esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Results
### Risk Assessment Calculation
Cloud map is to compare the shape position of cloud droplets in the integrated and evaluated cloud in the same vector space. The risk level corresponding to the cloud droplet with the highest closeness to the integrated cloud is the risk assessment level. The experiment was based on cloud map analysis, aided by similarity comparison using Equation (8). Considering the more visual representation of images and the tinier presentation and implementation of control countermeasures, the paper only shows the results of the LNG leakage risk analysis of the cloud map. As can be seen in Figure 11, the comprehensive cloud model is between level IV and level V. Clearly visible, the comprehensive cloud is in close proximity to the cloud model at level IV, with the highest density and concentration of cloud droplets in such region. Using similarity calculations to verify this, the similarity between the comprehensive cloud and level IV is 0.7602, and the similarity to level V is 0.2039. The results of the similarity comparison are consistent with the cloud map, so the risk level of the LNG tank leakage can be assessed as level IV, and the company needs to take the necessary control measures, including regular testing and evaluation. According to the results in Table 7, the risk levels of B1 and B5 can be assessed as level III with the above method, and the level of B2, B3, and B4 are level IV. The risk level of the  
Figure 11: Integrated cloud model for LNG leakage accident. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**First-Level Elements** & **Numerical Characteristics** & **Comprehensive Weights \(\mathbf{\omega}\)** & **Risk Level** \\ \hline High-risk locations B1 & (0.3770, 0.1018, 0.0281) & 0.038 & III \\ High-risk equipment B2 & (0.7517, 0.0993, 0.0268) & 0.529 & IV \\ High-risk processes B3 & (0.6974, 0.0769, 0.0272) & 0.251 & IV \\ High-risk personnel B4 & (0.7115, 0.0721, 0.0267) & 0.122 & IV \\ High-risk substances B5 & (0.4253, 0.0781, 0.0242) & 0.075 & III \\ \hline LNG storage tank leakage accident A & (0.7057, 0.0905, 0.0271) & & IV \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Numerical characteristics of LNG tank leakage accident. secondary index \"safety accessories\" is level V, which requires the enterprise to stop work immediately for maintenance and fully review safety accessories to reduce the risk to an acceptable range.
# Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning
## 2 Related Works
### Impact of Data Quantity in Time Series Forecasting  
In time series forecasting, the correlation between the quantity of data and model accuracy is particularly pronounced. Larger datasets are often essential for accurately modeling complex multivariate time series, as they capture a wider array of seasonal and cyclical patterns. For instance, models such as DeepAR [29] demonstrate enhanced performance when trained on extensive historical datasets, allowing them to identify intricate temporal dependencies. In financial forecasting, longer and richer multivariate time series provide the necessary context for models like long short-term memory (LSTM) networks, thereby improving predictive accuracy [30].  
However, the utilization of larger datasets is not without its challenges. Issues such as noise, missing data, and the computational complexity of handling vast datasets can hinder model performance. To address these concerns, techniques such as data imputation and noise filtering have been explored to enhance the quality of the data used for training.  
There is some concern in the literature about the number of observations needed to obtain good predictions, with some experimentation in this regard through performance measurement and variation of error metrics [31; 32], finding significant differences between the observations considered necessary.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 14 Enhancements
#### 2.2.2 Multicast Services  
The main objective of this mechanism is to optimize resources as well as transmission latency by addressing the data to a group of UEs at the same time rather than sending it multiple times to separate devices.  
Therefore in Release 14, Multimedia Broadcast Multicast Services (MBMS) is supported through single-cell point-to-multipoint (SC-PTM). In general, SC-PTM is an efficient dynamic mechanism for optimal radio resource usage as it allows broadcast or multicast services to a specific group based on real-time traffic load and user requirement. SC-PTM uses NPDSCH by mapping Single-cell MBMS Control CHannel (SC-MCCH) and Single-Cell MBMS Traffic CHannel (SC-MTCH) that carry control and data traffic to the physical layer scheduled by using the downlink control information (DCI).
# Corrosion-Fatigue Evaluation of Uncoated Weathering Steel Bridges
## 3 Corrosion Pitting Evaluation
### Proposed Model for Pitting Corrosion  
[PERSON] [16] proposed that the pitting flaw depth has a power relation with the time based on the test data, in which the power index is 1/3. The similar relation is also reflected by the well-known [PERSON]'s law established through electrochemistry [38; 39], as shown in Equation (1):  
\[\frac{dV}{dt}=\frac{Ml_{p}}{nF\rho}, \tag{1}\]  
where \(V\) is the volume of the pit; \(t\) is the corrosion time; \(M\) is the atomic mass of the material; \(n\) is the number of electrons released during corrosion of the metal; \(F\) is the Faraday constant; \(\rho\) is the density of the material and \(I_{p}\) is the pitting current related to the corrosive environment.  
The pitting current could be solved using Equation (2):  
\[I_{p}=I_{p0}\exp{(-\frac{\Delta H}{RT})}, \tag{2}\]  
where \(I_{p0}\) is the pitting current coefficient; \(R\) is the universal gas constant; \(T\) is the absolute temperature and \(\Delta H\) is the activation enthalpy.  
Obviously, the pitting volume is a natural index describing the degree of pitting corrosion. Thus, as shown in Equations (1) and (2), the degree is determined by factors such as the time exposed to corrosion, corrosive environment, temperature and activation enthalpy. In this study, the pitting  
Figure 2: Illustration of the pitting corrosion.  
flaw is idealized as a semi-ellipsoid with the round projection on the material surface, as a common practice [10]. The pitting volume could be calculated as shown in Equation (3):  
\[V=\frac{2}{3}\pi ab^{2}, \tag{3}\]  
where \(a\) is the pitting depth and \(b\) is the radius of the round on the project surface.  
Since the pitting depth is believed as the major index reflecting the pitting process [28], it can be solved by combining Equations (1) and (3), as shown in Equation (4):  
\[a=Bm_{asp}^{2/3}t_{p}^{1/3}t^{1/3}, \tag{4}\]  
where \(m_{asp}\) is the aspect ratio of the pitting flaw lateral section, i.e., \(a/b\); \(B\) is a material-related constant and \(t\) stands for the time exposed to corrosion.  
Studies show that the aspect ratio of pitting flaws is close to 1.0 [10,40]. On this end, the aspect ratio in this study was determined as 1.0. Meanwhile, the constant \(B\) can be determined by the aforementioned material properties, as shown in Equation (5):  
\[B=\sqrt[3]{\frac{3M}{2\pi nF\rho}}, \tag{5}\]  
where the parameters could be applied as \(M\) = 56 \(\times\) 10\({}^{-3}\) kg/mol, \(n\) = 2, \(F\) = 96,500 C/mol and \(\rho\) = 7850 kg/m\({}^{3}\), respectively.
# A Vehicle-Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion
## 4 Experiment
### Implementation Details  
In the experiments, the detection range is set to \(x\in[-50.4,50.4],y\in[-40,40]\), \(z\in[-3,1]\) for the DZGSet dataset, and \(x\in[-100.8,100.8],y\in[-40,40],z\in[-3,1]\) for the DAIR-V2X dataset. The communication range between vehicle and infrastructure is established at 50 m for the DZGSet dataset and 100 m for the DAIR-V2X dataset. Beyond this broadcasting radius, vehicles are unable to receive information from the infrastructure.  
For the PointPillars feature extraction network, the voxel size is set to 0.4 m for both height and width. All models are trained and tested on a platform equipped with a GeForce RTX 2080 Ti GPU. The training process spans 30 epochs with a batch size of 4. The Adam optimizer is employed, starting with an initial learning rate of 0.002 and a weight decay of 0.0001. A multi-step learning rate scheduler is utilized, reducing the learning rate by a factor of 0.1 at the 10 th and 15 th epochs. Furthermore, Kaiming Uniform is used for weight initialization.
# Estimating Algorithmic Information Using Quantum Computing for Genomics Applications
## 5 Application Framework  
The model of computation discussed in this research can estimate time-bound algorithmic metrics. Here, we present a framework for empirical experimentation on a quantum accelerator. This is shown in Figure 11. The output of the unitary \(U\) implementing the time-bound QPULBA model can be conditioned based on the required application. The last three cases presented in Section 3.3 conditions the programs with respect to the outputs, conditions the outputs with respect to a specific output, or conditions the final state with respect to a specific state. Thereafter, the conditioned register can be amplified using Grover search, or near-term variational quantum optimization approaches using parametric circuits such as QAOA [46; 47; 48]. Since the programs, outputs, and state qubits are entangled in a quantum associative memory, the amplitude of corresponding registers also evolves, such that the probability of measuring out the solutions increases. The results can then be sampled with high probability.  
Estimation of algorithmic properties of a dataset can be very useful as it points to mechanistic connections between elements of a system, even those that do not yield any regular statistical patterns that can be captured with more traditional tools based on probability theory and information theory. Theoretical applications of algorithmic metrics such as Kolmogorov complexity is widespread [10], finding various uses in artificial general intelligence, theoretical physics, psychology, data compression, finance, linguistics, neuropsychology, psychiatry, genetics, sociology, behavioral sciences, image processing, among others. However, estimating algorithmic information for practical datasets is often computationally intractable due to the large number of enumerations that needs to be executed. The exploration in EAIT is growing in popularity due to its ubiquity in tasks that can be modeled as inductive reasoning. It connects theoretical computer science to the real-world by a quasi-empirical approach to mathematics (popularized as metamathematics by [PERSON]). It involves enumerating and running programs to understand its statistical mechanics and is similar to [PERSON]'s computational universe approach [49].  
Figure 11: Application development framework for quantum experimental algorithmic information theory.
## 9 Fundamental Concepts Underlying Adaptive Filters
### Adaptive Filters: A Comprehensive SWOT Breakdown  
LMS and RLS adaptive filters represent advanced methodologies for removing artifacts from EEG signals, each with its specific advantages and limitations. The LMS filter is appreciated for its simplicity and adaptability, offering an effective method to address different noises in EEG data, but faces challenges in parameter optimization and can struggle with nonlinear artifacts. Nonetheless, integration with other techniques promises a more robust approach to artifact removal. Advances in machine learning and artificial intelligence could further improve the capabilities of the LMS filter. On the other hand, the RLS filter stands out for its high-precision and real-time update capability, making it ideal for applications requiring high signal fidelity. However, its computational complexity and the need for accurate calibration of initial parameters represent significant obstacles. The potential for integration with AI and the development of less computationally demanding variants could extend the applicability of RLS. Both filters address the challenge of the increasing complexity of EEG signals and the need for real-time processing, underscoring the importance of continuous innovations to maintain their effectiveness in removing artifacts across a wide range of scenarios. Table 6 displays the relevant SWOT matrix.
# Estimation of the Noise Source Level of a Commercial Ship Using On-Board Pressure Sensors
## 1 Introduction
Differently from [25], the boundary element integral equation was not modified, and an optimization technique was employed to find the locations and the strengths of the sources. Similar concepts can be found in the time difference of arrival (TDOA) method [27] or compressive sensing (CS) method [28; 29]. These have advantages over the inversion method, as they do not require prior knowledge of the acoustic pressure field. However, iterative calculations and optimization techniques are needed for the TDOA method and the CS method, respectively. For these reasons, it seems obvious that on-board sensors need to be used to constantly monitor the underwater radiated noise. In addition, as the computation time is an important factor, the estimation method should be simplified to some extent. In this regard, [PERSON] et al. [30] tried to estimate the underwater radiated noise from a research vessel using on-board sensors. With an empirical formula, comparisons between the measured hull pressure and radiated noise level were made for blade-passing frequency components. [PERSON] and [PERSON] [31] presented a nearfield beamforming method with minimum-variation distortionless response (MVDR) to localize the incipient cavitation. The method was validated in a model-scale test, and the results at full scale showed good agreement between the measured and estimated values from 4 to 16 kHz. This method has an advantage in the computational cost by assuming a single monopole source. This made the estimation process into a minimization problem that can simply be calculated in the region of interest. In this study, the beamforming method presented in [31] was employed without the MVDR. Although it is unlikely that the propeller of a commercial ship exhibits incipient cavitation at operating conditions, a single monopole source was assumed for simplicity. The method is briefly introduced and formulated in Section 2. For validation of the method, a model-scale measurement was carried out with a hydrophone sensor array at the hull surface above the propeller. Using a hydrophone at the bottom of the tunnel and a source of known transmitting voltage response (TVR), the source level was measured and compared with the estimated one. For the same ship, a full-scale trial was conducted with flush-mounted pressure transducers installed on the hull surface. The underwater radiated noise was measured using a buoy-type hydrophone array with three self-recording hydrophones, and the results were converted into the source level. Using the measured hull pressures, the source level was estimated and compared with the measured one. A parametric study was conducted to check the sensitivity of the measurement variables to the result.
# Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing
## 3 Methodology  
### Problem Specifications  
The motivating application was the design of an automatic vending machine for bulk goods packaged into plastic bags to be used by a retailer. As noted in the introduction, large-scale industrial applications, warehouse management, and manufacturing scenarios are not of interest since there are already available solutions. The targeted use case also imposes the requirement for inexpensive computing architecture, with cost and computing-power limitations.  
The problem addressed is depalletizing, which consists of removing goods from a pallet using a robotic arm. Of all the phases of the task, we focus on object identification to provide the location of the next best object to be removed. There is no assumption about the mechanical part (robotic arm and gripper). The robotic arm will be positioned according to the output of the algorithm, while the gripper will be tailored to the specific use case.  
We assume the palletized items are roughly convex, but not of regular shape, although we expect them to be similar to each other (we refer to the previous Figure 1 for examples). Likewise, we assume that the items are organized into mostly horizontal layers, although not necessarily regular. This allows us to employ methods that do not rely on 3D vision, but simply require assessing the z-order of items. A rangefinder can then provide the actual z coordinate.  
We also assume that the pallet is axis-aligned, which is a realistic assumption since it is a trivial task to axis-align a mostly rectangular target with image processing techniques. As for the items, when their geometric aspect ratio is not 1, we assume that they are mostly oriented along two directions. This is typically true for pallets containing items that do not have the same aspect ratio as the pallet itself and are organized so as to optimize their fit on the pallet's surface.  
Since we do not target manufacturing scenarios, which may have to deal with complex shapes and possibly random orientations, we consider the exact orientation of the items to be irrelevant. This will make it sufficient to locate items by their bounding box.
# Research on Large Divergence Angle Laser Ranging System +
## 2 Overall Solution  
To achieve a >1 km ranging performance on small targets, the solution adopts the time-of-flight (TOF) ranging methodology, which calculates the distance between the rangefinder and target by measuring the round-trip propagation time of laser pulses. The principle of pulse laser ranging is similar to that of radar ranging. It involves emitting a single laser pulse or a series of laser pulses toward the target using a pulsed laser. A counter measures the round-trip time of the laser pulse from the emitter to the target and back to the receiver, allowing for the calculation of the target's distance. The principle of pulse laser ranging is illustrated in Figure 1, and the ranging formula is as follows:  
\[d=\frac{ct}{2} \tag{2}\]  
Figure 1: Composition diagram of a laser ranging system.  
In the equation, \(d\) represents the measurement distance, \(c\) denotes the speed of light, and t refers to the round-trip time of the ranging signal [3].  
Compared to conventional laser rangefinders with a divergence angle generally less than 1 mrad, the divergence angle of the laser designed in this system is approximately 26 mrad. Additionally, the optical system's receiving field of view is designed to be approximately 30 mrad, which can meet the requirements for effective echo reception under conditions of large divergence angle emission.
# Multi-Objective Optimization of a Wireless Body Area Network for Varying Body Positions
## 6 Conclusions and Future Work  
### Conclusions  
In this paper, we have presented and discussed a methodology for optimizing a three-node WBAN design (node placement) with both on-body and off-body communication. Under this frame, the proposed approach lays the groundwork for a more general method for finding the optimal design of any WBAN configuration. The systematic exploitation of Pareto-like optimality is made possible by evolutionary computing linked with the FDTD full-wave electromagnetic simulation of antenna and radio channel performance. This computational method facilitates the search for innovative designs because it is able to identify non-trivial solutions, i.e., solutions improving the prototype performance with respect to all design criteria, which is difficult using the traditional trial-and-error approach. Specifically, in the research reported in this paper, we exploited our methodology to improve the operation of a WBAN on a human body in both sitting and standing positions by searching for the  
Figure 19: The history of the optimization process (2\({}^{\text{nd}}\) case) in the objective function space.  
best configuration of the nodes. Because we evaluate two objective criteria (multi) for two (many) body positions, the method can be qualified as many-multi-optimization. Our numerical experiments revealed significant improvements with respect to both design criteria, i.e., on-body and the off-body channel performance.
# Influence of Polypropylene Fiber on Tensile Property of a Cement-Polymer Based Thin Spray-On Liner
## 1 Introduction
### Fiber-Reinforced Materials and Fiber-Reinforced TSLs  
Fibers has been widely used to improve concrete properties, of which plastic fibers are of great advantages. According to [PERSON] [19], tunneling shotcrete with 0.6-0.8% volume content of polyamide-fiber-reinforced shotcrete can yield significant improvement in the flexural ductility, toughness, and ultimate load capacity. [PERSON] [20] reported that fiber addition prevents the formation of plastic shrinkage cracks in underground shotcrete. [PERSON] [21] reviewed that plastic fibers are synthetic fibers, which can be in the form of micro plastic fibers(with the diameter of 5 to 100 \(\upmu\)m and length of 5-30 mm) or macro plastic fibers (which have a length of 30-60 mm and cross section of 0.6-1 mm\({}^{2}\)). Polypropylene, polyester, and high density polyethylene (PP, PET, and HDPE) are the three main raw materials used in the production of plastic fibers, and polypropylene fibers have become the most common commercial products in fiber-reinforced concrete. [PERSON] [22] use polypropylene fiber as well as organic polymer to reinforce sand and concluded that through the interaction force among fibers and sand particles and the bonding force of polymer among sand particles, tensile strength of reinforced sand is greatly improved.  
Published studies on fiber-reinforced TSLs are rare. Resin-based TSLs with glass fiber (placed in several layers in the specimens) have been investigated by [PERSON] [8]. The results revealed that the tensile and flexural strengths of the polymers increased, owing to the fiber addition. [PERSON] [23] investigated cube samples containing glass fiber, and the results indicated that samples with fiber are stronger than those without fiber. In another study [24], the shear strength of the polymer material was significantly improved, owing to the addition (layer-wise) of glass fiber, which is beneficial for supports in underground mines. Cement-polymer based TSLs always behaved lower tensile performances compared with the resin-based ones in the aforementioned three tests. The tensile strength of cement-polymer TSLs were much lower than that of polyurethane at a 4 mm thickness [2]. However, many of the rock support mechanisms provided by sprayed liners depend substantially or completely on the tensile strength contribution of the liner [4]. Therefore, the improvement of tensile performance is of great necessity.  
A TSL is usually sprayed onto the rock substrate. Fiber with a small length and diameter may be of ease to be pumped and conveyed. Consequently, microfibers such as PP are preferred for TSL reinforcement in the perspective of application. In this study, the influence of fiber addition on the tensile performance of TSL samples was investigated by adding two different contents of polypropylene fiber to a cement-polymer based TSL product. Control samples were also investigated. Samples with different fiber content were tested at separate curing times of 1, 7, 14, and 28 days.
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 2 Development Stages of Pavement Distress Detection
### Manual Inspection Stage  
The initial stage depended entirely on manual field surveys using specialized equipment. Although providing direct observations, this method was labor-intensive and time consuming. Additionally, required lane closures often disrupted traffic, creating significant operational difficulties for large highway networks.