esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Preliminary Tests of Chlorophyll a Measurements
#### 4.4.3 Performance Testing and Calibration  
The performance of the proposed device was determined considering the measurement range, limit of detection and linearity. As such, an initial calibration was performed using standard solutions of chlorophyll a (from spinach, Sigma-Aldrich C5753-1 mg) dissolved in acetone 90%. Several dilutions of this standard solution were carried out with known concentrations in the range of 0.01-300 mg/L (Figure 13). As it can be seen from the presented results, the sensor exhibits a linear response (R\({}^{2}\) = 0.9963) with a 0.01 mg/L detection limit.  
Experimental tests were also performed in the device with collected seawater. Water samples were diluted (with filtered seawater) to obtain five different water samples with chlorophyll a concentration in the range 0.05-2 mg/L. This low concentration allowed assessing the limit of detection of the device using natural seawater samples (Figure 14). These measurements showed an excellent performance for outputting the chlorophyll a concentrations, from 0.05 to up to 300 mg/L, with in vivo phytoplankton, allowing to conclude the viability of the developed electronics illumination and readout circuits.
# Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis
## 2 Materials and Methods
### Evaluation, Visualization, Interpretation, and Optimization
#### 2.6.1 Evaluation  
When the computations for topic modeling are completed, it becomes necessary to validate the outcomes. [PERSON] et al. [51] enumerates various reasons for the potential appearance of suspiciousness in extracted topics. These include (1) the merging of two or more distinct themes into a singular theme; (2) the extraction of two themes that, according to human perception, appear as duplicates; (3) the existence of topic keywords extracted without apparent coherence; (4) topics encompassing an excessive number of generic terms; (5) the extraction of topics based on seemingly unrelated terms; (6) inconsistency between the extracted topics and human judgment; (7) the perception of extracted topics as irrelevant; (8) the absence of a clear correlation between the topics and associated documents, and (9) the extraction of multiple closely similar topics. Evaluating the topic models can often prove to be a challenging activity, as the interpretability of the outcomes is not consistently assured with unsupervised methodologies [35]. This culminates in the presentation of a list of words ranked by relevance, and assigning a label to the topic based on these words can indeed be intricate.  
[PERSON] and [PERSON] [19] delineated three approaches for validating the results of a topic model: statistical, semantic, or predictive. Statistical validation entails the application of statistical methods, such as topic perplexity and coherence [42] or [PERSON]'s kappa [52], to test quantitatively the model's underlying assumptions. Semantic validation involves comparing the topic model results with expert reasoning, ensuring that the outcomes align with semantic coherence. Put differently; it questions whether the clustering of documents into topics is conceptually logical and ideally verified by an expert. For instance, a method could involve the manual coding of papers and subsequent comparison of the coding to the topic model's output. Predictive validation is employed when an external event can be correlated with an incident not explicitly present in the papers.  
In our case, we opted for semantic validation through a two-step process. Initially, as [PERSON] [30] suggests, we leveraged the visualization capabilities provided by the BERTopic module to gain a holistic understanding of the underlying latent themes. Subsequently, we conducted an in-depth examination of the most representative documents within each topic to corroborate our initial perceptions.
## 3 Proposed Method
### Evaluation Criteria  
Pearson (PCC) and Spearman (SROCC) correlation coefficients were employed to evaluate the quality prediction of the introduced IQM. The coefficients were calculated between the subjective scores and the predicted image quality values for each viewing distance. A correlation coefficient of 1 indicates a perfect prediction and a correlation coefficient of 0 indicates no correlation.  
The predicted scores were mapped to the subjective scores through the following non-linear logistic function:  
\[Q=\beta_{1}(\frac{1}{2}-\frac{1}{e^{-\beta_{2}(Q_{p}-\beta_{3})}})+\beta_{4}*Q_ {p}+\beta_{5} \tag{1}\]  
where \(Q_{p}\) and \(Q\) are the predicted and the mapped scores. \(\beta_{1}\)-\(\beta_{5}\) are the fitting parameters.
# Advanced Medical Image Segmentation Enhancement:
## 3 Materials and Methods
### Integrating PSO and HE for Image Segmentation
#### 3.4.3 Applications and Considerations  
The integrated PSO-HE approach is strategically positioned for applications in medical image segmentation. Its adaptability to intricate details and nuanced intensity variations positions it as a formidable tool for achieving precise segmentation outcomes. As we traverse through the intricacies of the integration, considerations extend beyond algorithmic nuances to encompass the pragmatic realm of computational costs. The optimization of parameter settings not only caters to segmentation quality but also ensures computational efficiency: making the approach practically applicable in real-world scenarios.
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 1 Introduction
Some scholars have applied the DIC technique to ascertain the full-field mode shapes. [PERSON] et al. [24] used this technique with the phase-based motion magnification [25] to obtain the full-field vibration response of the laboratory cantilever beam and wind turbine blade. [PERSON] and [PERSON] [26] used the high-speed stereo-DIC method with a four-mirror adapter to determine the full-field mode shapes of a rectangular cantilever beam. Meanwhile, [PERSON] et al. [27] applied the phase-based motion magnification method to extract the coarse shapes of the first four mode shapes of a cantilever beam, and [PERSON] et al. [28] advanced this strategy to visualize the full-field mode shapes by employing the edge-detection algorithm. However, several limitations come with the DIC technique, such as the need to paint the structure surface with speckles, while the mode shapes identified by the motion magnification method are only for visualization and cannot be quantified for further applications such as structural condition assessment. The structure edge in images is a key feature for image segmentation, understanding, and recognition. By identifying and tracking the movement of the structure edge, it is possible to determine the high-resolution full-field mode shapes of the structure without a preset tracking target. Therefore, the present study proposes a CV-based method for the full-field mode shape identification of structures using subpixel edge detection [29; 30; 31] and tracking techniques. Firstly, the Canny operator is applied on each frame of the structure vibration video to extract the pixel-level edges, and the improved Zernike orthogonal moment (ZOM) subpixel edge-detection technique is adopted to relocate the precise structure edges. Then, all the detected edge points are tracked to obtain the full-field dense displacement time history, which is subsequently used to determine frequencies and compute full-field mode shapes of the structure by combining the covariance-driven stochastic subspace identification (SSI-COV) with the hierarchical cluster analysis [32]. Finally, the proposed method is verified on the aluminum cantilever beam in the laboratory and the Humen Bridge in the field.
## 3 Data and Methodology
### Urban LULC Modeling
#### 3.4.1 Cellular Automata-Markov Chain (CA-MC)  
It is a combination of a sophisticated method between cellular automata (CA) and Markov chain (MC). Firstly, CA is a discrete model from disciplines of physics and biology [38]. It adopts a natural adaptation from the [PERSON]'s Game of Life [36] to simulate and understand a complex behavior [39]. The CA consists of four important elements: (i) a grid cell, which is a spatial unit in a finite cellular space; (ii) the state, each cell also has an individual state in the system; (iii) a neighborhood, which is a set of surrounding cells in the grid system; and (iv) a transition rule, which applies a change to the cell state based on specific properties of the cell and its neighborhood. Secondly, MC is a stochastic process and widely useful technique in examining LULC changes [40]. MC uses the knowledge based probability to predict the entire state of a given time (t2) from an earlier time (t1) without historical consideration. A transition probability and area matrix are calculated to illustrate the likelihood of the changes in the pixel status and total area for the next time step [36]. Moreover, a set of condition probability images is also created to identify the probability that areas will be changed in the next generation. The CA-MC, built in IDRISI Selva version 17 [36], requires two different periods of LULC maps. The time interval for the future LULC prediction is based on a number of iterations determined by a user. The \(5\times 5\) mean filter of the contiguity constraint of the CA was used for the future land allocation.
## 3 Results and Discussion
### Characteristics of the Amine Immobilized GMA-g-NWF and IRP
#### 3.2.2 Elemental Composition Analysis  
The FTIR result of the immobilized amine samples was presented in Figure 3. Both GMA-g-NWF and IRP samples exhibits common stretching for polyethylene/polypropylene (PE/PP) at 2913, 2843, 1464, 1370 and 718 cm\({}^{-1}\) which attributed to C-H stretch (PP), C-H stretch (PE), CH\({}_{2}\) stretch (PP & PE), symmetric CH\({}_{3}\) deformation stretch (PP) and CH\({}_{2}\) stretch (PE) respectively, as similar described in other researcher [29]. Additional stretching detected at 1732 and 1140 cm\({}^{-1}\) assigned for C=O and C-O vibration which originates from -COO- ester group of GMA [30]. However, additional stretching was found such as N-H stretching at 3201 cm\({}^{-1}\), N-H bending at 1524 cm\({}^{-1}\), C-N alkyl group stretching at 1256 and 1156 cm\({}^{-1}\) which attributed from aliphatic amine groups [25]. Besides that, additional peaks at 1405, 1014 and 989 cm\({}^{-1}\) which presents onto IRP samples attributed to C-H stretch. These are typical stretching vibration band of the vinyl groups which associated with in-plane and out-of-plane deformation bands of the vinyl group [31]. This peaks specifically attributed to the crosslinking process with DVB via radiation [32].  
Figure 2: FESEM imaging of (**a**) EDA-GMA-\(g\)-NWF, (**b**) EDA-IRP, (**c**) DETA-GMA-\(g\)-NWF, (**d**) DETA-IRP, (**e**) TETA-GMA-\(g\)-NWF, (**f**) TETA-IRP, (**g**) TETA-GMA-\(g\)-NWF and (**h**) TEPA-IRP at experimental condition of 70% amine: 30% isopropanol, 60 \({}^{\circ}\)C, \(D_{\rm g}\)\(\sim\)100% and 2 h.
## 2 Deep Learning Models
### Long-Short Term Memory  
The conventional RNN can only memorize the short time series data. With the increasing amount of data and time steps, it will lose the important information of long term input, causing a vanishing gradient or exploding gradient problem. To tackle this, the LSTM method was proposed in Reference [18], whose structure is shown in Figure 2. LSTM processes the input time series by iteratively passing the transfer function, and adds the input gate (\(\Gamma_{u}\)), forget gate (\(\Gamma_{f}\)) and output gate (\(\Gamma_{o}\)) for maintaining valuable information of history data. The input gate determines the update of the hidden layer information, whereas the forget gate determines whether the updated information contains the information of the last moment. The output gate determines which part of the information will be selected. Eventually, the formulation of updating the cell states and parameters are as follows:  
\[\Gamma_{f}=\alpha\big{(}\omega_{f}[o_{t-1},x_{t}]+b_{f}\big{)} \tag{5}\]  
\[\Gamma_{i}=\sigma\big{(}\omega_{i}[o_{t-1},x_{t}]+b_{i}\big{)} \tag{6}\]  
\[\Gamma_{o}=\sigma(\omega_{o}[o_{t-1},x_{t}]+b_{o}) \tag{7}\]  
\[\overline{S_{t}}=tanh(\omega_{s}[o_{t-1},x_{t}]+b_{s}) \tag{8}\]  
\[S_{t}=\Gamma_{i}*\overline{S_{t}}+\Gamma_{f}*S_{t-1} \tag{9}\]  
Figure 1: Basic recurrent neural networks structure.  
\[o_{t}=\Gamma_{o}*tanh(S_{t}) \tag{10}\]  
where \(o_{t-1}\) is the output at t-1 time slot, and \(x_{t}\) is the input at current moment, \(\widetilde{S_{t}}\) is the new candidate values, and \(S_{t}\) is the memory from the current block, \(S_{t-1}\) is the memory from the previous block, w is the weights, b is the bias, and symbol * is the element-wise multiplier. \(\sigma\) is another activation function as follows:  
\[\sigma(x)=\frac{1}{1+e^{-x}} \tag{11}\]
## 3 Results
### Aggregated Transfer Coefficient (Tag)  
Based on the obtained results of isotope concentrations in lichen tissue and deposition rates, the Aggregated Transfer Coefficient (Tag) was determined, which is an empirical coefficient that determines bioaccumulation capacity and which can be used in predictive models for both natural and semi-natural ecosystems. It is defined by a specific expression [18]:  
\[\text{Tag}=\frac{\text{Activity concentration of the sample (A)}}{\text{Activity of the deposition per unit area (D)}}(\text{m}^{2}/\text{kg}) \tag{1}\]  
The average Tag value determined from the data for all sampling periods was not statistically significantly different between Aquaria A, B, and C for the three isotopes and was for \({}^{54}\)Mn (0.44, 0.40, 0.44), \({}^{57}\)Co (0.38, 0.41, 0.47), \({}^{60}\)Co (0.43, 0.42, 0.44), \({}^{65}\)Zn (0.44, 0.42, 0.54), and \({}^{137}\)Cs (0.50, 0.53, 0.64), respectively (Kruskal-Wallis test; \(p>0.05\)). Only for \({}^{241}\)Am (0.45, 0.53, 0.64) was Tag significantly higher in the aquarium with the highest deposition (Aquarium C) compared to Aquariums A (Kruskal-Wallis test; \(p<0.05\)) (Figure 4). Since one of the components needed to calculate the Tag is the activity in lichens, the coefficient's variation followed the same trend as activity in lichens.  
The Tag value is not fixed and may even rise over time under certain conditions. Even during a short experiment of 5 months, significant differences were observed in the calculated Tag depending on the contamination and sampling period (Figure 4, Table 4).  
Figure 3: Temporal change (7 days, 1 month, 2 months, 3 months, 5 months) in the activity concentrations of isotopes (\({}^{57}\)Co, \({}^{54}\)Mn, \({}^{65}\)Zn, \({}^{60}\)Co, \({}^{137}\)Cs, and \({}^{241}\)Am) in soil in relation to deposition (Aquarium A, Aquarium B, Aquarium C).  
Figure 4: Temporal change (7 days, 1 month, 2 months, 3 months, 5 months) of aggregated transfer coefficient (Tag) of isotopes (\({}^{57}\)Co, \({}^{54}\)Mn, \({}^{65}\)Zn, \({}^{60}\)Co, \({}^{137}\)Cs, and \({}^{241}\)Am) in _Cladonia uncialis_, in relation to deposition (Aquarium A, Aquarium B, Aquarium C).
# A Fast Recognition Method for Dynamic Blasting Fragmentation Based on YOLOv8 and Binocular Vision
## 4 Discussion
### Application Value  
The fully automatic blasting fragmentation recognition technology developed in this study not only demonstrates excellent accuracy and system stability, but also shows broad application value in actual production, significantly improving the intelligence level of mining operations. The system brings direct and tangible improvements and benefits to a wide range of users. For mining engineers and technicians, the system provides real-time, accurate particle size data, enabling a scientific evaluation of blasting performance and the optimization of key parameters such as charge amount, hole spacing, and row spacing. This, in turn, enhances the rationality and effectiveness of blast design. Meanwhile, the system's fully automatic analysis eliminates subjective errors from manual sketching and  
Figure 21: Shadow-specific data augmentation—comparison of recognition performance before and after shadow-specific data augmentation: (**a**) before optimization; (**b**) after optimization.  
Figure 20: Shadow-specific data augmentation: (**a**) original image; (**b**) clash enhancement; (**c**) gamma transformation; (**d**) histogram equalization; (**e**) Laplacian enhancement; (**f**) LoG (Laplacian of Gaussian) object enhancement; (**g**) Msr; (**h**) Ssr.  
visual estimation, strengthening objectivity and data support for technical decisions. For on-site managers and dispatchers, the system can provide real-time feedback on rock size information for each truckload. This helps dynamically adjust crusher loads, prevent blockages or idle running caused by uneven particle sizes, and improve equipment utilization and operational coordination efficiency. In addition, for safety and environmental personnel, the system can automatically detect and warn about the frequency of oversized boulders, effectively reducing risks associated with transportation and crusher operations. By precisely controlling particle size distribution, it also minimizes the need for secondary crushing and reduces energy consumption, thereby lowering environmental impacts such as dust and noise. In summary, the system offers significant improvements in technical, economic, and environmental aspects, making it a key tool in the intelligent transformation of mining.
# Deep Unsupervised Embedding for Remote Sensing Image Retrieval Using Textual Cues
## 3 Experimental Results  
### Dataset Description  
In this work we use two different benchmark datasets to validate the performances of the proposed method. The first one is the TextRS dataset [50], which consists of 2144 images collected from several scene datasets (i.e., AID [31], Merced [51], PatternNet [52], and NWPU [53]) (see Figure 5). In particular, this dataset is built by selecting randomly 16 images from each class of four popular heterogeneous scene datasets: AID (30 classes), UC Merced (21 classes), PatternNet (38 classes), and NWPU (45 classes). TextRS has 2144 images: 720 images with spatial resolution 0.2 to 30 m, 608 images with spatial resolution 0.062 to 4.7 m, 480 images with spatial resolution 0.5 to 8 m, and 336 images with spatial resolution 30 m as shown in Table 1. Each remote sensing image is annotated by five various sentences; therefore, the total number of sentences is 10,720. The second dataset is the Merced Land-Use dataset, which consists of remote sensing images with 21 classes [54]. Each class has 100 RGB images (256 \(\times\) 256 pixels). The total number of images is 2100, with every image labeled also with five different sentences (see Figure 5b).
# Current-Assisted Single Photon Avalanche Diode (CASPAD) Fabricated in 350 nm Conventional CMOS
## 4 Conclusions  
The device structure can still be improved in many ways, mainly in its center area. Layout and depth of diffusions and wells and the use of circular shapes can be further investigated. They all affect the CASPAD performance parameters like PDP, DCR, deadtime, and afterpulsing. Receiver circuitry can also be considered, thereby taking advantage of the very small detector capacitance with passive and/or active quenching. Use of enhanced CMOS technology can also improve device operation. Further, using the current assistance principle, it becomes probably feasible to make devices that mainly have only vertical current assistance (omitting the ring). In that way a thicker epi-layer of 20 \(\upmu\)m with a smaller CASPAD pitch (e.g., \(<\)10 \(\upmu\)m) could be envisaged for higher resolution, NIR-efficient, imager solutions.  
In summary, a detector which combines the principles of SPAD and current assistance has been presented. The device showed relatively good timing response for its larger area. Its basic operation has been demonstrated, in terms of its photon-detection probability (PDP), timing jitter, dark-count rate (DCR), and afterpulsing. Changes at the device level and integration with circuitry can still improve general performance.  
Conceptualization, G.J. and M.K.; methodology, G.J.; validation, G.J., H.I., and M.K.; formal analysis, G.J.; investigation, G.J.; writing--original draft preparation, G.J.; writing--review and editing, G.J., H.I., and M.K.; supervision, M.K.; All authors have read and agreed to the published version of the manuscript.  
This work was sponsored in part by Sony Depthsensing Solutions NV and in part by the Research Council of the Vrije Universiteit Brussel (SRP 19).  
The authors declare no conflict of interest.
# Mdpi
## 2 System Model
### Proposed Receiving Block  
In accordance with the iterative IN mitigation approach in Reference [22], the received signal is clipped by a pre-IN mitigation block. A multiplexer (MUX) is then used to select the input signal of the adaptive DWT block, which outputs the symbols for each subchannel. The MUX selects a less contaminated received signal as the input for the adaptive DWT block. We denote \(\hat{S}_{k}[n]\) as the output signal of the adaptive DWT block for the \(k\)-th subchannel. The signal \(\hat{X}_{k}[n]\) for each subchannel is then demapped and applied to the iterative IN reconstruction block to estimate the IN data \(\hat{u}[n]\). This makes it possible to obtain a received signal \(\hat{r}[n]\) of greater clarity by subtracting the estimated IN \(\hat{u}[n]\) from the input signals of the adaptive DWT block, i.e., \(\tilde{r}[n]\). Following this iterative procedure, a parallel-to-serial (P/S) block converts \(\hat{X}_{k}[n]\) into the estimated information source \(\hat{x}[n]\).