, vegetation indices, and biophysical variables of Sentinel-2, were first applied  
Figure 6: Predicted maps of above-ground biomass in the study site derived from (**a**) geographically weighted regression (GWR), (**b**) multi-layer perception neural network (ANN), (**c**) support vector machines for regression (SVR), and (**d**) random forests (RF). YJ, YQ, LJ, LS, and ED are the abbreviations of Yanjiang, Yongqing, Liangjiang, Lushuihe, and Erdaobaihe towns, respectively.  
simultaneously in AGB modeling, so that the texture characteristics and biophysical variables were outstanding compared to the other kinds of Sentinel-based predictors in this study. In a word, this study dug out vital and new information from the Sentinel series about forest AGB estimation.
## 4 Discussion
### Minerals  
The ash content represents the total minerals in Jameed. The obtained values did not differ from those reported in other studies [3; 17; 18; 19]. The difference in Ca content could be affected by the processing method because Ca can be drained and lost with the whey during the drying process of Jameed with elevated acidity. The lowest Ca content was observed in Ajloun because farmers in this region mix sheep milk with goat milk, which is less concentrated in Ca than sheep milk [3; 30]. The low values obtained in Eshobak and Mafraq are due to the processing of yogurt that is acidic, as preferred by the consumers, which facilitates the movement of Ca to the liquid phase of the gel and is lost with the whey during the concentration process [26]. Samples collected from Tafila and Mafraq were high in Mg (important for skeletal muscles and bone health) [3; 30; 31; 32]. The low values obtained in El Eshobak and Mafraq are due to the processing of yogurt that is acidic, as preferred by consumers, which facilitates the movement of Ca to the liquid phase of the gel and is lost with the whey during the concentration process [26]. In samples collected from Tafilaand Mafraq, Mg (important for skeletal muscles and bone health) [31; 32] was the highest, with 16% and 27% higher values than the average of collected samples, respectively. The P content in Jameed produced in Ajloun was the lowest of all regions, possibly due to mixing sheep milk with goat milk, which contains fewer minerals than sheep milk (Table 3). Both P and K values obtained in this study were in the range reported in the region for other similar types of dairy products [3].  
All samples had salt concentrations above the 12% prescribed in the Jordanian standards and regulations, which is confirmed in previous findings to be higher than 10% [3; 20; 23]. The average salt concentration in the current study was 15.68%, with regions in northern Jordan like Mafraq, Jarash, and Ajloun using less salt compared to regions in the south. Such a practice of adding more salt in the southern parts is an attempt by the processors to control product cracking due to CO2 production in hot regions by inhibiting bacterial activity during the drying process [25], but the high salt concentrations can also inhibit yeasts during the drying process.