esa-sceva/satcom-chunk-collection huggingface dataset:
# Probabilistic Analysis as a Method for Ground Freezing Depth Estimation
## 2 Material and Methodology
### Measurements of Soil Temperature
The process of ground freezing consists of solar radiation, the advection of warm air masses, and heat penetrating from deeper layers of soil. The timing of freezing of the first layers of soil, the length of permafrost persistence and the maximum freezing depth are of great importance in construction, agriculture, and water management. At weather stations measuring ground temperature at depths of 5, 10, 20, 50, and 100 cm, isotherm \"0\" in the ground is calculated. Isotherm (ET) \"0\" is the plane connecting the points at 0 \({}^{\circ}\)C in the ground [19]. Depending on the course of the weather, there may be one or more \"0\" isotherms in the ground. On the basis of measured everyday values of soil temperature personnel of weather stations calculate the position of the zero configrade temperature. Following formula is used:  
\[Z=A+\frac{a\cdot\Delta T_{A}}{\Delta T} \tag{2}\]  
where: \(A\)--the depth of the lowest place of thermometer which measured negative temperature; \(a\)--the distance between depths of thermometers measuring negative and positive temperature; \(\Delta T_{A}\)--temperature at the level \(A\); \(\Delta T\)--the difference between temperature values at the levels with zero configrade temperature between them. An example is presented in Figure 1 below: \(A\) = 0.2 m, \(a\) = 0.5 \(-\) 0.2 = 0.3 m, \(\Delta T_{A}\) = \(-\)0.4 \({}^{\circ}\)C, \(\Delta\)T = \(-\)0.8 \({}^{\circ}\)C. Zero-degree temperature is at the depth of 0.35 m.  
The problem may occur when the negative soil temperature exceeds 1.0 m depth. In such a case it is recorded as below 1.0 m. The true value can be defined using extrapolation of values measured at smaller depths. An example is presented in Figure 2. However, it must be pointed out that such cases were not frequent. Figure 1: Example of temperature of the soil at the meteorological station in Bialystok for one day (15 March 2005) adapted from [19]. When determining the position of a zero isotherm by extrapolation, it should be taken into account that as the temperature rises in the upper layers of the ground at an unchanged value at the depth of the largest measurement, the slope of the straight regression decreases. The use of such a set may lead to an error in the determination of the extrapolated value.
# Feasibility of Storing Latent Heat with Liquid Crystals. Proof of Concept at Lab Scale
## 4 Conclusions  
In this paper we propose the use of thermotropic liquid crystals based on aromatic carboxylic acid derivatives with intermolecular H-bonds as phase change materials for latent heat storage. From the data found in the literature, liquid crystals based on alkoxy-derivatives of benzoic, biphenylbenzoic, cinnamic and naphtotic acids seem to be adequate in terms of clearing phase change temperature. In this way, we present the preliminary results of preparation and characterization of 4\({}^{\prime}\)-decyloxybiphenyl-4-carboxylic acid. This compound fulfills quite well the requirements  
Figure 10: Dynamic viscosity of 10-BPhCOOH for the mesophase (@ 225 \({}^{\circ}\)C) and the isotropic state (@ 270 \({}^{\circ}\)C).  
Figure 9: Thermogravimetric (TG) and differential (dTG) curves of 10-BPhCOOH in Ar atmosphere.  
for being used as latent storage medium for applications in which steam is involved. It has a mesophase/isotropic transition temperature in the range of 251 \({}^{\circ}\)C, high enough clearing enthalpy (55 kJ/kg), good heat capacity (\(\sim\)2.4 kJ/kg) and low dynamic viscosity (<0.6 Pas). Although more characterization in terms of long-term thermal stability is required, these preliminary results are very promising and further studies on other liquid crystals with carboxylic acid as functional group are underway.  
The authors would like to acknowledge the E. U. through the 7 th FP for the financial support of this work under the STAGE-STE Project (Ctr. Nr. 609837), the Community of Madrid for its support under the ALCCONES Project (S2013/MAE-2985) through the Program of R&D activities between research groups in Technologies 2013 and the Spanish government in the framework of the DETECSOL project (Ref. ENE2014-56079-R) both co-financed by ERDF funds, the Spanish Ministry of Economy and Competitiveness through the Project with ref. CTQ2014-52796-P and the Community of Castilla y Leon through the Project with ref. VA302U13.  
[PERSON] and [PERSON] conceived the concept, performed the literature review, designed characterization strategy and coordinated the work between institutions; [PERSON], [PERSON] and [PERSON] designed the synthetic route, prepared the liquid crystal and performed some characterization (preliminary DSC measurements, IR spectra, elemental analysis, 1 HNMR and polarized light microscopy); [PERSON] performed DSC scans and cycles and also TG analysis; [PERSON] performed viscosity measurements ; [PERSON] wrote the paper and the rest of authors contributed with corrections and comments.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} PCM & phase change material \\ LC & liquid crystal \\
10-BPhCOOH & 4\({}^{\prime}\)-\(n\)-decyloxybiphenyl-4-carboxylic acid \\ DSC & differential scanning calorimetry \\ TG & thermogravimetry \\ dTG & differential thermogravimetry \\ C\({}_{p}\) & specific heat capacity \\ \end{tabular}
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 5 Experiments
### Performance Evaluation
#### 5.1.1 Accuracy (A)  
As shown in Equation (1), accuracy is the ratio of truly predicted levels to total levels.  
\[A=\frac{TP+TN}{TP+TN+FP+FN}\times 100 \tag{1}\]
## 2 Hardware Composition
### Design of Filling Valve  
The conventional filling systems utilized instruments based on the theory of mechanism and fluid dynamics and were composed of the counter pressure process, filling valve opening process, filing process, filing valve closing process, and snifting process ([PERSON] 2016). However, the filling method using a flow meter measures the filling flow rate of the filling valve and converts the electric signal into pneumatic pressure, which should open/close the filling valve or remove the residual pressure, etc. Therefore, even if the flow rate is measured precisely, there will still be a difference between the desired filling amount and the actual filling amount due to the time delay in the operating parts powered by a pneumatic system. Therefore, it is necessary to develop a high precision pneumatic control filling valve that has a higher response speed. As shown in Figure 1, a filler that uses a flow meter measures the filling volume with the flow meter and uses the electric signal from the flow meter to close the filling valve. Therefore, it is not possible to use the conventional, mechanical type. For this reason, it is necessary to operate each filling mode electrically in accordance with a series of pre-determined steps. In theory, if the system is filling at a rate of 150 gr/sec and there is a 30 ms time delay in the operation time, there will be a 4.5 gr difference in the filling volume. In order to reduce this difference, the authors added a low-speed filling control valve when it is close to the target filling level, and performed low-speed filling to reduce the gap.  
Figure 1: Components of a high-speed carbonated soft drink flow rate filling system.
## 3 Construction of Carbon Steel Corrosion Prediction Model  
### Model Establishment  
The existing carbon steel corrosion prediction model evolved based on semi-empirical models, as shown in Equation (4) [28].  
\[logRc=-7.545-\frac{3359.5}{T+273}-2.4622 log(P_{\text{CO}_{2}})+5.9977(7-pH_{ \text{CO}_{2}}) \tag{4}\]  
where \(Rc\) is the corrosion rate obtained by model.  
The shortcoming of this model is that it predicts a fixed corrosion rate, assuming that the corrosion depth is a linear function of time. However, the corrosion morphology continuously evolves throughout the corrosion process, leading to changes in the corresponding corrosion rate. Experiments and simulations demonstrate the impact of the medium flow rate on the pipeline, which cannot be ignored when evaluating the corrosion rate. Therefore, considering time and flow rate, we propose a new corrosion prediction parametric model, as shown in Equation (5).  
\[h=\left\{\begin{array}{l}At^{b1}\\ ln(A)=b_{2}+\frac{b_{2}}{Tem+273.15}+b_{4}ln(P_{\text{CO}_{2}})+b_{5}(7-pH_{ \text{CO}_{2}})+b_{6}ln(V_{l})\end{array}\right. \tag{5}\]  
where \(h\) (mm) is the remaining thickness of the tube wall, and \(b_{1}\) to \(b_{6}\) are the undetermined coefficients.  
The sampling method of the face-centered cubic design cannot be used to simulate the segmental phenomenon caused by temperature adequately. Therefore, only the one-stage model was fitted, which verified the feasibility of the constructed model. These results laid the foundation for developing a more precise segmental corrosion prediction model for carbon steel pipelines. Based on the experimental corrosion data listed in Table 2, the least squares method was used to obtain the undetermined coefficients, as shown in Equation (6).  
\[h=\left\{\begin{array}{l}At^{0.14761}\\ A=e^{(-5.6126-\frac{472.9510}{tem+273.15}+0.096 ln(P_{\text{CO}_{2}})-0.5827(7-pH_ {\text{CO}_{2}})-0.0408 ln(V_{l}))}\end{array}\right. \tag{6}\]  
Figure 8: Weight loss record under 25 different working conditions.  
The determination coefficient, \(R^{2}\), of the fitted model is 0.9573, close to \"1\", thus indicating the model's is accuracy. The carbon steel uniform corrosion depth and error curve calculated by this model are shown in Figure 9.  
Figure 9a shows that the experimental and simulation values are similar in terms of variation trend and value. In addition, the prediction error curve in Figure 9b shows minimal variation, fluctuating around \"0\", with a maximum error of 0.0006. This result confirms the feasibility of the proposed method and accuracy of the fitted model.
# On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
## 2 Proposed System Model and Techniques
### SI Cancellation Techniques
#### 2.2.1 Analog/RF SI Cancellation Techniques
In signal processing terms, a generic form of the Kautz filter function as per Figure 7 can be described by  
\[H(z)=\sum_{i=0}^{N}w_{i}G_{i}(z) \tag{39}\]  
where,  
\[G_{i}(z)=\frac{\sqrt{1-z_{i}z_{i}^{*}}}{z^{-1}-z_{i}^{*}}\prod_{j=0}^{i}\frac{z^ {-1}-z_{j}^{*}}{1-z_{j}z^{-1}}\]  
Here, \(i=\{0,1,\ldots,N\}\) and \(G_{i}(z)\) is determined by the chosen optimal set of poles \(\{z_{j}\}_{j}^{N}=0\) such that \(\left|z_{j}\right|<1\). The following assumptions are made for modeling a real response: (i) poles should be real or (ii) poles occur in complex conjugate pairs. Normalization terms of the Kautz filter are determined by the corresponding optimal pole pair \(\{z_{j},z_{j}^{*}\}\) and are described as:  
\[p_{i}=\sqrt{\frac{(1-p_{i})(1+p_{i}-r_{i})}{2}}, \tag{40}\]  
\[e_{m}(t,u)=e_{m-1}(t,u)+h(t,u)*[w_{m}(t,u)b_{m}(t,u)] \tag{34}\]0  
where \(r_{i}=-2 Re\{z_{i}\}\)_and_\(p_{i}=\left|z_{i}\right|^{2}\)  
\[e_{m}(t,u)=e_{m-1}(t,u)+h(t,u)*[w_{m}(t,u)b_{m}(t,u)] \tag{34}\]1 Analog/RF SI cancellation using the Kautz filter. Here, pole adaption is carried out using the BU method. This method is used because of its capability of producing stable and accurate poles, and this method operates in the time domain for signal processing.
## 3 Materials and Methods
### Data Preprocessing  
Figure 1 shows the overview of data preprocessing and feature extraction. We used the NeuroKit2 package to extract R-peaks from the raw ECG recordings and generate RRI sequences [44]. The NeuroKit2 package uses in-house algorithms for noise reduction and R-peak detection [45]. Electrocardiogram signals are often contaminated with different types of noise, including baseline wandering, power line interference, and instrumentation noise [46]. Therefore, noise reduction is required before performing R-peak detection to prevent false detection. Noise in the raw ECG signals was reduced using a high-pass filter (Butterworth, 5 th order, 0.5 Hz), followed by a powerline notch filter [45]. The QRS complexes are detected based on the steepness of the absolute gradient of the ECG signal [45]. R-peaks were detected as local maxima in the QRS complexes, which indicate ventricular depolarization and are the most prominent wave components in ECG signals measured using the lead II configuration [45]. The RRI sequences were generated by calculating the time differences between the R-peaks. Subsequently, RRIs were interpolated to 4 Hz.  
We applied a non-overlapping sliding window to the entire length of the RRI data obtained from each subject to generate 5 min RRI sequences. A total of 3606 5 min RRI samples were generated from 1093 subjects. The median number of samples extracted from each subject was 3 (1-7 samples from each subject). Table 1 lists the number of RRI samples acquired from each age group. Figure 2 shows a sample RRI sequence extracted from a subject in age group 1.  
Figure 1: Overview of data preprocessing and feature extraction.  
Figure 2: Sample 5 min RRI sequence from a subject from age group 1.
## 4 Results Discussion  
### The Sensitivity of Thermal Prediction to Absorptivity  
The temperature field distribution of the melt pool is a very important parameter, and the SLM process mechanism can be further explored and understood by studying the evolution law of the temperature field to provide help for optimizing the process and improving the forming quality. Compared with traditional numerical simulation and experimental research, the analytical method consumes less resources, and can obtain the temperature field distribution of the SLM process at a faster speed, and then obtain the optimized process parameters. The absorptivity for IN718 has been reported as constant by some researchers, which varies from 0.3 to 0.87. It can be found from the description in Section 2.2 that the absorptivity is temperature-dependent. In order to illustrate the influence of absorptivity value on the analysis results, analytical calculations were carried out and the results are shown in Figure 3. Here, \(A\) = 0.3 (the minimum value can be found in literature), \(A\) = 0.87 (the maximum value can be found in literature), \(A\) = 0.5 (the medium value can be found in literature), \(A\) (calculation by Equations (5) and (6) at room temperature).  
From Figures 3 and A1, Figures A2-A4 in Appendix A, we can find that the geometry of melt pool is significantly affected by absorptivity. When A is 0.3, the width, length and depth of the molten pool are 145 \(\upmu\)m, 375 \(\upmu\)m, and 130 \(\upmu\)m, respectively. However, when A is 0.87, the width, length and depth of the molten pool are 200 \(\upmu\)m, 1150 \(\upmu\)m and 265 \(\upmu\)m, respectively. Therefore, we can see that the higher the absorptivity, the larger the size of the molten pool can be obtained. It can be seen from Equation (2) that the width of the molten pool is proportional to the square root of the absorbed energy and it is also proportional to the square root of absorptivity. So the absorptivity cannot be considered as constant. As the composition of IN718 material is more complicated, the IN718 powder produced by different manufacturers has certain differences, so the absorptivity should also be different. It is recommended that when using analytical methods to predict the temperature field distribution of the melt pool, the measurement method proposed by [PERSON] [17] be used to measure the electrical resistivity of the raw materials used in AM processing. Then, absorptivity can be computed by Equation (5), thus the prediction of temperature field distribution of melt pool can be obtained by Equation (4).
## 7 Conclusions
**Data Availability Statement:** The datasets presented in this article are not readily available because the Brazilian government classifies the data as confidential since it is official government data. Access to the data was granted through a technical cooperation agreement between the University of Brasilia and the Attorney General's Office, a Brazilian public agency. The development team of this work signed a confidentiality agreement. **Acknowledgments:** The authors would like to thank the technical and computational support provided by the LATITUDE Laboratory of the University of Brasilia, TED 01/2019 of the Attorney General's Office (Grant AGU 697.935/2019), TED 01/2021 of the National Secretariat for Social Assistance--SNAS/DGSUAS/CGRS, TED 01/2021 of the General Coordination of Information Technology (CGTI) of the Attorney General's Office of the National Treasury--PGFN, and the SISTER City Project--Secure and Real-Time Intelligent Systems for Smart Cities (Grant 625/2022), the Project \"Control and Unification System for the Federal District Government--Sispro-DF\" (Grant 497/2023), the Dean's Office for Research and Innovation--DPI/UnB, and FAP/DF. **Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. **Abbreviations**  
The following abbreviations are used in this manuscript:  
U.S. United States of America  
AMLC Anti-Money Laundering Council  
IFRS International Financial Reporting Standards  
RDF Resource Description Framework  
OWL Web Ontology Language  
OTK On-To-Knowledge  
ASD Application Field and Scope Definition  
DCI Data Cluster Identification  
DKC Domain Knowledge Capture  
DKD Domain Knowledge Documentation  
ORR Existing Ontology Reusability Review  
EOA Existing Ontology Alignment  
OCO Ontology Construction  
SUS Sistema Unico de Saude  
TSE Tribunal Superior Eleitoral  
SENATRAN National Traffic Secretary  
CNH Cadastro Nacional de Condotores Habititados  
UIDAI Unique Identification Authority of India  
ANS National Supplementary Health Agency  
SISU Unified Selection System  
INEP National Institute for Educational Studies and Research  
CNPq National Council for Scientific and Technological Development  
CPF Cadastro de Pessoas Fisicas  
CAGED General Register of Employed and Unemployed  
RAIS Annual Social Information Report  
HMRC HM Revenue and Customs
# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 2 Methods  
The overall process is illustrated in Figure 1. Given an input RGB-D stream \(\{F_{t},t=1...N\}\), along with a segmentation mask for the object of interest provided only in the first frame, our method tracks the 6-DoF pose of the object across subsequent frames and reconstructs a textured 3D model, even under conditions of severe occlusion. Our approach employs a neural object field, like BundleSDF [12], to represent the reconstructed object and, importantly, does not require category-level priors, such as instance-level CAD models of the object.
## 6 Simulation Results
### Validaton of the MASSIVE Test-Bed  
#### 6.1.1 Validation of MASSIVE Waveforms  
One of the requirements to validate the implementation was the receiver performance in terms of packet loss ratio (PLR) as a function of the carrier-to-noise ratio (C/N) in the additive white Gaussian noise (AWGN) channel. To this end, the PLR vs. C/N curves obtained by using the actual receiver, where all the estimation algorithms were enabled, were compared with the theoretical curves, where ideal perfect estimators were assumed. The resulting implementation loss was fixed to 1 dB by requirement in the frame of MASSIVE. This was fully achieved, as shown in Figure 12 for waveforms identified by TFI#4 and TFI#17.
# Structural Health Monitoring for Prefabricated Building Envelope under Stress Tests
## 3 Results and Test Analysis
### Modal Analysis (Pre/Post)  
Two modal analyses were conducted to assess any changes in structure that might have occurred due to the conformance tests explained in Appendix A. The procedure for the evaluation of the structure was applied to the central module of the wall facade, and the geometry was configured based on the dimension of the module of interest: 54 nodes were created, as shown in Figure 15.  
An instrumental hammer was used for the excitation of the surface, and the dynamic response at the nodes reported in Figure 15 was measured with monoaxial accelerometers installed for measuring in Z, moved all over the nodes. The force exerted by the hammer and the acceleration were collected by means of a SIEMENS acquisition system (SCADAS). At each node, the frequency response function was calculated as a response over force ratio. The sum of all the FRFs acquired is reported in Figure 16.  
A modal analysis was performed on the FRF dataset collected to estimate the dynamic parameters of the structure under test (natural frequencies, damping loss factors, and mode shapes). A comparison between those parameters was estimated in the modal tests performed before and after the stress tests were performed. To compare mode shapes, the Modal Assurance Criterion (MAC) was used; to compare the variation in the natural frequencies, the Natural Frequency Difference (NFD) was estimated. The MAC and the SFD are reported in Figures 17 and 18, respectively.  
Figure 15: Modal analysis geometry consists of 54 nodes representing the modulus of the facade used for performing modal analysis.  
Two modes were selected to have an example of the structure movements at selected frequencies, as reported in Figure 19.  
Figure 16: FRF sum comparison between pre- and post-compliance tests.  
Figure 17: MAC (Modal Assurance Criterion) between the two performed modal analysis.  
Figure 18: NFD (Natural Frequency Difference) between the two performed modal analysis.  
The natural frequencies of all the mode shapes identified with the modal analysis performed on the FRF dataset acquired on the structure before and after the compliance tests are reported in Table 3.
# Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications
## 4 Experiments
### Results  
The experiments were conducted on the Tensorflow framework. First, we evaluate the impact of segment length on the resulting PRD and the computational complexity and determine the suitable segment length to our proposed model. Next, we try to increase the CR with a tolerable PRD. Third, we consider fixed-point and communication link quality issues for practical implementation.