esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Regarding memory usage, the rule of thumb and the 1 st and 2 nd degree equations show relatively similar results, especially for larger input sizes. This similarity may primarily be due to the large and consistent input and output array sizes. In a different way, the Magnus-Teten equation stands out with substantially higher memory usage, especially for the largest input size. The complex mathematical operations, including logarithms, likely contribute to this increased memory requirement. Furthermore, the results, shown in the figures, indicate that while using RAM for storing intermediate results provides the fastest access, alternative methods such as writing results to provisional files can introduce significant overhead, increasing the computation time due to slower disk I/O operations. Given the substantial memory usage observed with the Magnus-Tetens equation, the developed second-degree model demonstrates a significant advantage, maintaining efficiency without compromising accuracy. This makes it a feasible alternative in systems with constrained memory resources, where the Magnus-Tetens equation may not be sustainable, particularly for high-frequency data streams or real-time applications. Although the Thumb equation is widely used because it provides very good results with minimal errors at high temperatures, its performance is poor at low temperatures, regardless of the relative humidity ratio. This is problematic because accurate dew point modeling is critical at low temperatures, where the margin for error is smaller. In applications such as water harvesting, the dew point directly influences available production, and in other applications, it indirectly affects the energy consumption of the process. The second-degree equation presents more generally reliable results because it maintains high accuracy and low error rates across a wider range of temperatures and humidity conditions. It is able to represent complex relationships between temperature and humidity more effectively than simpler models. This consistency is particularly important in practical applications where varying environmental conditions can significantly impact performance. The comprehensive validation process and iterative refinement of the second-degree equation also contribute to its reliability, making it a suitable choice for accurate and dependable dew point modeling. Furthermore, these findings imply that in conditions requiring high precision, such as low-temperature scenarios in meteorological and industrial applications, the second-degree equation may offer critical advantages. Given its demonstrated accuracy and robustness, it stands out as a viable alternative for operational systems that require consistent performance across a diverse range of conditions, supporting a broad array of applications, including energy-efficient building climate control and agricultural monitoring systems.
## 5 Performance Analysis
### Residual Energy  
Data transmission, data reception, and communication with other nodes are the three main types of node energy consumption. The more time a node is online, the more evenly its energy use will be spread out. As seen in Figure 8, the suggested protocol's energy consumption is more consistent than the LEACH, CACP, CPCP, and CUCA protocols. Reduced energy usage across clusters and within each one can be achieved with this approach.
# Detection and Analysis of Ethereum Energy Smart Contracts
###### Abstract  
As blockchain technology advances, so has the deployment of smart contracts on blockchain platforms, making it exceedingly challenging for users to explicitly identify application services. Unlike traditional contracts, smart contracts are not written in a natural language, making it difficult to determine their provenance. Automatic classification of smart contracts offers blockchain users keyword-based contract queries and a streamlined effective management of smart contracts. In addition, the advancement in smart contracts is accompanied by security challenges, which are generally caused by domain-specific security breaches in smart contract implementation. The development of secure and reliable smart contracts can be extremely challenging due to domain-specific vulnerabilities and constraints associated with various business logics. Accordingly, contract classification based on the application domain and the transaction context offers greater insight into the syntactic and semantic properties of that class. However, despite initial attempts at classifying Ethereum smart contracts, there has been no research on the identification of smart contracts deployed in transactive energy systems for energy exchange purposes. In this article, in response to the widely recognized prospects of blockchain-enabled smart contracts towards an economical and transparent energy sector, we propose a methodology for the detection and analysis of energy smart contracts. First, smart contracts are parsed by transforming code elements into vectors that encapsulate the semantic and syntactic characteristics of each term. This generates a corpus of annotated text as a balanced, representative collection of terms in energy contracts. The use of a domain corpus builder as an embedding layer to annotate energy smart contracts in conjunction with machine learning models results in a classification accuracy of 98.34%. Subsequently, a source code analysis scheme is applied to identified energy contracts to uncover patterns in code segment distribution, predominant adoption of certain functions, and recurring contracts across the Ethereum network.  
distributed ledger; Ethereum; smart contract; transactive energy framework; machine learning; energy trading
# A Statistical Analysis of Industrial Penetration and Internet Intensity in Taiwan +
## 5 Results and Discussion
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Variables** & **Intensity of Internet Use (\(y_{i}\))** & **Select (\(z_{i}\))** \\ \hline \multirow{4}{*}{\(HHI_{j}\)} & 0.148 & \(-\)1.369 \\  & (3.660) & (0.065) *** \\  & [2.732] & [0.067] *** \\ \hline \multirow{4}{*}{\(export_{i}\)} & 1.086 & 3.807 \\  & (1.284) & (0.207) *** \\  & [1.336] & [0.057] *** \\ \hline \multirow{4}{*}{\(GHHI_{j}\)} & \(-\)2.774 & 0.051 \\  & (1.057) *** & (0.237) \\  & [5.237] & [0.201] \\ \hline \multirow{4}{*}{\(city_{i}\)} & 0.852 & \(-\)0.201 \\  & (0.523) * & (0.013) *** \\  & [0.378] ** & [0.010] *** \\ \hline \multirow{4}{*}{\(computer1_{i}\)} & 0.239 & \multirow{4}{*}{-} \\  & (51.880) & - \\  & [0.432] & \\ \hline \multirow{4}{*}{\(computer2_{i}\)} & 0.069 & \multirow{4}{*}{-} \\  & (0.119) & - \\  & [0.019] *** & \\ \hline \multirow{4}{*}{\(size_{i}\)} & 0.002 & \multirow{4}{*}{-} \\  & [0.002] & (0.001) *** \\  & [0.002] & [0.0002] *** \\ \hline \multirow{4}{*}{\(group_{i}\)} & \multirow{4}{*}{-} & 58.543 \\  & & (16.397) *** \\  & & [0.005] *** \\ \hline \multirow{4}{*}{constant} & 2.643 & \multirow{4}{*}{-57.606 \\  & (0.755) *** & (16.400) *** \\  & [0.882] *** & \\ \hline \multirow{4}{*}{Mills lambda (\(\lambda\))} & \(-\)7.229 & \multirow{4}{*}{-} \\  & (2.595) *** & \\  & [2.193] *** & \\ \hline \# of observations & 153,081 & \\ \hline \# of censored observation & 31,924 & \\ \hline Wald Chi2 (df) & 543.38 (32) & \\ \hline \hline \end{tabular} Notes: Bootstrapping standard errors are in parentheses, and standard errors without bootstrapping appear in square brackets. The asterisks ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. 2-digit industry dummies are included in the empirical equation to control heterogeneity, but not report in the table for saving space.  
\end{table} 
Table 5: Selection corrected internet intensity (with Herfindahl-Hirschman index (HHI)) for all industries.
# Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
## 4 Compensation Control Strategy for Delayed Information
### Compensation Control Strategy  
In this part, by modifying the guaranteed cost strategy through the above time delay compensation method, we obtain the compensation control strategy for the time delay scenario. Due to the existence of delay time \(\Delta t\), the information obtained at time \(t\) is the opponent's state information at time \(t-\Delta t\). Although the opponent's state information at time \(t\) is not known, the uncertainty set can be calculated. Then the center of the uncertainty set is taken as the opponent's estimation state at time \(t\). The compensation control strategies are given as follows:  
\[\left\{\begin{array}{l}\mathbf{U}_{P}(t)=-\rho_{P}\frac{\mathbf{B}_{P}^{T}\big{(}t_{f}-t \big{)}\mathbf{\xi}^{c}}{\|\mathbf{B}_{P}^{T}\big{(}t_{f}-t\big{)}\mathbf{\xi}^{c}\|}\\ \mathbf{U}_{E}(t)=\rho_{E}\frac{\mathbf{\zeta}_{E}^{T}\big{(}t_{f}-t\big{)}\mathbf{\xi}^{c} }{\|\mathbf{\zeta}_{E}^{T}\big{(}t_{f}-t\big{)}\mathbf{\xi}^{c}\|}\end{array}\right., \tag{34}\]  
where \(\mathbf{\xi}^{c}=\frac{\mathbf{Z}^{c}(t)}{\|\mathbf{Z}^{c}(t)\|}\), \(\mathbf{Z}^{c}(t)=D\mathbf{\Phi}\Big{(}t_{f}-t\Big{)}\hat{\mathbf{X}}_{PE}\). \(\hat{\mathbf{X}}_{PE}\) is the estimated relative state after compensation.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 3 Air Pollution Monitoring Equipment
### Particulate Matter Sensor  
The measurement of particulate matter (PM) is not straightforward and there are many techniques (used in conventional monitoring systems and TNGAPMSs) available for measuring the mass concentrations of PM. Due to the complex nature of PM, different measurement techniques may give different results [86]. Some conventional monitoring instruments use a heating element to eliminate the effect of changing humidity and temperature. However, the heating element evaporates the semi-volatile species and influences the measurement results. Therefore, some instruments use a special dryer instead of a heating element (e.g., the Nafion dryer [87]).  
The available techniques for measuring the concentration of PM can be grouped into two categories. One is direct reading instrument which provides continuous measurements (sampling interval is in seconds or minutes) on the concentration of PM in ambient air (see Table 8). The other one is filters-based gravimetric sampler, which collects the PM onto a filter that needs to be weighted periodically in lab. The weighting procedure is a time and human resources consuming task, which leads to a large delay (in days) between collection and reporting. However, the filters-based gravimetric technique is usually used as the reference method in government agencies. One should note that the reference methods are not the absolute methods but subject to many artifacts (temperature and humidity change and semi-volatile compounds).  
The commonly used continuous measurement techniques of PM in ambient air are listed as follows.  
Figure 4: Basic Electrochemical Sensor.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 4 ITGA Design
### Encoding  
The algorithm employs a non-repeating integer encoding scheme to address the hardware resource scheduling problem for satellite data transmission ground stations. Compared to other encoding methods, this approach generates chromosomes that more intuitively represent the hardware resource allocation sequence for satellite data transmission tasks. The chromosome structure is illustrated in Figure 5.  
The integer values within the chromosome individual correspond to the task id, denoted as \(k\) in Equation (1). Data transmission tasks that are positioned earlier in the sequence receive priority in the allocation of ground station hardware resources based on their demand and the constraints imposed by ground resources. If the required hardware resources for data transmission tasks positioned later in the sequence are insufficient, those tasks may be abandoned.
# A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks
## 7 Conclusions  
In this paper, we proposed PUDCRP, an energy-efficient multi-hop routing protocol based on particle swarm optimization (PSO) algorithm to form clusters adaptively. The PSO-based uneven dynamic clustering method divides the network area into circles with unequal sizes according to the number and distribution of nodes. The radius of the circles is determined by the distance between the center of the circles and the BS. The farther the distance from clusters to the BS, the larger the clusters, which can effectively solve the hotspot problem of WSNs. The proposed protocol improves the way clusters are created in a wireless sensor network. The key idea is to divide the network area into multiple clusters adaptively based on distribution of nodes to achieve more balanced energy consumption. Compared with the rectangular grid clustering method and the formula selection CH clustering method, the PSO-based uneven dynamic clustering method can significantly reduce the energy consumption of nodes. We further proposed a connecting line aided route construction method to improve the energy efficiency of data transmission between the BS and CH nodes. Simulation experiments showed that compared with UCCGRA, multi-hop EEBCDA, EEMRP, CAMP, PSO-ECHS and PSO-SD, PUDCRP achieves more balanced energy consumption, significantly prolongs the network lifetime, and has better scalability in both the various number of nodes and different size networks.  
**Author Contributions:** Conceptualization, D.R. and J.H.; Methodology, D.R.; Formal Analysis, D.R.; Investigation, D.R.; Resources, J.H.; Writing-Original Draft Preparation, D.R.; Writing-Review & Editing, J.H.; Visualization, D.R.; Supervision, J.H.  
**Acknowledgments:** This work is supported in part by the National Science Foundation of China grant number [61472139]; the key project of Shanghai Science and Technology Commission grant number [11511504403].  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Possibility of Bridge Inspection through Drive-By Vehicles
## 3 2D VBI Model
### Short Time Fourier Transform Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cases**} & \multirow{2}{*}{\begin{tabular}{c} **Real Mid** \\ **Location(m)** \\ \end{tabular} } & \multicolumn{6}{c}{**Identified Results**} \\ \cline{3-8}  & & \begin{tabular}{c} **Start** \\ **Location(m)** \\ \end{tabular} & \begin{tabular}{c} **End** \\ **Location(m)** \\ \end{tabular} & \
\begin{tabular}{c} **Mid** \\ **Location(m)** \\ \end{tabular} &
\begin{tabular}{c} **Ave Energy** \\ **(dB/Hz)** \\ \end{tabular} \\ \hline
1 m damage & & & & & & \\ at 1 m location & 1.5 & 2.5 & 3.45 & 2.975 & \(-101.06\) \\ \hline
1 m damage & & & & & \\ at 5 m location & 5.5 & 3.25 & 7.85 & 5.55 & \(-97.234\) \\ \hline
1 m damage & & & & & & \\ at 10 m location & 10.5 & 8.05 & 12.95 & 10.5 & \(-95.321\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Short-Time Fourier Transform (STFT) results of different damage locations on the bridge. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Cases**} & \multirow{2}{*}{ \begin{tabular}{c} **Real Mid** \\ **Location(m)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Real** \\ **Damaged** \\ **Length(m)** \\ \end{tabular}  } & \multicolumn{6}{c}{**Identified Results**} \\ \cline{3-8}  & & \begin{tabular}{c} **Landaged** \\ **Length(m)** \\ \end{tabular}  & \begin{tabular}{c} **Start Lo-** \\ **cation(m)** \\ \end{tabular}  & \begin{tabular}{c} **End Loca-** \\ **tion(m)** \\ \end{tabular}  & \begin{tabular}{c} **Mid Loca-** \\ **tion(m)** \\ \end{tabular}  & \ \begin{tabular}{c} **Damaged** \\ **Length** \\ **(m)** \\ \end{tabular}  & \begin{tabular}{c} **Ave** \\ **Energy** \\ **(dB/Hz)** \\ \end{tabular}  \\ \hline
1 m damage & & & & & & & \\ at 10 m location & 10.5 & 1 & 8.05 & 12.95 & 10.5 & 4.9 & \(-95.321\) \\ \hline
0.5 m damage at 5 m location & 10.25 & 0.5 & 8.05 & 12.4 & 10.225 & 4.35 & \(-93.278\) \\ \hline
0.1 m damage at 10 m location & 10.05 & 0.1 & 8.5 & 11.6 & 10.05 & 3 Figure 9 shows that the damage area's energy loss has a linear relationship with the damage level, which could be used to identify the damage level once the STFT spectrum of the vertical vehicle acceleration is generated. For multiple damages, 1 m damages are set at the 5, 10, and 20 m locations; the results of STFT on the vertical vehicle acceleration is shown in Figure 10.  
Figure 10 showed that for multiple damages, the STFT method can still work. Changes of frequencies and energy leaked at the damage zones can be identified according to the damage locations.
# Semi-Coprime Array with Staggered Beam-Steering of Sub-Arrays
## 4 Parametric Analysis
### Effect of \(\Delta\) for Fixed L and SLA  
The simulations were carried out to study the effect of \(\Delta\) with three different cases of \(\theta_{0}\) (\(0^{\circ}\), \(30^{\circ}\) and \(60^{\circ}\)). The SLA was fixed at 22.5 dB and the number of antenna elements (\(L\)) was fixed at 14. Figure 5(a) shows the results for \(\Delta\) vs. HPBW and directivity. It is obvious from the figure that HPBW decreases almost linearly with an increase in \(\Delta\) for all the values of \(\theta_{0}\). HPBW also increases when \(\theta_{0}\) is increased from \(0^{\circ}\) to \(60^{\circ}\). The figure also shows that directivity increases almost linearly with increasing \(\Delta\) until \(\Delta\) reaches a certain limit, after which directivity decreases rapidly. This limit is larger for larger values of \(\theta_{0}\). Thus, for \(\theta_{0}\) = \(0^{\circ}\) and \(30^{\circ}\), directivity is at a maximum at \(\Delta\) = \(0.9^{\circ}\) and \(1.1^{\circ}\), respectively. For \(\theta_{0}\) = \(60^{\circ}\), the point of maximum directivity is even beyond the range of \(\Delta\) shown in the figure. The initial increase in directivity with increasing \(\Delta\) is due to the reducing beamwidth. When \(\Delta\) is increased further, the beam patterns of SA1 and SA2 are staggered to an extent that their grating lobes start to overlap. This phenomenon causes side lobes to rise and the PSLR to degrade, consequently reducing directivity. This effect on the PSLR is shown in Figure 5(b). It is evident from this figure that the PSLR remains almost constant as \(\Delta\) increases, until it reaches a certain limit, after which the grating lobes overlap and the PSLR drops sharply. Figure 5(b) also shows that the power loss increases exponentially as \(\Delta\) increases. Moreover, the rate of exponential growth also increases as \(\theta_{0}\) increases from \(0^{\circ}\) to \(60^{\circ}\). The effect of \(\Delta\) on the performance metrics can be understood keeping the fact in view that \(\Delta\) controls the overlapping region of the main lobes of SA1 and SA2 if \(\Delta\) is kept small, otherwise it also causes and controls the overlapping of their grating lobes. The larger \(\Delta\) is, the narrower the overlapping region of the main lobes causing a smaller HPBW and a higher power loss.  
Figure 6: Performance metrics of the SCASS-C for variable \(\Delta\), fixed SLA = 22.5 dB and \(L\) = 14.
## 5 Evaluation
### Comparison between One-Class and Binary Classification
was measured using a test set that did not include any training data and did not contain the synthetic anomalies. Therefore, this experiment not only demonstrates the validity of the binary classifier in anomaly detection but also highlights the effectiveness of the synthetic anomaly. The results of this experiment were quite intriguing. Basically, accuracy was generally higher for one-class classification models in the case of HAI, while it was higher for binary classification models in the case of SWaT. However, since both HAI and SWaT are imbalanced datasets that overwhelmingly contain normal data, accuracy is not a reliable performance metric for these models. On the other hand, for both HAI and SWaT, the model with the highest F1 score was LSTM-BIN. In fact, most models except for LSTM-BIN exhibited higher recall than precision. This discrepancy is primarily due to the imbalance in the ICS datasets. Generally, a high recall indicates that the model correctly identifies actual positive samples (anomalies), whereas low precision means that the proportion of actual positives among the samples predicted as positive by the model is low. This suggests that, given the relative scarcity of positive class (anomalies) compared to the negative class (normal data), many samples predicted as positive by the model are likely to be false positives. Another possible interpretation is that the models, while relatively effectively identifying samples from the positive class, generate many false positives because the differentiation between the negative and positive classes is not pronounced. In other words, while the models successfully detect positive class samples, they frequently confuse them with the negative class, leading to lower precision. Despite this, LSTM-BIN demonstrated a relatively high F1 score because its recall and precision were not significantly different from each other. This indicates that LSTM-BIN, by using data from both classes, learned an optimal decision boundary that better distinguishes between positive and negative classes. Additionally, LSTM-BIN, being based on a neural network, can learn complex patterns in the data, including non-linearities, allowing it to form a more sophisticated decision boundary compared to other binary classification models. In contrast, LSTM-AE, which is trained only on normal data, learns a less refined decision boundary compared to LSTM-BIN. **The answer for RQ2. ** Comparing the performance of one-class classification models and binary classification models using the pure ICS dataset and the anomaly-augmented ICS dataset revealed that the neural net-based binary classifier exhibited slightly higher performance. In other words, it was confirmed that dataset augmentation through the generation of ICS time-series anomalies indeed aids in training sophisticated anomaly detection models.