esa-sceva/satcom-chunk-collection huggingface dataset:
# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 4 Heterogeneous Graph Structure Learning Model
### Model Complexity Analysis  
We present a detailed analysis on the proposed model. In the heterogeneous graph contrastive learning, the graph encoder takes \(O\big{(}\big{|}E\big{|}d+\big{|}V\big{|}d^{2}\big{)}\) time, including the message passing mechanism via each edge and the updating process of each node. The contrastive loss is usually calculated in a batch, denoted as B, so the contrastive loss takes \(O\big{(}B^{2}+B^{2}d\big{)}\) time, including the calculation of similarity and loss. The graph structure learning component takes \(O\big{|}\big{(}V\big{|}d^{2}+\big{|}E\big{|}d+\big{|}V\big{|}d^{2}\big{)}\) time, the first of which is the time of computing the similarities between two nodes, while the remaining part is regarding the graph encoders. In the sequence modeling, the sequence encoder takes \(O\Big{(}Ld^{2}~{}+~{}L^{2}d\Big{)}\) time, including computing the attention weights based on representations and the updating of nodes' representations. Combining them, the model complexity is \(O\Big{(}\Big{|}E\big{|}d+\Big{|}V\big{|}d^{2}~{}+B^{2}d+Ld^{2}~{}+~{}L^{2}d \Big{)}\), where \(\big{|}E\big{|}\) and \(\big{|}V\big{|}\) represents the number of edges and nodes in a graph, \(d\) is the representation dimension, \(B\) is the batch size, and \(L\) is the length of sequences.
###### Abstract  
The widespread use of journal bearings in rotating machinery has intensified the need to optimize their operational performance. A key determinant of bearing efficiency lies in the lubricant film thickness between the rotor and bearing surface. Recent studies demonstrate that strategically engineered surface textures can effectively modulate film thickness and enhance both static and dynamic characteristics of bearing. This investigation specifically examines how cubic, cylindrical, and semi-ellipsoidal texture geometries at varying depths influence the performance of noncircular two-lobe bearings. Through finite element analysis, the study evaluates critical performance parameters including load capacity, rotor attitude angle, critical mass threshold, and whirl frequency ratio to establish texture-depth relationships with system stability and operational efficiency. The analysis reveals that texturing the maximum pressure zone in lobe 2 significantly enhances bearing performance, with semi-ellipsoidal, cylindrical and cubic geometries, respectively. Also, the results demonstrate that texture geometry and depth significantly influence two-lobe bearing performance. Optimal enhancements in load capacity, whirl frequency reduction, and critical rotor mass occur at texture depths below the bearings clearance space width, with performance peaking before gradually declining as depth increases further. Notably, texture application in low-pressure or cavitation regions generally yields diminished or even counterproductive effects. The findings demonstrate that cubic textures provide optimal bearing performance across all depth ranges, with cylindrical and semi-ellipsoidal geometries ranking second and third, respectively, in comparative analysis.  
nocircular two-lobe journal bearing; hydrodynamic lubrication; surface texture; FEM; steady state performance; dynamic stability
## 4 Results and Discussion

Table 2: Object detection results for RTDETR with different training datasets. We further used RTDETR_non and YOLOv8x_non as baselines to calculate the improvement in detection performance for each rainy model. The results presented in Table 3 clearly confirm our hypothesis for this study: that no single method of generating rainy images is suitable for all pre-trained models. Additionally, the choice of pre-trained model is equally important. Training the same dataset with different pre-trained models can lead to vastly different results. The ability of pre-trained RTDETRx models to extract useful information for object recognition under the interference of rainy conditions, despite the degradation of visual feature information, is evidently superior to YOLOv8. We attribute this result to the contribution of Transformer characteristics. Compared to YOLOv8, RTDETR utilizes global information from images for object detection, rather than relying solely on local information like YOLOv8. These differences have minimal impact on object detection performance when images are clean. However, under rainy weather conditions, where images become blurred and objects are obscured or interfered with, the advantages of RTDETRx are highlighted. It can be observed that both RTDETR_NST and RTDETR_A demonstrate positive improvements in three out of four indicators. The only regret is that each model fails to exhibit a significant improvement in one indicator. However, the significance of the models' contributions to our study is not diminished by this. From the table, improvements of 13.89% and 15.50%, respectively, are achieved by these two models in terms of the mAP50-95 indicator. Additionally, a high improvement rate of 16.35% in the Recall (R) indicator is shown by RTDETR_NST. This suggests that the sensitivity of object detection is significantly enhanced by the model, and it provides more accurate object positioning under rainy weather conditions, which is meaningful for an object detection model. Based on the results of this study, we further infer that Transformer-based pre-trained object detection models are more suitable for establishing rainy weather object detection models compared to pre-trained models from the YOLO series. Additionally, training models using rainy image generation methods paired with the neural style transfer and analytical methods is recommended as one of the approaches.
# Revisiting the Analysis of Radiative Mid-Range Wireless Link for Pacemakers
## 3 Results  
The radiation characteristics of an antenna inside the human tissue are studied through the near-field distribution, where the antenna is immersed with different depths of human tissue for the pacemaker application. In the following subsection, the phenomenon of the reduction in the far-field gain, known as \"gain reduction factor\", is used to describe the radiation characteristics in the radiative near-field region. Based on the understanding of the gain reduction factors, the related formulas are presented to predict the power transmission made in the radiative near-field region.
# A Discrete Hidden Markov Model for SMS Spam Detection
###### Abstract  
Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naive Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each word in SMS, is unable to distinguish the importance of words, due to the length limitation of SMS. This paper proposes a new method based on the discrete hidden Markov model (HMM) to use the word order information and to solve the low term frequency issue in SMS spam detection. The popularly adopted SMS spam dataset from the UCI machine learning repository is used for performance analysis of the proposed HMM method. The overall performance is compatible with deep learning by employing CNN and LSTM models. A Chinese SMS spam dataset with 2000 messages is used for further performance evaluation. Experiments show that the proposed HMM method is not language-sensitive and can identify spam with high accuracy on both datasets.  
Keywords:short messaging service (SMS); spam detection; hidden Markov model (HMM); text classification; natural language processing (NLP) +
Footnote †: journal: Applied Sciences
# Modulation of Host Lipid Pathways by Pathogenic Intracellular Bacteria
## 2 Intracellular Bacteria That Replicate Inside a Vacuole during Infection
### Host Lipids Provide Structural Integrity to Pathogen-Associated Membranes
Similar to _Chlamydia_, the cholesterol within the _Salmonella_-PCV is actively recruited to the membrane and is an important regulator of host:pathogen protein interactions by providing structured \"hot spots,\" such as lipid rafts, on the PCV membrane that facilitate _Salmonella_ protein presentation and association with host proteins in the cytosol [64]. This active recruitment is achieved through the type III secretion system (T3 SS) effector protein, SseJ, which has acyltransferase activity [65; 66; 67; 68] (Figure 1). A mutation in _sseJ_ during mouse infection attenuates virulence and causes a decrease in PCV production, supporting the requirement and use of SseJ as an acyltransferase used to modify membrane lipids for incorporation into the PCV [68].
## 4 Methodology
### Data Segmentation
Following the padding method described in [38], the side of the analysis window with m sensor observations is padding so that the analysis window contains sensor observations \(\mathbf{x}_{1-m},\ldots,\mathbf{x}_{n+m}\) with a total number of \(n+2m\). This padding guarantees enough data to calculate the summary statistics at the window extremities, as well as results in a minimum latency of m sensor observations when executing a distinct window version of the algorithm. Within each window, an index variable, \(l,1<l\leq n\), is slid to subsequently calculate the summary statistics of the component distributions before and after \(l\). Particularly, we calculate the means of data samples, \(\mathbf{\tilde{f}}_{1}(l)\) and \(\mathbf{\tilde{f}}_{2}(l)\), as well as variance-covariance matrices, \(\mathbf{S}_{1}(l)\) and \(\mathbf{S}_{2}(l)\), which include the variance of data samples in the diagonals and the covariance in the off-diagonals. Having computed summary statistics before and after \(l\), the \(F\) statistic at position \(l\), \(F_{l}\), is calculated according to Equation (1), where \(n_{1}=m+l-1,n_{2}=n+m-l+1,b\) donates the number of variables and \(T^{2}\) donates the Hotelling T-squared statistic (calculated as Equation (2)), \(S_{p}\) donates the pooled variance-covariance matrix (calculated as Equation (3)). The point \(l\) which maximises \(F_{l}\) is finally selected as the most likely change point within a window. \[F_{l}=\frac{n_{1}+n_{2}-b-1}{b(n_{1}+n_{2}-2)}T^{2}, \tag{1}\]  
\[T^{2}=\big{(}\mathbf{\tilde{f}}_{1}-\mathbf{\tilde{f}}_{2}\big{)}^{\prime}\Big{\{}S_{ p}\Big{(}\frac{1}{n_{1}}+\frac{1}{n_{2}}\Big{)}\Big{\}}^{-1}\big{(}\mathbf{\tilde{f}}_{1 }-\mathbf{\tilde{f}}_{2}\big{)}, \tag{2}\]  
\[S_{p}=\frac{(n_{1}-1)S_{1}+(n_{2}-1)S_{2}}{n_{1}+n_{2}-2} \tag{3}\]  
At the second step, the data steam \(\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{q}\) is formally divided into segments of \(S_{1},S_{2},\ldots,S_{n}\), \(n<a\), in accordance with the indices of change points. As a result, the \(S_{i}\) segment contains the sensor observations \(\big{[}\mathbf{x}_{i},\ldots,\mathbf{x}_{c_{i+1}-1}\big{]}\).
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 3 XBeats System Architecture  
The design of XBeats addresses the limitations of existing solutions by providing a BLE-connected, real-time, and comprehensive ECG monitoring system. The architecture comprises a wearable and unobtrusive real-time ECG device which can be configured with three operation modes: (a) continuous mode, (b) triggered mode, and (c) offline mode.
## 3 Experiments  
### Droplet Manipulation  
In our experiment, we demonstrated highly accurate droplet manipulation using the EWOD device with dimple structures. Figure 3 shows the experimental setup for droplet manipulation. The six electrodes were connected to a voltage source (High Voltage DC-DC Boost Converter, 200-450 V, NoData, China) via six relay switches (G5V-1, Omron, Kyoto, Japan). The switching was controlled by a microcomputer (PIC16F628A) and a series of commands for the switching was issued using Python (Ver. Python 3.6.0, Python Software Foundation, Open source) in the control PC (ThinkPad X201s, Lenovo, Beijing, China). The voltage was set to 320 V and the switching interval was set to 1.0 s. The switching of the voltage allocation generated the electrostatic force to move the droplet, which was captured as MP4 data by a camera (EOS Kiss X8i, Canon Inc., Tokyo, Japan). The droplets used for verification were 10 and 20 \(\upmu\)L of distilled water. To visualize the actual path of the droplet, we extracted images every second from the MP4 data and measured the median points of the droplets using ImageJ (ImageJ Ver. 1.45s, public domain, Open source).
# Recognition of Eye-Written Characters Using Deep Neural Network
## 2 Materials and Methods
### Preprocessing  
The data were preprocessed in four steps: (1) resampling, (2) eye-blink removal, (3) resizing, and (4) normalization (Figure 3). First, the signal was resampled to 64 Hz because high sampling rates are unnecessary for EOG analysis (the signal was originally recorded at 2048 Hz). Second, eye blinks in the signals were removed using the maximum summation of the first derivative within a window (MSDW) [24]. The MSDW filter generates two sequences: the filtered signal of emphasizing eye blinks and selected window-sizes (\(W\)) at each data point. The MSDW filter utilizes a set of a simple filter (\(F\)), known as the SDW (summation of the first derivative within a window). An SDW filter with a window size of \(W\) is defined as follows:  
\[F_{W}(t)=S(t)-S(t-W), \tag{1}\]  
where \(S(t)\) is the \(t\)-th sample of the original signal and \(W\) is the width of the sliding window. For every time \(t\), the following steps are performed to obtain an MSDW output:  
Figure 1: Pattern designs of Arabic numbers [23]. The red dots denote the starting points. Reprinted with permission.  
Figure 2: Eye-written characters [23].  
1. Calculate SDWs with different \(W\)s, considering a typical eye blink range.
2. Choose the maximum SDW at time \(t\) as the output of MSDW if it satisfies the conditions below. 1. The numbers of local minima and maxima are the same within the range of \([t,\ t-W]\); 2. All the first derivatives from time \(t\) to \(t-W+1\) should be within \(S^{\prime}(t-W+1)\) and \(S^{\prime}(t)\), where \(S^{\prime}(t)\) is the first derivatives at time \(t\).  
The ranges of the eye-blink region (\(R\)) were determined using the following equation:  
\[R=\Big{\{}\Big{[}T\big{(}Max_{i-j}\big{)}-W_{\mathrm{T}(Max_{i-j})},\ T(Min_{i}) \Big{]}\Big{\}}, \tag{2}\]  
where \(W_{t}\) is the window-size at time \(t\) from MSDW filter output, and \(T(Max_{i})\) and \(T(Min_{i})\) are the time points of the \(i\)th local maximum and minimum, respectively [24]. The integer value \(j\) is determined to maximize \(Max_{i-j}-Min_{i}\). The detected regions were removed and interpolated using the beginning and end points of each range. Third, all the signals were resized to have the same length because the eye-written characters were recorded in varying time period from 1.69 to 23.51 s, and the use of convolutional layers require all signals to have the same length. All the signals were resized to length \(L\), where is the mean of all signal lengths of raw data. Finally, the signals were normalized such that they were within a \(1\times 1\) size box in 2D space, keeping the aspect ratio unchanged.  
The preprocessing procedure in [23] was used after the following simplification: the saccade detection and crosstalk removal were removed. This is because convolutional networks can extract features by themselves, and additional feature extraction methods often decrease the accuracy.
###### Abstract  
There are considerably fewer requirements for the quality of hydrogen combusted in an engine than its quality for fuel cells. Therefore, the analysis was carried out on the combustion of hydrogen-helium mixtures in an engine with a two-stage combustion system (TJ)--Turbulent Jet Ignition). A single-cylinder research engine with a passive and active prechamber was used. A hydrogen-helium mixture was supplied to the main chamber in proportions of 100:0, 90:10, 80:20, 30:70, and 60:40 volume fractions. The prechamber was fueled only with pure hydrogen. Combustion was carried out in the lean charge range (\(\lambda\) = 1.5-3) and at a constant value of the Center of Combustion (CoC = 8-10 deg aTDC). It was found that the helium concentration in the mixture affected the changes in combustion pressure, heat release rate and the amount of heat release. It was observed that increasing the proportion of helium in the mixture by 10% also reduces the IMEP by approximately 10% and reduces the rate of heat release by approximately 20%. In addition, helium influences knock combustion. Limits of MAPO = 1 bar mean assumed that knock combustion occurs in the main chamber at values of \(\lambda\) < 1.9. Increasing the excess air ratio results in a gradual reduction in the temperature of the exhaust gas, which has a very rapid effect on changes in the concentration of nitrogen oxides. Studies carried out on the helium addition in hydrogen fuel indicate that it is possible to use such blends with a partial deterioration of the thermodynamic properties of the two-stage combustion process.  
Keywords:hydrogen engine; two-stage combustion; TJ; hydrogen combustion; helium +
Footnote †: journal: _Sciences_
## 4 Results
### Results for Maritime Mobile Service
#### 4.2.2 Results for MMS with Directional Antenna  
Interference analysis for the system with the omnidirectional receiver for different distances showed that _l_/_N_ protection criteria is all cases with 14 dB margin. Figure 13 shows the distribution functions of _l_/_N_ when interfering from IMT to the System 2 MMS receiver.  
Figure 11: I/N distribution for AMS System 2 with directional antenna: (**a**) cumulative distribution function of _l_/_N_ for System 2 AMS receiver and (**b**) probability distribution function of _l_/_N_ for System 2 AMS receiver.  
Figure 12: I/N distribution for MMS System 1 with omnidirectional antenna: (**a**) cumulative distribution function of _l_/_N_ for System 1 MMS receiver and (**b**) probability distribution function of _l_/_N_ for System 1 MMS receiver.
## 5 Simulation Results and Analysis
### Sensitivity Analysis  
In Equation (7), there are two controllable parameters, \(p\) and Target Point, which are used to change the shape of the delay utility function by adjusting the value of \(k\). In order to investigate the sensitivity of these two variables, we simulate the average throughput and the average number of handovers with the changes of \(p\) and Target Point.  
As shown in Figure 7, the system average throughput and the handover times change with the variation of \(p\). In general, the value of \(p\) in our method has good results in the range of 0.75-0.85, which is similar to the description in Equation (7). Compared with the results obtained by different strategies in Figures 4 and 5, if \(p\) is not in the optimal range, its results may be worse than those of other strategies.  
Figure 6: The number of handovers under different schemes. (**a**) Service distribution 1, (**b**) Service distribution 2.  
We can know that the value of Target Point cannot exceed maximum delay tolerance from (7). Let the Target Point equals t times maximum delay tolerance, and the simulation results are shown as Figure 8. We can see that the value of Target Point also affects the outcome of the strategy.
## 3 Results and Discussion
### Drought Trend  
#### 3.2.1 Monthly  
For monthly assessment of drought, the SPI index was computed for the 17-year precipitation data of 42 points from the TRMM database, and the monthly conditions from October to June were considered. For example, Figure 4 shows the average SPI of January precipitation in the study area. Accordingly, in most years of study, 40% of the precipitation rate was normal in this month of the year. During the study, the wet periods occurred more often than dry periods, with frequencies of six and four months in the study period, respectively. The maximum value of SPI in this month was 1.36 and the minimum was \(-2.1\).  
Figure 4 shows that the most severe drought happened in January 2009. The zoning of drought severity during this month in the study area based on the meshed TRMM data is depicted in Figure 5. Although the least average SPI in January for the whole area was \(-2.1\), the drought distribution in the region was not uniform. Based on Figure 5, the severity of drought in the south and west of the study area was very extreme, so that the value of the selected index reached less than \(-2\). These various conditions were also observed in some central areas of the study area, such as Kermanshah city. In central and most eastern regions of the province, the drought was  
Figure 4: Average standardized precipitation index (SPI) for January in the study area.  
Figure 3: TRMM precipitation versus synoptic stations based on BIAS as a statistical indicator.  
between \(-1.5\) and \(-1.9\). The most moderate drought in January belonged to the northern parts of Kangavar city, with an SPI value of \(-1.3\), which is the manifestation of moderate drought in this area.  
By considering SPI values for different months during the study period, it was revealed that in most months of the year, the frequency and duration of dry periods were more than wet periods. Meanwhile, the wet periods enjoyed more severity compared with droughts. In recent years, the number of dry periods, the downward trend of the precipitation, was increased across the study area. Despite the higher frequency and duration of wet relative to dry periods in some months such as November, December, and January, the severity of dry periods was more apparent. There is no specific trend for precipitation in March, and with reference to May, as compared to most months of the year, the precipitation shows a tendency to increase, with a few dry periods in recent years.
# Blockchain Technology for Secure Communication and Formation Control in Smart Drone Swarms
## 6 Discussion
### Ethical Considerations  
Despite the significance and seriousness of such systems, particularly in military and defense activities, a number of ethical issues arise in relation to the use of such autonomous robotic swarms [34; 35; 36]. The first major issue is related to target discrimination. Such systems often fail to distinguish between an enemy target and an ally (or civilian), leading to an increased likelihood of accidental losses due to the increased number of aircraft in these clusters. This risk of collateral losses could be decreased by carefully and effectively programming the drones to distinguish between their targets. In addition, in combat situations, the level of force that is employed must be proportional to the target. Different tactics should be used for a military base as opposed to an isolated target.  
Another critical issue is about taking responsibility for autonomous drone decisions. Nowadays, the majority of these types of schemes are managed by people via a control center, and the operator takes responsibility for poor judgment and bad actions. Something like this would be challenging for automated systems (it is impossible to hold an autonomous drone responsible for its actions). Therefore, there must be specific rules to ensure that the \"operators\" (those who program, design, and maintain these systems) are held accountable for the decisions they make.  
Additionally, security and data privacy within these systems are definitely something to be concerned about. Drones are susceptible to malicious attacks, just like any other IoT device. The mission could end in disaster if successful attempts are made against them and the sensors they are carrying. Information and data leaks, illegal use of force against unarmed civilians, and other incidents can have a severe impact on the mission. Therefore, it is crucial to include strong security measures and encryption techniques to protect data integrity and lower the likelihood of harmful behavior.