esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
They concluded that uneven energy depletion is intrinsic to the system and no routing strategy can avoid energy hole around the sink when \(\alpha=2\). For larger values of \(\alpha\), the uneven energy consumption can be prevented by judicious system design and the energy consumption is suboptimally balanced across the network. [PERSON] _et al._[13] proposed the SSEP-Non-uniform Sensor (SSEP-NS) distribution model and the SSEP-NS routing protocol to increase the network data capacity. The SSEP-NS network is partitioned into sub-areas and the sub-areas are further partitioned into sub-regions. The closer a sub-region is to the sink, the higher the sensor density in the sub-region. To save energy, sensor nodes work in an active-sleep model. The SSEP-NS routing protocol is used to maitain the property of uniformly distributed active sensor nodes with the minimum density at any time. In this work, only energy consumption for data transmission considerd. [PERSON] _et al._[14] investigated the theoretical aspects of the nonuniform node distribution strategy used to mitigate the energy hole problem in WSNs. They concluded that it is impossible to achieve completely balanced energy depletion among all the nodes due to the intrinsic many-to-one traffic pattern in a circular multi-hop WSN. However, nearly balanced energy depletion in the network is possible by using their proposed nonuniform node distribution strategy, wherein the number of nodes grows in geometric progression from the outer coronas to the inner ones, except the outmost one. They assumed that the nodes in the network constantly report data to the sink and nodes are active all the time. Energy consumption for both data transmission and reception is considered. For their proposed nonuniform node distribution strategy, they also proposed the \(q\)-Switch routing algorithm, in which the relay node with the maximum residual energy among \(q\) relay nodes in the adjacent inner corona is selected. In their network model, the boundary between adjacent coronas is fixed and the network only lasts to the time when the first node is dead. As introduced in Section 1, in MOL-enabled [18] PMRC-based WSNs, the layer boundary between neighbor layers is dynamically changed during network lifetime. As such, the aforementioned solutions may not be suitable for the energy hole problem in MOL-enabled PMRC-based WSNs.
# Is Malware Detection Needed for Android TV?
## 3 Methods
### The Collection of Android TV Application Packages  
Previous research has typically only investigated smartphone applications due to their popularity. The Androzoo [20] dataset provides millions of Android applications collected from several sources, including the official Google Play application market. However, it is not possible to identify these TV applications without a further analysis since they are not labeled in the Androzoo [20] dataset. To the best of our knowledge, there is no publicly available dataset in the literature for Android TV applications, except the study from [PERSON] et al. [24], which contains the SHA-256 checksums of applications. We conducted a search for Android TV applications using the SHA-256 information provided in the Androzoo [20] dataset, but some of them were not found in the dataset. We were able to collect 1107 Android TV application packages from the list shared by [PERSON] et al. [24].  
Figure 1: Methodologies in our study.  
We also decided to create an Android TV application dataset from scratch and implemented a web scraper to collect applications from an alternative application market called APK Mirror, which listed applications for various devices (e.g., phones, watches, TVs, Auto, etc.). We scraped the links for 600 TV application packages listed on the APK Mirror website and downloaded all of them. A collection of 370 TV applications was created by eliminating the application packages for different versions of the same application and packages that were not in APK format among the downloaded packages.  
The average package size of the 1107 TV applications in the Androzoo dataset is 26.4 MB, while the minimum and maximum sizes are 23.5 kB and 145.2 MB, respectively. For the 370 TV applications in the APK Mirror dataset, the average package size is 28.2 MB, while the minimum and maximum sizes are 3.2 kB and 191.1 MB, respectively. The minimum, maximum, and average file sizes of the TV application packages in the Androzoo and APK Mirror datasets are presented in Table 2.
# A Metamaterial Inspired AMC Backed Dual Band Antenna for ISM and RFID Applications
###### Abstract  
This work presents the design and fabrication of a metamaterial-based stimulated dual band antenna on FR4 material (dielectric constant 4.3) to operate in Industrial, Scientific and Medical (ISM) and Radio-frequency Identification (RFID) applications. The antenna model had an overall dimension of \(70\times 31\times 1.6\) mm\({}^{3}\) with etched T-slots and L-slots for dual band resonance. The main objective of this work was to enhance the gain performance characteristic at the selected dual band frequencies of 0.915 GHz and 2.45 GHz. Initially, it achieved a narrow bandwidth of 0.018 GHz with a gain of 1.53 dBi at a lower frequency, and 0.13 GHz of bandwidth featuring 4.49 dBi of gain at a higher frequency. The antenna provided an impedance bandwidth of 2% (0.905-0.923 GHz) and 5% (2.382-2.516 GHz) at two resonating frequencies. The antenna was integrated with a designed novel AMC structure to enhance the gain in CST Microwave Studio software with the finite integration method. The characteristic features of the AMC unit cell were observed at 0.915 GHz and 2.45 GHz frequencies and after antenna integration, the final prototype achieved a gain of 2.87 dBi at 0.915 GHz and 6.8 dBi at 2.45 GHz frequencies.  
metamaterial; microstrip antenna; gain; dual band frequencies; AMC backing structure; wireless communication 2022
# Scalability Analyses of Premium Connections Characterized by a Surface Fractal Function
## 2 Analysis of Fractal Surface Contact Behavior
### Analysis of the Contact Behavior of the Full-Size Area
By comparing the changes in contact area and contact pressure between Figure 5a,b, the fractal dimension \(D\) increased from 2.1 to 2.9, and the contact area increased by 9 \(\times\) 10\({}^{-5}\) m\({}^{2}\). At the same time, the contact pressure decreased by 1.31 \(\times\) 10\({}^{9}\) Pa. Conversely, when the scale coefficient G increased from 1.6 \(\times\) 10\({}^{-9}\) to 2.4 \(\times\) 10\({}^{-9}\) m, the contact area decreased \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & \(D\) & \(G\) **(m)** & \(E\) **(Pa)** & \(H\) **(Pa)** & \(\gamma\) \\ \hline (a) & 2.1–2.9 & 2.0 \(\times\) 10\({}^{-9}\) & 7.58 \(\times\) 10\({}^{8}\) & 9.85 \(\times\) 10\({}^{8}\) & 1.5 \\ (b) & 2.5 & 1.6–2.4 \(\times\) 10\({}^{-9}\) & 7.58 \(\times\) 10\({}^{8}\) & 9.85 \(\times\) 10\({}^{8}\) & 1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fractal surface and material parameters. Figure 5: Contact pressure and contact area curves with different fractal parameters. (**a**) Fractal dimension \(D\). (**b**) Scale coefficient \(G\). by 2.5 \(\times\) 10\({}^{-5}\) m\({}^{2}\), while the contact pressure increased by 2.53 \(\times\) 10\({}^{8}\) Pa. It can be inferred that the fractal dimension \(D\) exerts a more significant impact on the actual contact area and pressure compared to the scale coefficient \(G\). Therefore, in the subsequent analysis, the focus is on the influence of the fractal dimension \(D\) on the contact mechanics and sealability of the premium connections. An error analysis of the model in this study revealed that the influence of deformation behavior on the surface asperities and the interaction between them was ignored, which is main reason for the observed errors. Both of these factors can reduce the stiffness of the sealing surface, and the greater the load, the more significant the influence [23].
# Conformal Test Martingale-Based Change-Point Detection for Geospatial Object Detectors
## 4 Experiments
### Comparison Experiments
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **Mean Delay** & **POFD** & **POD** \\ \hline Alg.1 & 40.0 & 0.0000 & 0.9801 \\ CTM-C(\(\epsilon=5\)) & 27.1 & 0.0002 & 0.9864 \\ CTM-C(\(\epsilon=10\)) & 26.6 & 0.0000 & 0.9867 \\ CTM-C(\(\epsilon=15\)) & 27.9 & 0.0000 & 0.9860 \\ Alg.2(\(r_{n}=0\)) & 40.0 & 0.0000 & 0.9801 \\ Alg.2(\(r_{n}=0.1\)) & 39.9 & 0.0000 & 0.9801 \\ Alg.2(\(r_{n}=0.5\)) & 37.7 & 0.0000 & 0.9812 \\ Alg.2(Adaptive) & **26.0** & 0.0000 & 0.9870 \\ \hline \hline \end{tabular} The lowest Mean Delay is shown in bold and underlined.  
\end{table} 
Table 4: Change-Point Detection From Bright to Dark Domain. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **Mean Delay** & **POFD** & **POD** \\ \hline Alg.1 & 53.6 & 0.0008 & 0.9573 \\ CTM-C(\(\epsilon=5\)) & 46.0 & 0.0000 & 0.9633 \\ CTM-C(\(\epsilon=10\)) & 37.3 & 0.0000 & 0.9703 \\ CTM-C(\(\epsilon=15\)) & 37.5 & 0.0000 & 0.9700 \\ Alg.2(\(r_{n}=0\)) & 53.5 & 0.0008 & 0.9574 \\ Alg.2(\(r_{n}=0.1\)) & 48.6 & 0.0949 & 0.9613 \\ Alg.2(\(r_{n}=0.5\)) & 37.4 & 0.2493 & 0.9702 \\ Alg.2(Adaptive) & **31.0** & 0.0008 & 0.9753 \\ \hline \hline \end{tabular} The lowest Mean Delay is shown in bold and underlined.  
\end{table} 
Table 3: Change-Point Detection From Blur to Clear Domain. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **Mean Delay** & **POFD** & **POD** \\ \hline Alg.1 & 51.1 & 0.0003 & 0.9687 \\ CTM-C(\(\epsilon=5\)) & 41.6 & 0.0002 & 0.9745 \\ CTM-C(\(\epsilon=10\)) & 36.7 & 0.0000 & 0.9774 \\ CTM-C(\(\epsilon=15\)) & 37.8 & 0.0000 & 0.9768 \\ Alg.2(\(r_{n}=0\)) & 50.8 & 0.0003 & 0.9689 \\ Alg.2(\(r_{n}=0.1\)) & 48.6 & 0.0153 & 0.9702 \\ Alg.2(\(r_{n}=0.5\)) & 42.9 & 0.0970 & 0.9737 \\ Alg.2(Adaptive) & **31.8** & 0.0003 & 0.9805 \\ \hline \hline \end{tabular} The lowest Mean Delay is shown in bold and underlined.  
\end{table} 
Table 5: Change-Point Detection From Dark to Bright Domain. All of the experiments demonstrate the worth of introducing conformal test martingale to the detection of change-point for geospatial object detection with few false alarms, and the proposed Alg.2(Adaptive) can achieve equivalent and in some cases superior performance to the state-of-the-art CTM-based methods for change-point detection. The experimental results also show that although the proposed approach violates validity, it empirically performs well in most cases with proper momentum parameters. Besides, since Alg.1 can test IID assumption with a theoretical basis, the experimental results of Alg.1 also verify our partition for different domains proposed in Section 3.1 statistically.
## 3 Results
### Recognition Results and Performance Analysis
#### 3.1.2 Classification Performance Comparison of Models  
To fully verify the accuracy of this method, a total of 1200 groups of data obtained from four kinds of defect models are selected as test samples. The MCNN is compared with the other six models in terms of performance, including three deep neural network models, AlexNet; VGG16; Resnet18; and three lightweight convolutional networks, including SqueezeNet1.0, DenseNet121, and MobileNetsV2. The recognition accuracy (Accuracy) is used as a unified index to quantitatively analyze the performance of each model, and the pattern recognition results are shown in Table 3.  
As can be seen from the data in Table 3, the overall recognition rate of the MCNN is 98.55%, which is significantly higher than that of the three kinds of depth convolution models: AlexNet, ResNet18, and VGG16 model. Compared with the MobileNetsV2 network without SE module, the MCNN designed in this paper has improved the recognition ability. Among the four compressed convolution models, the recognition accuracy of the model designed in this paper is the highest. In addition, the classification results obtained from the seven models are ideal, because the PRPD spectra are used as the input sample, and the spectra contain PD information over a period of time, so they can better reflect the characteristics of different types of PD and promote the recognition rate of each model generally higher. Among all the types of PD, the recognition rate of suspended discharge and tip discharge is relatively high. This is because the data analysis of PD detection in Section 2.1 shows that the signal spectra of these two types have obvious characteristics of amplitude and phase, while the statistical spectra of air gap discharge and surface discharge are similar.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Recognition Accuracy/\%**} & \multirow{2}{*}{
\begin{tabular}{c} **Average** \\ **Accuracy** \\ \end{tabular} } \\ \cline{2-2} \cline{4-6}  & **Tip** & **Surface** & **Air-Gap** & **Suspended** & **Discharge** & **Accuracy** \\ \hline AlexNet & 92.52 & 82.42 & 81.81 & 96.71 & 88.37\% \\ ResNet-18 & 94.32 & 88.42 & 90.22 & 100 & 93.24\% \\ VGG16 & 93.28 & 88.34 & 86.79 & 97.36 & 91.44\% \\ SqueezeNet1.0 & 91.01 & 82.17 & 80.64 & 95.33 & 87.29\% \\ DenseNet121 & 100 & 94.37 & 95.54 & 100 & 97.48\% \\ MobileNetsV2 & 99.41 & 90.51 & 93.24 & 100 & 95.79\% \\ OurModel & 100 & 96.31 & 98.53 & 100 & 98.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Core module construction of MOBILENETS.  
Figure 7: The influence of initial learning rates on the accuracy of MCNN.
###### Abstract  
The effects of the presence of perfluorocarbons (PFC) with a gas transport function in media with different phototrophic microorganisms on their growth rates and the accumulation of their biomass when using free and immobilized cells as inoculums were investigated. The significant increase in the average rate of biomass accumulation as well as levels of biomass accumulation in the presence of various PFCs were established for _Chlorella vulgaris_ cells. When 1 g/L glycerol was introduced into the growth medium with PFCs and _C. vulgaris_ cells, the increase in the rate of biomass accumulation was 9-32%. The maximum intracellular ATP concentrations corresponded to the combination of microalgae (_Chlorella vulgaris_) with bacterial cells (_Pseudomonas esterophilus_ and _Rblockoccus ruber_) obtained with a mass ratio of 25:1. It provided for the formation of a consortium, which was able to accumulate the maximum amount of microalgae biomass for 3 days in the medium with PFCs and organophosphorus pesticide. The obtained data allow, on the one hand, predicting the growth of microalgae under environmental conditions in media with PFC pollution and, on the other hand, developing approaches to regulation of phototrophic microorganisms' growth in order to obtain and use their high biomass yields for various purposes.  
microalgae; fluoroorganic compounds; gas transport function; biomass accumulation; bacteria-microalgae consortium; methyl parathion degradation Article
# Lightweight Path Recovery in IPv6 Internet-of-Things Systems
## 5 Performance Evaluation and Analysis
### Comparison of the Accuracy  
Figure 7 demonstrates the accuracy of the three algorithms in different network scenarios. As shown in Figure 7a, when the number of nodes is 300, and the percentage of fault nodes is 5%, the accuracy of the 3 algorithms improves as the active ratio of the nodes changes. This is because, with the active ratio of the nodes increasing, the more packet information the algorithm can utilize, the more beneficial it is to path recovery. The accuracy of the LiPa algorithm is consistently higher than iPath and CSPR, improving by over 30% on average. As shown in Figure 7b, when the percentage of fault nodes is 5% and the active ratio of the nodes is 30%, the accuracy of LiPa and CSPR is stable, while iPath decreases as the number of nodes changes. This is because the higher the number of nodes, the more complex the network topology is. iPath has a specific performance bottleneck by using only the path correlations at that moment. In contrast, LiPa and iPath can use the information for longer time durations to solve the complex topology. The accuracy of LiPa is still consistently higher than that of the other 2 algorithms, improving by over 50% on average compared with iPath and over 20% on average compared with CSPR. As shown in Figure 7c, when the number of nodes is 300, and the active ratio of nodes is 30%, the accuracy of the 3 algorithms decreases as the percentage of fault nodes changes. This is because the more fault nodes there are, the more dynamic the network topology is and the more difficult it is to recover the path. The accuracy of LiPa is still consistently higher than that of iPath and CSPR, improving by over 55% on average compared with iPath and by over 35% on average compared with CSPR.  
We can conclude that LiPa consistently outperforms iPath and CSPR in the above network scenarios. The reason for this is that LiPa takes similar features of both iPath and CSPR and makes further enhancements. On the one hand, both LiPa and iPath have the idea of path correlation. Still, LiPa uses the information of parent and grandparent nodes to extend the possible cases of correlation, using the information of a sliding window to perform path recovery. On the other hand, both LiPa and CSPR utilize temporal information, but LiPa processes this information by path correlations rather than compression-aware methods.  
Figure 6: The Performance Improvement of LiPa. (**a**) _Accuracy_ with varying number of nodes. (**b**) _Accuracy_ with varying active ratio of nodes. (**c**) _Accuracy_ with varying percent of fault nodes.
# Leveraging Large Language Models for Sensor Data Retrieval
## 3 Architecture of the System
### Data Gathering  
The aim of the first component is to gather sensor data and ensure that they are in a structured reusable format that is suitable for further exploitation by the second component. As mentioned previously, sensor data frequently originate from web pages [2], presenting challenges in their handling due to system and data heterogeneity. Consequently, effective management requires significant effort. The primary objective of this first component is to improve sensor data accessibility by harnessing the contextual comprehension and text generation capabilities inherent in modern LLMs to convert raw sensor data into a reusable format.  
This procedure comprises two steps: data identification and, if needed, employing a LLM to parse raw sensor data and convert them into a structured format suitable for sharing and reuse. Figure 1 presents a visual summary of this process. The following lines elaborate on each of these steps.
# Use Electroencephalogram Entropy as an Indicator to Detect Stress-Induced Sleep Alteration
## 4 Discussion
### Amplitude Entropy  
Using Fourier transform to measure the EEG features is a standard method for analyzing polysomnography findings during sleep. Fourier transform is temporally dependent. In Figure 2 we observed a linear correlation with the time variable by using Fourier transform. For instance, the EEG power of NREM sleep gradually attenuated as the function of time. We further noticed some nonlinear correlations. For example, the power for frequencies of each temporal bin (15 min) is not linear and the width of theta frequency during REM sleep was narrower in the footh shock group than that in the control group. Therefore, using the Shannon entropy can compromise this limitation. We thoroughly measured the entropies of instantaneous amplitudes, frequencies, and phases to depict the EEG profiles after the footh shock stimuli. The footh shock stimuli increased the amplitude entropy of NREM sleep, but decreased the amplitude entropy of REM sleep, which suggests that the footh shock affected the uniformity of amplitude. During NREM sleep, the amplitudes of EEGs were changed more frequently after the footh shock because their amplitude entropy increased. These results can be interpreted by two possible explanations. One possibility is the poor sleep quality affected by the footh shock stimuli, given that the amplitude of EEGs during NREM sleep corresponds to sleep quality. The various amplitudes may reflect that the rats entered various NREM sleep stages after the footh shock stimuli, although it is difficult to further differentiate the sleep stages of NREM sleep in rodents compared to humans. The other possible explanation is that there may be certain sleep compensation for the first hour of sleep deficit after the footh shock. This sleep compensation might result in greater amplitudes, indicating the sleep quality, rather than the regular baseline and the greater amplitudes, contributed to the lower uniformity of the amplitude. In contrast, the footh shock stimuli caused a low entropy of amplitudes during REM sleep, which may result from the weak power spectrum and the smaller amplitudes of REM EEGs, as indicated in Figure 2D,E. We believe the spectrogram of REM sleep in Figure 2D,E reflects this finding; that is, the power of theta waves between 6 to 8 Hz decreased after the footh shock stimuli, although it did not reach significant meaning. Some human [34] and rodent studies [4], also, report that PTSD patients or susceptible animals have a reduction in theta powers during REM sleep. Weak amplitudes contributed to lower amplitude entropies because the difference of amplitudes at different levels is low and would be sorted into the same level, which would, subsequently, exhibit a low entropy.
## 2 Material Properties
### Switching Properties  
In rewritable optical recording media and other PCM devices, the information storage utilizes either the large electrical or optical contrast between the two states for binary representation. As the operation for the typical memory devices shown in Figure 2, SET (conventionally linked to writing a logic '1') and RESET (writing a logic '0') states are controlled by a Joule heating process and are associated with amorphous-to-crystalline and crystalline-to-amorphous transitions, respectively. Since crystallization is a slower process than amorphization, the SET process in a device requires heating the material above its crystallization temperature for a sufficient length of time (tens to 100s of ns) so that the atoms rearrange themselves in crystalline order. A medium level laser or current (blue curve) for fairly long pulse times is used to re-crystallize the phase change material to its crystalline state. On the other hand, the RESET or amorphization process needs a higher temperature with a shorter duration to melt and convert the material to a liquid (amorphous) state then quickly quench the material such that the atoms do not have time to arrange in a crystalline fashion. The typical time required for RESET switching is shorter than a few tens of nanoseconds [22; 23]. In this process, the temperature of the material needs to drop down quickly, i.e., the fast quench should occur faster than the timescale for thermal diffusion from cell to neighboring cell, preventing reorganization into a crystalline structure. A much lower level laser or current with essentially no Joule heating is used for reading the state, differentiating between amorphous (low reflectivity, high resistivity) and crystalline (high reflectivity, low resistivity) states.  
These two approaches for switching the phase of a phase-change device, electrical and optical control, generate two formats of devices, driven by either electricity or light. For example, for PCRAM devices as in Refs. [24; 25; 26; 27], the phase change material, and a heater element are connected in series, with the heater inducing localized Joule heating aimed at switching the volume near the interface region (between the heater and phase change material elements). We will discuss the details of PCRAMs in Section 3.1. Optical control is another effective approach that generally uses an optical pump beam that is focused on the PCM film, leading to Joule heating by absorption, which induces the phase transition. The rewritable optical disk is one of the well known optical control PCM applications [1; 2].