esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Attitude Synchronization for Spacecraft Formation Flying via Event-Triggered Control
## 2 Preliminaries and Problem Statement  
### Preliminaries  
The following symbols are defined to express conveniently. \(I_{n}\) stands for the \(n\times n\) identity matrix. \(1_{n}\) represents a n-dimensional vector of all ones. For a vector or matrix, let \(\|\cdot\|\) and \(\|\cdot\|_{\infty}\) be its Euclidean norm and Infinity norm, respectively. For the given vector \(x=[x_{1},x_{2},\cdots,x_{n}]^{T}\in\mathbb{R}^{n}\), \(\dot{x}\) and \(\ddot{x}\) represent its first derivative and second derivative, respectively. For an arbitrary three-dimensional vector \(x=\left[x_{1},x_{2},x_{3}\right]^{T}\in\mathbb{R}^{3}\), the skew-symmetric matrix \(x^{\times}\in\mathbb{R}^{3\times 3}\) is defined as:  
\[x^{\times}=\left[\begin{array}{ccc}0&-x_{3}&x_{2}\\ x_{3}&0&-x_{1}\\ -x_{2}&x_{1}&0\end{array}\right]\]  
In this paper, a directed graph \(G=\{V,E,A\}\) is utilized to describe the information exchange of SFF systems composed of \(n\) spacecraft. The digraph \(G\) includes a set of nodes \(V=\{v_{1},v_{2},\cdots,v_{n}\}\), an edge set \(E\subseteq V\times V\), and an adjacency matrix \(A=\left[a_{ij}\right]\in\mathbb{R}^{n\times n}\). Edge \(\left(v_{i},v_{j}\right)\in E\) implies that node \(v_{j}\) can receive state information from node \(v_{i}\), but not necessarily vice versa. The weighted adjacency matrix \(A\) satisfies \(a_{ij}>0\) if \(\left(v_{j},v_{i}\right)\in E\), otherwise \(a_{ij}=0\). In addition, it is assumed that self-loops are not allowed, that is, \(a_{ii}=0\). For a directed graph, a directed path from \(v_{i}\) to \(v_{j}\) is composed of a sequence of ordered edges of the form \(\left(v_{i},v_{k}\right)\), \(\left(v_{k},v_{l}\right)\), \(\cdots,\left(v_{m},v_{j}\right)\) and if there is a node with a directed path to any other node, the digraph is said to have a directed spanning tree. The Laplacian matrix associated with the digraph \(G\) is represented by \(L=D-A\in\mathbb{R}^{n\times n}\), in which \(D=diag[d_{1},d_{2},\cdots,d_{n}]\) and \(d_{i}=\sum_{j=1}^{n}a_{ij}\). The Laplacian matrix \(L\) satisfies \(I_{ij}=-a_{ij}\leq 0(i\
eq j)\) and \(\sum_{j=1}^{n}l_{ij}=0(i=1,\cdots,n)\), showing that zero is an eigenvalue of \(L\) with an associated eigenvector \(1_{p}\).  
**Assumption 1**.: _The digraph \(G\) has at least one directed spanning tree._
## 3 Results and Discussion
### Evolution of the Standardized Yield Residual Series (SYRS)  
The analysis of SYRS evolution from 2003 to 2018 across the various MS provinces revealed diverse trends (Figure 6). In Al Haouz Province, the yields increased in 2003 (1.56), followed by periods of stability and moderate losses, with an exceptional peak in 2015 (1.73) and substantial losses in 2016 (\(-\)1.97). Chichaoua Province initially showed typical yields, with a notable peak in 2010 (2.02), followed by variations, including losses in 2012 and 2013. The subsequent years displayed a mixed trend: normal yields, moderate losses in 2016, and notable yields in 2018 (1.60). El Kelaa des Sraghna Province experienced fluctuating yields, ranging from slight to moderate losses, with an exceptionally high yield (2.48) recorded in 2009, followed by periods of stability and minor improvements. In Essaouira Province, losses were observed in 2008 and 2014 (\(-\)1.55, \(-\)1.66), while 2009 saw an increase in yield to 1.77. Marrakech Province exhibited variations, with stable years, high yields in 2009 (2.42) and 2015 (2.06), and moderate losses in 2012, 2016, and 2018. Similarly, Rehamna Province showed an increase in yield in 2009 (2.45) and 2010 (1.53). Safi Province displayed variations, with initially expected yields followed by moderate losses in 2005. A high yield (2.10) was recorded in 2009, followed by periods of stability and moderate increases. Youssoufia Province initially demonstrated normal yields with moderate losses in 2012 and 2014. In 2015, there was an increase to 2.16, followed by stability and further growth in 2018 (1.83) (Figure 6).
# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
## 4 Production Performance of Single Well Pair
### Sensitivity Analysis of Inter-Well Connectivity
#### 4.3.1 Permeability of Permeable Channels
Figure 9: Water cut profiles observed under different reservoir permeability channels.
## 3 Methodology
### Input Layer  
The input layer is mainly composed of word embedding and sentence encoding.  
(1) Word embedding: Given a sentence consisting of n words, s = {w\({}_{1}\),w\({}_{2}\),\(\cdots\),w\({}_{\text{T+1}}\),\(\cdots\),w\({}_{\text{T+m}}\),\(\cdots\),w\({}_{\text{n-1}}\),w\({}_{\text{n}}\)} where {w\({}_{\text{T+1}}\),\(\cdots\),w\({}_{\text{T+m}}\)}. The representation has m aspects and each word in the sentence is looked up through the word embedding matrix \(\text{L}\in\mathbb{R}^{|\text{V}|\times\text{d}_{\text{w}}}\) of pre-trained Glove [40], where \(|\text{v}|\) represents the size of the vocabulary and \(\text{d}_{\text{w}}\) represents the dimension of word embeddings. Thus each word is embedded in a low-dimensional vector.  
(2) Sentence encoding: In this paper, a bidirectional LSTM is used for encoding. In this way, the hidden representation of a given sentence, s, can be extracted. Given that two LSTM forward and reverse time series are connected to one output, this is expressed as follows:  
\[\overset{\rightarrow}{\text{H}}=\left\{\overset{\rightarrow}{h}_{1},\overset{ \rightarrow}{h}_{2},\cdots,\overset{\rightarrow}{h}_{\text{T+1}},\cdots \overset{\rightarrow}{h}_{\text{T+m}},\overset{\rightarrow}{h}_{\text{n-1}},\overset{\rightarrow}{h}_{\text{n}}\right\} \tag{1}\]  
\[\overset{\leftarrow}{\text{H}}=\left\{\overset{\leftarrow}{h}_{1},\overset{ \leftarrow}{h}_{2},\cdots,\overset{\leftarrow}{h}_{\text{T+1}},\cdots \overset{\leftarrow}{h}_{\text{T+m}},\overset{\leftarrow}{h}_{\text{n-1}},\overset{\leftarrow}{h}_{\text{n}}\right\} \tag{2}\]  
\[\text{H}=\left[\overset{\rightarrow}{\text{H}},\overset{\leftarrow}{\text{H}}\right] \tag{3}\]  
where H represents forward, H represents reverse, and H represents the final representation encoded by the bidirectional LSTM, which contains contextual information between aspect words and opinion words.
# Flexible Dispatch for Integrated Power and Gas Systems Considering Power-to-Gas and Demand Response
## 2 Scheduling Flexibility and Demand Response Model
### Scheduling Flexibility for Grid-Connected Wind Power
#### 2.1.1 Adjustable Capacity Supply  
In wind power grid-connected system, adjustable capacity is mainly provided by various flexible resources. Since the generator unit has a certain adjustable ability, it has been regarded as the main undertaker of dispatch flexibility. In addition to generator unit, the P2G facility, wind power curtailment and load reduction in the day-ahead stage are also considered as flexible resources to provide adjustable capacity for the system.  
When the net load changes, the generator unit provides adjustable capacity for system according to its output range and ramp rate limit. The maximum upward and downward adjustable capacity it can provide is as follows:  
\[\left\{\begin{array}{l}f_{\text{g},t}^{\text{u,max}}=\min\left(u_{i,t}\cdot P _{i,t}^{\text{max}}-P_{i,t},u_{i,t}\cdot R_{i}^{\text{u}}\cdot\Delta t\right) \\ f_{\text{g},t}^{\text{d,max}}=\min\left(P_{i,t}-u_{i,t}\cdot P_{i,t}^{\text{ min}},u_{i,t}\cdot R_{i}^{\text{d}}\cdot\Delta t\right)\end{array}\right. \tag{1}\]From the perspective of flexibility, the P2G facility can also be compared to \"conventional generator set\", that is, to provide the system with adjustable capacity up and down under specific operating range and climbing constraints. The expression is as follows:  
\[f_{\text{P2g,p,t}}^{\text{u}}=\sum_{p\in\Omega_{\text{P2g,p,t}}}\left(P_{\text{ P2g,p,t}}-u_{\text{P2g,p,t}}\cdot P_{\text{P2g,p,t,min}}\right) \tag{2a}\]  
\[f_{\text{P2g,p,t}}^{\text{d}}=\sum_{p\in\Omega_{\text{P2G}}}\left(u_{\text{P2g, p,t}}\cdot P_{\text{P2g,p,t,max}}-P_{\text{P2g,p,t}}\right) \tag{2b}\]  
In terms of demand response, through reasonable incentives, users can flexibly change their own power needs, thereby providing upward and downward adjustable capacity, as follows:  
\[f_{\text{dr,t}}^{\text{u}}=\sum_{m\in\Omega_{\text{DR}}}(v_{m,t}\cdot P_{ \text{DRE},m,t,\text{max}}-P_{\text{DRE},m,t}) \tag{3a}\]  
\[f_{\text{dr,t}}^{\text{d}}=\sum_{m\in\Omega_{\text{DR}}}(P_{\text{DRE},m,t}-v_ {m,t}\cdot P_{\text{DRE},m,t,\text{min}}) \tag{3b}\]  
During peak load hours, upward adjustable capacity \(f_{\text{plcur,t}}^{\text{u}}\) can be provided through load shedding, and when excess wind power cannot be absorbed, downward adjustable capacity \(f_{\text{pwcur,t}}^{\text{d}}\) can be provided through wind power curtailment. The expression is as follows:  
\[f_{\text{plcur,t}}^{\text{u}}=\Delta P_{\text{L},\text{b,t}} \tag{4a}\]  
\[f_{\text{pwcur,t}}^{\text{d}}=\Delta P_{\text{w,k,t}} \tag{4b}\]  
Therefore, the upward and downward adjustable capacity provided by the IGPS is shown in the following formula:  
\[f_{t}^{\text{u}}=f_{\text{g,t}}^{\text{u}}+f_{\text{P2g,t}}^{\text{u}}+f_{ \text{dr,t}}^{\text{u}}+f_{\text{plcur,t}}^{\text{u}} \tag{5a}\]  
\[f_{t}^{\text{d}}=f_{\text{g,t}}^{\text{d}}+f_{\text{p2g,t}}^{\text{d}}+f_{ \text{dr,t}}^{\text{d}}+f_{\text{pwcur,t}}^{\text{d}} \tag{5b}\]
## 2 Mathematical Model  
### ORC System Description  
In this paper, we chose a basic ORC system for waste heat recovery. The four main parts of a standard ORC system are the working fluid pump, expander, condenser, and evaporator, which together complete the work process. Its temperature-entropy (T-s) diagram is shown in Figure 1. The unsaturated liquid organic working fluid in process 2-5 becomes superheated after absorbing heat from the heat source in the evaporator. Then, the working fluid in processes 5-6 enters the expander, expands, and produces work. The overheated organic working fluid in the 6-1 process becomes a saturated liquid working fluid after passing through the condenser and dissipating system heat to the cold source. In the end, the pump presses it into the evaporator inlet, which is the 1-2 process. The dotted lines at 5-6s and 1-2s represent the ideal expansion and compression processes, respectively. The 2s and 6s points are the ideal state points. The open ORC system has a larger output power, but the closed ORC system has a higher thermal efficiency. In this paper, we selected an open ORC system as the working type.
# Spatio-Temporal Variations of Ecosystem Water Use Efficiency and Its Drivers in Southwest China
## 4 Discussion  
### Spatial Distribution Patterns of Ecosystem WUE in Southwest China  
Under the influence of latitudinal zonation, the climate of the study area transitions from subtropical to tropical monsoon systems from higher to lower latitudes, while the type of vegetation gradually transitions from subtropical evergreen broad-leaved forests to tropical rainforests. This resulted in similarities in the spatial distribution of multi-year mean values of GPP, ET, and ecosystem WUE with significant increases with decreasing latitude and altitude (Figure 2, left and right panels). This finding is consistent with the result of a previous study [34]. Similarly, from high to low latitudes, vegetation growth became better with increasing temperature and precipitation, which increases the GPP [35] and ET [36] of ecosystems. However, the rate of change of GPP dominates the rate of change of ecosystem WUE (Figure 3a), so this is why the interannual trend of ecosystem WUE in the study area is very similar to GPP in spatial distribution (Figure 3b,d).  
GPP, ET, and ecosystem WUE vary in longitude, but clearly decrease near a latitude of 104\({}^{\circ}\). Spatially, from Figure 1c, this low-value area also has a high concentration of karst landforms. Previous studies [37; 38; 39; 40] have reported that karst landforms are susceptible to drought and shallow soils with sparse vegetation due to precipitation infiltration and low carbon sequestration capacity as compared to non- karst landforms [41]; thus, the ecosystem WUE is also low and fluctuates in a \"W\" shape with longitude.
# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
###### Abstract
Specifically, several classification scenarios were examined, leveraging multi-temporal PolSAR and optical data from the Sentinel-1 and Sentinel-2 missions and a Support Vector Machines (SVM) classifier. Cloude-Pottier polarimetric decomposition was employed in order to extract polarimetric attributes from Sentinel-1 time-series data. Several studies have used some of the H/A/\(\overline{\alpha}\) polarimetric parameters in crop classification tasks. However, in this work we assess for the first time the usefulness of the complete set of 23 H/A/\(\overline{\alpha}\) decomposition polarimetric parameters, as provided by the PolSARpro software. In total, multi-temporal optical and polarimetric features formed a large feature space of 818 attributes. In an attempt to derive an optimal feature subset of this big feature space, we also implemented a GA as a feature selection technique. Apart from this, we also performed the computational demanding analysis of carrying out 100 independent GA experiments and recording the selection frequency of each individual feature as a means to identify the most informative attributes for the multi-class crop classification task at hand. In summary, the main contributions of this work are the following:  
1. The extraction of the complete set of \(\mathrm{H}/\mathrm{A}/\overline{\pi}\) polarimetric indicators from the Sentinel-1 time-series data and the assessment of their capability of classifying different crop classes using SVM, which yields promising results with an overall accuracy of more than 82%. 2. The demonstration of the added value that PolSAR Sentinel-1 data offer when combined with Sentinel-2 optical data for crop classification, in areas that suffer from extended cloud coverage. 3. The implementation of a custom but robust GA as a feature selection method, which provides the optimal feature sets for crop classification. 4. A statistical analysis of GA's feature selection results as a means to estimate features' relative importance and suggest optimal feature sets of reduced dimensionality (more than 85% decrease). We show that the spectral and polarimetric characteristics of these optimal features, in different temporal milestones, can be explained by the phenology evolution of the different crops included in the dataset.
## 2 Materials and Methods
### RNA Extraction, RNA-Seq, and transcriptomic Data Processing  
RNA extraction, RNA-seq, and transcriptomic data processing were accomplished by BGI Technology Services Co., LTD (Shenzhen, China). Three biological replicates for each sample were measured.  
Total RNA was extracted using TRIzol reagent (Invitrogen, Waltham, MA, USA) and purified with the Rio-Zero rRNA Removal Kit (Illumina, San Diego, CA, USA) following the manufacturers' instructions. The genomic DNAs were digested by DNase I (Fermentas, Waltham, MA, USA). Degradation and contamination of the as-prepared RNA were monitored on 1.5% agarose gels. RNA concentration was detected with Qubit(r) RNA Assay Kit in Qubit(r) 3.0 Fluorometer (Life Technologies, Carlsbad, CA, USA). RNA integrity was assessed with the RNA Nano 6000 Assay Kit in Agilent 2100 Bioanalyzer system (Agilent Technologies, Santa Clara, CA, USA) (RIN > 9.0). The RNA samples were added into fragmentation buffer for thermal fragmentation into 130-160 nt. Then, the obtained fragments were reverse transcripted into cDNA with random primers using First Strand Mix, and the second-strand cDNA was generated by Second Strand Mix. The resultant double-strand cDNA was purified, end repaired, amplified, ligated to sequencing adapters, and sequenced on the BGISEQ500 platform (BGI, Shenzhen, China). The sequencing data were filtered using SOAPnuke v1.5.2 and aligned using Bowtie2 ([PERSON], 2016). The gene expression level analysis and differential expression (DE) analysis were conducted, as described previously ([PERSON], 2016; [PERSON], 2016), using _L. casei_ Zhang (Firmicutes) (GenBank Accession No. NC\({}_{\text{-}}\)014334.2 and NC\({}_{\text{-}}\)011352.1) as reference genome. _p_-value < 0.001 and fold change >2.0 were standards to define differential expression.  
RNA-seq data were deposited in Sequence Read Archive under the accession numbers of SRR12378022 (SSF-1), SRR12378030 (SSF-2), SRR12378029 (SSF-3), SRR12378028 (LSF-1), SRR12378027 (LSF-2), and SRR12378026 (LSF-3).
# Secure Service Proxy: A CoAP(s) Intermediary for a Securer and Smarter Web of Things
## 3 Problem Statement and Research Goals
### Complex Application Features in LLNs  
Apart from security issues, there is another important category of problems that relate to the functionality at the application layer for constrained devices, which is targeted by this work. Firstly, the same constraints that prohibit offering extensive security features also apply to implementing application features on the constrained device. This is one of the reasons why the IETF has standardized special purpose protocols and data formats for use in constrained environments (e.g., CoAP and CoRE Link Format (CLF) [23]). However, traditional Internet hosts do not always implement these protocols and data formats. In these cases, a protocol and data format translation should occur that enables the Internet host to communicate with the constrained device (e.g., an HTTP/CoAP proxy and a JSON/CLF mapper). Such a translation has to be performed by an unconstrained third party system (e.g., gateway). Secondly, some types of functionality can be ineffective when they are offered on the constrained device. An example is caching the responses of a constrained server on the device itself, which will not save any network traffic. A second example is the aggregation of observation relationships by intermediaries; clearly, this has to be offered on an intermediary and not on a constrained node in order to have any effect. Note that conventional end-to-end security does not allow for response caching or observation aggregation, as all traffic passing at an intermediary is encrypted. Thirdly, some functionality can be inefficient when they are implemented on the constrained device. An example is storing verbose semantic descriptions on a constrained device, which will lead to significant amounts of network traffic every time these descriptions are requested. Another example of functionality that is inefficient to offer on constrained devices is access control. Typically, the LLN will have already spent a significant amount of resources delivering the request to its destination where it will end up being discarded. Clearly, discarding this request before the network has wasted its resources is more efficient. For these cases, this work will study how third party systems can support and optimize the operations of constrained devices and LLNs.
# A Novel Cooperative Opportunistic Routing Scheme for Underwater Sensor Networks
## 5 Experimental Results  
The details of our simulation study and also the performance results are presented in this section. We use Aqua-Sim [46], an NS-2-based simulating software for underwater acoustic networks, to evaluate OVAR against VBF, HHVBF and VAPR in a single-sink architecture.  
Figure 9: The relationship between the number of forwarding nodes and energy cost.
## 2 Model Development  
### Model Geometry  
Multiphysical field direct coupling analysis software COMSOL Multiphysics was selected to build and compute the PEM fuel cell models with different rib grooves. The schematic diagram of the modeling domains of the serpentine flow field PEM fuel cell is shown in Figure 1. The geometry was built with seven different layers from bottom to top, which are the anode flow field (A-FF), the anode gas diffusion layer (A-GDL), the anode catalyst layer (A-CL), the membrane (MEM), the cathode catalyst layer (C-CL), the cathode gas diffusion layer(C-GDL), and the cathode flow field (C-FF), respectively.  
As shown in Figure 2, in this work, the simulations were conducted using four types of serpentine flow field design with different rib grooves: (a) the conventional design, where the rib groove rate was 0, (b) 1/3 of the rib groove, where the rib groove rate was 1/3, (c) 2/3 of the rib groove, where the rib groove rate was 2/3, (d) full rib groove, where the rib groove rate was 1. To simplify the models, the rib grooves were only added to the cathode flow field. The rib grooves were established along the upper surface of the cathode gas diffusion layer. The depth of the rib groove was 0.1 mm, the angle of rib groove direction with the inlet direction was 45\({}^{\circ}\), and the distance between adjacent rib grooves was 2 mm. The geometric parameters of the PEM fuel cell model used in the simulation are listed in Table 1.  
Figure 1: Geometry of the fuel cell with a serpentine flow field.  
Figure 2: Geometry of the serpentine flow field. (**a**) Conventional design, (**b**) 1/3 of the rib groove, (**c**) 2/3 of the rib groove, (**d**) full rib groove.
# Proxy Re-Encryption Scheme for Decentralized Storage Networks
###### Abstract  
Storage is a promising application for permission-less blockchains. Before blockchain, cloud storage was hosted by a trusted service provider. The centralized system controls the permission of the data access. In web3, users own their data. Data must be encrypted in a permission-less decentralized storage network, and the permission control should be pure cryptographic. Proxy re-encryption (PRE) is ideal for cryptographic access control, which allows a proxy to transfer [PERSON]'s ciphertext to [PERSON] with [PERSON]'s authorization. The encrypted data are stored in several copies for redundancy in a permission-less decentralized storage network. The redundancy suffers from the outsourcing attack. The malicious resource provider may fetch the content from others and respond to the verifiers. This harms data integrity security. Thus, proof-of-replication (PoRep) must be applied to convince the user that the storage provider is using dedicated storage. PoRep is an expensive operation that encodes the original content into a replication. Existing PRE schemes cannot satisfy PoRep, as the cryptographic permission granting generates an extra ciphertext. A new ciphertext would result in several expensive replication operations. We searched most of the PRE schemes for the combination of the cryptographic methods to avoid transforming the ciphertext. Therefore, we propose a new PRE scheme. The proposed scheme does not require the proxy to transfer the ciphertext into a new one. It reduces the computation and operation time when allowing a new user to access a file. Furthermore, the PRE scheme is CCA (chosen-ciphertext attack) security and only needs one key pair.  
proxy re-encryption; blockchain; storage; proof-of-replication Article