esa-sceva/satcom-chunk-collection huggingface dataset:
# PK-APF: Path-Keeping Algorithm for USVs Based on Artificial Potential Field
## 4 Conclusions and Future Work  
In this paper, the PK-APF algorithm is proposed to solve the shortcomings of USVs in their failure to achieve a high-precision path and falling into the local minimum point by increasing virtual foot points and temporal forces, respectively. In the simulation environment of MATLAB and VRX, obstacles and wind force are added as external disturbance factors to represent a real environment. The experimental results show that the path-keeping precision of PK-APF is improved by at least 22% than APF. Therefore, USVs are of great significance in practical use when high-precision data collection is needed, such as grid data for obtaining water quality data and riverbed modeling.  
In the future, we will implement the proposed PK-APF algorithm in a real USV, and an advanced controller will be designed to help the USV achieve the high-precision path-keeping in real environments.  
Figure 14: Path-keeping error statistics.  
Figure 13: Path-keeping error for APF and PK-APF with different wind speeds.  
**Author Contributions:** Conceptualization, Y.C.; methodology, Y.C.; software, Y.C. and Z.W.; validation, Y.C. and Z.W.; writing--original draft preparation, Y.C., Z.W., Y.Y., X.Z., E.G.L. and P.P.; writing--review and editing, Y.C., Z.W., Y.Y., X.Z., E.G.L. and P.P.; funding acquisition, Y.Y. and X.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by by the Suzhou Science and Technology Project (SYG202122), the Key Programme Special Fund of Xi'an Jiaotong-Liverpool University (XJTLU) (KSF-A-19, KSF-E-65, KSF-P-02, KSF-E-54), and the Research Development Fund of XJTLU (RDF-19-02-23).  
**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.
## 3 Off-Line Compression by Longest-First Substitution
### Reducing Grammar Size  
In the above sections we considered text compression by longest first substitution, where we construct a context free grammar \(\mathcal{G}\) that generates only a given string \(w\). By Observation 1, for any production rule \(A_{k}\to x_{k}\) of \(\mathcal{G}\), \(x_{k}\) contains only terminal symbols from \(\Sigma\). In this section, we take the factors of \(x_{k}\) into consideration for candidates of LRFs, and also replace LRFs appearing in \(x_{k}\). This way we can reduce  
Figure 7: The left figure illustrates how to find \(v(j)\) from \(v(j-1)\). The right one illustrates a special case where \(v(j)=\mathit{leaf}_{j}\). Once \(v(j)=\mathit{leaf}_{j}\), it stands that \(v(k)=\mathit{leaf}_{k}\) for any \(j\leq k\leq p-1\). ```
Input: String w ending with a unique symbol Output: Set of grammar rules which produce w, greedily selected by substituting longest repeating factors
1 SLSTree \(:=\) sparse lazy suffix tree of w; bins \(:=\) bin-sorted nodes; len \(:=|\textsf{w}|\); rules \(:=\emptyset\);
2 whiletrued do
3 while\((\textsf{n}=\textsf{bins.getNextOfLength}(\textsf{len}))=\textsf{null}\)do
4 iflen\(\leq 2\)thenreturn rules;
5 foreach\(\textsf{x}\in\textsf{bins}(\textsf{len})\)doupdate x.min, x.max, x.card from children;
6 len\(-\) ;
7 update n.min, n.max, n.card from children;
8 ifn.max \(-\) n.min \(\geq\) n.pathlen /* n is repeating factor */ then
9 nonTerm \(:=\) new non-terminal symbol;
10 rules \(:=\) rules \(\cup\)\(\{\textsf{nonTerm}\to\textsf{n.path}\}\);
11 gso \(:=\) getGreedilySelectedOccurrences(n);
12 updateSLSTree(w, n.pathlen, nonTerm, gso, SLSTree, bins);
13
14
15
```  
**Algorithm 1**Recursively find longest repeating factors.
## 4 Experiments
### Implementation Details  
We implement the network using MMSegmentation [38] and Pytorch [39] and train the network using two Nvidia GTX3090 GPUs with 24 GB of memory. The network is trained for 270K iterations. The initial learning rate is 0.00005, with a weight decay of 0.05, and the learning rate is reduced by 90% at 210K and 250K iterations. We employ the backbone pre-trained by ImageNet-1K [40] following SegNeXt-tiny [6]. The batch size is 16 for ADE20K and COCO-Stuff and 8 for Cityscapes. Following [8], loss coefficients \(\lambda_{c}\), \(\lambda_{d}\), \(\lambda_{p}\), and \(\lambda_{s}\) are assigned values of 2.0, 2.0, 5.0, and 1.0, respectively. For all training cases,data augmentations such as random resize, random cropping, and random horizontal flipping, following the settings from MMSegmentation, are used to enhance data diversity for different datasets. During training on ADE20K and Cityscapes, the images are cropped to 512 \(\times\) 512, while for Cityscapes, the images are cropped to 1024 \(\times\) 1024. Finally, all results are reported on the validation set, maintaining a single-scale evaluation.
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 1 Introduction
For example, the target layer is the cable channel operation risk, the criterion layer may include environmental factors, equipment factors, human factors, etc., and the indicator layer is further refined, such as temperature and humidity under environmental factors [23; 24; 25]. Ref. [26] proposes a dynamic and comprehensive assessment method of community distribution network risk based on hierarchical analysis and a Bayesian network to realize the subjective and objective comprehensive assessment of community distribution network risk levels. Ref. [27] proposed a power cable quality evaluation method based on hierarchical analysis, calculating the quality weighted scores of the inspected products, combined with mathematical and statistical analysis methods to realize the supply quality ranking of qualified suppliers. Ref. [28] proposed a sustainable urban transport solution based on a fuzzy analytic hierarchy process to solve the optimization problem of a sustainable urban transport system in Ireland. Ref. [29] proposed an urban gas pipeline leakage risk assessment model based on a fuzzy-analytic hierarchy process, solving the problem of insufficient consideration of the interaction between disaster factors in the existing assessment methods. This provides a scientific basis for the safety management of urban gas pipelines. Although the hierarchical analysis method has a strong reasoning ability, the construction of the judgment matrix has randomness and cannot be combined with the assessment results. Therefore, the fuzzy comprehensive evaluation method is introduced, which can better solve the uncertainty existing in the risk assessment of the operation of the urban dense cable channel. The formation of the fuzzy-hierarchical analysis method is based on the operational risk assessment method of the urban dense cable channel. The assessment method includes qualitative assessment and quantitative assessment, which has a better effect on the assessment of multi-factor and multi-level complex problems. Aiming to solve the above problems, this paper analyzes the operational risk parameters of urban dense cable channels in detail, constructs the risk assessment index system, and uses the fuzzy-hierarchical analysis method to construct the risk assessment model. It puts forward a fuzzy-hierarchical analysis method based on the operational risk assessment method of urban dense cable channels. By constructing a three-layer evaluation system including a target layer, criterion layer, and indicator layer, using hierarchical analysis to calculate the weight of each attribute, constructing the corresponding fuzzy relationship matrix according to the affiliation degree of each factor, and finally arriving at the evaluation results through matrix operation, the method can effectively deal with the multi-factor, multi-level, and complex problems involved in the evaluation process, making the evaluation results more objective and accurate.
###### Abstract  
With the continuous increase in the number of in-orbit satellites and the explosive growth in the demand for observation targets, satellite resource allocation and mission scheduling are faced with the problems of declining benefits and stagnant algorithm performance. This work proposes a progressive optimization mechanism and population size adaptive strategy for an improved differential evolution algorithm (POM-PSASIDEA) in large-scale multi-satellite imaging mission planning to address the above challenges. (1) MSIMPLTS based on Multi-layer Objective Optimization is constructed, and the MSIMPLTS is processed hierarchically by setting up three sub-models (superstructure, mesostructure, and undersructure) to achieve a diversity of resource selection and step-by-step refinement of optimization objectives to improve the task benefits. (2) Construct the progressive optimization mechanism, which contains the allocation optimization, time window optimization, and global optimization phases, to reduce task conflicts through the progressive decision-making of the task planning scheme in stages. (3) A population size adaptive strategy for an improved differential evolution algorithm is proposed to dynamically adjust the population size according to the evolution of the population to avoid the algorithm falling into the local optimum. The experimental results show that POM-PSASIDEA has outstanding advantages over other algorithms, such as high task benefits and a high task allocation rate when solved in a shorter time.  
progressive optimization mechanism; population size adaptive strategy; differential evolutionary algorithm; multi-satellite imaging mission planning
###### Abstract  
Cloud-Radio Access Network (RAN) is one of the architectural solutions for those mobile networks that aim to provide an infrastructure that satisfies the communication needs of a wide range of services and deployments. In Cloud-RAN, functions can be flexibly split between central and distributed units, which enables the use of different types of transport network. Ethernet-based fronthaul can be an attractive solution for Cloud-RAN. On the one hand, the deployment of Ethernet-based fronthaul enables Cloud-RAN to provide more diverse, flexible and cost-efficient solutions. On the other hand, Ethernet-based fronthaul requires packetized communication, which imposes challenges to delivering stringent latency requirements between RAN functionalities. In this paper, we set up a hardware experiment based on Cloud-RAN with a low layer split, particularly between medium access control and the physical layer. The aim is to demonstrate how multi-path and channel coding over the fronthaul can improve fronthaul reliability while ensuring that: (i) latency results meet the standard requirements; and (ii) the overall system operates properly. Our results show that the proposed solution can improve fronthaul reliability while latency remains below a strict latency bound required by the 3 rd Generation Partnership Project for this functional split.  
Cloud-RAN; ethernet-based fronthaul; functional split; multi-path; latency; reliability Article
# Blockchain-Based Multimedia Content Protection: Review and Open Challenges
## 3 State-of-The-Art of Blockchain-Based Content Protection Systems
### Comparative Analysis
* The majority of the schemes use IPFS or data servers for storing and distributing the copyrighted content to the buyers upon a successful purchase. * All schemes are based on different content protection mechanisms and satisfy the basic security requirements described in Section 2.6. Figure 2 illustrates the 11 identified individual security properties (excluding the common security properties) of four multimedia content protection techniques (encryption, DRM, watermarking and fingerprinting), and the analyzed systems (18) that satisfy each of these objectives. It can be observed that the majority of the systems provide data integrity (12 systems) and protection against communication attacks (12 systems). In terms of tamperproofness, we can observe that many systems guarantee tampering resistance (8 systems). This is to be expected since this property is provided by the blockchain technology. It can be observed that fewer schemes address data confidentiality (7 systems), traceability (7 systems), authenticity (6 systems), and conditional access (6 systems). In terms of security against attacks, only a few schemes are robust against common signal processing (7 systems) and collusion/coalition (5 systems) attacks. Considering the quality tolerance objective, we observe that transparency of the copyrighted (watermarked/fingerprinted) content is evaluated by many fewer schemes (2 systems). * Most of the schemes presented in Table 2 are proof-of-concepts and have not been evaluated in a real-world scenario. In only three schemes [38; 41; 42], the total response time or the computational overhead in generating the copyrighted content is evaluated.
## 5 Conclusions  
In this study, the usability of arc light signals emitted during electric arc welding in the estimation of the welding current is investigated. A system was developed that allows the light sensor to move synchronously with the electrode holder for data acquisition. The arc light and current data acquired at maximum current levels of 140 A, 120 A, 100 A, and 80 A, and with two different electrode diameters, were analyzed using filtering and shifting processes. These processes ensured that the signals were denoised, data quality was improved, and estimation accuracy was increased. The ANFIS model was trained using the obtained arc light and current data, and then, current estimations were performed on the test data. The model results were enhanced through filtering, shifting, and current-limiting processes. The performance of the ANFIS models was evaluated with cross-correlation and R-squared values. Generally, the highest R-squared and cross-correlation values were achieved for the training and testing phases in ANFIS models, as shown in Table 2. ANN models showed lower R-squared values in both training and testing phases.  
As a result of comparing the performance metrics of the ANFIS and ANN models, better estimation results were obtained for ANFIS when compared to ANN. The study shows that successful results can be obtained in current estimation by effectively using the arc light signals emitted during the welding process with artificial intelligence methods.  
Conceptualization, Y.K.; project administration, Y.K. and Y.B.; methodology, Y.K.; software, Y.K.; sample preparation, Y.K., Y.B. and G.B.; laboratory experiments, Y.K. and G.B.; data curation, Y.K. and Y.B.; validation, Y.K., Y.B. and G.B.; formal analysis, Y.K. and Y.B.; resources, Y.K. and G.B.; investigation, Y.K., G.B. and Y.B.; visualization, Y.K. and G.B.; writing--original draft preparation, Y.K. and Y.B.; writing--review and editing, Y.K. and Y.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: The original contributions presented in the study are included in the manuscript; further inquiries can be directed to the author.  
The authors declare no conflicts of interest.
# Performance of UAV-to-Ground FSO Communications with APD and Pointing Errors
## 3 Symbol Error Rate Calculation
### SER Calculation of Weak to Moderate Links  
From Equations (27) and (33), the PDF of log-normal channels can be expressed for weak to moderate links as  
\[\begin{split} f_{\rm Y}^{\rm LN}(\rm Y)&=\exp\!\left(- \frac{\theta_{FOV}^{2}}{2\sigma_{AoA}^{2}}\right)\delta\!\left(\frac{\sigma_{n} \sqrt{\rm Y}}{P_{t}mG\Re}\right)\\ &+\Xi_{1}\!\left[\!1\!-\!\exp\!\left(-\frac{\theta_{FOV}^{2}}{2 \sigma_{AoA}^{2}}\right)\!\right]X^{\Xi_{2}}Q\left(\frac{\ln\!\left(\frac{ \frac{\sigma_{n}\sqrt{\rm Y}}{P_{t}mG\Re}}{A_{0}\exp(-\chi L)}+\Xi_{3}\right)} {\sigma_{R}}\right).\end{split} \tag{34}\]  
The SER expression of the UAV-based FSO systems in cases of weak to moderate links can be derived as  
\[\begin{split}\mathrm{SER}^{\rm LN}&=2q(M_{l})\int_ {0}^{\infty}\left(Q\Big{(}A_{I}\sqrt{\rm Y}\Big{)}\exp\!\left(-\frac{\theta_{FOV }^{2}}{2\sigma_{AoA}^{2}}\right)\delta\!\left(\frac{\sigma_{n}\sqrt{\rm Y}}{ P_{t}mG\Re}\right)\right.\\ &+\Xi_{1}\!\left[\!1\!-\!\exp\!\left(-\frac{\theta_{FOV}^{2}}{2 \sigma_{AoA}^{2}}\right)\!\right]\!X^{\Xi_{2}}Q\!\left(\ln\!\left(\frac{\frac{ \sigma_{n}\sqrt{\rm Y}}{P_{t}mG\Re}}{A_{0}\exp(-\chi L)}+\Xi_{3}\right)/\sigma _{R}\ \right)\right)\!dY\\ &+2q\big{(}M_{Q}\big{)}\int_{0}^{\infty}\!\left(Q\Big{(}A_{Q} \sqrt{\rm Y}\Big{)}\exp\!\left(-\frac{\theta_{FOV}^{2}}{2\sigma_{AoA}^{2}} \right)\!\delta\!\left(\frac{\sigma_{n}\sqrt{\rm Y}}{P_{t}mG\Re}\right)\right. \\ &+\Xi_{1}\!\left[\!1\!-\!\exp\!\left(-\frac{\theta_{FOV}^{2}}{2 \sigma_{AoA}^{2}}\right)\!\right]\!X^{\Xi_{2}}Q\!\left(\ln\!\left(\frac{\frac {\sigma_{n}\sqrt{\rm Y}}{P_{t}mG\Re}}{A_{0}\exp(-\chi L)}+\Xi_{3}\right)/ \sigma_{R}\ \right)\Bigg{)}dY\\ &-4q(M_{I})q\big{(}M_{Q}\big{)}\int_{0}^{\infty}\!\left(Q\Big{(} A_{I}\sqrt{\rm Y}\Big{)}Q\Big{(}A_{Q}\sqrt{\rm Y}\Big{)}\exp\!\left(-\frac{ \theta_{FOV}^{2}}{2\sigma_{AoA}^{2}}\right)\!\delta\!\left(\frac{\sigma_{n} \sqrt{\rm Y}}{P_{t}mG\Re}\right)\right.\\ &+\left.\Xi_{1}\!\left[\!1\!-\!\exp\!\left(-\frac{\theta_{FOV}^{2} }{2\sigma_{AoA}^{2}}\right)\!\right]\!X^{\Xi_{2}}Q\!\left(\ln\!\left(\frac{ \frac{\sigma_{n}\sqrt{\rm Y}}{P_{t}mG\Re}}{A_{0}\exp(-\chi L)}+\Xi_{3}\right)/ \sigma_{R}\ \right)\Bigg{)}dY.\end{split}\]
## 5 Discussion
### Theoretical and Practical Implications  
This study offers meaningful theoretical contributions to computer science education, prompt engineering, and human-AI interaction. It empirically demonstrates that not all interactions with LLMs are equally effective, rather, the type and clarity of the query significantly influence learning outcomes and task success. Specifically, the study found that students who formulated queries related to EF and CU showed statistically significant improvements in both the functionality and correctness of their code. These findings support and extend existing theories in cognitive learning science by suggesting that AI-assisted learning is most effective when it targets immediate problem resolution and conceptual clarity. Moreover, the research highlights that prompt formulation is not merely a technical interaction but a cognitively demanding task that affects how learners extract meaningful feedback from LLMs. This aligns with emerging frameworks in digital pedagogy that treat prompt engineering as a critical thinking skill in itself, reinforcingthe notion that generative AI does not eliminate the need for metacognitive effort but redistributes it toward interaction design. Thus, this work lays a foundation for future research into how different prompt categories map onto various levels of programming cognition (e.g., comprehension vs. synthesis).  
The practical implications of this study are particularly relevant for educators, curriculum designers, and developers of AI-based coding tools. First, the findings suggest that teaching students how to ask better questions, especially those related to debugging and code comprehension, can significantly improve their ability to produce runnable and correct code, even when prior programming experience is limited. This is critical in introductory programming courses where students frequently struggle with diagnosing problems and understanding complex code behaviour. Embedding targeted exercises in prompt formulation or using LLMs for conceptual clarification could be a low-cost, high-impact intervention to support student learning. Additionally, the study demonstrates that tagging or explicitly categorizing one's query type (e.g., EF, CU) can lead to more deliberate and structured interactions with LLMs. Tool designers could leverage this insight by integrating query-type selection interfaces or guided prompt templates into AI coding assistants to help users refine their input more effectively. From a broader organizational perspective, development teams that adopt LLMs into their workflows might consider offering internal training on prompt strategy or implementing lightweight tagging systems to improve the efficiency and reliability of AI-generated output. Ultimately, this research reinforces the idea that human factors, especially intentional communication, are central to unlocking the value of AI assistance in software development and education.
## 2 Materials and Methods
### Field Measurements  
Flux and meteorological instruments were mounted on a 32 m high tower erected at the center of the observation field in Changwu station, the tower is about 500, 1400, 1000, and 1800 m away from the surrounding steep ravines in the directions of east, south, west, and northwest, respectively [32; 33]. Two sets of eddy covariance (EC) systems were used for measuring latent heat flux (LE), sensible heat flux (H\({}_{\mathrm{s}}\)), CO\({}_{2}\) flux (F\({}_{\mathrm{c}}\)) at 2 m and 32 m above ground. The area around the experimental site is quite variable, but presumably statistically homogeneous at the meso-gamma scale which ranges roughly from 2 to 20 km, as the variabilities are constant in space, as described by [PERSON] (1998) [34] and [PERSON] et al. (2017) [33]. The details of the topography around the experimental site are given in [PERSON] et al. (2007) [32] and [PERSON] et al. (2017) [33]. According to the footprint study of [PERSON] et al. (2009) [35] at this site, when the measurement height changed from 2 m to 12 m above ground, the largest observation distance on prevailing wind direction (southeast) changed from 172.8 m to 1555.2 m, and it could be larger when the measurement height is increased [36]. Thus, the footprint of the EC system placed at 2 m covered the winter wheat continuous cropping field around the tower, while the footprint of the EC system at 32 m could represent the complex of wheat field, apple orchard, and other ecosystems (the AFE) within a distance of more than 2000 m in the direction of the prevailing wind (Figure 1b).  
An open-path infrared gas analyzer (LI7500, LI-COR, Inc., Lincoln, NE, USA) was used to measure fluctuations of CO\({}_{2}\) and H\({}_{2}\)O density. Wind velocity and virtual temperature fluctuations were measured using a three-dimensional sonic anemometer (CSAT3, Campbell Scientific, Inc., Logan, UT, USA). The sampling frequency of the eddy covariance system was 10 Hz. T\({}_{\mathrm{a}}\) and RH (HMP45C, Vaisala Inc., Vantaa, Finland), W\({}_{\mathrm{s}}\) (3-cup anemometer, Vector Instruments, Hudson, NH, USA) were all measured at 2 m and 32 m above the ground. P was measured using a tipping bucket rain gauge (RA-1, Ota Keiki Seisakusho Co., Ltd., Tokyo, Japan) at 0.4 m above the ground. PAR (LI190 SB, LI-COR, Inc.,  
Figure 1: The location of the study site on the Loess Plateau of China (**a**), general view of the area and location of the tower (**b**).  
Lincoln, NE, USA) was measured at 2 m above the ground. T\({}_{\rm s}\) and SWC were measured at 2, 10, 20, 40, and 80 cm below the ground using a thermometer (Campbell Scientific, Inc., Logan, UT, USA) and TDR sensor (IMKO, Ettlingen, Germany). Climatological and soil parameters were recorded at 30 min intervals with a CR10X data logger (Campbell Scientific, Inc., Logan, UT, USA). VPD and R\({}_{\rm n}\) were calculated based on the above parameters.