esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Proposed Fusion Approach
### Noise Elimination Process  
The image _g(x, y)_ of size \(M\times N\) is separated into rows, and the rows are concatenated to generate a 1-D vector data _g(x)_ of size _MN_[18]. It is shown in Algorithm 1.  
```
Input: Two-Dimensional Image (\(I\)), No. of Rows (\(M\)), and No. of Columns (\(N\)) Output: One-Dimensional Vector Data (\(I\)) Steps:  Begin \(I(2:2:END,:)=I(2:2:END,END:-1:1)\) \(I=RESHAPE(I^{\prime},1,M*N)\) End
```  
**Algorithm 1** Converting a two-dimensional array to a one-dimensional array  
By inversing the technique mentioned in Algorithm 2, the 2-D image could be restored from the 1-D vector data.  
```
Input: One-Dimensional Vector Data (\(I\)), No. of Rows (\(M\)), and No. of Columns (\(N\)) Output: Two-Dimensional Image (\(I\)) Steps:  Begin \(I=RESHAPE(I,M,N)^{\prime}\) \(I(2:2:END,:)=I(2:2:END,END:-1:1)\) End
```  
**Algorithm 2** Converting a one-dimensional array to a two-dimensional array  
Likewise, the size image _g(x, y)_ is separated into columns and these columns are concatenated to generate a 1D vector data with _g(x)_ a size of _MN_. The operation is \(I=\) C2 DT1D (\(I^{\prime},M,N\)).  
Figure 1: The flow diagram of the proposed qshiftN DTCWT-LP and MPCA-based image fusion algorithm.
# End-of-Life Scenarios for Mass Timber: Assumptions, Limitations and Potentials--A Literature Review
## 3 Results
### Cascading Use for EoL: Non-Demolition, Reuse, Recycling, Energy Recovery and Landfill
#### 3.6.1 Best EoL Scenario: Non-Demolition and Adaptive Reuse of Mass Timber Building  
The key in maximizing timber's climate benefits as a carbon sink is to prolong the life of timber structures as much as possible. According to [PERSON] [40], a structure used for 100 years would achieve carbon negativity for about half its life, whereas one demolished after 40 years would never become carbon-negative. This study also concluded that, even under ideal conditions, \"it is still better for the climate to build nothing at all than to construct a timber building\" [40]. This push for constructing more timber buildings is also criticized by [PERSON] [59], whose study argues that it represents a non-circular and linear approach. Instead, it advocates for producing and building less, focusing on developing solutions to utilize the existing stock of wood-based materials already embedded in our current buildings.  
Based on these zoomed-out perspectives, the best EoL scenario for existing mass timber buildings is actually to delay the release of carbon, through adaptive reuse of the entire structure avoiding major demolition. This thinking, emphasized by [PERSON] [59], sees mass timber not only at the product component scale, but also at a building and urban scale. [PERSON] et al. [60] have also addressed the idea of Design for Adaptation (DfA) for timber buildings in order to accommodate functional and structural changes over time. While the current lifespan of mass timber buildings is set at around 60 years, further studies could focus on extending this lifespan, thereby prolonging the carbon sequestration period. From an LCA perspective, adaptive reuse connects the C phase to the use modules B3, B4, and B5 (Repair, Refurbishment, and Reuse). Policies and standards could also explore ways to make phases B and C more interconnected. However, this ideal scenario and the consideration of time at mass timber's EoL are not currently reflected in industry EPDs, which either do not include it or classify it under reuse categories. Additionally, credits for extending the EoL are currently allocated to the next product system, but they could potentially be integrated into dynamic LCA calculations of the current one.  
Based on the cascading principle and the hierarchy of circular strategies outlined in Section 1 Introduction [3, 4], recovering mass timber materials for reuse is the second priority after the adaptive reuse of the entire mass timber structure. When it is no longer possible to reuse mass timber materials for the same purpose, recycling becomes the next preferred EoL scenario in the cascading hierarchy. If both reuse and recycling options  
Figure 4: Hierarchy of EoL Options according to the Cascading Approach.  
are fully exhausted, energy recovery can be selected to offset GHG emissions in the next system. Finally, landfill should only be considered as a last resort due to its potential negative environmental impacts.
# Shifting of Meteorological to Hydrological Drought Risk at Regional Scale
## 1 Introduction
Drought propagation information can aid in the prevention of significant economic losses as well as decision-making. Early warning systems are estimated to save hundreds of lives, save 2.7 billion Euros in natural disaster losses, and generate billions of supplementary benefits per year in Europe based on the optimization of economic production in the energy and agriculture sectors. In the near past, Pakistan has also experienced recurrent droughs and more severe droughs are anticipated in near future due to climate change [22]. The Soan River is a tributary of Pakistan's mightty Indus River Basin. It flows from the Murree Mountains into the Indus via the Dhoke Pathan hydrological station. It is the main hydrological unit for the Pothohar arid region of Pakistan. The importance of the Pothohar region cannot be repudiated as the region of Pothohar is rich in agriculture and agriculture is the backbone of the country's economy [30]. However, there are canal irrigation resources in this area and the crop production depends entirely on rainfall at suitable timings. Pothohar region gets rainfall during both the winter and summer seasons, which is the main source of crop germination, flowering, and maturity. If any of the seasons fail to bring rain, then soil moisture depletes resulting in drought and ultimately significant crop damage. More specifically, the crops in these areas depend on monsoon rains in summer and on western rains in winter. If the summer season fails to bring rains in the area, then it will cause huge crop yield loss in that specific year. Similarly, if the westerly system also fails to bring rains to the area, crops are affected badly, resulting in severe droughts. Historically, this Pothohar region was severely affected by substantial variation in the rainfall and prolonged rain shortage especially from 1999 to 2002 [31]. Hence, the objectives of the present study include the exploration of spatiotemporal evolutions of meteorological and hydrological droughts in the study basin, evaluation of the link between meteorological and hydrological droughts, identifying the lag time, and the investigation of the differences between hydrological and meteorological droughts.
# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
## 2 Related Work  
### Single Channel rPPG Method  
Green [11] demonstrated the feasibility of conducting remote photoplethysmography (rPPG) measurements using a consumer-level webcam. They observed that the green bandwidth of visible light exhibits the highest number of heart rate signals due to the significant absorption of light by hemoglobin in the green bandwidth. To extract a pulse signal from the time series data, they continuously recorded the average pixel value of a region of interest (ROI) corresponding to the skin pixels. The findings of this investigation validate the applicability of remote photoplethysmography in medical and sports domains. Moreover, they established that remote sensing using a monochrome camera is viable not only in the visible light spectrum but also in the infrared wavelength bands [11].  
DistancePPG [12] is a camera-based algorithm designed for non-contact estimation of vital signs by extracting photoplethysmography (PPG) signals using readily available cameras. The algorithm specifically focuses on utilizing the green channel within the RGB channels to capture skin color variations across different regions of the face. This is achieved by employing a weighted average approach, where the weights are determined based on the blood flow dynamics and the impact of light on specific facial regions. To calculate the weights for the time series signals obtained from multiple regions of interest (ROIs), a quality assessment of the pulse signals was performed [12].
# Influence of Thin Fluorine Resin Film on DUV LED Packaging Devices
## 4 Conclusions  
We have fabricated a thin amorphous fluorine-resin-film-packaged DUV LED device by using the drip-coating method. Tens of micrometers of thin fluorine resin film were formed by using a small amount of fluorine resin solution. Due to the existence of cracks, the enhancement of light output power cannot be increased continually with an increasing thickness of the fluorine resin film. In this condition, the maximum enhancement of light output power was \(\sim\)10%. At the same time, the consumption of the fluorine resin solution was only 8 \(\mu\)L, which means the cost of fluorine resin can be reduced to \(\sim\)10%. In addition, no effect on the wavelength and the voltage was observed, and no degradation was observed after aging for 1000 h on Chip A, for which the light output power was about 5 mW. However, when the light output power reached 12 mW or higher, as in Chip B and Chip C, at least 10% decay was observed after aging for 1000 h. Under these circumstances, the fluorine resin material is more applicable in low-power packaging.  
Figure 6: (a) Light output power of devices with Chip B and Chip C, (b) light output power decay for devices with Chip B and Chip C.  
**Author Contributions:** Conceptualization, W.L. and Q.S.; methodology, W.L.; software, W.L.; validation, W.L., M.F. and Q.S.; formal analysis, L.T.; investigation, Y.Y.; resources, Q.S.; data curation, G.L.; writing--original draft preparation, W.L.; writing--review and editing, W.L.; visualization, Z.Z.; supervision, Q.X.; project administration, Q.S.; funding acquisition, Q.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Key R&D Program of China (grant nos. 2022 YFB3604802, 2022 YFB3604300, 2021 YFB3601600, and 2022 YFB2802801); Guangdong Province Key-Area R&D Program (grant nos. 2019B090917005, 2020B010174004, 2019B090904002, and 2019B090909004); Natural Science Foundation of China (grant nos. 62274177, 62174174, 62074158, 62275263, and 61874131); Jiangxi Science and Technology Program (grant no. 20212 BDH80026); Strategic Priority Research Program of CAS (grant nos. XDB43000000 and XDB43020200); Key Research Program of Frontier Sciences, CAS (grant no.ZDBS-LY-JSC040); Bureau of International Cooperation, CAS (grant no. 121E32 KYSB20210002); Key R&D Program of Jiangsu Province (grant nos. BE20201051 and BE2020004-2); the Natural Science Foundation of Jiangsu Province (Grant No. BK20220291) and Suzhou Science and Technology Program (grant nos. SJC2021002 and SYC2022089).  
**Institutional Review Board Statement:** Not applicable.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 6 Transfer Learning between Datasets
### CNN-Based Models  
Transfer of the CNN-based model is explored as well. The results can be seen in Table 6. The model trained on the SPACE08 dataset perform decently well on the KWAUG14 dataset, although that does not work as well in reverse. This is a reasonable result, as the KWAUG14 dataset is fairly homogeneous in terms of environmental conditions while the SPACE08 data took place over several days with a wide variety of weather conditions.
## 4 Conclusions  
The work confirmed that the new L-FDM technique can change the properties of printed objects by directly introducing chemical compounds such as chemicals, dyes, radioactive substances, pesticides, antibiotics, nanoparticles, trace elements, fertilizers, phosphors, monomers for polymerization, proteins, peptides and active ingredients. In the process, there is no need to use traditional equipment for processing, mixing and machining composites. The revolutionary approach of the method developed by our team allows you to obtain completely new materials for 3D printing not only in industrial conditions, but also in laboratories or by private users without access to specialized equipment. Using this technique, simple, commercially available alcohols and organosilicon compounds were introduced into the polymer matrix. The tensile strength increased by 18.33% for PLA prints and 53% for PET-G prints. The prints were characterized by higher impact resistance and a more hydrophobic surface. Based on the presented EDS images, it was shown that the dispersion of the incorporated chemical in the polymer matrix varies depending on the specific chemical used.
## 2 Methodology and System Architecture
### LabVIEW API  
LabVIEW API follows the classical producer/consumer design pattern. This design pattern is used to process data being produced and consumed at different rates to avoid data loss. For example, the data exchange between the JOpenShowVar API is completed \"at a non-deterministic time with an average of 5 ms\". Since the process of uploading the data to the IoT platform is dependent on the speed of the internet connection, it could take hundreds of milliseconds to a few seconds. Figure 8 depicts an example of two loops running asynchronously. The producer loop actively polls the variables from both the PLC and the KRC.  
The producer loop shown in Figure 9 is a state machine with eight states:  
* Initialize: initializes TCP client sockets.
* Test_Internet: tests connection to the internet and its stability.
* Connect_IoT: connects devices to the IBM Watson IoT platform.
* Check_Status: checks the termination of the program.
* From_PLC: reads a data block from the S7 1200 and enqueues read variables.
* From_Kuka: reads from the KRC as well as enqueues the read variables.
* To_Kuka: writes to the KRC.
* Stop: stops program execution.  
In both From_PLC and From_Kuka states, the strings read are parsed and formatted according to a specific protocol. The consumer loop does not run until there are data in the queue. The data are then dequeued and parsed to be published to the IoT platform with their respective property and topic.  
Although the MQTT protocol runs over the TCP/IP, it is better to use a library that handles the details of the MQTT protocol. The Espotel IoT foundation library is chosen for IoT communications. The library eases the connection with IBM Watson IoT using MQTT. It contains blocks for connecting devices and applications, as well as publishing events and subscribing to events.  
Figure 7: (**a**). The API architecture of JOpenShowVar; (**b**). The API architecture of CrossComClient.
## 2 Distribution of Si in Plants  
All plants contain Si in their bodies, but its accumulation varies widely among species. Generally speaking, the Si content in a monocotyledonous plant is significantly higher than that in a dicotyledonous plant [28]. On the basis of the different Si content in plants, plants are divided into three types: high, such as horsetail and rice, containing 10-15% Si, intermediate, such as sugarcane, containing 1-3% Si, and low, such as tomato and pea, containing less than 0.5% Si [12]. In addition, the types of Si accumulation by plants can also be divided into three types: active, passive and rejective, on the basis of the Si/Ca ratio. An Si/Ca ratio greater than 1 indicates active uptake, ratios of 0.5-1 indicate passive uptake, and ratios less than 0.5 suggest exclusion [29].  
However, the Si concentrations in different parts of the same plant also show large differences. The Si distribution types in the same plant are as follows: (1) low-Si plants with approximately the same Si content as the root system or a slightly higher root system content, such as tomato and cabbage; (2) middle-Si plants in which the Si content in shoots is much lower than that in roots, such as crimson clover, which has a root level that is approximately eight times that in shoots; (3) high-Si plants in which Si mainly accumulates in shoots, such as rice and oats [30].  
Most of the Si in plants is distributed in the apoplast; consequently, Si in rice mainly accumulates in the cell wall, cell lumen and intercellular space or between the epidermal cells and the stratum corneum. The Si deposits in the epidermal cells of rice leaves forms a cuticle-Si double layer. In a leaf sheath, Si is deposited in the cell walls of the epidermis and parenchyma. In stems, it is mainly distributed in the cell walls of epidermal cells, vascular bundles, sclerenchyma and parenchyma. In inflorescence and rice husks, it is mainly deposited in the space between the cuticle and epidermal cells and in vascular bundles. The Si distribution in the roots is relatively uniform, but mainly concentrated in maturation zone, with less Si being deposited in the elongation zone [31].
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 5 Taxonomy for Task Scheduling Algorithms in Fog Computing
### Problem Solution Techniques
#### 5.2.6 Deep Reinforcement Learning-Based Approaches  
Deep Reinforcement Learning (DRL) integrates reinforcement learning principles with deep neural networks, empowering agents to learn and decide in intricate environments. When implemented in fog computing task scheduling, DRL facilitates adaptive and intelligent decision-making, accommodating fog environments' dynamic and diverse characteristics [19]. This strategy can potentially enhance resource utilization efficiency, minimize latency, energy consumption and boost overall system performance.  
This approach can help manage the system's variability and unpredictability in fog computing, such as dynamic workload and resource availability. Deep Reinforcement Learning-based approaches can handle complex scheduling scenarios and adapt to changing system conditions.
## 3 Results and Discussion
### Phosphorylation of Akt, mTOR, and S6 is Involved in the SG4-Induced Apoptosis of HT-29 Cells  
Previous studies suggest that phosphatidylinositol 3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR) pathway is a critical factor involved in the occurrence of human cancers. This pathway is also related to numerous cellular functions including proliferation, migration, adhesion, invasion, metabolism, angiogenesis, and survival [39]. A flow cytometric method was performed to determine the expressions of p-Akt, Akt1, p-mTOR, and p-S6 in cells. As shown in Figure 7a,b, when HT-29 cells were treated with 200 ug/mL SG4 for 48 h, the high fluorescent cell population in p-Akt, p-mTOR, and p-S6 groups significantly decreased (\(p<0.05\)) from 44.7% \(\pm\) 0.8% (control) to 29.1% \(\pm\) 2.3%, 53.3% \(\pm\) 1.2% (control) to 28.7% \(\pm\) 1.4%, and 53.0% \(\pm\) 1.6% (control) to 32.2% \(\pm\) 1.1%, respectively. These results indicate the occurrence of SG4-mediated dephosphorylation of Akt, mTOR, and S6. Moreover, it was found that the expression of Akt1 (total Akt) was not varied with respect to SG4 treatment (98.6% \(\pm\) 0.7% (control) to 96.7% \(\pm\) 0.3%). Therefore, our results show that dephosphorylation of Akt, mTOR, and S6 is involved in the SG4-induced cytotoxicity of HT-29 cells. These data were similar to previous findings, which suggested that fucoidan decreased p-Akt of DU-145 human prostate cancer cells in a concentration-dependent manner [40]. Further elucidation of the precise molecular mechanism as well as signaling cascade, especially using in vivo models, is warranted.  
Figure 6: The annexin V-FITC/PI staining analysis of HT-29 cells treated with 200 ug/mL SG4 for 48 h: (**a**) In the histograms, annexin-V fluorescence is displayed on the \(x\) axis and PI fluorescence on the \(y\) axis. The percentage of cells on each boundary (live, early apoptotic, late apoptotic, and necrotic) is indicated in each histogram; (**b**) the percentage of live, early apoptotic, late apoptotic, and necrotic cells is presented as determined for (**a**). The experiments were performed in triplicate. ***\(p<0.001\).
# Definition and Determination of Fin Substitution Factors Accelerating Thermal Simulations
## 3 Results  
### Definition of the Fin Substitution Factor  
A smooth surface should behave thermally sufficiently similar to a specific finned surface using the _FSF_. Thereby the smooth surface should have the same dimensions as the base plate of the finned array. Both surfaces are thermally similar if:  
* Identical temperatures exist for the smooth surface and the fin base
* Identical heat flows of the smooth and the ripped surfaces  
The _FSF_ results from the heat flows of the smooth and the finned surfaces, shown in Figure 1, whereby the focus in this work is just on natural convective heat transfer. The convective heat flow \(Q_{0}\) of a smooth surface is calculated by  
\[Q_{0}=h_{0}\cdot A_{0}\cdot(T_{s}-T_{\infty}) \tag{28}\]  
and depends on the difference between the surface \(T_{S}\) and the ambient temperature \(T_{\infty}\), the size of the surface itself \(A_{0}\), and the heat transfer coefficient \(h_{0}\)[13; 14]. The heat conduction in the fins must also be taken into account with a finned surface. This can be done via the thermal fin efficiency \(\eta\). The heat flow of a finned surface \(Q_{f}\) can be determined using the heat transfer coefficient \(h_{f}\), the areas of the fins \(A_{f}\) and the fin base \(A_{fb}\) and the temperature difference between the fin base \(T_{fb}\) and the ambient [13; 14].  
\[Q_{f}=h_{f}\cdot\Big{(}A_{fb}+\eta\cdot A_{f}\Big{)}\cdot\Big{(}T_{fb}-T_{ \infty}\Big{)} \tag{29}\]  
The _FSF_ is defined as the ratio of the heat flow from the finned surface to the heat flow emitted by the smooth surface.  
\[FSF=\frac{Q_{f}}{Q_{0}}=\frac{h_{f}\cdot\Big{(}A_{fb}+\eta\cdot A_{f}\Big{)} \cdot\Big{(}T_{fb}-T_{\infty}\Big{)}}{h_{0}\cdot A_{0}\cdot(T_{s}-T_{\infty})} \tag{30}\]  
Taking into account the objective that the temperature of the smooth surface should correspond to that one at the fin base, the definition of the _FSF_ can be simplified as follows  
\[FSF=\frac{Q_{f}}{Q_{0}}=\frac{h_{f}}{h_{0}}\cdot\frac{\Big{(}A_{fb}+\eta\cdot A _{f}\Big{)}}{A_{0}} \tag{31}\]  
On closer examination of this definition, the _FSF_ consists of parameters describing geometry (\(A_{fb}\), \(A_{f}\), \(A_{0}\)), heat conduction (\(\eta\)), and heat transfer (\(h_{f}\), \(h_{0}\)).