esa-sceva/satcom-chunk-collection huggingface dataset:
# TMCrack-Net: A U-Shaped Network with a Feature Pyramid and Transformer for Mural Crack Segmentation
## 3 Proposed Methods  
### Model Architecture  
TMCrack-Net consists of three main modules: (1) Top-down extraction of crack features using the ConvNext network as the backbone of the U-net network. (2) The AG-BiFPN module includes BiFPN and CCT, a feature fusion network, to exploit the multi-scale features of the image better. (3) An attention fusion module to better enhance useful features and suppress invalid ones. Figure 3 shows the TMCrack-Net with BiFPNet, Transformer, and FCA.  
In this paper, we consider the crack segmentation task of mural images as a pixel-level semantic segmentation problem, where 0 represents \"background pixels\" and 1 represents \"crack pixels\". Compared with natural images, the number of mural images is small, and the background information is complex, so the feature extraction ability of the backbone network directly affects the segmentation effect. Our backbone is the ConvNext [20] network proposed by Facebook AI Research and the University of California, Berkeley, which optimizes ResNet to reach the performance limits of pure convolutional models. As a pure convolutional network, it achieves 87.8% TOP-1 accuracy on the ImageNet dataset while maintaining the simplicity and efficiency of standard convolutional networks.  
ConvNext consists of downsample blocks and ConvNext blocks, repeatedly stacked. A ConvNext block consists of depthwise convolution, and ordinary Conv is combined, using the GELU function as the activation function. A downsample block consists of layer norm and \(2\times 2\) convolution. Figure 4 shows the structures of downsample and ConvNext blocks.  
With the small number of mural images and complex background information, low-level information and high-level semantic information are particularly important for crack segmentation. Using only the backbone network to extract features from the images does not make good use of the channel information at different scales, and a feature fusion network needs to be added to recycle the multi-scale features, but feature fusion confuse the localization of cracks in the images. Attention mechanisms need to be used to optimize the feature pyramid, and using attention computation at each layer of the pyramid is costly. Therefore, we introduced the CCT [19] module. By analyzing the information provided by each element layer, CCT can automatically determine the importance of different channels, increase the weights of channels that contribute to segmentation, and suppress channels that do not. Due to the ambiguity between the features of the codecs, in this paper, we design an FCA module based on a self-attentive model to filter the channel information and better fuse the features between them.
## 4 Evaluation Study
### Setup
#### 4.1.3 Analysis Tools
The score is higher when the clusters are dense and well separated, which relates to a standard concept of a cluster. Moreover, the score is computed fast. * **Davies-Bouldin index**[44]. The Davies-Bouldin index signifies the average'similarity' between clusters, where the similarity is a measure that compares the distance between clusters with the size of the clusters themselves. The index is defined as the average similarity between each cluster \(C_{i}\) for \(i=1,...,k\) and its most similar one \(C_{j}\). In the context of this index, the similarity is defined as a measure \(R_{ij}\) that trades off:  
* \(s_{i}\): the average distance between each point of cluster \(i\) and the centroid of that cluster (also known as cluster diameter). * \(d_{ij}\): the distance between cluster centroids \(i\) and \(j\). A simple choice to construct so that it is nonnegative and symmetric is:  
\[R_{ij}=\frac{s_{i}+s_{j}}{d_{ij}}\]  
Then the Davies-Bouldin index is defined as:  
\[DB=\frac{1}{k}\sum_{i=1}^{k}max(R_{ij})\]  
A lower Davies-Bouldin index relates to a model with better separation between the clusters. Zero is the lowest possible score. Values closer to zero indicate a better partition.
# Laplacian Controllability and Observability of Multi-Agent Systems: Recent Advances in Tree Graphs
### Literature Review  
Controllability and observability are topics of interest to a broad community, and they have been studied over the years using several different tools, which has led to different approaches and results. In this Section, we review the main methodologies available in the literature for the analysis and design of controllability and observability of a network. In general, it is worth mentioning that this topic has been widely discussed in recent years, and the interested reader may also refer to the survey papers [9; 10]. A careful and thorough overview of the approaches for the analysis of observability of network systems is [11].  
One milestone paper that gave rise to the ideas of most of the further literature and approaches on this topic is [12]. In this paper, the authors introduce and motivate the controllability property of a multi-agent system, and they derive some basic fundamental results using several different approaches that have been largely investigated over the years.  
One main direction stemming from this paper is the use of equitable partitions to detect uncontrollable graph topologies [13; 14]. However, this method is proved to be only sufficient (and thus it provides only necessary conditions for controllability and observability). Moreover, the investigation of the existence of equitable partitions in large graphs may be difficult.  
Other research directions exploring several different facets of controllability are the so-called structural controllability, namely the controllability analysis for almost all edge weights [15], and gramian-based controllability, where energy-based analysis is adopted to study the impact of control [16; 17].  
In paper [18], a brand new approach was introduced for the class of path and cycle graphs, considering the position of the control/measured nodes within the network, and necessary and sufficient conditions were derived based on simple modular algebra. This approach stimulated several reserch activities; in [19], the results of [18] were increasingly deepened and fully exploited.  
The approach of [20] is based on the definition of minimal perfect critical set. This method is adequate to capture the minimum set of leaders for the controllability of undirected graphs, and it is applied to two different classes of bipartite networks, namely deterministic scale-free networks and Cayley trees.  
In paper [21], path graphs and star graphs are considered, and the idea exploited is to also use second-order neighbors to improve controllability properties.  
In order to summarize the above scientific literature on the topic of interest, we briefly report the different approaches and their limitations in Table 1.
# Two Novel Multidimensional Data Analysis Approaches Using InSAR Products for Landslide Prone Areas
## 4 Results
In the image obtained in this way, the areas with deformation in both methods are visible as bright reflection values (white). Areas not identified as deformation in either method are shown in dark (black) color. The areas shown in cyan and red colors correspond to the deformation areas identified by InCROSS and MIRA, respectively. The visual comparisons of the two methods employed in this study are illustrated in Figure 9c. The consistency of these two methods was also computed and found to be 80.48%. In conclusion, Sentinel-1 SAR data demonstrate strong potential for monitoring slow-moving ground deformations such as landslides. Future studies are encouraged to integrate ground-based GNSS observations to improve model accuracy. Additionally, using high-resolution DEMs and increasing SAR image acquisition frequency may enhance analysis quality, particularly in densely vegetated areas.
###### Abstract  
In this study, a new route planning model is proposed to help ocean-going ships avoid dangerous weather conditions and ensure safe ship navigation. First, we integrate ocean-going ship vulnerability into the study of the influence of meteorological and oceanic factors on navigational risk. A multi-layer fuzzy comprehensive evaluation model for weather risk assessment is established. A multi-objective nonlinear route planning model is then constructed by comprehensively considering the challenges of fuel consumption, risk, and time during ship navigation. The International Maritime Organization (IMO) guidelines are highlighted as constraints in the calculations, and wind, wave, and calm water resistance to ships in the latest IITC method is added to the fuel consumption and sailing time in the objective function. Finally, considering the large amount of data required for ocean voyages, the bidirectional A* algorithm is applied to solve the model and reduce the planning time. Furthermore, our model is applied to the case of an accident reported in the Singapore Maritime Investigation Report, and the results show that the model-planned route is very close to the original planned route using the Towing Manual, with an average fit of 98.2%, and the overall meteorological risk of the model-planned route is 11.1% smaller than the original route; our model can therefore be used to plan a safer route for the vessel. In addition, the importance of risk assessments and the IMO guidelines as well as the efficiency of the bidirectional A* algorithm were analyzed and discussed. The results show that the model effectively lowers the meteorological risk, is more efficient than the traditional route planning algorithm, and is 86.82% faster than the Dijkstra algorithm and 49.16% faster than the A* algorithm.  
IMO; route planning; meteorological risk; multi-layer fuzzy comprehensive evaluation; bidirectional A* algorithm +
Footnote †: journal: Applied Sciences
## 2 Sensors
### State-of-the-Art Research on BSN Status  
In recent years, with the development of BSN applications in many fields, electrocardiograph (ECG) sensors, accelerometers, pressure sensors, and respiration sensors are gradually becoming the hotspots of BSN sensor research. A large amount of research has been done on improving the wearability of sensor nodes, and optimizing data processing algorithms. An in-depth study has been made on problems such as energy control, fault diagnosis, and sensor node number reduction. All of these works promote the development of BSNs in the direction of energy-efficiency and accuracy.
# An Efficient One-Dimensional Texture Representation Approach for Lung Disease Diagnosis
## 2 Related Works
### Chest X-Ray Datasets  
Many other studies used different DL techniques trained on various chest X-ray image datasets and showed different levels of detection accuracy [31; 47; 48; 49; 50; 51; 52; 53]. Some of these datasets have many images with high-image pixels for many patients. The ChestX-ray8 dataset has 108,948 images with 1024 \(\times\) 1024 pixels for 30,805 patients. The ChestX-ray14 dataset has 112,120 images with 1024 \(\times\) 1024 pixels for 32,717 patients. The CheXpert dataset has 224,316 images for 65,240 patients. The MIMIC CX dataset has 473,057 images with 2544 \(\times\) 3056 pixels for 63,478 patients. The Indiana dataset has 7470 images with 512 \(\times\) 512 pixels for 3996 patients. The Padchest dataset has 160,868 images for 67,000 patients. The PLCO dataset has 185,241 images for 56,071 patients. These large 2D chest X-ray datasets required intense supervision to make them suitable for multiple-layer DL techniques. They required annotated bounding boxes, adjusted image boundaries, a corrected batch size to minimize overfitting problems, and high computational power to feature augmentation, enhancement of image resolution, extraction, and one or more fully connected layers for the category classification of 2D images. These requirements are not available in many remote or underdeveloped countries' hospitals. Therefore, to overcome these challenges, an attempt was made to convert a 2D chest X-ray into 1D data to train deep learning algorithms to detect a cardiomegaly-level (enlarged heart) rapidly [39]; it showed high accuracy, reaching 98.00% higher than the traditional 2D chest X-ray approach. The results of this study are promising and may be used for other chest problems. However, the image transformation method used all image pixels instead of utilizing a statistical representation to reduce the image dimensions, carry meaningful information, and provide a simpler approach with more insights into the image. Moreover, it used pooling for downsampling to simplify data by taking maximum values, which may result in the loss of fine details. Therefore, simplifying the complexity of image processing and classification is crucial. This study aims to simplify the image processing and classification complexity by converting2D images in the Guangzhou Women and Children's Medical Center dataset [25] and the COVID-19, pneumonia, and normal chest X-ray PA dataset [27] into 1 DTR data. Then, the 1D data will train a deep learning classifier to classify pneumonia and COVID-19.
# Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation
## 3 Scalable Evolutionary Clustering Algorithms
### Isesc
Thus, \(p_{2}\) will also be  
Figure 4: ISESC overview, adapted from [13]. treated as evidence of a significant change (cluster merging). Note that _ls_s areas will never overlap since they grow towards the Voronoi diagram, and _k_-means always consider the minimum distance. The data point \(p_{3}\) is outside both boundaries and will be treated as evidence of a significant change. The last data point, \(p_{4}\), lies within both boundaries of \(C_{4}\) and does not represent evidence of a significant change. If evidence of change is detected, the model is incrementally updated. A reduce job assigns the data points to their closest clusters, aggregating their summaries and incrementally updating their information. Then, clusters not updated in the most recent time window, i.e., with _ts_ values older than the time window limit, are removed from the model. The remaining updated clusters compose the new model. Otherwise, if evidence of change is detected, the full-clustering component is executed over the data of the present time window. It consists of running SF-EAC over an initial population of pre-defined solutions built from the current model and derivatives. The current model reflects the known structure along the data stream. Based on this model, two methods are used to derive it: the first aims at increasing iteratively the number of clusters \(k\) until a limit _kmax_ is reached, selecting prototype candidates among the farthest objects from the existent centroids (inspired by [18]); the second method aims at decreasing \(k\) by merging the closest pairs of clusters repeatedly until a _kmin_ cluster is reached. The closest pair of clusters is estimated by their boundaries, i.e., the distance between centroids minus the sum of their radii values (_rd_). If necessary, both methods may use other arbitrary stopping criteria (the maximum population size, for example). Besides guided initialization, SF-EAC provides different strategies to diversify the population during the evolutionary search, estimating a new data-adjusted model. ISESC incrementally updates the model with a low computational cost when no significant change is detected. However, it runs the full-clustering component at any evidence of change, which is the most accurate (but costly) way to learn the current clustering model. Thus, ISESC prioritizes quality and is suitable for data sets with low concept drift, i.e., the clusters do not or rarely change abruptly. Otherwise, the full-clustering component may be triggered repeatedly, requiring computational resources.
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 2 Materials and Methods
### Data Acquisition
#### 2.2.1 Reference Measurements  
Reference measurements were collected under laboratory conditions for the 21 individual bunches and the 31 individual vines. Individual bunch mass (g) was recorded with a Mentor scale (Ohaus, Parsippany, NJ, USA), and individual vine mass (g) was recorded with a Viper SW scale (Mettler Toledo, Columbus, OH, USA). Bunch/vine volume measurements were recorded as the displacement (mL) of water when bunches were submerged in a container of water [24]. The mass and volume measurements were used as reference measurements for the estimated measurements derived from the two PRS techniques.
## 5 Experimentation and Performance Evaluation of Obfuscation Methods
### Performance Evaluation
In the absence of critical path obfuscation, the additional overhead incurred by the circuit is essentially none, so the impact is negligible. When using critical path obfuscation, various performance metrics of the circuit undergo alterations without compromising the functionality of the circuit. Timing, as a crucial metric for the critical path obfuscation method, demonstrates a significant delay with an average change value of 4.7%. The change in circuit overhead meets thecriteria expected from the experiment. The obfuscation rate consistently remains at 98% or higher, indicating successful coverage of a substantial portion of the code. The duration of obfuscation is expected to exhibit a positive correlation with the size of the RTL code content. However, the precise time is also influenced by the complexity associated with reconstructing the data. Critical path obfuscation differs from other obfuscation methods in that it changes the structure of the project and the evaluation of the obfuscation effect is more complex. After the critical path obfuscation, part of the code is changed, but the inserted modules ensure that the input data does not change, preserving functional equivalence. However, this definitely increases the various overheads of the circuit but is perfectly acceptable for developers who need it.
## 5 Experiments and Discussion
### Comparative Experiment  
The experimental data is real and effective, and it comes from the real drilling site environment of different coal mines. The same drilling rig data sets are used in the comparative experiments. In order to verify the effectiveness of the algorithm proposed in this paper, it is verified on the self-built drilling rig dataset. At the same time, the current mainstream target detection algorithm is compared in Figure 5 and Table 2. The model has the same depth and width. The resolution of the input image is 640 \(\times\) 640.  
Both the convergence speed and accuracy of YOLOV7-GFCA are better than the original YOLOV7 algorithm. The identification of drilling rigs has achieved an accuracy rate of 99.5%. Compared with other network models, the parameter quantities were reduced to 2.314 \(\times\) 10\({}^{6}\), 2.121 \(\times\) 10\({}^{6}\), 2.13 \(\times\) 10\({}^{6}\), and 2.325 \(\times\) 10\({}^{6}\), respectively, and the model volume reduced by 17.1 M, 15.9 M, 15.9 M, 17.5 M. The inference speed increased by 39 frames/s, 45 frames/s, 35 frames/s, and 33 frames/s, respectively. The results of the simulation under the different scenes is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]\%** & **FPS** & **Model** & **Parameter** \\
**Type** & **[EMAIL_ADDRESS]\%** & **FPS** & **Weight/MB** & **Amount/(10\({}^{6}\))** \\ \hline YOLOv3 & 96.2\% & 41 & 71.4 & 9.308 \\ YOLOv4 & 95.4\% & 35 & 70.2 & 9.115 \\ YOLOv8 & 93.8\% & 45 & 70.2 & 9.124 \\ YOLOv7 & 96.4\% & 47 & 71.8 & 9.319 \\ YOLOv7-GFCA & 99.5\% & 80 & 54.3 & 6.994 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between YOLOV7-GFCA and mainstream algorithms.  
Figure 5: Comparison between the YOLOv7-GFCA algorithm and the current mainstream target detection models.
## 5 Prediction Performance Evaluation  
The following formulated criteria are employed to quantify the performance of the devised dual-step integrated ML based WPP model.  
* Mean absolute percentage error (MAPE): \[\text{MAPE}=\frac{100}{N}\sum_{\text{h}=1}^{\text{N}}\left|\frac{\text{P}_{ \text{h}}^{\text{a}}-\text{P}_{\text{h}}^{\text{f}}}{\text{P}_{\text{h}}^{ \text{a}}}\right|\] (10) where \(\text{P}_{\text{h}}^{\text{a}}\) and \(\text{P}_{\text{h}}^{\text{f}}\) are the real and the prediction values of the power at hour h, respectively, and \(N\) is the forecasting horizon.
* Root mean squared error (RMSE): \[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{\text{h}=1}^{\text{N}}\left(\text{P}_{ \text{h}}^{\text{a}}-\text{P}_{\text{h}}^{\text{f}}\right)^{2}}\] (11)
* Normalized mean absolute error (NMAE): \[\text{MAE}=\frac{1}{N}\sum_{\text{h}=1}^{\text{N}}\frac{\left|\text{P}_{ \text{h}}^{\text{a}}-\text{P}_{\text{h}}^{\text{f}}\right|}{\text{P}_{\text{ max}}}\] (12)  
where \(P_{max}\) is the peak generation capacity of the wind generation system, which is 2500 kW in this paper.  
* Error Variance: The inconsistency of the WPP method after development is an index of the stochasticity of the method and is estimated by calculating the variance of the forecasting error. The prediction is considered to be highly accurate or certain if the result of the variance is lower [19]. Using Equation (11), daily error variance is expressed as:\[\sigma_{\text{e,day}}^{2}=\frac{1}{N}\sum_{\text{h=1}}^{N}\left( \left|\frac{\text{P}_{\text{h}}^{\text{a}}-\text{P}_{\text{h}}^{\text{f}}}{\text {P}_{\text{h}}^{\text{a}}}\right|-\text{e}_{\text{day}}\right)^{2}\right. \tag{13}\] \[\text{e}_{\text{day}}=\frac{1}{N}\sum_{\text{h=1}}^{N}\left|\frac{ \text{P}_{\text{h}}^{\text{a}}-\text{P}_{\text{h}}^{\text{f}}}{\text{P}_{\text {h}}^{\text{a}}}\right| \tag{14}\]  
\(\bullet\) Forecast skill (FS):  
The FS measure calculates the worth of the WPP models by referring the prediction error to the persistence output. For 24 h-ahead WPPs, the persistence outputs can be defined as:  
\[\text{P}_{\text{h}}^{\text{f}}(\text{t})=\text{P}_{\text{h}}^{\text{a}}(\text{ t}-24) \tag{15}\]  
The FS is estimated based on the relation of the RMSEs of forecast methods with reference to the persistence method [19; 36], and it is given as follows.  
\[\text{FS}=1-\frac{\text{RMSE}_{\text{Model}}}{\text{RMSE}_{\text{ Persistence}}} \tag{16}\]
## 2 Materials and Methods
The photocatalytic activity was studied under a mercury lamp with a power of 300 W. MO in pure solution or MO-Na\({}_{2}\)SO\({}_{4}\) mixed solution was the object of photocatalytic degradation. UV-VIS spectrophotometry (UV-VIS: Beijing Puxi Tu-1950) was used to determine the concentration of MO solution both before and after the photocatalytic experiment, with the maximum absorption wavelength being 459 nm. First, the prepared photocatalysts (4.5 cm \(\times\) 2.5 cm) were placed in methyl orange (MO) solution (15 mL) at a concentration of 10 mg/L. The dye solution was then held under dark conditions until it reached absorption-desorption equilibrium. Then, the dye solution containing photocatalysts was irradiated at a position 10 cm below the high-pressure mercury lamp (300 W). In keeping the experimental conditions unchanged, the photocatalyst was used to degrade the MO solution (10 mg/L) containing Na\({}_{2}\)SO\({}_{4}\) (12.5 g/L) (MO-Na\({}_{2}\)SO\({}_{4}\) mixed solution). Four cycles of testing were conducted in order to characterize the cycling stability of the films' photocatalytic performance. After photocatalytic degradation, the photocatalyst was taken out of the solution and cleaned by rinsing it with ultrapure water. Then, the film was dried at room temperature and reused for the photocatalytic degradation of MO. The degradation rate can be calculated with the following formula [20]:  
\[\eta\%=\frac{\text{C}_{0}-\text{C}_{\text{t}}}{\text{C}_{0}}\times 100\% \tag{1}\]  
where \(\eta\) is the degradation rate of MO in pure MO solution or MO-Na\({}_{2}\)SO\({}_{4}\) mixed solution, C\({}_{0}\) (mg L\({}^{-1}\)) is the initial concentration of the MO, and C\({}_{\text{t}}\) (mg L\({}^{-1}\)) is the concentration of the MO solution at time t (min).