esa-sceva/satcom-chunk-collection huggingface dataset:
# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 2 Materials and Methods
### Experimental Design and Configuration  
To compare the effectiveness of AdaptiveConv2d, we used U-Net as the baseline framework and replaced the convolution operations with different types: standard convolution, dilated (hole) convolution, Depthwise Separable Convolution, and Group Convolution, subsequently comparing these with AdaptiveConv2d. Additionally, to assess the applicability of AdaptiveConv2d, we compared its performance with several state-of-the-art(SOTA) deep learning segmentation methods, including U-Net, nn-UNet, UNet++ [40], TransUNet [19], and Attention U-Net [41]. The experiments were conducted using the PyTorch framework on an NVIDIA RTX 3090 GPU. The ratios of the training set, validation set, and test set was set to 8:1:1. During training, the learning rate was set to 0.0001, the batch size to 10, and the number of epochs to 200. All images, except those in the Blood Cell Segmentation Dataset, were resized to 256 \(\times\) 256 pixels; images from the Blood Cell Segmentation Dataset were resized to 1024 \(\times\) 1024 pixels. The Adam optimizer was used for training, and the loss function was a combination of Dice Loss and cross-entropy loss. To enhance data quality, improve model robustness, and ensure consistency in training conditions, we applied uniform data augmentation techniques across the datasets. The augmentation operations included the follow features:  
* Random Cropping: Images were cropped at random positions to generate sub-images of a specified height and width. This approach helps the model learn from different regions of the image, enhancing robustness to local features.
* Horizontal and Vertical Flipping: Images were randomly flipped horizontally and vertically, with each operation having a 50% probability of being applied. These transformations allow the model to adapt to various orientations and directions present in the images.
* Random Rotation by 90 Degrees: Images were rotated by 90 degrees with a certain probability. This operation further increased dataset diversity by introducing variations in orientation, making the model more stable when processing images with different angles.
* Color Jittering: The brightness, contrast, saturation, and hue of the images were adjusted to simulate different lighting conditions. This color transformation helps the model remain robust under varying lighting and color scenarios.
* Normalization: Pixel values were normalized using a specific mean and standard deviation. Normalization not only speeds up model convergence but also improves generalization.  
Through these data augmentation operations, we significantly improved the robustness of the model under various conditions, effectively mitigating the risk of overfitting.
# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
## 4 Experimental Works and Results
### Experimental Setup
#### 4.1.6 End-to-End Delay  
The end-to-end delay is the duration between when packet transmission is initiated in the fog layer and its arrival at the DAG root in the node layer. Our proposed mechanism is compared again with DCTM-RPL [28], RBAM-IoT [29], RPL-MRC [30], and DSH-RPL [25]. The results are shown in Figure 9 for rank, Sybil, and sinkhole attacks. The numbers of nodes are shown on the \(x\)-axis, and the end-to-end delay in milliseconds is shown on the \(y\)-axis. The average end-to-end delays during a rank assault for DCTM-RPL, MMTM-RPL, and RBAM-IoT were 77, 78, and 81 ms, respectively. During a Sybil assault, the average end-to-end latency for DCTM-RPL, MMTM-RPL, and RPL-MRC was around 139, 142, and 143 ms, respectively. The end-to-end delays during sinkhole attacks for DCTM-RPL, MMTM-RPL, and DSH-RPL were around 117, 118, and 177 ms, respectively. The simulation findings reveal that the additional security mechanisms incorporated into an RPL-based  
Figure 8: Comparison of attack detection time for different attacks: (a) Rank attack, (b) Sybil attack, and (c) Sinkhole attack.  
LLN induce high delay. Moreover, since MMTM-RPL has an upper fog layer, it has slightly more latency than the other techniques, despite having a lower hop count. This indicates that the shortest path may not guarantee that delays and latency are minimized. Moreover, the trust calculation and propagation in the other approaches are done by the node layer, which reduces the delay.
# Industry 4.0 and World Class Manufacturing Integration: 100 Technologies for a WCM-I4.0 Matrix
## 3 Methodology to Structure the WCM-I4.0 Matrix  
The proposed WCM-I4.0 matrix aims to highlight the technologies that can best serve and satisfy the needs of the WCM world. The idea comes from the need to map, in an intuitive and immediate way, the origin of technological opportunities and where they can positively impact on WCM pillars, helping in investment decision-making and allowing to identify the appropriate technological drivers.  
The WCM-I4.0 matrix reports the WCM pillars in each row, along with some of their characteristic elements. On the columns, the WCM-I4.0 matrix shows the I4.0 technological areas, rearranged into the following groups:  
* Cloud Computing;
* Cognitive Computing;
* Internet of Things;
* Machine-to-Machine (M2M);
* Mobile Technologies;
* Augmented Reality;
* Simulation;
* Additive Manufacturing;
* Advanced Robotics.  
As said, also Big Data, Cybersecurity, RFID areas are often cited within the Industry 4.0 universe [13; 14; 34]. Here, also according to the outcomes of the discussion with the panel of experts, these areas have been excluded from the analysis because:  
* Cybersecurity--Intended as the protection of computer systems and networks from the theft of or damage to their hardware, software, or electronic data--is to be considered a prerequisite for Industry 4.0 technologies, not an opportunity area for WCM pillars;
* RFID--Intended as the technology that allows the automatic identification and tracking of objects through radio-frequency tags--was considered as surpassed by, or at least included in, IoT paradigm;
* Big Data--Intended as the area that refers to analyzing and systematically extracting information from data sets that are too large or complex to be dealt with by traditional data-processing application software--can be considered a generic concept and a transversal approach to all the cited areas: for example, Big Data may originate from M2M applications or from mobile technologies, or from IoT. Thus we assumed that the capabilities of performing advanced data analytics methods and extracting value from data can be shared among several cited areas. Analogous consideration can be done for Cyber-Physical Systems, intended as a computer system in which a mechanism is controlled or monitored by computer-based algorithms.  
Finally, a set of 100 I4.0 technologies has been identified and represented into the WCM-I4.0 matrix: in each cell, the technologies belonging to a given I4.0 group and providing support to given WCM tools are listed.  
In the following paragraphs, the I4.0 groups (elements on the columns) and then the WCM tools (elements on the rows) are briefly recalled, showing the relevant literature that helped the authors in finding the links between the two dimensions.
## 2 Bio-Inspired Corrugated Core Sandwich Structure
(**a**) An original sinusoidal shape and cutting planes formed the unidirectional unit cell; (**b**) an original bi-sinusoidal shape and cutting planes formed the bidirectional unit cell; (**c**) unidirectionally corrugated core model; (**d**) bidirectionally corrugated core model. In the literature, several kinds of the UCS structure were investigated by experiment and simulation [11; 15; 32; 33]. [PERSON] et al. [18] reported that the BCS structure greatly reduced the risk of damage in the crushing test. The results from this numerical simulation revealed that the UCS structure deflected unsymmetrically, which could increase the localized boundary layer transition, as presented in Section 4. The UCS structure should be considered carefully for the skin structure of the metallic TPS panel. Therefore, the use of a bidirectional core structure is preferred over the unidirectional core structure for such a skin structure of a metallic TPS panel. Hence, we decided to fabricate and test the BCS structure in a thermomechanical experiment before it was employed in the TPS panel. In this study, the BCS structure was first fabricated by Doowon Heavy Industrial Company, Republic of Korea, using the design already explained. However, because of cost and usage constraints, the use of Inconel 625 material is limited. Thus, in this study, we fabricated a prototype of the BCS structure in which the corrugated core was made by bending the 0.2 mm thick stainless steel sheet in a mold. Then, the sandwich structure was assembled by brazing the corrugated core with the 0.3 mm thick stainless steel face sheets. A picture of the bidirectionally corrugated core sandwich structure is shown in Figure 5. The weights for the as-fabricated structures are listed in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & \(\mathbf{t_{\mathrm{o}}}\) & \(\mathbf{t_{\mathrm{I}}}\) & \(\mathbf{t_{\mathrm{C}}}\) & \(\mathbf{h}\) & \(\mathbf{w}\) \\ \hline Unit (mm) & 0.3 & 0.3 & 0.2 & 6 & 21.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geometrical parameters of a unit cell. Figure 4: Geometrical configuration of different corrugated cores: (**a**) unidirectional core, (**b**) bidirectional core, (**c**) unidirectionally corrugated core structure, (**d**) bidirectionally corrugated core structure. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Core Type** & \(\mathbf{Ix}\) & \(\mathbf{Iy}\) \\ \hline Unidirectional core & \(2.49\times 10^{-8}\) mm\({}^{4}\) & \(2.42\times 10^{-8}\) mm\({}^{4}\) \\ Bidirectional core & \(2.33\times 10^{-8}\) mm\({}^{4}\) & \(2.33\times 10^{-8}\) mm\({}^{4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Area moment of inertia of the corrugated core structures.
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 2 Machine Learning Tools and Signal Processing
### Machine Learning Tools  
Machine learning algorithms build a model that maps a certain set of features from an input X to output Y, where the (X, Y) pair is a training dataset, using sample data collected from a given distribution and limited in amount to perform prediction without expert intervention [16]. The three common types of machine learning paradigms are: supervised, unsupervised, and reinforcement [17]. The multilayer perceptron network and extreme learning machine, both of which are based on an artificial neural network (ANN), are briefly discussed below.
## 4 Experimental Measurements
### Measurement of Topographic Targets  
The measurements shown in Figures 8 and 9 were taken on 18 July 2014, after positioning the lidar system on the flat roof of the D4 building of the School of Agrifood and Forestry Science and Engineering (Spanish initials: ETSEA) of Universitat de Lleida, in Lleida (Catalonia, Spain) and aiming it at various surrounding topographic targets. Figure 8a corresponds to the signal backscattered by the crowns of a group of trees located 340 m from the lidar system. Two signal peaks separated by 15 m can be observed. This is because the crowns are comprised of a set of non-homogeneous elements which favour partial impact of the beam on them. In Figure 8b the measurement is shown of the wall of a building located 1125 m away. The SNR estimated for measurement of the trees is 71 (highest peak), while the corresponding SNR for the signal backscattered by the wall is 50. The procedure explained in [14] was used to calculate the SNR.  
Figure 9a shows the signal peak backscattered by a mountain located at a distance of 2275 m. The estimated SNR is 11, a far higher value than the threshold of 5 considered in the simulations of Section 2. This result shows that the system is capable of detecting with clarity topographic targets over 2 km away. The measurement shown in Figure 9b corresponds to the same mountain, but in this case averaging a  
Figure 8: Range profile of lidar signal backscattered by several topographic targets. (**a**) Peaks corresponding to the crowns of a group of trees; (**b**) Detection of a building located at 1125 m from the lidar system.  
Figure 9: Range profile of lidar signal backscattered by a mountain located at 2275 m from the lidar. (**a**) Return corresponding to a single laser shot; (**b**) Return after averaging 100 laser pulses.  
total of 100 shots, (PRF = 2 Hz, observation time of 50 s) in order to reduce the white noise. It can be observed how the SNR improves up to a value of 63. Due to their high dynamics, shot averaging cannot be used for the measurement of drift clouds in cases in which there is a desire to know their temporal evolution. However, this method raises the possibility of monitoring other cloud typologies with slower dynamics, as for example of atmospheric clouds (ceilometry) or those generated in forest fires.
## 3 Key Findings
### RQ1: What Are the Security Challenges of IIoT?
#### 3.1.1 RQ1-1: Architectural Design Loopholes of IIoTsec
Thus, the recommendation proposed in the study is to design an appropriate topology to ensure network coverage and instantaneous connectivity. Moreover, [PERSON] et al. [55] showed how security challenges the architectural design in emerging wireless communication systems like IIoT. To combat the effect, the authors proposed a boundary technique. They implemented its efficiency at the physical layer to decry delay and signal attenuation occasioned by the off-zone positioning of sensors.
# Application of Data Mining Techniques to Predict Luminance of Pavement Aggregate
## 1 Introduction
Therefore, the degree of complexity in predicting the luminance coefficient indicates that traditional regression methods basing on the method of least squares will be neither effective nor efficient. There are very few publications that attempt to apply DM techniques to luminance coefficient evaluation. In their paper, [PERSON] et al., used DM techniques to manage street lighting [24]. In addition, [PERSON] et al., applied the ANN algorithm to predict the daylight illumination of office buildings [25]. In contrast, in the paper [26], the authors attempted to ensure adequate luminance of an indoor sports court by determining it using DM techniques. It should be noted that it is possible to use traditional techniques, such as multiple regression, but these make it impossible to predict nonlinear effects. In the article by [PERSON] [27], a multiple regression model was used to calculate the perceived luminance in tunnel interior lighting conditions. It was pointed out that this type of regression technique is applicable only to the object under study and its generalization requires the consideration of more factors. If the number of factors is increased, the effectiveness of the multiple regression model will decrease significantly. It should be noted that a high luminance of the aggregate does not necessarily mean that a highly bright pavement will be obtained. This will definitely depend on the texture of the pavement, the amount of binder, and the pavement's surface condition. [PERSON] et al. [28] indicated a significant impact of the mixture type, its structure, and the time of exposure to climatic conditions on the value of the obtained reflectance coefficient. Therefore, in order to determine the final value of the luminance coefficient, the composition of the bituminous mixture should also be taken into account. Nevertheless, the aggregate is responsible for a substantial part of the luminance coefficient value. This was indicated in the work of [11], as well as in experimental tests performed on samples of bituminous mixtures [29]. Observing the limited number of publications, it can be concluded that regarding the luminance coefficient problem, very little attention was paid. However, the minimum requirements for the aggregate luminance coefficient are frequently defined as a precondition for designing, for example, pavements in tunnels. Thus, it should be emphasized that aggregate luminance is crucial for pavement brightening [11] and a reliable prediction model of the aggregate luminance coefficient is important; its results will be presented in this article.
# A Survey of the Applications of Text Mining for the Food Domain
## 6 Text Mining in Recipe Recommendation and Personalized Nutrition
### Cross-Modal Recipe Analysis  
Cross-modal food analysis involves synthesizing textual, pictorial, and multi-modal datasets to capture the essence of recipes and intricacies of ingredient preparation, offering a deeper understanding of cultural and health narratives. By dissecting the visual and procedural attributes of food, such as color and slicing techniques, this approach enables finely tuned, visually oriented food recommendations [95].  
Cross-modal methodologies can deduce unarticulated aspects of recipes. Studies, like the work by [PERSON] et al. [96], leverage imagery and textual content to correlate recipes with visual depictions, guiding users in the food-creation process based on visual cues from food photographs. By combining text and image data from diverse sources, a more nuanced system for recipe comprehension and recommendation can be engineered. Innovations, like the R2 GAN model introduced by [PERSON] et al. [97], generate food images from recipe descriptions, enhancing cross-modal recipe retrieval. This advancement benefits individuals seeking to identify dishes from recipes or recipes from images, opening possibilities for cross-modal retrieval in different domains. Cross-modal recipe retrieval, matching food images with recipe scripts or vice versa, has been explored in various studies [98; 99; 100; 101]. Looking ahead, as multimodal interaction evolves, additional modalities like auditory and gestural inputs may be integrated, providing users with more intelligent and bespoke food selection and guidance services.  
In conclusion, cross-modal analysis improves recipe understanding and marks a major advancement towards customized services and intelligent food recommendation systems by overcoming the limits of text-only data.
## 3 Data and Methods  
### Datasets  
The data set used in this paper was the data of two independent wells in Tarim Block, Xinjiang Oilfield, China, with a total length of 5500 m, including magnetic positioning, CBL, VDL, and cementing interpretation results of two wells. The shortest interpretation interval is 1m, and the longest interpretation interval is 802 m. However, the convolutional neural network needs to input data with consistent resolution. We divided the VDL into 1 m long depth segments.  
Concerning the five response characteristics of the CBL-VDL in Section 2.1.1, we developed a method for labeling the cementing quality training set, as shown in Table 1, and relabeled the training label for the data set. To ensure the accuracy of manual labeling as much as possible, we entrusted the data labeling work to three experts with years of experience in logging engineering and research, and took their interpretation results as the final data labels.
# Designing an Adaptive Underwater Visible Light Communication System +
## 6 Receiver Design
### Receiving Payload  
After the successful reception of the message signal, the PPM signal is decoded using the matched filter as shown in Figure 12. This filter identifies the peaks, which are then translated into bits. The signal reception process is described in detail in the flow chart presented in [4].  
Figure 11: Flow chart for the receiver model to find the angle.
# A Wireless Monitoring System for Cracks on the Surface of Reactor Containment Buildings
## 7 Conclusions  
The advent of wireless sensor networks brings both opportunities and challenges for structural health monitoring. We enjoy the convenience of wireless transmission but suffer limited resources at the same time. In this paper, we present a real-time monitoring system for cracks on the surface of reactor containment buildings during pressure tests based on wireless sensor networks. Sensors like crackmeters and temperature detectors are chosen and customized platforms are designed and implemented to meet the monitoring requirements. Meanwhile, software protocols like route discovery, time synchronization and data transfer are developed to take the limited resources into account. A graphical user interface is programmed to receive, store and display data. The monitoring system is evaluated with several experimental tests before real-life deployment. In addition, the full scale deployment of the system at Daya Bay Nuclear Power Station with 30 sensor nodes is quite successful. Despite the successful deployment, there are still some improvements that can be made for our system. Currently only crackmeters and temperature detectors are used in our nodes, and more sensors may be added for multiple purposes in the future. The topological structure of the monitoring network is established at the initialization stage and is static; thus, it is not robust to failures, especially for cluster head errors. Therefore, dynamic topology will be a better choice for the system. The system is mainly designed for low sampling frequency tasks as tailored time synchronization protocols are developed. However, it may need to be redesigned to suit some high sampling frequency monitoring projects. Similarly, the unreliable data transfer protocol only works well with low sampling frequency, reliable protocols or data recovery mechanisms that are required for high sampling frequency tasks. We believe our system can be easily adapted to many monitoring tasks if these improvements are made.  
This work has been supported by the National Natural Science Foundation of China under grant No. 41474005, and Key Laboratory of Advanced Engineering Surveying of National Administration of Surveying, Mapping and Geoinformation under grant No. TJES1202.  
Figure 8: Pressure changes during the pressure test.  
**Author Contributions:** The corresponding author [PERSON], who is responsible for the overall work, proposed the research idea, conducted the experiments and prepared the manuscript. The second author [PERSON] provided general guidance during the entire research period. The third author [PERSON] helped in design and implementation of hardware and software.  
**Conflicts of Interest:** The authors declare no conflict of interest.