esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Future Challenges by Intelligent Ship PHM
### Treatment of Uncertainty Problems  
The health management of ship mechanical systems is affected by internal uncertainties such as working conditions and degradation, and external ones such as navigation environment and abnormal external working conditions. Therefore, the decision-making of health management should not only consider the completion of internal performance analysis but also integrate multiple external information to realize comprehensive health assessment, to make scientific operation and maintenance decisions. This external uncertainty information and its impact mainly include:(1) Extreme weather. In extreme weather conditions such as typhoons, the mechanical system suffers an abnormal influence. For the HCM, it is necessary to solve the challenges of adaptive adjustment of the alarm threshold. In addition, these impact loads directly affect the service life of the mechanical system.  
(2) Special areas for navigation. When sailing through a special area, some systems and equipment need to change the working mode to meet the requirements of the convention, which will directly affect the accuracy of fault mode identification and failure prediction.  
(3) Sudden global abnormal information. These sudden problems include canal blockage, military control, COVID-19 pandemic, and so on. The current COVID-19 pandemic will directly affect the transportation of ship materials and equipment spare parts, loading and unloading goods at the port, personnel replacement. These influences will make maintenance decisions more difficult.  
To address these problems, the health management of marine mechanical systems should focus on building an adaptive baseline and threshold model to adapt to complex environments and working modes, scientifically divide health stages, and improve the accuracy of diagnosis and prediction. In addition, ships need more extensive information acquisition ability to enhance the acquisition ability of accident information. Receive real-time weather and port information, use the Internet of things technology to optimize resource allocation worldwide, integrate this online information into the health management model and seek the best solution. To sum up, the integrated health management system integrating multiple information is the major problem to be solved in the future.
## 2 Proposed TDoA-Based Method
### Phase Reconstruction Method
Nevertheless, we are dealing with a propagating signal, so changing the observation point we expect to observe a time shift on signal observations in Figure 2b. Similarly to (1), in the fit signal model the starting phase shift due to changing of observation point can be put as  
\[\beta_{\text{FIT}}=\overline{\mathbf{k}}_{\text{FIT}}(\vartheta,\varphi)\cdot (\overline{\mathbf{r}}-\overline{\mathbf{r}}_{0}) \tag{18}\]  
where \(\overline{\mathbf{r}}_{0}\) is the initial observation point, \(\overline{\mathbf{r}}\) the new one, and \(\overline{\mathbf{k}}_{\text{FIT}}\) the wave vector describing the equivalent fit signal propagation. Note that the latter can be defined as  
\[\overline{\mathbf{k}}_{\text{FIT}}=\left(\frac{2\pi}{\lambda_{\text{FIT}}} \right)\hat{\mathbf{k}}=2\pi\left(\frac{f_{\text{FIT}}}{\text{v}_{\text{ group}}}\right)\hat{\mathbf{k}}=2\pi\left(\frac{\text{B}}{M\cdot\text{v}_{\text{ group}}}\right)\hat{\mathbf{k}} \tag{19}\]  
where the vgroup parameter is still unknown. Despite this, Equation (1) can be rewritten as a typical single-tone propagating wave impinging on the i-th antennas of an array  
\[s_{\text{FIT}}(t,i)=A_{0}\,\text{e}^{\text{i}\left(2\pi\frac{\text{B}}{M}t- \overline{\mathbf{k}}_{\text{FIT}}(\vartheta,\varphi)\cdot\overline{\mathbf{ r}}_{\text{i}}\right)} \tag{20}\]  
where, exactly as in (1), \(\overline{\mathbf{r}}_{\text{i}}\) is the i-th antenna center of phase position vector with respect to reference system \(\overline{\mathbf{O}}\), as indicated in \[\
u_{\mathrm{group}}=2\pi\left(\left.\frac{\vartheta\left|\overline{\mathbf{k }}\right|}{\partial f}\right|_{f=f_{0}}\right)^{-1}\quad\mathrm{with}\;\; \overline{\mathbf{k}}=\mathrm{f}\left(A_{-\frac{M}{2}},f_{-\frac{M}{2}},\ldots,A_{\frac{M}{2}},f_{\frac{M}{2}}\right)\
eq\frac{2\pi}{\lambda_{0}} \tag{13}\]0  
Because the model in (20) exactly resembles the fundamental propagation model, to avoid the phase-wrapping issue the condition in (10), it can be put again that  
\[\
u_{\mathrm{group}}=2\pi\left(\left.\frac{\vartheta\left|\overline{\mathbf{k }}\right|}{\partial f}\right|_{f=f_{0}}\right)^{-1}\quad\mathrm{with}\;\; \overline{\mathbf{k}}=\mathrm{f}\left(A_{-\frac{M}{2}},f_{-\frac{M}{2}},\ldots,A_{\frac{M}{2}},f_{\frac{M}{2}}\right)\
eq\frac{2\pi}{\lambda_{0}} \tag{13}\]1  
but observing Figure 2b it is clear that \(\lambda_{\text{FIT}}>>\lambda_{0}\). Following (19), the equivalent wavelength for signal fit can be written as  
\[\
u_{\mathrm{group}}=2\pi\left(\left.\frac{\vartheta\left|\overline{\mathbf{k }}\right|}{\partial f}\right|_{f=f_{0}}\right)^{-1}\quad\mathrm{with}\;\; \overline{\mathbf{k}}=\mathrm{f}\left(A_{-\frac{M}{2}},f_{-\frac{M}{2}},\ldots,A_{\frac{M}{2}},f_{\frac{M}{2}}\right)\
eq\frac{2\pi}{\lambda_{0}} \tag{13}\]2  
so theoretically the condition in (21) can be relaxed considering a multi-tone transmitted signal as defined in (14), optimizing the number of tones \(M\) and the bandwidth B.
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 2 Modern ECG Signal Processing Techniques for Detection and Prediction of Ventricular Fibrillation
### Neural Networks and Deep Learning Algorithms
#### 2.2.2 Recurrent Neural Networks (RNNs)
The researchers developed and evaluated a model based on GRU networks for the continuous analysis of ECG signals collected from patients with various heart conditions. The GRU model demonstrated outstanding performance in ventricular arrhythmia detection with significant accuracy and adaptability for implementation in portable, continuous medical monitoring devices. This paper [30] exemplifies the application of recurrent neural networks for real-time ECG monitoring and evaluates their performance in detecting cardiac arrhythmias, being a well-cited and relevant source in the field of medical research. The paper [31] presents an algorithm for classifying electrocardiogram (ECG) beats using a combination of bidirectional recurrent neural network (BiRNN) and convolutional neural network (CNN) called BiRCNN. The end-to-end model was used, and the morphological features specific to the EXG signal were automatically extracted using a CNN and then processed using a recurrent bidirectional network. For the MIT-BIH arrhythmia database (MITDB), the authors obtained a sensitivity of 98.7% and a positive predictive value of 96.4% on average for the EBV class. For the SVEB class, the sensitivity was 92.8%, with a few percentages below the results reported at that time with other methods.
## 3 Results
\begin{table}
\begin{tabular}{l l l l} \hline  & **Occurrence** & & & \\  & **(\%)/N** & **OR (95\% CI 1) Crude HAF** & **OR (95\%CI) Age-Adjusted HAF** \\ \hline Healthy food intake (pHDI-10) & & & \\ \(\geq\)7 a day & (30)/10 & 2.8 (0.8; 10.5) \(p=0.11\) & 2.5 (0.7; 9.7) \(p=0.17\) \\ \(\geq\)5 a day & (48)/16 & 0.8 (0.3; 2.3) \(p=0.70\) & 0.7 (0.2; 2.0) \(p=0.46\) \\ \(\geq\)3 a day & (88)/29 & 1.4 (0.3; 6.2) \(p=0.60\) & 0.9 (0.2; 4.2) \(p=0.86\) \\ \hline Yoghurt and fermented drinks & & & \\ \(\geq\)1 a day & (24)/8 & 1.6 (0.4; 5.7) \(p=0.46\) & 1.2 (0.3; 4.7) \(p=0.76\) \\ \(\geq\)Every other day & (58)/19 & 1.4 (0.5; 3.7) \(p=0.55\) & 1.2 (0.4; 3.4) \(p=0.72\) \\ \hline Fresh cheese curd products & & & \\ \(\geq\)every other day & (55)/18 & 3.3 (1.1; 9.7) \(p=0.02\) * & 2.6 (0.8; 8.2) \(p=0.09\) \\ \(\geq\)once a week & (76)/25 & 2.1 (0.7; 6.3) \(p=0.18\) & 1.6 (0.5; 5.2) \(p=0.39\) \\ \hline Milk product intake & & & \\ \(\geq\)1 a day & (51)/17 & 3.5 (1.2; 10.5) \(p=0.02\) * & 3.2 (1.0; 9.9) \(p=0.04\) * \\ \(\geq\)every other day & (73)/24 & 3.5 (1.2; 10.2) \(p=0.02\) * & 3.9 (1.3; 12.3) \(p=0.01\) * \\ \hline Fish & & & \\ \(\geq\)once a week & (48)/16 & 1.4 (0.5; 3.9) \(p=0.49\) & 1.4 (0.5; 3.9) \(p=0.54\) \\ \hline White meat & & & \\ \(\geq\)every other day & (73)/24 & 1.1 (0.4; 3.5) \(p=0.81\) & 0.9 (0.3; 3.0) \(p=0.96\) \\ \(\geq\)once a week & (88)/29 & 1.0 (0.2; 5.1) \(p=0.88\) & 0.8 (0.2; 4.2) \(p=0.84\) \\ \hline Wholegrain bread & & & \\ \(\geq\)1 a day & (18)/6 & 1.4 (0.4; 5.9) \(p=0.60\) & 1.3 (0.3; 5.7) \(p=0.71\) \\ \(\geq\)every other day & (61)/20 & 1.5 (0.6; 4.3) \(p=0.39\) & 1.3 (0.4; 3.7) \(p=0.65\) \\ \hline Wholegrain pasta and rice & & & \\ \(\geq\)every other day & (61)/20 & 1.02 (0.4; 2.9) \(p=0.96\) & 1.1 (0.4; 3.1) \(p=0.60\) \\ \(\geq\)once a week & (73)/24 & 0.7 (0.2; 2.2) \(p=0.49\) & 0.6 (0.2; 2.2) \(p=0.46\) \\ \hline Fruits & & & \\ at least twice a day & (18)/6 & 0.7 (0.2; 2.5) \(p=0.61\) & 0.7 (0.2; 2.7) \(p=0.61\) \\ \(\geq\)2 a day & (61)/20 & 1.2 (0.4; 3.3) \(p=0.75\) & 1.1 (0.4; 3.2) \(p=0.83\) \\ \hline Vegetables & & & \\ \(\geq\)2 a day & (48)/16 & 1.2 (0.4; 3.4) \(p=0.68\) & 1.2 (0.4; 3.5) \(p=0.70\) \\ \(\geq\)1 a day & (61)/20 & 0.8 (0.3; 2.2) \(p=0.62\) & 0.7 (0.2; 2.0) \(p=0.49\) \\ \hline Legumes & & & \\ \(\geq\)every other day & (12)/4 & 0.7 (0.2; 2.9) \(p=0.61\) & 0.7 (0.1; 3.0) \(p=0.60\) \\ \hline \end{tabular}  
* \(p<0.05\). 1 Confidence Intervals.  
\end{table} 
Table 2: Odds ratio (OR) of pro-healthy food frequency intake in higher android/gynoid fat ratio patients (HAF).
## 4 DD Toposphere Delay and Ionosphere Delay Analysis
### DD Ionosphere Delay  
As shown in Equation (9), the DD ionosphere delay is slant and satellite-dependent. In order to obtain precise DD slant ionosphere delay, after PPP-AR at each station, the geometry-free phase observation is used [20]. Then, the DD slant ionosphere delay for Frequency 1 (in meters) is also constructed by using between-satellite single-difference and between-receiver single-difference. Figure 3 plots the DD slant ionosphere delay for six baselines, with an elevation angle mask of 15\({}^{\circ}\). It can be observed that, compared to DD troposphere delay series, DD ionosphere delay series variation is more severe. The DD ionosphere delay of some satellites can be significant, with values still exceeding 0.5 m when the elevation angle is greater than 40\({}^{\circ}\). However, the majority of satellites exhibit a negative correlation between the DD ionosphere delay and the elevation angle.  
Taking the baseline HZDQ-HZLA as an example, the RMSE of DD ionosphere delay for all ambiguity-fixed satellites is calculated, as shown in Figure 4. It can be seen that the DD ionosphere delay RMSE of all satellites is less than 0.3 m, with only four satellites having an RMSE greater than 0.2 m. The average RMSE for all satellites is 10.7 cm, which is smaller than the quarter-wavelength of the wide-lane wavelength; thus, it can be ignored when performing wide-lane ambiguity resolution.  
Furthermore, considering the simultaneous impact of ionosphere delay and troposphere delay on the WL ambiguity resolution, we provide time series of float WL ambiguities based on Equation (8), using the HZDQ-HZLA as an example. Here, only ambiguities within \(\pm\)5 cycles are plotted, as shown in Figure 5. It can be observed that the float WL ambiguities fluctuate mainly around integer cycles, demonstrating relatively good stability, indicating the feasibility of simultaneously neglecting DD ionosphere and troposphere delay residuals.  
Figure 2: DD troposphere delay for HZDQ-HZLA, HZDQ-HZXS, HZXS-HZLA, WHJA-EZEC, EZEC-WHJX, and WHJX-WHJA. Each colored line represents a satellite.  
Figure 4: The RMSE of the DD ionospheric delay of all ambiguity-fixed satellites for the baseline HZDQ-HZLA.  
Figure 3: DD ionosphere delay of HZDQ-HZLA, HZDQ-HZXS, HZXS-HZLA, WHJA-EZEC, EZEC-WHJX, and WHJX-WHJJA. Each colored line represents a satellite.
# Performance Modeling Framework for IoT-over-Satellite Using Shared Radio Spectrum
## 5 Optimal Operating Parameters
### Finding the Minimum SF in Different Region  
LoRa modulation employs the Chirp Spread Spectrum (CSS) modulation, which transmits the data by spreading information into the wide-band linear chirp pulses. By applying different SF into the modulation, it adopts different transmission ranges, payload sizes, and data rates to enable the LoRa CSS modulation [7]. As a result, changing SF is a trade-off between ranges, payload sizes, and data rates. Hence, in order to understand the trade-off between those factors and to increase the spectral efficiency of LoRaWAN in IoT-over-Satellite communication, we find the minimum SF in the simulated area by applying the PER mapping parameters in Table 4 into Figure 7 so that we can find out the minimum SF ensuring the PER is less than \(10^{-7}\) allowing IoT-over-Satellite communication. The result is shown in Figure 13.  
Figure 11: An example of SINR vs antenna beamwidth with different SINR threshold. The red line represents the optimal antenna beamwidths (\(\theta^{*}\)) when maximum link-availability achieved.  
Figure 12: The link-availability at different SINR threshold when the optimal beamwidth is adopted. The markers represent the result of the simulation, while the plots are the approximation of link-availability using (18).  
Figure 13: The minimum Spreading Factor that suitable for the simulated area based on the result of average SINR over a week.
###### Abstract  
Both a decrease of sea ice and an increase of surface meltwater, which may induce ice-flow speedup and frontal collapse, have a significant impact on the stability of the floating ice shelf in Greenland. However, detailed dynamic precursors and drivers prior to a fast-calving process remain unclear due to sparse remote sensing observations. Here, we present a comprehensive investigation on hydrological and kinematic precursors before the calving event on 26 July 2017 of Petermann Glacier in northern Greenland, by jointly using remote sensing observations at high-temporal resolution and an ice-flow model. Time series of ice-flow velocity fields during July 2017 were retrieved with Sentinel-2 observations with a sub-weekly sampling interval. The ice-flow speed quickly reached 30 m/d on 26 July (the day before the calving), which is roughly 10 times quicker than the mean glacier velocity. Additionally, a significant decrease in the radar backscatter coefficient of Sentinel-1 images suggests a rapid transformation from landfast sea ice into open water, associated with a decrease in sea ice extent. Additionally, the area of melt ponds on the floating ice tongue began to increase in mid-May, quickly reached a peak at the end of June and lasted for nearly one month until the calving occurred. We used the ice sheet system model to model the spatial-temporal damage and stress on the floating ice, thereby finding an abnormal stress distribution in a cracked region. It is inferred that this calving event may relate to a weakening of the sea ice, shearing of the tributary glacier, and meltwater infiltrating crevasses.  
frontal collapse precursors; high-resolution remote sensing; ice sheet system model; Petermann Glacier  
1  
[PERSON], [PERSON], [PERSON], R. Hydrological and Kinematic Precursors of the 2017 Calving Event at the Petermann Glacier in Greenland Observed from Multi-Source Remote Sensing Data. _Remote Sens._**2021**, _13_, 591. [[https://doi.org/10.3300/rs13040591](https://doi.org/10.3300/rs13040591)]([https://doi.org/10.3300/rs13040591](https://doi.org/10.3300/rs13040591))  
1
## 4 Conclusions  
In this paper, an industrial PSS field repair kit optimization case study of was presented. The objective of the case study was to cost optimize the composition of a field repair kit placed at a customer's site, while maintaining a specific level of PSS availability. A high fidelity PSS simulation model was created and utilized to construct the PSS availability regression model. The availability model was used with other spare parts information to cost optimize the field repair kit, subject to the PSS availability requirements. Monte Carlo simulation was performed to assess the confidence level at which the optimized PSS field repair kit can meet the availability requirements.  
Results showed that the PSS field repair kit can be cost optimized to satisfy the availability requirements imposed by the contract with the customer. It was also revealed that the marginal cost of availability improvement was very high, resulting in an almost twofold increase in cost to improve the availability by 5%. The investigation of the optimized PSS repair kit composition showed that the PSS should never shut down for a low cost spare part, but should only shut down for a very expensive spare part that is too expensive to be included in the field repair kit. Finally, Monte Carlo simulation results showed that the optimized PSS field repair kit performed better than the originally proposed field repair kit, meeting the availability requirements at a higher level of confidence. The overall case study results were encouraging in that the modeling and optimization process shown in this paper is applicable to other complex systems.  
Future works regarding the improvement of PSS can expand into multiple directions. The latest advances in big data, information and communications technology (ICT), and internet of things (IoT) can greatly improve preventive and predictive maintenance practice for PSS. This can be accomplished through better prediction of parts failure interval and failure modes through analysis of historical data and real-time feedback from the PSS. Big data and IoT can be utilized to design the improved spare parts supply chain as well, providing information for an appropriate inventory level, stocking locations, and reorder points based on PSS location and usage. Another direction the research can take is the improvement of the PSS design: how can PSS be architected for optimum preventive or predictive maintenance policy? How does the PSS architecture impacts spare parts logistics or its supply chain structure? In addition to the topics mentioned, another fundamental direction that the PSS related research can take is to explore ways to reduce the limitation imposed for implementing PSS, such as willingness to adopt the PSS by companies, willingness to accept the PSS by customers, and dealing with environmental implications. All these are very promising future research topics to explore.  
This research received no external funding.  
The work presented in this paper in its entirety was conducted while employed at Xerox Corporation.  
The author declares no conflict of interest.
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 2 NB-IoT
### NB-IoT Key Technologies
#### 2.2.6. PSM  
Some IoT devices do not require to frequently send packets and receive a packet from the network sporadically. For instance, in the case of a fire alarm, data transmission is mostly triggered from the temperature sensor, and it is not necessary to monitor paging except for firmware updates and keepalive. Thus, for some applications, the downlink latency is not important, and it is enough to monitor paging every several days. If paging does not need to be monitored for a long time, the UE is able to deactivate, besides the modem hardware, most parts of the device such as memory refresh. Figure 6 illustrates the relationship between the PSM operation and the UE power state. The PSM interval is negotiated and decided via a tracking area update (TAU) procedure with a maximum interval of several months.
# Aggression Detection in Social Media from Textual Data Using Deep Learning Models
## 2 Related Work
In [35], the authors performed experiments to identify 'bully' traces on the social network. They used syntactic and semantic features for textual data based on emojis to identify 'bully' traces on the Twitter dataset. Furthermore, they tested their model with other social media datasets, such as YouTube and Formspring. Ref. [36] composed a model with three pragmatic features: user tagging, negative emoticons, and positive emoticons to identify sarcasm in tweets. They also compared their model performance with human sarcasm identification to clarify the difficulty of sarcasm detection. The author in [37] performed experiments to find sarcasm and irony in an English language Twitter dataset. Features included emoji and TF-IDF vector techniques to express text into vectors. Furthermore, the author also discussed the relationship among sarcasm, irony, and cyberbullying. The authors in [38] collected English cyberbullying tweets from Twitter. Then, they established an auto-detection model of cyberbullying detection based on sentiment score, readability, and text to predict the tweets. Moreover, the authors used a curse word dictionary to find the emotional score in the text using the number of occurrences of negative emotions in a tweet. Ref. [17] also used the English dataset to identify aggression detection using the Bert model. As deep learning has become widely used in many application domains, new usages of deep learning for NLP purposes have also come into focus. Various deep learning techniques are proven to be more efficient than certain machine learning techniques. Researchers in [21] performed experiments to differentiate between hate speech and profanity. They applied ensemble learning, which achieved 87% accuracy. Tuning hyperparameters of meta classifiers can improve this work. Moreover, ref. [39] used CNN to identify hate speech in textual data. They used Word2 Vec and character four-gram as feature extraction techniques. This work can be improved by identifying the sequential nature of data using LSTM and BiLSTM. To learn high level classification, ref. [7] performed character level classification using logit-boost and LSTM. Similarly, ref. [40] performed experiments on the Bangla text using LSTM, BiLSTM, and GRU to identify cyberbullying. An empirical analysis was performed by [41] to evaluate the performance of deep learning in the detection of cyberbullying across social media. LSTM, BiLSTM, GRU, and RNN are used for experiments.
# External Slot Relationship Memory for Multi-Domain Dialogue State Tracking
## 3 Model
### Context Encoder  
In recent years, BERT [22], a pre-trained language model, has demonstrated powerful contextual semantic representation capabilities in various downstream tasks. Therefore, in this paper, we adopt the BERT pre-trained model for encoding the context. At dialogue turn T, the dialogue history is defined as \(D_{T}=R_{1},U_{1},\ldots,R_{T},U_{T}\), which is a collection of system responses R and user utterances U, where \(R={R_{t}}^{T}_{t=1}\) and \(U={U_{t}}^{T}_{t=1},1\leq t\leq T\). We define \(E_{T}=B_{1},\ldots,B_{T}\) as the dialogue state at each turn, where each \(E_{t}=(S_{1},V_{1}),\ldots,(S_{j},V_{j})\) represents a collection of J slot-value pairs.The context encoder takes the dialogue history up to turn t, which can be represented as \(X_{t}=D_{1},\ldots D_{t},E_{t-1}^{\prime}\), as input and generates the context vector representation \(H_{t}\) as its output. For each slot \(S_{j}\) and its corresponding value \(V_{j}\), we encode them using \(BERT_{sv}\), where we use the output vector corresponding to the special token (CLS) as the slot representation. During training, we keep the model parameters fixed.  
\[H_{t}=BERT_{context}(X_{t}) \tag{2}\]  
\[H_{S_{j}}=BERT_{sv}(S_{j}) \tag{3}\]  
\[H_{V_{j}}=BERT_{SV}(V_{j}) \tag{4}\]  
Figure 2: Our ER-DST architecture overview. The diagram on the right depicts the overall structure of ER-DST, while the diagram on the left illustrates the internal structure of the external slot relationship memory. The gray BERT encoder represents frozen parameters that are not fine-tuned, while the blue BERT encoder represents fine-tuned parameters.