esa-sceva/satcom-chunk-collection huggingface dataset:
# TSCH Multiflow Scheduling with QoS Guarantees:
## 3 Results
### Packet Inter-Arrival Time and Deadline Satisfaction Ratio  
PIAT is a metric that verifies the effect of all the traffic flowing through the network on a specific data flow. The closer this value is to the Packet Period, the more deterministic the network is, since the delay added by the intermediate hops is constant. In this case, Figure 6 shows the results for different values of the Packet Period on each of the TSCH schedulers.  
Figure 5: Radio duty cycle for each scheduler with different packet period.  
For Figure 5(a) SDN WISE-TSCH, 3 clearly differentiated lines can be observed, corresponding to each of the flows (70 ms, 140 ms and 500 ms). These are constant and coincide with the packet period because the packets are classified and sent by the specific resources assigned to them. In AMUS Figure 5(b) the only flow that maintains a constant delay is the 70 ms flow, beacause its frequency always has packets in queue. The other flows show variations that even exceed the established deadline because there is no synchronisation between TSCH scheduling and packet generation. For Orchestra in Figure 5(c) all the flows have variations, produced by the saturation of the nodes. However, the low saturation of Orchestra buffers allows it to outperform AMUS since more than 100% of the packets are below 500 ms. In Figure 5(d) the MSF behaviour has a high randomness for flows with higher frequency, 70 ms and 140 ms, because when using the shared slots collisions can occur at any hop. In low saturation conditions, it has more stable behaviour with 80% of the packets at around 500 ms.  
The DSR makes it possible to cumulatively observe the total number of packets that are received in a time less than or equal to the packet period. A high DSR value entails a constant and predictable flow between the source and the destination, which leads to greater determinism. In Figure 6, SDN WISE-TSCH achieves a deadline satisfaction rate of 100% in all cases due to the fact that in this scheduler the flows are differentiated and the TSCH scheduling adapted to be below the deadline. AMUS achieves 98%, 90% and 84% due to the lack of synchronisation between generation and scheduling, which can cause the transmission to be delayed with the next slotframe. When this happens the control packets can be sent by the cells assigned to the data traffic. Orchestra maintains a high DSR in all cases 95%, 90% and 95% but like AMUS, it is affected by control traffic that it cannot differentiate. However, for high packet generation periods it is not affected because the bandwidth does not depend on the scheduled traffic, it depends on the slotframe length. Therefore, in this case Orchestra has a larger amount of bandwidth available. MSF reaches 79% under favourable conditions, when network congestion is low.  
Figure 6: Packet Inter-Arrival Time for each Scheduling protocol with different Packet Period.  
Figure 7: Deadline Satisfaction ratio for each protocol.
## 3 Operation Regions  
According to the switching frequency, the LLC converter operation can be divided into three regions, as shown in Figure 2.  
Either the input or output voltage fluctuation in the LLC converter yields the intense decrease in its efficiency [59]. In order to have a constant DC voltage in the output side, the LLC converter has to work in the ZVS region in which the switching frequency (\(f_{SW}\)) is smaller than the resonant frequency (\(f_{r}\)), i.e., region 2 in Figure 2. In this region, the primary and secondary side switches work in ZVS and ZCS situations, respectively. In order to ensure ZVS in region 2, the sufficient peak of the magnetizing current and enough deadtime to charge or discharge all parasitic capacitors are required [6]. Due to ZVS in the primary side and ZCS in the secondary one, region 2 is commonly used for DC transformer applications [60].  
Also, region 1 in Figure 2 provides ZVS in a larger frequency range. If the LLC resonant converter is used in a wide output voltage range application by switching modulation frequency, region 1 is preferred. Theoretically, region 1 ensures wider switching modulation ranges, while the load varies in a wide range. It should be noted that if the input DC voltage is increased, ZVS can be partially maintained for primary side switches and the remaining hard-switching yields more heat and power loss [6]. Various control methods have been proposed to improve light load efficiency in LLC resonant converters [34; 61; 62; 63].  
In order to adjust the output power, the LLC converter normally operates with varying switching frequency, instead of duty cycle control, above the resonant frequency [24]. Increasing frequency equals to reducing the output power.  
When the switching frequency is lower than the resonant frequency, i.e., \(f_{SW}/f_{r}<1\), the circulating power is enhanced, resulting in the increase of conduction loss and current stress [63].  
Figure 2: LLC resonant converter operation regions.
# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 3 Methods and Materials
### Methodology
This study used the second variant of the NearMiss algorithm to address class imbalance by removing majority class (normotypic) samples closest in mean distance to the farthest minority class samples. This approach ensures a balanced representation of both classes, crucial for effective model training. The NearMiss algorithm balances reducing class imbalance with minimizing the loss of informative data. In this study, 90% of the majority class samples were removed, retaining 10% closest to the farthest minority class samples. The final training set had 1106 normotypic and 996 SMS samples in study case 3. * Phase 6: Model training. To ensure robust performance evaluation, a 10-fold cross-validation approach is used, partitioning data into ten equal folds. The CNN trains for twenty epochs per fold, iteratively refining its parameters. This process is repeated for all ten folds, ensuring diverse data evaluation and mitigating biases. To address imbalanced datasets, sixteen SMOTE variations generate synthetic minority class data, balancing the class distribution. The training protocol is executed for each SMOTEvariation, resulting in 160 training iterations (10 folds \(\times\) 16 SMOTE variations). This comprehensive approach ensures a statistically reliable evaluation of the methodologies. * Phase 7: Dumping results. Due to the numerous methods used, outcomes are automatically dumped into a spreadsheet. Each SMOTE method has its own Microsoft Excel spreadsheet with two tables: one for audio data (including speaker and audio identifiers, hits, and misses) and another for the confusion matrix and performance metrics (sensitivity, specificity, balanced accuracy). This approach allows comprehensive analysis and comparison across iterations, aiding decision making on SMOTE methods. A structured results repository is created for easy comparison of methodologies. It includes a second table identifying the most and least successful audio samples, offering insights into factors affecting model performance.
## 2 Data and Methodology
### Lyapunov Method  
The Lyapunov concepts [68; 69] are a useful mathematical tool for analyzing non-linear and chaotic systems. The Lyapunov (\(\lambda\)) exponent [70] determines how the presence of chaos addressees the predictability of the future [71].  
As the \(H\) index is related to the fractal dimension (\(d_{A}\)) of a TS by the condition \(d_{A}=2-H\), a relation between the Hurst and Lyapunov exponents can be estimated from the global dimension (\(d_{G}\)). This is used to find the neighboring points in the TS and must be at least or greater than \(2d_{A}\) in order to avoid false neighbors in the calculation [70]. Then, one can write [70; 72]  
\[d_{G}\geq 4-2H. \tag{13}\]  
In this work, we obtain the Lyapunov exponent from a TS by means of the algorithm developed by [PERSON] et al. [71]:  
\[\lambda_{i}(t)=\frac{1}{t_{M}-t_{0}}\sum_{k=1}^{M}\log_{2}\left(\frac{\varepsilon _{i}(t_{k})}{\varepsilon_{0}(t_{k-1})}\right), \tag{14}\]  
where \(M\) is the total number of replacement steps and \(t_{k}-t_{k-1}=\Delta\) is the time step.  
The signs of the Lyapunov exponents provide information about the system dynamics. Indeed the value of \(\lambda\) is an important index to diagnose chaotic motion since a positive value indicates that the system is chaotic. Besides, the Lyapunov exponent can be an indicator of how far into the future forecasts can be made in a TS [73]. Bearing this fact in mind, we applied such technique to calculate the Lyapunov exponent for the price TS.
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 5 Methodology
### Configurations under Test  
Several tests have been designed and performed to evaluate different aspects of the communication stack and the dependency with the channel parameters.  
* AWGN channel test. It is a test of the whole transmission and reception chain with a flat channel and AWGN noise. It is the base reference test, used as a reference for the rest of the performed tests, to relate how changes in the different parameters affect the system behavior. Results are described in Section 6.1.
* Synchronous impulsive Noise channel test. The test inserts in the propagation channel synchronous impulsive noise of variable duration. This test aims at evaluating the robustness of the system to synchronous impulsive noises of different temporal distribution of the noise power. Results are described in Section 6.2.
* Selective channel test. The purpose of this test is to check the robustness of the communications in the presence of an important attenuation on some of the subcarriers. Results are described in Section 6.3.
* Abrupt Phase channel test. For this particular channel, the distortion to be tested is an abrupt phase shift of \(\pi\)_rad_ in the center of the band. Results are described in Section 6.4.  
Table 4 summarizes the detailed information of the configuration for each test whose results are detailed in Section 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & **Channel** & **Noise** & **Subcarrier** & **PSDU** \\
**Configuration** & **Response** & **Model** & **Modulation** & **Length** \\ \hline Channel simulation TX-RX & & & & \\ - AWGN & Flat & AWGN & All & 256 bytes \\ - Impulsive Noise & Flat & Impulsive & \(DBPSK_{F}\) & 256 bytes \\ - Selective Channel & Selective & AWGN & \(DBPSK_{F}\)–\(D8 PSK_{F}\) & 256 bytes \\ - Abrupt Phase Channel & Abrupt Phase & AWGN & \(DBPSK_{F}\)–\(D8 PSK_{F}\) & 256 bytes \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test configurations studied.
## 3 Research Method
### Data Collection
#### 3.1.1 GF-1 Satellite Data  
This study utilizes data from the GF-1 satellite to identify disturbance boundaries of SDAs in HSRs, which was obtained from the Department of Water Resources of Guangdong Province. GF-1, a pivotal satellite in China's high-resolution Earth observation system, was launched on 26 April 2013. The satellite payload specifications are shown in Table 1. The satellite is equipped with high-resolution panchromatic and multispectral cameras, along with various multispectral cameras offering different spatial resolutions, enabling ground observations up to 800 km in width. GF-1's extensive ground coverage and high spatiotemporal resolution allow for the frequent acquisition of data over the same area within short intervals, making it particularly effective for monitoring dynamic environmental changes. The high-resolution imagery provided by GF-1 is crucial for capturing fine surface details, while its temporal resolution enables continuous tracking of surface disturbances. These attributes make GF-1 an optimal choice for accurately identifying and monitoring disturbance boundaries in SDAs, especially in complex terrain regions.  
Figure 1: Research framework of monitoring SDAs in HSRs.
# Panchromatic and Multispectral Image Fusion Combining GHS, NSST, and PCA
## 3 Evaluation Metrics
### Evaluation Metrics with References  
The evaluation model with references down-samples the original MS and PAN images using the cubic convolution method (the down-sampling factor is obtained based on the resolution ratio of the MS image to the PAN image), and the sampled images are fused. In this way, the original MS image is used as the reference image for assessing the image quality, and a method evaluation can be performed using the full reference method. The image quality assessment indexes used in this paper include the average gradient (AG), structural similarity (SSIM), correlation coefficient (CC), universal image quality indexes (UIQI) [20], spectral angle mapper (SAM) [21], and erreur relative global adimensionnelle de synthese (ERGAS) [22], where AG can be used to measure the spatial quality of the fused image, and a larger value of AG indicates a clearer image; SSIM indicates the structural similarity between the two scenic images, and a higher SSI value indicates that the structure of the fused image is more similar to the reference image and that its spatial quality is better; the size of CC indicates the degree of correlation between the two images; UIQI is used to evaluate the degree of structural preservation of the image, and its optimal value is 1; SAM reflects the size of the spectral distortion between the reference image and the fused image, and a smaller value of SAM indicates a better spectral quality of the fused result; and ERGAS reflects the overall quality of the fused image, and a smaller value indicates a better quality of the fused image.
# Operational Optimisation of a Non-Recuperative 1-kWe Organic Rankine Cycle (ORC) Engine Prototype
## 3 Data Analysis
### Components Performance
#### 3.3.2 Expander Performance  
The expansion device is a key component of the system, the performance of which strongly influences that of other components and that of the ORC engine. The scroll expander used in the set of experiments presented and analysed in the present study is a positive-displacement expansion machine. Its performance can be further described through components-scale indicators, defined in Equations (11) and (12).  
The isentropic efficiency, \(\eta_{\rm exp,is}\), informs about the degradation of the energy quality in this steady-flow device:  
\[\eta_{\rm exp,is}\,=\,\frac{\dot{W}_{\rm exp}}{\dot{m}_{\rm wf}\,\Big{(}h_{\rm exp,in}\,-\,h_{\rm exp,out}\,\rm{is}\Big{)}}\quad, \tag{11}\]  
where \(h_{\rm exp,out}\,\rm{is}\) is a fictive ideal specific enthalpy that would be delivered at the exhaust of an isentropic expander operating between the inlet and outlet pressures: \(h_{\rm exp,out}\,\rm{is}\,=\,h\,\left(P_{\rm exp,out},\,S_{\rm exp,in}\right)\).  
The filling factor, \(\phi_{\rm exp}\), is the ratio between the actual and theoretical mass flowrate in given operating conditions:  
\[\phi_{\rm exp}\,=\,\frac{\dot{m}_{\rm wf}}{\rho_{\rm exp,in}\,\omega_{\rm exp} \,V_{\rm exp,disp}}\quad, \tag{12}\]  
where \(\rho_{\rm exp,in}\) is the expander intake density, \(V_{\rm exp,disp}\) the expander built-in displacement and \(\omega_{\rm exp}\) the scroll spindle speed, to be expressed in Hz when used in the above equation.
# Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
## 4 Simulation Results and Discussion
### Environmental Conditions  
To design the prototype UAV for Mars exploration, environmental conditions become a necessary consideration. Atmospheric pressure, air temperature, gravity, speed of sound, and air density are some of the mandatory parameters that are discussed below.
# Wavelet Packet Method for Locating Critical Slip Surface Using the Strength Reduction Method
## 3 Wavelet and Wavelet Packet Transformation and Reconstruction
### Multi-Resolution Analysis  
[PERSON] first proposed a multi-resolution analysis (MRA) and presented a fast algorithm based on wavelet transformation and reconstruction, named the Mallat algorithm. According to the Mallat algorithm, if \(f_{k}\) contains the sampling information of \(f(t)\), and \(a_{0,k}=f_{k}\), the orthogonal wavelet decomposition formula is  
\[\left\{\begin{array}{ll}a_{j,k}=\sum_{n}a_{j-1,k}h(n-2k),&k=0,1,2,\ldots,N-1 \\ d_{j,k}=\sum_{n}a_{j-1,k}g(n-2k),&k=0,1,2,\ldots,N-1\end{array}\right. \tag{8}\]  
where \(c_{j,k}\) is the scaling coefficients, \(d_{j,k}\) is the wavelet coefficients, \(j\) is the layer of decomposition, and \(N\) is the sampling points.  
In this, \(h\) and \(g\) are orthogonal filters generated by the scale function \(\phi(t)\) and wavelet function \(\psi(t)\), respectively. These are defined as  
\[\left\{\begin{array}{ll}h(k)=\sqrt{2}\int_{-\infty}^{+\infty}\phi(t)\phi(2 t-k)dt\\ g(k)=\sqrt{2}\int_{-\infty}^{+\infty}\psi(t)\phi(2t-k)dt\end{array}\right. \tag{9}\]  
The reconstruction procedure is an inverse transform of the decomposition. The reconstruction formula is  
\[a_{j-1,k}=\sum_{n}a_{j,n}h_{k-2n}+\sum_{n}d_{j,n}g_{k-2n} \tag{10}\]  
A wavelet has the property of multi-resolution, which can treat signal data in multi-resolution analysis and decompose a signal interwoven with different frequencies into different frequency sub-signals.
# The First Result of Relative Positioning and Velocity Estimation Based on CAPS
## 1 Introduction
CAPS is also an independent system with a PNT service capability, and it is currently in a development and testing phase [9]. The navigation signal on the C1 frequency of CAPS can be tracked by our self-developed receiver, and the code, carrier-phase and doppler measurements can be produced. The performance of Standard Point Positioning (SPP) based on CAPS was assessed by the simulated and real data in the previous studies [5; 10] and the results showed that CAPS can independently achieve the SPP with an accuracy of 10 to 20 m, which was comparable to the SPP performance of traditional GNSSs. To further validate the potential performance of CAPS in high-precision positioning and velocity estimation, this contribution is the first that assesses the relative positioning and velocity estimation performance of CAPS based on real data. A brief introduction to the system's architecture, the observation equations and the relative positioning model of CAPS are described in Section 2. In Section 3, the noises of code and carrier-phase measurements are analyzed with the data from a zero-baseline experiment, and the accuracies of the relative positioning and velocity estimation are estimated, respectively, in static and kinematic modes. The conclusion and future work are drawn out in the final section.
# An Enhanced Distributed Data Aggregation Method in the Internet of Things
## 4 Performance Evaluation
### Upward Average Delay Evaluation  
The upward consumed time for a packet to reach the sink node is known as a function of the distance to the sink node. According to Figure 16, as anticipated, due to the degree restriction, the upward delay in reaching the sink node in the proposed method is less than those of other approaches. The reduction  
Figure 14: The results of routing control overhead evaluation, while network traffic is set to 5, 10, and 15 packets per minute.  
Figure 15: The results of average path length evaluation, while the successful transmission rate of links varies between 0.3–0.9.  
of network nodes overflow rate as well as the aggregated packet transmissions has brought about a trade-off; such that the slight impact of packets aggregation delay is evident in the network.  
In RPL, M-RPL and ARPL approaches, due to the occurred congestion on parent nodes, the delay of seeking the root node is gradually increased. However, as compared to BD-RPL, the degree restriction in the proposed LA-RPL method has caused some time duration to be consumed for aggregation, as well as the common existing packet reception delay, processing delay, transmission delay, and propagation delay. On the other hand, since the packet transmission delay is longer than the aggregation delay, less total time is consumed for packet transmissions by the proposed method in comparison with BD-RPL.  
\[AD=\frac{\sum_{i=2}^{n}(D_{Process}+D_{Queue}+D_{Aggregation}+D_{Trans}+D_{ Propagate})}{\sum_{i=2}^{n}(Hop)} \tag{12}\]  
In the proposed method, \(D_{\text{Aggregation}}\) is a nonzero value. This value is equal to zero for all other approaches.
# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 3 Materials and Methods
### Data Collection and Preparation
#### 3.1.2 Data Pruning  
The extracted tweets are in raw form, that contain \"noise\" or \"undesired data\" in the form of irrelevant terms, symbols, links, and punctuation marks. These irrelevant terms in the data are not useful for the model and may reduce the performance of classifiers. To remove such undesired data from the corpus, we perform some preprocessing tasks on data that are described in the subsequent sections (see Figure 3).  
Tokenization  
In order to eliminate undesired terms, and to construct a word vector for the model, we parsed every tweet into tokens. Tokenization transform the tweet text into words segment such as if we had a tweet like [@pajhwok Former president slams Pakistan's airstrike in Kunar], it will be segmented with the rule that each word in the tweet is separated by a space or special character in the tokenization phase. The process of tokenization converts the sentence into tokens and generate the following output like [@pajhwo] [Former] [president] [slams] [Pakistan's] [airstrike] [in] [Kunar].  
Character Transformation  
It is possible that the same token may appear in multiple tweets with different text case; lower or uppercase. Letter transformation converts each token into a standard format to avoid duplicate feature, herein we converted all the tokens into lowercase.  
Stop Words Removal  
Tweets contain many unrelated words that lead to an increase in the dimensionality of features. This increase in dimensionality increases computational complexity of classification models. Few terms are entirely useless in tweets for computation algorithm i.e., \"the\", \"an\", \"is\", \"am\", \"how\", \"to\", etc. It is a set of frequent words that carry less important meaning. Such tokens are removed because of non-informative and unnecessary increase in training time and memory overhead.  
Figure 3: Data preprocessing steps.  
Removing Irregular Terms  
While posting tweets many user tag URL links and images with the combination of text, which also becomes part of tweet message. Here, we only focus on written content; therefore, we removed all the contents which are in uneven form.  
Stemming  
Due to grammatical reason, people use a different inflected form of words such as kill, killed, and killing. A stemming process is applied for token normalization where the goal is to reduce inflectional form of words to a common base form. Stemming removes affixes from a word and uses the beginning of the word to represent a common base form. For example, the stem of study, studies, and studying is _studi_.