esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Direct Shear Test Results
#### 3.1.1 Evolution Rule of Shear Stress
The variation trend of permeation grouting silty clay is generally consistent when the cement content is less than 15%. Each curve reaches or approaches its stable state before the maximum shear displacement, and the shear stress does not increase at this time. However, the shear stress-shear displacement curve increases approximately linearly when the cement content is 15%, and then the growth rate decreases gradually. The shear stress decreases after reaching the peak and finally remains at the stress value after specimen failure. This result indicates that the cement content of the slurry can change the failure mode of silty clay, and the original elastic-plastic failure will gradually change to brittle failure after grouting.
# Optimization Method for Improving Efficiency of Thermal Field Reconstruction in Concrete Dam
## 3 Optimization Method for Thermal Field Reconstruction  
The optimization method proposed in this study, as illustrated in Figure 3, comprises three key components: interpolation algorithm evaluation and selection, optimization of the number of monitoring points, and analysis of the impact of monitoring positions. Initially, the optical fiber layout scheme is determined based on actual construction conditions. This step is primarily influenced by the constraints of construction organization, structural characteristics, and temperature-control requirements. Next, for the specific structure of concrete dam blocks, an analysis of the applicability and accuracy of interpolation algorithms is condupcted. Following this, a sensitivity analysis of the number and positions of monitoring points is performed, guided by the selected interpolation algorithm and optical fiber layout scheme, to inform the selection of monitoring data.  
Figure 3: Optimization method for improving efficiency of thermal field reconstruction.
## 4 System Development and Experimental Verification
### Experimental Verification
#### 4.2.3 Multi-Modal Data Fusion Technology Experimental Verification  
As shown in Figure 20, based on the radar data frame time, the image in the image sequence closest to it was matched to achieve time synchronization, and then through spatial registration, the radar point cloud and image pixel points were matched one by one, obtaining the remapping image of the tunnel contact network frame point cloud and its camera image. Through actual measurement, the maximum error of time fusion using the nearest time matching strategy is about 27 ms, which is far less than the interval between radar data frames (100 ms) and will not cause error accumulation, and the maximum matching error of the two types of data is 1.3 cm. Therefore, the results fully meet the system requirements.
# Wireless Data Acquisition System with Feedback Function
Wired channels provide high-speed transmission of large amounts of data, but due to the high cost of cable products and the complexity of installation, they are not suitable for building low-power GEPP monitoring systems, in which high transmission speeds are usually not required, and data volumes are in the tens or hundreds of bytes. The limited applicability of PLC technology for monitoring GEPP operation is proven, in particular, by recent studies [28], which confirm that the level of noise and interference in power line communication (PLC) is much higher than in any other communication network. Using a wired connection on GEPPs is not always acceptable due to the additional wiring required. At the same time, wireless networks have evolved significantly over the past few years, providing low-cost and energy-efficient solutions with reliable data collection and sufficient real-time performance, ensuring lossless data transmission to the final destination. Therefore, monitoring system developers are increasingly choosing wireless solutions. In [18; 19; 20], communication is based on the GSM network. In particular, they use the short message service (SMS). However, it should be noted that this channel in places with unstable communication is not always capable of providing data transmission in real time, since in the GSM network, voice communication has the highest priority, followed by data service, and SMS transmission has the lowest priority [29]. In [21; 22], a wireless technology based on ZigBee is proposed to provide online monitoring systems for photovoltaic power plants. However, to build a ZigBee network, microcontrollers with a flash memory capacity of 128 kB or more are required, as well as the purchase of licensed software in the form of the so-called ZigBee stack [30], which makes their use for building low-power DASs for GEPPs economically unfeasible. The widespread availability of inexpensive energy-saving radio modules (ESRMs) with convenient interfaces for interfacing with microcontrollers (MCUs) gives developers of wireless data acquisition systems new opportunities, and in simple, unbranched low-power GEPP networks, their use may well compete with ZigBee technology. In [23] and some others, data are sent to the user's PC in the form of a Wi-Fi signal, which limits the range compared to ESRMs in places far from routers. For instance, the range of the popular and inexpensive ESRM type HC-12 with a convenient UART interface is 1000 m [31]. In more recent works [24; 25; 26], the authors adopted Internet of Things (IoT) technology for solar energy monitoring. Despite the fact that IoT is a modern and seemingly perfect solution for monitoring solar power plants, this technology may be at risk of cyberattacks and operational failures [32; 33; 34].
# Towards Harnessing the Most of ChatGPT for Korean Grammatical Error Correction
## 4 Experimental Settings
### Implementation Details  
We analyze the performance of ChatGPT, powered by the gpt-3.5-turbo-0301 model, utilizing the OpenAI API. Experiments are conducted in both zero-shot and few-shots settings. In the few-shots configuration, we explore performance across four different numbers of examples: 1, 4, 8, and 16. We also investigate the influence of temperature on the model's performance by setting it to three distinct values: 0.2, 0.5, and 0.8. For performance assessment, we employ the BLEU [45] and GLEU [46] scores, widely used evaluation metrics in various spelling and grammar correction studies. BLEU is a metric used to assess the performance of machine translation systems by comparing the machine-translated output with human-translated references. It operates on the principle of n-grams (sequences of n consecutive words) and can be applied to any language pair. One advantage of BLEU is that it allows for rapid calculation. Similar to BLEU, there is another metric called GLEU, proposed by [PERSON]. GLEU requires human annotators to rewrite the original source sentence to create a corrected version. Unlike BLEU, GLEU takes the source sentence into account and is specifically designed to evaluate the performance of grammatical error correction systems.
# Monitoring System Enhancing the Potential of Urban Bekeeping
## 2 Methods
### Monitoring System--Installation in the Urban Apiary for Tests  
The monitoring system was tested in an urban apiary, which is shown in Figure 4. The apiary consists of three self-made wooden hives. The beehive construction type is Wielkopolski, slightly extended. Frames are arranged in parallel to the beehive entrance. Hives were populated by inoffensive colonies of honey bee (_Apis mellifera_).  
The hives are located on the roof of the four-floor building (Poland, Wroclaw University of Science and Technology, building C-6). They were mounted on the fenced-in, elevated openwork technical platform, as shown in Figure 4. A special harness was designed, see Figure 4, to protect them from overturning by the wind or sliding from the base.  
The devices dedicated to inside-beehive monitoring, i.e., the multi-sensor device and the endoscopic camera, were mounted in the measuring frame. It was a normal hive frame, but empty, without the comb. The part of the frame hosting the multi-sensor device was separated from the beehive air by a transparent, stainless steel mesh, as shown in Figure 5. This partition was intended to prevent the device from bees clogging the gas inlets and to assure free access of the beehive air to the sensors. The part of the frame hosting the endoscopic camera was left open on both sides. The initially applied side plexiglass covers were removed due to the light reflection effect distorting the camera view. The camera was free to move upside-down and left-right as well as turn around so that the watched section of the adjacent comb could be changed. Power and Ethernet cables were pulled through the top of the frame. The measurement frame was mounted among regular frames as the  
Figure 4: Urban apiary where the monitoring system was installed and tested.  
fourth one from the beehive entrance. The distance from the neighbouring comb, watched by the camera, was twice the normal between-frame distance.  
Outside-beehive cameras were mounted on the railing of the beehive's hosting platform. The cameras for beehive-entrances monitoring were located opposite beehive entrances at the side so as not to obstruct the bees' pathway to/from the beehive. The camera overlooking the apiary as well as the auxiliary equipment, such as the electric switchboard, and the components of the data transmission system were also mounted on the railing. All cables were routed under the platform.
## 1 Introduction  
It is still an open question what the network of the future will look like. Although a plethora of proposals have been submitted and discussed, there is no clear contender for the first prize. Some proposals even call for thousands of future networks in parallel [1]. However, one of the issues that any future network will have to address is access control, to both the network and network services offered therein.  
It is probably fair to say that the general consensus is that this kind of mechanism should  
* be easy to understand and use, _i.e._, user-friendly,
* work from different geographic locations (roaming),* work with different devices (versatility and portability),
* allow only legitimate users to access the network or a given service.  
Controlling access will become an ever greater issue in future as a multitude of devices will be connected to the Internet (possibly leading to an \"Internet of Things\"), and quite likely via different access technologies.  
We believe now is a good time to design new access control mechanisms for the network that, unlike today, fulfill the requirements listed above and can be applied to any type of new network. Our proposal is based on the idea of using home networks as a natural point of reference and trust anchor.  
The remainder of this article is organized as follows: first, we present our authentication and identification scheme for home networks in next generation networks and describe two candidate technologies that can be applied together (Section 2). In Section 3, we elaborate on a way to increase the security of home networks, and thus the security of the system as a whole, by using Trusted Computing technology. We discuss benefits and disadvantages of our concept in Section 4. We conclude with a summary in Section 5.
# Dynamic Simulation Model-Driven Fault Diagnosis Method for Bearing under Missing Fault-Type Samples
## 4 Experimental Verification
### Case 1: Bearing Dataset with One Missing Failure Sample
#### 4.1.3 Comparison of Neural Network Model Training Results  
To accurately demonstrate how superior the proposed approach is. Each method received twenty experiments, and the classification accuracy results of Datasets A and B are displayed in Figure 10. For Dataset A, the average classification accuracy and standard deviation of WGAN-GN are 97.45% and 0.27%, respectively, and the two corresponding values are 91.27% \(\pm\) 1.01% for WGAN and 81.19% \(\pm\) 1.49% for GAN. The average accuracy of GAN is 81.78% \(\pm\) 1.16% for Dataset B, while the average accuracy of WGAN is 90.88% \(\pm\) 1.05%. In contrast, the average accuracy of WGAN-GN is 96.95% \(\pm\) 0.49%. The comparison shows that the proposed WGAN-GN is significantly better than other methods. The strategy proposed in this study can improve the accuracy of diagnosis to a certain extent when the real measured signals are used as the data set. However, when the simulation signal obtained from the kinetic model is used as one of the fault signals, the experimental accuracy is also not significantly decreased, indicating that the obtained simulation signal can replace the real measured signal to a certain extent for fault diagnosis.  
T-SNE [19] is used for the visual operation of dimension reduction, and the results of Dataset A are shown in Figure 11. As be observed, GAN is only able to appropriately identify RF and OF samples, and there are different degrees of confusion among other fault samples. As Figure 10b, WGAN has some classification ambiguity, but the aggregation among samples is relatively good. In contrast, the proposed WGAN-GN fully isolates all bearing failure samples, and the samples of the same type are strongly aggregated, which  
Figure 9: Extracted features of the generator for NC samples: (**a**) the 1 st layer, (**b**) the 2 nd layer, (**c**) the 3 rd layer, (**d**) the output layer, (**e**) the raw sample.  
illustrates the efficiency of the proposed method. In addition, when using simulation signal as the training data as in Figure 12, WGAN-GN still performs far better than the other two approaches. As a result, it is concluded that, the constructed model has the best ability for fault classification compared to the other methods. Meanwhile it also proves that the obtained simulation signal can replace the real measured signal to a certain extent for fault diagnosis.
## 3 Methodology
### Metrics
* **False Positives (FPs)**: These are cases where there is no drone in the scene, but the model incorrectly detects something (false positive). This metric is essential for evaluating the performance of a model in the absence of a target to detect, which is particularly important in scenarios where false positives are costly or disruptive. * **Inference Time and FPS**. Inference time refers to the time that a model requires to process a single image from a video and generate a prediction. This metric is vital for real-time applications, such as drone detection, where every millisecond is significant. Related to this is Frames Per Second (FPS), which indicates the number of images a model can process per second. A higher FPS denotes that the model can operate at increased speeds, which is essential in scenarios where immediate detection and reaction are critical, such as in aerial surveillance. Both metrics, inference time and FPS, are utilized to assess the efficiency of the model and its suitability for real-time scenarios.
# Equalization Methods for Out-of-Band Nonlinearity Mitigation in Fiber-Optic Communications
###### Abstract  
In recent years, it has been established that the adverse effects of nonlinear interference noise (NLIN) can be mitigated using adaptive equalization methods. As such, a wide variety of adaptive equalization methods have been used to treat nonlinearity, in different transmission scenarios. This paper reviews the principles of out-of-band nonlinearity mitigation using adaptive equalization. Statistical properties of NLIN that can be exploited for mitigation are discussed, as well as the cost and benefit of various types of equalizers. In particular we describe the equivalence between the NLIN and time-dependent inter-symbol-interference (ISI) and discuss ways in which the ISI coefficients can be characterized theoretically and experimentally. We further discuss the effectiveness of existing ISI mitigation algorithms, and explain the need for designing customized algorithms that take advantage of the various correlation properties characterizing the ISI coefficients. This paper is intended to be a practical reference for researchers who want to apply equalization algorithms or design new methods for nonlinearity mitigation.  
optical fiber communication; nonlinear Interference; time varying inter symbol interference; nonlinearity mitigation +
Footnote †: journal: Applied Sciences  
_Applied_
## 2 Methods
### Introduction of Network Architecture
#### 2.2.1 Introduction of CNN and CNN-Based Depth Estimation
However, its output range is shifted from (0, 1) to (\(-\)1, 1), and the derivative range is extended to (0, 1). The mathematical expression for the Tanh function is  
\[\text{Tanh}(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{10}\]  
The ReLU (Rectified Linear Unit) function, also known as the Modified Linear Unit or Linear Rectifier, is a piecewise function with the following mathematical expression:  
\[Rectifier= \begin{cases}x,&x\geq 0\\ 0,&\text{other}\end{cases}  \tag{11}\]  
ReLU-Like FunctionAlthough the ReLU activation function is not flawless, its advantages make it widely adopted. To address the \"neuron death\" problem in ReLU, researchers have proposed several improvements, primarily focusing on modifying the behavior for \(x<0\). The Leaky ReLU function modifies the \(x<0\) region by using a small, non-zero slope instead of setting it to zero. Its mathematical expression is  
\[f(x)=max(ax,\ x) \tag{12}\]  
where \(\alpha\) is a constant. When \(\alpha\) is set too small, the function is close to the ReLU function and does not work; when \(\alpha\) is set large, it loses the ability to sparsify the input data and weakens the nonlinearity of the function. 1. PReLU function To improve the selection of \(\alpha\), the PreLU (Parametric ReLU) function was proposed. In this function, \(\alpha\) is treated as a learnable parameter that is updated during training to adapt to the data and the network. 2. RReLU function The RReLU (Randomized Leaky ReLU) is a variant of the Leaky ReLU function. During network training, it generates a random \(\alpha\) from a Gaussian distribution for each activation, which is fixed during testing. 3. ELU function None of the three variants above address the issue of producing zero-mean outputs. As a result, the ELU (Exponential Linear Unit) function was introduced, with the following mathematical expression:  
\[f(x)= \begin{cases}x,&x\geq 0\\ \alpha(e^{x}-1),&\text{other wish}\end{cases}  \tag{13}\]  
The ELU function employs a nonlinear expression with soft saturation for \(x<0\). Moreover, its output approaches zero for negative values of x, which aids in accelerating convergence during training.
# Considerations for Developing Robot-Assisted Crisis De-Escalation Practices
## 3 Basic Requirements for Social Robots and Working Model for De-Escalation  
Human interactions are characterized by a high degree of adaptivity. People instinctively change their actions according to the perceived cognitive and affective state of their interactive partners and environmental context. This social adaptability is made possible through the complex interplay of real-time sensory, cognitive, affective, and behavioural systems that comprise the central nervous system. For robots to successfully enter the social sphere, they will similarly require the ability to perceive and adapt to their interactive partners and the environmental context. Robots will need to be able to perceive and express emotions, sense verbal and non-verbal signals from humans and respond adequately, comprehend and generate natural language, have memory and reasoning capacities, plan actions and execute movements, and demonstrate various social competencies according to what is required by the specific context [15]. Research in computer science and engineering has focused on designing cognitive architectures and adaptive behavioural models inspired by the human brain to enable artificial intelligence in robotic agents and enable them to exhibit intelligent behaviours, learn new tasks, and adapt to changes in their environment [16]. Robots capable of de-escalation will require sufficient mastery of these baseline skills needed for social robots, in addition to computational models geared specifically towards the de-escalation process.
## 4 Experimental Results and Analysis
### Degradation Analysis of Stiffness  
The stiffness of specimens is shown in Table 1. The stiffness degradations of uniformly corroded specimens under various working conditions are shown in Figure 11. Table 1 and Figure 11 show that the stiffness of specimens decrease with the increase of eccentricity. The reductions of the stiffness of specimens increase with the increase of corrosion ratio. The stiffness degradations of uniformly corroded specimens are linear with the corrosion ratios, and the slopes are greater than 1. That is, the reductions of the stiffness of specimens are greater than that of material strength. The slope of specimens with eccentricity of 35 mm is the largest, that is, the stiffness degradation of specimens with eccentricity of 35 mm is the largest at the same corrosion ratio.
# Outside Dynamic Evacuation Routes to Escape a Wildfire: A Prototype App for Forest Firefighters
## 2 State of the Art
### Tools for Fighting Wildfires  
In cases of wildfires with surrounding populated areas, people have the option of escaping or defending their homes [28; 29]; however, that decision may come a little late based on people's predictions due to different variables. It is not only about decidingwhether to escape and leave all their belongings, but there may also be primary factors such as children, the elderly, pets, and material goods (car and house) [30]. In addition, forest fires spread more or less quickly due to various factors, e.g., wind speed, wind direction, terrain inclination, humidity, and more, which can affect people's decisions.  
Wildland firefighters advise people to evacuate homes when fires threaten them. However, evacuations can become complicated as the fire spreads. In addition, effective evacuations require specific protocols to be followed that the population may not be familiar with, causing complications when protecting people in emergencies [31]. Forest firefighters and rescue teams use various tools to control and fight forest fires, both physical and digital. For example, Pulaski is a versatile tool that combines an ax and a hoe in the same head; it has a rigid handle made of wood, plastic, or glass. This tool performs multiple tasks, including cutting and hauling brush, excavating, and cutting wood to create firewalls [32]. OruxMaps is a digital tool that allows visualization routes designed on online/offline maps and navigating accurately to destination locations in a three-dimensional way. It efficiently manages geographic and topographic data, is widely used among firefighters to become familiar with the terrain, and analyzes evacuation routes when they need to escape danger [33; 34].  
The projects, tools, and applications described in the previous subsections are excellent and very useful in various scenarios, especially WSN which allows real-time monitoring of study areas. However, there is no algorithm or digital tool on the market that considers variables such as physical elevations, wind speed, and direction or the type and density of vegetation on the ground to predict in real time the wildfire spread and simultaneously create outdoors dynamic evacuation routes. This algorithm's design would help relief agencies execute a better control and combat plan, decrease reaction times, take timely actions, and efficient decisions based on the development of the incident and the terrain characteristics.