esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
**Spatial Analysis of Navigation in Virtual Geographic Environments**  
**[PERSON] 1,3,*(r)**, [PERSON] 2,3,*(r), [PERSON] 1, [PERSON] 1,3(r), [PERSON] 1(r) and [PERSON] 3(r)**  
1 Department of Geography, Faculty of Science, Masaryk University, Kotlarska 267/2, 61137 Brno, Czech Republic; [EMAIL_ADDRESS] (L.H.); [EMAIL_ADDRESS] (Z.S.); [EMAIL_ADDRESS] (P.K.)  
2 Centre for Experimental Psychology and Cognitive Sciences, Department of Psychology, Faculty of Arts, Masaryk University, Arna Novaka 1/1, 60200 Brno, Czech Republic  
3 Division of Information and Library Studies, Faculty of Arts, Masaryk University, Arna Novaka 1/1, 60200 Brno, Czech Republic; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] (P.U.); [EMAIL_ADDRESS] (V.J.); Tel.: +420-549-49-7608 (P.U. & V.J.)  
Received: 6 April 2019; Accepted: 30 April 2019; Published: 7 May 2019  
**Featured Application: Methods of analyzing user behavior in virtual environments, such as spatial movement and interaction patterns in individuals or within groups, user interface usage.**  
**Abstract:** Human performance and navigation activity in virtual environments can be measured and assessed with the aim to draw specific conclusions about human cognition. This paper presents an original virtual geographic environment (VGE) designed and used for this purpose. The presented research is rooted in an interdisciplinary approach combining knowledge and principles from the fields of psychology, cartography, and information technologies. The VGE was embedded with user logging functionality to provide a basis from which conclusions about human cognitive processes in a VGE could be drawn. The scope of this solution is introduced, described, and discussed under a behavioral measurement framework. An exploratory research design was adopted to demonstrate the environment's utility in proof-of-concept user testing. Twenty participants were observed in interactive, semi-interactive and non-interactive tasks, their performance and individual differences were analyzed. The behavioral measurements were supplemented by Object-Spatial Imagery and a Verbal Questionnaire to determine the participants' cognitive styles. In this sample, significant differences in exploration strategies between men and women were detected. Differences between experienced and non-experienced users were also found in their ability to identify spatial relations in virtual scenes. Finally, areas for future research areas and development were pinpointed.  
**Keywords:** spatial cognition; spatial navigation; virtual geographic environment; visual cognitive styles; spatial data collection; spatial data analysis; exploration strategies
## 4 Experiments  
### The Dataset Used for Experimental Validation and Experimental Settings  
4.1.1. The Dataset Used for Experimental Validation  
* AnyLogic Simulation datasets:Many previous studies have utilized public datasets like ETH and UCY for validation, which mainly contain sparse pedestrian trajectory data from outdoor settings. To delve into the nuances of indoor pedestrian movements, we utilized AnyLogic to create simulations of real-world indoor transportation environments. The Chengdu Shuangliu Airport Metro Line 10 T2 Terminal Station is a multi-level transportation hub with comprehensive three-dimensional scene graph information, providing a solid foundation for research. Our model replicates the Chengdu Shuangliu Airport's Terminal 2 station on Metro Line 10, taking into account passenger flow, platform congestion, and queuing behaviors. The layout of our simulation features four entrances/exits, two self-service ticketing machines, three gates, and two intersections, providing a detailed and accurate platform for studying the dynamics of indoor pedestrian traffic.  
Since the simulation can be of any length, a 2 h simulation length was used to simplify the problem. The sampling frequency of pedestrian trajectory data is set at 0.4 s intervals. The experimental verification dataset includes 20,910 pedestrian trajectories.  
The lengths of the observed trajectory series and the predicted trajectory sequence are fixed at 8 frames and 12 frames, respectively.  
* Publicly available dataset:  
In addition to the aforementioned work, we utilized a scene (UNIV dataset) from the publicly available UCY [50] dataset to validate our model's performance in predicting pedestrian trajectories in densely populated, real-world scenarios, as shown in Figure 5. This dataset comprises a video of students in motion on a university campus, recorded with a camera positioned from a bird's-eye perspective. It includes 21,813 trajectory data of 415 pedestrians.
###### Abstract  
Precisely segmenting the hippocampus from the brain is crucial for diagnosing neurodegenerative illnesses such as Alzheimer's disease, depression, etc. In this research, we propose an enhanced hippocampus segmentation algorithm based on 3D U-Net that can significantly increase hippocampus segmentation performance. First, a dynamic convolution block is designed to extract information more comprehensively in the steps of the 3D U-Net's encoder and decoder. In addition, an improved coordinate attention algorithm is applied in the skip connections step of the 3D U-Net to increase the weight of the hippocampus and reduce the redundancy of other unimportant location information. The algorithm proposed in this work uses soft pooling methods instead of max pooling to reduce information loss during downsampling steps. The datasets employed in this research were obtained from the MICCAI 2013 SATA Challenge (MICCAI) and the Harmonized Protocol initiative of the Alzheimer's Disease Neuroimaging Initiative (HarP). The experimental results on the two datasets prove that the algorithm proposed in this work outperforms other commonly used segmentation algorithms. On the HarP, the dice increase by 3.52%, the mIoU increases by 2.65%, and the F1 score increases by 3.38% in contrast to the baseline. On the MICCAI, the dice, the mIoU, and the F1 score increase by 1.13%, 0.85%, and 1.08%, respectively. Overall, the proposed model outperforms other common algorithms.  
Keywords:hippocampus segmentation; dynamic convolution; attention mechanism; 3D U-Net +
Footnote †: journal: Medical Imaging
# Mean Estimation on the Diagonal of Product Manifolds
## 3 Diffusion Mean Estimation
### Fermi Bridges to the Diagonal  
Consider a Brownian motion \(X_{t}=(X_{1,t},\ldots X_{n,t})\) in the product manifold \(M^{n}\) conditioned on \(X_{1,T}=\cdots=X_{n,T}\) or, equivalently, \(X_{T}\in N\), \(N=\text{diag}(M^{n})\). Since \(N\) is a submanifold of \(M^{n}\), the conditioned diffusion defined above is absolutely continuous with respect to the Fermi bridge on \([0,T)\)[8, 28]. Define the \(FM\)-valued horizontal guided process  
Proof.: From [15] Theorem 8 and [28],  
\[\mathbb{E}[f(X)|X_{T}\in N]=\lim_{t\uparrow T}\mathbb{C}\mathbb{E}^{\mathbb{P}^{F} }\left[f(Y^{F})\varphi(Y_{t}^{F})\right].\]  
\(\square\)  
Since \(N\) is a totally geodesic submanifold of dimension \(d\), the results of [8] can be used to give sufficient conditions to extend the equivalence in (17) to the entire interval \([0,T]\). A set \(A\) is said to be polar for a process \(X_{t}\) if the first hitting time of \(A\) by \(X\) is infinity a.s.  
**Corollary 2**.: _If either of the following conditions are satisfied_  
1. _the sectional curvature of planes containing the radial direction is non-negative or the Ricci curvature in the radial direction is non-negative;_
2. \(\operatorname{Cut}(N)\) _is polar for the Fermi bridge_ \(Y^{F}\) _and either the sectional curvature of planes containing the radial direction is non-positive or the Ricci curvature in the radial direction is non-positive;_  
_then_  
\[\mathbb{E}[f(X)|X_{1,T}=\dots=X_{n,T}]=\mathbb{C}\mathbb{E}^{\mathbb{P}^{F}} \Big{[}f(Y^{F})\varphi(Y_{T}^{F})\Big{]}.\]  
_In particular, \(\frac{\varphi(Y_{T}^{F})}{\mathbb{E}^{\mathbb{P}^{F}}\left[\varphi(Y_{T}^{F}) \right]}d\mathbb{P}^{F}\propto d\mathbb{P}^{*}\)._  
Proof.: See [8] (Appendix C.2). \(\square\)  
For numerical purposes, the equivalence (17) in Theorem 2 is sufficient as the interval \([0,T]\) is finitely discretized. To obtain the result on the full interval, the conditions in Corollary 2 may at first seem quite restrictive. A sufficient condition for a subset of a manifold to be polar for a Brownian motion is its Hausdorff dimension being two less than the dimension of the manifold. Thus, \(\operatorname{Cut}(N)\) is polar if \(\dim(\operatorname{Cut}(N))\leq nd-2\). Verifying whether this is true requires specific investigation of the geometry of \(M^{n}\).  
The SDE (16) together with (17) and the correction \(\varphi\) gives a concrete simulation scheme that can be implemented numerically. Implementation of the geometric constructs is discussed in Section 4. The main complication of using Fermi bridges for simulation is that it involves evaluation of the radial distance \(r_{N}\) at each time-step of the integration. Since the radial distance finds the closest point on \(N\) to \(x_{1},\dots,x_{n}\), it is essentially a computation of the Frechet mean and thus hardly more computationally efficient than computing the Frechet mean itself. For this reason, we present a coordinate-based simulation scheme below.
# A Lesson for the Future: Will You Let Me Violate Your Privacy to Save Your Life?
## 7 Discussion and Suggestions  
In this study, in addition to investigating the privacy and security of the Canadian CTA, we aim to investigate and disclose the public's perception of contact tracing apps and their most significant concerns that prevent individuals from adopting and utilizing these apps. Hence, besides the questions discussed in the previous sections, we devoted some questions to users' concerns during the pandemic.  
Figure 8: Participants’ opinions about sharing their data with the government and third-party companies. (**A**) “I trust the government to collect my geolocation data”. (**B**) “I trust a private company to collect my geolocation data”.  
Figure 7: Participants’ opinions after the presentation of findings. (**A**) “I wouldn’t mind sharing my geolocation data with a trusted party or government to prevent the spreading of the virus and save more lives”. (**B**) “I would advise others to download the contact tracing app by sharing this information”.  
Our study demonstrates that more than 95% of the participants had received at least one dose of the COVID-19 vaccine. We asked this question to discover relationships between their personal choices and opinions. Furthermore, more than 73% (45.4% agreed and 27.9% strongly agreed) of the participants were highly concerned about their wellbeing. Moreover, more than 65% (44.1% agreed, 21.1% strongly agreed) followed the COVID-19 instructions, infection rates, and death toll closely. Considering the provided statistics, to our surprise, about 40% of the participants indicated that they had NEVER heard about COVID-Alert, the Canadian CTA, before participating in our study. This could be one of the factors that drastically impact the effectiveness of a potential contact tracing application. Recall that, in Section 1, we discussed that an adoption rate of at least 60% is required For CTAs to be effective [2], and considering that about 40% of the participants had not heard about it before will make the 60% adoption rate goal an even more difficult objective to achieve.  
Moreover, about 55% of the participants indicated they were concerned about their privacy if they wanted to use a contact tracing app. As a result, about 50% of the participants indicated that they never used or installed any type of CTAs on their personal devices, such as cell phones.
## 6 Application
Communication among sensors and between sensors and Meshlium is performed using ZigBee interfaces, while communication among Meshlium and smartphone and tablets is performed using WiFi technology. The experimental scenario has been covered with a chain of eight nodes, one of them acting as chain initiator (node 1) and another one acting as gateway. In order to evaluate different environmental situations, a dozen of experiments have been conducted on different dates and environmental conditions, obtaining minimal differences between them. Each experiment implies that the initiator node sends a message to its successor every 10 s, up to a total of 10,000 messages. During each cycle,  
Figure 19: Experimental scenario (**left top**), application (**bottom left**) and devices of the WSN (**right**). messages travel from a given mote, where information is aggregated, to its next one until the sink (gateway), which transmits the message to the Meshlium device where information is stored. The loss of a message along the chain implies that the cycle is not completed successfully. Figure 20 illustrates the results obtained during the experimentation. It is observed that only 845 of the cycles (0.70%) are not accomplished successfully. According to the dozen of experiments performed, one can observe an average of 0.70% of messages lost over the total of messages sent, with a standard deviation of 0.22% and a median of 0.68%, implying a very low level of loss. Network has been constructed so that if the gateway node detects that after ten consecutive cycles without accomplishing any iteration, the gateway node triggers an automatic process in charge of chain reconstruction. However, this loss condition has not occurred during the experimentation, so the chain reconstruction process has never been triggered. Figure 21 shows the rate of messages lost by node. Note that the first mote never loses any message since it acts as initiator of the chain. Figure 20: Messages received and lost during the experimentation. Figure 21: Rate of messages lost by node. Although loss rate is very low, one can note that two motes have a failure rate that is double than others. The second mote and the last one act as stoppers, the first one due to the initial flow of the network, and the last one due to its status of gateway to the Meshlium. In both cases, one can observe a limited contention effect, since motes do not have multithreading capability, and therefore, they must serialize their tasks.
## 3 Influence Factors of \(R_{s}\)
### Influence of Environmental Factors on \(R_{s}\)
#### 3.1.2 Temperature  
Temperature, as one of important factors in the calibration of passive samplers, plays a crucial role in the determination of the \(R_{s}\). Several studies have reported that the \(R_{s}\) values for some polar organic contaminants generally increased with the temperature [27; 64; 65], although these effects were relatively small [50]. The effects of temperature on the \(R_{s}\) might be associated with the solubility and mobility of analytes, the octanol-water partition coefficient (\(K_{ow}\)), and the permeability of polymers in membrane [66]. Theoretically, the increase in temperature would cause an enhancement in mobility and solubility of analytes, thus facilitating the transfer of analytes from aqueous solutions to adsorbents in the POCIS. Equation (9) shows the relationship between \(R_{s}\) and temperature, which can be used to calibrate the error caused by temperature effect.  
\[\ln R_{s}=\ln a-\frac{\Delta E_{a}}{\text{RT}} \tag{9}\]  
where \(R\) is the universal gas constant (kJ/mol/K), \(a\) is the pre-exponential factor expressing the maximum uptake rate at infinite temperature, \(T\) is the absolute temperature (K), and \(\Delta E_{a}\) is the activation energies (kJ/mol).  
On the other hand, the temperature affected the permeability of polymers, and thus determined the diffusion coefficient and solubility of analytes in polymers [67], which could be described by using Arrhenius equations:  
\[S =S_{0}\;exp\!\left(-\frac{H_{s}}{\text{RT}}\right) \tag{10}\] \[D =D_{0}\;exp\!\left(-\frac{E_{d}}{\text{RT}}\right)\] (11) \[P =P_{0}\;exp\!\left(-\frac{E_{p}}{\text{RT}}\right) \tag{12}\]  
where S\({}_{0}\), D\({}_{0}\), and P\({}_{0}\) are the standard solubility, diffusion coefficient, and permeability coefficient, respectively; H\({}_{s}\) is the heat of sorption; E\({}_{d}\) is the activation energy for diffusion; and E\({}_{p}\) is the activation energy for permeation. Through combination of Equations (10)-(12), the Ep can be expressed as Equation (13), where two parameters, Hs and Ed, depend on the net change of permeability of polymers with temperature.  
\[E_{p}=E_{d}+H_{s} \tag{13}\]  
It has been shown that the diffusion coefficient of analytes in the polymer polydimethylsiloxane (PDMS) increased with temperature, whereas the partition coefficient decreased with temperature [68].  
For polymers poly(tetrafluoroethylene) (PTFE), poly(ethersulfone (PES), and polyethylene, an enhanced permeability was found with increasing temperature [69].
## 3 Results
### RT-ddPCR Analyses of the isaB, isaA, and hlgB Genes  
An additional RT-ddPCR analysis for genes coding IsaB (with 59-fold higher abundance on SDBF_24 h than EDBF_24 h), IsaA (with 32-fold higher abundance on PL8 h than PL96 h cells), and HlgB (WP_000595617.1) with 61-fold higher abundance on PL8 h than PL96 h cells was conducted  
Figure 4: (**A**) THP-1-mediated phagocytosis/intra-cellular killing of the fully matured EDBF and SDBF cells at three and 24 hpi. Biofilm cells were detached from the polystyrene support and washed prior to the in vitro test. An unpaired \(t\)-test was used to compare the means of both groups. (**B**) Relative abundances of _isaA_, _isB_, and _hlgB_ specific transcripts in the PL8 h, PL96 h, EDBF_24 h, and SDBF_24 h cells. (**C**) Relative transcript (squares) versus the protein abundance (bars) levels for IsaA, IsaB, and HlgB in planktonic bacteria at 8 and 96 h time points. (**D**) Relative transcript (squares) versus protein abundance (bars) levels for IsaB during planktonic and biofilm growth. The error bars in panels B, C, and D indicate standard deviations for \(\geq\)2 protein label-free quantification (LFQ) intensity values and relative transcript abundances of two independent ddRT-PCR assays.  
to compare transcriptional and post-translational level abundances. Figure 4B indicates the relative abundance of each transcript in PL8 h, PL96 h, EDBF_24 h, and SDBF_24 h cells. Among these, the relative abundance of _isaB_ transcripts were the highest in planktonic cells at both time points, and the lowest in biofilm cells. The _isaA_ transcripts displayed a time-dependent decrease in planktonic cells, whereas in biofilm cells the relative _isaA_ abundance was higher in SDBF than in EDBF_24 h cells. The highest level for the _hlgB_ transcripts was observed in PL8 h cells and the lowest was in PL96 h and the biofilm cells. Figure 4C compares the transcriptional and protein abundance levels from the PL8 h and PL96 h cells. Between the metabolically active PL8 h and levofloxacin-tolerant PL96 h cells, IsaA and HlgB demonstrate parallel reduction in both the protein abundance and corresponding transcript abundance. For IsaB the protein abundance increased towards the 96 h time point, whereas the corresponding transcript level was either constant or only slightly increased towards the end of the growth in planktonic cells. In biofilm cells, the relative amounts of _isaB_ and the corresponding protein abundance changes in biofilm cells are markedly different; IsaB abundance increased sharply from EDBF to SDBF_24 h biofilms, a change that was not detected at the transcript level (Figure 4D). Thus, the transcription and protein expression profiles correlated in planktonic but not in biofilm cultures.
# Biohydrogen from Microalgae: Production and Applications
## 2 Hydrogen Metabolism in Green Microalgae: Biophotolysis
### Direct and Indirect Biophotolysis  
Hydrogenase can receive electrons from different metabolic sources deriving upstream from the biophotolysis of water. At the PSII level, the splitting of water simultaneously produces O2 and electrons, which, in lighting conditions, reach the Fd and are processed for carbon fixation through the ferredoxin-NADP\({}^{+}\) reductase. Under anoxic conditions, oxygen depletion generates a favorable environment for the expression of hydrogenases that start to receive photosynthetic electrons. This process is the direct biophotolysis.  
Biophotolysis can also indirectly feed the hydrogen evolving process through the electrons deriving from the breakdown of complex stored carbohydrates, such as starch, which reach Fd and, finally, the hydrogenase. Moreover, the electron load is transferred to the PQ pool via a specific type-II calcium-dependent NADH dehydrogenase (Nda2), and then is transferred to Fd, bypassing PSII without oxygen cogeneration. This process is called indirect biophotolysis [22; 23]. Hydrogen evolution is triggered and promoted by modulating different growth strategies, largely experimented on _Chlamydomonas reinhardtii_ strains. The most commonly adopted strategy is sulfur deprivation that forces cells to reduce protein synthesis and recover sulfur (S) from protein degradation, such as PSII linked protein D1, impairing the photosystem functions. Exposure to light after dark incubation causes the formation of electrons, but also reactive oxygen species near the defective photosystems with potentially harmful consequences on cell structures. In this context, hydrogenase activity works as a safety valve to preserve structures from oxidative stress, combining electrons with protons to produce hydrogen [24].
## 5 Discussion  
Most studies this work discusses focus on distinguishing materials, such as classifying steel and wood. However, our approach emphasizes leveraging material differences to distinguish objects with similar appearances without focusing on their exact material. This is especially critical in the field of autonomous driving, where accurately identifying humans is a vital safety issue. By utilizing a SWIR camera, we demonstrated the ability to distinguish between visually indistinguishable objects in the visible spectrum. This is achieved by exploiting differences in material reflectivity within the SWIR range, showcasing SWIR imaging's transformative potential for safety-critical applications.  
An essential contribution of this study is the assessment of the SWIR range wavelengths for developing an object detection model that harnesses material reflectivity rather than visual features. Using a diverse dataset, including mannequins, humans, and banners, our model effectively recognized material-based distinctions, even in scenarios where appearance cues were insufficient. To further validate this capability, we created and publicly released a novel fruit and vegetable dataset comprising visually similar objects. The classification task on this dataset confirmed that SWIR imaging enables material-based differentiation in a manner not achievable with visible-spectrum data.  
To our knowledge, this work represents the first publicly available SWIR dataset, addressing a notable gap in the research community. Due to the lack of existing resources, we independently acquired data for both model training and inference. Additionally, while ideal dataset construction would involve objects with closely matching material properties  
Figure 14: Confusion matrix of human vs. others object detection evaluation.  
and appearances, such as wax mannequins alongside human subjects, practical constraints made this infeasible at scale. Instead, we used standard mannequins, banners, and people, acknowledging that this introduces some appearance-based differences. However, our additional fruit and vegetable dataset experiment, where objects share identical visual appearances, strongly supports our claim that the model primarily learns distinctions based on material properties.