esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Data Collection and Processing
#### 2.2.3 Other Data  
The elevation data were derived from SRTMDEM 90M product, using the Geospatial Data Cloud ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/))). Shuttle Radar Topography Mission (SRTM) was measured by the National Aeronautics and Space Administration (NASA) and National Imagery and Mapping Agency (NIMA) [33]. The digital maps including ecological function protection areas, vegetation types, soil types, and ecogeographic zoning of China were downloaded from the Resource and Environment Data Cloud Platform ([[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn))). The vegetation types in northeast China were summarized as follows: coniferous and broadleaf mixed forest (CBMF), broadleaf forest (BF), coniferous forest (CF), herbosa (HE), shrubland (SH), swamp (SW), meadow (ME), cropland (CR), grassland (GR), and others (OT, such as water area). Soil included the following 23 types: brown coniferous forest soil (BCFS), brown forest soil (BFS), dark-brown forest soil (DBFS), albic soil (AS), cinnamon soil (CS), phaeozem (PH), gray forest soil (GFS), chernozem (CH), chestnut soil (CHS), chestnut cinnamon soil (CCS), alluvial soil (ALS), aeolian sandy soil (ASS), andisol (AN), lithosol (LI), skeletal soil (SS), meadow soil (MS), fluvo-aquic soil (FAS), bog soil (BOS), peat soil (PS), solonchak (SO), marine solonchak (MAS), solonetz (SOL), and paddy soil (PAS). Vegetation type and soil type data (with a scale of 1:1,000,000) were resampled to 0.25\({}^{\circ}\) by nearest-neighbor interpolation.  
Daily precipitation for 2007-2015 at a 0.5\({}^{\circ}\) spatial resolution was obtained from the National Meteorological Information Center of China ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))). The gridded datasets were produced through the thin-plate spline (TSP) interpolation method in ANUSPLIN software using the meteorological data from 2472 national meteorological stations across China. We applied the MVC method to generate eight-day precipitation data.
## 2 Construction and Correlation Properties of OPRC
According to [34], when two code sequences are multiplied, the correlation function of the resulting sequence is equal to the product of the individual correlation functions of the two original code sequences. Thus, the correlation function of the OPRC can be expressed as:  
\[C_{\mathbf{S}_{i}\mathbf{S}_{j}}(\tau)=\frac{1}{L_{S}}\sum_{n=1}^{L_{S}}S_{i}(n)S_{j}(n +\tau)=\left\{ \begin{aligned} 0,&i\
eq j\\ 1,&i=j,\ \tau=0\\ \lambda_{i}(\tau),&i=j,\ \tau=kL_{m}(k=1,\ \ldots,\ L_{h})^{\prime}\\ (-1/L_{m})\lambda_{i}(\tau),&i=j,\ \tau\
eq kL_{m}(k=1,\ \ldots,\ L_{h})\end{aligned} \right. \tag{5}\]  
where we define \(<\!n+\tau\!>\) as \([(n+\tau)\bmod L_{s}]\), \(\lambda_{i}\left(\tau\right)\) is the auto-correlation value of \(\mathbf{H}_{i}\) that is used to generate \(\mathbf{S}_{i}\), and \(1\,\lambda_{i}\left(\tau\right)\leq 11\,\). For Equation (5), when \(i=j\), \(C_{\mathbf{S}_{i}\mathbf{S}_{i}}\left(\tau\right)\) represents the periodic auto-correlation function (PACF) of \(\mathbf{S}_{i}\). When \(i\
eq j\), \(C_{\mathbf{S}_{i}\mathbf{S}_{j}}\left(\tau\right)\) represents the periodic cross-correlation function (PCCF) of \(\mathbf{S}_{i}\). The OPRC has a zero-valued PCCF at any chip shift and a multi-valued PACF with high peaks equal to \(1\). Given the excellent correlation properties of OPRC, we further explore its potential in constructing ACDM-based VLP systems. We also develop a more advanced VLPC system design that integrates OPRC with the SICD scheme, which can leverage the unique characteristics of OPRC and SICD to mitigate MAI caused by asynchronous transmissions. The specific principles of the proposed system will be detailed in the following sections.
# Toward Low Time Fragmentation of Equipment: A Double-DQN Based TT&C Task Planning Approach
## 5 Low Time Fragmentation-Oriented TT&C Task-Planning Algorithm
### Feasible Scheme Set Construction  
A TT&C task may have many feasible schemes, which increase the complexity of TT&C task planning of space network. To better solve the TT&C task-planning problem of space networks, this paper constructs the feasible scheme set of all tasks in advance. Specifically, we model the feasible planning orders for visible windows of the same task as a directed graph model. Then, this paper transforms the problem of constructing the set of feasible schemes into a path searching problem in directed graph. Finally, we use the traversal search algorithm to find all the feasible schemes of all TT&C tasks.  
Let directed graph \(G_{i}(V_{i},E_{i})\) represent all visible windows of satellite \(s_{i}\) as well as their relationship in a planning horizon. Vertex set \(V_{i}=W_{i}\cup\{B,E\}\) in the graph represents the vertex set corresponding to all visible windows between satellite \(s_{i}\) and the TT&C equipment within a planning horizon. The start vertex \(B\) and the end vertex \(E\) are two virtual visible windows of the TT&C task \(om_{i}\). The directed edge of directed graph \(G_{i}\) represents the sequential scheduling relationship between two visible windows for task \(om_{i}\). For instance, windows \(w^{k}_{i,m},w^{r}_{i,n}\in W\) are two visible windows for task \(om_{i}\). If there is no overlap between the two windows and the ascending or descending orbit alternate condition is satisfied, the two windows can be scheduled sequentially for task \(om_{i}\). That is to say, \(E_{i}\) in the graph represents the connection relationship between vertices, which is defined as follows:  
1. \(\forall w^{k}_{i,j}\in V_{i}\), exists \((B,w^{k}_{i,j})\in E_{i}\), and \((w^{k}_{i,j},E)\in E_{i}\);
2. \(\exists w^{k}_{i,m},w^{r}_{i,n}\in V_{i}\), if the two windows do not overlap and one is an orbit ascending window and the other is an orbit descending window (without loss of generality, we assume that \(ws^{r}_{i,n}>we^{k}_{i,m}\)), then \((w^{k}_{i,m},w^{r}_{i,n})\in E_{i}\). As shown in Figure 5, vertex \(B\) and vertex \(E\) represent the virtual start window and virtual end window, respectively. \(w^{1}_{1,1}\), \(w^{2}_{1,1}\), \(w^{1}_{1,2}\), \(w^{3}_{1,1}\), \(w^{4}_{1,1}\), \(w^{2}_{1,2}\), \(w^{3}_{1,2}\), and \(w^{4}_{1,2}\) represent different visible windows, respectively. A path that satisfies the number of windows and the window of ascending or descending requirements represents a feasible scheme within the planning period. Since the aim of TT&C tasks of space networks is to track, telemeter, and command satellites, different tasks are independent of each other in the construction of feasible schemes. That is to say, the directed graphs of each satellite are independent of each other, and the problem of constructing feasible schemes is transformed into the path problem of each independently directed graph. This paper uses the traversal search algorithm on the graph to complete the construction of the feasible scheme set [19; 20; 21].
# Enhanced Antifungal Activity of Engineered Proteins via Swapping between Thiodexin H2 and H3
## 1 Introduction  
Thiodexins (Trxs) are small ubiquitous proteins that participate in a wide variety of biological functions [1]. Several subgroups of Trxs, including Trx-m, -f, -x, -y, -z, -o, and -h are found in _Arabidopsis thaliana_[2]. Trx H-type proteins are the most abundant Trx proteins in _A. thaliana_ and can perform multiple functions, such as redox regulation, antifungal activity, and chaperone functions, in various organisms [3; 4; 5; 6; 7]. In particular, the immune response of _A. thaliana_ Trx-H3 and Trx-H5 against pathogenic microorganisms is well known [6; 8; 9]. Trx proteins contain a common motif 'Trx-fold' with a conserved active site sequence Trp-Cys-Gly-Pro-Cys (WCGPC) or Trp-Cys-Pro-Pro-Cys (WCPDC) [10]. As AtTrx-H2 and AtTrx-H3 contain two different active site motifs--WCGPC and WCPDC, respectively--their functions and structures are also substantially different [11].  
Chimeric proteins prepared by swapping of two or more functional domains may exhibit newly evolved functions [12]. For example, several chimeric proteins exhibit enhanced or reducedanticribobal activity [13; 14; 15], chaperone activity [11; 16], protein solubility [17], and self-assembly properties [18]. Previous studies have demonstrated that AtTrx-H2, AtTrx-H3, and two chimeric proteins (AtTrx-H-2N3C and AtTrx-H-3N2C designed by switching the N- and C-terminal regions of AtTrx H2 and AtTrx H3, respectively) undergo functional switches due to changes in their hydrophobicity and structure [11]. _A. thaliana_ Trx isoform proteins have distinct structural and functional specificities. While AtTrx-H2 formed as low-molecular-mass structures showed only disulfide reductase activity, AtTrx-H3 with high-molecular-mass complexes functions as both a disulfide reductase and as a chaperone.  
Here, we hypothesized that a change of the unique structural feature of each AtTrx H-type protein might result in variation in its functional properties. Interestingly, the Trx-like proteins Trx-H5 and OsTDX, are used as antimicrobial agents against pathogenic fungus [6; 19]. In this study, we sought to determine the effects of swapping the N- or C-terminal domains of AtTrx-H2 and AtTrx-H3 on their antifungal activities. To monitor the structure-function relationship, we first constructed two proteins domain-swapped between the N- and C-terminal regions of AtTrx-H2 and AtTrx-H3. Then, we investigated changes in the antifungal activity and compared the action mechanism of four recombinant proteins--AtTrx-H2, AtTrx-H3, AtTrx-H-2N3C, and AtTrx-H-3N2C--against pathogenic mold and yeast species.
# The Effects of Train Passes on Dwell Time Delays in Sweden
## 1 Introduction
### Delay Management  
Delays can be avoided through effective timetabling and dispatching decisions of traffic controllers. In [6], the authors defined timetable resilience as the flexibility of a timetable to prevent or reduce secondary delays using dispatching actions. Management of unexpected delays depends on the ability of the train dispatchers to make accurate decisions in advance by taking into consideration the inter-dependency between trains. However, dispatching measures are often sub-optimal as train dispatchers have a limited view of the effects of conflict resolution methods and are unable to compare alternative solutions based on various performance indicators due to the limited time available for real-time decision-making [7]. In [8], the authors also found that the dispatching algorithms are less efficient if only one station ahead instead of two is considered when calculating the best dispatching solution.  
Extensive efforts have been made to develop decision support tools to monitor delays in the rail network to effectively reschedule trains during operations. In [9], the authors proposed a dispatching optimisation method based on a greedy algorithm to obtain an optimal relationship between capacity and operational quality. To optimise train schedules on single line rail corridors, in [10], the authors devised a mathematical programming model that prioritises trains in conflict based on remaining crossing delays, overtaking delays, and current delays. To generate a periodic network-optimal timetable, [11] optimised the train order, run and dwell times, and overtaking locations of a line plan, and then evaluated the timetable's stability. In [12], a delay root cause discovery model that used machine learning to anticipate the delay root cause by analysing scheduled and unscheduled train meetings, overtaking behaviours, and the consequent delay propagation was proposed.  
Different dispatching strategies, such as re-timing [13; 14], reordering [15], rerouting, and even train cancellations [16; 17], have been used to solve the train rescheduling problem, that is, to minimise deviation from original timetables. However, research into the effects of train passes on delays is relatively scarce. In [18], it was found that congestion-related factors such as meets and passes have the largest effect on congestion delays. In [19], the impacts of train meets and passes to dwell time delays through a novel measure of the excess probability of delays was captured. The study found that meets and passes of the train are not evenly distributed geographically in Sweden and tend to increase the risk of delays by around 30%.
# A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images
###### Abstract  
The geostationary remote sensing satellite has the capability of wide scanning, persistent observation and operational response, and has tremendous potential for maritime target surveillance. The GF-4 satellite is the first geostationary orbit (GEO) optical remote sensing satellite with medium resolution in China. In this paper, a novel ship-tracking method in GF-4 satellite sequential imagery is proposed. The algorithm has three stages. First, a local visual saliency map based on local peak signal-to-noise ratio (PSNR) is used to detect ships in a single frame of GF-4 satellite sequential images. Second, the accuracy positioning of each potential target is realized by a dynamic correction using the rational polynomial coefficients (RPCs) and automatic identification system (AIS) data of ships. Finally, an improved multiple hypotheses tracking (MHT) algorithm with amplitude information is used to track ships by further removing the false targets, and to estimate ships' motion parameters. The algorithm has been tested using GF-4 sequential images and AIS data. The results of the experiment demonstrate that the algorithm achieves good tracking performance in GF-4 satellite sequential images and estimates the motion information of ships accurately.  
GF-4 satellite; ship detection and tracking; RPCs correction; data association +
Footnote †: journal: Sensors
# Panchromatic and Hyperspectral Image Fusion Using Ratio Residual Attention Networks
## 2 Related Work  
Recent advancements in deep learning have led to significant progress in hyperspectral pansharpening. For instance, [PERSON] et al. [18] proposed a spatial-spectral residual network that effectively captures spatial and spectral features using 3D convolutions. [PERSON] et al. [19] introduced a distributed fusion framework based on residual CNNs, achieving improved fusion quality by leveraging multi-scale features. [PERSON] and [PERSON] [20] developed a multistage dual-attention guided fusion network, which employs spectral and spatial attention mechanisms to enhance fusion performance. Additionally, [PERSON] et al. [21] presented the MS/HS Fusion Net, a model-based deep learning approach that integrates observation models with deep networks for effective fusion.  
In addition to these methods, several other deep learning approaches have also demonstrated promising results. For example, [PERSON] et al. [22] introduced a densely connected network that enhances feature reuse and alleviates gradient vanishing, improving the detail preservation in fused images. [PERSON] et al. [23] proposed an attention-based fusion network that adaptively weights spectral and spatial features, further boosting fusion accuracy. Moreover, GAN-based methods such as the one by [PERSON] et al. [24] utilize adversarial training to generate more realistic and spectrally consistent fused images.  
On the other hand, traditional model-based fusion methods such as Coupled Non-negative Matrix Factorization (CNMF) [7] and Hyperspectral Super-resolution (HySure) [8] remain important baselines. CNMF jointly decomposes the hyperspectral and panchromatic data matrices to effectively integrate spatial and spectral information, while HySure employs a convex optimization framework incorporating prior knowledge and data fidelity terms to enhance spectral fidelity. Additionally, multi-resolution analysis techniques [25] have been employed to better capture features at different scales, which lays a solid foundation for subsequent deep learning fusion frameworks.  
Despite these significant advances, challenges persist in simultaneously preserving fine spatial details and accurate spectral information. Many existing methods either sacrifice spectral fidelity for spatial sharpness or vice versa. To address these limitations, our proposed method introduces a novel ratio transformation combined with a residual attention network. This approach aims to effectively capture the complementary characteristics of spatial and spectral domains, leading to enhanced fused image quality with improved spatial detail and spectral fidelity.  
Despite these advancements, challenges remain in effectively preserving both spatial details and spectral fidelity. Our proposed method addresses these challenges by introducing a novel ratio transformation combined with a residual attention network, aiming to enhance both the spatial and spectral aspects of fused images.
# Assessment of Student Music Performances Using Deep Neural Networks
## 3 Material and Methods
### Pre-Processing
#### 3.3.1 Pitch Contour  
Pitch contours are \"time continuous sequences of fundamental frequency (F0) values\" [49]. They are compact representations of the high level melodic content present in pitched musical audio and are motivated by the human perception of pitch in auditory streams [50]. While pitch and F0 are distinct physical entities, for the purpose of simplicity, we use them synonymously. Pitch estimation is considered as an essential pre-processing step in many MIR tasks such as melody extraction [49] and music transcription [8; 9]. Since pitch contours capture frequency deviations, they are capable of characterizing important expressive aspects of musical performances such as bends, vibrato, etc., which make them suitable for use as an input representation for feature learning [51].  
To compute the pitch contours from the input audio, the following steps were used (see Figure 3). First, the F0 estimate in Hz is computed from the audio recordings per block using the pYin F0 tracking algorithm [52]. pYin is a widely used F0 estimation technique for monophonic audio (all wind instruments considered in this work are monophonic). A block size of 23.2 ms and a hop size of 5.8 ms is used. The obtained frequency values are then converted to the MIDI domain using the following equation:  
\[m=69+12\text{log}_{2}\left(\frac{f}{440\,\text{Hz}}\right)\,, \tag{1}\]  
where \(f\) is the F0 value in Hz and the \(m\) is the resulting MIDI pitch. This is followed by a normalization step. Since all the instruments under consideration have a pitch range from note C2 (\(m=32\)) to C8 (\(m=104\)), all MIDI pitches are first subtracted by 32 and then divided by 72. This results in a pitch contour range from 0 to 1 while minimizing sparsity in the input space to some degree. This is defined as the PC input representation.
# Time-Sensitive Networking Mechanism Aided by Multilevel Cyclic Queues in LEO Satellite Networks
## 2 Related Work
### Latency-Based Schemes  
Besides routing schemes, several approaches have been proposed in recent studies for optimizing latency. [PERSON] et al. [20] proposed a latency-optimal scheduling algorithm that could achieve near-global-optimum solutions with a limited number of iterations and a constrained search space. [PERSON] et al. [21] introduced L2D2, a geographically distributed ground station design that employs low-cost commodity hardware to provide low latency and robust downlink. Another approach, by [PERSON] et al. [22], is the TOMRA algorithm, which minimizes the latency associated with task offloading and processing in GEO-LEO networks. Additionally, [PERSON] et al. [9] investigated network delay and the age of information (AoI) in a multihop satellite network, and developed a model for latency patterns in satellite networks. However, these studies did not consider the different time delay requirements of various time-sensitive services. Therefore, further research is necessary to explore how to differentiate traffic management for varying time delay requirements.
## 3 Survey of Techniques for Updating
### Dissemination  
The protocols developed for disseminating a software update in a wireless sensor network are based on data dissemination protocols, with key examples being Directed Diffusion [21], RMST, [22], PSFQ [23], Infuse [24], and Sprinkler [25]. An overview of WSN transport protocols is presented in [26]. The key differences between protocols for data collection and for updates are:  
1. the data flow for software updating is from the gateway to the nodes (see Figure 2),
2. typically, intermediate nodes both store and forward the data,
3. the data transfer must be reliable and achieve network-wide coverage.  
Dissemination may also be performed _out-of-band_ (for example, see Deployment Support Network (DSN) [27]), or over an existing network infrastructure--but the vast majority of protocols assume an _ad-hoc_ network, and use a form of controlled flooding. Some protocols (see for example, CORD [28]) build a spanning-tree as the first phase of dissemination.  
The _advertisement_ pattern (Figure 3a) for dissemination protocols typically has four steps: advertisement of available software, selection of a source, requesting the update, and reliable download to the target. The target may then become a source in turn. This may be reversed by use of a _subscription_ approach (Figure 3b), where the targets subscribe to sources for new software updates, but for WSN's this results in significantly increased overhead at the source, as every client subscription need to be tracked.  
The following subsections provide a summary of the operation of the most significant software update dissemination protocols for WSNs (presented in alphabetical order). Each protocol is briefly described, and then autonomic support is reviewed from the following aspects:  
Figure 3: (**a**) Advertisement Pattern for Dissemination Protocols; (**b**) Subscription Pattern for Dissemination Protocols.  
Figure 2: Update Dissemination.  
support for pre-dissemination planning, including an energy model and update simulation;
* energy-aware and policy-driven dissemination of the update;
* policy-driven activation of the update;
* feedback.