esa-sceva/satcom-chunk-collection huggingface dataset:
# Damage Characteristics of Thermally Deteriorated Carbonate Rocks: A Review
## 2 Thermal Dilatancy and Alteration in Rock Fabric
### Reasons behind the Thermal Expansion  
The crystal structure of a dolomite mineral is intermediate between calcite (CaCO\({}_{3}\)) and magnesite (MgCO\({}_{3}\)). However, it differs from calcite in terms of two aspects: alternating layers of calcium and magnesium in the unit cell and slight tilting of the carbonate group [49]. Thermal expansion of calcite along the a-axis is observed negative. On the other hand, in dolomite, it is measured positively along both the a-axis and c-axis [50]. Single carbonate crystals, such as calcite, show typical strength-temperature behavior (strength decrease with the temperature). Whereas, double carbonate crystals, such as dolomite, exhibit a different behavior that is shown by the calcite. It is attributed to the thermal vibration of the carbonate group that hinders the dislocation movement against increasing temperature [51]. In dolomite, the octahedral system of CaO\({}_{6}\) and MgO\({}_{6}\) plays an important role in stabilizing the crystal structure. The Ca-O bond length is found larger in dolomite as compared to that in calcite [49]. The variations in the bond length of Ca-O relative to temperature is different in both single carbonate crystals and double carbonate crystals. At the temperature range of 24 to 600 \({}^{\circ}\)C, in dolomite, a linear elongation in Ca-O bond length is observed at a faster rate. Whereas, in the case of calcite, an exponential trend is noted in the expansion of Ca-O bond length at a slower rate [52]. The bond strength can be expressed in terms of thermal expansion. Longer bonds (in dolomite) show less resistance and expand rapidly. There are different trends reported in the literature that explain the thermal behavior of polyhedral crystals in terms of volume expansion (VE) and quadratic elongation (QE). In dolomite, Ca-octahedron shows a very slight or no change in QE. Whereas, in Mg-octahedron, QE increases substantially. This behavior shows a contrast with the QE trends observed in calcite and magnesite. The QE under increasing temperature varies sharply for Ca-octahedron but, in the case of Mg-octahedron, exhibits
## 5 Experimental Results
### Experiment 1: Applying ParsBERT, BERT, and GCN on Our Dataset  
Under two scenarios, we examined the performance of our proposed models. The first scenario involved a two-class dataset with two labels: \"recommended\" and \"not-recommended\". The second scenario dealt with a three-class dataset which had three labels: \"recommended\", \"not-recommended\", and \"no-idea\". For both scenarios, we evaluated the model's performance and accuracy in two settings: one with unbalanced data and the other with balanced data. The results of these experiments are presented in Table 3. For every case in which the dasaset was imbalanced, we also calculated \(F_{1}\), precision, and recall along with the accuracy. In the three-class dataset, 51,961 data points were allocated for training, while 5774 data points were designated for testing. The number of extracted word was 44,758, so the number of nodes was 102,493. For the two-class dataset, 43,149 data points were assigned to training, while 4795 data points were reserved for testing. The number of extracted words was 40,304, so the number of nodes was 88,248.  
As is made evident from the results in Table 3, the performance of ParsBERT was better than BERT in both balanced and imbalanced data scenarios. The key insight here is that this enhancement can be attributed to the nature of our data being in the Persian language. Since ParsBERT was trained on a large collection of Persian texts, this allowed for ParsBERT to learn better textual representation vectors for Persian text compared to BERT, resulting in a significantly higher accuracy. In both cases of using BERT and ParsBERT, and in both balanced and imbalanced data scenarios, when employing graph neural networks such as GCNs, the model's performance increases; this is due to the advantage of utilizing graph neural networks.  
Regarding the statistics of the above models, we repeated the above experiments several times, each time with a different model weights initialization. As mentioned in Table 3, for example, the mean of the results (accuracies) of the strongest model in the above table, namely ParsBERT+GCN, is 91.1 (on 2 Class) with a standard deviation of less than 0.06. For the BERT+GCN model (on 2 Class), the standard deviation is just marginally higher (0.08) and the mean is also slightly (around 0.5%) lower than the ParsBERT+GCNmodel, which can again be due to the fact that the representations of ParsBERT in the Persian language are marginally better than BERT's.
## 4 Results and Discussions  
### Thermal Model Validation  
Validation of the thermal model was performed using Syltherm 800 as a base fluid. The obtained results showed high accuracy compared to the Sandia National Laboratory (SNL) results for the LS2 PTC model, which were determined by [PERSON] [44]. Different cases under different operating conditions have been selected to validate the deviations in the efficiency and outlet temperature for the base fluid, Syltherm 800. As presented in the following table, the results showed a high accuracy with a maximum deviation of 1.5% and 3.8% for the efficiency and outlet temperature, respectively Table 6.  
In addition, the current model was validated by comparing the outlet temperature results of the use of alumina oxide/water nanofluid under a 1% volume concentration with the experimental results determined by [PERSON] et al. in their study [63]. Figure 2 presents the validation of the present model outlet temperatures with the experimental results; all the results showed high accuracy, with a maximum deviation of less than 0.47% for the outlet temperature.
## 3 Results and Discussion
### Non-Metric Multidimensional Scaling (NMDS)  
Non-metric multidimensional scaling analysis is a ranking method applicable to ecological research [25]. A total of 1647 to 1931 OTUs were found to be present in the samples; 235 were identified, using the KW filter, to have significantly different abundances between the single cultivated amelina varieties and varieties intercropped with peas and vetch. The result of the NMDS analysis, based on the sequencing results (OTUs), is in Figure 6. The NMDS based on Bray-Curtis dissimilarities was conducted to reflect the soil bacterial beta diversity of the rhizosphere soil with amelina compared to soil with co-cultivation of amelina with legumes. The results showed that OTUs were clustered within the inter-cropping system of Cs3.Vs.S.Pro and Cs3.Ps.S.Pro (Figure 5), indicating a clear separation of soil bacterial communities with single cultivations of amelina (Figure 6), which showed distance (stress = 0.0). This result indicated that the three amelina varieties Luna, Lenka, and local Bulgarian landrace, when solely cultivated, congregated in different rhizosphere microbial communities.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Soil Sample** & **Observed\_Species** & **Shannon** & **Simpson** & **Chao1** & **ACE** & **Goods\_Coverage** & **PD\_Whole\_Tree** \\ \hline Cs1.S.Pro & 1647 & 7.791 & 0.975 & 1770.6 & 1828.321 & 0.992 & 134.844 \\ Cs2.S.Pro & 1782 & 8.982 & 0.996 & 1896.5 & 1901.636 & 0.994 & 138.570 \\ Cs3.S.Pro & 1849 & 9.094 & 0.995 & 1976.1 & 2008.721 & 0.993 & 146.101 \\ Cs3.Vs.S.Pro & 1931 & 9.226 & 0.996 & 2037.4 & 2051.597 & 0.994 & 148.461 \\ Cs3.Ps.S.Pro & 1802 & 9.150 & 0.996 & 1934.4 & 1910.993 & 0.994 & 139.961 \\ \hline \end{tabular}
\end{table}
Table 1: Alpha diversity according to six indices.
## 2 Materials and Methods  
This work frames the results of several projects conducted by this research team to monitor the pasture in the Montado ecosystem in nine experimental fields (Figure 3). The characteristics of the experimental fields used in this study are presented in Table 1. These are typical biodiverse dryland pastures that usually grow under a low density plantation of Holm oak or Cork oak, and they are mainly used for grazing by sheep or cattle in a rotational or permanent basis. The following data were used: (i) pasture moisture content (PMC), CP, and NDF in experimental field \"MIT_1,\" collected between February and December 2018; (ii) PMC, CP, and NDF in 8 experimental fields (\"AZI,\" \"CUB,\" \"GRO,\" \"MIT_2,\" \"MUR,\" \"PAD,\" \"QE,\" and \"TAP\") collected in May 2018 (\"MIT_2,\" \"QE,\" and \"TAP\") and between January and February 2019; (iii) PMC, CP, NDF, the NDVI, and the NDWI in 8 experimental fields (\"AZI,\" \"CUB,\" \"GRO,\" \"MIT_2,\" \"MUR,\" \"PAD,\" \"QE,\" and \"TAP\") collected between January and May 2019.  
Figure 2: Schematic representation of the experimental methodology used in this study.  
_Appl. Sci._**2020**, _10_, 4463  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Coordinates** & **Pasture Type** &
\begin{tabular}{c} **Predominant** \\ **Trees** \\ \end{tabular} & **Animal Species** \\ \hline \multirow{3}{*}{“AZI”} & \multirow{3}{*}{38°6.2\({}^{\prime}\) N; 8°26.9\({}^{\prime}\) W} & Permanent; biodiverse & \multirow{3}{*}{Holm oak and} & \multirow{3}{*}{Sheep in rotational} \\  & & (predominance of & & \\  & composites) & & & \\ \cline{1-1} \cline{3-5}  & & Annual; biodiverse & & \\ \cline{1-1} \cline{3-5}  & & (mixture of grasses and & & \\ \cline{1-1
# Outpatient Total Knee Arthroplasty: A Meta-Analysis
###### Abstract  
**Introduction:** Outpatient total knee arthroplasty (TKA) is attracting growing interest. This meta-analysis compared patient reported outcome measures (PROMs), infection, readmission, revision, deep vein thrombosis (DVT), and mortality rates of outpatient versus inpatient TKA. **Methods:** This meta-analysis was conducted according to the 2020 PRISMA statement. In August 2021, the following databases were accessed: Pubmed, Web of Science, Google Scholar, Embase. All the clinical trials comparing outpatient versus inpatient (>2 days) TKA were considered. Studies which reported data on revision settings were not considered, nor studies which included patients discharged between one and two days. **Results:** Data from 159,219 TKAs were retrieved. The mean follow-up was 5.8 \(\pm\) 7.6 months. The mean age was 63.7 \(\pm\) 5.0 years and the mean BMI 30.3 \(\pm\) 1.8 kg/m\({}^{2}\). Comparability was found in age (\(p\) = 0.4), BMI (\(p\) = 0.3), and gender (\(p\) = 0.4). The outpatient group evidenced a greater Oxford knee score (\(p\) = 0.01). The inpatient group demonstrated a greater rate of revision (\(p\) = 0.03), mortality (\(p\) = 0.003), and DVT (\(p\) = 0.005). No difference was found in the rate of readmission (\(p\) = 0.3) and infection (\(p\) = 0.4). **Conclusions:** With regards to the endpoints evaluated in this meta-analysis, current evidence does not support outpatient TKA. However, given the limited data available for inclusion and the overall poor quality of the included articles, no reliable conclusion can be inferred. Further high quality clinical trials with clear eligibility criteria are required.  
Keywords:total knee arthroplasty; inpatient; outpatient +
Footnote †: journal: MedicalTKA. The outcomes of interests were the rate of infection, readmission, revision, deep vein thrombosis (DVT), mortality, and patient reported outcomes measures (PROMs). We hypothesized that outpatient TKA in selected patients performed similar to the inpatient procedure and can be safely performed.
# A Low-Complexity Security Scheme for Drone Communication Based on PUF and LDPC
## 3 Security Architecture
### QC-LDPC Encoding
\begin{Bmatrix}p_{i}^{1 st},p_{i}^{2 nd},\ldots,p_{i}^{pth}\end{Bmatrix} \), \(0\leq i\leq n-k-1\) to obtain a new matrix \(P^{T}_{L}\) of size \((n-k)\times\rho\) composed of indices  
\[P^{T}_{L}= \begin{bmatrix}p_{0}\\ p_{1}\\ \vdots\\ p_{n-k-1}\end{bmatrix} = \begin{bmatrix}p_{0}^{1 st}&p_{0}^{2 nd}&\cdots&p_{0}^{pth }\\ p_{1}^{1 st}&p_{1}^{2 nd}&\cdots&p_{1}^{pth}\\ \vdots&\vdots&\ddots&\vdots\\ p_{n-k-1}^{1 st}&p_{n-k-1}^{2 nd}&\cdots&p_{n-k-1}^{pth}\end{bmatrix}  \tag{7}\]  
During program execution, it is only necessary to calculate the symbol bits supervised by a certain parity bit and to perform the XOR operation, \(w_{i}=\oplus_{n\in\{1,2,\ldots,p\}}m_{i_{n}^{th}}\). If the base matrix \(A\) also has the characteristics of cyclic shifts, then all the matrix information can be embedded in the program. For encoding, each time a parity bit is computed, a computation is required. Considering that the program running memory space on embedded chips is generally more limited than program storage space, this method of trading computation time for RAM space is acceptable. The basis matrix \(A\), for example,  
\[A= \begin{bmatrix}1&0&1&0&0&0&0&1\\ 1&1&0&1&0&0&0&0\\ 0&1&1&0&1&0&0&0\\ 0&0&1&1&0&1&0&0\\ 0&0&0&1&1&0&1&0\\ 0&0&0&0&1&1&0\\ 0&1&0&0&0&0&1&1\end{bmatrix}  \tag{8}\]  
has a grith of six. The cyclic parameter matrix is \[C= \begin{bmatrix}0&\infty&1&\infty&\infty&\infty&\infty&2\\ 0&2&\infty&4&\infty&\infty&\infty&\infty&\infty\\ \infty&0&3&\infty&6&\infty&\infty&\infty\\ \infty&\infty&0&4&\infty&8&\infty&\infty\\ \infty&\infty&\infty&0&5&\infty&10&\infty\\ \infty&\infty&\infty&\infty&0&6&\infty&12\\ 0&\infty&\infty&\infty&\infty&7&14&\infty\\ \infty&0&\infty&\infty&\infty&8&16\end{bmatrix} . \tag{9}\]  
Utilizing submatrices \(E_{32}\) for expansion, a matrix of size \(8\times 8\), with a girth of eight and a size of \(256\), is obtained. The shape of matrix \(P^{T}\) used in this study can be seen in Figure 2, and the Generation of LDPC parity matrix \(P^{T}_{L}\) is shown in Table 5. The row index is calculated in a similar way to the column index formula.
## 4 Results, Comparison, and Limitations  
### Results  
The proposed system, called DTL-AFI-VHA, is designed to identify animals in several stages. First, the muzzle point of the animal is extracted using a Yolo object detection model, which has demonstrated mean average precision of 99.5% for detecting the face of the animal and 99.7% mean average precision for detecting the nose or muzzle point of the animal. Next, the features of the muzzle are detected as keypoints and descriptors, which are then stored in a database. For the recognition process, FLANN is used to match the keypoints and descriptors of the animal's muzzle with those in the database. The proposed system has been tested using images from the THDD dataset and has achieved 100% accuracy in animal muzzle point feature matching. All of the animals in the THDD dataset were successfully recognized by the system.
# Evolution towards Smart and Software-Defined Internet of Things
## 5 Evolution of IoT Architecture
### SDN IoT with Fog Computing  
[PERSON] et al. in [79] state that, due to the exponential increase in data due to IoT, bigger data pipes to the data center are required, which is not economically feasible and also increases the latency for delay-sensitive applications. In addition, [PERSON] et al. in [80] offer an IoT platform-as-a-service (PaaS) architecture based on WSO2 Stratos, an open-source PaaS solution as depicted in Figure 5. This technique is proposed on top of gateways to enhance the heterogeneity of the structure by mediating interfaces between gateways.  
The PaaS IoT optimizes the architecture's scalability and efficiency while providing multi-tenancy. However, further research is needed on system resource optimization and QoS. To counter this, they propose a distributed SDN architecture with geo-distributed fog nodes in an intermediate layer as depicted in Figure 6. The novelty of the system is that the fog orchestration is delegated to SDN controllers [79] to achieve higher efficiency, while the SDN scalability issue is relieved by delegating some controller tasks to fog nodes. This system provides a high degree of scalability, QoS, real-time delivery, and mobility. However, security and energy-efficient scheduling are not addressed explicitly.  
Figure 5: IoT PaaS Architecture.
## 2 Material and Methods
### Sentinel-2 Reflectances  
Sentinel-2 reflectances are used to create a reference SIC dataset for evaluation of the merged, MODIS and AMSR2 SIC. The EU Copernicus Sentinel-2A/B satellites were launched in June 2015 (Sentinel-2A) and March 2017 (Sentinel-2B). They follow a sun-synchronous orbit at 786 km altitude and 10:30 am equator crossing time. Their phase is shifted by 180\({}^{\circ}\). Their payload, the Multispectral Instrument (MSI), features 13 bands in the visible and shortwave infrared spectral region. To obtain a reference dataset, we downloaded 79 cloud-free scenes from [[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)) (last access 12 September 2020). We made sure that they are cloud-free by checking every scene visually. Figure 1 and Table 1 offer a spatial and temporal overview over the scenes. Scenes with more than two hours time lag towards the next MODIS/AMSR2 overflight are not used for the intercomparison, but are included in the paper because of their possible relevance for other validation studies. The scenes were recorded in the East Siberian, Laptev, Kara, Barents, Beaufort Sea and in the Fram Strait between 22 February 2019 and 27 th May 2019. The scenes comprise mainly first-year ice with smaller amounts of young ice. Ice-type maps based on Advanced Scatterometer (ASCAT) and AMSR2 data ([12; 13; 14], [[https://seaice.uni-bremen.de/multiyear-ice-concentration/](https://seaice.uni-bremen.de/multiyear-ice-concentration/)]([https://seaice.uni-bremen.de/multiyear-ice-concentration/](https://seaice.uni-bremen.de/multiyear-ice-concentration/)), last access 12 September 2020) show some multiyear ice in the East Siberian, Fram Strait and Beaufort Sea (not explicitly shown here). For this study, Level 1 C reflectances at 665 nm (band 4) with a spatial resolution of 10 m are used. Level 1 C data are top-of-the-atmosphere reflectances which are projected onto a Universal Transverse Mercator projection.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region** & **Feb** & **Mar** & **Apr** & **May** & **Total** \\ \hline East Siberian Sea & 0 & 10 (3) & 8 (5) & 0 & 18 (8) \\ Laptev Sea & 3 & 4 & 2 & 10 & 19 \\ Kara Sea & 3 & 7 & 10 & 9 (8) & 29 (28) \\ Barents Sea & 0 & 1 & 0 & 2 & 3 \\ Fram Strait & 0 & 1 (0) & 2 (1) & 1 & 4 (2) \\ Beaufort Sea & 1 & 1 & 2 & 2 & 6 \\ All & 7 & 24 (16) & 24 (20) & 24 (23) & 79 (66) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of Sentinel-2 scenes for the respective region and month. If there is a number in brackets, it gives the number of scenes after discarding Sentinel-2 scenes for which no cloud-free MODIS measurement is available within two hours of the acquisition. In those without brackets all scenes could be used.
## 4 Experiment
### The Influence of the Feature Embedding Dimensions  
We analyze the effect of the feature embedding dimensions on the three datasets, and the experimental results are shown in Figure 7. According to the research findings, the results of the precision and recall indicators show a general increase when the feature embedding dimension is from 32 to 64. However, as the embedding dimension further increases, the performance gradually decreases, possibly due to overfitting. The experimental results indicate that the larger the embedding dimension, the greater the complexity of the model, resulting in a downward trend in the MIMA's performance indicators. Therefore, when choosing feature embedding dimensions, there is a trade-off between algorithm complexity and model performance that needs to be balanced.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 7 Evaluation
### Evaluation Environment
#### 7.2.1 Test Applications  
When studying the performance overhead and disruption impact of reconfiguration tactics and the controller on clusters and running applications, we select two test applications, a Cassandra database and a SaaS application, representing two classic Kubernetes workloads, Statefulset and Deployment:  
* **Cassandra Database**: The Cassandra database is a distributed, high-performance and highly available NoSQL database widely used in the industry [52]. We evaluate our research questions by measuring the latency of CPU-intensive write operations at various workload levels (i.e., number of writes/sec);
* **Configurable SaaS Application**: This SaaS application can be configured to perform a combination of CPU-, disk- or memory-intensive operations [53]. Again, here, we measure the latency of CPU-intensive operations.  
We use K8s-Scalar as our traffic generator to send requests to these applications and measure the latency. K8-Scalar is originally designed to evaluate autoscaling methods for containerized services in a cluster [54]. Here, we take advantage of its traffic generator that can generate workload fluctuations (in terms of number of concurrent users) based on a declarative specification.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 5 Potential Research for ISAC towards 6G ISTNs  
In this section, we speculate on the potential research directions and connections between ISAC in the ISTNs and other emerging communication technologies.
## 4 Discussion  
Nowadays, the development of remote sensing technology has provided an effective way to obtain crop phenotype information in a large area and quickly without loss, and the use of remote sensing technology to monitor the changes in crop phenotype parameters is the future development trend. In this study, the quantitative inversion of the vertical distribution of nitrogen content based on the remote sensing platform was carried out from the demand of nitrogen content monitoring. It was found that the correlation between spectral reflectance and nitrogen content of different leaf positions was significantly enhanced after FD and SD processing of the original spectrum, and the correlation coefficients could reach up to 0.89. This was mainly because the spectral information is affected by atmospheric effects and environmental factors during hyperspectral data acquisition, resulting in noise interference in the original spectrum, which can affect the extraction of sensitive information. Spectral differentiation technology could partially eliminate the influence of atmospheric effects and environmental factors, reflecting the nature of vegetation characteristics, which is consistent with the previous conclusions of [PERSON] et al. [20]. In recent years, wavelet transform, as an emerging discipline applied in remote sensing science, is one of the very promising techniques in hyperspectral information extraction, and reflects more and more advantages in data dimensionality reduction, crop identification, and inversion of crop physical and chemical parameters [21]. Our study obtained the highest prediction accuracy by constructing a nitrogen content prediction model using wavelet coefficients. This was because wavelet variations can extract the weak information hidden in the spectral signal and effectively use the overall structural characteristics of the spectral information. It has been found that nitrogen is easily transported within the plant, resulting in significant differences in the nitrogen content of leaves at different leaf positions [22]. Therefore, studying the vertical distribution of nitrogen content within the plant is of great significance for growth monitoring, making fertilization decisions, guiding agricultural production, and monitoring pests and diseases. In this study, nitrogen content prediction models for different leaf positions were constructed based on machine learning algorithms,