esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis
## 4 Discussion  
Various metrics of the presented algorithm were evaluated and analyzed. The comparison study in Table 8 reveals that the model produces encouraging findings, and these results indicate that it is possible to apply the model in healthcare facilities to support physicians and pathologists. Saving patients' lives can be achieved with support from the proposed method. This method can identify, outline, and segment the potential RoIs appropriately as shown in Figures 7 and 8. Moreover, counting of the number of blood cells takes place in this algorithm. The presented framework's performance was assessed as shown in Table 6; the obtained results show that exquisite findings were reached and considered promising. In addition, this framework was compared against some developed works from the literature. This comparison is reported in Table 8, and it shows that the implemented framework outperforms all models except those from [1; 9]. This assessment demonstrates that the proposed framework shows higher performance than most of the current techniques.  
Numerous experiments were performed in this research to exhibit and validate the attained performance results by the presented framework by modifying and adjusting the applied configurations of the hyperparameters and preprocessing methods. The execution time reduced from 8.5 s per image to 5.536 s, which means the improvement was nearly 34.87%. In addition, the number of the total parameters went down from 63.82 million to approximately 48.44 million; the percentage of this reduction is 24.1%. Moreover, the number of FLOPS decreased by approximately 28%. The performed changes affected the performance quantities positively, since accuracy was raised to nearly 98.76%, precision increased to 97.84%, recall went up to 98.02%, specificity enhanced to 97.47%, F-score was raised to nearly 99.21%, and Jaccard Index and DSC increased to 98.61% and 98.76%, respectively.  
Investigating the effectiveness of data augmentation on the proposed framework was explored. This investigation showed that applying the framework without data augmentation affected its performance negatively since accuracy dropped to 94.11% due to limitations in capturing the complexities between data. Moreover, other metrics went down significantly. The lowest result was around 90.34% for specificity.  
The major limitation/obstacle that this research faced was the availability of all leukemia types as the applied datasets contain only two types, ALL and AML, as stated earlier. In this regard, these datasets are believed to be difficult in many attributes. Firstly, only two types exist with different sizes, which require rescaling for each input. Secondly, rescaling all images takes time, and this results in the elapsed time by the framework. Lastly, these datasets entail multi-class classification on the pixel level of the binary image mode, which forces us to deploy various methodologies to reach the final findings.
# Optical Camera Communication as an Enabling Technology for Microalgae Cultivation +
## 5 Results
### Indoor Experiment
However, it can be seen that the relative intensity contribution for angles above 30 degrees is higher in the borders than the centre (Figure 8). For these angles, the radiation intensifies relatively. This effect is also observed in Figure 9b, where the edge region appears brighter than the centre. This outcome is related to the SPF of the _A. platensis_ and the internal shape of the container. Regarding the SNR, Figure 10 shows the SNR for different viewpoints either when the signal is received from the centre (dashed lines) or the side (dotted lines). The columns represent the biomass concentration, increasing from left to right. The rows represent the image channel, from top to bottom: red, green and blue. Each graph shows the SNR against the camera viewing angle (from zero to 85 degrees). The most evident result extracted from these graphs is that the SNR in the blue channel decreases much faster than in the other channels. Furthermore, the SNR starts to decay at 40 degrees at the edges, approximately 20 degrees earlier than in the red and green channels. This was discussed previously in terms of the light intensity emitted by the surface. Nevertheless, another result can be extracted. The differences between the central  
Figure 10: Red, green and blue SNR for small areas located at the centre and the border of the container. and outer SNRs are reduced progressively as the biomass concentration increases. _A. platensis_ scattering slowly contributes to distributing the optical signal more evenly across the surface. Besides, the difference in power also gradually decreases, at least in the red and green channels. Therefore, slow attenuation combined with increasing scattering is beneficial, mainly because it reduces the gap between the two regions, increasing the effective signal reception area. Finally, regarding the sensing of the biomass concentration and culture's growth state, it is concluded that it slightly depends on the viewing angle. The relative differences in attenuation per angle for each channel as a function of concentration are non-linear. As the concentration increases, the radiant intensity diagram varies slightly in shape, relatively different for each channel. Therefore, to estimate the biomass correctly using only the pixel values inside a given area, it is necessary to consider the viewpoint. However, this radiation's behaviour provides valuable information that can be favourably exploited for more accurate crop parameters' estimation. The larger the variations, the better the estimation will be. In this sense, Case II, which proved to be the ideal candidate for a real deployment, has the added advantage that it analyses the strain from different angles. If all containers are interconnected through pipes and the strain is transferred from container to container periodically, its sensing will be more accurate.
## 2 Materials and Methods
### Technical Discussions on Related Works
#### 2.1.3 Energy-Efficient Wireless Routing Strategies and Protocols
The research contributions are extended for different types of networks, such as generic sensor networks, body sensor networks, personal sensor networks, health sensor networks, surveillance sensor networks, agricultural sensor networks and IoT-based sensor networks [74]. Each existing technique is specifically formed with static assumptions under WMAC and routing policies [76; 77; 78]. More insights into energy-aware routing in WSN, MIMO systems, 4G and 5G networks are also briefly discussed in [79; 80; 81; 82; 83]. However, the need to integrate irregular cross-layer functions in order to monitor individual energy consumptions is a more important quality. These cross-layer solutions and integrated energy plans are not effectively taken into consideration for research practices. With this in mind, the proposed experimental survey has been executed for justifying the solutions against energy issues. The reason for continuous energy depletion is related to listening (active and passive) activities, data transmission policies, data aggregation policies, network deployment strategies, routing policies, WMAC practices and physical problems in WSNs.
###### Abstract  
Role-based access control (RBAC), which has been regarded as one of the most popular access-control mechanisms, is featured by the separation-of-duty constraints, mutually exclusive constraints, and the least-privileges principle. Role mining, a bottom-up role-engineering technology, is an effective method to migrate from a non-RBAC system to an RBAC system. However, conventional role-mining approaches not only do not consider the separation of duty constraints, but also cannot ensure the security of a constructed RBAC system when the corresponding mined results violate the separation of a duty constraint and/or the least-privileges principle. To solve these problems, this paper proposes a novel method called role-mining optimization with separation-of-duty constraints and security detections for authorizations (RMO_SODSA), which mainly includes two aspects. First, we present a role-mining-optimization approach for satisfying the separation of duty constraints, and we constructed different variants of mutually exclusive constraints to correctly implement the given separation of duty constraints based on unconstrained role mining. Second, to ensure the security of the constructed system and evaluate authorization performance, we reduced the authorization-query problem to a maximal-satisfiability problem. The experiments validate the effectiveness and efficiency of the proposed method.  
role engineering; role mining; separation of duty constraints; user authorization query +
Footnote †: journal:  
0000-0001-6697-3899][PERSON]
## 4 Numerical Analyses
### Modelling of the Transformation Process  
The case with 25% clay slurry was simulated first. The velocity and the run-out distance at the toe of the slurry front are presented in Figure 5. In the run-out process, the maximum velocity at the leading edge of the slurry reaches 0.6 m/s in 6 s. Then, the velocity decreases due to the frictional resistance along the interface between the slurry bed and slurry wall. The velocity contour of the run-out at 6 s is shown in Figure 6. Due to the difference between the velocity of the front section and that of the rear section, the thickness of the slurry is continuously reduced during the run-out process, together with its elongation [56]. The body of the slurry interacts with the ambient water. As described above, shearing mainly occurs on the upper front surface between the slurry and the ambient water, as shown in Figure 7. Due to the difference between the velocity of the slurry and that of the ambient water at the stagnation point, shearing occurs at the stagnation point. The shearing force gradually erodes the slurry material from the front to the upper surface. In this process, there is continuous mixing of the eroded slurry and the ambient water, shaping a wake zone with a volume fraction ranging from nil and unity. Based on its definition, the volume fraction of a turbidity current is usually below 0.3. Thus, the transformation ratio \(T\) of the debris flow to the turbidity current can be estimated as follows:  
\[T=A_{0.3}/A, \tag{11}\]  
where \(A_{0.3}\) and \(A\) are the areas with volume fractions smaller than 0.3 and unity, respectively. The change in the transformation ratio \(T\) as a function of time throughout the whole process is presented in Figure 8. The volume fraction of the transformed turbidity current increases in the first 6 s, reaching a maximum value of 40%. The remaining 60% of the original slurry is deposited after 6 s, as seen in Figure 8. The front parts of the slurry with higher migration velocities are almost entirely transformed. In the transformation process, the generated turbidity current with a smaller density is entrained into the ambient water and gradually diffused, as shown in Figure 9.  
Figure 5: Changes in the velocity and run-out distance as a function of time during the run-out process.  
Figure 6: Velocity contour of the slurry at 6 s.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 3 System Model
### Simulation Models
#### 3.7.1 Mobility Model  
The devices' mobility in the D2D-based IoT 5G network could be described through the position values and the devices' speed. The mobility of devices provides information regarding the varying topology and the link failure of the network. The random waypoint model (RWP) has been utilized for mobility models in the simulation of the D2D-based IoT 5G network. It selects a random destination and speed for each device from 0 to \(S_{max}\). The destination point was selected randomly in the network area. Thus, in order to compute the distance between the nodes, the distance between the nodes is calculated.  
Figure 2: Flowchart of the computation route in MBMQA scheme.  
the effect of devices' mobility on the proposed scheme's performance, the parameter values of the devices were considered based on mobility.
# Towards Sentiment Analysis for Romanian Twitter Content
## 3 Data Processing Methodology
### Text Preprocessing  
Dealing with bad language is mandatory in a SA task over microblogging content [10], thus, in this subsection we describe specific text preprocessing efforts done with this respect. As indicated by [PERSON] et al. [50], individualized pre-processing of the tweets is required in order to better exploit the hidden information of the input data. We developed a specialized preprocessing module, containing the following steps, applied in this specific order:  
1. Extra white space removal (language-independent);
2. Custom word lemmatization and tokenization (language-dependent);
3. URL identification and removal (language-independent);
4. Emoji identification and replacement (language-independent);
5. Social media mention identification and removal (language-independent);
6. Extra consecutive character removal (language-independent);
7. Abbreviation replacement (language-dependent);
8. Stop-word removal (language-dependent);
9. Lower case capitalization (language-independent);
10. Punctuation mark removal (language-independent).  
Language-independent steps can be applied in the same manner in both English and Romanian. In contrast, language-dependent steps implies that specific knowledge of Romanian or English is requested.  
For building the BERT-based classifier, we performed only steps 3 and 5, and we performed an additional sentence-level tokenization. Next, we called to the BertTokenizer to obtain the specific BERT encodings.  
All steps, excluding 2 and 4, are commonly used for text preprocessing and were applied in our work in the recommended fashion. In step 2, we instructed [PERSON] [57] to not lemmatize social media-specific tagged words, to not split the tagged word during the tokenization phase, and to not remove the negation element during the stop-word removal.  
Step 4 is critical for our microblogging context, as we deal with emojis, as a sort of Twitter bad language. [PERSON] et al. [60] reports that about 4% of Tweets contain emojis and their sentiment polarity does not depend on the language. In this respect, they constructed the Emoji Sentiment Ranking lexicon containing the 751 most frequently used emojis, each annotated with the sentiment polarity (negative, neutral or positive). Here, we verify whether a token is found in the Full Emoji List ([[https://unicode.org/emoji/charts/full-emoji-list.html](https://unicode.org/emoji/charts/full-emoji-list.html)]([https://unicode.org/emoji/charts/full-emoji-list.html](https://unicode.org/emoji/charts/full-emoji-list.html)), accessed on 3 September 2022) and whether it has an associated sentiment in the Emoji Sentiment Ranking lexicon mentioned above. If true, the token will be replaced with its polarity and a special prefix and suffix.
## 4 Preparation of Fiber Optic OFDR Sensor  
A fiber optic OFDR sensor, which is already fabricated from our previous work [12], is prepared in order to measure the distributed strain of the bearing plate. The schematic diagram of the fiber optic OFDR sensor is shown in Figure 4a. A tunable laser source (TLS, Agilent 8164B, Santa Clara, CA, USA) makes the light launched in a 5:95 coupler (C1). This tunable laser sweeps from 1545 to 1551 nM in which the frequency sweep range is almost 500 GHz. This sensor has two interferometers: a main interferometer and an auxially interferometer (AI). The auxiliary interferometer gives the light frequency spacing information during laser sweeping. A clock signal from this interferometer is generated to remove frequency tuning nonlinearity. The main interferometer is constructed by a 50:50 coupler (C2) with a local oscillator (LO) and a sensing fiber. When the light of the tunable laser source scans the sensing fiber, the Rayleigh backscattering (RBS) is generated and beat with the LO signal. This beat signal is the frequency domain data throught the sensing fiber. Therefore, when this beat signal is processed by inverse Fast Fourier Transform, then the time domain data are obtained. This time domain data has the location information through the sensing fiber line. That is, a data set that divides time domain data into small segments is a bit signal at each position of the sensing optical fiber. Accordingly, by obtaining a difference in correlation between the reference data set and the data after a certain period of time, it is possible to obtain the frequency shift, which is the signal output of the fiber optic OFDR sensor due to a change in temperature or strain applied to the sensing optical fiber. A polarization beam splitter used to reject the polarization effect on the beat signal [13]. The strain coefficient is about 0.14 GHz/micro-strain, and also the temperature coefficient is about 1.45 GHz/degree Cellicius, when fiber optic OFDR is operated with a single mode optical fiber. In this study, the segment size, which is almost the same as the gage length of strain pages, is selected as a 5 cm. Strain data are obtained by moving at 1-mm intervals using distance data corresponding to each segment. With this segment size, theoretical correlation data is obtained at a 2-GHz interval, and these correlation data are fit with the curve to determine a precise frequency shift, which means a strain resolution can be less then 1.4 \(\mu\epsilon\)[14]. The strain measurement accuracy was already studied as less than \(\pm\) 2 \(\mu\epsilon\) from our previous study [12]. This fabricated OFDR system was shown in Figure 4b.  
Figure 4: Fiber optic OFDR sensor. (**a**) Schematic diagram. (**b**) Fabricated OFDR.
# Understanding of Customer Decision-Making Behaviors Depending on Online Reviews
## 1 Introduction  
Numerous reviews are being made and uploaded through the Internet today. This applies not only to tangible products, such as fans and vacuum cleaners, but also to intangible consumer goods, such as concerts and movies. These reviews are generated by consumers and have a huge impact on consumers' purchasing decisions [1, 2]. Consumers perceive reviews as more reliable and engaging than information provided by vendors because they are created by fellow consumers who have already purchased and have been using the products [3]. From these reviews, companies obtain valuable information from the consumers' perspectives of their products [4]. In addition, online reviewing has been integrated by many companies because it is an effective way to not only engage consumers directly with the products, but also influence consumers [5].  
There is a large diversity of reviews due to reflecting consumers' perceptions, and there is usually a variety of consumer perspectives even for a single product. In a related study [6], it was confirmed that the tendency to be affected by review data varies depending on the consumer type of how many expectations are made for the product. Depending on the type of consumer, there are differences, such as being more vulnerable to negative reviews or being affected by the selection. In the case of user types in which the reliability of reviews may be seen as a measure, factors, such as sources, are also affected [7]. From a seller's point of view, naturally, they want their products to have a positive perception. However, it is not advantageous to consumers for only the large number of good reviews to have high visibility, which is often the case. Reviews should be transparent and unbiased.  
User reviews implemented by companies have two main characteristics. The first is the numerical review, such as a one to five-star rating, and the second is the text review, such as a comment. In most cases, star ratings and comments are shown together. Company sites that provide consumer goods with reviews, such as Amazon, Walmart, and Movietickets provide star ratings and comments together. Yet, it is unclear which review component, either star ratings or comments, holds more weight to the consumer. Do consumers prefer consumer goods with high star ratings but poor comments or vice versa? What do consumers think when they look at the star rating to make a purchase decision? This paper explores the decisions that consumers make when presented with both review components simultaneously. Thus, three tasks were devised through our user study to simulate consumers' reading and interpretation of reviews. As a result of the user study, we had the following observations; (1) Differences in the importance of the review components according to the purpose of reading them, and (2) arbitrary interpretation of reviews by consumers that were different from the intention of the reviewers.
# Re-Pair in Small Space +
## 2 Sequential Algorithm
### Algorithmic Ideas
If \(F\) becomes empty, we move to the \((k+1)\)-st round and create a new \(F\) for storing \(f_{k+1}\) frequencies. Otherwise (\(F\) still stores an entry), we can be sure that \(F\) stores a most frequent bigram. In both cases, we recurse with the \((i+1)\)-st turn by selecting the bigram with the highest frequency stored in \(F\). We show in Algorithm 1 the pseudo code of this outlined algorithm. We describe in the following how we update \(F\) and how large \(f_{k+1}\) can become at least. ```
1\(k\gets 0,i\gets 0\)
2\(f_{0}\leftarrow\mathcal{O}(1)\)\(T_{0}\gets T\)whilehighest frequency of a bigram in \(T\) is greater than onedo\(\triangleright\)during the \(k\)-th round
3\(F\leftarrow\) frequency table of Lemma 2 with \(d:=f_{k}\)\(t_{k}\leftarrow\) minimum frequency stored in \(F\)while\(F\
eq\emptyset\)do\(\triangleright\)during the \(i\)-th turn
4\(\texttt{bc}\leftarrow\) most frequent bigram stored in \(F\)\(T_{i+1}\gets T_{i}\).replace\((\texttt{bc},X_{i+1})\)\(\triangleright\) create rule \(X_{i+1}\rightarrow\texttt{bc}\)\(i\gets i+1\)\(\triangleright\) introduce the \((i+1)\)-th turn
5 remove all bigrams with frequency lower than \(t_{k}\) from \(F\) add new bigrams to \(F\) having \(X_{i}\) as left or right character and a frequency of at least \(t_{k}\)\(f_{k+1}\gets f_{k}+\max(2/\beta_{i},(f_{k}-1)/(2\beta_{i}))/\alpha_{i}\)\(k\gets k+1\)\(\triangleright\) introduce the \((k+1)\)-th round
6
7 Invariant: \(i=m\) (the number of non-terminals)
```  
**Algorithm 1**Algorithm outline of our proposed algorithm working on a text \(T\) with a growing frequency table \(F\). The constants \(\alpha_{i}\) and \(\beta_{i}\) are explained in Section 2.3. The same section shows that the outer while loop is executed \(\mathcal{O}(\lg n)\) times.