esa-sceva/satcom-chunk-collection huggingface dataset:
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 3 Data Caching and Data Sharing Security in 5G
### Permission Delegation  
Permission delegation allows the assigning of access rights to an end-user by an administrative user or another end-user. It requires a user to have the ability to use the access right being delegated but not the administrative user [43]. A federated delegation mechanism can be applied to the capability generation and propagation process for authorization and capability revocation management. Using ABAC and CBAC with FId in a content-aware mobile network could efficiently address challenges in access control strategy processing. By delegating part of the authentication and authorization tasks to other users in different security domains, it supports the 5G security and services access objectives. Processing validation of capability in the HN and third-party SPs enables a flexible, elastic, context-aware, and fine-grained access control mechanism in 5G.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 2 The Energy-Efficient Internet-of-Things Model
### Radio Model and Energy Budget Computation
#### 2.2.1 Energy Budget for Sensing Sensor Tags by HSNs  
HSNs dissipate a significant amount of energy when interrogating the sensor tags. The total energy dissipated is directly dependent on the density of the sensor tags within the sensing region, which is assumed to be an ellipse, as illustrated in Figure 1. The higher number of sensor tags interrogated, the higher the amount of energy dissipation. Let \(S=\{s_{1},s_{2},\ldots,s_{t}\}\) denote the set of sensor tags in the network and \(H=\{h_{1},h_{2},\ldots,h_{t}\}\) the set of HSNs within reading range of the sensor tags. Then, let \(S_{i}\) denote a subset of \(S\) that represents the set of sensor tags randomly distributed within the reading area of \(h_{i}\). In a given region, the set \(S_{i}\) can only have a maximum of \(t\) sensor tags. Each sensor tag generates \(k\) bits of data. Assuming that \(h_{i}\) successfully receives the data from all \(t\) sensor tags, then by following the principle in Equation (1), the energy consumption of \(h_{i}\) for interrogating \(t\) sensor tags would be:  
\[E_{i}=\sum_{j=1}^{t}\left[(E_{elec}*k+\xi_{amp}*k*d_{ij}^{\text{st}})+E_{elec}*k\right] \tag{3}\]  
where \(\xi_{amp}\in\{\xi_{FS},\xi_{TR}\}\) and \(0<d_{i,j}\leq max\{d_{1},\ldots,d_{t}\}\) is the distance between \(h_{i}\) and sensor tag \(j\) within its reading range, while \(\alpha\) is the propagation loss coefficient with values of two or four, depending on the value of \(d\), as defined in Equation (2).
# Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation
## 4 Results
### Carbon Fluxes from the Calibrated and Assimilated Model  
Five years of GLASS LAI products were assimilated into the calibrated Biome-BGC model using the EnKF with an assimilation window of eight days. When the size of the ensemble was larger than 100, the R\({}^{2}\) and RMSE between predicted GPP and EC measurements reached approximately stable values. The errors (from the model and GLASS LAI) were determined through LAI sites all over the world reported by [PERSON] et al. (2017). The error of GLASS LAI products ranged from \(-\)0.008 to 0.12. Then the variance (0.032) was calculated and used in the EnKF algorithm. The model error was estimated at the same time, and the value ranged from \(-\)0.32 to 0.44, with a variance of 0.616 in EnKF.  
Figure 5 shows the comparison between GLASS LAI and simulated LAI, depicted as: LAI_sim (LAI obtained from the calibrated Biome-BGC without data assimilation) and LAI_DA (LAI simulated by the assimilated Biome-BGC model using the EnKF). It is noticeable that LAI_sim generally underestimated LAI compared with the observations, especially in growing seasons. LAI_sim increased rapidly at the beginning of the growing season and the values were higher than the remote sensing observations at the end of the growing season. The LAI_DA agreed well with the GLASS LAI, except for a few fluctuations in winter.  
The measured GPP profile from 2003 to 2007 is shown in Figure 6a, together with those obtained from the default (GPP_Default), calibrated (GPP_Calibrated) and assimilated Biome-BGC model (GPP_DA). The original model was incapable of simulating GPP behavior, especially in summer and winter (Figure 6b, R\({}^{2}\) = 0.72, RMSE = 2.419 gC\(\cdot\) m\({}^{-2}\cdot\)d\({}^{-1}\)). After calibration, the underestimation during summer had significantly diminished by adjusting the key parameters (FLNR, SLA, G\({}_{\text{max}}\), etc.), with R\({}^{2}\) = 0.87, and RMSE = 1.583 gC\(\cdot\) m\({}^{-2}\cdot\)d\({}^{-1}\) (Figure 6c). However, some fluctuations still remained, especially in spring. Therefore the time series GLASS LAI was assimilated into Biome-BGC. The LAI was calculated from leaf carbon in Biome-BGC, and the assimilation directly affected the  
Figure 4: A comparison of eight-day GPP values for the growing season obtained using the optimized MOD_17 model and those derived from the calibrated Biome-BGC model.  
photosynthesis, thus affecting the performance of GPP. Figure 6d shows that GPPs from the assimilated Biome-BGC agreed well with the EC measurements.  
Figure 5: Comparison of simulated LAI and GLASS LAI.  
Figure 6: \(Cont\).
## 3 Test Results and Discussion  
### Influence of Heterogeneity  
The influence of heterogeneity on the mechanical behavior of the tested sandstone was analyzed through the test results for three intact sandstone specimens (Int-1, Int-2 and Int-3). As depicted by the stress-strain curve shown in Figure 3, the uniaxial compressive strength (UCS) of Int-1, Int-2 and Int-3 are 76.64 MPa, 76.94 MPa and 71.50 MPa respectively. The standard deviation of the data is 2.50 MPa, and the coefficient of variation (the ratio between the standard deviation and the average  
Figure 2: Photographic view of the testing system.  
value) is 3.33%. Similarly, the standard deviation of peak strain is 0.19, the standard deviation of elastic modulus is 0.68 GPa, and their coefficient of variation are 2.95% and 5.22%, respectively. According to the above comparison, it can be seen that the influence of heterogeneity on strength and deformation behavior of the tested sandstone specimen is small and thus can be ignored.
# Named Entity Recognition for Sensitive Data Discovery in Portuguese
## 4 Experiments and Results
### Named Entity Recognition Component Validation  
The last evaluation performed for this work consisted of the validation of the Named Entity Recognition Component development. Its main goal was to assess both the performance of the NER component and the quality of the recognition of sensitive entities. For this evaluation we used the DataSense NER Corpus, previously annotated. Before performing the evaluation tests, there was a set of decisions that were made in order to understand which models would be part of the NER Component. For the Named Entity Recognition Module, and considering the results of f1-score and performance for each of the tests, a set of different tasks was chosen in order to cover all classes of entities required for the DataSense Project and the best results for each entity. In Table 4, we can see the named entity recognition methods chosen for each class of entities.  
The evaluation of DataSense NER Corpus was performed with this set of models. Its results are presented in Table 5.  
The NER Component had an f1-score of 83.01% in the DataSense NER Corpus and took 1716 s to complete the processing of all 78 documents. In Figure 2, we can see the detailed analysis of the f1-score results for each class of entities.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Entity Classes** & **Used Models** \\ \hline Personal Identifications Numbers & \\ CodigoPostal & Rule-Based Models \\ EnderecoElectronico & \\ \hline Profissao & \\ Med & Lexicon-Based Models \\ Tempo & \\ Valor & \\ \hline Pessoa & \\ Local & Machine Learning Models (Bi-LSTM) \\ Organizacao & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Method of Named Entity Recognition (NER) used by a class of entity for NER Component.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entity Class** & **Lexicon-Based Model** & **CRF Model** & **RF Model** & **Bi-LSTM Model** \\ \hline TEMPO & **91.7\%** & 65.9\% & 75.2\% & 71.27\% \\ \hline VALOR & **62.8\%** & 34.6\% & 18.6\% & - \\ \hline PESSOA & 39.5\% & 69.4\% & 60.2\% & **80.78\%** \\ \hline LOCAL & 51.9\% & 77.5\% & 58.4\% & **80\%** \\ \hline ORGANIZACAO & - & 47.1\% & 34.1\% & **80.5\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation results by entity class by model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Results** \\ \hline Precision & 87.60\% \\ Recall & 79.02\% \\ F1-score & 83.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: DataSense NER Corpus evaluation results.  
From the Figure 2, we can conclude that there are some classes, such as Profissoa (Job) and Med (Medical data), which have much lower results when compared to the other classes, because these two classes have been implemented with lexicon-based models and these lexicons are very small and very lacking in these two entities. However, in general, all classes presented good results.
# Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions
## 2 Dataset
### Study Data
#### 2.2.2 Environmental and Climatic Data  
In this study, five environmental factors, namely the annual average atmospheric CO\({}_{2}\) concentration (CO\({}_{2}\)), the growing season (May to September), total nitrogen deposition (Ndep), average temperature (Tmean), total precipitation (Pre), and average water vapor pressure difference (VPD), were selected as growing season total GPP impact factors. The annual mean atmospheric CO\({}_{2}\) concentration (CO\({}_{2}\)) was derived from the annual average atmospheric CO\({}_{2}\) concentration data released by the Mauna Loa Observatory in Hawaii, USA, from 1982 to 2010 [37]. The nitrogen deposition (Ndep) data were derived from the historical nitrogen deposition database (1850-2014) in the CCMI nitrogen surface fluxes in support of CMIP6--version 2.0 [38], with a temporal resolution of months and a spatial resolution of 0.5\({}^{\circ}\). The average temperature (Tmean), the total precipitation (Pre), and the average water vapor pressure difference (VPD) were calculated from the CRU TS (version 4.0.5) [39] global meteorological data from 1901 to 2017 provided by the National Centre for Atmospheric Science, with a monthly temporal resolution and a spatial resolution  
\begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **Description** & **Resolution** \\ \hline EC-LUE GPP & 1982–2017 global GPP dataset corrected using the EC-LUE model & 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\); 8-day \\ NIRv GPP & 1982–2018 global GPP dataset estimated using novel vegetation & 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\); monthly \\  & index NIRv & 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\); monthly \\ CO\({}_{2}\) concentration & Data on annual average atmospheric CO\({}_{2}\) concentrations from 1982 & \\  & to 2010 published by the Mauna Loa Observatory—ESRL Global & Yearly \\  & Monitoring Laboratory & \\ Nitrogen deposition (Ndep) & The historical nitrogen deposition database (1850–2014) in the & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\); monthly \\  & input4 MIPs dataset & \\  & Calculated from the CRU TS (version 4.0.5) global meteorological & \\  & data from 1901 to 2020 provided by the National Centre for & 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\); monthly \\  & Atmospheric Science & \\ Stand age map & Constructed from forest inventory data in 2010 & 1 km \(\times\) 1 km \\ Stand type map & Constructed from CAS in 2006 & 1 km \(\times\) 1 km \\ Site class index map & Constructed from forest inventory data in 2010 & 1 km \(\times\) 1 km \\ \hline \end{tabular}
\end{table}
Table 1: Description of the data used in this study.  
Figure 2: GPP of the annual growing season in Heilongjiang Province, China, calculated by different remote sensing products.
## 2 Materials and Methods
### ECA-DenseNet
#### 2.4.1 Batch Channel Normalization  
When processing spectral data, traditional Batch Normalization (BN) [19] is limited by the batch size, restricting the exploration of the model capacity and affecting the accuracy when the training and test distributions are inconsistent. In contrast, BCN [20] adopts a channel--independent normalization mechanism, which is more suitable for the characteristics of spectral data.  
In each training batch, BCN calculates an independent mean value \(\mu_{j}\) for each channel j  
\[\mu_{j}=\frac{1}{N\times H\times W}\sum_{i=1}^{N}\sum_{d=1}^{H}\sum_{w=1}^{W}x_{ ij,j,h,w} \tag{1}\]  
and variance:  
\[\sigma_{j}^{2}=\frac{1}{N\times H\times W}\sum_{i=1}^{N}\sum_{d=1}^{H}\sum_{w= 1}^{W}\left(x_{ij,j,h,w}-\mu_{j}\right)^{2} \tag{2}\]  
N represents the batch size; that is, the number of samples in a batch: H and W are the height and width of the feature map, respectively. For one-dimensional spectral data, H can be regarded as 1, and W corresponds to the feature dimension of the spectral data, namely the number of wavelengths; \(x_{ij,j,h,w}\) represents the feature value at the position (h,w) in the j-th channel of the i-th sample. In this way, BCN can accurately capture the distribution characteristics of each channel.  
Next, BCN performs a normalization operation on each channel:  
\[\hat{x}_{n,c,1}=\frac{x_{n,c,1}-\mu_{c}}{\sqrt{\sigma_{c}^{2}+\epsilon}} \tag{3}\]  
In this study, \(\epsilon\) is set to \(10^{-5}\), which is used to avoid division by zero, standardizing the data within the channel to a distribution with a mean of 0 and a variance of 1, reducing the problem of internal variable distribution shift.  
Finally, the normalized features are adjusted through learnable scaling parameter \(\gamma_{c}\) and offset parameter \(\beta_{c}\):  
\[y_{n,c,1}=\gamma_{c}*\hat{x}_{n,c,1}+\beta_{c} \tag{4}\]  
Figure 4: Diagram of ECA-DenseNet model.  
Channel-wise normalization accelerates convergence, regularizes implicitly, and boosts spectral robustness.
# Behind the Door: Practical Parameterization of Propagation Parameters for IEEE 802.11 ad Use Cases
## 4 Experimental Results
### Case 2: Power Distribution with an Angle in the Presence of People in the Vicinity of the STA  
In this case, six pedestrians move freely and randomly (similar to the works [4; 7]) in the vicinity of the transmitter and receiver. They are 20- to 23-year-old male students, with 1.70-1.75 m heights. Thus, it is reasonable to think that this traffic may generate variable shadowing over time. We present the results in Figures 4 and 5. In these figures, we observe the standard deviation of the received power as a function of the angle for the positions in Figure 3. The average received power per angle is shown in the polar diagrams of the figure mentioned above in the blue data series.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Position**} & \multirow{2}{*}{\(\theta_{TX}(^{\circ})\)} & \multicolumn{2}{c}{\(\theta_{MaxPR}(^{\circ})\)} & \multicolumn{2}{c}{\(\Delta_{PR}\) **(dB)**} & \multicolumn{2}{c}{\(\Delta_{\theta}(^{\circ})\)} \\ \cline{3-8}  & & **NP** & **P** & **NP** & **P** & **NP** & **P** \\ \hline Pos 1. (NLOS) & 220 & 117 & 245 & 11 & 13 & 103 & 25 \\ Pos 2. (NLOS) & 245 & 219 & 227 & 13 & 8 & 26 & 18 \\ Pos 3. (NLOS) & 290 & 315 & 299 & 8 & 6 & 25 & 9 \\ Pos 4. (NLOS) & 256 & 254 & 257 & 0.6 & 0.5 & 2 & 1 \\ Pos 5. (LOS) & 270 & 274 & 266 & 0.9 & 0 & 4 & 4 \\ Pos 6. (LOS) & 270 & 273 & 272 & 0.6 & 0.5 & 3 & 2 \\ Pos 7. (NLOS) & 280 & 312 & 293 & 11 & 16 & 32 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Difference table between \(\theta_{TX}(^{\circ})\) and \(\theta_{MaxPR}(^{\circ})\). NP represents the case without people and P the case with people.  
Figure 4: Standard deviation of positions 1–4: **(A)** Pos. 1 (NLOS), **(B)** Pos. 2 (NLOS), **(C)** Pos. 3 (NLOS), and **(D)** Pos. 4 (NLOS).  
The largest fluctuations recorded were 6-7 dB, more frequent in NLOS links. This differs significantly from Case 1, where the angular distribution of the standard deviation did not exceed 1 dB (magenta series). Although the power fluctuations produced by the movement of the person seated inside the offices could be considerable, this does not occur uniformly for all angles. In general, we observed that the largest standard deviation was recorded at angles close to \(\theta_{TX}\). Their position, as well as the dominant directions and secondary lobes, is coherent with the results in Case 1.
## 2 Key Steps in a FRB Search Pipeline
### Rejection of Radio Frequency Interference
#### 2.1.4 IQRM  
A method that more explicitly removes slowly varying trends in the data than the thresholding described above, and detects RFI outliers, is the Inter-Quartile Range Mitigation (IQRM) algorithm. IQRM identifies all the frequency channels affected by narrow-band RFI. The first step is to characterise the entire chunk of data by a single statistical metric per frequency channel and then to determine the channels whose statistical metric does not conform to clean data. IQRM is characterised by two aspects. First, any spectral statistic to decide which channels are contaminated by RFI is acceptable, as long as higher values of the metric indicate a larger contamination of the data by RFI. Second, the threshold beyond which data are rejected is estimated from data which are assumed to vary slowly with the observing frequency.  
The algorithm works as follows: for each sample in a frequency channel \(i\), we compute the chosen statistical metric \(s_{i}^{0-N}\). The total number of samples considered are dictated by a radius parameter and a threshold parameter (in units of \(\sigma\)) that the metric needs to exceed to be considered an outlier. Using these parameters, the algorithm computes the difference, \(\Delta\ s_{i}^{j}\), over the radius \(r\) such that \(0\leq\ j\leq\ r\). Finally, all the \(\Delta\ s_{i}^{j}\) values are filtered based on the threshold defined by the user. Any values above this threshold will be flagged as RFI by the algorithm.  
Figure 2 shows an example of data before and after flagging by IQRM. It clearly demonstrates the ability of IQRM to pick out time variable narrow-band RFI. IQRM has been used for various time-domain surveys [11]. Since the algorithm is dependent on a statistical metric that is expected to be robust to RFI, it is bound to be ineffective in cases where RFI affects a significant fraction of the data that is used to compute the statistical metric. Hence, IQRM is not an effective tool against long-term, broad-band RFI.
## 3 Results
### Placental Gene Expression  
The mRNA expression of VDR, CYP27B1 and CYP2R1 was studied in 100 human placentas using quantitative real-time PCR. The expression levels of both genes--CYP27B1 (1.17 vs. 1.05 in controls, \(p\) = 0.006) and CYP2R1 (2.01 vs. 1.89 in controls, \(p\) = 0.039)--were significantly higher in preeclamometric placentas compared to the control group. Interestingly, VDR expression was significantly lower in placentas from the PE group (1.15 vs. 1.20 in controls, \(p\) = 0.030) (Table 3). After dividing all preeclamptic women into subgroups according to delivery before and after the 34 th week of gestation, significantly higher placental expression in the LOPE subgroup compared to healthy controls was observed only for the CYP27B1 gene (_p_ = 0.012) (Figure 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multicolumn{2}{c}{**Preclampsia**} & \multicolumn{2}{c}{**Controls**} \\  & \(n\) = **41** & \(n\) = **59** & \(p\) \\ \hline Maternal age (years) & 30.73 \(\pm\) 5.00 & 30.98 \(\pm\) 4.54 & 0.7988 \\ Gestational age (weeks) & 33.78 \(\pm\) 3.24 & 39.02 \(\pm\) 1.05 & \textless{}0.001 \\ Systolic blood pressure (mmHg) & 164.15 \(\pm\) 18.39 & 107.50 \(\pm\) 9.51 & \textless{}0.001 \\ Diastolic blood pressure (mmHg) & 105.49 \(\pm\) 11.21 & 66.85 \(\pm\) 7,27 & \textless{}0.001 \\ Pre-pregnancy BMI (kg/m\({}^{2}\)) & 25.16 \(\pm\) 5.68 & 21.59 \(\pm\) 3.52 & \textless{}0.001 \\ Post-pregnancy BMI (kg/m\({}^{2}\)) & 30.12 \(\pm\) 5.08 & 26.87 \(\pm\) 3.90 & \textless{}0.001 \\ Caesarean section \(n\) (%) & 41 (100.00) & 40 (67.80) & \textless{}0.001 * \\ Primipara \(n\) (%) & 24 (58.54) & 26 (44.07) & 0.2223 * \\ Infant birthweight (g) & 1948.61 \(\pm\) 877.50 & 3476.55 \(\pm\) 417.89 & \textless{}0.001 \\
1 min Apgar score & 9 (7-10) & 10 (10-10) & \textless{}0.001 \# \\
5 min Apgar score & 9 (8-10) & 10 (10-10) & \textless{}0.001 \# \\ Placetna weight (g) & 385.31 \(\pm\) 163.07 & 565.96 \(\pm\) 103.94 & \textless{}0.001 \\ Urea (mg/dL) & 32.33 \(\pm\) 12.25 & — & — \\ Uremic acid (mg/dL) & 16.67 \(\pm\) 8.68 & — & — \\ Creatinine (mg/dL) & 0.77 \(\pm\) 0.31 & — & — \\ Total protein (g/dL) & 5.42 \(\pm\) 0.51 & — & — \\ Proteinuria (mg/dL) & 256.36 \(\pm\) 206.14 & — & — \\ Proteinuria (g/24 h) & 4.21 \(\pm\) 3.50 & — & — \\ ALT (IU/L) & 36.69 \(\pm\) 46.84 & — & — \\ AST (IU/L) & 43.95 \(\pm\) 54.98 & — & — \\ Early-onset PE \(n\) (%) & 20 (48.78) & — & — \\ Late-onset PE \(n\) (%) & 21 (51.22) & — & — \\ \hline \hline \end{tabular} Values are presented as mean \(\pm\) SD unless indicated # median (IQR) or \(n\) (%), \(p\) value—Student’s _t_-test, * [PERSON]’s test. Abbreviations: ALT—Alanine aminotransferase, AST—Aspartate aminotransferase, PE—precalmpsi.  
\end{table}
Table 2: Basic characteristics of preeclampsia cases and normotensive controls.
## 3 Results and Discussion
### Characterization of PEI-MNPs  
The magnetic response speed of MNP and PEI-MNPs were tested under the action of a magnet, and the results are shown in Figure 2a. PEI-MNPs achieved separation in less time (1 min), while MNPs took twice as long to achieve separation (2 min). This may be the result of partial adhesion of PEI-MNPs (in the synthesis of PEI-MNPs, one PEI molecule connected multiple MNPs). A faster magnetic response speed will facilitate the rapid separation of bacteria in samples. The Zeta potential of PEI-MNPs measured in a wide pH range (from 4 to 10) gradually decreased with the increase of pH due to the deprotonation of the surface groups [32], and the isoelectric point was around 8.6 (Figure 2b). The potential of PEI-MNPs was positive in a wide pH range (from 4 to 8), which was conducive to the electrostatic adsorption of PEI-MNPs on the negatively charged bacterial surface [33]. Moreover, MNPs and PEI-MNPs exhibited excellent superparamagnetism and fast magnetic response speed, with high magnetic saturation strengths of 30.9 emu/g and 27.8 emu/g, respectively (Figure 2c). MNPs and PEI-MNPs were observed to be uniformly spherical by SEM (Figure S1). Furthermore, PEI-MNPs with a particle size much smaller than that of pathogenic _Listeria_ spp. were able to absorb on the surface of target bacteria in large quantities (Figure 2d), which was due to the fact the PEI-modified MNPs could interact with abundant functional groups in the surface components of pathogenic _Listeria_ spp. The above results indicated that PEI-MNPs were able to capture _Listeria_ rapidly.