esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 8 Challenges
### Challenge 4  
Smart healthcare devices from different manufacturers may not be able to communicate with each other, which can make it difficult to optimize QoS across the entire system. One approach to achieving interoperability in smart healthcare applications in the context of QoS improvement is to use a standard for data sharing, such as HL7 FHIR (fast healthcare interoperability resources) [78]. This standard makes it easy to transfer data between different platforms and systems while guaranteeing that the data is accurate and current. Making sure that data is consistently formatted and easy for different systems to interpret can also be accomplished with the use of a standard data model, such as the one provided by HL7 FHIR. Another approach is to implement a distributed architecture, which would allow for decentralized communication between various services, lessen dependency on a central hub, and boost robustness and scalability. The use of reliable and secure communication protocols, such as MQTT, CoAP, etc., could be an additional solution [79]. Regarding high throughput, low power consumption, and short latency, these protocols are designed to maximize service quality. Overall, building interoperability in smart healthcare applications while enhancing QoS requires the use of standardized datainterchange formats and protocols as well as the creation of a robust and adaptable design that can adapt to the changing requirements of the healthcare system.
## 5 Experimental Analysis
### Objective Function and Optimal Solution Set Optimization  
#### 5.1.1 Objective Function Comparison  
In the experimental testing of objective function optimization, the proposed third-generation non-dominated sorting genetic algorithm (NSGA-III) based on reference points for LEO satellite jamming was tested under the constraint of at least 90% jamming coverage. Subsequently, it was compared with the constraint-based dual-population evolutionary algorithm (c-DPEA), multi-task constraint multi-objective optimization algorithm (MTCMO), multi-strategy improved continuous explosive algorithm (MSCEA), and constraint-based multi-objective optimization algorithm utilizing unconstrained and constrained Pareto front relationships (URCMO). After 100 iterations, the multi-objective Pareto frontier sets for power, delay, and frequency compensation were obtained, as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline LEO Satellite Frequency Bands & 37.5–42.5 GHz \\ Altitude of LEO Satellites & 900 km \\ Number of Visible LEO Satellites & 11 \\ Number of Jammers & 20 \\ Altitude of Jammers & 10–20 km \\ Jamming Radius & 20 km \\ Area Size & 20 km \(\times\) 20 km \\ Number of Target Central Points & 25 \\ Population Size & 1000 \\ Maximum Iteration Count & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Jamming scene parameter settings.  
Figure 6: Pareto frontiers generated with different optimization algorithms.  
From Figure 6, it can be observed that the objective function values generated with the URCMO algorithm were scattered and did not converge to a stable Pareto frontier, while the other four algorithms all converged to a stable Pareto frontier, with the Pareto frontier generated with the MSCEA algorithm being relatively divergent. In terms of total jamming power, the NSGA-III algorithm optimization produced a jamming power of \(-125\) dBm, while the jamming powers optimized with the MSCEA, c-DPEA, and MTCMO algorithms were all around \(-128\) dBm, thus proving that the NSGA-III algorithm maximized the jamming power. Under similar total frequency compensation, the NSGA-III algorithm produced a smaller total delay compared to the other three algorithms, for instance, at a total relay delay of \(1.2\) us with a total frequency compensation of \(724\) KHz for low-Earth-orbit constellations, while those of the other algorithms were all around \(1.76\) us. Similarly, under a similar total delay, the NSGA-III algorithm produced smaller total frequency compensation compared to the other three algorithms; for example, at a total delay of \(1.4\) us, a total frequency compensation of \(695\) KHz was obtained for low-Earth-orbit constellations, while those of the other algorithms were around \(800\) KHz, thereby demonstrating the effectiveness of the NSGA-III algorithm in optimizing in the power, time, and frequency domains for distributed jamming of the LEO satellites' downlink.
## 6 Conclusions  
Results of the present study indicate the high potential of Sentinel-2 data for applications in applied forestry and vegetation analysis despite the deca-metric spatial resolution. Our proposed workflow achieved overall classification accuracies of 88% and 85% for the study area and in a transferability study respectively, indicating its robustness and potential for scaling to a larger level. The design and backend technology allow the use on large datasets due to the concept of raster functions that are capable of on-the-fly and distributed processing.  
A comparison of SVM and RF classifiers using PB and OBIA hierarchical classification approaches showed, that OBIA approaches are better suited than PB approaches in this setting. The slightly better performance of the SVM classifier is probably due to the training data used, as discussed in the previous section. Along these lines, machine learning algorithms are strongly dependent of the quality of the training data (here: inventory data) as well as on the selected areas. Thus, future studies are needed to evaluate the workflow in other forest areas to assess the effect of different forest structures and other tree species. Testing of different band combinations for improving the classification results, highlighted the importance of band 8 in combination with the red edge bands as well as the other 10 m resolution bands of the spring and summer scenes. The red edge part shows clearly visible differences in the spectral profiles, but higher resolution 10 m bands were crucially important for good results. The lower spatial resolution (20 m) for the red edge region (bands 5 to 7) somewhat diminishes its capability to correctly classify vegetation on a species or even tree-patch level while using PCs of the summer scene together with the May scene shows best results for broad-leaved tree types.  
We conclude that the proposed design is well-suited to be used on larger areas with a similar forest structure and allows a streamlined workflow for applied forestry by providing analysis results directly to mobile applications for validation and data collection in the field. We showed that Sentinel-2 data is a suitable, cost-free alternative to commercial satellite data with higher spatial resolution for classifying trees at a stand level using machine-learning algorithms.  
Conceptualization, M.W., M.B. and D.T.; Methodology, M.W., M.B.; Supervision, M.B. and D.T.; Writing-original draft, M.W.; Writing-review & editing, M.B. and D.T.  
This research received no external funding  
We would like to thank the Bayerische Staatsforsten for providing the forest inventory data used in this study and the Bayerische Vermessungsverwaltung for their support with orthophotos. Furthermore, we would like to thank [PERSON] and [PERSON] for discussions and for helping with the provision of all datasets. The Open Access Publication Fund of the University of Salzburg supported this publication.  
The authors declare no conflict of interest.
## 3 Conversion Method for CCA-Secure KEM  
### Kem  
#### Syntax  
We follow the syntax of KEM from the work in [16]. A key encapsulation mechanism \(\mathsf{KEM}=(\mathsf{KEM}.\mathsf{Setup},\mathsf{KEM}.\mathsf{Encap}, \mathsf{KEM}.\mathsf{Decap})\) consists of three algorithms: The setup algorithm \((\mathsf{PK},\mathsf{SK})\stackrel{{\$}}{{\leftarrow}}\mathsf{ KEM}.\mathsf{Setup}(\lambda)\) takes as input the security parameter \(\lambda\) and outputs a key pair \((\mathsf{PK},\mathsf{SK})\). The encapsulation algorithm \((\mathsf{K},\mathsf{CT})\stackrel{{\$}}{{\leftarrow}}\mathsf{ KEM}.\mathsf{Encap}(\mathsf{PK})\) generates a key \(\mathsf{K}\) and a ciphertext \(\mathsf{CT}\) from input \(\mathsf{PK}\). The decapsulation algorithm \(\mathsf{K}\leftarrow\mathsf{KEM}.\mathsf{Decap}(\mathsf{PK},\mathsf{SK},\mathsf{ CT})\) takes as input a public key \(\mathsf{PK}\), a private key \(\mathsf{SK}\), and a ciphertext \(\mathsf{CT}\) and outputs a key \(\mathsf{K}\).
## 2 Literature Review
### Wind-Probability-Distribution-Function-Based Wind Speed Prediction Model  
Wind-probability-distribution-function-based WSP models were developed by assuming that wind speed follows the same distribution for the next time period. This approach is aligned with the concept behind the persistence model (PM), according to which, any future wind speed value is equal to its last known value of wind speed due to the high autocorrelation on the behavior of the wind speed [7]. Despite its simplicity, the PM produces excellent WSP results and is used to assess the quality of new WSP methods [23]. PMs forecast wind speed (\(u_{t+h}\)) at any future time, as \(t+h\), \(h>0\) is the same as wind speed (\(u_{t}\)) at current time \(t\).  
\[u_{t+h}=u_{t},\hskip 14.226378 pth>0 \tag{1}\]  
Wind speed follows non-negative and right-skewed distribution rather than the normal distribution [24]. Several probabilities distribution functions are right-skewed and non-negative and are used for modeling wind speed. Weibull distribution and Rayleigh distribution are the most common probability distribution functions for wind speed modeling [25]. Although the Weibull distribution function is the most widely used function, there is no consensus on which best describes wind speed data for a specific case study site. Therefore, in this study, we consider both Weibull- and Rayleigh-based wind speed modeling and forecasting.
## 1 Introduction
* The suggested approach attempts to improve communication between AVs and destinations (AV2X) in terms of energy efficiency (\(EE\)) and achievable data rate (\(R\)). This can be accomplished by identifying the shortest possible distance between AVs and anything in order to maximize total EE and R. Alternately, other factors should be taken into account like transmission power, interference distances that could arise from existing transmission devices sharing the same spectrum, the necessary signal-to-interference-plus-noise ratio (\(SINR_{th}\)), and path loss. * Through the deep learning model, AVs will be able predict the maximum suitable permissible transmission distance while taking into consideration various environmental conditions. * The proposed approach is examined in terms of energy efficiency and overall achievable data rate under various environmental conditions, such as transmission power, transmission ranges, and needed \(SINR_{th}\) values. These discoveries allow for the enhancement of AV networks' performance. The following is the order of the sections: The relevant work will be presented in Section 2. Section 3 will go over the specific proposed strategy in depth. The experimental and analytical work of the suggested approach will be displayed in Section 4. Finally, Section 5 will present the conclusion.
# Inertial Parameter Identification in Robotics: A Survey
## 8 Introducing the BIRDy Matlab Toolbox: A Benchmark for Identification of Robot Dynamics
### Trajectory Data Generation  
To avoid data rank deficiency of the observation matrix \(\mathbf{W}\), the different components of the model should be sufficiently excited during the experiments. This can be achieved by means of specific joint trajectories, referred to as _exciting_. The design of excitation trajectories has been widely investigated over the past two decades, see e.g., [99; 100; 101; 23; 23; 38; 53; 98; 102]. From these works, it appears that the conditioning of the observation matrix \(\mathbf{W}\) is a relevant quality indicator for the generated trajectory. In our case, joint trajectories are obtained  
Figure 6: Block diagram of \(BIRDy\), the Benchmark for Identification of Robot Dynamics. In this figure, \(\bar{\mathbf{q}}_{d},\dot{\mathbf{q}}_{d},\mathbf{q}_{d}\) refers to the desired trajectory data, obtained from the trajectory interpolator, \(\bar{\mathbf{q}},\mathbf{\dot{\tau}},\bar{\mathbf{t}}\) respectively denote the noisy joint positions, torques and (in the case of a real robot) drive current measurements. The actual data \(\bar{\mathbf{q}}_{m},\dot{\mathbf{q}}_{m},\mathbf{q}_{m},\mathbf{\tau}_{m}\) used for identification, is obtained after a short pre-processing step of the raw experiment data.  
by parametrization of finite Fourier series as presented in [9; 23; 101]. The following cost criterion \(J_{t}\) is minimized over the experiment time horizon using a standard nonlinear programming solver (in our case the \"_fmincon_\" and \"_ga_\" Matlab functions):  
\[J_{t}=k_{1}\cdot\text{cond}(\mathbf{W}^{\top}\mathbf{W})+\frac{k_{2}}{\sigma_{min}}\,, \tag{67}\]  
where \(\text{cond}(\cdot)\) refers to the condition number of a matrix; \(\sigma_{min}\) is the smallest singular value of \(\mathbf{W}\); and \(k_{1},k_{2}\in\mathbb{R}_{+}\) are tuning gains defined by the user. It should be emphasized that this method also allows the generated trajectories to be constrained to comply with the physical limitations of the considered robot, such as joint, velocity, torque or operational space limits. The generated trajectory is eventually stored in a dedicated data container. We provide multiple interpolation routines, based on the \"_interp1_\" Matlab function, so that relevant trajectory data can be obtained at any epoch, from a limited number of points, either generated by the optimizer or coming from a pre-existing file, created outside BIRDy. Please note that any third-party trajectory file containing a time series of joint values can be imported into the benchmark, provided its data are first stored in one of its predefined data containers.
# Raman Spectroscopy to Monitor the Delivery of a Nano-Formulation of Vismodegib in the Skin
## 3 Results and Discussion
### VDG Determination in Skin by UV-Visible Spectrophotometry  
VDG was detected throughout all sections of the human and pig skin: shallow SC (1-5), medium SC (6-10), deep SC (11-20), and VED (Figure 2). Remarkably, while in the samples of human origin VDG were predominantly distributed in the first part of the SC, with a minor penetration percentage into the VED, in the pig skin samples, the opposite occurred, with a higher presence of the drug in the VED and lower retention in the SC.  
While the literature generally validates the use of _ex vivo_ porcine ear skin as a surrogate for human skin [19,20], our results, along with previous findings, draw attention to certain differences. A previous study regarding the penetration of fluorescently-labeled transfersomes in both human and pig skin [21] had shown some differences between these skin models for the penetration of this type of carrier, which was reaffirmed here with the profile of T + VDG. This is also consistent with observations made in previous studies [22], where in vivo human skin was compared to _ex vivo_ pigskin at room temperature. In that case, higher permeability for pigskin to substances of different solubilities was noted. In this case, with both _ex vivo_ samples incubated at body temperature, that trend is maintained. It is important to emphasize that the penetration of transfersomes depends on the existence of a moisture gradient in the skin. This should be taken into account regarding excipients that could be used in final formulations, as the literature shows how the Raman signal is affected at different depths of the skin by substances that can remove water, causing dehydration [23].
## 2 General Model
### Users  
Users want to subscribe to a sensor-data service offered by the IoT-SPs. The number of users is \(M\), where \(M\gg 1\). The utility of a user making the choice \(j\) is based on [41; 21]  
\[U_{u_{j}}=\alpha\log\left(\frac{\beta R_{j}}{f_{j}}\right)+\kappa_{u_{j}}, \tag{5}\]  
where the first part of the expression is deterministic and is related with the market parameters while the second part \(\kappa_{u_{j}}\) is treated as a random variable that models the unobserved user-specific part of the utility. The random variable \(\kappa_{u_{j}}\) follows a Gumbel distribution of mean 0. The human behavior is hard to predict and usually users within the same population do not have the same preferences. For instance, while some users always prefer the cheapest option others only will change their decision if the difference in the perceived utility is high enough. All these unknown effects are aggregated in the random variable \(\kappa_{u_{j}}\). In the deterministic part \(R_{j}\) is the quality of the data provided by the WSN to the IoT-SP\({}_{j}\), \(f_{j}\) is the price per time unit that users pay to the IoT-SP\({}_{j}\) for its service, \(\beta\) is a conversion factor and \(\alpha>0\) is a sensitivity parameter that models the relative importance of the rate \(R/f\). Larger values of \(\alpha\) increase the impact of the rate \(R/f\) in users' choices, while lower values of \(\alpha\) reduce the impact. In our model we set the conversion factor \(\beta=1\). We obtain the expression for \(R_{j}\) assuming that the quality of the information is proportional to the number of sinks sending data to the IoT-SP\({}_{j}\)  
\[R_{j}=x_{1}rN_{j}. \tag{6}\]  
The logarithmic relation between physical magnitudes and the human perception observed in (5) has been justified in telecommunications through the Weber-Fechner Law [42; 43; 44].  
The users will choose the IoT-SP\({}_{j}\) that provides him the highest utility \(U_{u_{j}}\geq U_{u_{k}}\ \forall\ k\
eq j\). The distribution of users in the system is described by the vector \(X_{u}=(y_{0},y_{1})\), where \(y_{0}\) is the fraction of users not subscribed to the IoT-SP and \(y_{1}\) is the fraction of users subscribed to IoT-SP. Note that \(y_{0}+y_{1}=1\).
# Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data
## 1 Introduction  
Feature selection is a fundamental challenge in analyzing high-dimensional data, where datasets often contain numerous irrelevant or redundant features. Removing these features enhances predictive accuracy, reduces computational complexity, and improves model interpretability. By selecting the most relevant subset, models can focus on informative data, leading to more efficient and accurate predictions.  
In bioinformatics, feature selection helps identify key biological markers or genes associated with specific processes or diseases, enabling deeper insights into biological mechanisms while improving predictive reliability. It is also widely applied in fields such as finance, healthcare, and text classification, where efficient handling of high-dimensional data is essential.  
As a crucial step in data preprocessing, feature selection reduces overfitting, enhances generalization, and decreases computational costs. Methods for feature selection are typically categorized into filter, wrapper, and embedded approaches [1, 2].  
Filter-based feature selection methods utilize statistical measures in order to rank features or to give various features in a dataset a numerical rank in order to be able to discard irrelevant features with a low statistical score. Filter-based methods are often computationally efficient, but they sacrifice accuracy due to the fact that they look at the features individually without taking into account the relationships between features. For example, two features may be insignificant on their own, but when paired together, they might heavily affect the data.  
Wrapper methods are often referred to as trial-and-error methods because they take into account every possible combination of features and choose the best one. As a result, wrapper methods for feature selection are often the most computationally expensive, but yield the most accurate results. The work being performed focuses on performing feature selection for highly dimensional data (large number of features), so wrapper-based approaches take an incredibly long time to run; thus, testing them has proven to be difficult.  
Embedded methods perform feature selection during the model training process. The benefit of these methods is that they take into account interactions between features, something that filter-based approaches completely ignore. Embedded methods often provide a good balance between computational cost and accuracy.  
A hybrid method can be any combination of two types of feature selection methods or even a feature selection method and another algorithm that may not generally be used for feature selection.
###### Abstract
There is also an application from the company Philips (a vital signs camera) that uses an iPad2 to measure the heart and breathing rates, as well as an application based on the MIT technology developed by Cardiio Inc., which measures the heart rate from a distance using a mobile device camera to measure the heart rate on the iOS platform. Based on these technological acquisition principles, we created our mathematical method and procedure to acquire PPM using a mobile device. The technique presented here contributes to new advances in technology and health because it is a non-invasive technique that allows one to estimate the heart rate through the use of common mobile devices, which, through cameras and algorithms, are able to easily determine real-time variations in skin color that are imperceptible to the eye. This method provides a new application for the conventional mobile devices that we have today within our reach, allowing us to monitor the vital signs of users and, in turn, facilitating the prevention of diseases. This non-invasive contactless application allows users to determine their health condition without the need for medical equipment. In addition, this tool is able to be used on patients without limbs, since it does not require direct contact. This paper presents a methodology, using a mobile application, for using images obtained by a video camera on a mobile device in the real-time estimation of heart rate based on the changes of tonality that occur on the skin. These readings are performed without the necessity for physical contact with the patient.