esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Performance Analysis of Satellite Network Moving Target Defense System with Petri Nets
## 4 SPN-Based Modeling and Performance Analysis  
In this section, to investigate the impact of MTD on the performance of satellite networks, we give two satellite network communication scenarios based on conventional state and moving target defense state separately. Furthermore, we describe our proposed SPN models and demonstrate theoretical reasoning in detail.
## 4 Experiments
### Datasets
#### 4.1.7 MSL and SMAP  
The Mars Science Laboratory rover (MSL) and the Soil Moisture Active Passive satellite (SMAP) are real-word datasets acquired from the spacecraft, which were annotated by experts of NASA. MSL dataset includes the 2160 normal samples and the 2731 samples under attack and SMAP includes the 2556 normal samples and the 8071 samples under attack. There are 55 and 27 sensors in SMAP and MSL datasets, respectively.
# Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model
## 2 Methodology
### Methods Trained with Input Data at Various Sampling Rates  
In this study, we implemented and compared three methods trained with input data at various sampling rates, as follows:  
* Method I: The model is trained with data from various sampling rates [27].
* Method II: The model is trained with data of 16 samples per cycle (960 Hz), and the input data are resampled to 16 samples per cycle.
* Method III: The model is trained with data of 4 samples per cycle (240 Hz), and the input data are resampled to 4 samples per cycle.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameters** & **Value** \\ \hline Number of Parameters & 68,791 \\ Encoder Layers & 3 \\ Encoder Dimension & 128 \\ Attention Heads & 3 \\ Convolution Kernel Size & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of the model hyper-parameters for Conformer.  
Table 3 summarizes the model parameter settings for the three methods employed in this study. The primary distinctions between the methods lie in the number of epochs and batch sizes utilized during training. Method I requires a higher number of epochs to achieve convergence, primarily due to its batch size of 1, which is necessary to maintain uniform data length for each batch. In contrast, Method II and Method III adopt a larger batch size of 128, as the input data are resampled to a consistent rate. This permits the utilization of larger batch sizes, which can improve training efficiency. This choice is further supported by evidence suggesting that using a small batch size may increase training time [28], leading to longer training and inference times for Model I. Despite these variations in training setups, the classification results among the three methods are relatively similar, as will be demonstrated in the subsequent section.  
Another technique to enhance the performance of the Transformer model is to use a warmup step and a learning rate scheduler [29]. The warmup step refers to the initial training phase where the learning rate is gradually increased from 0 until a predefined condition is met. Our project's first 2 epochs of model training are dedicated to the warmup phase. Afterward, the learning rate follows a cosine function schedule for gradual reduction, as depicted in Figure 9.
## 4 Results
### Analysis of Urban Eco-Efficiency in Shandong Province
#### 4.2.3 Spatial Differences in Urban Eco-Efficiency in Shandong Province  
To reveal the spatial differences in eco-efficiency and their sources in Shandong Province, this study analyzes urban eco-efficiency based on the Gini coefficient. The analysis results are shown in Figure 7 and Tables 3-5. The overall Gini coefficient in Shandong Province shows an upward trend, and the overall difference in urban eco-efficiency rises by 4.5% on average. It rose sharply during 2014-2018, after which it declined with small fluctuations and reached a minimum value of 0.387 in 2020, indicating that the eco-environmental protection synergy mechanism has achieved some success in recent years. Subregionally, the Gini index of the Provincial Capital Economic Circle continued to rise before 2018 and showed a stable state after 2018 with a small decline, indicating that the gap between the eco-efficiency of the cities in the Provincial Capital Economic Circle has been narrowing in recent years. Combined with the previous section, it can be seen that there is a synergistic downward trend in the eco-efficiency of the Provincial Capital Economic Circle. The Jiaodong Economic Circle and the Provincial Capital Economic Circle have roughly the same trend, and the Gini coefficient overall shows a rising trend. After 2018, there is a downward trend. Combined with the analysis of Figure 3, it can be seen that the eco-efficiency of the Jiaodong Economic Circle as a whole is at a high level, and the eco-efficiency of the city of Weihai rebounded in the 21 st year, which may be the reason for the decline in the Gini coefficient in the 21 st year. The distribution of the Gini coefficient of the Lunan Economic Circle and other economic centers is obviously different; the overall tendency is a downward trend, with an average of 19.6% and a significant decline of 62% in 2018-2020, indicating that there is a synergistic decline in eco-efficiency.  
Analyzing the trend of the evolution of the differences between different economic centers in Figure 7, it can be seen that the differences between different economic centers show an upward trend, and the fluctuations are roughly the same. Among them, the difference between the provincial capital and the Lunan Economic Circle is the largest, and the difference between Jiaodong and Lunan is smaller.  
Figure 7: Evolutionary trends of eco-efficiency differences and their contribution in Shandong Province, 2014–2021. (**a**) Overall and regional differences; (**b**) regional differences; (**c**) differential contribution.  
Figure 7 reflects the contribution of intrasubgroup differences, intersubgroup differences, and hypervariable density to the overall differences, from which it can be seen that the intersubgroup differences are the largest, indicating that the level of differences between regions is large, which is consistent with the effect presented in Figure 6. The hypervariable density has the smallest contribution, indicating that the differences due to the cross-overlapping parts of different subgroups are small.
## 2 Results and Discussion
### Organic Compound Degradation and Biosurfactant Production  
Numerous studies have shown that bacteria belonging to the genus _Pseudomonas_ are known for their ability to degrade aliphatic and/or aromatic organic compounds [4; 92]. For example, [PERSON] et al. [93] reported that the _P. aeruginosa_ strain GOM1 degraded 96% of the aliphatic fraction (C12-C38) of crude oil during a 30-day incubation period, exhibiting high activity against long-chain alkanes and expressing alkane hydroxylases. In another study, [PERSON] et al. [94] described that _Pseudomonas_ sp. BUN14 could grow on \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gene** & **Accession Number** & **KO Numbers** & **Gene Product** & **Activity** \\ \hline _alg44_ & MBG8560012.1 & K19291 & Mannuronan synthase (EC:2.4.1.33) & \\ _alg8_ & MBG8560013.1 & K19290 & Mannuronan synthase (EC:2.4.1.33) & \\ _algD_ & MBG8560014.1 & K00066 & GDP-mannose 6-dehydrogenase (EC:1.1.1.132) & \\ _algH_ & MBG8561216.1 & K07735 & Putative transcriptional regulator & \\ _algR_ & MBG8558298.1 & K08083 & Two-component system. Lytr family. Response & \\  & & & regular algr & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gene** & **Accession Number** & **KO Numbers** & **Gene Product** & **Activity** \\ \hline _katE_ & MBG8562679.1 & K03781 & Catalase (EC:1.11.1.6) & Oxidative stress \\ _katG_ & MBG8559566.1 & K03782 & Catalase-peroxidase (EC:1.11.1.21) & response \\ _sol2_ & MBG8560099.1 & K04564 & SOD2; superoxide dismutase. Fe-Mn family & \\  & & & (EC:1.15.1.1) & \\ _ggt_ & MBG8558271.1 & K00681 & Gamma-glutamyltranspeptidase/glutathione & \\  & & & hydrolase (EC:2.3.2.2 3.4.19.13) & \\ _gst_ & MBG8558300.1 & K00799 & Glutathione S-transferase (EC:2.5.1.18) & \\ _gppx_ & MBG8560587.1 & K00432 & Glutathione peroxidase (EC:1.11.1.9) & \\ _gsr_ & MBG8561128.1 & K00383 & GSR; glutathione reductase (NADPH) (EC:1.8.1.7) & \\ _oxyR_ & MBG8558139.1 & K04761 & Lysr family transcriptional regulator. Hydrogen & \\  & & & peroxide-inducible gene activator & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Genes potentially involved in response to oxidative stress in the ZCR6 genome. and utilize various aromatic hydrocarbons, including pyrene, naphthalene, phenanthrene, carbazole, dibenzofuran, dibenzothiophene, biphenyl, pristane, fluoranthene, crude oil, octadecane, and tetradecane, as sole carbon and energy sources. It is worth noting that the effectiveness of phytoremediation supported by bacteria depends mainly on the activity of microorganisms carrying catabolic genes [95; 96]. To investigate the potential of the ZCR6 strain to degrade both aliphatic and aromatic hydrocarbons, we explored its genome in search of genes related to the corresponding degradation pathways. The ZCR6 genome contains crucial genes involved in the degradation of these compounds (Table 8), which emphasizes that this strain is an excellent candidate for bioremediation.
## Materials and Methods
### Waterborne Sand Test Method  
The waterborne sand test method developed in this program was specially designed to evaluate the abrasion resistance of concrete surfaces subjected to the impact and impingement of water flow that carries a predetermined amount of sand. To accurately model the damage mechanisms, a carefully thought-out test procedure was conducted by combining the impact load of a water jet and the shear/friction forces of sand particles that accompany it.  
To understand the interfacial bonding behaviors between coarse aggregate and mortar, a specially designed and fabricated 10 mm \(\times\) 200 mm rectangular nozzle sufficiently large to cover the maximum aggregate size was used in the waterborne sand tests (Figure 2). A rectangular nozzle was used because it produces a water-jet flow over a spillway in the field as opposed to a circular flow.  
The test water was made by mixing quartz river sand no coarser than 5 mm to formulate a slurry mixture. During the tests, the nozzle was held at 90\({}^{\circ}\) and 45\({}^{\circ}\) angles in relation to the test slab to evaluate the effects of flow angles on the impact abrasion.  
Figure 2: (**a**) Test apparatus for waterborne sand flow impact abrasion, (**b**) Schematic diagram of nozzle.  
An abrasion chamber measuring 1260 mm \(\times\) 1150 mm \(\times\) 1170 mm was installed. The chamber can accommodate four individual pumps that can simultaneously shoot four separate water flows of different sand mixtures at different velocities onto the test slabs that were positioned above water level.  
Fresh sand supply was used to form the designed water flows composed of angular quartz-rich river sand with a Mohs-hardness (Hp) of 8 and specific gravity of 2.64. In general, sand was gradually poured in and mixed for 5 min until the mixture reached 300 kg/m\({}^{3}\) sand content.  
During each water jet test, the cavitation index was first assessed and found to be 0.2. In accordance with reference (ACI 210R-03, 2003), a cavitation index of 0.2 is small enough to be ignored. Throughout the 2 h water jet test, the water velocity was controlled at 10 m/s, which is equivalent to a 0.17-MPa pressure on the test slab, and the water temperature was maintained at 30 \({}^{\circ}\)C.  
Immediately after the test, the loose materials were flushed and collected to determine their mass with a precision of \(\pm\)0.05 g. This study also measured the mass of the slab before (m1) and after (m2) the test to determine the impact abrasion loss (percent by mass); moreover, the impact abrasion rate (IAR, in g/h) and average IARs we determined from the specimen's mass loss versus test period and total mass loss versus test time, respectively. A minimum of three measurements were used to establish the IAR. The range among the test results for the three specimens should be no greater than 45% of their average to quickly determine whether the test process is successful.
## 4 Results and Discussion
### Analysis of Prediction Statistics of Historical Landslide Data
Assuming that the respective landslide occurrence times are correct and rainfall was correctly recorded without rain gauge malfunction, these incidents indicate that the prediction of artificial slope failure may require the consideration of additional failure mechanisms and various uncertainties caused by human influence and slope-design factors. Figure 11 shows the results of reclassifying predicted historical landslide data for each of the eight IC zones in Busan. While IC zones 1, 2, and 8 were regions with the least number of recorded landslides, IC zones 3 and 4 represented regions with the highest number of recorded landslides. As mentioned in Section 3.2., differences in the number of landslides depend on whether the regions were located in densely populated and urbanized areas. IC zones 1, 2, and 8 are located in the outskirts of Busan, where risk elements (people and infrastructure such as buildings and roads) are sparse. Therefore, the lower number of landslides in these areas compared to other IC zones do not necessarily indicate that they are geologically or meteorologically more stable. Although SEM-LEWS results satisfactorily predicted landslides that occurred in IC zones 3, 4, and 6, considering the large proportions of the landslides that occurred under the \"Alert\" and \"Emergency\" levels, the SEM-LEWS results did not effectively predict landslides in IC zone 5, as a large proportion of landslides occurred at relatively low warning levels (\"Watch\", \"Attention\", and \"Null\" levels). Figure 10: Historical slope failures that occurred within the “Null” level including (**a**) soil loss of a cut slope in 2012 (captured from a Korean news video clip) and (**b**) failure of an embankment within a housing area in 2016.
## 1 Introduction  
Axially symmetric lens systems are often required to be aplanatic to provide sharp imaging at least in a central portion of the system's field of view. The current meaning of the term aplanatic is freedom from spherical aberration and coma aberration. However, the meaning can be defined as absence of uniform and linear field-dependent aberrations. Aplanatic is a very desirable property because it provides sharp imaging and ease of lens alignment. Because of lens fabrication and assembly errors, there are not in practice perfect axially symmetric systems but perturbed optical systems [1; 2; 3].  
When the axial symmetry is lost the first aberrations that can be present, in addition to the aberrations of axially symmetric systems, are uniform astigmatism, uniform coma, linear astigmatism, field tilt, and smile and keystone distortion [4]. Then, under perturbation and to keep aplanation one must correct for uniform astigmatism, uniform coma, and linear astigmatism aberration. Because uniform astigmatism depends on the square of the surfaces tilt with respect to the nominal optical axis, and given small system perturbations, then uniform astigmatism turns out to be negligible. If there is any significant uniform astigmatism it is likely due to surface figure error or lens strain. Thus to keep the anplanation of a perturbed aplanatic system one must correct or mitigate for uniform coma and linear astigmatism.  
Uniform coma, sometimes called central coma [5], is often a problem in high numerical aperture systems and can be corrected by decentering a lens element. This has been done in assembling microscope objectives and in some early projection lenses for micro lithography. Another way to correct for uniform coma is reprocessing a system surface at the stop aperture location to introduce the opposite amount of uniform coma and rely on coma cancellation. Still another way to mitigate for uniform coma is to disassemble and reassemble the lens system several times until uniform coma is found to be reduced. In this case one relies on the statistical nature of the lens aberration residuals as a function of lens assembly.  
On the other hand linear astigmatism [6] is a problem in wide angle lenses. This paper discusses several methods to mitigate linear astigmatism aberration in a system that is nominally axially symmetric but that due to fabrication and assembly errors suffers from this aberration.  
There has been interest in understanding the behavior of perturbed lens systems in relation to tolerancing a nominally axially symmetric system [7; 8; 9; 10; 11; 12; 13; 14; 15]. Our study focuses on the control of linear astigmatism aberration. Other techniques for aberration mitigation in projection lenses not discussed in this paper are using off-axis illumination and image processing [16].
# NIRS-EMG for Clinical Applications: A Systematic Review
## 4 NIRS-EMG: Review of Studies in Clinical Practice  
The aim of this literature analysis is to review all available studies which have combined EMG and NIRS for clinical practice, targeting applications of the techniques for patients with motor impairment or oxygen metabolism diseases. The design of this research attempts to answer the question: \"Have NIRS and EMG been effectively combined, on muscle, in clinical practice?\". With this aim, the following procedure was employed for the literature screening. A collection of articles were obtained by screening PubMed, PubMed Central, and Google Scholar, using the following keyword combinations: (1) \"EMG\" and \"NIRS\" and \"Rehabilitation\": 225 papers; (2) \"EMG\" and \"NIRS\" and \"Clinical\": 354 papers; (3) \"EMG\" and \"NIRS\" and \"Patient\": 369 papers; (4) \"EMG\" and \"NIRS\" and \"Instrument\": 48 papers; and (5) \"EMG\" and \"NIRS\" and \"Device\": 140 papers. The papers found in the literature research were screened, one by one, for inclusion. The further inclusion criteria were that papers had to be published in journals with impact factor; include measures on EMG and NIRS; the measurements had to be done not only on healthy volunteers, but performed also on people with orthopedic or neurological diseases (\"patients\", not \"subjects\"); and, in case of NIRS, the measurements had to refer to muscles (not the brain).  
According to the proposed inclusion criteria, only 25 studies were eligible for inclusion in this paper. They were further classified into three subgroups. In the first subgroup, we included all the works (n = 9) where both EMG and NIRS signals were acquired, interpreted, and correlated to each other, in order to represent the physiological muscular status. In these studies, both EMG and NIRS were outcome measures (see Section 4.1, NIRS and EMG). In the second subgroup, we gathered studies (n = 8) where only the EMG signal was used to verify that a movement had effectively occurred, or to understand whether a certain muscular work threshold was reached. In these studies, the EMG signal was employed as a feedback (or as a methodological measure) to support NIRS-based findings (see Section 4.2, NIRS > EMG). Finally, the third subgroup regards the works (n = 7 + 1 review) in which integrated NIRS-EMG hardware systems were developed or employed (see Section 4.3, NIRS and EMG HW).
## 4 Results
### Protocol Evaluation
This, in turn, implies that it is more likely to find routes requiring less power. The results show that the use of higher \(\omega\) values leads to a clear benefit in terms of the transmitted power. Furthermore, the same effect can be observed for the overall transmitted power. Then, Figure 15b exhibits a behavior similar to the one seen for the standard deviation of the transmit power. Figure 13: Power transmission statistics upon variation of \(\omega\). (**a**) Standard deviation. (**b**) Total power transmitted. We can finally conclude that, for the considered network topologies, the use of the dynamic metric that accounts for the accumulated exposure yields a reduction of the total power transmitted by the complete network. This reduction is achieved by means of a fairer distribution of the exposure induced by the different nodes. This exposure distribution avoids concentrating traffic in some particular nodes or geographical areas, so leading to a certain traffic load balancing between the available paths. In turn, this reduces the number of wireless collisions and traffic retransmissions, causing the power savings that are evinced in the analyzed scenarios. On the other hand, we believe that this effect may not appear in other scenarios with lower density, and that it may have some impact in other QoS parameters, such as delay. A further analysis of this effect will be tackled in our future work. In addition, we have also observed that the the proposed solution yields a certain traffic balance between the available paths. We have also validated the correct operation of the EA-AODV protocol, which provides an open implementation to include the EMF exposure in the routing procedure, where costs can be customized for both the accumulated exposure and instantaneous transmission power.
# A High-Robust Sensor Activity Control Algorithm for Wireless Sensor Networks
## 5 Evaluations and Results
### Convergence Time and Successful Ratio Analysis  
Figure 8 shows the execution result of the Gur Game; the target value was increased from 0 to 100. It can be observed from the experimental results that convergence to the target value occurred only when the target value was between 25 and 75. Therefore, when the target value is set too large or too small, the number of active sensors cannot successfully converge to the target value. In other words, the Gur Game has restrictions on its use.  
Figure 9a,b shows the execution results of the ACK algorithm. When the probability of the \(P_{1}\) state of the state machine is set to 0.01, the success ratio can reach 100%, in which case the average convergence time does not exceed 250 rounds. The success ratio cannot reach 100 when \(P_{1}\) is set to 0 or 0.05. Therefore, in subsequent experiments, \(P_{1}\), \(P_{2}\), and \(P_{3}\) were set to 0.01, 0.1, and 1 for the ACK algorithm.  
Figure 10a,b shows the results of SACA, where the target value was increased from 0 to 100. We set the activation parameters to 0.001 and 0.0001, respectively, and compared the activation parameters to the sensor convergence speed impact. According to the experimental results, when the activation parameter was set to 0.0001, the convergence success ratio of the SACA was close to 100%. The convergence time of SACA was shortened by approximately one-tenth that of Gur Game, which shows that SACA has strong self-organization and fast convergence speed ability.  
A WSN usually consists of a large number of sensors. Therefore, when the number of sensors increases, the strong self-organization ability can avoid the waste of power resources and provide real-time environmental monitoring quality according to the requirements of the application. To further highlight the faster convergence speed of SACA compared to the ACK algorithm, we increased the total number of sensors from 1000 to 10,000. The target values were set at 30% and 70% of the total number of sensors, respectively. As shown in  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Parameter** \\ \hline Total number of sensors, \(m\) & \(10^{2}\), \(10^{3}\)–\(10^{4}\) \\ Number of the sink & 1 \\ Target value, \(Q_{0}\) & 0–100 \\  & 0.3, 0.7 (\(Q_{0}/m\)) \\ Adjustment parameter, \(\Delta\tau\) & 1 \\ Activation parameter, \(\sigma\) & 0.001, 0.0001 \\ Number of experiments & 100 \\ Execute rounds for each set of experiment & 3000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameter settings.  
Figure 8: Average convergence time and success ratio in the Gur Game approach.  
Figure 11, when the number of active sensors reached 3% of the target value, it indicated a successful convergence to the target value.