esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing
## 5 Conclusions  
In this paper, we presented a fine-tuning method for image classification of large-scale remote sensing datasets. We showed that the adoption of a linear decay learning rate schedule or Cyclical Learning Rates, combined with regularization techniques, like label smoothing, could produce state-of-the-art results in terms of overall accuracy. Summarizing, SVM with linear or RBF kernel presented more accurate results than softmax when using 10% and 20% training data splits. This behavior is expected, since SVM is known to be more robust in the presence of of small training sets [60]. The above discussion is giving us valuable information for researching more competitive methods to provide progress in remote sensing image classification. After this, we suggest the following directions: (1) assess the method with different types of pre-trained CNNs with different types of neural network architectures, (2) include learning rate finder [39] in order to determine optimal boundaries for cyclical learning rates or initial learning rate for linear decay scheduler, and (3) improve the results by fine-tuning only some layers of pre-trained CNNs, in contrast with unfreezing the whole network architecture for training.  
Conceptualization: B.P., T.A.-P. and E.Z.; Methodology: B.P., T.A.-P., R.C., P.L. and E.Z.; Software: B.P., P.L. and E.Z.; Validation: B.P., R.C., P.L., P.M. and E.Z.; Formal analysis: B.P. and P.M.; Investigation: B.P., R.C., P.L. and E.Z.; Writing--original draft preparation: B.P., R.C., P.L. and E.Z.; Writing--review: B.P., R.C., P.M., P.L. and E.Z.; And editing: [PERSON], P.M., P.L. and E.Z. All authors have read and agreed to the published version of the manuscript.  
P.M. acknowledges the support of the projects TALIsMAN (ARS01_01116) funded by the Ministry of Education, Universities and Research (MIUR) and MAESTRA (ICT-2013-612944) funded by the European Commission. E.Z. and P.L. acknowledge the support of Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, North Macedonia.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 All-Fiber Lasers Mode-Locked with m-SWCNT Saturable Absorber
### Holmium-Doped Fiber Laser  
Lastly, a holmium-doped ring laser cavity was constructed. The resonator was pumped with an in-house-made linear thulium-doped fiber laser emitting a 1940 nm continuous wave radiation. The laser produced 717 fs pulses at a wavelength of 2082.4 nm (Figure 6a,b). The spectral width of the solitons was 6.35 nm, which corresponds to the time bandwidth product of 0.315, showing that the pulses were transform-limited. Up to 19.8 mW of the average output power was generated for a pump power of 1.1 W.  
Figure 4: Optical spectrum (a) and autocorrelation trace with a sech\({}^{2}\) fitting (b) of the generated pulses.  
Figure 5: Optical spectrum (a) and autocorrelation trace with a sech\({}^{2}\) fitting (b) of the generated pulses.  
The all-fiber cavities were designed to keep a comparable length of 3.8-4 m, which corresponds to repetition frequencies of 54.5, 52.2, and 54.3 MHz for the erbium, thulium, and holmium-doped fiber lasers, respectively. The radio frequency spectra of the fundamental repetition frequency, along with the spectra measured in a 1.25 GHz span (insets), are presented in Figure 7a-c. The high signal-to-noise ratios indicate the stable operation of the investigated mode-locked lasers. No pre- and postpulsing was observed in any of the cases.  
Performance of the mode-locked lasers was characterized with the use of the following equipment: APE Pulsecheck autocorrelator (erbium laser), Femtochrome FR-103 XL autocorrelator (thulium and holmium lasers), Yokogawa AQ6375 optical spectrum analyzer, Keysight EXA N9010A RF spectrum analyzer, and Discovery Semiconductors DSC2-50S 16 GHz photodiode.
# Parameter Optimization of Orbital TIG Welding on Stainless Steel Pipe
## 3 Results and Discussion
### Taguchi Analysis  
Based on the Taguchi analysis method and Minitab software 19.1, the results from the Taguchi method are shown in Table 8, Figure 8, and Table 9.  
Table 8 displays the response for the signal-to-noise ratio. The welding current has the highest impact rank, followed by the wire feeding diameter. The welding speed ranks in the lowest position. The direct impact of the welding current on the HI value could explain the high-ranking effect of the welding current on the tensile strength. The optimal results in Figure 8 are presented in Table 9: D\({}_{\text{w}}\) of 1.0 mm, welding current of 110 A, and welding speed of 4.5 mm/s, with the corresponding HI of these parameters of 0.32 kJ/mm. The predicted tensile strength from these parameters is 432.4 MPa, which is lower than the highest UTS value of 562 MPa.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Level** & **D\({}_{\text{w}}\) (mm)** & **I (A)** & **V\({}_{\text{f}}\) (mm/s)** \\ \hline
1 & 45.66 & 41.95 & 45.62 \\ \hline
2 & 49.04 & 47.42 & 47.76 \\ \hline
3 & 44.14 & 49.47 & 45.46 \\ \hline Delta & 4.90 & 7.53 & 2.30 \\ \hline Rank & 2 & 1 & 3 \\ \hline \end{tabular}
\end{table}
Table 8: Response for signal-to-noise ratios (more prominent is better).  
Figure 7: Optical micrographs showing the microstructure of the weld zone and fusion zone of samples S2 and S6.
###### Abstract  
Most Internet of Things (IoT) resources are exposed to security risks due to their essential functionality. IoT devices, such as smartphones and tablets, have a limited network, computation, and storage capacity, making them more vulnerable to attacks. In addition, the huge volume of data generated by IoT devices remains an open challenge for existing platforms to process, analyze, and discover underlying trends to create a convenient environment. As a result, to deliver acceptable services, a new solution is necessary to secure data accountability, increase data privacy and accessibility, and extract hidden patterns and usable knowledge. Moving the Internet of Things to a distributed ledger system might be the most effective way to solve these issues. One of the most well-known and extensively utilized distributed ledger systems is the blockchain. Due to its unique properties, such as privacy, accountability, immutability, and anonymity, blockchain technology has recently attracted a lot of interest. Using IoT in conjunction with blockchain technology can bring several benefits. This paper reviews the current state of the art different BIoT architectures, with a focus on current technologies, applications, challenges, and opportunities. The test findings prove that the decentralized authentication platform-based blockchain-based IoT (BIoT) device-to-device architecture has a significantly higher throughput than the gateway-based architecture. To encrypt the elliptical curve cryptographic (ECC) and to generate keys, the Chinese remainder theorem (CRT)-based scheme is proposed and compared with the secure hash algorithm (SHA-256). Finally, ECC-CRT is used to access system performance in terms of latency, throughput, and resource consumption, simulated through the Contiki Cooga (CC) simulator, and alter order and peer nodes for performance study in BIoT. A comprehensive analysis and simulation results show that the proposed scheme is secure against a variety of known attacks, including the man-in-the-middle (MiM) attack, and outperforms the SHA-256 cryptographic algorithm. Moreover, the significance of blockchain and IoT, as well as their analysis of proposed architecture, is discussed. This paper will help readers and researchers understand the IoT and its applicability to the real world.  
IoT; blockchain; security; BIoT architecture; ECC-CRT; SHA-256 +
Footnote †: journal:  
_Applied_  
_sciences_
# Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk Heritage
## 3 STMaps Visualization Tool  
The main (and original) goal of the visualization system proposed in this paper is to show the information gathered by the SILKNOW project by means of spatio-temporal maps. For this reason, we call it STMaps. Nevertheless, it is important to emphasize that we have designed the STMaps application to be multi-purpose and able to work in different contexts, so it is not restricted to be used within the context of the SILKNOW project, nor is it limited to CH-related content. To do so, we have based the system on ontologies, both for storing the data and for visualization purposes.  
SILKNOW is a multidisciplinary project aimed at preserving and promoting the heritage of silk textiles. For this reason, digital data about silk textiles from the databases of several institutions (museums, manufacturers, associations etc.) have been processed and analyzed with Artificial Intelligence (AI) techniques [67]. These data have been incorporated into the SILKNOW's knowledge graph in order to allow queries from a public web site and also to virtually weave the textile with a software called Virtual Loom [68; 69]. The results of these queries are presented in standard web format, but it is also possible to create a visual representation of these data in the form of an interactive map. This component is the STMaps tool. STMaps receives datasets from the SILKNOW's knowledge graph (or any other knowledge graph from other DO) and shows them on a map. The data should have spatio-temporal information and be previously filtered, as is done by SILKNOW's search tool--called ADASilk [70]--so that the items visualized correspond to the interest of the user.
## 3 Chemical Composition of _Hyssopus officinalis_ L. and _Agastache foeniculum_ (Pursh) Kuntze EOs
### Antimicrobial Activity of Agastache foneiculum (Pursh) Kuntze
#### 4.2.1 Antifungal Activity  
[PERSON] et al., 2010 [49] showed the fungistatic effect of _A. foneiculum_ EO on _A. flavus_, _F. solani_, and _A. fumigatus_. Also, they demonstrated a fungicidal effect on the first two strains. [PERSON] et al., 2024 [19] demonstrated antifungal activity against two strains of yeasts: _C. albicans_ and _S. cerevisiae_. Meanwhile, [PERSON] et al., 2017 [40] showed antifungal effects on _A. flavus_ and _A. niger_. The data are comprehensively detailed in Tables 7 and 8.  
\begin{table}
\begin{tabular}{l c c} \hline
**Microorganism** & **MIC** & **MBC/MFC** & **Reference** \\ \hline \hline \multirow{6}{*}{_Staphylococcus aureus_ ATCC 6538 & 125 \(\mu\)g/mL & 125 \(\mu\)g/mL \\  & 500 \(\mu\)g/mL & 1000 \(\mu\)g/mL \\  & 125 \(\mu\)g/mL & 250 \(\mu\)g/mL \\  & 625 \(\mu\)g/mL & 125 \(\mu\)g/mL \\  & 625 \(\mu\)g/mL & 125 \(\mu\)g/mL \\  & 250 \(\mu\)g/mL & 250 \(\mu\)g/mL \\  & 250 \(\mu\)g/mL & 500 \(\mu\)g/mL \\  & 150 \(\mu\)g/mL & 250 \(\mu\)g/mL \\ \hline \hline \end{tabular} (40)  
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**Fungal concentration: \(10^{6}\) spore/mL**} \\ \hline \hline \multicolumn{1}{c}{**Food-borne fungal strains:**} & 20 \(\mu\)L EO & 5 \(\mu\)L EO \\  & 200 ppm & 400 ppm \\  & 400 ppm & 800 ppm \\ \hline \hline \end{tabular}
\end{table}
Table 7: Minimum inhibitory concentration and minimum bactericidal/fungicidal concentrations of _Agastache foneiculum_ EO.
# Supporting Privacy of Computations in Mobile Big Data Systems +
## 4 Secure and Verifiable Cloud Computing for Mobile Systems
### Construction and Evaluation of Garbled Circuits
#### 4.1.2 Secure Multiparty Computation of an Entry, \(A_{ab}\)  
Assume that \(n\) parties need to compute the value of an arbitrary function of their private inputs, namely \(f(x_{1},x_{2},\ldots,x_{n})\) without revealing their private inputs to one another. Assume that the function \(f(x_{1},x_{2},\ldots,x_{n})\) is expressed as a Boolean circuit (\(B^{\prime}\)) (Boolean circuit \(B^{\prime}\) is different from Boolean circuit \(B\). While \(B\) is a circuit that corresponds to the computation requested by the client (e.g., addition of two numbers), \(B^{\prime}\) is a circuit that creates the entries such as \(A_{ab}\) in the garbled tables of the garbled circuit \(GC\)) using \(XOR\) and \(AND\) gates only.  
We briefly describe the secure multiparty computation protocol of [PERSON] [8; 9] as follows. For each wire in the Boolean circuit, the actual binary value which corresponds to the \(XOR\)-sum of shares is distributed among the \(n\) parties.  
Evaluation of each \(XOR\) gate in the circuit, requires no communication--it is carried out locally; each party merely performs an \(XOR\) operation over its shares for the two input wires to obtain its share for the output wire.  
Evaluation of each \(AND\) gate in the circuit, however, requires communication between all pairs of parties. For the two inputs wires of the \(AND\) gate, let \(a_{i},\ b_{i}\) denote the shares of party \(p_{i}\); and let \(a_{j},\ b_{j}\) denote the shares of party \(p_{j}\). Then, the output of the \(AND\) gate is expressed as follows:  
\(\big{(}\oplus_{i=1}^{n}a_{i}\big{)}.(\oplus_{i=1}^{n}b_{i})=[\bigoplus_{1\leq i <j\leq n}((a_{i}\oplus a_{j}).(b_{i}\oplus b_{j}))]\oplus_{i=1}^{n}((a_{i}.b_ {i}).I)\), where \(I=n\ mod\ 2\).  
Each party \(p_{i}\) computes \(((a_{i}.b_{i}).I)\) locally; and the computation of each _partial-product_, \(((a_{i}\oplus a_{j}).(b_{i}\oplus b_{j}))\), is realized using 1-out-of-4 oblivious transfer (OT [15; 16]) between \(p_{i}\) and \(p_{j}\), such that no party reveals its shares to the other party [8; 9].  
Following the above procedure, the \(n\) parties evaluate each gate in the circuit. Thus, in the end, for the BMR protocol, as we have described above, each server \(p_{i}\ (1\leq i\leq n)\), obtains the share, \((f(x_{1},x_{2},\ldots,x_{n}))_{i}=(A_{ab})_{i}\), such that \(f(x_{1},x_{2},\ldots,x_{n})=A_{ab}=\bigoplus_{i=1}^{n}(A_{ab})_{i}\).
## 3 Results
### Comparison among Results from Different Models
#### 3.3.2 Nonforest Sites  
Air temperature is not usually a limitation factor in temperate croplands for GPP estimations, so R\({}^{2}\) values for AVM and TG models are lower than those for VPM, GR, and MOD_ALG. VPM gives the best performance (R\({}^{2}\) = 0.84 and RMSE = 42.24%), indicating that water may be another important influencing factor.  
AVM and TG models show satisfactory GPP estimates for grasslands (R\({}^{2}\) = 0.91 and 0.9, respectively); RMSE values from both models are lower than MOD17/MOD_ALG. [PERSON] _et al._[12] reported that AVM may be the optimum model to reflect GPP estimates when compared with the other four models in grasslands. The GR model generally overestimates GPP values due to the lack of EVI\({}_{\text{based}}\); VPM shows large uncertainty at higher GPP values. Therefore, EVI and temperature are more sensitive factors in GPP estimation in grasslands.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline  & \multicolumn{3}{c}{MF} & \multicolumn{3}{c}{Croplands} & \multicolumn{3}{c}{ENF} & \multicolumn{3}{c}{EBF} & \multicolumn{3}{c}{Grasslands} \\ \cline{2-13} Model & Bias & RMSE\% & R\({}^{2}\) & Bias & RMSE\% & R\({}^{2}\) & Bias & RMSE\% & R\({}^{2}\) & Bias & RMSE\% & R\({}^{2}\) & Bias & RMSE\% & R\({}^{2}\) \\ \hline AVM & \(-\)7.01 & 31.83 & 0.96 & \(-\)9.80 & 64.52 & 0.68 & \(-\)14.63 & 46.96 & 0.67 & \(-\)15.75 & 55.18 & 0.27 & \(-\)0.39 & 51.42 & 0.91 \\ GR & 6.31 & 46.77 & 0.84 & 11.51 & 56.13 & 0.80 & \(-\)12.76 & 44.43 & 0.61 & \(-\)14.67 & 54.18 & 0.25 & 6.63 & 107.15 & 0.85 \\ TG & \(-\)3.69 & 30.37 & 0.93 & \(-\)2.94 & 53.64 & 0.74 & \(-\)10.93 & 40.76 & 0.61 & \(-\)6.27 & 42.92 & 0.34 & 1.48 & 55.72 & 0.90 \\ VPM & \(-\)6.52 & 40.89 & 0.89 & \(-\)2.16 & 42.24 & 0.84 & \(-\)14.70 & 46.82 & 0.68 & \(-\)12.24 & 48.79 & 0.34 & 1.43 & 56.25 & 0.90 \\ MOD17 & \(-\)8.41 & 59.23 & 0.76 & \(-\)4.83 & 80.87 & 0.80 & \(-\)11.89 & 48.64 & 0.44 & \(-\)10.07 & 53.48 & 0.21 & \(-\)1.70 & 72.91 & 0.90 \\ MOD\_ALG & \(-\)7.64 & 58.04 & 0.75 & 2.85 & 48.43 & 0.79 & \(-\)17.53 & 56.97 & 0.48 & \(-\)25.48 & 72.82 & 0.16 & 1.43 & 55.02 & 0.90 \\ \hline \end{tabular}
\end{table}
Table 5: Performance of AVM, GR, TG, VPM, MOD17, and MOD_ALG models over the study period. Unit of bias: gC/m\({}^{2}\)/eight-day.
# Light-Responsive Polymeric Micellar Nanoparticles with Enhanced Formulation Stability
## 3 Results
### Measurement of Light-Induced Destabilization of Micellar Nanoparticles  
To investigate the light responsiveness of **DTX-NP\({}_{\text{DNQ}}\)**, the micellar nanoparticles were irradiated by 365 nm UV light, and DLS measurements were conducted to confirm the light-triggered destabilization process of **DTX-NP\({}_{\text{DNQ}}\)**. DLS results showed a change in the micellar diameter in response to UV light (Figure 5b). The irradiated **DTX-NP\({}_{\text{DNQ}}\)** (50 mW/cm\({}^{2}\), 10 min) showed a decrease in its hydrodynamic size from 143 nm prior to irradiation to 18 nm and additionally, 63% of DTX was released into the solution during the light-triggered process; however, the DTX release profile after 1, 3, and 5 min of UV irradiation could not be obtained due to the destabilization of **DTX-NP\({}_{\text{DNQ}}\)** (i.e., partial IC from DNQ) during dialysis. These results revealed that the micelles dissociated partly under UV light, which was attributed to the Wolff rearrangement of hydrophobic DNQ moieties into hydrophilic IC molecules (Figure S2). The change in the hydrophilic-hydrophobic balance of **NP\({}_{\text{DNQ}}\)** then destroyed the structure of the micelles.  
The light-triggered effect of the micelles was studied with MCF-7 cells (Figure 5c,d); Cells were incubated with **DTX-NP\({}_{\text{DNQ}}\)** for 2 h, washed with fresh media, irradiated by 365 nm UV light for 10 min (50 mW/cm\({}^{2}\)), and then further incubated for 24 h. Prior to irradiation, the cell viability of **DTX-NP\({}_{\text{DNQ}}\)** (400 ng/mL of DTX) was 55%. Upon irradiation with UV light, the cell viability was decreased to approximately 36%, similar to the cell viability of free DTX (25%), while there was no significant difference in the MCF-7 cell viability of **NP\({}_{\text{DNQ}}\)** with UV irradiation (Figure 5c). The study showed that  
Figure 5: (a) Cytotoxicity of HUVECs with **NP\({}_{\text{DNQ}}\)** (green bar), **DTX-NP\({}_{\text{DNQ}}\)** (blue bar), and DTX (red bar); (b) DLS measurement of size change of **DTX-NP\({}_{\text{DNQ}}\)** upon UV light irradiation (365 nm, 50 mW/cm\({}^{2}\), 10 min) in PBS; (c) MCF-7 Cells Viability with Light Irradiation (**NP\({}_{\text{DNQ}}\) & **DTX-NP\({}_{\text{DNQ}}\)**); (d) Cytotoxicity of MCF-7 cells with different concentrations of **DTX-NP\({}_{\text{DNQ}}\)** (blue bar), **DTX-NP\({}_{\text{DNQ}}\)** with UV light irradiation (365 nm, 10 min, 50 mW/cm\({}^{2}\)) (green bar) and DTX (red bar). Data are means \(\pm\) SD, \(N\) = 4, the concentrations of NPs were adjusted to the concentration of DTX by UV-Vis spectroscopy, asterisks indicate \(p<0.05\).  
DTX-NPDNQ** was more cytotoxic than **NPDNQ** with UV irradiation because most of **DTX-NPDNQ** were disrupted by UV irradiation and then released DTX into MCF-7 cells.
# The Relationship between Radon and Geology: Sources, Transport and Indoor Accumulation
## 4 Risk Assessment and Mitigation
### Geographical and Seasonal Variations in Radon Concentration
#### 4.2.3 Daily Variations  
The concentration of radon can also vary throughout the day due to daily fluctuations in temperature and atmospheric pressure, in addition to seasonal variations [185]. Although daily variations are generally smaller than seasonal variations, they can still be significant in some cases. The daily variations in radon concentration are influenced by various factors such as the rate of radon production, ventilation, and soil moisture. For example, during the day, the temperature and pressure typically rise, leading to increased soil gas flow and radon transport [187; 188]. Conversely, at night, the temperature and pressure usually drop, resulting in less soil gas flow and reduced radon transport. The knowledge of these daily variations is important for understanding radon exposure patterns and designing effective mitigation strategies. In their 2009 study, [PERSON], [PERSON], and [PERSON] explored the daily variations in indoor air ion and radon concentrations, highlighting their opposite effects on human health [189]. They found radon, a potentially lethal gas due to its association with lung cancer, to be a significant generator of air ions in the lower troposphere. The team measured air ion and radon concentrations in both outdoor and indoor environments, as well as their vertical gradients in residential buildings. Their study revealed a substantial correlation between positive air ion and radon concentrations indoors, with a correlation coefficient of approximately 0.7. Importantly, they observed that peak values for both outdoor and indoor concentrations occurred simultaneously, and a clear vertical gradient of concentrations was apparent in indoor measurements. The researchers concluded that the concentration of positive air ions could serve as an alternative method for assessing radon activity concentration, offering a valuable tool for monitoring this potentially hazardous gas.