esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Effects of Atmosphere and Soil Drying on
## 2 Materials and Methods
### Methods  
#### 2.2.1 Estimation of the Stomatal Conductance  
Based on the Penman-Monteith formula, vegetation stomatal conductance can be estimated using flux measurement [35]. The formula is as follows:  
\[\text{Gs}=\frac{\gamma LEg_{a}}{\Delta(R_{n}-G)-LE(\gamma+\Delta)+\rho C_{p}VPDg _{a}} \tag{1}\]  
In the equation, Gs is the stomatal conductance (m s\({}^{-1}\)), \(\gamma\) (kPa \({}^{\circ}\)C\({}^{-1}\)) is the hygrometer constant, \(LE\) (W m\({}^{-2}\)) is the latent heat flux, \(\Delta\) (kPa \({}^{\circ}\)C\({}^{-1}\)) is the slope of saturated vapor pressure and temperature, \(R_{n}\) (W m\({}^{-2}\)) is the net radiation flux, \(G\) (W m\({}^{-2}\)) is the soil heat flux, \(\rho\) (kg m\({}^{-3}\)) is the air density, \(C_{p}\) (J kg\({}^{-1}\)\({}^{\circ}\)C\({}^{-1}\)) is the specific heat capacity of air, _VPD_ (kPa) is the saturated water vapor pressure difference, and g\({}_{a}\) (m s\({}^{-1}\)) is the aerodynamic conductivity. In this paper, the relationship between canopy height and wind speed proposed by [PERSON] and [PERSON] is used to estimate \(g_{a}\)[36]:  
\[g_{a}=\frac{U\text{K}^{2}}{\left[\text{ln}(\frac{z_{m}-z_{d}}{z_{0}})\right]^{ 2}} \tag{2}\]  
where \(U\) (m s\({}^{-1}\)) represents the wind speed, K is the von [PERSON] constant (K = 0.4), \(z_{m}\) (m) is the measured height, \(z_{d}\) (m) is the zero plane displacement, and \(z_{0}\) (m) is the momentum roughness length. \(z_{d}\) and \(z_{0}\) are taken as 0.67 h and 0.1 h, respectively, while h is the canopy height.  
Figure 1: Global distribution of 36 flux stations (5 PFTs are included: CRO, DBF, ENF, GRA, and SAV).
# Photonic Packaging: Transforming Silicon Photonic Integrated Circuits into Photonic Devices
## 1 Introduction
Figure 1: Schematic of a Si-PIC (photonic integrated circuit) packaged with a multi-channel quasi-planar coupled (QPC) fiber-array, a hybrid-integrated laser source based on a micro-optic bench (MOB), a vertically integrated electronic integrated circuit (EIC), and a thermo-electric cooler. Electrical connections between the PIC and PCB (printed circuit board) are made by wire-bonds, while the connections between the PIC and EIC are made using copper pillar bumps (CPBs).
# Evaluation of the Mid-Latitude Ionospheric trough Using GRACE Data
## 3 Results and Discussion
### Dependence on the SYM-H Index  
The SYM-H index is similar to the Dst-index but has a different time resolution and method to determine the base value. The temporal resolution of the SYM-H index is 1 min. [PERSON] et al. [28] found that the SYM-H index is accurate and should be used as a replacement in future studies. Figure 18 shows the dependence of Tm, width, and depth according to SYM-H index. The SYM-H index shows a clear correlation with the main trough parameters, and in the northern hemisphere, this dependence is more prominent than in the southern hemisphere. With a decreasing SYM-H index, the trough moves equatorward. The trough becomes narrower and deeper with a decreasing SYM-H index. Our results confirm the previous findings by [PERSON] et al. (2016) [19], who showed that the trough dynamics differ depending on the magnetic storm category and that the shift in the position of the trough in the northern and southern hemispheres during magnetic storms is symmetrical.  
Figure 17: Bx (blue), By (orange), and Bz (green) component dependence of the trough minimum position, trough width, and trough depth for the northern and southern hemispheres.
## 3 System and Methodology
### System Overview  
The overall framework of the proposed system is illustrated in Figure 1. The RGB-D camera equipped on the UAV takes the image every 0.5 s and then transmits the image  
Figure 2: UAV (**left**), the Percipio RGB-D camera (**right**).  
to the server via WIFI. We applied the Mercury-MW150R router to transmit the signal from the UAV to the server, which theoretically has an effective communication range of 300 m indoors and 800 m outdoors. On the server, these images are used to detect, track, segment objects, and estimate 6D poses. The position, size, and 6D pose parameters of the target were transmitted back to the UAV to provide visual assistance for handling tasks. The following sections describe the detection, segmentation, and object 6D pose estimation algorithms.
# 5G Network Slice Isolation
## 4 Model of Network Slice Isolation  
In this section, we develop a mathematical model to help MNO identify the cost of deploying the right network slice isolation points. We use the 3 GPP protocol stack [22, 23] as a network slice logical deployment representation. We begin with the case where the isolation points can be independently selected for every individual slice. Ideally, those isolation points would not have any impact on the performance nor implementation cost of other slices. Therefore, we define a mathematical model to provide a rational representation. We start with the following notations:  
We consider the set of all \(N\) slices \(\mathcal{N}=\{1,2,3,\ldots,N\}\), where every slice needs to implement a full stack \(\mathcal{P}=\{1,2,3,\ldots,P\}\) of protocol layers. Every individual slice \(n\in\mathcal{N}\) can independently and flexibly choose the method to implement each protocol layer, either in a physical way or in a virtual way. This can be formulated with a binary indicator for every pair of network slice \(n\) and protocol layer \(p\):  
\[v_{n,p}=\begin{cases}1&n\text{ virtualizes }p\\ 0&\text{otherwise}\end{cases},\quad\forall n\in\mathcal{N},p\in\mathcal{P}. \tag{1}\]  
For each protocol layer \(p\) of a specific network slice \(n\), call its isolation level \(i_{n,p}\), the tenant control level \(t_{n,p}\) can be selected from a finite discrete set \(\mathcal{T}_{n,p}\subseteq\mathcal{T}\), where \(\min(\mathcal{T}_{n,p})=0\) and \(\max(\mathcal{T}_{n,p})=t_{n,p}^{\max}\in(0,1)\). We also define the MNO control level \(m_{n,p}=1-t_{n,p}\), the operations cost \(c_{n,p}^{\text{op}}\) and the infrastructural cost  
\[c_{n,p}^{\text{ifr}}=c_{n,p}^{\text{P}}v_{n,p}+c_{n,p}^{\text{V}}\big{(}1-v_{n,p}\big{)}, \tag{2}\]  
where \(c_{n,p}^{\text{P}}\) and \(c_{n,p}^{\text{V}}\) are the cost to implement \(p\) for \(n\) physically and virtually, respectively. Their values in practical systems are determined by the specific hardware and software used by the MNO. Generally, given an arbitrary fixed \(i_{n,p}\),  
\[c_{n,p}^{\text{P}}>c_{n,p}^{\text{V}},\quad\forall(n,p)\in\mathcal{N}\times \mathcal{P}. \tag{3}\]  
Figure 5: The controls of MNO and tenant are exclusive to each other.  
through linear interpolation. Such linear programs are guaranteed to be efficiently solved with polynomial time complexity. Thereafter, the optimal solution to the original problem in Equation (5a)-(5d) can be obtained by rendering the optimum relaxed LP, e.g., with the well-known branch-and-bound or cutting-plane algorithms.
This research paper is therefore intended to address the gap by investigating the degree of sustainability after the adoption of E-procurement practices, identify the factors that are significant for E-procurement as an instrument of sustainable supply chain performance, and the relationship between E-procurement technology (EPT) and green procurement practices (GPP). For many years, TAM has indeed been competent in forecasting whether or not users would adopt technological changes [57]. This study attempts to understand the antecedents of E-procurement adoption, as well as their implementation and the possible effect on green procurement practices in ISO 14001-certified firms by employing TAM theory [58]. There is a scarcity of studies focusing on a comprehensive study of how these two disciplines may be linked, which offers some challenges when pursuing procurement effectiveness and profitability. As a result, this study fills the gap by developing an integrated program that enables a platform to encourage research involvement in the future, therefore supporting both goals. The relationships between EPT and SPP were explored and investigated [33; 43]. The limited literature search for EPT and SPP was emphasized [33; 43], eventually showing that the linkages between some components of the social assurance of sustainability in the procurement process and E-procurement seemed less recognized, although support mechanisms may be implemented to boost EPT [59; 60; 61; 62; 63]. This shows the need for an improved alignment of both activities to promote productivity and sustainability in the procurement process and sustainable supply chain management. Our goal is therefore to fill the research gap in electronic procurement and its impact on green procurement for sustainable supply chains in ISO 14001-certified firms through the following:  
* Assessing the level of sustainability after the adoption of E-procurement technology;
* Assessing the factors portraying as a tool for green procurement practices;
* Assessing the effect of E-procurement technology on green procurement practices for the sustainable supply chain. This study contributes significant information to scientific knowledge and the community as a whole, in line with the specific objectives stated above. This study provides scientific knowledge that will assist the organization in developing and implementing procurement policies. A guide to promoting EPTs and GPP in the ISO industries towards sustainable developments is provided by this study to industry practitioners and policymakers. As a result of this study, many companies will have a benchmark for improving their procurement capabilities; they can gain insight that their competitors cannot access, contributing to a competitive advantage over the long run. Research agendas for continual improvement in project procurement are shaped by elements revealed in the integration framework. Lastly, this study will help employees understand and apply digital procurement and green procurement opportunities that will result in improved supply chain performance.
# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 2 Study Area, Data, and Methodology
### Methods
#### 2.3.6 Model Performance Evaluation  
To comprehensively assess the performance of the developed models, this study utilized several widely adopted and practical evaluation metrics, including sensitivity, specificity, precision, and F1 score [54]. These metrics are derived from the four fundamental values in a confusion matrix: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) [55]. Each metric evaluates the model's classification performance from a different perspective, providing nuanced and holistic insights, especially when dealing with imbalanced datasets. The formulas for the evaluation metrics used in this study are defined as follows:  
\[Sensitivity=\frac{TP}{TP+FN} \tag{3}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{4}\]  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[F1\ Score=2\times\frac{Precision\times Sensitivity}{Precision+Sensitivity} \tag{6}\]  
These metrics collectively evaluate model performance from complementary perspectives. Sensitivity measures the model's ability to detect landslide-triggering events, thereby minimizing missed alarms. Specificity quantifies the accuracy in identifying non-triggering events, reducing false alarms. Precision reflects the reliability of positive predictions by assessing how many predicted landslides are actual events. The F1 score combines precision and sensitivity, offering a balanced measure, which is particularly crucial for imbalanced datasets.  
Additionally, this study also utilized the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) as evaluation metrics [56; 57]. The ROC curve visualizes the relationship between the True Positive Rate (TPR) and the False Positive Rate (FPR) across various classification thresholds, providing an intuitive assessment of model performance under different decision conditions. Meanwhile, the AUC quantifies the area under the ROC curve, where values closer to 1 indicate superior model performance in distinguishing between classes.
## 2 Method
### Obstacle Prediction Using Fusion Prediction
#### 2.1.3 Integration of Prediction Methods  
We consider integrating the physics-based prediction CVM and pattern-based prediction KMP to provide a comprehensive obstacle prediction system.  
Suppose at the \(i\)-th time step, the mean and covariance of the moving target are \(\hat{\xi}_{i}^{o}\) and \(\hat{P}_{i}^{o}\), respectively. The challenge is to compute the mean and covariance at the next time step \(i+1\) using both the CVM and KMP.  
The physics-based prediction CVM updates the mean and covariance as follows:  
\[\begin{split}\hat{\xi}_{i}^{o,\text{CVM}}&=\hat{ \xi}_{i}^{o}+\vec{u}_{n}\\ \hat{P}_{i}^{o,\text{CVM}}&=\hat{P}_{i}^{o}+Q_{i} \end{split} \tag{14}\]  
where \(\vec{u}_{n}\) and \(Q_{i}\) are obtained according to (3).  
The pattern-based prediction KMP updates the mean and covariance as follows:  
\[\begin{split}\hat{\xi}_{i}^{o,\text{KMP}}&=\mathbf{ E}(f^{pred}(\hat{\xi}_{i}^{o}))\\ \hat{P}_{i}^{o,\text{KMP}}&=\mathbf{D}(f^{pred}(\hat{ \xi}_{i}^{o}))\end{split} \tag{15}\]  
To integrate the predictions from both methods, we use a weighted fusion approach. Let \(\vec{W}_{i}\) be the weighting factor for the CVM and \(\vec{I}-\vec{W}_{i}\) be the weighting factor for the KMP. The fused mean \(\hat{\xi}_{i+1}^{o}\) and covariance \(\hat{P}_{i+1}^{o}\) at time step \(i+1\) are computed as follows:  
\[\begin{split}\hat{\xi}_{i+1}^{o}&=\vec{W}_{i}\hat{ \xi}_{i}^{o,\text{CVM}}+(\vec{I}-\vec{W}_{i})\hat{\xi}_{i}^{o,\text{KMP}}\\ \hat{P}_{i+1}^{o}&=\vec{W}_{i}\hat{P}_{i}^{o,\text{ CVM}}\vec{W}_{i}^{T}+(\vec{I}-\vec{W}_{i})\hat{P}_{i}^{o,\text{KMP}}(\vec{I}- \vec{W}_{i})^{T}\end{split} \tag{16}\]  
We seek \(\vec{W}_{i}\) that minimizes \(\hat{P}_{i+1}^{o}\) by setting \(\frac{\partial\hat{P}_{i+1}^{o}}{\partial\vec{W}_{i}}\) to \(\vec{0}\), i.e.,  
\[\frac{\partial\hat{P}_{i+1}^{o}}{\partial\vec{W}_{i}}=\hat{P}_{i}^{o,\text{ CVM}}\vec{W}_{i}+\hat{P}_{i}^{o,\text{KMP}}(\vec{I}-\vec{W}_{i})=\vec{0} \tag{17}\]  
Solving this equation, we derive the optimal \(\vec{W}_{i}^{*}\) as  
\[\vec{W}_{i}^{*}=\hat{P}_{i}^{o,\text{KMP}}(\hat{P}_{i}^{o,\text{ CVM}}+\hat{P}_{i}^{o,\text{KMP}})^{-1} \tag{18}\]  
Using \(\vec{W}_{i}^{*}\), we can compute the optimal fused mean \(\hat{\xi}_{i+1}\) and covariance \(\hat{P}_{i+1}^{o}\):  
\[\begin{split}\hat{\xi}_{i+1}^{o}&=\vec{W}_{i}^{*} \hat{\xi}_{i}^{o,\text{CVM}}+(\vec{I}-\vec{W}_{i}^{*})\hat{\xi}_{i}^{o,\text{ KMP}}\\ \hat{P}_{i+1}^{o}&=\vec{W}_{i}^{*}\hat{P}_{i}^{o, \text{CVM}}\vec{W}_{i}^{T}+(\vec{I}-\vec{W}_{i}^{*})\hat{P}_{i}^{o,\text{KMP}}( \vec{I}-\vec{W}_{i}^{*})^{T}\end{split} \tag{19}\]  
This integration approach leverages the strengths of both the CVM and KMP to provide a robust prediction system for moving obstacles. It should be noted that to predict a sequence of state transitions, successive one-step predictions are performed to construct an extended long-term trajectory.
## 4 Performance Analysis of the BOC Signal and BPSK Signal  
In this section, we investigate the performance of the composite signal in terms of the acquisition time, tracking performance, bit error ratio (BER), and switching time  
Figure 7: The time sequence of device C in the fast-switching process.  
for different ratios of BOC and BPSK signals. In addition, we will give the hardware implementation of the novel BOC signal.
# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 5 Experimental Evaluation
### Simulation Results
#### 5.2.4 Generalization Ability of NDR-Net
To mitigate the impact of CFO on the channel estimation accuracy, the CFO needs to be compensated at the receiver side. This is what we need to study in the future. Figure 14: Generalization performance of different channel estimators when the test channel is TDL-C, SNR = 15 dB, Velocity = 350 Km/h. We also consider the case where the PAPR of the transmitted OFDM signal is high. To reduce the impact of high PAPR, the signal is clipped before it passes through the channel. Figure 16 represents the proposed algorithm trained at TDL-B-120 Km/h without signal clipping. The simulations are performed under the conditions of signal without clipping, signal clipped to 6 dB, 7 dB and 8 dB, respectively. Firstly, as can be seen from the simulation results, when the modulated signal is clipped to 6 dB, its MSE curve appears flat when the SNR is greater than 30 dB, so the SNR loss is gradually enlarged compared with the estimated performance tested under unclipping. Secondly, there is almost no SNR performance loss when the MSE is \(10^{-2}\). For example, when the MSE is 0.019, the SNR performance loss is about 5 dB. Therefore, in general, the proposed algorithm is robust to PAPR. Figure 16: Generalization performance of NDR-Net trained without signal clipping, tested under the conditions of signal without clipping, signal clipped to 6 dB, 7 dB and 8 dB, respectively. Figure 15: Generalization performance of NDR-Net trained at CFO = 0 and tested under the conditions of CFO = 0, 0.01, 0.05, 0.1, respectively.
### Classifiers  
#### 3.3.1 Statistical Classifier  
In references [45; 46; 48], a multi-class classifier was proposed, applied, and optimized for the recognition of ornate granites in digital images. Its experimental results were very high, being a promising classifier in image recognition. This research proposes its use in the identification of cervical cancer images. The classifier is statistically based and consists of two phases: learning and recognition. In the learning phase, a prototype vector is generated for each known carcinogenic image. Meanwhile, in the recognition phase, a vector is generated for a test carcinogenic image. This resulting vector is compared against all of the known prototype vectors, and, ultimately, the carcinogenic image is assigned to the class whose textural characteristics are most similar.  
To train the classifier, the following process is employed, considering that the task is the classification of binary carcinogenic images in \(M\) classes where each binary image is a class. From a carcinogenic image of non-homogenous texture, a \(P\) series of \(S_{m}^{\alpha}\ (m=1,2,3,\dots,M)\), \((\alpha=1,2,3,\dots,P)\) sub-images are sampled at random, and then the \(F_{m}^{\alpha}[k]\) characteristic vector is obtained for each sub-image. Subsequently, each carcinogenic image of \((S_{m})\) class is characterized through its \(F_{m}^{\alpha}[k]\) spectra.  
\[F_{m}[k]=\frac{1}{P}\sum_{\alpha=1}^{P}F_{m}^{\alpha}[k] \tag{8}\]  
\(F_{m}[k]\) is considered the prototype vector of the cervical cancer image (class \(m\)) in the textural spectrum employed, LBP, OC-LBP, CS-LTP, ICS-TS, or CCR. In the recognition phase, a characteristic vector from a test carcinogenic image is estimated, and the minimal  
Figure 5: CCR texture unit.  
distance is deployed to assign it to the class with the most similar prototype. For the characterization of the \(\mathcal{S}_{T}\) test cervical cancer image, a series of \(K\) sub-images \((\beta=1,2,3,\dots,K)\) of equal size are employed; in concurrence with the training phase, the \(F_{T}^{\beta}[k]\) characteristics vector of each \(\mathcal{S}_{T}^{\beta}\) sub-image is obtained. The carcinogenic image \(S_{T}\) is characterized through the \(F_{T}^{\beta}[k]\) functions as shown in the following section:  
\[F_{T}[k]=\frac{1}{K}\sum_{\beta=1}^{K}F_{T}^{\beta}[k] \tag{9}\]  
The test carcinogenic image is assigned to class m if and only if the distance between the vector and prototype vector of class m is minimal; the distance is calculated with  
\[d(F_{T}[k],F_{m}[k])=\sum_{k}|F_{T}[k]-F_{m}[k]| \tag{10}\]  
The classification result is illustrated in a confusion matrix \(C=\{C_{mm}\}\) whose rows correspond to the test images, the columns entail the classes, the main diagonal presents the correct predictions, and the elements circumventing the diagonal represent the classification errors or incorrect predictions [49].
# Sentiment Analysis Based Requirement Evolution Prediction
## 2 Related Work
### Requirements Evolution Prediction through Sentiment Analysis  
A number of studies have focused on requirement extraction for mobile applications [1]. [PERSON] et al. investigated users' reviews' Sentiment Analysis (SA) in order to extract user requirements to build new applications or enhance existing ones [2]. The authors analyzed users' feedback to extract the features and sentiment score of a mobile app in order to find useful information for app developers [3]. [PERSON] et al. presented an exploratory study of Twitter messages to help requirements engineers better capture user needs information for software evolution [4]. [PERSON] et al. used text mining technology to evaluate quantitatively the economic impact of user opinions that might be a potential requirement of software [5]. [PERSON] and [PERSON] proposed an automated approach to extract the user sentiments about the identified features from user reviews. The results demonstrated that machine learning techniques have the capacity to identify relevant information from online reviews automatically [6]. The analysis of user satisfaction is useful for improving products in general and is already an integral part of requirements evolution. An approach was presented for mining collective opinions for comparison of mobile apps [7]. To determine important changes in requirements based on user feedback, a systematic approach was proposed for transforming online reviews to evolutionary requirements [8]. A text mining method was proposed to extract features from precedents, and a text classifier was applied to classify judgments according to sentiment analysis automatically [9]. The authors proposed incorporating customer preference information into feature models based on sentiment analysis of user-generated online product reviews [10]. A semi-automated approach was proposed to extract phrases that can represent software features from software reviews as a way to initiate the requirement reuse process [11].