esa-sceva/satcom-chunk-collection huggingface dataset:
# PV Tracking Design Methodology Based on an Orientation Efficiency Chart
## 2 Materials and Methods
### EFO Chart of Installation Latitude  
For a specific latitude, it is necessary to evaluate whether designing a new solar tracker with low precision but high efficiency is possible or whether this strategy must be replaced with another design method, as described in [14]. For this purpose, determining the PV efficiency loss due to misalignment error is necessary, using an EFO chart for the specific latitude (\(\varphi\)). Equations (1) and (2) are used to determine the EFO in relation to the inclination (\(\beta\)) and azimuth (\(\alpha\)) alignments [25] to ultimately determine the efficiency loss due to misalignment.  
\[EFO=100-100\times\left[1.2\times 10^{-4}(\beta-\varphi+10)^{2}+3.5\times 10^{-5} \alpha^{2}\right]\quad\ for\qquad 15^{\circ}<\beta<90^{\circ} \tag{1}\]  
\[EFO=100-100\times\left[1.2\times 10^{-4}(\beta-\varphi+10)^{2}\right]\quad\ for \qquad\beta\leq 15^{\circ}\quad\text{and}\quad\alpha=0\ ^{\circ} \tag{2}\]  
An EFO graphic representation for 27.5\({}^{\circ}\) latitude is shown in Figure 2. For this case, the proposed EFO facilitates the incorporation of mechanisms with low resolution that can maintain the tracking errors along the azimuthal axis within the range of \(\pm 30^{\circ}\) and the tracking errors for the tilt axis within the range of 25\({}^{\circ}\) while maintaining the collection efficiencies within the range of 95-100% EFO.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Design Parameters** & **Value** \\ \hline Latitude & 27.5\({}^{\circ}\) \\ Efficiency as a function of the orientation (EFO) & 95–100\% \\ Maximum wind speed & 33.3 m/s \\ Capacity & 1 kW \\ Cost & Lowest available \\ \hline \hline \end{tabular}
\end{table}
Table 1: Solar tracker design parameters.  
Figure 2: EFO chart for a photovoltaic (PV) system installed at 27.5\({}^{\circ}\) latitude.
# Comprehensive LCA of Biobased Sustainable Aviation Fuels and JET A-1 Multiblend
## 1 Introduction
### Scope of the Study  
The introduction of alternative aviation fuels, in particular multiblend JET A-1 built and tested under real conditions, has also led to outstanding questions relating to environmental assessment that need to be answered through further research.  
What is the environmental performance of the used SAF? What are the main drivers of the environmental impacts? How can the supply chains be optimized? Based on these questions, the overall objective of this study was to answer the following question: What is the GHG mitigation potential of using multiblends of conventional JET A-1 with either two or three sustainable aviation fuels that have been built and tested under real conditions?  
While the environmental impact assessment of sustainable aviation fuels has been addressed in several studies [12] either by individual consideration of different types of renewable and alternative fuels for aircraft [13; 14] or in scenario analyses [15], the environmental impact of the production and use of multiblends taking into consideration the current mixing restrictions has rarely been assessed. Existing studies often refer to a 1:1 substitution of fossil fuels with SAF [16] or discuss blending rates discussed in a more qualitative manner [17; 18].  
In this context and to answer the scoping question, a comprehensive life cycle assessment (LCA) of multiblends was conducted, as described in subsequent parts of the article. The analysis covered all relevant aspects of the multiblends, starting from SAF production to mixing of different SAF with conventional JET A-1 and finally the use of the produced multiblend under real conditions.
## 2 Materials and Methods
### Sites and Application
#### 2.3.4 Floating Artificial Wetlands  
Floating artificial wetlands are a \"photo-technology\", where emergent macrophytes grow on a buoyant platform and support remediation of eutrophication on several levels [38]. On the one hand, roots in the water column directly adsorb nutrients and incorporate them into plant tissue through biosynthesis. On the other hand, a biofilm on the roots enhances bacterial denitrification. In addition, floating islands attenuate wave energy and water flow and are consequently able to enhance particle settling and nutrient burial [38; 39; 40]. Floating wetlands with native emergent macrophytes were tested for the first time in Baltic logoons in 2018 [39; 40] and turned out to be a valuable refuge for aquatic fauna such as the endangered eel [33].  
Each measure was formulated as a scenario, and two scenarios were always evaluated for one target area (Figure 3). Within the Danish coastal waters (Limfjord and Horsens Fjord), a commercial mitigation approach (Limfjord) and the IMTA approach (Horsens Fjord) were formulated as scenarios (Figure 3 (1)). For the Curonian Lagoon, a commercial mussel farm as well as a beach mussel farm were set as scenarios. Due to the reduced salinity of the Curonian Lagoon, zebra mussels were cultivated (Figure 3 (2)). For the German coastal waters, the Greifswalder Bodden was used as the scenario site. Here, a beach mussel farm and floating wetland scenario were assessed (Figure 3 (3)).  
Figure 3: Different scenarios and their sites (five-pointed star), which were assessed in different ESAs. (1) Two mussel farm scenarios in Danish coastal waters: (a) commercial mitigation farm in the Skive Fjord vs. (b) the IMTA approach in the Horsens Fjord; (2) two mussel farm scenarios in the Curonian Lagoon: commercial mitigation farm vs. beach mussel farm; and (3) beach mussel farm scenario vs. floating macrophytes islands in Greifswalder Bodden.
# Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention-Informer Algorithm
###### Abstract  
In response to the problems that abnormal yaw position causes during the yawing process--on the one hand leading to the accumulation of yaw position errors, affecting the accuracy of yawing to the wind or safety due to excessive cable twisting, and on the other hand, with the phenomena of frequent position jumps or frequent short-term position maintenance generating certain yaw errors, affecting the stability of yaw control, thus resulting in a high occurrence frequency of yaw system failures and high operation and maintenance costs--a data-driven fault diagnosis method is proposed to give early warnings for abnormal conditions of the yaw position of the wind turbine unit. Firstly, for the massive data in the SCADA (Supervisory Control and Data Acquisition) system, the ReliefF feature algorithm based on standardized interaction gain (Standardized Interaction Gain and ReliefF, SIG-ReliefF) is used for accurately identifying and screening the characteristic parameters that have a greater impact on the yaw system failure of wind turbines. The advantage of this method lies in its ability to effectively consider the correlation between features and retain the relevant features and interaction features of yaw system failures to the greatest extent. Then, an Informer yaw position prediction model is established, combined with the two-stage attention mechanism (two-stage attention and Informer, TSA-Informer), and the distribution of residuals is statistically analyzed through the sliding window method to determine the fault threshold. Finally, the validity and accuracy of the proposed method are verified through examples, and through comparison with other algorithms, it is verified that it has better abnormal early warning performance. Relevant conclusions can provide a reference for the fault diagnosis of the actual yaw system.  
wind turbine; yaw system; interactive information; ReliefF; Informer; abnormal diagnosis Article
## 3 The Decoding-Complexity, Rate, and Distortion Relationship
### The Decoding-Complexity, Rate, and Distortion Space  
In HEVC, the optimum coding parameter combination of a CU is derived by using a Lagrangian optimization approach with the rate-distortion (RD) cost function  
\[\min_{p\in\mathcal{P}}\Big{\{}D(p)+\lambda R(p)\Big{\}}, \tag{1}\]  
where \(\lambda\geq 0\) is the empirically determined Lagrangian multiplier, \(p\) is a coding structure in the set of combinations \(\mathcal{P}\), and \(D(p)\) and \(R(p)\) represent the distortion (squared error per pixel) and bit rate (bits per pixel), respectively. Each \(p\) in (1) results in different decoding-complexities at the decoder [24; 44; 45; 46] that remain unknown to the encoder. In order to assess the impact of each \(p\) on the decoding-complexity, we first redefine the optimization function in (1) as  
\[\min_{p\in\mathcal{P}}\ J_{\text{CRD}}\ \Big{|}\ J_{\text{CRD}}D(p)+\lambda_{r}R(p)+ \lambda_{c}C(p), \tag{2}\]  
where \(C(p)\) is the relative decoding-complexity (cycles per pixel) of \(p\) obtained from [43; 44; 45]. Here, \(\lambda_{r}\) and \(\lambda_{c}\) are bit rate and decoding-complexity trade-off parameters analogous to \(\lambda\) in (1), respectively. The ranges of \(\lambda_{r}\) and \(\lambda_{c}\) define the decoding-complexity-rate-distortion space spanned by the coding parameter combinations in \(\mathcal{P}\). Next, we analyze this relationship and derive a model of these parameters for use in a joint decoding-complexity and rate-controlled encoding algorithm.
## 3 Methodology
### Simulation Setup  
The proposed scheme uses NS-2.35 for simulating the network model of NBMS. The initial setting of all the networks is considered the same to fairly evaluate the performances of all the considered networks. The simulation parameters are shown in Table 4.
## 3 Defining clique minimal separator decomposition  
### Definitions and examples  
The decomposition by clique minimal separators can be defined by the following algorithmic process on input graph \(G\): define a collection of subgraphs (which at the beginning contains only \(G\)) by repeatedly applying the following decomposition step on a subgraph which has a clique minimal separator, until none of the subgraphs has a clique minimal separator.  
**Decomposition Step 3.1**: _Given a graph \(G^{\prime}\), find a clique minimal separator \(S\) of \(G^{\prime}\) and a full component \(C\) of \(S\); replace \(G^{\prime}\) with the following 2 subgraphs: \(G_{1}=G^{\prime}(S\cup C)\), \(G_{2}=G^{\prime}(V-C)\)._  
The _decomposition_ is the set of subgraphs obtained in the end, which are called _atoms_.  
The decomposition is unique, the resulting set of atoms does not depend of the order in which the decomposition steps are executed. This is because of the following property of clique minimal separator decomposition:  
**Property 3.2**: _Let \(\mathcal{S}\) be the set of minimal separators of a graph \(G^{\prime}\), let \(S\) be a clique minimal separator of \(G^{\prime}\); after application of one decomposition Step 3.1, each other minimal separator is a minimal separator of either \(G^{\prime}_{1}\) or of \(G^{\prime}_{2}\)._  
Thus in particular, any clique minimal separator will be a clique minimal separator of \(G_{1}\) or of \(G_{2}\). Note that the clique minimal separator which was used, \(S\), may or may not still be a minimal separator of \(G_{2}\), depending on whether \(S\) had more than two or only two full components.  
The atoms obtained are characterized as follows:  
**Characterization 3.3**: _An atom of graph \(G\) is a maximal connected subgraph containing no clique minimal separator._  
**Example 3.4**:
* _Figure_ 6 _gives the decomposition of a tree._
* _On any chordal graph, this decomposition yields the set of maximal cliques of the input graph._
* _In a non-chordal graph, some of the atoms will not be cliques. Let us use our graph_ \(G\) _from Figure_ 1 _as an example: The clique minimal separators of_ \(G\) _are:_ \(\{c,k\}\)_,_ \(\{\)_j,k\(\},\{d,j,k\}\)_. Figure_ 7 _shows a decomposition tree and the set of atoms._
## 2 Related Work
### Foundation Model Inference Partitioning  
Due to the mismatch between the computing power required for foundation model inference and the computational resources of edge servers, how to decompose foundation model inference has become a crucial issue that urgently needs to be addressed. To solve this problem, several related solutions have been proposed.  
In response to the above issues, many people have put forward the idea of cloud-edge collaboration. [PERSON] et al. [18] proposed the JALAD method, a joint accuracy and latency-aware execution framework. By decoupling the structure of deep neural networks, JALAD enables part of the model to be executed on edge devices and the rest to run in the cloud, minimizing data transmission. This method aims to reduce latency while maintaining model accuracy, improving the performance and user experience of deep learning services. [PERSON] et al. [19] proposed a real-time DNN inference acceleration method based on edge computing. The core idea of this method is to utilize the powerful computing capabilities of edge servers to offload part or all of the DNN inference tasks to the edge servers for processing, thereby reducing the computational burden of mobile devices and improving inference speed and accuracy.  
However, numerous researchers and practitioners also believe that the effect of direct dismantling large model inference is better. [PERSON] et al. [20] proposed the EdgeFlow method. EdgeFlow is a distributed inference system that provides natural support for DAG (Directed Acyclic Graph) structural models. The main design goal of this system is to divide and distribute the model to different devices while maintaining complex layer dependencies to ensure correct inference results. [PERSON] et al. [21] proposed MoDNN--A locally distributed mobile computing system for deep neural network applications. MoDNN can divide trained DNN models into multiple mobile devices to accelerate DNN calculations by reducing device-level computing costs and memory usage. Two model partitioning schemes are designed to minimize non-parallel data transmission time, including wake-up time and transmission time. [PERSON] et al. [22] introduced DeepSlicing, a collaborative and adaptive inference system that adapts to various CNNs and supports customized flexible fine-grained scheduling. By dividing the model and data, they also designed an efficient scheduler--Proportional Synchronous Scheduler (PSS), which realizes the trade-off between computation and synchronization. [PERSON] et al. [17] proposed DeepThings, a framework for adaptive distributed execution of CNN-based inference applications on resource-constrained IoT edge clusters. DeepThings used fused tile partitioning to minimize memory occupancy while exposing parallelism.
# Integrated Hydrological Analysis of Little Akaki Watershed Using SWAT-MODFLOW, Ethiopia
## 3 Results and Discussions
### Redarge Estimation
#### 3.1.1 Basin Average Recharge Rates  
The average monthly and annual long-term groundwater recharge in the Little Akaki watershed was simulated by using the SWAT hydrological model. The average monthly recharge distribution indicates clearly that the recharge amount and timings are correlated with the precipitation amount and months. In the watershed, most of the groundwater  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **p-Factor** & **r-Factor** & \(\mathbf{R^{2}}\) & **NSE** & **PBIAS** & **RSR** \\ \hline Calibration & 0.69 & 0.89 & 0.85 & 0.81 & \(-11.5\) & 0.44 \\ Validation & 0.58 & 0.61 & 0.85 & 0.8 & 8.0 & 0.45 \\ Performance Rating & & & & & Very good & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model performance evaluation statistics and performance rating.  
Figure 4: Monthly simulated water balance components.  
Figure 5: Average monthly water components.  
recharge occurs during the wet/rainy season, from June and September of the summer season (Figure 6). The average annual recharge estimated for the Little Akaki watershed is 179 mm (Figure 7), about 15% of the annual precipitation of the area (1204 mm). Based on that, the annual average volumetric groundwater recharge in Little Akaki watershed was estimated at about 24.3 Million cubic meters. The average annual simulated groundwater recharge is in line with the average groundwater recharge reported by Reference [9], 150 to 250 mm/year, less than the 265 mm recharge estimated by Reference [10] from selected points in the Akaki Catchment, and by far greater than the regional groundwater recharge estimation for Ethiopia, 39 mm/year [25].
# Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks
## 1 Introduction
A network suffering from grey hole attacks either forwards packets to the destination node in the period of process discovery as \"true\" behaviour, and then, when the network behaves maliciously, be made to drop almost every received packet [11]. Figure 1: A basic structure of Vehicular ad hoc networks. Rushing attacks or sudden attacks are seen as an emerging type of Denial of Service (DoS) attacks that have a direct and negative effect on the action of the routing protocols, particularly on AODV and Dynamic Source Route [10]. The source vehicle floods road requests (RREQ) to the destination vehicle, via VANETs in the road discovery phase. The rushed vehicle receives at this point the RREQ and moves the packet directly to destination vehicles without delay, i.e., a zero delay [12]. The original packet will automatically be removed by the destination node as a copy packet, because the node has already accepted the packet from the rushing attack. Such attacks are particularity effective when they are near the source or destination vehicles [12]. In this paper, we propose an IDS to protect external communication in autonomous and semi-autonomous vehicles from grey hole and rushing attacks. This security system is capable of detecting malicious behaviour in real time, thereby preventing the malicious vehicle from communicating with other vehicles. In addition, we propose a novel response system to switch infected vehicles into a \"safe mode\" without delay to protect passengers'/drivers' lives and even the vehicles themselves. This work is presented in the following sequence: Section 2 discusses similar research in the area of protection for attacks on VANETs. Next, Sections 3 and 4 explore the methodology while Section 5 gives details of the experimental results. Section 6 discusses these experimental results. Finally, Section 7 gives the conclusions and directions on future research on this topic area.
## 5 Security Analysis
### Data Confidentiality
#### 5.1.2 Security Analysis of SubMAC  
In our scheme, the data packet reserves 4 bytes for MAC. The security of a 4-byte MAC is quantified as 2\({}^{4\text{-}8}\) because an adversary has a 1 in 2\({}^{4\text{-}8}\) chance in blindly forging the MAC. While increasing the size of MAC is also increasing the communication overhead, the subMAC which has the size of 32/(\(T+1\)) bits is employed in this scheme, and \(T+1\) subMACs computed by \(T+1\) nodes form a MAC. Hence, an adversary can successfully forge a valid MAC if it finds all \(T+1\) subMACswith the probability of 1 in \(2^{32/(T+1)}\) for each subMAC. Thus, the probability that the false data are not detected by the MAC is \((1/2^{32(T+1)})^{T+1}=1/2^{32}\).
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 2 Disclose the Valuable Information
### Dimensionality Reduction  
Principal component analysis (PCA) is used to map data from high dimensional space to low dimensional subspace, to mitigate dimensionality of the data, where the variance of the data could be comprehended in the best possible way [37]. [PERSON] et al. considered the PCA method to extract features from voltage and current signals [38]. The PCA approach is employed on wavelet coefficients and principal components, used for fault-type classification and location finding. The feature extraction method is proposed in [39] based on random dimensionality reduction projection (RDRP) to reduce the dimensionality of the original vector in the Gaussian random matrix, making RDRP an independent of training data. Further, small memory is required as feature extraction is furnished with matrix multiplication [39].
## 4 Bayesian Game among Community Operators
### Game Formulation with Complete Information  
In the game with complete information, the grade of DR resource participating in the bidding market is well known to all other community operators. Furthermore, each community operator tries to maximize its own payoff in the bidding market by acknowledging other community operators' strategies. Consequently, the complete information game among community operators can be formulated as follows [30].  
1. Players: All community operators who willing to participating in the bidding market.
2. Strategies: each community changes its bidding strategy \(x_{n}^{t}\) to maximize its payoff.
3. Payoffs: the payoff of each community operator is defined as \[P_{n}\left(x_{n},x_{-n}\right)=W_{n}^{\text{DR}}+K_{n}^{\text{GB}}W_{n}^{\text {GB}}+K_{n}^{\text{ES}}W_{n}^{\text{ES}}\] (11) where \(x_{n}=\left[x_{n}^{1},x_{n}^{2},\cdots x_{n}^{T}\right]\) is the bidding strategy set of community operator \(n\) and \(x_{-n}=\left[x_{1},\cdots,x_{n-1},x_{n+1},\cdots,x_{N}\right]\) is the bidding strategy set of all community operators except operator \(n\); \(K_{n}^{\text{GB}}=1\) and \(K_{n}^{\text{ES}}=1\) represent community operator \(n\) has equipped gas boiler and energy storage, otherwise \(K_{n}^{\text{GB}}=0\) and \(K_{n}^{\text{ES}}=0\).  
Community operator \(n\) is willing to schedule bidding amount of DR resource to maximize its own payoff on basis of it opponents' strategies until no community operator wants to change. This equilibrium state is called Nash equilibrium, which can be expressed as following formula  
\[P_{n}\left(x_{n}^{*},x_{-n}^{*}\right)\geq P_{n}\left(x_{n},x_{-n}^{*}\right) \tag{12}\]  
where \(\left(x_{n}^{*},x_{-n}^{*}\right)\) is Nash equilibrium of community operators in the bidding market. Once the Nash equilibrium is reached, the payoff for any community operator \(n\) will be reduced by changing from equilibrium \(x_{n}^{*}\).