esa-sceva/satcom-chunk-collection huggingface dataset:
# A Local Feature Engineering Strategy to Improve Network Anomaly Detection +
## 5 Proposed Strategy  
The Local Feature Engineering (LFE) strategy proposed in this work introduces a series of new features in the set of features \(F\) that characterizes each network event. The addition of new features is then followed by a discretization process [91] applied on them, which is aimed to reduce the number of potential event patterns, since the result is the aggregation of the similar ones.  
Since the computational complexity related to the definition of a classification model in the intrusion detection domain [92] is proportional both to its feature space (in terms of number of features) and its data (in terms of number of events), such a pattern reduction has a positive effect to the computational load.  
The high-level architecture of the proposed LFE strategy is shown in Figure 5. It indicates how such a strategy is based on two different data paths: the first one is related to the preprocessing of set \(E\) (classified events) and set \(\hat{E}\) (unclassified events) through the LFE strategy (i.e., feature addition and values discretization), whereas the second one is aimed to detect the optimal \(\delta\) value to use for the previously mentioned discretization process. This process is denoted as \(\circlearrowright\), and it is performed by using only \(E\). As last step, the evaluation model is trained by using set \(E\), previously preprocessed according to the \(\delta\) value we defined. The unevaluated events in set \(\hat{E}\), whose features have also been preprocessed by using the same \(\delta\) value, are then classified on the basis of the defined evaluation model. All the implementation details are reported in Sections 5.1 and 5.2.
# Fourier Neural Operator Network for Fast Photoacoustic Wave Simulations
## 3 Results  
### Breast Vasculature Simulation  
An FNO network with the hyperparameters of 5 channels and 64 Fourier modes was trained using the breast vasculature dataset. The trained FNO network was used to predict and simulate photoacoustic wave propagation for \(n=100\) initial photoacoustic sources in the breast vasculature testing dataset. The photoacoustic wave simulations produced by the FNO network and k-Wave were remarkably similar and essentially identical to the naked eye (Figure 3). The FNO simulations successfully maintained the sharp edges and fine image details of the acoustic waves as they propagated throughout the medium. This demonstrated that the FNO network can model the broadband and high-frequency waves required for photoacoustic simulations.  
Errors in the FNO network were quantitatively measured using the RMSE. The distribution of normalized photoacoustic pressures decreased as the simulation continued forward because energy dissipated within the medium as the acoustic waves propagated and exited the medium (Figure 4). For the testing dataset, the RMSE was several orders of magnitude smaller than the distribution of photoacoustic pressures in the simulations. This indicated that the errors in the FNO network simulations were small compared to the actual acoustic pressures, and the FNO network simulations were highly accurate approximations of the k-Wave simulations for photoacoustic wave propagation. Here, the RMSE was orders of magnitude smaller than the photoacoustic pressure distribution in the simulations, which can be attributed to the inherent model properties of globally learning frequency domain features and using the entire Fourier mode without any truncation. Therefore, the broadband frequency components originally distributed in the photoacoustic time series data can be described by sufficient frequency components.  
A key advantage of data-driven PDE solvers over traditional ones is the vast reduction in computation time. In general, the time required to solve the photoacoustic wave equation largely depends on the discretization of the computational grid. For a computational grid of 64 \(\times\) 64, k-Wave on average required 1.63 s to complete the simulation on a GPU. The FNO network on average required 0.061 s to complete it on the same GPU. This was approximately a 26\(\times\) reduction in computation time.  
Figure 4: RMSE and standard deviation bands between the k-Wave and FNO simulations on the breast vasculature test dataset. Distribution of pressures (25 th, 50 th and 75 th percentiles) are provided as a frame of reference.  
Figure 3: Visual comparison of the ground truth (**Top** Row) using k-Wave and the FNO network. (**Bottom** Row) simulated photoacoustic wave propagation for an example vasculature image in a homogeneous medium at t = [1, 20, 40, 60, 80] time steps. The RMSE over all time steps was 3.8 \(\times\) 10\({}^{-3}\) for this example.
## 6 A Practical Example for GDM with IVIFPRs
### A Practical Example of Enterprise Innovation Partner Selection
Through Equation (36), the scores function \(S(w_{i})\) and accuracy degrees of \(H(w_{i})\) of \(w_{i}(i=1,2,3,4)\) are obtained as \(S(w_{1})=0.2454\), \(S(w_{2})=0.3092\), \(S(w_{3})=0.2747\), \(S(w_{4})=0.2122\), \(H(w_{1})=0.9394\), \(H(w_{2})=0.9466\), \(H(w_{3})=0.9444\), \(H(w_{4})=0.9344\),  
Thus, descending scores function \(S(w_{i})(i=1,2,3,4)\) and accuracy degrees \(H(w_{i})(i=1,2,3,4)\), the ranking order of alternatives is \(x_{2}>x_{3}>x_{1}>x_{4}\), and \(x_{2}\) is the best partner. Additionally, when \(\varphi\) takes different values, we get the associated calculation results and ranking orders shown Table 1. For different consistency threshold \(\mathrm{MCI}_{0}\), the corresponding computation results are listed in Table 2.  
It can see from Tables 1 and 2 that the ranking order results are always \(x_{2}>x_{3}>x_{1}>x_{4}\) and the best partner is \(x_{2}\). Moreover, the ranking order result derived from the absolutely multiplicative consistent IVIFPR (i.e., \(\mathrm{MCI}_{0}=0\) and \(\varphi=0.5\)) is the same as other situations in Table 2. Consequently, it may be robust and applicable to directly adopt the proposed algorithm in this paper for addressing practical GDM with IVIFPR.
# Privacy and Utility of Private Synthetic Data for Medical Data Analyses
## 4 Medical Use Case
### Secondary Use Scenario  
In this work, a scenario of secondary use of medical data are considered. Within this scenario, several individuals donate their data to a research project which aims to analyze the data for certain properties. Fitness trackers and wearables are becoming increasingly popular, as users of these devices generate a large amount of health data that could also be of interest to researchers. The most common data collected by such trackers are the heart rate and step count. Continuous heart rate monitoring can provide deep insights into an individual's health. One property that can be identified by tracking the heart rate is the presence of what is known as tachycardia [33]. Tachycardia can be diagnosed if the heart rate exceeds 100 beats per minute (BPM). In most cases, this can be explained through physical activity. However, tachycardia at rest can be an indicator of more serious health conditions. In our scenario, wearable users donate their heart frequency data, which are then analyzed for tachycardia. Although this seems like a superficial analysis, this paper aims for a real-world use case where accurate medical data are needed.  
To simulate this scenario, a dataset of crowdsourced data from Fitbit health trackers is used [34] that was created by the RTI International Institute. The data were collected through a survey on Amazon Mechanical Turk. This dataset contains continuous heart rate measurements obtained by Fitbit wearables from 30 participants over a one-month period. In detail, the approximately 15 million data points including heart rate measurements (5- or 10-s interval), step counts (60-s interval) and the duration of sleep. Each entry of the dataset has a timestamp and a participant ID. For privacy reasons, there is no additional information or demographic data about the participants. It should be noted that only the heart frequency measurements are used in this work (heartrate_seconds_merged.csv). Figure 1 shows an exemplary plot of one of the heart rate measurements of the dataset.  
These measurements follow a typical scheme: There are some phases with a higher heart rate (during daytime) and lower heart rate at night. Furthermore, there are some peaks that may indicate exercise or other physical activity. Even such a small subsample of data shows that it is possible to detect the activity patterns of individuals which might not be obvious when sharing these data.
## 2 Materials and Methods
### MP Identification and Quantification
#### 2.4.3 Micro-Raman Analysis  
Micro-Raman microscopy was conducted using a fully integrated confocal Raman microscope instrument (Labram SoleilTM, Horiba Scientific, Lyon, France) equipped with two gratings by default, a multichannel detector and a computerization package. Raman spectra were obtained with a 532 nm wavelength laser and power at 1.9-3 mW at variable magnifications of 50\(\times\) to 100\(\times\). The Raman spectra were collected in the 600-3800 cm\({}^{-1}\) range. The integration time was 20-200 s (usually 200 s), and the cumulation number was 1-10 times (usually 5). To obtain the spectrum with low fluorescence and the highest possible quality, we used different Raman settings for the same spot or collected multiple spectra at different spots on the same sample. Collected spectra were baseline corrected to enhance the quality of spectra and matched between recorded spectra and reference from the KnowItAll library database with >70% identification. Finally, the Particle FinderTM software was applied to biota samples, an application that allows automated location of particles, analysis of key particle parameters, such as size and mass, and subsequent chemical characterization with Raman. It is especially beneficial in cases where the number of particles is large, like MPs, where manually locating and marking each particle would be time-consuming, outweighing the benefits that Raman can offer.
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
###### Abstract  
Internet of Things (IoT)-based devices consist of wireless sensor nodes that are battery-powered; thus, energy efficiency is a major issue. IEEE 802.15.4-compliant IoT devices operate in the unlicensed Industrial, Scientific, and Medical (ISM) band of 2.4 GHz and are subject to interference caused by high-powered IEEE 802.11-compliant Wireless Local Area Network (WLAN) users. This interference causes frequent packet drop and energy loss for IoT users. In this work, we propose a WLAN Aware Cognitive Medium Access Control (WAC-MAC) protocol for IoT users that uses techniques, such as energy detection based sensing, adaptive wake-up scheduling, and adaptive backoff, to reduce interference with the WSN and improve network lifetime of the IoT users. Results show that the proposed WAC-MAC achieves a higher packet reception rate and reduces the energy consumption of IoT nodes.  
wireless sensor networks; medium access control; IEEE 802.15.4; IEEE 802.11
## 3 Results and Discussion
### Influence of Feed Rate and Applied Voltage  
We study experimentally how the hole diameter depends on the feed rate and the applied voltage, and conclude from the measurements and the calculated average values that the diameter is between 162.00 \(\upmu\)m and 284.67 \(\upmu\)m. Figure 6 shows that the diameter increases with decreasing feed rate and increasing applied voltage.  
Figure 4: Shape of holes drilled with different duty cycles: **(a)** 50%; **(b)** 30%; **(c)** 20%; **(d)** 10%.  
Figure 5: Shape of holes drilled with different frequency: **(a)** 10 kHz; **(b)** 20 kHz; **(c)** 50 kHz; **(d)** 100 kHz.  
Using multivariate regression analysis, the quantitative relationship among the diameter \(D\), the feed rate \(V\), and the applied voltage \(U\) is:  
\[D=22.13+15.57U+15.17V+1.3U^{2}-4.819 UV+3.208\mathrm{V}^{2} \tag{1}\]  
Figure 7a shows a three-dimensional shape achieved by cubic spline interpolation based on experimental results. Figure 8a shows a contour plot of the diameter, showing how the feed rate and the applied voltage influence the diameter. Figures 7b and 8b show the results corresponding to Figures 7a and 8a, but plotted using Equation (1). The left side of the yellow lines in Figures 7b and 8b are generated by equation, which cannot be acquired by experiments. On the contrary, the right side can be actually obtained. The fitting error between the two sets of results is less than \(\pm 7\) um, and shown in Figure 7c. Then, we could find a reasonable parameter combine from the equation or the contour plot when drilling a specific diameter micro-hole.  
Figure 8: Contour plot: (**a**) Generated by cubic spline interpolation; (**b**) plotted using Equation (1).  
Figure 6: Relationship between diameter and feed rate for different values of applied voltage.  
Figure 7: Three-dimensional surface plot: (**a**) Generated by cubic spline interpolation; (**b**) plotted using Equation (1); (**c**) fitting error between (**a**) and (**b**).
## 3 Proposed CNN-NAP Method  
In Reference [25], 29 common features of the signal were firstly extracted; then, these features of the signal under different working conditions were projected by NAP; finally, a back propagation neural network (BPNN) was adopted for classification. The method in Reference [25] has achieved a good classification effect, but the structure is complex. In this paper, a more intuitive and concise method using NAP for fault diagnosis is presented.  
The proposed CNN-NAP method is based on the combination of NAP and CNN, and the structure is shown in Figure 2. The proposed method cancels the step of extracting signal features and directly applies the CNN to the training of original signal classification. Nuisance attribute projection is seen as an FC layer added before softmax regression. In the back propagation, in addition to the cross entropy between softmax outputs and the labels, the objective function also add Equation (8) to minimize. The difference is that since NAP is extended to multiple faults, the value of \(\mathbf{W}\) here is:  
\[\mathbf{W}_{ij}=\left\{\begin{array}{ll}1&workingcondition(f_{i})\
eq working condition(f_{j})\\ 0&workingcondition(f_{i})=workingcondition(f_{j})\\ 1&faultpattern(x_{i})=faultpattern(x_{i})\\ 0&faultpattern(x_{i})\
eq faultpattern(x_{i})\end{array}\right.. \tag{10}\]  
In addition, note that the projection matrix \(\mathbf{P}\) minimizing the nuisance attribute in the sub-space must satisfy the idempotent condition (i.e., \(\mathbf{P}^{\mathbf{T}}\mathbf{P}=\mathbf{P}\)). Thus, finally, the objective function is:  
\[argmin(CE+PE+IC), \tag{11}\]  
where \(CE\) represents the cross entropy between softmax outputs and the labels, it is expressed by:  
\[CE=-\sum y_{i}\mathit{lna}_{i}, \tag{12}\]  
where \(y\) represents real value, \(a\) represents the output value of softmax, and \(i\) represents the node number of the fault category. \(PE\) is the value obtained by Equation (8), \(IC\) is the value of \(\|\mathbf{P}^{\mathbf{T}}\mathbf{P}-\mathbf{P}\|\). In this way, by minimizing the objective function can not only train the network but also obtain a better projection matrix. In the next section, the structure of the model will be introduced according to the measured data.
# Secure Cognitive Radio Vehicular Ad Hoc Networks Using Blockchain Technology in Smart Cities
## 2 Literature Review
Likewise, in [13], an approach to managing traffic routes for fog-based VANETs was proposed using homomorphic encryption to encrypt driving routes and send the encrypted information to the fog node. The traffic management center uses decrypted messages to manage traffic without knowing the precise route of each vehicle. The scheme was evaluated on the Ethereum test network and showed better performance than other schemes. To detect malicious nodes, [PERSON]. [14] employed blockchain-based deep learning techniques. Similarly, [PERSON]. [15] proposed a framework that is inspired by blockchain to ensure safe routing in multiple domains of the internet of things. In order to enable the routing of device owners in autonomous systems and gain significant control over inter-domain networks, a blockchain-based self sovereign identity system has been proposed in [16]. All the schemes discussed above have failed to address the problems of spectrum scarcity, routing, and communication security simultaneously by mutually sharing the spectrum between primary and secondary users and assessing delivery time and overhead consumption as performance metrics. Hence, to the best of our knowledge, this is the first work that incorporates blockchain technology to ensure secure communications by simultaneously considering spectrum scarcity and routing. As vehicle communication is necessary to inform vehicles on the road about any upcoming problem in a timely manner, we need to make sure that drivers take part in the idea of making the road safety experience even better. Why would someone want to invest their time in informing someone of a potential roadblock ahead? Why would someone spend money for the betterment of other people on the roads if they do not know whether other people will do the same for them or not? To inspire them do this, we need to reward them for taking part in the betterment of our society. This research aims to explore the efficiency of implementing blockchain technology to enhance security in cognitive vehicular communication and to evaluate how a fair reward mechanism can induce vehicles to participate in road safety and help reduce road incidents.
# MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System
## 4 Deep Learning-Inspired Sleep Control Strategy
### MECS Sleep Control Vector Determination  
After collecting \(\{Q_{i}(t),\tilde{\lambda}_{i}(t),F_{i}:\forall i\in\mathcal{M}\}\) at the beginning of each time slot \(t\), a controller determines an MECS sleep control vector by using GA. To exploit GA, we define a fitness function for a combination \(x\in\Omega(t)\) as \(f(x)=-E(t)\) if \(x\) satisfies the queue length constraint \(Q_{i}(t)<Q_{th},\forall i\in\mathcal{M}\). When \(x\) violates the queue length constraint, we set \(f(x)=-\infty\).  
We create a population \(X_{c}\) by randomly selecting \(n\) combinations from the possible combinations of \(\Omega(t)\). Among the combinations in \(X_{c}\), we find the best combination \(x_{c}^{*}\) as follows.  
\[x_{c}^{*}=argmax_{x\in X_{c}}f(x). \tag{11}\]  
To construct a crossover set \(Z_{1}\) from \(X_{c}\), we create a temporary set \(Y\) by selecting the best \(\lfloor n/2\rfloor\) combinations from \(X_{c}\) based on the fitness values of \(\forall x\in X_{c}\). Then, we randomly select two combinations \(x\) and \(y\) from \(Y\) and crossover them to make two children, \(z_{1}\) and \(z_{2}\). Specifically, we randomly select an index \(k\) from \([1,m]\), where \(m=|\mathcal{M}|\) is the number of MECSs in an MEC system. Then, we make a child combination \(z_{1}\) by concatenating \(x[1:k]\) and \(y[k+1:m]\), where \(x[1:k]\) denotes the first \(k\) elements in \(x\) and \(y[k+1:m]\) represents the last \(m-k\) elements in \(y\). We also make another child combination \(z_{2}\) by concatenating \(y[1:k]\) and \(x[k+1:m]\) and put both \(z_{1}\) and \(z_{2}\) into the crossover set \(Z_{1}\). To construct a mutation set \(Z_{2}\), we mutate the children combination \(z_{1}\) and \(z_{2}\) as follows. After randomly selecting an index \(k\in[1,m]\), we mutate \(z_{a}(a\in\{1,2\})\) by changing \(z_{a}[k]\) from 0 to 1 or vice versa. Then, we add the mutated \(z_{1}\) and \(z_{2}\) into the mutation set \(Z_{2}\).  
After repeating the crossover process and the mutation process \(\lfloor n/2\rfloor\) times, we find the best combination \(x_{n}^{*}\) in \(X_{c}\cup Z_{1}\cup Z_{2}\). If \(f(x_{c}^{*})<f(x_{n}^{*})\), we replace \(x_{c}^{*}\) with \(x_{n}^{*}\). We construct a new population \(P_{n}\) by randomly choosing \(n\) combinations from \(X_{c}\cup Z_{1}\cup Z_{2}\) and replace \(P_{c}\) with \(P_{n}\). We repeat the whole process \(n_{g}\) times and output the final \(x_{c}^{*}\) as \(\Omega^{*}(t)\). We summarize the MECS sleep decision algorithm in Algorithm 1.
## 3 Methods  
### Experimental Setup  
In order to answer the research questions, a series of tests have to be carried out. All the tests use the same setup to ensure a fair comparison between the models. The experiments run on an Acer Predator Helios 300 laptop (Acer Inc., New Taipei City, Taiwan) with a 12-core Intel Core i7-10750H 2.60 GHz CPU, 32 GB of RAM, and an NVIDIA GeForce RTX 2070 GPU with 8 GB of GDDR6 memory. The laptop runs Windows 11 with Ubuntu 24.04 LTS via Windows Subsystem for Linux (WSL). It has Python 3.12.4, PyTorch 2.3.1, and CUDA 12.1.0 installed. The experiments use the same configuration for both models to maintain consistency and fairness in the comparison.  
We intentionally chose a consumer laptop instead of a high-end computing system to evaluate the models' performance in a real-world scenario that is more accessible to general users, particularly to medical professionals who work with systems featuring diverse hardware configurations depending on the specific hospital or healthcare facility. This decision allows us to assess whether these models can be efficiently deployed on consumer-grade hardware, which is crucial for wider adoption in practical settings. Furthermore, considering that medical images contain sensitive patient data, it is essential to ensure that segmentation processes can be executed on dedicated medical hardware systems. By using a typical consumer laptop, we aim to demonstrate the feasibility of running advanced medical image segmentation models on affordable and widely available equipment rather than on specialized servers that may not be as readily accessible.  
Different pre-trained checkpoints are available for each model. Table 1 contains the names of the models and their respective sizes for reference, with the chosen models'checkpoints in bold. The greater the size of the model, the better it performs. The choice to test SAM is ViT-L, as the laptop's GPU cannot load the ViT-H checkpoints due to its GPU memory limitations. However, in the case of SAM 2, the large version of the model can run without problems. Although the test machine cannot run the best version of SAM, it can run the best version of SAM 2. Note that the best SAM 2 model size is 28.14% smaller than the best SAM model and 64.99% smaller than the one used for the comparison.