esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Constant False Alarm Detector Based on Composite Fuzzy Fusion Rules
## 1 Introduction
VIHCEMOS-CFAR has better detection performance than VI-CFAR in a multiple-target environment, but poor false alarm control ability in a clutter edge environment. New variability index CFAR is proposed in Ref. [14]. In new variability index CFAR, SO-CFAR of VI-CFAR is replaced by ordered statistic smallest of CFAR, which improves detection performance in a multiple-target environment. However, it is susceptible to information loss due to using only half of the reference window information in a multiple-target environment. In addition, VI class CFAR detectors are prone to misclassifying homogeneous or clutter edge environment as a multiple-target environment, which places high demands on the robustness of the detection methods used by Class VI detectors. Fuzzy logic fusion detector (FUMCA-CFAR) is proposed in Ref. [15]. In FUMCA-CFAR, discrete thresholds are replaced by continuous thresholds to avoid information loss, improving the adaptability and robustness of the detection method. To further enhance the detection performance of the CFAR detector, a composite fuzzy fusion rules KLQ-CFAR detector (CFKLQ-CFAR) is proposed to improve the detection performance in multiple-target environment. Two sensors are employed to collect environmental information, and four fuzzy fusion rules (MAX, MIN, algebraic sum, algebraic product) are applied for target detection, CFKLQ-CFAR performs a composite judgment on the detection results to improve the probability of target detection. An adaptive constant false alarm rate detector based on the composite fuzzy fusion rules (CFVI-CFAR)detector is designed by combining the variability index. In homogeneous and clutter edge environments, CA-CFAR detector and GO-CFAR detector are used for target detection, respectively. The performance of CFVI-CFAR is improved while maintaining good false alarm control ability.
## 7 Future Research Directions
### Motion Control System  
Most of the reviewed literature has focused on UAVs and UGVs. Future research should study other robots with different types of locomotion in the inspection and monitoring of the built environment. Amongst the promising categories of robots are legged robots, which can navigate stairs and avoid small obstacles, making them better suited for construction environments. These robots have not been thoroughly studied, owing to the lack of many commercial options on the market. Quadruped robots that are currently on the market, e.g., Spot by Boston Dynamics, are costly. A cost-benefit and return-on-investment analysis of the quadruped robot is required to build the case for industry adoption in the future. Although legged robots are more adaptable than wheeled UGVs, their adaptability to unstable and unfinished terrain must also be assessed. A comparative analysis of different types of robots in undertaking inspection and monitoring of the built environment in multiple scenarios can uncover the potentials and limitations of the robot types.  
More research on hybrid robots is also needed. As discussed in Section 4.8, hybrid robots combine multiple motion control systems, such as wheels, legs, suction mounts, and rotors. Built-environment structures vary significantly in shape and size [3]. Requirements for a bridge inspection robot can be significantly different from the requirements for a building inspection robot. Similarly, indoor and outdoor inspections require significantly different motion capabilities. A versatile robotic platform suitable for a comprehensive inspection of a wide range of structures should possess multiple motion capabilities, such as wheeled legs with rotors. Future research can develop more types of hybrid robots and study their usability in the construction and operation of buildings and infrastructure.
# Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting
## 2 Forecasting Model and Missing Data Imputation Methodology
### Missing Data Imputation Methods
#### 2.2.4 Multivariate Imputation by Chain Equations  
The MICE algorithm designed by [PERSON] and [PERSON] [33] is based on a Markov chain Monte Carlo method wherein the state space is the collection of all imputed values [13]. The merit of MICE is that the results are computed after a comparatively few numbers of iterations. As per some studies [33; 34], in general, five iterations are usually sufficient. The MICE algorithm [35] for the imputation of multivariate missing data consists of the following steps in Algorithm 1.  
```
1. Execute a simple imputation such as the mean imputation for every missing value in the dataset. These mean imputations are called \"place holders.\"
2. Set the \"place holder\" mean imputations about one variable (\"var\") to \"missing\" again.
3. Develop a regression model (e.g., linear, logistic, Poisson, etc.). Here, the dependent variable is \"var,\" while all others are independent variables. We note that the imputation model may or may not consist of all of the variables in the dataset.
4. Replace missing values for \"var\" with prediction values. After \"var\" is used as an independent variable in the regression models for other variables, all the observed and the abovementioned imputed values are used.
5. Repeat Steps 2-4 for each variable that has missing data. The cycling of each variable is defined as one iteration or cycle. At the end of one cycle, all of the missing values have been substituted by predictions from regressions that are related to the observed data.
6. Repeat Steps 2-4 for a number of cycles, with the imputations being updated at each cycle.
```  
**Algorithm 1**MICE algorithm for imputation of multivariate missing data [35].
## 5 Conclusions  
We have proposed a dual-branch cross-attention feature fusion network that combines ResNet50 and Swin Transformer to improve weld seam recognition accuracy and enhance adaptability in complex environments. The network uses a dual-branch structure, which includes two encoders: one based on ResNet50 and the other on Swin Transformer, along with an innovative CaFF module. The ResNet50 encoder focuses on capturing multi-scale local features, such as weld seam details and texture information. The Swin Transformer encoder extracts global features by modeling long-range dependencies using a self-attention mechanism, allowing it to capture the overall weld seam information. The CaFF module fuses features from both branches through a three-stage cross-attention process, dynamically balancing their contributions. This significantly improves the feature representation ability and enhances the network's robustness.  
Extensive qualitative and quantitative experiments show that the proposed DRST-Net model has significant capability advantages. In the qualitative experiments, DRST-Net accurately captures weld seam boundary details and maintains strong robustness even under noisy conditions. In the quantitative experiments, the model outperforms existing methods in key metrics such as \(mloqu\), \(mRecall\), and \(F1\_score\). Additionally, the ablation experiments confirm that the dual-branch structure and the CaFF module play a critical role in performance improvement, further validating the validity of the proposed design.  
Future work will focus on optimizing the model architecture to handle complex welding environments, such as varying lighting conditions and material reflections. We will also expand the model's ability to recognize diverse weld seam shapes, including irregular and discontinuous seams. Additionally, lightweight techniques will be explored to improve the model's capability, ensuring that it meets the higher real-time effectiveness and accuracy requirements of automated welding production.  
Conceptualization, Y.L.; methodology, Y.L.; software, Y.L.; validation, Y.L.; formal analysis, Y.L.; investigation, Y.L.; resources, Y.L.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L. and Q.H.; visualization, Y.L. and Q.H.; supervision, Q.H.; project administration, Q.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
## 1 Introduction
In this paper, to realize reliable video transmission of long-distance optical wireless communication, a joint source-channel coding scheme based on residual distributed compressive video sensing is proposed for the underwater photon counting communication system with high bit error rate. Signal extraction from single photon pulses, data frame and data verification are specifically designed. Experimental results show that this scheme greatly reduced the amount of data at the transmitter, transfered the coding complexity to the receiver, and enhanced the anti-channel error ability.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature Set** & **Number of Features** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **PPV (\%)** & **NPV (\%)** \\ \hline Proposed Features & 43 & 92.91 & 89.94 & 91.38 & 90.29 & 91.45 \\ SGLD (F1) & 8 & 88.42 & 87.89 & 69.24 & 72.33 & 71.95 \\ Depth–Width Ratio (F2) & 1 & 89.32 & 65.97 & 91.28 & 70.39 & 68.87 \\ Number of Depressions (F3) & 1 & 89.52 & 54.28 & 90.24 & 77.87 & 75.27 \\ Orientation (F4) & 1 & 58.29 & 57.22 & 55.56 & 54.17 & 55.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative Performance Analysis of Various Feature Sets. Figure 9: Classification performance of breast tumor using five objective indices. An experimental outcome indicating a larger AUC suggests a superior method compared to those with lesser areas. Figure 11 illustrates the classification efficacy of our suggested features against others, as measured by AUC. The result demonstrates that the AUC for our suggested features stands at 92.4% and surpasses that of other features, such as \(F_{1}\), \(F_{2}\), \(F_{3}\), and \(F_{4}\), by 6.8%, 10.1%, 10.5%, and 34.6%, respectively. Hence, our proposed approach exhibits the highest capability for discrimination.
# Diagnosis by SAM Linked to Machine Vision Systems in Olive Fitting Machines
## 3 Results
### Validation of SAM Segmentation Accuracy
#### 3.3.1 Metrics and Computation Workflow  
1. Ground-truth masks (manual) were extracted from the red-outlined images in contornos_Gordal and contornos_Hoijblanca by thresholding the white interior (R, G, B > 200).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Variety/File** & **n** & **SW W** & **SW p** & **DP p** & **AD p** & **JB p** & **sk** & **ku** & **Decision** \\ \hline Gordal (black)—informe_neggras_09052024a.txt & 1305 & 0.9982 & 0.185 & 0.271 & 0.159 & 0.260 & \(-\)0.107 & 2.95 & Normality not rejected \\ Hoijblanca (green)—informe_hoijblancas.txt & 9150 * & 0.9930 & 6.3 \(\times\) 10\({}^{-15}\) & \textless{}0.001 & 0.0005 & 0.001 & 0.058 & 3.79 & Normality rejected (slight deviation) \\ \hline \end{tabular}
* SW on random sub sample of 5000 values.  
\end{table}
Table 3: Normality test results for olive area distributions (\(\alpha=0.05\)). SW applied to full sample when \(n\leq 5000\); otherwise to a random 5000 item subset.  
SAM masks were those stored in aceitunas_Gordal_mascara_relleno and aceitunas_Hoijblanca_mascara_relleno.
3. Masks were mutually trimmed to the common FoV to prevent size mismatches.
4. For each pair we recorded:  
Two binary masks are analyzed: the SAM prediction \(M_{\text{SAM}}\) and the ground-truth annotation \(M_{\text{GT}}\). Let the logical operators \(\wedge\) (AND) and \(\
eg\) (NOT) act pixel-wise, and let \(\sum\) denote the sum over all pixels in the common field-of-view. We define  
* \(\mathit{TP}=\sum(M_{SAM}\wedge M_{GT})\)--true-positive pixels (olive correctly identified).
* \(\mathit{FP}=\sum(M_{SAM}\wedge\
eg M_{GT})\)--false positives (background labelled as olive).
* \(\mathit{FN}=\sum(\
eg M_{SAM}\wedge M_{GT})\)--false negatives (olive pixels missed by SAM).  
From these counts, we derive the standard overlap and detection metrics:  
\[IoU=\frac{\mathit{TP}}{(\mathit{TP}+\mathit{FP}+\mathit{FN})};\]  
\[\mathit{Dice}=\frac{2\cdot\mathit{TP}}{(2\cdot\mathit{TP}+\mathit{FP}+\mathit{ FN})};\]  
\[\mathit{Precision}=\frac{\mathit{TP}}{(\mathit{TP}+\mathit{FP})};\]  
\[\mathit{Recall}=\frac{\mathit{TP}}{(\mathit{TP}+\mathit{FN})}.\]  
IoU (intersection-over-union) and Dice quantify spatial overlap, whereas precision and recall disentangle over- from under-segmentation.
5. Area metrics (MAE, MAPE, RMSE, Pearson \(r\), \(R^{2}_{y=x}\)) compared the pixel counts of each mask.
6. The analysis was performed with two MATLAB scripts: pixel_metrics_SAM_vs_manual.m and valida_area_SAM.m.
# The Low-Loss Spot Size Converter for Alignment with Cleaved Single Mode Fiber  
[PERSON]  
The Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China; [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (Z.W.); [EMAIL_ADDRESS] (Y.S.)  
[PERSON]  
The Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China; johnnie_029@shu.  
damaged. In order to reduce coupling difficulty and costs, one of the trends in SSCs is to change from alignment with lensed fibers to alignment with cleaved SMFs. When coupling with the cleaved SMF, the coupling loss of SSC using an inversely-tapered Si waveguide is up to 3.5 dB for transverse electric (TE) mode [8]. To reduce the coupling loss, additional structures are usually required besides the tapered Si waveguide, such as subwavelength grating structures [5], overlapped tapers [9], thin-film stack-based lenses [10], and polymer tapered waveguides [11]. However, the coupling loss of SSC when coupling with cleaved SMF is difficult to achieve below 1 dB. Although the coupling loss between SSCs and cleaved SMFs can be as low as 0.75 dB using a subwavelength grating structure, the length of the device is more than 1500 \(\upmu\)m, which is hard for the integration with other devices on the single chip [5]. It can be seen that achieving low-loss coupling between SSC and cleaved SMF is difficult. In addition to this, the tapered waveguides in SSCs typically have taper tips smaller than 200 nm, which is not suitable for SSCs to be fabricated at the current commercial silicon photonics fab.  
In this paper, a design of SSC integrating the ion-implanted GRIN waveguide and the 2-D inversely-tapered waveguide has been proposed for coupling the light between the cleaved SMF (MFD: 8-10 \(\upmu\)m) and the Si waveguide. The tip width of the tapered waveguide is 200 nm and the height is 60 nm, which is compatible with commercial silicon photonics technology. The coupling loss of the SSC is obtained by using finite difference time domain (FDTD) simulation. When the MFD is 8 \(\upmu\)m and the wavelength is 1550 nm, the lowest coupling loss is 0.29 dB for TE mode and 0.27 dB for transverse magnetic (TM) mode. Meanwhile, the starting Si tip width of 200 nm reaches the level manufactured at the commercial silicon photonics fab.
## 4 Results and Discussion
### Baseline Performance Analysis and Bottleneck Identification (Pre-Optimization)
#### 4.2.3 Duration by Activities  
The analysis of station durations revealed significant variations in operational hours across the manufacturing line (Figure 8). The four bottleneck stations (202-205) showed the highest operational durations, each exceeding 10,000 h:  
* Station 202: 9906 h
* Station 203: 9922 h
* Station 204: 9970 h
* Station 205: 10,003 h  
In contrast, non-bottleneck stations (206-209) operated for significantly fewer hours, with Station 209 showing the lowest duration at 3680 h. The average duration across all stations was 9094.1 h.
# Fabrication of an Anti-Icing Aluminum Alloy Surface by Combining Wet Etching and Laser Machining
###### Abstract  
The phenomenon of icing on the surface of the fuselage while aircraft pass through clouds has an impact on flight safety. This aircraft tcing may adversely affect metrological conditions and cause aerodynamic mechanical effects, resulting in a threat to flight safety. This research aims to fabricate an anti-icing surface on a 2524 aluminum alloy material by combining laser machining and wet etching. The microstructure surfaces were obtained by laser, the nanostructured surfaces were obtained by wet etching, and the hierarchical structures were prepared through a combination of these two processes. The contact angle, icing delay performance, icing adhesion, and dynamic water repellency of three kinds of textured surfaces and smooth surface were tested and compared comprehensively through experiments. The experimental findings have shown that the hierarchical surface has the best superhydrophobic properties, and the freezing time of droplets on the hierarchical surface can be extended 10 times. In addition, the ice adhesion on the surface of the hierarchical structure decreased by approximately 75% compared to the untreated samples. The surface of the hierarchical structure showed the best dynamic water repellency. The proposed hybrid laser machining-wet etching fabricating method has the potential to avoid aircraft tcing.  
aluminum alloy; micro/nanostructure; anti-icing performance; superhydrophobic surface
###### Abstract
R2R3-MYB transcriptional factors are the largest family in plants and play an important role in the regulation of secondary metabolites and terpenoids. VvMYB5b transcriptional factors increased the accumulation of terpenoids, as previously described [26]. _SmMYB97_ TFs belong to medicinal plants (_S. milliorrhiza_) and increase the contents of the terpenoids (tanshinones) in their overexpression lines [27]. _SmMYB98_ belong to the R2R3-MYB subgroup 20, which are involved in the biosynthesis of phenolic acid and terpenoids. _SmMYB98_ accumulate the terpenoids (tanshinones) in overexpressed lines [28]. In light of the above evidence, MYB transcriptional factors play an important role in the synthesis of terpenoids in higher plants. In microalgae, a few studies have been reported on the regulation of MYB transcriptional factors in terpenoid synthesis. For example, _MYB44_ positively regulate the carotenoid biosynthesis in _U. prolifera_, which is known as marine green algae [29]. However, the role of MYB transcriptional factors in the regulation of terpenoids in _C. reinhardtii_ is not yet functionally characterized. In _C. reinhardtii_, the biosynthesis of squalene is excepted through the MEP pathway [30]. In the initial stage, squalene is synthesized by squalene synthase (SQS) from farnesyl diphosphate (FPP). A two-step reaction produces squalene by reductive dimerization of FPP with SQS. This reaction continues through head-to-head coupling of two FPP molecules to produce squalene through a stable cyclopropylclarbinyl diphosphate intermediate [31]. In the current study, by using advanced synthetic biotechnology approaches, heterologous overexpression of MYB TFs was achieved in _C. reinhardtii_, which might play an important role in the production of squalene (Figure 1). To optimize MYB TFs' expression in _C. reinhardtii_, it was completely redesigned. The vectors were constructed with the goal of expressing and accumulating the targeted protein in _C. reinhardtii_'s nucleus.
# Optimal Mixture Designs for Heavy Metal Encapsulation in Municipal Solid Waste Incineration Fly Ash
## 3 Results and Discussion
### Heavy Metal Leaching
#### 3.3.2 The Encapsulation of Cr  
Figure 5 shows the Cr concentrations and the encapsulation percentage at all ages for all groups. All mixtures significantly reduce Cr concentrations, and the Cr amounts are always below the EPA drinking water limit, in contrast to the MSWIFA and water mixture, where the Cr amount is double the limit. The LB and GB groups show greater Cr encapsulation than the other groups, with the encapsulation percentage above 90%. The BM and CB groups show somewhat lower Cr encapsulation, with the percentage ranging from \(-14\%\) to 90% at 28 days. A negative value of encapsulation here and for other ions is likely caused because of the very small concentrations, sometimes below 1 ug/L, and small errors may cause negative encapsulation percentages. The encapsulation increases with time, and at 180 days, for all mixtures except one (B30M60), the encapsulation is over 89%. These findings suggest that the LFS increases Cr encapsulation at early and later ages, likely due to high amounts of calcium aluminate hydrates, ettringite and Friedel's salt in the mixtures with LFS. In mixtures without LFS, the encapsulation process is slower. The Cr encapsulation mechanism may be explained using the literature [6; 9; 10; 11; 22; 24; 28; 29; 30; 31; 32; 33; 34; 35; 36]. It is known that calcium sulfoaluminate hydrates play an exceptional role as Cr immobilizers. Cr can partially replace the aluminum in ettringite [22] and can also be integrated into the Friedel's salt layers, either through ionic substitution or by surface adsorption [9; 10; 24; 34; 35; 36]. Cr can also be incorporated into the C-S-H as insoluble chromium hydroxides [6; 32].  
Figure 4: Concentrations of MSWIFA powder leachate and EPA drinking water limit. In the rest of this section, the encapsulation of each heavy metal is discussed.
# Robust Speed Control of a Multi-Mass System: Analytical Tuning and Sensitivity Analysis
## 3 Development of the Control Algorithm
### Controller Design
The results are found in Equation (21). The value of precompensation F gain in the case of \(LQI\) is equal to 320.5. Accordingly, the PI controller gains are tuned until the dominant poles of the PI controller system are as close as possible to the \(LQI\) controller system. Consequently, the PI controller gains are set to \(k_{p}\) equals 260, and \(k_{i}\) is 2050. With that achieved, it is expected that both systems would have similar time responses. Figure 7 shows the closed-loop poles related to each controller. The \(LQ\)-controlled system has 10 stable poles, while employing \(LQI\) produced 11 stable poles, and the result with the PI controller is only six stable poles. This observation means that all controllers do not have the zero pole from the original plant. \[\mathbf{Q}_{i}= \begin{bmatrix}1\times 10^{8}&0&0&0&0\\ 0&1&0&0&0&0\\ 0&0&5\times 10^{6}&0&0&0\\ 0&0&0&1&0&0\\ 0&0&0&0&1\times 10^{7}&0\\ 0&0&0&0&0&1\times 10^{10}\end{bmatrix} ,R_{i}=1500 \tag{20}\]  
\[\mathbf{K}_{LQI}= \begin{bmatrix}256.1&-1602.7&13.1&328.9&51.3&2582\end{bmatrix} ,\ \mathbf{L}_{LQI}= \begin{bmatrix}410.29\\ 0.01\\ 105.91\\ -0.04\\ 12.31\end{bmatrix}  \tag{21}\]  
Figure 6: Block diagram representation of the plant system and the I-SS controller. In addition, the low-frequency resonance, attained from complex poles \(-2.65\pm 108\), is also successfully eliminated by all controllers. Therefore, all the developed controllers can stabilize the closed-loop system since they have successfully eliminated the 0 and \(-2.65\pm 108\) poles. Figure 8 shows the dominant poles in Figure 7. A subtle difference exists between \(LQ\) and \(LQI\) poles, although the same \(\mathbf{Q}\), \(R\) values are employed for both. This difference results from tuning the additional factor in the diagonal matrix \(\mathbf{Qi}\). Moreover, \(LQI\) has an additional pole on the real axis (i.e., without a complex conjugate part), representing the integral action effect. Therefore, the poles related to the integrator action of both \(PI\) and \(LQI\) are located approximately at the same location. Therefore, the two underdamped poles of the PI-controlled system are placed as near as possible to other poles of \(LQ\) and \(LQI\) in order to obtain a comparable dynamic response. However, \(PI\) poles cannot be placed arbitrarily in the space (i.e., they could not be placed closer to \(LQ\) or \(LQI\) poles, no matter how many more tuning iterations are performed). This result confirms the same conclusion in [10; 14] that PI controller is more constrained than SS controllers.