esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The aim of this study was to compare the three-dimensional dentoskeletal effects of Haas-type and Hyrax-type expanders using primary teeth as anchorage for rapid palatal expansion (RPE). Thirty-four subjects in mixed dentition were divided according to their expander's type: Hyrax (n = 16; 6F, 10M; mean age 8 \(\pm\) 3 years) or Haas (n = 18; 14F, 4M; mean age 8 \(\pm\) 2 years). Each patient underwent CBCTs before (T0) and after RPE (T1). Dentoskeletal changes were collected. A paired sample t-test and independent t-test were used to compare each variable within the same group and between groups, respectively, with a 5% significance. The Hyrax group showed an increase in all dentoskeletal parameters; skeletal expansion was significantly increased anteriorly (1.76 mm) and posteriorly (1.93 mm). The greatest dental expansion was observed in the anchorage unit (6.47 mm), about twice as much as permanent molars (3.42 mm). The same statistical significance of Haas group measurements was observed; anteriorly skeletal expansion (2.97 mm) was greater than posteriorly (1.93 mm) and dental expansion was greater on anchored teeth (6.80 mm) than non-anchored teeth (4.57 mm). No statistical significance was observed between Hyrax and Hass groups. CBCT analysis showed that, in RPE, the dental expansion was greater than skeletal expansion. No significant or clinical changes were observed between Hyrax and Haas appliances anchored to primary teeth.  
CBCT analysis; deciduous teeth; orthodontic anchorage; rapid palatal expansion; randomized clinical trial +
Footnote †: journal: Applied Sciences
## 2 The Challenge of Cloud-Native INCCOM
[17] proposes the vision of deploying certain critical network functions in the core network on satellites to reduce latency. Ref. [18] further elaborates on this vision by distributing all or some of the network functions of the 5G core network across multiple satellites in different orbits and leveraging the \"service set\" mechanism in 5G to alleviate the significant signaling pressure between satellites and the ground. Ref. [19] addresses the issue of signaling storms introduced by satellite mobility and presents the in-orbit stateless core network case. It is important to note that the focus above is on the overall architectural research of the mobile core network in the satellite-ground fusion network scenario, with a lack of research on actual deployment algorithms for distributed core networks. In a typical network environment, the authors of refs. [20; 21] implemented a cloud-native Service Function Chaining (SFC) framework that provides traffic steering mechanisms for establishing end-to-end network services. In [22], the authors propose a horizontal scaling algorithm that leverages Control Theory to dynamically adjust the number of instances of the Access and Mobility Management Function (AMF) based on traffic load. To conserve resources required for User Plane Function (UPF) instances, the authors of ref. [23] propose a UPF instance scaling mechanism based on deep reinforcement learning. Although these efforts reduce the traffic overhead in the control plane of the core network deployed in the form of virtual machines, containers, or slices, there is a lack of consideration for the relationship between fluctuating service requests, deployment costs of the core network at multiple edge nodes, and inter-node control signaling overhead. Clearly, to simultaneously minimize deployment costs and control signaling overhead while adapting to the dynamic nature of user service requests, an intelligent CNFs deployment architecture is required. This architecture provides deployment strategies for CNFs by learning the spatiotemporal relationships between user service requests, deployment costs, and control signaling overhead.
###### Abstract  
The main goal of the study was to obtain new experimental data on the seasonal variability of carbon dioxide (CO\({}_{2}\)) and methane (CH\({}_{4}\)) fluxes in a subarctic palsa mite in north-central Siberia, as well as to assess the sensitivity of the CO\({}_{2}\) and CH\({}_{4}\) fluxes to environmental changes. The results of field measurements in 2017 and 2018 showed that the palsa mite served as a sink of CO\({}_{2}\) from the atmosphere for the period between mid-June to the end of August for both years. Maximum daily CO\({}_{2}\) uptake rates in 2017 were observed at the beginning of July (up to 4.5 gC m\({}^{-2}\) d\({}^{-1}\)), mainly due to high incoming solar radiation, optimal air temperature and sufficient soil moisture conditions. Seasonal variability of CH\({}_{4}\) fluxes was relatively high and governed mainly by weather conditions. During both growing seasons the palsa mite served mostly as a CH\({}_{4}\) source for the atmosphere. The periods with prevailed CH\({}_{4}\) emission alternated with periods of CH\({}_{4}\) uptake and the fluxes varied between \(-8.3\) to \(13.6\) mgC m\({}^{-2}\) per day in 2017 and between \(-4.5\) to \(21.8\) mgC m\({}^{-2}\) per day in 2018.  
carbon dioxide and methane fluxes; eddy covariance; palsa mite; north-central Siberia; northern tiaiga; forest-tundra acetone; permafrost +
Footnote †: Presented at the 5 th International Electronic Conference on Atmospheric Sciences, 16–31 July 2022; Available online: [[https://ecas2022.sciforum.net/](https://ecas2022.sciforum.net/)]([https://ecas2022.sciforum.net/](https://ecas2022.sciforum.net/)).  
Footnote †: Presented at the 5 th International Electronic Conference on Atmospheric Sciences, 16–31 July 2022; Available online: [[https://ecas2022.sciforum](https://ecas2022.sciforum)]([https://ecas2022.sciforum](https://ecas2022.sciforum)).  
Palsa mires are complex wetland ecosystems with permanently frozen peat components [7]. They are often situated in high latitude environments of Fennoscardia, Russia, Canada and Alaska and may have a significant influence on local and regional atmospheric processes.
## 6 Security Evaluation

Table 7: Reentracy attack result varying the number of addresses. Figure 4: Reentrancy attack model. Figure 5: Truffle console showing the processed transactions. From the tests performed, we realized that the contract developed is tamper-proof from Gas Limit DoS. Again, due to the simplicity of the smart contract, when a function is called multiple times, it is processed instantly, not allowing DoS attacks. However, we realized that the complexity of the algorithm directly influences this security issue. The complexity of the developed algorithm is classified as \(O(1)\), which makes the attack impossible. Table 8 presents the probability of a DoS attack occurring according to the complexity of the algorithm when the contract design is not done correctly. We used the Surya tool to generate a graphic representation of the contract developed and illustrate its complexity. Surya is a utility tool for smart contract systems that provides a number of visual outputs and information about the structure of the contracts. It also supports querying the function call graph for manual inspection of contracts. As shown in Figure 7, the contract functions do not interact with each other and cannot be called externally by other smart contracts. This approach reduces the complexity of contracts and avoids security issues. As the last experiment, we used the audit tool Mythril to assess the security of the developed smart contract. Mythril is a security analysis tool for Ethereum Virtual Machine bytecode. It detects security vulnerabilities in smart contracts built and uses symbolic execution and Satisfiability Modulo Theories (SMTs) solving and taint analysis to detect a variety of security vulnerabilities. This tool searches for pieces of code that could lead to security inconsistencies and can detect vulnerabilities in smart contracts for Ethereum and other platforms. Figure 8 presents a screenshot of the report generated by Mythril showing the result obtained. The report produced by the Mythril tool displays the security warning MythX SWC-103. A floating pragma is set, which is issued when we denote in the contract a different version of the pragma used in the compiler. The developed contract was built using version 0.6.4 (line 1 of Figure 3), while the Solidity compiler installed on the computer applied in the tests used version 0.6.7. This type of approach can be harmful as outdated versions of the pragma can generate bugs in the execution of the contract. To solve this problem, we need to change the contract to use the same version of the compiler installed on the machine. \begin{table}
\begin{tabular}{c c} \hline \hline
**Complexity** & **Probability of DoS** \\ \hline \(O(1)\) & Impossible \\ \(O(n)\) & High \\ \(O(n^{2})\) & Extremely high \\ \(O(2^{n})\) & Extremely high \\ \(O(n!)\) & Extremely high \\ \hline \hline \end{tabular}
\end{table} 
Table 8: DoS probability in relation to contract complexity. Figure 6: Gas Limit Denial of Service attack model.
## 4 Results
### Geological Cross-Section Survey  
The geological cross-section is located on the south slope of Taipingshan mountain (Figure 9a). In this cross-section, there are good outcrops along the mountain road and appropriate terrain for measuring geological cross-section. Data collection for the cross-section was conducted based on the Qmini A5 PDA. The orientation and distance of the transverse line were derived by the high-precision positioning system. The attitude of strata was obtained by the digital compass in the PDA. The cross-section data collected in the field were then inputted into the cross-section module in the DGSInfo to calculate the truth thickness of the strata and the apparent inclination. Based on the direct and derived parameters, the cross-section could be drawn on grid paper automatically (Figure 9b). A stratigraphic histogram could also be drawn based on the true thickness of the strata and the collected data in the field.  
Figure 8: A comparison of geological mapping results between professional geologists and the undergraduates. (**a**) the geological map created by professional geologists. (**b**) a primitive geological map in the fold development area of A created by undergraduates, (**c**) a primitive geological map in the fault development area of B created by undergraduates.  
Compared with the conventional method, the PDA-based geological section survey is efficient for data collection and producing cross-section maps. In terms of labor intensity, one undergraduate can use the PDA-based method to finish the data collection for a geological cross-section. The conventional method needs a team of 4-5 undergraduates to complete the same work. The time spent on this work is also powerful evidence to prove that the PDA-based method is more efficient. During field training, the undergraduates usually spend half of the day conducting the geological section survey. The PDA-based method, in comparison, only needs one and half hours. The automatic mapping technology for creating a cross-section map and stratigraphic histogram can also save a lot more time compared to the traditional hand drawing method.
# The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting
## 4 Comparison between Transformations
### Root Mean Squared Forecast Error (RMSFE)  
The most commonly adopted approach for comparing the predictive accuracy between forecasts is to compute and compare the RMSFE from out of sample forecasts. The RMSFE can be defined as  
\[RMSE_{h}=\sqrt{\frac{1}{h}\sum_{t=N+1}^{N+h}\left(x_{t}-\hat{x}_{t}\right)^{2}}, \tag{10}\]  
where \(h\) is the forecast horizon, N is the number of observations, \(x_{t}\) is observed value of time series, and \(\hat{x}_{t}\) is the forecasted value.  
The application of data transformation prior to forecasting with SSA may significantly affect the forecasting outcome and the affect may vary based on the properties of a time series. Thus, we need to examine the effect of data transformation on RMSFE along with the differing properties of time series. Comparisons between the RMSFE of the original and transformed time series can be used to learn about the forecasting performance of a model for a given time series. However, comparison of RMSFE for a pool of time series with different characteristics is not straightforward. We compute \(RMSE_{h}\) for \(h=1,3,6,12\) (\(h=1\) for a short-term forecast, \(h=12\) for a long-term forecast, and \(h=3,6\) as a medium-term forecasting horizon) for each of the time series in the pool and examine the effect of transformation by using statistical tests.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 6 System Design and Implementation  
To conduct experimental studies to determine the accuracy of the proposed algorithm, in this research, we built a measurement system that includes a BLE signal transmitter and a scanner device. The transmitter is based on the Arduino BLE 33 sense board (shown in Figure 6), which has a small form factor with an ARM cortex M4F microcontroller and an on-board nRF52480 Bluetooth module. The nRF52840 chipset on this version of the Arduino board supports Bluetooth v5.0 with up to 8 dBm transmission power and data rates of up to 2 Mbps. Using the readily available Arduino libraries, it is programmed to broadcast Bluetooth advertisement packets at a set interval with a specific transmission power and identifiers required for the scanner device to discover it. A smartphone with an inbuilt IMU sensor and a GPS receiver is selected as the scanner device, which also allows for the effortless development of a scanner application with an intuitive user interface.
# An Adaptive Throughput-First Packet Scheduling Algorithm for DPDK-Based Packet Processing Systems
## 4 Results  
In this section, we evaluate our proposed method with RR, the original HRW, and CRC32. The test server we use is an Intel server equipped with two 12-core Intel(R) Xeon(R) Silver 4116 processors running at 2.10 GHz. We use Ixia as the packet generator and the traffic is mirrored to our test server through specific ports on the switch by a 10 Gb/s fiber-based link. The DPDK version we use is 17.05.2. Since our proposed method depends on the Log cores to handle the issue of out-of-order, we assume that the processing capacity of the Log cores is not the bottleneck of the system throughput, which can be easily achieved by reserving enough cores.
## 3 Results and Discussion
### Overall Variability and Relationships Involving the Apparent Optical Properties
#### 3.2.2 Irradiance Reflectance
Although this may not seem surprising given the common use of the blue-to-green ratio of ocean surface reflectance (typically the remote-sensing reflectance rather than irradiance reflectance) for estimating surface \(Chl\) in the context of remote sensing applications, e.g., [103; 104], it is important to emphasize a distinct difference which is that our data represent irradiance reflectance and \(Chl\) measured at different discrete depths within the entire euphotic layer down to depths as high as \(\sim\)80 m.  
Figure 11: Irradiance reflectance \(R\) in the red spectral bands (650 and 683 nm as indicated) plotted as a function of depth \(z\) using data from all stations and depths. Solid lines represent the best-fit regression functions.
# Atlas of Defects within a Global Building Inspection System
## 2 Materials and Methods  
Considering the objective of creating a catalogue to ease matching building degradation phenomena with defects in a classification list and with a level of urgency of repair, a database was devised for reference during fieldwork. Previously developed components of the global building inspection system were used, namely the classification list of defects [11] and the detailed files of defects, where the urgency of repair was characterised.  
First, the structure of such a database was designed. The atlas of defects has several pages, each corresponding to a defect listed in the classification of defects of the global building inspection system. Each page is arranged as a table with columns corresponding to types of building elements/materials and rows to levels of urgency of repair. The number of columns varies according to the field of application specified in the detailed file of the defect (Figure 1). The levels of urgency of repair are also established in the files of defects (Figure 1), varying from 0 to 4, where level 0 is the most urgent and level 4 the least urgent. So, the table has five rows. In each cell of each page's table, an exemplifying photograph and a concise description are available, establishing the criteria to identify a defect in a type of building element/material with a specific level of urgency of repair.  
The levels of urgency of repair are defined as follows [11]:  
* 0: imminent danger, contingency measures needed;  
Figure 1: Excerpt of the detailed file of defect “A-B4 Blistering/bulging” showing the fields “field of application” and “urgency of repair”.  
* 1: need for immediate intervention;
* 2: need for intervention in the short-term;
* 3: need for intervention in the long-term;
* 4: no urgent need, assess in the next inspection.  
During fieldwork, the surveyor attributes a level of urgency of repair to each detected defect based on the application of algorithms. In other words, each level of urgency of repair corresponds to a set of conditions determined for each type of building defect according to the type of building element/material.  
With the structure of the atlas of defects laid down (Figure 2), all the pages were filled with graphical and descriptive contents. The photographic content of the atlas of defects was collected from the authors' research team, mainly from inspection campaigns, or taken specifically to fill the atlas of defects.
###### Abstract  
This study reports the comparative deviations in experimental viscosity, density and ultrasonic velocity of two new ethanol-based binary liquid mixtures (ethanol + 1-hexanol and ethanol + 1-octanol) at 303.15 K by applying various theoretical models ([PERSON] relation (\(\eta\)H), [PERSON] and Monroe relation (\(\eta\)K-M), [PERSON] relation (\(\eta\)B), [PERSON] relation (\(\eta\)AE), Croenauer-Rothrus Kermore relation (\(\eta\)CRS) and [PERSON] relation (\(\eta\)G)). Typically, the experimental densities are compared with theoretical methods like the [PERSON] model (\(\rho\)MNM), [PERSON] and Thomson model (\(\rho\)HT), [PERSON] and Gunn model (\(\rho\)YG) and [PERSON] et al. (\(\varrho\)R) model. Additionally, the experimental ultrasonic velocities are compared with various theoretical models like the [PERSON] relation (UN), [PERSON] and [PERSON] relation (UIMR), Impedance relation (UIR), [PERSON]'s specific velocity relation (UR) and Junjie relation (UJ). The average percentage of deviation (APD) is determined to identify the most suited model that can closely agree to the experimental values of the specified property (viscosity, density and ultrasonic velocity). From the APD values, it may be concluded that the \(\eta\)K-M model is the most suitable theoretical method for estimating the viscosity for the ethanol + 1-hexanol system, and the Gambrill model is the suitable method for estimating viscosity for ethanol + 1-octanol liquid systems. Similarly, the model of [PERSON] et al. and [PERSON]'s relation are the most suited theoretical models to predict the density and ultrasonic velocity of the binary liquid systems, respectively. Form the experimental data, various molecular interaction properties like adiabatic compressibility, intermolecular free length, free volume, internal pressure, and viscous relaxation time are analysed. The results of this study are expected to be useful in predicting the suitable molecular proportions that can be suited for industrial application (flavouring additive, insecticide, in the manufacture of antisepctics, perfumes for 1-hexanol based mixtures and flavouring, and as an antifoaming agent for 1-octanol based liquid mixtures).  
molecular binary mixtures; liquid; theoretical models; thermophysical properties; deviation +
Footnote †: journal: Applied Sciences
# Overview of the Development of 3D-Printing Concrete: A Review
## 1 Introduction  
Concrete material is a mixture made by mixing cementitious materials, water, coarse and fine aggregates in appropriate proportions. It is widely used in construction, water conservancy, bridges, highways, railways and urban infrastructure construction which is one of the important civil engineering materials. As the demand for concrete increases, the problems of high pollution and high energy consumption in production and application process have become increasingly prominent, restricting the green, healthy and sustainable development of concrete materials. At the same time, the increasingly complex concrete structures have put forward higher requirements on the strength and durability of concrete materials due to the environment and stress characteristics.  
For the past few years 3D-printing technology has been widely used in architectural design, industrial manufacturing, aerospace, biological engineering, cultural relics protection and other industries with its advantages of low cost, high efficiency, strong design, and reliable quality [1, 2]. Especially the 3D-printing technology combined with concrete technology that provides new ideas for the development and application of concrete materials. However, 3D-printing concrete technology has strict requirements in different performance domains, and further, deeper research is still needed.  
This article introduces the research status of 3D-printing concrete technology, summarizes the impact of concrete material properties, key preparation technologies, 3D-printing control parameters and other factors on 3D-printing concrete technology. Based on the current status and problems, looks forward to the research direction of 3D-printing concrete technology and development trends.
## 3 Data and Methods
### Methods  
The comprehensive AI detection workflow is depicted in Figure 3. Unlike the procedural framework presented in [PERSON] et al. [30], the seismic phase data used in training the RNN model were manually picked by experts. In contrast, the sample dataset in [PERSON] et al. [30] was obtained through detection by the PAL-picker. The AI model in this study integrates a hybrid CNN and RNN structure, an approach previously developed by [PERSON] et al. [14]. Within this combined CNN and RNN model, the CNN deep neural network is composed of eight convolutional layers, Rectified Linear Unit (ReLU) non-linear activation functions, Max Pooling layers, and fully connected layers. The forward propagation procedure is defined by a loss function based on the L1 norm, while the backward propagation utilizes the Adam optimizer. The RNN features two bidirectional Gated Recurrent Unit (GRU) layers, which process data both forward and backward in time. In the RNN, each layer's current state is influenced by both the input at the present time step and the hidden state from the preceding time step.  
Figure 2: Histograms of signal-to-noise ratio, epicentral distance, azimuth coverage, and travel time-to-epicentral distance relationship in the training dataset. (**a**,**b**) depict the distribution of signal-to-noise ratio for earthquake events and noise samples in the CNN training set. (**c**–**f**) represent histograms of the travel time-to-epicentral distance relationship, azimuth coverage, epicentral distance, and signal-to-noise ratio in the RNN training set.  
Phase association incorporates clustering analysis in both time and space domains [22]. Temporal association is achieved by searching for clusters of earthquake occurrence times. Spatial association is accomplished through grid searching for the hypocenter position with the minimum travel time residual. The phase association procedure ensures the detection of the same seismic signal by a minimum of four stations, thereby reducing the likelihood of misidentified signals. The magnitude estimation is determined by calculating the body wave magnitude using the S-wave amplitude. The earthquake localization utilizes Hypoinverse [7] for absolute location and HypoDD [10; 31] for relative relocation.