esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis
### Outage Probability
#### 4.2.2 Co-Carrier-Based Selection Combination  
For the outage probability of UT \(k\) in the co-carrier-based SC scheme, we have the following theorem.  
**Theorem 5**: _The outage probability of the k-th beam-edge UT in the co-carrier-based SC scheme is given by_  
\[\Pr_{k}^{\prime}=\left\{\begin{array}{ll}(1-e^{-\frac{\Gamma_{k}}{|g_{k}|^{2 }\delta(1-\pi k)}})^{M},&1-\Gamma_{k}\alpha>0\\ 1,&1-\Gamma_{k}\alpha\leq 0\end{array}\right. \tag{36}\]  
The outage probability of UT \(k\) in SC method can be defined as  
\[\Pr_{k}^{\prime} =\mathbb{P}(SINR_{k}^{\prime}<\Gamma_{k})\] \[=\mathbb{P}(\max\{SINR_{1,k}^{\prime},\ldots,SINR_{M,k}^{\prime} \}<\Gamma_{k})\] \[=\mathbb{P}(\frac{|g_{k,1}|^{2}\delta}{|g_{k,1}|^{2}\alpha\delta +1}<\Gamma_{k}),\ldots,\mathbb{P}(\frac{|g_{k,M}|^{2}\delta}{|g_{k,M}|^{2} \alpha\delta+1}<\Gamma_{k}). \tag{37}\]  
According to (26), we can derive (36).  
The theorem is proved.  
Since the beam-center UT in the co-carrier-based SC method receives one signal path as well, the outage probability of beam-center UT is the same as that in the co-carrier-based DC method shown in (31).
## 3 General Findings
### Key Findings Addressing the Research Questions
#### 3.2.2 Identified Determinants for the BIS Adoption and Acceptance  
Identification of potential determinants is very important for successful implementation of BIS as it will guide the decision-makers to remove the barriers in the adoption and acceptance of BIS. The content analysis of models and theories has been done by rigorous review of the literature regarding the identification of the determinants. Total 93 determinants are identified within the given subject of the study. The most frequent determinants are presented in Table 6 that have influenced the adoption and acceptance of BIS in organizations. The significant determinants are presented with name, rank, frequency, and category code in Table 6. The analysis reveals clearly that determinant \"organization size\" ranked the top of the list with a total frequency of 13, industry size; company size, business size, and organization size are merged in one determinant \"organization size\". Relative advantage is ranked at second with usage frequency of 12, followed by determinant complexity with frequency 11, competitive pressure 9, compatibility, and cost with frequency 8, and organization readiness and top \begin{table}
\begin{tabular}{c c} \hline \hline
**Theories/Frameworks/Models** & **References** \\ \hline Diffusion of Innovation (DOI) & [41, 47, 51, 52, 54, 55, 58, 61, 68, 73, 75, 76, 82, 86, 100, 101] \\ Technology-Organization -Environment (TOE) & [19, 41, 47, 54, 55, 61, 68, 69, 73, 75, 76, 80, 84, 101] \\ Institutional Theory & [41, 43, 47, 54, 55, 61, 73, 76, 101] \\ Unified Theory of Acceptance and Use of Technology (UTAUT) & [25, 50, 57, 100] \\ Theory of Planned Behavior (TPB) & [25, 59, 82, 98] \\ Technology Acceptance Model (TAM) & [36, 39, 51, 57, 60, 98] \\ Resource-Based View (RBV) Theory & [59, 72, 75] \\ Critical Success Factor (CSF) Theory & [59] \\ Maturity Model & [59] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Theories and models used for business intelligence system (BIS) adoption and acceptance. Figure 6: Theories and frameworks/models used for the BIS adoption/acceptance. management support with frequency 7 and 6 respectively. Technology cost, organization cost, and affordability are considered as one determinant under the name of determinant \"Cost\".
## 2 Background  
### Code-Based GNSS Inter-User Ranging Techniques  
By considering a pair of moving users (i.e., spacecraft) \(i\) and \(j\), at a given time instant \(k\), relative ranging technologies allow to estimate their _inter-user range_ (a.k.a. _baseline length_)  
\[d_{ij,k}=||\mathbf{d}_{ijk}||=||\mathbf{x}_{j,k}-\mathbf{x}_{i,k}|| \tag{1}\]  
where the operator \(||\cdot||\) is the Euclidean norm and \(\mathbf{x}_{i,k}\), \(\mathbf{x}_{j,k}\) are the actual locations of the users in a given Cartesian reference frame. While radio-frequency ranging solutions directly provide an estimate of Equation (1) by means of signal Time of Flight (ToF) or Round-trip Time (RTT), DGNSS techniques first estimate a displacement vector, a.k.a. _baseline vector_  
\[\mathbf{d}_{ij,k}=\left[\begin{array}{cc}\Delta x_{ijk,k}&\Delta y_{ijk,k}& \Delta z_{ijk,k}\end{array}\right]. \tag{2}\]  
Through DGNSS techniques, the inter-user range estimation can be obtained by applying the Euclidean norm to an estimate of Equation (2), namely \(\hat{\mathbf{d}}_{ij,k}\).  
The DGNSS estimation of the inter-user range can be achieved through a few differential methods [23], based on the combination of pseudorange measurements from independent, networked GNSS receivers, following the exchange of these data from one user, defined as the _aiding user_, to another, the _aided user_, as depicted in Figure 0(a). Different algorithms can be selected according to their capability of canceling the systematic biases at stake (i.e., receiver and satellite clock biases and satellites orbit biases) [47], at the cost of an increased output variance [48]. Despite the fact that ionospheric and tropospheric errors can be reasonably neglected at lunar distances, other common sources of error may affect the spaceborne receivers and could be effectively compensated through these techniques.  
Given the high dynamics inherent in a differential GNSS scenario involving lunar orbiters, the effect of the time offset between the GNSS observables of the two receivers is amplified, meaning that the baseline will have changed significantly over this time offset. Therefore, in all the techniques presented here below, the pseudoranges of the _aiding user_ are considered to be shared to the _aided user_ such that they can subsequently be subject to a  
Figure 1: Pictorial view of differential GNSS ranging both for terrestrial applications and space scenarios. The symbol * denotes the user id where it can be either \(i\) or \(j\).  
time-compensation to be extrapolated to the _aided user_ measurements timestamps, before estimating the _aiding user_ SPP position.  
Therefore, the present section defines the pseudorange measurements, the time-compensation technique, the SPP estimation technique and all the GNSS ranging techniques that the present contribution applied to infer the Inter-Spacecraft Range (ISR) in a lunar scenario of DGNSS.  
In the following, the inter-user range will be referred to as ISR or baseline length and the inter-user vector as ISV or baseline vector.
# A Context-Aware Neural Embedding for Function-Level Vulnerability Detection
## 4 Related Work
### Software Vulnerability Detection  
Rules derived from the experience of knowledgeable individuals are applied for detecting potential buggy/vulnerable code that did not conform to the rules or best practices [37; 57; 58]. However, designing rules to cover all possible programming flaws is infeasible. The ML techniques offer new solutions for bugs/vulnerabilities detection by learning rules/patterns automatically. For instance, [PERSON] et al. [59] applied features from imports and function calls as indicators of vulnerable software components. API usage patterns were used by [PERSON] et al. [60] as features for predicting functions containing potential vulnerabilities. [PERSON] et al. [6] extracted code-based metrics and meta-data retrieved from open-source projects to predict commits that lead to vulnerabilities. Nevertheless, the aforementioned studies still depend on the manual process for engineering features, which can be time consuming and also error prone.  
Recently, deep learning techniques were applied for bug/vulnerability detection. It is hypothesized that the deep architecture is capable of automatically learning the latent patterns, which can be more effective and generalizable than the features driven by human knowledge. Based on this hypothesis, [PERSON] et al. [13] used deep belief networks for learning semantic patterns for bug discovery. [PERSON] et al. [14], and [PERSON] et al. [49] leveraged LSTM network for learning high-level representations for detecting vulnerabilities on C software projects. [PERSON] et al. [61] adopted LSTM network to learn vulnerable patterns on so-called \"code gadgets\", which are the explicitly-defined data flows. The data flow can either be within function boundary or span multiple functions. In this paper, we combine the ELMo module and LSTM network to learn latent source-sink patterns directly from the source code, and thus no other code analysis tools are required. In terms of learning source-sink patterns that are associated with taint-style vulnerabilities, [PERSON] et al. [33] extended the code property graph (CPG) [62] for analyzing the vulnerable source-sink patterns. They converted source code to graphs and formulated the vulnerable source-sink patterns as graph traversal queries for matching potential vulnerabilities. Our work utilizes the deep neural network for learning vulnerable source-sink patterns automatically, thus significantly simplifying the code analysis processes.
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 4 Displacement of Large Diameter Monopile under Scour
### Discussion  
1. Figure 14a shows the relationship between scour depth and pile head displacement under each level of load. In the higher load level, with the increase of scour depth, the pile displacement increases nonlinear obviously; at the lower load level, the pile displacement changes little with the increase of scour depth, or can be approximately linear correlation.
2. It can be seen from Figure 14b that the displacement and load are nonlinear, whether or not scour occurs. When the load is less than 200 kN, the effect of local scour is not obvious. When the scour depth is 0.5D, the pile head displacement after scour can reach 1.4 times of that before scour. When the scour depth is 1.5D, it can reach 3.6 times of that before scour.
3. According to the position of the minimum positive and negative displacement, the point of apparent fixity (\(y=0\)) could be determined by linear interpolation. Displacement under each level of load was collected. It can be seen from Figure 15 that the depth of apparent fixity decreases with the increase of scour depth. Taking the scour depth 2D as an example, when \(F=200\) kN, the apparent fixity depth increases by 1.2 m; when \(F=400\) kN, the depth increases by 0.4 m; and when \(F=600\) kN, the depth only increases by 0.1 m. In other words, the greater the load, the smaller the impact of scour on the apparent fixity depth. That is to say, at a high load level, scour has less influence on the apparent fixity depth. At a low load level, scour has a greater influence on the apparent fixity depth. This is because, at a high load level, the soil deformation has been fully developed and the pile displacement and rotation are mainly affected by the amplitude of load, so the influence of scour is relatively limited, thus the depth of apparent fixity  
Figure 14: Pile head displacement of monopile under scour. (**a**) Scour depth–displacement under different load levels; (**b**) load–displacement under different scour depths.  
Figure 15: Scour depth–apparent fixity depth under different load levels.  
has no obvious change under scour. Meanwhile, at a low load level, the soil deformation is small, and scour deteriorates the soil conditions and reduces the embedment depth; thus, compared with load amplitude, scour also has a great influence on the depth of the apparent fixity.
# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 2 Related Work
### Image Alignment and Registration  
Image alignment, also known as registration, aims to find the spatial transformation that maps points or regions between images or a template and an image. This process is crucial for normalizing the view of a visual element's instance (such as a road marking or an industrial label) relative to a canonical template or a digital model, enabling consistent measurement and comparison. Classical feature-based methods, like SIFT [3], SURF [11], and ORB [4], detect local invariant features and use robust estimators like, RANSAC [5], to compute transformations. While effective in many general vision tasks, these methods often struggle significantly when applied to template images of structured elements, such as road markings. Such templates frequently possess simple geometric shapes with limited texture or distinct corner features. Traditional feature detectors designed for complex images often detect only a sparse set of points on these plain structures.  
Furthermore, standard SIFT and ORB, while offering some robustness to scale and rotation, are not fully invariant to the general affine transformations commonly encountered in viewpoint changes. This combination of difficulty in detecting a sufficient number of features on simple template structures and limited invariance to affine distortions makes traditional feature-based methods unreliable for robust template-to-transformed-template alignment, let alone the more complex template-to-real-world instance problem. Direct template matching based on correlation is similarly sensitive to geometric variations [12]. Deep learning has also been applied to image alignment; Spatial Transformer Networks (STNs) [13] learn implicit transformations, and more recent deep keypoint correspondence networks learn robust features [14; 15]. Our work falls into this broader category but is specifically tailored for the challenge of learning keypoints on structured templates, using a self-supervised approach.
# Multiscale Feature-Based Infrared Ship Detection
## 5 Conclusions  
In this paper, based on the idea of \"step-by-step accuracy\", we propose a novel multiscale feature-based infrared ship-detection method (MSFISD). The proposed method can efficiently and effectively achieve infrared ship detection in complex scenarios, which may provide assistance in applications such as night surveillance or remote piloiting. First, CRs are extracted from the whole image by extracting the sea-sky line and ROI and are refined by incorporating the edge features of the ship to reduce false alarms. Finally, by analyzing the shape features, the ship targets are finally accurately extracted. Experimental results demonstrate that compared with the other five methods, our method has a higher detection accuracy with a lower false-alarm rate and shows excellent detection performance. However, our method still has limitations. Firstly, in scenes with large-scale interferences, such as islands or bright and dark bands, it is a challenging task to accurately extract the sea clutter. In these scenarios, large areas of islands or reefs may obscure parts of the sea-sky line in images, resulting in ocean boundaries with potentially intricate shapes. Secondly, under certain lighting conditions in which the sea surface exhibits severe fluctuations, it becomes difficult to distinguish clutter, which resembles the grayscale and shape of ships. In future work, we will extend the method of sea-sky line extraction to a more universal marine area extraction and improve and innovate both the single-frame detection and multi-frame detection algorithms to achieve more accurate, stable, and robust infrared ship detection.  
Conceptualization, D.L.; methodology, H.T.; software, H.T.; validation, J.T. and L.T.; formal analysis, M.W.; investigation, L.T.; resources, J.T.; data curation, M.W.; writing--original draft preparation, H.T.; writing--review and editing, D.L.; visualization, Z.T.; supervision, D.L.; project administration, L.W.; All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data is contained within the article. Conflicts of Interest: The authors declare no conflicts of interest.
# Computer Architecture for Industrial Training Evaluation
## 3 Methodology
### Phase 1. Guidelines for the Architecture
#### 3.1.2 Correlation Between ADFs and ISO/IEC/IEEE 42010 Standard  
This standard specifies the requirements for the structure and expression of an architectural description (hereinafter AD) for several entities, including software, systems, enterprises, systems of systems, families of systems, products (goods or services), product lines, service lines, technologies, and business domains [36].  
The ISO/IEC/IEEE 42010 standard describes the requirements for the use of architectural concepts and their relationships as expressed in an AD. It also specifies the requirements for an architecture description framework (ADF), an architecture description language (ADL), architectural viewpoints, and model types to support the development and use of an AD.  
According to ISO/IEC/IEEE 42010, for the proper implementation of an architecture, an ADF must be selected to respond to the Stakeholders' concerns.  
The list of ADFs presented in this research is from Annex F of the ISO/IEC/IEEE 42010 standard [36]. Table 1 shows the details of the ADFs and their compliance with each one of the requirements that according to the standard they should have.  
Table 1 qualitatively describes the fulfillment of the requirements, that is, if the requirement is not met, meaning partially described or fully met. In order to identify which are the most complete ADFs, a quantitative assessment scale is proposed to transform the matrix in Table 1:  
* Value 0, if the requirement is not met.
* Value 0.5, if the requirement is partially met.
* Value 1, if the requirement is fully met.  
The implementation of the scale in Table 1 can be described as follows. For example, for the Zachman ADF, the first requirement, \"Information identifying the ADF\" in the standard indicates \"Yes\" meaning that requirement is fully met and is replaced by the value of 1. The same happens with the requirements \"Stakeholder identification\" and \"Stakeholder perspectives\" that will be replaced in the quantitative matrix by 1. In the case of the requirements \"Concern identification\", \"Architecture Viewpoint\", \"Model kinds\", \"Legends and correspondence methods\" and \"Framework methods\", as the requirement is partially fulfilled, the criterion of using half the scale value 0.5 is applied. In the case of the specifications that are \"No\", it is understood that this characteristic is not addressed in the ADF, and 0 is placed. All those specifications different from these parameters are assumed as clarifications and that the requirement is present, as is the case of \"Aspects\" that appear in the matrix as Called \"interrogatives\"; for this typology, a value of 1 is placed. This assessment is performed for each ADF, and the values presented in Table 2 are obtained.  
The ADFs with the highest scores are GERA and ArchiMate. However, no ADF meets all the requirements.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 3 Energy Management for an Energy-Harvesting Sensor Node  
In this section, we introduce a new EM for EH-WSNs, whose task is to dynamically adjust the performance of the node, evaluated in this work by the throughput, according to the current residual energy. The proposed EM can be used in collaboration with various MAC protocols. Later, we show the benefits of combining this novel EM with the SNW-MAC protocol leveraging WuRx proposed in Section 4.  
We assume that the time is divided into time slots of equal duration \(T\), and the EM is executed at the end of each slot to set the throughput of the node for the next slot \(k\). At each execution, the EM measures the current residual energy denoted by \(e_{R}\) and sets the frequency at which the node performs sensing and sends the so-obtained data. The EM sets this frequency by adjusting the wake-up interval for the next slot denoted by \(T_{WI}\), i.e., the time between two consecutive sense-and-send operations. Two submodules compose the proposed EM as shown in Figure 1. The Energy Budget Computation (EBC) module evaluates the energy that the node can consume in the next time slot \(k\) to remain sustainable. This amount of energy is called the energy budget and is denoted by \(e_{B}[k]\). The inputs to the EBC are the residual energy at the end of the slot \(k-1\) and the variation of residual energy, respectively denoted by \(e_{R}[k-1]\) and \(\Delta e_{R}[k-1]\), defined by:  
\[\Delta e_{R}[k-1]=e_{R}[k-1]-e_{R}[k-2]. \tag{1}\]The second module is the Throughput Computation (TC) module, which calculates the wake-up interval \(T_{WI}[k]\) according to the energy budget \(e_{B}[k]\). When the topology is a star network, the only task of a node is to perform a measurement and to send the so-generated data to the sink. In multi-hop networks, each node must also relay packets sent by other nodes. Only the TC is specific to star network applications, and the EM can therefore easily be adapted to multi-hop scenarios by designing a module replacing the TC that allocates the energy budget among the sensing and the relaying tasks, required by multihop networks.
## 4 Simulation and Performance Evaluation
### Performance Analysis under Different Parameters
#### 4.2.5 The Impact of Velocity of Sink  
In this section, we study the impact of velocity of sink on the performance of network. The parameters we set are shown as follows: (1) the number of all nodes is 500; (2) \(r_{e}\) is 30 m\({}_{i}\) (3) _Th_ is 70%; (4) the event area changes by period, and the period is 800 s; (5) one source node produces one data packet in one second. The velocity of sink varies from 2 m/s to 16 m/s. The result of experiment is shown in Figure 18.  
The black line represents network's lifetime. Network's lifetime increases fast at first, and it gets a peak value at 8 m/s. Then network's lifetime decreases slowly. In our algorithm, if sink moves slowly, then nodes around sink may consume much energy. Hot spot problem will occur. If sink moves fast, the network needs to update frequently. The operation of updating also consumes energy, and the energy consumed of updating is much less than energy consumed of transmitting data, thus there will be a point where the most balanced situation is achieved, and 8 m/s is the point. Network's lifetime increases fast from 2 m/s to 8 m/s. That is because when sink moves faster, the energy consumption will not be always concentrated in a region. Hence network is becoming more and more balanced, and lifetimes becomes longer. When more than 8 m/s, there is a slight decline. That is the impact of too frequent updating operation. The red line represents average residual energy. Corresponding with the black line, average residual energy is low when network's lifetime is high. The blue line represents the  
Figure 17: The impact of velocity of source data on the network performance.  
Figure 18: The impact of velocity of sink on the network performance.  
variance of residual energy, and it is decreased in the mass. That means the network is more balanced when sink moves faster.  
According to the data obtained from this experiment, the network gets the best performance at 8 m/s, and the performance of network is relatively good when velocity of sink is greater than 8 m/s.