esa-sceva/satcom-chunk-collection huggingface dataset:
# Weakly Supervised Learning for Object Localization Based on an Attention Mechanism
## 5 Discussion and Conclusions  
In this paper, we introduced a WSOL method that used the pre-FC layers of VGG-16 and Inception-v3 as feature extractors, as well as using an attention mechanism. In order to minimize the learning parameters and minimize preprocessing, the proposed method in this paper simplified the model by only introducing an attention mechanism, rather than using the methods applied in existing studies, in order to avoid extensive sub-optimal localization problems. In this experiment, the proposed method showed higher performance on the CUB-200-2011 dataset with 75.21% top-1 classification accuracy and 55.15% top-1 localization accuracy. To establish why our proposed object localization outperformed other existing algorithms even without adding preprocessing and convolutional layers, we generated two attention maps from different feature maps and minimized the difference between these attention maps. The results provided vindication that the use of two attention maps makes it possible to cover a wider area than the area covered by attention maps generated by one classifier.  
Conceptualization, N.P.; Formal analysis, N.P.; Project administration, H.K.; Supervision, H.K.; Writing--original draft, N.P.; Writing--review & editing, H.K.; All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Top-1 Loc** & **Top-1 Clas** \\ \hline Inception V3-CAM [1] & 43.67 & 73.80 \\ Inception V3-DANet [13] & 49.45 & 71.20 \\ Inception V3-Ours & 52.71 & 75.21 \\ VGG-CAM [1] & 34.41 & 67.55 \\ VGG-AcoL [14] & 45.92 & 71.90 \\ VGG-ADL [15] & 52.36 & 65.27 \\ VGG-CCAM [11] & 50.07 & 73.20 \\ VGG-Ours & 55.15 & 73.51 \\ \hline Best-Performance & 55.15 & 75.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative experiments using other baseline algorithms.  
Figure 8: Result of the learning process per epoch.  
**Funding:** This research is supported by the Government-Wide R&D Fund Project for Infectious Disease Research (GFID), Republic of Korea (grant number: HG19C0682).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A UAV-Based Visual Inspection Method for Rail Surface Defects
## 3 Experiment Results
### Experiment Setup
#### 3.1.2 Experiment Environment  
As shown in Figure 8, the UAV equipped with an aerial camera was used to capture image or video information of rails as it flies overhead. Then, customized image processing software was used to analyze the captured UAV images. To avoid interference of obstructions, the flight height was set to 30 m. The images of rail tracks from the UAV were acquired on the freight line near the Badding railway south station and the freight line near Nansihuan in Beijing. One of the experiment environments is shown in Figure 7. The speed of the UAV and the actual length of the rail track in an UAV image are 2 m/s and 1 m, respectively. Therefore, to cover every part of the rail tracks, the frequency of the camera shutter is set to 2 frames per second. Each image captured by an UAV has corresponding POS information that contains the coordinates of the aircraft at that moment, and therefore, the location of rail defects can be found based on these coordinates.
## 5 System Model
### VLC Channel Model  
In this study, it is assumed that the LOS path is the sole means of communication between the photodiode (PD) and the LED. The expression for the LoS direct current (DC) channel gain in VLC is  
\[H_{VLC}=\begin{cases}\dfrac{A_{pd}L_{0}(\theta)}{D^{2}}cos(\psi)T_{s}(\psi)g_{ s}(\psi),&0<\psi<\psi_{c}\\ 0,&\psi>\psi_{c},\end{cases} \tag{9}\]  
where \(A_{pd}\) denotes the area of the photodiode, \(L_{0}\) represents the LED radiation distribution function, \(D\) is the distance between the transmitter and the receiver, \(T_{s}(\psi)\) signifies the filter gain, \(g_{s}(\psi)\) represents the concentrator gain, and \(\psi\) denotes the FoV of the photodiode.  
The radiation distribution function, considering the number of radiation patterns, \(m\), and the radiation angle of the LED, \(\theta\), can be expressed as  
\[L_{0}(\theta)=\frac{m+1}{2\pi}\cos^{m}(\theta). \tag{10}\]
## 4 Sound Recognition of Trunk Borer Using SVM  
The reasons for using SVM as the classification model in this study are as follows:  
* First of all, although deep neural networks and other related methods are preferred in most harsh acoustic environments, the artificial selection of features with strong robustness to noise, combined with the SVM classification model, is even better than the recognition results of the deep neural network model in some fields of sound recognition [27; 28];
* Secondly, the object discussed in this paper is the sound detection of trunk borers in a noisy environment. Compared with the deep neural network, the artificial selection feature combined with machine learning classification method requires fewer data, and the model is relatively easy to train. It is easier to verify the feasibility of the method;
* Thirdly, at present, the main machine learning methods commonly used for audio signal classification are GMM, HMM and SVM. The typical application of support vector machine is to solve the problem of binary classification, that is, to judge whether the test sample belongs to a positive class or negative class. The purpose of this study  
Figure 6: This picture expresses the frequency components of the audio signal of clear trunk borers, decomposed by different wavelet systems.  
is to identify the signals of trunk borer under an adverse acoustic background. In essence, it is a two-classification problem. In addition, the improved PNCC feature is a high-dimensional feature. Therefore, support vector machine is more suitable. Furthermore, SVM's strong generalization ability makes it more suitable for solving problems in practical applications;
* Finally, in this study, when comparing the accuracy of MFCC and PNCC in the sound recognition of trunk borer under the condition of noise, background sound, channel distortion, reverberation interference, or non-synchronous training test environment, the use of SVM is more contrastive.  
This paper uses the LIBSVM [29], developed by Dr. [PERSON] from National Taiwan University.
# An Overview of Self-Heating Phenomena and Theory Related to Damping and Fatigue of Metals
###### Abstract  
This paper presents an overview of the self-heating phenomena and the continuum thermodynamics framework related to the damping and fatigue of metals. The self-heating process under cyclic loading generally undergoes three phases: Phase I with gradually increasing temperature to a stabilized or steady-state in Phase II, followed by Phase III with an accelerated temperature increase until the test sample ruptures. Although energy dissipation and heat generation are all captured by the first law of thermodynamics, the functional form of the heat source(s) with entropy change is not formulated for engineering materials. Experimentally, infrared (IR) thermographic techniques can measure the surface temperature variation during constant-amplitude fatigue testing. The observed relationship between the stabilization temperature or temperature increase rate and the applied stress amplitude is often used to infer the fatigue endurance limit, above which point heat generation from \"damage\" leads to acceleration of self-heating. The IR thermographic fatigue testing offers a rapid alternative method to assess the material's fatigue strength. But, the full physical interpretation of the phenomena remains a challenge. On the other hand, the Tanaka-Mura-Wu model is introduced to describe fatigue crack nucleation via accumulation of dislocation dipole pile-up, which provides a class-A prediction (forecast before even happening) for fatigue crack nucleation life in terms of the material's elastic modulus, Burgers vector, surface energy, and the loading parameter such as cyclic stress/strain range. Then, the release of dislocation dipole pile-up energy to form new crack surfaces is brought into the energy argument. With the inclusion of crack formation energy in the first law of thermodynamics, a unified framework of deformation, damping, fatigue, and self-heating may be established for structural design.  
fatigue; self-heating; dissipation; damping; thermodynamics +
Footnote †: journal: Applied Physics Letters B  
19 January 2022  
Accepted: 14 March 2022  
Published: 17 March 2022
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 3 O-RAN in Smart IoT Systems
### Smart Energy Systems
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference, Year** & **Objective** & **Gap** & **Contribution** & **Remarks** \\ \hline Ref. [62], 2023 & \(X_{3},Y_{1}\) & 5G facilitates the seamless connectivity required by remote healthcare services but with additional cost & Employs O-RAN to extend remote healthcare services to rural areas cost-effectively & Smart healthcare solutions will remain incomplete if end-to-end privacy and security of patients data is not considered \\ \hline Ref. [83], 2022 & \(X_{3},Y_{2}\) & 5G-based inactivity timer prediction methods are not adaptive & Employ O-RAN concept to propose an adaptive policy that embeds intelligence in the RAN, guiding the efficient energy utilization of remote narrowband medical devices & The effort is conceptual; needs to be transited to real implementation \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of O-RAN in smart healthcare systems. leverage the O-RAN to promote openness and intelligence in addition to virtualization, scalability, and flexibility provided by the proprietary C-RAN and V-RAN. The openness in the edge offers the opportunity for heterogeneous computation and communication tasks to be jointly optimized and executed for latency reduction, which translates to the cost-effective energy consumption of participating IoT devices. Taking the computation offloading case [86] as an example, offloading points, processing speed, offloading ratio, and transmission power were jointly optimized within the edge. Since computation is carried out in the O-RAN-based edge and only the results are sent back to the participating nodes (IoT devices) via the downlink channel; this avoids traffic congestion, drastically reducing the energy consumption of IoT devices. Illustratively, in the smart transportation system, by minimizing the offloading delay in an open collaborative edge [64], the convergence leads to a massive communication latency reduction, a stringent requirement for the effective communication of autonomous vehicles. Likewise, in the smart healthcare system, the convergence can considerably reduce the cost associated with remote healthcare giving, just like the case of the OpenCare5G project [62], where the O-RAN concept was used to realize an interoperable architecture housing hardware and software, virtual machines and cloud-based open interfaces developed by different healthcare vendors.
## 3 Results and Discussion
### Luminescence Behavior of Fabricated Sol-Gel Materials
In this way, to cover the energy gap in silicate host, four Si-OH groups or twelve Q\({}^{3}\) units were needed. The second luminescence lifetime component, \(\tau_{2}\), was related with the remaining part of Eu\({}^{3+}\) ions which were successfully incorporated into CaF\({}_{2}\) nanocrystals during the controlled heat-treatment process. Indeed, due to low-phonon energy of the CaF\({}_{2}\) crystal lattice (\(\sim\)466 cm\({}^{-1}\)[40]), about 27 Ca-F phonons were needed to cover the \({}^{5}\)D\({}_{0}\)\(\rightarrow\)\({}^{7}\)F\({}_{6}\) energy gap. The probability of non-radiative relaxation from the \({}^{5}\)D\({}_{0}\) state was greater within the silicate framework than in the CaF\({}_{2}\) crystal lattice due to higher-phonon energies. Moreover, the probability of radiative relaxation was lower in more symmetric CaF\({}_{2}\) than in asymmetric sol-gel host. Therefore, the \(\tau_{2}\) components were significantly prolonged compared to \(\tau_{1}\). Moreover, we suggested that the tendency of the Eu\({}^{3+}\) ions to migrate into CaF\({}_{2}\) could be promoted by a similar ionic radii of Ca\({}^{2+}\) (1.00 A) and Eu\({}^{3+}\) (1.07 A). It should be also noted that the \(\tau_{1}\) luminescence lifetime component of fabricated CaF\({}_{2}\):Eu\({}^{3+}\) GCs was prolonged compared with the \(\tau\) lifetime estimated for xerogel. Both of the presented lifetimes were related with Eu\({}^{3+}\) ions, which were dispersed inside the silicate sol-gel host. To explain the differences in luminescence lifetime values, we used the results from the IR-ATR spectroscopy. As was evidenced by IR measurements, the huge amounts of OH groups with high vibrational energies (above 3000 cm\({}^{-1}\)) were identified in the structure of fabricated xerogels. Therefore, such numerous OH groups in the nearest vicinity of Eu\({}^{3+}\) ions were mainly responsible for non-radiative quenching of luminescence from the \({}^{5}\)D\({}_{0}\) excited state, which resulted in a relatively short lifetime. Simultaneously, the infrared measurements carried out for CaF\({}_{2}\):Eu\({}^{3+}\) glass-ceramic indicated a strong reduction of OH groups, hence, the amount of effective 'quenchers' in the nearest surrounding of Eu\({}^{3+}\) ions in the silicate sol-gel network was smaller than in xerogels. In consequence, the \(\tau_{1}\) luminescence lifetime was prolonged due to smaller amounts of OH quenchers.
## 2 Materials and Methods
### Characterization  
\({}^{1}\)H NMR of the oxidized starch (see Section 2.1) was undertaken at 600 MHz in D\({}_{2}\)O, using a Bruker 600 MHz Avance III HD (Bruker, Billerica, MA, USA) equipped with a 5-mm cryogenic CP-TCI z-gradient probe. Data were reprocessed using ACDLabs spectrus software (version 2019.0.1, ACD/Labs, Toronto, ON, Canada, 2019) and the spectra referenced to the residual solvent peak (4.79 ppm for D\({}_{2}\)O). 1H NMR (600 MHz, D\({}_{2}\)O, ppm): 3.50-4.40 (m, 5 H), 4.50-4.88 (m, 0.5 H), 4.90-5.50 (m, 1.5 H), 8.48 (s, 0.03 H), 9.31 (s, 0.03 H), 9.74 (m, 0.03 H).  
Optical microscopy images were collected using an Olympus BX43 equipped with an Olympus XM-10 digital camera (Olympus Corporation, Tokyo, Japan). Capsule size measurements were performed using a Beckman Coulter LS230 Laser Diffraction Particle Size Analyzer (Beckman Coulter Inc., Brea, CA, USA) with water as the mobile phase.  
GC MS analysis of peppermint: The samples were analysed on a Gas Chromatography--Mass Spectrometry (GC-MS) system from Agilent Technologies (Santa Clara, CA, USA). The GC-MS system consisted of an Agilent 7890A gas chromatograph and an Agilent 5975C mass spectrometer. The GC was equipped with a split/splitless inlet. The inlet was run in split mode with a split ratio of 25:1, temperature was set at 250 \({}^{\circ}\)C. The column used in this analysis was an Agilent J&W DB-WAX GC Column, 30 m, 0.25 mm, 0.25 \(\upmu\)m. The column was run with constant pressure mode at 16.1 psi. The oven was programmed with a temperature gradient: 40 \({}^{\circ}\)C for 4 min, then 5 \({}^{\circ}\)C/min to 50 \({}^{\circ}\)C for 1 min, then 18 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C for 0 min, then 25 \({}^{\circ}\)C/min to 245 \({}^{\circ}\)C for 1 min. The total runtime was 18.33 min. The mass spectrometer was operated in scan mode, with a scan range from 35-550 mass to charge ratio (\(m/z\)). Data analysis was performed with Agilent MassHunter Quantitative Analysis (Version B.09.00, Agilent Technologies, Santa Clara, CA, USA, 2017).  
The microencapsulation efficiency (\(MEY\)) was calculated as the percentage of peppermint oil entrapped in capsules relative to the total amount added according to Equation (1).  
\[MEY\ (\%)=\left(\frac{Microencapsulated\ peppermint\ oil}{Peppermint\ oil\ initially\ added}\right)\times 100 \tag{1}\]
## 3 Methods
### Optimization  
During the optimization phase, the system calculates the optimal schedule of home appliances that can be moved throughout the day. The optimization is based on the expected energy production from the solar system (previously calculated with the \(GHI\) value predicted by one of the two methods previously described, namely, the statistical or meteorological method), the user's energy consumption and appliance usage preferences, and the amount of energy in the energy storage system (\(ESS\)).  
The data described above were processed by a genetic algorithm (\(GA\)) that has the purpose of generating optimal scheduling of shiftable household appliances that will prefer the use of energy from the solar system rather than energy purchased from suppliers. The scheduling of shiftable household appliances, also called population, is first randomly generated by the algorithm, and then based on the user's preferences. Then, from the previous population, new generations are made to identify the best solution. This is defined as the solution that maximizes the fitness function.  
The fitness function used is as follows:  
\[fitness=\frac{1}{C_{T}} \tag{8}\]  
where:  
\[E_{T}(h)=E_{a}(h)-E_{PV}(h)\pm\ E_{bat}(h) \tag{9}\]  
And  
\[C_{T}=\sum_{h=0}^{23}E_{T}(h)C_{e}(h) \tag{10}\]  
where:  
* \(E_{T}(h)\) is the total consumed energy in a specific hour of the day.
* \(E_{a}(h)\) is the energy consumed by household appliances at a specific hour of the day. It is important to note that energy consumption is aggregated on an hourly basis because the raw data were sampled at a frequency of six seconds.
* \(E_{PV}(h)\) is the energy produced by the photovoltaic system at a specific hour of day.
* \(E_{bat}(h)\) is the energy present in the ESS at a specific hour of the day. The sign \"\(\pm\)\" in (Equation (9)) indicates that the ESS could be in discharging (+) or charging mode (-). It is important to highlight that the ESS is not charged by the energy that comes from the grid.
* \(C_{T}\) is the total cost of energy for the day.
* \(C_{e}(h)\) is the cost of energy for electricity at a specific hour of the day.
# High-Speed Directly Modulated Laser Integrated with SOA
## 3 Device Characterization
This indicates improved signal quality and reduced signal degradation due to SOA integration. As shown in Figure 6b, TDEC reaches its maximum at \(I_{S}=0\) and rapidly decreases as \(I_{S}\) increases to 10 mA for a fixed filter bandwidth. This improvement is attributed to the increased optical power from the SOA, which enhances the signal-to-noise ratio (SNR), leading to a clearer distinction between logic levels and a larger eye opening, as visually confirmed by the eye diagrams in Figure 6. The clear eye diagrams after 2 km and 10 km transmission highlight the potential of the SOA-integrated DML for passive optical network (PON) applications. In contrast, the DML without SOA exhibits eye diagrams with broader transition regions, frequency chirp, and asymmetric edge slopes, indicative of significant chirp. For the DML with SOA, when  
Figure 5: Spectrum of the proposed DML at room temperature (25 \({}^{\circ}\)C) with \(I_{S}=30\) mA and varying \(I_{M}\). the SOA is inactive (\(I_{S}=0\)), carrier transport minimizes the modulation impact on the DFB section, reducing chirp. Activating the SOA (with \(I_{S}>0\)) reduces carrier transport, increasing the DFB section's modulation sensitivity and potentially leading to increased chirp, although the improved SNR and power still result in overall better performance as evidenced by the TDEC and eye diagrams. It should be noted that, in theory, the dynamic gain response of the SOA may introduce a certain degree of chirp. However, in our experiments, no significant performance degradation attributable to chirp was observed. As shown in Figure 6b, the TDEC value continuously decreases with increasing SOA bias current, and the eye diagram in Figure 6c also exhibits a clear improvement in opening. These results indicate that the overall signal quality is effectively enhanced. Therefore, under the current device structure and bias conditions, the output power gain and signal amplification provided by the SOA play a dominant role in performance improvement. The positive effect of increasing the signal-to-noise ratio (SNR) clearly outweighs the potential negative impact of chirp.
## 3 Results and Discussion
### Texture Evolution of Alloy during Annealing
#### 3.2.2 Annealing Temperature Effect
The size of the reverse plastic zone under plane-stress condition can be estimated thus:  
\[r_{c}\approx\frac{1}{\pi}\left(\frac{\Delta K_{l}}{2\sigma_{0.2}}\right) \tag{2}\]  
For a tension-tension fatigue loading, compressive stress exists in this reverse plastic zone that has an inhibitory effect on the crack growth. Generally, the larger the size of the reverse plastic zone, the lower the FCGR. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Direction** & \(\sigma_{0.2}\) **(MPa)** & \(\sigma_{\text{th}}\) **(MPa)** & \(\delta\) **(\%)** \\ \hline \multirow{2}{*}{Sample 1} & RD & 305.5 \(\pm\) 6.9 & 375.7 \(\pm\) 5.6 & 20.6 \(\pm\) 0.6 \\  & TD & 284.8 \(\pm\) 4.3 & 358.6 \(\pm\) 4.5 & 21.1 \(\pm\) 0.5 \\ \multirow{2}{*}{Sample 2} & RD & 266.5 \(\pm\) 4.1 & 353.7 \(\pm\) 6.3 & 23.5 \(\pm\) 0.7 \\  & TD & 253.8 \(\pm\) 3.5 & 344.6 \(\pm\) 3.1 & 24.3 \(\pm\) 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mechanical tensile properties of two T3-tempered samples. Figure 15: Fatigue crack growth rates (FCGRs) vs. applied \(\Delta\)K of Sample 1 and 2, and an industrial 2524-T3 alloy. Previously, [PERSON] et al. [2011] investigated the texture evolution of a hot-rolled Al-Cu-Mg plate, annealing at temperatures from 300 \({}^{\circ}\)C to 400 \({}^{\circ}\)C, finding that the fracture toughness (\(\Delta K_{l}\)) of the alloy is reduced with the increase in the volume fraction of Brass texture. Additionally, as shown in Table 6, this Brass texture can increase \(\sigma_{0,2}\) to a significant extent; both these effects can lead to a decrease in \(r_{\text{c}}\) and thus a higher FCGR. This may be due to the lower fatigue crack growth resistance of Sample 1.