esa-sceva/satcom-chunk-collection huggingface dataset:
# FLINT: Flows for the Internet of Things
## 5 Evaluation
### Levels of Interoperability
#### 5.1.2 Device and Network Interoperability  
The lowest level of interoperability that FLINT can guarantee, is device and network interoperability. Device interoperability ensures that _high-end IoT devices_, such as smartphones, can communicate with resource constrained, _low-end IoT devices_[4]. The _Mapper_ interface provides a queue and supports bidirectional communication over multiple network interfaces. Both _low-end devices_ and _high-end devices_ can exchange information using different communication technologies with diverse downlink patterns. Figure 6 shows an excerpt of the complete FLINT configuration that provides device and network interoperability for a _low-end device_ with a Sigfox and LoRaWAN interface. The Sigfox adapter forwards packet \(p\) together with the device's MAC address to the _Mapper_ adapter. The routing scheme is calculated based on the information in the TD. Packets coming from these networks are SCHC compressed and forwarded to the SCHC router. This adapter handles requests to, and responses from, the IPv6 network. The response from the IPv6 network (packet \(f\)) is stored in the queue of the _Mapper_ adapter, since downward traffic for these devices can not flow continuously. Finally, due to a network change, the device transmits packet \(s\) over its LoRaWAN interface. The _Mapper_ adapter will immediately dequeue packet \(f\) and forward it to the corresponding LoRaWAN adapter. Packet \(s\) is also delivered to the SCHC router.  
As illustrated, the _Mapper_ adapter is required to provide _device and network interoperability_.
# Image-Based Malicious Network Traffic Detection Framework: Data-Centric Approach
###### Abstract  
With the advancement of network communication technology and Internet of Everything (IoE) technology, which connects all edge devices to the internet, the network traffic generated in various platform environments is rapidly increasing. The increase in network traffic makes it more difficult for the detection system to analyze and detect malicious network traffic generated by malware or intruders. Additionally, processing high-dimensional network traffic data requires substantial computational resources, limiting real-time detection capabilities in practical deployments. Artificial intelligence (AI) algorithms have been widely used to detect malicious traffic, but most previous work focused on improving accuracy with various AI algorithms. Many existing methods, in pursuit of high accuracy, directly utilize the extensive raw features inherent in network traffic. This often leads to increased computational overhead and heightened complexity in detection models, potentially degrading overall system performance and efficiency. Furthermore, high-dimensional data often suffers from the curse of dimensionality, where the sparsity of data in high-dimensional space leads to overfitting, poor generalization, and increased computational complexity. This paper focused on feature engineering instead of AI algorithm selections, presenting an approach that uniquely balances detection accuracy with computational efficiency through strategic dimensionality reduction. For feature engineering, two jobs were performed: feature representations and feature analysis and selection. With effective feature engineering, we can reduce system resource consumption in the training period while maintaining high detection accuracy. We implemented a malicious network traffic detection framework based on Convolutional Neural Network (CNN) with our feature engineering techniques. Unlike previous approaches that use one-hot encoding, which increases dimensionality, our method employs label encoding and information gain to preserve critical information while reducing feature dimensions. The performance of the implemented framework was evaluated using the NSL-KDD dataset, which is the most widely used for intrusion detection system (IDS) performance evaluation. As a result of the evaluation, our framework maintained high classification accuracy while improving model training speed by approximately 17.47% and testing speed by approximately 19.44%. This demonstrates our approach's ability to achieve a balanced performance, enhancing computational efficiency without sacrificing detection accuracy--a critical challenge in intrusion detection systems. With the reduced features, we achieved classification results of a precision of 0.9875, a recall of 0.9930, an F1-score of 0.9902, and an accuracy of 99.06%, with a false positive rate of 0.65%.  
**Keywords:** feature engineering; deep neural network; convolutional neural network; network security; malicious network traffic detection
# Datamator: An Authoring Tool for Creating Datamations via Data Query Decomposition
## 2 Related Work
### NLIs for Data Visualization  
Over the past decade, various NLIs for data visualization have been explored within the research community [28; 29; 30; 31; 32; 33; 34; 35; 36; 37] and industry [38; 39]. While NLIs provide flexibility in posing data-related questions, inherent characteristics of natural language such as ambiguity and under-specification make precisely understanding user intentions a challenging task. To overcome this obstacle, NLIs are designed to either guide users to provide a more concrete nature language query [31; 35; 40] or untangle ambiguities in the query [30; 36; 41] to capture user intent.  
One approach to parsing natural language uses predefined grammar. Flowsense [40] and Eviza [31] depend on predefined grammar to capture query patterns. Articulate [41] allows people to generate visualizations by deriving mappings between tasks and data attributes in user queries, and the translation of imprecise user specification is based on a natural language parser enriched with machine learning algorithms that can make reasoned decisions. ncNet [42] builds a Transformer model to translate natural language queries to visualization specifications.  
DataParticles [9] is designed to author data stories with animated unit visualizations. In contrast to our system, DataParticles necessitates manual specification of each block of the data story rather than automatically organizing the entire one. Another recent approach that shares similarities with ours is Urania [43], an NLI for exploratory data analysis that interprets the analysis process. Different from Urania, Datamator, to the best of our knowledge, is the first of its kind, providing an authoring tool designed to facilitate datamation design and generation. Our system enables users to effortlessly generate an initial datamation by specifying a data query in natural language and refining it through interactive editing. The model integrates a knowledge calibration network to enable the model output to be continuously updated based on user feedback.
## 3 Results and Discussion
### RNA Degradation  
Genes encompassed within the GO term \"RNA degradation\" were significantly enriched in the DAVID analysis (Figure 2). These genes, which encode proteins that are components of the RNA degradosome, were differently regulated by HXT. Thus, _lp_0792_ or _enoA1_ (enolase), _lp_2122_ (RNase J) and _lp_2278_ or _rhe3_ (RNA helicase highly homologous to the CshB RNA helicase from _Bacillus subtilis_) were downregulated whereas _lp_1898_ or _pfk_ (6-phosphofructokinase), _lp_0842_ or _ppk_ (polphosphate kinase) and _lp_0728_ or _groEL_ (chaperonin GroEL), were upregulated. The RNA degradosome is believed to degrade RNA with high efficiency [31]. This protein complex has been suggested as a mechanism potentially degrading oxidized RNA [32], but it is also involved in maintaining the typical half-lives of RNA transcripts so that relatively low amounts of oxidized RNA molecules should be degraded to maintain a functional RNA repertoire. In view of this, downregulation of _lp_2122_ (RNase J) and _lp_2278_ or _rhe3_ (RNA helicase) may have helped to adjust transcript abundance to adapt _L. plantarum_ to HXT stress whereas upregulation of genes coding for accessory proteins of the degradosome (_pfk., groEL_) may have stabilized the protein complex and remove poly-P (_ppk_), which is inhibitory for RNA degradation.
## 4 Experimental Results and Analysis
### Modeling of Initial Health Assessment Model Based on MSET
#### 4.1.3 Data with the Same Fault Degree at Different Speeds  
Input data with fault 1 and data with fault 2 under four speed conditions into the health assessment model, and the output deviation distance and the evaluated health score are shown in????:  
Figure 7 shows that the calculated deviation distance of data with fault 1 under four operating speeds of 1730 r/min, 1750 r/min, 1772 r/min and 1797 r/min is concentrated around 80, based on which the health score calculated is 86. Figure 7 shows that the calculated deviation distance of fault 2 under four operating speeds of 1730 r/min, 1750 r/min, 1772 r/min and 1797 r/min is concentrated around 126, based on which the health score calculated is 67. The data of the same fault evaluation remain consistent under different speed conditions. It is verified that the deviation distance calculated by the health  
Figure 5: (**a**) Deviation distance of 3 kinds of faults at 1730 r/min; (**b**) Health score of 3 kinds of faults at 1730 r/min.  
Figure 6: (**a**) Deviation distance of data with fault 1 at different speeds; (**b**) Health score of data with fault 1 at different speeds.  
assessment model is little affected by the velocity condition, and MSET can reduce the influence of the speed condition.
## 3 Examples of New Empirical Laws from \(y_{2\text{A}}(x)\)
### Nuclear Magnetic Resonance Soundings  
The application of nuclear magnetic resonance (NMR) is widely accepted in many fields (e.g., medicine), and its application and validity for the characterisation of geological media have increased in recent decades. Apart from the technological advances that have allowed its implementation, NMR values allow estimating some properties of the subsurface, such as porosity and permeability. The most relevant formulation is the one that allows an estimation of medium permeability from the free porosity \(\mathcal{O}_{\text{mr}}\) obtained using NMR, \(k_{\text{nmr}}=\text{C}\cdotp_{nr}\cdot\text{T}_{1}^{2}\), where C is an adjustment factor, \(\text{T}_{1}\) is the NMR relaxation time, and \(p\) is an exponent with two possible values. These two values come from the fact that the relationship \(k_{\text{nmr}}(\mathcal{O}_{\text{,T}1})\) has two distinct sources: (1) a theoretical-empirical origin [28], which concludes that \(p=1\); and (2) a purely empirical character [29], which concludes that the best fit is obtained for \(p\approx 4\). Clearly, the selection of either exponent value radically alters the obtained results; thus, the values of C vary by several orders ofmagnitude, which greatly reduces the application of the NMR technique for estimating permeability.  
Under the hypothesis that this exponent is a characteristic parameter of each geological medium, [PERSON] et al. [6] resolved this limitation by developing an empirical relationship to obtain this exponent in unconsolidated detrital media. The exponents that yield a minimum deviation with respect to the directly measured hydraulic data were obtained for each aquifer using the empirical data of 23 magnetic resonance soundings measured in four detrital aquifers of four continental basins. Because the \(p\) values do not correlate well with any of the parameters provided by the NMR technique, a new variable \(\zeta_{\text{M}}\) was defined: \(\zeta_{\text{M}}=\overset{\text{h}}{\text{mr,M}}\text{T}_{1,\text{M}}^{2}\), where \(\mathcal{O}_{\text{mr,M}}\) and \(\text{T}_{1,\text{M}}\) denote the average \(\mathcal{O}_{\text{mr}}\) and \(\text{T}_{1}\) values in each aquifer, respectively, and \(h\) is an adjustment coefficient close to 1. \(\zeta_{\text{M}}\) provides a high correlation (\(R^{2}=0.95\)) with the empirical data, and the fitting to a monotonic function reaches a maximum for \(h=0.87\). The developed function of double asymptote (Figure 4) presents a mean deviation of 1.5%. The function of double asymptote obtained thus provides the expected asymptotic values, which would not have been otherwise obtained using linearisable functions. The determination of \(h\) and the coefficients of the developed relationship in other geological media will allow this relationship to be applicable to any porous lithology; thus, it could be considered a general empirical law or its application range is limited to unconsolidated detrital formations.
## 3 Approximation of the IRM Hit Ratio for FIFO
### Precision of the FIFO Approximation for Zipf-Distributed IRM Requests  
We compare the exact FIFO hit ratio analysis with the approximation for cases of Zipf-distributed IRM requests [31] with \(p_{k}=\alpha\,k^{-\beta}\) for \(k=1,\ldots,N;\alpha=1/\Sigma_{k}\,k^{-\beta}\). Zipf-distributed request pattern has been reported manifold in content distribution on the Internet for the popularity of files, videos, etc. [31; 32; 33]. Figure 2 shows results for \(\beta=1\) and for varying object catalogue sizes \(N=10,\ldots,10^{6}\), with cache sizes \(M\) over the entire range [\(1,\ldots,N\)]. The largest deviations of up to \(1.7\%\) are encountered for the smallest \(M\), \(N\). For large \(N\), \(M\), the deviations are often tiny, going down to the range of \(10^{-6}\)-\(10^{-7}\), where asymptotical exactness can be expected due to a statistical multiplexing effect. As a general trend, we experience the deviations to decrease with the variance of the popularity distribution. Thus, for independent Zipf-distributed requests, the deviations are increasingwith the shape parameter \(\beta\) and decreasing with \(M\), \(N\).  
Figure 2: Deviations of FIFO approximations of Equations (4) and (5) from exact results of Equations (2) and (3).
# Reversible Data Hiding for DICOM Image Using Lifting and Companding
## 6 Performance Evaluation
A robust Multi-Carrier-Code-Division-Multiple-Access (MC-CDMA) based fingerprinting scheme against time-varying collusion attack, which is analogous to fading, is proposed in [37]. The algorithm uses multicarrier approach for codeword generation (i.e., Hadamard-Walsh codes), time-varying channel response for colluder weight evaluation, and the Maximal Ratio Combining (MRC) detector [36]. It is quite reasonable to accept fading operation as collusion-like as colluders would develop an average watermarked DICOM frame through variable weights instead of equal weight to remove their identities. To simulate collusion operations, five different watermarks (each watermark (\(W_{i}\)) is a pseudo-random binary sequence generated based on secret Key (K)) are embedded in host DICOM images and five different watermarked images are obtained. In the present scheme, we have tested the anti-collusion performance of the proposed reversible watermarking algorithm by transmitting first (in fact it is a random choice from the total set) four (e.g.) watermarked DICOM frame using MC-CDMA [35] through Rayleigh fading wireless channel. Transmission is done at different Signal-to-Noise-Ratio (SNR) values changing from 50 dB to 100 dB. In the present test, we keep SNR value to very high as DICOM images are very sensitive to noise than traditional images. The resultant received watermarked \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Wavelet Type**} & \multicolumn{8}{c}{**Payload (in Bits)**} \\ \cline{2-10}  & \multicolumn{2}{c}{**2500**} & \multicolumn{2}{c}{**10,000**} & \multicolumn{2}{c}{**40,000**} & \multicolumn{2}{c}{**62,500**} & \multicolumn{2}{c}{**65,536**} \\ \cline{2-10}  & **M** & **P** & **M** & **P** & **M** & **P** & **M** & **P** \\ \hline haar & 1 & 71.42 & 0.9998 & 65.31 & 0.9997 & 59.33 & 0.9996 & 57.40 & 0.9995 & 57.17 \\ db1 & 1 & 71.33 & 0.9998 & 65.36 & 0.9997 & 59.33 & 0.9996 & 57.35 & 0.9995 & 57.17 \\ sym2 & 1 & 70.25 & 0.9998 & 63.52 & 0.9996 & 56.57 & 0.9994 & 54.70 & 0.9994 & 54.56 \\ cdf1.1 & 1 & 71.29 & 0.9998 & 65.33 & 0.9997 & 59.27 & 0.9996 & 57.34 & 0.9995 & 57.16 \\ bior1.1 & 1 & 71.35 & 0.9998 & 65.33 & 0.9997 & 59.28 & 0.9996 & 57.32 & 0.9995 & 57.18 \\ rbiol.1 & 0.9998 & 65.34 & 0.9993 & 59.31 & 0.9988 & 53.29 & 0.9981 & 51.34 & 0.9979 & 51.11 \\ bs3 & 0.9999 & 67.26 & 0.9997 & 61.21 & 0.9994 & 55.25 & 0.9991 & 53.29 & 0.9990 & 53.09 \\ coif1 & 0.9994 & 60.79 & 0.9976 & 54.53 & 0.9962 & 49.23 & 0.9939 & 47.21 & 0.9931 & 46.95 \\ rbs3 & 0.9995 & 60.14 & 0.9981 & 54.49 & 0.9970 & 49.20 & 0.9952 & 47.24 & 0.9947 & 47.02 \\
9.7 & 0.9998 & 63.17 & 0.9990 & 56.61 & 0.9981 & 50.06 & 0.9972 & 48.20 & 0.9970 & 48.01 \\ r9.7 & 0.9998 & 65.22 & 0.9994 & 59.02 & 0.9988 & 52.36 & 0.9983 & 50.50 & 0.9982 & 50.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of various lifting based wavelets and variation of PSNR (dB), MSSIM for different payload size (Lung image). M: MSSIM; P: PSNR (in dB).
## 3 Results
### Correlation Charts of Fluxes and Vegetation Indices  
A correlation matrix of midday and daily aggregates of fluxes with all vegetation indices is shown in Figure 4. Correlations were evaluated for the wet phase (Figure 4, blue line), dry phase (red line) and the entire growing season. The good relationship among most vegetation indices, as previously mentioned, is confirmed by the high correlation coefficients. Only MNDWI and NDSVI were poorly correlated with the other vegetation indices.  
Figure 3: Temporal profile of vegetation indices calculated in this study (see Table 2). Different symbols represent different years.  
Figure 4: Correlation charts between fluxes and vegetation indices. Values in black, blue and red represent Pearson correlation coefficients for a single fit, the wet phase (VPD \(\leq\) 1500 Pa) and the dry phase (VPD \(>\) 1500 Pa), respectively. The remark \"_24\" is used to distinguish daily average fluxes from midday average fluxes. \", \", \" indicates correlations significant at \(p<\) 0.05; \(p<\) 0.01 and \(p<\) 0.001, respectively.  
On the correlation charts, only separate fit lines are shown (wet and dry phases) in order not to make the graphs overloaded (left bottom of the matrix). However, correlation coefficients were computed also for the whole season (values in black in the matrix). Low correlation coefficients were obtained for the whole season whereas the consideration of the two separate phases in the growing season resulted in higher correlation coefficients.  
All the greenness-related VIs (NDVI\({}_{\text{PV}}\), NDVI, EVI, SAVI and GNDVI) and LSWI gave higher correlation coefficients with GPP and midday NEE in the wet phase compared to the dry phase. In contrast, for daily NEE aggregates, the VIs, except GNDVI, gave a better correlation coefficient during the dry phase. MNDWI gave high correlation coefficients during the dry phase for GPP and NEE, both for midday and daily aggregates. NDSVI gave better correlation coefficients during the wet phase than the dry phase for GPP and NEE with both aggregates, but generally lower than correlation coefficients observed with other VIs.
## 3 DePaaS: Architecture and Design
### Functional Description  
A functional description of the DePaaS platform is shown in Figure 1. It was developed to describe DePaaS to industry practitioners.  
DePaaS shall be hosted on a public cloud as a publicly consumable service. It shall consist of a set of SDP models that can be accessed by software development teams using a front-end application. SDP models are built on feature sets from public datasets and cross-project/cross-version/cross-release defects data held on DePaaS. SDP models offer several AI based prediction techniques including-machine learning or search-based or hybrid techniques [8] and produce results of varying accuracy.  
A model selector algorithm shall help selecting the most suitable SDP model matching the project on hand and such an algorithm might resemble the meta-learning solution proposed by [PERSON] et al. [18]. DePaaS shall also have a model tuner application that shall help fine-tuning parameters of the chosen prediction model and to train-the model on a new dataset in near-real-time.  
Software project teams, with the help of the front-end application, upload the meta-data of their current project including values for select software metrics. The front-end application hand holds the software development team through the SDP process which produces various outputs such as list of defective modules, module wise count of defects, severity of defects, etc. These results are offered to software development teams and are also stored in the DePaaS database.  
DePaaS shall also contain SDP model improvement application that could be built to continuously improve the performance of DePaaS including the SDP model collection. It shall enable evolutionary learning within SDP techniques used in DePaaS and its continuous improvements shall help users to get the best performance over time.  
As this schematic was developed for the consumption of software practitioners, other users such as SDP Model Provider, Dataset Uploader, DePaaS Admin, etc., are not depicted in this diagram.
## 4 Results and Discussion
### Performance Evaluation
#### 4.2.4 \"Hall\" Video Sequence
Figure 5 shows that DIVCOM and PDIVCOM outperform the Intra H.264 codec in coding efficiency and PSNR gain for all quantization points. DIVCOM achieves a better coding efficiency ranging from 62 kbps to 94.4 kbps, while its PSNR performance also improves and with a gain of around 0.06 dB. The graph analysis indicates that PDIVCOM achieved a bit rate saving ranging from 61.16 kbps to 91.73 kbps over Intra H.264 for all quantization points. In terms of PSNR gain, PDIVCOM outperforms Intra H.264, by 0.04 dB to 0.12 dB.
# Privacy and Security in Digital Health Contact-Tracing: A Narrative Review
## 3 Results
### Perceived Benefits and Usefulness
#### 3.5.3 Effectiveness  
Effectiveness refers to the degree to which something achieves its intended outcome. It tells how well a process fulfils its purpose. In this context, effectiveness reflects how the contact-tracing apps act to stop the pandemic by timely identification and notification of the infected individuals.  
A study conducted in the USA strongly emphasised the importance of the effectiveness of digital contact-tracing, with 42% of users ranking the platform's ability to accurately identify potential contacts as the most crucial characteristic of a contact-tracing tool [26]. In a study conducted in France, the perceived effectiveness of the application was positively associated with the use of contact-tracing apps [80].  
In a study conducted in New Zealand, less than half of the respondents agreed that the contact-tracing app was speedy or operated efficiently [84]. Among those who did not download the contact-tracing app (53%), the most frequent reasons that would have motivated them to download it were the desire for more evidence of its effectiveness (23%) [27].  
In the UK, among the 27.4% of participants who did not intend to download the app, one of the most prevalent reasons was the belief that the app would not be effective [28]. Similarly, the same reason was cited as one of the two main reasons for non-adoption in Germany [43; 118]. The primary reason for not using it was the belief that it was neither effective nor useful (17.6%) [100]. The belief that the contact-tracing app was not effective was cited as a reason for not downloading or lack of download intention in several countries. [18; 22; 54]. Reasons for being unwilling to use a contact-tracing app included concerns about app efficacy, ranking among the top ten reasons [61]. Regarding perceived efficacy, the contact-tracing app was considered ineffective, primarily due to its low adoption rate [25]. In one of the UK studies, the main concerns about contact-tracing systems were often related to the need for other people to use it rather than privacy concerns [93]. Further, a study conducted in nine European countries revealed that people expressed doubts regarding the ability of existing public health systems to effectively accommodate digital contact-tracing technologies [102].