esa-sceva/satcom-chunk-collection huggingface dataset:
# DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking
## 3 Proposed Framework
### Attention Network  
In practical applications, watermarking algorithms are required to have good imperceptibility and robustness. To achieve this goal, we wanted to find regions for watermark embedding that had little impact on the HVS and focus on watermark features during watermark extraction, so an attention network was introduced. This intuition was derived from the classic attention mechanism CBAM [18], which sequentially infers attention values along both channel and spatial dimensions and generates an attention feature map.  
Figure 2 shows the flow chart of the attention network. The input is the cover image with a size of \(M\times N\); through a \(3\times 3\) convolution, the cover feature with 64 channels is generated. Then, features are extracted by max-pooling and average pooling operations on the cover feature map respectively, and then they are input into a fully connected (FC) layer to generate attention eigenvalue vectors. The two vectors are summed element by element to obtain the channel attention weight parameters, which are multiplied by the cover feature map to obtain the channel attention feature map. The spatial relationship of the channel attention feature map is extracted by max-pooling and average pooling layers. Then, the features are connected by a \(3\times 3\) standard convolution layers to generate spatial attention weights. Multiply the spatial attention weights with the channel attention feature map to generate an attention feature map with a size of \(M\times N\times 64\).  
Although the network structure is only slightly different from CBAM, the purpose of the implementation is very different. CBAM is used to locate salient regions of an image. In contrast, the proposed attention network is used to locate nonsignificant regions that are insensitive to the human eye during watermark embedding and to assign greater weight to them, selecting such regions to embed the watermark to improve imperceptibility. When extracting the watermark, this attention network is used to focus on the features of the watermark and suppress noise signals in the attacked image to improve robustness.  
Figure 1: Flow chart of DARI-Mark.  
Figure 2: Attention network: preprocessing of the cover image.
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 4 Adaptive GLAN Using Reinforcement Learning
### Reinforcement Learning in AGLAN with aDQN  
Figure 7 depicts the structure of \(aDQN\) in our system. When the UAV swarm delivers geographic location and data communication information to the server, the server creates a simulation environment based on the information and covers the expected UAV mobility by giving changes less than the mobility size to all UAVs at every episode. The proposed system provides an optimized zone over the existing forwarding region through learning. In \(DQN\), overestimation problems often occur when the action value becomes excessively large during learning. To prevent this, in this paper, an estimation function called the pseudo-attention layer is added to give a relative value to each action through the evaluation of the action in the current state. This pseudo-attention serves to direct the routing closer to the destination. Even considering random mobility from the source UAV to the destination UAV, we reduce the reward weight for actions that overload the network. Through this process, we can learn at a relatively fast speed by reducing the learning that was overestimated.  
Algorithm 2 is the integrated pseudocode of \(aDQN\) including experience replay and the target network. A buffer called replay memory is created to store samples generated at each step, and randomly extracted samples are used for Q-update learning. By using experience replay, data efficiency can be increased as one sample can be used for multiple model updates, and the update variance can be reduced by removing sample correlation by randomly extracting data. In addition, the behavior policy is averaged to suppress the oscillation and divergence of parameters during learning, increasing the learning stability. It replicates the existing Q-network identically to create a dual structure of the main Q-network and target network. By having a dual network structure through the target network, learning instability due to the moving target value is improved. The Q-network is used to obtain the action value Q, which is the result value, using the state and action, and the parameters are updated at every step. The target network is used to obtain a target value, which is the reference value for an update. Since the target value network is also parameterized with \(\theta\), as with the main Q-network, it is not updated every time, but the model is updated in the desired direction in synchronization with the main network at every c step.  
Figure 7: \(aDQN\) target network structure of AGLAN system.
# Internet of Things in Marine Environment Monitoring: A Review
## 2 Overview of IoT in Marine Environment Monitoring  
This section provides an overview of IoT in marine environment monitoring, including various applications, common system architectures, typical sensing nodes and sensing parameters, and related wireless communication technologies.
## 4 Proposed Solution
### Network Model  
The considered MANET consists of nodes having similar features such as transmission range, pause time and mobility in terms of m/s. The nodes can roam randomly or remain stationary in a location for an arbitrary period of time. The nodes perform in a cooperative fashion using peer-to-peer communication over the shared bandwidth-constrained and error-prone wireless channel. All the nodes in the network have bi-directional links. In order to have ease of identification, all nodes are assumed to have a unique non-zero node ID. Unlike some existing security frameworks, in the proposed solution, the network does not contain any CA, but all nodes perform extra functions such as key sharing and maintaining backup queues in order to ensure security and high performance. With the presence of a CA, the network may have a single point of failure. To demonstrate the attack, there may exist a varying number of blackhole nodes in the network at different locations.
# Coordinating Entrainment Phenomena: Robot Conversation Strategy for Object Recognition
## 5 Experiment 1
### Results
#### 5.2.3 Change of Referencing Style through Interaction  
We proposed the robot's confirmation behavior to entrain the redundant reference based on the consideration the humans' desirable reference behavior to improve the performance of indicated object recognition is the redundant reference, that is the reference behavior with the speech that contains as much information as possible that is valuable to identify the object and with pointing gestures as described in Section 2.4. To verify whether the frequency of the such redundant reference depending on the robot's confirmation behavior, we analyzed the humans reference behavior from the point of view of reference redundancy of speech and with or without pointing gestures.  
The reference redundancy of speech is defined as the difference between the number of object attributes in the participant's references and the minimum number of attributes required to uniquely identify the indicated objects in the environment. The objects we used in the experiment have three attributes: color, symbol, and letter. Hence, the number of attributes for the object in the participant's references was defined as 0-3. For example, if the participant did not referred to any of the attributes, the number of object attributes is 0, while the participant referred to all of the attributes (i.e., color, symbol, and letter), the number of object attributes is 3. In the environment, the minimum number of attributes required to uniquely identify the indicated object ranges from 1 to 3. Accordingly, reference redundancy ranges from \(-\)3 to 2. Suppose a participant refers to a book with no attributes (e.g., \"that book\" or \"this book\") in the environment where all of the attributes are needed to uniquely identify objects. In this case, minimum number of attributes is 3 and, thus, the reference redundancy is \(-\)3. Likewise, if a participant refers to a book with all three attributes in the environment where there is one red book and only color is needed to uniquely identify the object, the minimum number of attributes is 1, and the reference redundancy is 2.  
The redundant reference is defined as the reference that of reference redundancy of speech is more than 0 and with a pointing gesture, and we measured the rate of the redundant reference per sessions, that is 10 object reference conversations. Figure 13 shows the results. We conducted a two-factor repeated measure ANOVA and main effects of the confirmation factor were close to significance (F(1,23) = 3.641, \(p=0.069\), \(\eta_{\rm p}^{2}=0.137\)). No effect was observed in main effects of the arrangement factor (F(2,46) = 0.727, \(p=0.489\), \(\eta_{\rm p}^{2}=0.031\)) and interaction between confirmation \(\times\) arrangement factor (F(2,46) = 0.312, \(p=0.734\), \(\eta_{\rm p}^{2}=0.013\)). These results suggest the trend that if the robot confirms an indicated object with minimum information to identify the object by following the proposed method, humans tend to use the redundant reference.
## 2 Study Area and Data  
### Study Area  
The study area is South Korea where more than 70% of the land is forested areas (Figure 1). While the mean altitude is 448 m, the mean slope is 5.7\({}^{\circ}\), which is about 2\({}^{\circ}\) steeper than the overall average of 3.9\({}^{\circ}\) in East Asia including Japan, the east part of Mongolia, and China [29]. Most forest fire cases have occurred in the spring season when the weather is dry and warm, and hikers frequently visit forested areas. On the other hand, the number of forest fire occurrences in summer is very low due to the highly concentrated precipitation and relatively high humidity. Among the forest fire events that occurred from 2010 to 2019, 58% occurred in spring, followed by winter (23%) [30]. The frequency and intensity of forest fires continue to increase as on-going climate change tends to increase extreme weather conditions [30].  
Figure 1: The study area of South Korea with (**a**) elevation and (**b**) land cover data. The elevation data come from ALOS World 3D 30m v3.1 [31]. The land cover data were provided by the Ministry of Environment [32].
# LogRESP-Agent: A Recursive AI Framework for Context-Aware Log Anomaly Detection and TTP Analysis
## 2 Related Works
### LLM-Based AI Agent  
The rapid advancement of Large Language Models (LLMs) has opened new avenues for intelligent log analysis. Unlike traditional methods, LLM-based agents can support contextual reasoning, tool coordination, and natural language explanation, offering the potential for more adaptive and interpretable security workflows. Recent research has leveraged these capabilities to automate tasks such as anomaly detection, threat triage, and incident response.  
However, existing systems often rely on rigid, prompt-driven execution flows and lack the autonomy required for complex investigations. For example, IDS-Agent [26] combines LLM reasoning with external tools for intrusion detection, but its pipeline follows a fixed sequence and cannot revise its course mid-execution. Similarly, Security Event Response Copilot (SERC) [27] integrates Retrieval-Augmented Generation (RAG) into SIEM, enriching alerts with external intelligence--but only in response to predefined events, without proactive detection or recursive reasoning.  
A more advanced design is found in Audit-LLM [39], which distributes analytical tasks across multiple agents (e.g., goal decomposers, script generators, executors). While this supports limited iteration, the overall flow remains pre-structured, and agent behaviors are tightly scoped. These architectures cannot dynamically restructure goals, reselect tools, or adapt to ambiguous or evolving evidence without human intervention.  
Overall, these approaches demonstrate the potential of LLM agents in automating security analysis but remain limited by single-pass logic, rigid workflows, and a lack of self-directed reasoning. They struggle with heterogeneous, semi-structured logs where threat indicators are fragmented or ambiguous. As highlighted in recent studies [23; 25], current agents lack the ability to adapt mid-process or refine analytical goals based on evolving context. While meta-reasoning frameworks such as Reflexion [40] and AgentBench [41] offer promising architectures for recursive planning and self-directed reasoning in open-ended tasks, they are not designed for security log analysis and lack integration with domain-specific tools and knowledge bases.  
To address these gaps, we introduce LogRESP-Agent--an LLM-based framework that supports recursive reasoning, dynamic tool coordination, and multi-step threat interpretation through a Thought-Action-Observation loop. Unlike prior models, it autonomously adapts to new evidence during analysis, enabling scalable and interpretable log investigations in complex environments. A comparative summary of key architectural characteristics across related models is presented in Table 1 to highlight the broader system-level distinctions of LogRESP-Agent.
# Assessment of MicroPET Image Quality Based on Reconstruction Methods and Post-Filtering
## 2 Materials and Methods
### Phantom Imaging and Measurement Methods  
A mouse image quality phantom (NEMA NU-4) offered image resolution, uniformity, and contrast information for microPET imaging. The NEMA NU-4 image was acquired for 20 min with a 3.7 MBq (100 Ci) 18F solution. In the phantom image, microPET performance was assessed via image accuracy, RC, uniformity, and SOR values. The resolution portion contains hot rods of 5, 4, 3, 2, and 1 mm diameter in a cold background for evaluating contrast resolution. The contrast portion contains water- and air-filled cylinders of 8 mm diameter in a hot background for evaluating accuracy of attenuation and scatter corrections. The uniformity portion contains a hot background with no structures for evaluating noise and nonuniformity artifacts.  
The image accuracy was measured with a full width at half maximum (FWHM) of 5 fillable rods in the resolution area. Five fillable rods were used to determine the _RC_, defined as the ratio of the measured activity concentration in the rods and the activity concentration measured in the uniform area (UA).  
\[Recovery\ Coefficient\ (RC)=\frac{Rod_{max}}{UA_{mean}} \tag{1}\]  
where \(Rod_{max}\) is the maximum value of rods and \(UA_{mean}\) is the mean of uniformity area.  
The fillable uniform area of the phantom was used to measure the _uniformity_ as a measure of noise. A lower %\(SD\) indicated a lower variability in the dataset. Equally, a higher %\(SD\) showed that the dataset exhibited variations.  
\[\%SD=\frac{UA_{sd}}{UA_{mean}}\times 100\ (\%) \tag{2}\]  
where \(UA_{sd}\) is the standard deviation of uniform area.  
The \(SOR\) of water- or air-filled area was calculated as follows:  
\[Spillover\ Ratio\ (SOR)=\frac{WA_{mean}\ or\ AA_{mean}}{UA_{mean}} \tag{3}\]  
where \(WA_{mean}\) or \(AA_{mean}\) are the mean of water or air area.  
A circle ROI with a 2.5 mm radius was drawn in a hot rod of 5 mm diameter for calculation of peak signal-to-noise ratio (\(PSNR\)). The peak signal (\(PS\)) and mean square error (\(MSE\)) were measured in ROI. The PSNR was calculated as follows:  
\[Peak\ signal-to-noise\ ratio\ (PSNR)=10\times\log_{10}\frac{PS^{2}}{MSE}\ (dB) \tag{4}\]
# Antibacterial Activity of Green Synthesised Silver Nanoparticles on _Saccharomyces cerevisiae_
## 2 Materials and Methods
### UV-Spectrophotometry  
To understand the kind of silver in the supernatant and yeast lysate, the samples were examined using a spectrophotometric method. This study used the DS-11 FX + spectrophotometer to measure the absorption spectra. The obtained spectra were analysed with the results of studies from the scientific literature. The results of UV spectroscopy confirmed that the obtained samples contained silver nanoparticles matching the values of the plasmon resonance of silver nanoparticles in the range of 400-430 nm.
## 3 Autonomous Collision Avoidance Method
### Optimisation Evaluation Function  
According to the provided algorithm, a variety of decisions in the dynamic feasible manoeuvring interval should be made at any given time, which can safely avoid obstructions and ships. The two criteria of economy and timeliness are also taken into consideration when choosing collision avoidance decision options for ships as evaluation indicators on the basis of safety.  
The ship's timeliness can be measured by how long it takes to complete the collision avoidance action. There is less risk of collision during an encounter stage when the time value is short. The function of timeliness can be shown as follows:  
\[f_{1}=\frac{\Delta t_{(i)}}{max(\Delta t)} \tag{18}\]  
where \(\Delta t_{(i)}\) means the period consumed by the collision avoidance action of \(i\) and \(f_{1}\) denotes the timeliness.  
The economy mostly pertains to the ship's fuel usage during collision avoidance, which is primarily shown in the steering amplitude, which can be determined as follows:  
\[f_{2}=\Delta c \tag{19}\]  
where \(\Delta c\) means the amplitude of altering course, and the lower the amplitude, the less energy the ship avoidance operation costs.  
In conclusion, the optimisation evaluation function is calculated decision-making in terms of ship timeliness and economy, which can be expressed as follows:  
\[F=af_{1}+bf_{2} \tag{20}\]  
where \(a\) and \(b\) represent the weights of timeliness and economy indicators, respectively, and \(a+b=1\).  
The smaller the evaluation function \(F\), the better the decision option. When the weight \(a\) of timeliness is greater, the ship takes less time in avoidance decision, which means that the potential danger in the whole process of collision avoidance is decreased. When  
Figure 7: Decisions in different encountering situations: (**a**) Overtaking situation; (**b**) Head-on situation; (**c**) cross situation.  
the weight \(b\) of the economy is greater, the steering amplitude is greater, which means the safety distance between ships is larger. But the time of ship avoidance increases, and the potential danger also increases. Therefore, it satisfies different situation requirements to adjust the corresponding weights of timeliness and economy indicators according to different scenarios.
## 5 Discussion
### Objective Function
#### 5.3.5 Goal Programming Method  
* Advantages: Goal programming enables concurrently considering various goals, possibly assigning different priorities. This adaptability proves advantageous in intricate decision-making contexts, where navigating trade-offs among conflicting objectives is essential.
* Disadvantages: This approach depends significantly on the decision-maker's capacity to establish and rank goals precisely. Such reliance can inject subjectivity into the process, potentially skewing the results.  
A fundamental multi-objective optimization concept is the Pareto front, which represents the trade-offs between conflicting objectives. It draws attention to dominated and non-dominated solutions, demonstrating the trade-offs necessary to achieve optimal results. Each approach to optimizing objectives presents specific advantages and challenges, from the weighted sum to trade-off analysis. The choice of methodology depends on project priorities, so balancing multiple criteria is essential to designing efficient and sustainable microgrids. In addressing uncertainties in renewable generation and demand, advanced optimization techniques such as chance-constrained programming (CCP) have shown potential for real-time adaptation in complex energy systems. Research [146] demonstrates that a CCP-based strategy balances user comfort and system economy in a multi-agent energy system by adjusting confidence levels to manage renewable generation and external temperature uncertainties. This approach could be explored further in microgrid optimization, especially for enhancing cost, reliability, and sustainability trade-offs under uncertainty. The following section presents the key conclusions drawn from this review, highlighting the main contributions and potential avenues for future research.
## 3 Methodology
### Data Preparation
#### 3.3.3 Ratio  
This feature measures the downlink to uplink throughput values over time. The ratio, denoted by \(R\), is calculated as shown in Equation (4):  
\[R[i]=\frac{N_{\text{received}}[i]}{N_{\text{sent}}[i]}, \tag{4}\]  
where \(i\) denotes the current time index, \(N_{\text{received}}[i]\) represents the downlink throughput, and \(N_{\text{sent}}[i]\) represents the uplink throughput at time \(i\).  
The ratio quantifies the asymmetry between the sent and received throughput, revealing the unidirectional or bidirectional nature of each application. This characteristic helps the model distinguish between scenarios where byte transfer is required for both uplink and downlink communication and those where it is essential only for specific directions. In Figure 9, the received to sent throughput ratio of YouTube traffic is demonstrated.