esa-sceva/satcom-chunk-collection huggingface dataset:
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 4 Experimental Evaluation
### Computational Efficiency  
We evaluated the computational efficiency of our framework on a workstation with an Intel Xeon E5-2690 v4 CPU and an NVIDIA Tesla V100 GPU. Table 7 reports the inference time and model size for different methods.  
Our method has a slightly higher computational cost compared to baseline approaches due to the additional components (MSGF and RFDM). However, the difference in inference time is acceptable (108 ms vs. 103 ms for RRFS), making our method suitable for real-time food safety inspection systems.
## 4 Digitalization of MGs
### Internet of Things (IoT)  
The IoT has emerged as a game-changing technology that is revolutionizing various industries, including the energy sector. In particular, IoT applications have become increasingly relevant in MGs [138; 139; 140]. MGs can offer a range of benefits, such as improved energy access, reliability, and cost-effectiveness. The integration of IoT devices in MGs can further enhance these benefits, by enabling real-time monitoring, analysis, and optimization of energy systems. One of the most significant advantages of IoT in MGs is improved energy efficiency. IoT sensors and controllers can collect real-time data on energy consumption, production, and distribution, which can be analyzed and used to optimize microgrid operations [73]. For example, IoT devices can detect when energy consumption is at its peak and adjust the power supply accordingly, reducing the risk of overloading and minimizing energy waste. Additionally, IoT-enabled MGs can integrate REs by managing their fluctuating output and ensuring their efficient use. By using IoT technology in MGs, energy providers can improve their energy management, reduce energy waste, and reduce costs, leading to a more sustainable and cost-effective energy system. Another benefit of IoT in MGs is improved reliability and resilience. IoT sensors can detect faults or anomalies in the system, enabling quick identification and resolution of potential issues. This is particularly important in MGs, which rely on localized power generation and distribution, and are more vulnerable to disruptions and failures than the main grid. By using IoT technology, MGs can improve their monitoring and maintenance processes, reducing the risk of downtime and power outages. Additionally, IoT-enabled MGs can incorporate energy storage systems which can store surplus energy for later use and provide backup power during outages. By
# Secure Transmission in mmWave Wiretap Channels: On Sector Guard Zone and Blockages
## 5 Numerical Results
Figure 8 presents the effects of transmit power allocation factor \(\mu\) and the eavesdropper density \(\lambda_{E}\) on the secrecy throughput. From the simulation results, it shows that there exists an optimal transmit power allocation factor \(\mu\) for maximizing the secrecy throughput with the changing \(\lambda_{E}\). On the  
other hand, when the power allocation factor is smaller, no matter how high \(\lambda_{E}\) is, the connection is interrupted, and the reliability of the system is not guaranteed. As both \(\mu\) and \(\lambda_{E}\) are sufficiently high, the secrecy outage occurs and the security is not guaranteed. It reveals that the power allocation of the information signal and AN need to be properly set depending on different system parameters for increasing the secrecy throughput. Figure 9 presents the effects of the \(\eta\) versus the blockage density \(\beta\) with different \(r_{D}\). We observe that the secrecy throughput of \(r=80\) m is always superior to that of \(r=60\) m under the same system parameter settings. The reason is that, with the increase of \(r\), if eavesdroppers exist in the sector secrecy guard zone and there is an appropriate \(\lambda_{E}\), the transmitter will transmit AN interference to eavesdroppers, so the secrecy performance will be enhanced. In addition, increasing blocking intensity \(\beta\) does not always result in a strict decline in the secrecy throughput of mmwave wiretap networks. This shows that blockage plays an important role in the transmission of mmWave, which can be utilized to improve secrecy performance. From Figure 9, there exists an optimal \(\beta\) for maximizing the secrecy throughput at the different \(r\). It is a meaningful conclusion that choosing sector secrecy guard zone according to the density of physical barriers and the distance of receiver can improve the secrecy throughput. As we improve \(\beta\) to the optimal point, the secrecy throughput attenuates, as NLOS communication dominates mmwave wiretap networks, using the multipath signals at the receiver. However, when the environment is full of physical obstacles, it is highly difficult for the message to reach the receiver, thus the secrecy throughput is gradually declining.
## 3 Results and Discussion
### Dependence on Biomass Type  
Figure 3 displays (on the same scales) time series of simultaneously measured H\({}_{2}\)O, gas temperature and K(g) at HAP 4 mm above softwood (Figure 3a), Miscanthus (Figure 3b), wheat straw (Figure 3c) and Salix (Figure 3d) particles with initial mass of 500 \(\pm\) 10 mg. In accordance with the energy density data shown in Table 1, the pyrolysis of woody-type fuels (softwood, Salix) takes longer than that of straw-based fuels (wheat straw, Miscanthus). Softwood has the highest energy density and longest devolatilization time (-60 s), while Miscanthus has the lowest energy density and shortest pyrolysis time (-40 s).  
The measured H\({}_{2}\)O concentrations and flame temperatures are comparable for all fuels, except for wheat straw, which shows a higher temperature. With regard to softwood (Figure 2), for all fuels, significant K(g) levels are present only during the devolatilization stage. This is due to the flame, as the decomposition rates of gaseous KOH and KCl to atomic K increase with temperature, and the presumably fuel-rich conditions close to the particle, where the released compounds displace the air/oxygen. The detected K(g) concentrations are several orders of magnitude lower during char combustion, probably because of the lower gas temperature and higher oxygen availability favouring the formation of gaseous KOH instead of K(g). Similar trends for K(g), H\({}_{2}\)O and temperature were previously observed [12; 14; 16; 23; 37].  
that of softwood according to the table, the measured difference in K(g) concentration (and total release) is significantly smaller than 30. This is most likely because the potassium is released as KCl (high Cl content) or KOH rather than K(g), and that the concentrations of Si and Ca in wheat straw lead to the formation of K-containing compounds in the solid (ash) or liquid (slag) phase, such that less K is released to the gas phase. Interestingly, the study reveals distinct differences in K(g) release between the fuels during char conversion. For the woody biomass, K(g) concentrations were overall higher and increasing, whereas the opposite behaviour was found for the straw-based fuels, with the lowest concentrations observed for Miscanthus.
# Cascade Speech Translation for the Kazakh Language
## 1 Introduction
Our research makes several significant contributions in the field of speech translation (ST):  
* Firstly, we created the ST-kk-ru dataset. This dataset includes original transcriptions in Kazakh, which we carefully translated into Russian, providing valuable language resources for the Kazakh-Russian language pairs. * Additionally, we conducted extensive experiments to optimize the ASR component specifically for the challenging Kazakh language. We explored various advanced ASR techniques to achieve the most accurate word error rate (WER) results, ensuring reliable speech-to-text transcription. * Furthermore, we focused on enhancing the MT process for the Kazakh-Russian language pair. Through rigorous experimentation, we employed different MT methods  
Figure 1: Cascade (**above**) and end-to-end (**below**) approaches to speech translation. and strategies, aiming to maximize the BLEU scores, thus ensuring high-quality translations. * Finally, to assess the overall performance and effectiveness of our speech translation pipeline, we rigorously evaluated its performance using a comprehensive test set, encompassing diverse speech and translation samples. This evaluation provides valuable insights into the system's capabilities and highlights its potential for practical applications in real-world scenarios. This paper is organized as follows: Section 2 provides an overview of the existing research on cascade speech translation methods, highlighting the relevant work in the field. In Section 3, we will delve into the details of the Kazakh-to-Russian speech translation dataset, explaining the data collection process and presenting relevant corpus-related statistics. Section 4 comprehensively describes the distinct methodologies employed to construct the ASR and MT modules of the cascade approach, shedding light on the technical aspects and considerations involved. Moving forward, Section 5 elucidates the experimental setup deployed in our study, encompassing the key parameters. We further present the obtained results and provide a detailed analysis of the outcomes. Section 7 focuses on the analysis and interpretation of the obtained outcomes. Finally, in Section 6, we draw the main conclusions derived from this research endeavor, summarizing the findings and implications and discussing potential future directions for further exploration in this field.
# Determining the Quality of a Dataset in Clustering Terms
## 2 Materials and Methods
### Methodology
#### 2.2.1 Cluster Analysis  
Cluster analysis aims to divide a dataset  
\[O=\{x_{i}=(x_{i1},\ \ldots,\ x_{in}):i=1,\ \ldots,\ N\},\]  
composed of \(n\)-dimensional feature vectors \(x_{i}\) into clusters (subsets, groups, classes) with respect to a certain criterion [16]. The division must meet two assumptions:  
* Homogeneity within the groups (elements forming the group should be as similar as possible);
* Heterogeneity between clusters (elements from different clusters should be as dissimilar as possible).  
We obtain a division of the set \(O\) consisting of \(N\) elements into \(k\) clusters and the matrix \(B_{k\times N}\), whose successive elements \(b_{ic}\) are the belonging degrees of the element \(x_{c}\) to the group \(G_{i}\). In the classical cluster analysis, a hard split is used in which each element \(x_{i}\) belongs to only one group. Due to the clustering methods, cluster analysis algorithms are most commonly divided into hierarchical and nonhierarchical.
## 1 Introduction
These two types of models are therefore often combined to predict the thermal comfort of an average person under different environmental conditions [13; 15]. Many advanced mechanistic thermoregulation models, such as the \"Fiala thermal Physiology and Comfort\" (FPC) model, were developed to predict the thermal comfort status of humans [16]. By implementing thermoregulation models in wearable devices connected to the body, thermal comfort can be monitored and, in a further stage, even controlled. However, for real-time applications, such models are too complex and have a high computational cost, thus making them less suitable for monitoring and control applications. Data-based models, on the other hand, are less complex and thus more adequate for real-time monitoring and control purposes. [PERSON] et. al. [17] demonstrated that such compact data-based mechanistic models are promising for modelling body temperature response using metabolic activity alone or metabolic activity and skin temperature as inputs by means of, respectively, a single-input single-output (SISO) or a multiple-input single-output (MISO) discrete-time transfer function model. Recent developments in compact wireless sensors allow the implementation of sensors in wearable devices such as bicycle helmets. Considering this, bicycle helmet design should be optimised for thermal comfort, so that bicycle helmets not only allow monitoring an individual's thermal comfort but also support its active control. In this reported research, we aimed at  
1. identifying a general model to estimate thermal comfort based on a few variables, the measurements of which can be integrated in helmets;
2. developing and testing a prototype of a smart helmet based on the identified general thermal comfort model; and
3. introducing the framework of calculation for an adaptive personalised reduced-order model to predict a cyclist's under-helmet thermal comfort using nonintrusive, easily measured variables.
## 3 Findings and Discussion
### Comparative Analysis  
After analyzing 24 literature sources on the topic, it can be concluded that there is no solid correlation in the statements of the literature. The experiments were conducted with varying numbers of samples, slightly different experimental setups with similar methodologies, and different age groups, which makes it difficult to draw a definitive conclusion. Additionally, the frequency ranges analyzed in the same GSM (Global System for Mobile Communications) or UMTS (Universal Mobile Telecommunications)-based analysis also differed in these articles. As a result, further research with standardized experimental protocols and consistent sample sizes is needed to provide a clearer understanding of the potential correlation between exposure to MP/BS (mobile phone/base station) emitted electromagnetic radiation and cognitive performance. It can be observed that, for different types of exposure, such as GSM, UMTS, or LTE (long-term evolution), there is a lack of consistency in the concluding remarks for all the selected articles. Furthermore, there is no significant correlation as there are no solid statements provided in all of these articles. Table 3 describes the comparative analysis of the articles that have been reviewed in this paper.
## 2 Related Work  
An assessment of the utility of Mixed Reality (MR) in medical education is presented in [2]. This study examines the pedagogical value of virtual reality methods in medical education. The authors surveyed 258 people, who were staff of a medical college, regarding potential applications of MR in the medical curriculum. They asked them to complete a questionnaire containing eight questions. The results showed that most of the volunteers think that MR-supplemented education is advantageous over classical instruction. The 3D visualization capabilities of MR, especially in anatomy classes, were most highly valued. A valuable conclusion extracted from this study is that MR expands the capabilities and effectiveness of remote learning, which was normalized during the COVID-19 pandemic, to ensure effective student and patient education. MR-based lessons, or even select modules, provide a unique opportunity to exchange experiences inside and outside the medical community.  
In [3], a technique for an orthopedic surgical process is proposed, with the establishment of the Virtual Surgical Environment (VSE) for educational purposes of the medical staff. The proposed method is used to tackle fractures of the femur and is called Less Invasive Stabilization System (LISS) surgery. The entire approach involved first getting the appropriate knowledge of the LISS plating procedure via interactions with proficient orthopedic surgeons and utilizing information-centric models. These models present a well-defined base to design and develop the emulator. A haptic interface is incorporated to support training activities in order to allow users to feel, touch, and interact with the surgical tools through these trainings.  
The study in [4] demonstrates the research progress and developments in Virtual Reality (VR) and Augmented Reality (AR) technologies in the manufacturing industry. During the fourth revolution, the penetration of new immersive technologies is of main importance and focuses especially on human machine interaction. However, critical challenges in VR/AR technology implementation at a hardware and software level still exist.
# Towards Efficient Sink Mobility in Underwater Wireless Sensor Networks
## 5 Simulation Results
### Packet Drop Ratio  
Figure 7 shows that the packet drop ratio in the proposed scheme is less when compared to the SEEC. In the proposed scheme, mobile sinks are utilized efficiently, which results in less packet drop ratio. As discussed in Section 3 that the SEEC categorizes the dense and sparse region at the initial stage and the mobile sink is moved on the basis of a sparse region searched array. This strategy of SEEC makes the movement of mobile sinks inefficient because, due to the water currents, it is not necessary that the sparse regions remain the same as they were in the initial stage. The movement of the mobile sink in constant searched array results in a high packet drop ratio. On the other hand, the restricted movement of mobile sink in the proposed scheme results in less packet drop ratio.
# Experimental Study on the Impact of CO2 Treatment on Different Lithofacies in Shale Oil Reservoirs
## 3 Results and Discussion
### Micomorphology Analysis on Different Lithofacial Types of Shale during CO\({}_{2}\) Treatment
Due to the large sizes of mineral particles in laminas, the sizes of new dissolution pores can reach 50 \(\upmu\)m in diameter. Furthermore, the dissolution of biogenic calcium carbonate minerals results in many dissolution microfractures, which can significantly improve the porosity and pore connectivity of shale and increase the flowability of shale oil. Although the precipitation and migration of minerals caused by CO2 treatment partially block pores and throats, the porosity and pore connectivity of the shale samples is still improved significantly by the presence of large-sized dissolution pores and fractures. So, after CO2 treatment, the permeability of shale samples can be improved significantly.
## 2 Materials and Methods
### Data
#### 2.1.3 Soil Moisture Data  
Soil moisture directly affects soil evaporation as it is one of the key determinants of water transfer from the soil to the atmosphere [43]. Soil moisture plays a crucial role in soil evaporation estimation. Therefore, soil moisture data are important for achieving accurate evapotranspiration partitioning. However, the soil moisture data in the FLUXNET2015 dataset has a large number of missing values, which is insufficient to support soil evaporation modeling. Therefore, the soil moisture data were obtained from the ERA5-LAND hourly dataset with a spatial resolution of 10 km. This is a climate reanalysis dataset produced by the European Center for Medium-range Weather Forecast (ECMWF). The soil moisture data of the ERA5-LAND dataset were obtained from the Google Earth Engine platform ([[https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)]([https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)) (accessed on 17 May 2023)) [28], including volumetric_soil_water_layer_1 (0-7 cm, SWC1), volumetric_soil_water_layer_2 (7-28 cm, SWC2), volumetric_soil_water_layer_3 (28-100 cm, SWC3), and volumetric_soil_water_layer_4 (100-289 cm, SWC4). To be consistent with the temporal coverage of the eddy covariance data, the soil moisture data from 2000 to 2020 were selected and then linearly interpolated into half-hourly intervals.
# World-Models for Bitrate Streaming
## 3 World-Models  
Model-based RL encompasses a wide variety of methods, including world-models. In this project, world-models refer to models of the environment that can be fully substituted in place of the real environment. Some instances of model-based learning may learn the components for a full world-model, but they may not use the model to replace the real environment for training. World-models typically involve two components, the model of the environment, \(M\), and a controller, \(C\)[4]. A model learns from real interactions with an environment, and these interactions must be gathered from a controller or logs of previous interactions. A world-model must learn to predict the next state \(s_{t+1}\) and reward \(r_{t+1}\), given the current state \(s\) and action \(a\). \(M\) can be substituted in place of the real environment as a world-model once a world-model has learned \(P(s_{t+1},r_{t+1}\mid s,a)\). Figure 1 shows a visualization of a world-model pipeline. First, a world-model is learned from recorded observations. A controller then learns inside the world-model's hallucinated episodes of the real environment, which can be transferred back to the real environment during deployment. A controller may learn policies fully inside of the world-model [6], use the world-model for planning [23], or use a world-model to augment the real environment [5]. Neural networks can be used as function approximators for modeling the environment. In deterministic, predictable environments, feedforward neural networks can approximate the world-model. However, these networks tend to fail in POMDP problems [24]. Memories of previous events can assist with the difficulties of POMDPs. The Recurrent Neural Network (RNN) architecture is a class of neural network models that process temporal data. The following subsection provides an overview of the RNN architecture.  
Figure 1: World-model pipeline. An agent trains inside the learned world-model and then is deployed back into the real environment.
## 4 Results
### Organization of the VTS Systems
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Name of VTS**} & \multicolumn{2}{c}{**Selected Decision-**} & \multirow{2}{*}{**Selected Allied**} & \multirow{2}{*}{**Type of Services**} & \multirow{2}{*}{**Selected Traffic Management Approaches**} \\  & & **Making** & & & & **of VTS** & **Provided** & **Management Approaches** \\  & & **Participants** & & & & & & \\ \hline \multirow{9}{*}{VTSZatoka} & 1. & VTS Zatoka & & & & & 1. & Assignment of recom- \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & 2. & Gdynia & & & & & & \\  & & Harbor & & & & & & \\  & & Master’s & 1. & Pilot Station & & 1. & Information & 2. & Two-way traffic is \\  & & Office & 2. & SafeSeaNet & & 2. & Traffic & & carried out in \\  & 3. & Gdarsk & 3. & Search and & & & Organization & & dedicated \\  & & & & Rescue & 1 & & Services & & lanes \\ Gdarska & Harbor & 4. & National & & 3. & Navigational & & separated by a separation \\  & & Master’s & & & service & & & & zone \\  & 4. & Gdarsk & & & & & & 3. & The port and \\  & & Northern & & & & & & coastal traffic \\  & & Port Harbor & & & & & & & takes place in the internal \\  & & Master’s & & & & & & the internal \\  & & Office. & & & & & & & traffic zone \\ \hline \multirow{9}{*}{VTS Lawica} & 1. & VTS Lawica & 1. & SafeSeaNet & & & & & \\  & & & 2. & Search and & & & & & \\  & & & & Rescue & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & 1. & VTS & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & 1. & VTS & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & 2. & VTS Szczecin & 1. & Pilot Station & & & & & \\  & & & & & & & traffic is \\  & & & & & & & & carried out in \\  & & & & & & & & one lane of \\  & & & & & & & & the fairway \\  & & & & & & & & \\  & & & & & & & & \\  & 5. & Port & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of the VTS systems in Poland according to the criterion organization of the system (own elaboration based on [55; 56]).