esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Blockchains with the IoT: A Review of Architectures and Marine Use Cases
## 4 Applications and Challenges of Blockchains in MIoT and IoUT
### Solutions for Blockchain-Enabled MIoT and IoUT Systems
To decide which modulation method to use, it utilizes real-time feedback, specifically the Packet Delivery Ratio (PDR), which measures how successfully data are being transmitted and received. This method optimizes communication efficiency by addressing dynamic connection quality issues, which are often a bottleneck for blockchain transaction finalization in IoUT networks. The paper's results show that AMUSE outperforms other learning-based approaches, demonstrating its efficiency in adapting transmission characteristics to ever-changing underwater environments while maintaining simplicity. Furthermore, [PERSON] and [PERSON] [54] proposed a Deep Reinforcement Learning (DRL)-based Medium-Access Control (MAC) protocol for UACNs, showcasing how asynchronous learning can leverage long propagation delays to significantly improve the network throughput and packet success rates. Their protocol allows underwater nodes to exploit these delays and improve the network throughput. More specifically, it provides greater flexibility by adjusting transmission start times, thereby improving the throughput and packet success rates. The DRL-based protocol has been shown to outperform traditional methods such as TDMA (Time Division Multiple Access) or ALOHA (Additive Links On-line Hawaii Area) by optimizing the transmission timing. These studies collectively illustrate the potential of integrating various optimization approaches with blockchain-based frameworks to improve scalability and the overall efficiency in MIoT and IoUT systems. By utilizing such approaches, new BIoT solutions can be developed, making these systems more suitable for real-world marine applications and possibly increasing their adoption rate.
# Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils
## 2 Materials and Methods
### Comparison between the Proposed Models and Empirical Formulae  
In order to test the effectiveness of the most appropriate _FS-RF_ model, a comparative study was performed with some empirical formulae suggested by the previous studies for predicting \(C_{s}\). These formulae were presented in Table 1. Table 11 illustrates the statistical results of \(C_{s,estimated}/C_{s,measured}\) for aforementioned empirical equations in comparison with the proposed _FS-RF_ and _FS-DNN_ models. The mean and standard deviation of the ratio \(C_{s,estimated}/C_{s,measured}\) could be useful evidence for evaluating the predictive capacity. The closer the mean value to one and the standard deviation to zero, the better is the model. The results show that the most appropriate _FS-RF_ model is the one with the minimum standard deviation \(\sigma_{FS-RF}=0.25\), in addition to being the closer mean value to 1 average(_FS-RF_) = 1.07. The other equations indicate a poor predictive capacity, yielding a mean value in the range of 0.45-1.7, and standard deviation value between 0.26-0.99. Equally, the box plot of \(C_{s,estimated}/C_{s,measured}\) of aforementioned formulae is displayed in Figure 7. This graphical representation provides an overview of the dispersion and skewness of every model. The scattering of the most appropriate _
## 5 Light-Weight Distributed Security Scheme
### Implementation of the Security Scheme  
In this subsection, we will focus on the security scheme and authenticating the participating nodes while establishing the paths in the clustered WMSN network. As we mentioned before, all the nodes in the network have the preloaded master key (K\({}_{m}\)), then each one of them compute the unique-node key shared between the node and BS. Before BS starts broadcasting its BS-Msgs, it first authenticates itself by properly calculating the MAC tag of these messages using the master key (K\({}_{m}\)) and including this MAC in one of the fields of BS-Msg, as follows:  
\[BS\to S_{j}:BS-Msg,MAC_{K_{m}}(BS-Msg) \tag{8}\]  
Upon receiving this message, the node S\({}_{j}\) checks the MAC using the same master key before processing the other data to extract the routing information. If the MAC is verified, BS will be added in the routing table of node S\({}_{j}\) along with all relevant security information such as nonce identifier (random number used once composed of random number and time stamp) and sequence number used to avoid replay attacks, and pair-wise key will be generated. On the other hand, when the verification process is valid, these nodes reply to BS by sending back Ack-Msgs authenticated with MAC using the unique-node key to avoid impersonation attempts and to authenticate themselves to the BS as 1 st-level cluster heads. In the same manner, cluster heads include in their CH-Msgs the MAC tag using the master key and other relevant security information in order to authenticate the communication between them and other cluster heads and their group members and to exchange data securely:  
\[CH\to BS:Ack-Msg,MAC_{K_{i}}(Ack-Msg) \tag{9}\]  
\[CH\to S_{j}:CH-Msg,MAC_{K_{m}}(CH-Msg) \tag{10}\]  
At the same way, each node S\({}_{j}\) behaves as a group member in any cluster of the network sends back an Ack-Msg to its CH which is also properly authenticated by including the MAC tag using the pair-wise key.  
\[S_{j}\to CH:Ack-Msg,MAC_{K_{ij}}(Ack-Msg) \tag{11}\]  
After the network is built up and each node knows its parent (if it is a cluster head) and its cluster head (if it is a group member), all the cluster heads and the group members compute the unique-cluster key of their clusters. This key will be used later on for authenticating any cluster head messages for broadcasting messages or disseminating commands within the cluster. Finally, the master key should be deleted from the memory of all nodes since the security of our protocol depends on the deletion of the master key at the end of the process. We should take care that the deletion is unrecoverable, for example by overwriting the master key (in practice several times). Now, the communication between the nodes and the base station has been secured and the sources can send their data securely:  
\[S_{j}\to CH:Data-Msg,MAC_{Kij}(Header|E_{Kij}(Data)) \tag{12}\]
## 2 Results
### Selectivity Studies
#### 2.3.3 Selectivity of **1**-MIPs: Implication on Their Applications  
The poor-to-moderate selectivity of **1**-MIPs against **18**, **19** and **20** could initially be construed as unfavourable; however, these results indicate that **1**-MIPs are capable of broad-based capture of blends of illicit substances with, rather than specifically for, **1**, comparable to the 'dummy' MIP approach. The high cross-reactivity against **20** means that **1**-MIPs could potentially extract 1-(3-trifluoromethyl-phenyl)piperazine (TFMPP, **3**), a derivative of **20** most commonly blended with **1**, and other known substituted phenylpiperazine blend ingredients such as **4**, **5** (Figure 1) and 4-fluorophenylpiperazine [64]. Other pills are also known to be mixed with cocaine (**17**) [16; 65] and ephedrine (**19**) [64], which, according to our selectivity studies, could also be extracted by **1**-MIPs.  
Illicit drugs are never pure, and conducting an analysis of their composition, including minor ingredients and adulterants, is essential in drug profiling studies [66]. The competitive uptake of **18**, **19** and **20** by **1**-MIPs means that, when present in trace amounts, they could be pre-concentrated within the MIP, which could enhance their detection. We speculate that other minor non-piperazine derivatives blended with **1** could also be captured and pre-concentrated with **1**-MIPs. Unfortunately, due to the nature of these analytes, we could not easily obtain real samples to test.  
Our results suggest no significant difference in the selectivity between self-assembly and semi-covalent **1**-MIPs. Thus, the MIP materials could be prepared by the synthetically simpler self-assembly imprinting using cheap commercially available monomers (monomer **7** and crosslinkers), thus making these materials very competitive over other analytical extraction methods.  
Our **1**-MIP materials are suited to laboratory test setting, with potential as the recognition element for in-field sensing devices. Sample preparation would be simple, and with their enhanced enrichment capability, due to the presence of imprinted sites, they would be useful for the capture of illicit drugs, particularly in biological samples. MIP-bound drug analytes could be re-extracted and differentiated by a number of analytical procedures--notably, chromatographic methods--preferably with MS detection and capillary electrophoresis, among others.
# The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation
## 3 Experimental Results
### Impact of the Distribution  
Subsequently, we aimed to assess the attainable accuracy while minimizing the number of GCPs needed. To achieve this, we subdivided the GCPs depicted in Figure 7 into GCPs and check points, as depicted in Figure 14. Note that the check points were also distributed across the entire image. Following this, we assigned zone numbers from one to nine to the remaining GCPs, as illustrated in Figure 4.  
Utilizing the GCPs with assigned zone numbers, we conducted an analysis of achievable accuracy using RPC bias compensation by varying the number of GCP zones. Figure 15 illustrates the RMSE error for each Kompsat-3A image. As the number of zones increases, the accuracy improves. However, accuracy stabilizes from three or four zones, indicating the attainment of accuracy at the one-pixel level.  
Figure 14: Distribution of GCPs and check points (triangles: GCP, circle: check).  
For a more detailed error analysis, an error histogram is provided for Image 3 as an example in Figure 16. The error spans from \(-5\) pixels to +5 pixels in the case of three zones, indicating a considerable range. To achieve higher accuracy, such as within \(\pm 2\) pixels, a minimum of five zones is necessary for this scenario.
# An Improved Method of Polyp Detection Using Custom YOLOv4-Tiny
## 4 Proposed Architecture  
### Overall Architecture of the Proposed Model  
In the initial stage, we planned to use only the custom YOLO version to detect polyps, but we did not expect to encounter some missing data while doing so because the gastrointestinal tract is home to various diseases. To address these issues, we decided to develop a new approach that enhances the model architecture and reduces the number of missing polyps during detection. YOLO, proposed by [PERSON] et al. [29], has been widely used for real-time object detection. Compared with the other models, the YOLO series model simplifies the object detection task into a single regression task and can therefore detect objects in real time. YOLOv4 [32] is an improved version of YOLOv3 [33]. Furthermore, YOLOv4-tiny [5] is a modified version of YOLOv4 that is simpler and faster than the original version. The number of features that could be used for classification and regression detection was reduced from 60 million to 6 million with the application of the CSPOSANet (CSP) backbone.  
In this study, we propose a new custom model for wireless endoscopic image analysis based on the improved YOLOv4-tiny model with the Inception-ResNet-A block. We replaced the previous CSP module [34] with our custom CNN block to increase the classification accuracy for the detection and localization of polyp areas. Figure 3 shows the overall architecture of our proposed model architecture. The original version of the YOLOv4 backbone is the Darknet53-tiny, which receives a 608 \(\times\) 608 \(\times\) 3 input size and sends it to the 32 \(\times\) 3 \(\times\) 3 convolutional layer in the first step. In the next step, three CSP blocks come with max pooling of the baseline SCP block, which is shown in the next part of Figure 3. The CSP block divides the features of the base layer into two parts; the main function of the first part is to directly form the residual edge, and the final output can be obtained by convolution using the splicing of the second part. This network structure can reduce computational complexity while maintaining network performance.  
Figure 3: Backbone of original YOLOv4-tiny. Left side: Darknet53-tiny backbone architecture. Right side: CSP block.  
Inception-ResNet-A block was modified by combining one of the blocks of the traditional Inception-ResNet architecture [35] with the ResNet model [36]. The ResNet and Inception blocks can be used in combination because the ResNet module consists of residual connections that can be used to train a deep neural network, while the inception block can handle more information from input images. Generally, the architectures of several versions of Inception-ResNet, such as A-, B-, and C-modularized Inception-ResNet blocks, the original CSPOSA block, and the updated Inception-ResNet-A block, include stem blocks as shown in Figure 4. Inception-ResNet-A block is modified as Inception 1 \(\times\) 1 convolution block connected with ReLU activation.
## 3 Methods and Data
### Construction of the WaveNet Network Model  
This article provides a detailed introduction to a residual convolutional neural network called \"WaveNet,\" enhanced by a channel attention mechanism. WaveNet is designed to effectively process complex remote sensing images while achieving high classification accuracy and robustness. It achieves greater network depth by sequentially combining convolutional layers and pooling layers, enabling autonomous learning and the capture of essential features in remote sensing images. Simultaneously, it employs a channel attention mechanism to assign varying weights to channels within the feature map. These learned features are subsequently consolidated through a fully connected layer to produce the final classification result. The experimental findings presented in Section 4 unequivocally demonstrate the outstanding performance of WaveNet in diverse image classification tasks. Consequently, this method holds immense potential for widespread application in remote sensing image processing and is poised to contribute significantly to advancements in the field of remote sensing image classification.
## 5 Discussion and Implications
### Adversarial Robustness and Model Vulnerabilities  
Adversarial robustness emerged as a significant concern during evaluations. All models experienced performance degradation under FGSM perturbations (\(\epsilon=0.01\)) with XCeption maintaining a relatively higher adversarial accuracy (79.1%) compared to ResNet-50 (64.2%) and VGG16 (74.3%). These results reinforce prior findings [7] that CNN-based detectors can be systematically deceived through minor pixel-level modifications even when they excel in non-adversarial conditions. Consequently, there is a critical need for integrating adversarial training, defensive distillation, and improved preprocessing workflows to bolster reliability. The rise of diffusion-based forgery techniques [21] further underscores the urgency for advanced defenses tailored to increasingly sophisticated manipulations.
## 3 Materials and Methods
### Evaluation of Total Antioxidant Capacity  
#### 3.4.1 Trolox Equivalent Antioxidant Capacity Assay (TEAC)  
The TEAC assay measures the inhibition of the radical cation ABTS\({}^{+}\) by antioxidant compounds, which is compared to the activity of a reference antioxidant standard (Trolox). The spectrophotometric method proposed by [PERSON] et al. [18] was used. ABTS reagent (7 mM) and K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) (140 mM) were mixed and maintained at room temperature in darkness for 16 h to generate the ABTS\({}^{+}\) stock solution. Then, it was diluted in ethanol until an absorbance of 0.700 \(\pm\) 0.020 at 734 nm and 30 \({}^{\circ}\)C to obtain the ABTS\({}^{+}\) working solution. Proper dilution of each fish extract to achieve a percentage of absorbance inhibition of approximately 50% was required. A range of Trolox standard solutions (0-300 \(\mu\)M) were prepared. The absorbance of 2 mL of ABTS\({}^{+}\) working solution was considered the initial point of reaction (A\({}_{0}\)). Then, 100 \(\upmu\)L of diluted extracts or Trolox standards were added immediately. After 3 min of reaction, the absorbance was measured and considered the final point (A\({}_{I}\)). All measures were conducted in a thermostatized UV-vis spectrophotometer. The percentages of absorbance inhibition were calculated using the following equation: 1 - (A\({}_{I}\)/A\({}_{0}\)) \(\times\) 100 and were compared to the Trolox standard curve. The results were expressed as \(\upmu\)M Trolox Equivalents.
# Application of Artificial Intelligence in Wind Power Systems
## 6 Dangers of Using Artificial Intelligence (AI) in Wind Turbines
For example, converting raw sensor measurements into torque signatures or vibration envelopes increases the signal-to-noise ratio and reduces the detrimental effects of class imbalance. Advanced learning architectures such as matching contrastive learning (MCL) and hybrid neural network/fuzzy logic designs are promising when adapted to wind turbine data with sparse fault events [85]. Ensemble methods (e.g. XGBoost, random forest) can also be configured with class weighting or cost-sensitive loss functions to correct biases in favor of majority classes. As the operating conditions in wind farms change over time, continuous retraining based on newly acquired, high-quality data is still essential. Complemented by active learning, where the model queries human expertise to flag uncertain cases, this retraining process helps to ensure the long-term robustness of predictions and close emerging gaps in data quality [23]. By integrating these approaches, AI-based systems can detect faults more accurately and optimize turbine performance, ultimately leading to more reliable fault prediction and timely maintenance actions. * Cyberattacks: AI systems can be targeted by cyberattacks, which can lead to wind turbine outages or even physical damage. Attackers can exploit vulnerabilities in AI systems to take control of wind turbines. For example, manipulated sensor data could trick AI into ignoring blade cracks or bearing failures, risking catastrophic failures [13]. This is why cyber-protection systems are needed. An example of a cyber-secure wind turbine control system involves the integration of a support vector machine algorithm with an Hoo controller to identify communication attacks, combined with a machine learning algorithm to address errors resulting from communication or data injection attacks. * Ethical issues: The application of AI can raise ethical issues, such as data privacy and accountability for decisions made by AI. It is important to ensure that AI systems are used in a way that is ethically acceptable and transparent. The use of AI in decision-making can lead to ethical dilemmas, especially when it comes to decisions that affect human lives or the environment. A lack of transparency in how AI makes decisions can lead to distrust among users and the public. * System complexity: AI systems can be very complex, which can make them difficult to understand and manage. This can lead to problems in maintaining and optimizing the system. * The issue of liability for accidents: In the event of accidents or malfunctions caused by decisions made by AI, it is difficult to determine who is responsible--whether it is the manufacturer, the developer or the user of the system. This ambiguity can lead to a lack of accountability and reduce motivation to develop safer and more efficient systems
## 6 Architecture Preliminary Validation via Prototype
### Experimental Results
We can observe that the redirection of path has beneficial effects on the overall load of switches since the number of transmitted bytes is more fairly-distributed among all the available switches. This is in line with the IETF guidelines for the control functionalities governing the service function chaining [51]. It is worth highlighting that while addressing data delivery performance, our approach preserves the perceived quality of the services since no packets are lost during the tear-down of the data delivery paths that are rapidly re-established through unloaded switches. However, this enhanced performance is obtained at the cost of the increased number of exchanged messages and the presence of redirection time, as shown in Table 5 in comparison with a case without redirection considered in the literature [43]. The redirection feature increases the number of messages exchanged internally since every time a redirection is performed, a new path is calculated and new flow entries are setup which necessitates further communication messages (e.g., path delivery set-up, path teardown, _etc._). Moreover, these operations require a certain time, evaluated to 0.3 s in these tests, which is acceptable with respect to the overall flow duration.
# Laser Inter-Satellite Link Visibility and Topology Optimization for Mega Constellation
## 4 Results and Discussion
### Link Connection Policy
However, provided the laser beam's narrow beam width and the differing relative velocities of the two satellites that connect the intersecting orbital planes, establishing some temporary LISLs is challenging, and a more precise ATP system needs to be developed. In the future, the failure of a single satellite can also be utilized as a case study. The law analyzed in this paper is presented to probe a link reconstruction algorithm with a high degree of connectivity and a low delay, as well as the optimal routing protocol based on the shortest path principle.