esa-sceva/satcom-chunk-collection huggingface dataset:
# Fabricating a Novel Three-Dimensional Skin Model Using Silica Nonwoven Fabrics (SNF)
## 2 Materials and Methods
### Skin Corrosion Test by MTT Assay  
The skin corrosion was evaluated using the MTT assay [36]. The MTT test was performed on a skin model using NHEK and NHDF cells. The model was removed from the CO2 incubator and 100 \(\upmu\)L of 14.4% hydrochloric acid (Fujifilm Co., Ltd., Tokyo, Japan) and 8 N KOH (anhydride ratio 0.85, Fujifilm Co., Ltd., Tokyo, Japan) were added. Distilled water (100 \(\upmu\)L) was used as the negative control. Each model was incubated for 3 min at room temperature and for 60 min in incubator. The test and control items were then dosed with PBS approximately five times around the insert and on the model surface. Then, 300 \(\upmu\)L of 1 mg/mL MTT solution was applied to the model surface and incubated for 3 h [36]. After 3 h, the whole membrane filter of each insert was cut with a scalpel and the cultured epidermal fragments were placed in 1.5 mL microtubes. The microtubes were filled with 1 mL of dimethyl sulfoxide (DMSO) and washed with a biomasser (Power Masher II, Nippi Inc., Tokyo, Japan) for 1 min. The microtubes were then subjected to sonication (38 kHz, Ultrasonic Cleanser US-104N, SND Co., Ltd., Nagano, Japan) for 10 min and mixed thoroughly. The extracted formazan solution was placed in 96-well microplates (TPP AG., Trasadingen, Switzerland) in three wells with 200 \(\upmu\)L in each well, and the formazan products were quantified by measuring absorbance at 530 nm with absorption spectrophotometry (POWERSCAN HT, DS Pharma Biomedical Co., Ltd., Osaka, Japan). The cell viability was calculated as follows:  
\[\%\text{Viability}=\frac{\text{Evaluation substance absorbance}-\text{ Blank }(\text{DMSO})\text{ absorbance}}{\text{Negative control absorbance}-\text{ Blank }(\text{DMSO})\text{ absorbance}} \tag{1}\]  
In accordance with OECD Test Guideline 431, the following acceptance criteria must be met [36]: the condition of corrosiveness occurs when the survival rate is \"less than 50% after exposure for 3 min or greater than 50% after 3 min exposure and less than 15% after 60 min exposure and the condition of non-corrosiveness is when the survival rate is >50% after 3 min of exposure and >15% after 60 min of exposure\". Two samples were considered for each condition (\(n=2\)).
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 8 Use Case Study: The DIAS Architecture
### Saffron Cultivation  
The saffron cultivation is considered to be of extreme importance for the Western Macedonia citizens, due to the activity over saffron cultivation by a large number of citizens, the limited farming geography, the unique production process to plant the saffron seeds, and the unique production risk that this operation faces. The only kind of saffron that is systematically cultivated around the world, for at least ten centuries, is the edible saffron or as it known in scientific (botanical) terminology Crocus Sativus Linneaus.  
The cultivation process includes harvesting the flowers, as presented in Figure 2, splitting the stigmas and stamens from the petals, drying and sorting of saffron. The picking of 1000 flowers is process of a duration around 45-55 min, while additional 100 to 130 min are required for removing the stigmas for drying. In total, 370 to 470 h are required to produce 1 kg of dried saffron [52]. The flowers are picked exactly when they are fully bloomed, and the saffron strand or stigma is at its reddest. The harvesting process begins shortly after dawn to minimize the further sunlight exposure to the crops, since they may lose their color and even flavor. Saffron cultivation requires dry soil with specific levels of moisture, controlled irrigation and right amounts of nitrogen, phosphorus and potassium. Cultivation is commonly disturbed by mice, moles and rats wrecking the stems. Furthermore, fungi may cause specific diseases at the early stages of saffron growth.  
The DIAS platform aims to improve the production process, by offering the ability of immediate interference in case of animal, disease or wild weeds detection. The farmer will be able to monitor the fields in real time and getting alerts in case of unwanted incidents regarding the state of the plants during all cultivation period, while raising profits. To satisfy these goals, the monitoring process will be aligned specifically with the farmers concerns and actions during each month of cultivation as presented in Figure 3.
# Ion Mobility-Mass Spectrometry Approach for the Comparison of Sheep and Goat Milk Lipidomes
###### Abstract  
In this work, we report an analytical procedure to investigate the lipid compositions of sheep and goat milk. This approach is based on an ion mobility-high-resolution mass spectrometric method to facilitate the identification of complex lipid species and their regiochemistry. A common triacylglycerol profile was observed for sheep and goat milk samples, while a higher abundance of medium-chain fatty acids was observed at the sn-2 position for sheep milk. Furthermore, differences can be also observed in the levels of saturated and unsaturated fatty acids at the sn-2 position. In terms of lipid classes, goat milk showed higher levels of triacylglycerols, phosphatidyl and ether-linked phosphatidylethanolamine, while sheep milk showed higher levels of free fatty acids, lysophosphatidyllethanolamines, lysophosphocholines and non-hydroxy fatty acid-dihydroxyphenylingosine ceramides when compared with goat milk.  
lipidomics; triacylglycerols; lipid regiochemistry; IM-QTOF-MS analysis Article
## 3 Network Model
### CPSR-HQ KDN Routing Scheme
Therefore, as shown in Figure 4, the diagram represents a hybrid network topology with two types of QKD links. It includes trusted relay nodes, untrusted relay nodes, and backbone nodes functioning as either senders or receivers. As shown in the figure, when backbone node 1 wants to forward category N packets to backbone node 5 via some intermediate nodes, there are two candidate paths: 1-2-3-4-5 and 1-6-7-8-5. The following is the routing weight target function for category N packets. \[W=\alpha*\sum_{l\in L}\frac{TD_{l}}{2}+\beta*\sum_{n\in N}S(n),t=t_{last}\in T \tag{1}\]  
In Equation (1), \(W\) represents the weight to be considered in the algorithm, which is determined by the transmission delay of the path in the previous time slot and the quantity of packets to be forwarded by the nodes along the path. \(\alpha\) and \(\beta\) are two adjustment factors that must satisfy \(\alpha+\beta=1\), thus the weight \(W\) obtained should be minimized. The following Table 2 is the Dijkstra algorithm based on the minimum weight \(W\). The difference lies in the situation where, within a time slot t, when each port is processing packets requiring quantum keys, there is insufficient key availability, resulting in subsequent packets collectively waiting. This leads to a waste of time slot resources. In Figure 5, when such a situation occurs, it is allowed to set the waiting packets requiring quantum keys to the Blocked state and prioritize the forwarding of category N packets by allowing them to jump the queue. Simultaneously, a key aspect of the proposed scheme involves the routing decision for category \(H\) and category \(L\) packets. As shown in Figure 5, when backbone node 1 needs to send a category H packet to backbone node 5 via intermediate nodes, its routing is restricted to using quantum keys generated by the MDI-QKD protocol. Therefore, the path can only be 1-2-3-4-5. Conversely, when backbone node 1 needs to send a category \(L\) packet to backbone node 5 via intermediate nodes, its routing is restricted to using quantum keys generated by the BB84 protocol. Hence, the path can only be 1-6-7-8-5. Under these conditions, the proposed scheme also takes into account key-related factors such as the key update rate, key consumption rate, key utilization rate, and remaining key quantity. Figure 4: Analysis of Different Types of Packet Routing Cases. \[p_{kl}*(p_{n}-1)\leq\sum_{l\in r}kr_{l},r\in R \tag{2}\]  
When a request to send an encrypted data packet is forwarded to the SDN controller, the controller needs to estimate whether a path exists in the network that satisfies the key consumption requirements of the data packet, such that it can reach the destination node. If there is no suitable path available, the request is rejected. Equation (2) represents the aforementioned logic, where \(R\) denotes the set of optional paths and \(r\) represents a single path in set \(R\). \[QBER_{l,t}=\left(1-\frac{EB_{l,t}}{QB_{l,t}}\right)*100\%,\forall t\in T,l\in L \tag{3}\]  
\[SNR_{l,t}=\left(\frac{SP_{l,t}}{NP_{l,t}}\right)*100\%,\forall t\in T,l\in L \tag{4}\]  
\[QBER_{l,t}\geq\rho \tag{5}\]
# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 6 Conclusions  
This paper introduces a novel method for image dehazing employing the DCP-based transmission map estimation technique. Our approach incorporates several innovative elements to derive an enhanced transmission map, such as employing multiple \(1\times 1\) DCPs derived from multiscale hazy, inverted, Euclidean difference images, guided filtering, airlight estimation, and adaptive gamma correction. The proposed method outperformed [PERSON]'s method by 9 points and achieved a 3.03 point higher DHQI score than the second-best approach. Additionally, it significantly reduced distortion in the sky region, surpassing all existing state-of-the-art techniques. However, effectively eliminating highly dense haze remains a persistent challenge, highlighting the need to explore further fog removal techniques tailored to diverse environmental conditions.  
Conceptualization, J.K.; methodology, J.K.; software, J.K.; validation, T.-S.N.; formal analysis, J.K.; investigation, J.K.; resources, J.K. and A.B.J.T.; data curation, J.K. and T.-S.N.; writing--original draft preparation, J.K.; writing--review and editing, J.K., T.-S.N. and A.B.J.T.; visualization, J.K.; supervision, A.B.J.T.; project administration, A.B.J.T.; funding acquisition, A.B.J.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (no. NRF-2022R1A2C1010710).  
Not applicable.  
This literature involves one publicly available dataset, the RTTS [[https://sites.google.com/view/reside-dehaze-datasets/reside-standard?authuser=3D0](https://sites.google.com/view/reside-dehaze-datasets/reside-standard?authuser=3D0)]([https://sites.google.com/view/reside-dehaze-datasets/reside-standard?authuser=3D0](https://sites.google.com/view/reside-dehaze-datasets/reside-standard?authuser=3D0)) (accessed on 12 December 2017) [15].  
The authors declare no conflicts of interest.
# A New Architectural Approach to Monitoring and Controlling AM Processes
## 5 Case Study: In-Process Melt-Pool Monitoring
### Applying Clustering to our Melt-Pool Monitoring Problem  
There are still many challenges that exist for monitoring and controlling the AM process. Melt-pool-shape monitoring is one of the key features used for quality estimation in AM. In-situ-based, monitoring systems are reliable approaches used by different researchers for real-time melt-pool monitoring. A high-speed camera is used to monitor the melt-pool in real-time during the AM manufacturing process.  
After capturing the melt-pool images during manufacturing, the next step is to remove the noise and to extract the key features from the melt-pool. Completing these two functions in a timely manner is another challenge for real-time feedback control. In our case, in the next step, we needed to find the boundary of the melt-pool to estimate its area. After estimating the area, we used the elbow method to find the number of and members of clusters automatically within that area. The k-means methods clusters nearby points to find defects in the melt-pool.  
In addition to the part quality, the melt-pool provides important information about the stability of the AM process. Based on the melt-pool shape, we can predict whether the AM process is functioning normally. Two principal parameters that affect the shape of the melt-pool are laser power and scan speed. It is possible to readjust these parameters based on the melt-pool-shape analysis. It means that, by monitoring the melt-pool size and shape, we can make control decisions that will positively impact the quality of the fabricated AM part.  
As noted above, since our focus in this study is near-real-time control, we use the K-means Cluster algorithm on two different build layers called layer1 and layer2, with 2269 and 2215 data points, respectively. We used each layer's data points and their associated melt pool area as inputs to the elbow method. We used this method to find the appropriate number of clusters (see Figure 5). For layer1, the optimal number of clusters is shown in the left top Figure 5. By observing each cluster's shown histogram, we find that, in clusters C2, C3, and C4, the shapes and sizes of the corresponding melt-pool shapes are very different from the other histograms.  
Therefore, these clusters are considered abnormal and represent two patterns of defects. Based on the very high values of C2 and C4 (drawn in blue and cyan color in the left bottom figure), their likelihood of being real defects is quite high. Given the value for C3, its likelihood is much lower. From the perspective of near-real-time control, these patterns must be identified and their quality impact must be assessed before any decision can be made.  
Figure 5: Clustering result.
# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
## 4 Discussion
All authors have read and agreed to the published version of the manuscript. This work was supported jointly by the Youth Cross Team Scientific Research Project of the Chinese Academy of Sciences (JCTD-2021-10), the National Natural Science Foundation of China under Grant no. 42074042 and 41775034, the Feng Yun 3 (FY-3) Global Navigation Satellite System Occulation Sounder (GNOS and GNOS II) Development and Manufacture Project led by the National Space Science Center, Chinese Academy of Sciences (NSSC/CAS), and Strategic Priority Research Program of Chinese Academy of Sciences, Grant no. XDA15007501. FY3D datasets were analyzed in this study, which can be found here: [[http://www.nsmc.org.cn](http://www.nsmc.org.cn)]([http://www.nsmc.org.cn](http://www.nsmc.org.cn)) (accessed on 9 January 2022). We also used ECMWF operational analyses in experiment 1, which are available at: [[https://www.ecmwf.int/en/forecasts/dataset/operational-archive](https://www.ecmwf.int/en/forecasts/dataset/operational-archive)]([https://www.ecmwf.int/en/forecasts/dataset/operational-archive](https://www.ecmwf.int/en/forecasts/dataset/operational-archive)), with the permission of ECMWF. The FNL datasets can be found here: [[https://rda.ucar.edu/datasets/ds083.2/#access](https://rda.ucar.edu/datasets/ds083.2/#access)]([https://rda.ucar.edu/datasets/ds083.2/#access](https://rda.ucar.edu/datasets/ds083.2/#access)) (accessed on 9 January 2022). The ERA5 datesets are available at: [[https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5)]([https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5)) (accessed on 9 January 2022). We acknowledge the support and funding from the National Space Science Center, Chinese Academy of Sciences (NSSC/CAS). We appreciate the valuable and constructive suggestions from reviewers. The authors declare no conflict of interest.
## 5 Simulations Setup and Results  
We use the same simulation parameters as in our previous publication [20]. To measure the performance of our newly introduced protocol, we have programmed the LAPP protocol in C++ to test it using the GreenCloud simulator. The open source simulator was loaded on an Ubuntu-based computer. The simulations were performed on a computer with 6 GB of RAM and an Intel Pentium Multi-Core CPU (4 GHz). For the testing system, we have used Windows 10 (64 bit). The data center scenarios were designed and simulated using the GreenCloud simulator. A highly scalable network was designed and consisted of \(1600\times 1600\) square meters. 2003-line cards, 2600 chassis switches, and 49 ports in the core layer (C1). The proposed LAPP protocol was installed on layer (C2) that consisted of 145-line cards, 234 chassis switches, and 53 ports. The data center covered 15,000 cloud clients, and each CC performs a maximum of 20 tasks/second. Each task consisted of 12,000 Bytes. The network with supported data center used 6 L3, 128 L2, and 106 hosts in each rack. Each host used eight processors with 32 GB memory, 240 GB virtual disk, and 180 GB storage. The switches' positions were set securely to avoid any possible malicious threat. The L3 is set at the center of the network with degrees of 60 at X-axis, Y-axis is at 250, and Z-axis at 0.  
We have used different bandwidths at each layer; 32 for L1, 128 for L2, 264 for L3, and 512 for L4. The bandwidths elapsed and changed after half of the simulation time and became approximately half of the initial bandwidth. Real-time traffic was used so that the processing delay was ignored and only considered propagation delay that was 0.024 milliseconds, 0.005 milliseconds, and 0.0004 for L1, L2, and L3, respectively. The queue delay, burst time, and idle time were set as 0.005 seconds, 0.0056 seconds and 0.0032 seconds, respectively. The priority queue was used for buffering of the packets. We have used TCP Westwood for communications, and the packet size was 1260 bytes. The total experimental simulation time was set to 30 min. The output task size was not extended to more than 250,000 packets. The remaining parameters are summarized in Table 2.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Details** \\ \hline Number of chassis switches at L4 & 1920 \\ Line cards at L4 & 1630 \\ Ports at L4 & 72 \\ Number of racks at L4 & 16 \\ Number of chassis switches at L3 & 432 \\ Line cards at L3 & 164 \\ Ports at L3 & 48 \\ Number of racks at L3 & 128 \\ Used virtual machines & 1800 \\ Number of servers & 64 \\ Maximum number of cloud service users & 18,000 \\ Hosts in each rack & 132 \\ Network size & \(1600\times 600\) square meters \\ Each host supports & 16 processors \\ Memory with each processor & 256 GB \\ Storage memory & 512 GB \\ Virtual disk memory & 430 GB \\ Bandwidth for L4 & 256 GB/s \\ Bandwidth for L3 & 128 GB/s \\ Bandwidth for L2 & 64 GB/s \\ Bandwidth for L1 & 16 GB/s \\ Queue delay & 0.005 s \\ Burst time & 0.0056 s \\ Idle time & 0.0032 s \\ Packet Size & 1260 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.
## 4 Literature Review: Other Research on CWSN
### Energy Efficiency
#### 4.2.3 Energy Efficient Modulation Technique  
[PERSON] _et al._[82] proposed an energy-efficient and adaptive modulation technique for CW-WSNs to achieve power efficiency. The authors proposed a subcarrier detection mechanism, where the user determines its optimal subcarrier, and minimize power consumption at each node by adjusting the constellation size using a modulation technique.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
###### Abstract  
Due to the unpleasant and unpredictable underwater environment, designing an energy-efficient routing protocol for underwater wireless sensor networks (UWSNs) demands more accuracy and extra computations. In the proposed scheme, we introduce a mobile sink (MS), _i.e._, an autonomous underwater vehicle (AUV), and also courier nodes (CNs), to minimize the energy consumption of nodes. MS and CNs stop at specific stops for data gathering; later on, CNs forward the received data to the MS for further transmission. By the mobility of CNs and MS, the overall energy consumption of nodes is minimized. We perform simulations to investigate the performance of the proposed scheme and compare it to preexisting techniques. Simulation results are compared in terms of network lifetime, throughput, path loss, transmission loss and packet drop ratio. The results show that the proposed technique performs better in terms of network lifetime, throughput, path loss and scalability.  
energy consumption; underwater wireless sensor network; direct transmission; mobile sink; courier nodes +
Footnote †: journal: Sensors
## 5 Experimental Verification
At 25% loading condition the efficiency of the conventional PFC converter is experimentally about 95.9% and for the interleaved PFC converter is about 93.8%, when the load is increased to about 100% loading condition, the conventional PFC converter efficiency is about 97%, and for the interleaved PFC converter is about 97.6%. It is observed that, at a low loading condition the conventional PFC converter offers higher efficiency than the interleaved converter, but when the loading power is increased the current stress and thermal losses in the conventional PFC boost inductor and Mosfet is increased which increased the thermal power losses and reduced the conversion efficiency, also increasing the current distortion factor, increasing the THD, and reducing the input power factor. On the other hand, interleaving technique in the PFC converters improve the converter thermal performance, reducing the voltage, and current stresses in the boost converter components at higher loading condition which increase the conversion efficiency, and reducing the total harmonic distortion of the input current at high power loading condition as shown in Figure 26c, where the THD is reduced from 5.6% in the conventional PFC converter to about 3.2% with using of the interleaved PFC converter. Usually, in the PFC converter circuits, the input power factor is depending on the displacement factor and the  
Figure 26: Performance measurements with different loading condition: (**a**) power factor (PF); (**b**) conversion efficiency; (**c**) THD (total harmonic distortions). distortion factor [5], thus, reducing the current distortion factor leads to an increase in the input power factor from 99% to about 99.85% as shown in Figure 26a.