esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of NoSQL Document Databases: Couchbase, CouchDB, and MongoDB
###### Abstract  
NoSQL document databases emerged as an alternative to relational databases for managing large volumes of data. NoSQL document databases ensure big data storage and good query performance and are essential when the data scheme does not fit into the scheme of relational databases. They store their data in the form of documents and can handle unstructured, semi-structured, and structured data. This work evaluates the top three open-source NoSQL document databases: Couchbase, CouchDB, and MongoDB with Yahoo! Cloud Serving Benchmark (YCSB), which has become a standard for NoSQL database evaluation. The performance and scale-up of document databases are assessed using YCSB workloads with a different number of records and threads, where the runtime is measured for each database. In the experimental evaluation, we concluded that MongoDB is the database with the best runtime, except for the workload composed by scan operations. In addition, we identified CouchDB as the database with the best scale-up when varying the number of threads.  
NoSQL document databases; Yahoo! Cloud Serving Benchmark; Couchbase; CouchDB; MongoDB +
Footnote †: journal: _Algorithms_  
0000-0001-999-2299-  
scanning, and read-modify-write operations. With this benchmark, we aim to know the behavior of the database engine when scale-up by varying the number of threads and records by using a single node. We evaluated the three databases using an ordinary and limited personal computer rather than a specialized computer.  
Considering the runtime in the tests performed, MongoDB was the database with the best performance. However, MongoDB was the database with the worst runtime in the workload composed of scan operations. On the other hand, CouchDB was the database that showed the best scale-up with a variation in the number of threads, and it was the database with the best runtime with the scanning operation.  
The following points are the main contributions of this paper:  
* Analysis of the main characteristics of NoSQL document databases;
* Performance and scale-up evaluation of NoSQL document databases using YCSB benchmark;
* Study the impact of query execution time on the databases using a different number of threads and various records size.  
The rest of this paper is organized as follows: Section 2 reviews related work about NoSQL database evaluation. Section 3 describes the three NoSQL document databases used in this work. Section 4 explains the Yahoo! Cloud Serving Benchmark. Section 5 presents the experimental results of YCSB using the databases. Finally, Section 6 presents the conclusions and future work.
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 1 Introduction
### Organizations  
The remainder of this paper is organized as follows. The related works are demonstrated in Section 2. In Section 3, we introduce the system model. In Section 4, we formulate the problem by introducing the freshness and fidelity metric. Closed-form sub-optimal solution is given based on appropriate approximations in Section 5. Numerical results are provided in Section 6. Finally, conclusions are drawn in Section 7.
## 5 Attributes and Characteristics of FL
### Non-IID Data in FL
#### 5.8.1 Understanding Non-IID Data  
In an FL framework, non-IID data are characteristic of situations whereby the participating devices, whether clients or nodes, possess data distributions that are not a balanced representation of the broader population. Several factors can lead to this imbalance. For instance, different devices might adopt varying data collection procedures, reflecting the diversity and variability in devices. Further complicating factors include user-specific behaviors, which can create unique data environments. Challenges like device-specific characteristics and interactions could further skew the data.
## 1 Introduction
The endogenous melatonin content in fruits and vegetables exhibits considerable variation, ranging between 0.1 and 1000 ng\(\cdot\)g\({}^{-1}\), while exogenous melatonin demonstrates remarkable ability to regulate both climacter and non-climacteric fruit ripening through complex interactions with ethylene and abscisic acid signaling components [18]. Recent scientific investigations have particularly focused on melatonin's effectiveness in managing browning reactions during storage and on the shelf life of post-harvest horticultural products [19]. This versatile compound has shown exceptional promise in regulating various metabolites associated with improving nutritional quality, maintaining product integrity under diverse storage conditions, and preventing post-harvest diseases and decay through its potent antioxidant properties [20]. Furthermore, research since 2017 has increasingly concentrated on melatonin's role in enhancing fruit production and preserving post-harvest fruit quality, with numerous studies highlighting its potential as a natural preservation agent [21]. The role of melatonin in regulating post-harvest hormone levels in fruits and vegetables has been extensively documented in multiple studies [15; 16]. Notably, melatonin significantly delays fruit and vegetable senescence by inhibiting ethylene and abscisic acid accumulation [17; 18; 19]. The multifaceted biological functions of post-harvest melatonin are intricately linked to its interactions with reactive oxygen species, plant hormones, and other signaling molecules. Exogenous melatonin application enhances antioxidant defense systems, providing protection against oxidative damage and preserving post-harvest fruit energy. Its effect on maintaining fruit flesh firmness is primarily attributed to the inhibition of enzymes associated with cell wall and starch degradation. Furthermore, melatonin reduces fruit discoloration by inhibiting polyphenol oxidase enzyme activity [17; 18; 19; 20]. While extensive research exists documenting melatonin's effects on fresh fruits, including apricots, systematic studies examining its impact on processed fruits, particularly sun-dried apricots, remain notably limited. Given the significant economic importance of dried apricot production and the growing demand for natural preservation methods, this study aims to investigate the effects of melatonin application in dried apricot production, focusing on quality preservation and color retention throughout the drying process. The primary objectives include examining the reduction of ROS accumulation and analyzing changes in oxidative stress indicators (MDA and H2O2) during the drying process under melatonin treatment. Additionally, this paper evaluates the potential benefits of exogenous melatonin (N-acetyl-5-methoxytryptamine) application as a bio-stimulant in commercial-scale dried apricot production, including the optimization of treatment parameters and the assessment of long-term stability during storage.
# Label-Setting Algorithm for Multi-Destination \(K\) Simple Shortest Paths Problem and Application
## 3 Methodology  
Let \(G(N,A)\) be a directed network with a set of nodes \(N=\{1,2,3,\ldots,n\}\) and a set of links \(A=\{1,2,3,\ldots,m\}\) such that the number of nodes and number of links are \(n\) and \(m\), respectively. Links in \(A\) are ordered tuples of nodes such that \(A\subset N\times N\). A directed simple path from node \(s\in N\) to node \(t\in N\) is defined as a sequence of nodes \(\gamma_{s,t}=(u_{1}=s,u_{2},\ldots,u_{r}=t)\) with \((u_{l},u_{l+1})\in A\ \forall\ 0\leq l<r\), and every node along the path is visited exactly once, i.e., \(u_{l}\
eq u_{p}\ \forall\ 0\leq l<p\leq r\). Let \(C_{i,j}\) be the cost of the link \((i,j)\in A\). The link costs are assumed to be non-negative, i.e., \(C_{i,j}\geq 0\ \forall\ (i,j)\in A\). The cost of the path \(\gamma_{s,t}\), \(C(\gamma_{s,t})\), is defined as the sum of the cost of its constituent links such that \(C(\gamma_{s,t})=\sum_{(i,j)\in A}C_{i,j}\delta_{i,j}^{T_{s,t}}\), where \(\delta_{i,j}^{T_{s,t}}=1\) if link \((i,j)\) belongs to the simple path \(\gamma_{s,t}\) and \(0\) otherwise.
# An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning
## 4 Simulation Result
### One-Hop Simulation Result  
In the last section, we formulated the single-hop problem of IAB network planning for the random distribution of donors. To solve the formulated optimization problem, we adopted the Python-based Gurobi library, which has been shown to be efficient in previous location coverage problems [21].  
Figure 6 is a testimonial to the simulation's effectiveness. With nine donors, randomly positioned, the simulation strategically orchestrated the deployment of a mere eight additional nodes to achieve full grid coverage.  
To analyse the relationship between the number of donors and the number of feasible optimization models. We simultaneously monitor the accumulation of both feasible and infeasible optimization models until a total of 100 models is reached. In Figure 7, we illustrate the number of feasible models as the number of donors varies between 5 and 20. Feasible models representing the Gurobi optimizer can successfully find an optimal solution. The plot reveals a corresponding increase in the number of feasible models, highlighting the positive impact of the donor quantity on achieving practical solutions. As the number of donors increases, there is a greater likelihood of obtaining a feasible solution for the given coverage constraints and the number of donors required to cover the area, leading to a reduction in the marginal benefits delivered as the donor density increases further.  
The number of IAB nodes deployed when the number of donors increases from 5 to 20 is shown in Figure 8. The figure demonstrates a decline in the average nodes deployed as the quantity of donors escalates. The standard deviation around the mean indicates that the number of nodes deployed may be affected by factors including donor location. Furthermore, a deeper analysis of the results unveils a substantial increment in the total number of deployments, embodying both donors and nodes. Despite the surge in donors, the comprehensive number of deployments increases. This suggests that an increase in the number of donors does not necessarily deter the growth in the total deployments.  
Figure 6: Optimization one-hop deployment result on a 1000 m \(*\) 1000 m area with predefined donor positions, each donor/node’s coverage radius is 200 m, and the backhaul radius is 300 m.  
Figure 7: Number of feasible models of one-hop simulation for different numbers of donors.
## 2 Related Work
### Feature Fusion  
Incorporating multi-scale features is crucial for boosting performance in various tasks [38; 39]. In the CNN backbone networks, recent studies generally agree that lower-level features possess higher resolution and more detailed information, yet due to the limited number of convolutional layers, they contain less semantic information and more noise. Conversely, the higher-level features exhibit the opposite characteristics. The module in the Inception network consists of four parallel branches: 1 \(\times\) 1 convolution, 3 \(\times\) 3 convolution, 5 \(\times\) 5 convolution, and 3 \(\times\) 3 max pooling [40]. The output feature maps of the branches are concatenated along the channel dimension, combining information from multiple scales to enhance feature representation. Similarly, U-Net [41] and the feature pyramid networks (FPN) [42] approach involve pooling or upsampling features at various scales before prediction.  
There is a paucity of research on the issue of multi-scale feature fusion in continuous sign language recognition, despite its fundamental importance in sign language video understanding. There are still some studies that employ multi-modal data for feature fusion. [PERSON] et al. took RGB image and optical flow for hand regions as input to combine the appearance and motion information. The authors summed the two stream outputs generated by CNN modules for fusion [37]. On the other hand, a spatial multi-cue module in STMC was specifically designed for spatial representation and employs a self-contained pose estimation branch to explicitly decompose the visual features of different cues [7]. This approach also models and fuses each cue using sequence modeling, enriching the spatiotemporal information. The results demonstrate the benefits of feature fusion in learning sign language semantics. However, it is worth noting that current methods still have certain limitations. First, acquiring optical flow data can be challenging, and their accuracy is difficult to determine due to their sensitivity to noise [43; 44]. Second, the STMC method relies on a pose estimator for keypoint detection. However, in practice, it can be difficult to accurately detect keypoints in motion images, which can lead to errors [45; 46]. Therefore, it is necessary to explore more concise and reliable methods to address these issues.  
Based on previous research, we propose a spatial feature extraction module for multi-scale feature fusion. This module uses a Transformer encoder to process feature maps of different receptive fields and concatenate them, effectively combining low-level details with high-level semantic information. This not only enhances the model's ability to capture fine-grained features but also improves its understanding of complex semantic structures. This approach avoids the need for additional data and modules and enables end-to-end training.
## 3 Results and Discussion
### Impregnation Methodology
#### 3.1.2 Effect of the Amount of Leca in Contact with the TiO\({}_{2}\) Suspension  
In order to optimize the dispersion of TiO\({}_{2}\) suspension over the Leca surface, the impact of the Leca amount put in contact with TiO\({}_{2}\) suspension was evaluated. The TiO\({}_{2}\) suspension used consisted of 3.6%TiO\({}_{2}\) dispersed in ethanol, while the tested amounts of Leca were 10, 15, 20, and 30 g. [PERSON] et al. [25] used a suspension with the same TiO\({}_{2}\) amount in contact with 30 g of Leca to produce a catalyst for the ammonia abatement. The Leca amount is an important parameter since the suspension volume is constant. Figure 1a shows that the increase in the Leca amount leads to a decrease in the TiO\({}_{2}\) impregnated mass per gram of Leca. The best results were obtained using 10 g of Leca, where about 19 mg of TiO\({}_{2}\)/g of Leca was impregnated. The turbulence generated during the boiling of solvent improves the contact between Leca and TiO\({}_{2}\) suspension, enhancing the redistribution of TiO\({}_{2}\) along with the support particles. However, this process is more effective for the case of 10 g of Leca since all the particles are always in contact with TiO\({}_{2}\) suspension. For higher loads, some particles of Leca are not immersed in the TiO\({}_{2}\) suspension, which reduces the solid/liquid contact. In Figure 1b, the percentage of TiO\({}_{2}\) impregnated was evaluated as a function of the ratio of Leca mass per solvent volume for different amounts of Leca. The solid/liquid (S/L) ratio varied from 0.5 to 1.5 g/mL. At a 0.5 ratio, 34% of TiO\({}_{2}\) in the suspensions was impregnated, while 47% was verified in the case of 1.5 due to the higher number of Leca particles in contact with the suspension. Increasing the S/L ratio three times does not mean a consequent increase in the TiO\({}_{2}\) impregnation yield (Figure 1b). Consequently, the increase in the Leca load significantly decreases the TiO\({}_{2}\) impregnated weight per g of Leca (Figure 1a). Considering these results,the impregnation method that used 10 g of Leca in contact with the TiO\({}_{2}\) suspension was selected in the subsequent tests.
## 6 Experimental Evaluation
### Simulation of Device Heterogeneity  
To reflect realistic deployment scenarios, we simulated a heterogeneous mix of devices, including IoT devices, edge devices, and smartphones. Each device was modeled with varying levels of battery capacity and privacy sensitivity, based on typical characteristics observed in real-world applications. The simulation was conducted using Google Co  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **MNIST \& Fashion-MNIST** & **EMNIST** & **CIFAR-10** \\ \hline Optimizer & [PERSON] & Adam & [PERSON] \\ \hline Learning Rate & 0.001 & 0.001 & 0.001 \\ \hline Batch Size & 32 & 64 & 64 \\ \hline Epochs & 20 & 30 & 50 \\ \hline Convolution layer 1 & 32 filters, 3 \(\times\) 3, ReLU & 32 filters, 3 \(\times\) 3, ReLU \\ \hline Convolution layer 2 & 64 filters, 3 \(\times\) 3, ReLU & 64 filters, 3 \(\times\) 3, ReLU & 64 filters, 3 \(\times\) 3, ReLU \\ \hline Convolution layer 3 & — & 128 filters, 3 \(\times\) 3, ReLU & 128 filters, 3 \(\times\) 3, ReLU \\ \hline Fully connected layer & 128 units, ReLU & 256 units, ReLU & 512 units, ReLU \\ \hline Output layer & 10 units (softmax) & 62 units (softmax) & 10 units (softmax) \\ \hline Loss function & Categorical crossentropy & Categorical crossentropy & Categorical crossentropy \\ \hline \hline \end{tabular}
\end{table}
Table 5: Key model parameters used across different datasets.  
lab, which provides virtual machines equipped with NVIDIA Tesla T4 GPUs, Intel Xeon CPUs, and approximately 12.7 GB of RAM. The FL experiments employed Flower (Flwr) version 1.19.0, an open-source framework for FL. The environment ran Python 3.8 with TensorFlow 2.12 for model training and evaluation. Additional libraries such as NumPy 1.23.5 and Matplotlib 3.7.1 were used for data processing and visualization. This setup enabled efficient simulation of client-server interactions and dynamic training rounds under realistic conditions. In each simulation run, and for every tested client pool size (e.g., 10, 25, 50, etc.), we ensured that the selected clients were a mix of different device types. This heterogeneous grouping was meant to reflect real-world scenarios where various devices with different capabilities participate in training. The mixture of devices in each pool allowed us to evaluate how battery levels, privacy sensitivity, and contribution to accuracy influence participation and overall training performance. The participation behavior of clients determined by their profile directly impacted the global model's accuracy, average battery consumption, and privacy loss.
## 3 Monitoring Results and Analysis
### Comparison of GSS with Different Pre-Supports  
Section DK70+829 with a buried depth of 16 m and section DK70+869 with a buried depth of 12.4 m were selected as the representative sections for the pipe roof and the leading ductule sections. The arch settlement, horizontal convergence, and GSS at the tunnel centerline of section DK70+829 were 31.7 mm, 17.4 mm, and 43.1 mm, respectively. These corresponding results for section DK70+869 were 32 mm, 17.7 mm, and 43.2 mm, respectively. Comparing the results, it can be concluded that the arch settlement and the GSS at the tunnel centerline of section DK70+869 were slightly larger than these values for section DK70+829. The GSS values at the tunnel centerline were almost equal in the two sections, while the buried depth of section DK70+869 was less than that of the DK70+829 section. According to stochastic medium theory [23], for unlined shallow-buried tunnel, the GSS at the tunnel centerline is positively correlated with tunnel displacement, while it is negatively correlated with the tunnel buried depth. This suggests that the reinforcement circle formed by the pipe roof provides a greater effect on hindering the deformation development of the upper ground.
# Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC-WPT System
## 3 System Model
### Energy Harvesting Model  
The multi-antenna AP in the multi-user MEC system can use the energy beamforming to transfer power to the MD through WPT technology, and the single antenna of the MD can receive the energy transmitted by the AP to charge its battery. Although the current energy harvesting technology is not very mature, we consider the relatively sufficient battery power. Therefore, we assume that MDs have sufficient battery capacity to store harvested energy. Then the energy collected by the MD during energy harvesting is:  
\[E_{i,j}=v_{j}p_{i,j}^{a}h_{i,j}^{a}t_{i}^{har}\quad i\in\mathbf{N},j\in\mathbf{M} \tag{6}\]  
where \(0<v_{j}<1\) represents energy conversion efficiency, \(h_{i,j}^{a}\) represents the channel gain of the downlink channel from the AP to the \(j\)-th MD and is a constant during the harvest time, \(p_{i,j}^{a}\) represents the AP transmission power, \(t_{i}^{har}\) represents the energy harvesting time of the MD in the \(i\)-th time block and the energy harvesting time of the MDs in this time block is equal.  
According to our model, the MD harvests energy first, and then offloads the task during the rest of the block to meet the time constraint of time \(T\) in each time block. In our system, multiple MDs perform energy harvesting and task-offloading in each time block. Therefore, the energy harvesting time and task-offloading time must meet the following constraints:  
\[t_{i}^{har}+\sum_{j=1}^{M}t_{i,j}^{off}C_{j}\leq T\quad i\in\mathbf{N} \tag{7}\]  
The above are the three aspects of the system model for which we have carried out a detailed description and explanation.
# Segmentation of Liver Tumor in CT Scan Using ResU-Net
## 3 Material and Method
### Image Normalization  
Image intensity normalisation is a technique for reducing inter-patient variability in the intensity distribution of images in a dataset [30]. It will also increase patient image comparability and result in improved segmentation findings. As a result, liver CT scan results are subjected to intensity normalisation.
## 4 Results and Analysis
### Performance of GPP Estimation Results Based on Flux Tower-Based GPP
The data used here are easier to obtain than those required by the multilayer GPP estimation method, and the performance results were also better than those when not considering vertical stratification. Modeled GPP and flux tower-based GPP values still differ to some degree, as illustrated in Figure 10. On a daily scale, the multilayer GPP model has a lower mean bias than the single-layer GPP model. Figure 10b demonstrates that the multilayer method performs well during the growing season (DOY 180 to 280) when the understory LAI is large. On some days when the shortwave incident radiation is high, GPPm was overestimated because the understory PAR was overestimated, but GPPm  
Figure 9: Differences in GPP estimations in different seasons. Subfigure (**A**) showed the seasonal GPP variance in DHS, while subfigure (**B**) showed the condition in TMK. Here, we used three different types of LUEmax parametric approaches to model GPP using [PERSON]’s method. GPPmW is the multilayer result using two or three different LUEmax inputs for GPP estimation. GPPsW is the single-layer result using an EBF-based LUEmax. GPPPaW is the average LUEmax input in GPPmW, using the average LUEmax for the different components. showed little variance when the understory LAI was low (Figure 10a,b DOY 290 to 360). Most of the results estimated by GPPs were lower than the GPP measured by the flux towers because the understory LUEmax was underestimated. Therefore, the GPP estimation method that considered vertical structure would be useful when the understory LAI is high (>1 m\({}^{2}\)/m\({}^{2}\)). Generally, adding the understory vegetation GPP to the total GPP reduces the underestimation when the understory LAI is high.