esa-sceva/satcom-chunk-collection huggingface dataset:
# Shear Wave Elastography Implementation on a Portable Research Ultrasound System: Initial Results
## 4 Discussion
### Phantom Experiments
In spite of that, the SWS maps of the various inclusion types and diameters (Figures 12-14) provided a brief overview of the system capabilities in terms of resolution. For instance, the 6.5 mm type III inclusion could barely be seen in the estimated SWS map due to poor contrast, whereas the type IV and type II inclusions were easily detectable. In addition, along with degrading the spatial resolution, increasing the lateral kernel size could also cause some level of averaging and shift the reconstructed inclusion values toward the SWS value of the background. This was in line with the observations of [PERSON] et al. [28]. This averaging effect could explain the inclusion average SWS results, which varied with respect to the inclusion diameter. This study had some limitations. First of all, for the purpose of clarity, there was no systematic analysis of the push beam design parameters in the evaluated methods, such as focusing depth, the number of parallel beams of focal points or center frequency. Only arbitrarily applied settings were used, which might not have been fully representative of a given technique. However, an analysis of the impacts of those parameters on the SWE imaging quality has been covered in detail in other works. Secondly, only a single homogeneous phantom was used, so the evaluation of the performance of the three methods in the homogeneous experiments was limited. Figure 17: The histograms of the SWS estimates that were obtained within the ROIs in the homogeneous phantom experiments using the various beam generation techniques: (**a**) FCUSE; (**b**) SSI; (**c**) FCUSE-SSI. The red dotted line in each histogram shows the real value of the SWS (as declared by the phantom producer).
## Appendix B Sample implementation guideline for security controls.  
### Auditing and Accountability  
In WBAN applications, it is necessary to keep track of each activity performed by an authorized and/or unauthorized user. Auditing is the process which will keep track of different types of event including password changes; failed log-on, key management, query parameters and file access. This audit record can be used make a user accountable.  
Source:  
NIST 800-53 r5: AU-2, AU-3, AU-5, AU-6, AU-7, AU-8, AU-9, AU-5  
ISO IEC 27002/ISO 27799: 12.4.1, 12.4.2  
Guidelines:  
* Define the list of parameters that will be captured as part of audit records and use a centralized platform to configure and manage these list of parameters (AU-3, 12.4.1)  
user IDs.  
system activities.  
dates, times and details of key events, e.g., log-on and log-off.  
device identity or location if possible and system identifier.  
records of successful and rejected system and other resource access attempts.  
changes to system configuration.  
use of privileges.  
use of system utilities and applications.  
files accessed and the kind of access.  
network addresses and protocols.  
alarms raised by the access control system.  
activation and de-activation of protection systems, such as anti-virus systems and intrusion detection systems.  
records of transactions executed by users in applications.  
Limit the capturing of PHI and/or PHR data in audit records to minimize the privacy risk. If required anonymize the PHI and/or PHR data records before capturing in the audit log (AU-3, 12.4.1).  
Provide a warning to respective roles or owner within an organization when allocated audit record storage volume reaches the maximum audit record storage capacity (AU-5, 12.4.2).  
Provide a real-time alert if the system failed to capture audit record in a time-period (AU-5).  
Implement an automated process to review and analysis the audit log which followed by generating report. Use this report to investigation and response to suspicious activities (AU-6).  
Implement the capability to sort and search audit records for an event based on the content fields of audit records (AU-7).  
Use internal system clocks to generate the timestamp for audit records (AU-8).  
Implement cryptographic mechanisms to protect the integrity of audit records and ensure only authorized users obtain access to these audit records. If required, create an authorized user with read-only permission to audit record (AU-9).  
Initiate session audits including automatically file transfer, user request/response at the system start-up (AU-14).
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 6 Conclusions  
In this paper, we presented and discussed a security framework from the perspective of QoS by using MANET routing protocol, OLSR. The framework aimed to address sinkhole attacks during OLSR routing and to maximize network performance. The architecture of the QoS-aware security framework was presented, and its unique set of components explained with the aim of QoS delivery in video streaming applications. The functions within the framework were explained under their respective interconnected layers. Most approaches to network security do not consider the aspect of QoS, hence our contribution to knowledge by developing a QoS-centric framework that will not only manage the security aspect of the network but also the QoS delivery capacity of the network. The implemented lightweight IDS enhances performance as it is customized to work with the routing process  
Figure 19: Delay for 100 nodes.  
Figure 20: Jitter for 100 nodes.  
of OLSR in the MAC and network layers. A custom flow chart of OLSR was also presented as part of our contribution to QoS-aware adaptive routing. The evaluation results showed the effectiveness of the proposed framework and its strengths. In real life situations, the three scenarios can be likened to three environments (banks, companies or institutions, vehicular highway, smart cities etc.) that are low, medium, and highly populated within a given space size (say 1000 m \(\times\) 1000 m grid in our experiment). The sensor nodes are computers, smart vehicles, smart houses, etc., which communicate to share secret and vital information. The malicious nodes are intruders, hackers, phishers, etc., of the same devices wanting to break into the system by masquerading as a genuine member of the system. Our framework scenario is set up in such a way that irrespective of the environment and density, any masquerading devices are detected and denied access to the network. In all cases of performance measurement, our framework has a higher throughput, reduction in delay to transmit packets, and less jitters enabling sent signals (packets) to be received without distortions. As future work, the ultimate intention is to further customize our framework for high-speed nodes, VANETs, and their routing protocols. We will also consider the battery power of each sensor node and how to manage them for efficient power utilization.  
The authors declare no conflict of interest.
# Characterization of Shear Strain on PDMS: Numerical and Experimental Approaches
## 4 Results and Discussion
### Numerical  
As previously mentioned, the four constitutive models presented were implemented in the numerical analysis. Although all the hyperelastic constitutive models present similarities in their results, it is essential to highlight the main differences. For example, Figure 11 shows the shear strain field determined numerically (Mooney-Rivlin constitutive model) for both simulations (2 mm and 4 mm of PDMS thickness). The steel is much more rigid than PDMS, and for this reason, the values of shear strain in the steel plates are small when compared with the PDMS plate. The values of shear strain in the top and bottom of the PDMS plate are lower than in the central region due to the edge effect.  
Figure 12 shows the evolution of shear strain (Exy) along y-direction for 2 mm PDMS thickness. The average value for shear strain from the Yeoh, polynomial, and Gent models presented the same result, \(-0.09680\). However, the Mooney-Rivlin constitutive model resulted in \(-0.09684\), a difference of \(0.4\times 10^{-3}\).  
Figure 13 shows the results found of shear strain for a 4 mm thickness PDMS. The constitutive models of Yeoh, polynomial and Gent presented the same result, \(-0.02060\). While the Mooney-Rivlin Model gives \(-0.02056\), again showing the same difference as the previous simulation.  
The results of maximum, minimum, and average values of shear strain found in the FEM simulation are shown in Table 2. For the simulation with 2 mm of PDMS, the highest and lowest values of shear strain were found for the polynomial 3 rd Order constitutive model. In the case of the simulation involving 4 mm of PDMS, all constitutive models presented the same maximum and minimum value for the shear strain. In terms of percentage is possible is possible to observe that for simulation with 2 mm of PDMS have an average around 9.968% while for 4 mm of PDMS the average shear strain decreases to 2.056%.  
It is important to emphasize, as shown in Figure 5, the most suitable hyperelastic models are the Mooney-Rivlin and polynomial models. The constitutive model of Gent tends to move away at the end of the curve, which means that the values of shear strain found by the hyperelastic Gent model can be higher than the experimental ones, and some points are slightly above or below the experimental result at the end of the Yeoh curve. However, the results from in the Mooney-Rivlin and polynomial  
Figure 11: Shear strain field obtained by numerical simulation using the Mooney–Rivlin constitutive model. (a) 2 mm and (b) 4 mm of PDMS thickness.
## 1 Introduction  
Climate warming is currently receiving considerable attention and has become a global research focus. Forests are the main vegetation cover in terrestrial ecosystems, and forest productivity and biomass play an important role in global climate change and circulation of substances [1]. Via photosynthesis, forests fix atmospheric CO\({}_{2}\) and accumulate carbon in their biomass, litter, and soil. In addition, due to biological respiration, litter decomposition, soil carbon degradation, and natural or artificial disturbances, forests emit carbon into the atmosphere in the form of CO\({}_{2}\). Although the forest area only accounts for about one third of the total terrestrial ecosystem area, carbon storage in aboveground forest vegetation is equivalent to 80% of the aboveground total organic carbon of terrestrial ecosystems; belowground carbon storage accounts for 40% of that of the entire terrestrial ecosystem [2].  
The Three Gorges Reservoir area (TGRA) of the Yangtze River is a new concept derived from the construction of the Three Gorges Dam Project since 1992. It refers to the region submerged due to the water level rise caused by the Three Gorges Dam (water level 175 m; height of the dam wall 185 m). The Three Gorges Reservoir Area (TGRA) of the Yangtze River is the most ecologically sensitive and fragile area in China with complex terrain and frequent natural disasters, which is located in the transition zone between a mountainous region and watershed region. As a special human activity disturbance, the Land Use/Cover Change (LUCC) was seriously affected by the construction of the reservoir, including the establishment of new towns and cities, migration of millions of people and the extended railway and highway nets [3]. In recent decades, the built-up land increased the most, largely and rapidly, followed by water bodies. They were transformed from croplands, forests and grasslands.  
Against the background of climate change and land use change, the present situation of the forest ecosystem in the Three Gorges Reservoir area, as well as its response to global climate change, has become the focus of a large number of studies both in China and around the world [4; 5; 6]. This study focused on the temporal and spatial variations in forest productivity and carbon storage in the TGRA as well as the corresponding influencing factors. The research results are significant in revealing the extent and scope of human disturbance effects on forest productivity and forest carbon storage and to provide evidence for predicting the response of forest productivity to future climate change scenarios. Therefore, a locally parameterized Biome-BGC model was used to simulate net primary productivity (NPP) and carbon density of the vegetation layer, the litter layer, and the soil layer for various forest types from 1992 to 2012 in the TGRA in order to reveal the changes.
## 5 Thinking modules
### Service and Content Management modules
#### 5.4.1 Monitoring, filtering, storing, discovering and composition module  
This module on the basis of the application protocols (inputs from the Service/Content layers), as well as of the Present Context, is in charge of _jointly_ performing the following fundamental and strictly related tasks:  
* dynamically managing the _advertisement information_ about the _potentially available metadata_: these last are the network/user/service/content data/parameters which could be monitored by means of the sensing functionalities. As a matter of fact, the potentially available metadata need to be advertised and indexed in order to be recovered whenever they are needed. Note that the advertisement information has to be properly exchanged among Cognitive Managers so that any Cognitive Manger can be aware of (i) which metadata are potentially available in the various networks, (ii) which Cognitive Managers handle the potentially available metadata, (iii) how and at which conditions it is possible to actually avail of such metadata;
* dynamically deciding which metadata (among the potentially available ones, which have been advertised as mentioned in the previous issue) have to be _discovered_ and the relevant _filtering_ and _storing_ criteria; then, this module has to accordingly control the discovery, filtering and storingfunctionalities (included in the Metadata Handling block represented in Figure 2), which are in charge of actually discovering, filtering and, possibly, storing the above-mentioned metadata. The best algorithms which the discovery and filtering functionalities have to put into operation, are dynamically selected by the Supervisor module;
* dynamically deciding the criteria for the aggregation of the discovered metadata in order to generate the _aggregated metadata_ which form the _Present Context_ which, as already explained, is the multi-layer, multi-network information serving as key input for all thinking modules. These criteria are put into operation by the composition functionalities included in the Metadata Handling block represented in Figure 2 which generates the Present Context.
## 2 Related Works
### Semantic and Sentiment Analysis  
As texts are unstructured data, we often convert text into numerical or vector forms for generating relevant computations. In general, the conversion of texts into semantic vectors can be divided into word vectors and sentence vectors. There are two types of word vector conversion: (1) One-hot representation and (2) Distributed representation. One-hot representation adopts 0 and 1 to represent a vector, where 1 represents the presence of a specific word. The number of document words represents the dimensionality of the vector. In the text vector of each word, only one dimension is 1, while most of the dimensions are 0. Word embedding is commonly used to convert words into word vectors with Distributed representation [24; 28]. This method is trained from the corpus to build word vectors and preserve word-to-word relationships; the vectors are of a fixed dimension. Common approaches for word embedding are Word2 Vec [29] and GloVe [30]. Word2 Vec builds word vectors based on the continuous bag-of-words (CBOW) model and skip-gram. GloVe considers both local and global information in the lexicon and uses distributed statistics to build word vectors. These two algorithms have been applied in many studies to generate word vectors and perform sentiment analysis [7; 9; 19; 31].  
Sentence vectors can be generated by summing or averaging all word vectors in a sentence to represent the semantic vector of the sentence. [PERSON], [PERSON], and [PERSON] [24] used three filters of different lengths (1, 2, and 3) in CNN to analyze the sentences in an article. The important features of the three filters, i.e., unigram, bigram, and trigram, are then extracted by max-pooling. Finally, these three features of different lengths are averaged for the sentence vector. [PERSON] et al. [32] combined sentiment analysis and genre-based similarity in the collaborative filtering method; it uses Bidirectional Encoder Representation from Transformers (BERT) to extract genres and features from user reviews in the deep-learning-based sentiment analysis in order to improve the recommendation quality of streaming platforms.  
The dictionary method is often used in sentiment analysis to analyze the sentiment features in the text. To quantify the sentiments implied by a text, many studies adopt sentiment dictionaries to identify the sentiment-related words in the text and calculate the sentiment intensity and polarity of the text. Commonly used emotion dictionaries include ANEW [14], SentiWordNet [33], SentiNet [34], and HowNet [35]. The higher the sentiment intensity score, the more positive the sentiment expressed in an article; conversely, the lower the sentiment intensity score, the more negative the sentiment expressed in the article [12]. [PERSON] and [PERSON] [36] applied SentiWordNet for sentiment analysis in product reviews and used averaging and summing to calculate the sentiment intensity scores of words. Their study found that averaging the sentiment word scores in an article derived better analysis results.
## 5 Conclusions  
This paper proposes a model-free ACC speed- and distance-switching prescribed performance control algorithm. The algorithm consists of two parts: speed-prescribed performance control and collision avoidance distance-prescribed performance control. If the follower is far from the leader vehicle, a speed-prescribed performance controller is used to adjust the follower vehicle's speed to the desired predefined speed according to the prescribed range. If the follower vehicle approaches the leader vehicle, the collision avoidance distance-prescribed performance control adjusts the distance between the follower and leader vehicle to keep a safe distance between the two vehicles.  
(1) Regarding the information required for the control algorithm, the existing ACC algorithm must establish an accurate system model, while strictly ensuring safety, and obtain the position, speed, or acceleration information of the leader vehicle. The proposed model-free adaptive cruise control algorithm only requires speed information of the controlled vehicle and distance information from the vehicle in front.  
(2) Regarding the control effect of the control algorithm, the developed algorithm ensures that the vehicle's operating speed and distance strictly change smoothly within the prescribed spatial range, and there is no overshoot phenomenon during the initial state adjustment, emergency braking, and frequent start and stop stages. It can meet the various control requirements of ACC for speed, spacing, and collision avoidance.  
Similar to numerous current methods in robust control, the controller we have developed is not immediately compatible with hardware platforms and requires integration with additional power electronics techniques. Going forward, a complete ACC physical framework will be set up under the control paradigm proposed in this study.  
Conceptualization, J.S.; methodology, P.J.; software, P.J.; writing--original draft preparation, P.J.; writing--review and editing, J.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fundamental Research Funds for the Central University of China (No. Z1090122053).  
Not applicable.  
We thank the anonymous reviewers for their valuable comments and suggestions.  
The authors declare no conflict of interest.
# An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour
## 4 Experimental Results and Analysis
### Qualitative Results  
In this section, we present the results of the 2-Opt++ algorithm after deploying the graph compression technique for all three categories of problems: small, medium, and large. The results of small category problems are provided in Table 4, and the resultant comparison graph is depicted in Figure 7. For comparison's sake, we also present the results of the nearest neighbour algorithm along with the results of the 2-Opt++ algorithm. An important point to be noted is that for all small category problems, our proposed algorithm produced an error margin percentage of below 7%. We would like to mention that only one of the problems, p264, produced a sub-par 13% error margin rate. However, once we added the number of active edges, it provided a much improved error rate of 3.78%. This means that all the problems were successfully managed under an error margin of 7%. In Figures 7-9, we can clearly see that the variance of the error is lower in the 2-Opt++ algorithm as compared to nearest neighbour. We only considered NN for the comparison of larger problem instances (\(>\)1000 vertices) because, for the other algorithms, the resource and computation time requirements were simply huge. There are some techniques available to deal with larger problem instances in the literature, but they are implementation-dependent [27].  
Figure 6: Error comparison between 2-Opt++ algorithm and NN, GA, SA, TS, TPO, and ACO in small TSP instances.  
Figure 8: Error comparison of 2-Opt++ algorithm with NN in medium category.  
Figure 7: Error comparison of 2-Opt++ algorithm with NN in small category.
## 3 Data Source and Analysis
### Using a Neural Network to Build an Early Warning Model
#### 3.2.5 Training the Model  
During the training process, the neural network toolbox divides the samples into the following three parts: training samples, validation samples, and test samples. The variation in the mean square error with the number of iterations is shown in Figure 4. The training result demonstrates good performance for the fourth iteration, with a mean squared error of 0.019765.