esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Despite its perennial canopy, the Amazonian tropical evergreen forest shows significant canopy growth seasonality, which has been represented by optical satellite-based observations. In this paper, a new Microwave Temperature-Vegetation Drought Index (MTVDI) based on Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sensors was used to capture the canopy seasonality from 2003 to 2010 in comparison with four climatic dryness indicators (Palmer Drought Severity Index (TVDI), Climatological Water Deficit (CVDI), Terrestrial Water Storage (TWS), Vapor Pressure Deficit (VPD)) and two photosynthesis proxies (Enhanced Vegetation Index (EVI) and Solar-Induced chlorophyll) Fluorescence (SIF)), respectively. Our results suggest that the MTVDI shows opposite seasonal variability with two photosynthesis proxies and performs better than the four climatic dryness indicators in reflecting the canopy photosynthesis seasonality of tropical forests in the Amazon. Besides, the MTVDI captures wet regions that show green-up during the dry season with mean annual precipitation higher than 2000 mm per year. The MTVDI provides a new way for monitoring the canopy seasonality of tropical forests from microwave signals.  
Microwave Temperature Vegetation Drought Index (MTVDI); tropical evergreen forests; canopy growth index; photosynthesis seasonality; tropical phenology; Amazon +
Footnote †: journal: Remote Sensing  
1  
The Novel Microwave Temperature Vegetation Drought Index (MTVDI) captures Canopy Seasonality across Amazonian Tropical Evergreen Forests. _Remote Sens._**2021**, _13_, 339. [[https://doi.org/10.3390/rs1300339](https://doi.org/10.3390/rs1300339)]([https://doi.org/10.3390/rs1300339](https://doi.org/10.3390/rs1300339))
## 2 Materials and Methods
### Aroma Compounds Analysis
#### 2.5.2 Gas Chromatography-Orbitrap-Mass Spectrometry (GC-Orbitrap-MS)  
A Thermo Scientific Trace 1300 gas chromatography coupled with a Thermo Scientific Q-Exactive Orbitrap mass spectrometer (GC-Orbitrap MS, Thermo Scientific, Bremen, Germany) was used to analyze sulfur volatile compounds in these passion fruit wine samples. A DB-WAX capillary column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m thickness, J&W Scientific, Folsom, CA, USA) was used to separate sulfur volatiles under a carrier gas (helium) flow rate at 1 mL/min. The oven temperature gradient was programed as follows: 40 \({}^{\circ}\)C held for 2 min, then ramped from 40 \({}^{\circ}\)C to 230 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min and then 230 \({}^{\circ}\)C held for 15 min. A 70 eV voltage ionization energy was used in Orbitrap-MS under an electron impact mode with a mass scanning range of 30 to 3000 amu under a selective ion mode. The retention indices were carried out using a C7-C24 n-alkane series (Supelco, Bellefonte, PA, USA) under the same chromatographic conditions. Sulfur volatiles with available standards were identified through comparing their mass spectrum and retention indices with NIST11 database and their reference standard. Sulfur volatiles without available reference standard were tentatively identified through comparison of their mass spectrum with NIST11 database.  
For quantitation, a synthetic passion fruit wine model containing 9 g/L tartaric acid, 12% \(v\)/\(v\) ethanol and 10 g/L glucose was prepared. The model solution followed the same extraction and GC-Orbitrap MS analysis as the passion fruit wine samples. Sulfur volatiles were quantified using their corresponding reference standard when the reference standards were available. For sulfur volatiles without available standards, they were quantified using the standard, with their chemical structure or carbon atoms similar to the volatiles [21; 22]. Each sample was analyzed on GC-Orbitrap MS in duplicate.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Defense at the Network Level  
#### 3.4.1 Secure Communication Protocols  
Network security is an important aspect of end-to-end IoT security. As devices communicate with one another and with assorted cloud services, it is critical to ensure the secure transmission of data and prevent unauthorized access. This protection can be achieved by using secure communication protocols, such as datagram transport layer security (DTLS), to furnish secure communication among IoT devices [54].  
In the network layer security, encrypted communication is seen as the cornerstone of securing data transmission [54]. This is achieved through the use of strong encryption algorithms such as transport layer security (TLS) or a secure sockets layer (SSL) [55] for secure website accesses (HTTPS) [56], and other network services that require a secure channel [57]. The network-level defense aims to ensure that data are not intercepted or tampered with during transmission and that mutual authentication is implemented between the client and server.  
Although the TLS and DTLS protocols share the same goal of securing data in transit, they are each applicable to different network protocols and scenarios. TLS--a standard encryption protocol dedicated to providing communication security--is primarily used on TCP (transmission control protocol) [58] connections to offer security protection for the communication between web browsers and servers, e-mail, instant messaging, and other services. DTLS is a variant of TLS designed for the UDP (user datagram protocol). Since the UDP is a connectionless protocol, it is mainly used in applications that require high-speed transmission and low latency such as video conferencing. DTLS facilitates TLS-like security in these situations, including encryption, authentication, and data integrity protection.
# Prosthesis Embodiment in Lower Extremity Limb Loss: A Narrative Review
## 4 Results  
### Appearance and Ownership  
The visual appearance of a prosthesis plays a crucial role in promoting embodiment, as recognized by tools such as the PEMbS-LLA scale [14]. Prostheses with natural, anthropomorphic designs enhance embodiment by engaging neural pathways, even with limited training [32]. Visual continuity between the prosthetic limb and the rest of the body is particularly important, as prostheses that closely resemble natural limbs elicit stronger feelings of embodiment compared to less organic designs [33]. Conversely, when prosthetic devices fail to meet aesthetic expectations, users may face significant challenges, including reluctance to use the device, heightened anxiety, and disrupted well-being [34].  
The aesthetic design also significantly impacts the acceptance of the prosthesis by shaping the body image and social confidence [7]. Many users report feeling more comfortable in social interactions and personal relationships when their prosthesis appears natural and visually appealing. This sense of comfort often results in reduced anxiety, improved self-esteem, and a stronger sense of integration between the prosthesis and the user's identity. Positive results encourage users to participate more actively in community life, highlighting the profound influence of prosthesis appearance on social participation and quality of life [7].  
Furthermore, the alignment of the prosthetic appearance with the gender identity of the user is essential to promote body image and social comfort [35]. For example, prostheses that do not accommodate the ability to wear high heels can negatively affect the sense of gender identity and self-expression in someone who feels heels are affirming [36]. Furthermore, while some patients prefer prosthetic devices with a realistic appearance that visually blend with their body, others favor a more technological aesthetic, incorporating design elements such as carbon fiber or brushed metal [7]. Since visual realism is a known factor in promoting embodiment during rubber hand illusion experiments, a deeper understanding of the lower limb of the relationship between visual aesthetics, prosthetic integration into one's identity, and the sense of embodiment remains absent yet critical for providing patients with devices that best meet their individual needs. These findings underscore that the appearance of the prosthesis deeply influences overall well-being and satisfaction. When users feel positive about their prosthesis' design, they are more likely to embrace it as part of their identity, fostering a sense of ownership and embodiment.
## 2 Statistical Modeling and Some Theoretical Results
### Analysis of Covariance (ANCOVA)
For testing \(H_{0}:\gamma=0\) we use a test statistic  
\[t=\frac{\widehat{\gamma}}{\widehat{\sigma}/\sqrt{S_{xx}}} \tag{8}\]  
which has a standard \(t\)-distribution with \(n-3\) degrees of freedom under \(H_{0}\), and \(H_{0}\) is rejected at significance level \(\alpha\), if \(|t|>t_{a/2,n-3}\). A \(100(1-\alpha)\%\) confidence interval for \(\gamma\) is obtained as  
\[\widehat{\gamma}\pm t_{a/2,n-3}\frac{\widehat{\sigma}}{\sqrt{S_{xx}}}. \tag{9}\]  
**Inference on \(\Delta_{x}=(\mu_{E}+\gamma x)-\mu_{C}\)**  
Let \(x\) denote some pre-specified level of engagement for which we would like to estimate the expected response in the experimental group, \(\mathrm{E}(Y|x)=\mu_{E}+\gamma x\), and make a prediction for a new observation, \(Y(x)\). We have \(\widehat{Y}(x)=\widehat{\mu}_{E}+\widehat{\gamma}x=\overline{Y}_{E}+\widehat {\gamma}(x-\overline{x}_{E})\), \(\mathrm{var}\Big{(}\widehat{Y}(x)\Big{)}=\sigma^{2}\Big{(}\frac{2}{n}+\frac{( x-\overline{x}_{E})^{2}}{S_{xx}}\Big{)}\), and therefore, we can obtain a \(100(1-\alpha)\%\) confidence interval for \(\mathrm{E}(Y|x)\) as  
\[\overline{Y}_{E}+\widehat{\gamma}(x-\overline{x}_{E})\pm t_{a/2,n-3}\widehat{ \sigma}\sqrt{\frac{2}{n}+\frac{(x-\overline{x}_{E})^{2}}{S_{xx}}}, \tag{10}\]  
and a \(100(1-\alpha)\%\) prediction interval for a new observation \(Y(x)\) as  
\[\overline{Y}_{E}+\widehat{\gamma}(x-\overline{x}_{E})\pm t_{a/2,n-3}\widehat{ \sigma}\sqrt{1+\frac{2}{n}+\frac{(x-\overline{x}_{E})^{2}}{S_{xx}}}, \tag{11}\]  
Furthermore, the contrast \(\Delta_{x}=(\mu_{E}+\gamma x)-\mu_{C}\) is estimated as \(\widehat{\Delta}_{x}=\overline{Y}_{E}-\overline{Y}_{C}+\widehat{\gamma}(x- \overline{x}_{E})\), and for testing \(H_{0}:\Delta_{x}=0\) we use a statistic  
\[t=\frac{\overline{Y}_{E}-\overline{Y}_{C}+\widehat{\gamma}(x-\overline{x}_{E })}{\widehat{\sigma}\sqrt{\frac{4}{n}+\frac{(x-\overline{x}_{E})^{2}}{S_{xx}}}}. \tag{12}\]A \(100(1-\alpha)\%\) confidence interval for \(\Delta_{x}\) is obtained as  
\[\overline{Y}_{E}-\overline{Y}_{C}+\widehat{\gamma}(x-\overline{x}_{E})\pm t_{ \alpha/2,n-3}\widehat{\sigma}\sqrt{\frac{4}{n}+\frac{(x-\overline{x}_{E})^{2} }{S_{xx}}}. \tag{13}\]  
Note that Equation (13) with \(x=0\) is equivalent to Equation (7). In practice, an investigator may be interested in the following question: What is the smallest level of engagement that results in the statistically significant difference between the experimental and the control groups? This question can be answered by solving the appropriate inequality, e.g., if \(\widehat{\gamma}<0\), we are interested in \(x\) for which the upper limit of the \(100(1-\alpha)\%\) confidence interval in Equation (13) is \(<0\); or, if \(\widehat{\gamma}>0\), we are interested in \(x\) for which the lower limit of the \(100(1-\alpha)\%\) confidence interval in Equation (13) is \(>0\). In general, these inequalities can be solved analytically, but they are quite tedious. In practice, once the estimates are available, the solution can be obtained numerically, as we illustrate by example in Section 3.
## 1 Introduction
In recent years, several attempts have been made to address this need through the employment of a traceable'reference material' or'material measure' suitable for a range of different optical measurement systems. Calibration is achieved through the comparison of optically measured deformations and theoretical predictions. A number of material measures have been proposed for evaluating both static, in-plane strains [15] and out-of-plane displacements during dynamic loading [16; 17]. In all cases, however, a metallic material measure was used, which significantly limits the maximum strains and displacements that can be evaluated. Given the range of applications for which DIC is used, it would be beneficial to be able to evaluate strains associated with much larger deformations. An alternative approach to creating a physical material measure is to apply artificial deformations to a reference image. One method is to use an image from an experiment as the reference image as this provides a genuine representation of a real speckle pattern. Synthetic patterns have also been created using software packages as this technique allows greater control of speckle characteristics [13; 18]. Either the real or synthetic image is then deformed artificially in a known manner, although care needs to be taken to minimize additional errors introduced through the transformation procedure, such as the interpolation technique [19]. This approach has been successfully used to assess errors due to the correlation principle [20], but a physical embodiment of the deformed images would be required to evaluate a complete system. [PERSON] et al. [21] attempted to do this by presenting synthetic images on an LCD screen that were then captured by a stereo camera system; however, any errors introduced in the presentation of the images are unknown. The aim of this paper is to create an experimental method to enable a traceable measurement calibration to be established for a complete DIC system in a real-world environment. The proposed method will enable the calibration of full-field measurements of large deformations. Although surface deformations can occur in three dimensions, due to the difficulties in creating traceable non-planar strain states, this method will focus on calibrating for in-plane deformations only.
# Comparing Augmented Reality-Assisted Assembly Functions--A Case Study on Dougong Structure
## 4 Major Assisted Functions
### Part Search  
* Interactive 3D model display: the 3D display of the components to be assembled is more intuitive than their 2D drawings. The user can rotate the component models freely to observe their details (such as assembly features) at different view angles. Such rotation functionality solves the possible ambiguity of presenting a model only at a fixed view angle in the paper-based instructions. The assembly result is presented using a similar method.
* Part identification with instant feedback: according to previous research [6], whether or not the user has chosen the correct part is critical in most assembly tasks. This is often a significant reason for the prolonged assembly process. A part identification function implementing automatic object recognition was proposed to solve this problem. The identification result can guide the user to choose correct components through an AR interface. A typical use scenario of this function is that the user places an actual component in front of a camera, and the system determines whether the component is correct for the current assembly step. A confirmation message is instantly sent back to the user. This design can reduce the possibility of human cognition errors.
# Aromatic Potential of Diverse Non-Conventional Yeast Species for Winemaking and Brewing
## 2 Materials and Methods
### Comparison of Microscale to Labscale  
_S. cerevisiae_ NIZO 900 was pre-cultured overnight at 30 \({}^{\circ}\)C in GPY (2% glucose, ScharlauChemie, Spain; 1% bacteriological peptone, Oxoid, UK; 0.5% yeast extract, Oxoid, UK). This strain was selected due to its good performance in fermenting alcoholic beverages. Afterwards, yeast cultures were centrifuged at 1000\(\times\)_g_ for 5 min, the medium was decanted, and the cells were subsequently  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compound** & **Aroma** & **ST Wine * (mg/L)** & **ST Beer ** (mg/L)** \\ \hline \multicolumn{4}{c}{_Vicinal diketones_} \\ \hline Diacetyl & buttery & - & 0.10–0.15 \\
2,3-Pentanedione & toffee & - & 0.90 \\ \hline \multicolumn{4}{c}{_Aldehydes_} \\ \hline Acetaldehyde & herbaceous, sherry, nutty, & & \\  & bruised apple & 100 & 20–25 \\ \hline \multicolumn{4}{c}{_Higheralcohols_} \\ \hline Isobutyl alcohol & solvent, fusel, spiritous & 40 & 100 \\ Isoamyl alcohol & harsh, nail polish, banana & 30 & 50–65 \\ Phenylethyl alcohol & floral, rose, sweet & 10 & 40 \\ \hline \multicolumn{4}{c}{_Acetateesters_} \\ \hline Ethyl acetate & nail polish, fruity & 7.50 & 30–33 \\ Isoamyl acetate & banana, pear & 0.03 & 1.60–2.00 \\ Phenylethyl acetate & flowery, rose, fruity, honey & 0.25 & 3.80 \\ \hline \multicolumn{4}{c}{_Ethyl Esters_} \\ \hline Ethyl caproate & green apple, aniseed & 0.05 & 0.23 \\ Ethyl caprylate & sour apple, sweet soap & 0.02 & 0.50–0.90 \\ Ethyl caprate & apple, floral, soap & 0.20 & 1.50 \\ \hline \multicolumn{4}{l}{\({}^{\star}\)ST wine: sensory thresholds in wine (10% ethanol) extracted from [13]; ** ST beer: sensory thresholds in beer extracted from [14] and [15].} \\ \end{tabular}
\end{table}
Table 2: Sensory descriptors and thresholds of the aroma compounds investigated in the microfermentations.  
re-suspended in triplicate in either 200 mL (bottles-lab scale) or 5 mL (microplates, MTPs-microscale) of Syrah must (Sofralab, Magenta, France) at a final OD\({}_{600\,\mathrm{nm}}\) of 0.1. Priortoinoculation, wine must was sterilized overnight with 1 mL/L dimethyl dicarbonate (Sigma-Aldrich, St. Louis, MO, USA). Samples were taken after 1, 3, 7 and 10 days of fermentation, centrifuged (4 \({}^{\circ}\)C, 1000\(\times\)\(g\), 5 min) and frozen immediately at \(-\)20 \({}^{\circ}\)C, except for OD measurements.
# Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System+
## 2 Assessment of Hidden Ecological Factors  
Climate factors are the drivers of malaria transmission [14]; however, a study analysing the causal ecological relationship among the climatic factors that affect the incidence of malaria is still lacking, particularly in Sub-Saharan African countries.  
The malaria ecosystem comprises four main components: human host, mosquitoes vector, parasites and environmental condition (see Figure 1).  
Figure 1: Conceptual framework of the malaria ecosystem describing the dynamic stages of malaria transmission from humans and mosquitoes under the influence of environmental factors. The boxes colored blue indicates the dynamics development of malaria parasite and its interaction between human host with mosquito vector and ecology. While the box colored red is the main scheme for malaria prevention and control indicating the intervention measure taken to mitigate the burden imposed on the human population.  
These components are very dynamic in nature due to the inherent characteristics of ecology and the anticipatory change to biodiversity because of global warming. The works by [15; 16; 17] reported that ecological changes would adversely affect human health in some ways that are both obvious and obscure. However, the growing evidence also suggests that due to the rise in temperature as a result of the anticipated global warming, some previously unexposed regions of malaria transmission would have a 50% chance of experiencing it due to the link between malaria incidence and ecological factors [18]. The relationship between environmental changes and human health cannot be overemphasized because of the inherent variability and complexity of human nature. In many circumstances, grasslands and forest are converted for agriculture to reduce communicable disease, including wetland drainage for the prevention and control of malaria [17]. These activities can either lead to unintended negative health effects or succeed in the designed purpose. Also, transforming forest to augment food production may, in the long run, lead to the creation of a suitable environment for disease-causing agents such as mosquitoes for malaria transmission [19].
###### Abstract  
The effects of operational conditions such as permeate recirculation velocity, mixing intensity, and trans-membrane temperature on the performances of hydrophobic polyethylene (PE) hollow-fiber membrane were investigated by operating the submerged direct contact membrane distillation (SDCMD) process treating a synthetic low-strength wastewater. Permeate flux of the membrane increased with increasing a permeate recirculation velocity through the fiber lumen. However, the effectiveness was less pronounced as the velocity was higher than 0.5 m/s. Increasing rotational speed to 600 rpm, which can lead to mixing intensity from a bulk wastewater toward hollow-fiber membrane, enhanced permeate flux. Feed temperature played a more significant role in enhancing permeate flux rather than a permeate temperature under constant trans-membrane temperature. The SDCMD process treating a synthetic low-strength wastewater achieved an excellent rejection efficiency which is higher than 97.8% for both chemical oxygen demand (COD\({}_{\text{Cr}}\)) and total phosphorus (T-P) due to the hydrophobic property of membrane material which can allow water vapor through membrane. However, the rejection efficiency of the ammonia nitrogen (NH\({}_{3}\)-N) was relatively low at about 87.5% because ammonia gas could be volatized easily through membrane pores in SDCMD operation. In a long-term operation of the SDCMD process, the permeate flux decreased significantly due to progressive formation of inorganic scaling on membrane.  
direct contact membrane distillation; organic wastewater; nutrients; inorganic scale +
Footnote †: journal: Applied Sciences
## 4 Simulation Results and Discussion
### Implementation of JS-MFO for Solving OPF Problems
Figure 11 indicates the voltage magnitude profile for each optimal state for cases 4-6, describing that the voltage magnitude is within the predefined limits between 0.92 p.u. and 1.08 p.u. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**State Variables** & **Case 4** & **Case 5** & **Case 6** \\ \hline
**Generated Reactive Power** & **JS-MFO** & **JS-MFO** & **JS-MFO** \\ \hline QG1(MVAR) & \(-79.6379\) & \(-104.3660\) & \(-99.8009\) \\ QG8(MVAR) & \(-13.7852\) & \(-6.2700\) & \(-17.1219\) \\ QG1G(MVAR) & \(-15.8118\) & \(-6.1590\) & \(-8.9077\) \\ QG23(MVAR) & \(-7.8579\) & \(-5.8648\) & \(-11.5592\) \\ QG24(MVAR) & \(-5.9939\) & \(-25.0052\) & \(-17.6271\) \\ QG25(MVAR) & \(0.3272\) & \(5.1091\) & \(4.5488\) \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Reactive power generation in the optimal state for each case—RIM 27-bus power system. Figure 10: Convergence characteristics of JS, MFO, and JS-MFO algorithms for case 5.
## 3 Electromagnetic Performances Comparison and Analysis
### Overall Comparison  
The overall comparison of four cases is listed in Table 6. The torque variation range is signifcantly smaller than the flux voltage regulation ratio. In Table 6, the 12/7 design has the best flux voltage regulation ratio, and has a torque density of over 20 kNm/m\({}^{3}\). Therefore, the 12/7 design is selected as the best design among the four cases.
# Temperature Effect on Stability of Tunnel Face Under Unsaturated Seepage Condition
## 4 Parametric Studies
### The Impact of Flow Rate q  
Figure 5 demonstrates the variation in the safety factor FS for the three types of soils with respect to changes in flow rate q under different temperature conditions. As can be seen from the figure, clay exhibits extremely high sensitivity to changes in flow velocity, silt shows the next level of sensitivity, and sand continues to display relatively low sensitivity to flow velocity variations, which can be reflected in Figure 4 as well. Combining Figures 4 and 5, it can also be concluded that silt has a strong sensitivity to temperature, followed by clay, while sand is hardly affected.