esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
Reliability was assessed using intraclass correlation coefficient (ICC) and standard error of measurement (SEM). ICC and SEM were calculated between the first two trials of the protocol (both normal walking), to assess the test-retest reliability for each measurement system. For further calculations, the second trial was used as a baseline, because the IMUs  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Sensors** & **Positioning** & **Description of Position** \\ \hline
1 & Lumbar & Centered on the low back, at the base of the spine. \\  & & Superior aspect of the posterior sacral surface. \\
2 & Upper leg & Lateral aspect of thigh, midline right over the \\  & & iliotibial band, between the muscular tissue, \\  & & one hand’s width above the knee. \\  & & Medial to the front of the tibia, on the flat surface \\
2 & Lower leg & of the bone, high enough for the strap to wrap just \\  & & above the widest part of the calf muscle. \\
2 & Foot & Centered on top of the foot, aligned with the \\  & & second metatarsal. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of sensors, positioning, and description of position of the IMUs (Opal sensors) used.  
were kept in place from the second trial onwards. ICC estimates and their 95% confidence intervals were calculated based on a mean-rating (k = 2), absolute-agreement, 2-way mixed-effects model. The SEM was calculated as \(\text{SEM}=\text{SD}*\sqrt{1-R_{xx}}\), using the standard deviation (SD) and the reliability of the test (\(R_{xx}\)). The SEM value can range from 0 to the value of the standard deviation, with a higher value indicating a lower test reliability. The \(\Delta\)FPA is calculated by subtracting the baseline FPA per leg from the gait strategy (toe-out gait or toe-in gait). Finally, the minimal detectable change (MDC) was determined by \(\text{MDC}=1.96*\text{SEM}*\sqrt{2}\). The MDC is described as the least amount of change that is not the result of measurement error [13].  
Validity was assessed by calculating repeated measures ANOVA (analysis of variance) test, ICC, and Bland-Altman plots. ANOVA tests were performed with one dependent variable, FPA, and two independent variables: \"gait type\" (baseline/toe-in/toe-out) and \"measurement instrument\" (IMU/PSW). [PERSON]'s test indicated that the assumption of sphericity had been violated for both independent variables. Greenhouse-Geisser corrections were applied to the degrees of freedom, such that a valid critical F-value could be obtained. ICC was calculated per system for the two baseline measurements. Bland-Altman plots were created using the mean and the difference in FPA between both measurement systems. Then, 95% confidence intervals (95% CI) were added as the limits of agreement. Finally, a scatterplot was created using the mean and the difference in FPA between the systems for all gait types, combined with a linear regression to identify proportional bias.
# Effect of NS-DBD Actuator Parameters on the Aerodynamic Performance of a Flap Lifting Device
## 4 Simulation Results and Analysis  
### Comparisons of Discharge Frequency  
First, the effect of discharge frequency on the flap lift device is analysed, and two discharge frequencies, 100 and 200 Hz, are selected. The effects of different discharge frequencies at three different discharge positions (the upper surface, upper trailing edge, and lower trailing edge) under the 10\({}^{\circ}\) flap deflection angle are studied in this section. Other parameters, such as pulse width (50 ms) and discharge energy density (10\({}^{13}\) W/m\({}^{2}\)), remain unchanged. Since the pulse action process is similar when only the discharge frequency is changed, in the comparative analysis of the flow field structure, only the vorticity distributions of the flow field after the first, 5 th, 10 th, and 20 th pulse, which have more obvious distinctions, are selected.
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 3 Application of Terpenoid Microbial Cell Factories in the Food Industry
### Acting as Medicinal Health Food (MHF)
#### 3.4.3 Patchoulol  
Patchoulol, which has a wooden, earthly scent, is a valuable sesquiterpene with anti-inflammatory, anti-cancer, anti-depressant, and other therapeutic properties [103]. It is widely used in the food, cosmetic, and pharmaceutical industries. Patchoulol is the major active ingredient in the Chinese medicine _Pogostemon cabin_ ([PERSON]) [123]. Since the natural yield of patchoulol is limited, efforts have been made to produce patchoulol using microorganisms.  
It was reported that patchoulol synthase (PTS) is a promiscuous enzyme capable of producing at least 22 different sesquiterpene compounds with various structures, including patchoulol. Scientists have conducted research on its catalytic mechanism and product spectrum [132; 133]. [PERSON] et al. performed a semi-rational design and obtained a PTS mutant PTS2C415F/H454A with higher substrate-binding affinity and a 2.9-fold increase in the titer of patchoulol. After deleting competing pathways and strengthening the efflux pathways, the engineered _E. coli_ strain produced nearly 1 g/L of patchoulol in a 5 L bioreactor [103]. In further research, they obtained another PTS mutant, PTS3 mut4, with 11-fold higher than PTS2C415F/H454A. By deleting the cyclic AMP synthesis pathway to alleviate glucose repression and dynamically regulating glycolysis based on pyruvate biosensor, they achieved a titer of 1675 mg/L patchoulol in _E. coli_[104]. Similarly, [PERSON] and co-workers rationally engineered PTS and obtained PTS4045, which could reduce the production of the concomitant byproducts of patchoulol. Combining modifying off-pathway, enhancing the ergosterol pathway, regulating NADPH supply, and decreasing intracellular ROS level, they obtained an _S. cerevisiae_ strain with a titer of 1632 mg/L patchoulol in a two-stage fermentation [105]. To our surprise, appropriately enhancing the by-pass synthesis of ergosterol not only did not reduce the flux of patchoulol synthesis but also increased cell density and accumulation of patchoulol in their research. This provides us with a new perspective for further breaking through the bottleneck of terpenoid synthesis.
# Digitalization of Public Services--An Input Output Logit Analysis
###### Abstract  
In 2000, Sri Lanka designed an ambitious plan for the introduction of information and communication technology (ICT) in most government functions and in the public service delivery (PSD) system in the country. This process started in the early 2000s and gained momentum with several local and internationally funded initiatives. A systematic innovation concept was incorporated within the ICT regime, which ensured bottom-up learning for a smooth transformation from paper to digitized PSD systems. Towards this end, the Information Communication Technology Agency (ICTA) and Lanka Government Network (LGN) were established. ICT incorporation covered the operations of most government agencies and departments to improve governance and PSD. We analyzed the efficiency of the ICT regime to understand its impact on public service employee output as well as on services to the public. We collected service delivery data from both the employees and their clients using a Likert-scale questionnaire. The questionnaire enquired about the utility of the ICT regime introduced in various departments and ministries (DMs) of the Sri Lankan government. This paper analyzes the overall and relative effectiveness of the ICT regime in terms of the inputs incurred and the outcomes realized. First, we calculated the Cronbach's alpha to test the robustness of the data. Second, we applied ordinal logistics analysis to understand the interrelations among various measures (inputs) and their impacts (outcomes). Finally, we conducted specificity, sensitivity, and predictive value analysis to assess the accuracy of the investigative model. Our findings suggest a positive correlation between the inputs and the outcomes of the ICT regime introduced to digitalize PSD. Our results further indicate that although the inputs and the outcomes are positively corelated, this correlation is not sufficiently strong, and the ICT implementation measures need further emphasis to demonstrate any significant impact on user confidence in this regime.  
ICT; logit regression; public service delivery; Sri Lanka; system innovation +
Footnote †: journal:
# Adaptive Vector Quantization for Lossy Compression of Image Sequences +
## 2 The Adaptive Vector Quantization (AVQ) Algorithm
### Logical Architecture
This heuristic adds two new blocks to the dictionary, if possible. The first block and the second block are obtained by adding a row to the matched block, in the already coded part of the image, as shown in Figure 4.
* _Deletion Heuristic_ (_DH_)  
A DH is used when all of the entries of the dictionary \(D\) are used. Such a heuristic defines which blocks in the dictionary should be deleted in order to make space.
## 2 Materials and Methods
### Texture Analysis  
The MRI images obtained with T1 post-contrast sequences and the ADC maps before and after RT were imported into 3D slicer (www.3 dslicer.org (accessed on 12 February 2022)) v10.4.2 software. The parotid and submandibular glands located on the same side of the oropharyngeal cancer, corresponding to the irradiated side, were segmented for the entirety of their volumes by a radiologist with 3-years' experience in head and neck cancer using the \"segmentation wizard\" extension for 3D slicer (Figure 3). Specifically, the segmentation of the gland located on the irradiated side was performed on CE-T1 sequences both before and after RT. The same method was carried out on the ADC maps  
Figure 2: Region of interest (ROI) definition for functional MRI. (**A**) DCE-PWI ROI definition for kinetic parameters drawn on both the parotid glands. The graph shows the contrast agent concentration over time for the two ROIs (left and right parotid gland, respectively). (**B**) ADC map ROI definition for local ADC values (see purple circle ROIs on the left parotid gland) with mean ADC values.  
thus resulting in a total of 4 different segmented volumes per gland. This process resulted in a total number of 216 volumes being segmented with 4 submandibular and 4 parotid gland volumes investigated for each of the 27 patients. Texture features were analyzed and extracted from such volumes using the extension \"Pyradiomics\" for 3D slicer.  
A total of 107 radiomics features were extracted, belonging to the following categories: First Order, Shape-based (2D and 3D), Gray Level Co-occurrence Matrix, Gray Level Size Zone Matrix, Gray Level Run Length Matrix, Gray Level Dependence Matrix, and Neighboring Gray Tone Difference Matrix. The detailed explanations of the texture subclasses can be found in Table 1 (Appendix A).
## 3 Results and Discussion
### Infrared and Raman Spectral Analysis
#### 3.7.1 C-H Vibrations  
The aromatic rings exhibit C-H stretching as well as in-plane and out-of-plane bending vibrations. Aromatic compounds commonly exhibit multiple weak bands in the 3100-3000 cm\({}^{-1}\) region [22] due to ring C-H stretching vibrations. These vibrations are not strongly affected by the nature of the substituents [23,24]. All of the C-H stretching vibration modes exhibit very weak intensities. In the current study, (2\(E\))-HIPC exhibits 10 C-H stretching vibrations. Among the four expected C-H stretching vibrations, three were observed at 3048, 2930, and 2908 cm\({}^{-1}\) in the FT-IR spectra, and the same types of vibrations were observed at 3023, 2980, 2930, and 2880 cm\({}^{-1}\) in the FT-Raman spectra. These vibrations are shifted downfield due to the imidazole moiety and the side-chain substitutions. All of the bands were observed as weak vibrations.  
The vibrational modes due to C-H in-plane bending vibrations were observed in the 1000-1300 cm\({}^{-1}\) region [25]. For the title compound, the C-H in-plane bending vibrations were observed at 1198, 1180, 1165, and 1152 cm\({}^{-1}\). The wavenumbers obtained using the B3 LYP/6-311++G(d,p) level of theory and subsequently rescaled were in good agreement with the experimentally observed data. The C-H out-of-plane bending vibrations appear in the 1000-700 cm\({}^{-1}\) region [26]. The vibrations identified at 740, 730, 713, and 700 cm\({}^{-1}\) were attributed to C-H out-of-plane bending for (2\(E\))-HIPC.
## 3 Results
### SF-36  
AMI patients demonstrated a significant improvement with a score increase across all SF-36 sub-scores as well as the aggregate Physical and Mental Component Scores (PCS and MCS) from baseline to 12 months post-amputation. The mean Physical Functioning score increased from 29.2 \(\pm\) 4.9 (SE) to 70.4 \(\pm\) 5.4 (SE) (mean difference 41.2 [95% CI 30.3 to 52.2]; \(p\) < 0.0001). The Role Limitations Due to Physical Health score increased from 11.4 \(\pm\) 7.0 (SE) to 46.3 \(\pm\) 7.9 (SE) (mean difference 35.0 [95% CI 16.6 to 53.3]; \(p\) = 0.0002). The Role Limitations Due to Emotional Problems score increased from 55.6 \(\pm\) 6.5 (SE) to 84.3 \(\pm\) 7.4 (SE) (mean difference 28.8 [95% CI 11.0 to 46.5]; \(p\) = 0.0017). The Energy/Fatigue score  
Figure 4: Mean PROMIS T-scores for negative PROMIS dimensions at 0 to 48 months post-operation for patients undergoing a modified AMI amputation including standard error bars.  
Figure 5: Mean PROMIS T-scores for positive PROMIS dimensions at 0 to 48 months post-operation for patients undergoing a modified AMI amputation including standard error bars.  
increased from 43.1 \(\pm\) 4.0 (SE) to 67.9 \(\pm\) 4.4 (SE) (mean difference 24.8 [95% CI 16.0 to 33.6]; \(p\) < 0.0001). The Emotional Well-being score increased from 66.8 \(\pm\) 3.2 (SE) to 80.2 \(\pm\) 3.5 (SE) (mean difference 13.3 [95% CI 6.6 to 20.1]; \(p\) = 0.0001). The Social Functioning score increased from 45.8 \(\pm\) 4.6 (SE) to 81.1 \(\pm\) 5.2 (SE) (mean difference 35.3 [95% CI 23.4 to 47.1]; \(p\) < 0.0001). The Pain score increased from 30.1 \(\pm\) 4.1 (SE) to 70.0 \(\pm\) 4.6 (SE) (mean difference 39.9 [95% CI 29.2 to 50.5]; \(p\) < 0.0001). The General Health score increased from 54.1 \(\pm\) 4.3 (SE) to 72.1 \(\pm\) 4.7 (SE) (mean difference 18.0 [95% CI 10.4 to 25.6]; \(p\) < 0.0001). The aggregate Physical Component Score (PCS) increased from 26.4 \(\pm\) 1.8 (SE) to 43.3 \(\pm\) 2.0 (SE) (mean difference 16.9 [95% CI 12.7 to 21.1]; \(p\) < 0.0001). The aggregate Mental Component Score (MCS) increased from 48.0 \(\pm\) 1.9 (SE) to 55.1 \(\pm\) 2.1 (SE) (mean difference 7.1 [95% CI 2.5 to 11.7]; \(p\) = 0.0028) (Figure 6).
# Software Fault Localization Based on SALSA Algorithm
## 2 Related Work
### Program Dependency Graph  
The Program Dependency Graph (PDG) is a graph structure used to represent the dependencies between various statements in a program. It is an extension of the Control Flow Graph (CFG), encompassing not only the execution path information of the program but also the dependencies of data flow and control flow. It has been widely applied in software testing and program slicing.  
[PERSON] et al. [25] summarized the necessity of weak and strong syntactic dependencies for certain inter-statement relationships and pointed out that these dependencies can be efficiently computed to guide activities such as test data selection and code inspection. [PERSON] et al. [26] proposed a fault classification and fault injection method based on the Program Dependence Graph (PDG). This method further characterizes faults by analyzing the differences between the PDGs of the original and faulty programs, categorizing faults into structural and statement-level faults. By performing transformations on the PDG, it generates different types of faults. The Control Dependency Graph (CDG), as a part of the Program Dependency Graph (PDG), specifically represents the control dependency relationships between program statements. This paper takes the CDG as the research object to accomplish the task of fault localization.
# Perception of Risks and Usefulness of Smart Video Surveillance Systems
## 2 Method
### Materials and Procedure  
The exploratory study (repeated measurement design) was conducted using an online survey. On average, the participants needed about 19 min to complete the questionnaire. First, the participants were informed about the study and provided their informed consent. Subsequently, the subjects received an introduction to the smart video surveillance system, whereby no technical knowledge was required to answer the questions. Overall, the questionnaire consisted of three parts: the assessment of usefulness of smart video surveillance using digital skeletons, questions recording the perceived privacy risk of the system, as well as the perceived safety. After that, the participants were asked about socio-demographic information. Finally, there was a farewell.  
The following independent variables (IVs) are used. IV1: gender (male, female), as well as the repeated measures IV2: time of day (day, night), IV3: public area (see Table 1) and IV4: data privacy levels of a smart video surveillance system (exclusive analysis of body movements using digital skeleton, exclusive analysis of possible crime weapons, analysis of video recordings, but censoring of biometric features (e.g., faces), exclusive analysis of biometric features (e.g., facial recognition)).  
\begin{table}
\begin{tabular}{l l l} \hline (AP) Amusement Parks & (LE) Large Events & (SM) Shopping Malls \\ \hline (AR) Airports & (LS) Low-Traffic Sidewalks & (SS) Shopping Streets \\ \hline (AS) Areas Surrounding & (PG) Parking Garages & (TS) Train Stations \\ \hline (AW) Areas Surrounding & (PL) Parking Lots & \\ Workplace & (CL) City Squares & (PLG) Playgrounds \\ \hline \end{tabular}
\end{table}
Table 1: Overview of public areas and their acronyms. The entries are shown column-wise in alphabetical order.  
The dependent variables (DVs) are described as follows. DV1: perceived usefulness. Participants indicated the extent to which they would perceive the smart video surveillance useful, using a 5-point scale ranging from not at all useful (1) to exceptionally useful (5). DV2: perceived privacy risk. The following scenario was described to the participants and rated for different data privacy levels (see IV4) with a 5-point scale, ranging from not at all risky (1) to exceptionally risky (5): The evaluated method (see IV4) detected a crime. Based on this suspicion, police officers shall be sent to the crime scene. Therefor, the data are only accessible at the time of video recording and are analyzed in real time.
# Method for Constructing a Facade Dataset through Deep Learning-Based Automatic Image Labeling
## 3 Research Methodology  
### Research Direction  
In this study, we attempted to automatically label facade images to quickly generate large-scale facade datasets with much less effort than by the existing research methods. Therefore, the main direction of this study is to utilize deep learning technology to accurately auto-label non-quantified street facade images existing in road-view images generated from web portals such as Google Street View [9] and Naver Street View [10]. It is also necessary to verify that the required data can be obtained using the facade database to identify the characteristics of the building design on the street.  
The direction of this research, shown in Figure 2, was as follows:  
1. Definition of Facade Dataset Information Defining the required information in a dataset based on the existing characteristics of the facade.
2. Acquisition Method of Dataset Sources Selection of the source of the dataset from road-view images and image distortion correction technology.
3. Conversion Method of Dataset Sources For the selection of converted datasets using deep learning model-based automatic labeling technology, the road-view images were corrected using image distortion technology, which was converted into computer-recognizable datasets. The deep learning method included the SegNet model training data acquisition using the CMP database [18], training data optimization, and securing a facade automatic labeling algorithm based on the SegNet model [19].
4. Dataset Acquisition Dataset was secured according to the conversion and acquisition methods
5. Reliability Analysis of Dataset A reliability analysis was conducted by comparing the facade information obtained with ground truth on the four categories (Dominant color, Secondary Color, Number of Floors, Number of Buildings) of facade information that can be calculated through the datasets.
6. Verify Efficiency and Application of Dataset  
Figure 1: Concept of semantic segmentation and instance segmentation.
# Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage
## 5 Experimental Results and Analysis
### Comparison of the Deblurring Performance  
The parameters of the six algorithms are adjusted by the traversal to achieve the best deblurring indicators. The test results for the different images are presented in Tables 2 and 3, where the values in bold refer to the optimal indicator values. Table 2 presents the deblurred results obtained by the six different models for degraded images with Gaussian blur (G, 5, 5, 7) and Gaussian noise standard deviations of 1, 3, and 5, respectively. Table 3 presents the results obtained by the six different models for degraded images with motion blur (M, 10, 10) and Gaussian standard deviations of 1, 3, and 5, respectively. Figure 5 depicts the changes in the methods in terms of the evaluation metrics with respect to the increase in the iteration number. Figure 3: Effects of variations on the peak signal-to-noise ratio (PSNR) for the “Car” images degraded by the motion blur kernel (M, 10, 10) with Gaussian noise standard deviations 1, 3, and 5 respectively. (**a**) \(u_{1}\); (**b**) \(u_{2}\); (**c**) \(u_{3}\);(**d**) \(u_{4}\); (**e**) \(u_{5}\). Figure 4: Effects of variations on the PSNR for the “Car” images degraded by the motion blur kernel (M, 10, 10) with Gaussian noise standard deviations 1, 3, and 5 respectively. (**a**) \(p_{1}\); (**b**) \(p_{2}\); (**c**) \(p_{3}\); (**d**) \(p_{4}\); (**e**) \(p_{5}\).