esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Chromosomal Localization and Gene Duplication  
The complete and partial/pseudo _RcTPSs_ and _RcTPTs_, along with nine _RcNUDX1_ genes were mapped to the seven chromosomes of the _R. chinensis_ (Figure 3). RcChr5 contained the largest number of TPT and TPS genes, including 18 _RcTPSs_, 10 _RcTPS-p_ genes,  
\begin{table}
\begin{tabular}{l l l c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Id**} & \multirow{2}{*}{**Chr**} & \multirow{2}{*}{**Amino**} & \multirow{2}{*}{**Exon**} & \multicolumn{2}{c}{**Localization**} & \multirow{2}{*}{**Conserved Motif**} \\  & & & & & & **Acid** & **Number** & **TargetP** & **LOCALIZER** \\ \hline \multirow{8}{*}{SC 1} & _RcGCPPS1_ & RchiOHHmChr2g0102671 & 2 & 363 & 1 & C 3 & C & DDX\({}_{(2-a)}\)D, DDXXD, CXXXC \\  & _RcGCPPS2_ & RchiOHHmChr2g0493601 & 3 & 360 & 1 & C & C & DDX\({}_{(2-a)}\)D, DDXXXD, CXXXC \\  & _RcSSUll_ & RchiOHHmChr2g0279181 & 6 & 329 & 2 & C & C & DDX\({}_{(2-b)}\)D, DDXX, CXXXC, CXXXC \\  & _RcGPDS_ & RchiOHHmChr5g0014811 & 5 & 426 & 12 & M 4 & M & DDX\({}_{(2-d)}\)D, DDXXD \\  & _RcFPS1_ & RchiOHHmChr5g000321 & 5 & 342 & 12 & / & / & DDX\({}_{(2-a)}\)D, DDXXD \\  & _RcFPS2_ & RchiOHHmChr5g0075621 & 5 & 342 & 11 & / & / & DDX\({}_{(2-a)}\)D, DDXXD \\ LC 2 & _RcSPPS_ & RchiOHHmChr5g0028851 & 5 & 421 & 6 & / & C & DDX\({}_{(2-d)}\)D, DDXXD \\ \hline \hline \end{tabular}  
* SC: short-chain; 2 LC: long-chain; 3 C: Chloroplast; 4 M: Mitochondria.  
\end{table}
Table 1: Members of full-length _RcTPT_ genes and their sequence characteristics.  
\begin{table}
\begin{tabular}{l l c c c|c c c c} \hline \hline \multicolumn{1}{c}{**Name**} & \multirow{2}{*}{**Id**} & \multirow{2}{*}{**Chr**} & \multirow{2}{*}{**Sub-**} & \multirow{2}{*}{**Amino**} & \multirow{2}{*}{**Lacid**} & \multirow{2}{*}{**Chr**} & \multirow{2}{*}{**Sub-**} & \multirow{2}{*}{**Amino**} \\  & & & & & **Family** & & **Acid** & & **Family** & **Acid** \\ \hline _RcTPS1_ & RchiOHHmChr1g0313881 & 1 & a & 562 & _RcTPS24_ & RchiOHHmChr5g023471 & 5 & e/f & 799 \\ _RcTPS2_ & RchiOHHmChr1g032651 & 1 & a & 581 & _RcTPS25_ & RchiOHHmChr5g0234611 & 5 & e/f & 724 \\ _RcTPS3_ & RchiOHHmChr1g0326061 & 1 & a & 580 & _RcTPS26_ & RchiOHHmChr5g036921 & 5 & a & 561 \\ _RcTPS4_ & RchiOHHmChr1g0326071 & 1 & a & 546 & _RcTPS27_ & RchiOHHmChr5g037011 & 5 & a & 555 \\ _RcTPS5_ & RchiOHHmChr1g0326521 & 1 & a & 556 & _RcTPS28_ & RchiOHHmChr5g034 _RcTPTS_ and 6 _RcTPT-p_ genes, which suggest the multiple duplication and recombination events on this chromosome [44]. Most TPS-p genes were distributed near the putative full-length TPS genes. Six tandemly duplicated TPS genes were present in the _R. chinensis_ genome, which occurred in the TPS-a, -b, and -g subfamilies, forming five gene clusters. Some of the _RcTPS_ genes were localized in the vicinity of RcTPT genes, which indicated that some _RcTPS_ and _RcTPT_ genes probably evolved together through genomic duplication [30]. Additionally, no segmentally duplicated _RcTPS_ or _RcTPT_ genes were detected in the _R. chinensis_ genome.
# Mechanical and Acoustic Emission (AE) Characteristics of Rocks under Biaxial Confinements
## 3 Results and Discussion
### AE Characteristics
#### 3.4.2 AF-RA Distribution  
The AF-RA (where AF is the average frequency of AE waveform) density relationships of granite, marble, and sandstone are shown in Figure 10, in which the red area has higher data density, and the blue or purple area is with lower data density. The data distribution changes from sparse to dense with the color changing from blue-purple to red. It was proved that tension cracks generated during loading with a long side along with AF and a short side along with RA, while shear cracks with a short side along with AF and a long side along with RA [37].  
It can be known from the figures that the high-density area gradually converges to the AF axis having low RA value and a large range of AF value with the increase of \(\sigma_{2}\), which indicates that the greater the \(\sigma_{2}\) is, the more the tensile cracks generate in rock specimens during biaxial loading condition. The range of AF value in the red area of granite is almost 50 and 100 kHz more than that of marble and sandstone, respectively, which indicates that the tensile failure degree of granite is the highest while that of sandstone is the lowest.  
The behavior of two typical AE parameters with time for the different types of rock is depicted in Figure 11. The RA values of granite, marble, and sandstone specimens mainly maintain at a low level of 0-100 ms/V during loading and show a strong increase before specimen failure, which indicates that few shear cracks in the rock are formed before the failure stage, but the number of shear cracks increases obviously during the failure stage. Moreover, the AF is distributed in the whole range of 0-450 KHz and the density gets higher as the loading going on, which demonstrates that the tension cracks in the rock are generated during the entire loading process, but the rate is increasing with the increase of  
Figure 10: AF-RA dot density diagrams: (**a**–**c**) granite; (**d**–**f**) marble; (**g**–**i**) sandstone.
## 3 Results
### Phase 1--GNSS and Behavioural Recordings of Alpacas and Ewes
#### 3.1.4 Alpaca Behavioural Results  
During behavioural recordings each of the alpacas spent time 'out of sight' due to the layout of the field and the position of the observer; however, for 92% of the observation time they were within sight of the observer.  
When examining if the two alpacas differed in time spent exhibiting state behaviours compared to each other, the only difference found was in relation to sleeping behaviour. There was a significant difference in the time spent sleeping between the two alpacas during the 09:00 h time period (\(p<0.05\)), with one alpaca sleeping for 7% of their time during this time period compared to the second alpaca  
Figure 5: Livestock residency index maps for ewes and alpacas from 22/10/17 to 31/10/17. ‘Day’ was considered to be 06:00–19:00 h while ‘Night’ was 20:00–05:00 h. (**a**) ewe day; (**b**) alpacas day; (**c**) ewe night; (**d**) alpacas night. Dark blue grids indicate areas of heaviest stock density residency and green represents areas of lowest stock density residency.  
at 2%. Overall, there were no other differences found between the alpacas in relation to their state behaviours exhibited during the different time periods (\(p>0.05\) for all state behaviours).  
When examining differences in specific state behaviours for the different time periods, we found that the alpacas spent a significantly longer time grazing during the later time periods of the day compared to the earlier time periods (Table 3), with significant differences in grazing patterns occurring between 07:00 and 18:00 h (\(p<0.01\)), 09:00 and 18:00 h (\(p<0.05\)) and a trend between 07:00 and 16:00 h (\(p=0.07\)). For resting behaviour the alpacas spent a significantly longer time resting during the earlier time periods of the day compared to the later time periods (Table 3), with significant differences in resting patterns occurring between 07:00 and 18:00 h (\(p<0.01\)) and 09:00 and 18:00 h (\(p<0.05\)) and a trend present between 07:00 and 15:00 h (\(p=0.07\)). The alpacas also spent a significantly longer time standing at 09:00 h compared to 18:00 h (\(p<0.05\)) and a trend towards longer standing time at 09:00 h compared 15:00 h (\(p=0.05\)). There were no significant differences between time periods in relation to time spent alert, walking and sleeping.  
When examining event behaviours exhibited by the alpacas, the most frequent behaviour was scratching (Table 3). One alpaca was observed to scratch more frequently than the other (226 vs. 446 events, \(\chi^{2}(1)=72.02\), \(p<0.0001\)). When investigating alpaca's response to newborn lambs, both alpacas demonstrated a significantly greater number of sniffing events towards lambs than towards ewes (alpaca 1: 117 vs. 16 events, \(\chi^{2}(1)=76.7\), \(p<0.0001\); alpaca 2: 71 vs. 14 events, \(\chi^{2}(1)=38.2\)\(p<0.0001\)). The average frequency of all event behaviours are presented in Table 3.
# Envisaging a European Digital Building Renovation Logbook: Proposal of a Data Model
## 3 Results
### Technical Systems Information  
This category, like the previous one, is highly relevant in the case of renovation since the replacement of the buildings' technical systems is one of the main lines of action to improve the energy efficiency of buildings. The category is made up of five entities where domestic hot water (DHW), heating, ventilation, and air conditioning (HVAC), and lighting systems are described (Figure 10). Attributes are described in Appendix A.6.  
Figure 9: Construction and materials category of the proposed conceptual relational data model for the DBrL  
* In \"DHWSystem\", \"HeatingSystem\", \"CoolingSystem\", and \"MechanicalVentilation\" entities, the information that describes the technical system of the building or building units is stored. This information include description, age, energy vector and consumption, efficiency, energy label, and information on inspections. It also includes the unequivocal identification of the building or building part that is served by the system.  
Figure 10: Technical systems category of the proposed conceptual relational data model for the DBrL.  
* \"LightingSystem\" collects the description and energy consumption of the lighting system installed. Its main purpose is to identify the type of lighting elements being used, with the aim of their eventual replacement by more efficient ones.  
\"DHWSystem\", \"HeatingSystem\", \"CoolingSystem\", and \"MechanicalVentilation\" may have a double linkage with \"Building\" and \"BuildingUnit\" tables from the \"core\" category by means of 1:N relationships, where each building or unit can be linked to several systems. The reason for making this double connection, which can be used or not, with \"Building\" and \"BuildingUnit\" lies in the need to consider both individual and centralised systems. Centralised systems are very common in the cluster of multi-family buildings from the last third of the 20 th century, a cluster that is on the focus of renovation due to its poor energy performance [52], whereas individual systems are the most common today due to their higher efficiency.  
When a technical system is installed to serve common parts of the building, the system needs to be assigned to common areas, treating them as building units.  
In the case of the lighting system, only the connection to \"BuildingUnit\" is established, by means of a 1:N relationship.
## 2 System Model and Complexity Analysis
### Linear Block Code Representation of the Multi-Way Relaying System  
Based on [12], this scheme can be represented by a \((K+L,K)\) binary linear block code with a systematic generator matrix:  
\[\mathbf{G}_{sys}=\begin{bmatrix}\mathbf{I}_{K\times K}&\mathbf{P}_{K\times L} \end{bmatrix}=\begin{bmatrix}1&0&\cdots&0&p_{1,1}&p_{1,2}&\cdots&p_{1,L}\\ 0&1&\cdots&0&p_{2,1}&p_{2,2}&\cdots&p_{2,L}\\ \vdots&\vdots&\ddots&\vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&\ldots&1&p_{K,1}&p_{K,2}&\ldots&p_{K,L}\end{bmatrix} \tag{7}\]  
where the identity matrix \(\mathbf{I}_{K\times K}\) corresponds to the first \(K\) phases involving user-to-user transmissions, and and the parity check submatrix \(\mathbf{P}_{K\times L}\) corresponds to the subsequent \(L\) phases involving network-coded transmissions from relays. The element at the \(i\)th row and \(j\)th column of \(\mathbf{P}\) is denoted by \(P_{i,j}\), which can assume the values 0 or 1. The elements 1 on the \(j\)th column of \(\mathbf{P}\) correspond to the terminals in the relevant group of \(R_{j}\) ( i.e., \(T_{i}\) is in the relevant group of \(R_{j}\) if \(P_{i,j}=1\)).  
The codeword of one transmission time slot is denoted by \(\mathbf{x}=(x_{1},x_{2},\ldots,x_{K+L})\), and the information bits from the terminals are denoted by \(\mathbf{m}_{T}=(m_{1},m_{2},\ldots,m_{K})\). With all operations performed in the binary field \(\mathbb{GF}(2)\), the vectors \(\mathbf{x}\) and \(\mathbf{m}_{T}\) are related by the following:  
\[\mathbf{x}=\mathbf{m}_{T}\mathbf{G}_{sys}=(\mathbf{m}_{T},\mathbf{m}_{T} \mathbf{P}) \tag{8}\]  
Therefore, for \(1\leq j\leq K\), \(x_{j}=m_{j}\). For \(K+1\leq j\leq K+L\),  
\[x_{j}=\sum_{i=1}^{K}P_{i,j}m_{i}. \tag{9}\]
# Influence of Frost Growth and Migration in Cryogenic Heat Exchanger on Air Refrigerator
###### Abstract  
Frost formation degrades the performance of heat exchangers greatly, thus influencing the cryogenic refrigerator. Different from frost formation on the evaporator surface, the growth and migration of frost layer inside the heat exchanger is of low temperature and humidity. In addition to the constantly changing boundary conditions, the effective prediction is difficult. In the present study, a numerical model was proposed to analyze the frost formation in the cryogenic heat exchanger of a reverse Brayton air refrigerator. Under small amounts of moisture, the growing of frost layer was simulated through the numerical heat and mass transfer by adopting semiempirical correlations. The frost formation model was inserted into the transient model of refrigerator, and numerical calculations were performed on heat and mass transfer rates, and growth and migration of frost layers in forced convection conditions. Experiments were conducted under different air humidity to investigate the frost formation and verify the numerical model. Through the model, the influences of frosting on the refrigerator were evaluated under different moisture contents and running time. It can be used to predict the performance of air refrigerators with low humidity and provide a basis for improving the system operation and efficiency.  
air refrigerator; cryogenic heat exchanger; frosting; simulation; experiment; cooling performance +
Footnote †: journal: Applied Physics Letters
## 5 Discussion  
### Data Accessibility  
The multi-source data acquisition for this article is easy. The required data is detailed below. In the study, the use of remote sensing images with low cloud noise and close time before earthquakes is something that should be considered. DSM data can be extracted by remote sensing technology using high spatial resolution satellites and UAV. POI data exist in software such as Baidu Map and AMap. Population data are generally available from local government statistics. Post-earthquake images are selected from UAV with high timeliness. Hence, the data involved in the proposed method are relatively easy to obtain. The data can be applied to most of the geographical areas, unlike the other methods, which require precise data from manual research.
## 2 INS/GNSS Concepts
### Extended Kalman Filter (EKF)  
The Extended Kalman Filter (EKF) is a widely used algorithm for state estimation in nonlinear systems. It extends the traditional Kalman filter (KF), which is designed for linear systems, by linearizing around the current estimate to handle the nonlinearity. The following subsections presents the EKF equations for both direct and indirect navigation modes.
###### Abstract
However, during an earthquake the abutments would move together with the superstructure, which induces a complex soil-structure interaction between the abutment-backfill and the pile-foundation [30; 31], and plastic hinges tend to form at the top of the abutment piles under severe seismic events [32; 33]. Some researchers have also studied the possibility to put EPS Geofoam between the abutment walls and the backfill, in order to avoid the complex soil-structure interaction during earthquakes [24]. Currently, seismic design and analyses of integral abutment bridges only involve the far-field non-pulse type ground motions [21; 22; 30; 31], while the large amplitude and long-period velocity pulses of the near-fault pulse type ground motions might have quite different influences on bridge seismic response, especially considering the complex soil-structure interaction. Therefore, this paper presents a study on the nonlinear seismic response of skewed integral abutment bridges (SIABs) under near-fault pulse and far-field non-pulse type ground motions, with parametric studies on bridge skew angles and the compactness of abutment backfill. Three-dimensional numerical models of SIABs were built using the analysis and design software SAP2000 [34], with nonlinear soil springs around the piles and behind the abutments. Three near-fault pulse ground motion records were selected based on the bridge site condition, and three corresponding far-field non-pulse artificial ground motions were fitted by the response spectra of the near-fault records. Nonlinear time-history analyses were conducted on the SIABs model, and a seismic response was obtained including abutment and deck displacement, soil pressure behind the abutment, deflection and internal forces of the piles, and internal forces of the main girder, etc. This study could provide a reference for future studies on the seismic behavior and design of SIABs in near-fault regions.
## 4 UVSQ-SAT First Observations  
Since February 2021, UVSQ-SAT has been measuring, among other things, solar radiation reflected from the Earth and OLR. UVSQ-SAT does not have an active attitude control system. Therefore, it is equipped with several photodiodes and ERS sensors on all sides to perform scientific measurements. Six photodiodes allow to measure both the TSI and the OSR. The albedo is obtained from these measurements considering the TSI as known and measured with other space-based instruments. Twelve ERS sensors allow to measure the TSI, the OSR and the OLR with a wide FOV in a complete hemisphere. The whole absorbed radiations generate temperature gradients on the ERS thermopile which delivers a voltage proportional to the absorbed flux (Equation (1)). Six of the twelve ERS thermopiles are coated with carbon nanotubes in order to absorb all incident radiation from short to long wavelengths (absorption very close to 1). The BRDF of the carbon nanotubes coating was measured with a goniophotometer. The optical coating shows a weak angular dependence. During eclipses, these detectors measure only the OLR. Out of eclipses, the carbon nanotube-based thermopiles measure the TSI, the OSR, and the OLR. There are also six ERS thermopiles, which are coated with optical solar reflectors to mainly measure the OLR.
## 4 Applications of ML-Based Routing Algorithms in WSNs
### Smart Home  
A smart home is based on intelligent living systems that can help people improve their quality of life. Sensors are embedded in home appliances and furniture and connected to the Internet through wireless networks. The sensors deployed in the room can sense the humidity, temperature, light, air composition, and other data, so as to automatically control the doors, windows, air conditioners, and other home appliances, providing people with an intelligent and comfortable living environment. The use of remote monitoring systems can achieve remote control of home appliances. Meanwhile, WSN can monitor the potentially dangerous behavior of infants and elderly people living alone and provide early warnings to prevent tragedies.  
The home healthcare scheduling and routing problem (HHCSRP) was explored [81], and an offline learning method was proposed that can process all the information needed to make decisions for HHCSRP. Considering that neural networks have been widely used in the complex information processing of decision-making algorithms, shallow neural networks was adopted to approximate the action value function of offline model-free RL algorithms to develop a more intelligent routing decision algorithm and solve the home care routing problem. The experimental results show that the neural network approximator enables the agent to make effective routing decisions based on the complete information of the environment, reducing unnecessary energy loss.
## 5 Case Study
### Definition of Damage Criterion  
Four potential damage states were considered, ranging from minor (DS1) to moderate (DS2), extensive (DS3), and complete (DS4). This study closely followedthe proposal ofLagomarsinoandGiovinazzi [11]. Minor damage occurs when 70% of the spectrum exceeds the displacement at the yield point (i.e., 0.70 Sdy); the threshold for moderate damage, which is higher than that for extensive damage, is defined as (0.75 Sdy + 0.25 Sdu). The threshold for extensive damage is defined as the average between the spectral yield point displacement and ultimate displacement (i.e., (Sdy + Sdu)/2), and it is considered complete when the ultimate displacement is exceeded (i.e., Sdu) [50].
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 3 Proposed TL-Based UWA OFDM Receiver
### Pre-Trained Model
#### 3.1.2 Pre-Trained Model Architecture  
The pre-trained model extracts features from received signal data using a structured input of \([1,Nt\times 2,1]\), where \(Nt=23\) represents the features in the received signal, consisting of real and imaginary components. Feature extraction is performed through convolutional layers: _conv1_ (kernel size \(1\times 5\), \(8\) filters, stride \(10\)) captures fundamental patterns, followed by _conv2_ (kernel size \(1\times 5\), \(16\) filters), _conv3_ (kernel size \(1\times 3\), \(16\) filters), _conv4_ (kernel size \(1\times 3\), \(32\) filters), and _conv5_ (kernel size \(1\times 3\), \(32\) filters), progressively refining features with smaller kernels and more filters. Each layer uses ReLU activation. Fully connected layers (fc1 to fc4) map extracted features to higher dimensions, with fc1 (64 units) using L2 regularization. fc2 and fc3 (32 units each) refine the learned representations, and fc4 outputs two class predictions.  
Figure 4: The process of data collection.  
Figure 5: The process of data concatenation.
## 2 Materials and Methods
### Feature Extraction
#### 2.3.3 Topological (\(T\)) Feature  
Betti numbers are invariant for topological spaces based on connectivity. The topological analysis was applied to the three-dimensional CT and DD images. In three-dimensional image, two values (b0 and b2) were included for the Betti numbers. b0 represents the number of connected components and b2 is the number of cavities. b0 and b2 were calculated from binarized CT and DD by changing the threshold of the CT value (range: mean CT value in CT \(\pm\) 50, increment: 1 Hounsfield unit [HU]) and percentage prescribed dose (range: 95-105%, increment: 0.5%). In other words, b0 and b2 were obtained from each CT, and the DD was binarized with various thresholds. Moreover, the ratio of b0 and b2 (b2/b0) and area under the Betti number curve (AUBC) were employed as \(T\) features. AUBC is a definite integral of a curve describing the change in b0, b2, and b2/b0 as a function of the threshold value. An example of the Betti number curves in the CT of the four cases is shown in Figure 3. The Betti number curves varied depending on each case and may be able to express the differences in cancer traits.  
Figure 3: Example of Betti number curves describing the change in b0 (**left**), b2 (**middle**), and b2/b0 (**right**) as a function of threshold value in CT for four cases. CR: complete response, TF: treatment failure.