esa-sceva/satcom-chunk-collection huggingface dataset:
## Case Study
### A Practical Distribution System with 173 Nodes
#### 4.1.2 The Typical Scenarios Partitioning of the 173 Nodes System  
There are eight typical topology scenarios in the historical database from year 2011 to 2015, and the sum of the duration accounts for 94.85% of the total running time of the system. The rest time is accounted for other topology scenarios. The specific information of the typical topology scenarios are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Capacitors** & **Connected Bus** & **Compensation** & **Capacitors** & **Connected Bus** & **Compensation** \\
**Name** & **Number** & **Capacity/Kvar** & **Name** & **Number** & **Capacity/Kvar** \\ \hline Qc1 & 3 & 2000 & Qc8 & C19 & 600 \\ Qc2 & 5 & 2000 & Qc9 & C27 & 1200 \\ Qc3 & A19 & 1200 & Qc10 & D11 & 2000 \\ Qc4 & A44 & 1000 & Qc11 & D21 & 1200 \\ Qc5 & B13 & 1200 & Qc12 & E10 & 2000 \\ Qc6 & B26 & 800 & Qc13 & E25 & 800 \\ Qc7 & C7 & 600 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The configuration of capacitors in 173 nodes system.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & \multicolumn{6}{c}{**The State of Tie Switches**} & \multicolumn{6}{c}{**The Disconnected**} & **Scenario** & **The Ratio of** \\ \cline{2-9}  & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **Feeders** & **Duration/h** & **Duration** \\ \hline Typical topology & & & & & & & & & \\ Scenario 1 & Open & Open & Open & Open & Open & / & 10,512 & 23.99\% \\ \hline Typical topology & & & & & & & & & \\ Scenario 2 & & & & & & & & & \\ \hline Typical topology & & & & & & & & & \\ Scenario 3 & & & & & & & & & \\ \hline Typical topology & & & & & & & & & \\ Scenario 4 & & & & & & & & & \\ \hline Typical topology & & & & & & & & & \\ Scenario 5 & & & & & & & & & \\ \hline Typical topology & & & & &According to the information of seasons, weekdays and weekends, there are eight typical load scenarios, and the specific scenario information is shown in Table 3.  
The total load forecast curve of one day is shown in Figure 6, and three different load levels are selected out for reactive power optimization calculations, respectively at 2 o'clock, 10 o'clock and 17 o'clock.  
Take the high load level (at 17 o'clock) for example to illustrate the calculation processes. Firstly, it is determined that the topology at the time belongs to the first type of the typical topology scenarios according to Table 2, and the typical scenario lasts for 10,512 h from year 2011 to 2015, which can be converted to 438 days. Besides, the day belongs to the typical summer weekdays according to the date, and the typical load scenario lasts for 328 days from year 2011 to 2015. Take the intersection of the two typical scenarios, and there are 217 days in the database that the topology scenario and load scenario are both belonged to the same typical scenario with time to be optimized.
# Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review
## 3 Applications in Medical Decision-Making
### Synergy of QC and ML
Hybrid quantum-classical models could enhance medical imaging by combining quantum algorithms for image processing with classicalML techniques for disease detection, potentially improving early diagnosis in fields like radiology and pathology [64]. However, clinical implementation remains at least a decade away due to hardware limitations, error rates, and scalability challenges. In the next 5 years, progress will be limited to small-scale experiments and simulations, while in 10 years, early clinical testing may emerge as quantum hardware improves. Widespread adoption will require fault-tolerant quantum systems, regulatory approval, and clear advantages over classical methods, making large-scale clinical deployment a long-term goal. QML can also be applied to optimize radiotherapy treatment plans, where quantum algorithms can handle the computationally intensive optimization of radiation dose distribution, while classical models process patient data and medical history. This synergy could lead to more precise and effective radiation therapy, minimizing side effects and improving patient outcomes [65].
## 5 Simulation Results
### Multi-Domain Coverage Planning in Static Environments  
#### 5.1.1 Path Searching  
Firstly we clustered the random spots into groups via KMEANS solver. Then, the coordinate and set number of each spot were put into the GLNS algorithm. The coordinates of the center of each spot were the input of ACO.In our work, the parameters selected for ACO were as in Table 2.  
Figure 14: An example of current fields. The field are for underwater and aerial environments. We made the assumption that the surface of the water will remain flat during transitions of the vehicle from air to water, and vice versa.  
A comparison is illustrated in Figure 15. The output of GLNS is shorter than that of ACO.  
The total weighted distance of the path output by GLNS is much shorter than that of ACO, whereas ACO was faster. Furthermore, we present the weighted lengths of paths under five scenarios. The numbers of sets and information spots and lengths of GLNS and ACO outputs are in Table 3.
# A Novel Shape-Adjustable Surface and Its Applications in Car Design
## 2 SQ-Coons Surface
### Mathematical Definition
Special surface \(R_{1}\) is a surface of Bezier basis functions in its \(u\)-direction and CE-Bezier basis functions in its \(v\)-direction (with the shape parameters \(\alpha_{v}\), \(\gamma_{v}\)). As shown in Figure 2a, when \(\alpha_{v}\) and \(\gamma_{v}\) are valued as \(-1\), the boundary curves \(v=0\) and \(v=1\) of \(R_{1}\) interpolate the Bezier curves (the black curves), while the boundary curves \(u=0\) and \(u=1\) interpolate the CE-Bezier curves (the red curves that are further away from control points). \(R_{1}\) is defined as:  
\[R_{1}=\sum_{i=0}^{3}\sum_{j=0}^{3}B_{i,3}(u)\cdot C_{j,3}(v)\cdot p_{ij} \tag{6}\]  
Analogously, another special surface \(R_{2}\) in Figure 2b is a surface of CE-Bezier basis functions in its \(u\)-direction (with the shape parameters \(\alpha_{u}\), \(\gamma_{u}\) valued as \(-1\)) and Bezier basis functions in its \(v\)-direction:  
\[R_{2}=\sum_{i=0}^{3}\sum_{j=0}^{3}C_{i,3}(u)\cdot B_{j,3}(v)\cdot p_{ij} \tag{7}\]  
In addition, four new CE-Bezier curves are generated by the shape parameters mentioned above and the control points of curves \(p_{u0}\), \(p_{u1}\), \(p_{v0}\), \(p_{v1}\):  
\[\left\{ \begin{array}{l}p_{u0}^{\prime}=\sum_{i=0}^{3}C_{i,3}(u)\cdot p_{0i} \\ p_{u1}^{\prime}=\sum_{i=0}^{3}C_{i,3}(u)\cdot p_{3i}\\ p_{v0}^{\prime}=\sum_{j=0}^{3}C_{j,3}(v)\cdot p_{j0}\\ p_{v1}^{\prime}=\sum_{j=0}^{3}C_{j,3}(v)\cdot p_{j3}\end{array} \right. \tag{8}\]  
A new bilinearly blended Coons patch \(T\) in Figure 2c is constructed by the four new CE-Bezier curves:  
\[T=-\left[ \begin{array}{ccc}-1&u&1-u\\ \end{array} \right]\left[ \begin{array}{ccc}0&p_{u0}^{\prime}&p_{u1}^{\prime} \\ p_{0v}^{\prime}&p_{00}^{\prime}&p_{03}^{\prime}\\ p_{1v}^{\prime}&p_{30}^{\prime}&p_{33}^{\prime}\end{array} \right]\left[ \begin{array} []{c}-1\\ v\\ 1-v\end{array} \right] \tag{9}\]In this research, the surface \(S\) is defined as a bicubic shape-adjustable quasi-Coons surface based on CE-Bezier basis functions (SQ-Coons surface for short in the following sections), and the final construction step is as follows:  
\[S = R_{1}+R_{2}-T \tag{10}\]  
The surface \(S\) is combined with the characteristics of the bilinearly blended Coons surface and CE-Bezier surface, and it is extremely suitable for modelling design due to its outstanding geometric properties.
# Life Cycle Assessment on a Biorefinery Approach to Pyrosis Oil for Wood Modification Treatment
## 2 Materials and Methods  
The LCA was performed in accordance to the International Organization for Standardization (ISO) 14040 and ISO 14044, using the International Reference Life Cycle Data System (ILCD) handbook as guidance, with the exception of the review process [24]. The review of this LCA was performed by a single external review (in addition to the peer review for this journal), rather than a panel review. The review was performed by Blonk Consultants and the review statement can be found in the Supplementary information (Document S3: LCA review statement by Blonk Consultants). Simapro version 8.5.2.0, released in May 2018, was used as the modeling software. Recipe 2016 was used as the impact assessment method, using the hierarchist perspective with a time horizon of 100 years.  
The boundaries of the LCA were divided in two parts as shown in Figure 1. Firstly, to determine the impact of different feedstocks for the production of pyrolysis oil, the boundary was set in accordance with as a cradle-to-gate LCA. As functional unit 1 GJ pyrolysis oil was selected. Secondly, to compare the use of fossil-based creosote to pyrolytic sugars, the boundary was set to encompass the entire system from feedstock to wood modification. Here, the functional unit was 1 m\({}^{3}\)a of modified wood, i.e., 1 m\({}^{3}\) of modified wood multiplied by the time of use of the modified wood in years. This included the production, use, and end of life of 1 m\({}^{3}\) of wooden poles for 1 year.  
Life cycle inventory (LCI) data was obtained from within the Dutch-German Interreg project Groen Goud as well as the Horizon2020 projects Bio4 Products and Residue2 Heat. Additional data was obtained from open literature and the Ecoinvent database (version 3.4, compiled in April 2018). The LCI  
Figure 1: System boundaries of the life cycle assessment (LCA). Blue dotted line: The system boundaries of the pyrolysis oil LCA. Green solid line: The system boundaries of the wood modification LCA.  
of creosote-based wood modification was based on the work of [PERSON] et al. [13] and [PERSON] et al. [25], where the end of life LCI was supplemented with data from [PERSON] et al. [26].  
The LCA was classified as category A (microlevel decision support), in accordance to the ILCD handbook, and set up as an attributional LCA. The co-production of steam and electricity was allocated on an energy basis, where the coproduction of multiple fractions from pyrolysis oil was allocated on a mass-basis. The uncertainty analysis was performed by a Monte Carlo analysis with 10.000 calculations, results were deemed significantly different when there is no overlap between the 95% confidence interval of both numbers. The impact of the assumed end of life of the modified wood was researched by a sensitivity analysis on the end of life.
# Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks
## 3 Optimized Node Selection Scheme  
In the traditional sensor nodes' selection scheme, each sensor node is selected by the same probability, which doesn't consider the accuracy of reconstruction results and only guarantees that the signal can be reconstructed. Based on the Bayesian estimation scheme, an optimized sensor nodes' selection scheme can be driven by considering the corresponding MSE and the network lifetime.
# ARIBC: Online Reporting Based on Identity-Based Cryptography
## 2 Related Work
The main motivation for this approach is to eliminate the need for certificates and their management; this is why such schemes are called _certificates IBC schemes_ and are considered simpler and less resource-demanding solutions than certificate-based PKIs. Even though [PERSON] proposed the idea, he was only able to develop an identity-based signature (IBS) scheme based on the RSA primitive. Only in the early 2000's did the emergence of cryptographic schemes based on pairings on elliptic curves result in the construction of feasible and secure IBC schemes [3; 20; 21]. An early survey of IBC schemes appeared in [22]. IBC schemes have found use in many diverse applications, including mobile ad-hoc network security [23]; secure e-mail implementations [24; 25]; cloud security [26]; healthcare systems [27; 28; 29]; e-Government environments [30]; smart grid security [31]; and aviation and maritime navigation and tracking [32; 33]. The distinguishing features, the benefits and the drawbacks of IBC against those of the more traditional PKI have been discussed in [34; 35]. The Sakai-Kasahara Key Encryption (SAKKE) scheme [3], and a variant of the Elliptic Curve Digital Signature algorithm (ECDSA) optimized for use with the Sakai-Kasahara scheme is part of the MIKEY-SAKKE protocol [36; 37; 38], that was proposed in 2016 by the National Cyber Security Centre of the UK [39]. MIKEY-SAKKE \"is designed for government and relevant enterprises to enable secure, cross-platform multimedia communications\" and integrates the MIKEY (Multimedia Internet KEYing) framework [40]. In this work the Sakai-Kasahara IBC schemes [3] and the BLMQ signature scheme proposed in [41; 5] are used. Further, the guidelines of the IEEE 1363.3-2013 \"Standard for Identity-Based Cryptographic Techniques using Pairings\" [42] have been followed. The standard describes IBC schemes that use pairings to implement data encryption, digital signatures, data signcryption, and exchanges of symmetric ciphers keys. Additionally, the standard presents formalized algorithms for calculating pairings with the appropriate parameters to satisfy industry-standard security requirements as defined in [43]. The security of the algorithms and techniques employed in this paper has been analyzed in [4; 42; 44].
# Offloading of Atomic Tasks in Satellite Networks:
## 3 Task Offloading Model
### Traffic Model  
Suppose a given satellite network \(G=(V,E,T)\), \(V\) denotes all satellites, \(E\) denotes the visible time window that can be established between satellite nodes in the network \(G\) at time \(t\). If satellite nodes \(u\) and \(v\) can establish a time window, then this implies that an edge \((u,v)\) exists between two satellites. Otherwise, it is considered that no edge exists. Suppose the task \(Q=\{1,2,\ldots,q\}\). To describe the problem easily, we assume that the number of satellite antennas is 1. That is, only one communication link can be established between satellites at a certain time. After the inter-satellite visible time window is successfully established, an atomic task needs to continue to occupy the time window, and other tasks cannot interrupt the existing atomic task until the atomic task is completed. Let \(P_{s_{i}}\) represent the data transmission power of satellite \(s_{i}\), and the data transmission rate \(r_{u,v}\) between satellite \(u\) and satellite \(v\) in slot \(t\) can be defined as follows,  
\[r_{u,v}=w_{u,v}\log_{2}\biggl{(}1+\frac{P_{s}h_{u,v}}{\sigma^{2}}\biggr{)} \tag{1}\]  
Among them, \(\sigma^{2}\) represents noise power, \(w_{u,v}\) represents the channel bandwidth between satellite \(u\) and satellite \(v\), \(h_{u,v}\) is a constant which denotes the channel gain between satellites.
## 2 The Proposed Approach for Fault Diagnosis of Wheelset Bearing
### The Regular Distribution of the Periodic Peaks after AC Process  
In addition to the property of noise reduction, AC process [41] can also change the arrangement of periodic impulses. Provided a periodic signal \(x(n)\) has a fault period \(2L\), it can be expressed by the sum of the cosines and sines as:  
\[\begin{array}{ll}x(n)&=a_{0}+\sum_{k=1}^{N}\left(a_{k}\cos(\frac{2\pi k}{L} n)+b_{k}\sin(\frac{2\pi k}{L}n)\right)\\ &=a_{0}+\sum_{k=1}^{N}\left[\sqrt{{a_{k}}^{2}+{b_{k}}^{2}}\cos(\frac{2\pi k}{ L}n-\arctan\frac{b_{k}}{a_{k}})\right],\;N\geq 1\end{array} \tag{9}\]  
The AC process of \(x(n)\) can be described as:  
\[\begin{array}{ll}R_{xx}(\tau)&=\frac{1}{2L}\sum_{n=-L}^{L-1}x(n)x(n+\tau)\\ &=\frac{1}{2L}\sum_{n=-L}^{L-1}\left[a_{0}+\sum_{k=1}^{N}\left(\begin{array}[ ]{c}a_{k}\cos\frac{2\pi k}{L}n\\ +b_{k}\sin\frac{2\pi k}{L}n\end{array}\right)\right]\left[a_{0}+\sum_{k=1}^{N} \left(\begin{array}{c}a_{k}\cos\frac{2\pi k}{L}(n+\tau)\\ +b_{k}\sin\frac{2\pi k}{L}(n+\tau)\end{array}\right)\right]\\ &=a_{0}^{2}+\sum_{k=1}^{N}\left(\frac{a_{k}^{2}+b_{k}^{2}}{2}\cos\frac{2\pi k} {L}\tau\right)\end{array} \tag{10}\]  
The AC of a periodic signal is still a periodic signal and it shares a same period \(2L\) with the original signal. In addition, another quality of AC process is revealed that the initial phase of the AC signal at time lag \(\tau=0\) is zero, which implies the AC signal is not changed with the initial phase of the original signal. Hence, the positions of the periodic peaks are rearranged in the AC domain, and the first peak will be adjusted to the position of the time lag \(\tau=2L\) (fault period) no matter where the first periodic peak appears in the original signal.  
In order to better highlight the advantages of AC process, a simple example is generated by a simulated wheelset bearing fault signal with a periodic fluctuation of \(2\%\) and a signal-to-noise ratio (SNR) of \(-15\) dB (the bearing model, not explained here, will be displayed in Equations (14) and (15) below). The fault frequency is set as \(54\) Hz, the resonance frequency band is set as \(3200\) Hz and he sampling frequency is set as \(25.6\) kHz. The squared envelope signal resulted from the optimal IFB and its corresponding AC process signal are displayed in Figure 1.  
From Figure 1b,c, it is evident that the residual in-band noise is further reduced and fault-related peaks are enhanced after AC process. Furthermore, as depicted in Figure 1c, the positions of the periodic peaks are rearranged and first periodic peak is transferred to the position of the fault period, which is consistent with the theoretical derivation above.
# Yeast Fermentation for Production of Neutral Distilled Spirits
## 2 Overview of Neutral Distilled Spirits Production
### Neutral Spirit Distillation  
A neutral spirit is produced by removing most of the congeners during the distillation of the fermented wash. Multiple distillation steps using a multi-column continuous system is typically used to achieve the desired concentration of alcohol and the level of congeners. Following the primary distillation that consists of a stripping and rectifying column, the spirit may be processed through an extractive distillation step (hydro-selection), a secondary distillation (stripper and rectifier), fused-oil recovery, and a methanol reduction step. Table 3 demonstrates the efficiency of a multi-column distillation system for producing a spirit with barely any detectable congeners present. In comparison, a pot-distilled new-make whisky spirit is typically rich in congeners. The reader is directed to [PERSON] [17] for more comprehensive coverage of distillation technology for distilled spirit production.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compound** & **New-Make Whisky** & **Grain Neutral Spirit** \\ \hline Amyl 3 & 212.4 & \textless{}0.1 \\ \hline Isobutanol & 78.0 & \textless{}0.2 \\ \hline Amyl 2 & 64.6 & \textless{}0.1 \\ \hline Ethyl acetate & 36.9 & \textless{}0.2 \\ \hline \(n\)-Propanol & 17.4 & \textless{}0.1 \\ \hline Acetaldehyde & 11.2 & 0.2 \\ \hline Ethyl palmit & 7.3 & \textless{}0.1 \\ \hline Acetal & 5.6 & \textless{}0.1 \\ \hline Methanol & 5.2 & \textless{}0.1 \\ \hline Furfural & 3.1 & \textless{}0.1 \\ \hline Ethyl caprate & 2.1 & \textless{}0.1 \\ \hline Ethyl lactate & 1.2 & \textless{}0.2 \\ \hline Ethyl laurate & 1.0 & \textless{}0.1 \\ \hline Ethyl caprylate & 0.8 & \textless{}0.1 \\ \hline Ethyl palmitoleate & 0.8 & \textless{}0.1 \\ \hline \(n\)-Butanol & 0.7 & \textless{}0.1 \\ \hline Ethyl myristate & 0.4 & \textless{}0.1 \\ \hline Isoamyl acetate & 0.4 & \textless{}0.1 \\ \hline Ethyl caproate & 0.2 & \textless{}0.1 \\ \hline Methyl Acetate & \textless{}0.1 & \textless{}0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Congener profile of new-make whisky and neutral spirits. Values are presented as grams per hectolitre of alcohol. Values with the symbol \(<\) are the test-detectable limits and indicate that the compound was not detected. Analysis courtesy of Tatlock & Thomson (by Leven, UK) on samples provided by Arbikie Distilling Ltd. (Arbroath, UK).  
Figure 2: PCA of congener data obtained following corn-whisky fermentation at 20 \({}^{\circ}\)C, 33 \({}^{\circ}\)C, and 38 \({}^{\circ}\)C using a neutral yeast strain (circles) and a whisky yeast strain (diamonds). (**a**) Scores plot (**b**) Loadings plot. Analysis courtesy of Lallemand Biofuels & Distilled Spirits R&D, Montreal, QC, Canada.
## 2 Related Work  
### Unsupervised Intrusion Detection Techniques  
Intrusion detection mainly includes signature-based intrusion detection and anomaly-based intrusion detection. In detail, signature-based intrusion detection is also known as rule-based intrusion detection. Signature-based intrusion detection has a high detection rate for known attacks, but has no ability to detect attack behaviors that are not in the rule base [4]. Anomaly-based intrusion detection is also known as behavior-based intrusion detection. This algorithm has the ability to detect unknown attack behavior [5]. Feature extraction based on accurately preprocessed network packets in anomaly-based intrusion detection is the basis for good performance. At present, it is easy to collect a large amount of unlabeled data in intrusion detection, and it is difficult to obtain a large amount of labeled data [16; 17; 18]. Therefore, an unsupervised algorithm for intrusion detection has been introduced.  
In recent years, there have been some studies based on unsupervised intrusion detection techniques. In 2007, [PERSON] et al. used a hierarchical PCA model to detect intrusion behavior on the KDD99 dataset [8]. In 2017, [PERSON] et al. used RBM to detect intrusion behavior on the ISCX2012 dataset [7]. In 2018, [PERSON] et al. used Deep Auto-encoder to detect intrusion behavior on the KDD99 dataset [9]. The common point of the above studies [7; 8; 9] is that the preprocessing ignores the similarity between network packets. Network packets with strong similarity relationship are closer in the similarity relation graph and have similar local proximity embedding [10]. The preprocessing algorithm in the above studies ignores the similarity relationship between network packets, resulting in low accuracy of intrusion detection. Therefore, we introduce a graph representation leaning algorithm to solve the above problem. The network packet is preprocessed by using a graph representation leaning algorithm. In the proposed algorithm, the original features of the network packet are combined with the local proximity features describing the similarity relationship between the network packets as the input of the neural network. The proposed algorithm adds effective features by considering the similarities between network packets.
## 2 Materials and Method
### Datasets
#### 2.2.2 Satellite Datasets
In the development of the GSMaP precipitation products, the instantaneous precipitation rate is first archived from the PMW radiometers based on various satellite platforms, such as GMI, advanced microwave sounding unit-A (AMSU-A), advanced microwave scanning radiometer-2 (AMSR-2), TRMM Microwave Imager (TMI), microwave humidity sounder (MHS), and special sensor microwave imager/sounder (SSMIS). Further, the gaps between the PMW-based estimates are propagated using atmospheric moving vector (cloud motion vector) calculated from successive IR images. In addition, a new Kalman filter model is applied to refine precipitation rates after the propagation [61]. Finally, forward and backward propagated precipitation estimates are weighted and combined to produce the GSMaP-MVK product. GSMaP-MVK also uses IR to correct satellite estimates but adopts different PMW imagers and sounders. In addition to PMW and IR, GSMaP-MVK estimates are adjusted using daily 0.5\({}^{\circ}\) NOAA/CPC gauge-based analysis to develop GSMaP-Gauge precipitation product; this reduces precipitation biases and has a latency of 3 days [64]. In addition to the GPM/DPR database, orographic rain correction classification also introduced in the algorithm of GSMaP-V07. In the current study, the Version 07 satellite-only (GSMaP-MVK) and gauge adjusted (GSMaP-Gauge) hourly datasets with a 0.1\({}^{\circ}\) spatial resolution were used. An overview of the selected datasets in the current study is provided in Table 1.