esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Method
### Variable Selection Methods
#### 2.4.2 Penalized Regression Methods
ALASSO was tuned over 100 values of \(\lambda\) and four values of \(\gamma\) (0.5, 1.0, 1.5, and 2.0), resulting in a total of 400 tuning parameter values. Two-dimensional 10-fold CV, AIC, and BIC were used to select a pair (\(\lambda\), \(\gamma\)) of tuning parameters. Relaxed Lasso  
The RLASSO was proposed to address two weaknesses of the lasso: the over-shrinkage of nonzero coefficients and the selection of too many noise variables when CV is used to select the tuning parameter [16]. The RLASSO estimator combines lasso estimates (\(\hat{\beta}^{lass}(\lambda)\)) and unpenalized OLS estimates (\(\hat{\beta}^{OLS}(\lambda)\)) for the variables selected by the lasso at the tuning parameter \(\lambda>0\). The vector (\(\hat{\beta}^{OLS}(\lambda)\)) includes the OLS estimates for the selected variables and zero values for the coefficients of unselected variables to match the zero coefficients in the lasso estimates. This ensures that the lengths of the lasso and OLS estimates are identical [40]. For a linear regression with standardized covariates and a centered response variable, the RLASSO estimator is defined as:  
\[\hat{\beta}^{relax}(\lambda,\gamma)=\gamma\hat{\beta}^{lass}(\lambda)+(1- \gamma)\hat{\beta}^{OLS}(\lambda),\lambda>0,0\leq\gamma\leq 1.\]  
The RLASSO has two tuning parameters, \(\lambda\) and \(\gamma\), which serve two different purposes. The \(\lambda\) determines which variables are included in the model, similar to lasso, while \(\gamma\) controls the amount of shrinkage applied to the coefficients. When \(\gamma=1\), RLASSO produces estimates which are identical to those of lasso, and when \(\gamma=0\), it produces estimates which are identical to the OLS estimates for the variables selected by the lasso. It is important to note that the \(\lambda\) value for RLASSO can differ (and may be larger in order to select simpler models) from that of ordinary lasso, except when \(\gamma=1\). RLASSO was tuned over 100 values of \(\lambda\) (the same as lasso) and five values of \(\gamma\) (0, 0.25, 0.50, 0.75, and 1.00), as per the default in glmnet R package (version 4.1-9) [55], resulting in a total of 500 combinations of tuning parameter. Two-dimensional 10-fold CV, AIC, and BIC were used to select the optimal pair (\(\lambda\), \(\gamma\)).
## 3 Background on 5G System
### The 5G Architecture  
As illustrated in Figure 1, the 5G system architecture is composed of three main parts: the user equipment (UE), the radio access network (RAN), and the 5G core network (5 GC). The figure highlights the main SPIs on the control plane and the reference points involving NFs working on the user plane [21]. The presented architecture refers to the 5G stand-alone nonroaming scenario. This architecture can be used both in the case of private and public networks.  
The RAN is a mesh network of gNodeBs (gNBs), which facilitates wireless connectivity for 5G users to 5 GC via the Uu air interface. The gNodeB engages with the UE through New Radio Radio Resource Control (NR RRC) protocols in the control plane (CP) and forwards traffic data to the user plane function (UPF) in the user plane (UP). Additionally, it establishes a relay bridge for the transmission of non-access stratum (NAS) messages between the UE and the AMF, to manage the establishment of communication sessions and to maintain continuous communications with the UE as it moves.  
UE denotes the mobile equipment (ME) equipped with a Subscriber Identity Module (SIM). The ME, typically represented by smartphones, serves as a cellular device capable of establishing connections with gNB. The SIM can take the form of a physical Universal SIM  
Figure 1: 5G system architecture.  
(USIM) or a virtual eSIM. It stores subscriber data identical to that found in the operator's Unified Data Management (UDM) database, including the globally unique Subscriber Permanent Identity (SUPI), cryptographic keys, and security algorithms.  
The 5 GC incorporates an array of NFs, each serving distinct roles. Noteworthy, on the control plane among these are the following:  
* The Network Exposure Function (NEF) facilitates the exposure of NF functionality externally, fostering interaction with third-party applications.
* AMF orchestrates access control and mobility management.
* The Network Slice Selection Function (NSSF) facilitates the selection of network slices for user-requested services.
* The Session Management Function (SMF) is responsible for session setup and management in alignment with network policies.
* The Policy-Control Function (PCF) provides policy management capabilities.
* UDM stores subscriber data and profiles.
* The NF Repository Function (NRF) enables registration and discovery of NFs for interfunction communication.
* The Authentication Server Function (AUSF) is responsible for user authentication.  
On the data plane, only UPF provides all the functions necessary for user packet routing and forwarding. UPF corresponds to the gateway concept in 4G, interfacing the 5G systems with the external data networks.  
Of note, NEF and NRF functions stand as noteworthy additions in the 5 GC compared to the 4G EPC. Although both fulfill similar roles, NEF targets external network exposure, while NRF serves internal 5 GC functions.
## 5 Materials and Methods
### Evaluation Metrics  
To evaluate and compare the performance of the different developed models of breast cancer detection using AMs and deep neural networks in thermal breast images, these evaluation metrics were used: accuracy, sensitivity, specificity, precision, and F1-score. The equations for these metrics are given as follows [36]:  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN}) \tag{1}\]  
\[\text{Specificity}=\text{TN}/(\text{TN}+\text{FP}) \tag{2}\]  
\[\text{Sensitivity}\text{ (also known as Recall)}=\text{TP}/(\text{TP}+\text{FN}) \tag{3}\]  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{4}\]  
\[\text{F1-score}=2\text{ (Precision}\times\text{Recall)}/(\text{Precision}+\text{ Recall}) \tag{5}\]  
where TP, TN, FP, and FN indicate true positive, true negative, false positive, and false negative, respectively.  
The accuracy is the proportion of the total number of correctly classified cases. It is easy to understand but can be misleading, especially if you have an uneven class distribution. Specificity is the fraction of negative occasions. Precision is the ratio of correctly positively labeled (abnormal) cases to all positively identified cases. Sensitivity (recall) is the proportion of positive (abnormal) cases correctly identified as positive. On the other hand, the F1-score is considered a better indicator of the classifier's performance than accuracy. It sums up the predictive performance of a model by combining the two competing metrics, precision and recall [36].  
The model's ability to distinguish between classes is measured by the AUC ROC, which typically has a value range between 0.5 and 1. In the medical context, it assesses the model's diagnostic performance; values closer to 1 indicate good test results, while values closer to 0.5 indicate poor test results [24]. Moreover, the developed model was compared with the state-of-art studies discussed in Section 4, Related Work.
## 2 Material and Methods
### Bacterial Strains and Antimicrobial Potential  
To obtain the water extract, 100 mg of freeze-dried _C. sativa_ leaves were added to sterile ultra-pure water to a final concentration of 100 mg/mL. The suspension was boiled at 100 \({}^{\circ}\)C for 30 min. In the next step, the suspension was centrifuged at 12,000 RPM for 10 min and then the supernatant was transferred to a new eppendorf tube. The supernatant was collected and stored until use at \(-\)20 \({}^{\circ}\)C. To obtain the ethanolic extract, 100 mg of freeze-dried _C. sativa_ leaves were added to 80% ethanol to a final concentration of 100 mg/mL. The samples were then incubated at 37 \({}^{\circ}\)C for 24 h with shaking (1000 RPM). They were then centrifuged at 12,000 RPM, RT, for 10 min. The supernatants were collected and stored until use at \(-\)20 \({}^{\circ}\)C.  
The antimicrobial potency of _C. sativa_ leaf extracts was evaluated on the following microorganisms: four strains of Gram-negative (_Escherichia coli_ PCM 2209, _Klebsiella pneumoniae_ PCM 1, _Pseudomonas aeruginosa_ DSM 1980, Serratia marcescens PCM 501) and three strains of Gram-positive (_Staphylococcus aureus_ PCM 458, _Listeria monocytogenes_ PCM 2191 and _Enterococcus faecalis_). Additionally, the antifungal activity of the tested extracts was checked using the _Candidida albicans_ ATCC 14053 strain. Clinical strains _Enterococcus faecalis_ were received from the collections of microorganisms of the Frederic Chopin Provincial Specialist Hospital in Rzeszow, Poland which were obtained during routine diagnostic cultures. The microorganisms were cultured on Nutrient Agar NA (meat extract--10 g/L, peptone--10 g/L, sodium chloride--5 g/L, agar--20 g/L, pH--7.0) and on YPD medium (yeast extract--10 g/L, peptone--20 g/L, glucose--20 g/L, pH--7.2). The water and ethanolic extracts were used to prepare dilutions, i.e., 1:10, 1:100 with which the tested microorganism species were treated. Antibacterial and growth inhibitory properties of _C. sativa_ leaf extracts against selected microorganisms were tested using a spot-on-lawn method. Briefly, microorganism assay plates were prepared whereby the tested bacteria and yeast (8-log CFU/mL) were seeded. Then, 5 uL of each _C. sativa_ leaf extract was surface-spotted onto the indicator lawn. In the case of ethanol extracts, positive controls of appropriately diluted ethanol, i.e., 80%, 8%, 0.8% were also surface-spotted onto the indicator lawn. Plates were allowed to incubate overnight. Growth inhibition was evaluated after incubation (24 h at 37 \({}^{\circ}\)C--bacteria and 29 \({}^{\circ}\)C--yeast) by observations of the zone of inhibition around the spots with the test organism and were recorded using a Canon EOS 600D camera (Canon, Warsaw, Poland).
## 2 Materials and Methods  
### Csc  
_G. sylvestre_ seeds were obtained from Malayandi Kamaraj, Department of Botany, Jamal Mohamed College, Tiruchirappalli, TN, India. The seeds were disinfected with 2% NaOCI solution for 20 min,followed by 0.5% HgCl\({}_{2}\) solution for 5 min and five washes with sterile deionized water (H\({}_{2}\)O). Disinfected seeds were grown in MS medium [30] containing 8.0 g/L phytoagar and 30 g/L sucrose. Cultures were placed in a plant growth chamber at a \(24\pm 2\)\({}^{\circ}\)C with a light/dark cycle of 16 h/8 h for 4 weeks. Leaf segments were incubated on a medium containing 2,4-dichlorophenoxyayacetic acid (2,4-D; 0.5-3 mg/L) combined with 0.1 mg/L kineti (KIN) for callus development. The cultures were kept in a plant growth chamber with the aforementioned temperature and light/dark conditions for 21 days. Friable callus (1 g) was cultured in conical flasks containing MS basal salt solution containing 0.1 mg/L KIN and 2 mg/L 2,4-D for initiation of CSC. The flasks were aerated in a shaking incubator (110 rpm) using the aforementioned light/dark cycle at \(25\pm 2\)\({}^{\circ}\)C. The cultures were re-passaged every 7 days intervals of the new passage of culture. The growth of cell suspensions in terms of fresh cell mass (FCM), dry cell mass (DCM), and phytochemical contents were evaluated after 6, 12, 18 and 24 days of culture.
## 3 Results
### Driving Factors of GPP on a Seasonal Scale  
The possible drivers related to the GPP variations in the NYRD are investigated by the RF model in Figure 5 to assess their relative contributions. NDVI was the most important factor in modulating GPP, accounting for 56% of the overall variable importance. As illustrated in Figure 6, GPP showed the strongest positive correlation with NDVI, with the highest Pearson correlation coefficient (\(r\)) of 0.74, which was consistent with the variable importance value in Figure 5. In addition to NDVI, there were another three dominant variables--namely, LAI, DSR, \(T_{\text{max}}\) and FPAR, with importance values of 13%, 10%, 8% and 3%, respectively. NDVI and LAI were important indicators of the phase of terrestrial photosynthesis, which tracked well the crop phenological dynamics over time [22; 48; 50]. DSR and FPAR covariated with light to a large degree--the source of energy for photosynthesis in vegetation [51]. \(T_{\text{max}}\) played a critical role in the chemical reactions of biological processes [17]. In contrast, the impact of \(T_{\text{mean}}\), \(T_{\text{min}}\), VPD and RH on GPP was not obvious, exhibiting the lowest relative importance, with values of 2, 2, 2 and 2%, respectively. Although FPAR was highly correlated with NDVI (Figure 6), the importance of FPAR was very low in the RF model. This was because NDVI was directly derived from the satellite spectrum, while FPAR was indirectly calculated based on LAI and the physical models. The uncertainties in the MODIS LAI product can be attributed to the input data (surface reflectance or radiation data), model imperfections, and the inversion process [52]. The Pearson correlation coefficient for \(T_{\text{mean}}\) (\(r\) = 0.64) lay in the range of those for \(T_{\text{max}}\) (\(r\) = 0.65) and \(T_{\text{min}}\) (\(r\) = 0.60), as \(T_{\text{mean}}\) incorporated both day- and nighttime conditions (Figure 6). Generally, all these predictors were involved to different degrees in CO\({}_{2}\) exchange processes. Vegetation indices (i.e., NDVI and LAI), related to the phenological properties of the plants, had the greatest influence on GPP variations. In terms of meteorological factors, DSR, \(T_{\text{max}}\) and FPAR carried the information  
Figure 4: (**a–c**) Eight-day averaged gross primary production (GPP) measured by EC (GPP\({}_{\text{EC}}\)) and MOD17A2H (GPP\({}_{\text{MOD}}\)), and (**d–f**) seasonal cumulative GPP for rice and wheat growth seasons.  
of the light-dependent reactions of photosynthesis, which had a moderate effect on the GPP changes. In particular, \(T_{\text{mean}}\), \(T_{\text{min}}\), VPD and RH showed weak influences on the GPP cycles.  
Figure 5: Feature importance for the random forest model in the North Yangtze River Delta region.  
Figure 6: Correlations among the GPP (gross primary productivity) and Input variables. The correlations were calculated by all training data from the Dongtai and Shouxian sites.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 1 Introduction  
In an underwater constrained environment, existing communication mechanisms consist of single medium and single band technology for transferring data through wireless communication. Therefore, it is difficult to apply various types of applications underwater. In existing underwater wireless communication systems, it is hard to satisfy the real time performance and reliability requirements while maintaining the connection with various heterogeneous networks beyond the application domain. In order to overcome this, a method for bundling underwater wireless media and underwater wireless bands which can adapt with the existing communication is proposed. Based on the characteristics of each medium such as acoustic, optical, IR, Magnetic Field (MFAN), etc., the adaptation layer for underwater multi-media/multi-band is proposed.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 4 Framework Architecture
### Modeling the Parameters
#### 4.1.1 Graph Size  
Graphs are a valuable tool for modeling networks mathematically. Since network connections are typically bidirectional, the graphs are assumed to be undirected in this  
Figure 1: An overview of all the paramters used in the framework and their possible values. The boxes represent parameter categories, and the bold text below them depicts the concrete parameters.  
paper. The _graph size_ describes how many nodes are part of the network. Here, we allow an arbitrary integer \(\geq 2\) as the number of nodes for the network since this allows for the creation of any kind of network desired.
# Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios
## 2 Thermal Modeling
### Finite Volume Thermal Model  
The temperature field of the object is computed using the finite volume open source code OpenFOAM. The code takes as input the geometry of each component of the target in the.stl file format. The actual computational mesh is created using such files and for each of the components, the material properties are assigned as well as the thermal contact resistances. The solar radiation direction and intensity are provided as input, and starting from a guessed initial temperature field for each component, the finite volume method is applied to solve for the temperature field. The code offers the geometrical accuracy required to create realistic thermal images and the flexibility needed to interface with the rendering engine. For a detailed description of the code and the numerical testing, the reader can refer to [8; 9]. The code has been compared to real thermal vacuum test data in a CubeSat scenario proving to be reliable in computing the temperature field; moreover, preliminary mono-material cases using such code for thermal image generation are presented in [2; 10; 11].  
For the multi-region case presented in the article, different properties are assigned to the different parts of the object. From the resulting temperature field, each mesh face temperature is available together with the face orientation, which is fundamental for the view factor computation as reported in the next sections.
# Geometric Parameter Self-Calibration Based on Projection Feature Matching for X-Ray Nanotomography
## 2 Materials and Methods
### Geometric Parameter Calibration Process  
The nanotomography geometric parameter self-calibration algorithm based on projection feature matching mainly includes projection image preprocessing, pairwise projection image registration based on feature points, and solving geometric parameters. The flow of geometric parameters is shown in Figure 2. The preprocessing of projected images mainly uses the normalization of projected image gray value, non-local average denoising and histogram equalization. Feature-matching screening is mainly achieved by extracting feature points from SURF and designing feature triangles from projection images. The filtered high-quality matching feature points are then used to achieve the proposed parameter fitting, and the parameters are used to achieve projection correction. Finally, 3D reconstruction of the projection image using the Feldkamp-Davis-Kress (FDK) reconstruction algorithm is used to obtain the voxel data.
## 1 Introduction
Two different methods of NDT were proposed recently to identify a gap under a concrete slab track based on an impact echo test method with wavelet transform analysis [12] and on wave activated stiffness [13], respectively. A new impact echo analysis method was adapted recently where the signals recorded from the microphones were analyzed using percentage of energy information to detect in situ damages. Specially, in this method, an artificial neural network (ANN) was used in order to test the feasibility of using the automaticity of the impact echo test method [14]. One of the NDT methods, GPR (ground penetrating radar), has been used for detecting reinforcement bars in concrete structures and locating underground utility in general. GPR uses radio waves to locate objects and subsurface structures in materials such as soil, rock, concrete, and asphalt pavement. It was found in several studies that a change of properties (dielectric permittivity and relative conductivity) between concrete and air was sensitive enough in cases of voids existence under the concrete structure since strong reflections were measured in GPR data [15; 16]. However, restriction of using the GPR method for detecting a gap generated under concrete structures is high interference at shallow depths due to the presence of rebar in the concrete structures that causes a lot of chaotic reflections. This means that GPR is not a proper NDT method to detect gaps between railway track slabs and tracked soil foundations accurately since densely woven reinforcement rebar exist at the bottom of the concrete slab. In this study, first, finite element analysis was performed to investigate the possibility, applicability, and effectiveness of using the NDT-RAW method as a sensing method to detect gaps existing immediately underneath concrete slabs of railway tracks. ABAQUS, a general finite element analysis software, was used for this purpose. An imaginary impact load was simulated and applied to the top surface of a concrete slab in order to generate air waves in the air and elastic waves in the concrete slab simultaneously. Leaked acoustic Rayleigh waves transmitted from the top surface of the concrete slab to the air and elastic waves generated in the concrete slab by the imaginary impact load were analyzed with a size and a depth of an imaginary gap underneath the concrete slab and with a surrounding soil stiffness. Second, for verification purposes, a small concrete slab was built on a test site for simulating a slab of a railway concrete track and for obtaining wave propagation simulation by applying an impact load to the surface of the slab. Leaky acoustic wave and elastic wave were obtained from the impact test and were analyzed to obtain useful information that can be used for developing a new NDT-RAW technique for the detection of gaps between slabs of railway tracks and trackbed soil foundations.
## 5 Evaluation Metrics and Cross-Validation for Cardiovascular Disease Prediction
### Evaluation Metrics
Therefore, assessing machine learning models for cardiovascular disease prediction requires a combination of general and specific evaluation metrics, considering the trade-off between sensitivity and specificity. Balancing the trade-off between minimizing false positives and false negatives is particularly significant in healthcare applications. By adjusting the classification threshold, one can balance sensitivity and specificity according to the specific requirements of the application. In addition, ensemble techniques like bagging and boosting can help to improve the performance of models on imbalanced data by combining multiple models to make predictions.
# An Efficient Distributed Coverage Hole Detection Protocol for Wireless Sensor Networks
## 3 System Model  
Let us consider a wireless sensor network in which \(n\) number of wireless sensors are deployed randomly over a rectangular monitoring region \(R\). Let \(R_{c}\) and \(R_{s}\) be the communication and sensing range of a sensor, respectively, such that \(R_{c}=2R_{s}\). It is assumed that some part of the network is densely deployed with a large overlap of the sensing range, whereas some other part of the network is sparsely deployed, which is obvious due to the random deployment of the nodes. The part of the network in which nodes are deployed densely can have coverage overlapping, and the part of the network where nodes are sparsely deployed can have coverage holes; and the whole network is well connected irrespective of the presence of holes. Besides, coverage holes may be created due to the predictable death of the nodes, as some nodes may die due to power exhaustion. Similarly holes may be created due to unpredictable death of the nodes, as nodes may die due to hardware and software failures. Let \(S=\{S_{1},S_{2},...,S_{i},...,S_{n}\}\) be the set of \(n\) nodes, where \(S_{i}=(X_{i},Y_{i})\), \(i=\) 1,2,3,...,\(n\) is the location of each sensor. It is assumed that every node knows its location information through GPS or some location services, and the coordinates of the monitoring region \(R\) are also known. As shown in Figure 2, \(A(x_{1},y_{1})\), \(B(x_{2},y_{2})\), \(C(x_{3},y_{3})\) and \(D(x_{4},y_{4})\) are the coordinates of the monitoring region \(R\).