esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
However, sensor nodes are uniformly deployed and this state is difficult to maintain as nodes move with ocean tides. The proposed RAR and RACAA schemes are different and unique from the existing schemes in that they both consider the connectivity of the complete path to water surface in routing packets. In addition, they both calculate the probability of successfully advancing packets over a route under consideration for routing. These strategies collectively enhance the packets delivery to sea surface. Furthermore, RACAA also adds cooperative routing to packets delivery. In this type of routing, a destination gets multiple data copies: from an original data broadcasting node and an intermediate relay node. The destination then combines these packets to extract the meaningful data.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 2 Study Area and Data  
### Study Area  
The study area is located in Anji County, Zhejiang Province, China (Figure 1), with a total area of approximately 1900 square kilometers, situated between 119\({}^{\circ}\)14\({}^{\prime}\)-119\({}^{\circ}\)53\({}^{\prime}\) east longitude and 30\({}^{\circ}\)23\({}^{\prime}\)-30\({}^{\circ}\)53\({}^{\prime}\) north latitude. Zhejiang Province is one of China's major tea-producing regions, with a tea planting area exceeding 200,000 hectares, serving as one of the significant sources of income for local farmers. Anji County features a subtropical monsoon climate, with an average annual temperature of 16.1 \({}^{\circ}\)C, annual precipitation of 1423.4 mm, and annual sunshine duration of 1771.7 h. The climate in the study area is variable and susceptible to the influence of cloudy and rainy weather, which limits the extraction of tea planting areas based on satellite remote sensing. Anji stands out as a distinguished producer of white tea within Zhejiang Province, benefiting from its acidic red soil, which furnishes an optimal growth milieu for tea plants. Garnering recognition as the cradle of Chinese white tea, Anji's tea industry witnessed an annual output value surpassing 1.33 billion yuan by 2019 [27]. Serving as a vital revenue stream for local farmers, the tea industry underscores the importance of timely and precise information on tea plantation areas, crucial for augmenting farmers' income and fostering agricultural advancement. Tea cultivation within the study area is predominantly managed by small-scale farmers, concentrated in the central and northern hilly and mountainous terrains, notably in towns like Dipu, Meixi, and Tianhuangping. The picking period of Anji white tea generally startsfrom early March to mid-April and lasts for 15 to 20 days. After the picking is completed, Anji white tea is often pruned heavily, and the tea tree is pruned more than 20 cm above the ground. This allows the branches to store nutrients more fully, and then in the new branches that grow before the next picking, the concentrated nutrients are used for the growth of new leaves, thereby improving the quality and yield of the tea leaves.
## 3 Wireless Sensor Networks
### Bluetooth vs. ZigBee  
Table 1 provides a comparison between ZigBee and Bluetooth. For applications where higher data rates are important, Bluetooth clearly has the advantage since it can support a wider range of traffic types than ZigBee. However, the power consumption in a sensor network is of primary importance and it should be extremely low [28]. Bluetooth is probably the closest peer to WSNs, but its power consumption has been of secondary importance in its design. Bluetooth is therefore not suitable for applications that require ultra-low power consumption; turning on and off consumes a great deal of energy. In contrast, the ZigBee protocol places primary importance on power management; it was developed for low power consumption and years of battery life. Bluetooth devices have lower battery life compared to ZigBee, as a result of the processing and protocol management overhead which is required for ad hoc networking [28, 34]. Also, ZigBee provides higher network flexibility than Bluetooth, allowing different topologies. ZigBee allows a larger number of nodes - more than 65,000- according to specification. Thus, the suitability of ZigBee for monitoring in agriculture and food industry has been proposed by various authors [14,28,34-36].
# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 2 Methods
### Hierarchical Encoder-Decoder Architecture  
The overall network follows whole follows a coder-decoder architecture and contains two encoders and one decoder (denoted green, orange, and blue in Figure 1, respectively).  
The two encoders are a pair of twin neural network branches that share the same network structure weights, which are used for feature extraction on remote sensing images \(X_{1}\) and \(X_{2}\), respectively. The encoders use the ResNet-18 network [28], where the last three layers used for classification are removed. Each encoder branch consists of five stages that output five levels of features \(F_{i}^{i}\), where \(t\) corresponds to two moments of the images \(X_{1}\) and \(X_{2}\), and the resolution sizes of the features are \(C_{i}\times(H/2^{i-1})\times(W/2^{i-1})\), and the number of feature channels for each level is \(\{C_{1},C_{2},C_{3},C_{4},C_{5}\}=\{64,64,128,256,512\}\), respectively. In this way, the two encoders are able to extract features independently for bi-temporal data, and each sample pair in a multi-temporal image can be represented by hierarchical features.  
The expressive ability of a CNN model has a close relationship with its receptive field. Usually, shallow networks with small receptive fields can meet the localization needs of fine targets, and large-scale targets need deep networks to provide large receptive fields for localization. A single receptive field makes it difficult to meet the needs of different targets. In order to be able to accurately identify the changes of different scale targets, EGHNet connects the features extracted by the encoder with the features generated by the decoder by jump connection which maximally strengthens the multi-scale feature characterization ability of the building change detection network. Each layer of features in the decoder consists of two parts: one part is the features extracted by the encoder, which uses the edge-guided module to increase the weight of the target edges in the features, and the other part is the features with the same resolution obtained from the up-sampling in the decoder. These two parts of the features are jump-joined, spliced in channel dimension, and input to the next level after passing through a convolutional layer. The output layer of the network is normalized by a Softmax layer, and the final output is a change map corresponding to the two remotely sensed images.
## 2 Related Work
### Game Theory and Multipath Transmission for Network Resource Management  
In satellite networks, in addition to protocol stack optimization, optimization algorithms, and machine learning approaches, game theory and multipath transmission techniques are widely utilized to enhance resource allocation and network reliability. Game-theoretic models typically simulate the interactions among multiple participants (e.g., satellites or users) to derive optimal resource allocation strategies [24; 25]. For example, in [24], researchers developed a Nash-equilibrium-based model to address resource competition, achieving significant improvements in utilization efficiency and fairness. Such models effectively resolve resource conflicts in competitive environments, while optimizing overall network performance. Simultaneously, multipath transmission has emerged as a key technology for improving network fault tolerance. Multipath traffic scheduling algorithms mitigate the impact of single-path failures on performance by dynamically routing traffic through multiple paths [26; 27; 28]. For instance, in [26], a dynamic multipath routing algorithm was introduced, leveraging real-time network conditions to minimize packet loss and latency. Furthermore, multipath scheduling approaches actively select the optimal paths, to improve reliability and efficiency under varying network loads [29; 30; 31]. As an example, [PERSON] et al. [29] proposed a task-aware multipath scheduling framework that exploits path diversity, ensuring high reliability and reduced latency even in scenarios with significant traffic variability. These advancements collectively demonstrate the potential of game theory and multipath transmission in addressing the challenges of dynamic satellite network environments.  
However, game theory and multipath transmission methods face challenges in large-scale networks, often suffering from high computational complexity and slow convergence speeds. Additionally, balancing load among multiple paths and ensuring efficient scheduling under rapid topology changes remain significant challenges for effective resource management.
## 4 Results and Analysis
### Overview of Simulation Results
#### Key Performance Indicators  
To evaluate the success of the ONSSO framework, several key performance indicators (KPIs) were established. The MATLAB/Simulink simulations assessed the following KPIs:  
* **Latency**: the time taken to process user requests and allocate resources. This is measured in milliseconds (ms).
* **Throughput**: the amount of data successfully transmitted through the network. This is measured in megabits per second (Mbps).
* **Resource utilisation**: the efficiency of resource allocation relative to total available resources. This is expressed as a percentage.
## 2 Proposed Scheme
### Image Interpolation  
First, the encrypted image \(E\) is divided into non-overlapping 2 \(\times\) 2 blocks. Next, each 2 \(\times\) 2 block is interpolated to a 3 \(\times\) 3 interpolated block. Notably, the four original pixels in the 2 \(\times\) 2 block are rearranged to the four corners of the 3 \(\times\) 3 interpolated block. The mapping between each 2 \(\times\) 2 block and its corresponding 3 \(\times\) 3 interpolated block is illustrated in Figure 2. After all 2 \(\times\) 2 blocks in the original image have undergone the interpolation operation defined in Equation (4), the final interpolated image \(C\) is obtained.  
The Nearest Neighbor Mean Interpolation (NMI) technique is applied to generate interpolation image blocks. For boundary pixels, the neighboring pixel values are copied to fill the gap. For internal pixels, the average value of neighboring pixels is used. It is worth noting that in the interpolation image block shown in Figure 2, all original pixels \(O\)(0,0), \(O\)(0,1), \(O\)(1,0) and \(O\)(1,1) located in the interpolation image block will not be changed even during the embedding stage, while the interpolated pixels \(C\)(0,1), \(C\)(1,0), \(C\)(1,1), \(C\)(1,2), and \(C\)(2,1) will be influenced by the embedded secret information. The definitions for the five interpolated pixels are defined as  
\[\left\{\begin{aligned} C(0,\,j)=\frac{(O(0,\,j-1)+\,O(0,\,j))}{2} \\ C(i,\,0)=\frac{(O(i-1,\,0)+\,O(i,\,0))}{2}\\ C(n,\,j)=\frac{(O(n-1,\,j-1)+\,O(n-1,\,j))}{2}\\ C(i,\,m)=\frac{(O(i-1,\,m-1)+\,O(i,\,m-1))}{2}\\ C(i,\,j)=\frac{(O(i-1,\,j-1)+\,O(i-1,\,j)+\,O(i,\,j-1)+\,O(i,\,j))}{4} \end{aligned}\right. \tag{4}\]  
For ease of explanation, in the subsequent stages of data hiding and extraction, the four original pixels copied from the 2 \(\times\) 2 block within the interpolated image block will be uniformly represented as \(O\), while the remaining five interpolated pixels will be uniformly represented as \(C\).
# Optimization Strategies in Quantum Machine Learning: A Performance Analysis
## 2 Literature Review and Background
### Challenges and Limitations in Quantum Machine Learning Optimization  
Despite the promising advancements in quantum machine learning, several challenges and limitations persist, particularly in the context of optimization.  
1. Noise and Decoherence  
Noisy Intermediate-Scale Quantum (NISQ) devices are inherently prone to errors due to noise and decoherence. These factors can significantly impact the performance of optimizers, especially those relying on precise gradient evaluations. For example, ADAM's sensitivity to noise can lead to suboptimal convergence, as observed in our results. Future work should explore noise-resilient optimization techniques, such as error mitigation strategies and stochastic gradient descent estimation methods [26; 27].
2. Scalability  
While quantum computing holds the promise of scalability, current NISQ devices are limited in terms of their qubit count and coherence time. This limitation restricts the size and complexity of models that can be trained effectively. Optimizers like COBYLA, which require fewer iterations, are advantageous in this context but may not scale well to larger problems. Developing scalable optimization algorithms that balance computational efficiency and accuracy remains a critical area of research [28].
3. Hybrid Approaches  
Hybrid quantum-classical optimization approaches, such as VQAs, offer a practical solution for leveraging quantum resources in the NISQ era. However, the interplay between quantum and classical components introduces additional complexities. For instance, the choice of ansatz and optimizer must be carefully aligned to ensure effective parameter tuning. Future research should focus on designing adaptive hybrid frameworks that dynamically adjust to the problem's characteristics [28].  
Benchmarking and Standardization  
The lack of standardized benchmarks for evaluating quantum optimizers poses a significant challenge. Current studies often rely on custom datasets and metrics, making it difficult to compare results across different works. Establishing a unified benchmarking framework would facilitate more rigorous evaluations and accelerate progress in the field [29].
## 4 Research Findings on Identified Gaps
Previous studies have shown that there is a lack of information system research based on theoretical background for expert finding systems, such as developing user satisfaction and system success models. Additionally, there is a need for qualitative research in expertise seeking to explain how human-related factors influence the expert finding problem. There is a further need to develop novel expertise seeking models for particular tasks and domains. Finally, several factors affected the source selection in expertise seeking; the most frequently used factors are quality and accessibility, but their interactions are not yet understood. Many studies have investigated task- and seeker-related factors and determined the manner in which source accessibility and quality were affected by the various moderating factors. These factors moderate the effect of source quality and accessibility.
# Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate
## 5 Conclusions  
The present study evaluated the relative importance (weight) of different input variables influencing PR. Initially, different supervised ML approaches were compared aiming to identify the most accurate modeling approach. Five supervised ML techniques were selected and evaluated, i.e., KNN, SVM, NN, CART, and CHAID. These modeling approaches were selected because of their ability to handle continuous target variables. A variety of ML techniques were conducted to predict TBM PR using a database comprising of 209 datasets including the most important parameters on TBM PR. Performance of the five mentioned ML techniques was evaluated using a simple ranking technique and some performance indices, i.e., R\({}^{2}\), RMSE, VAF, and MAE. According to the obtained results, the KNN predictive model was identified as the optimum model. A total ranking value of 32 was obtained by the KNN model, while other techniques obtained ranking values of 25 (NN), 28 (SVM), 24 (CART), and 14 (CHAID). R\({}^{2}\), RMSE, VAF, and MAE values of (0.907, 0.204, 89.574, and 0.157) and (0.962, 0.116, 96.226, and 0.081) were recorded for the developed KNN, as the selected predictive model of this study. Besides, the results of KNN are enhanced concerning the previously published results in the field of TBM performance prediction. It can be concluded that the KNN predictive model should be applied in similar conditions to examine the capability of this technique in the field of geotechnical engineering. Furthermore, \"importance\" (weight) results of the various input variables show that USC and RPM are the most and the least important input variables, respectively, when applying the KNN model.  
Conceptualization, D.J.A.; data curation, M.M.T.; formal analysis, J.Z.; supervision, P.G.A.; writing--review & editing, H.X.  
This work is financially supported by the National Foundation of China (No. 41807259) and the Sheng Hua Lie Ying Program of Central South University.  
The authors declare no conflict of interest.
# Recent Advances in Spectroscopy Technology for Trace Analysis of Persistent Organic Pollutants
## 2 Applications of Spectroscopy Technology in Detection of POPs
### Some Considerations in Spectroscopy Technology Detection of POPs  
Conventional environmental analysis generally requires accurate determination and quantification of low-concentration chemical pollutants in complex surroundings. As a result, the development of spectroscopy technology should consider some specific detection properties, such as sensitivity, selectivity, and reproducibility. The above-mentioned spectroscopy technologies possess the potential to meet these requirements because of their unique features.  
Though each of these spectroscopy technologies have some defects and drawbacks, there are some notable achievements that have already put forward a series of effective strategies to address these problems. Some of the methodologies were as follows: (1) Novel nanostructures, which could offer large surface areas and great amounts of functionalized binding sites for targeted molecules, which could greatly enhance detection sensitivity. In addition, surface hydrophobicity of the substrates by chemical and physical methods can also effectively enrich the targeted molecules; (2) exquisite specificity and high affinity of antibody-antigen interactions lead to dramatic sensitivity enhancement and can reduce the detection limit of POPs to sub-nanomolar levels; (3) excellent reproducibility, which can validate the reliability and repeatability of the experimental results, benefits from well-ordered and uniform nanostructure and morphology of the substrates of optical sensors. Finally, the miniaturization of the optical instruments and the development of spectroscopy technology facilitates portability for field detection.
# A Review of Parallel Heterogeneous Computing Algorithms in Power Systems
## 3 PHC in Power Systems
### CPU Clusters, Fog and Cloud Computing, and FPGA
#### 3.2.4 Smart Grids  
Smart grids are the next application with PHC references [135; 136; 137; 138; 139; 140; 141; 142; 143; 144; 145; 146]. The authors in ref. [136] presented how cloud computing can manage the smart grid data measured from front-end intelligent devices. Cloud computing fits big data applications since it provides scalability, agility, and flexibility. A data security solution was proposed based on identity-based encryption, signature, and proxy re-encryption. In ref. [138], the day-ahead energy resource scheduling of a smart grid with high penetration of distributed generation and electrical vehicles was optimized to satisfy the demand for sensitive loads.  
The optimization was developed with a multi-objective model constituted by a PSO and a deterministic technique based on Mixed-Integer Linear Programming. The objective functions are the distributed generator energy production costs, the external suppliers' energy costs, demand response program costs, the energy storage system and electrical vehicle discharging costs, the non-supplied demand costs, and the generation curtailment power costs. Each optimization problem defined in the multi-objective model was solved in an independent computer core.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 4 Aggregation-Based Compressed Sensing
### Data Aggregations
#### 4.3.1 Formation of a Sparse Data Matrix  
As per the network model in the proposed framework, the forest region is divided into \"N\" regions. These regions are not of equal size, since the nodes are randomly deployed in the hilly region. For each region, an aggregator is chosen based on its transmission range. If there are \"N\" regions, there will be \"N\" aggregators. The representation for an nth aggregator is specified by Agg\({}_{n}\) where n \(\epsilon\)\(\phi\) Agg. Agg\({}_{n}\) gathers the sensor data from the deployed heterogeneous sensors in its region. Assume that there are \"m\" nodes in the region \"n\" and \"ith\" node senses the environmental data d\({}_{ni}\) and transmits to the Agg\({}_{n}\) where (0 \(\leq\) i \(\leq\) m)M.  
The sensor data are generated and transmitted with some specified limited interval time. For a time period \"t\", the data sent by the sensors to the aggregator node Agg\({}_{n}\) in the region are given by Equation (1).  
\[\mathrm{D}_{n}=[\mathrm{d}_{n1},\ \mathrm{d}_{n2},\ \mathrm{d}_{n3},\ldots \ldots\ldots\ldots\mathrm{d}_{nM}]^{\mathrm{T}} \tag{1}\]  
where  
\[\mathrm{D}_{n}\ \in\ \mathbb{R}(\mathrm{M}\times 1)\]  
\(\mathrm{D}_{n}\) is M-dimensional real-time data. The data collected from the sensors with respect to time are not sparse at all. The sparse basis is calculated for the sensed data as given in Equation (2).  
\[\mathrm{D}_{n}=\mathrm{I}_{n}\times\delta_{n} \tag{2}\]  
\[\mathrm{D}_{n}=\ \left[\ \mathrm{I}_{n1}\ \ \ \mathrm{I}_{n2}\ \ldots\ldots\ \ \ \mathrm{I}_{nM}\ \right]\ \left[\ \delta_{n1}\ \ \ \delta_{n2}\ldots\ldots\ldots\ \ \ \delta_{nN}\ \ \right]^{\mathrm{T}}\]  
where  
\[\delta_{n}=\mathrm{I}_{n}^{\mathrm{T}}\ \times\ \mathrm{D}_{n}\]
# Nano- and Micropatterning on Optical Fibers by Bottom-Up Approach: The Importance of Being Ordered
## 3 Bottom-Up Fabrication of Periodic Structures on OFs
### Colloidal Self-Assembly
From the morphological images, the degree of order was limited to a short range. Nonetheless, the fabricated fiber optic samples were successfully employed as a SERS probe and label-free biosensor. The same authors exploited this technique to create a Pd pattern of CPA-SR around a capillary tube for hydrogen sensing [60].