esa-sceva/satcom-chunk-collection huggingface dataset:
# Verifiable (2, \(n\)) Image Secret Sharing Scheme Using Sudoku Matrix
## 3 Research Methods  
In this section, we propose a (\(t\), \(n\))-threshold secret sharing scheme using the Sudoku matrix, where \(t\leq n\). The secret image can be distributed to \(n\) different participants. After this, any \(t\) participants together can completely reconstruct the secret image, except in the case where participants whose number of shares minus the result is a multiple of three. An authentication mechanism is also proposed to authenticate the integrity of the proposed scheme. The new proposed Sudoku matrix can markedly improve the embedding capacity of secret data. The proposed approach can be divided into the following parts: (1) the procedure for hiding and extracting secret images, (2) the authentication mechanism, and (3) the reconstruction of the secret image and authentication.  
Based on the (\(t\), \(n\))-threshold secret sharing method put forth by [PERSON] and [PERSON] in 1979, the matrix that we propose incorporates this concept as a (\(2\), \(n\))-SIS scheme. In our method, there is no limit on the number of shareholders, and each two of the holders can completely retrieve secret data. As the overflow shows in Figure 2a, there can be several cover images to embed secret data, while the same number of shares are produced. Any two of the shares can reconstruct the secret image. For example, as shown in Figure 2b, if \(n\) is \(29\), we select two of the shares, such as the first and the last ones, to reconstruct the secret image.  
If two of the random shares are real, as shown in Figure 3a,b, the secret data can be recovered completely, as in Figure 3c. On the contrary, if any one of the shares is fake, as in  
Figure 2: Schematic diagram of the proposed (\(2\), \(n\))-SIS scheme. (**a**) Secret sharing process. (**b**) Recovery process.  
Figure 3e, the secret data cannot be retrieved, and the image in the authentication result will appear incomplete, as shown in Figure 3f.
###### Abstract  
We examined the relationship between satellite measurements of solar-induced chlorophyll fluorescence (SIF) and several meteorological drought indices, including the multi-time-scale standard precipitation index (SPI) and the Palmer drought severity index (PDSI), to evaluate the potential of using SIF to monitor and assess drought. We found significant positive relationships between SIF and drought indices during the growing season (from June to September). SIF was found to be more sensitive to short-term SPIs (one or two months) and less sensitive to long-term SPI (three months) than were the normalized difference vegetation index (NDVI) or the normalized difference water index (NDWI). Significant correlations were found between SIF and PDSI during the growing season for the Great Plains. We found good consistency between SIF and flux-estimated gross primary production (GPP) for the years studied, and synchronous declines of SIF and GPP in an extreme drought year (2012). We used SIF to monitor and assess the drought that occurred in the Great Plains during the summer of 2012, and found that although a meteorological drought was experienced throughout the Great Plains from June to September, the western area experienced more agricultural drought than the eastern area. Meanwhile, SIF declined more significantly than NDVI during the peak growing season. Yet for senescence, during which time the reduction of NDVI still went on, the reduction of SIF was eased. Our work provides an alternative to traditional reflectance-based vegetation or drought indices for monitoring and assessing agricultural drought.  
drought monitoring; solar-induced chlorophyll fluorescence (SIF); drought indices; gross primary production (GPP); Great Plains; hyperspectral remote sensing 2016
# SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark +
## 2 Related Work
### Sketch as Queries  
Sketch-based image retrieval is a key task aimed at retrieving photos of the same category [16; 17; 67] or corresponding instances [18; 19; 20; 21; 22] based on a query sketch. The Sketch-a-Classifier model [55] generates a photo classifier from a sketch of an unseen category, while Sketch-a-Segmenter [56] similarly creates a pixel-level classifier from a sketch input. SGL [54] proposes generating object proposals relevant to the sketch query, and DIY [68] uses sketch queries for few-shot class incremental learning. The power of sketches has been effectively harnessed not only in static images but also in the video domain, where they have shown significant potential for fine-grained sketch-based video retrieval at both the instance [57] and scene levels [58]. Recent developments in sketch-based video object localization [69] have demonstrated how sketches can accurately localize specific targets within video content. However, these approaches have largely focused on class-level retrieval and detection.  
Our previous work [25] pioneered the task of sketch-based video object segmentation (SKVOS), introducing a novel framework that fuses sketch information with the first video frame and establishing a large-scale dataset alongside a baseline method. However, the baseline primarily focused on single-frame fusion, neglecting the rich temporal information inherent in video sequences. Building upon this foundation, we address these limitations by proposing advanced techniques to leverage temporal continuity and enhance sketch-video fusion, aiming to improve segmentation accuracy and robustness in dynamic video scenarios. This work not only advances the state of the art in SKVOS but also broadens the perspective on sketch-video interaction, challenging the conventional view that sketches are limited to class-level guidance for retrieval and detection tasks [57; 58; 69].
# Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study
## 5 Identified Challenges and Gaps  
Based on the systematic mapping study, we found that there is still a wide gap in some areas concerning the sentiment analysis of students' feedback that need further research and development. The following list shows some of the prominent issues, as presented in Table 10.  
* Fine-grained sentiment analysis: Most studies have focused their attention on a complete review to determine a sentiment rather than going deeper into identifying fine-grained teaching/learning-related aspects and sentiments associated with them;
* Figurative language: Identifying figurative speech, such as sarcasm and irony, from student feedback text in particular is lacking and needs further exploration;
* Generalization: Most of the techniques are domain-specific and thus do not perform well in different domains;
* Complex language constructs: There is an incapability to handle complex language involving constructs such as double negatives, unknown proper names, abbreviations, and words with dual and multiple meanings;
* Representation techniques: There is a lack of research effort on the use of general-purpose word embedding as well as contextualized embedding approaches;
* Scarcity of publicly available benchmark datasets; there is a lack of benchmark datasets and an insufficient dataset size. Although there are a few open datasets available, there is no benchmark dataset that is useful for testing deep learning models due to the small number of samples those datasets provide;
* Limited resources: There is a lack of resources such as lexica, corpora, and dictionaries for low-resource languages (most of the studies were conducted in the English or Chinese language);
* Unstructured format: most of the datasets found in the studies discussed in this survey paper were unstructured. Identifying the key entities to which the opinions were directed is not feasible until an entity extraction model is applied, which makes the existing datasets' applicability very limited;
* Unstandardized solutions/approaches: We observed in this review study that a vast variety of packages, tools, frameworks, and libraries are applied for sentiment analysis.
## 4 Results
### Sunspot Data  
The number of darker spots on the sun's surface has long been a subject of curiosity for humanity, so we have a solid history of records going back several hundred years. An observatory in Belgium keeps records of current sunspot activity and has an archive of historical numbers [26]. The sunspot series is often referred to as [PERSON]'s sunspot data because [PERSON] first compiled the time series [30]. Because of its crucial influence on various fields, from climate to telecommunications, statisticians have studied sunspot data extensively. However, identifying a functional relationship in the sunspot series has been challenging because of the nonlinear, nonstationary, and non-Gaussian nature of the series [31; 32]. In addition, the high level of complexity in the series has made sunspot data a yardstick for evaluating forecasting models.  
The sunspot series used in this study comprises 288 annual observations, covering 1700 to 1987. The data set was split into two parts for training and testing purposes. The test data include 221 observations from 1700 to 1920, and the remaining portion, from 1921 to 1987 (67 data points), is used for testing. In parallel with previous studies on this series, we followed a two-step hybrid model approach to model the series [5; 6; 25].  
An autoregressive model of order nine, AR(9), and an ANN architecture of \(4\times 4\times 1\) are used in basic and secondary models. The decision to select these models is based on the previous research work done on the problem [5; 33; 34].  
Figure 4 shows the forecast and actual data over the test period. For most of the observations, the difference between the forecast and the actual remains small. Table 1 compares the performance of various methods in terms of MAD (mean absolute deviation). The list indicates that the proposed method performs better than previous models.
However, these standards do not include any provisions for Web service assurance; rather, they are aimed at satisfying pre-defined security requirements that service designers identify at development time. Some well-known packages for service security, such as Apache Rampart ([[http://axis.apache.org/axis2/java/rampart/](http://axis.apache.org/axis2/java/rampart/)]([http://axis.apache.org/axis2/java/rampart/](http://axis.apache.org/axis2/java/rampart/))) and Java WSIT ([[http://wsit.java.net](http://wsit.java.net)]([http://wsit.java.net](http://wsit.java.net))), provide basic facilities for monitoring Web service security mechanisms; however, few if any assurance techniques based on such facilities have been proposed in the literature. This paper explores the idea that machine-readable certificates of security and privacy properties of Web services can provide a way to bound, transfer, and alleviate security risks at run time. In 2008, the Software Engineering Institute (SEI) published a requirements document on the service certification process for the U.S. Army's Chief Information Office/G-6 (CIO/G-6) organization that takes charge of the information management function of the Army to address security and provisioning concerns the Army foresees in its development of Service-Oriented Architecture (SOA) environments [2]. This document presents a methodology for certifying Web services to assure their compliance with stated non-functional requirements. This work represents a first step in defining methods and techniques to certify that the service satisfies specific security requirements, producing security assertions and documents that could be accepted as a proof of service assurance. It is important to note that certification of Web service security properties involves both the container and the code implementing the service itself. Even if a Web service container is certified to support, say, the confidentiality of data flowing through a service interface, no inference can be done on the internal implementation of the service, where information leaks could happen. [PERSON] _et al._ introduced container- and service-level security properties [3]. Their work aims to support the definition and run-time handling of certificates stating that a set of security properties are satisfied by Web services deployed in a given container. However, handling context changes involving the container, the underlying hardware, or the service itself remains an open issue. This paper is organized as follows: Section 2 gives an introduction on how to express security requirements. Section 3 discusses our approach to security certification of services and presents different types of certificates. Section 4 describes the concept of Adaptable Security Certification using a practical example and introducing a conceptual architecture for adaptable assurance-based service discovery. Section 5 presents related work and Section 6 concludes the paper.
## 5 Experimental Methodology
### Trie Implementation and Address Generation  
Our implementation strictly follows the Ethereum Yellow Paper [3] specifications for address generation and trie construction. In Ethereum, addresses are derived through the following process:  
* Generate a 256-bit private key: \(PrivateKey=CSPRNG(256)\);
* Compute the public key using secp256k1 elliptic curve: \(PublicKey=secp256k1\) (\(PrivateKey\));
* Take the Keccak-256 hash and extract the last 20 bytes: \(Address=rightmost20(Keccak256\) (\(PublicKey\))\).  
Our implementation reproduces this exact process using cryptographically secure random number generation (CSPRNG) and the official Keccak-256 hash function implementation to ensure uniform distribution across the \(2^{160}\) address space.  
The trie construction follows Ethereum's Modified Merkle Patricia Trie (MPT) specification (EIP-158) [29], implementing the following:  
python Copy class Node: def \_init\_\_(self): self.children = \{\} #Hexadecimal character mappings self.is\_end = False # Terminal node indicator This structure mirrors Ethereum's state trie implementation, where
* Each node represents a nibble (4 bits) of the address;
* The trie maintains the same 16-ary branching factor as Ethereum;
* Path compression is implemented through single-child node merging.  
The resulting trie structure is functionally equivalent to Ethereum's state trie, ensuring our experimental results accurately reflect real-world blockchain behavior.
# Reconfiguration Decision-Making of IoT based Reconfigurable Manufacturing Systems
## 5 Decision-Making System
### Decision-Making Process
#### 5.1.1 Reconfiguration Situations  
Based on the data collected by the IoT sensors, current factory information, external information, and factory status were identified, and reconfiguration decisions were made. Without the data obtained by the IoT sensors, it would take longer to recognize a situation.  
The situations that need reconfiguration decision-making are as follows: The first situation is changed in external factors such as changed sales strategies, revised government regulations, changed ratios of products, new products, and the introduction of new processes. In this case, it is required to make a reconfiguration decision to meet a given external goal.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Sensor** & **Collecting Item** \\ \hline \multirow{4}{*}{Machine status} & Thermometer & Internal temperature of a machine \\  & 3-Axis vibration transducer & Vibration during machining \\  & Time of flight sensor & Assembly status of machining tools \\  & 3-Axis tilt sensor & Tilted angle of an installed machine \\  & Surface roughness sensor & Surface wear of tools \\  & & Cracks of tools \\  & & Expansion of tools or parts \\  & & Whether tools and parts are in place \\  & Thermometer & Temperature of factory \\  & & Hydrometer \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of Internet of Things (IoT) sensors in the RMS.  
Figure 3: Structure of the data acquisition module.  
The second situation is changed in internal factors such as failures of productivity goals, machine failures, and predicted failures of productivity. In this case, it is necessary to execute reconfiguration to deal with the situation. Table 2 presents the causes of situations that need reconfiguration decision-making.
# Techno-Economic Analysis of Flare Gas to Hydrogen: A Lean and Green Sustainability Approach
## 1 Introduction  
Global energy consumption has been steadily increasing due to the need for industrialization and the ongoing growth of the human population, resulting in a consistent rise in CO\({}_{2}\) emissions [1]. This growth is primarily driven by increased demand in emerging and developing economies, particularly in sectors such as industry and transportation, which subsequently leads to considerable environmental impacts. Some scholars have argued that human growth and the emergence of industrialization are linked to the leading causes of climate change [2, 3, 4]. According to the International Energy Outlook report, global energy consumption is projected to increase by nearly 50% by 2050 [5]. This estimated increase in global energy consumption has heightened the need for sustainable and clean energy solutions, which are necessary to reduce CO\({}_{2}\) emissions and achieve the target goal of below 2 \({}^{\circ}\)C, as outlined in the United Nations Framework Convention on Climate Change [6, 7].  
Hydrogen has been identified as a promising and key solution to achieving the net-zero greenhouse gas emissions commitments announced in recent years; it is a versatile andeco-friendly energy carrier with numerous applications in power generation, transportation, and industrial sectors [8; 9; 10]. The global industrial percentage of hydrogen consumption is shown in Figure 1.  
The potential of hydrogen to decarbonize several industries has led to a recent surge in the demand for hydrogen as a clean energy carrier on a global scale [12; 13]. Its global demand reached 94 million tonnes (MT) in 2021 and is estimated to rise to 115 million tonnes (MT) by 2030 [14]. Hydrogen can be produced from various sources, and the color of the hydrogen distinguishes its production method as shown in Figure 2[15; 16].  
While natural gas remains the primary source for hydrogen production, accounting for 75% of the world's supply through the SMR process, which is significantly higher than the electrolysis method [17], this method emits up to 10 kg of CO\({}_{2}\) per kg of hydrogen produced [18]. However, this CO\({}_{2}\) is significantly reduced when SMR is used with CCS, producing what is known as blue hydrogen [19; 20; 21]. Nigeria has a natural gas reserve of 5.94 trillion standard cubic meters [22], offering a promising market for hydrogen production, which could play a crucial role in alleviating the nation's ongoing challenges with sustainable gas flaring.  
Figure 1: Global hydrogen consumption by industries (adapted from [11]).  
Figure 2: Hydrogen colors, sources, and production methods.
## 6 Conclusions  
This study demonstrates that roof structure plays a critical role in water-resisting capacity restoration in shallow coal seam goafs. Our experimental results confirm that roof lithological composition represents a fundamental engineering-geological control factor. Through systematic investigation of compaction-seepage characteristics (CSCs) for four distinct lithological combinations in shallow mining conditions, we establish the following key findings:  
1. In the shallow buried stress range, the compaction characteristics of broken rock in goaf are affected by many factors, such as lithology, strength, softening characteristics, layered thickness, number of interfaces, accumulation structure, and initial porosity.
2. The seepage characteristics of lithologic combination samples under low axial stress are unstable, and the curves of water pressure gradient and flow velocity show tortuous characteristics. The change trend and change times of CSC of lithology combination are related to the order and number of interfaces of lithology combination.
3. The contribution for restoration of water-proofing function of the rock sample compaction of the combination lithology from high to low is mudstone, sandstone, and coal. Although the strength of coal and mudstone is low, the softening effect of mudstone by water is stronger. The coal compaction process mainly breaks and seals the large pores, and the mudstone also has the advantage of cement sealing the small pores.
4. The'stress-void-seepage' analysis model of compaction seepage of lithologic combination broken rock considering compaction and re-breaking is established, and the permeability PV sensitivity index is analyzed. The restoration of the water-proofing function of mudstone and sandstone combined compaction is the most obvious.
5. In the engineering site, the permeability reduction at the interface of the broken rock combination can promote the formation of a stable water-proof rock layer in the goaf.  
Conceptualization, methodology, funding acquisition, writing--original draft preparation, writing--review and editing, K.M. and S.T.; validation, review, and editing, W.L.; formal analysis and visualization, J.L.; validation, investigation and data curation, J.T., H.Z. and J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Jiangsu Funding Program for Excellent Postdoctoral Talent, (Nos. 2024 ZB531) and the National Natural Science Foundation of China (Nos. 51874281).  
Not applicable.  
Data Availability Statement: The data presented in this study are available upon request from the corresponding author.  
Acknowledgments: Thank [PERSON] and [PERSON] engineers in Shanxi Jinshen Shaping Coal Mining Co., Ltd. for their help in obtaining materials used for experiments. Conflicts of Interest: Author [PERSON] was employed by Shaanxi Energy Fengjiata Mining Operation Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A New Flow Control Method of Slat-Grid Channel-Coupled Configuration on High-Lift Device
## 2 Flow Control Optimization Strategy for Leading-Edge Slot of the Airfoil 30P30N Based on Grid Fin  
The grid-slat configuration can improve the flow field level of the slot by increasing grid flow channels. Its basic principle is that, in addition to increasing the flow path of the leading-edge slats, the slats themselves generate additional lift. More importantly, a new additional slot is formed on the basis of the original single channel, which can enhance the flow characteristics of the original slot, improve its disturbance characteristics to the flow of the main wing, and thus enhance the flow control effect. However, since there are many design parameters involved due to the addition of slots in the slats, this paper constructs an aerodynamic optimization design method for grid-slat flow control based on the DOE algorithm.