esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Implementation of Multilayer Perceptrons: Reducing Execution Time and Memory Consumption
## 3 Results
### Operation Time
#### 3.2.1 Personal Computer  
All tests were conducted on a 2017 macbook pro with i5-7360U CPU @ 2.30 GHz (Apple, Cupertino, CA, USA) and with the clang compiler in the clang-900.0.39.2 version [21].  
These first times have been taken without using any of the optimization options provided by the compiler. The results can be checked in Table 4, and Figure 5 shows the increased speed. The reduction in time can be observed in all tested runs, showing a pattern of our proposed model of performance improvement at a higher network complexity.  
Figure 4: Percentage of saved memory using the proposed method instead of the traditional one for the networks specified in Table 2.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & \begin{tabular}{l} **Network** \\ **Topology** \\ \end{tabular} & \begin{tabular}{l} **Traditional** \\ **Approach** \\ \end{tabular} & \
\begin{tabular}{l} **Proposed** \\ **Approach** \\ \end{tabular} &
\begin{tabular}{l} **Memory** \\ **Saving** \\ \end{tabular} \\ \hline Iris & 4, 5, 3 & 644 B & 308 B & 52.17\% \\  & 4, 5, 7, 3 & 1544 B & 560 B & 63.73\% \\  & 4, 4, 5, 5, 3 & 1876 B & 588 B & 68.66\% \\ \hline Cancer & 9, 7, 1 & 1244 B & 508 B & 59.16\% \\  & 9, 7, 3, 1 & 1704 B & 616 B & 63.85\% \\  & 9, 12, 8, 4, 1 & 4788 B & 1400 B & 70.76\% \\ \hline Ionosphere & 34, 9, 1 & 7940 B & 1812 B & 77.18\% \\  & 34, 9, 4, 1 & 9432 B & 1988 B & 78.92\% \\  & 34, 12, 8, 4, 1 & 14188 B & 2900 B & 79.56\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Memory in bytes used to store ANNs. The second column indicates the topology of the network. The third and fourth columns represent the memory used in bytes to store the network using the traditional and proposed methods, respectively. The last column indicates the amount of memory saved by using the proposed method instead of the traditional method.  
The times obtained by using compiler optimization options have also been analyzed. Specifically, the following parameters were used: \"-O3-fast-math-funroll-loops-free-vectorize-march=native\". The results can be checked in Table 4, and Figure 5 shows the increased speed. Once again, the results obtained in the execution of the problems on a server will follow the pattern observed in our proposed model. It is worth noting that the improvement achieved in execution on a personal laptop using a compiler optimization versus not using the optimization is still significantly better on average.  
Figure 5: Speed-up improvement of the proposed method vs. the traditional method in network execution on a personal computer. (**a**) Without using the compiler optimization flags. (**b**) Using the compiler optimization flags “-O3-fast-math-funroll-loops-free-vectorize-march=native”.
## 3 Results and Discussion
### Transformer Noise Separation Test in a Substation
#### 3.3.2 Analysis of Test Results
Therefore, the distance between the microphone line array and the transformer should be selected appropriately according to the transformer's characteristic size when separating the transformer noise using the phase conjugation-based separation technique. 2. Application of test results  
After the noise separation test, we are able to obtain sound pressure spectrums and contributions of each transformer in the substation, which can be used to guide the vibration and noise reduction for the substation. The final noise separation results of three transformers at all seven test points (the total sound pressure spectrums, the separated sound pressure spectrums, and the noise contributions of each transformer in Condition 2) are shown in Figure 11. Figure 16 only illustrates the final noise separation results at test point 1 as an example. Considering the final results, it is possible to quantitatively give the effect magnitude of the three transformers on the noise at any test point among their working frequencies. For example, if the location of test point 1 is a residential building, we should carry out vibration and noise reduction measures to make noise at this location reach a set standard, and then only transformer 1 needs to be designed for vibration and noise reduction because of its maximum noise contribution to test point 1, as illustrated in Figure 16.  
In this subsection, a substation noise separation test is carried out using the noise separation technique based on the phase conjugation method proposed in the present paper. Figure 16: Final noise separation results of 3 transformers at test point 1 in the substation. Figure 15: Total A-weighting SPL and the test-point-to-source distance of 7 test points (Condition 2): (**a**) transformer 1, (**b**) transformer 2, (**c**) transformer 3. Through accuracy and application analysis, the test results prove that this noise separation technique can realize the separation of strong coherent noise signals and contributes analysis of multiple transformers at any location and frequency in the substation. The test results can be used to guide transformer vibration and noise reduction, which can greatly reduce the cost of designing vibration and noise reduction for all transformers one by one because of the inability to separate the noise sources. To sum up, the noise separation technique using the phase conjugation method proposed in this paper is of vital importance for engineering applications regarding substation vibration and noise reduction.
## 4 Materials and Methods
### Isolation and Purification of Phages  
Stems, leaves and soil from tomato plants presumptively infected with _Xanthomonas_ showing symptoms of bacterial spot, as well as water samples from two irrigation channels (C1 and C2) were collected in tomato crops in the farm \"Chaparral\" located at Culiacan, Sinaloa, Mexico (24.603894, N-107.579580 coordinates) in June 2018. Stems, leaves and soil samples were mixed with sterile distillated water and shaken at 150 rpm at room temperature overnight. All samples were centrifuged at 8000\(\times\)\(g\) for 15 min and the supernatant filtered through 0.22-\(\upmu\)m sterile syringe filters of cellulose acetate membrane (GVS, USA). Phages were enriched as previously reported by [PERSON] et al. [56] with some modifications. Briefly, 25 mL of overnight culture of XvA or XvB strains was supplemented with 5 mL of sample and incubated at 27 \({}^{\circ}\)C for 18 h. Then, 250 \(\upmu\)L of chloroform were added and culture was incubated 1 h to lyse bacteria. Bacterial culture was centrifuged 5000\(\times\)\(g\) for 30 min and the supernatant collected and filtered through 0.22 \(\upmu\)m pore cellulose acetate membrane. Phages were detected by the double agar technique, in which 1 mL of XvA or XvB was added to 3 mL of molten TSB top agarose (0.4%) and mixed with 100 uL of enrichment filtrate, spread on TSA plates and incubated at 27 \({}^{\circ}\)C. Then, clear and non-turbid individual plaque was selected and recovered from the TSA plates with Pasteur pipettes. The plaque was resuspended in 1 mL of nanopure water and diluted to repeat the double agar for purifying the individual plaque. This step of purification was done at least eight times to obtain single-plaque isolates.
# Detection and Localization of Failures in Hybrid Fiber-Coaxial Network Using Big Data Platform
## 5 Failure Detection and Localization Based on Big Data
### Failure Localization
FF:3C:2A:1C:FE:AA, AMP_cc143
2. FF:1B:A7:C9:D9:34, AMP_rq501
3. FF:AE:FE:2B:98:99, AMP_sd211
4. FF:B4:93:64:E7:AE, AMP_yt1
5....
6. FF:9E:FC:98:86:F1, AMP_bcc89  
In step 1 of the failure localization algorithm, all CPE devices that are connected to the given MAC domain are pinged. Modems that respond (records with \"ttl\" and \"time\") are online, while others are considered offline. An example of pinging result is as follows:  
1. ping 10.0.1.17, Request timeout for icmp_seq 0
2. ping 10.0.1.22, Request timeout for icmp_seq 0
3....
4. ping 10.0.1.63, Request timeout for icmp_seq 0
5....
6. ping 10.0.1.64, 64 bytes from 10.0.1.64: icmp_seq=0 ttl=64 time=0.053 ms
7. ping 10.0.1.214, 64 bytes from 10.0.1.214: icmp_seq=0 ttl=252 time=22.557 ms  
In step 2, the algorithm joins results from step 1 with the network topology and calculates the percentage of offline modems. An example of step 2 results is as follows:  
1. AMP_cc143, 100
2. AMP_rq501, 100
3. AMP_sd211, 100
4. AMP_yt1, 0
5. AMP_bcc89, 11.11
6. AMP_ng2, 100
7. AMP_pp929, 8.10
8. AMP_as218, 71.67
9. AMP_gy88, 0
10. ON_br21, 62.7  
In steps 3 and 4, the algorithm travels bottom-up through the topology and attempts to find the top element under which all modems are offline. In this example, which is based on network topology in Figure 3, AMP_ng2 is the root cause.
## 6 Discussion
[61], who used hybrid network structures of conventional feature extraction techniques such as STFT analysis combined with 1D and 2D convolutional neural networks. An accuracy of 94.3% was reported as the best performance accuracy obtained from the hybrid deep CNN model. One of the important aspects of improving the accuracy of epileptic seizure detection systems is the preprocessing stage. In this work, we employed a bandpass filter for the elimination and removal of the artifacts embedded in the epileptic EEG signals. However, other advanced preprocessing methods could be deployed in future work to improve the performance of the model aimed at the development of low-cost EEG monitoring and BCI systems. Recently, work presented in [62; 63] has demonstrated an efficient and advanced method for the elimination of eye blink artifacts using variational mode extraction (VME) with DWT and clustering algorithm approaches based on variational mode extraction (VME) and smoothed nonlinear energy operator (SNEO), respectively. Another factor to be considered for improving the efficiency of our method is to investigate the optimal values of the nonlinear features by tuning the appropriate parameters to obtain an optimal value. This work employed a \(t\)-test statistical method to optimize the parameters based on their lowest \(p\)-value. Other parameter optimization methods should be considered in future work to properly tuned some parameters to their optimal values. Few previous works have reported on the application of localization for epileptogenic areas in the brain EEG signals using a deep learning approach, as it is mostly applied in areas of image processing. This work aims to leverage the application of deep learning networks in the analysis of EEG epileptic seizure signals. The work includes a comprehensive analysis of various feature extraction techniques proposed by various researchers; these features are either from the EEG signals themselves or from the sub-bands of the signals. The performance of conventional classifiers, such as SVM, was compared with that of recent deep neural networks, such as the CNN architecture. We tried to explore how the hand-crafted features could be combined with DNN architectures as well as how these DNN architectures could be used as a stand-alone classifier without the need for hand-crafted features. We have also seen how the learnable feature maps in our proposed DCNN network properly discriminate and characterize the information hidden in the EEG signals to detect and classify focal and non-focal EEG signals.
## 2 Materials and Methods
### Definition of Inputs  
The proposed model requires input values, for which we can consider the health personnel positive for COVID-19, evidenced by a rapid test or PCR, after which the staff is isolated and cannot be counted on for care, causing a decrease in staff. As the pandemic continues, there are various scenarios, such as the recovery of staff and new cases of infection. Each scenario is different from one day to the next, changing the probability of infection, recovery, and minimum personnel to care for patients, among other considerations.  
In the model, we propose to define a scenario as real as possible. Hence, we identify two groups of inputs, the first related to a group of known factors and a second group considered as estimated factors, which are those related to the possible probabilities of occurrence:  
**Group of known factors:**  
1. **Number of personnel available for attention**: the total number of health professionals, including physicians, nurses and auxiliary and health personnel.
2. **Number of interactions**: the number of interactions to be performed in the simulation is considered.  
The inputs considered as known factors are related to the values that we know and that we can have control over. In a real environment, the number of health personnel can vary if we consider hiring health personnel, which increases the number of health personnel. Regarding the number of iterations, it is an average value that we can consider in order to have a larger number of possible situations.  
**Group of estimated factors:**  
1. **Accepted percentage of contagion** is the percentage of contagion of health personnel that we can accept and that does not affect patient care.
2. **Average percentage of contagion** is the average percentage of contagion of health personnel, which is continuously occurring.
3. **Average percentage of personnel recovered from infection** is the average percentage of health personnel who have completed the isolation process and are returning to work to attend to patients.  
Figure 1: The methodology in building blocks.  
Average percentage of personnel needed for care is the average percentage of personnel needed to care for patients; it is considered as the minimum number of personnel that does not affect the care of patients.  
The inputs considered as a group of estimated factors include all the probabilistic values that may occur and directly affect the health personnel, affecting the ability to comply with daily patient care. All values in this group are percentage probability values.
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 5 Key Challenges and Future Research Directions
### Credibility of Data Acquisition
Depending on their characteristics, different sensors can have different losses in them, such as magnetization losses in current transformer-based sensors, thermal runaways in optical sensors, the high sensitivity of magnetic sensors, etc. These losses, which are specific to the type of the sensors deployed, should be accounted for while processing the acquired raw grid parameter data. To summarize, the research directions should be focused on addressing the challenges of sensor node identification, heterogeneity, losses, and synchronicity. Ref. [155] suggests dealing with these issues by designing lightweight protocols to facilitate identity address allocation, the detection of address collision and address duplication, address recycling, address translation, and address auto-configuration for scalable and seamless ubiquitous connectivity among sensor nodes.
## 3 Case Simulation and Result Analysis
### Simulation and Results Analysis
#### 3.2.3 The Influence of Different Sonar Parameters and Target Parameters on Evaluation Indicators
When the target disappears at position \((-a,0)\), the Multi-sonar system takes the shortest time to approach the target, the instantaneous detection probability peak appears first, and the cumulative detection probability begins to increase first; When the target disappears at positions \((0,0)\), \((0,a)\), and \((0,-a)\), the time it takes for the multi-sonar system to approach the target is centered, the time when the instantaneous detection probability peak occurs is centered, and the time when the cumulative detection probability begins to increase is centered. When the target disappears at position \((a,0)\), the multi-sonar system takes the longest time to approach the target, the instantaneous detection probability peak appears last, and the cumulative detection probability finally begins to increase. 2.4 Analysis and Simulation Study on the Influence of Each Sonar on the Overall Detection Capability  
The correlation coefficient (CC) can be used to measure the degree of association between two random variables, while mutual information (MI) can reflect the degree of dependence and information transmission between two random variables. The instantaneous detection probability vector only reflects the detection capability of sensors at different times, and is more suitable for analyzing the real-time dynamic characteristics of multi-sensor collaboration. The cumulative detection probability vector integrates the detection results of all members throughout the entire mission process, making it more suitable for evaluating the overall detection efficiency under long-term missions. In order to analyze the influence of each sonar on the overall detection capability during the entire target search task, this section calculates the correlation coefficient and mutual information between the cumulative detection probability vector of each sonar and the overall cumulative detection probability vector, and analyzes and simulates the influence of each sonar on  
Figure 13: The influence of the target disappearance position on evaluation indicators.
# Metallurgical Aspects in the Welding of Clad Pipelines--A Global Outlook
## 3 Microstructure Evolution
### Heat-Affected Zone
#### 3.4.4 Bimetallic Interface After Welding  
The interface between the 316L SS clad and high-strength, low-alloy (X60/X65) base metal has already been discussed in Section 2, as the majority of the elemental diffusion and segregation has taken place during the clad pipe production. In hot-roll (metallurgical) bonding of the clad pipe, carbon diffuses at elevated temperatures (\(>\)\(A_{3}\) point in the Fe-C diagram) from the carbon-rich ferrite to the austenite, with subsequent carbide precipitation of M\({}_{23}\)C\({}_{6}\), mainly Cr\({}_{23}\)C\({}_{6}\)[90]. This diffusion results in a softer, carbon-depleted zone in the ferrite (decarburized layer in the pipe steel close to the interface), and a hard, carbon-enriched zone (carburized layer) in the 316 SS (austenite) clad [91]. Moreover, this phenomenon makes the clad sensitized, and the existence of Cr-depleted zones may promote intergranular corrosion [92].  
After welding, interface cracking has been found between the BM (X60) and the 316L SS clad material, as shown in Figure 19a. It was observed that the cracking was usually initiated in the HAZ. Upon further inspection, it was found that there is a grain boundary precipitation on the clad side near the interface (see Figure 19b) [93]. The electron microprobe analysis revealed high carbon content in the clad near the interface, which increases the risk of cracking [21]. This possibly takes place due to additional stresses caused by welding; i.e., residual stresses added to production thermal stresses because of thermal expansion mismatch between the 316L stainless steel clad and the pipe steel. Fortunately, the cracks occurred in the direction following the pipe length, where the load is low, and were not regarded as representing any risk of pipeline failure. The cracking problem can be prevented using a Ni interlayer [17]. To the authors' knowledge, similar cracking problems with Inconel 625 have not been reported.  
Figure 19: (**a**) Cracking near the bimetallic interface in the clad and (**b**) grain boundary precipitates, as indicated by the red arrow, near the interface [21].
## 6 Conclusions  
This paper proposes a Separated Transmission and Control ALOHA (STCA) protocol, which enhances network performance by introducing airborne access control nodes (ACNs) and separating transmission and control functions. Simulation results show that STCA outperforms existing protocols in key metrics.  
STCA with SIC achieves a throughput of 70%, significantly higher than the 37% of Slotted ALOHA. Under high traffic loads (>100%), STCA's TTC channel utilization approaches 100%, while IRSA's remains at 20%. Under 100% load, IRSA results in 40% packet loss, whereas STCA ensures that nearly all traffic reaches the satellite. Additionally, STCA solves IRSA's issue of long frame times by using minislots, optimizing efficiency. These three STCA variants (without SIC, with SIC, and with variable frame length) all maximize satellite channel utilization, offering flexible options for different device capabilities.  
Figure 10: (**a**) Channel utilization of Slotted ALOHA algorithm. (**b**) Channel utilization of STCA algorithm with SIC. (**c**) Throughput of IRSA algorithm. (**d**) Throughput of STCA algorithm without SIC.  
In the future, we will extend the STCA protocol to multi-ACN scenarios, focusing on exploring inter-ACN coordination mechanisms to address issues like load imbalance and spectrum interference between adjacent ACNs. Additionally, we will validate the protocol in more realistic contexts by incorporating real-world satellite communication impairments such as channel fading and rain attenuation into the simulation model, aiming to enhance the robustness of STCA in actual deployments.  
Conceptualization, C.H. and X.M.; methodology, X.M.; software, C.H.; validation, X.X., X.M. and W.H.; formal analysis, W.H.; investigation, Y.D.; resources, W.H.; data curation, X.M.; writing--original draft preparation, C.H.; writing--review and editing, X.M.; visualization, Y.D.; supervision, X.M. and X.X.; project administration, W.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Basis Research Plan in Shaanxi Province of China (grant No. 2025 JC-YBMS-674); in part by the Technological Innovation Guidance Program (Fund)--Three Reforms--Performance-Based Evaluation (grant No. 2025 ZJC-YYDP-51); in part by the Shaanxi Key Industrial Innovation China Project in Industrial Domain (grants No. 2023-ZDLGY-15 and No. 2023-ZDLGY-51); in part by \"Scientist + Engineer\" team building in Qinchuangyuan (grant No. 2025 QCY-KQJ-169); and in part by the Key Research and Development Program of Shaanxi (program No. 2025 CY-YBSM-059).  
Not applicable.  
All data generated or analyzed during this study are included in this article.  
The authors declare no conflicts of interest.