esa-sceva/satcom-chunk-collection huggingface dataset:
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 2 Literature Reviews
### 1-D CNN Deep Learning and Data Acquisition  
The compact 1-D CNN excels in extracting features from vibration data with minimal training required [18; 19]. The vibration data (i.e., time-history and SVD responses) are one-dimensional arrays with inherent order. The 1-D CNN model is suitable for processing 1-D and sequential input data, as it can effectively learn the ordered patterns by capturing local dependencies. [PERSON] et al. [14] applied 1-D CNNs trained using vibration signals from a numerical model to detect damage in a steel bridge. [PERSON] et al. [15] employed a CNN model to quantify the dynamic stiffness of railway tracks using vibration data. [PERSON] et al. [16] implemented an adaptive CNN model for bearing-fault diagnosis in noisy environments.  
The vibration data for the CNN model can be obtained by two approaches. In the first approach, vibration data are collected directly from damaged caissons after extreme events such as storms or tsunamis. These data are labeled according to the severity of the damage and used for training. In the second approach, vibration data are collected indirectly through foundation damage simulation. [PERSON] et al. [13] proposed a method called pseudo-damage simulation, where concrete weights were applied on top of the caisson to mimic the effect of foundation damage. In their study, experiments were conductedunder laboratory conditions. The location, shape, and severity of foundation damage levels were predetermined.
# Blind Channel Estimation Method Using CNN-Based Resource Grouping
## 2 System Model  
### NR Frame Structure  
In New Radio (NR), transmitted data is allocated to time-frequency resources at the physical layer for transmission. Uplink and downlink data can be assigned to a single or multiple orthogonal frequency division multiplexing (OFDM) symbol(s) in the time domain within a single slot, as well as to a single or multiple RB(s) in the frequency domain. A single slot can consist of 14 OFDM symbols, and a single RB consists of 12 subcarriers [14].
# Development of New Spectral Amplitude Coding OCDMA Code by Using Polarization Encoding Technique
## 5 Performance Analysis  
In this part, a theoretical analysis of FDW code is compared with previous code, such as Hadamard, modified frequency hopping (MFH), DW code, MDW code, and EDW code. The comparison was particularly made by observing the BER in each case. Each DW family has a unique code that is improved from time to time. A good coding scheme must have high autocorrelation, low cross-correlation (less than 1), and the BERs of less than \(10^{-9}\) for  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Degree of** & & & & & & & \\
**Rotation**\({}^{\circ}\) & & & & & & & \\ \hline
0 & 2 & 1 & 0 & 1 & 1 & 0 \\
90 & 3 & 0 & 1 & 0 & 1 & 1 \\
90 & 4 & 1 & 0 & 1 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Compression of FDW \(W=3\) for polarization encoding technique.  
Figure 1: Polarization encoding–decoding scheme block design.  
the system to work properly. To compare each DW family code, a few parameters have been fixed, which are shown in Table 7.  
We used a coherent source such as a light emitting diode (LED), and the line width for an LED used in this system is 3.75 THz, which is equivalent to 30 nm. Based on recent studies, the line width of LED can go up to 110 nm, which is equivalent to 13.7 THz for the center wavelength of 1550 nm [24]. To calculate the line width of the LED, the center wavelength is set to 1550 nm. The calculation of the line width requires the bandwidth wavelength (\(\lambda\)), center wavelength (\(\lambda_{O}\)) and speed of light (\(c\)). The center wavelength determines the line width of an LED, which is based on the equation below,  
\[Linewidth=\frac{c}{\lambda_{O}-\frac{\lambda}{2}}-\frac{c}{\lambda_{O}+\frac {\lambda}{2}} \tag{9}\]
# A Comparative Analysis of Big Data Frameworks:
## 4 Big Data Processing Frameworks
### 4.5. Flink
#### 4.5.3 Scheduling  
There are three scheduling strategies in Flink for resource allocation: all at once/eager scheduling, lazy from sources scheduling and pipelined region scheduling. All at once/eager scheduling tries to allocate required resources at once when the job starts. This strategy is primarily targeted for streaming jobs because in case of batch processing, acquiring all resources in advance will lead to resource underutilization as any resources allocated to subtasks that could not run at any time due to blocking results will be idle and therefore considered wasted. In order to solve the blocking effect and ensure that consumers are not deployed before completing the operations of the respective producers, Flink provides different scheduling strategy for batch processing. The lazy from source scheduling, starts from source and deploys subtasks according to their topological order, this ensures that a subtask can only be deployed if all of its inputs are ready. One limitation of this approach is that it operates on individual tasks and thus treating all tasks in a similar fashion, without considering the subtasks that are non-blocking and can be executed in parallel. To address this limitation, the pipelined region scheduling is introduced in Flink, that analyzes Job graph and identify pipelined regions before deploying tasks and subtasks. It schedules each region only when all of its predecessors are executed thereby making all of its inputs ready. If there are enough available resources, JobManager will try to execute as many pipeline regions in parallel as possible [57].
## 3 Methodology
### System Model
#### 3.1.9. Neighbor Head Node Methodology  
The neighbor head node methodology considers the amplify and forward (AF) method, in which the neighbor head node (\(Ri\)) multiplies the acknowledgment signal from \(Si\), from the beginning of transmission to the destination (\(Di\)), by a gain factor \(G\), i.e., \(Y-rd=G(Y-sr)\). This method implies that the neighbor head node energy consumption is equivalent to that of the sending node during the first hop. Equations (8) and (9) can be used to express the amplifier parameter \(G\) in the presence of a channel state information (CSI) surge on the neighbor node.  
\[G=(|h_{SR}|^{2}+\frac{1}{y_{0}})^{-1} \tag{8}\]  
where \(y_{o}=p_{s/N_{o}}\) is the common SNR of each link without fading: \(Y-rd=G(Y-sr)\).  
\[y_{RD}=h_{RD}\text{Gy}_{SR}+N_{RD} \tag{9}\]  
The notation \(h,n\in(S_{D},S_{R},R_{D})\) represents the fading channel magnitude.
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 5 Simulation and Evaluation
### Simulation Results
#### 5.2.1 Statistical Results over The Lifetime Based on Various Methods
This scheme evaluates the network lifetime performance of four algorithms by fixing the number of sensors and varying the number of sinks, where Figure 6c depicts a scenario in which the number of sensors is N = 500 and the number of sinks ranges from 5 to 15, and Figure 6d depicts a scenario in which the number of sensors is N = 1000 and the number of sinks ranges from 5 to 15. It is obvious that the network's lifetime increases as the number of sinks increases, resulting in most of the sensor nodes adopting direct transmission to the sink. Therefore, the number of intracommunications is diminished. Overall, Figure 6c,d  
Figure 6: Network lifetime vs. (**a**) number of sensors, (**b**) number of cluster heads, varying the number of sinks from 5 to 15 when fixing the number of sensors (N)., (**c**)-N = 500, (**d**)-N = 1000.  
indicate how the network's lifetime improves as the number of sinks increases according to the suggested model. As the total number of sinks grows, the overall number of sensors that communicate directly to the sink increases, implying that the load on the sensors is reduced, the effect of the energy hole phenomena is diminished, and the network's lifetime is extended. As an outcome, expanding the number of sinks for a given number of sensors extends the sensor's lifetime. Conversely, compared to our proposed method, the network's lifetime acquired by the LEACH, HEMP, and DP clustering approaches is poor in each case. The proposed approach provides the most extended lifetime. It outperforms the results of other algorithms by at least 4%.
## 1 Introduction
Moreover, the investigation has extended to more complex anionic entities, including anionic silica (SiO(OH)\({}_{3}\)\({}^{-}\)) [28] and larger polyoxometalate ions, as well as hexacyanoferrate complexes (Fe(CN)\({}_{6}\)\({}^{n-}\)) (see e.g., [29, 30, 31, 32, 33, 34, 35]). The unique vibrational fingerprints associated with each of these anions are discernible through their respective infrared and Raman spectra. These spectral signatures provide crucial insights into the interactions and substitutions occurring within the hydrdcalcite matrix. Through rigorous analysis using these spectroscopic techniques, researchers have been able to correlate the presence of specific anions with structural modifications within the LDH framework. This knowledge not only enhances the understanding of the properties of LDH but also informs potential applications in fields such as catalysis, ion exchange, and environmental remediation, where the selective uptake and release of ions play a critical role. Such advancements underscore the relevance of infrared and Raman spectroscopy in characterizing the multi-faceted nature of hydrdcalcite and their layered structures. X-ray photoelectron spectroscopy (XPS) has established itself as the predominant technique for investigating surface phenomena. However, there remains a notable paucity of research focusing on the bulk atomic structure and chemical states of minerals. This oversight is particularly striking given that the vast majority--often exceeding 90%--of the intensity of XPS lines is derived from the bulk material when employing conventional laboratory X-ray sources, such as the Al K\(\alpha\) source at 1486.6 eV. This discrepancy invites further inquiry into the collective properties of minerals that are not fully captured by surface-centric methodologies. Historically, the XPS analysis of various mineralogical components has been documented in the literature, encompassing a diverse range of materials, including feldspars, clays, aluminum oxy(hydroxide) phases, arsenates, and phosphates. Notable contributions have highlighted the unique electronic and structural characteristics present in these minerals, but comprehensive studies that simultaneously account for both surface and bulk properties remain limited. It is essential to broaden the scope of investigation to include these often-ignored bulk properties to achieve a better understanding of mineral behavior. So far, limited use has been made of XPS as an analytical tool to study LDH in detail, and it generally stops at just providing binding energy values (see e.g., [36, 37, 38, 39, 40, 41, 42]). As part of an ongoing study on LDH, this study aims to better understand the X-ray photoelectron spectroscopy (XPS) of the interlayer anions in LDH to complement the existing Infrared and Raman spectral data of LDH.
## 8 Open Research Challenges and Research Directions
### Cybersecurity  
Cybersecurity is one of the major challenges facing IoMT. As IoMT devices become more interconnected and accessible, they become more vulnerable to cyber-attacks. It refers to the interconnected network of medical devices, sensors, and other healthcare technology that is connected to the internet. While IoMT has the potential to revolutionize healthcare and improve patient outcomes, it also presents significant cybersecurity challenges. Medical devices and healthcare systems are often vulnerable to cyber-attacks, which can result in the theft of sensitive patient data, the manipulation of medical records, or even physical harm to patients. This is because many medical devices and systems were not designed with cybersecurity in mind and are often not regularly updated with security patches. Additionally, many healthcare providers have not yet fully integrated cybersecurity into their overall risk management strategies, which leaves them vulnerable to cyber threats. As a result, it is important for healthcare providers and medical device manufacturers to prioritize cybersecurity in order to ensure the safety and security of patient data and the integrity of medical devices and systems. This may involve implementing strong encryption and authentication protocols, regularly updating and patching systems, and investing in cybersecurity training and awareness for staff.
# CitiusSynapse: A Deep Learning Framework for Embedded Systems
## 3 Deep Learning Core and Accelerator Engine
### Deep Learning Core
#### 3.1.1 Data Structure with Unified Memory  
As shown in Figure 3a, a standard PC has a separate memory for the CPU (host) and GPU, in which the OpenCL buffer that is generated in the CPU needs to be copied to the GPU in order to execute OpenCL in the GPU. Moreover, data also need to be copied from the GPU to the CPU in order to import the result of operations executed in the GPU. Notably, recent CPUs have a built-in GPU, and share memory through the built-in GPU structure. However, in most PCs, the CPU and GPU are configured independently for the efficiency of deep learning operations.  
In contrast, embedded systems have a unified memory structure, in which the CPU and GPU share a single memory space, as shown in Figure 3b; thus, data can be shared between the CPU and GPU without transfer. The memory required for deep learning  
Figure 3: Comparison of memory architecture with and without unified memory. (**a**) Memory architecture without unified memory, (**b**) Memory architecture with unified memory.  
operations can be minimized, because the CPU and GPU do not need to create separate memory spaces. In particular, for an embedded system with limited bandwidth for memory size and data transfer, it is necessary to minimize the operational costs required for deep learning operations, using the characteristics of unified memory.  
However, the data structure of conventional deep learning frameworks does not support the unified memory of an embedded system, and thus requires an unnecessarily costly data transfer between the CPU and GPU. To overcome this drawback, our framework, an OpenCL buffer, is generated in the device memory, as shown in Figure 3b, and the memory is shared using the memory mapping and unmapping functions of OpenCL. In other words, the generated buffer is mapped if the OpenCL buffer is to be used in the CPU, while the generated buffer is unmapped if the OpenCL buffer is to be used in the GPU.  
When executing operations in the hidden layers of a DNN, only the OpenCL buffer for the GPU is generated, and used without mapping or unmapping. Due to the fact that the GPU result needs to be returned to the CPU when the final output is returned, it is unnecessary to return the GPU result to the CPU through mapping when the operations are executed in hidden layers. Moreover, the acceleration performance of OpenCL is reduced when the operation result is returned from the GPU to the CPU during operation execution in hidden layers, because the asynchronous operation processing structure of OpenCL cannot be applied. The asynchronous operation processing structure of OpenCL is explained in Section 3.2.2. Also, the performance improvement by unified memory is explained in detail in Appendix A.
## 4 Conclusions  
In this paper, exergy analysis based on both the conventional and advanced technology is utilized to a novel CCHP system based on TC-CCES. Some important conclusions are drawn as follows.  
(1) Based on the results obtained from conventional exergy analysis, the biggest exergy destruction exists in cold storage, followed by compressor 1 and heat exchanger 3. Heat exchanger with supercritical working fluid produces much more irreversible loss than that with gaseous working fluid. The system keeps an overall exergy efficiency of 56.25%.  
(2) More interesting features can be obtained from advanced exergy analysis. Dividing exergy destruction into exogenous /endogenous parts clarifies that the component interactions in the system are not very sound but rather complex. Dividing exergy destruction into avoidable/unavoidable parts  
Figure 8: Effects of compressor inlet pressure: (**a**) conventional exergy analysis results; (**b**) advanced exergy analysis results.  
Figure 9: Effects of ambient temperature: (**a**) conventional exergy analysis results; (**b**) advanced exergy analysis results.  
uncovers the real improvement potential and identifies heat exchanger 3 as the eighth component to be improved, amending the misleading conclusion deduced based on conventional exergy analysis results. The overall system exergy efficiency is 74.79% under the unavoidable condition, indicating a great improvement potential to the CCHP system based on TC-CCES.  
(3) Sensitivity analysis demonstrates that an increase in turbomachinery efficiency and a reduction of pinch temperature in the heat exchanger and cold storage within technological permission benefits the system characteristics in terms of decreasing exergy destruction and increasing system exergy efficiency. Moreover, the efficiency in turbine keeps a higher influence on overall system exergy destruction than compressor efficiency. In addition, a smaller pressure drop in throttle valve 2 and a larger storage pressure are helpful for improving system exergy efficiency, and there exists optimum value through evaluating compressor inlet pressure and ambient temperature.  
The application of advanced exergy analysis to the CCHP system based on TC-CCES provides more valuable and detailed information for the optimization of the system. The advanced exergy analysis can be considered as a meaningful supplement to the conventional exergy analysis.  
Conceptualization, W.S. and Z.L.; methodology, W.S. and Z.L.; validation, W.S. and Z.L.; formal analysis, W.S. and Z.L.; writing--original draft preparation, W.S.; writing--review and editing, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
###### Abstract  
In this paper, a novel approach for raindrop size distribution retrieval using dual-polarized microwave signals from low Earth orbit satellites is proposed. The feasibility of this approach is studied through modelling and simulating the retrieval system which includes multiple ground receivers equipped with signal-to-noise ratio estimators and a low Earth orbit satellite communicating with the receivers using both vertically and horizontally polarized signals. Our analysis suggests that the dual-polarized links offer the opportunity to estimate two independent raindrop size distribution parameters. To achieve that, the vertical and horizontal polarization attenuations need to be measured at low elevation angles where the difference between them is more distinct. Two synthetic rain fields are generated to test the performance of the retrieval. Simulation results suggest that the specific attenuations for both link types can be retrieved through a least-squares algorithm. They also confirm that the specific attenuation ratio of vertically to horizontally polarized signals can be used to retrieve the slope and intercept parameters of raindrop size distribution.  
raindrop size distribution; rain attenuation; low Earth orbit satellites; microwave propagation; polarization +
Footnote †: journal: 2021  
1
Footnote 1: institutetext: Department of Electrical, Electronic and Computer Engineering, The University of Western Australia,  
Perth 6009, Australia; [EMAIL_ADDRESS]
## 3 Model Problems and Numerical Results
### Numerical Results for Model Problem 2
#### 3.4.2 Number of Samples  
Figure 26 shows the number of samples for a high and a moderate stochastic dimension for model problem 2 for a user requested tolerance on the RMSE of \(5\times 10^{-5}\). Similar conclusion can be drawn as for model problem 1. Here, our p-ML(Q)MC simulations only need four levels while h-ML(Q)MC requires at least five.
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 2 Materials and Methods  
### Study Area  
The Sloneczne Lake, an artificial, flowing water reservoir, is located in the western part of Szczecin city within the Gumience district. Created in the 1930s on marshy riverbeds alongside the Bukowa River, the lake forms part of the wider Bukowa River system, which extends through the urban expanses of Szczecin before merging with the Western Oda River [21; 22].  
The catchment area for Sloneczne Lake covers an extensive region of 129 km\({}^{2}\). The land composition of this catchment is mainly residential, accounting for 70% of the total area. The remaining area includes fields and recreational plots (20%), green spaces (5%), and industrial zones (5%). Several points of sewage discharge have been identifiedwithin this catchment, particularly from the fields, recreational plots, and industrial plants, contributing to the inflow of pollutants to the lake [21; 23].  
The lake itself spans approximately 4.8 hectares, stretching about 1.3 km in length, with the deepest point nearing 2 m. The water-retention time in the lake, based on surface runoff, varies between 14 to 33 days [24].  
Soneczne Lake's geographical coordinates are 53 deg25'/29'' N latitude and 14 deg29'54'' E longitude. Its water level is at 16.9 m above sea level, and the total capacity is approximately 59,400 cubic meters. The lake exhibits an average depth of 1.1 m, extends 640 m at its longest point, and measures 90 m at its widest. The coastline stretches about 1320 m [21; 24].  
This unique composition of Sloneczne Lake and its catchment, combined with its significant residential influence and multiple points of sewage discharge, make it an insightful subject for studying water-quality dynamics in an urban [21; 23].