esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
## 5 Experiments and Results
### Image Dehazing Result
The base model achieved better visual results. However, there were color distortions in some regions of the outdoor image, and the restored indoor image was blurred. Our model generated very natural colors in the outdoor image. The sky and trees were almost the same as those in the clear image. Our model also generated a much clearer indoor image than the base model (see the bookshelves). Figure 9 shows some visual results using the SOTS dataset. DCP and BCCR both had the same problem of color distortion. The dehazed images were dark. The base model performed better than AOD-Net, but the sky did not look natural and some haze was not removed. Our proposed model produced high quality haze removal. As compared with the results of the base model, our proposed model generated dehazed images with no color distortions in the sky. The buildings were very similar to those in the clear images. In the previous experiment, we demonstrated that the framework of cGAN outperformed various hand-crafted and deep learning-based methods with image dehazing datasets. In the second experiment, we aimed to evaluate some cGAN models in order to select the best model. We compared two versions of our cGAN model with the base model on custom datasets of road scene images. Table 7 shows the results using the rain dataset. Table 8 shows the results using the fog dataset with three more evaluation parameters: the visual contrast measure (VCM), color naturalness index (CNI) and fog reduction factor (FRF) [41]. Fog is a more serious defect than rain. In general, the evaluation metrics obtained on the fog dataset were lower than those of the rain dataset. We also compared our proposed models with two state-of-the-art image dehazing methods: FFA-Net [18] and MSBDN [20]. Figure 10 shows some visual results obtained using our proposed model 2, FFA-Net and MSBDN. With the two modifications in our design, both versions of our cGAN model achieved better performance than the base model (more than 0.6 dB in PSNR with the rain dataset and more than 0.7 dB in PSNR with the fog dataset). Furthermore, with the adoption of Mish as the activation function in the discriminator, model 2 could further improve the dehazing as compared with model 1. \begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Inference Time (s)** \\ \hline Base model [36] & 0.029 \\ \hline Our model & 0.044 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Inference time per image. Figure 8: _Cont_. Figure 8: Visual results using NYU/RESIDE dataset—first row: clear images; second row: hazy im-ages; third row: images dehazed using DCP; fourth row: images dehazed using AOD-Net; fifth row: image dehazed using base model; last row: images dehazed using our proposed model. Figure 9: _Cont._  
Figure 9: Visual results using SITS dataset—first row: clear images; second row: hazy images; third row: images dehazed using DCP; fourth row: images dehazed using AOD-Net; fifth row: images dehazed using base model; last row: images dehazed using our proposed model.
## 6 Case Study and Discussion
### Simulation System Construction  
For simplification of the simulation, a multi-microgrid system with two RMGs and one OBMG is constructed. All EV owners are living in the two RMGs, identified as RMG1 and RMG2, and working in the OBMG. Exchanging power limit of the RMGs is \(-95\) kW \(\leq P_{i}^{G}(t)\leq 95\) kW, and the limit for OBMG is \(-250\) kW \(\leq P_{i}^{G}(t)\leq 250\) kW. Since the three microgrids are geographically near, PV generation and wind generation curves are similar and are forecasted by artificial neural networks using historical weather data derived from local weather records. Load demands of the three microgrids are derived from data of real microgrids with similar sizes.  
A time-of-use electricity price is adopted as the energy exchanging prices with the main grid. The prices are demonstrated in Figure 5[23]. In order to guarantee the benefits of the EV owners, charging and discharging prices of EVs should be restricted, as in (18)  
\[p_{C}^{EV}(t)\leq\eta_{C}\cdot\eta_{D}\cdot p_{D}^{EV}(t) \tag{18}\]  
where \(p_{C}^{EV}(t)\) and \(p_{D}^{EV}(t)\) are charging and discharging prices of EVs. They are assumed to be time-invariable in this paper. Additionally, to encourage participation of microgrids, discharging prices of EVs should be lower than the highest exchanging price in OBMG, and charging prices should be higher than the lowest exchanging price in RMG.  
\[\left\{\begin{array}{l}p_{C}^{EV}(t)\geq\min(p_{RMG}^{G}(t))\\ p_{D}^{EV}(t)\leq\max(p_{OBMG}^{G}(t))\end{array}\right. \tag{19}\]  
For the EV model, BYD E6 is selected in the simulation. The capacity of the EV battery is 80 kWh and charging and discharging power limits are both 7 kW in normal charging mode. Charging and discharging efficiency are also identical, at 0.9. The daily cost of installing an EVBCDI is 0.45\(\lx@sectionsign\). Some other data are referred to in [25].
## 6 Results and Discussion  
### Comparison  
A model evaluation was provided to better understand and compare the performance of the forecasting models. The comparison of the performances was made by using three different error metrics in a multi-step scenario, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-Square (\(R^{2}\)), defined in Equations (19)-(21), respectively, where \(y_{i}\) is the observation, \(\hat{y}_{i}\) is the prediction for \(y_{i}\), and \(\bar{y}\) the mean of the observed data. RMSE is useful to measure the stability of the forecast and is sensitive to outliers. MAE measures the average of the forecast error values, where all the values are forced to be positive, and it is also very robust to outliers. It is very useful for training datasets corrupted by many outliers. The R-square metric represents how much of the variance for a dependent variable is explained by an independent variable or variables. An \(R^{2}\) score of 0.50 means that approximately half of the observed variation can be explained by the  
Figure 14: Flow Diagram of Seq2 Seq Models. (**a**) Seq2 Seq model flow diagram. (**b**) Attention model flow diagram.  
model. As higher the \(R^{2}\) coefficient, the higher the correlation between forecast and real values is.  
\[\text{MAE}(y,\hat{y})=\frac{1}{n}\sum_{i=0}^{n-1}\lvert y_{i}-\hat{y}_{i}\rvert \tag{19}\]  
\[\text{RMSE}(y,\hat{y})=\sqrt{\frac{1}{n}\sum_{i=0}^{n-1}(y_{i}-\hat{y}_{i})^{2}} \tag{20}\]  
\[R^{2}(y,\hat{y})=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}( y_{i}-\hat{y})^{2}} \tag{21}\]  
Every model was trained 20 times and evaluated in the testing set using the defined metrics. Table 1 presents the averaged prediction scores of the models when performing iterative predictions over the normalized testing data.  
Table 1 shows that the model with the best performance in every metric was the Attention model. The Seq2 Seq without Attention falls behind the others, being the less accurate one. This is because it has to compress all the information into a single vector, which makes it weaker to long sequences. Figure 15 shows a comparison between the predictions made by the models and the real temperature values. The comparison shows how the values predicted by the Attention model are closer to the real ones. Figure 16 shows the prediction made with the Attention model in the testing set of the three time series.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **MAE** & **RMSE** & **R-Square** \\ \hline LSTM & 0.054004 & 0.075457 & 0.913425 \\ GRU & 0.058296 & 0.083487 & 0.851738 \\ CNN & 0.053656 & 0.08737 & 0.831054 \\ DNN & 0.072263 & 0.095527 & 0.727253 \\ Seq2 Seq & 0.115742 & 0.171256 & 0.750568 \\ Seq2 Seq + Attention & 0.023398 & 0.045421 & 0.958567 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prediction evaluations.  
Figure 15: Comparison of models predictions with real values.
# Widely-Tunable Ring-Resonator Semiconductor Lasers
## 6 Conclusions  
We have presented an extensive overview of chip-scale widely-tunable lasers that use ring resonators for tuning and filtering out a single longitudinal mode. Wide-tunability is commonly achieved by utilizing the Vernier effect, where two or more rings have slightly different radii. The use of the Vernier effect results in increased tuning efficiency. We analyze and measure four different laser cavity architectures having rings with two different radii, and point out the advantages and disadvantages of each approach. The use of ring resonators, provided that the utilized waveguide platform offers sufficiently low propagation losses (which both Si and SiN waveguides do, with losses in dB/cm or sub-dB/cm range), has an advantage, as the effective laser cavity length at ring resonance is significantly enhanced, directly influencing linewidth. We show approaches for further reducing the linewidth, either by using external on-chip delay, or by the introduction of a third ring resonator with high-\(Q\) factor. Introduction of the third ring increases the number of controls in the laser, but we show that by using on-chip monitor photodetectors, such lasers are straightforward to control and tune. Lastly, we have compared the linewidth performance of all chip-scale widely-tunable lasers, and have shown that the ring based tunable lasers offer superior linewidth performance with integrated linewidths approaching few kHz and instantaneous linewidths in the sub-kHz range, while at the same time providing wide tuning (40+ nm), and high output power (up to 20 dBm). A fully-integrated widely-tunable ring-resonator based laser realized in heterogeneous silicon process, is a perfect candidate to be used in future complex photonic integrated circuits for communications and sensing.  
This work was supported by a DARPA EPHI and DODOS contracts. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing official policies of DARPA or the U.S. Government. [PERSON] was in part supported by NEWFELPRO Grant 25. [PERSON] acknowledges the support from the Fundamental Research Funds for the Central Universities (No. 2016 YJS030) and China Scholarship Council. The authors would like to thank [PERSON], [PERSON] and [PERSON] for helpful discussions.
## 2 Results and Discussion
### In Vitro Antioxidant Activity of SBP-III-3  
#### 2.3.1 HO\(\bullet\) Scavenging Activity  
HO\(\bullet\) is a highly reactive radical to the organism because it can destroy virtually all types of macromolecules including carbohydrates, nucleic acids (mutations), lipids (lipid peroxidation), and amino acids (e.g., conversion of Phe to m-Tyrosine and o-Tyrosine). As shown in Figure 2A, the HO\(\bullet\) scavenging rate of SBP-III-3 showed a dose-response relationship, and the EC\({}_{50}\) of SBP-III-3 was 0.867 mg/mL, which was lower than those of Pro-Ser-Tyr-Val (PSYV) (2.64 mg/mL) [18], Pro-Ser-Lys-Tyr-Glu-Pro-Phe-Val (PSKYEPFV) (2.86 mg/mL) [19], Pro-Tyr-Ser-Phe-Lys (PYSFK) (2.283 mg/mL), Gly-Phe-Gly-Pro-Leu (GFGPL) (1.612 mg/mL), Val-Gly-Gly-Arg-Pro (VGGRP)(2.055 mg/mL) [20], Phe-Ile-Met-Gly-Pro-Tyr (FIMGPY) (3.037 mg/mL), Gly-Pro-Ala-Gly-Asp-Tyr (GPAGDY) (3.92 mg/mL), and Ile-Val-Ala-Gly-Pro-Gln (IVAGPQ) (5.03 mg/mL) [10] from protein hydrolys of weatherfish load muscle, grass carp muscle and skin, and skate cartilages. SBP-III-3 showed good HO\(\bullet\) scavenging activity, which demonstrated that it could serve as a scavenger for reducing the damage induced by HO\(\bullet\) in biological systems.  
Figure 1: (**A**) Gel filtration chromatography of SBP-III on a Sephadex G-25 column; and (**B**) HO\(\bullet\) scavenging activities of subfractions from SBP-III. All of the values were mean \(\pm\) SD (\(n=3\)). \({}^{a-d}\) Columnwise values with the same superscripts of this type indicated no significant difference (\(p>0.05\)).
## 8 Future Directions
### Multi-Hop Relay Networks  
Implementing multi-hop relay networks where multiple UAVs act as intermediate relays extends the communication range and improves coverage. Each UAV decodes and forwards the signal to the next UAV or ground station. A cooperative communication strategy where UAVs use decode-and-forward (DF) relays to enhance signal quality and reliability over long distances can ensure robust communication even in challenging environments.
## 3 Spectrum Management Issues
### A. Carrier Aggregation
#### 3.1.1 Resource Sharing  
The authors in [91] have discussed the RRM shortcomings of the currently utilized CA technique. The issues persist with the CC selection and Resource Block (RB) allocation of selected CCs, as mentioned in 4G standards. Nevertheless, a method is developed on the basis of the head of line delay and delay  
Figure 2: Carrier Aggregation types.  
threshold, which enhances the overall data rate and Quality of Service (QoS) of the user, with low computational complexity. However, no significant outcome is observed in user fairness. In [92], researchers suggest fewer studies that have been undertaken on the findings of several performance parameters on the packet scheduling algorithm in the CC system. Therefore, a newer and orderly scheduling algorithm of packets is considered for multiple CC systems on fair criteria equivalence for downlink propagation. The technique supports real and non-real-time traffic with good energy efficiency and better utilization of spectrum resources. However, the performance results of the designed algorithm based on overall system throughput, fairness, and mean delay largely improve in comparison with those algorithms of independent CC without aggregated. The resource allocation and joint optimization of CC selection are observed in the current 5G CA system in the study [93], and the researchers have delivered a greedy-based algorithm method to resolve the issue. Therefore, the proposed technique helps improve performance by comparing existing schemes and computational complexity at an acceptable level. In another study [94], authors have worked on an enhanced method of CC selection algorithm to overcome the issues of the existing CC selection scheme. An innovative channel quality and traffic load algorithm approach are used in each CC. The simulation output shows that downlink performance is vastly improved as a newly designed CC selection approach provides high throughput and good QoS to all user equipment. However, the method still suffers from power equality when the number of users in a cell is high.
## 2 Safety Analysis Framework for Complex Electromechanical Equipment System Based on STAMP
### System-Theoretical Process Analysis (STPA)  
STPA is a new hazard analysis technique based on system safety theory. According to the above-mentioned description, an accident is defined as the result of a complex process in which the system behavior goes against the safety constraints. The main steps of STPA can be shown as follows:  
Step 1: Identify improper control actions of the system that may cause danger,  
Step 2: Determine how potential hazard controls may occur in step 1.  
The goal of STPA is to find comprehensive causes of accidents, and those actions which can happen or exist to affect safety are unsafe control actions (UCA). UCAs fall into four general types in STPA:  
(1) An unsafe control action is provided that creates a hazard,  
(2) A required control action is not provided to avoid a hazard,  
(3) A potentially safe control action is provided too late, too early, or in the wrong order,  
(4) A continuous safe control action is provided too long or is stopped too soon.
# Cyanobacterial Potential for Restoration of Loess Surfaces through Artificially Induced Biocrusts
## 3 Results
### Screening of Cyanobacterial Strains for Toxin, Biomass and Polysaccharide Production
#### 3.1.2 Quantification of Biomass Production  
The biomass production and the effects of nitrate presence in the culture medium are summarized in Table 2. The highest biomass (501.4 \(\pm\) 25.6 mg/L) in the medium BG11 N+ was produced by the strain _Nostoc_ sp. L5. The strain _Tolytotrix_ sp. L4 produced the highest biomass in medium BG11 N\(-\) (89.2 \(\pm\) 6.0 mg/L). Regarding the effects of nitrate on biomass production, four tested strains (_Chroococcidiposis_ sp. L1, _Nostoc_ sp. L2, _Tolytotrix_ sp. L4, _Nostoc_ sp. L5) were observed to have a higher biomass production when cultivated in the medium supplemented with nitrate, while one strain (_Nostoc_ sp. L6) showed higher biomass production under diazotrophic cultivation conditions. The continuous growth of all strains throughout the cultivation period of seven weeks was observed in the strains cultivated in the medium BG11 N+. However, three cyanobacterial strains (_Chroococcidiposis_ sp. L1, _Nostoc_ sp. L6, each in the 4 th, 5 th and 6 th weeks of cultivation, respectively) reached the highest cell density quicker when cultivated under diazotrophic conditions. A gradual decrease in biomass production followed afterward.
# A Fine-Tuning Based Approach for Daily Activity Recognition between Smart Homes
## 2 Related Work
### Methods of Daily Activity Recognition Based on Heterogeneous Smart Home Environments  
[PERSON] et al. proposed a method called XLearn [9]. This method is based on ontology and feature migration. Firstly, the similarity of sensors and active samples are calculated separately using ontology. The sensor and activity space remapping is completed using the similarity. Then, clustering and integrated learning methods are used to label the activities.  
[PERSON] et al. proposed a method called FSR [10]. Firstly, the common feature space of source and target domains is constructed using meta-features. Then, the similarity of the instance meta-features is calculated by the formula. Additionally, the source domain feature with the highest similarity to the target domain is selected as the mapping. Finally, activity recognition is then performed using ensemble learning.  
[PERSON] et al. proposed a method called SEMINAR [11]. Initially, the semantics of the sensor and active words are learned through word embedding. This creates a common semantic feature domain, which solves the problem of mapping different domains of sensors and activities. Then, regression fitting is performed through the long short term memory networks (LSTMs).  
[PERSON] et al. used binary to encode the sensor profile to obtain a vector of activities [12]. Then, the two activity vectors are correspondingly subtracted to obtain the similarity of the two activities. Finally, based on the similarity metric, a graph matching algorithm is used to automatically compute the appropriate feature mapping to complete daily activity recognition.  
[PERSON] et al. proposed a new transfer learning framework [13]. To begin with, they use web knowledge to retrieve keywords related to daily activities for sensor and activity mapping. Then, a maximum mean difference (MMD) metric is used to obtain the similarity of activities between two domains. Lastly, the similarity is used as the weight of daily activity features in the source domain to complete the activity mapping.  
[PERSON] et al. used an algorithm to mine the target data and extract the activity model from both spaces [14]. The activity model is then mapped from the source environment to the target environment using a semi-EM framework to complete daily activity recognition.  
[PERSON] et al. also contributed to the direction of instance-based transfer. They proposed a method called CDAR [15]. Firstly, network search technique and MMD metric are used to obtain the similarity between activities in two domains. Secondly, the source domain labels are mapped to the target domain with confidence, i.e., similarity, to generate pseudo-training data. Finally, a multi-class SVM model is trained for daily activity recognition.
## 3 Results
### Accuracy Analysis of the Integrated STP Pseudo-Observations  
To analyze the influences of the dynamic models and the sampling interval on the accuracy of integrating STPs, we choose the one-day reduced-dynamic POD solution of the GOCE satellite (orbital altitude of approximately 260 km) [31; 32], and the GRACE A/B satellites (orbital altitude of approximately 500 km) provided by the Jet Propulsion Laboratory [38]. The RD POD solutions are used as the true orbit.
# A Watermarking Technique for Biomedical Images Using SMQT, Otsu, and Fuzzy C-Means
## 2 Related Works  
Several researchers work in the area of digital biomedical image watermarking [4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. Most of them use the watermark technique for confidentiality, authentication, and improved security. [PERSON] et al. proposed a model for digital image watermarking using DWT [4]. [PERSON] et al. proposed a model for an invisible watermarking technique in biomedical images using DWT, discrete cosine transform (DCT), and singular value decomposition (SVD) [5]. [PERSON] et al. used a combination of DWT and DCT algorithms for embedding and extracting watermarks [6]. Both of the experiments were evaluated using PSNR. [PERSON] et al. created a method of watermarking using fuzzy image [7]. They embedded a watermark in a fuzzy segmented image. [PERSON] et al. implemented a method using particle swarm optimization (PSO) and DWT [8], where the host image quality is enhanced using PSO. After that, the watermark is embedded using DWT. [PERSON] et al. proposed a watermarking technique using singular value decomposition (SVD) and stationary wavelet transformation (SWT), where the host image is divided into several bands using SWT and then processed by SVD for embedding the watermark in the image [9]. [PERSON] et al. proposed a watermarking algorithm where first the biomedical image is divided into two regions: a region of interest (ROI) and a region of background (ROB) [10]. Then, the watermark is embedded into the ROI because it provides a better result. [PERSON] et al. [11] and [PERSON] et al. [12] proposed a model where the input images are divided into two regions: ROI and region of non-interest (RONI). This model focuses on avoiding disturbances by embedding the watermark in the ROI. A different approach was taken by [PERSON] et al. for securing the transmission of watermarked biomedical images [13]. [PERSON] and [PERSON] have proposed a Hamming code-based error detection using a graphics processing unit (GPU) for accelerating the transmission requirements of a watermarked biomedical image [4; 8]. Most of the research results are evaluated using PSNR, which shows how robust the image is. [PERSON] et al. proposed a model for watermarking based on hybridization of compression and cryptography algorithms [14], which consists of the region-growing algorithm and Hash algorithm-256 for embedding and extracting the watermark in the biomedical image.