esa-sceva/satcom-chunk-collection huggingface dataset:
# Retrofitting a Building's Envelope: Sustainability Performance of ETICS with ICB or EPS
## 3 Case Study--Insolution Cork Board (ICB)  
The model building named Hexa comprises a ground floor for commerce and six residential floors [21] that is representative of the Portuguese buildings, either in constructive or in architectural practices [5]. The flat on the right in Figure 2, located in a middle floor without buildings adjacent to the east facade, is the subject of the study. Evora was chosen as the location for Hexa in this study, in the South of Portugal.  
North and South facades are the external walls of the flat that were studied. \"A square metre of external wall\" (being the East facade considered the same as wall W1--see Table 1--for all alternatives) is the declared unit. The reference study period is 50 years [21].  
In order to consider the energy renovation of the \"Hexa\" building facades with ETICS using ICB, two reference solutions without insulation were considered: One with a single-leaf hollow fired-clay bricks wall of with 0.22 m (W1) and another one with a cavity wall of the same material with two leaves of 0.15 m and 0.11 m (W8). Then, six improved solutions using ETICS with ICB were used for the single-leaf walls and six were used in the cavity walls (Table 1).  
The energy renovation of reference walls (W1 and W8) is important. However, the heating and cooling needs (in terms of final energy consumption) of the flat depend, in each year of the study period, not only on the lower U-value after this intervention but also on the surface (internal or external) of the external wall where the insulation is applied. In fact, the effect of a lower U-value on decreasing the energy needs for heating and cooling is maximum if the application of the insulation material is made on the external surface of this wall.  
The maintenance, repair, and replacement operations of each external cladding and internal coating over the life-cycle after the renovation operation are described in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Wall Type**} & \multirow{2}{*}{**Designation**} & \multicolumn{2}{c}{**Thickness (m)**} & \multirow{2}{*}{**U-Value (W/(m\({}^{2}\cdot\)C))**} \\ \cline{3-4}  & & **ICB Board** & & **Total** \\ \hline \multirow{8}{*}{Single-Leaf} & W1 & - & 0.26 & 1.36 \\  & W2 & 0.04 & 0.30 & 0.58 \\  & W3 & 0.05 & 0.31 & 0.51 \\  & W4 & 0.06 & 0.32 & 0.45 \\  & W5 & 0.07 & 0.33 & 0.41 \\  & W6 & 0.08 & 0.34 & 0.37 \\  & W7 & 0.09 & 0.35 & 0.34 \\ \hline \multirow{8}{*}{Cavity Wall} & W8 & - & 0.35 & 0.95 \\  & W9 & 0.04 & 0.39 & 0.49 \\ \cline{1-1}  & W10 & 0.05 & 0.40 & 0.44 \\ \cline{1-1}  & W11 & 0.06 & 0.41 & 0.39 \\ \cline{1-1}  & W12 & 0.07 & 0.42 & 0.36 \\ \cline{1-1}  & W13 & 0.08 & 0.43 & 0.33 \\ \cline{1-1}  & W14 & 0.09 & 0.44 & 0.30 \\ \hline \end{tabular}
\end{table}
Table 1: Designation, thickness and thermal performance (U-Value) of the ETICS applied.  
Figure 2: Residential flat model used in the study (right).
## 4 Results and Discussion  
### Ethanol Production Ability  
To assess the efficiency of alcoholic fermentation for each strain (W303 wild type, W_M4_558, W_M4_533), samples collected at various time points during the fermentation process were subjected to analysis using R.F.G.C. The peak height, denoted as \(H\), was extracted from the recorded chromatograms, as it has been established that \(H\) linearly corresponds to the ethanol concentration \(c\)[48]. Specifically, \(H\) represents the distance from the baseline to the maximum height at the conclusion of the R.F.G.C. experiment. The results were obtained after three repetitions at each temperature and for each yeast strain.  
The graphical representation of \(H\) versus time at a fermentation temperature of 12 \({}^{\circ}\)C revealed that substituting the serine residue with alanine at position 533 of the Msn4p transcription factor, while not influencing ethanol production, results in a 3-fold reduction in the time required for completion compared to the wild-type strain. Moreover, substituting the serine residue with alanine at position 558 demonstrates a 2.5-fold decrease in the time required for completion compared to the wild-type strain (Figure 3A). This can be attributed to the fact that the genetic modifications of the wild type lead to more psychrophilic and alcoholic-resistant yeast strains.  
Figure 3: Graphical representation of ethanol production, illustrating the relationship between the peak height (\(H\)) and time, accompanied by sigmoidal fitting curves for each strain. In each graph, the error bars after three replicates are also given. Alcoholic fermentation was conducted at **(A)** 12 \({}^{\circ}\)C, **(B)** 18 \({}^{\circ}\)C and **(C)** 25 \({}^{\circ}\)C.  
At temperatures of 18 \({}^{\circ}\)C and 25 \({}^{\circ}\)C, both wild-type and genetically modified yeast strains exhibit a shorter duration for alcoholic fermentation compared to the time required at 12 \({}^{\circ}\)C, as anticipated [49]. Upon comparing genetically modified yeasts with the wild-type strain, it is discerned that, despite the genetically modified strains displaying a shorter fermentation duration than the wild type, these differences do not attain statistical significance (\(p<0.05\), one-way ANOVA) (Figure 3B,C).  
The above findings do not entirely align with previously published results [50] regarding the use of the same genetically modified yeasts in fermenting grape must. While the outcomes for strain W_M4_533 exhibit similarities, there are notable discrepancies in comparison to the other modified strain W_M4_558.
# Near-Space Wide-Area and High-Resolution Imaging System Design and Implementation
## 2 Wide-Area and High-Resolution System Design  
The near-space ball-borne wide-area and high-resolution imaging system mainly consists of a photoelectric sphere, a high-resolution camera, and a control system. The high-resolution camera is installed inside the photoelectric sphere and achieves high-resolution imaging with a large field of view through the rotation of the photoelectric sphere. The control system mainly generates the image collection and maintains control of the high-resolution camera, as well as the motion control of the photoelectric sphere. The composition diagram of the system is shown in Figure 1.  
When the system is powered on, the high-resolution camera and the micro-industrial computer start working. The camera image data is sent to the micro-industrial computer automatically, and the micro-industrial computer stores the image data at the specified address. This address is set as a shared address, and the image data can be captured through the Internet. The main control module can set the parameters of the camera, such as the camera focal length, exposure time, and the pitch and azimuth angles of the photoelectric sphere. It is also possible to control the power up/down of the photoelectric sphere and the camera.  
The ground console sends commands through the RS422 serial port, and the command data is transmitted to the public IP through the serial server. The ball-mounted serial port server obtains data from the public IP through satellite-Internet communication and sends commands to the main control module through the RS422 serial port. The main control module collects information, such as the angle and attitude of the photoelectric sphere, transmits it to the onboard serial port server through the RS422 serial port, and then transmits it to the public IP through satellite communication.  
The main indicators of the system are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Data** \\ \hline Resolution & 0.2 m@20 km \\ Azimuth angle of photoelectric sphere & \(-180^{\circ}\) to \(180^{\circ}\) \\ Pitch angle of photoelectric sphere & \(-140^{\circ}\) to \(140^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main performance indicators.  
Figure 1: Near-space-based wide-area and high-resolution imaging system.  
Section 2.1 mainly introduces the design and analysis of high-resolution cameras, including their optical design and analysis, thermal analysis, tolerance analysis, and structural and electronic design. Section 2.2 introduces the design of the photoelectric sphere and swing imaging schemes.
## 2 Methods
### Mean Energy Loss  
Mean energy loss, also referred to as stopping power, is a quantity associated with the amount of energy lost during an inelastic scattering event. This quantity is experimentally determined and necessary to calculate inelastic cross-sections. For ice, this value is experimentally known to be 39.3 eV [20]. However, such values are not available in the literature for amorphous carbon, crystalline silicon, and amorphous silicon dioxide. Therefore, they must be calculated using available spectra from electron energy loss spectroscopy (EELS). The following equation describes how mean energy loss can be calculated from EELSspectra in the low energy loss range, which was defined in this study as approximately 10-90 eV, beginning immediately after the zero-loss peak centered at 0 eV.  
\[\langle\Delta E\rangle=\frac{\int_{0}^{\infty}\left(\Delta E\right)I_{inel}d \Delta E}{\int_{0}^{\infty}I_{inel}d\Delta E} \tag{5}\]  
where \(I_{inel}\) is the signal intensity at energy loss \(\Delta E\).  
The 200 kV crystalline silicon and amorphous silicon dioxide EELS data were provided by [PERSON] with Universite Grenoble Alpes. The 200 kV amorphous carbon EELS data were provided by [PERSON] with Natural Resources Canada. Note that the spectral data for carbon were complete up to approximately 45 eV, so to remain consistent with integration for other materials, this curve was extended using a power series fit to 90 eV. Using Equation (5), the following values were calculated for mean energy loss: 54.65 eV for crystalline silicon, 52.55 eV for amorphous silicon dioxide, and 32.55 eV for amorphous carbon.
# A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings
## 3 Experimental Results
### Hardware Design  
In Figure 2, a hardware platform is realized, including a microcontroller, an accelerometer port, a display port, and a serial port. The vibration signal is obtained from the accelerometer port. The sampling frequency is 10.24 kHz and the sample time is 5 s. An ARM Cortex-M4 32-bit RISC core is selected as the microcontroller, which is a low computing power platform. The Cortex-M4 core operates at a clock speed of 168 MHz with 1 MB of Flash memory and 192 KB of SRAM. Usually, it is noted that the traditional hardware is only designed to collect signal data. Different from that, our platform achieves completed signal collection with diagnosis inference, which decreases the communication consumption with high efficiency.
## 4 Identified Factors and Discussed Discussion
### Factors: York ICT-1
#### 4.1.2 Absence of Event-Specific Information  
York is different in nature. The significant reason was the lack of sharing of event-specific information among authorities and flood victims. The issue is the origin of many issues such as loss of valuable time to figure out what went wrong, where it went wrong, who was the victim, how to help, what plan to adopt, etc., which in turn caused uncertainty and delays in the execution of operations by the authorities. Also, the spread of misinformation and miscommunication happened among those vulnerable, which made the situation worse, and trust in authorities was lost. Even though there was a possibility of a weak connection (bond of trust) between authorities and people during floods, unfortunately, that was not shown. For example, when York's Foss barrier and pumping station were overwhelmed by flood water, a sudden decision was made to raise the barrier [52] and immediate amendments to the flood prevention plan were executed. This caused more than 600 properties to flood [53] and hundreds of people to evacuate because neither people nor authorities were expecting this situation.  
Figure 5: Factor loading plot in rotated factor space for (a) York ICT-1, (b) York ICT-2, (c) Head-Marala ICT-1, (d) Head-Marala ICT-2.  
Authorities probably had the resources and plans to tackle any challenging situations, but people did not. Therefore, in such situations, the exchange of event-specific information is necessary to utilize the best possible efforts to deal with unpleasant circumstances. For example, someone is unable to evacuate their house, is stuck in a building, their car is flooded with water and they cannot come out of it, has taken a wrong turn on the road and is unable to recognize the way out [37; 54; 55], and similar. It is only possible when we deploy every possible ICT available and effective in that area in an optimum manner and monitor it to improve transport systems' resilience in floods. This can be conducted to bridge the gap between individuals of communities and authorities through the sharing of information. The resilience can be monitored by further creating response maps of that particular area that identify the rate of participation in information pools.  
Experts revealed that the main activities from previous floods, e.g., the time schedule for past floods' coping activities, were reviewed by authorities to improve the system for the future [40; 56] but new challenges arose every time because each flood is unique and brings its own provocations.
# Research on Characteristics of Flow Noise and Flow-Induced Noise
## 4 Noise Calculation of Cylindrical Shell Model
### Flow Noise Calculation  
The acoustic field calculation was built on the foundation of the flow field computation, and in this study, Virtual Lab acoustic simulation software was employed for the analysis. The calculated CGNS files were imported into the acoustic simulation software. The boundary element method transformed the 3D volume mesh into 2D surface integral solution by combining the Helmholtz equation with the acoustic field control equation, and acoustic analysis could be performed at any point in the acoustic field.  
The acoustic directivity analysis referred to selecting points at a certain distance on the required solution surface, solving the acoustic pressure values at these points, and finally plotting the relationship between the acoustic pressure values and the angles relative to the reference axis in a polar coordinate system. This could effectively obtain the spatial distribution characteristics of flow noise.  
We first analyzed the flow noise directivity of the shell. By taking the center of the shell (0, 0, 0.105 m) as the center, a circle with radius of 4 m was made on the vertical section, and an acoustic signal detection point was placed every 10\({}^{\circ}\) to obtain the spatial distribution characteristics of flow noise in the vertical section. Taking 2 m away from the aft shell, i.e., (0, 0, 0.355 m) as the center, a circle with radius of 4 m was made on the horizontal section, and an acoustic signal detection point was placed every 10\({}^{\circ}\) to obtain the spatial distribution characteristics of flow noise in the horizontal section.  
From Figure 7a, it can be observed that the spatial distribution of flow noise on the vertical section was circular in shape. Because the shell was simplified as a cylinder, it had very good spatial symmetry. In the far-field, the flow noise that radiated from the shell was evenly distributed. On the horizontal section, it was difficult to directly observe the distribution characteristics of flow noise. Therefore, the flow noise values were reduced by 40 dB for observation. It can be seen from Figure 7b that the flow noise at the bow and stern decreased significantly. Because of the influence of the incoming flow and wake flow, the flow velocity at the bow and stern was lower. Hence, the noise is relatively reduced. The flow velocity in the middle part of the shell was faster, so the noise was relatively large. Because the shell structure was symmetrical on the left and right, the flow noise showed a symmetrical distribution.  
Figure 6: Simulation model and computational domain mesh: (**a**) simulation model mesh and (**b**) computational domain mesh.
## 2 Materials and Methods
### Modeling Process Duration with Risk  
The credibility of the project model depends on the correctness of the input, in this case, the types and parameters of probability distributions of the process durations. The labor productivity benchmarks developed for the whole industry are estimated with the assumption of \"average conditions\". They do not account for unique conditions of a particular organization, project, location, actual composition and qualification of work gangs, and inevitable fluctuations of their performance, or weather. Most \"standard labor productivity rates\" are published as single values, with no hint on the scale of variability observed during data collection. Therefore, their use in the simulations is limited.  
In practice, the types and parameters of process durations are assumed based on historical data or expert opinions. Due to the unique nature of construction projects, historical data are of limited use. Collecting productivity data in the course of a project and recording them together with data on particular conditions is time-consuming and expensive. The results become unreliable when the technical and organizational conditions change, and the use of statistical forecasting methods is risky, especially when the values of the forecasted parameters exceed the range of available data.  
The quality of experts' estimates depends on their individual experience and subject to bias. Experts from the client's side tend to be over-optimistic, while the contractor prefers to be \"on the safe side\" and schedule processes to take longer. To balance opinions, group decision-making methods are applied [50].  
The experience prompts that the probability density function of construction processes duration is right-skewed. According to [PERSON] [11], the triangular distribution--described using simple analytical dependencies understandable to practitioners--provides an adequate approximation of the beta distribution used in the PERT, and the results of the risk assessment do not differ significantly. Many authors (e.g., [PERSON] [11], [PERSON] and [PERSON] [13]) recommend defining the parameters of triangular distribution based on the mode and quantiles \(t_{a,p}\) and \(t_{b,r}\) of order \(p\) and \(r\) (typically \(p=0.10\)\(q=0.90\) or \(p=0.05\), \(q=0.95\) ). The method by [PERSON] [24] may be used to determine the parameters of the triangular distribution of construction processes duration under various conditions.
## 1 Introduction
Moreover, it was claimed that by manipulating the bacterial supernatant, the downstream processing during the AgNPs synthesis would become more straightforward and effortless [21]. However, the study using cell biomass from the LAB for the production of AgNPs is scant. Research into the ability of LAB cell biomass as a nanofactory may also shed some light on its potential application in decontamination and bioremediation applications. In this present study, the biosynthesis of AgNPs was investigated using the cell biomass of probiotic _Lactobacillus plantarum_ TA4. The probiotic strain was isolated from local fermented food (\"tapai pult\") in our previous study, which demonstrated zinc-tolerant properties at a higher concentration and simultaneously developed zinc oxide nanoparticles (ZnO NPs) [10]. Therefore, based on our prior findings, here we demonstrate the potential of cell biomass of _L. plantarum_ TA4 to interacts with Ag\({}^{+}\) and its potential to produce AgNPs. The ability of _L. plantarum_ TA4 to tolerate various Ag\({}^{+}\) concentrations were determined, and the formation of AgNPs was checked by UV-Visible spectroscopy. Later, the resulting AgNPs were characterized by dynamic light scattering (DLS), Fourier-transform infrared (FTIR), and high-resolution transmission electron microscope (HR-TEM). The biopotential applications such as antimicrobial and antioxidant activity were also investigated.
# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 4 Analysis
### Analysis of Research Question 4: Network Architecture  
#### 4.3.1 Introduction  
Network architecture plays a crucial role in shaping the performance of network diffusion algorithms, particularly in IoT and Space IoT configurations. Key factors such as the network's structure, topology, device heterogeneity, and the dynamic nature of these environments heavily influence how information spreads throughout the network. This  
Figure 3: Algorithm category distribution.  
section explores the effects of various network topologies, heterogeneity, dynamicity, and layered network structures on the diffusion processes.  
As illustrated in Figure 4, the distribution of network topologies is shown through a pie chart, with Small World and Random Topology comprising the largest segments. For a detailed analysis of network topologies, heterogeneity, dynamicity, and layered structures, refer to Tables 5-7, which provide a comprehensive overview of these critical factors. These tables categorize networks based on their topology: whether they are homogeneous or heterogeneous, dynamic or static, weighted or unweighted, and whether they are interdependent or independent. The classification helps in understanding how different topological structures influence information propagation, resilience to failures, and the efficiency of network diffusion processes in IoT and Space IoT configurations. The insights gained from these tables allow researchers to evaluate which topologies and configurations are best suited for various IoT applications, such as sensor networks, epidemic modeling, or social network-based diffusion.  
Figure 4: Network topology distribution: This pie chart represents the percentage share of various network topologies, with Small World (SW) and Random Topology (RT) making up the largest portions. Other topologies include Scale-Free (SF), Mesh Networks (Mesh), Lattice (Lat), and Tree (Tree), among others.