esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This paper has presented a dual-channel acquisition method that is based on the extended replica folding algorithm (DC-XFAST) method for long PN-code acquisition. To make up for the SNR deterioration resulting from folding and false alarm clues to peak position ambiguity, the proposed method uses multi-peak verification to guarantee a low false alarm probability and applies max-valuedetection to achieve a high detection probability. Compared with the unfolding method ZP, DC-XFAST takes advantage of reducing the code phase to be searched by folding the local PN-code to shorten the mean acquisition time. Compared with the folding methods XFAST and DF-XFAST, DC-XFAST has a stable detection probability and a lower false alarm probability. Benefiting from its multi-peak secondary verification, DC-XFAST requires much less mean acquisition time than other methods. The detailed structure of this method is described and its performance is analyzed.  
The enhancement of DC-XFAST with respect to DF-XFAST, XFAST and ZP has been demonstrated by numerical results. The best acquisition probability gain of DC-XFAST method would be about 7 dB compared with XFAST and 2 dB compared with DF-XFAST when the folding number is 20 and the threshold is 4. In addition, with the advantage of higher detection probability and lower false alarm probability, the mean acquisition time of DC-XFAST would be about 1/10 that of XFAST, 1/5 that of DF-XFAST and 1/50 that of zero-padding.  
Numerical results show that the performance of DC-XFAST is optimized as a whole. In the situation of complex electromagnetic interference and low SNR, this method can achieve a stable detection probability and a low false alarm probability. Moreover, this method takes much less acquisition time than the conventional methods when the time-slot is short, therefore, DC-XFAST is a viable solution for long code acquisition in inter-satellite links.  
Conceptualization, H.Z. and Y.C.; Methodology, H.Z. and Y.C.; Software, Y.C.; Validation, W.F., H.Z. and C.Z.; Formal Analysis, Y.C.; Investigation, W.F.; Resources, H.Z.; Data Curation, W.F.; Writing-Original Draft Preparation, Y.C.; Writing-Review & Editing, H.Z., Y.C. and C.Z.; Visualization, Y.C.; Supervision, W.F.; Project Administration, W.F.; Funding Acquisition, H.Z. This research was funded by the Chinese National Natural Science Foundation under Grant No. 91438116.  
This research is supervised by [PERSON] and [PERSON] from Beihang University. Thanks also go to the authors of the papers mentioned in Reference.  
The authors declare no conflict of interest.
# Photovoltaic Solar Cells: A Review
## 2 Solar Cells
### Materials Employed in PV Cells  
A concise comparison between the different materials used in PV cells nowadays with their advantages and drawbacks is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Sub-Material** & **PCE** & **Advantages** & **Problems** \\ \hline \multirow{6}{*}{c-Si} & & & & (1) & (c-Si) technology is too expensive for large-scale operation [38]. \\  & & & & (2) & (c-Si) modules have a weak interaction with light as Si is an indirect bandgap semiconductor [54]. \\  & Single crystal & 20\% [53] & & (1) & Moncrystalline solar cell is generally used due to its high-efficiency level as compared to the multi-crystalline cell [54]. \\  & Single crystal & 20\% [53] & & (2) & Occupy a small space [55]. \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{6}{*}{Polycrystal} & \multirow{2}{*}{16\% [60]} & \multirow{6}{*}{Cost-effective as compared to the monocrystalline module [54].} & & (1) & Less efficiency than the monocrystalline module as it is made of impure Si [60]. \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{6}{*}{a-Si} & \multirow{6}{*}{11.3\% [61]} & (1) & much cheaper than (Si-c) technology [55]. & & Lower efficiency than the mono-crystalline and poly-crystalline solar cells [9]. \\  & & & (2) & Long-term instability [8]. \\ \cline{1-1} \cline{2-5}  & & & (1) & Good match with the solar spectrum to get energy at shorter wavelengths compared to Si modules [10]. & \\ \cline{1-1} \cline{2-5}  & & & (2) & Cadmium is abundant [62]. & \\ \cline{1-1} \cline{2-5}  & & & (1) & CIGS have high efficiency that is similar to that of (c-Si) PV cells. & \\ \cline{1-1} \cline{2-5}  & & & (2) & Less expensive as CIGS can absorb light using only \(\sim\)2.0–2.5 mm layer thickness, which decreases the use of raw materials. & \\ \cline{1-1} \cline{2-5}  & & & (3) & Easy to fabricate compared to (c Si) based PV cells [64]. & \\ \hline \multirow{6}{*}{GaAs} & \multirow{6}{*}{Over 30\% [66]} & (1) & High resistivity to heat damage [67]. & (1) & Ga is an expensive material because of its low abundance [69]. \\  & & & & & \\ \cline{1-1} \cline{2-5}  & & & (2) & As is highly toxic [70]. \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison between the typical types of materials used in PV solar cells.
## 3 Results
### Integration Strategies for Nanosensors in Bridge Maintenance
#### 3.6.4 Environmental and Long-Term Considerations  
Consider the impact of various environmental factors (such as corrosion, material degradation, biofouling, etc.) on sensor performance and devise strategies to mitigate these effects to ensure long-term reliability of the SHM system.  
* Protective Coatings and Housings: Employ protective coatings and sensor housings that shield them from corrosive and abrasive elements without compromising data quality.
* Long-Term Calibration: Implement methods to ensure that sensors remain calibrated over extended periods and establish procedures for regular recalibration and maintenance.
* Material Compatibility: Ensure that the materials of the sensors are compatible with the bridge materials to prevent adverse reactions that could compromise structural integrity or sensor performance.
###### Abstract  
The power-generation mix of future grids will be quite diversified with the ever-increasing share of renewable energy technologies. Therefore, the prediction of electricity demand will become crucial for resource optimization and grid stability. Machine learning- and artificial intelligence-based methods are widely studied by researchers to tackle the demand forecasting problem. However, since the COVID-19 pandemic broke out, new challenges have surfaced for forecasting research. In such a short amount of time, significant shifts have emerged in electricity demand trends, making it apparent that the pandemic and the possibility of similar crises in the future have escalated the complexity of energy management problems. Motivated by the circumstances, this research presents an hour-ahead and day-ahead electricity demand forecasting benchmark using Deep Polynomial Neural Networks (DNN) and Gene Expression Programming (GEP) methods. The DNN and GEP algorithms utilize on-site electricity consumption data collected from a university hospital for over two years with a temporal granularity of 15-minute intervals. Quarter-hourly meteorological, calendar, and daily COVID-19 data, including new cases and cumulative cases divided by four restriction levels, were also considered. These datasets are used not only to predict the electricity demand but also to investigate the impact of the COVID-19 pandemic on the electricity consumption of the hospital. The hour-ahead and day-ahead nRMSE results show that the DNN outperforms the GEP by 8.27% and 14.32%, respectively. For the computational times, the DNN appears to be much faster than the GEP by 82.83% and 78.56% in the hour-ahead and day-ahead forecasting, respectively.  
electricity; demand; forecasting; Deep Polynomial Neural Networks (DNN); Gene Expression Programming (GEP); COVID-19 pandemic
## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Understanding Impacts on Physical Properties via DL
#### 4.1.2 DL for Predicting Ocean Warming Trends and Associated Extreme Weather Events
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Area** & **Reference** & **Application** & **Data** & **Model** & **Results** \\ \hline \hline \multirow{8}{*}{Ocean pH} & [PERSON] et al. [99] & pH Estimation & SST, SSS, dissolved oxygen, nitrate, phosphate, silicate, spatial coordinates, time & ANN & pH RMSE = 0.04. \\  & & & (cruises); SST, SSS, dissolved oxygen, nitrate, phosphate, silicate (FVCOM) & & \\ \hline [PERSON] et al. [100] & pH Estimation & pCO\({}_{2}\), SSS, SST (LDEO); & RF, GRNN, & Global pH Product: \\  & & & pCO\({}_{2}\), SSS, ST, TA, pH & FFNN & High-resolution \\  & & & (GLODAP); Chl, SST, RRS & & (0.25\({}^{\circ}\times\) 0.25\({}^{\circ}\)) monthly \\  & & & (MODIS-Aqua) U-wind, V-wind (CCMP); & & \\  & & & MLD (CMES) & & & \\ \hline [PERSON] et al. [101] & pH Estimation & SST, SSS, Chl, pCO\({}_{2}\), pH & LR, RF, BP-NN & R\({}^{2}\) = 0.9702, \\  & & & (CMEMS); pH (KEO, CCE1, & & RMSE = 0.0074. \\  & & & Kaneohe stations) & & \\ \hline [PERSON] et al. [103] & pH Estimation & DIC, TA, fCO\({}_{2}\) (GOMECC); & GOM-NNpH & pH RMSE = 0.008. \\  & & & SST, SSS, Chl, pH, nitrate, oxygen, pressure (BGC-Argo) & & \\ \hline [PERSON] et al. [107] & TA Estimation & SST, SSS, nitrate, TA & MLP, RF, & TA RMSE = 3.08 umol/kg, \\  & & & (GLODAP); SST & TabNet & R\({}^{2}\) = 0.99. \\  & & & (MODIS-Aqua); nitrate & & \\  & & & (WOA2018); SSS (MMOI-SSS) & & \\ \hline [PERSON] et al. [106] & pH \& TA & DIC, TA, pH, SST, SSS & ANN & Produced high-resolution \\  & Estimation & & (OCADS); Wind speed, & & (0.04\({}^{\circ}\times\) 0.04\({}^{\circ}\)) daily maps \\  & & & direction, stress (ASCAT); Chl, & & of DIC, TA, and pH. \\  & & & RRS (MODIS-Aqua), PIC, & Model Accuracy: \\  & & & POC (VIRRS); SSS (SMOS); & & TA bias = 4 μmol/kg, \\  & & & SST (OISST); MLD (CMEMS) & & pH bias = \(-\)0.025. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selected studies on DL applications in ocean physical property analysis.
## 3 Method
### Model Fusion  
In this paper, RL-Fusion employs two model fusion methods (TIES [13] and Slerp [30]) as the basic fusion methods improved by reinforcement learning to optimize the fusion parameters for model fusion. Unlike traditional SLERP, which is limited to interpolating between two models and requires predefined interpolation factors, RL-Fusion dynamically adjusts these factors using reinforcement learning to achieve optimal fusion configurations. Similarly, while TIES focuses on resolving parameter redundancy and sign conflicts through fixed density and weight parameters, RL-Fusion enhances TIES by leveraging real-time feedback to refine density and weight settings, ensuring better alignment with task-specific objectives. By integrating reinforcement learning, RL-Fusion not only overcomes the limitations of these methods but also introduces a dynamic evaluation mechanism that continuously adapts the fusion process based on performance feedback. This approach ensures a more intelligent, adaptive, and efficient model fusion process compared to static methods like SLERP and TIES.
# 3D Behavior Recognition Based on Multi-Modal Deep Space-Time Learning
###### Abstract  
This paper proposes a dual-stream 3D space-time convolutional neural network action recognition framework. The original depth map sequence data is set as the input in order to study the global space-time characteristics of each action category. The high correlation within the human action itself is considered in the time domain, and then the deep motion map sequence is introduced as the input to another stream of the 3D space-time convolutional network. Furthermore, the corresponding 3D skeleton sequence data is set as the third input of the whole recognition framework. Although the skeleton sequence data has the advantage of including 3D information, it is also confronted with the problems of the existence of rate change, temporal mismatch and noise. Thus, specially designed space-time features are applied to cope with these problems. The proposed methods allow the whole recognition system to fully exploit and utilize the discriminatory space-time features from different perspectives, and ultimately improve the classification accuracy of the system. Experimental results on different public 3D data sets illustrate the effectiveness of the proposed method.  
space-time characteristics; feature fusion; 3D skeleton +
Footnote †: journal: Applied Sciences
# Occlusion Avoidance in a Simulated Environment Using Reinforcement Learning
## 3 The RL Environment
### Reward Function  
The final element of the environment is the reward function that is used for training the reinforcement learning agent. Here, our goal was to ensure that the agent found a pose that avoided all occlusion in as few steps as possible. To ensure this, in every step when the occlusion is not resolved, we give a small penalty (\(-0.2\)) to the agent. Once the occlusion has been successfully avoided, the agent receives a reward of 5.  
However, we felt that giving only sparse rewards at the end of the optimization would result in subpar performance compared to using a dense reward system. By giving direct feedback to the RL agent after every step, the training procedure can establish the relationship between actions and rewards much more quickly. To do this, we compare the amount of occlusion before and after the action is taken, and add this to the reward.  
However, this addition presents a new problem. The amount of occlusion is measured in pixels and has a variance that is several orders of magnitude higher than the fixed rewards introduced earlier. Moreover, the amount of occlusion varies significantly due to the size of the objects in the environment, which is out of the agent's control. To solve this problem, we normalize the size of this component with the size of the objects in the given scene. For our test. we investigated the following two normalization schemes:  
1. Union-based normalization: the occlusion-based reward is divided by the total area of the object silhouettes in the first frame.
2. Intersection-based normalization: the occlusion-based reward is divided by the area of the occlusion in the first frame.  
To summarize, the reward function is defined as:  
\[R=L_{Oct}^{t-1}-L_{Oct}^{t}+P(L_{Oct}^{t}), \tag{7}\]  
\[P(L_{Oct}^{t})=\begin{cases}5,&\text{if }L_{Oct}^{t}=0\\ -0.2,&\text{otherwise}\end{cases} \tag{8}\]  
where \(L_{Oct}^{t}\) is the amount of occluded pixels in timestep \(t\), and \(P\) is the additional penalty term.
## 2 Materials and Methods
### Analysis of Audio Recordings  
Audio recordings were made at all measurement points using a condenser microphone with omnidirectional characteristics. The microphone sensitivity was set to its highest value, and the sampling frequency was configured at 48 kHz. For environmental recordings, the sampling frequency should be chosen based on the recording's purpose and the required level of detail. A sampling frequency of 48 kHz ensures sufficient quality to capture the general nature of the environmental sounds [55]. The recordings were made simultaneously with the measurements taken by a noise meter. In a subsequent step, the recordings were analyzed concerning the components of sound phenomena, primarily regarding their origin: natural and anthropogenic sounds, as defined in [56].
## 4 Conclusions  
A multilayer bandpass filter with high selectivity and wide stopband is proposed in this letter. TZs introduced by anti-parallel coupling network as well as discriminating coulpling between quarter- and half-wavelength resonators improve the selectivity and out-of-band suppression of the BPF.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **CF (GHz)** & **FBW (\%)** & **N** & **TZ@Sizeband** & **Size (\(\lambda_{g}*\lambda_{g}\))** & **Mental Layers** \\ \hline
[1] & 2 & 10 & 2 & 0.85,1.15 & 0.79 \(\times\) 0.06 & 1 \\
[4] & 2.4 & 3.5 & 3 & 0.83,1.08 & - & 1 \\
[7] & 2.75 & 3.6 & 4 & 0.89,1.16 & 0.18\(\times\)0.29 & 1 \\
[9] & 8.7 & 16 & 3 & 0.99,1.16 & 0.29 \(\times\) 0.57 & 1 \\
[10] & 13.53 & 3.9 & 3 & 0.97,1.04 & 0.84 \(\times\) 0.84 & 1 \\
**This work** & **2.48** & **8.1** & **5** & **0.84,1.08** & **0.20 \(\times\) 0.22** & **3** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance Comparisons with previous works  
Figure 6: Simulated and measured results of the proposed BPF (solid lines: measured results; dash lines: simulated results) (**a**) Simulated and measured amplitude of S parameters (dB). (**b**) Measured group delay (ns). (**c**) Simulated and measured phase of S parameters (deg).  
To verify the design, a prototype BPF is fabricated centered at 2.49 GHz. Both simulated and measured results manifest the performance with good selectivity and extended stopband. With these features, The proposed BPF is attractive in modern wireless system.  
Conceptualization, J.C. and H.C.; methodology, J.C.; software, H.C.; validation, J.C., H.C.and R.Z.; formal analysis, J.C.; investigation, H.C.; resources, R.Z.; data curation, J.C.; Writing--Original draft preparation, H.C.; Writing--Review and editing, R.Z.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by National Natural Science Foundation of China 62001232, 61971224 and Jiangsu Provincial Natural Science Foundation under Grants Nos. BK20180457.  
The authors declare no conflict of interest.
## 2 Taxonomy
### Machine Learning Detection  
Since tunnel technology is densely cultured and complex, fingerprint-based methods have required maintaining a huge database with limited patterns, and statistical feature methods have not been able to deal with traffic camouflage dynamically in recent years. Tunnel traffic detection has gained new vitality with the advent of machine learning (ML).  
There are three types of ML: supervised, unsupervised, and semi-supervised. In supervised learning, accuracy is measured by constantly comparing prediction results to training data. Supervised learning includes decision trees such as C4.5, RF (random forest), SVM (support vector machine), etc. Additionally, its training process requires a great deal of manual labels and selecting features is highly dependent on expert knowledge. Unsupervised learning, also known as clustering, distinguishes between categories based on unlabeled data in large datasets. In reality, this method is simpler, but its accuracy is not high due to the large amount of unlabeled raw tunnel traffic in real networks. Moreover, semi-supervised learning, which combines advantages from both supervised and unsupervised learning, is more accurate than unsupervised learning, and manual involvement is reduced to some extent.  
Since deep neural networks do not extract features in the training process, which can automatically learn high-dimensional abstract data (called E2E), they are extremely popular for the classification of tunnel traffic on large scales. RNN (recurrent neural network), LSTM (short- and long-term memory networks), etc., can handle the temporal features of tunnel traffic well, CNN (convolutional neural networks) can handle the spatial features of tunnel traffic well, and the GAN (generative adversarial network) can be used to alleviate class imbalance in datasets. A deeper and more complex network model structure also means that parameter adjustment is more time-consuming.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Traditional**} & \multicolumn{3}{c}{**ML**} \\ \hline
**Fingerprint** & **Statistic** & **Supervised** & **Unsupervised** & **Semi-Supervised** \\ \hline \hline \#Match [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24] & \#Markov [25; 26] & \#SVM [16; 27; 28; 29; 30; 31; 32; 33] & \#CAE [34] & \#VAE [35] \\ \hline \#N-gram [28; 36; 37] & \#MaMPF [38] & \#Decision Tree (C4.5/5.0) [29; 32; 33; 39; 40; 41; 42; 43] & \#OLDBSC [44] & \#Bi-GRU [35] \\ \hline \#MD5[24] & \#ICNN [40; 43; 45; 46] & \#RF [29; 37; 43; 47; 48] & \\ \hline \#Degree Distribution [49] & \#Naive Bayes [17; 27; 32; 48] & \#LSTM [50; 51] & \\ \hline \#MNB [52] & \#Bi-LSTM [53; 54] & \\ \hline \#ki-means [55; 56; 57] & \#CNN [34; 50; 58; 59] & \\ \hline \#TF-IDF [60] & \#Linear Regression [29] & \\ \hline \#GP [42] & \#MLP [51] & \\ \hline \hline \multicolumn{2}{c}{AdaBoost [42; 61; 62]} & \#RIPPER [61] & \\ \hline \#RBFN [43; 57] & \#RFN [43; 57] & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Traditional and ML models of tunnel detection.
## 3 Results and Discussion
### Microbial Diversity  
The fungal flora in MFHS are mainly _Ascomycota, Basidiomycota, Mortierellomycota, Mucornycota, Chytridiomycota, Basidiobolomycota, Neccallimastigomycota_, and _Glomeromycota_; _Ascomycota_ and _Basidiomyccota_ are the dominant microorganisms. _Ascomycota_ increased from 10.16% to 90.78%, and Basidiomyccota decreased from 76.15% to 7.55% after the Monascus addition. The addition of Monascus changed the dominant fungal microorganisms in the raw surimi, and their relative abundance fluctuated with the fermentation time (Figure 7a).  
At the genus taxonomic level, according to the species annotation and abundance information of all samples at the genus level, the top 30 fungal species in the surimi were selected for analysis (Figure 7b). The fungal flora in the blank group were mainly _Haglerozyma_ (59.36%), _Cryptococcus_ (4.70%), _Cutaneotrichosporon_ (4.39%), _Apiotrichum_ (2.97%), _Candida_ (1.98%), and _Tichosporon_ (1.18%). After adding _Monascus_, the fungal flora in surimi  
Figure 7: Relative abundance levels of fungal community in MFHS. ((**a**)—phylum, (**b**)—genus).  
were mainly _Monascus_ (89.95%) and _Haglerozyma_ (6.60%), and the relative abundance of _Haglerozyma_ gradually decreased to 0.28% after 5 h. At the same time, the original genera _Cryptococcus_, _Cutaneoustichosporon_, _Apiotrichum_, _Candida_, and _Trichosporon_ were inhibited and remained extremely abundant during fermentation. It can be concluded that the genus _Monascus_ dominates the activity of fungal microorganisms during the Monascus fermentation, and the addition of _Monascus_ inhibits the activities of _Cryptococcus_, _Cutaneoustichosporon_, _Apiotrichum_, Candida, and _Trichosporon_.
## 3 Methodologies and Research Framework
### Blind Spot Setting for Vehicle Detection  
The main methodology to detect a vehicle in a blind spot is that, once a vehicle enters a blind spot, which is defined based on x and y axes, a blue rectangle box detects a vehicle using the sliding window technique. The size of the video image, which was recorded using the set up in Figure 4, is approximately 1300 px \(\times\) 800 px. In this video image, we manually and visually checked that a blind spot has a range of 400-800 px in x-axis and 450-550 px in y-axis, as shown in Figure 8.  
One thing to keep in mind is that this system is not intended for object classification; that is, this system does not intend to identify whether the detected object is a vehicle or not. For this reason, a rectangle box focuses on detecting vehicle features; for instance, once the front view of a vehicle enters a blind spot, a license plate, radiator grill, headlights, etc. can be possibly detected by a rectangle box. In the case of a side view of a vehicle, a tire, alloy wheel, side mirror, etc. can be detected. Therefore, once any of the vehicle features is detected in a blind spot, a rectangle box is displayed on the screen. However, the size of the rectangle box, also known as rectangle threshold, is associated with false positive reduction, and this point is discussed in the next section.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Epoch** & **Training Loss** & **Training Accuracy** & **Testing Loss** & **Testing Accuracy** \\ \hline
1 & 0.5111 & 78.8\% & 0.2019 & 87.8\% \\
2 & 0.1750 & 93.4\% & 0.1337 & 95.6\% \\
3 & 0.1254 & 96.3\% & 0.1014 & 96.9\% \\
4 & 0.0982 & 97.3\% & 0.0817 & 97.4\% \\