esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determination of DPPH Radical Saxenging Activity  
AuNPs were screened for DPPH free radical scavenging activity by the method described by [PERSON] et al. [31] with little modification. Briefly, 0.5 mL of 1.5m MDPPH was mixed with different concentrations (31-1000 mg/mL) of gold nanoparticles and incubated in dark for 30 min. After incubation, the absorbance of the samples was determined by UV spectrophotometer (Shimadzu UV-1650, Shimadzu, Kyoto, Japan) at 517 nm. DPPH methanol reagent without sample was used as a control, and Vitamin C was used as a standard.
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 2 Materials and Methods
### Determination of the Antioxidant Activity  
#### 2.8.1 DPPH Radical Scavenging Capacity  
1,1-diphenyl-2-picryl hydrazyl (DPPH) radical scavenging activity was evaluated following a modified method [22]. Briefly, 40 \(\mu\)L of sample extract (Section 2.1) was mixed with 160 \(\mu\)L purified water and 40 \(\mu\)L freshly prepared DPPH solution (Sigma-Aldrich, St. Louis, MO, USA). After incubation in darkness at 25 \({}^{\circ}\)C for 30 min, absorbance was measured at 517 nm using a microplate reader (SpectraMax(r) iD3, San Jose, CA, USA). A control was prepared identically, substituting the sample with purified water. The radical scavenging activity (%) was calculated using the following equation:  
\[\text{DPPH radical scavenging activity (\%)}=[1-(\text{A}_{\text{sample}}/\text{A}_{\text{blank }})]\times 100 \tag{3}\]
# Acceleration of OCT Signal Processing with Lookup Table Method for Logarithmic Transformation
###### Abstract  
Real-time Fourier domain optical coherence tomography (FDOCT) has been widely used in clinical applications. In order to accelerate the imaging processing and display of FDOCT, an alternative lookup table-based strategy for logarithmic transformation was presented. In this paper, real-time and high-quality FDOCT imaging display of biological tissues at an A-line rate of 62 kHz was demonstrated by optimizing the logarithmic calculation.  
optical coherence tomography; lookup table; logarithmic transformation; real-time imaging display +
Footnote †: journal: Applied Physics Letters
# Investigation on Vibration Influence Law of Double-Shield TBM Tunnel Construction
## 4 Analysis of Vibration Monitoring Results of Double-Shield TBM Construction
### Frequency Analysis of Vibration Monitoring Results  
The sampling hole Z-4 is located above the right tunnel vault, and the three measuring points of Z-4 are selected for frequency analysis. Through the fast Fourier transform [32; 33] of the vibration data, the frequency spectrums of the three measuring points are obtained. The three measuring points' frequency spectrums of sampling hole Z-4 are shown in Figure 8.  
Figure 7: Time-domain curve of monitoring point Z-4 03.  
From Figure 8a, we can note that the peak of the amplitude spectrum is located at 15.72 Hz in the X-direction (Channel 1-28), the peak in the Y direction (Channel 1-29) is 32.13 Hz, and the Z-direction (Channel 1-30) is 19.92 Hz. Seeing the whole frequency range, the vibration frequency of measuring point 01 is mainly distributed in 10-40 Hz. However, the obvious difference is that the frequency of measuring point 02 is distributed in 20-50 Hz, and the amplitude spectrum peaks in the X, Y, and Z-directions are located at 40-50 Hz, as seen in Figure 8b. As can be seen in Figure 8c, the frequency of measuring point 03 is distributed in 10\(\sim\)0 Hz. However, the frequency spectrums in the X, Y, and Z-directions have no obvious peaks. In addition, it can be obtained that the vibration acceleration amplitude increases with frequency. It is significantly different from the frequency in medium and coarse sand and plain fill.  
Figure 8: Frequency spectrums of sampling hole Z-4. (**a**) Measuring point 01; (**b**) Measuring point 02; (**c**) Measuring point 03.  
Comparing the frequency spectrums in the X, Y, and Z-directions, it can be clearly seen that spectrums in the same rock mass are basically approximate. Combined with the strata where the measuring points are buried, we can obtain that the main vibration frequency of the plain fill is 20-30 Hz, and the main frequency of medium and coarse sand is 40-50 Hz.
# Next-Generation Blockchain Technology:
## 1 Introduction  
A blockchain is an online global database accessible to anyone via the Internet at any time [1]. In blockchain databases, the information is recorded, maintained, and shared by a community. The blocks within a blockchain contain information about transactions. A typical transaction is a data structure that defines a transfer of information or value. In this sense, a transaction can be the operation of storing information to the block, extracting information from the block, transferring value from one entity to another, a contract, and so on. Blockchain technology combines many other technologies, like cryptography, peer-to-peer networks, smart contracts, and consensus mechanisms, to make it nearly impossible to hack or tamper with the transactions and information stored within the blocks. Crypto currencies such as Bitcoin [2] are an application of the blockchain, which are related to day-to-day digital payment-based systems. In order to create a new block or to verify a transaction, computers must solve complicated math problems in a process called mining. The node that completes the mathematical puzzle the fastest receives a small Bitcoin reward and acquires the right to certify the transaction. Blocks are then appended to the blockchain in chronological order, i.e., as they are created.  
The main issue with the blockchain technology is the ever-increasing size of the blockchain, which makes its storage and validation process more complicated. A single block in the Bitcoin blockchain is around 1-2 Mb in size, while the entire Bitcoin blockchain today is 578.48 Gb (on 12 June 2024), making it almost impossible to run on conventional laptops and desktops [3]. Moreover, its daily growth rate is around 0.04%, and its annual growth rate is 16.91%.  
Another issue is the increasing computational power requirements of mining blocks and performing the validation. The proof-of-work consensus mechanism uses a lot of power. For example, the electricity consumption of Bitcoin is estimated to be 172.26 terawatt-hours (TWh) per year (on 12 June 2024), which amounts to 96.08 Mt CO\({}_{2}\) equivalent. The average energy consumption of Bitcoin per transaction is 766.64 kilowatt-hours (kWh), which has a carbon footprint of 427.60 kg CO\({}_{2}\)[4]. These huge numbers have a direct impact on the power requirements necessary to run the blockchains, and they also have a measurable environmental impact.  
In this article, we propose a novel method that allows for the scalability of blockchain databases in the download, validation, and confidentiality process, developing a lightweight blockchain technology called Entropic Blockchain for simultaneous one-way data compression and encryption, while also facilitating a unique methodology for reaching consensus in real time. This novel method uses [PERSON]'s information entropy (IE) [5] to generate the entropic barcode of a dataset, and it is the subject of a recent patent application [6].
## 3 Materials and Methods
### Data Preparation
#### 3.3.2 Topographical Factors  
The slope and elevation altitude are geographical characteristics that influence high accelerated runoff potential (high altitude, high slopes) and water stagnation areas that are highly prone to flooding, generally at low altitudes and slope. These factors determine how stable the topography is to the slope related hazard, such as flooding, landslides, and erosion. With respect to building siting, many studies have identified the slope and altitude as vital conditioning factors [67; 68; 69].  
Like the slope and altitude, the plan curvature influences the runoff potential of any topography. It is described as the surface perpendicular to the direction of the maximum slope [70]. A negative plan curvature indicates areas where there is a convergent (accelerated) overdland flow, while positive values show the divergent areas with a decelerated overload flow.  
The stream power index (SPI) is a secondary product of the DEM that indicates the power of flowing water and erosion potential based on the presumption that the erosive power of a topography is related to the quantity of water discharge to the definite catchment area. The SPI is computed from the combination of the information of the catchment area and slope gradient [71] using the formula introduced by [PERSON] and [PERSON] [72].  
\[SPI=A\tan\beta/b \tag{1}\]  
where \(A\) is the specific catchment area and \(\beta\) is the local slope gradient computed in degrees.  
The topographic wetness index (TWI) is a secondary derivative of the DEM obtained from the flow accumulation and flow direction. High values of this index is indicative of areas favoring water accumulation. It has been widely used to measure the effect of topography in terms of the location and size of the saturated source of water runoff [73]. The TWI can be calculated using the equation [74]:  
\[TWI=\log_{e}\left(\frac{A}{b\tan\beta}\right) \tag{2}\]  
where \(A\) denotes flow accumulation in square meters, \(b\) refers to the pixel width through which water flows in meters, and \(\beta\) (radian) represents the slope.  
The topographic roughness index (TRI) is also a secondary derivative of a topographic product that characterizes the variability in elevation within a spatial unit [71]. This factor is used to define landform components. The terrain roughness is usually considered with other terrain attributes in order to understand and describe the landform process that differentiates the geomorphological units [75]. The TRI is derived using the following equation proposed by [PERSON] [76].  
\[TRI=\sqrt{|x|(max^{2}-min^{2})} \tag{3}\]  
where \(max\) denotes the pixel largest value in nine rectangular altitude neighborhoods, and \(min\) denotes the minimum value.
## 3 Experimental Results
### Measurement of the Critical Fredericksz Voltage \(V_{c}\)  
The critical voltage defined in Figure 5 was measured with a 50 \(\upmu\)m-thick sample. With this thickness, the measured voltage \(V_{c}^{\prime}\) is equal to \(V_{c}\) to better than 1% if \(L<0.25\)\(\upmu\)m, a condition well fulfilled experimentally in my experiments at all temperatures, as I will check _a posteriori_ (see Figure 11a below). The measurements were performed by increasing very slowly the voltage by increments of 10 mV with a time interval of 30 s between each increment. A capacitance curve is shown in Figure 5. This curve shows that the measured capacitance is perfectly constant below the onset of instability, showing that there is no pretilt in my sample. The measured critical voltage as a function of temperature is shown in Figure 6.  
Figure 4: Dielectric anisotropy \(\gamma\) calculated by taking the value of \(\varepsilon_{\parallel}\) extrapolated at \(V=0\) V. The agreement with the measurements of [PERSON] et al. [33] is excellent.  
Figure 5: Capacitance curve measured in a planar sample of thickness \(d=50\)\(\upmu\)m at \(T=38\,^{\circ}\)C.  
Figure 6: Critical Fredericksz voltage as a function of temperature measured in a planar sample of thickness \(d=50\)\(\upmu\)m. The solid line is just a guide for the eye.
## 3 Results
### Ground Truth 1
#### 3.1.1 Connectivity Detection
TRGC (\(Mdn\) = 1), however, outperformed all ANN models in terms of Sensitivity (\(p\)-values denoting differences with all other methods < 0.001). Finally, while no significant difference was found between LSTM-NUE (\(Mdn\) = 0.67) and Conv2D (\(Mdn\) = 0.67) with two time series as predictors (\(p\) = 0.239), LSTM-NUE performed significantly better than Conv2D with three time series as predictors (\(Mdn\) = 0), \(p\) < 0.001). Rankings are described below in Figure 7 to provide qualitative comparisons. Note that in Figure 7, mean scores \(M\) for each dipole condition is still reported, given that the current results were obtained with small sample sizes. Hence, differences between dipole conditions may still appear once statistical power is increased (i.e., by using more data sets) and given that differences between dipole conditions were, to some extent, expected. With regard to Precision, the results of a Scheirer-Ray-Hare Test with method and dipole condition as factors were not significant, albeit a marginally significant result for method (\(H\) (4,40) = 8.79, \(p\) = 0.07) was obtained. Hence, no follow-up tests were carried out. Thus, we rely upon rankings only for our qualitative description (in terms of mean scores \(M\), taking dipole condition into account) of the data. In the Far-Superficial dipole condition, Conv2D with TS = 2 and LSTM-NUE obtain both a Precision of \(M\) = 0.75 (\(\pm\)0.15, 0.17, respectively), followed by TRGC (\(M\) = 0.58 \(\pm\) 0.05) and TCDF (\(M\) = 0.50 \(\pm\) 0). Precision is lowest in Conv2D with TS = 3 (\(M\) = 0.40 \(\pm\) 0.55). However, in the Far-Deep dipole condition, TRGC obtains perfect Precision (\(M\) = 1 \(\pm\) 0), followed by LSTM-NUE (\(M\) = 0.88 \(\pm\) 0.14) and Conv2D with TS = 2 (\(M\) = 0.63 \(\pm\) 0.41). The qualitatively lower Precision score of TRGC in the Far-Superficial condition turned out to be mainly due to two consistently observed false-positive connections that were not detected in the Far-Deep dipole condition. When summarizing the results in terms of F1-scores, the following ranking was obtained for the Far-Superficial condition: TRGC (\(M\) = 0.73 \(\pm\) 0.04) = Conv2D with TS = 2 (\(M\) = 0.73 \(\pm\) 0.09) > LSTM-NUE (\(M\) = 0.70 \(\pm\) 0.07) > TCDF (\(M\) = 0.40 \(\pm\) 0.0) > Conv2D with TS = 3 (\(M\) = 0.20 \(\pm\) 0.27). For the Far-Deep condition, the F1-score ranking was as follows: 1 \(\pm\) 0 (TRGC) > 0.75 \(\pm\) 0.17 (LSTM-NUE) > 0.47 \(\pm\) 0.31 (Conv2D with TS = 2) > 0.20 \(\pm\) 0.27 (Conv2D with TS = 3) = 0.20 \(\pm\) 0.27 (TCDF).
## 5 Experimental Results
The same clear correlation can be observed between the quantitative estimates of cumulative rain provided by UPAL and the measurement of the in-situ rain gauge (Figure 6c): UPAL was able to detect rain in all hours in which reference measurements evidenced precipitation. The error related to its cumulative amount compared to the radar estimates is less than the one compared to the rain-gauge: this could be explained as UPAL gives an estimate of rainfall on a wide area, like radar does, whereas the rain-gauge on the buoy provides a point measurement. Figure 5: Gamma-ray spectra acquired by the KATERINA II system with and without rain. The gamma-ray rate is plotted versus channels (raw data, bottom axis) and keV (energy calibrated, top axis). In most cases, rainfall led to increases of the counting rates whereas some acoustic data variations are attributed to other sources. Combination of the results from radon progenies counting rates and from the processing of acoustic data identified five cases of intense precipitation events. Considering the total gamma-ray rate, additional precipitation events can be identified that are well detected by acoustic samples. However, there is at least one rainfall event identified by the other sensors and gauges without significant variation of radioactivity level. This could be explained as follows: radon is an inert gas and after its exhalation from the Earth's crust is transported by wind and air movements; consequently, several meteorological parameters, e.g. wind speed and direction, pressure and temperature, should be taken into account as they contribute to radon concentration variation in the atmosphere before and during a rainfall event. On the other hand, acoustic data identify episodes when no rain was observed as rainfall events. It is worth noting that, analyzing the rainfall phenomena both with gross gamma-ray rate from KATERINA II and acoustic samples from UPAL, it is possible to identify correctly all the events, discarding any false alarm. In these cases, UPAL can provide very satisfactory approximation of rainfall amount in the area of the deployment. Further statistical analysis of the results is expected to reveal interesting new findings concerning rainfall and cloud characteristics (e.g., intensity, cloud high, age of raindrops) establishing radon progenies and acoustic observations as useful, alternative tracers of rainfall events.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 4 Wireless Network Architectures for Energy-Efficient DFL
### DFL in UAV Communication Networks  
While CFL employs distributed topologies for model parameter exchange, it remains centralized, relying on a central server for model aggregation. In contrast, the server-based DFL approach faces a single point of failure in UAV networks [73]. For example, in a UAV DFL framework, a leading UAV typically serves as the central server for model aggregation, with other UAVs sending their local models to this server. However, if the central UAV becomes unreachable due to issues with air-to-air links, attacks, or battery depletion, the system may fail. To mitigate this risk, Ref. [112] proposed a DFL framework for UAV networks (DFL-UN), as shown in Figure 7. In this framework, each UAV trains its local model and integrates models from neighboring UAVs, eliminating the need for a central aggregation entity. Recognizing the importance of aggregation methods for enabling model sharing and combining updates among UAVs, the authors in [113] proposed two novel aggregation methods within a DFL framework tailored for UAV networks.  
These methods address server dependency, energy consumption, and communication costs by optimizing local training and communication rounds.  
To address security challenges in DFL-UN, such as unreliable connections, high mobility, and energy inefficiency, authors in [114] proposed the use of peer-to-peer trust policies, asymmetric encryption, and an intrusion detection mechanism to detect hijacking attempts. Additionally, they integrated location-based energy-efficient routing to predict link breakage and optimize routing paths. In [115], the authors extended the UAV systems into a coordinated swarm, using natural swarm behavior algorithms and introducing a leader election-assisted, spiking neural networks-driven DFL framework. This approach optimizes FL model training while minimizing energy consumption and training time.
## 3 Results
### Volatile Compounds Detection Using GC-MS
#### 3.1.1 Esters  
The solid-state fermentation of traditional Chinese vinegar favors ester accumulation, substantially improving the aromatic complexity [33]. This study revealed eleven esters in the Chinese vinegar samples, including ethyl acetate, ethyl propionate, n-propyl acetate, isobutyl acetate, isoamyl acetate, 1,2-propanediol,2-acetate, trimethylene acetate, ethyl benzoate, diethyl succinate, ethyl phenylacetate, and \(\beta\)-phenethyl acetate (Table 4). B1 exhibited high 1,2-propanediol,2-acetate (344,314.42 \(\mu\)g/L) and B2, B3, B4, B5, and B6 all displayed high ethyl acetate concentrations of 625,514.35 \(\mu\)g/L, 783,331.79 \(\mu\)g/L, 571,951.58 \(\mu\)g/L, 304,167.86 \(\mu\)g/L, and 467,917.75 \(\mu\)g/L, respectively. Meanwhile, ethyl phenylacetate was the lowest (trace amounts) in all samples.  
Esters represent essential volatile compounds providing vinegar with floral or fruity aromas [34]. Ethyl propionate, denoting sweet, fruity, grape, ether, rum, and pineapple notes, contributed most to the aroma of B1 (OAV = 118.177) (Table 5) B2 (OAV = 170.431), and B3 (OAV = 173.433). Ethyl acetate (OAV = 114.390), ethyl benzoate (OAV = 73.764), and trimethylene acetate (OAV = 263.524) were more representative of the aroma of B4, B5, and B6, respectively. The diethyl succinate (OAV = 5.698) concentration only reached an odor threshold in B5, while isobutyl acetate and ethyl benzoate failed to reach an odor threshold in B3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**KI**} & \multirow{2}{*}{**Method of**} & \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Quantitative**} & \multirow{2}{*}{**Aroma Description**} & **Odor** \\  & & & & & & & **(ng/L)** \\ \hline
43 & 5-acetyldlihydrofuran-2(3H)-one & 2160 & MS, RI & Furan & 85 & sweet, lemon green & 65 \\
44 & 2-methyllyrazine & 1266 & MS, RI & Pyrazine & 94 & nuts, peanuts, roasted incense, & 60 \\
45 & 2,3-dimethyl pyrazine & 1356 & MS, RI & Pyrazine & 67 & mildew, roasted, creamy, nuts, & 2500 \\
46 & 2,3,5-trimethylene & 1415 & MS, RI & Pyrazine & 42 & baked potatoes, fried peanuts, nuts, earthly & 400 \\
47 & 1,3-dioxalone,2,4,5-trimethyl- & 967 & MS, RI & Others & 44 & notes, fermented & — \\
48 & 1,3-dioxane, 2-methyl- & 1044 & MS, RI & Others & 87 & — \\
49 & naphthalene & 1744 & MS, RI & Others & 128 & aromatic odor, coal tar smell & 1500 \\
50 & 2-methylinphthalene & 1839 & MS, RI & Others & 142 & aromatic odor, coal tar, camphor, chemicals & — \\
51 & 2-phenylthiophene & 2124 & MS, RI & Others & 160 & — & — \\
52 & 4-acetoxy-3-methoxystyrene & 2235 & MS, RI & Others & 150 & — & — \\ \hline \hline \end{tabular}  
* MS: mass spectra; RI: retention indice; Both agreed with database of NIST11; Odor thresholds were from the literature [32].  
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**No.**}
## 5 Delays within a Domain
### Calculation for Example Route  
The optimized route shown in red in Figure 1 contains 6 nodes and can be used to investigate the implications of the values in Tables 2 and 3.  
For a typical security policy, the domain gateway routers (ingress & egress) may have thousands of rules configured. In addition, each internal router may have 100 rules configured, possibly on both ingress and egress interfaces within each router. Using these figures, it is possible to calculate the cumulative delay for a packet which is required to traverse all routers shown in the example.  
Figure 7 shows a simplified version of the route and Table 4 shows the expected delay for a packet using both a basic and advanced version of the OS. Analyzing the delay within a domain will depend on the route selected, which can be expressed as the summation of delays through the components in the route. A number of options are calculated, the worse case being all routers configured with ACLs and the basic OS utilized.  
By simply using an advanced OS in all the routers, an improvement of 250% can be obtained over a basic OS. Optimizing the route based on the conditions derived from Tables 2 and 3, the best case is using advanced OS everywhere an ACL is applied and a basic ACL where no ACLs are applied. If this principle is applied to a trusted domain as described in the introduction then routers loaded with the advanced OS would be used at the ingress/egress points and routers with basic OS and no ACLs within the rest of domain as seen in Figure 8. The best case shows an improvement of 320% over using a basic OS everywhere in the domain and 30% over using an advanced OS everywhere.