esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Level Attention-Based Categorical Emotion Recognition Using Modulation-Filtered Cochleagram
## 4 Experimental Results and Analysis
### Dataset Description and Evaluation Metrics  
In this study, the IEMOCAP database is used in the experiment for categorical emotion recognition. Only four emotional categories are used in this database: happy, sad, angry, and neutral. Since the speech from scripted data may contain an undesired relationship between linguistic information and the emotion labels, we only use the improvised data. We calculate MCG features from the speech signal within the IEMOCAP database and split those MCG features into 2 s segments. Each segment split from one sentence retains the same emotion label of the original sentence. The 2 s segments are performed during the training stage, while the entire sentences are used for evaluation during the testing stage.  
Figure 7 illustrates the data distribution, revealing the following counts for each category: neutral (1099), happy (947), angry (289), and sad (608). Because the class distribution of the IEMOCAP database is not balanced, the number of utterances belonging to happy/neutral is more than 3-times that of angry. In this paper, unweighted accuracy (UA) is used as the performance metric of the proposed model to avoid bias towards the larger class. The calculation formula of UA is as follows:  
\[acc_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}}\times 100\%, \tag{15}\]  
\[UA=\frac{1}{E}\sum_{i=1}^{E}acc_{i}\times 100\%, \tag{16}\]  
where \(TP_{i}\) and \(FP_{i}\) represent the true and false positive of each category, \(acc_{i}\) represents the accuracy of each category, and \(E\) represents the number of emotional categories.  
Additionally, the \(F1\) score is also calculated to evaluate the performance of the model, as it takes into account the harmonic mean of precision and recall, providing a comprehensive measure of accuracy. The calculation formula of \(F1\) score is as follows:  
\[F1=\frac{1}{E}\sum_{i=1}^{E}\frac{2 TP_{i}}{2 TP_{i}+FP_{i}+FN_{i}}\times 100\%, \tag{17}\]  
where \(FN_{i}\) represents the false negative of each category.  
Figure 6: Flowchart of multi-level attention module. The shape of feature maps is denoted by [W, H, T, C].
## 4 Discussion
One limitation of this study is the limited sample size and its retrospective nature. In addition, we used the CPR impairment on [15O]water PET as a surrogate parameter for the risk of stroke, instead of assessing the factual occurrence of strokes. It is important to emphasize that we pragmatically rated the CPR on [15O]water PET using a 4-point score and arbitrarily decided to binarized each ACA/MCA territory regarding severe CPR impairment (visual CPR score of \(\geq\)3) or a normal/not severely impaired CPR (visual CPR score \(<\) 3). This binarization was performed to provide comparability to the statistics of the study of [PERSON] et al. However, a stepwise linear regression analysis with a 4-point-scored CPR as an independent variable (without binarization) yielded similar results. The aim of this evaluation was to show the strong correlation between the MARS-MMA score and the CPR. The MARS-MMA grades currently generated from the MARS-MMA scores should therefore be seen as a preliminary suggestion for further prospective studies. Another limitation is that, due to the limited sample size, we were unable to validate the MARS-MMA score in an independent control cohort to avoid overfitting. Future studies should validate the results in a larger number of patients and evaluate the extent to which the score estimates the true risk of stroke by prospectively comparing the initially attributed score and grade with the occurrence of future TIA and strokes. Figure 5: Flowchart of MARS-MMA score calculation and recommendations for therapeutic and diagnostic procedures for each of the 3 grades. Note: All suggested clinical decisions must be correlated with the clinical findings for final decision-making in each case.
## 3 Experiment Design  
### Procedure  
We evaluated three input interactions in four sessions (two scenarios with two AV actions) and one NDRT for (N = 32) participants within-subject in a driving simulator. Our goal was to evaluate the use of gaze-head input compared to two input interactions (touch and voice inputs) while drivers perform an NDRT in the simulated AV as shown in Figure 1a. Participants range in age from 26-35 years (_Age_: \(M\) = 29.1; \(SD\) = 3.96; _Gender_: _Male_ = 19, _Female_ = 13) and all had a driver's license, which guaranteed that they were aware of our tested driving scenarios. Participants were recruited from our university and have a similar education level. They were recruited with a counterbalancing design (\(3\times 4\)) between the three input interactions and the four automated driving actions considering the learning effects. They were informed about the experiment, completed a consent form, then answered preliminary questions regarding personal information, including to self-report mobility problems related to the experiment design; no participants reported problems in moving their eyes, nodding head, talking, or touching. Calibration for eye-tracking was performed prior to a practice session to familiarize them with all three input interactions. After that, the main experiment was conducted and interim-questionnaires were completed after each session. After completing the final interim-questionnaire of the last session, a post-questionnaire was provided to collect participant's preferences of input interactions and how their preferences effected by the engagement of the NDRT combined with open-ended opinions. Participants were compensated with approximately $10 after completing the entire experiment.  
Figure 1: Experiment setup (**a**) Simulation environment (motion platform, full windshield head-up display (HUD); gaze-head input interaction (eye-tracker); touch input interaction (touch panel); voice input interaction (microphone); participant reading from a smartphone (non-driving related task) while sitting in the autonomous vehicle (AV) simulator motion platform); (**b**) destination and 2D user interface map implemented in Unity that shows two driving scenarios and four AV actions (two actions for each scenario).
## 3 Results and Discussion
### The Splitting Tensile Strength and Tension-Compression Ratio of AN-LAC  
As shown in Figure 6a, the splitting tensile strength of AN-LAC at 28 d ranged from 4.08 to 5.27 MPa. With the decrease in the average particle size of NSLA, the splitting tensile strength of AN-LAC increased. AN-LAC-1 exhibited the highest splitting tensile strength among all groups, whereas AN-LAC-3 displayed the lowest strength at 3 d, 7 d, and 28 d.  
In single-particle size gradation groups, the splitting tensile strength of AN-LAC-1 and AN-LAC-2 at 28 d was 22.6% and 9.5% higher than that of AN-LAC-3, respectively. In combined-particle size gradation groups, AN-LAC-6 with larger average particle sizes showed a relatively low strength. The splitting tensile strength of AN-LAC-4, AN-LAC-5, and AN-LAC-7 at 28 d was 12.9%, 5.7%, and 7.5% higher than that of AN-LAC-6, respectively. This phenomenon could be explained by the fact that NSLA with a smaller particle size was important for AN-LAC to have a reliable particle size distribution and possess excellent packing, thereby increasing the splitting tensile strength of concrete [38]. In addition, NSLA with a smaller particle size possessed higher cylinder compressive strength. Thus, the splitting tensile cracks of specimens could bypass a NSLA having a small particle size and more complex and tortuous paths more, improving the splitting tensile strength [39].  
Moreover, the ratio of splitting tensile strength to compressive strength, abbreviated as the tension-compression ratio, is an important index that reflects the toughness of concrete. A lower value of this ratio indicates the greater brittleness of concrete. The regression equations of the tension-compression ratio of high-strength concrete and ceramiste concrete, which were based on the conclusions of ACI 363R-92 [40] and [PERSON] et al. [41], were used to fit the experimental data of AN-LAC. It was found that the fitted values were lower than the experimental values and the correlation was weak, indicating that AN-LAC possessed a relatively high toughness. Based on their conclusions, the regression equation for the tension-compression ratio that was applicable to AN-LAC was proposed. Equations (5) and (6) were used to fit the experimental data of single-particle size gradation groups and combined-particle size gradation groups, respectively. Figure 6b exhibited an excellent correlation:  
\[f_{\text{ts}} =0.03f_{\text{cu}}^{1.27} \tag{5}\] \[f_{\text{ts}} =0.28f_{\text{cu}}^{0.71} \tag{6}\]
# High-Sensitivity Displacement Sensor Using Few-Mode Optical Fibers and the Optical Vernier Effect
## 4 Results and Discussion
### Harmonic OVE Analysis
To verify the high repeatability of this behavior, five additional MZFI concatenations were performed with \(F\!M\!F_{R\!f}\) lengths longer than \(F\!M\!F_{Sen}\), followed by reversed lengths with \(F\!M\!F_{R\!f}\) shorter than \(F\!M\!F_{Sen}\), as detailed in Table 4. The results are plotted in Figure 14. To ensure consistency and uniformity in analysis, all devices were tested over a displacement range of 0 to 3 mm, with increments of 1.5 mm. The devices were labeled according to the observed envelope displacement. Labels in Figure 14a,c,e,g,i correspond to configurations where \(F\!M\!F_{R\!f}\) lengths are greater than \(F\!M\!F_{Sen}\) lengths, and the red arrows indicate a Blueshift. Conversely, labels in Figure 14b,d,f,h,j correspond to configurations where \(F\!M\!F_{R\!f}\) lengths are shorter, and the blue arrows indicate a Redshift. It should be noted that slight variations in arm lengths cause phase differences that generate complex interference patterns, thereby affecting the regularity of the envelope curve without impacting the accuracy of the displacement measurement, as observed in Figure 14. \begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **Experiment** \\ \end{tabular} & **Label in Figure 14** & \
\begin{tabular}{c} \(F\!M\!F_{R\!f}\) \\ **(cm)** \\ \end{tabular} &
\begin{tabular}{c} \(F\!M\!F_{Sen}\) \\ **(cm)** \\ \end{tabular} \\ \hline \multirow{2}{*}{1} & (a) & 17.5 & 18.5 \\  & (b) & 18.5 & 17.5 \\ \hline \multirow{2}{*}{2} & (c) & 26.5 & 27.5 \\  & (d) & 27.5 & 26.5 \\ \hline \multirow{2}{*}{3} & (e) & 34.5 & 35 \\  & (f) & 35 & 34.5 \\ \hline \multirow{2}{*}{4} & (g) & 41.5 & 42.5 \\  & (h) & 42.5 & 41.5 \\ \hline \multirow{2}{*}{5} & (i) & 50.5 & 51.5 \\  & (j) & 51.5 & 50.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Main parameters of the five devices used to demonstrate Blueshift and Redshift. Figure 13: Sensitivity of the first four harmonics of the first device: (**a**) first harmonic; (**b**) second harmonic; (**c**) third harmonic; (**d**) fourth harmonic.
## 3 IEEE 802.15.6: The Key-Enabling Standard for WBANs
### IEEE 802.15.6 MAC Layer
#### 3.3.2 MAC Access Mechanisms and Techniques  
The standard provides greater flexibility on the users to adapt MAC according to their requirements. With regards to medium access mechanisms, WBANs Coordinator can decide to operate in one of the following access modes:  
* _Beacon mode with superframe boundaries_: A beacon is transmitted at the beginning of every beacon period, as shown in Figure 9. In this way a common time base is established, to enable time referenced allocations. There are two Exclusive Access Phases (EAP; EAP is added in this standard to support the high priority and emergency traffic.) and two Random Access Phases (RAP, used for regular non-emergency traffic.), which can be configured together based on the application requirements as EAP1 and EAP2 respectively. For the medium access only contended allocation is possible which means either CSMA/CA or Slotted Aloha can be used during EAP, RAP and CAP periods. Whereas, during the managed access phase (MAP) there are scheduled, unscheduled and improvised access options as shown in Figure 10.
* _Non-beacon mode with superframe boundaries_: In this mode beacons are not transmitted but superframe and allocation slots boundaries are established which provides time referencing to the medium access. Figure 11 shows the layout of the non-beacon mode with Superframe boundary, where it can be seen that only managed access is possible in this mode
* _Non-beacon mode without superframe boundaries_: In this mode beacons are not transmitted, superframe and allocation slots boundaries are not established because there is no time reference involved in accessing the medium. Figure 12 shows the non-beacon mode without superframe medium access. It is based on unscheduled access with type-II polled uplink and post allocation for downlink by using CSMA/CA mechanism.  
Figure 10: Allocation Interval and Access Methods Available in Managed Access Phase [9].  
Figure 9: Layout of Access Phase in a Beacon Period (Superframe) for Beacon Mode [9].
## 3 Results and Discussion
### Is MWCB-Phylase Promising for Monogastric Animal Feed?  
Bacterial display is a technology for expressing proteins linked to a biological surface that offers various advantages, such as high yield and productivity [61, 62]. Several proteins have been displayed on bacteria, which have exhibited potential as cell-based systems for applications in biotechnology and biomedicine, ranging from whole-cell biosensors and biocatalysts to cell-based platforms for antibody screening [63, 64, 65, 66, 67, 68, 69].  
The gastrointestinal tract of monogastric animals is influenced by natural factors such as microbiota diversity and environmental pH fluctuations [20, 69], which contribute to complex physiological conditions that can affect the phytic acid degradation. Therefore, MWCB-phyrase may be suitable for animal feed applications, as _E. coli_ (a commensal in the GIT of numerous mammals, including monogastric animals [70, 71]) has evolved to provide stability to surface proteins under complex physiological conditions [72, 73, 74].  
In this study, _E. coli_ Lemo21(DE3) cells were engineered to surface-display the AppA enzyme, which promises immediate use after harvesting and easy reuse after storage. At the laboratory scale, these bacteria can dephosphorylate phytic acid under simulated poultry GIT conditions, making them alternative biocatalysts (i.e., MWCB-phytase) for use as feed additives. Furthermore, this bacterial system offers the prospect of generating functionalized outer membrane vesicles ready for biocatalytic applications (such as the production of OMV-phytase) [75]. However, further studies are required to translate this system to animal nutrition.  
Figure 5: Functional analysis of MWCB-phytase under simulated poultry GIT conditions. (**a**) Effect of total bacterial number (OD\({}_{650}\)) on the dephosphorylation of phytic acid (presented as micromolar concentrations of released inorganic phosphate). The data (n = 3) were analyzed using a typical nonlinear method and fitted to a one-site binding model (r\({}^{2}\) = 0.906). (**b**) Functional assessment of MWCB-phytase as a feed additive. Simulated digestion: phytic acid (PAC, blue), ground corn flakes (GCF, red), and mimicked poultry feed (MFP, magenta).  
The use of genetically engineered bacteria (GEB) has resulted in significant advances in food production (i.e., increasing efficiency, reducing waste, and optimizing resources), allowing beneficial innovations. However, international regulatory agencies must review the safety of GEB and the food goods produced to ensure that both microorganisms and the resulting products are safe for the environment, as well as human and animal health [76, 77, 78].
# A Feature Selection Algorithm Performance Metric for Comparative Analysis
## 8 Conclusions  
The goal of this study was to propose a novel performance metric: the baseline fitness improvement (BFI) measure for feature selection algorithms, which is unbiased and can be used for comparative analysis across feature selection problems. The BFI is a normalised measure that allows performance comparison of algorithms across independent datasets, a critical component to the algorithm selection model.  
Using BFI, the experimental results showed that there is no algorithm that is consistently the worst or the best. This observation remained valid for a variety of different scaling constants. All algorithms did perform the best for at least one problem.  
The experimental analysis shows that there is performance complementarity for feature selection algorithms using BFI as the performance measure. Performance complementarity for feature selection algorithms is supported by other experiments carried out in the feature selection community [28].  
The per-instance algorithm selection problem is of particular importance when there is a high level of performance complementarity between the set of algorithms \(A\) on a set of problem instances \(I\). The use of per-instance algorithm selectors shows promising opportunity to achieve better algorithm performance on problem instances for classification problems [15]. The performance complementarity of feature selection algorithms encourages further research in automated algorithm selection for feature selection algorithms.  
The experimental study in this paper was based on a set of 29 datasets with up to 148 features and 699 instances. In the era of big data, there are datasets that have many more features and instances. Further work could include experimentation on larger datasets to investigate the relative performance of different algorithms on large datasets and to confirm that performance complementarity still holds. Future work also includes using the BFI measure to determine the effect of fitness landscape characteristics on the performance of feature selection algorithms and to develop methods for landscape aware automated feature selection algorithm selection.  
Conceptualization, W.M., K.M.M. and A.P.E.; methodology, W.M., K.M.M. and A.P.E.; software, W.M.; validation, W.M.; formal analysis, W.M.; investigation, W.M.; writing--original draft preparation, W.M.; writing--review and editing, K.M.M. and A.P.E.; visualization, W.M.; supervision, K.M.M. and A.P.E. All authors have read and agreed to the published version of the manuscript.  
The contribution to this research by K.M.M. was funded by the National Research Foundation of South Africa (Grant Number: 120837).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Best** & **Worst** \\ \hline (5) \(k_{c}=1.0\), \(k_{p}=0.0\) & & \\ \hline AMSO & 2 (4\%) & 7 (20\%) \\ GAFS & 21 (42\%) & 0 (0\%) \\ SBFS & 11 (22\%) & 2 (5\%) \\ SFFS & 10 (20\%) & 8 (22\%) \\ PCFS & 1 (2\%) & 6 (17\%) \\ IGFS & 4 (8\%) & 12 (34\%) \\ \hline \hline \end{tabular}
\end{table}
Table 11: _Cont._
# Attack Categorisation for IoT Applications in Critical Infrastructures, a Survey
## 6 Discussion
### Challenges
#### 6.1.1 Intrusion Detection Systems  
In [21], the authors defined the various challenges revolving around Intrusion Detection Systems (IDS), in particular around the protection of Industrial Control Systems and the use of Intrusion Evasion Detection. In the former, the unique architecture and operational importance of Industrial Control Systems cause complications for IDS due to the different attack variants. In various cases, many systems use outdated Microsoft Legacy operating systems, which increase the complexity the protect these patch-less zero-day vulnerable systems.  
An historical example of such issues is the WannaCry Ransomware, which devastated the National Health Service in the United Kingdom, where many of the systems were still running unprotected Windows XP [117]. The other challenge of IDS, the use of Intrusion Evasion Detection techniques, complicates attack detection by the use of evasion methods by attackers to mask their attacks. This complicates the detection of the attack signature but also the creation of an after-event signature to complement the attack detection arsenal, impacting the robustness of IDS when facing evasion techniques.
## 4 Materials and Methods
### Transcript Structure Analysis  
Potential coding sequence (CDS) regions within the transcripts were predicted using TransDecoder ([[https://github.com/TransDecoder/TransDecoder/releases](https://github.com/TransDecoder/TransDecoder/releases)]([https://github.com/TransDecoder/TransDecoder/releases](https://github.com/TransDecoder/TransDecoder/releases)) accessed on 13 May 2022). Simple sequence repeats (SSRs) within the transcriptome were identified using MISA ([[http://pgrc.ipk-gatersleben.de/misa/](http://pgrc.ipk-gatersleben.de/misa/)]([http://pgrc.ipk-gatersleben.de/misa/](http://pgrc.ipk-gatersleben.de/misa/)) accessed on 13 May 2022). LncRNAs were screened via the coding-non-coding-index (CNCI) with default parameters [41] and coding potential calculator (CPC) with the NCBI eukaryotes protein database (E-value \(<\) 1e\({}^{-10}\)) [42]. Each transcript was translated in three possible frames, and a Pfam Scan with default parameters of -E 0.001 -domE 0.001 was utilized to determine whether there exists a domain of a known protein family. Transcription factors (TFs) were predicted using iTAK software from putative protein sequences [43].  
Illumina cDNA Library Construction and Second-Generation Sequencing for Transcriptome of Fruit Development and Ripening Stages  
Eighteen cDNA libraries (six representative stages \(\times\) three biological replicates) were constructed and used for second-generation high-throughput sequencing. RNA extraction and quality detection, cDNA synthesis, library preparation, high-throughput sequencing, identification of DEGs, functional categorization, and pathway analysis of DEGs followed our previously published protocol [9].