esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### GIS Development  
The archiving system created with a GIS approach allowed the extraction and rapid selection of information in an integrated form between alphanumeric and graphical data. The results were not a simple list because the system allows users to check all the information and data available in a single environment. By obtaining detailed information from each single element constituting the external cladding of the building, it also allowed the realization of quantitative, qualitative and spatial analyses.  
The system and the data infrastructure created can be defined as a \"decision support system (DSS)\". The stored information is always available, searchable, comparable, reinterpretable, and implementable [8].  
Figure 2: (**a**) Map of the cathedral and bell-tower complex with the side reference (A to L); (**b**) the relative photoplanes.  
To place each element in the drawing space, a \"Reference system\" must be defined. In the present work, for each photoplane, the coordinate reference system was the plane xz with the x set at an arbitrary zero left of the photoplane and the z corresponding to the datum above sea level.  
Each ashlar of the cladding was defined as single stone element and, by a CAD wire-frame, was imported into the GIS system in the correct coordinates in relation to all the other modelled objects. For the purpose of implementation in GIS, the vector data transmitted were all transformed into shapefiles optimized and, when possible, unified in order to make management and use easier. The available table information was reworked and linked to the relative spatial data in order to have as much information as possible available.  
Once positioned in the space of the reference system and in relation to the other basic elements that make up the individual facades of the monument, each geometry, ashlar, and more was \"attributed\" by assigning it an ID and the type of stone material, thus making each element identifiable (Figure 4).  
Subsequently, for each basic geometric unit we proceeded to the compilation of attributes, closure of polygons, the subdivision of the different elements on distinct layers, and the decomposition of complex objects into simple primitives.
## 2 Service-oriented and Self-managed Optical Networks
### Self-managed Optical Networks to Handle Elephant Flows  
In the above-described service-oriented optical networks, either users or applications are in charge of requesting the establishment of new lightpaths when they need to transmit data, if this is not  
Figure 1: Service Oriented Optical Network (SOON)’s service plane building blocks and interfaces. Adapted from [7].  
performed by the network managers as in currently deployed optical networks. In self-managed networks, it is the optical network that infers when new lightpaths are needed by detecting elephant flows and by subsequently requesting the establishment of lightpaths to offload these flows to the optical layer. Figure 2 illustrates the architecture of a self-managed optical network.  
The key to this approach is flow characterization, not service characterization as in service-oriented networks. There are several research works on flow characterization, most of which are based on the analysis of traces and on the comparison of observation points. These works, e.g., [13, 14, 15], are mainly focused on estimating precise flow size distribution or packet distribution. Note that data for flow characterization can be collected at one or more observation points or can be obtained by measurement techniques such as packet sampling. The metrics considered for analyzing flows are typically packets, bytes and port distribution, and some works also consider effects on the flow size distribution, e.g., packet counts, SYN information and sequence number information.  
Other relevant works are those of [PERSON] _et al._[16] and [PERSON] _et al._[17]. In [16], the authors focus on the analysis of the volume (size) of big IP flows as well as on their historical behavior. The authors argue that the flow volume may be very volatile, which results in different load values captured at different time intervals. In [17], the authors characterize big flows by observing their size, duration, throughput (rate), and burstiness, as well as by examining their correlation considering the traditional 5-tuple flow definition: group packets with the same source and destination IP addresses, same transport protocol, and same transport protocol source and destination port numbers. Alternatively, [1] proposes a novel set of parameters to predict the traffic volume generated by elephant flows, which are summarized in Table 1. Note that volume prediction is employed in the self-management approach to reduce the cost of high-volume flows staying at the IP level, in terms of consumption of network resources, by detecting them in a timely manner so that they can be transferred to the optical layer.  
Figure 2: Architecture of a self-managed optical network. Extracted from [12].
# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
###### Abstract  
Existing low-light image enhancement methods often struggle to effectively enhance space targets in deep-space contexts due to the effects of extremely low illumination, stellar stray light, and Earth halos. This work proposes a low-light image enhancement method based on multi-image fusion, which integrates features of space targets with the Retinex theory. The method dynamically adjusts contrast by detecting luminance distribution and incorporates an adaptive noise removal mechanism for enhanced image quality. This method effectively balances detail enhancement with noise suppression. This work presents experiments on deep-space background images featuring 10 types of artificial satellites, including AcrimSat, Calipso, Jason, and others. Experimental results demonstrate that the proposed method outperforms traditional methods and mainstream deep learning models in qualitative and quantitative evaluations, particularly in suppressing Earth halo interference. This study establishes an effective framework for improving the visual quality of spacecraft images and provides important technical support for applications such as spacecraft identification, space target detection, and autonomous spacecraft navigation.  
Keywords:low-light image enhancement; image fusion; deep-space background; halo suppression 2020
# Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models
## 3 Results and Discussion
### Model Generalization  
The model's generalization is assessed by the model's ability to forecast samples beyond the training range. Figure 9 depicts the relative error variation in each of the Prophet and Holt-Winters models. The relative error percentage of the Prophet model outperforms the Holt-Winters model. The percentage of maximum relative error in the Prophet model is less than 16%, whereas the Holt-Winters model's relative error reached 30%. Besides, the SHF results can be used to show the generalizability of both models. The Prophet model shows better generalizability than the Holt-Winters model, as suggested by the MAPE values and the relative error percentage.  
Figure 8: Smoothed mean absolute percentage errors for the Prophet and Holt-Winters models.  
Figure 9: Relative errors percentage for Prophet and Holt-Winters models.
## 2 Action Mechanism and Calibration of the Optical-Fiber-Embedded Beam (OFEB)  
### Principle of DPP-BOTDA  
Herein, a distributed optical fiber strain monitor based on DPP-BOTDA technology was used [40]. Figure 1 shows the injection of two pump pulses of different widths (\(\tau_{1}\), \(\tau_{2}\)) into the same test optical fiber. In addition, a differential Brillouin signal, \(I_{0}\) (\(\Delta\tau\)), was obtained by subtracting the Brillouin signals \(I_{0}(\tau_{1})\) and \(I_{0}\) (\(\tau_{2}\)) before constructing the differential Brillouin gain spectrum. Two pump pulses with small widths can achieve high spatial resolution [19].  
Figure 2 shows the schematic of the DPP-BOTDA system, where the laser module emits a laser with a wavelength of 1550 nm, which is split into two parts by a 3-dB coupler at the exit. A light beam was modulated using an electro-optical modulator (EOM), which was then amplified in the sensing fiber using an erbium-doped fiber amplifier (EDFA). Another light beam was modulated into a multifrequency light using an EOM containing an effective detecting light that differs from the original light frequency in the Brillouin frequency shift range. This light beam was then passed through the fiber Bragg grating filter, which effectively filters out the detecting light entering the sensing fiber segment. The two modulated laser beams then interact in the sensing fiber to generate stimulated Brillouin scattering, and the resulting Brillouin signal light, recorded by a high-speed capture card, is transformed into a digital signal by using a photoconverter. The detailed performance indicators of the designed instrument are listed in Table 1.  
Figure 1: Principle of the DPP-BOTDA sensing system. CW: continuous wave; SBS: stimulated Brillouin scattering; BGS: Brillouin gain spectrum; \(\tau\): pulse width; \(I_{0}(\tau)\): Brillouin signal; \(\Delta z\): spatial resolution; \(v\): Brillouin frequency shift.
# Directly Synthesized Graphene-Based Photonics and Optoelectronics Devices
## 2 Growth Mechanism of Graphene
### In-Situ Growth  
To synthesize graphene on an optical device under ambient conditions, a transfer-free, in-situ growth process has been proposed that can grow graphene directly onto single-mode optical fiber end facets [82] and on the flat surface of a side-polished single-mode fiber (SMF) under ambient atmospheric conditions [83] as schematicized in Figure 3a,b, respectively. In this method, on the end facet and polished region, a polycrystalline Ni film was pre-deposited, which acted as a catalyst for interfacial growth as well as a host for the C atoms. The authors first deposited a 100 nm thin film of polycrystalline Ni on the ferrule end facet and on the flat surface of a side-polished SMF using the electron beam evaporation method. Two CW lasers at 1552 (pump) and 1539 (probe) nm were coupled such that the incident pump and probe laser could be reflected back from the Ni layer or interacted with the Ni layer and monitored accordingly. During light irradiation, the optical power of the pump laser was tuned from 0 to 7 dBm by a 0.5 dB step every 30 min, while the power of the probe laser was fixed at \(-\)1.26 dBm. With the absorption of the light-induced heat, carbon atoms will segregate and precipitate on the grain boundaries of the Ni film [66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86].  
Figure 2: (**a–d**) Graphical representation of the experimental procedure of interfacial growth of graphene without any source feeding; (**e**) Raman spectrum of as-grown graphene on SiO\({}_{2}\)/Si substrate from (**a–d**); (**f**) Temperature functional growth of graphene on SiO\({}_{2}\)/Si substrate and evolution of Raman spectra. (**a–e**) Reproduced with permission from [71]. Copyright American Chemical Society, 2012; (**f**) Reproduced with permission from [78]. Copyright American Chemical Society, 2016.  
After light irradiation, a 0.2 M iron (III) chloride (FeCl3) solution was used to etch out the Ni layer by placing the laser-exposed parts in the solution for 30 min. This process yielded the SMF parts grown with interfacial graphene. The graphene-incorporated device was employed for ultrafast pulse generation and optical switching in furtherance.
## 2 System Description
### Two-Echelon Service Logistics System Description  
A two-echelon service logistics system is composed of three Base locations and two Depot locations. As indicated in Figure 2, the fleet system executes duties at the Base locations.  
Figure 1: Two-indenture system structure.  
The assumptions of the two-echelon service logistics system are as follows [20]:  
1. Each maintenance location has a set of resources, including spare parts, maintenance workers, maintenance equipment, and tools. The maintenance capability is proportional to the resources available for maintenance.
2. The discard decision is only considered in the Depot.
3. A failed (sub)component may not be shipped to a lower echelon level. When a failed component with echelon level \(e\) should be repaired, the corresponding subcomponent must be maintained at echelon level \(f\geq e\), rather than a lower level.
4. There are no lateral transshipments between locations at the same echelon level or emergency shipments from locations at a higher echelon level; functioning spare parts are only supplied from one specific location at the next higher echelon level.  
When the Base location cannot repair the failure components, they must be transferred to the Depot location for maintenance. At this point, the Depot location must decide whether to repair or discard the failed component. Due to the (\(s\)-1, \(s\)) continuous review inventory control policy, minimizing the expected number of backorders is a good approximation of maximizing the availability [26].
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 4 Experimental Simulation
### Testing Performance
#### 4.3.2 Comparison of Algorithms Test
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multicolumn{4}{c}{**Average Scheduled Rate 1**} \\ \cline{2-5}  & **GA** & **ALNS** & **DRL-LNS** & **DRL** \\ \hline Area\_50 & 0.2640 & 0.4680 & 0.5000 & 0.4080 \\ Area\_100 & 0.2400 & 0.3760 & 0.4020 & 0.3640 \\ Area\_150 & 0.2080 & 0.3267 & 0.3747 & 0.3107 \\ Area\_200 & 0.0000 & 0.3010 & 0.3520 & 0.2840 \\ World\_100 & 0.6000 & 0.6620 & 0.7000 & 0.7020 \\ World\_200 & 0.0000 & 0.6150 & 0.6400 & 0.6660 \\ World\_300 & 0.0000 & 0.6093 & 0.6320 & 0.6640 \\ World\_400 & 0.0000 & 0.5795 & 0.5940 & 0.6350 \\ \hline \hline \end{tabular} 1  
\end{table} 
Table 5: Comparison of the iteration times. Figure 7: Optimization Scheduled rate performance contrast. (**a**) Scene: Area. (**b**) Scene: World. faster extraction of relevant features from the data. These enable the DRL method to obtain satisfactory suboptimal solutions in a single inference. The results indicate that the D3 RL-based approaches (DRL-LNS, DRL method) have faster convergence speeds and higher solution quality than the traditional heuristic algorithms (GA and ALNS). And the DRL-LNS is more suited to scenarios with regionally dense targets on a small scale (less than 200), while the DRL method is better-suited to scenarios with large-scale global targets (more than 200). Figure 8 displays a detailed comparison of the algorithm iteration curves for both _Area_ and _World_ instances. Figure 8: _Cont._
# Quantum Stream Cipher Based on Holevo-Yuen Theory
## 2 Current Status of Quantum Communication Security Technology
### Quantum Cryptography
The Y-00 signals of the length \(m\) (number of slot) are described as follows:  
\[\mid\Psi(X,K_{s},R_{p})>=\mid\alpha_{i}(X,f_{g}(K_{s}),R_{p})>_{1} \tag{1}\] \[\otimes\mid\alpha_{j}(X,f_{g}(K_{s}),R_{p})>_{2}\ldots\ldots.\] \[\otimes\mid\alpha_{k}(X,f_{g}(K_{s}),R_{p})>_{m}\]  
where \(\mid\alpha_{i}(X,f_{g}(K_{s}),R_{p})>\) is coherent state with amplitude \(\alpha(\cdot)\), \(i,j,k=1,2,3,\ldots 2M\), \(X\) is plaintext, \(f_{g}(K_{s})\) is a mathematical pseudo random function of secret key \(K_{s}\), and \(R_{p}\) is additional randomization. The set of these coherent states is designed to be strong non-orthogonal property, even if each amplitude of the signals is \(\mid\alpha_{k}(X,f_{g}(K_{s}),R_{p})\mid\gg 1\). A legitimate receiver with the knowledge for communication base to which the data is sent can ignore the quantum nature of the data, because it is a binary transmission by high-power signal. That is, one can receive the error-free data. On the other hand, an eavesdropper, who does not know the information of the communication base, must receive a sequence of a ultra-multi-valued optical signal that consists of non-orthogonal quantum states of Equation (1). The quantum noise generated by quantum measurement based on the Holevo-Yuen theory on quantum detection [8; 9; 10] masks the received signal, resulting in errors. Thus, even if the eavesdropper tries to record the ciphertext, the masking effect of the quantum noise makes it impossible to accurately recover the ciphertext. This fact is a novel function in the cryptology. Figure 2 shows the scheme of Y-00 principle (Appendix A).
# Low-Latency QC-LDPC Encoder Design for 5G NR
## 6 Conclusions  
In this paper, a 5G QC-LDPC encoder including CRC calculation is proposed. The designed hardware architecture has high parallelism and flexibility. Through parameter configuration, LDPC codes of various code lengths and code rates in the 5G standard can be encoded. This is achieved by improving the cyclic shift structure, the encoder uses the maximum lifting sizes of the 5G standard as the hardware scale and uses encoding parameters to constrain the hardware to achieve effective compatibility of the encoder.  
In addition to architecture innovation, the encoder includes the parallelization of CRC calculations and designed a 256-bit parallel CRC calculation architecture based on look-up tables. Therefore, the encoder can perform the complete process of 5G LDPC encoding, which is an innovation compared to existing work.  
The hardware implementation of the encoder is based on SIMC 28 nm CMOS technology. Compared with existing similar LDPC encoders, the proposed encoder achieves higher throughput, lower encoding latency, and the equivalent bit operation of each clock is also improved. Hence, our design achieves a better balance among flexibility, coding efficiency, and hardware power consumption and is suitable for 5G eMBB and URLLC scenarios.  
Figure 11: 5G NR QC-LDPC encoder layout.  
**Author Contributions:** Conceptualization, D.L. and Y.T.; methodology, D.L. and Y.T.; funding acquisition, D.L. and Y.B.; software, D.L.; validation, Y.T., Y.B. and D.L.; investigation, Y.T.; project administration, D.L.; resources, Y.B. and D.L.; data curation, Y.T.; writing--original draft preparation, Y.T., Y.B. and D.L.; writing--review and editing, Y.B. and D.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Hainan University project funding KYQD (ZR)1974, National Natural Science Foundation of China under Grant 61961014, and Hainan Provincial Natural Science Foundation of China under Grant 620 RC556.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Boundary-Inner Disentanglement Enhanced Learning for Point Cloud Semantic Segmentation
## 3 Methods
### Implementation Details  
Current segmentation networks generally follow the encoder-decoder paradigm, where different LAOs and subsampling strategies are used in encoding layers to extract multilevel local features, skip connections, and up-sampling operations employed in decoding layers to achieve end-to-end semantic segmentation. LAOs can be classified into three types: MLP-based, pseudo-grid-based, and adaptive-weight-based [5]. The latter two types have become the mainstream due to their excellent local feature extraction ability. Pseudo-grid-based methods preplace some pseudo-kernel points in the neighborhood and learn their convolutional weights directly. However, the pseudo-kernel points must be defined artificially, which can limit the generalizability and flexibility of models. In contrast, adaptive-weight-based methods learn convolutional weights indirectly from the relative position and features of the center point and its neighboring points. KPConv [31] and  
Figure 3: Detailed illustration of the Boundary–Inner Disentanglement Enhanced Learning (BIDEL) framework.  
RandLA-Net [28] are classic representatives of pseudo-grid-based methods and adaptive-weight-based methods, respectively. Both follow the encoder-decoder paradigm. RandLA-Net obtains a lower segmentation accuracy than KPConv, but has a marked drop in memory overhead and computation cost due to the mechanism of random sampling.  
To validate the benefits of the proposed BIDEL across different LAOs, this paper refers to KPConv and RandLA-Net as baselines. The overall architecture is depicted in Figure 4, where BIDEL is applied to optimize the representations learned by LAO in the first encoding layer.  
This paper sets the loss weight \(\lambda\) = 1 for \(L_{BIDEL}\), and ten follows the same training settings as the baselines for fair comparisons. Specifically, for KPConv, the optimizer, initial learning rate, and maximum training epoch are set to Momentum, 0.01, and 500, respectively; for RandLA-Net, the optimizer, initial learning rate, batch size, maximum training epoch, and the number of nearest points are set to Adam, 0.01, 4 \(\times\) 40,960, 100, and 16, respectively.  
The mIoU, OA, and mACC are considered as the evaluation metrics to quantitively demonstrate the benefits of BIDEL, in line with most point cloud semantic segmentation works. The experimental configurations are detailed in Table 2.
## 3 Numerical Modeling Procedure
### Parametric Study on the Fracture Intensity of the Fractured Zone  
To investigate the tensile failure development in the FZ after the longwall mining had been started, the zones with tensile failure states were identified and are marked in Figure 6. Many tensile failure zones occurred in the FZ, which was consistent with the description in Section 2.3. Such extensive rock mass failure in tension requires the consideration of the weakening effect on the rock mass behavior due to failure and fracturing in tension.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Strata** & **Lithology** & **\(K\) (GPa)** & **\(G\) (GPa)** & **\(c\) (MPa)** & \(\sigma_{t}\) (MPa)** & \(\phi\) (deg.) & \(\rho\) (N/m) \\ \hline \multirow{3}{*}{Roof} & Sandstone & 9.1 & 5.9 & 3.9 & 2.3 & 45 & 28263 \\  & Sandy & 5.2 & 3.1 & 3.2 & 1.8 & 40 & 25392 \\  & mudstone & 2.4 & 1.1 & 2.1 & 0.8 & 35 & 25509 \\  & Coal & 1.3 & 0.6 & 1.4 & 0.3 & 31 & 14063 \\  & Sandy & 7.2 & 4.0 & 3.4 & 2.2 & 37 & 25764 \\  & mudstone & 9.6 & 6.5 & 4.2 & 3.5 & 47 & 26989 \\ \hline \hline \end{tabular}  
* \(K\) is the bulk modulus; \(G\) is the shear modulus; \(c\) is the cohesion; \(\sigma_{t}\) is the tensile strength; \(\phi\) is the friction angle.  
\end{table}
Table 1: Rock mass mechanical properties \({}^{\text{b}}\).  
Figure 5: Flowchart of the numerical modeling approach for longwall mining.  
Therefore, to investigate the stress distribution and strata behavior with respect to various tensile fracturing intensities, a parametric study of the tension weakening model was performed with four different \(GSI_{t}\) values (as shown in Table 2), where \(E_{r}\) was the residual Young's modulus of rock mass after tensile failure, \(E_{m}\) was the Young's modulus of rock mass before failure, and a high \(GSI_{t}\) indicated high fracture intensity and consequently a low residual Young's modulus of rock mass according to Equation (6).