esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Films Characterization
#### 3.1.4 Thermal Characterization  
TGA and DSC tests were carried out to study the influence of the addition of PA and PB on the thermal stability and crystallinity of LDPE-based films. All samples presented a characteristic DSC curve showing exothermic crystallization and endothermic melting transitions at around 98 \({}^{\circ}\)C and 108 \({}^{\circ}\)C, respectively. Melting and crystallization temperatures and enthalpies and calculated crystalline contents for LDPE samples are summarized in Table 2.  
As it is shown, the addition of PA and PB to the LDPE did not modify the melting temperature (T\({}_{\text{m}}\)). However, a significant change in the melting enthalpy was detected, which corresponded to the decrease in the weight fraction of LDPE in the copolymer because of incorporation of the additives. Similar results were obtained by [PERSON] et al. [38]  
Figure 2: Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) spectra of PE, PE/PA, PE/PA/PB5 and PE/PA/PB13 films.  
related to LDPE/thermoplastic starch composites. Regarding crystallization content (\(X_{\rm c}\)), this parameter slightly increased with the addition of PB (\(p\)! 0.05) due to the interaction between PB and the polymer matrix showing some plasticizing effect as indicated previously by the mechanical properties. [PERSON] et al. (2019) reported similar results working with LDPE denoting that the increase in the chain mobility promoted by the plasticizing effect enhanced the ability of the polymer to crystallize.  
Regarding TGA, the initial degradation temperatures (T\({}_{\rm ini}\)) determined at 5% weight loss and the maximum degradation temperatures (T\({}_{\rm max}\)) of the studied films are reported in Table 2. Developed films showed one degradation step with a T\({}_{\rm max}\) around 475 \({}^{\circ}\)C. No statistically significant differences in T\({}_{\rm max}\) were observed among all the studied samples. However, the incorporation of both additives, PA and PB at different concentrations led to a significant reduction on T\({}_{\rm ini}\) values of the films (\(p\)! 0.05). In this sense, when compared to neat LDPE, the addition of 5 wt % of PA, resulted in a 15 \({}^{\circ}\)C reduction in T\({}_{\rm ini}\) whereas the addition of 5 wt % and 13 wt % of PB confirmed a decrease in T\({}_{\rm ini}\) of 19 and 26 \({}^{\circ}\)C, respectively. These results could be explained by the plasticizing effect of the additives, as already suggested by mechanical and DSC results. In this sense, it has been reported that the addition of plasticizers to different polymer matrices result in some decrease in the thermal properties ([PERSON] et al., 2019).
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Whisper
#### 3.4.1 Scope and Objectives  
The goal of this project was to repetitively transmit pulse signals to compute the impulse response of the HF channel in wide bandwidths and study some features, such as signal time of flight, number of propagation modes, time dispersion, frequency dispersion and Doppler shift, fading statistics, absolute signal power, noise and interference. The point was to find out those channel characteristics that might impair wide-band transmission and then design proper signals characteristics to compensate most of them. A particular purpose was to investigate whether the Watterson [52] uncorrelated Rayleigh fading model, used to represent narrowband channels (\(\leq\)12 kHz) was applicable to wideband channels.  
The main motivation of this project is to increase the HF link throughput to support user application growths. Previous research effort focused on increasing throughput on narrow-band HF channels (e.g, Reference [53]), but few studies ([54]) investigated using wider bandwidths. However, there was no consensus on which channel model was able to describe both the large channel features (e.g., Doppler shift and spread and multimode propagation) and the fine properties of these channels such as fading statistics (inter and intra mode). Therefore they proposed increasing the data rate of the HF channel not by increasing spectral efficiency in a standard 3 kHz bandwidth but using a wider bandwidth in order to use several simultaneous standard channels.
# Unraveling the Impact of Class Imbalance on Deep-Learning Models for Medical Image Classification
## 3 Results and Analysis
### Results and Analysis for One Layer
Upon analyzing this model, which achieves an overall accuracy of 81% in tests, Table 4 provides estimates of 302, 95, and 118 samples for the examined classes based on their respective quantities. However, the actual predictions per class are 359, 67, and 92. Therefore, it would be inaccurate to claim that this model achieves an 81% accuracy in class prediction, as the actual class-specific prediction percentages are 96.25%, 56.78%, and 63.01%, thus demonstrating the limitations of using a global metric as an indicator of detailed class performance. This analysis concludes by highlighting a significant discrepancy between the overall accuracy in tests and the class-specific accuracy within models. It is emphasized that a high level of overall accuracy does not necessarily ensure equitable performance across all classes. Models exhibit considerable variability in predicting labels for specific classes, suggesting that overall accuracy might mask significant shortcomings in class-specific accuracy. Additionally, phenomena such as overfitting and targeted improvements in the prediction of particular classes are not necessarily reflected in the overall accuracy. This underscores the importance of conducting a detailed evaluation of class-specific performance to gain a comprehensive understanding of a model's effectiveness, particularly in situations where maintaining a balance among classes is crucial. \begin{table}
\begin{tabular}{l r r r r r r r r r r r r r r r r r} \hline \hline  & & & & & & & & & & & & & & & & & & & \\ keras\_30 & 85 & 0.71 & 373 & 264 & 450 & 118 & 83 & 187 & 146 & 103 & – & 70.78 & 120.64 & 70.34 & 158.47 & 70.55 & – \\ (0 keras\_3 & 1000 & 0.75 & 373 & 279 & 368 & 118 & 88 & 23 & 146 & 109 & 84 & 74.80 & 98.66 & 74.58 & 19.49 & 74.66 & 57.53 \\ keras\_150 & 77 & 0.68 & 373 & 253 & 373 & 118 & 80 & 264 & 146 & 99 & – & 67.83 & 100.00 & 67.80 & 223.73 & 67.81 & – \\ keras\_150 & 1000 & 0.77 & 373 & 287 & 325 & 118 & 90 & 44 & 146 & 112 & 120 & 76.94 & 87.13 & 76.27 & 37.29 & 76.71 & 82.19 \\ keras\_cnn\_40 & 88 & 0.78 & 373 & 290 & 462 & 118 & 92 & 60 & 146 & 113 & 115 & 77.75 & 123.86 & 77.79 & 50.85 & 77.40 & 78.77 \\ keras\_cnn\_40 & 1000 & 0.81 & 373 & 302 & 399 & 118 & 95 & 67 & 146 & 118 & 92 & 80.97 & 96.25 & 80.51 & 56.78 & 80.82 & 63.01 \\ keras\_cnn\_450 & 53 & 0.80 & 373 & 298 & 380 & 118 & 94 & 90 & 146 & 116 & 167 & 79.89 & 101.08 & 79.66 & 76.27 & 94.15 & 14.13 \\ keras\_cnn\_450 & 1000 & 0.80 & 373 & 298 & 362 & 118 & 94 & 57 & 146 & 116 & 93 & 79.89 & 97.05 & 79.66 & 48.31 & 79.45 & 63.70 \\ neuralnet\_100 & 62 & 0.60 & 373 & 223 & 317 & 118 & 70 & 243 & 146 & 87 & 66 & 59.79 & 84.99 & 59.32 & 205.93 & 59.59 & 45.21 \\ neuralnet\_100 & 1000 & 0.58 & 373 & 216 & 241 & 118 & 68 & 66 & 146 & 84 & 43 & 57.91 & 64.61 & 57.63 & 55.93 & 57.53 & 29.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of model performance with actual vs. expected quantities for 1000 training epochs.
## 1 Introduction
### Slicing for Additive Manufacturing  
The special geometrical characteristics of thin-walled structures mean that the focus is on the contour boundary region. Although it does not usually need to be filled internally, it still needs to go through a layered slicing phase due to the layer-by-layer buildup. In AM, the common format used for transferring and reading part models is STreeoLithography (STL). Therefore, the input data for the slicing algorithm are usually in an STL file, which contains an unordered collection of triangular facets to fit the model surface, each with the attributes of vertex coordinates and normal vectors [5]. Initially, a uniform planar slicing algorithm emerged, which was based on the principle of using a set of equally spaced planes as cross sections to intersect the part model [6; 7]. These types of methods suffer from a step effect that affects the surface quality when dealing with surfaces. This led to the emergence of adaptive slicing algorithms [8; 9] dedicated to improving this problem, which use variables instead of constant layer thickness parameters. However, the adaptive algorithm not only fails to take into account the balanced transition of layer thicknesses but also affects the subsequent process flow, and thus, it has not been widely used and disseminated [10]. Later, non-planar slicing algorithms appeared, mainly including cylindrical slicing [11], spherical slicing [12], and free-form surface slicing [13]. Cylindrical and spherical slicing are designed to meet the process requirements of variable-curvature machining substrates, while free-form surface slicing is usually realized based on vector fields, which are difficult to control, and the high degree of uncertainty between neighboring layers does not apply to metal AM.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 2 System Model
### uRLLC Traffic
#### 2.5.2. uRLLC Mean Packet Delay  
In this system, the deadline interval of uRLLC UE \(l\) associated with BS \(b\) is contributed by the time processing of backhaul and RAN, are represented as \(T_{bl}^{WBH}\) and \(T_{bl}^{RAN}\), respectively.
## 3 Methodology
### Loss Function  
In the network model, the chosen loss function integrates Binary Cross Entropy (BCE) [38] and Dice coefficients [39]. The task of water body delineation falls under binary classification within semantic segmentation, targeting the discernment of water bodies from their surrounding terrain in images. BCE, a standard in binary classification scenarios, is adopted as the foundational loss metric. This loss criterion quantifies the discrepancies at the pixel level between the predicted outputs and actual labels, employing natural logarithm calculations as detailed in Equation (4):  
\[L_{BCE}=-\frac{1}{N}\sum\
olimits_{i=1}^{N}(y_{i}log\hat{y_{i}}+(1-y_{i})(1- log\hat{y_{i}})) \tag{4}\]  
where \(N\) denotes the number of samples; \(y_{i}\) denotes the true label value; \(y_{i}\in\{0,1\}\); and \(\hat{y_{i}}\) denotes the predicted probability of a particular area being classified as a water body.  
However, BCE loss focuses on pixel-level classification accuracy, posing challenges when subjects are minimal or sparse, resulting in the loss value being disproportionately influenced by the background imagery. Particularly in water body identification tasks, images from mountainous or urban regions frequently feature limited water areas. To address this imbalance, the Dice Loss function is incorporated, computing the congruence between actual labels and predictive outputs from the model. Dice Loss alleviates the detrimental effects arising from foreground-background area discrepancies within the samples, as delineated in Equation (5):  
\[L_{DICE}=1-\frac{2|Y\cap P|}{|Y|+|P|} \tag{5}\]  
where \(Y\) denotes the true labeled value; \(P\) denotes the model predicted value; and \(\mid\cdot\mid\) denotes the number of elements in the set.  
The overall loss function of the network model can be expressed as in Equation (6):  
\[Loss=\lambda_{1}\times L_{BCE}+\lambda_{2}\times L_{DICE} \tag{6}\]  
where \(\lambda_{1}+\lambda_{2}=1\), the performance of the model is optimized through the calibration of the weights attributed to the two loss functions during the training process.  
Figure 8: Structure of FPA.
# Interference Mitigation for GNSS Receivers Using FFT Excision Filtering Implemented on an FPGA
## 3 Proposed Methodology
### Windowing
Analysis of Window Types  
Figures 9 and 10 show the effect of different types of windowing when a fixed frequency interference is applied to the filter depicted in Figure 8. Some windows, such as Hann and BlackmannHarris, provide a high level of interference suppression (Figure 9) while the Hamming window provides the best spectral containment (Figure 10) with reasonable suppression. The specific interference scenario determines which is the better choice. For example, the Hamming window performs well with multiple jammers as it removes less of the usable spectrum in the passband. However, if only one or two jammers are present, a BlackmannHarris window is preferable. Figure 8: Proposed FFT mitigation algorithm.
## 3 Internet of Things Applications for Healthcare Systems
IoT platforms are able to support pervasive healthcare by adopting wearable devices for data collection and mobile devices to upload the data to servers, for communication and interfacing with sensors that allow measurement of physiological parameters [99]. Healthcare systems based in IoT architectures provide an effective way to monitor and store health and well-being continuously [2] but further offer protocols to be ubiquitous and fully modified for personalized healthcare [100; 101]. UT-GATE offers native capabilities for well-being supervision software, such as the local repository, compression, signal processing, data standardization, web services, protocol translation and tunneling, firewall, data mining, and notifications, being proposed by [102]. The Health-IoT (in-home healthcare IoT architecture) delivers a business technology with co-design organization for cross-boundary incorporation forin-home medical systems based in IoT but has a deficiency of interoperability [103]. The correlation of a diversity of healthcare systems and sensors could provide earlier medical interventions rather than a detection of advanced stage diseases and provide preventive care [100]. Table 2 aims to analyze a number of studies of the current state of the art, which are categorized by application, sensing, configuration setting, connectivity, access to data, results, and limitations. Furthermore, Table 2 presents a comparison summary, which will be analyzed afterwards in order to extract lessons and common threads on the enhanced healthcare systems research. Table 3 presents a review summary on the communication technologies used for the design of healthcare systems. The Wi-Fi communication is commonly used to provide Internet connection; however, due to energy consumption concerns, it is not used in battery-powered systems. The GPRS/3G/HSDPA and 4G are incorporated in the majority of smartphones and are significant to provide Internet connection when Wi-Fi networks are not available. Several healthcare systems are designed to use the smartphone as a gateway, where the data is collected using wearable sensors and is transferred to the smartphone. In these scenarios, the interface commonly used is Bluetooth since this is a low-consumption communication technology. Furthermore, RFID and NFC are used to provide identification of the patients or the devices and rarely serve the purpose of transmitting data from one device to the other. From the analysis of Table 3, it is possible to conclude that the least-used technology for data communication in these scenarios is cabled Ethernet, followed by ZigBee, this being used in three studies. The most used technologies, used in six studies, are the Bluetooth protocol and the GPRS/3G/HDSPA/4G, with Wi-Fi in second place. The conclusions are aligned with the requirements for low power consumption, portability, and lightweight of data communication technologies.
## 3 Design and Construction of Laser Detection System  
According to the measurement experiment of the reflection spectra and statistical analysis of the data, the values of the most sensitive wavelength \(\lambda_{1}\) and least sensitive wavelength \(\lambda_{0}\) of all cells are determined, and then a laser detection system can be built for measure the color of polycrystalline silicon cells. The system is composed of an optical laser for measurement with the central wavelength \(\lambda_{1}\), an optical laser for reference with the central wavelength \(\lambda_{0}\), two optical shutters, two collimating beam expanding systems, an optical coaxial system, a sample platform for the polycrystalline silicon cell, a collecting lens, and an optical power meter or optical sensor, as shown in Figure 8.  
The generally chosen optical lasers for measurement and for reference of this system are either a solid-state or semiconductor laser with a small volume and easy adjustment. Two laser beams from optical laser for measurement and for reference pass through two optical shutters, two optical filters, and two collimating beam expanding systems respectively to realize the switch control, light filter, aberration correction, collimation and expansion of the beams. The collimating beam expanding system is realized by a pair of convex lenses with different focal lengths. The purpose of beam expanding is to increase the spot size of the laser beams to fit the surface size of the sample. The optical axis coincidence of two laser beams after collimation and expansion is realized in the optical coaxial system. The optical coaxial system is composed of a specially coated reflection mirror, which has full transmission effect on the optical laser for reference and total reflection effect on the optical laser for measurement. By adjusting the positions of the optical elements on the optical path, the optical laser for reference and for measurement can be adjusted to the same optical axis, so as to ensure the incident of two laser beams to the same position of the sample surface, so as to reduce the errors caused by the irregular surface of the sample. Two laser beams on the same optical axis incident on the sample platform are reflected or scattered due to the condition of the sample surface. For the irregular surface of a polycrystalline silicon cell, scattering generally occurs. Reflected or scattered light is collected through a lens with a high numerical aperture and received by optical power meter or optical sensor. The wavelength range of the optical power meter or optical sensor needs to match the wavelengths of the optical beams both for measurement and for the reference. When scattering occurs, the position of collecting lens and optical power meter is placed in the area with strong scattered light according to the scattering situation of the sample surface. When the sample is actually detected, the results of the optical beams for measurement and for reference exposure to the sample can be obtained respectively by controlling the light shutter.  
Figure 8: The schematic diagram of light path of the laser detection system.
# A Survey on Botnets, Issues, Threats, Methods, Detection and Prevention
## 2 Definition of Botnets  
[PERSON] et al. [3] defines botnets as one of the main methods of transporting malware throughout networks. This methodology is responsible for a multitude of cyber-attacks such as Distributed Denial of Service (DDoS) attacks. Botnet DDoS attacks are performed by infecting a target network device. The infected device would then become known as a \"zombie\" a device under a threat actor's control. By using the zombie, bot-masters can then spread the botnet throughout the network and reach other connected devices. For DDoS attacks, the botnet can cause the network's bandwidth to be used up more than what is intended and will disrupt all services that each host would contain [4]. Since network services may be in failure, this may compromise data availability. [PERSON] et al. [5] defines a botnet as a series of malware-infected devices that then create a network of said devices to propagate the target network further. The spread can be used to prevent services and disrupt the data availability. Therefore, denying permitted users to access specific information when it is needed. Botnets are utilized in a variety of cyber-crimes, be it enabled (where technology is used to commit them but are not relied on) or dependent (where technology is required to do so, such as DDoS or stealing sensitive information of their choice). [PERSON] et al. [5] further mentions that the hacker instigating a botnet attack would be referred to as the bot-master, who is also responsible for controlling the bot network. [PERSON] et al. [3] and [PERSON] et al. [5] provided similar definitions into what a botnet is, along with discussing the rise in such attacks. Ref. [3] emphasizes that hackers would normally target Peer-to-Peer networks, a type of network that has computers with similar roles connected to the network with their data shared via wired networks lacking servers. Such infrastructure can be useful for Local Area Networks (LANs) [6]), as there would be more connectivity between network devices so the malware can go from one infected device to another device at a faster rate.  
Businesses are common targets for botnets to commit information theft [7] enabling bot-masters to perform further criminal activities [3,5]. In the past, zombies (i.e., infected victim computers) were originally used for Internet Relay Chats (IRCs), allowing assistance with messaging throughout the internet [7]. An IRC channel enables users to join and communicate with other users who exist on that channel, behaving very similar to social media chat rooms. If the channel had many users, then bots would be created to monitor the channel. However, in such a case, the bot was not an infected machine as known today; rather, it was an automated program for IRC channel administration. Over time, botnets were used to attack IRC servers by sending a large number of network packets resulting in DDoS attacks as the traffic was too much for the target IRC server to manage.
## 3 Methodology
### System Model
#### 3.1.2 Power Efficiency Modeling  
If we consider a physical machine \(h\) with a CPU capacity of \(mips(h)\) (expressed in Million Instructions Per Second, or MIPS) and a maximum power consumption of \(\mathbb{P}^{max}(h)\) (measured in watts) reflecting the overall power draw from all the PM dimensions (CPU, RAM, network, and disk storage), we can define the power efficiency of \(h\) as follows:  
\[\mathbb{P}^{e}(h)=\frac{mips(h)}{\mathbb{P}^{max}(h)}. \tag{2}\]  
This metric denotes the computational power provided by a specific physical machine per watt of energy consumed. For instance, the machine in our running example from Table 2, Lenovo ThinkSystem SD535, V3 boasts a computational power of 288,000 MIPS and a maximum power consumption of 1321 watts, yielding a power efficiency score of 218.02. Conversely, Lenovo ThinkSystem SR850 V3 offers a computational power of 456,000 MIPS and a maximum power consumption of 1916 watts, resulting in a power efficiency score of 237.99. Consequently, Lenovo SR850 V3 demonstrates superior power efficiency compared to ThinkSystem SD535.