esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules
## 2 Literature Review  
FBE channel access has received less interest from researchers than LBE. Most existing papers evaluate FBE performance in long term evolution (LTE) license-assisted access (LAA) networks, the predecessor of NR-U, and typically in LTE LAA and Wi-Fi coexistence scenarios [19; 20; 21; 22; 23]. Both [PERSON] et al. [24] and [PERSON] et al. [25] evaluate the limits of FBE with Markov chains and conclude that the strict duty cycle scheme of FBE causes domination by the competing (Wi-Fi) devices. [PERSON] et al. [26] provide an analytical model of FBE to determine latency. [PERSON] et al. [27] consider a fully coordinated FBE and LBE approach, where each FBE node calculates its uplink/downlink ratio during idle periods and communicates this to a central controller, which calculates an appropriate frame structure for the connected FBE nodes. [PERSON] et al. [28] propose dynamic switching between LBE and FBE in which the transmitting nodes switch the channel access mechanisms based on frame priority. Furthermore, [PERSON] et al. [29] design an algorithm for the joint channel assignment problem and transmission duration optimization for FBE in Wi-Fi coexistence scenarios while [PERSON] et al. [17] design a machine-learning-based adaptive duty cycling mechanism for NR-U coexisting with other radio technologies. The challenges and opportunities of the available channel access mechanisms are summarized in [5]. In terms of stand-alone FBE analysis, [PERSON] et al. [30] and [PERSON] et al. [31] optimize FBE channel access considering power consumption. Furthermore, there have been proposals of FBE variants, e.g., discussed in 3 GPP meetings [32; 33; 34] and in the literature [35; 36].  
We distinguish ourselves from previous work by assessing the channel access performance of FBE in terms of channel utilization and fairness among contending devices, both for the standardized version of FBE and its variants. Furthermore, we provide a coexistence study between FBE variants that is also lacking in the literature.
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 3 Identifying Challenges in RSA: Literature Review
### Network and External Issues  
Early implementations of RSA services suffered from the network connection and limited cellular bandwidth [42]. Although cellular connection has improved over the years, the problem remains for indoor navigation [43], which could lead to large delays or breakdowns of video transmissions [13; 94; 111]. Additionally, an external factor such as low ambient light conditions at night causes the poor quality of the video feed.
## 1 Introduction
Their results show that BA converges faster than GA, while GA is more favorable in terms of repeatability of the algorithm. Also, [PERSON] et al. [20] compared particle swarm optimization (PSO) and ant colony optimization (ACO) using different criteria. The study found that PSO outperformed ACO in terms of quality of solutions, better convergence, and consistency. [PERSON] et al. [21] applied a modified particle swarm optimization (PSO) algorithm combined with a simulated annealing (SA) algorithm to derive solutions using the hybrid bi-level model and conventional multi-objective model for shelters location-allocation problems. The hybrid bi-level model was proven to be useful for optimal shelter allocation. As mentioned in the study by [PERSON] et al. [22], the multi-objective approaches are less used and more advanced algorithms are needed to solve many problems in DOM including evacuation. This paper aims to compare the performance of four metaheuristic algorithms extended from the standard algorithms of simulated annealing (archive multi-objective simulated annealing--AMOSA), artificial bee colony (multi-objective artificial bee colony--MOABC), genetic algorithm (non-dominated sorted genetic algorithm-II--NSGA-II), and particle swarm optimization (multi-objective version of standard particle swarm optimization--MSPSO) for evacuation planning. The four algorithms along with geographic information systems (GIS) are used to solve an urban evacuation problem on a study area in the city of Kigali, Rwanda. Their performance is evaluated based on effectiveness, efficiency, consistency, and computational time for each algorithm. The remainder of this paper is organized as follows: In Section 2 we review the metaheuristic algorithms and give an overview of the tested four algorithms; Section 3 describes the study area and data preparation; Section 4 explains the methodology used in this study; Section 5 presents the results and analysis, and Section 6 concludes the paper and provides future research directions.
## 4 Discussion
### Biochar Effects on Plant Growth  
The effects of biochar on plant growth could be positive, null and negative [6; 42; 43], depending on the types of biochar and the incorporation rates. Incorporating biochar made from woodchips of _Pinus densiflora_ and _Quercus acutissima_ and rice husk at 20% (by vol.) with growing media, _Zelkova serrata_ seedlings showed better performance than the control in a containerized production system; however, biochar made from crab shell had negative effects on seedling growth [44]. In this study, seedling biomass increased with the SBB (10-100%) and HB (10-50%) incorporation rate, which is slightly different from [PERSON]'s results [31]. Tomato plants from all biochar-amended mixes had significantly lower FDW (yield), basil plants from biochar-amended mixes (except for 10% SBB, 30% SBB and 50% SBB) had similar LDW (yield) to the control.  
Even though the effects of biochar on plant biomass can be variable [6; 42; 43], the effects of biochar on plant GI is more often positively reported [6; 27; 45]. The GI of plants can be an important parameter for landscape plants such as _Magnolia_, _Ilex_, _Lagerstroemia_ and other species [46]. Biochar has also been reported to have positive effects on some ornamental plant GIs, such as poinsettia, Easter lily and \"Firework\" _Gomphrena_[6; 27; 45]. In this study, even though seedlings grown in biochar-amended mixes (SBB-, 10%, 30% and 70% HB-amended) had significantly lower TDW than the control, after growing in CS1 for four weeks, plants from biochar-amended mixes (except 30% SBB for tomato, 10%, 30% SBB for basil) all had a similar GI to the control. As landscape plants need more time to grow from seedling to a marketable size, the biochar-amended mixes used in this study might be used more successfully for landscape plants seedling production. More biochar studies on landscape plants should be conducted in the future.  
Detailed studies on biochar-root interactions are few [47], but plant roots are the first contacting points to biochar particles. Plants with longer root length, larger surface area and more root tips may be able to obtain more nutrients and grow better [47; 48]. In this study, root length, surface area and the number of tips of seedlings grown in biochar-amended mixes (except for 50%HB-amended) were all shorter, smaller or less than those grown in the control, which can explain why seedlings grown in most biochar-amended mixes did not perform as well as the control.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 3 Methodology
### Benchmark Models  
#### 3.6.1 CNN 2D-1 Layer [21]  
This model employs a sequential Convolutional Neural Network architecture, starting with a 2D convolutional layer with 32 filters and a 2 \(\times\) 2-pixel kernel size. It uses Rectified Linear Unit (ReLU) activation to introduce non-linearity, processing input tensors of shape (3, 3, 2). The feature maps are then flattened into a one-dimensional vector, connecting to a fully connected layer with 16 hidden units that also use ReLU activation. The network ends with a dense layer of 2 units, applying Softmax activation to generate probability distributions for target classes. This design allows the network to capture hierarchical features through convolution while keeping a lightweight parameter count due to its limited filters and hidden units.  
Figure 8: Architecture of the feature fusion and classification head of the proposed model.
# Sulfur Development in the Water-Sediment System of the Algae Accumulation Embry Area in Lake Taihu
## 3 Results and Discussion
### Oxygen Profiles and Uptake Rates Across the SWI  
The OPD was higher during winter (January) and spring (April) (Figure 2) with a lower temperature and less blooming algae. The OPDs of all the sites were similar during the investigation (analysis A2 in Table S1, \(p=0.432\)), and site S5 had the highest OPD during the investigation. In January, the OPDs ofthe study sites were \(>\) 2 mm, with the highest OPD of 5 mm observed in S5. The size of OPDs at each site was as follows: S5 \(>\) S4 \(>\) S2 \(>\) S3 \(>\) S1. The OPDs decreased gradually from January to July. In July, the sediment cores of the sites S1, S2, and S3 were in totally anoxic conditions. The DO concentrations at sites S1, S2, and S3 were nearly 0 mg L\({}^{-1}\) across the SWI. The DO concentration at site S4 was less than 2 mg L\({}^{-1}\) and decreased sharply from the overlying water to the sediment, with the OPD being less than 2 mm. In October, the DO concentrations at sites S1, S2, S3, and S4 increased, while the OPDs were still low (\(<\) 2 mm). The OPD at S1 was still 0 mm in October. The OPDs at site S5 were higher in July and October, but were still \(<\) 2 mm. A significant difference (\(p\) = 0.018) in the OPDs between the seasons was discovered.  
The OURs were calculated according to the DO profiles. From January to October, the OURs at the study sites generally increased (Figure 3). The OURs for sites S1, S2, S3, and S4 were at or nearly 0 mg m\({}^{-3}\) s\({}^{-1}\) in July, because there was nearly no oxygen across the SWI (Figure 2). The increased OURs from January to October indicated that there was an increased demand for, and depletion of, oxygen across the SWI, with the increased temperature and algal blooming. During the highest algal blooming season (July and October), the excessive accumulation of algae led to the death and deposition of algal particles on the surface sediments [43], consuming oxygen quickly and leading to the depletion of oxygen across the SWI [23; 54]. Therefore, the OURs in October were much higher than those in January and April (Figure 3). The increased OURs and the associated depletion of oxygen across the SWI might influence the development of sulfur and other elements [25; 40].  
Figure 2: Dissolved oxygen (DO) profiles distributed across the sediment–water interface (SWI) during the field investigation period.
# Antioxidant and Antimicrobial Activity of Porcine Liver Hydrolyates Using Flavourzyme
## 2 Materials and Methods
### Antioxidant Activity
#### 2.5.2 Oxygen Radical Absorbance Capacity (ORAC)  
The ORAC method was assayed following [PERSON] et al. [30] with some adaptations. This assay relies on the measurement of fluorescence decay caused by the presence of oxidants, while the existence of antioxidants inhibits the loss of fluorescence. Reactions were performed in 75 mM phosphate buffer (pH 7.4), where the final volume of the reaction mixture was 200 \(\upmu\)L. The mixture of 150 \(\upmu\)L fluorescein (substrate) 0.8 \(\upmu\)M plus 25 \(\upmu\)L of properly dilute sample (antioxidant) were incorporated into the wells of 96-well microplate reader (FluoroNunc(tm) F96-MicroWell(tm) plate). They were quickly incubated for 30 min at 37 \({}^{\circ}\)C inside the fluorescence microplate reader. Then, 25 \(\upmu\)L of 184 mM AAPH (heat-labile azo-initiator compound), which undergoes thermal decomposition to generate peroxyl free radicals, was quickly added into the through the injectors of the fluorometer. Before each measurement, samples were stirred. Fluorescence was recorded using filters for an emission wavelength of 528 nm and an excitation wavelength of 485 nm. The ORAC values were calculated as mg Trolox Equivalent (TE)/g pork liver, considering the differences of areas under the fluorescence curve between samples/standard and blank (phosphate buffer).
## 5 Conclusions  
This study validated and optimized the performance of MODIS-derived GPP compared with EC-observed GPP at seasonal and annual time scales for the main arid ecosystems relying on flux networks constructed in arid and semi-arid ecosystems in China. Our study revealed that the current MODIS GPP products were significantly underestimated, as compared with the tower-observed GPP for most types of ecosystems in the arid region of China, especially the irrigated cropland and forest ecosystems, due to uncertainty of meteorological data and model parameters. Using ground-based meteorological data and updated land use data can improve GPP estimation. In addition to the light-use efficiency parameters, the temperature-limited stress factors and the VPD-limited factors also need to be recalibrated for ecosystems in arid regions. After using the proper model parameters, great improvements to the GPP model can be performed through a Bayesian approach. However, it is difficult to estimate GPP accurately in desert ecosystems because of the uncertainty of remote sensing vegetation products in arid regions. Hence, improvements in modelling GPP in desert ecosystems are needed in future studies. Moreover, this study implies that the current MODIS-derived GPP product requires further improvements to provide accurate monitoring of terrestrial ecosystem productivity in arid regions worldwide.  
[PERSON] and [PERSON] designed the research, analyzed data and prepared the manuscript. [PERSON] and [PERSON] provided suggestions to data analyses. All of the authors thoroughly reviewed and edited this paper.  
This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA20100104), the National Natural Science Foundation of China (grant numbers: 91425303, 41871250, 41601482, 41471448), and the China Postdoctoral Science Foundation funded project (Grant No. 2016T90960).  
We thank the two reviewers' insightful and helpful comments and suggestions to our manuscript. We are grateful to [PERSON] for the help of English editing of our revised manuscript and appreciate the availability of the MODIS data from the Oak Ridge National Laboratory Distributed Active Center (ORNL DAAC) website.  
The authors declare no conflict of interest.
## 5 Implementation of Multimedia Independent Multipath Routing Algorithms
### Algorithmic Implementation Details  
#### 5.2.1 Sink End Angle Allocation Algorithms  
In order to reduce the interference of wireless signals, the receiving angle range is allocated to each node according to the weight of source nodes, which makes the paths between nodes independent. First, we need to calculate the weight of each source node, the weight = traffic \(\times\) priority. Then, the angle range that nodes can allocate is equal to node weight/all node weight and \(\times\) 360. The formula is as follows:  
\[Q_{i} =P_{i}\times F_{i} \tag{29}\] \[Q_{total} =\sum\
olimits_{i=1}^{n}Q_{i}\] (30) \[Angle_{i} =\frac{Q_{i}}{Q_{total}}\times 360 \tag{31}\]  
where \(P_{i}\) is the priority of the \(i\)th node, \(F_{i}\) is the traffic of the \(i\)th node, \(Q_{i}\) is the weight of the \(i\)th node, \(Q_{total}\) is the sum of all node weights, \(Angle_{i}\) is the angle range allocated by the \(i\) th node.
## 1 Introduction  
Local differential privacy (LDP) [1; 2] combined with the deep generative models [3; 4] can simultaneously ensure the provable and controllable upper bound of information leakage and hold realistic utilities [5; 6] upstream of data processing. Therefore, we consider that this combination can be a realistic tool to anonymize and then share private medical images among researchers in the public domain. On the other hand, the total dimension of data modeled by deep generative models for images is typically limited to, equivalent to, or below 2,097,152 (see the section on related works for details). However, to diagnose patients with volumetric medical images, medical doctors usually want those images to have a resolution of at least \(256\times 256\times 256=16\),777,216 total dimensions. On the basis of these backgrounds, the purpose of the present study is to build a deep generative model that can learn volumetric images with state-of-the-art high-resolution (\(256\times 256\times 256\)) and sample from the probabilistic distribution of those images to apply LDP algorithms with it for high-resolution volumetric medical images. Specifically, we adopt diffusion models as the deep generative models because the diffusion models can divide the training problem for the probabilistic distribution of images to virtually infinite subproblems to enhance the power of expression. With reference to this division, the stochastic differential equations for the diffusion models [7] are an interesting topic; however, we do not handle those equations in the present study. More strictly, we adopt pixel-space diffusion models [3] because they can straightforwardly handle real images without encoders, as in generative adversarial networks (GANs) [4] and latent-space diffusion models [8], which is an important and favorable feature in LDP processing. Furthermore, a pair of an encoder and a decoder for the latent-space diffusion models requires a significantly large GPU memory when we scale up the models to high-resolution volumetric images. Finally, we not only show unconditional image generation from a pseudo-random noise with the proposed diffusion model, but also apply the proposed model to generate high-resolution LDP volumetric medical images from a real image. Because differential privacy (DP) provides provable privacy protection, we decided to concentrate on clarifying the utility of those generated images, including LDP-processed images with visual evaluations by three medical doctors. We adopt head magnetic resonance (MR) images taken at the University of Tokyo Hospital throughout the present study. In summary, this study enhances diffusion models to facilitate modeling high-resolution volumetric images, contributing to the construction of practical medical systems. Additionally, we apply this improved diffusion model to propose and validate a method for sharing medical images with guaranteed privacy.
## 2 Background and Related Work
### Cyber Security of Smart Health Domain
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{\begin{tabular}{c} **Survey (S) or** \\ **Research (R)** \\ \end{tabular} } & \multicolumn{2}{c}{**Employs Deception**} & \multirow{2}{*}{\begin{tabular}{c} **Scope of the Study** \\ **(Yes—, No—X)** \\ \end{tabular} } \\  & & & & & \\ \hline ([PERSON] et al., 2023), [1] & R & X & \begin{tabular}{c} Present a cybersecurity framework developed for \\ medical images in a smart healthcare system based on \\ cryptographic techniques. \\ \end{tabular} \\ \hline ([PERSON] et al., 2023), [2] & S & X & \begin{tabular}{c} The study discusses the challenges of implementing \\ an e-health system, particularly in terms of security \\ and privacy due to the large volume of sensitive \\ medical data involved. It examines the vulnerabilities \\ of traditional systems for exchanging medical data \\ and proposes blockchain technology as a \\ potential solution. \\ \end{tabular} \\ \hline ([PERSON] et al., 2019), [3] & S & X & \begin{tabular}{c} Smart health monitoring systems, which rely on \\ wireless networks, are highly vulnerable to various \\ cyberattacks. In this regard, this study focuses on \\ specific threats like denial of service, data snooping, \\ router attacks, and sensor tampering and analyzes the \\ impact of these attacks on health monitoring systems \\ and proposes countermeasures. \\ \end{tabular} \\ \hline ([PERSON] et al., 2022), [10] & R & ✓ & \begin{tabular}{c} The authors explore the use of honeypot technology to \\ enhance security awareness in medical device \\ networks. It specifically focuses on how honeypots \\ can detect attacks targeting medical devices by \\ analyzing sensor data collected during different \\ attack phases. \\ \end{tabular} \\ \hline ([PERSON] et al., 2021), [13] & S & X & \begin{tabular}{c} The study investigates the security and privacy \\ concerns associated with the MIoT. It analyzes current \\ vulnerabilities, potential attacks, and existing \\ countermeasures. Additionally, the study identifies \\ challenges in MIoT security and outlines future \\ research directions to address these issues. \\ \end{tabular} \\ \hline ([PERSON] et al., 2018), [14] & S & X & \begin{tabular}{c} The authors examine the security and privacy \\ challenges in the MIoT. It analyzes the current state of \\ MIoT security, identifies common attack methods, \\ proposes potential countermeasures and solutions, \\ and discusses future research directions to address \\ emerging threats. \\ \end{tabular} \\ \hline ([PERSON] et al., 2022), [15] & S & X & \
\begin{tabular}{c} The authors provide a review of security and privacy \\ concerns pertaining to MIoT. \\ \end{tabular} \\ \hline ([PERSON] and [PERSON], 2019), [16] & S & X &
\begin{tabular}{c} The authors provide a systematic survey on different \\ methodologies and approaches used by researchers to \\ implement secure smart health care. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Summarization of the recent related state-of-the-art.
# Utility Analysis about Log Data Anomaly Detection Based on Federated Learning
## 2 Related Works  
### Federated Learning  
Unlike existing artificial intelligence learning, federated learning does not directly collect user data in the central server; instead, learning proceeds in each user's environment. Then, the weights derived through learning in each user environment are transmitted to the central server, and the global model updates and aggregates the transmitted weights. Thus, data anonymity is guaranteed because the weights are transmitted rather than the data [8].  
Regarding the global model update method, two methods can be applied, federated stochastic gradient descent (FedSGD) and federated averaging (FedAVG). First, FedSGD collects parameters learned from one update communication from a central server, then calculates the average parameter values for all clients to update the global parameters, sending the results back to all local servers. Afterward, the corresponding process is repeated until the convergence condition of the corresponding parameter set is satisfied [9]. Although these methods are simple and computationally efficient, one local update must be reflected every time in a single learning process, and FedAVG has been proposed for this purpose. Unlike FedSGD, FedAVG can reduce global update time by delivering parameter values to servers after each local server repeatedly learns a certain number of rounds. Therefore, the experiments on federated learning were conducted by applying this method [10].  
Federated learning can be classified into horizontal federated learning, vertical federated learning, and federated transfer learning, in addition to the commonality that data is generated in the user environment and that it is coordinated and managed overall on a central server. Horizontal federated learning is when the same feature is used for more than one user dataset, and vertical federated learning is when the dataset is the same but the features used are different. Federated transfer learning is a technique that improves performance through similar learning methods due to a lack of commonality in both data and features [9]. The method applied to the model proposed in this paper is horizontal federated learning, as it extracts the same features for different log datasets from two or more local servers.