esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Predictive Power & Observer Localization
Similarly but less extreme, the inflation model produces a universe that is vastly larger than its visible part, and different regions may have different properties. Egocentric to Geocentric model.Consider now the \"small\" scale of our daily life. A young child believes it is the center of the world. Localization is trivial. It is always at \"coordinate\" (0,0,0). Later it learns that it is just one among a few billion other people and as little or much special as any other person thinks of themself. In a sense we replace our egocentric coordinate system by one with origin (0,0,0) in the center of Earth. The move away from an egocentric world view has many social advantages, but dis-answers one question: Why am I this particular person and not any other? (It also comes at the cost of constantly having to balance egoistic with altruistic behavior.) Geocentric to Heliocentric model.While being expelled from the center of the world as an individual, in the geocentric model, at least the human race as a whole remains in the center of the world, with the remaining (dead?) universe revolving around _us_. The heliocentric model puts Sun at (0,0,0) and degrades Earth to planet number 3 out of 8. The astronomic advantages are clear, but dis-answers one question: Why this planet and not one of the others? Typically we are muzzled by questionable anthropic arguments [8, 18]. (Another scientific cost is the necessity now to switch between coordinate systems, since the ego- and geocentric views are still useful.) Heliocentric to modern cosmological model.The next coup of astronomers was to degrade our Sun to one star among billions of stars in our milky way, and our milky way to one galaxy out of billions of others. It is generally accepted that the question why we are in this particular galaxy in this particular solar system is essentially unanswerable. Summary.The exemplary discussion above has hopefully convinced the reader that we indeed lose something (some predictive power) when progressing to too large universe and multiverse models. Historically, the higher predictive power of the large-universe models (in which we are seemingly randomly placed) overshadowed the few extra questions they raised compared to the smaller ego/geo/helio-centric models. (we're not concerned here with the psychological disadvantages/damage, which may be large). But the discussion of the (physical, universal, random, and all-a-carte) multiverse theories has shown that pushing this progression too far will at some point harm predictive power. We saw that this has to do with the increasing difficulty to localize the observer.
# Temperature-Dependent Dynamic on Resistance in Gamma-Irradiated AlGaN/GaN Power HEMTs
## 3 Results and Discussion
### Irradiation at a Low Temperature (53 \({}^{\circ}\)C)  
In the case of irradiation with a lower temperature, regardless of the applied voltage stress, a decrease in the drain current was observed, which was mainly caused by a reduction in the source-substrate current of around 60%. There was no increase in the gate leakage current; however, differences were observed depending on whether the DUT was stressed with V\({}_{\text{DS}}\) = 500 V and V\({}_{\text{GS}}\) = \(-\)5 V or V\({}_{\text{DS}}\) = 500 V and V\({}_{\text{GS}}\) = 0. The devices that were stressed during irradiation with V\({}_{\text{DS}}\) = 500 V and V\({}_{\text{GS}}\) = \(-\)5 V (G6 and G7) showed a higher reduction (60%) in the drain leakage current than the devices (G8 and G10) that were only stressed with V\({}_{\text{DS}}\) = 500 V (45%). With a negative gate voltage, there was an indicative reduction in the gate leakage current that was additional to the reduction in the source-substrate leakage current, which was due to the compensation of the hole traps by the existence of additional electron traps under the gate that were induced by the negative gate bias during irradiation (Figure 8).  
In off-state conditions, trapping could occur both at the gate-drain surface and the buffer. The increase in R\({}_{\text{ON}}\) mainly originated from buffer traps. This process could be explained by two different mechanisms: an electrostatic effect of the high negative  
Figure 8: The leakage currents versus drain voltages in the off state (V\({}_{\text{GS}}\) = 0 V) before (black lines) and after (magenta lines) gamma radiation with a total ionising dose of 374 krad(SiO\({}_{2}\)) at a base temperature of 53 \({}^{\circ}\)C. (**a**) Device G6 stressed during irradiation with V\({}_{\text{DS}}\) = 500 V and V\({}_{\text{GS}}\) = \(-\)5 V. (**b**) Device G7 stressed during irradiation with V\({}_{\text{DS}}\) = 500 V and V\({}_{\text{GS}}\) = \(-\)5 V. (**c**) Device G8 stressed during irradiation with V\({}_{\text{DS}}\) = 500 V and V\({}_{\text{GS}}\) = 0. (**d**) Device G10 stressed during irradiation with V\({}_{\text{DS}}\) = 500 V and V\({}_{\text{GS}}\) = 0.  
gate-drain voltage that depleted the hole traps in the buffer and the flow of vertical drain current.  
Our results, in concordance with previous investigations [25], showed an improvement in the electrical properties of the devices due to the decrease in leakage current when gamma irradiation was applied. This reduction could be attributed to the reduction in the density of traps, along with the ordering of native defects, but it needs to be verified by scanning transmission electron microscopy (STEM) to characterise the defects and elemental distributions. The reduction in the drain-substrate leakage helped to reduce on-state trapping in the device and, therefore, the dynamic R\({}_{\text{ON}}\).
## 6 Simulation Results
### Overview of AODV-EHA  
In route discoveries of AODV-EHA [30], the expectations of data transmission cost (in terms of energy) are computed for all routes while considering energy harvesting technology. The route with the least energy cost approximation is chosen for data transmission. In any specific route, let \(E_{m}\) represent the estimation of energy cost after a data packet travels from the \(m\)-th node to its next hop is successfully delivered, and then total energy cost of the entire route, represented by \(E_{route}\), is as follows.  
\[E_{route}=\sum_{m}E_{m} \tag{23}\]  
AODV-EHA is an improved version of the AODV protocol involving the aforementioned energy cost estimation. This means that AODV-EHA chooses the most energy-efficient route with the minimum \(E_{route}\).
## 5 The PMOD Scheduler
### Transforming the Class Table into a Scheduling Matrix
The average latency is computed as follows: there are two node pairs with a maximum latency of \(Q-1\). Without loss of generality, we can assume that they are distributed in two rows of \(SM\), producing a total latency of \(2*(Q-1)\) time units. These two pairs determine the overall latency of the communication steps defined in these \begin{table}
\begin{tabular}{c c} \hline \hline
**Latency \(\ell_{q}\)** &
\begin{tabular}{c} **Number of Communicating Nodes** \\ **Exhibiting This Latency** \\ \end{tabular} \\ \hline \(\ell_{0}=0\) & \(Q\) \\ \(\ell_{1}=1\) & \(2(Q-1)\) \\ \(\ell_{2}=2\) & \(2(Q-2)\) \\ \(\ell_{3}=3\) & \(2(Q-3)\) \\ \(\ell_{4}=4\) & \(2(Q-4)\) \\ \(\vdots\) & \(\vdots\) \\ \(\ell_{q-1}=Q-1\) & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Communication latencies among \(Q^{2}\) node communications. rows, as they have the maximum latency. Similarly, the four elements with a cost of \(Q-2\) are, on average, distributed in four (provided that \(Q\geq 4\)) rows, and we determine the cost of four halves out of these four rows (in the average case, the latencies of half of these pairs are \"absorbed\" by the maximum latencies of \(Q-1\); in other words, two of the pairs with a cost of \(Q-2\) are in the same row as the pairs with a maximum cost of \(Q-1\)). Thus, we have an added latency from these steps equal to \(2(Q-2)\). There remain \(Q-4\) rows to be examined. Continuing in this manner, the six elements with a cost of \(Q-3\) are, on average, distributed in six rows (provided that \(Q\geq 6\)) and we determine the latencies of \(\frac{6}{2}=3\) (provided that \(Q-4\geq 3\); otherwise, they determine the latencies of \(<3\) rows) out of these six rows (in the average case, the latencies of half of these pairs are \"absorbed\" by the larger latencies of \(Q-1\) and \(Q-2\); in other words, three of the pairs with a cost of\(Q-3\) are in the same row as the pairs with larger costs of \(Q-1\) and \(Q-2\)). Working similarly, we find that, on average, the total latency of the steps defined by the \(SM\) can be computed by Equation (11). Let us return to our example to see how Equation (11) applies:  
\[\mathcal{C}_{SM} =2(Q-1)+2(Q-2)+\chi_{1}(Q-3)+\chi_{2}(Q-4)+\cdots+\chi_{v}(Q-1)\] \[=2\times 5+2\times 4+1\times 3+1\times 2=23\]  
where \(\chi_{1}=\chi_{2}=1\). The next subsection describes how the \(SM\) will be used to implement the pipelined scheduling.
## 2 Materials and Methods
### Calculations of LET\({}_{\text{D}}\)
It follows from [PERSON]'s investigation [33] that the relative variance of the imparted energy (\(V_{\epsilon}\)) is:  
\[V_{\epsilon}=V_{\text{LET}}+V_{I}+V_{\text{LET}}V_{I}+V_{\delta}, \tag{7}\]  
where \(V_{\text{LET}}\) is the relative variance of an ion's LET, \(V_{I}\) is the relative variance of the ion's path length and \(V_{\delta}\) is the relative variance of the energy loss straggling. The relative variances are additive and the additional term \(V_{\text{LET}}V_{I}\) enhances the combined effect of LET and path-length variations [33]. The relative variance of a random variable (\(x\)) can be expressed as:  
\[V_{x}=\frac{\sigma_{x}^{2}}{\overline{x}^{2}}=\frac{x_{\text{D}}}{\overline{ x}}-1, \tag{8}\]  
where \(\sigma_{x}\) is the standard deviation of the variable, \(\overline{x}\) is the mean value and \(x_{\text{D}}\) the weighted average. If \(V_{\epsilon}\), \(V_{\text{LET}}\) and \(V_{I}\) are replaced in Equation (7) from Equation (8) and the relative variance of the energy loss straggling is taken as: [33],  
\[V_{\delta}=\frac{\delta_{2}}{\overline{\epsilon}}, \tag{9}\]  
where \(\delta_{2}\) is the dose-averaged deposited energy by the particle in one collision, it follows that  
\[\frac{\epsilon_{\text{D}}}{\overline{\epsilon}}-1=\frac{\text{LET}_{\text{D}} }{\overline{\text{LET}}}-1+\frac{l_{\text{D}}}{\overline{l}}-1+\left(\frac{ \text{LET}_{\text{D}}}{\overline{\text{LET}}}-1\right)\left(\frac{l_{\text{D }}}{\overline{l}}-1\right)+\frac{\delta_{2}}{\overline{\epsilon}}. \tag{10}\]  
If the dose-averaged lineal energy is taken as \(y_{\text{D}}{=\epsilon_{\text{D}}/\overline{l}}\), and by using the mean imparted energy from Equation (6), an expression that relates \(\text{LET}_{\text{D}}\) with \(y_{\text{D}}\) can be obtained:  
\[\text{LET}_{\text{D}}=\frac{1}{f_{ion}\,l_{\text{D}}}(\overline{l}\,y_{\text{ D},direct}-\delta_{2}). \tag{11}\]  
Equation (11) is valid under the short track-segment condition, i.e., when the dimensions of the volume of interest is small enough that the ion's LET can be considered constant along its entire path within the volume [33]. The evaluation of \(y_{\text{D},direct}\), \(\delta_{2}\) and \(f_{ion}\) are described in the next section.
## 3 Optimal Service Provisioning Framework
### Customized Mist-Edge-Cloud Framework
#### 3.1.5. Cloud Application Layer  
The topmost layer presents healthcare dashboards, analytics interfaces, and visualization tools for medical personnel. It supports telemedicine consultations, alerts for emergency services, and the integration of personalized health insights into the hospital workflow. This layer is also the point of orchestration for deploying new healthcare applications across the lower tiers.
## 1 Introduction
* The four sensors are installed with different heights, which can collect different pyroelectric infrared information from corresponding parts of the human body. * The effective data is fused by multiple channel signals which are collected from the four sensor modules. * Extracting different pyroelectric infrared features of the human target by different algorithms can help establish different target identification model databases. In this paper, the major goal of the work is to develop a novel wireless distributed pyroelectric sensor system which can precisely identify human targets in a confined area. With the increasing distance between the pyroelectric sensor and the human target, the target recognition rate of a single sensor module is showed in a downward trend. Thus, a novel fusion algorithm to fuse the data of pyroelectric sensors with different heights is proposed to improve recognition rate. Firstly, Fast Fourier transform (FFT), Short Time Fourier Transform (STFT), Wavelet Transform (WT) and Wavelet Packet Transform (WPT) are used to extract the pyroelectric features of the human body, which is detected by Single Pyroelectric Infrared (PIR) sensor. Then, the dimensionality of pyroelectric features of the human target is reduced through the Principal Component Analysis (PCA). Additionally, the preliminary recognition rate of single sensor module is obtained by using the Support Vector Machine (SVM) algorithm. Finally, the Fuzzy Comprehensive Evaluation method (FCE) is applied to fuse the preliminary recognition rate of the four PIR sensor modules, and then the final recognition result can be obtained. The experiments proved that the proposed scheme has stronger robustness on the human height and the detection distance between the human target and the pyroelectric sensor. The remainder of the paper is organized as follows. The related work is illustrated in Section 2. Section 3 introduces sensor modules and the deployment structure. Section 4 presents the proposed human target recognition system, and a novel recognition strategy is presented in Section 5. The experimental results with the various collected data are presented and analyzed in Section 6. Section 7 concludes the paper.
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 2 Related Works
### Spatial Acoustic Technology  
Various methods have been proposed for spatial acoustic technology. MPEG-H, which is currently being standardized by the International Organization for Standardization (ISO) and the Moving Picture Experts Group (MPEG), a working group of the International Electrotechnical Commission (IEC). MPEG-H Part 3 includes a section on 3D audio [41]. The three spatial acoustic methods allowed by MPEG-H 3D audio are channel-based, object-based, and HOA (higher-order ambisonics) [42]. Channel-based audio records and edits acoustic data tied to the playback environment based on information regarding the number of speakers and their position relative to the viewer. When moving in an acoustic space, it is necessary to prepare other acoustic data associated with the new environment or modify the original acoustic data. Therefore, expressing sound movements following the viewer's actions is challenging and lacks flexibility and interactivity.  
On the other hand, in object-based audio, the 3D location information of the sound source is recorded as metadata and acoustic data. Flexible and interactive sound reproduction is possible by rendering the acoustic data according to the positional information of the sound source and the listener and the environment of the playback space. The main applications are Dolby Atmos [43; 44] and DTS:X [45], which have been introduced in movie theaters and home theaters. AuroMax [46] employs object-based audio or combines it with conventional channel-based audio.  
Accessing each acoustic object in the sound field remains challenging [47] because spatial information in HOA/ambisonics is not explicit geometric metadata, as it is in object-based audio. Meanwhile, the recorded data reproduces a spherical sound field centered on the microphone and thus can easily follow the rotation of the listener's head. This characteristic is similar to that of a video recorded by a 360\({}^{\circ}\) camera. Recently, consumer-use 360\({}^{\circ}\) cameras such as the Rich THETA Z1 and THETA V [48] have been equipped with ambisonic microphones. YouTube and Facebook's 360\({}^{\circ}\) video services also support HOA/ambisonics.
## 4 Results and Discussion
### Dataset Description  
The evaluations of a framework are performed using the UNSW-NB15 dataset [37; 38; 39]. This dataset is a very popular IoT-based intrusion detection dataset that mainly covers the features of diverse network attacks. It is a public source dataset and can be downloaded using the following URL [[[https://www.kaggle.com/datasets/mrwellsavid/unsw-nb15](https://www.kaggle.com/datasets/mrwellsavid/unsw-nb15)]([https://www.kaggle.com/datasets/mrwellsavid/unsw-nb15](https://www.kaggle.com/datasets/mrwellsavid/unsw-nb15)), accessed on 7 March 2023]. It was fundamentally introduced by the Cyber Range Lab of the Australian Center for Cyber Security (ACCS) in 2015 and is often used in the research community. This work evaluates the framework using the raw network packets generated using the IXIA perfect storm program. It includes nine attack features: analysis, backdoor, DoS, exploits, fuzzes, generic, reconnaissance, shellcode and worms, and normal, benign features. Forty-nine network traffic features are extracted along with their class labels using the Argus and Bro-IDS programs with the assistance of 12 developed algorithms. All the extracted features are kept in a separate.csv file named UNSW-NB15_features.csv.  
This dataset includes 2,540,044 streams, where 2,218,761 streams fall under the benign streams, and 321,283 streams fall under the aggressive streams. All these data streams are placed in four separate.csv files named UNSWNB15_1.csv, UNSWNB15_2.csv, UNSW-NB15_3.csv and UNSWNB15_4.csv. The ground truth table of this dataset is presented in the UNSWNB15_GT.csv file, and an event list of this dataset is presented in the UNSW-NB15_LIST_EVENTS.csv file. The major attack features needed in the implementation phase are divided and placed in two separate training and testing files named UNSW-NB15_trainingset.csv and UNSWNB15_testinget.csv. The training set of this dataset includes 175,341 data records, and the testing set includes 82,332 data records covering both normal and attack features. The dataset distribution based on different attack categories is presented in Table 3.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Values** \\ \hline SVM & \\ \hline Kernel function & Linear \\ Gamma & 0.25 \\ \hline FSA & \\ \hline Initial population & 50 \\ Maximum iterations & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Training Set** & **Testing Set** \\ \hline Normal & 56,000 & 37,000 \\ Analysis & 2000 & 677 \\ Backdoor & 1746 & 583 \\ DoS & 12,264 & 4089 \\ Exploits & 33,393 & 11,132 \\ Fuzzers & 18,184 & 6062 \\ Generic & 40,000 & 18,871 \\ Reconnaissance & 10,491 & 3496 \\ Shellcode & 1133 & 378 \\ Worms & 130 & 44 \\ Total & 175,341 & 82,332 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset distribution based on attack categories.
# Assessing the Efficacy of Seawater Batteries Using NASICON Solid Electrolyte
## 4 Discussion
### The NASICON Principles Used in seawater Electrolytic Processes
The positive electrode may simultaneously experience different oxidation reactions, such as OER or Na deintercalation. The sodium that is deposited by the negative electrode is subject to oxidation and released into the water by NASICON during subsequent discharge. \[2 Na(s)+2H_{2}O(aq)\to H_{2}(g)+2 NaOH(aq)\ E^{0}=2.22\ V\ vs.\ Na^{+}/Na \tag{10}\]  
As shown in Equation (9), hydrogen is stored and collected while charging, and when it is discharged, it is produced, and energy is recovered. The SWB is a good option for hydrogen storage as it has a remarkable volumetric capacity of 42 to 218 g L\({}^{-1}\), even with standard atmospheric pressure (1 atm) and ambient temperature (25 \({}^{\circ}\)C). The volumetric energy density of hydrogen at ambient conditions is approximately 0.0824 kgm\({}^{3}\), which means that this exceeds the inherently low volumetric energy density, which is approximately 0.01 MJ L\({}^{-1}\) H\({}_{2}\). Both methane and gasoline have a volumetric energy density of 0.04 MJ L\({}^{-1}\) H\({}_{2}\), which is much higher than hydrogen's, which is a challenge to their commercial viability [20].
# Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network--A Survey
## 2 Error Correction Scheme (ECS)
### Forward Error Correction (FEC)
The iterative decoding was further classified depending on the input to the decoding algorithms. If the decoder received the hard decision from the channel and the hard decision information used during the decoding iteration, then the decoder is referred to as hard-decision decoder. Whereas, if the decoder received the soft decision from the channel and the soft decision information used during the decoding iteration, then the decoder is referred to as soft-decision decoder. Soft decision decoder has better performance (i.e., coding gain) than the respective hard decision decoder. However, the soft decision decoder has higher complexity than the respective hard decision decoder. Binary message passing (BMP) decoding or Weighted Bit Flipping (WBF) is the hard decision version of SPA. The SPA also has lower complexity versions, called min-sum decoding [27]. The FEC scheme involves PHY layer of OSI model.