esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique
## 2 Data and Methods
### Selection Thresholds
The values for all three parameter sets are detailed in Table 3.  
The validation results for the three parameter sets are presented in Table 4, which details the following classification outcomes:  
* **P** (actual positive): spectrograms visually classified as containing whistler waves;
* **N** (actual negative): spectrograms visually classified as not containing whistler waves;
* **TP** (true positive): events confirmed as whistlers by the visual inspection and correctly classified as whistlers by WhISPER;
* **FN** (false negative): events confirmed as whistlers by the visual inspection but incorrectly classified as non-whistlers by WhISPER;
* **FP** (false positive): events confirmed as non-whistlers by the visual inspection but incorrectly classified as whistlers by WhISPER;
* **TN** (true negative): events confirmed as non-whistlers by the visual inspection and correctly classified as non-whistlers by WhISPER. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Total Population** & \multicolumn{2}{c}{**Set 1**} & \multicolumn{2}{c}{**Set 2**} & \multicolumn{2}{c}{**Set 3**} \\
**P + N = 1346** & & & & & \\ \hline
**P** & **TP** & **FN** & **TP** & **FN** & **TP** & **FN** \\
1232 & 1180 & 52 & 453 & 779 & 1232 & 0 \\ \hline
**N** & **FP** & **TN** & **FP** & **TN** & **FP** & **TN** \\
114 & 7 & 107 & 4 & 110 & 113 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix for the three parameter sets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\diamond\) & \(\boldsymbol{T_{1}}\) (nT\({}^{2}\)) & \(\boldsymbol{T_{2}}\) (nT\({}^{2}\)) & \(\boldsymbol{T_{3}}\) & \(\boldsymbol{T_{4}}\) (TW) \\ \hline
**Set 1** & 0.29 & 0.25 & 0.5 & 2 \\ \hline
**Set 2** & 0.9 & 0.6 & 0.65 & 2 \\ \hline
**Set 3** & 0.1 & 0.05 & 0.3 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The three sets of treshold parameters. The comparative performance of the parameter sets, as detailed in Table 4, indicates that Sets 1 and 3 achieve a high true positive count. However, Set 3 exhibits a poor performance in detecting true negatives. Conversely, Set 2 excels in true positive detection but exhibits a significantly elevated false negative rate. To quantify the accuracy of each parameter set, we employed the metric \(\text{ACC}_{\text{k}}=\frac{\text{TP}_{\text{k}}+\text{TN}_{\text{k}}}{\text{P}_{ \text{k}}+\text{N}_{\text{k}}}\), where \(k=1,2,3\) denotes the Parameter Set. Set 1 demonstrated superior accuracy, achieving \(\text{ACC}_{1}\simeq 96\%\), compared to \(\text{ACC}_{2}\simeq 42\%\) and \(\text{ACC}_{3}\simeq 92\%\) for Sets 2 and 3, respectively. Accuracy and other important metrics utilized for the evaluation of the three parameter sets are reported in Table 5. As evident from the table, Parameter Set 2 demonstrates excellent performance in True Negative Rate (TNR) but exhibits a low True Positive Rate (TPR). Conversely, Parameter Set 3 shows the opposite trend, while Parameter Set 1 achieves outstanding performance in both TPR and TNR.
# Tracking Mobile Sinks via Analysis of Movement Angle Changes in WSNs
## 3 Details of Damst
### Single DAMST Angle Analysis Phase
In Scenario C, the mobile sink node chooses F as a footprint when approaching F, and updates its DAMST point list to include B, C, E and F, meanwhile updates the DAMST angle list to include angle 1, angle 4 and angle 5. Similarly, for 90\({}^{\circ}\) < angle 5 < angle 4, the mobile sink deletes E from the DAMST point list. Subsequently, the mobile sinks updates its DAMST angle list to include angle 1 and angle 6 as well as DAMST point list to include B and C, as shown in Scenario D. For angle 6 > angle 1 > 90\({}^{\circ}\), the DAMST angle analysis is finished at F. Through the DAMST angle analysis, the nodes D and E on detours are cancelled and a better data transmission path A-B-C-F, is established from RA to F.  
The whole DAMST angle analysis process is shown in Algorithm 1. After each movement, the mobile sink puts the new footprint and new DAMST angle into the DAMST point list and the DAMST angle list, respectively, and then executes the single DAMST angle analysis and adjacent DAMST angle analysis repeatedly, to cancel the useless footprints on detours, until no more detours are found. The DAMST point list updated by Algorithm 1 should be included in the query generated by the mobile sink for guiding the query forwarding. Figure 7: An example of adjacent DAMST angle analysis. ```
1.Input P and A;//Input the DAMST point list and the DAMST angle list
2.\(S_{A}\) = sizeof (A); m = 1;//Obtain the size of DAMST angle list
3.while(m\(\
eq\)0)//Start DAMST angles analysis
4. {j=0;
5.for(i=S_{A};i\(\geq\)1;i- - )//Start the single DAMST angle analysis phase
6. {
7.if(A[i] < 90\({}^{\circ}\))//A[i] < 90\({}^{\circ}\) means a longer detour through P[i]
8.cancel P[i] ;j++;//Cancel P[i] from the DAMST point list
9.endif//Finish the single DAMST angle analysis phase
10.} 11.Update P and A ; \(S_{A}\) = sizeof (A) ; k = 0 ;//Update the DAMST point list and the DAMST angle list
12.for(i=S_{A};i\(\geq\)2;i- - )//Start adjacent DAMST angles analysis phase
13. {
14.if(A[i] < A[i-1] )//A[i] < A[i-1] means a longer detour through P[i]
15.cancel P[i] ;k++;//Cancel P[i] from the DAMST point list
16.endif//Finish adjacent DAMST angle analysis phase
17.} 18.Update P and A ; \(S_{A}\)= sizeof (A) ; m =\(S_{A}\);//Update the DAMST point list and the DAMST angle list
19.if(j=0&k=0 )
20.m = 0 ;//Finish the DAMST angle analysis
21.endif//Finish the DAMST angle analysis
22.} ```  
**Algorithm 1**The Damst angle analysis algorithm.
## 4 Feature Extraction and Control Methods
### Feature Extraction
#### 4.2.3 Simulation  
According to the principle of these two methods, two simulation programs were developed based on the LabVIEW platform.  
The amplitude of the initial signal and phase were set to 4 \(\mu\)m and 140\({}^{\circ}\), respectively. To quantitatively compare the extraction errors between these two methods, a noise signal was introduced to simulate the actual working conditions by adjusting its standard deviation. The comparison results are shown in Table 3.  
Two conclusions could be drawn from the results: (1) the extraction error increased gradually with the enhancement of the noise signal; and (2) the extraction error of the tracking-filter method was less than that of the cross-power method, which was limited by the sample rate. Therefore, when it is hard to increase the sample rate as determined by the data collection equipment, it is better to use the tracking-filter method to extract the unbalance vibration.  
Additionally, the phase from these two methods is the phase of initial vibration, but in the actual balancing process, the required parameter is the peak phase of the vibration. After using the two methods to extract the vibration phase \(\theta\), the value needs to be converted to the peak phase \(\delta\), and the relationship between the two phases is shown in Figure 8. The transformation function can be expressed by  
\[\delta=\left\{\begin{array}{ll}90^{0}&(0^{0}\leq\theta<90^{0})\\ 450^{0}-\theta&(90^{0}\leq\theta<360^{0})\end{array}\right. \tag{29}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Standard** & \multicolumn{2}{c}{**Phase Error (\({}^{\circ}\))**} & \multicolumn{2}{c}{**Amplitude Error (\(\mu\)m)**} \\
**Deviation (\(\mu\)m)** & **Tracking-Filter** & **Cross-Power** & **Tracking-Filter** & **Cross-Power** \\ \hline
0.2 & 0.27 & 0.34 & 0.026 & 0.029 \\
0.5 & 0.73 & 1.02 & 0.044 & 0.044 \\
1.0 & 1.48 & 2.03 & 0.110 & 0.146 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measurement error analysis of the vibration signal.
# A Systematic Literature Review of Various Control Techniques for Active Seat Suspension Systems
## 8 Control Methods Used in the Active Seating Suspension System
### Adaptive Neuro Fuzzy Interference System  
Adaptive Neuro Fuzzy Interference System (ANFIS) is a type of artificial intelligence controller that combined the features of both the fuzzy logic theory and neural network methodology. The fuzzy logic theory provides effective solutions to the information from the collected data while neural network methodology uses the learning capability of the method to improve the solutions. Therefore, the fuzzy membership functions and fuzzy rules can be adjusted adaptively through the NN learning process [98].  
ANFIS consists of 5 layers as illustrated in Figure 16. Layer 1 receives two crisp inputs namely error \(e\) and change of the error \(de\) to perform the fuzzification process. The node of the layer one is an adaptive node. Layer 2 contains the rule layer and its node is a fixed node which provides the output of cross multiplication of the input signals. Layer 3 is a fixed node with normalization layer. Layer 4 is an adaptive layer which generates the defuzzification layer. It takes the \(e\) and \(de\) as an input associated with layer 3 output. Layer 5 is a fixed node which generates the ANFIS controller output. The ANFIS has the numerical and linguistic knowledge, which make it an advantage for many researchers.  
ANFIS has been used in the active vehicle suspension system to enhance the ride comfort of the driver [99; 100; 101; 102]. It was proposed to actively control the seat suspension system [64]. It was implemented under three different cases: the active quarter main vehicle suspension, active seat suspension and the active control system integrated within both the vehicle and passenger seat suspensions. The ride comfort was evaluated using the weighted RMS acceleration and VDV. The simulation result has indicated that the active control system integrated within both the vehicle and seat suspensions provides excellent ride comfort.
###### Abstract  
In this study, we aimed to optimize the cutting parameters that affect the minimum temperature and power consumption in the turning of AISI 52100 bearing steel. For this, the Box-Behnken experimental design method, which was used for the lowest number of experiments in the experimental systems created using the response surface method (RSM), was used. The cutting parameters affecting the turning of the AISI 52100 bearing steel were determined as the cutting speed, depth of cut, and feed rate based on a literature research. The temperature and power consumption values were obtained via analyses according to the experimental design method determined by the finite element analysis (FEM) method. The results obtained were analyzed in Design Expert 13 software. According to the analysis results, the parameter values were determined for the minimum temperature and power consumption. The temperature and power consumption variables were affected by all three parameters, namely the cutting speed, depth of cut, and feed rate. For the minimum temperature and power consumption, a cutting speed of 162.427 m/min, depth of cut of 1.395 mm, and feed rate of 0.247 mm/rev, as well as the feed rate parameters, affected both the temperature and power consumption the most. In addition, it was determined that the cutting speed parameter had the least effect on both the temperature and power consumption variables. In addition, validation experiments were carried out in a real experimental environment with optimum values for the cutting parameters. The results showed that the output values obtained within the limits of the study with the obtained equation were quite close (3.3% error for temperature, 6.6% error for power consumption) to the real experimental outputs.  
AISI 52100; response surface method; cutting parameters; FEM analysis Article
# Integrated Replay Spoofing-Aware Text-Independent Speaker Verification
## 3 Integrated Speaker Verification
### End-to-End Monolithic Approach  
We first propose an E2E monolithic approach. This architecture simultaneously trains all components, including SID, PAD, and ISV, using a common feature, as illustrated in Figure 1a. The loss function for training the proposed E2E architecture comprises three components: a categorical cross-entropy (CCE) loss for SID, a binary cross-entropy (BCE) loss for PAD, and a two-class BCE loss for ISV. When a mini-batch is input for training, the proposed system first conducts SID and PAD with an MTL framework. Then, it composes a number of trials. A trial consists of two embeddings: one for enroll and the other for test. The ISV prediction is made by feed-forwarding the two embeddings through a few fully-connected layers. The entire DNN is jointly optimized using the sum of three loss functions. The objective function _Loss_ is defined as follows,  
\[Loss=Loss_{SID}+Loss_{PAD}+Loss_{ISV} \tag{1}\]  
where \(Loss_{SID}\) refers to the CCE loss for SID, \(Loss_{PAD}\) is the BCE loss for PAD, and \(Loss_{ISV}\) denotes the CCE loss for ISV.  
However, we find consistent tendencies that make it difficult to extract a common representation, i.e., feature, for performing both SV and PAD tasks through experiments. Therefore, we hypothesize that, although SV and PAD tasks are closely related in the scenario, the discriminative information required for each task collides. Speaker embeddings for the SV task requires robustness to device and channel difference; meanwhile, representation for the PAD task uses such information [22]. Additionally, both bona fide and replayed utterances include the same speaker information, making it a less discriminative factor for the PAD task; meanwhile, it is key information for the SV task. The study of [PERSON] et al. [13] supports our hypothesis, which states that the SV and PAD tasks should exist independently. To validate our hypothesis, we conduct experiments using separately trained SV and PAD systems and MTL-based systems. We further detail these elements in Section 4.3.
## 3 Reducing Transmission Overhead without Compromising Throughput
### LARep Design  
This section presents the design of LARep, and is organized as follows. Section 3.2.1 presents PoiTrace, a mechanism for determining the most significant locations visited by nodes from GPS information obtained on-the-go. In order to facilitate replication decisions with this information, Section 3.2.2 proposes MPTrack, a mechanism for gaining knowledge about the progress messages have made towards their destination nodes. Section 3.2.3 proposes the LAMeD mechanism, which provides a policy for dropping messages by considering the progress made towards their destinations. In Section 3.2.4, LASnF, a locality aware algorithm for determining the number of copies to generate for each message and when to generate a copy, is presented.
## 5 Conclusions  
The JSATS is a nonproprietary sensing technology developed by the U.S. Army Corps of Engineers, Portland District, for detecting and tracking small fish. Each cabled system is synchronized to a universal GPS clock, resulting in timing accuracy of 250 ns within a single system and 500 ns across multiple systems. The JSATS cabled system was evaluated extensively in both a controlled laboratory environment and a near-dam noisy field environment. The testing demonstrated the JSATS detection range of more than 500 m in an ideal laboratory environment. The system detection efficiency of 99.5% up to 107 m and average decoding efficiency of 64.5% resulted in a detection range greater than 107 m in the near-dam noisy field environment. The JSATS cabled systems have been deployed successfully on several major dams to acquire information for salmon protection and to develop more \"fish-friendly\" hydroelectric facilities.
## 2 Proposed Method
### Obtaining the Adjusting Factor for RUL Prediction
#### 2.2.2 Adjusting Factor  
In this section, the trained ELM is used to predict the capacity data of the target battery. Then, the predicted capacity data are compared with the real capacity data; thus, the adjusting factor based on the differences between the predicted capacity data and real capacity data can be obtained.  
Assume the capacity of the \(ith\) battery samples for training is \(C_{train}=[c_{m}^{i}]\) (\(m=1\), \(2\), \(3\ldots\)). The available capacity of the target battery is \(C_{pre}=[c_{pre,j}]\) (\(j=1\), \(2\), \(3\ldots\)). The capacity degradation rate of the battery samples \(L_{train}=[l_{m}^{i}]\) (\(i=1\), \(2\), \(3\ldots\), \(m=1\), \(2\), \(3\ldots\)) for training can be expressed as:  
\[l_{m}^{i}=[\frac{c_{m}^{i-1}-c_{m}^{i}}{c_{m}^{i-1}}]\;i=1,2,3,\ldots \tag{9}\]  
The capacity degradation rate of the target battery \(L_{pre}\)= \([l_{pre,j}]\) (\(i=1\), \(2\), \(3\ldots\)) can be expressed as:  
\[l_{prej}\!=[\frac{c_{pre,j-1}-c_{pre,j}}{c_{pre,j-1}}]\;j=1,2,3,\ldots \tag{10}\]After obtaining the capacity data and calculating the degradation rate data, train an ELM network to construct the relationship between the capacity and the degradation rate. Suppose the quantity of the input nodes of the ELM is q, and the output node is one. At time point \(t\), the input nodes consist of q capacity data [\(c_{m}^{l-q+1},c_{m}^{l-q+2},\ldots,c_{m}^{l-1},c_{m}^{l}\)], which are the capacity value at the moment and \(q-1\) numbers of data before it. The output nodes are the degradation rate at the moment [\(l_{m}^{l}\)].  
After training an ELM neural network, we use the steps shown in this section. Suppose the quantity of the input nodes of the ELM is q. Then, use the first existing q degradation rate [\(l_{pre,j}\)] (\(j=1,2,3\ldots q\)) to make a multi-step prediction.  
The steps of multi-step prediction are shown in Figure 6. The output of the prediction is actually the predicted value for the next time point. Then, integrate the new value into the sequence, place it at the bottom of the sequence to generate the new sequence, and use the new sequence to continue to make the prediction.  
First, input the q degradation rate data of the target battery into the trained ELM network to obtain the predicted (\(q+1\))th capacity data. Then, the predicted capacity data are used to generate the (\(q+1\))th degradation rate. In the next prediction step, the obtained (\(q+1\))th degradation rate data will be input into the ELM network again to obtain the (\(q+2\))th predicted data. Repeat the above steps until the predicted value reaches the failure threshold of the target battery. The predicted capacity data of the target battery is \(C_{pre}^{\prime}=[c_{pre,j}^{\prime}]\) (\(j=1,2,3\ldots\)).  
Based on the predicted capacity data \(C_{pre}^{\prime}\) and the real capacity data \(C_{pre}\) of the target battery, the adjusting factor \(\lambda\) can be calculated based on the consequence above:  
\[\lambda=\frac{\sum_{k=1}^{l}C_{pre}^{k}}{\sum_{k=1}^{l}C_{pre}^{k}} \tag{11}\]
# An Overview of Transcranial Magnetic Stimulation and Its Application in Multiple Sclerosis
## 2 Basic Principles of Transcranial Magnetic Stimulation (TMS)
### Safety Profile  
TMS is a generally well-tolerated and safe technique. The majority of side effects arising from TMS, especially single- and paired-pulse studies, are typically mild and transient, with headaches, scalp pain, neck pain, and small changes in hearing sensitivity being the most common [7; 8]. In studies employing rTMS, other side effects such as transient acute hypomania induction have been reported in very rare cases. Other side effects such as histotoxicity and structural brain changes have inconsistent data associated with their frequency [8]. By far the most severe adverse event associated with TMS is the induction of seizures. However, the reported frequency is extremely low, with a survey by [PERSON] et al. suggesting a rate at less than 1 event per 60,000 sessions [9]. An important risk factor for seizures associated with TMS applications includes having a neuropsychiatric disease, such as epilepsy. Other general factors may include sleep deprivation, stress, anxiety, and medical conditions that can lower one's seizure threshold [10]. According to 2020 expert guidelines from [PERSON] et al., a history of epilepsy or ferromagnetic metal implants within the head typically serve as exclusionary criteria for TMS application, but this is not necessarily an absolute contraindication [10]. In these cases, it is important for clinicians and researchers to consider the necessity of TMS, as well as the particular protocol employed.  
In the case of TMS being used in conjunction with MRI, most cases involve TMS being conducted before or after imaging. However, for concurrent use, it is important that dedicated, MRI-approved TMS coils that do not contain ferromagnetic materials are employed. Manufacturer guidelines may also require these coils to be maintained regularly [8; 10].
## 3 Results and Discussion
### Removal of Subsurface Impurity Defects by HF Acid Etching
#### 3.3.3 Laser Damage Resistance of Etched Fused Silica Surface
This indirectly validates the simulation results that the much higher temperature and stress caused by impurity defects in comparison to defect-free surfaces are the potential underlying mechanisms for explaining the low laser damage threshold of polished fused silica optics. The decrease of LIDT as shown in Figure 14a and the initiation of laser damage shown in Figure 14c should be blamed for the appearance of subsurface cracks and scratches. To solve this issue, two other parallel processing techniques, CO\({}_{2}\) laser conditioning [8] and thermal annealing [9; 10], have been also being developed to close and heal the cracks and scratches by heat softening for mitigating the negative effect of subsurface defects, which is beyond the scope of the present work. It is worth noting that though the amount of CeO\({}_{2}\) defects as shown in Figure 13 can be reduced by more than a half, some residual impurity defects are still adsorbed on the etched surfaces or hidden beneath the subsurface cracks. The residual impurity defects would limit the capacity of the HF-based acid etching technique in promoting the laser damage resistance of fused silica optics. Therefore, based on HF-based etching, some new techniques (e.g., assistance of megasonic agitation and addition of chelating agent adoption) are being explored and developed to further remove the subsurface impurity defects of fused silica optics. In the experiments, the variation of the impurity defect population with respect to the increase of etching time is indirectly derived by characterizing the etching rate, impurity element content, laser damage threshold, surface roughness and morphology of etched silica surfaces. The errors in the etching process would bring in potential deviations of these characterized features to the actual case. Hence, the simulation results of the negative effects of impurity defects have been not quantitatively and accurately validated, and new techniques of testing the transient high temperature and analyzing the exact impurity defect content should be developed in the future to further justify the role of impurity defects in decreasing the laser damage resistance of the polished fused silica optics.
# Performance of Satellite-Based Evapotranspiration
## 2 Study Area and Datasets
### Ground Data  
Air temperature (T\({}_{\text{a}}\)), Relative Humidity (RH) and Wind speed (u) measured at 2 m above the surface were obtained from 20 agrometeorological stations of the Chilean Agricultural Research Institute (INIA; available at: [[http://agromet.inia.cl/](http://agromet.inia.cl/)]([http://agromet.inia.cl/](http://agromet.inia.cl/)). Moreover, the automated weather station at Oromo Calibration Site (OCS) from LAB-Net [47] was used to validate surface temperature (T\({}_{\text{s}}\)) and actual evapotranspiration (ET\({}_{\text{a}}\)) estimates. Unlike the agrometeorological stations, this station also records radiative variables of the surface energy budget such as infrared temperature (TIR), Net Radiation (R\({}_{\text{n}}\)) and soil heat flux (G). The OCS data can be freely obtained from [[http://biosfera.uchile.cl/In_oromo.html](http://biosfera.uchile.cl/In_oromo.html)]([http://biosfera.uchile.cl/In_oromo.html](http://biosfera.uchile.cl/In_oromo.html)).