esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The objective of this paper is to analyze two parameterizations for LUE to estimate the GPP with Sentinel-2 imagery in two different ecosystems in the Donana protected area: (i) marshes, which are seasonal wetlands flooded in winter, and (ii) xeric Mediterranean scrub adapted to drought, using the LUE model. Two LUE model approaches will be applied, depending on how the light use efficiency is parameterized: (i) using meteorological data, and (ii) focusing on water availability. The results will be evaluated with data collected by two flux towers (eddy covariance, EC) installed by the Singular Scientific and Technological Infrastructure of the Donana Biological Reserve (ICTS-DBR) in both ecosystems from 2020 to 2022 for the xeric Mediterranean scrub, and 2021-2022 for the Donana marsh.
## 3 Results  
### Structural Model of NADPH-Bound Mtb FAS-I  
Mtb FAS-I is a large \(\alpha\)\(\alpha\) subtype barrel-shaped complex consisting of six long polypeptide chains (\(\alpha\) chains). Each \(\alpha\) chain is 3069 amino acids long and comprised of seven catalytic domains [16]. The overall architecture depicts the whole complex as a central wheel capped by domes on each side. The seven catalytic domains are namely acetyl-transferase, enoyl reductase (ER), dehydatase, malonyl transacylase, ketoacyl reductase, ketoacyl synthase, and acyl carrier protein. The Mtb FAS-I was found to be similar to fungal FAS which is a homolog that retains the barrel shape complex [37]. The comparison of catalytic elfts between mycobacterial and fungal domains is given in detail [16]. Furthermore, the crystal structure of fungal FAS-I has been reported in complex with NADP+ and FMN (PDB:4V59). The structural comparison revealed that the enoyl reductase domain of Mb has a wider catalytic cleft and FMN is more exposed due to local amino acid composition. The ER domain is involved in the catalysis of the last step of fatty acid elongation cycle through FMN-dependent reduction of enoyl-ACP intermediate to saturated acyl-ACP. The ER is embedded in the FAS-I complex so that it allows easy access of NADPH to the binding site from outside of the FAS-I complex, while the catalytic centre is accessible from the inside of the reaction chamber. A two-step ping-pong mechanism had been proposed for catalytic mechanism of the ER of fungal FAS-I [47]. Moreover, analogs of pyrazinamide have been reported to be competitive inhibitors of NADPH binding to MbF FAS-I [14]. Hence, we were intrigued to identify putative inhibitors of MbF FAS-I. The availability of the Mtb FAS-I structure paved the way for docking-based screening. Taking in consideration the close homology with fungal FAS-I, NADPH was extracted from fungal FAS-I and docked to the binding site of MbF FAS-I (Figure 1a). The residues involved in the binding site of NADPH were studied and compared (Table 1) to the NADPH binding site in fungal FAS-I. The residues were found to be conserved and thus indicates the suitability of NADPH binding. The residues Tyr636, Lys1026, and pro1027 hold adenosine moeity; Asp 1045 interacts with pyrophophophate part, while His 751 acts as a catalytic residue in fungal FAS-I. Similarly, the corresponding residues in Mtb FAS-I have been tabulated (Table 1). Notably, the residue His584 in Mtb seems to act as a catalytic residue and can play an important role in the function of the enoyl reductase domain.
## 3 Evaluation Criterion
### Tracking Performance  
Gabor bandwidth and code tracking errors are important technical indexes for evaluating the tracking performance. Based on a coherent early-minus-late (EML) code tracking loop, the code tracking errors in additive white Gaussian noise (AWGN) are defined as [18]:  
\[{\sigma_{\epsilon}}^{2}=\frac{{{B_{L}}{\left({1-0.5{B_{L}}{T_{i}}}\right)} \left[{{\int_{-{B_{L}}{T_{i}}}^{{B_{L}}}{G(f)\sin^{2}(\pi\!\!\!/d)df}}}\right.}}{{{ \left({2\pi\!\!\!/\,{X_{0}}}\right)}^{2}{\left({C\,/\,{X_{0}}}\right)}{\int_{-{B _{L}}{T_{i}}}^{{B_{L}}}{f(f)\sin(\pi\!\!\!/d)df}}}}} \tag{9}\]  
Figure 2: **(a)** PSD of CPM signals with different \(g(t)\) ; **(b)** PSD of CPM signals with different \(h\) ; **(c)** PSD of CPM signals with different \(L\) ; **(d)** PSD of CPM signals with different \(M\).  
where the \(B_{L}\) denotes the tracking loop bandwidth, \(G(f)\) is the PSD of the signal that is normalized to unit power over infinite transmission bandwidth and symmetric to the carrier frequency, \(d\) denotes the correlation time spacing between the early and late reference signals, \(C\) / \(N_{0}\) is the carrier-to-noise ratio (CNR), \(B\) is the receiver pre-filtering bandwidth and \(T_{i}\) is the coherent integration time. Assuming that the signal is ideal and \(B_{L}T_{i}\) is small enough, the Equation (9) in the limit defined as \(d\) is vanishingly small and becomes:  
\[\begin{split}\sigma_{\epsilon,\ d\to 0}^{2}\cong\sigma_{ CRB}^{2}=&\frac{B_{L}\int_{-B/2}^{B/2}G(f)(\pi fd)^{2}df}{(2\pi)^{2} (C\) / \(N_{0})[\int_{-B/2}^{B/2}\pi f^{2}dG(f)df]^{2}}\\ =&\frac{B_{L}\tau^{2}d^{2}\int_{-B/2}^{B/2}f^{2}G(f) df}{(2\pi)^{2}(C\) / \(N_{0})\pi^{2}d^{2}[\int_{-B/2}^{B/2}f^{2}G(f)df]^{2}}\\ =&\frac{B_{L}}{(2\pi)^{2}(C\) / \(N_{0})\int_{-B/2}^{B/2}f^{2}G(f)df}\end{split} \tag{10}\]  
with:  
\[\Delta f_{Gabor}=\sqrt{\int_{-B/2}^{B/2}f^{2}G(f)df} \tag{11}\]  
where \(\sigma_{CRB}^{2}\) and \(\Delta f_{Gabor}\) are referred to as the Cramer-Rao lower bound and Gabor bandwidth, respectively. From Equation (10), it is obvious that the Gabor bandwidth can be approximately interpreted as Cramer-Rao lower bound and the greater the Gabor bandwidth, the better the code tracking accuracy.
## 2 Methodology  
A comprehensive description concerning our study method is provided in this section. The outline of this study was divided into three stages: data collection; modelling and computation work; and results analysis. In Stage 1, measurement work and data collection on-site were undertaken using soil measurement equipment. The apparent resistance of the soil was measured for Towers T42, T48 and T50, which were chosen for the case study. This was followed by modelling and computation work in Stage 2, using the Current distribution, electromagnetic field, grounding and soil structure analysis (CDEGS) software, a platform to compute results based on the input from Stage 1. Finally, in Stage 3, results were analysed and discussed before the conclusion of all findings and contributions.  
The analysis carried out included three modelling components: soil, earthing and EEC. The methods of all models included several phases in sequence. The modelling and simulations were all performed using the SESCAD tool for CDEGS. The CDEGS software was sufficiently powerful to interpret complex soil profiles from field measurement data and to design earthing systems [24]. The first step of this analysis was to use the RESAP module to determine the soil profile based on the real data from Peninsula Malaysia. Subsequently, the tower earthing structure was developed and evaluated using the MALT module based on the soil structure built in the first step. After the earthing structure was developed, the calculation of the EEC model followed. This section also provides a real case study demonstrating how a proper simulation of soil, earthing and EEC was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Countries** & **Earthing Design** & **References** & **Remarks** \\ \hline Indonesia & [14] & Octagon shape \\ \hline Thailand & [14] & Ring shape \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._developed and analysed. The quantitative and qualitative analyses of the earthing system were determined based on the earthing resistance obtained under steady-state conditions.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 1 Introduction
### Contributions  
In this paper, we propose Adaptive-DecayRank, a novel real-time and adaptive anomaly detection model for dynamic graphs. This model utilizes a modified version of the dynamic PageRank algorithm [9] tailored to adapt and continuously update node scores based on recent structural changes in the graph. In Adaptive-DecayRank, we introduced a novel **decay factor** formula with Bayesian updating; this was inspired by the _\"Weight Decay Regularization in ADAM.\"_[10], where the authors propose decoupling gradient-based updates from weight decay in both the SGD [11] and Adam [12] optimizers. The _decay factor_ in Adaptive-DecayRank is strategically incorporated into the dynamic PageRank algorithm and is dynamically adjusted based on the observed changes of edge influx at each time step of the graph, utilizing a Bayesian updating mechanism.  
The decay factor formula determines how quickly past information (nodes and edges) is discounted, allowing the algorithm to react swiftly to recent changes. By incorporating Bayesian updating inference, our model dynamically adjusts the decay factor for each node  
Figure 1: An illustration of graph representation: (**a**) Static graph, \(G\), and (**b**) evolving dynamic graph, \(\mathcal{G}=(V_{t},E_{t},\mathcal{T})\), showing a series of graph snapshots with edge insertions, deletions, and node insertions over time.  
based on observed changes in node scores. This ensures that nodes with rapid changes in connectivity receive higher scores, reflecting their increased importance and potential anomalous behavior.  
The motivation behind Adaptive-DecayRank lies in its ability to capture abrupt structural alterations in dynamic graph networks using the adaptive decay mechanism. We consider anomalous behavior as sudden changes in the structural composition of node scores, specifically the rapid addition of new edges between unrelated nodes as these networks evolve over time. For example, a sudden increase in node scores might signify a surge in activity, suggesting a noteworthy event or a potential security threat.  
Our contributions are summarized as follows:  
* We introduce Adaptive-DecayRank, a modified dynamic PageRank with a Bayesian updating mechanism and an adaptive decay factor for fast anomaly detection and real-time adaptation to graph changes.
* We demonstrate that our Adaptive-DecayRank prioritizes recent edge changes and assigns higher anomaly scores to structural deviation, thereby improving detection accuracy.
* We conducted extensive experiments on real-world dynamic graph datasets, demonstrating that Adaptive-DecayRank outperforms state-of-the-art baselines in both precision and AUC while maintaining competitive runtime performance.
## 2 Mathematical Formulation of Object Localization and Tracking Algorithms
### Object Localization and Tracking Algorithm
#### 2.4.2 EKF Algorithm for Object Localization and Tracking  
In this section, the EKF algorithm is used to estimate the position and velocity of the target. Suppose the state \(\mathbf{\theta}= \begin{bmatrix}x_{1}&x_{2}&\hat{x}_{1}&\hat{x}_{2}\end{bmatrix} ^ {\intercal}\) where \(\mathbf{x}= \begin{bmatrix}x_{1}&x_{2}\end{bmatrix} ^{\intercal}\) is the position coordinate and \( \begin{bmatrix}\hat{x}_{1}&\hat{x}_{2}\end{bmatrix} ^{\intercal}\) is the velocity of target. The state transition equation can be written as follows:  
\[\mathbf{\theta}^{(k)}=\mathbf{A}\mathbf{\theta}^{(k-1)}+\mathbf{w}^{(k-1)} \tag{31}\]  
where  
\[\mathbf{\theta}^{(k)}= \begin{bmatrix}x_{1}^{(k)}\\ x_{2}^{(k)}\\ \hat{x}_{1}^{(k)}\\ \hat{x}_{2}^{(k)}\end{bmatrix} ,\ \mathbf{A}= \begin{bmatrix}1&0&t&0\\ 0&1&0&t\\ 0&0&0&1\end{bmatrix} ,\ \mathbf{\theta}^{(k-1)}= \begin{bmatrix}x_{1}^{(k-1)}\\ x_{2}^{(k-1)}\\ \hat{x}_{1}^{(k-1)}\\ x_{2}^{(k-1)}\end{bmatrix}  \tag{32}\]  
where \(t\) is the transition time, the notations \((k)\) and \((k-1)\) denote the \(k\)th and \((k-1)\)th time steps, and \(\mathbf{w}\in\mathbb{R}^{4\times 1}\) is the state noise, which is Gaussian distribution with zero mean and diagonal covariance matrix \(\mathbf{Q}\in\mathbb{R}^{4\times 4}\). The measurement equation is:  
\[\mathbf{u}^{(k)}=\mathbf{h}^{(k-1)}+\mathbf{v}^{(k-1)} \tag{33}\]  
where \(\mathbf{h}= \begin{bmatrix}h_{t1}(\mathbf{x})\ldots h_{tm}(\mathbf{x})\end{bmatrix} ^ {\intercal}\), and \(\mathbf{v}\in\mathbb{R}^{m\times 1}\) is the observation noise, which is Gaussian distribution with zero mean and diagonal covariance matrix \(\mathbf{R}\in\mathbb{R}^{m\times m}\). When the TOFs are obtained from each channel, outliers may occur when the echo distortion is under serious conditions, such as complex shaped targets or target movement, affecting the performance of the EKF. A reliable and efficient method is provided in [34]for outlier detection. The prediction step (31) and (33) in the proposed EKF are modified as follows:  
\[\tilde{\mathbf{\theta}}^{(k)} =\mathbf{A}\mathbf{\theta}^{(k-1)} \tag{34}\] \[\tilde{\mathbf{u}}^{(k)} =\tilde{\mathbf{h}}^{(k-1)}\] (35) \[\tilde{\mathbf{p}}^{(k)} =\mathbf{Q}^{(k)} \tag{36}\]  
where \(\tilde{\mathbf{\theta}}= \begin{bmatrix}\tilde{x}_{1}&\tilde{x}_{2}&\dot{\tilde{x} }_{1}&\dot{\tilde{x}}_{2}\end{bmatrix} ^{\intercal}\), \(\tilde{\mathbf{h}}= \begin{bmatrix}h_{t1}(\tilde{\mathbf{x}})\dots h_{tm}(\tilde{\mathbf{x} })\end{bmatrix} ^{\intercal}\), \(\tilde{\mathbf{x}}= \begin{bmatrix}\tilde{x}_{1}&\tilde{x}_{2}\end{bmatrix} ^{\intercal}\), and \(\mathbf{Q}^{(k)}\) is the noise variance of \(\mathbf{\theta}^{(k)}\).
## 3 Materials and Methods
### Anomaly Detection System
#### 3.5.1 EfficientAD Model Architecture  
The EfficientAD model was selected as our primary anomaly detection approach due to its effectiveness in detecting sensor attacks without requiring prior knowledge of attack patterns. EfficientAD employs a student-teacher framework that identifies anomalies based on the discrepancy between teacher and student network predictions. The architecture consists of the following:  
* A teacher network that learns robust feature representations from normal sensor data,
* A student network that attempts to replicate the teacher's feature extraction capabilities, and
* A discrepancy measurement module that quantifies differences between teacher and student outputs to generate anomaly scores.  
This architecture is particularly suitable for sensor attack detection, as it can identify subtle deviations from normal sensor behavior patterns without requiring examples of attacks during the training phase.
## 3 Semantic Service Description and Context-Based Orchestration
### Construction of Ontologies  
Based on the requirements derived from the use cases described in the last section, we developed a structure of modular ontologies, which depict the basis for the semantic description of services provided by field devices and which are used for semantic reasoning in matching and selection processes. In the following sections, this ontological structure is illustrated and the different contained ontologies are described in detail.
# Hybrid Zoning Algorithm to Optimize Overhead in Smart Mobile Communication
## 4 OLSR Protocol
### Default Forwarding Rules of OLSR  
OLSR uses two types of messages to build and maintain the topology over the network: HELLO and TC messages. The HELLO messages discover the neighborhood and sense links between adjacent  
Figure 4: MPR computation function of original OLSR.  
nodes in order to build the local topology. The TC messages transport the local information to the entire network in order to build global knowledge of the topology. TC messages are generated by MPR nodes every five seconds. They are forwarded from a MPR to another until covering the whole network. When a MPR \(m\) broadcasts a \(TC_{m}\) message that contains links between itself and its MPR selectors in \(MPR^{-1}(m)\), all its neighbors in \(N1(m)\) will receive and treat this message, and they will update their routing tables accordingly. However, only a subset of neighbors will forward the \(TC_{m}\): the nodes that are selected as MPRs by \(m\). If \(u\) is a MPR and \(m\in MPR^{-1}(u)\), \(u\) rebroadcasts the \(TC_{m}\) message towards all nodes in \(N1(u)\). This flooding mechanism is repeated in order to build global knowledge of the topology. The default forwarding rules of OLSR protocol forward TC messages based on a topology-based heuristic. The DFR take into account only the source of the TC message. Some recipient nodes receive redundant messages since they are neighbors of multiple MPRs. This redundancy consumes sufficient network resources so as to congest the network and decrease the reliability. The default forwarding rules of OLSR are summarized in Figure 5.
## 3 Results
### Power Production  
In the sample-water reaction, it was identified that AlN (as detected by XRD)- containing samples (SOW and RM) would form ammonia. Thus, using PEM fuel cell would not be viable for these types of materials as ammonia would degrade and poison the fuel cell. Any contamination must be avoided if generated gas is used in a fuel cell. Ammonia (even at ppm level) is an actual contaminant to the PEM hydrogen fuel cell [19]. A 15 h exposure to 30 ppm NH\({}_{3}\) in the anode fuel caused a rapid drop in cell performance. It was expected to detect ammonia in gas samples, but as we saw in Tables 1 and 2, no NH\({}_{3}\) was identified; on the other hand, methane was. Thus, the sample generated from HDC was not used; on the other hand, the samples that contained no methane were used for power production via a fuel cell, where a constant load was applied to measure the output.  
In Figure 5, we see the power output of the FC from tested samples. In these tests, 4 g of sample was used to measure the power output in a small FC stack. We see that Alw1 power production was initiated very quickly, as expected from the reaction rate constant and previous experience. As the sample is relatively pure compared to the white dress samples, there is a rapid increase in power production with constant maximum output for a set period and an equally rapid fall of power due to the end of the reaction. In this case, a simple setup for power production can provide power in 1.5 s after the start of the reaction, reaching a maximum in 1 s. On the other hand, the SOW sample showed a very slow reaction, and functionally, no power production was below 0.05W. RM showed a slower onset of hydrogen generation and thus power production; on the other hand, the produced power was more significant than Alw1 or SOW.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 7 Conclusions and Future Work  
In this work, we propose Adaptive-DecayRank, an unsupervised, real-time anomaly detection approach for identifying node-level and structural anomalies in dynamic graph streams. Adaptive-DecayRank is a modified dynamic PageRank algorithm with a _decay factor_ and Bayesian updates, which enable rapid adaptation to sudden, short-term deviations and facilitate fast, accurate anomaly detection in graph streams. Our experiments demonstrate that Adaptive-DecayRank outperforms baseline approaches by **11.28-15.27%** in terms of precision and **4.65-40.63%** in terms of AUC. While processing 4.6\(M\) edges across 1463 snapshots, our model achieves a processing rate of 1 million edges in just 14.23 --making it **4.53** times faster than the baseline approaches (SedanSpot and DynAnom). For future work, we will explore more real-world applications, such as financial transactions, medical data, and drug discovery. Additionally, we aim to investigate how GPUs could be leveraged to optimize linear scaling for larger streaming edge sets.  
Conceptualization, O.A.E. and W.E.; Methodology, O.A.E. and W.E.; Software, O.A.E. and J.C.; Validation, O.A.E., W.E. and J.C.; Formal analysis, O.A.E., W.E. and J.C.; Resources, W.E.; Data curation, J.C.; Writing--original draft, O.A.E.; Writing--review & editing, W.E.; Visualization, O.A.E.; Supervision, W.E.; Project administration, W.E. All authors have read and agreed to the published version of the manuscript.  
This research is partially supported by the National Science Foundation (NSF-REU, #2349104).  
Not applicable.  
The original contributions, including the code and datasets presented in this study, are openly available in the Adaptive-DecayRank GitHub repository at [[https://github.com/EkleTony/Adaptive-DecayRank](https://github.com/EkleTony/Adaptive-DecayRank)]([https://github.com/EkleTony/Adaptive-DecayRank](https://github.com/EkleTony/Adaptive-DecayRank)). Further inquiries should be directed to the first author, O.A.E.  
The authors wish to thank the College of Engineering, the Machine Intelligence and Data Science Center, and the Computer Science Department at Tennessee Tech University for providing the resources and funding for this work.  
The authors declare no conflicts of interest.
## 2 Methodology
### Vehicle Dynamic Model
#### 2.2.2 Motor Module  
The motor module is to convert the required vehicle speed and torque to the required battery power, and then generates the corresponding torque based on the battery capacity. The motor model was established based on the motor voltage, torque, power and operating characteristic equation. With torque and power being the outputs, the input is the speed connected to the transmission system and motor control signal. When the signal is positive, the motor is actuated, and the motoring torque was calculated based on the motor speed, control signal and voltage of the battery; whereas, when the control signal is negative, the transmission system drives the motor to regenerate electricity. The power \(P_{m}\) under motoring and regenerating condition follows Equation (1). The electricity transmission losses from both the motor control unit (MCU) and the motor itself were taken into account by experimentally testing the efficiency of the motor system (i.e., motor body + motor controller) under different working modes. The data acquired was then applied in the model. An equivalent battery charging efficiency of 98% was also used to account for the losses from battery charging and conversion from AC to DC:  
\[P_{m}=\begin{cases}\frac{T_{m}\omega_{m}}{\eta_{m}}&\quad\text{ \emph{motoring}},\\ T_{m}\omega_{m}\eta_{m}&\quad\text{\emph{regenerating}},\end{cases} \tag{1}\]  
Figure 4: Flow diagram of the vehicle dynamic model of a hybrid electric vehicle employed in this study.  
where \(\eta_{m}\) is motor efficiency, \(\omega_{m}\) is the motor rotating speed connected with the transmission system, and \(T_{m}\) is the motor torque.
## 5 Experiment and Results
### Experiments and Discussion
#### 5.4.2 PASCAL VOC Object Detection Results  
The PASCAL VOC dataset primarily comprises images captured from real-world scenarios, each featuring multiple objects. Each object in the image is annotated with a bounding box and labeled with its corresponding category. The dataset encompasses 20 distinct object categories, such as humans, vehicles, and structure. Consequently, our study aims to leverage this dataset to assess the efficacy and resilience of various models.  
Table 3 and Figure 18 demonstrate that the model introduced in this paper outperforms other models with similar parameters in the VOC dataset comparison experiment. Moreover, our pruned model possesses fewer parameters, rendering it well-suited for deployment on embedded platforms with constrained memory resources while maintaining high accuracy.  
Upon verifying the PASCAL VOC dataset, it was demonstrated that the proposed model not only excels in the task of meter reading recognition but also surpasses the general target detection task. These results evidently show that DAMP-YOLO possesses outstanding capabilities in feature learning and generalization.