esa-sceva/satcom-chunk-collection huggingface dataset:
# Human Annotated Dialogues Dataset for Natural Conversational Agents
## 2 Related Datasets  
Dialogue systems have been categorized into two groups [16]: task-oriented systems and non-task oriented systems (also known as chatbots). The aim of task-oriented systems is to assist users to complete specific tasks by understanding the inputs from the user. Manual handcrafted rules in such systems make it not only expensive and time-consuming but also limit the systems to a particular domain. Non-task oriented dialogue systems can communicate with humans on open domains, thus they can be used in real-world applications.  
Dialogue systems are mainly task-oriented, designed to complete specific tasks such as airline tickets booking [17], bus information searching [18], restaurant booking [10] or railroad goods shipping [19]. Such systems perform well when the task is simple and explicit intentions of the users are well-calibrated to the system capabilities. Moreover, some of the popular domain specific datasets are bAbI simulated restaurant booking dialogues [10], Movie dialog dataset [20] and Ubuntu Dialogue Corpus dataset [4]. The Ubuntu dataset consists of large-scale unstructured dialogues with a multi-turn conversation between two persons, where negative turns of the dialogue are created by sampling. Major drawbacks of the Ubuntu dataset are that it has no annotation on context-reply pairs and no different replies for the same context.  
Many recent works use conversational models for open-domain datasets such as Twitter Dialog Corpus [21] and the Chinese Weibo dataset [22] that are posts and replies from social networking sites. PERSONA-CHAT dataset [23] introduces chit-chat dialogues between crowd-sourced participants based on their given profile. The DailyDialog [24] dataset uses manual labeling with three human experts to develop a dataset with communication intention and emotion information. Moreover, the other large-scale datasets that are often used to train neural network-based conversational models are movie-subtitles datasets such as OpenSubtitles [25], Cornell Movie-Dialogue Corpus [7], Movie-DiC [26] and Movie-Triples [27]. In this work, we selected 4750 dialogues from the Cornell movie dialogue dataset as our base dialogues which are then used for human annotation as explained in the next section. A comparison of the HUMOD dataset with the existing movie dialogue datasets is shown in Table 1.  
Figure 1: A sample of 6-turn dialogue context with positive (actual) reply and candidate negative (sampled) reply and two examples of human generated replies for the dialogue context.  
In previous works of dialogue response evaluation, different unsupervised metrics have been adopted [28] to evaluate (or how not to evaluate) dialogue systems with human judgments in the Ubuntu and Twitter corpus dataset. In addition, supervised metrics [29] have been implemented that are trained to predict human judgments with or without reference reply. In this paper, we perform a correlation of human judgment with both supervised and unsupervised metrics in order to provide preliminary benchmark metrics on the HUMOD dataset.
# Plasma Surface Modification of PDMS-Glass Microfluidic Chips for Oil Recovery Studies
## 3 Results and Discussions  
### Surface Modification  
#### 3.1.1 PDMS  
Initially, the effect of plasma treatment on the wettability characteristics of the PDMS substrate was studied. Figure 5 shows a photograph of water droplets on the original and processed substrate during the advancing process.  
As can be seen, plasma treatment makes it possible to radically change the wetting characteristics. The dependence of the water wettability characteristics of the PDMS surface on the treatment time in the plasma chamber in a humidified atmosphere was obtained (Figure 6). It can be seen that the advancing contact angle decreases from 120\({}^{\circ}\) to 10\({}^{\circ}\) in 400 s, and the receding one decreases from 70\({}^{\circ}\) to <1\({}^{\circ}\). The data obtained indicate the possibility of changing the wetting properties of PDMS using electric plasma in a fairly wide range, from hydrophobic to superhydrophobic.  
Figure 4: Microfluidic chip topology.  
PDMS is a porous material, but its untreated surface contains mainly only methyl groups and, as a result, its surface is hydrophobic. That is, filling the pores of the raw PDMS is only possible with non-polar liquids such as oil or crude oil. In the plasma of a water-moistened atmosphere, methyl groups are replaced by methanol and hydroxyl groups, which are polar [23]. Nearby hydroxyl groups can form a vicinal complex, that is, they will have a common hydrogen and such a complex will have a charge.  
We associate the decrease in advanced contact angles with the ionization of the surface: the higher the charge, the greater the distance from which water molecules begin to be attracted to the surface; receding contact angles are related to the adhesion of liquid molecules to the surface. The strength of this compound is largely affected by the number of polar molecules; ions, on the other hand, form layers of bound water around themselves, and the strength of the connection with the \"large\" drop will be due to the cohesion of water. Thus, between 60 and 120 s, we observe a slight increase in the receding angle and, at the same time, a decrease in the advancing angle. This may be due to the fact that some of the polar molecules formed vicinal complexes. That is, the number of polar molecules decreased (the receding angle increased), and the number of ions increased (the advancing angle decreased). These considerations require verification by modeling and analysis of the chemical composition of the surface.  
Figure 5: An advancing drop of water on the surface of PDMS before (**a**) and after (**b**) treatment in electric plasma for 400 s.  
Figure 6: Dependence of the contact angle of wetting the PDMS surface with water on the time of treatment with electric plasma in the manufactured laboratory instrument.
## 3 Methods
### Relation Classification Module
\[\mathrm{Z_{i,j,h}^{r}}=\mathrm{softmax}(\frac{\mathrm{Q_{i,j,h}^{r}}\times \mathrm{K_{ij,h}^{r}}^{\top}}{\sqrt{\mathrm{N_{a}}}})\mathrm{V_{ij,h}^{r}} \tag{15}\]  
Figure 4: Structure of the Multi-head Self-Attention encoder and the self-attention encoder. The colors in the left part of the image correspond to the elements in Figure 1.  
where \(\rm N_{a}\) is the feature dimension of input \(\rm a_{i,j}^{r}\). The output \(\rm Z_{i,j,h}^{r}\) obtained from each self-attention layer is stitched together to obtain the output \(\rm Z_{i,j}^{r}\) of the multi-head self-attention layer:  
Figure 3:0  
The output is processed using residual joins as well as layer normalization. Figure 3:1  
Considering that the attention mechanism is not sufficient for fitting complex processes, a feedforward connection layer is used to enhance the model fitting ability. After the feedforward layer, the final output was obtained by using residual connections again, and layer normalization. Figure 3:2  
The predicted results of the relation classifier are defined in Equation (19):  
Figure 3:3  
where T denotes the the transformer encoder. \(\rm\hat{y}_{i,j,k}^{r}\) is the predicted probability of the kth relation from span \(\rm s_{i}\) to \(\rm s_{j}\). \(\sigma\) is the sigmoid function. \(\rm N_{r}\) is the number of relation types. If all the \(\rm\hat{y}_{i,j,k}^{r}\) for \(\rm N_{r}\) relations were below threshold (generally 0.5), the model would predict no relation from \(\rm s_{i}\) to \(\rm s_{j}\). If any \(\rm\hat{y}_{i,j,k}^{r}\) was above the threshold, the model would select all kth relations as the predicted relations.
# Experimental Assessment of the Effect of Vertical Earthquake Motion on Underground Metro Station
## 3 Test Results and Discussion  
### Influence of RVH on Soil Acceleration  
To investigate the influence of the RVH on the vertical propagation of the waves in the ground, as an example, Figure 7 compares the horizontal acceleration time histories of the soil at different measuring points (AM-1, AM-2, AM-3, AM-4, and AM-5) between RVH = 0 and RVH = 1. It is seen that at any measuring point their horizontal acceleration always behaves consistently in the trend, and the differences mainly lie in the amplitude. For both RVH = 0 and RVH = 1, the horizontal acceleration of the soil increases with decreasing depth.  
Figure 6: Comparison between targeted base motions and achieved base motions both in the horizontal and vertical components for all test cases.  
To better evaluate the trend of the horizontal acceleration amplitude, the acceleration amplification factor (AAF) is obtained by dividing the peak horizontal acceleration at each measuring point by the peak horizontal acceleration of input motion. Therefore, the AAF at the base of the model (at soil depth of 1.6 m in Figure 3a) is equal to unity (1). This definition also applies to the following parts. Figure 8 shows the AAF of the ground along the depth under different RVH. The horizontal acceleration amplitude of the soil tends to increase from the bottom to the ground surface. Under the four conditions the AAFs on the ground surface are about 2.3-3.1 and the soil presents significant amplification. This is reasonable since the amplitude of all the input motions are relatively low.  
Figure 7: Comparison of horizontal acceleration time histories of soil between RVH = 0 and RVH = 1.  
In terms of the acceleration levels in this study, with increasing RVH, soil horizontal acceleration increases and the increase in the amplitude grows significantly with increasing RVH on the whole. It demonstrates that soil horizontal acceleration is more sensitive to higher RVH or higher vertical acceleration.
# Diagenesis of Deep Low Permeability Reservoir in Huizhou Sag and Its Influence on Reservoirs
## 4 Results
### Diagenetic Processes
#### 4.2.3 Dissolution  
The dissolution of all soluble components in sandstone occurs under specific environmental conditions in secondary pores, which can improve the reservoir performance of sandstone. In particular, dissolution is of great significance in low intergranular pore sandstone reservoirs in forming secondary pores and developing \"sweet spots\" [55; 56]. In the study area, the tight sandstone reservoirs in the Enping Formation section obviously undergoogenetic modification through dissolution. Specifically, from the scanning electron microscope analysis, secondary dissolution pores, predominantly pores in the grain, are feldspar dissolved along the cleavage plane, and the unstable components in the cuttings are dissolved, forming pores with honeycomb and patch distribution (Figure 6H,I). In the Enping Formation, the contribution of potassium feldspar mineral particle dissolution to secondary dissolution pores is primary, and that of debris is secondary, whereas the proportion of dissolution fractures is rare (Figure 5).
# A Systematic Review of the Effects of Nutrient Intake in Handball Players on Exercise Performance
## 3 Results
### Characteristics of the Studies  
A summary of the characteristics of the studies linking nutritional aspects in samples of handball athletes is included according to the variables analysed in Tables 1-4. Seven studies were related to hydration in handball athletes, five of them with hydration habits and water/liquid intake throughout the day or at times close to training or matches [18; 19; 20; 21; 22] and another two with water and electrolyte demands during training and competition [23; 24]. Twenty-two studies were related to the intake of energy, macronutrients and fibre in samples with handball players present; 20 of them referred to the mean energy, carbohydrate, fat, protein and/or fibre intake of these athletes in different phases of the season [18; 19; 22; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41], with only one of them making specifications by playing position [35], while five focused on analysing the energy and different macronutrient demands [22; 37; 42; 43; 44]. In addition, 13 other studies analysed, together or not, energy, fibre or nutrient intake; dietary habits and consumption of different food groups in different handball athletes [19; 26; 27; 28; 34; 36; 39; 40; 45; 46; 47; 48; 49]; as well as the effects of a nutrition education programme on dietary habits and consumption of different foods in one of them [28]. Twenty-three studies evaluated micronutrient status in handball athletes, 17 articles analysing the nutritional intake of athletes [18; 22; 25; 26; 28; 29; 30; 31; 32; 33; 35; 36; 45; 50; 51; 52; 53], and 14 in conjunction or not with different biochemical analyses in samples of interest [22; 29; 31; 32; 33; 34; 50; 51; 53; 54; 55; 56; 57; 58; 59]. Finally, four studies provided information on the nutritional knowledge and main sources of information of handball athletes [21; 40; 46; 49], and two additional articles on eating disorders in these athletes [60; 61]. In those studies, dealing with interventions or presenting samples using dietary supplements [41; 50; 53; 59], only data excluding or prior to the use of supplements were included. Figure 1: Study retrieval process flowchart.
# IoT-Based Smart Surveillance System for High-Security Areas
## 3 Results
### IoT-Based Generated Call and SMS  
The SS-HSA monitors home security in real-time and notifies the end-user to take proactive action against a suspected intruder. Arduino UNO is simultaneously connected with GMS and GSM to generate real-time notifications in order to inform the concerned entity via a message and a call when an object is detected.
# Energy Saving in Wireless Sensor Networks via LEACH-Based, Energy-Efficient Routing Protocols
## 2 LEACH Protocol
Consequently, nodes with low energy reserves have the same probability of becoming cluster heads as those with ample energy. Also, in LEACH, clustering is random and consequently unbalanced, because not only the number but also the position and the formation of clusters are unsystematic. Moreover, LEACH may not be suitable for networks deployed in wide areas due to its reliance on single-hop routing, which is impractical for large-scale WSNs. Furthermore, the dynamic clustering approach is associated with higher overhead.
# OpenCare5G: O-RAN in Private Network for Digital Health Applications
###### Abstract  
Digital Health is a new way for medicine to work together with computer engineering and ICT to carry out tests and obtain reliable information about the health status of citizens in the most remote places in Brazil in near-real time, applying new technologies and digital tools in the process. InovaHC is the technological innovation core of the Clinics Hospital of the Faculty of Medicine of the University of Sao Paulo (HCFMUSP). It is the first national medical institution to seek new opportunities offered by 5G technology and test its application in the first private network for Digital Health in the largest hospital complex in Latin America through the OpenCare5G Project. This project uses an Open RAN concept and network disaggregation with lower costs than the traditional concept used by the telecommunications industry. The technological project connected to the 5G network was divided into two phases for proof-of-concept testing: the first with an initial focus on carrying out examinations with portable ultrasound equipment in different locations at HCFMUSP, and the second focusing on carrying out remote examinations with health professionals in other states of Brazil, who will be working in remote areas in other states with little or no ICT infrastructure together with a doctor analyzing exams in real time at HCFMUSP in Sao Paulo. The objective of the project is to evaluate the connectivity and capacity of the 5G private network in these the proof-of-concept tests for transmitting the volume of data from remote exams with higher speed and lower latency. We are in the first phase of the proof of concept testing to achieve the expected success. This project is a catalyst for innovation in health, connecting resources and entrepreneurs to generate solutions for the innovation ecosystem of organizations. It is coordinated by Deloitte with the participation of the Escola Politecnica da USP (The School of Engineering--University of Sao Paulo), Airspan, Itait Banks, Siemens Healthineers, NEC, Telecom Infra Projet, ABDI and IDB. The use of 5G Open RAN technology in public health is concluded to be of extreme social, economic, and fundamental importance for HCFMUSP, citizens, and the development of health research to promote great positive impacts ranging from attracting investment in the country to improving the quality of patient care.  
5G; B5G; digital health; Open RAN; private network +
Footnote †: journal:
# Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
## 7 Conclusions and Future Research  
This study examines the robustness of existing ML-based network traffic classification models against adversarial attacks. A novel framework called DVars is proposed to enhance the resilience of these models. DVars, which introduce randomness into the input data by generating distinct variables from the baseline dataset, play a crucial role in strengthening the robustness of models against adversarial attacks. Notably, the key distinction between the proposed approach and traditional adversarial training lies in the emphasis on utilizing DVars rather than AEs. The evaluation conducted herein reveals that the integration of DVars in the training of ML models considerably improves their robustness when faced with adversarial attacks. Moreover, the practicality and scalability of DVars are underscored by their computational efficiency and independence from AEs.  
This research not only highlights the potential of DVars as an effective defense mechanism against adversarial attacks but also provides a viable avenue for further research.  
Figure 10: The confusion matrix showing robustness of DVars against AEs with respect to low perturbation intensity (\(\epsilon=0.01\)).  
Further research is warranted to assess the applicability of DVars to deep learning-based network traffic classification models.  
Conceptualization, J.M.A. and G.L.; methodology, J.M.A.; software, J.M.A. and J.Z.; validation, J.M.A., G.L. and J.Z.; formal analysis, H.M.B. and N.W; investigation, J.M.A.; resources, G.L.; data curation, N.W.; writing--original draft preparation, J.M.A. and J.Z.; writing--review and editing, J.M.A.; visualization, H.M.B.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R & D Program of China (2021 QY0700); in part by National Natural Science Foundation of China (U21B2003); and in part by the Startup Foundation for Introducing Talent of the Nanjing University of Information Science & Technology, China (2023r014).  
The dataset used in the experiments is the CSE-CIC-IDS-2018 dataset, which is cited in the article.  
All authors would like to thank the editors and reviewers for their advice.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 2 Methods
### Numerical Indexing of Related Spatial Factors
#### 2.2.4 Driving Category (Rainfall)  
The density of rainfall data collected by rainfall stations is much lower in mountainous areas than that in urban areas. Relevant studies have mostly used distance as an interpolation reference to obtain the rainfall in a whole area through grid interpolation. This study considered the distance and elevation factors of rainfall stations and added the aspect factor to construct a rainfall interpolation model, as shown in Equation (12). In this model, the elevation parameter \(\alpha\), distance parameter \(\beta\), and aspect parameter \(\gamma\) are obtained through the least squares adjustment, and the parameter weight is shown in Equation (13).  
\[\mathrm{I_{rain}}=\alpha\left(\sum_{\mathrm{iH}}\mathrm{W_{iH}}\cdot\mathrm{R_ {i}}\right)+\beta\left(\sum_{\mathrm{iL}}\mathrm{W_{iL}}\cdot\mathrm{R_{i}} \right)+\gamma\left(\sum_{\mathrm{i0}}\mathrm{W_{i0}}\cdot\mathrm{R_{i}}\right) \tag{12}\]  
\[\mathrm{W_{iH}}\propto\frac{1}{\Delta\mathrm{H}^{2}};\ \mathrm{W_{iL}}\propto \frac{1}{\Delta\mathrm{L}^{2}};\ \mathrm{W_{i0}}\propto\frac{1}{\Delta\mathrm{ \theta}^{2}} \tag{13}\]  
where \(\mathrm{I_{rain}}\) is the rainfall index, \(\mathrm{W_{iH}}\) is the elevation weight, \(\mathrm{W_{iL}}\) is the distance weight, \(\mathrm{W_{i0}}\) is the aspect weight, \(\mathrm{R_{i}}\) is the rainfall observation at Station i, \(\alpha\) is the elevation parameter, \(\beta\) is the distance parameter, \(\gamma\) is the aspect parameter, \(\Delta\mathrm{H}\) is the elevation difference, \(\Delta\mathrm{L}\) is the distance difference, and \(\Delta\mathrm{\theta}\) is the aspect difference.
## 4 Discussion
### Attribution of Differences between Remote Sensing Metrics
Because the influence of climate factors on animal diversity varies with different regions which leads to an offset on global-scale analysis, we expect that explained variance of LST are higher in regional analysis. For the ecosystem texture category, texture metrics have been proved to be important drivers of animal species richness [11; 25; 48; 71; 109], but the relationship pattern of texture metrics with animal species richness is still controversial. Various studies have found that species richness exhibited positive relationships with environmental heterogeneity, while negative relationships between environmental heterogeneity and species richness have also been reported [27; 43; 110; 111; 112; 113]. [PERSON] and [PERSON] [25] validated the performance of 14 heterogeneity metrics, using North American bird richness. They showed that the texture metrics combined with net primary productivity (NPP) can explain up to 35.8% of the variance. In addition, homogeneity could still explain about 8% of the variance by itself. In the ecosystem texture category, our results showed that the CV was the strongest metric on a global scale, with an explained variance of 22%. Compared with metrics in energy/productivity and climate category, this study showed that texture metrics exhibited low explained variance on a global scale, while high correlations were observed at smaller scales. In fact, the pattern of the relationship between animal species richness and habitat heterogeneity was impacted by many factors. Previous studies have showed that the possible effects of heterogeneity may also vary relative to the structural variable measured, and the effect of habitat heterogeneity for one species group may differ in relation to the spatial scale [43; 71]. In this study, the high-correlation areas were mainly covered shrub lands, grasslands, and savannas, while the explained variance of texture metrics were relatively low in forests (Table S1). This is probably because the texture dataset we used mainly focused on the horizontal structure of an ecosystem, while forest biome has more complex vertical structures than shrub lands, grasslands, and savannas. Moreover, different regions showed various major driving texture metrics. For example, DIS, ENT, and HOM exhibited high explained variance in grasslands and savannas. Moreover, previous studies found that the predictive performance of texture metrics would vary across different spatial scales [114]. Overall, texture metrics should be primarily considered when determining the species distribution at regional scales. For different animal classes, we found a gap in the explained variance between bird species richness and the species richness of the other two animal classes, among the climate metrics. The explained variance of PET on bird richness was much higher than that of both amphibian and mammal richness, which indicated that radiance and temperature played a more important role in bird richness distribution than in the other two animal classes.