esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Geopolymer concrete (GC), also known as green concrete, contains slag, silica fume, and fly ash as binders. The absence of cement in concrete is critical to protect the world from the environmental impacts of cement production. In addition, exposure to high temperatures is a critical parameter that causes loss of strength in concrete. In this study, Geopolymer concrete samples were prepared with 10 different samples containing different proportions of slag, silica fume, and porous ash and subjected to various physical, mechanical, and optical tests. The sample (GS90) with optimum workability and compressive strength, which also showed high performance in water absorption, freeze-thaw, and UPV tests, was used in high-temperature tests. Portland cement concrete (PCC) was used as a control sample. This study investigated the effect of high temperatures on the physical and mechanical properties of fiber-free GCs containing 2%, 5%, and 10% by volume of ceramic fibers. Therefore, fiber-reinforced, fiber-free, and PCC specimens were subjected to high-temperature tests at 100, 300, 600, and 900 \({}^{\circ}\)C. As a result of the observation of crack growth, color changes, and compressive strength parameters in the samples subjected to high-temperature tests, the thermal resistance of the 10% ceramic fiber geopolymer concrete sample was 2.5% higher than other samples. There is no study in the literature that examines the behavior of ceramic fiber-reinforced geopolymer concrete at high temperatures. This research revealed an important finding by proving that ceramic fiber reinforcement increases the compressive strength of geopolymer concretes at a remarkable rate after high-temperature impact.  
geopolymer concrete; ceramic fiber; high temperature; slag; silica fume; fly ash +
Footnote †: journal: Applied Sciences
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 1 Introduction
### The Current Model Is Empirical  
The man-made noise values given in P.372, on frequencies up to 300 MHz, are based on measurements data dating back half a century. P.372 provides an empirical man-made noise model. Only one model is available, supposedly valid for the entire world. This does not seem very realistic, as equipment emissions will be higher in countries with less stringent emission rules or enforcement of these rules. And regional differences in construction style and material will influence the wall attenuation of buildings [16], and hence the emission per house. It would therefore seem logical to include parameters in these models based on the local situation. So far, this is not the case. In the last two decades, several member states have provided man-made noise measurements to the ITU Radio Noise Data Bank, and a few attempts have been made to fit curves through these data points to update the model in P.372. So far, these attempts have not been successful. In fact, it can be shown that corruption of the man-made noise model is very likely, unless the distribution of the measurement data over measurement year, frequency spectrum, geographical location, and environment category is accounted for.  
This article opposes this approach. It will be shown that using the data indiscriminately to construct an empirical model will result in strong biases as a result of the uneven distribution of the measurements over the globe, and due to data scarcity in several dimensions of the data set. A better approach to arrive at a trustworthy man-made noise model will be proposed, along with other suggestions to improve the quality of the ITU radio noise data set.  
The article is structured as follows. Section 2 introduces the ITU radio noise model, the desire to update this model and the measurement data collected in the ITU Radio Noise Data Bank. This data set is analyzed for gaps in geographical coverage, coverage of environment types, spectral coverage and temporal distribution in Section 3. As discussed in Section 4, it will be shown that despite its size, this data set must be considered too sparse for the creation of an empirical model, and a better approach is proposed in Section 5. Conclusions can be found in Section 6.
## 1 Introduction
The contributions of this paper can be further categorized into two major sections:  
* Firstly, we present a comprehensive analysis of various OFDM-based image communication systems for CV applications. The analysis includes the use of high-order M-QAM systems to increase data rates, different channel models to simulate real-world environments, and different channel estimation techniques to evaluate the tradeoff between image quality and system complexity. Performance evaluation of the communication systems and the quality of recovered images transmitted over these systems are analyzed using various performance matrices. * Secondly, we conduct robustness analysis of DL-based CV applications in smart cities, specifically, traffic sign recognition (classification task) in ITS environments. Here, the respective performances of two distinct DL models are analyzed, on recovered images from various OFDM-based image communication systems implemented in the first contribution. Additionally, we have utilized different augmentation techniques to improve the performance of the DL models for the CV application. The rest of the paper is organized as follows. In Section 2, we present related works on OFDM-based image communication system and robustness of DL models on image perturbation. Section 3 introduces the methodology of the study followed by simulation results and discussion in Section 4. Finally, we conclude the paper with limitations of the study and future research direction.
# Observing Pictures and Videos of Creative Products: An Eye Tracking Study
## 4 Data Acquisition and Elaboration
### Data Elaboration  
TVD data has been summarized in Table 3. In the table, the form of representation (picture/video) of the AOIs analyzed is presented in the fourth column. It is possible to notice the alternation of pictures and videos of the same AOI. The TVD sum is the amount of time (in seconds) spent to the specific AOI by the 14 participants. The TVD Average is the average time (in seconds) spent by each participant to the specific AOI, while the TVD SD is the resulting standard deviation. In order to compare the two forms of representation, the variable TVD Diff% has been created and it is shown in the penultimate column of Table 3. The TVD Diff% is a variable considered within each AOI and it has been calculated as in Equations (1) and (2) for pictures and videos, respectively. \[\text{TVD Diff}\%_{\text{picture}}=100*\frac{TDV\ Sum_{\text{picture}}-TDV\ Sum_{\text{video}}}{TDV\ Sum_{\text{picture}}} \tag{1}\]  
\[\text{TVD Diff}\%_{\text{vVideo}}=100*\frac{TDV\ Sum_{\text{Video}}-TDV\ Sum_{ \text{picture}}}{TDV\ Sum_{\text{Video}}} \tag{2}\]  
From the above equations, it can be noticed that the variable TVD Diff% indicates the extent to which the considered form of representation has extended the TVD of the same AOI. For instance, a high value of TVD Diff% for an AOI referred to a video, e.g., +453% for the AOI Water in the product Equilibrium, means that the element depicted in that AOI was dedicated more attention to in presence of a dynamic stimulus (5.53 times in the illustrative example). On the other hand, as for the same product, the picture as form of representation was able to drive the attention on the AOI Hand for a much longer time (TVD Diff% = +591). Table 3 includes indications on the significance of the increase of the TVD when pictures or videos are displayed. Clearly, the stars depicting the level of significance of the increases as a common rule of thumb can be present just in one of the two rows standing for the same AOI of the same product. For the sake clarity, significant TVD decreases are not indicated, but they can be inferred for videos (pictures) when significant TVD increases emerge for pictures (videos). Figure 4: Boundary frames for the creation of dynamic Areas of Interest (AOIs) for the product OnPot. With the aim of shading light on the major TVD differences inferable from Table 3 ([TVD Diff%] \(>\) 100), a graphical representation of these differences is presented in Figures 5-10. In these figures, the distribution frequencies of the TVD on specific AOIs are presented through violin plots. Blue violins refer to pictures, whereas red violins denote videos. The width of each violin represents the proportion of the TVD located there. Each of these figures considers a specific product.
# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 7 Challenges and Limitations of AI Chatbots in Infectious Disease Outbreaks  
The integration of chatbots and large language models (LLMs) into healthcare communication during epidemics offers some notable advantages such as innovative ways to both collect and disseminate information and opportunities for resource optimization.  
Figure 5: The role of chatbots in epidemic response.  
While these potential benefits can significantly support doctors, policymakers, and citizens by providing timely information and aiding in resource management, this class of software applications also present important limitations and risks in the context of infectious diseases, which must be known to allow for their cautious and responsible implementation.  
The challenges and risks discussed below are similar to those associated with medical and health issues affecting individuals. However, they are intensified by the scale of those impacted, the uncertainty surrounding the nature of symptoms, the relationship between symptoms and disease severity, and the ambiguity regarding modes of disease transmission and mitigation. In other words, in what follows, we will survey problems that are known to be related to the use of conversational agents in the management of health issues, but exacerbated by the contexts in which epidemics and pandemic occur, that is, in contexts of heightened severity, spread, and uncertainty.  
The main limitations and risks can be grouped in two broad ambits: capacity and alignment. Capacity refers to the intrinsic abilities of these systems in terms of their functions and skills. This domain encompasses quality dimensions such as accuracy, reliability, and effectiveness. Limitations in capacity involve technical shortcomings like misinformation propagation, the inability to interpret complex or ambiguous user inputs, and challenges in processing real-time updates. Essentially, capacity-related issues regard the tasks these AI systems are not capable of carrying out as expected and the kind of failure they can exhibit.  
Alignment, on the other hand, pertains to the extent to which the capacities of these AI systems are coherent with the values, principles, and norms of the society and community they serve. This includes compliance with ethical standards, cultural sensitivities, and legal regulations such as data protection laws and medical practice guidelines. Limitations in alignment involve ethical dilemmas, privacy violations, lack of transparency, and potential misuse of technology. In essence, alignment-related issues regard shortcomings of these systems in adhering to societal expectations and legal requirements. For both ambit, we will discuss four main challenges, as they are reported in Table 3. The following sections will explore each of these challenges in detail, highlighting the critical issues that emerge in infectious disease contexts.
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 2 Technology Overview
LoRaWAN permits two kinds of end device activation, namely Over-the-Air Activation (OTAA) and Activation by Personalization (ABP). The OTAA process permits end nodes to validate and secure access to the network with security credentials. ABP is a simpler and less secure process which skips the join procedure. The LoRaWAN protocol does not support direct communication between end nodes [10]. Security is a huge challenge in IoT networks. The authors in [11] provided an extensive  
Figure 1: Long-Range Wide Area Networks (LoRaWAN) architecture [10]. security risk analysis of the LoRaWAN v1.1 protocol and discussed numerous countermeasures to the security risks outlined. They presented a \"threat\" catalogue, as well as propositions and analyses regarding the magnitude, effect, and possibility of each threat. Other suggestions of improving the network security have been suggested in [12; 13; 14].
## 2 Tissue-Engineered Scaffolds with GBM and Neural Tissues  
There are several scaffolds that can be applied to nerve repair and regeneration of the CNS and PNS. At present, there is more evidence to support the treatment of PNI secondary to the PNS high regenerative capacity after damage, in contrast to the CNS [34]. For NTE to progress, further research is required investigating the optimum parameters to develop tissue-engineered scaffolds that substitute the grafting procedure. The main challenges to improve neural regeneration involve bypassing the harsh microenvironment (e.g., inflammatory, and inhibitory) created following nerve injury. Following trauma or neurological disease, the myelinated fiber tracts are damaged gradually and the blood cells cross the broken BBB, invading the medullary tissues that lead to inflammatory responses. The excitatory neurotransmitters accumulation and inflammatory cytokines cause formation of an inhibitory extracellular matrix (ECM) [35; 36]. Chemical signals, such as bacterial endotoxins and cytokines, direct macrophage inflammatory (M1) and M2 phenotypes [37]. M1 promotes cell recruitment and proliferation, while M2 encourages differentiation. The polarization phenotype, switching from M1 to M2, is influential in promoting functional tissue regeneration [38]. Therefore, TE scaffolds can be applied to stimulate endogenous nerve migration and control inflammatory cell infiltration.  
GBMs have recently gained a lot of attention as promising candidates for the treatment of nerve injury and nervous tissue regeneration. Beneficial properties of GBMs include supplying electrical conductivity, which enhances mechanical strength and cellular behavior. The GBMs incorporation has been shown to improve the mechanical properties of bio-composites; higher concentrations of GBMs result in more suitable mechanical properties, which are adjustable by concentration for the target tissue [39; 40; 41]. In addition, the hydrophobicity of NTE structures enhances with increasing G amounts, which affects nerve cell attachment, proliferation, and differentiation. Further research should focus on ideal concentrations of G to achieve optimal results [15; 31; 42].  
This paper focuses on the effects of GBMs and their potential for NTE. G-based scaffolds are categorized based on their structure: electrospun fiber, film (membrane), hydrogel, foam, 3D printed or bioprinting, and conduit (Figure 3), which are listed in Tables 1 and 2. There are two concepts for GBMs in developing TE scaffolds promoting nerve regeneration: (1) evaluating the effect of GBMs concentration on characteristic outcome (prominently with electrospun fiber s) and the (2) addition of a certain amount of GBMs to optimize the scaffold by enhancing the mechanical strength, cellular behavior, and electroconductivity (commonly nerve guidance conduit and hydrogel).
# Time-Domain Dynamic Modeling and Analysis of Complex Heavy-Duty Gearbox Considering Floating Effect
###### Abstract
In large-scale mechanical systems, heavy-duty gearboxes with planetary gear sets are adopted extensively due to their unique advantages of high power density and high transmission ratio; however, this yields complex configurations and characteristic serving circumstances. Thus, more stringent design criteria are desired than for common industrial gearboxes, and the dynamic forces become the major limiting factor to achieving better performance [24]. In previous studies concerning gearboxes, the assessment of dynamic behavior is usually centered on the frequency domain. Hence, a study of dynamic behavior in the time domain becomes significant, as it can confirm the dynamic loading factors, which are usually chosen by experience from a manual according to the traditional design code for common gearboxes. In this paper, an appropriate and generally applicable modeling approach of heavy-duty gearboxes, mainly the planetary gear set, is proposed to investigate dynamic behavior in the time domain, and a LTC model of a 2 MW WTG was built in which the time-varying meshing stiffness, bearing stiffness, torsional stiffness, and floating factor were considered. The model was then verified by experiments on a test rig, and dynamic characteristics of the WTG were finally revealed on the verified model.
# Experimental Study on Anisotropic Mechanical Characteristics of Shale under Triaxial Loading
## 5 Discussion
Within the confining pressure range of the test, the anisotropic index of compressive strength basically decreases with the increase in confining pressure, while the anisotropy index of elastic modulus increases gradually. At present, it is generally believed that under a sufficiently high stress environment, the anisotropy of the rocks will gradually disappear and eventually tend to be isotropic, resulting in the anisotropy index of its mechanical parameters being close to 1. Therefore, combined with the experimental results in this paper, it can be concluded that with the increasing confining pressure, the anisotropy index of the mechanical parameters of shale does not monotonically decrease to 1. (5) With the increase in confining pressure, the evolution of the failure modes of shale samples with different bedding angles under uniaxial and triaxial compression is as follows: 1. For the 0\({}^{\circ}\) bedding angle sample: The failure mode changes from a composite mode that includes both splitting failure perpendicular to the bedding planes and shear failure through the rock matrix, a shear failure mode through the rock matrix; 2. For the 30\({}^{\circ}\) bedding angle sample: The failure mode changes from a composite mode that includes both local splitting failure and shear failure through the rock matrix, a shear failure mode through the rock matrix; 3. For the 45\({}^{\circ}\) bedding angle sample: The failure modes of the samples are both a mixed mode that includes shear failure through the shale matrix and along the bedding planes; 4. For the 60\({}^{\circ}\) bedding angle sample: The failure mode changes from a composite mode that includes shear failure through the shale matrix and along the bedding plane to a shear failure mode along the bedding planes; 5. For the 90\({}^{\circ}\) bedding angle sample: The failure mode of the sample gradually evolved from the splitting failure along the bedding plane to the shear failure mode through the rock matrix. Therefore, it can be found that under different confining pressure conditions, although the failure modes of shale samples with different bedding angles have different evolution rules, the failure modes of shale samples all tend to evolve in the direction of shear failure mode through the rock matrix with the increase in confining pressure, and the anisotropy of the failure modes of shale samples with different bedding angles is gradually weakened.
# A Review on Recent Advancements in FOREX Currency Prediction
## 2 Interests of the Study  
People are invested more in trading of currencies nowadays. Among these markets, two of the biggest trading platforms are cryptocurrency trading and the FOREX market. Cryptocurrency trading, an emerging and growing platform of currency trading, is sometimes considered as similar to the FOREX but has some key differences. Cryptocurrency trading is a relative newbie in the market, while FOREX is a large-scaled, well-set practice. In each trade, FOREX often involves third-party brokers, whereas cryptocurrency lacks them. Another drawback of cryptocurrency trading is the unavailing of the liquidity. Although cryptocurrency promises higher returns than FOREX, FOREX provides stable, highly secured, and highly regulated trading compared to cryptocurrency trading.  
Along with other fields, predicting the FOREX market has been a key target of investigators over the previous couple of decades. There are two alternative ways to forecast the foreign exchange market: fundamental research and technical research. Fundamental research considers many factors, such as the economy and political state of a country, the reputation of a company, all internal and external trading news, etc. Technical research solely predicts the FOREX market based on a company's historical data, in particular, the highest price, lowest price, opening price, and closing price of a currency and the volume traded on a particular day. There's evidence that people use both of those methods to forecast the exchange rates [9]. The fundamental analysis was used by [PERSON] to find out the effectiveness [10]. They studied the emergence of bubbles, excess volatility, and volatility clustering. They found that currency rate control had the tendency to scale back the volatility and deviations. Technical analysis has seen its fair share of use in recent years as well [11; 12; 13]. Research proves that foreign exchange market currencies have some similarities and they are clustered with each other [14].  
The emergence of artificial neural networks played a huge role in foreign exchange rate prediction. Due to its predictive capabilities, many researchers used neural networks for their prediction models [15; 16; 17; 18; 19]. In the past, the evolutionary computation method was a popular choice [20; 21; 22; 23] along with fuzzy logic [24; 25; 26; 27] which is popular still now. With the passage of time, newer approaches have been applied and newer models have arisen. However, along with the past methods, we need to consider the latest methods that have been arisen recently.  
The intention of this study is to provide a better understanding of these models that were used in research on the area of FOREX forecasting. This paper analyzes the research methods and articles which are directly related with FOREX prediction that have been published in the previous three years in \"Elsevier\", \"Springer\", and \"IEEE Xplore\". One of the objectives of this paper is to give an intuition into the recent conducted methods and also provide a future direction for researchers.
# Biochemistry of Terpenes and Recent Advances in Plant Protection
## 3 Role of terpenes in Plant Protection
### Antimicrobial (Phytoalexin) Activities of Terpenes
oryzae_ and _R. solani_ in rice plants and subsequently hindered the growth of _M. oryzae_ under in vitro conditions. A related study confirmed their accumulation in abundance within necrosis sites of the _M. oryzae_ infections [99]. Two labdane-related diterpenes, sclareol and cis-abienol, isolated from tobacco, exogenously inhibited bacteria wilt diseases in tobacco, tomato, and Arabidopsis plants. Microarray analysis implicated genes encoding mitogen-activated protein kinase (MAPK) cascade components, ATP-binding cassette (ABC) transporters, and biosynthesis and signaling defense-related molecules [100]. These events showed that host immune factors were responsible for inhibiting wilt disease. Epoxydolabranol, a caprotricyclic tetradecahydrophenanthrene diterpene elicited in maize roots, effectively inhibited the pathogenesis of _Fusarium graminearum_ and _Fusarium verticillioides_ simultaneously [101]. Another maize terpenoid, zealexins, is an effective antimicrobial agent. Zealexin A1 resisted the growth of _A. flavus_, _F. graminearum_, and _R. microsopres_. Zealexins A3 and A4 also exhibited inhibitory effects against _A. flavus_ and _F. graminearum_[102,103, 37]. The Antimicrobial properties of terpen are illustrated in Figure 4(b) and Table 2.