esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Microfibrillated cellulose (MFC) is a material of high interest due to its sustainability and biodegradability, and unique properties such as, mechanical robustness, barrier properties, large surface area, and lightness [1; 2]. Over the past decade, there has been explosive growth in MFC research, including improved MFC production technologies, surface functionalization, characterization techniques, composites processing, self-assembly, optical properties, and barrier properties. The applications of MFC are already numerous including supercapacitors, transparent flexible electronics, batteries, barrier/separation membranes, and antimicrobial films [3].  
A frequently noted issue in the processing of MFC suspensions is their complex rheological behavior. MFC suspensions tend to form a strong gel, which shows, for example, yield stress, shear thinning, hysteresis, and thixotropy already in low mass concentrations. Rheological information is critical in the design and operation of, for example, pumping, mixing, storage, and extrusion processes. MFC is also commonly used as a rheology modifier, for example,in cements, inks, drilling fluids and cosmetics. Thus, the bulk rheology of MFC suspensions has been a popular subject of discussion [4; 5; 6].  
Due to a high aspect ratio of fibres and strong interfibrillar forces, MFC fibres flocculate easily and form a highly entangled network already at relatively low concentrations. Thus, in addition to rheology, the flocculation tendency of MFC fibres has been of interest [7; 8; 9]. As summarized in [9], at the macroscale, that is, at the rheometer scale, changes in the floc structure of MFC correlate with a change in the shear stress. At low shear stress, flocs are attached to each other. When shear stress is increased, the floc structure starts to yield via flocs separating from each other. At high shear stresses, fibrils flow in individual, detached flocs, the size of which is inversely proportional to the shear stress.  
Previously the flocculation of MFC has been studied in a transparent cylindrical rheometer geometry [7; 8; 9]. Here, we analyze the flocculation of a MFC suspension in a pipe flow, which is a more realistic geometry for many practical applications. The flocculation measurements were performed when preparing our recently published article Ref. [10]. In that paper, we used a combination of pipe flow, pressure loss measurement, and a high-speed, sub-micron resolution Doppler optical coherence tomography (DOCT) device for investigating the rheology of 0.5% MFC suspension. DOCT was used in measuring the stationary velocity profiles of the MFC flow in the near-wall region of a straight tube. In addition to analysing the bulk rheology (yield stress and viscous behavior) using the concept of velocity profiling rheometry, the wall/depletion layer dynamics of the suspension was studied there in detail. The novel flocculation analysis presented in this paper is based on the structural information obtained simultaneously with the velocity information. The results in [10] that are relevant to this study are briefly presented.
# A FBG Intensity Modulation System Combined with an Optical Whispering Gallery Mode Edge Filter
## 2 The Operating Principles of the Whispering Gallery Mode (WGM) Edge Filter
### Phase Difference Formula  
The strength of the coupling between the WGM and the fundamental mode depends on the coupling distance \(d\), which is shown in Figure 2. The coupling distance \(d\) is related to the bend radius and wavelength and is expressed by the following equation [13]:  
\[d=R[(\beta\lambda/2\pi n_{cl})-1] \tag{6}\]  
The total power coupled from the WGM and the fundamental mode over the bending length is not only dependent on the coupling distance but also on the phase differences between these two modes. When the light is transmitted in the core of the bending fiber, some rays would radiate to the cladding and travel in a direction that is tangential to the radiation caustic. The points B and D in Figure 1 show that the reflection occurs between the cladding and the air. The reflected ray would transmit tangentially along the radiation caustic to the next reflection point. Then the radiation forms the WGM, which is confined by the cladding interface. Because the fundamental mode and the WGM transmit in different optical paths, the phase change would occur when the coupling is generated. The phase difference can be calculated as [13]:  
\[2\pi n_{cl}\dfrac{(I_{AB}+I_{BC})}{\lambda}+\phi-Z\beta=m2\pi \tag{7}\]  
\[I_{AB}=I_{BC}=[(R+f)^{2}-(R+d)^{2}]^{1/2} \tag{8}\]  
where \(\varphi\) is the phase change, \(\beta\) is the propagation constant, and \(m\) is an integer of value 1, 2, 3, _etc._ The curve \(Z\) of the optical fiber core can be expressed as:  
\[Z=2R\text{tan}^{-1}[I_{AB}/(R+d) \tag{9}\]  
Figure 2: Schematic diagram of the microbending translation system for the whispering gallery mode (WGM) edge filter. SLD: superluminescent diode; OSA: optical spectrum analyzer.  
On substituting Equations (8) and (9) into Equation (7), we would obtain a phase change function of bend radius \(R\) when the coupling occurs at the bend radii determined by the integer m.
# Conscious Mobility for Urban Spaces: Case Studies Review and Indicator Framework Design
## 3 Research Methodology
### Research Criteria  
A systematic literature review (SLR) identifies, selects, and critically appraises research to answer a clearly formulated question [31]. The SLR was performed through academic and scientific databases and publishers such as Google Scholar, MDPI, and Elsevier, among others. Data and technical reports from organizations, websites, and previously developed documentation were also retrieved, gathering quantitative and qualitative data for analytical and exemplification purposes.  
The next parameters and baselines were considered to gather useful and relevant information for the research:  
1. Documents published from 2012 to 2022 were considered as part of the predetermined period of research, due to the high amount of public and private initiatives, with a focus on transportation systems, urban mobility, sustainable mobility, smart mobility, electric mobility, mobility behaviors, and the interaction of transportation systems with urban environments. Moreover, documentation published between 2017 and 2022 was prioritized regarding the SOA on transportation systems and technologies.
2. [PERSON] (2017) was referenced to review the state-of-the-art in mobility and conceptualize the conscious mobility dimensions [1].  
Figure 2: Soft methodology scheme of conscious mobility.  
3. Documents [25; 26] were selected as a repository for existing conscious mobility projects based on electric mobility due to their broad overview of cases in different stages and regions but specifically in America. However, individual case study data gathering was performed to fulfill missing information and contrast several points of view. Just the main case study was outside the repositories, selected to broaden the perspective of the objectives pursued by conscious mobility projects.
4. Frameworks [32; 33] were used to fill the gap within the selection and creation of suitable indicators for the case study of Monterrey. These frameworks were used just to extract and adapt indicators that best fit with the proposed dimensions of conscious mobility.
5. Document [34] was used to understand the sustainable transport framework for the Metropolitan Monterrey Area (MMA), its challenges, and future strategies. Document [35] and its actualization [36] were also used to understand the current mobility status in DistritoTec and diagnose conscious mobility dimensions, laying the foundations for future conscious mobility projects.
6. Given the qualitative approach of the research, extensive analysis of quantitative data was not a priority; besides, most of the databases consulted for Latin American countries are no longer available, or the information is not registered in a centralized database. It is intended that this research and final result serve as a skeleton and the starting point of discussion to initialize a correct data gathering process, which can be visualized by the community and ensure the correct feeding of the indicator framework.
# Deep Interest Network Based on Knowledge Graph Embedding
## 5 Conclusions  
Through research, we found that most recommendation models based on knowledge graphs take the static interaction matrix as the user's behavior and interest characteristics, ignoring the user's behavior and interest characteristics, which is a dynamic process. Aiming to resolve this problem, this paper proposes a deep interest network based on knowledge graph (DINK) to fuse users' dynamic interest features into recommendation tasks. DINK consists of three parts: an embedding layer, a dynamic interest reasoning layer and a prediction layer. In the embedding layer, the user interaction sequence is mapped into the recommended scene knowledge graph, and the features of the first-order neighbor nodes are extracted by the aggregator. In the dynamic interest reasoning layer, DINK calculates the similarity score between the user interaction sequence and the candidate item as the attention weight and then uses the weighted center aggregation vector as the input of the GRU to obtain the user's dynamic interest feature. In the prediction layer, the user  
Figure 5: Performance in Different Lengths of Sequence.  
Figure 6: Performance in Different Dimensions.  
vector, candidate item vector and dynamic interest vector are spliced and pooled to predict the score of the candidate item. We conduct click-through rate prediction experiments, Top-k recommendation experiments, and performance experiments on three public datasets: Movielens-1M, Book-Crossing, and Last.FM. Experimental results show that, compared with other models, DINK can more effectively capture the dynamic interest features of users and can effectively alleviate the cold start problem.  
Conceptualization, D.Z. and H.W.; methodology, H.W., J.L., Y.M. and D.Z.; investigation, A.R. and X.Y.; visualization, H.W., A.R. and J.L.; project administration, D.Z.; writing--original draft preparation, H.W., D.Z., J.L. and A.R.; writing--review and editing, D.Z., H.W., Y.M. and A.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by (i) Natural Science Foundation China (NSFC) under Grant No. 61402397, 61263043, 61562093 and 61663046; (ii) Open Foundation of Key Laboratory in Media Convergence of Yunnan Province under Grant No. 220225201. (iii) Practical innovation project of Yunnan University, Project No. 2021234, No. 2021y128 and 2021y129.  
This research was supported by the Yunnan Provincial Key Laboratory of Software Engineering, the Kunming Provincial Key Laboratory of Data Science and Intelligent Computing and the Key Laboratory in Media Convergence of Yunnan Province.  
The authors declare no conflict of interest.
### Background
#### 1.1.7 Multilayer Perceptron  
Multilayer Perceptron classification utilizes a neural network. Each neuron in a layer is connected to neurons in the previous layer. In the forward step, each neuron computes a weighted sum of the inputs it received from the previous layer, then adds a threshold value, and finally applies an activation function to produce an output to send to the next layer of neurons. Once the algorithm has completed the forward step by progressing through every layer, an error is computed as a function of the weights.  
In the backward step, or backpropagation, this error propagates backward via the layers to update the weights. The cycle of forward-step and backpropagation continues until the error is sufficiently reduced [8].
## 2 Methods
### Statistical Analysis  
The statistical analyses were performed using IBM SPSS Statistics (version 29 for Windows) [29], Jamovi (version 2.6.23.0 for Windows) [30], and JASP for data visualization (JASP version 0.19.3.0 for Windows) [31]. Prior to the main analysis, normality was assessed for all variables. Variables that met the assumption of normality, as assessed by the Shapiro-Wilk test--namely, maximum speed achieved (km/h), Distance Zone 1, accelerations \(\geq\) 2.0 m/s\({}^{2}\), and decelerations \(\leq-\)2.0 m/s\({}^{2}\)--were subjected to parametric testing via one-way analysis of variance (ANOVA) to assess differences across playing positions. When statistically significant effects emerged, [PERSON]'s post hoc procedure was employed to identify pairwise differences. For the remaining non-normally distributed variables that violated the normality assumption--specifically, total distance (km), number of sprints, total sprint distance (km), relative distance covered (m/min), Distance Zone 2, Distance Zone 3, and Distance Zone 4--non-parametric Kruskal-Wallis tests were applied. In cases where significant group differences were detected, pairwise post hoc analyses were performed to further explore the inter-positional contrasts. Effect sizes (ES) were computed in accordance with [PERSON]'s [32] benchmarks: \(\eta^{2}\) was used for parametric outcomes, while \(\epsilon^{2}\) was calculated for non-parametric data. The thresholds for interpreting ES were defined as small (0.01-0.06), moderate (0.06-0.14), and large (>0.14) [32].  
A subsequent analysis was conducted to compare performance between the first and second halves of the match. For this purpose, normality checks were repeated for each variable after splitting the dataset in half, and variables that followed normal distribution--such as accelerations (\(\geq\)2.0 m/s\({}^{2}\)) and decelerations (\(\leq\)\(-\)2.0 m/s\({}^{2}\))--were analyzed using the independent samples t-test. Variables that deviated from normality, including the total distance (km), Distance Zone 1-4, relative distance covered (m/min), number of sprints, total sprint distance (km), and maximum speed achieved (km/h), were examined using the Mann-Whitney U test. Corresponding effect sizes were reported: Cohen's d for parametric tests and the rank-biserial correlation for non-parametric outcomes. The interpretation of effect sizes followed [PERSON]'s classification: small (0.20-0.49), moderate (0.50-0.79), and large (\(\geq\)0.80). Furthermore, within-position comparisons across match halves were also conducted. For variables maintaining normality, paired samples t-tests were used (i.e., accelerations and decelerations), while the Wilcoxon signed-rank test was utilized for non-normally distributed variables. The level for statistical significance was set at \(p\) < 0.05.
## 5 Evaluation of FFNN-Based VIIRS Dust Detection off CALIOP Track
### Off-Track Case Studies
The same as the previous two cases, the FFNN agrees very well with the CALIOP on the CALIOP track (Figure 11e). It also detects large areas of dust in this granule, for example over the eastern part of the granule where the AOD is high and Angstrom exponent is small. In comparison, the PHYS method does not label the aerosols in this region as dust. There could be multiple reasons for these differences such as the strong surface signal overpowering the atmospheric dust signal. An important one could be that all 16 M-bands of VIIRS are used in the FFNN method, whereas only 3 M-bands are used in the PHYS model. Figure 11: A case study over Arabian peninsular on day 075, at 13:36. (**a–d**) depict images from Wisconsin’s VIIRS quicklook, where (**a**) represents the true color, (**b**) Clear Sky confidence, (**c**) Aerosol Optical Thickness, and (**d**) Angström Exponent; (**e**) FFNN results on the same geolocation. Results are plotted in a \(0.1\times 0.1\) latitude and longitude grid, and the percentage of dust within the grid are shown as colors. Thin magenta lines show the CALIOP swath of this region. Pixels within the magenta lines are the CALIOP product; (**f**) same as (**e**), but for PHYS product.
## 3 A Brief Overview of AHL Systems in _Burkholderia_
### Burkholderia pseudomallei _Group_
#### 3.2.1 Burkholderia mallei  
The genome of _B. mallei_ contains two _luxI_ and four _luxR_ homologs arranged as two complete _luxI/R_ AHL QS systems and two orphan/solo _luxR_ homologs [59]. The complete QS systems in _B. mallei_ are called Bmal/R, and their _luxR_ homologues _bmaR1_ and _bmaR3_ respond to signals produced by adjacent _luxI_ homolog genes _bmaI1_ and _bmaI3_, respectively [60]. BmaI\(1/\)R1 shares sequence similarity with BpsI\(1/\)R1 in _B. pseudomallei_, producing and responding to C8-HSL [60]. _bmaI\(1\)-bmaR1_ are transcribed in opposite orientation to each other and are separated by an intergenic ORF  
Figure 4: Typical arrangements of QS genes in chromosome II of the _Burkholderia cepacia_ complex (BCC) group. (**A**) Arrangement of QS genes in the BCC members with _cciR1_ genes (for example _B. cenocepacia_ J2315 and _B. cenocepacia_ MC0-3); (**B**) Arrangement of QS genes in BCC members having just one pair of _luxR/I_ homologs (_cepR/I_) (for example _B. cenocepacia_ AU1054, _B. cenocepacia_ HI2424, _B. cepacia_ GG4, _etc._). OriC denotes the origin of replication, solo _luxR_ genes (_i.e._ those without adjacent I genes) are denoted by black and white ovals, respectively, with the latter indicating two adjacent _luxR_ homologues.  
which contains a homolog of _rsaM_. The second LuxI/R homolog pair of _B. mallei_ is BmaR3/I3. The _bmaR3-bmaI3_ pair is transcribed in the same direction and do not contain ORFs in the intergenic region. BmaI3 produces multiple AHL molecules and includes abundant levels of _N_-3-hydroxy-octanoyll-HSL (3 OHC8-HSL). In addition to 3 OHC8-HSL, BmaI3 produces _N_-3-hydroxy-hexanoyll-HSL (3 OHC6-HSL), and _N_-3-hydroxy-decanoyll-HSL (3 OHC10-HSL) in minor amounts. BmaR3 responds to the most abundant product 3 OHC8-HSL [59]. The role of orphan/solo LuxR homologs, BmaR4 and BmaR5 are currently unknown. In this genus, all the QS genes are present on chromosome 2.
## 3 Results and Discussion
### Non-Drone Target Detection  
Here, we presented a non-drone target crossing the baseline of the DVB-S-based passive FSR system to establish that a non-rotating target can be differentiated from a target with micro-Doppler component. This provides further evidence of a key feature to distinguish the drone from other targets. A fixed object without any micro movement was made to cross the baseline, and the corresponding signature is presented in Figure 15.  
Our aim here is view the Doppler signature without the micro-Doppler component; we therefore considered that only the IMF seems to be representing the Doppler component. The signal of Figure 15 was further decomposed via similar procedures discussed earlier to obtain the corresponding IMF components present. Figure 16 depicted only IMF4 out of the 16 vector components present in the original sampled signal after the sifting process.  
Based on the IMF4 representation depicted in Figure 16, both the time domain and the time-frequency, STFT representation do not contain any micro-Doppler components surrounding the main Doppler, as in the earlier-presented drone signature. For a clearer classification between targets (bird inclusive), other processing techniques may be employed. This served as a future area for further studies.  
Figure 16: Extracted Doppler Component IMF4 (**a**) time domain and (**b**) its corresponding STFT plot.  
Figure 15: (**a**) Received signal in time domain at the output of ADC, and (**b**) its time–frequency representation of the sampled signal (1–6 s) denoted as target signature.  
Although the DVB-S-based FSR system is limited to the narrow coverage area, it can easily be integrated into a conventional passive bistatic DVB-S system and enjoys the benefit of FSR. FSR will leverage its high Doppler resolution for target recognition. This study may open a basis for comprehensive aerial monitoring that could be utilized in, but not limited to, applications such as air traffic surveillance, border monitoring and perimeter protection. Figure 17 illustrates some potential applications of the DVB-S FSR system, with the possibility of integrating with passive bistatic radar, among which may include air traffic surveillance and control.
## 3 Analysis and Results
### Analysis of Realistic EEG in Mental Arithmetic Tasks
Since the NOTE value is inversely correlated with the coupling strength, in the subsequent analysis, the NOTE values were reversely processed (1 minus the value of NOTE), so that they would adhere to our intuition. EEG signals were first detrended using the singular value decomposition (SVD) method. Then, the detrended EEG signal was filtered using the harmonic wavelets in the frequency range of 1 to 42 Hz. The obtained resting and arithmetic state EEG signals from seven channels were segmented using a sliding time window with a width of 2000 samples and a moving step of 500 samples. Within each window, the NOTE was estimated between any two of the seven EEG channels using the CBTN method. With the NOTE value as the edge weight, the complex network was constructed and the average clustering coefficient and the global network efficiency of the complex network were calculated. Following the same procedure, all sliding windows were analyzed in turn to obtain the average aggregation coefficient sequence and the global network efficiency sequence of the subject in a state. Since the distribution of the obtained sequences were unknown, the nonparametric permutation test (1000 repeated arrangement sampling) was used to assess the significance between the same sequences in the two  
cognitive load during the arithmetic state. Figure 6 shows the analysis results of extracting the features of the complex network constructed by the CPE method under two states. In order to verify whether there was significant difference between the two states at the group level, the results was statistically analyzed using a paired sample \(t\)-test. The significance level was set at \(p=0.05\), and statistical analysis was performed on IBM SPSS25.0. The results of the statistical analysis showed that there was a significant difference in the mean clustering coefficients (\(p=0.0013\)) and in the global network efficiency (\(p=0.0017\)) between the two states using the CBTN method (Figure 5). The results of the statistical analysis also showed that there was a significant difference in the mean clustering coefficients (\(p=0.0056\)) and in the global network efficiency (\(p=0.0061\)) between the two states using the CPE method. Although both methods can distinguish between the two states, the CBTN analysis was significantly better than the CPE analysis. This suggests that a complex network based on the CBTN using electrodes in the prefrontal and frontal lobe can distinguish well between the two cognitive states, demonstrating the validity of the CBTN method in practical applications.