esa-sceva/satcom-chunk-collection huggingface dataset:
# An Untraceable Data Sharing Scheme in Wireless Sensor Networks
## 5 Secure Data Sharing between the Source Node and Sink Node
### Cloud Construction Based on Message Shares  
Different from existing schemes, we employed the message shares rather than the original message to construct an anonymous cloud with irregular shape. For a message \(X\), a set of message shares are derived, and together they recover \(X\). To decrease time delay of message \(X\), the best strategy is transmitting the shares to the sink node at the same time. In our scheme, the source node first assigns a random number \(k\) to each share and the share can walk exactly for \(k\) steps. Then, the source node sends the shares of a message to the neighbors randomly at the same time. If a node receives a real share, it first updates \(k\) by \(k-1\) and hence the share can be further transferred for \(k-1\) steps. Meanwhile, the node generates a set of fake shares with the same length with the real share. Moreover, these fake shares are randomly delivered to the neighbors of the node and they can also walk in the cloud for \(k-1\) steps. If a node receives a fake share, the node first checks the number of steps and if the share cannot further walk in the cloud, it just drops the share. Otherwise, the node subtracts the step number of the share by one and then sends the share to its neighbors. As shown in Figure 5, an example of the cloud with two real shares is provided and the shares can randomly walk for 4 and 3 steps, respectively.  
To make the nodes in the cloud statistically indistinguishable, all the nodes in the cloud employ the scheme in [6] to send the real shares or fake shares with a proper time delay. Specifically, the time delay is generated by a random distribution. However, to decrease time delay, the node transfers the real shares as soon as possible without breaking the statistical rule. In this way, all the nodes in the cloud have similar radio behaviors and hence the adversary cannot locate the real source node easily.
###### Abstract  
In wireless sensor networks (WSNs) with holes, designing efficient routing protocols, which prolong the network lifetime, is one of the most critical issues. To this end, this paper proposes a new geographic routing protocol for WSNs named the load Balanced and constant Stretch protocol for bypassing Multiple Holes (i.e., BSMH). In contrast to the existing works in the literature, the design of BSMH simultaneously takes into account the three factors that impacted the network lifetime, including routing path length, control packet overhead, and load balance among the nodes. Moreover, BSMH aims at minimizing the routing path length and the control overhead, while maximizing the load balance. We theoretically prove the efficiency of BSMH and extensively evaluate BSMH against the state-of-the-art protocols. The evaluation results show that the proposed protocol outperforms the others in various investigated metrics, not only network lifetime, but also routing path stretch, load balance, and control overhead. Specifically, BSMH prolongs the network lifetime by 30% compared to the existing protocols while guaranteeing that the routing path stretch is under 1.3.  
wireless sensor network; hole bypassing; network lifetime maximization; forwarding 20202002506; doi:10.3390/s20092506
## 2 Data and Methods  
### Data Set  
Different from the common practice in the field of super-resolution, where high-resolution (HR) images are downsampled to obtain low-resolution (LR) images, we applied deep learning mapping studies by aligning solar astronomical images captured at the same time but using different hardware facilities. This method provides LR and HR image comparisons that more closely match actual conditions, hence gathering data more suitable for real-world application scenarios, which is extremely beneficial for enhancing the generalization ability of the model. In the experiments presented in this paper, the LR images were sourced from the Atmospheric Imaging Assembly [3] (AIA) of the Solar Dynamics Observatory [4] (SDO), selecting images at two different wavelengths of 171 angstroms and 193 angstroms. These bands can effectively capture phenomena such as coronal mass ejections. The HR images are from NASA's High-Resolution Coronal imager (Hi-C) project, which includes all 114 high-definition coronal images from both Hi-C 1 and Hi-C 2.1. Hi-C 1 captured 36 coronal images at 193 angstroms from 16:52 to 16:55 on 11 July 2012, while Hi-C 2.1 captured 78 coronal images at 172 angstroms from 16:56 to 17:01 on 29 May 2018. To expand our dataset, we performed random cropping, horizontal flipping, rotation, and translation operations, ultimately selecting 440 image pairs as the training set. The resolution of HR images is 2048 \(\times\) 2048, while the resolution of LR images is 512 \(\times\) 512. In addition, we selected another 80 pairs of images as the validation set and the test set, with data from Hi-C 1 and Hi-C 2.1's two different wavebands divided in a 2:1 ratio.
## 4 Discussion
### Limitations  
The articles reviewed offer insights into the application of digital health interventions for the management of asthma and associated psychological factors, yet there are existing limitations. The breadth of the review is confined to a particular set of studies, owing to the paucity of studies in the literature specifically addressing digital health interventions for managing asthma and related psychological factors. The efficacy of these interventions may exhibit variability across different populations and settings, and the need for their personalization warrants further exploration. This review does not tackle the technological hurdles in adopting these interventions, nor does it contemplate ethical considerations, privacy concerns, and data security issues. Additional research is necessary to address these limitations and further the comprehension of digital health interventions in the realm of asthma management.
# Design of a Device Based on an LED Matrix for Water Sterilization
## 2 Materials and Methods
### Antibacterial Activity of the LED Array Prototype  
Cultures of _E. coli_ were washed and resuspended at a concentration of \(5\times 10^{3}\) CFU/mL in 1 and 5 L of water, respectively. These suspensions were then exposed to UV irradiation using the prototype LED array for varying durations. The experiments were conducted under both static and stirring conditions. The surviving bacteria were quantified using methods outlined in previous sections. Furthermore, this test was extended to include a mixture of all bacteria in a 1:1 ratio, suspended in 5 L of water, and subjected to the same experimental conditions.  
To assess the prototype's effect on an environmental sample, water samples were collected from the Piles River (Gijon, Spain), an urban natural environment with high contamination [7]. Samples of 5 L of river water were exposed to UV irradiation produced by the LED array prototype for different durations, under both static and stirring conditions, and the surviving viable microorganisms were quantified as in previous sections. The river water was not filtered before UV-C LED exposure. The assays were conducted under both aerobic and anaerobic conditions, for which an atmosphere generator system was used to maintain anaerbiosis. BHI medium was used under aerobic conditions, while TSB medium was used under anaerobic conditions.
## 3 Deployment Dynamics  
### Modeling of Kinematic joints and Latching Mechanism  
To present the deployment motion, dynamic modeling was conducted with the aid of commercial multibody dynamics (MBD) software, RecurDyn. There were three kinds of joints: revolute joints for one-axis revolution, fixed joints to eliminate rigid-body motion, and on-off joints for latching truss-links. In contrast to the two-dimensional type-2 design, in the actual 3D design, when many truss-links were connected at a rotation joint (see Figure 4a) such as joint F, the over-constrained mechanisms cannot be avoided according to Equation (1). To address this problem, we used the bushing force function of RecurDyn instead of the revolute joint [25]. The bushing force can model any joint with 6 DOF by adjusting the stiffness of six springs independently. In the case of a revolute joint with the bushing force, the translation stiffness was set to be sufficiently high that the translation motion would be constrained; one of the rotational stiffnesses was set to zero  
Figure 4: Configuration of truss-link mechanism: (**a**) 3-D truss, (**b**) truss-links with connection angles \(\theta\).  
Figure 5: Configuration of latching mechanism: (**a**) Neodymium magnet, ball plungers (**b**) Latching system.  
for the revolution while the other rotation stiffness was set high. The proposed truss-link mechanism must be latched on each joint, B, E, and H, in a fully deployed state at the end of the deployment process. The on/off joints provided by RecurDyn are used to address these problems, which allows operation when certain conditions are met [26], as shown in Table 6. Using this, we modeled the two links connected to the joint to be fixed without generating additional motion when they are aligned.
## 5 Discussion  
### Potential Improvement of FFSAR in Water Level Monitoring  
This study highlights the substantial reduction in error achieved by FFSAR-derived WL products compared to the current operational processing chain of Sentinel-6 UFSAR L2 data. Across our study areas, which encompass small to medium targets, FFSAR-derived WL\({}_{a}\)s for each pass exhibited an average relative error reduction of 85% compared to UFSAR-derived WL\({}_{a}\)s using the OCOG retracker, and 88% compared to those using the ocean retracker, resulting in an average median absolute deviation (MAD) of approximately 4 cm. Regarding the error in the WL\({}_{i}\) estimation of each retracked waveform, we found that 73.8% of FFSAR measurements had an error below 20 cm, in contrast to only 9.6% for UFSAR.  
In discussing the error reduction achieved by FFSAR, it is crucial to compare it with WL\({}_{a}\)s derived from UFSAR over the same targets to avoid misleading interpretations based solely on absolute error values. Previous studies have demonstrated UFSAR errors below 5 cm over large reservoirs or ocean-like bodies [17; 22].  
The presented WL\({}_{a}\) filter effectively handles sparse distributions of WL\({}_{i}\)s, which are often encountered over small to medium targets due to the presence of untargeted bright scatterers. However, its effectiveness may be compromised if the prominence of untargeted bright scatterers surpasses that of the targeted water surface. The degree of sparseness in WL\({}_{i}\) distributions may also be influenced by the power of the retrieved waveforms along the pass over the target, particularly if there is low reflected power from  
Figure 14: (**Top** panel) Overpass of Sentinel-6’s track 111 highlighted in red over the Cinca River (background provided by Esri World Imagery). The yellow rectangles indicate observed water level jumps. The blue and orange lines indicate the shoreline and centerline, respectively. (**Bottom** panel) Water level jump detected at the middle of the river section using FFSAR-derived data shown as black dots. The dashed black lines represent the average of each cluster. DEM values obtained along the shoreline and the centerline of the river are represented in blue and orange.  
the targeted water surface, leading to contamination from off-nadir bright surfaces within the radar's footprint.  
Sparse WL\({}_{i}\) distributions, which do not conform to Gaussian- or Cauchy-like functions, cannot be directly analyzed using previously proposed state-space models [21]. However, an extension of the state-space model to incorporate FFSAR data and sparse distributions could enhance error reduction and improve outlier filtering. One crucial consideration for WL\({}_{a}\) filters that consider the time axis is the retrieval of periodic measurements. Unfortunately, as demonstrated, incorrect positioning of the window and/or a WL\({}_{t}\) dynamic range of the reservoir broader than the window's width can result in the loss of satellite passes.
# Automated Gluten Detection in Bread Images Using Convolutional Neural Networks
## 3 Materials and Methods
### Model Training
* EfficientNetV2L: This is a CNN architecture developed by Google Brain as an improved version of EfficientNet, designed for better efficiency and faster training in image classification tasks [52]. EfficientNetV2L builds upon EfficientNet's compound scaling method, which optimally balances depth, width, and resolution to enhance accuracy while minimizing computational cost. Compared to its predecessor, EfficientNetV2 introduces fused MBConv layers, which reduce memory usage and improve training speed. EfficientNetV2L (Large variant) consists of approximately 120 million parameters and is optimized for high-performance classification tasks while maintaining efficiency [52]. All six models were trained with the following configuration. We excluded the top classification layer (include_top = False) and used input shapes of (150, 150, 3) for the images. This resolution was chosen to balance the computational efficiency and sufficient detail for effective feature extraction in the pre-trained models. Each model begins with a frozen convolutional base to leverage pre-trained features while preventing weight updates, ensuring faster training, and reducing the risk of overfitting. A 'GlobalAveragePooling2D' layer has been added to reduce dimensions, followed by a dense layer with 128 units and ReLU activation for feature extraction, and a dropout layer for regularization, where half of the neurons in the layer are randomly deactivated for each forward pass in training. Lastly, a dense layer with a sigmoid activation function was applied for binary classification. At first, the convolutional layer was frozen. However, during fine-tuning, the convolutional layer was unfrozen to update the weights and adapt the pre-trained features to the dataset. The models were compiled using the Adam optimizer with a learning rate of 0.0001 and binary cross-entropy loss. The models were trained for 300 epochs, and EarlyStopping was used to monitor the validation loss (val_loss) with a patience of 30 epochs, thereby avoiding unnecessary computations and overfitting. Additionally, ReduceLROnPlateau dynamically adjusted the learning rate by a factor of 0.2 when the validation loss did not improve for 5 consecutive epochs, with a minimum learning rate of 0.00001.
## 4 Results
### The Effect of TSS on the GPS and PLdB on the Ground, L/R = 12.5  
To assess the PLdB impact on the ground, we were guided by the approach proposed in [19; 20]. According to the calculated near-field, the Thomas waveform parameter method [19] allows approximating the pressure profile in the far zone by solving the ODE system, which approximates the ground pressure signature (GPS) after the signal has passed through the atmosphere. The GPS is characterized by the rates of change in the sound speed, the density of the environment, and the propagation velocity depend on the trajectory of the signal.  
The profiles of NFPS from Figure 15 were processed with the use of the software packages from [19], which realizes the Thomas waveform parameter method to simulate the passage of a signal through the atmosphere. We evaluated the impact of only the \"N-wave\" in the profiles; besides, the profiles were shifted to the coordinate origin.  
Figure 16 shows the processing of the NFPS profiles and the final GPSs resulting after using the numerical code from [19]. The GPSs are expressed in pounds per square foot. A comparison is shown at M\({}_{\infty}=2\) and M\({}_{\infty}=1.5\) for the cases of the absence of the TSS action (blue) and of the maximal effect of the TSS on the NFPSs (\(N=7.5\)) (orange) at the level of \(r=16.8\). The flight altitude was supposed to be equal to 10,000 m or 32,808.4 ft.  
The corresponding values of the PLdB impact on the ground evaluated using the open-source package PyLdB [21] are presented in Table 4. The PyLdB code implements the perceived loudness level calculation determined using the algorithm Mark VII, where the perceived value is correlated with the table of the corresponding range compiled by [PERSON], and the value of the perceived amplitude is converted to the PldB, according to the additional table [22].  
One can see that at M\({}_{\infty}=1.5\)-\(2\), the TSS, which has the maximum effect on the front of the BSW (and on the NFPS as well), does not increase the impact of the ground sound pressure. Thus, it can be concluded that controlling the flow (past an AD model of the considered shape) using the TSSs with the considered parameters does not increase the PLdB impact on the ground.  
Besides, from these simulations, one can conclude that changing the surface pressure on the AD body due to drag reduction does not necessarily cause a change in the PLdB on the ground.
# DIKWP-TRIZ: A Revolution on Traditional TRIZ Towards Invention for Artificial Consciousness
## 4 Mapping and Coverage Analysis of TRIZ Rules in DIKWP
### Mapping the 40 TRIZ Principles to the DIKWP Model
#### 4.1.13 Knowledge (K) \(\rightarrow\) Knowledge (K)  
The transformation process emphasizes the targeted nature of Information processing, ensuring that the acquisition, analysis, and presentation of Information all serve specific objectives. The following are several TRIZ principles related to this transformation process and their specific applications:  
* **Principle 22: Blessing in Disguise** Utilize challenges or limitations in knowledge as opportunities to develop deeper understanding or new methods. This approach emphasizes the potential for breakthroughs in solving problems and obstacles, advancing knowledge through critical thinking and innovation. For example, in technological innovation, traditional file systems cannot meet business needs, and companies developed their own file systems [81,86]. Therefore, by exploring alternative solutions or developing new methods, they promote the progress of related technologies and expand people's understanding of file systems.
* **Principle 34: Discarding and Recovering** Update Knowledge by eliminating outdated concepts and incorporating new findings, maintaining a robust and up-to-date Knowledge base. This method underscores the importance of Knowledge renewal, retaining core Knowledge while continuously discarding obsolete parts and adding the latest research outcomes. For example, in medical education, with the release of new clinical research results, teaching content should be promptly updated by removing disproven theories and including the latest medical practices [87].
## 2 Background
### Literature Review  
In recent years, machine learning techniques have been applied to assist in the resolution of intractable problems with metaheuristics [7], leveraging the high volume of data that can be obtained from solved problem instances to approximate both algorithmic decisions and design decisions. In ref. [8], supervised learning was applied to determine whether linearizing a quadratic programming problem would reduce solving time. With a similar approach, a metaheuristic's computational load can be reduced by approximating costly calculations with a regression model. Supervised learning can also be used to evaluate parametrizations on novel instances, potentially producing a parametrization better adapted to the particular instance.  
An unsupervised learning approach for combinatorial optimization is presented in [9]. Here, the authors evaluate the use of clustering in evolutionary algorithms to reduce the cost of evaluating populations (Fitness Imitation) and the use of clustering algorithms to group problem instances to obtain a set of quality metaheuristic parameters per group.  
Relevant conclusions are presented in [10], where the authors propose investigating the application of machine learning techniques to predict the evolution of dynamic instances to efficiently update a metaheuristic's parameters. The authors also reflect on the potential of using unsupervised learning to group problem instances to discriminate their underlying distributions to optimize the search of parameters.  
ISAC (Instance Specific Algorithm Configuration) [9] proposes the use of a clustering algorithm to configure the parameters for each group of similar problem instances. When a new problem instance needs to be solved, the automatically configured parameters of the most similar instance group are used. Here, instances were clustered according to the similarity of their extracted features. Similarly, the authors of [11] propose the construction of a portfolio of parameter configurations. Novel instances would then be solved by sampling from said portfolio with several methods, including a reinforcement-learning agent.  
Figure 1: Capacitated Vehicle Routing Problem with time windows scheme.  
For metaheuristic parameter tuning, ref. [2] presents three ways in which machine learning techniques can be applied. Here, a supervised learning approach is used, and a feature set must be constructed to train a machine-learning model for parameter prediction. While some feature sets for TSP and VRP problems have already been proposed [12, 13], no current feature set includes both capacity-related and time-related features.  
In the literature, there are some machine learning applications similar to our proposal, such as [14, 15, 16, 17, 18, 19, 20]. However, there are important differences in the selected machine learning algorithms. For example, in [PERSON]'s future work, it is mentioned that artificial neural networks could be considered in the design and comparison with their proposal. Moreover, no work was found that uses k-nearest neighbors to predict metaheuristic parameters.