esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### The Proposal and Integration of DenseDDSSPP into the Network
#### 3.5.1 Selection of Suitable Backbone Network  
As described above, the DenseDDSSPP module requires an input feature map, represented by the light-blue cube in Figure 3, to process through its layers and output a dense feature map. As mentioned in Section 3.4, DeepLabV3+ utilizes a backbone network to generate two feature maps from the input: one, denoted as \(F_{1}\), serves as the input for the DenseDDSSPP module, and the other, denoted as \(F_{2}\), is propagated to the decoder (illustrated by the yellow-colored sharp dotted line in Figure 3). Given its important role, selecting a suitable backbone network is essential for the overall performance of the DeepLabV3+ architecture. The present study performed an experimental evaluation of different networks to determine the suitable one. After careful consideration, Xception was selected as the ultimate backbone network for this work. Specifically, the _block3_sepconv2_layer of Xception was chosen to generate \(F_{2}\), while the _block13_sepconv2_bn_ layer was used to generate \(F_{1}\). The feature map, \(F_{1}\), was subsequently fed into the DenseDDSSPP module, producing the enhanced feature map \(F_{1}^{\prime}\). A visualization of these layers is presented in Figure 4. Consequently, the process following this step aligns with the latest DeepLab series, i.e., DeepLabV3+ [13], where \(F_{1}^{\prime}\) and \(F_{2}\) are concatenated with necessary feature enhancement and upsampling in the decoder to form a multi-scaled and dense feature map, \(D\), as depicted in Figure 3.  
Figure 3: Architecture of DeepLabV3+ with DenseDDSSPP module. The SE block in the decoder operates on a tensor of dimensions 1*1*1792, where 1*1 represents the spatial dimensions and 1792 denotes the number of channels.
## Derivation of Oscillators from Biquadratic Band Pass Filters Using Circuit Transformations  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 2 and [PERSON] 1**  
* [1] Department of Electronic Engineering, National Kaohsiung University of Applied Science, Kaohsiung 807, Taiwan; E-Mails: [EMAIL_ADDRESS] (H.-D.T.); [EMAIL_ADDRESS] (Q.-M.N.); [EMAIL_ADDRESS] (C.-Y.L.)
* [2] Department of Optometry, Chung Hwa University of Medical Technology, Tainan 717, Taiwan; E-Mail: [EMAIL_ADDRESS]
* [*] Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-7-381-4526 (ext. 5640); Fax: +886-7-381-1182.  
External Editor: [PERSON]  
_Received: 26 July 2014; in revised form: 12 September 2014 / Accepted: 22 September 2014 / Published: 29 September 2014_  
**Abstract:** Network transformations are the techniques to obtain new functional schemes from available circuits. They are systematic methodologies, since each transformation technique can be applied to many circuits to obtain the desired functions or characteristics. A convenient network transformation method, exploiting different circuit transformations, for deriving linear sinusoidal oscillators from biquadratic band pass filters is proposed. This method with generality can be applied to any band pass filter. The oscillation frequency of the new obtained oscillator is identical to the center frequency of the original band pass filter, and the useful properties of the selected band pass filter can be retained. Two examples are illustrated to confirm the feasibility of the proposed approach. The workability of the obtained oscillators is verified with PSPICE simulations.  
**Keywords:** band pass filter; circuit transformation; sinusoidal oscillator; sensitivity
## 1 Introduction
It gives excellent Low Probability of Recognition (LPR) characteristics and covert communication is possible in all ranges at high SNR. First covert experiment mimicking cetacean vocals to our best knowledge was conducted by [PERSON] et al. by dolphin clicks and whistles [19]. [PERSON] et al. used high frequency dolphin clicks as an information carrier for covert communication [20]. Initially they attained a 37 bps data rate, which later increased to 69 bps [21]. Dolphin whistles are mimicked using differential pattern time delay shift coding for clandestine communication [22]. [PERSON] et al. conducted successful tests for covert communication by using white sided dolphin and long fined pilot whale whistles [23]. A sea lion vocal was analyzed by [PERSON] et al. for mimicry communication and data was encapsulated. Simulation results showed a BER of almost zero [24]. Another novel method of covert mimicry communication was presented and tested using sea piling sounds using pulse position modulation [25]. A portable modemhas also been developed and tested which is suitable for transmitting mimicked covert signals [26]. Our research focuses on the development of standard mimicry codes similar to Morse code [27] based on sea natural noise. Conventional Morse codes are detected by the enemy and may be decoded. Our innovative coding technique is based on a carrier of humpback whale songs. Male humpback whales are famous for producing loud and long duration songs. They emit a vocal termed as a song in the frequency range of 8 Hz-8 kHz lasting from several minutes to hours. It is composed of several segments which are frequency and amplitude modulated. The whale song segments are mimicked into bionic Morse codes using information entropy for the characters of the English language. It givesperfect LPR constraint covert communication as the imitated communication signals have no chance of being recognized as a communication signal as it is composed of a natural humpback whale song. Our novel concept has been compared with Morse codes and better performance is achieved with a perfect covert prospective. A covert data rate of one character per second is achieved with negligible chances of detection. The novel concept has been verified at distances of five km and less than \(10^{-3}\) BER is achieved. This proves that the proposed method is feasible for LPR constraint covert underwater acoustic communication for long distances. This paper is organized as follows: Section 2 describes the technical characteristics of humpback whale songs. Section 3 highlights the contribution for the development of bionic Morse codes and comparison with Morse codes. The process of bionic Morse coding is briefly demonstrated in Section 4. Section 5 proves the novel concept feasible through the simulation experiment. Finally Section 6 concludes the paper and highlights the future research direction.
###### Abstract  
Face recognition using a single sample per person is a challenging problem in computer vision. In this scenario, due to the lack of training samples, it is difficult to distinguish between inter-class variations caused by identity and intra-class variations caused by external factors such as illumination, pose, etc. To address this problem, we propose a scheme to improve the recognition rate by both generating additional samples to enrich the intra-variation and eliminating external factors to extract invariant features. Firstly, a 3D face modeling module is proposed to recover the intrinsic properties of the input image, i.e., 3D face shape and albedo. To obtain the complete albedo, we come up with an end-to-end network to estimate the full albedo UV map from incomplete textures. The obtained albedo UV map not only eliminates the influence of the illumination, pose, and expression, but also retains the identity information. With the help of the recovered intrinsic properties, we then generate images under various illuminations, expressions, and poses. Finally, the albedo and the generated images are used to assist single sample per person face recognition. The experimental results on Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), Celebrities in Frontal-Profile (CFP) and other face databases demonstrate the effectiveness of the proposed method.  
face recognition; single sample per person; sample enriching; intrinsic decomposition Article
## 3 Results and Discussion
### SY Detection Using GCE/ERGO-NiNPs Electrode  
DPV measurements using GCE/ERGO-NiNPs electrodes were carried out at different SY concentrations from 10-1000 nM (Figure 7A). The plot of SY response versus concentration represents a linear variation following the equation Ip (uA) = (0.0066 \(\pm\) 0.0001) \(\times\) C (nM)-(0.031 \(\pm\) 0.014) (R2 = 0.996) (Figure 7B). The limit of detection (LOD) was calculated to be 3.7 nM from linear regression in a limited range from 20-200 nM following Equation (9) [60].  
\[\text{LOD}=3\times\text{Sy/b} \tag{9}\]  
where Sy represents the standard deviation of the response, and b is the slope of the calibration curve for a limited range from 10-200 nM.  
Figure 7: (**A**) DPV responses of GCE/ERGO-NiNP-modified electrode in acetate buffer pH 4 with varying SY concentrations from 10–1000 nM, namely 10, 20, 50, 100, 200, 350, 500, 700 and 1000 nM.  
(**B**) Plots of SY response versus SY concentration. Optimised DPV conditions: accumulation times of 2 min at 0 V/ECS, scan rate v = 20 mV s\({}^{-1}\), Es = 6 mV, ts = 300 ms, Ep = 30 mV, tp = 120 ms and Idt = 60 ms.  
The performance of this sensor is compared with some of the recent reported electrodes for SY quantification (see Table 1). It suggests that the GCE/ERGO-NiNP sensor exhibits good performance with a low LOD and a high sensitivity of 7 uA/uM with respect to other modified electrodes such as GCE/ERGO-AuNPs (1.5 uA/uM), GCE/ZnO-cystic acid (2.4 uA/uM), and GCE/GO-MWCNT (0.63 uA/uM).
## 3 Results
### Water Sorption and Solubility
#### 3.1.2 Water Solubility  
The means and standard deviations of water solubility values are shown in (Figure 6). Zendura showed the lowest water solubility in Groups 1 and 2 and the highest water solubility in Group 3. Essix Plus showed the highest water solubility in Groups 1 and 2, and it was the lowest in Group 3 (Figure 6). Water solubility (mean \(\pm\) SD) values varied from \(-3.01\)\(\pm\) 0.19 \(\mu\)g/mm\({}^{3}\) to \(-0.06\)\(\pm\) 0.06 \(\mu\)g/mm\({}^{3}\), \(-1.42\)\(\pm\) 0.19 \(\mu\)g/mm\({}^{3}\) to \(-0.11\)\(\pm\) 0.08 \(\mu\)g/mm\({}^{3}\), and \(-0.01\)\(\pm\) 0.24 \(\mu\)g/mm\({}^{3}\) to 0.55 \(\pm\) 0.11 \(\mu\)g/mm\({}^{3}\) in Groups 1, 2, and 3, respectively (Figure 6).  
The combined effects of the aging methods used and the type of thermoplastic retainer material on water solubility were evaluated using two-way ANOVA. There was a  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Source** & **Type III Sum of Squares** & **df** & **Mean Square** & **F** & **Sig.** \\ \hline Corrected Model & 1720.8  & 17 & 101.23 & 907.82 & 0 \\ \hline Intercept & 10,485 & 1 & 10,484.98 & 94,032.67 & 0 \\ \hline Group & 12.56 & 2 & 6.28 & 56.3 & 0 \\ \hline Material & 1661.6 & 5 & 332.32 & 2980.35 & 0 \\ \hline Group \(\times\) Material & 46.7 & 10 & 4.67 & 41.87 & 0 \\ \hline Error & 10 & 90 & 0.112 & & \\ \hline Total & 12,215.85 & 108 & & & \\ \hline Corrected Total & 1730.87 & 107 & & & \\ \hline \end{tabular}
\end{table}
Table 2: Two-way ANOVA (Tests of Between-Subjects Effects).  
Figure 6: Comparison of the water solubility between the groups. Groups with similar small letters have no significant difference at (\(p<0.05\)).  
statistically significant interaction between the type of aging and the type of thermoplastic retainer material on water solubility at (\(p<0.05\)), as shown in (Table 3).  
A statistically significant difference in the water solubility between the three groups was only observed in Zendura material (Figure 6). Statistically significant differences between Groups 1 and 3 and Groups 2 and 3 were found in Duran Plus, Essix ACE, and Imperelon S pd. Tru-Tain DX showed a statistically significant difference only between Groups 2 and 3 (Figure 6). The statistically significant difference in water solubility between the materials among the three groups is shown in Figure 7.
## 5 Conclusions  
The presented work demonstrated the importance of pH control, especially in terms of nutrient availability for microalgae, in order to ensure that bioremediation takes place via microalgal growth rather than by ammonium loss to the atmosphere. While looking at upscaling this technology, lower pHs of 6-6.5 should be maintained to minimise these ammonium losses, which are bound to occur in larger-scale systems. This study also emphasised the necessity for strain screening when considering digestate bioremediation; indeed, strains can perform differently on different digestates from different feedstock. Here, a pigmenture-based digestate yielded better dry weights in both _S. obliquus_ (0.23 \(\pm\) 0.002 g L\({}^{-1}\)) and _C. vulgaris_ (0.49 \(\pm\) 0.012 g L\({}^{-1}\)). Additionally, environmental factors and specific inhibitors can also influence performances and should be carefully considered. Finally, this study shed some light on acclimation processes in microalgae and on the resulting biomass composition, showing some advantages in terms of lipids and carbohydrate composition, where nitrogen starvation resulted in 6.94% DW of fatty acids in _S. obliquus_. The harvesting time was also found to be an impactful factor when targeting high-value compounds, and hence the importance of this work, which revealed some of the dynamics linked to nitrogen uptake from digestate. The presented work showed promising results and current findings could be utilised to further the field of digestate bioremediation using microalgae, especially for larger-scale applications and for high-value ingredient production within a circular economy.  
**Supplementary Materials:** The supporting informations are available online at: [[https://www.mdpi.com/article/10.3390/app12115429/s1](https://www.mdpi.com/article/10.3390/app12115429/s1)]([https://www.mdpi.com/article/10.3390/app12115429/s1](https://www.mdpi.com/article/10.3390/app12115429/s1)).
* **Author Contributions:** Conceptualisation, F.F., A.S. and D.d.l.B.; data curation, F.F. and J.I.G.-P.; formal analysis, F.F. and R.V.K.; investigation, F.F.; methodology, F.F. and J.I.G.-P.; supervision, C.A.L.; validation, F.F.; visualisation, F.F.; writing--original draft, F.F.; writing--review and editing, F.F., A.S., R.V.K. and C.A.L. All authors have read and agreed to the published version of the manuscript.
* **Funding:** This research was funded by the ALG-AD project funded under the INTERREG North-West Europe program (project number: NWE 520).
* **Institutional Review Board Statement:** Not applicable.
* **Informed Consent Statement:** Not applicable.
* **Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors gratefully acknowledge the scientific support of [PERSON] in discussing the experiment conceptualisation of this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 4 RSS-Based Localization Algorithms
### Fingerprint-Based Localization  
Fingerprinting is a localization technique that is well established in indoor localization applications. The reason for this is that the first phase, i.e., the building of a training database, requires a significant amount of time and effort. However, recent crowd-sourced database initiatives can tackle this problem. Therefore, in this paper, we optimize an ML-based fingerprinting algorithm for localization in an outdoor environment.  
As in many ML-based approaches, the localization data set described in Section 3 is split into 70% training messages, 15% test messages and 15% validation messages. The actual fingerprinting technique is split into two phases, as visualized in Figure 5. The first and offline phase consists of collecting training messages into a fingerprint database. Each entry of the fingerprint database consists of the RSS value to the serving eNB, together with the Cell Global Identity (CGI) and GPS coordinate of the ground-truth location. Consequently, in the second and online phase, a test fingerprint is being matched to the earlier collected fingerprints, stored in the training database. Using the \(k\)NN algorithm, we calculate the distance between the test sample and each training sample in signal space. Afterwards, the location of the UE is estimated by computing the centroid of the ground truth locations of the \(k\) nearest neighbors. Additionally, we optimize several \(k\)NN-based algorithms in this work. In brief, we iterate over four different RSS representations, 31 distance metrics and several values of \(k\), similar to what we did in [18]. After the most optimal parameter configuration is found, we validate our results using the messages in the validation data set.  
Figure 4: Visualization of the RSS-based ranging algorithm with sector information. A single receiving NB-IoT base station antenna covers a sector of 120 degrees. The RSS from that antenna is translated into a distance \(d\). The location of the UE is estimated to be at the center of the resulting arc.
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 5 Experiment and Analysis
### Analog Noise Image Test
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\eta\)} & \multicolumn{4}{c}{**Ceramic**} & \multicolumn{4}{c}{**Cameraman**} \\ \cline{2-11}  & **Noise** & **TV** & **CNN** & **MHE** & **Ours** & **Noise** & **TV** & **CNN** & **MHE** & **Ours** \\ \hline
0.02 & 30.15 & 28.24 & 34.31 & 24.36 & **36.16** & 29.85 & 31.24 & 34.26 & 30.21 & 36.48 \\
0.04 & 27.06 & 29.63 & 30.52 & 26.81 & **32.69** & 26.52 & 29.72 & 32.84 & 29.42 & **35.84** \\
0.10 & 24.57 & 28.39 & 26.75 & 25.65 & **29.76** & 24.13 & 31.25 & 32.59 & 30.34 & **35.47** \\
0.15 & 18.34 & 22.67 & 25.34 & 21.36 & **28.64** & 17.25 & 24.56 & 30.48 & 23.19 & **33.27** \\
0.20 & 12.19 & 19.34 & 24.89 & 16.68 & **28.94** & 10.57 & 20.31 & 24.65 & 16.34 & **28.76** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Peak signal to noise ratio (PSNR) (dB) results for ceramic and cameraman. Figure 10: Filtered results for cameraman corrupted by stripe noise with \(\eta\) = 0.17. (**a**) Clean; (**b**) Noisy; (**c**) total variation algorithm (TV); (**d**) convolutional neural network (CNN); (**e**) midway histogram equalization algorithm (MHE); (**f**) Ours. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\eta\)} & \multicolumn{4}{c}{**Ceramic**} & \multicolumn{4}{c}{**Cameraman**} \\ \cline{2-11}  & **Noise** & **TV** & **CNN** & **MHE** & **Ours** & **noise** & **TV** & **CNN** & **MHE** & **Ours** \\ \hline
0.02 & 0.876 & 0.921 & 0.905 & 0.916 & **0.976** & 0.957 & 0.974 & 0.962 & 0.982 & **0.993** \\
0.04 & 0.728 & 0.845 & 0.801 & 0.826 & **0.945** & 0.872 & 0.970 & 0.954 & 0.968 & **0.991** \\
0.10 & 0.543 & 0.878 & 0.579 & 0.835 & **0.927** & 0.684 & 0.962 & 0.859 & 0.958 & **0.986** \\
0.15 & 0.247 & 0.756 & 0.325 & 0.769 & **0.921** & 0.426 & 0.957 & 0.769 & 0.952 & **0.982** \\
0.20 & 0.134 & 0.723 & 0.187 & 0.743 & **0.908** & 0.243 & 0.952 & 0.654 & 0.942 & **0.979** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Structure similarity (SSIM) results for ceramic and cameraman.
## 3 Results  
Out of 40 patients, 32 (80%) patients were below 30 years of age while only 8 (20%) patients were above the age of 30 years. The gender distribution showed that 88 (58.7%) of the patients were male and 62 (41.3%) were female. Most of the patients, 26 (65.0%), were nonsmokers. The size of the preoperative periapical lesion was arbitrarily categorized into 6 mm or less and more than 6 mm for this study. The results showed that half of the patients had a periapical lesion size of 6 mm or less, while another half had more than 6 mm. Preoperative pain was present in 22 patients (55.01%) (Table 1).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & \(N\) = 40 (\%) \\ \hline
**Age** & \\ \textless{}30 years & 32 (80.0) \\ \textgreater{}30 years & 08 (20.0) \\
**Gender** & \\ Male & 28 (70.0) \\ Female & 12 (30.0) \\
**Smoking** & \\ Nonsmaker & 26 (65.0) \\ Smoker & 14 (35.0) \\
**Preoperative pain** & 22 (55.0) \\
**Periapical lesion size** & \\ \textless{}6 mm & 20 (50.0) \\ \textgreater{}6 mm & 20 (50.0) \\
**Postoperative healing** & 29 (72.5) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive Statistics for Demographics.  
Figure 5: (**A**). Immediate postoperative periapical radiograph showing periapical radiolucency around the apex of Tooth 12. (**B**). Periapical radiolucency decreased in six months. (**C**). Complete resolution of periapical radiolucency in one year.  
There was a statistically significant association between periapical healing and age (_p_-value = 0.025), smoking (_p_-value = 0.029) and the size of periapical lesion (_p_-value < 0.001). After periapical surgery, healing was observed in 29 patients, in which 26 (81.3%) patients were below the age of 30 years while only 3 (37.5%) patients were above 30 years of age on one year follow-up. Similarly, 22 nonsmoking patients (84.6%) revealed periapical healing compared to 7 (50%) smoking patients. Although the success of healing was higher in males, at 78.6% (22/28), compared to females, at 58.3% (7/12), no statistically significant relationship was found between gender and healing (_p_-value = 0.254). Likewise, no significant association between the presence of preoperative pain and postoperative healing (_p_-value = 0.723) was observed (Table 2).
## 4 Results and Analysis
### Analysis of Influencing Factors
#### 4.2.1 Effect of CP  
From the results of A1\(\sim\)A3 and B1\(\sim\)B3 (Table 1), the change of the curtain's position had little effect on RWT, and it only had influence on the stable-time of water after the set of TCC. Thus, in practical engineering, the position of TCC should be determined after considering the complex terrains, economic condition, waterway transport, and other specific cases in actual engineering.
# Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager
## 3 Results
### Importance Analysis of Different Features to Identify IHSPAs  
To estimate the relative importance weights of different features in identifying IHSPAs, the built-in functions of the SVM and neighborhood component analysis (NCA) [56] were used. First, the importance weights of the feature factors of the radial kernel function (RBF) SVM model were ranked using SVM built-in functions. The weights were listed in order from highest to lowest: NDBI, T\({}_{1}\), NDWI, T\({}_{3}\), NDVI, T\({}_{2}\), RI\({}_{2}\), RI\({}_{3}\), RI\({}_{4}\), and RI\({}_{1}\).  
NCA is supervised learning based on stochastic KNN, which uses the leave-one-out method to measure the similarity of nearest neighbors with the help of metric learning and cross-validation. It can effectively use objective optimization to reduce the conditionality of the input data and can be used to calculate the feature weights. The importance results based on NCA are listed in Table 6. They show that T\({}_{1}\) had the largest weight, followed by the NDBI, RI\({}_{2}\), RI\({}_{3}\), NDWI and T\({}_{2}\). The importance of thermal features in model training was demonstrated by both evaluation methodologies.