esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterisation Techniques  
Impedance spectroscopic studies of the smart composite were performed by introducing a sinusoidal voltage to all through-thickness facing pairs of carbon fabric strips. The excitation signal had the form of Equation (1).  
\[\mathrm{E_{t}=E_{0}\sin(\omega t)} \tag{1}\]  
where \(\mathrm{E_{t}}\) (V) is the voltage at a specific time, \(\mathrm{E_{0}}\) (V) is the voltage amplitude and \(\omega\) (rad/s) is the angular velocity. The relation between angular velocity and frequency (f) is described by the equation \(\omega=2\pi f\).  
The formation of a local electric field at the junction points of the carbon fibre strips that serve as a local sensing element provides the response of the material under test to the excitation field. The output current has the same frequency as the excitation voltage, with a phase delay \(\varphi\) which is described by Equation (2).  
\[\mathrm{I_{t}=I_{0}\sin(\omega t+\varphi)} \tag{2}\]  
Finally, the impedance is estimated by applying an expression analogue to [PERSON]'s general law for direct current \(\mathrm{R=E/I}\), Equation (3).  
\[Z=\mathrm{E_{t}/I_{t}=(E_{0}\sin(\omega t))/(I_{0}\sin(\omega t+\varphi))=\mid Z \mid(\sin(\omega t))/(\sin(\omega t+\varphi))} \tag{3}\]  
The real and the imaginary part of the impedance are calculated according to Equations (4) and (5).  
\[Z^{\prime}=\mid Z\mid\cos(\theta) \tag{4}\]  
\[Z^{\prime\prime}=\mid Z\mid\sin(\theta) \tag{5}\]  
Impedance measurements were performed using the Advanced Dielectric Thermal Analysis System (DETA-SCOPE) supplied by ADVISE, Chios, Greece. The copper wires of the electrodes were connected to a manual in-house manufactured multiplexing switch which directed the selected electrode pair to the spectroscope. A sinusoidal voltage of 10 V was applied to the electrodes. The frequencies of the performed scans were from 1 to 100 kHz. Every channel was measured individually and the duration of each measurement was 25 s.
## 4 Simulation and Verification
### Sensitivity Analysis  
In the abovementioned simulation results, the external system impedance is set as 0.5 \(\Omega\) by default. When the external system impedance varies, the short-circuit current would change. The short-circuit current of branch 1-2 considering the fluctuation in loads and DGs are shown in Figure 12 in case 3 for sunny days.  
As seen in Figure 12, the short-circuit current of branch 1-2 varies with the external system impedance. Since the DGs with LVRT strategies would inject larger current under lower node voltage, the short-circuit current of branch 1-2 becomes larger with the increase in the equivalent system impedance.  
In the meantime, a phase-to-phase short-circuit current calculation and phase-to-phase-to-ground short-circuit current calculation can also be conducted via our proposed method. When a phase-to-phase short-circuit fault occurred at node 20, the short-circuit current of branch 1-2 considering the fluctuation in loads and DGs in case 3 is shown in Figure 13. When a phase-to-phase-to-ground short-circuit fault occurred at node 20 with a fault resistance of 50 \(\Omega\), the short-circuit current in case 3 is also shown in Figure 13.  
The short-circuit current of branch 1-2 under phase-to-phase faults is smaller than that under three-phase solid faults. Meanwhile, when a phase-to-phase-to-ground short-circuit fault occurred at node 20 with a fault resistance of 50 \(\Omega\), the fault currents decreased rapidly. The ratio of the positive-sequence fault current to the normal current varies from 0.86 to 1.03. In other words, how to design a novel protection algorithm under high fault impedance is still a challenging issue in the distribution networks with high-penetration DGs.  
Figure 12: Short-circuit currents of branch 1–2 under different equivalent system impedance.  
Therefore, the calculated currents in some branches are influenced by the real-time output power of DGs and electricity loads. In our proposed method, these factors can be fully taken into account in the short-circuit currents calculation.  
Based on the abovementioned discussion, when a fault occurred at node 20 with a fault resistance of 0.1 \(\Omega\), the fault current considered in traditional calculations can be regarded as the fault current at 4:00 because of the zero-output power of DGs. And when the fluctuation in electricity loads is considered in our proposed method, as well as the output power of DGs, the fault current would vary with time. As given in Tables 6 and 7, the difference between these currents in the fault loop is in the range of [0.001, 0.023] of the traditional fault current.  
Therefore, in the analyzed scenarios, the difference between these currents would not lead to the failure of the over-current protection. We think our research results can help power supply utilities to realize the real-time short-circuit variation characteristics and provide the theoretical knowledge to obtain the reasonable setting calculation of relay protection, as well as the parameter selection of power equipment, such as circuit breakers.
## 4 Experimental Results  
### Dataset and Exploratory Analysis  
#### 4.1.1 Dataset  
We utilized a publicly accessible dataset sourced from Reddit, which was obtained from the Kaggle platform [20]. This dataset is comprehensive, encompassing a total of 232,074 posts in English, which were collected from the SuicideWatch subreddit. The term \"SuicideWatch\" is typically associated with a vigilant monitoring process aimed at preventing suicide attempts. This procedure is commonly implemented in environments such as jails, hospitals, mental health facilities, and military bases. It is specifically designated for individuals who are perceived to exhibit warning signs of suicide, suggesting a heightened risk of self-harm. The subreddit 'SuicideWatch' is a digital manifestation of this concept, where individuals potentially exhibiting suicidal tendencies or thoughts share their experiences and seek support. The timeframe for this data collection spans from 16 December 2008 to 2 January 2021. The dataset is meticulously balanced, consisting of 116,037 posts identified as suicidal and an equal number of posts classified as non-suicidal. The average length of each post in the dataset is approximately 430 words. Figure 4 illustrates the dataset, which consists of two columns: \"test\", which provides the content of user postings, and \"class\", which contains the label associated with the text.  
Figure 4: Dataset example.  
To evaluate the performance of both the proposed model and the baseline models, the dataset was stratified-split into three distinct subsets: training, testing, and validation, adhering to a proportional division of 70:20:10, respectively. The training subset, consisting of 162,452 samples, was utilized to train the models. The testing subset, which included 46,415 samples, served the purpose of evaluating the models' performance and generalization capabilities on unseen data. Lastly, the validation subset, containing 23,207 samples, was employed as an intermediary check during the model training process, aiding in the optimization of hyperparameters and helping to prevent overfitting.
# A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
###### Abstract  
In this paper, a novel anti-jamming technique based on black box variational inference for INS/GNSS integration with time-varying measurement noise covariance matrices is presented. We proved that the time-varying measurement noise is more similar to the Gaussian distribution with time-varying mean value than to the Inv-Gamma or Inv-Wishart distribution found by Kullback-Leibler divergence. Therefore, we assumed the prior distribution of measurement noise covariance matrices as Gaussian, and calculated the Gaussian parameters by the black box variational inference method. Finally, we obtained the measurement noise covariance matrices by using the Gaussian parameters. The experimental results illustrate that the proposed algorithm performs better in resisting time-varying measurement noise than the existing Variational Bayesian adaptive filter.  
Keywords:anti-jamming technique; black box variational inference; time-varying measurement noise covariance matrices; Gaussian distribution with time-varying mean value +
Footnote †: 2021: 11, 3664. [[https://doi.org/10.3390/app11083664](https://doi.org/10.3390/app11083664)]([https://doi.org/10.3390/app11083664](https://doi.org/10.3390/app11083664))
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 3 Results  
### Channel Frequency Response  
The transmitted and received signals are shown in Figure 8 as spectrograms over frequency and time. The undersampling introduces harmonic interference outside of the transmission band of the recorded signal, which are not physically present in the medium itself. Those phantom bands are removed on downsampling, by narrow bandpass filters.  
The power levels are more clearly visible in the averaged plots of Figure 9. The noise floor confirms the assumption of AWGN outside of the transmission, with an approximate SNR of 65 dB. The interference caused by the transmission itself raises the average power outside of the transmission band for about 30 dB.  
\begin{table}
\begin{tabular}{l r r l} \hline \hline
**Parameter** & \multicolumn{2}{c}{**Value**} & \multicolumn{2}{c}{**Description**} \\ \hline \multicolumn{2}{c}{_Single_} & \multicolumn{2}{c}{_Dual_} & \\ \hline \(N\) & 96 & 64 & Transmitted bits \\ \(N_{\text{lo}}\) & 1 & 2 & Number of sub-channels \\ \(N_{\text{ferm}}\) & 3 & 3 & Number of packages sent \\ \hline \(B\) & 5.0 kHz & 2.50 kHz & Bandwidth per channel \\ \(T\) & 10 ms & 10 ms & Length of a single chirp in time \\ \(f_{\text{c}}\) & 67.5 kHz & [66.25, 68.75] kHz & Frequency offset to band center \\ \hline \(T\,B\) & 50 & 25 & Time-bandwidth product \\ \hline \end{tabular}
\end{table}
Table 2: Experiment Waveform Parameters.  
Figure 8: _Cont._
## 4 Results and Analysis  
### \(Psnr\) and \(Mse\)  
The quality check of the decrypted image is initially analyzed by the two primary metrics, i.e., \(PSNR\) (peak signal-to-noise ratio) and \(MSE\) (mean square error) [8]. Mathematically, they are expressed as:  
\[PSNR=10\log_{10}\left[\frac{256\times 256}{MSE}\right] \tag{19}\]  
\[MSE=\frac{1}{MN}\sum_{i=0}^{M-1}\sum_{j=0}^{N-1}\left[f^{\prime}(i,j)-f(i,j) \right]^{2} \tag{20}\]  
Results of \(PSNR\) and \(MSE\) of the proposed method for different test images are shown in Table 1.
# Evaluation of Biodentine Tricalcium Silicate-Based Cement after Chlorhexidine Irrigation
## 1 Introduction
Chlorheximide is particularly useful during revised endodontic treatment, as it is active against pathogens responsible for the failure of primary endodontic treatment--_Enterococcus faecalis_ and _Candida albicans_[24]. When accidentally pushed beyond the apical foramen of the tooth, it has no toxic effect [23]. It cannot be used as the main irrigant during root canal treatment due to the inability to dissolve organic tissues (e.g., remnants of dead pulp) and insufficient activity against Gram-negative bacteria [23]. However, in some clinical protocols it is recommended as an adjuvant rinsing agent [25]. Efficacy of irrigants can be improved via the ultrasonic activation of the liquid known as passive ultrasonic irrigation (PUI). For a long time it has been the most popular activation method [26]. Its major advantage is the ability to dislodge debris from the canal walls and disrupt bacterial aggregations via the acoustic streaming generated at the oscillating device's tip [27]. Ultrasonic waves agitate the irrigant within the main canal, helping to transport it deeper into the more remote regions of the root canal system [26]. The process of mechanical canal wall cleaning is further enhanced by an increase in the irrigating solution's temperature and microcavitation [28]. The sonochemical effects can be triggered, under certain conditions, by the rapid fluctuations in irrigant pressure that causes the production of the shockwaves, the shear stress applied to the walls, and the localized increases in pressure and temperature [26]. The objective of this in vitro study was the assessment of the impact of chlorheximide on the structure and chemical composition of Biodentine with the use of ultrasounds. The null hypothesis was adopted that chlorhexidine, either ultrasonically activated or not, should not alter the structure of the tested biocement, because it targets organic matter and does not affect inorganic substances.
## 5 Conclusions  
This study conducted an experimental investigation to analyze the causes of the sinkhole phenomenon, which led to abnormal behavior of heat release rate and combustion efficiency, and evaluate the influence of opening shape on fire phenomena. For this purpose, the ISO 9705 compartment reduced to 1/4 scale was utilized, and a wide range of global equivalence ratios, including over- and under-ventilated fire conditions, were considered. To analyze the fire characteristics according to the global equivalence ratio and the opening shape, the heat release rate, temperature, and chemical species concentration were measured. The conclusions of the present study are as follows:  
1. The air inflow rate varied with the shape of openings designed to have a constant ventilation factor. Consequently, under identical ventilation factor and fuel supply conditions, the thermal and chemical environment inside the compartment considerably differed according to the opening shape. To accurately understand compartment fire phenomena, theories for predicting air inflow rates must be improved.
2. The cause of the sinkhole phenomenon was analyzed through the measurement of the temperature and the chemical species concentration of the discharge flow. The sinkhole phenomenon occurs because the fuel that is not burned due to the local combustion reaction occurring inside the compartment does not reach the lower flammable limit even outside the compartment. The sinkhole phenomenon ended simultaneously with the combustion reaction outside the compartment, i.e., flame eruption.
3. The sinkhole phenomenon caused by incomplete combustion in the over-ventilated condition leads to a higher fire risk than predicted. In particular, the generation of toxic substances due to incomplete combustion poses a huge threat to human safety, so this must be considered in fire risk assessments.  
Figure 13: Measurement results of chemical species volume fraction contained in the discharge flow.  
**Author Contributions:** Conceptualization, S.-Y.M., H.-S.Y. and C.-H.H.; methodology, S.-Y.M., H.-S.Y. and C.-H.H.; validation, H.-S.Y. and C.-H.H.; formal analysis, H.-S.Y.; investigation, S.-Y.M. and H.-S.Y.; resources, S.-Y.M., H.-S.Y. and C.-H.H.; data curation, H.-S.Y.; writing--original draft preparation, H.-S.Y.; writing--review and editing, H.-S.Y. and C.-H.H.; supervision, C.-H.H.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper was supported by the \"National Fire Agency\" R&D program (20016433).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
### Spectrum Sensors with Omnipresent Perception  
RF autonomy due to AI systems created using deep learning (DL) and reinforcement learning (RL) are expected to generate a deep knowledge--that is \"perception\"--about the sensed electromagnetic environment [4]. The National Spectrum Strategy (The White House, 11/2023) calls for initial studies in the 3.1 GHz to 3.45 GHz, 5.030 GHz to 5.091 GHz, 7.125 GHz to 8.400 GHz, and 18.1 GHz to 18.6 GHz bands toward DSA between commercial and DoD entities. AI-enabled perception calls for RF spectrum sensing and the detection of waveform parameters (e.g., modulation and perhaps even coding parameters [5]), carrier aggregation and bandwidth parts or similar information, propagation directions, power levels, and channel behaviors such as multipath, Doppler, and inter-symbol interference (ISI) metrics [6]. Typical spectrum sensors likely do not provide actionable intelligence on the spectrum except for usage statistics [7; 8; 9; 10], where the detection of \"spectrum holes\" determines sub-band(s) possibly available for SUs [11; 12; 13; 14; 15; 16; 17]. Spectrum fraud is a major issue [18]. The presence of harmful RF interference (RFI) can only be proven by independent third-party sensing and perception. To internalize a model of the spectral landscape, we will need a good understanding of the measured RF environment; simple energy binning using an FFT is too limiting for future use cases. Mitigating RFI events everywhere all the time is challenging and calls for the detection of jamming, pulse weapons, and spectrum fraud. We need real-time perception using machine intelligence, as events happen everywhere all the time at rates of thousands of events per second, thus ruling out any human-in-the-loop approaches. The fully AI-enabled perception of RF environment is of critical importance and serves as an intelligent information source to drive automated DSA [19].
## 4 Discussion
### Soil Analysis  
The soil chemistry might affect the concentration and composition of the secondary metabolites of plants [71]. The present investigation revealed that the percentage of soil organic C and P and K content were higher in the soil samples collected at the highest altitude of 2172 m with a soil pH of 7.0 whereas the total N content was higher in the soil samples collected at an altitude of 1620 m with a soil pH of 6.9. In addition to elevation factors, the physiochemical properties of the soil may also influence the quantity and quality of secondary metabolites in medicinal and aromatic plants [72]. Various chemical elements present in the rhizosphere of plants enter the enzymatic composition and affect the biochemical processes of plants [71]. Hence, the soil chemistry can affect secondary metabolite production in plants. In the present investigation, it was also revealed that the percentage of soil organic C, P and K content, and the total N content were found to be the lowest in the soil samples collected at the lowest altitude of 1583 m with a soil pH of 8.2. Hence, the soil composition may also play an important role in the chemical profiling of secondary metabolites. Furthermore, it was also observed that there was an increase in the percentage of soil organic C and the P and K content with an increase in the altitude, except for the total N content, which did not show any trend in the soil parameters concerning the variable altitude. This means that the environmental factors that are suitable for one component might not be favorable for another component. Secondary metabolite production in plants is directly related to edaphic factors and the quantity and quality of secondary metabolite production are affected by genetic and environmental factors. Therefore, the distribution of secondary metabolites and soil components in the present study may be attributed to the different geographical conditions.
## 3 Thermal Deformation Model for Steel Joint
### Study on Analytical Method of Steel Joint's Thermal Deformation
 \tag{11}\]  
Integrating the equation, we have  
\[\frac{d(r\cdot u_{r})}{dr}=r\alpha(1+\mu)T+rC \tag{12}\]  
Integrating the equation again, there would be  
\[r\cdot u_{r}=\alpha(1+\mu)\int_{a}^{r}Trdr+C_{1}r^{2}+C_{2} \tag{13}\]  
The displacement function is as follows:  
\[u_{r}=\frac{\alpha(1+\mu)}{r}\int_{a}^{r}Trdr+C_{1}r+\frac{C_{2}}{r} \tag{14}\]  
Getting the derivative of \(r\) for Formula (14), there would be  
\[\frac{du_{r}}{dr}=\alpha(1+\mu)\left(T-\frac{1}{r^{2}}\int_{a}^{r}Trdr\right)+ C_{1}-\frac{C_{2}}{r^{2}} \tag{15}\]  
With Formula (14) divided by \(r\), there would be  
\[\frac{u_{r}}{r}=\frac{\alpha(1+\mu)}{r^{2}}\int_{a}^{r}Trdr+C_{1}+\frac{C_{2}} {r^{2}} \tag{16}\]  
Formulas (15) and (16) are then substituted into the \(\sigma_{r}\) of Formula (5). \[ \begin{array}{ll}\sigma_{r}&=\frac{E}{1-\mu^{2}}\left[\alpha(1+\mu)\left(T- \frac{1}{r^{2}}\int_{a}^{r}Trdr\right)+C_{1}-\frac{C_{2}}{r^{2}}\ +\frac{\mu\alpha(1+\mu)}{r^{2}}\int_{a}^{r} Trdr+\mu C_{1}+\frac{uC_{2}}{r^{2}}\right]-\frac{E_{2}T}{1-\mu}\\ &=\frac{E}{1-\mu^{2}}\left[\alpha(1+\mu)T-\frac{\alpha(1-\mu^{2})}{r^{2}} \int_{a}^{r}Trdr+(1+\mu)C_{1}-\frac{(1-\mu)C_{2}}{r^{2}}\right]-\frac{E_{2}T} {1-\mu}\\ &=-\frac{E_{2}}{r^{2}}\int_{a}^{r}Trdr+\frac{EC_{1}}{1-\mu}-\frac{EC_{2}}{r^{2 }(1+\mu)}\end{array}  \tag{17}\]  
Owing to the boundary conditions \(\left.\sigma_{r}\right|_{r=a}=0\) and \(\left.\sigma_{r}\right|_{r=b}=0\), the equations containing integration constants \(C_{1}\) and \(C_{2}\) are:  
\[\left\{ \begin{array}{l}\frac{EC_{1}}{1-\mu}-\frac{EC_{2}}{a^{2}(1+\mu)}=0\\ -\frac{E}{r^{2}}\int_{a}^{b}Trdr+\frac{EC_{1}}{1-\mu}-\frac{EC_{2}}{b^{2}(1+\mu )}=0\end{array} \right. \tag{18}\]  
We solve the above equations:  
\[\left\{ \begin{array}{l}C_{1}=\frac{(1-\mu)\alpha}{b^{2}-a^{2}}\int_{a}^{b} Trdr\\ C_{2}=(1+\mu)\frac{a^{2}}{b^{2}-a^{2}}\int_{a}^{b}Trdr\end{array} \right. \tag{19}\]  
\(C_{1}\) and \(C_{2}\) are substituted into Formula (14), and the radius-directional thermal deformation function follows:  
\[u_{r}=\frac{\alpha}{r}\left[(1+\mu)\int_{a}^{r}Trdr+\frac{(1-\mu)r^{2}+(1+\mu )a^{2}}{b^{2}-a^{2}}\int_{a}^{b}Trdr\right] \tag{20}\]
# Evaluating the Impact of Pre-Configured Uplink Resources in Narrowband IoT
## 4 Experiments
### Packet Size  
NB-IoT is a crucial technology in IoT applications due to its low power consumption. However, the energy efficiency of this technology is significantly influenced by the size of the packet, as shown in Figures 16 and 17.  
Figure 16 illustrates the energy consumption for two different methods of packet transmission: PURs and the default RAP. The PUR method efficiently handles packet sizes up to 125 bytes, maintaining relatively stable energy consumption. However, once the packet size exceeds 125 bytes, the system switches to the default RAP, resulting in a sharp increase in energy consumption. For packet sizes between 125 and 200 bytes, the energy consumption remains high but stable, indicating the less energy-efficient mode of the default RAP.  
Figure 16: PUR fallback to default RAP when transport block size exceeds assigned resources.  
Figure 17 shows the trade-off between different coverage levels and energy consumption for UEs that use PURs. When UE need to cover a wider area, they use more energy, especially when the packet size is larger. This becomes especially noticeable when a UE has to use PURs to send packets larger than 125 bytes, causing the packets to be split and sent in multiple PUR occasions as PURs only support a maximum packet size of 125 bytes.  
When packets exceed 125 bytes, an additional PUR occasion is necessary. This requires another round of transmission and acknowledgment, effectively doubling the energy consumption compared to when the packet size remains at or below the threshold. Despite this, using PURs for packet transmission is still more energy-efficient than the default RAP, especially for CE Levels 0 and 1, as shown in Figure 16. However, it is important to note that for CE Level 2, PURs' energy consumption increases. The reason is that a UE with poor coverage ends up sending more repetitions of the same packet in the lower layers, as shown in Table 1, to increase the probability of the successful transmission of the packet. This has implications for the deployment of NB-IoT solutions, as UE operating in areas that require CE Level 2 will have shorter battery life and may need more frequent battery replacement. This increased energy consumption can be attributed to the higher power requirements for signal transmission and reception and a higher number of repetitions for the same packet at lower layers.