esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Works  
This paper presents a novel GPU implementation of MOC on single GPU card for accelerating the simulation of hydraulic transients. CPU is mainly responsible for executing logical operations and GPU is mainly responsible for executing floating operations. The MOC kernel, which is mainly responsible for calculation, is executed on GPU and invoked by an odd-even alternate form by CPU. The configuration parameters of the MOC kernel are organized as 1D linear structure to fit the line structure of the pipes.  
The benchmark single pipe water hammer problem is simulated to verify the accuracy and efficiency of GPU-MOC method. It is found that the numerical results of GPU-MOC are identical with CPU-MOC, but the computational efficiency is much higher, and the obtained speedup ratios of GPU-MOC are up to hundreds compared to CPU-MOC. Two computing-intensive problems, the large-scale water distribution system and the long-distance water transmission system, are simulated, which successfully validate the high computational efficiency of the GPU-MOC method. Moreover, the GPU-MOC method can achieve more significant performance gains as the size of the problem increases. Therefore, finer calculation sections and high-order methods can be used to simulate large-scale practical problems for getting more accurate results.  
The GPU-MOC method has great value for practical applications because the algorithm is simple, high-efficiency, easy to program, and convenient to popularize. Future work will focus on further optimization of the present algorithm and more computational intensive transient simulations like 2D or 3D and on multiple GPUs.  
Conceptualization: W.M. and Y.C.; methodology: W.M. and J.W.; software: W.M. and J.W.; validation: W.M., Y.C., and Z.Y.; writing--original draft: W.M. and Y.C.; writing--review and editing: W.M., Y.C., Z.Y., and S.S.  
This research was funded by the National Natural Science Foundation of China (NSFC) (grant nos. 51839008, 51579187, and 11172219).  
Our grateful thanks to [PERSON] for his valuable advice for revising the manuscript. Thanks also to Editor [PERSON] for correcting my English, editing and proof reading the text.  
The authors declare no conflict of interest.  
Figure 9: Performances of GPU-MOC and CPU-MOC for the two interpolation methods.
# Compression of Next-Generation Sequencing Data and of DNA Digital Files +
## 2 Ngs
### File Formats  
In bioinformatics today, we have to deal with many different file formats often ambiguously or poorly defined.  
_Flat files_ contain unstructured records. The interpretation of the content of a flat file depends on the software that will read that file.  
The _FASTA_ file format is for DNA and amino acid sequences. It was proposed by [PERSON] and [PERSON] in the FASTA suite [PERSON] and [PERSON] (1993).  
A FASTA file begins with a line describing the sequence. The description lines (define) are separated from the sequence lines with a 'greater than' symbol (>) at the beginning of the line.  
The _FASTQ_ file format has been developed as an extension of FASTA, including information on the trustworthiness of the readings.  
FASTQ is extremely simple and it is widely used, but this file format is not clearly or unambiguously defined. Therefore, there are many incompatible FASTQ variants--about one for each NGS sequencing machine.  
The _SAM_ format is the file format used for intermediate and final outputs of alignment software. SAM stands for Sequence Alignment/Map.  
The _BAM_ format is a compressed format for SAM files that uses the _BGZF_ (Blocked GNU Zip Format) format--a block compression format implemented through the gzip standard.  
Finally, the _VCF_ format [9] is a file format used for storing genetic variants paired with free annotations.
## 2 Materials and Methods
### Empirical Safety Stock Estimation
#### 2.2.4 GARCH Models  
To cope with the time-variation of lead time forecast errors' standard deviation the generalized autoregressive conditional heteroscedastic (GARCH) models can be used for capturing the 'fat' tails and heteroscedasticity [40].  
The expression of GARCH (p, q) models is given by the conditional variance of the forecast error at time t + 1. This variance is given by Equation (17).  
\[\sigma_{\mathrm{t+1}}^{2}=\omega+\sum_{\mathrm{i=1}}^{\mathrm{P}}\mathrm{a}_{ \mathrm{i}}\epsilon_{\mathrm{t}}^{2}+\sum_{\mathrm{j=1}}^{\mathrm{q}}\mathrm{ \beta}_{\mathrm{j}}\sigma_{\mathrm{t}}^{2} \tag{17}\]  
where q is the lagged conditional variance terms (\(\sigma_{\mathrm{t}}^{2}\)) and p is the lagged squared error terms (\(\epsilon_{\mathrm{t}}^{2}\)).  
We can notice that the SES method in Equation (9) is a particular case of the GARCH model when p = q = 1. Knowing that a GARCH (1,1) is given by Equation (18).  
\[\sigma_{\mathrm{t+1}}^{2}=\omega+\mathrm{a}_{\mathrm{1}}\epsilon_{\mathrm{t}}^ {2}+\mathrm{\beta}_{\mathrm{1}}\sigma_{\mathrm{t}}^{2} \tag{18}\]  
then replacing the one-step-ahead forecasting error with the cumulative lead time forecast error, Equation (11) can be rewritten as Equation (19)  
\[\sigma_{\mathrm{L,t+1}}^{2}=\omega^{\prime}+\mathrm{a}_{\mathrm{1}}^{\prime} \epsilon_{\mathrm{L,t}}^{2}+\mathrm{\beta}_{\mathrm{1}}^{\prime}\sigma_{ \mathrm{L,t}}^{2} \tag{19}\]  
Then, the safety stock is computed using the Equation (20)  
\[\mathrm{SS}=\hat{\mathrm{D}}+\mathrm{Q}(\mathrm{CSL})\hat{\mathrm{\sigma}}_{ \mathrm{L}}-\mathrm{point}\;\mathrm{forecast} \tag{20}\]  
where  
\(\hat{\mathrm{D}}\) is demand forecast for a certain lead time L,  
\(\hat{\mathrm{\sigma}}_{\mathrm{L}}\) is the standard deviation of the forecast error for a certain lead time L,  
\(\mathrm{Q}(\mathrm{CSL})\) is the quantile of standard normal distribution at CSL and the point forecast is given in Equation (9).
## 2 System Model and Problem Formulation
### Performance Metrics
\[L_{tether,(t)}(z) \tag{18}\] \[=\exp\biggl{\{}-2\pi\lambda_{u}\int_{0}^{\infty}[1-\] \[E_{|h_{n,u}|^{2}}\Bigl{(}\exp\Bigl{(}-zP_{u}|h_{n,u}|^{2}x^{- \zeta}\Bigr{)}\Bigr{)}\Bigr{)}\Big{]}xdx\biggr{\}}\] \[=\exp\Biggl{\{}-2\pi\lambda_{u}\int_{0}^{\infty}\Biggl{[}1-\frac{ 1}{1+\frac{\theta L_{n,u}(t)^{-\zeta}P_{U}x^{-\zeta}}{P_{U}\eta\eta}}\Biggr{]} xdx\Biggr{\}}\] \[=\exp\Biggl{\{}-2\pi\lambda_{u}\int_{0}^{\infty}\Biggl{[}\frac{ \theta r_{u}{}^{\zeta}P_{U}}{\theta L_{n,u}^{-\zeta}(t)P_{U}+P_{U}\eta\,x^{ \zeta}}\Biggr{]}xdx\Biggr{\}}\] \[=\exp\Biggl{\{}-\pi\lambda_{u}\int_{0}^{\infty}\Biggl{[}\frac{1}{1+ \Bigl{(}\frac{\theta}{\beta}\Bigr{)}^{-1}L_{n,u}^{-\zeta}(t)x^{\zeta}} \Biggr{]}dx^{2}\Biggr{\}}\]  
Let \(\delta=\Bigl{(}\frac{\theta}{\eta}\Bigr{)}^{-1}L_{n,u}^{-\zeta}(t)x^{\zeta}\), then we have  
\[L_{tether,(t)}(z) \tag{19}\] \[=\exp\Biggl{[}-\pi\lambda_{u}\biggl{(}\frac{\theta}{\eta}\biggr{)} ^{\frac{2}{\zeta}}L_{n,u}^{2}(t)G_{\zeta}(0)\Biggr{]},\]  
where \(G_{\zeta}(x)=\int_{x}^{\infty}\frac{1}{1+\delta^{\frac{2}{\zeta}}}dt\). Then, by substituting (24) and (26) into (23), the expression for the successful transmission probability can be obtained as follows  
\[P_{u,n}^{suc}(t)=\int_{Z_{u}(t)}^{R_{u}}\exp\Biggl{[}-\pi\lambda_{u}\biggl{(} \frac{\theta}{\eta}\biggr{)}^{\frac{2}{\zeta}}L_{n,u}^{2}(t)G_{\zeta}(0)\Biggr{]} \frac{L_{n,u}(t)}{{R_{u}}^{2}}dL_{n,u}(t) \tag{20}\]  
Mathematically, the content cache hit ratio can be expressed as  
\[Pr_{h}(t)=\frac{\sum_{n=1}^{N}min\{1,\sum_{u=1}^{U}\sum_{k=1}^{K}P_{u,n}^{suc} (t)(1-q_{n}(t))g_{n,k}(t)x_{n,k}(t)\}}{\sum_{n=1}^{N}(1-q_{n}(t))} \tag{21}\]  
This metric quantifies the effectiveness of the caching strategy [49].
# Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
###### Abstract  
This paper studies the privacy of wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect transmissions of interest. There exists one transmitter that transmits to its receiver in the presence of an eavesdropper. In the meantime, a cooperative jammer (CJ) with multiple antennas transmits carefully crafted adversarial perturbations over the air to fool the eavesdropper into classifying the received superposition of signals as noise. While generating the adversarial perturbation at the CJ, multiple antennas are utilized to improve the attack performance in terms of fooling the eavesdropper. Two main points are considered while exploiting the multiple antennas at the adversary, namely the power allocation among antennas and the utilization of channel diversity. To limit the impact on the bit error rate (BER) at the receiver, the CJ puts an upper bound on the strength of the perturbation signal. Performance results show that this adversarial perturbation causes the eavesdropper to misclassify the received signals as noise with a high probability while increasing the BER at the legitimate receiver only slightly. Furthermore, the adversarial perturbation is shown to become more effective when multiple antennas are utilized.  
deep learning; covert communications; signal classification; adversarial attack
## 5 Signal Feature Extraction Methods for Different Vessels  
### Feature Extraction Based on the PE Values of the IMF with the Highest Energy  
The differences are primarily concentrated in the first three energy levels by analyzing the PE values of IMF components at various orders for the three different electric field signals. The IMF components in the first energy level contain the main features of the signal, and there are significant differences in PE values. Although the PE values for the second and third energy levels differ considerably, the comprehensive signal feature components  
Figure 11: PE values of IMF components sorted by energy.  
are not as pronounced as in the first energy level. Therefore, this study utilizes the IMF components from the first energy level to determine their features.  
\[\hat{E}n_{\max}=\max\{\hat{E}n_{1},\hat{E}n_{2},\cdots,\hat{E}n_{j}\} \tag{11}\]  
where \(\hat{E}\) represents the average energy of each IMF component after EMD. According to Equations (9)-(11), the highest average energy and PE values for each IMF component of Vessel A, Vessel B, and Vessel C are calculated and presented in Table 1.  
According to the analysis of Table 1, the maximum average energy levels of signals after decomposition are different for different vessels, with an average energy difference greater than 0.02 and a PE difference greater than 0.3.
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Printer-Associated Problems
#### 3.1.4 Clogged Nozzle  
A sign of clogged nozzle is that the filament is only extruding a little and does not curl nicely or do not extrude anything at all. It is usually caused by an incorrect print temperature that results in the material buildup, due to a malfunctioning fan or build-up of old materials, especially when dealing with composite materials. Solutions include the following:  
1. If the filament is not extruding at all, heat the nozzle to the recommended print temperature for the loaded filament. Push a small needle into the nozzle and repeat until the plug unclogs. Reload a new filament to observe if the filament can extrude smoothly.
2. Perform a cold pull [70]. If the filament is pouring out a little or does not curl nicely, heat up the nozzle according to the material used (Table 3). Wait for 5 to 10 min and load the filament again. The new filament should push out the stuck material from the nozzle.
3. Check that there are no loose or broken wires from the main-board to the extruder assembly, check the transistors of the main-board. Replace the wires or the main-board if required [71].
4. When the clog is due to material build-up, replace the nozzle with a new one; clean the old one with a suitable solvent.
# Noninteracting Control Design for 6-DoF Active Vibration Isolation Table with LMI Approach
## 5 Conclusions  
In this paper, the authors suggested an effective control strategy for 6-DoF AVIS. This study focused on the configuration of the control system for this complex dynamic plant based on the concept of the noninteracting control design. The resulting closed-loop system was represented with a completely diagonal structure; that is, each output is independently controlled by the corresponding reference signal. The analytical methodology of deriving the noninteracting controller was proposed, and the optimal control law was obtained using the LMI technique. In addition, an ITS control system was designed to compare its performance with the proposed controller.  
The efficiency of the proposed control system was evaluated through two sets of simulations: vibration isolation and platform positioning. According to these simulation tests, the vibration isolation performance of both control schemes at low-frequency ranges improved significantly. However, the ITS control system generated additional and unwanted translations and rotations, whereas the proposed noninteracting controller isolated the AVIS without creating any additional motions. Similar results were obtained in the platform positioning simulation test. The analysis of the Bode magnitude plots revealed the interaction between the DoFs of the AVIS, especially with the ITS control system. On the other hand, the simulation results using the noninteracting controller proved the efficiency of this controller in decoupling the mutual interferences effectively while providing good positioning performances.  
In future work, an experimental study will be conducted with the proposed control system to evaluate the vibration isolation performances.  
Conceptualization, Y.-B.K.; methodology, T.H.; validation, T.H. and Y.-B.K.; formal analysis, T.H.; investigation, T.H. and D.-H.L.; writing--original draft preparation, T.H. and D.-H.L.; writing--review and editing, T.H., D.-H.L., S.C. and H.-C.P.; supervision and funding, H.-C.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 5: Frequency response of the platform positioning performance.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 3 Preliminary
### Homomorphic Encryption  
HE is a form of encryption that allows third parties to perform arithmetic operations directly with ciphertexts, and the HE scheme can be utilized to develop PPML in fields where data privacy is important. First, partial homomorphic encryption (PHE) capable of only addition or multiplication was developed. For example, the property of the AHE scheme, which can only perform addition operations, is represented as follows in [19]:  
\[D_{sk_{i}}(E_{pk_{i}}(m_{1})\cdot E_{pk_{i}}(m_{2}))=m_{1}+m_{2}, \tag{1}\]  
where \(D_{sk_{i}}(\cdot)\) is a decryption function using a private key \(sk_{i}\), \(E_{pk_{i}}(\cdot)\) is an encryption function using a public key \(pk_{i}\), and \(m_{i}\) is a plaintext. The cloud server can perform homomorphic addition operations without decryption using (1). Subsequently, fully homomorphic encryption (FHE) capable of both addition and multiplication operations was established in [20] to overcome the limitations of PHE, which is challenging to implement various homomorphic operations in [21]. FHE enables a variety of operations to be implemented using addition and multiplication operations. These HE technologies have led to the development of PPML algorithms in the cloud and machine learning fields.
## 3 Results and Discussion  
### Results of Modeling Nitrogen Ion Implantation  
Figure 9 presents the depth profiles for the cases of N\({}^{+}\) + N\({}_{2}\)\({}^{+}\) and ACS in W-C-Co substrate material, for the acceleration voltage of 50 kV and the fluence of 2.5 \(\times\) 10\({}^{17}\) cm\({}^{-2}\). All detailed data, i.e., the values of peak volume dopant concentrations, projected range, range straggling, kurtosis and skewness, characterizing the modelled depth profiles of the implanted nitrogen, have been presented in Table 2.  
Figure 8: EDS tests on the tooth surface.  
Figure 7: Classic wear curve showing the evolution of wear during machining.  
It is evident that N\({}^{+}\) + N\({}_{2}\)\({}^{+}\) profiles for W-C-Co and W-C substrates are very similar. The same situation is observed for their ACS. It is also indicated by the very similar values of the parameters shown in Table 2, for both described cases (the data for ACS cases are in the round brackets). This means that in practice, a simpler substrate model for modelling can be adopted, without significantly affecting the results. A greater difference is observed for the Co substrate, both compared to the W-C-Co and W-C substrates, and also to the ACS for Co. However, because this difference is about 7 nm, both for the projected range and range straggling, and cobalt content is only a few percent, this difference can be neglected. The values of the skewness and the kurtosis are higher for the N\({}^{+}\) + N\({}_{2}\)\({}^{+}\) case due to the two types of implanted ions. The shift of the peak maximum towards the surface is observed. The ACS profiles are closer to the normal distribution.  
The calculated values of sputtering yield are relatively low and again very similar for W-C-Co and W-C substrates. The value of this parameter is about two-fold higher for cobalt. This means that the sputtering of this element will be easier.
## 5 Experimental Results and Analysis
### Comparison and Analysis of the Routing Control Strategy
When the load rate is small, due to the algorithmic complexity, the average transmission delay of the MPSRCS is slightly higher than those of the OI-SRS, SPRS and HCARS. When the load increases, the proposed MPSRCS demonstrates the best delay regulation performance. Time delay for the SPRS is large and this is because the segmented route assignment adds extra time in the process. Delay regulation of the HCARS is acceptable. However, this strategy is likely to fall into the local optimal route since the bandwidth occupancy rate of some certain neighboring nodes have great influence on the real-time status of the downlink. Compared with other strategies, the proposed MPSRCS strategy is more stable and less volatile. Figure 16: Comparison of the average transmission delay of LoRaWAN downlink communication. Figure 17 shows the comparison of the average bandwidth usage for downlink communication. As can be seen from Figure 17, due to the random nature of route assignment, the shortest route may have multiple data streams at the same time, which may cause uneven allocation of link resources and link congestion. This is the reason that the occupancy rate of the SPRS is saturated firstly in the four strategies. The occupancy rate performance of the HCARS is better than the OI-SRS. When the link is congested, the HCARS policy can adjust the downlink bandwidth occupancy rate in time, and gradually adjust the average requested bandwidth to a larger value and therefore relieve the link congestion. However, when the requested bandwidth is further increased, congestion still can be observed in the HCARS. The proposed MPSRCS strategy is able to predict the bandwidth occupancy rate in the link. The evaluation mechanism is calculated in advance for the potential future congestion situation, and the idle resources in the link are redistributed reasonably. Therefore, the average bandwidth occupancy rate is significantly higher than those of the SPRS, OI-SRS and HCARS, which makes the network resource allocation mechanism of downlink communication more effective.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Large-Scale Data Handling
#### 5.2.3 Evaluation of the Existing Solutions  
Regarding the pros and cons of the three methods mentioned above. Firstly, Hadoop's MapReduce is highly effective in distributed data environments and large-scale batch processing, offering significant scalability and cost-effectiveness. However, it requires considerable setup and maintenance efforts, and its performance in real-time data processing may be limited [100]. Secondly, Apache Spark provides fast analysis through in-memory processing, which is particularly beneficial for iterative algorithms, but it requires substantial memory resources, which can be expensive and challenging to manage for extremely large datasets [101]. Thirdly, data mining techniques offer valuable insights through pattern recognition and predictive modeling, aiding personalized care and risk prediction. However, these methods require high-quality, well-labeled datasets and may face challenges with data heterogeneity and model interpretability [104]. Overall, Hadoop is more suitable for large-scale, distributed data processing, while Spark is ideal for high-speed, iterative analysis. Data mining techniques are crucial for extracting actionable insights and developing predictive models to improve patient outcomes.  
In addressing the challenges of large-scale data handling, it is essential to consider the comparative performance of existing frameworks like Hadoop and Spark. Based on the experimental results from multiple studies, it has been consistently demonstrated that Spark outperforms Hadoop in various scenarios, particularly in terms of execution speed and efficiency. For instance, one study conducted in a controlled laboratory environment compared Hadoop and Spark using WordCount and TeraSort workloads, revealing that Spark achieves up to two times speedup in WordCount and up to 14 times in TeraSort when parameters are appropriately tuned [105]. Another comprehensive evaluation highlighted that Spark's in-memory processing allows it to significantly outpace Hadoop's disk-based MapReduce, resulting in much faster query execution. Additionally, Spark's versatility, with built-in modules for stream processing, machine learning, and graph processing, further solidifies its superiority for complex applications [106]. However, it is important to note that Hadoop-MapReduce remains more suitable and cost-effective for truly massive batch processing tasks, reflecting the necessity of selecting the right tool based on the specific data handling requirements. These comparative analyses underscore the importance of choosing frameworks that align with the nature of the workload, thereby optimizing resource utilization, execution time, and overall efficiency [107].
# Smart Clothing Framework for Health Monitoring Applications
## 3 Proposed Framework for Smart Clothing (Leggings)
### Data Collection, Data Analytics and Decision Support  
Recent research efforts on data analytics present various healthcare frameworks. Most of the frameworks describe from the stage of data collection up to decision making. However, there is still lack of research providing the details of each stage in their frameworks. For smart clothing, data collection entails capturing raw biosignals from the human body such as ECG, heart bit, blood oxygen, knee movement, etc. The collected raw data are stored in a local hardware-based system or cloud-based system and pre-processed through filtering (eliminating outlier) transforming, categorizing and turning it into a structured form such as an CSV or text file with the use of statistical techniques. An example of ECG data filtering can be found in the author's previous publication [46]. The accurate processing and management of physiological data allows health/fitness monitoring systems to make holistic assessments of patients, personalize treatments, and improve communication. The processed data are then analysed by applying advanced machine learning techniques such as classification, clustering, regression analysis, and deep learning. Finally, a decision is made by the carer or medical advisor based on the outcome from the analysis with the help of a graphical user interface.  
Figure 18: (**a**) Musical jacket. (**b**) Fabric keypad with circuit board placed behind it [89].