esa-sceva/satcom-chunk-collection huggingface dataset:
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 12 Applications of HCFs in Networks and Systems
### Low Latency Applications
#### 12.1.4 Data Centers and Computers  
The number of fiber optic data links in a large (hyperscale) data center can exceed a million. The total length of fibers in such a facility, assuming a 10 m average length of a patch cord (most connections are within a single rack or row of racks) and duplexconnection with 2 fibers, is 20,000 km or more. Actual link lengths vary between 2 m (intra-rack connections) and 200-400 m (cable bundles linking distant rows and sections of facility). Short- and medium-length connections, up to ca. 200 m, are carried out with 50/125 m OM3 or OM4 multimode fibers [66]. Longer ones include single-mode fibers conforming to the ITU-T G.652.D [29] (sometimes G.652.A/ISO OS1 instead) or G.657.A [79] standards.  
Besides short links and large number of connectors, another peculiarity of data centers and supercomputer facilities is their large proportion of cables terminated with multi-fiber connectors, mostly of MPO type with a capacity from 12 to 64 fibers, employed for duplex parallel data transmission. Data centers builders are large buyers of fiber optic cabling, predominantly as factory-assembled patch cords, bundles and fanouts; more than 80% of fiber optic connectors are installed in data centers, not in telecom networks.  
A recent presentation of a Google engineer during OFC-2021 [124], known only from a short summary, included an estimate that latency in cabling between blocks and subsystems in a large data center reduces its throughput by 20-25%. The replacement of current fiber optic and twisted pair data cables with HFC cables and a reduction in latency by \(\approx\)31% shall increase throughput by \(\approx\)8% with the same equipment and power consumption, or reduce data center CAPEX (servers, power supply system, heating, ventilation and air conditioning equipment, floor space) and OPEX (power, cooling, maintenance) by 6-8% with respect to a facility of equal performance, but with traditional cables.  
This means a large prospective market for HCFs exists, but with a low-price ceiling.
# Experimenting the Automatic Recognition of Non-Conventionalized Units in Sign Language
## 1 Introduction  
Sign Languages (SLs) constitute one the most elaborate kind of human gestures. Originating in the communication between deaf people, they can be considered as a form of natural _oral_ language, in the sense that they include both expressive and receptive channels. However, because of the very specific visual-gestural modality, SLs hardly fall into the linguistic frameworks used to describe vocal languages. Perhaps partly because of misconceptions about SLs and the fact that SLs are poorly endowed languages, the field of Sign Language Recognition (SLR) has mostly focused on the recognition of lexical signs, which are conventionalized units that could loosely be compared to words. Yet, this approach is bound to be ineffective if SLR is considered as a step towards Sign Language Translation (SLT). Indeed, SLs are much more than sequences of signed words: they are iconic languages, which use space to organize discourse benefiting from the use of multiple simultaneous language articulators.  
In this introduction, we develop on the linguistic descriptions of SLs (Section 1.1), then, we summarize past and recent works on SLR (Section 1.2), highlighting the main limitations (Section 1.3). In Section 2, three major improvements are subsequently proposed: more relevant SL corpora made by linguists (Section 2.1), with the special case of Dicta-Sign-LSF-v2, that includes fine and consistent annotation;in Section 2.2, a broader formulation of Continuous Sign Language Recognition (CSLR) is formally introduced (Section 2.2.1), then relevant performance metrics are proposed (Section 2.2.2); in Section 2.3, a complete pipeline including a generalizable signer representation (Section 2.3.1) and compact learning framework (Section 2.3.2) is laid out. Experiments of this broader definition of CSLR were carried out on Dicta-Sign-LSF-v2, with results and further discussion in Section 3.
## 2 System Model  
In this Section, we describe the system model (Figure 1) containing a transmitter, a channel, and a receiver. The channel model and receiver imperfections are described in greater detail.
###### Abstract
Different plants have different reflection spectral characteristics, and the same plant has different reflection spectral characteristics under different growth stages conditions [21; 22]. Such spectral characteristics vary depending on the type of plant, the growth stage, the chlorophyll content, and the health status (whether or not it is affected by diseases, insect pests or parasitic plants) [21; 22; 23; 24]. Plant functional traits refer to a series of internal physiological functions and external morphological characteristics gradually formed during the long-term interaction between plants and environmental factors, thus avoiding and reducing the adverse effects of the environment on them to the greatest extent [25]. In 2004, [PERSON] et al. put forward the concept of leaf economics spectrum (LES) for the first time by analyzing the correlation between leaf functional traits. LES is the general internal relationship among functional traits of leaves [26; 27]. Leaf functional traits can objectively reflect the influence of environmental changes on plants and the adaptability of plants to the environment [27; 28; 29]. Therefore, we speculate that leaf functional traits can also be used to diagnose the relationship between biological interactions. At the same time, spectral remote sensing technology and the change information of plant spectral characteristics can provide a reliable basis for large-scale monitoring of pests and diseases. Related researches based on forestry hyperspectral mainly focuses on plant yield, crop seed vigor, plant diseases, plant feature extraction and so on [29; 30; 31; 32; 33; 34]. However, there are few studies on the response of plant leaf functional traits and leaf spectral characteristics to parasitic plant invasion. As plants are harmed by parasitic plants, they will grow badly or even die within a certain period time [35; 36; 31]. Therefore, how to monitor and acquire the growth status and the invasion of the damaged vegetation is the key to effectively control parasitic diseases. To fully understand the changing characteristics and laws of leaf reflectance and leaf functional traits of host plants after being parasitized, and further explore the response mechanism of leaf reflectance spectrum and plant traits to plant-parasitic stress. In this study, _Osmanthus fragrans Lour._, a typical greening tree species in China, was taken as the research object. Spectral reflectance characteristics and leaf functional traits of _Osmanthus fragrans_ leaves before and after being parasitized by _Cuscuta japonica Choisy_ and different parasitic areas were analyzed, and sensitive bands of _Osmanthus fragrans_ response to parasitic stress were obtained. The results provide a reference for the monitoring and early warning of the parasitism of _Cuscutta japonica Choisy_. At the same time, it provides a new experimental basis for different measures to control _Cuscutta japonica Choisy_ and provides a theoretical basis for an in-depth understanding of the parasitic damage mechanism of _Cuscutta japonica Choisy_ and its control strategies.
# Regression of Likelihood Probability for Time-Varying MIMO Systems with One-Bit ADCs
## 3 Proposed Problem
### Sample-Based Likelihood Probability  
This subsection details the derivation of the sample-based likelihood probability constructed based on the received signals. The probability \(p_{r,k}\) in (7) can be defined using the conditional probability formula:  
\[p_{r,k}=\mathbb{P}(q_{r}[n]=1|\mathbf{s}[n]=\mathbf{s}_{k})=\frac{\mathbb{P} (q_{r}[n]=1,\mathbf{s}[n]=\mathbf{s}_{k})}{\mathbb{P}(\mathbf{s}[n]=\mathbf{s }_{k})}, \tag{13}\]  
where each probability term in (13) is defined as follows:  
\[\mathbb{P}(q_{r}[n]=1,\mathbf{s}[n]=\mathbf{s}_{k}) =\lim_{\sum\limits_{m\leq n}1\left\{k[m]=k\right\}\rightarrow\infty }\frac{\sum\limits_{m\leq n}\bar{q}_{r}[m]\,\mathbb{1}\{k[m]=k\}}{n},\] \[\mathbb{P}(\mathbf{s}[n]=\mathbf{s}_{k}) =\lim_{\sum\limits_{m\leq n}1\left\{k[m]=k\right\}\rightarrow\infty }\frac{\sum\limits_{m\leq n}1\left\{k[m]=k\right\}}{n}. \tag{14}\]  
where \(\bar{q}_{r}[n]\) is a function that assumes the value of \(1\) if \(q_{r}[n]=1\) and \(0\) otherwise.  
As the transmitted symbol index \(k[m]\) is not available at the receiver, the probabilities in (14) cannot be computed directly; instead, they are approximated using the detected symbol index \(\hat{k}[m]\), given by  
\[\mathbb{P}(q_{r}[n]=1,\mathbf{s}[n]=\mathbf{s}_{k}) \approx\frac{\sum\limits_{m\leq n}\bar{q}_{r}[m]\,\mathbb{1}\{ \hat{k}[m]=k\}}{n},\] \[\mathbb{P}(\mathbf{s}[n]=\mathbf{s}_{k}) \approx\frac{\sum\limits_{m\leq n}\mathbb{1}\{\hat{k}[m]=k\}}{n}. \tag{15}\]  
These approximations converge to the true probabilities when \(n\) becomes sufficiently large [19].  
However, the detected symbol index \(\hat{k}[n]\) is often inaccurate compared to the true symbol index with a high probability. To address this, symbol refinement is proposed, in which the decoder's capability is exploited to estimate the true symbol index \(k[n]\). Thus, unlike in [19], the re-detected symbol index \(\hat{k}_{\text{dec}}[n]\) is used to obtain the sample-based probability as follows:  
\[p_{r,k}^{\text{sample}}[n]\approx\frac{\sum\limits_{m\leq n}\bar{q}_{r}[m] \,\mathbb{1}\{\hat{k}_{\text{dec}}[m]=k\}}{\sum\limits_{m\leq n}\mathbb{1}\{ \hat{k}_{\text{dec}}[m]=k\}}. \tag{16}\]  
Notably, the proposed likelihood probability in (17) occurs after each data block and the decoder output is available to calculate the sample-based likelihood probability.
# Precise Orbit Solution for Swarm Using Space-Borne GPS Data and Optimized Pseudo-Stochastic Pulses
###### Abstract  
Swarm is a European Space Agency (ESA) project that was launched on 22 November 2013, which consists of three Swarm satellites. Swarm precise orbits are essential to the success of the above project. This study investigates how well Swarm zero-differenced (ZD) reduced-dynamic orbit solutions can be determined using space-borne GPS data and optimized pseudo-stochastic pulses under high ionospheric activity. We choose Swarm space-borne GPS data from 1-25 October 2014, and Swarm reduced-dynamic orbits are obtained. Orbit quality is assessed by GPS phase observation residuals and compared with Precise Science Orbits (PSOs) released by ESA. Results show that pseudo-stochastic pulses with a time interval of 6 min and a priori standard deviation (STD) of \(10^{-2}\) mm/s in radial (R), along-track (T) and cross-track (N) directions are optimized to Swarm ZD reduced-dynamic precise orbit determination (POD). During high ionospheric activity, the mean Root Mean Square (RMS) of Swarm GPS phase residuals is at 9-11 mm, Swarm orbit solutions are also compared with Swarm PSOs released by ESA and the accuracy of Swarm orbits can reach 2-4 cm in R, T and N directions. Independent Satellite Laser Ranging (SLR) validation indicates that Swarm reduced-dynamic orbits have an accuracy of 2-4 cm. Swarm-B orbit quality is better than those of Swarm-A and Swarm-C. The Swarm orbits can be applied to the geomagnetic, geoelectric and gravity field recovery.  
Swarm; precise orbit determination; reduced-dynamic method; space-borne dual-frequency GPS data; pseudo-stochastic pulses; orbit accuracy +
Footnote †: journal: Sensors
# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## 5 Discussion  
The transmission and storage of shipborne radar digital video is of great significance to realize remote unmanned ships. In a marine environment with limited network bandwidth and storage resources, the interaction of ship-shore information and the use of efficient compression algorithms to process video data are the key methods to address information transmission and storage, in addition to the future development trend. Therefore, the compression delay has become one of the important factors affecting real-time communication between ships and the shore. We consider that intra-frame coding is the main reason for the compression delay. Considering the instability of maritime channel transmission, this study used a convolutional neural network to convert the CTU division depth problem of the coded radar image into a classification problem, which reduces the intra-frame computational complexity of coded radar images and optimizes the transmission delay. Next, on the basis of the existing research, we will examine the optimization of the computational complexity and control the time delay caused by inter-frame coding, and further reduce the transmission bit rate.  
The ongoing research on unmanned ships is resulting in a gradual increase in various sensing devices on ships. The fusion of multi-sensor data promotes the development of ship automation, makes the display content of radar images richer, and also provides more reference information for remote driving. Therefore, the shipborne radar video dataset we collect will continue to be updated and improved in the future.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 5 Discussions and Future Directions  
### Limitations  
The study's methodological framework, while demonstrating promising results, has several limitations that warrant critical discussion. First, while the Metasploitable environment provided a controlled platform for initial validation, its generic nature imposes limitations on extrapolating results to operational satellite systems. For example, satellite ground stations often employ real-time operating systems (RTOSs) and proprietary protocols (e.g., CCSDS standards) not fully represented in Metasploitable. Additionally, hardware-specific vulnerabilities--such as radiation-induced bit flips in field-programmable gate arrays (FP-GAs) or side-channel attacks on cryptographic modules--remain beyond the scope of this study. These limitations underscore the need for future experiments involving hardware-in-the-loop simulations and space-qualified software stacks to validate SatGuard's efficacy in operational satellite environments. Nevertheless, the current framework's success in identifying protocol-level and service-layer vulnerabilities demonstrates its foundational applicability to satellite cybersecurity, particularly in scenarios where legacy IT components intersect with space infrastructure.  
Second, the reliance on LLMs (GPT-4 and DeepSeek-R1) trained on data up to 2023 introduces constraints in adapting to emerging threats. While these models excel in parsing historical vulnerabilities, their performance may degrade for zero-day exploits or novel attack patterns evolving after their knowledge cutoff. Furthermore, vulnerabilities associated with post-quantum cryptographic algorithms (e.g., lattice-based or hash-based signatures), which are gaining prominence due to advancements in quantum computing, remain underrepresented in the models' training corpus. This temporal gap underscores the need for continuous LLM retraining with real-time threat intelligence feeds to maintain relevance in dynamic threat landscapes.  
Third, the experimental scope omitted validation of optical communication links (e.g., CCSDS 142.0B), which are increasingly critical for high-bandwidth space-terrestrial data transmission. The absence of optical link testing precludes insights into vulnerabilities unique to laser-based systems, such as signal jamming in low Earth orbit or quantum key distribution flaws. These limitations underscore the need for caution when generalizing SatGuard's efficacy to fully representative satellite ecosystems.
## 2 Methodologies
### Feature Extraction
#### 2.2.3 Modified Multi-Scale Entropy  
Although MSE can quantify complex dynamics of time sequence over multiple scales, there are still some limitations. Considering time sequence with a length of 100 points, MSE in scale 5 is calculated by only 20 coarse-grained points. However, a precise complexity analysis relies heavily on time sequence with sufficient samples. MMSE was proposed for complexity measurement of short-term time sequence [18] and has been applied for signal complexity analysis [24; 25]. In this work, entropy features were extracted directly from wavelet coefficients. The iterated downsampling operations of DTDWT greatly reduce the lengths of coefficient sets in high level decomposition. Accordingly, MMSE is preferred rather than MSE to estimate complexity of short wavelet coefficients. For MMSE, the coarse-grained time sequence \(\{y^{(\tau)}(j)\}\) is calculated through moving-average strategy as follows:  
\[y^{(\tau)}(j)=\frac{1}{\tau}\sum_{i=j}^{j+\tau-1}x(i),1\leq j\leq N-\tau+1. \tag{7}\]  
Instead of utilizing disjoint time window to divide original time sequence into coarse-grained sample points, for MMSE, an overlapping moving window was applied. Therefore, the length of coarse-grained time sequence increases from \(N/\tau\) to \(N-\tau+1\). According to previous study [26], \(m=1\) or \(m=2\) with \(\tau\) fixed at a value between 0.1 and 0.25 of the standard deviation (STD) of time sequence was suggested. Many applications of sample entropy selected parameters following this criterion [27]. Specifically, \(r=0.15\times STD\) was selected in [28]. In our method, \(m=2\), \(r=0.15\times STD\) and \(\tau=\{1,2,3,4,5\}\) (5 scales) were selected for MMSE calculation.
## 3 Results  
### Effect of Chilling Stress on the Physiological Indices of Leafy Mustard  
As shown in Table 2, the mean Chl content, SS content, Pro content, SOD activity, CAT activity, MDA content, NPQ, and Rfd of mustard were greater under low-temperature treatment at 5\({}^{\circ}\)C compared to the control (25\({}^{\circ}\)C), with the average values of each index increasing by 21.12%, 82.40%, 108.39%, 87.58%, 49.17%, 103.72%, 29.37%, and 7.78%, respectively. The mean POD activity, \(\text{Fv}/\text{Fm}\), \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\), \(\Phi\)PSII, and qP decreased, with average decreases of 25.00%, 1.13%, 3.85%, 15.56%, and 11.48%, respectively. Among them, SS, Pro, and MDA showed greater variation and greater coefficients of variation, at 70.58%, 108.06%, and 226.85%, respectively. \(\text{Fv}/\text{Fm}\) and \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) showed less variation and smaller coefficients of variation at 5.22% and 6.53%, respectively. Chilling Tolerance Coefficients of Various Indices and Correlation Analysis in Different Leafy Mustard Cultivars  
The chilling tolerance coefficients (CTC) of different leafy mustard cultivars for each index are shown in Table 3, from which it can be seen that 46 cultivars showed an increase in Chl content (CTC > 1) under chilling stress, of which No. 23 showed the highest increase in Chl content, which was 14.98 times that of the control. Forty-seven cultivars showed an increase in SS content, among which No. 5 showed the highest increase in SS content, which was 24.43 times more than the control. Fifty-one cultivars showed an increase in Pro content, with No. 54 showing the highest increase in Pro content, which was 14.30 times that of the control. Forty-five cultivars showed an increase in MDA content, of which No. 41 showed the highest increase, with its content being 9.19 times that of the control. Sixty-three mustard cultivars showed elevated SOD activity under chilling stress, of which No. 64 showed the greatest change in enzyme activity, with a change in SOD enzyme activity that was 4.51 times that of the control; No. 8 showed the least change in SOD enzyme activity. Twelve mustard cultivars showed elevated POD activity with chilling tolerance coefficients ranging from 0.36 to 1.58, with No. 31 being the highest and No. 16 the lowest. Forty-six mustard cultivars showed elevated CAT activity, of which No. 57 showed the highest change in enzyme activity, with a change in CAT enzyme activity of 9.49 times that of the control; No. 2 showed the lowest change in CAT enzyme activity. In addition, under chilling stress, \(\mathrm{Fv/Fm}\) and \(\mathrm{Fv^{\prime}/Fm^{\prime}}\) varied little, and the highest chilling tolerance coefficients were found in No. 19 and No. 56, with 1.15 and 1.11, respectively. The chilling tolerance coefficients of \(\mathrm{\PhiPSII}\) and \(\mathrm{qP}\) ranged from 0.31 to 2.67, with No. 65 being the highest and No. 58 the lowest, and the chilling tolerance coefficients of NPQ and Rfd were in the range of 0.27-3.27, with No. 4 and 19 being the highest, and No. 12 the lowest.
## 4 Optimization Mechanism Design
### General Design of DDAR
#### Phase 1:
3:For each \(\mathcal{S}_{\sigma^{i-1}}\) received by the senisng node do
4:if\(\mathcal{S}_{\sigma^{i}}==\mathcal{S}_{\sigma^{i-1}}\)then
5:\(Dest\Big{(}\sigma^{i}\Big{)}=\sigma^{i-1}\)
6:Return
7:Endif
8:Endfor
9:If there is an aggregator with no service tag
10:\(\mathcal{S}_{\sigma^{i}}=\mathcal{S}_{\sigma^{i-1}}\)
11:\(Dest\Big{(}\sigma^{i}\Big{)}=\sigma^{i-1}\)
12:Return
13:Endif
14:If there is no aggregator whose \(\mathcal{S}_{\sigma^{i-1}}\) is equal to \(\mathcal{S}_{\sigma^{i}}\)
15:\(\operatorname{index}=\mathcal{S}_{\sigma^{i}}-1\)
16:While\(\operatorname{index}>0\)do
17:For each \(\mathcal{S}_{\sigma^{i-1}}\)do
18:If\(\mathcal{S}_{\sigma^{i-1}}==\operatorname{index}\)then
19:\(Dest\Big{(}\sigma^{i}\Big{)}=\sigma^{i-1}\)
20:Return
21:Endif
22:Endfor
23:\(\operatorname{index}-\)
24:Endwhile
25:\(\operatorname{index}=\mathcal{S}_{\sigma^{i}}+1\)
26:While\(\operatorname{index}\leq\mathcal{Q}\)do
27:For each \(\mathcal{S}_{\sigma^{i-1}}\)do
28:If\(\mathcal{S}_{\sigma^{i-1}}==\operatorname{index}\)then
29:\(Dest\Big{(}\sigma^{i}\Big{)}=\sigma^{i-1}\)
30:Return
31:Endif
32:Endfor
33:index++
34:Endwhile
35:Endif
```  
**Algorithm 3** Establishment of Routing for an Aggregator in Level \(i\)\(\sigma^{i}\). The complexity analysis is described below:  
The number of the aggregators in the network is set as \(N\). the time that aggregator broadcasts and receives message is \(O(1)\). Then the aggregator selects an aggregator with the corresponding tag. The overhead of this step is \(O(N)\). If there is no this kind of aggregator, the aggregator finds one aggregator with no tag. The cost is \(O(N)\). If there is no such an aggregator, the sensor would go through the tags twice at most. Therefore, the time complexity of an aggregator selects an aggregator is as Equation (8):  
\[O(1)+O(N)+O(N)+O(N)+O(N)=O(N) \tag{8}\]  
_Phase 5_: Service tag rotation  
The different service requirements in the network cause the different frequency of data aggregation and transmission. Aggregation and transmission are the primary energy consumption methods of nodes. Consequently, nodes frequently transmitting data packets consume energy fast. As a consequence, the early death of these nodes has. So service tag rotation needs to be adopted in DDAR. Each aggregator uses the tag opposite to the current tag as its service tag (for example, there are five service requirements, the current tag of node \(\sigma\) is 1, the tag after tag rotation is 5), the operations phase 2 to phase 4 would be performed again after the rotation.