esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
## 5 Challenges and Recent Advances
### Improvements in Sensing and Data Acquisition  
Advancements in sensor technologies and data recording systems increasingly aid in higher-efficacy subsurface AE monitoring under adverse conditions. Notable progresses encompass the following:  
1. Micro-electro-mechanical system-based ultra-compact sensors with broad 20 kHz to 1 MHz bandwidths, low self-noise, resonance frequencies up to 1 GHz, and diameters less than 1 mm for dense embedding [112].
2. Fiber-optic-distributed acoustic sensing utilizing Rayleigh backscatter in optical fibers for continuous stroke monitoring over extended lengths, circumventing signal attenuation limitations of traditional point sensors [134].
3. Wireless battery-powered low-power sensor nodes streamlining sensor arrays through autonomy, avoiding cabling and overcoming field installation complexities [135].
4. Cloud-hosted data repositories augmenting computation and storage capacities facilitating advanced pattern recognition, machine learning, and visualization tools, empowering data-driven interpretation [136].  
In general, continuous progress in sensing and data acquisition systems holds the potential to enhance the capabilities of AE techniques for the more accurate monitoring of subsurface defects across large areas, even in challenging conditions. This evolution includes the development of advanced quantification and autonomous methodologies.
###### Abstract  
Smart cities need technologies that can be really applied to raise the quality of life and environment. Among all the possible solutions, Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) have the potentialities to satisfy multiple needs, such as offering real-time plans for emergency management (due to accidental events or inadequate asset maintenance) and managing crowds and their spatiotemporal distribution in highly populated areas (e.g., cities or parks) to face biological risks (e.g., from a virus) by using strategies such as social distancing and movement restrictions. Consequently, the objective of this study is to present an IoT system, based on an IoT-WSN and on algorithms (Neural Network, NN, and Shortest Path Finding) that are able to recognize alarms, available exits, assembly points, safest and shortest paths, and overcrowding from real-time data gathered by sensors and cameras exploiting computer vision. Subsequently, this information is sent to mobile devices using a web platform and the Near Field Communication (NFC) technology. The results refer to two different case studies (i.e., emergency and monitoring) and show that the system is able to provide customized strategies and to face different situations, and that this is also applies in the case of a connectivity shutdown.  
s mart city; emergency management; monitoring; social distancing; neural network; internet of things; wireless sensors network; near field communication technology; computer vision +
Footnote †: journal:  
0000-0001-4500-3399/10/0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-  
preparedness and risk reduction, disaster response, recovery) can be boosted if proper information sharing, which includes different systems and stakeholders, is carried out [3].  
Furthermore, emergency conditions are defined as the presence or the spreading of high concentrations of pollutants (environmental risk), or of diseases and epidemics caused by bacteria or viruses (biological risk). In the first case, thresholds can be determined, and pollutants can be monitored (e.g., using ICT solutions based on fixed and mobile sensor nodes that actively include citizens in the information gathering [4]) to verify that the pollutants' concentrations are under the given thresholds, or to trigger predefined alarms. In the second case, it is crucial to manage the spatiotemporal distribution of the crowds by using technologies that allow for the implementation of specific and well-designed measures, such as physical distancing (or social distancing) and movement restrictions [5], which are well-known strategies to reduce infection and mortality risks [6; 7].
# Cross-Layer and Energy-Aware AODV Routing Protocol for Flying Ad-Hoc Networks
## 5 Performance Evaluations
### Results and Discussion
#### 5.2.4 Packet Drop Ratio Calculation  
Figure 12 shows the PDR comparative graphical output of the proposed work CLEA-AODV method with earlier works such as the AODV, SOC-GSO, and EENFC-MRP protocols. From the graph, it is understood that the proposed work produced very few PDR compared with the earlier works. By using the CLEA-AODV method, the number of dropped packets from the overall simulation was 243. In comparison, the packet lost in the others such as the AODV, SOC-GSO, and EENFC-MRP methods were 694 packets and 524 packets, respectively. The cooperative between GSO-based CH selection methods reduced the energy consumption and delay. As a result of this reduction, it led to an increase in the PSR and reduced the PDR in the network.  
Figure 11: The E2E delay with the number of UAVs.  
Figure 10: The E2E delay with time.  
Figure 13 represents the packet drop calculation of the network with various scenarios in terms of the number of UAVs and simulation time. The drop produced by the CLEA-AODV protocol was very low in all scenarios such as 30, 60, 90, 120, 150, and 180 UAVs compared with the earlier approaches. The drop produced by the produced work ranged from 38 to 45 packets on average in the proposed work, whereas the earlier works ranged from 70 to 130 packets, respectively. The cross-layer approach is the primary reason the CLEA-AODV protocol achieved better outcomes than other protocols. This research presented an efficient CH selection approach that significantly increased the cluster lifetime and decreased the energy usage in FANETs, where multiple types of information transmissions occurred.
# Radiometric Identification of Signals by Matched Whitening Transform
## 4 Results
### Comparisons  
A comprehensive comparison of SVM, CNN and D(eep)NN are reported for six radios in [13]. The correct classification rates are 44.8% (SVM), 82.4% (CNN) and 71.9% (DNN). However, in the absence of accepted benchmarks for radiometric identification, which do not exist, pure numerical comparison are not conclusive. Factors such as the complexity of the algorithm, processing speed, training data size and other assumptions are considered, the comparison is difficult. Even the choice of radios or protocols are not common. The reported training sample size in [13] is 10% whereas it is 1.4% here. More importantly, no carrier recovery step reported. By assuming perfect phase and frequency alignment at the local oscillator, no mitigation for constellation smearing of the kind reported here has been carried out. This is a significant omission. There is also no noise in the system. Dealing with high dimensionality is another factor. The whitening transformation is featureless thus bypassing the dimensionality reduction whereas feature vectors extracted in [10] have 960 dimensions. RF device fingerprinting in the cognitive Zigbee networks shows good accuracy (\(\approx\)90%) but at high SNR (\(\geq\)20 dB) [15]. In [19], the input data are preprocessed as Hilbert spectrum gray-scale images, and achieves acceptable accuracy under moderate SNR levels (Avg 70% accuracy rate for SNR of 15 dB).
## 4 Applications
### Sensors Design  
MWCNTs have been extensively incorporated into electrochemical (bio)sensors' design, regardless of the detection scheme and the target analyte, because of their inherent properties. Their high conductivity, catalytic properties, high surface area, chemical stability, and biocompatibility promote a significant increase in the sensitivity, lifetime, and overall performance of the devices, as concluded in the contribution of [PERSON] et al. [12]. Nevertheless, the authors of [12] also concluded, after analyzing the published data from the 2013-2018 period concerning the new generation of sensors, that further technical developments are still needed in order to lower the cost of production of high quality MWCNTs. Moreover, the lack of comprehensive characterization of the toxicity of MWCNTs was also identified as an issue for the increase of the in vivo MWCNTs based sensors usage.
# A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data
## 4 Experimental Study  
This section describes the evaluation of our approach and comparison with other approaches. We first describe the setup of our experimentation and dataset used. For performance evaluation we measure the response time, maintenance cost and cache hit rates. We conducted the experiments on OpenLink Virtuoso Server 07.10 with 4 xAMD A8-7650K Radeon R7 and 64 bit Ubuntu 16.04.2 LTS and 32 GB of RAM with 7200 RPM. There are numerous datasets available in _Biomedicine_ that originate from different sources. In our experimentation, we utilized two datasets--LinkedCT ([[https://www.w3.org/wiki/HCLSIG/LODD/Data](https://www.w3.org/wiki/HCLSIG/LODD/Data)]([https://www.w3.org/wiki/HCLSIG/LODD/Data](https://www.w3.org/wiki/HCLSIG/LODD/Data))) and DYLDO ([[http://swse.deri.org/dyldo/data/](http://swse.deri.org/dyldo/data/)]([http://swse.deri.org/dyldo/data/](http://swse.deri.org/dyldo/data/))). Both of these datasets are serialized in triple format, we use NxParser ([[https://www.w3.org/2001/sw/wiki/NxParser](https://www.w3.org/2001/sw/wiki/NxParser)]([https://www.w3.org/2001/sw/wiki/NxParser](https://www.w3.org/2001/sw/wiki/NxParser))) to parse these datasets. For query evaluation, we utilized the queries extracted from the logs of LinkedCT and DYLDO [35]. In our evaluation, we have utilized the queries extracted from the public available SPARQL endpoints provided by the Linked SPARQL Queries (LSQ) ([[https://aksw.github.io/LSQ/](https://aksw.github.io/LSQ/)]([https://aksw.github.io/LSQ/](https://aksw.github.io/LSQ/))) datasets.  
The _Linked Clinical Trials (LinkedCT)_ dataset [36] is a Linked Data representation of the open dataset _ClinicalTrials.gov_. The original dataset is published in XML format and the main benefit of its linked representation is that it facilitates SPARQL queries. This dataset contains information about governmental and privately funded clinical trials in approximately 9.8 million triples. This dataset also contains links to external datasets such as _DBpedia_ and _Bio2 RDF.org_ via SPARQL endpoints..  
The Dynamic Linked Data Observatory _DYLDO_[10; 37] monitors the evolution of Linked Data over time. It collects snapshots of the Web of Data using 149 weekly crawls of the Linked Data Cloud. The average size of a snapshot is about 1.35 GB. The data collected during three years adds up to approximately 36 GB. DYLDO includes various well known sources such as _DBpedia.org_, _identi.ca_ and _DBtropes.org_.
# Optimization of Motion Planning and Control for Automatic Machines, Robots and Multibody Systems
###### Abstract  
The optimization of motion and trajectory planning is an effective and usually costless approach to improve the performance of dynamic systems, such as robots, mechatronic systems, automatic machines and multibody systems. Indeed, wise motion planning allows increasing precision and machine productivity, while reducing vibrations, motion time, actuation effort and energy consumption. On the other hand, the availability of optimized methods for motion planning allows for the cheaper and lighter construction of the system. Hence, it is also an effective tool towards a more economically and environmentally sustainable industry.  
Strictly related to motion planning is motion control, and employing a well-tuned control scheme is of primary importance. One the one hand, it allows for precise tracking of the optimized trajectory, thus boosting the achievement of the desired performances. On the other hand, it could compensate for a bad planned motion reference, whenever advanced feedback control schemes are adopted.  
The authors of this Editorial have been involved in several theoretical and experimental studies in these fields of research in recent years, by investigating some novel techniques targeted to different goals. For example, in the field of motion planning, they have proved the benefits of motion planning in the following fields:  
* Precise path tracking in underactuated multibody systems (see e.g., [1]).
* Vibration reduction in flexible link multibody systems (see e.g., [2]).
* Jerk reduction in robotic and mechatronic systems (see e.g., [3]).
* Reduction of energy consumption (see e.g., [4]).
* Optimization of the quality of manufacturing processes (see e.g., [5]).  
In some of the aforementioned applications, the authors have also proved the benefits of feedback control through some original numerical and experimental studies. For example, the following can be quoted:  
* Vibration reduction in flexible multibody systems (see e.g., [6]).
* Precise path tracking in underactuated multibody systems (see e.g., [7]).
* Control of the coordinated motion of hydraulic systems (see e.g., [8]).  
In the light of the increasing use of servo-actuated and servo-controlled systems, the optimization of motion planning and control can be beneficial in an even wider range of mechatronic and robotic systems. To collect and disseminate a meaningful collection of these applications by providing the most recent advances in this challenging research area, this book is proposed, which includes a series of 14 novel research studies that cover different sub-areas, in the framework of motion planning and control.
## 4 Methodology
### General Architecture
#### 4.1.3 Sender  
The sender is the user's OP. The sending data needs to go through the input buffer, data processing (encryption, data slicing, redundant coding, and other operations), and the output buffer, as shown in Figure 3. Tor's data processing and forwarding functions are controlled by libevent's read and write events [44]. When a read event is scheduled, the OP reads data from the input buffer and encrypts it with the session key of the three-hop ORs. The encrypted data will be converted into decimal units of 8 bits to facilitate the subsequent encoding process, and the converted data will be cut into **s** slices. Next, the slices will be redundantly encoded. Considering the actual demand and network quality, the redundancy degree **s** can be adjusted, which is determined by the sender. The detailed slicing and redundancy process is shown in Figure 3. After the data is processed by redundancy encoding, it will be split according to the number of circuits and the cell header field will be updated in real-time. Finally, a complete cell of 512 bytes in length is written to the cell queue for sending. If there is still data in the input buffer, the above operation is repeated until all the data is read. The write event is scheduled to write as many elements in the queue as possible to the output buffer. After that, another write event is called to write the elements from the output buffer to TLS layer encryption and converge them to the middle OR through the m-circuits.  
Figure 3: Sender data processing.
## 2 Materials and Methods
### Hyperparameter Tuning
#### 2.3.1 SMBO Tuning Setup  
In order to find the optimal parameters for the HRT algorithm, we employed Gaussian Process Regression (GPR) [19] as a surrogate model in the context of SMBO. GPR, also known as the Kriging model, is the default surrogate choice for SMBO within the SPOT framework. To drive this optimization process, we chose the Differential Evolution (DE) [20] algorithm as our optimization method.  
Figure 4: Recorded vibration for each data sample in the test set. The X-axis represents the number of the current sample in the test set, whereas the Y-axis is related to the vibration. The vibration data were measured in analog values between 0 and 27,648, related to a range between 0–25 [\(\frac{\text{mm}}{\text{s}^{2}}\)]. The peaks and drops in the time series were intentionally induced by shaking and striking the machine during the measurement process.  
The optimization bounds, along with the default values assigned to the tuned hyperparameters, are outlined in Table 2. Notably, the optimization process is time limited, with a maximum duration of 100 min. It is important to emphasize that this countdown starts only after the initialization of the initial surrogate model design, which in our specific case consisted of 50 data points. Due to the fact that our dataset was very large (almost 1.5 million samples), we used only 2% of the trainings and test set for the HPT. The remaining data were subsequently used for the actual training and evaluation of the tuned and default model. The goal of the optimization is to minimize a combined value of MAE, computation time, and memory usage. These values are weighted differently (MAE: 1, memory consumption: \(1\times 10^{-3}\), calculation time: \(1\times 10^{-3}\)).
# Review of Machining Equipment Reliability Analysis Methods based on Condition Monitoring Technology
## 2 Multi-Source Information during Cutting Process
### Quality Characteristics Information of Processed Products  
The quality characteristic information of processed products is mainly based on a class of quality characteristic data with a slower change frequency. This type of data mainly includes processing dimensions, tolerances, ambient temperature and other information that need to be inspected in the process. There are two main ways to monitor product quality information: Quality control technology based on statistical process control and data-driven product quality monitoring technology.  
Statistical Process Control (SPC) is an important research content in product quality control and design. It can reduce the scrap rate caused by fluctuations in various factors in the process. The actual production process usually uses statistical analysis methods to analyze and monitor the measured data of key processes at the site. Therefore, it is possible to grasp the abnormal fluctuation of product quality and take timely measures to control it. The effect of the control chart is largely determined by the recognition of out-of-control conditions from a pattern recognition perspective. Pattern recognition is an important issue in SPC. In the field of process quality control, many researchers use eight control chart modes to represent different processing states. For example, normal mode (NOR), hierarchical mode (STA), system mode (SYS), periodic mode (CYC), upper step mode (US), lower step (DS), trend up (UT), and trend direction Next (DT) [30; 31; 32; 33; 34; 35; 36]. In the application of control charts for the processing quality of parts, a lot of exploration has been done. Much analysis and research have been done on the causes and physical meanings of various abnormal control chart modes [37].  
Data-driven product quality monitoring methods can generally be divided into direct methods and indirect methods. In the direct method, direct detection of surface quality can be achieved by laser technology, ultrasonic technology, and the fringe field capacitive method [38; 39; 40]. However, direct monitoring methods are very sensitive to the test environment. They require special testing equipment. And these devices are very difficult to install and use. For indirect detection methods, people often use some sensing signals related to the manufacturing process, including vibration, cutting force, noise, acoustic emission, temperature, spindle torque, spindle voltage and power, to indirectly monitor the surface quality of the product.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
###### Abstract  
The 5G cellular network comes with a promise to provide a very high data rate at low latency, which is becoming critical for advancing technologies. Mobile operators are currently deploying the 5G cellular network worldwide. However, because of limited coverage and high susceptibility of the 5G network to obstacles, handoffs from 5G to 4G and vice versa frequently occur, especially when the user equipment (UE) is moving. These handoffs often cause significant delays in data transmission due to packet losses and retransmissions. A promising solution can be to use both 4G and 5G networks simultaneously, which can solve this problem and yield a better throughput. Multipath transmission control protocol (TCP) is an effective solution for this problem, but it requires significant performance evaluation before practical deployment. In this study, we implement an MPTCP testbed based on NS3-DCE that enables to test the performance of MPTCP schedulers and congestion control algorithms (CCAs) in both 3 GPP and non-3 GPP networks. Through extensive simulation experiments in a scenario where a UE simultaneously utilizes both 4G and 5G networks, we found that blocking estimation (BLEST) scheduler implemented with balanced linked adaptation (BALIA) CCA can produce the highest throughput and lowest delay. Finally, we showed how received signal to interference and noise ratio (SINR), congestion window, throughput, and packet losses are interconnected.  
multipath TCP; MPTCP; MPTCP schedulers; MPTCP congestion control algorithms; 4G; 5G +
Footnote †: journal: Sensors  
17 August 2022  
Accepted: 26 September 2022  
Submitted: 3 October 2022
## 5 Validation  
To validate the model, k-fold cross-validation was conducted. K-fold cross-validation is useful to conduct error evaluations in case limited datasets are used, as well as to prevent the overfitting problem [31]. Five cases, from the 180 cases, were randomly excluded from the CBR repository for every verification, and their properties were then entered into the CBR model to verify that the model would retrieve a similar case. Additionally, the cost of each case was compared with the cost of each retrieved case in order to validate the accuracy of the model. In this study, the verification was performed 10 times, and an average of the error rate was used for testing each individual model. Furthermore, multiple regression analysis (MRA) was selected, as it is one of the most widely used methods in statistics [32]. Table 4 presents the error rate for the two analysis methods. The test result shows that the proposed model is able to estimate the replacement cost of windows. The CBR verification produces an error rate value of 26.37%. This value of error variance is 0.69% lower than the MRA, the error rate of which was at 27.06%. This difference would not provide evidence for superiority of the proposed model over the regression model, but would be evidence that the model has an analogous prediction performance to that of the MRA model. Moreover, if the cases are sufficiently accumulated, the error rate range of the model should narrow down further.