esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
## 6 Discussion
### Implications and Uncertainties of This Study  
For ET estimation, both LAI and \(\Omega\) are required for describing complex canopy architecture. Both LAI and \(\Omega\) can be acquired from remote sensing. Nevertheless, there are still uncertainties in the values of the two parameters. By now, several global LAI products with moderate resolution (250 m to 7 km) are available based on different algorithms [63]. Depending on field measurements and models used in the algorithm, some LAI products are close to L\({}_{\text{e}}\)[64; 65] while some are close to true LAI [7; 37; 66]. If field-measured LAI is based on allometric equations or acquired from destructive sampling, the LAI would be close to true LAI. If optical instruments (i.e., LAI-2200 or DHP) are used to measure LAI without the clumping correction, the LAI is effective LAI [23]. Some remote sensing LAI products (i.e., GA-TIP and JRC-TIP) were produced based on data assimilation retrieval from albedo without the clumping collection, and thus they are effective LAI instead of the true LAI [63]. Clumping correction is needed for these LAI products before they are used for global ET simulation.  
Although the GLOBMAP LAI product used in this study has incorporated the leaf clumping effects, there are still biases in the global CI map used for the GLOBMAP LAI production due to using the RossThick-LiSparse Reciprocal (RTLSR) model with a constant \(\theta\) of 0\({}^{\circ}\)[37; 52]. CI of slight to middle clumped canopies was underestimated while they were overestimated for sparsely vegetated areas by this BRDF and SZA configuration [39]. The CAS-CI product performed better than the CI product produced by [PERSON] et al. [52], and it has a lower bias when validated with ground measurements. However, the CAS-CI product does not perform well over very sparse crop sites due to the substantial impacts of exposed soil, which impair the derivation of CI from remote sensing data using the NDHD method [27; 39]. Moreover, the NDHD method has a disadvantage of underestimating the true CI for tropical forests as satellite sensors are majorly detecting the thick overstory canopy, which would keep off the understory canopy [67]. The influences of frequent clouds would also impair the CI retrievals over tropical areas [27].  
There are only a few CI validations for extremely sparse and dense vegetation conditions [27]. Thus, in the spatial patterns of leaf clumping effects on ET, the largest uncertainties would exist in these areas. More field observations are required for further evaluation of the CI estimation over these land cover types. Shrubs and other vegetation types would also have large uncertainties as these PFTs have not been extensively validated in the BEPS model.
## 6 Future Challenges
This aspect is a key enabler to develop novel decentralized smart applications (i.e., DApps) and to leverage new artificial intelligence paradigms such as big data, machine learning, or deep learning. These paradigms need to rely on trustworthy datasets in order to reach their full potential and produce new data model-based applications. Nevertheless, smart campus designers have to use blockchain with caution, considering their advantages and disadvantages. In addition, the incorrect use of smart contracts can be a problem, since they are able to trigger certain automatic behaviors that can have serious economic or personal consequences. * Lack of smart campus standards and public initiatives. Although, in the last years, smart city initiatives have proliferated worldwide, there are only a few specifically related to smart campuses and universities. In addition, there is not a common framework for designing or deploying them, so future developers will have to keep compatibility and interoperability issues in mind. * Seamless integration of outdoor and indoor smart campus applications. Due to their communications needs, outdoor and indoor applications may differ in the underlying technologies, so it is necessary to design architectures and devices that allow for switching between communications transceivers. This means that, although the lower layers of the communications protocol may differ, the upper layers are compatible so that they are able to provide seamless communications among users, IoT objects, and the computing devices scattered throughout a campus.
# Logistics Service Provider Lifecycle Model in Industry 4.0: A Review
###### Abstract  
Supply chain and logistics management is of tremendous importance for multinational organizations. Logistics Service Providers (LSPs) provide logistics services and smooth logistics operations between suppliers, manufacturers, distributors, and customers. This paper uses a Systematic Literature Review (SLR) to identify the current trends and future developments of LSPs and the underlying (smart) logistics operations connected to the concept of lifecycle management. An SLR review was conducted to identify relevant research papers in the areas of LSPs and logistics lifecycle management. Out of 288 papers analyzed, 81 were identified as highly appropriate for in-depth analysis. The LSP Lifecycle Model (LSLM) was then developed by combining logistics service characteristics and the lifecycle management concept, including Product Lifecycle Management (PLM), Service Lifecycle Management (SLM), and Product Service System (PSS). The LSLM consists of three phases: The Beginning of Life (BOL), the Middle of Life (MOL), and the End of Life (EOL). The LSLM is characterized by three phases, eight criteria, and seventeen sub-criteria. This paper aims to fulfil customer requirements through a product or service in the whole lifecycle of the logistics service provider. The findings further present an adaptable LSLM by focusing on various logistics services and integrating sustainability factors to meet market trends. Logistics cost factors can also be used to evaluate logistics services in the MOL stage. The EOL shows the trend of risk management, evaluation, and decomposition, which is determined by new or re-designed logistics products and services.  
systematic literature review; Logistics 4.0; smart logistics; lifecycle model; logistics service provider; Industry 4.0 +
Footnote †: journal: Applied Sciences
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 2 OFDM Systems
### Equivalent Channel Model  
Substituting (12) into (11), we obtain the equivalent channel that data \(d_{n}\) employ to traverse from BS to user \(k\) as:  
\[Y_{k,n}=\|\tilde{\mathbf{H}}_{k}^{n}\|d_{n}+Z_{k,n}. \tag{13}\]  
The performance of the system can be determined using this model. For example, when \(d_{n}\) is drawn from the QPSK constellation, the average BER is then obtained by [33]:  
\[\text{BER}^{\text{OFDM}}=\mathbb{E}\Bigg{[}\frac{1}{2}\text{erfc}\Bigg{(}\sqrt {\|\tilde{\mathbf{H}}_{k}^{n}\|^{2}\frac{E_{\text{b}}}{N_{0}}}\Bigg{)}\Bigg{]}, \tag{14}\]  
and the average mutual information is obtained by [34]:  
\[\text{MI}^{\text{OFDM}}= \ 2-\frac{1}{\ln 2}-\frac{1}{4}\sum\limits_{i=1}^{4}\mathbb{E} \left[\log_{2}\left(\sum\limits_{i=1}^{4}\text{e}^{-\frac{\left\|Z_{k,n}+\left\| \tilde{\mathbf{H}}_{k}^{n}\right\|(i_{1}-i_{j})\right\|^{2}}{e^{2}}}\right)\Bigg{]} \tag{15}\] \[= \ 2-\frac{1}{\ln 2}-\mathbb{E}\left[\log_{2}\left(\sum\limits_{i=1} ^{4}\text{e}^{-\frac{\left\|Z_{k,n}+\left\|\tilde{\mathbf{H}}_{k}^{n}\right\|(i_{1} -i_{j})\right\|^{2}}{e^{2}}}\right)\right], \tag{16}\]in bits/symbol, where \(s_{i},s_{j}\in\Omega\) are QPSK symbols. The second equality (16) is due to the symmetric property of the QPSK constellation.
## 4 Results and Discussion
### Comparison of Convergence for MTL-AMWO, MTL-WO, and M-FNAS  
The compositional space of HEAs is vast. If the convergence speed of algorithm training can be improved, it would reduce the computational resources required for HEAs and achieve the desired accuracy in a shorter time, thereby improving the prediction accuracy of the model and accelerating the development of materials. To verify the convergence of MTL-AMWO, the experimental results of MTL-AMWO on the six datasets are compared with MTL-WO and M-FNAS in this section. The convergence curves of MTL-AMWO, MTL-WO, and M-FNAS in the six datasets are shown in Figure 7. The red line shows the convergence curve of MTL-AMWO. The blue line represents the convergence curve of MTL-WO. The black line shows the convergence curve of M-FNAS.  
As shown in Figure 7, (a) represents D\({}_{1}\), (b) represents D\({}_{2}\), (c) represents D\({}_{3}\), (d) represents D\({}_{4}\), (e) represents D\({}_{5}\), and (f) represents D\({}_{6}\). In the six subfigures, the convergence speed of MTL-AMWO is the fastest and can achieve the expected accuracy in a shorter time. The convergence speed of MTL-AMWO is faster than MTL-WO. The reasons may be as follows: firstly, MTL-AMWO employs an adaptive migration strategy that allows the AMWO in MTL-AMWO to adaptively migrate walnuses based on changes in the population's average fitness value over multiple iterations, thereby avoiding falling into a local optimum. Secondly, the BRDPC algorithm in MTL-AMWO can further improve the effectiveness of the transfer by clustering the samples in the source and target domains into several clusters with similar distributions, respectively. The convergence speed of MTL-WO is faster than M-FNAS. The reason may be that MTL-WO adopts a transfer strategy to transfer the optimal solution set of similar historical problems in the source domain to the target problem, thereby improving the population's search speed and efficiency in the search process.  
Figure 6: The quantities of SS, IM, SS + IM, and AM in the quinaries, senarios, and septenaries HEA samples in the six datasets.
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 3 Experiment and Results
### Result
#### 3.2.6 Comparison of CNNs  
Figure 14 shows the comparison results of transformers and Unet [27], DeepLab V3+ [39], and MAnet (multiscale attention net) [40] for the Vaihingen data set segmentation results. SwinUnet and SETRnet were significantly better than CNN for large-scale feature segmentation, which further proves that a transformer is beneficial to improve the large-scale feature-learning ability [41]. Regarding the case of feature confusion, SwinUnet had fewer such occurrences, and the problem of feature misclassification confusion is commonly found in CNNs, mainly because the feature-extraction ability of convolution is not as good as that of transformers. However, the segmented edges in both transformer networks appear to be less fine than those of convolutional networks, and even the results of TransUnet containing a CNN are better than those of the other two transformers. This indicates that the transformer still needs improvement in edge-extraction capability, and it is necessary to improve the spatial feature-information-learning capability.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Data Set** & **Kappa** & \
\begin{tabular}{l} **Average** \\ **Difference** \\ \end{tabular} &
\begin{tabular}{l} **Standard Value** \\ **of Difference** \\ \end{tabular} & **Cohen’s d** \\ \hline \multirow{3}{*}{Vaihingen data set} & SETRnet 73.80\% & & & \\  & SwinUnet 77.50\% & & & \\  & TransUnet 80.54\% & & & \\ \hline \multirow{3}{*}{Potsdam data set} & SETRnet 72.07\% & & & \\  & SwinUnet 76.47\% & & & \\ \cline{1-1}  & TransUnet 68.18\% & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Kappa coefficient effect scale.  
Figure 13: Training process of transformers on the Vaihingen data set.
# A Security-Oriented Data-Sharing Scheme Based on Blockchain
## 4 Experiment and Analysis
### Analysis of Complexity  
The system is primarily composed of four components: key generation, file uploading, file downloading, and record query.  
Key generation primarily relies on the efficient elliptic curve algorithm for the creation of identity keys, which is with a constant time complexity, denoted as \(O(1)\).  
File uploading is implemented with three modules: file uploading to IPFS, hash encryption, and uploading to blockchain. The file uploading to IPFS is influenced by the size of the file, the number of file blocks, and the size of each block. Hence, the time complexity can be noted as \(O(n+K\times B)\), where \(K\) represents the number of file blocks and \(B\) represents the size of each block. The time complexity of hash encryption  
Figure 11: Data-sharing records kept by NFT.  
depends on the size of the plaintext. In our scheme, the hash returned by IPFS is a fixed 64-bit hexadecimal number; thus, the encryption time complexity is constant, denoted as \(O(1)\). The time complexity of uploading to blockchain is determined by the length of the encrypted file, which is fixed, and therefore the time complexity for uploading to blockchain is at a constant as well, denoted as \(O(1)\). Thus, the time complexity for this part is \(O(n+K\times B)+O(1)+O(1)\), which simplifies to \(O(n)\).  
File downloading relies on three modules: file information retrieval, decryption of the encrypted file, and file downloading from IPFS. The retrieval of file information is with a time complexity ranging from \(O(1)\) to \(O(logn)\), where \(n\) is the amount of data in the blockchain. The decryption of the encrypted file, due to the fixed length of the encrypted file, has a time complexity of \(O(1)\). The downloading of files from IPFS relies on the size of the file, the number of file blocks, and the size of each block; hence, the time complexity can be noted as \(O(n+K\times B)\), where \(K\) is the number of file blocks needed and \(B\) is the size of each block. Therefore, the time complexity for this part is also \(O(n+K\times B)+O(1)+O(1)\), which simplifies to \(O(n)\).  
Record query is the process of reading data from the blockchain's ledger. Hence, the time complexity ranges from \(O(1)\) to \(O(logn)\), where \(n\) is the amount of data in the blockchain. Thus, the time complexity for this part is \(O(1)\).  
Therefore, the overall time complexity of the proposed scheme is \(O(1)+O(n)+O(n)+O(1)\), which simplifies to \(O(n)\), where \(n\) represents the size of the file.  
In this scheme, the primary contributors to space complexity are the associated cryptographic operations, including hashing, encryption, and decryption. Although these operations are complex, the data handled are with a fixed length; hence, it is generally accepted that the space complexity is \(O(1)\). Consequently, the spatial complexity of the proposed scheme is denoted as \(O(1)+O(1)+O(1)\), simplified to \(O(1)\).
# Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey
## 8 Challenges and Future Directions
### Generative AI and Adversarial Attacks in Blockchain Anomaly Detection
#### 8.2.2 Potential Approaches Related to Adversarial Attacks  
Potential approaches encompass developing robust anomaly detection algorithms resilient to adversarial attacks, implementing mechanisms to ensure the integrity of blockchain data, exploring advanced privacy-preserving techniques, and promoting user awareness and education regarding privacy best practices in blockchain systems. Addressing the above challenges necessitates a multifaceted approach encompassing technical innovations, robust defense mechanisms, and user empowerment.  
Unsupervised learning frameworks offer remarkable adaptability as the boundary between normal and anomalous behavior evolves dynamically. In this regard, deep learning assumes a pivotal role in this paradigm, capable of identifying anomalous activity by tapping into the latent features embedded in blockchain transactions.  
The future of anomaly detection in blockchain networks is likely to embrace a fusion of typical unsupervised learning and deep learning methodologies, where techniques like autoencoders and GANs can unveil hidden anomalies in blockchain data assisting the overall endeavor. Additionally, delving into temporal aspects such as time series analysis and recurrent neural networks can enhance the accuracy of anomaly detection mechanisms.  
In addition, future research should generate robust datasets encompassing various facets of blockchain transactions, serving as benchmarks for evaluating the efficacy of anomaly detection algorithms and fostering interdisciplinary collaboration.
# SASLedger: A Secured, Accelerated Scalable Storage Solution for Distributed Ledger Systems
## 3 Background
### Hyperledger Fabric
#### 3.1.3 Storage Structure of A Peer  
The storage structure of an HLF peer comprises a chain of blocks and several local databases. In HLF, a block consists of three parts [21]:  
* Block Header comprises three fields
* Block Number is the sequence of the current block. The block number is counted from 0. The first block is called genesis block.
* Data Hash is the hash value of all the transactions that are recorded in the current block.
* Previous Hash is the Data Hash of its previous block.  
Figure 1: The structure of Hyperledger Fabric network. The Ordering Service Nodes (OSN) order blocks and deliver ordered blocks to each organization (Org). Each organization contains several peers.  
* _Block Data_ contains the ordered transactions. The transaction creators' and the endorsers' signatures are attached with each transaction. The count of transactions stored in a block is called the \"block size\" which can be configured before the network is started.
* _Block Metadata_ contains the validation codes used to verify transactions and the signature of the block creator.  
The blocks are chained together through the \"Previous hash\", and they are stored in the file system as block files. The size of block files is configured before the network is started so that the number of blocks that each block file contains is determined.  
The blocks preserve all original information pertaining to each transaction, but for data retrieval, local databases are needed to provide the necessary functionality:  
* _StateDB_ records the newest status of all the objects defined in the applications of the blockchain system. The status is recorded as \(<\)Key, Value\(>\) pairs. It can be chosen that whether CouchDB [40] or LevelDB [41] is used as the state DB before the HLF network is started.
* _IndexDB_ indicates where to find each block and transaction in the block files. It records the sequence number of the block file retaining the indexed blocks and transactions and offsets of those blocks and transactions.
* _HistoryDB_ can be used to track each change for each key. The sequence numbers of key-related blocks and transactions are recorded.
## 5 Discussion
### Implications and Future Research  
The limitations of this study and potential future research avenues primarily encompass the following aspects: Firstly, a sentiment analysis might not be sufficiently precise. The sentence-level sentiment analysis method we employed assumes that the sentiment expressed in a sentence corresponds to the attributes mentioned within it. However, this approach can be limited when a sentence contains multiple attributes and varying emotions. Future research should focus on employing more fine-grained sentiment analysis methods, such as aspect-level sentiment analysis, to enhance the precision of sentiment analysis. Second, the way we determine costs is not necessarily accurate. As our attribute improvement costs are determined through the voting of six experts, this sample size may not be sufficient and introduces a degree of subjectivity, potentially leading to inaccuracies in cost judgments. Therefore, a comprehensive and thorough investigation into the costs associated with course improvements is crucial. Thirdly, we have employed only one method for multi-attribute decision-making, namely TOPSIS. However, there may be other, more suitable methods available. It is imperative to explore and discuss whether other multiple-attribute decision-making techniques might outperform TOPSIS in certain scenarios. Fourthly, our experiments were limited to only two courses: C language and Java language. Consequently, the experimental results obtained may not be universally applicable. To validate the generalizability of our method, it is necessary to apply it to courses across diverse fields.
## 2 Overview of Autonomous Vehicles (AVs)  
The reviewed research papers delve into the various aspects of autonomous vehicles (AVs), ranging from sensor technologies to user acceptance. [PERSON] et al. provided insights into the roles of RADAR, LiDAR, ultrasonic cameras, and GNSS sensors in AVs and highlighted their performance under different weather conditions [15]. [PERSON] et al. comprehensively evaluated the technologies integral to AVs, encompassing environment detection, pedestrian identification, path planning, motion control, and cyber security [14]. [PERSON] et al. underscored the criticality of testing deep-neural-network-driven autonomous cars to expose and rectify behaviors that could trigger potentially fatal incidents [16]. Lastly, [PERSON] et al. investigated the multitude of factors influencing public acceptance of AVs, including perceived ease of use, attitude, social norms, trust, perceived usefulness, risk perception, compatibility, safety, performance-to-price value, mobility, symbolic value, and environmental friendliness [17]. In essence, these studies present a holistic understanding of the complexities, technologies, and societal factors contributing to the development and adoption of autonomous vehicles.  
In terms of an overview, autonomous vehicles represent the convergence of advanced technologies aimed at revolutionizing transportation. These vehicles leverage a spectrum of sensors and artificial intelligence to perceive the surrounding environment and make independent decisions. From enhancing road safety by minimizing human error to providing mobility for individuals unable to operate traditional vehicles, AVs hold significant promise. However, their successful integration into everyday life hinges on overcoming various challenges, including robustness against diverse weather conditions, foolproof cyber security, and public acceptance. As they stand on the precipice of widespread adoption, autonomous vehicles represent a transformative potential that could redefine the landscape of transportation.  
Figure 1: Autonomous vehicle overview.