esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Underwater rock-plug blasting is a special blasting technique for excavating underwater inlets. In the process of rock-plug blasting excavation, the blasting-block movement from the difference in water pressure inside and outside the tunnel is one of the key factors for successful construction. Laboratory underwater rock-plug blasting experiments were conducted using small explosive charges, and a high-speed camera was adopted to observe and study block motion. Then, numerical simulations were conducted for the model experiment based on the Fluent and Engineering Discrete Element Method (EDEM) coupling program developed using the user-defined function (UDF) interface to reveal the mechanism underpinning the penetration of underwater rock-plug blasting. The results showed that the process of block motion in underwater rock-plug blasting can be divided into two stages. In the first stage, broken blocks move to two sides along the axis of the rock plug under the blast load. A blasting crater is formed on the downstream end face of the rock plug under the effects of the free face, while the upstream end face is loosened, or blocks are ejected under the influence of the water pressure. In the second stage, blocks flow to the broken-rock pit under the effects of water scouring and gravity, and, finally, the rock plug is penetrated. The larger the head of water and the opening angle of the rock plug are, the better the penetration effect for the rock plug is. The Fluent-EDEM coupling algorithm was in good agreement with the experimental results in terms of the rock-plug blasting effect and the velocity curve of the blocks, indicating that the coupling method had a favorable effect in simulating the interaction of blocks and water during underwater rock-plug blasting. The findings are expected to promote the application and popularization of the rock-plug blasting technique and can provide a reference for rock-plug blasting in water-intake and water-diversion projects.  
underwater rock-plug blasting; Fluent-EDEM; fluid-structure interaction; block motion Article
# Thermal Hydrolysis of Sewage Sludge: A Case Study of a WWTP in Burgos, Spain
## 3 Results and Discussion
### Total Self-Consumption, Electric Power Balance and Specific Energy Parameters
Thus, the gas available for valorisation is 0.8% lower, reducing, in this same value, the net electrical energy produced and decreasing the thermal energy by a value of 6.9%. Conversely, the amount of available biogas to be used as fuel by the engines is 8.6% higher in Scenario 3 (due to the lower demand of live steam because a less amount of sludge is pretreated). The increment in the biogas available for the engines causes the start-up of a third engine, which represents a significant decrease in the efficiency of these units. As a result, only a 0.2% increase is obtained in gross electricity production. This effect also results in a 6% decrease when considering the net electricity produced. In a similar way, Scenario 4 presents an increase in the biogas available for valorisation of about 19.2%, but the increment is a gross value, and net electrical energy is 12.9 and 3.1%, respectively. Finally, Scenario 5 reports the highest values for the analysed parameters, showing an increment in available biogas for the engines of 42.5%, thus proving to be the best option. The majority of reports found in bibliographies usually focus on the parameters associated with biogas increase, sludge solubilisation and centrate characteristics [33]. However, when considering the installation of a hydrolysis unit, other parameters become relevant as it involves the use of biogas for the boiler and engines. Batch processes are highly penalised due to the amount of biogas that is diverted for steam production; this, therefore, reduces the production of electricity. Another additional factor that highly influences the outcome of the different scenarios is the efficiency of engines based on fuel loading. Thus, increasing the amount of biogas available to the engines may not always translate into a direct increase in electricity production due to the lower efficiency of the engines if an additional one needs to be started up.
# Efficient Video Frame Interpolation Using Generative Adversarial Networks
## 4 Experimental Results and Analyses  
### Dataset  
The UCF101 dataset [37] is a large diversified video dataset, which includes 13,320 realistic videos from 101 action categories with various moving objects in static and dynamic environments. The videos in the UCF101 dataset have a fixed frame rate of 25 FPS (frames per second) and a resolution of 320 \(\times\) 240 pixels. [PERSON] et al. [37] divided these videos into five types, Body-Motion Only, Human-Human Interaction, Human-Object Interaction, Playing Musical Instrument, and Sports.  
In our experiment, we use a part of Body-Motion Only videos for both training and testing. The training dataset includes 377 videos, while the testing dataset comprises 59 videos. These videos come from the following action categories: BabyCrawling, BlowingCandles, BodyWeightSquats, HandstandWalking, JumpingJack, PullUps, RopeClimbing, Surfing, Swing, TrampolineJumping, and WallPushups. The selected videos cover a wide range of scenes with diverse motion and texture characteristics.  
We also perform the outside test by using the pre-trained model on other datasets, such as other types in the UCF101 dataset, the CUHK dataset [38], and the high-speed Sintel (Sintel) dataset [39]. The CUHK dataset contains 150 FPS videos of resolution of \(640\times 360\) pixels, while the Sintel dataset includes 1080 FPS videos of resolution of \(2048\times 872\) pixels.
# A Tweak-Cube Color Image Encryption Scheme Jointly Manipulated by Chaos and Hyper-Chaos
## 2 Preliminaries  
### The Logistic-Fraction Hybrid Chaotic Map  
In this section, a new chaotic map called the Logistic-Fraction hybrid chaotic map (LFHCM) is recommended, which is defined as Equation (1). The LFHCM is derived from the classical Logistic map and Fraction map [25], and they can be mathematically represented as Formulas (2) and (3).  
\[\mathrm{x}_{\mathrm{k+1}}=\mathrm{L}(\mathrm{x}_{\mathrm{k}})=\mathrm{ax}_{ \mathrm{k}}(1-\mathrm{x}_{\mathrm{k}})^{2}\times\frac{1}{\mathrm{x}_{\mathrm{ k}}^{2}+1} \tag{1}\]  
\[\mathrm{y}_{\mathrm{k+1}}=\mathrm{L}(\mathrm{y}_{\mathrm{k}})=\mathrm{by}_{ \mathrm{k}}(1-\mathrm{y}_{\mathrm{k}}) \tag{2}\]  
\[\mathrm{z}_{\mathrm{k+1}}=\mathrm{F}(\mathrm{z}_{\mathrm{k}})=\frac{1}{ \mathrm{z}_{\mathrm{k}}^{2}+0.1}-\mathrm{cz}_{\mathrm{k}} \tag{3}\]  
where control numbers \(\mathrm{a}\in[0,11.5]\)\(\mathrm{b}\in[0,4]\), and \(\mathrm{c}\in[0,1]\). In addition, the output ranges of the three maps are also different, \(\mathrm{x}_{\mathrm{k}}\in[0,1.56]\), \(\mathrm{y}_{\mathrm{k}}\in(0,1)\), and \(\mathrm{z}_{\mathrm{k}}\in[-10.0025,10.0025]\). Then, the analytical tests can verify that the LFHCM has more complex dynamic characteristics.
# Visual Enhancement Capsule Network for Aspect-Based Multimodal Sentiment Analysis
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.3 Aspect-Based Multimodal Sentiment Analysis
Figure 4 sorts the average text lengths and image numbers of samples in two datasets, respectively, and the statistics of their 91 st to 99 th quantiles are listed in Table 6. Taking the statistics in the typical 95 th quantile as an example, we can see that the average text length of MultiZOL is 803, while that of MTCom is only 146, and the average number of images in Multi-ZOL is 14, while that of MTCom is only 7. This is because, for expensive electronic products such as mobile phones, people tend to use longer text to describe all aspects of them in detail, as well as supplementing their reviews with corresponding images for almost every aspect. However, for the daily consumption of food and beverage, people usually do not spend a lot of time giving long reviews unless the dining experience is very good or very bad. So the text lengths and image numbers of reviews in the MTCom dataset are shorter and less than those in the Multi-ZOL dataset, and more representative of the majority of review data. Analysis of limitations.The analysis of the datasets also verifies our design idea from another aspect, that is, in the task of multimodal sentiment analysis, text information is dominant, and an image is auxiliary rather than a complete ideographic unit. Furthermore, VECapsNet proposed in our method uses capsule networks to model the local sentimental features, so it has more advantages for processing short text than MIMN, which uses bidirectional LSTMs for capturing long-distance dependencies of context. Although the effectiveness of VECapsNet can be verified by the overall experimental results, for long text content and a small number of image comments with almost no explicit emotion, the performance of VECapsNet is shown to be degraded, which may be due to the fact that text-guided image feature learning may not be able to well mine the induced emotion in images. We also point out that for the direction of our further research, we still need to strengthen the emotion analysis for long text compared with the uncertainty of image emotion. We may consider decomposing the long text into short texts according to syntactic structures or integrating the structural information of long text into the capsule features in MMCapsNet for sentiment prediction, so as to capture more sufficient semantic features from the global context. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Quantile**} & \multicolumn{2}{c}{**Multi-ZOL**} & \multicolumn{2}{c}{**MTCom**} \\ \cline{2-5}  & **Textual Length** & **Image Number** & **Textual Length** & **Image Number** \\ \hline
91 & 537 & 9 & 105 & 6 \\
93 & 648 & 9 & 122 & 7 \\
95 & 803 & 14 & 146 & 7 \\
97 & 1195 & 21 & 185 & 8 \\
99 & 1922 & 34 & 285 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The quantile statistics of context length and image number of samples on two multimodal datasets.
## 2 System Architecture
### The Dual-Axis Rotary Platform Mechanism  
The design of externals for the proposed dual-axis rotary device in this research is shown in Figure 1. There are two axes: The lateral axis is the \(x\)-axis (yaw) and the longitudinal axis is the \(y\)-axis(pitch). With the rotation of yaw and pitch, the movement of the image (the picture acquired by the thermal imaging or full-color camera) will be driven. The image provides flying drone detection with a viewing angle range of 360\({}^{\circ}\) for yaw rotation and 180\({}^{\circ}\) for pitch rotation. In order to improve the detection capability of the drone, there is a mechanism frame inside the dual-axis mechanism, which is equipped with a fine-tuning device (three sets of laser rangers), a nine-axis attitude meter, a thermal imaging camera, and a full-color camera for drone image capture and position detection. The fine-tuning mechanism is used to provide drone tracing, and the laser ranger is adopted for focusing precision adjustment. In the drone image focus and distance measurement, because the image acquisition lens and the laser rangers are not on the same line, the distance measuring of the drone for high-altitude flight needs the fine-tuning mechanism for the focus. First, the photographic lens is adopted as the focus point of the center of the image frame, then the three laser rangers are set close to the lens, before the fine-tuning mechanism is modified with the focus distance of the drone to be acquired by the tracing device, so that the hitting spot of the laser ranger on the drone overlaps with the spot of the center of the image. GPS and LoRa antennas are placed outside the tracing device due to the need to avoid metal disturbance and prevent from signal-shelter in the thick casing of the tracing device.
# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
## 3 Materials and Methods  
The main purpose of the inspection robot is to traverse a set inspection route, take images of devices at designated inspection locations, analyze these images utilizing a recognition algorithm, and subsequently generate readings of meter indicators. The methodology detailed in this paper consists of several essential stages: object detection employing YOLOv11-OBB for pointer rotation, mapping of meter pointer coordinates through template matching, and utilization of an angle resolution algorithm for calculating reading. The complete framework of the proposed method is illustrated in Figure 1.  
Step 1: Pointer Rotation Target Detection Based on YOLOv11-OBB.  
The YOLOv11-OBB detection model is an extension of the YOLOv11 object detection algorithm. It integrates an oriented bounding box (OBB) detection technique to precisely identify the vertex coordinates of meter pointer rotation boxes and dial center coordinates in inspection images. This approach enhances the ability to perceive orientation and improves the accuracy of detecting rotating pointer targets through angle-sensitive feature extraction and an adaptive rotating anchor frame mechanism.  
Step 2: Implementation of Meter Pointer Coordinate Mapping by Template Matching.  
This paper presents the creation of a template library comprising diverse pointer meters. The registration images of standard inspection points are gathered to facilitate the automated spatial alignment of inspection and template images through a feature point matching algorithm. An affine transformation matrix is computed with high accuracy to precisely align the coordinates of the pointer vertex and dial center from real-time images to a standardized template space. This method effectively reduces positioning errors caused by angular deviations and illumination variations.  
Step 3: Angle Analysis Algorithm to Realize Reading Recognition.  
Utilizing the angle analysis algorithm, a mathematical mapping model of the pointer deflection angle with the instrument range has been developed to enable precise calculation of the pointer reading.  
The template image represents the standard model for the pointer meter, captured under optimal conditions with uniform lighting, free from distortion and clearly defined. It provides precise geometric characteristics and benchmark scale data essential for the system. Registration images, obtained under real-world conditions, corresponding to specific inspection points, often face challenges such as variable illumination, background noise, distortion, and blurring. Multiple registration images may relate to the same template image, merging inspection point data with the template. This integration establishes a foundation for accurate and efficient reading recognition of pointer meters in complex environments.
###### Abstract  
Oil pipeline monitoring is having a significant role in minimizing the impact on the environment and humans during pipeline accidents. The real-time monitoring of oil pipelines empowers the authorities to have continuous supervision of the oil pipeline. The Internet of Things (IoT) provides an opportunity for realizing the real-time monitoring system by deploying the IoT-enabled end devices on the oil pipeline. In this study, we propose a hybrid architecture based on 2.4 GHz-based Zigbee and LoRa communication for oil pipeline monitoring. Moreover, customized end devices and LoRa based gateway are designed and implemented for sensing the critical parameters of an oil pipeline. Here, we have performed the simulation of Zigbee communication on the OPNET simulator for evaluating the parameters such as packet delivery ratio (PDR), retransmission attempts, throughput, medium access (MAC) queue size, and queue delay. Furthermore, the distinct evaluation metrics of LoRa such as bit rate, link budget, and receiver sensitivity are also included. Finally, a real-time experiment is implemented with customized end devices and a gateway for evaluating the proposed architecture. In the real-time experiment, the devices and gateway are logging the pressure sensory data into the Cayenne cloud.  
Cayenne cloud; IoT; oil pipeline; Zigbee; LoRa; OPNET simulator 2021 13 10226. 10.3900/sut31810226  
1
## 2 Background
### Problem Definition  
We define the path planning algorithm similarly to [18]. Let \(\text{C}\subseteq\mathbb{R}^{n}\) be the state space of the system. Path planning is to search for an optimal continuous path from the starting state \(x_{start}\) to the target state \(x_{tar}\) in the given space C. To ensure accomplishment of task safely, the mobile robot should avoid collision with any obstacles or threats in the region \(C_{obs}\subseteq C\), and should be in the set of the permissible state \(C_{free}\subseteq C\) throughout navigation. Here, \(C_{free}\) and \(C_{obs}\) are complementary, and constitute the state space of the mobile robot.  
We define the path function \(\pi^{*}[x_{start},\ x_{tar}]=(x[\tau])\) to be the optimal path between \(x_{start}\) and \(x_{tar}\) as:  
\[\pi^{*}[x_{start},\ x_{tar}]=\text{argmin}\{\pi\mid x[0]=x_{0},x[\tau]=x\}, \tag{1}\]  
where \(x\) is the robot position at time \(\tau\). It has nothing to do with the boundary and obstacles.  
We define the cost function \(c^{*}[x_{start},\ x_{tar}]\) to be the energy loss of the search time and path length between \(x_{start}\) and \(x_{tar}\) as:  
\[c^{*}[x_{start},\ x_{tar}]=\min\{\pi\mid x[0]=x_{0},\ x[\tau]=x_{1}\}c[\pi], \tag{2}\]  
where \(c[\pi]\) is the cost of the path.  
For \(0<t<\tau\), \(\pi^{*}[x_{0},\ x_{1}]\) can be expressed as:  
\[\pi^{*}[x_{0},x_{1}]=\pi^{*}[x_{0},x[t]]+\pi^{*}[x[t],x_{1}] \tag{3}\]  
which indicates that the optimal collision-free trajectory between the starting point and the target point of the robot \(\pi^{*}_{free}\) can be connected with the best trajectory among a set of discrete subset states \((x_{start},x_{1},x_{2},x_{3},\ldots,x_{tar})\).  
To search for the shortest path length in state space C, the IRRT* algorithm improves the subset state \(X_{f}\supseteq X_{f}\) with the current solution \(c_{best}\). The \(X_{f}\) can be expressed as  
\[X_{f}=\{x\in X\ |\ \|\ x_{start}-x\ \|_{2}+\ \|\ x-x_{tar}\ \|_{2}\leq c_{ best}\} \tag{4}\]  
The subset space can also be described as a special hyperellipsoid whose cross section is shown in Figure 1, in which the \(x_{start}\) and \(x_{tar}\) are the focal points. The \(c_{best}\) is the transverse diameter of the ellipse and the \(\sqrt{c_{best}^{2}-c_{min}^{2}}\) is the conjugate diameter.
# 4 KSecure: A Universal Method for Active Manipulation Detection in Images of Any Resolution
## 3 Method
### Evaluation
#### 3.4.2 Detection Metrics Obtained
Table 2 compares the results of the 4 KSecure algorithm with other significant methods for active image integrity verification published in the scientific literature in recent years. All the included methods are based on the concept of embedding integrity markers into an image before its publication, enabling subsequent manipulation detection. One of the key challenges in reliably comparing these methods is the lack of a widely accepted dataset for evaluating active image protection systems. As mentioned in Section 3.3.1, to the best of the authors' knowledge, no standard dataset exists that allows for a comprehensive end-to-end evaluation of these methods. In particular, there is no dataset that provides a structured approach to embedding a given signature in an image, applying distortions in a controlled manner and generating a corresponding ground truth mask for the later assessment of the protection's effectiveness. For this reason, a proprietary dataset was used for the analysis, allowing for evaluation of the method in a standardized environment, and thanks to a large number of examples, it should ensure an appropriate generalization of the results. \[accuracy=\frac{TP+TN}{TP+TN+FP+FN}\]5 Sample images from the dataset and the key pipeline processes applied to them (1). \[accuracy=\frac{TP+TN}{TP+TN+FP+FN}\]6 Sample images from the dataset and the key pipeline processes applied to them (2). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method Name** & **Year** & \begin{tabular}{c} **Dataset Used** \\ **for Train/Test** \\ \end{tabular} & \begin{tabular}{c} **Image Size** \\ **(Used in Paper)** \\ \end{tabular} & **AUC** & **SSIM** & **PSNR [dB]** & **Comments** \\ \hline FaceSigns [51] & 2022 & \begin{tabular}{c} CelebA [52] \\ FFHQ [53] \\ \end{tabular} & \(256\times 256\) & 0.996 & 0.975 & 36.08 & \begin{tabular}{c} This method primarily \\ works with face \\ datasets (CelebA, \\ FFHQ, etc.). \\ \end{tabular} \\ EditGuard [54] & 2023 & \begin{tabular}{c} COCO [55] \\ CelebA \\ COCO \\ CelebA \\ \end{tabular} & \(512\times 512\) & 0.933 & 0.949 & 37.77 & \\ MaLP [56] & 2023 & \
\begin{tabular}{c} COCO \\ CelebA \\ MIR-FlickR [58] \\ \end{tabular} & \(256\times 256\) & 1.0 & 0.7312 & 23.02 & \\ OmniGuard [57] & 2024 &
\begin{tabular}{c} MIR-FlickR [58] \\ \end{tabular} & \(512\times 512\) & 0.991 & 0.989 & 41.78 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison of transparency results achieved by the 4 KSecure algorithm (at full resolution) with other significant works in the field of active image integrity verification.
## 2 Materials and Methods
### Monitoring of Water Radioactivity and Physical-Chemical Parameters
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameter** & **Analytical Method** & **WHO Guideline** & **US-EPA Limit** & **EU** & **National Limit** & **Ref.** \\  & & **Value** & **Value** & **Limit Value** & **Value** & **Ref.** \\ \hline Radon (Rn-222, \({}^{22}\)Rn) & Alpha spectroscopy (pulse-counting ionization chamber) & 100 Bq/L & 11.1 Bq/L & 100 Bq/L & 100 Bq/L & [40] \\  & & & & & [32] \\ \hline Gross alpha activity & Alpha spectrometry & 0.5 Bq/L & \(\leq\)0.1 Bq/L & 0.1 Bq/L & 0.1 Bq/L & [41] \\  & & & & 0.5 Bq/L & 0.1 Bq/L & [40] \\  & & & & & [32] \\ \hline Gross beta activity & Beta spectrometry with scintillation detector & 1 Bq/L & 0.15 Bq/L & 1 Bq/L & 1 Bq/L & [41] \\  & & & & & [40] \\  & & & & & [32] \\ \hline Ammonia & UV-VIS spectrophotometry & 0.5 mg/L & 0.3 mg/L & 0.5 mg/L & 0.5 mg/L & [45] \\  & & & & & [47] \\ \hline Free chlorine & UV-VIS spectrophotometry & 0.6-1 mg/L & & \(\geq\)0.1\(\leq\)0.5 mg/L & [48] \\  & & & & & [38] \\ \hline Nitrates & UV-VIS spectrophotometry & 50 mg/L & 50 mg/L & 50 mg/L & 50 mg/L & [45] \\  & & & & & [32] \\ \hline Al, Fe & UV-VIS spectrophotometry & 200 mg/L & 200 mg/L (Al), 300 mg/L (Fe) & 200 mg/L & 200 mg/L & [47] \\  & & & & & [31] \\ \hline Pb & atomic absorption spectrometry & 10 mg/L & 10 mg/L & 10 mg/L & 10 mg/L & [45] \\  & & & & & [47] \\  & & & & & [32] \\ \hline Cd & Atomic absorption spectrometry & 3 mg/L & 5 ug/L & 5 ug/L & 5 ug/L & [45] \\  & & & & & [32] \\  & & & & & [31] \\ \hline Mn & Atomic absorption spectrometry & 100 mg/L & 50 mg/L & 50 mg/L & 50 mg/L & [38] \\  & & & & & [47] \\ \hline pH & Electrode method & \(\geq\)6.5–\(\leq\)9.5 & \(\geq\)6.5–\(\geq\)9.5 & \(\geq\)6.5–\(\geq\)9.5 & \(\geq\)6.5–\(\geq\)9.5 & [45] \\  & & & & & [32] \\  & & & & & [31] \\ \hline Conductivity & Electrode method & 2500 μs/cm & 2500 μs/cm & 2500 μs/cm & 2500 μs/cm & 2500 μs/cm & [45] \\  & & at 20 °C & at 20 °C & at 20 °C & [47] \\  & & & & [31] \\ \hline \end{tabular}
\end{table} 
Table 1: The applied methodology for monitoring radioactivity and the physical and chemical parameters of the investigated water sources intended for human consumption, and parametric limit values.