esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials, Chemicals, Methods and Cell Line
### Method Development and Validation
#### 2.4.8 Method Selectivity, Specificity, Accuracy and Recovery from Market Tablets  
The selectivity, specificity and ability of the analytical method to quantify the individual analytes in marketed products were evaluated using the addition method [42]. Briefly, 10 tablets of each formulation were separately crushed, and a known amount of the pure therapeutic was added to the powder of the relevant crushed tablets containing the same therapeutic (metformin was added to crushed Diaformin(r) 500 mg tablets, diplizide was added to crushed Minidaib(r) 5 mg tablets and gliclazide was added to crushed APX-Gliclazide(r) 80 mg tablets). The accuracy of the analytical method to quantify and recover every therapeutic agent among tablet excipients was assessed at 3 concentration levels--1 mg/mL, 2 mg/mL and 5 mg/mL--of every analyte separately. The % RSD was recorded for the triplicate determinations (n = 3, \(\pm\)SD) of theses concentration levels.  
2.4.9 Quantification of Metformin Uptake by the House Ear Institute-Organ of Corti HEI-OC1 Cell Line  
Using T75 flasks and under permissive conditions (10% CO\({}_{2}\) and 33 \({}^{\circ}\)C, Heracell VIOS 1601 CO\({}_{2}\) incubator, ThermoScientific, Langenselbold, Germany), cells were cultured in high glucose (Dulbecco's Modified Eagle's Medium, DMEM, Sigma Aldrich) supplemented with 1% penicillin/streptomycin solution and 10% foetal bovine serum. The medium was changed every two days until the cells reached 75% confluency. Cells were trypsinised, collected and seeded in a 96-well plate at 1000 cell/well density for 24 h to attach. Cells were then treated for 24 h with metformin at different concentrations (1 mM, 2.5 mM, 5 mM and 10 mM) in quadruplets for every concentration. DMEM containing residual metformin from the 96-well plate was collected and transferred into HPLC vials; 1 mL of mobile phase mixture was added, vortexed for 1 min and freeze-dried for 24 h using (ScanVac Coolsafe 55-4 pro freeze dryer, Lynge, Denmark). The dried content of the HPLC vials was reconstituted in 1 mL of the mobile phase mixture with vigorous shaking, vortexed for 5 min and filtered through a syringe filter before HPLC quantification. For reliability and reproducibility, the cell seeding and metformin treatment procedures were conducted on two separate experimental days, and metformin uptake by HEI-OC1 was calculated through Equations (3) and (4). Data are presented as mean (n = 4, \(\pm\)SD) for every metformin concentration over the two experimental days.  
\[\textbf{Mu}=\textbf{Mi}-\textbf{Md} \tag{3}\]  
where **Mu** is Metformin uptake (\(\upmu\)g/mL)  
**Mi** is the Initial metformin added to treat HEI-OC1 cells (\(\upmu\)g/mL)  
**Md** is the metformin detected in DMEM dried vials (\(\upmu\)g/mL)\[\%\ \mathbf{Mu}=\frac{\mathbf{Calculated\ Mu}}{\mathbf{Mi}}\times\mathbf{100} \tag{4}\]
# Seareed Exhibits Therapeutic Properties against Chronic Diseases: An Overview
## 6 Pharmacological Properties of Seaweed to Overcome Chronic Disease
### Obesity
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algal Source** & **Constituents** & **Study Type** & **Biological Effects** & **Ref.** \\ \hline _Fucus vesisulosus_ & Fucoidan & In vitro & Inhibited lipid accumulation & [181] \\  & & & Inhibited adipogenic and lipogenic genes & \\ _Petalonia binghamiae_ & Water-soluble extract & In vivo & expression & [182] \\  & & & Reduced body weight & \\ _Ecklonia stolonifera_ & Fucosterol & In vitro & Inhibited adipogenic and lipogenic gene & \\  & & & expression & [183] \\ _Ecklonia stolonifera_ & Fucosterol & In vitro & Inhibited adipogenesis via FoxO1 & [184] \\  & & & pathway modulation & [184] \\ _Ecklonia cava_ & Phlorotannin & In vitro & Inhibited adipogenic expression & [185] \\ _Ecklonia cava_ & Polyphenol Extract & In vivo & Inhibited lipogenesis & [186] \\ _Undaria pinnattifia_ & Fucoxanthin & In vivo & Inhibited lipogenesis & [105] \\ _Eisenia bicyclis_ & 6,6”-Bieckol & In vitro & Inhibited adipogenesis & [96] \\ \hline \multicolumn{5}{c}{Red seaweed (Rhodophyta)} \\ \hline _Gelidium amansii_ & Polysaccharide-rich & In vivo & Decreased triglyceride and total & \\ (formerly G. _elegans_) & extract & In vivo & cholesterol levels & [43] \\  & extract & & Decreased body and adipose tissue & \\  & & & weights & \\ (formerly G. _elegans_) & Ethanolic extract & In vivo & Inhibited adipogenesis & [187] \\ _Plocamium telfairiae_ & Ethanolic extract & In vitro and in vivo & Inhibited adipogenic and lipogenic gene expression & [188] \\  & & & & Modulated gut microbiota & \\ _Sarconema filiforme_ & Carrageenan & In vivo & Reduced body weight and lipid & [84] \\  & & & accumulation & \\ \hline \multicolumn{5}{c}{Green Seaweed (Chlorophyta)} \\ \hline _Codium cylindricum_ & Siphonaxanthin & In vitro and in vivo & Accumulated in stomach, small intestine, & \\  & & & liver, and adipose tissues & \\  & & & Modulated gut microbiota & \\ _Codium fragile_ & Crude extract & In vivo & Reduced body weight and accumulation & \\  & & & of cholesterol and glucose & \\ _Codium cylindricum_ & Siphonaxanthin & In vitro and in vivo & Reduced oxidative and somatic stress on & \\  & & & obese mice & [189] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._erides, total glucose, fatty liver, and white adipose tissue in the tested animals [43; 188]. Frequently bioactive compounds that are found in seaweed for anti-obesity activity are fluorescent [183; 184] and phlototannin [96; 185]. Previous studies have reported that siphonaxanthin, a carotenoid from _Codium cylindricum_, can restore antioxidative capacity and reduce somatic stress in obese mice [189].
# Characteristics and Applications of Spatiotemporally Focused Femtosecond Laser Pulses
## 3 Applications of the SSTF Technique in Femtosecond Laser Micromachining  
In 2010, [PERSON] et al. firstly demonstrated that the SSTF technique allows internal processing in glass with 3D isotropic resolution using a single objective lens. They fabricated micro-channels with circular cross-sections along different writing directions, as shown in Figure 6 [18]. It can be seen that the symmetrical cross-section can be achieved regardless of translation direction of the femtosecond laser beam. By changing the beam size and the average power of the femtosecond laser, it is found that the axial resolution of the fabrication can be continuously controlled. In addition, the aspect ratio of the cross-section can be controlled by changing the spatial chirp before the objective lens through theoretical simulation [19]. More recently, [PERSON] et al. reported that a 3D isotropic spatial resolution of 100 \(\upmu\)m can be achieved with a low numerical aperture lens by utilizing the spatiotemporal focusing technique [31]. This leads to significant enhancement of the fabrication efficiency due to the realization of high-throughput 3D femtosecond laser fabrication. Figure 7 demonstrates the fabrication of a complex, large-scale 3D model of the China Pavilion of EXPO 2010 (world exhibition of exposition) using the SSTF technique [31]. Despite its large size and the low repetition rate of the femtosecond laser used in the experiment, the structure was completed within a few minutes thanks to the large spherical focal spot produced by SSTF.  
Since the SSTF technique has been proposed, its applications in the field of laser micromachining have attracted significant attention. For instance, [PERSON] et al. demonstrated that the SSTF pulses are able to selectively ablate the back surface of a sample with low numerical aperture through 6 mm  
Figure 6: (**a**) Experimental setup of fabrication of microfluidic channel in glass using SSTF technique. The cross-sections of (**b–e**) \(x\)- and (**f–i**) \(y\)-oriented microfluidic channels. Reproduced with permission from [18], Copyright The Optical Society, 2010.  
Figure 7: Images of the China Pavilion written in Foturan glass fabricated by SSTF scheme. Reproduced with permission from [31], Copyright The Optical Society, 2016.  
thick transparent materials [32]. The underlying mechanism can be attributed to the suppression of self-focusing out of geometrical focus due to the broadening of the pulse duration [32]. Additionally, such characteristics can also benefit the precision enhancement in femtosecond laser processing for tissue ablation, which is particularly important in fs-ophthalmic surgery [13; 14]. More interestingly, it has been discovered that when SSTF pulses are employed for direct writing in transparent materials, a nonreciprocal writing effect occurs, i.e., the fabrication quality highly depends on the writing direction [23]. Although the mechanism of such an effect has not been fully understood, its connection to the PFT has been confirmed by several recent investigations [23; 33; 34].
## 5 Performance Evaluation and Discussion  
### Experiment Setup  
We conducted our simulation on a desktop computer with 16 GB of RAM and using an Intel(r) Core(TM) i7-4770 processor clocking at 3.4 GHz. Regarding the environment, there are 100 mobile devices distributed across five GBSs and three UAVs, and each mobile need to process an intensive application. In addition, there are \(0.6\times 10^{9}\) cycles per second on each mobile device, \(10\times 10^{9}\) cycles per second on edge servers, and \(1\times 10^{12}\) cycle per second on the cloud server. The transmission power for the mobile device is set to 0.2 Watts, and 10 MHz of bandwidth is available between the mobile device and edge servers. Each mobile device has randomly assigned \((10,30)\) MB as the input data size, which follows the random distribution and has also assigned with 1900 cycles per byte for the computation requirements. Moreover, a further analysis was made of the time  
Figure 3: Architecture of the proposed distributed deep reinforcement learning-based algorithm.  
and energy for each mobile, which was determined to be \(4.75\times 10^{-7}\) seconds per bit and \(3.25\times 10^{-7}\) Joules per bit, respectively [46]. It is estimated that there is a propagation delay of 15 ms between the edge servers and cloud. We have set the weight for both the execution time as well as the energy consumption as \(w_{i}^{t}=0.5\), and \(w_{i}^{e}=0.5\), meaning that the each device is considering both metrics. Furthermore, regarding the deep-learning algorithm's parameters, four layers are considered in each DNN, in which two of them are hidden layers with 120 and 80 neurons and the episode size, mini-batch size, learning rate and memory size are set as 20,000, 32, 0.01, and 1024, respectively. Following these specifications, we ran 50 simulations round and calculated average values.
# Smartphone-Based NFC Potentiostat for Wireless Electrochemical Sensing
## 1 Introduction
[PERSON] et al. presented a radio-frequency identification (RFID)/NFC wireless sensor tag capable of performing potentiometric measurement for use with pH and ion-selective electrodes [40]. For amperometric measurement, [PERSON] et al. demonstrated a wireless potentiostat that communicated with a smartphone by NFC. This potentiostat is semi-autonomous and able to store chronoamperometric data and perform data analysis externally on the smartphone [41]. In addition, NFC-based chemical sensors have been used to determine uric acid and glucose concentration. [PERSON] et al. developed smart bandage biosensor interfaces with a wearable potentiostat that wirelessly transfers data for uric acid levels, a wound biomarker, to a computer, tablet or smartphone by RFID or NFC. The smart bandage provides highly sensitive and specific uric acid detection, which is useful for home-based patients and healthcare service providers [42]. [PERSON] et al. demonstrated a wireless sensor system used to determine glucose concentration in tear fluid by chronoamperometry. This system transfers the measurement data wirelessly to a handheld reader using RFID and NFC [43]. For the application in cyclic voltammetry, [PERSON] et al. reported a sensor strip with an integrated circuit (IC) powered via NFC for cyclic voltammetry. The strip consists of an antenna, a chip, an electrode connector and an oscilloscope that monitors the output electrode voltages [44]. Therefore, we introduce an NFC potentiostat, a battery-free and flat card-sized device, which interfaces with a smartphone through near-field communication. In this study, we demonstrate the design and performance of an NFC potentiostat compared with a commercial benchtop potentiostat in chronoamperometry and cyclic voltammetry techniques. In this work, the NFC potentiostat was used to evaluate the concentration of ascorbic acid in deionized water and commercial drinking water.
## 3 Machinability of AM Ti6 Al4V Alloy
### Tool Wear Rate  
Tool wear is a gradual breakdown or failure of a cutting tool due to regular machining operation, and it can be identified as edge chipping, built-up edge, fracture, flank wear, etc. Tool life can be enhanced by the appropriate selection of cutting speed, feed rates, and cutting environment. Tool wear can be monitored by vibration signal measurement during the machining process. Tool material is expected to possess high hardness at elevated temperatures to withstand the stresses involved, chipping resistance, toughness, and fatigue resistance to resist the chip segmentation process, and good thermal conductivity to minimize thermal shock on the cutting tool. Another report showed that mixed ceramic silicon shows the highest wear rate of 2304 m/min [43]. This paper primarily focuses on a specific cooling strategy utilizing liquid nitrogen, without exploring alternative cooling methods such as air cooling, oil-based cooling, or hybrid cooling techniques. This limited scope may restrict the applicability of the findings to broader machining contexts and does not provide a comprehensive understanding of the various cooling options available.  
During high-speed machining of titanium alloys, the presence of compressive solid loads and elevated temperatures near the cutting edge significantly influences tool wear. Consequently, plastic deformation emerges as a critical factor in the wear analysis of various tool materials [54]. Turning titanium alloy at 51 m/min with TiCN or CrN-coated inserts developed high flank wear in the flank edges, as shown in Figure 4, in addition to notch wear [52]. One of the disadvantages of uncoated tools is that they reach their maximum process temperature easily compared to coated tools. A common sign that a tool's life has ended is flank wear [61]. The advantage of employing cutting fluids was found to increase the tool life [24].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Cutting Condition** & **Carbide Tool** & **Cast Alloy Tool** & **HSS Tool** \\ \hline Cutting speed (SFPM) & 75–275 & 75–275 & 30–50 \\ Feed rate (IPR) & 0.003–0.01 & 0.002–0.005 & 0.002–0.005 \\ Depth of cut (In) & 0.0003–0.03 & 0.003–0.03 & 0.0003–0.03 \\ \hline \end{tabular}
\end{table}
Table 6: Guidelines from manufacturers for turning heat-treated titanium alloy [59; 60].  
Figure 4: SEM image of a tool insert’s wear, with a contact length of \(\lambda_{c}\)[3].  
An H13 uncoated carbide insert was employed in turning titanium parts and optimizing four responses: SCE, Ra, R, and MRR [62]. Straight carbide insert (WC/Co) cutting tools were reported to be superior when machining titanium alloys, irrespective of the wear mechanism [63]. Straight-grade WC is the best tool material for cutting tool life [64]. The report shows that coated carbide offers no significant advantage due to its increased wear rates [42,65].
## 2 Proposed Method
### Proposed Method Process
#### 2.4.6 Break-Glass Key Extraction  
In this, the ECM sends a break-glass request to CSP or HC. Once this request is sent, the password for CSP and HC from Algorithm 5 is sent to the ECM. The ECM performs the operations in Algorithm 6 to extract the final key.  
```
Input:\(P_{CSP}\), \(P_{HC}\), patient identity, \(PW\) and other parameters used from CSP and HC Output: BGK  Begin Select s from ZP  Calculate R using \(R=H1(\textit{patient identity},\ PW)\)  Calculate r1 and r2 \(r1=(P_{CSP})^{R}\cdot G_{1}^{-s}\) \(r2=(P_{HC})^{R}\cdot G_{2}^{-s}\)  From r1 and r2 calculate bgk1 and bgk2 \(bgk1=K1\cdot(R1\cdot r1)^{\textit{theta}}1\) in CSP \(bgk2=K2\cdot(R2\cdot r2)^{\textit{theta}}2\) in HC  Final BGK is as follows \(BGK=(\ bgk1\cdot bgk2)\cdot(P_{CSP}\cdot P_{HC})^{-s}\) End
```  
**Algorithm 5**Break-Glass Key Generation  
From this algorithm, the break-glass key for extracting the ciphered EHR is obtained, and it is decrypted on the destination side. Using this process, the MCP-ABE-BG is implemented in a smart healthcare IoT system.
## 5 Results and Analysis
### WPK1 and WPK2
#### 5.3.3 Effective Memory Bandwidth
It was difficult for us to calculate these numbers and therefore we approximated \(e\_brw\_WPKx\) as below:  
\[e\_brw\_WPKx=(12m\times anpr)+4(5m+3n)+\frac{m}{8}. \tag{12}\]  
The effective memory bandwidth for the SURAA scheme can be calculated using the \(brw\) formula as below:  
\[e\_brw\_SURAA=(2m+1+nnz)\times 4+(nnz+m+n)\times 8. \tag{13}\]  
Figure 9 plots the calculated effective memory bandwidth for double precision SpMV for CSR, HYB, SURAA, CSR5, SELL-P, WPK1, and WPK2. The maximum theoretical memory bandwidth of K20M is 208 GBytes/s. On average, SURAA delivers a bandwidth of 50.2% of the theoretical maximum as compared to 44.41%, 43.7%, 34.62%, 30.15%, 28.1%, and 24.02% of the theoretical maximum for CSR5, WPK2, HYB, CSR, WPK1, and SELL-P, respectively. The highest effective memory bandwidths delivered by SURAA were 226.25 (_torso1_), 164.03 (_TSOPF_RS_b2383_), and 140.76 (_rail4284_) GB/s which are 109%, 78.9%, and 67.7% of the peak, respectively. A higher bandwidth, more than the theoretical bandwidth, signifies an efficient use of the cache and shared memory for SURAA when executing the matrix \(forso1\). A bandwidth, for SpMV computations, higher than the peak memory bandwidth has also been reported earlier; see, e.g., [58; 74]. The highest bandwidths obtained by the other techniques in descending order are: 160.75 GB/s (_ASIC_680k_s) for SELL-P, 138.87 GB/s (_TSOPF_RS_b2383_) for CSR5, 138.48 GB/s (_TSOPF_RS_b2383_) for CSR, 137.55 GB/s (_TSOPF_RS_b2383_) for WPK2, 112.7 GB/s (_thermal2_) for HYB, and 108.39 GB/s (_RM07R_) for WPK1. Clearly, SURAA provides significantly higher effective memory bandwidth over all the schemes.
# Towards Mirror-Less Graphene-Based Perfect Absorbers
## 3 Graphene Perfect Absorbers without External Mirror  
As mentioned in the previous section, back-reflection mirrors are often either lossy (e.g., metallic mirrors) or require additional fabrication efforts (e.g., DBR). Therefore, under single-sided illumination, the possibility of achieving perfect absorption without the aid of backing mirrors is highly attractive and could open up many engineering possibilities [45]. Over the past decade, three types of GPAs schemes without external mirror have been proposed: 'Degenerate critical coupling absorber', 'All-pass filter-based absorber', and 'One-port mimicking absorber'.
# Multivariate Network Layout Using Force-Directed Method with Attribute Constraints
## 5 Evaluation
### User Study  
To verify the effectiveness of our method in the representation of attribute information, we designed a user study. We defined three tasks:  
* Community task: can users intuitively distinguish the number of attribute communities?
* Node task: Based on the nodes that the user is interested in, can users intuitively judge the proportion of nodes with the same attribute and nodes with different attributes in its neighbor nodes?
* Edge task: Based on the node that the user is interested in, can users intuitively explore other nodes with the same attribute connected to the structure along the edge?  
We compared it with FR, linLog, and FA2. We invited five graduate students interested in the field of network layout as users. Users scored the experimental results of the four methods on multiple datasets according to the above tasks. The highest score is 5, which means it is easy to complete the task, and the lowest score is 1 which means it is difficult to complete the task. We averaged all the user scores. The experimental results are shown in Table 6.  
It can be seen from the experimental results that our method has obvious advantages in helping users to distinguish the number of attribute communities and judge node neighborhood relations. In terms of edge exploration, due to the influence of attribute community force, our method has more interference between edges in a larger graph, resulting in a lower overall score. In conclusion, our method can provide effective help for users to explore attribute information in the network.
## 2 Method  
### Definitions and Notations  
Before introducing the details of EFSDAF, some important definitions and notations are given here for convenience and clarity.  
\(T_{B}\) and \(T_{P}\) define the base date and the prediction date, respectively. \(S^{2}\) is the number of fine resolution pixels within one coarse resolution pixel. \(m\) represents the \(m\)-th endmembers within one coarse resolution pixel or one fine resolution pixel. \((x_{i},y_{i})\) and \(\left(x_{ij},y_{ij}\right)\) denote the coordinate index of the \(i\)-th coarse resolution pixels and the \(j\)-th fine pixels within the \(i\)-th coarse pixels, respectively, and \(j=1\)\(\ldots\)\(s^{2}\). The coarse resolution images observed at \(T_{B}\) and \(T_{P}\) are stored in \(C_{B}(x_{i},\ y_{i},b)\) and \(C_{P}(x_{i},y_{i},b)\), and the fine resolution images observed at \(T_{B}\) and \(T_{P}\) are stored in \(F_{B}\left(x_{ij},y_{ij},b\right)\) and \(F_{P}\left(x_{ij},y_{ij},b\right)\). \(b\) is the number of bands for coarse resolution images and fine resolution images. \(A_{P}^{B}\left(x_{ij},y_{ij},m\right)\) and \(A_{P}^{P}\left(x_{ij},y_{ij},m\right)\) denote the abundances of the fine resolution pixels at \(T_{B}\) and \(T_{P}\), and \(A_{C}^{B}(x_{i},y_{i},m)\) and \(A_{C}^{P}(x_{i},y_{i},m)\) denote the abundances of the coarse resolution pixels at \(T_{B}\) and \(T_{P}\), respectively.
# Deduplication-Aware Healthcare Data Distribution in IoMT
## 5 Results and Analysis
### Computational Overhead at Fog Server  
The CDs transmit the collected information to the fog server. This fog server performs some processing to refine the data before transmitting them to the cloud repository. The computational overhead is high in the WCA-BF + ASE scheme due to the extensive operation of encryption, involving mathematical operations, hashing, and digital signatures, placing a high computation load on the fog server. In the RAM, the number of comparisons and unconditional branches increases the overhead. In DSW, the reason for the high computational overhead is the frequent re-evaluation of the breakpoints using complex conditions and the processing of additional mechanisms for deduplication, which further increase the computational complexity and resources required. The DICA and CWCA schemes dynamically set the chunk size based on the data characteristics, ensuring that only essential data are processed without needless computations that increase the overhead. The proposed algorithm ensures that the fog servers can handle large-scale data processing  
Figure 6: Throughput.  
without compromising performance. Figure 8 shows the computational overhead at the fog node for all schemes under different numbers of data chunks. The RAM scheme consumes the most computational time of 740 ms; the CWCA, DSW, and WCA-BF + ASE schemes consume 530 ms, 600 ms, and 720 ms; and the proposed scheme, DICA, consumes only 500 ms.