esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Audio and Non-Visual AR Interfaces  
The audio feedback features in our proposed prototype demonstrate considerable potential for broad application scenarios. Compared to traditional solutions like braille and tactile graphics, our audio feedback mechanisms offer an easier, more intuitive way for users to perceive and interact with content without the need for extensive learning. These features can transform how common books or textbooks convey images, making them accessible through audio descriptions of visual elements and spatial layouts.  
The implementation of audio feedback for spatial virtual objects not only enhances the AR experience for users by providing them with an understanding of objects and their spatial relationships but also addresses common challenges in conveying spatial information in AR applications. This is achieved through varying sound volumes to simulate the proximity and orientation of virtual objects within the user's environment.  
In the findings from our study, we see a marked preference for the Natural Interaction-Based Audio Interface (NAI) over the Screen-Touch Interaction-Based Audio Interface (TAI), which offers significant insights into the future of AR interface design.  
The quantitative data derived from the SUS and UES unequivocally favored the NAI. This preference underscores the assertion that users are seeking more intuitive and natural ways to interact with technology--a trend that is particularly relevant in the context of AR. Our results indicate that when users engage with interfaces that leverage natural gestures coupled with audio feedback, they report a significantly enhanced user experience. This resonates with the broader discourse on human-computer interaction, where the move towards more 'natural' interfaces has been identified as a key driver for user satisfaction.  
Qualitatively, the insights gathered point towards an immersive interaction experience that users perceive as more intuitive and lifelike when using the NAI. Participants described the NAI as an innovation that seamlessly blends the digital with the physical, providing aricher, more engaging experience. This feedback is invaluable as it highlights the immersive potential of AR when it is freed from the constraints of traditional touch-based interactions.  
Furthermore, our study contributes to the conversation on inclusivity within AR. By focusing on audio-based, non-visual interfaces, we recognize the necessity of creating AR experiences accessible to users with visual impairments. The qualitative feedback particularly emphasized the enhanced spatial awareness provided by the NAI, an aspect critically important for users who rely on auditory cues to interact with their environment.
## 3 Research Methodology
### The Mathematical Model of the Pneumatic Classification Process  
The concentration of a fine fraction in space and time is described by the following first-order partial differential equation [23]:  
\[\frac{\partial\mathcal{C}}{\partial t}=\overset{\rightarrow}{U}\text{-}grad \leavevmode\
obreak\ C+K(C_{*}-C)\leavevmode\
obreak\, \tag{3}\]  
where \(\overset{\rightarrow}{U}=\{U,\,V,\,W\}\) is a vector of particle velocity with components \(U,\,V,\) and \(W\) (m/s), \(C_{*}\) is the limiting concentration of a fine fraction in the suspended layer (kg/kg), and \(K\) is the entrainment rate (s\({}^{-1}\)).  
For a relatively small width of the cross-section in comparison with the apparatus height, it can be considered that \(\partial/\partial X=0,\) and \(\partial/\partial Y=0,\) where \(X\) and \(Y\) are coordinates of the cross-section. In this case, Equation (3) can be rewritten in the following form:  
\[\frac{\partial C}{\partial t}=W\frac{\partial C}{\partial Z}+K(C_{*}-C)\leavevmode \
obreak\, \tag{4}\]  
where \(W\) is a vertical component of the velocity in the \(Z\)-direction (Figure 1).  
Equation (4) determines the kinetic removal of fine particles from a suspended layer for the case of small rotating velocity in comparison with the gas flow rate.  
Figure 1: Design scheme of the pneumatic classification process.  
The consequent use of the direct and inverse Laplace transforms allows obtaining the general solution of the first-order partial differential Equation (4) describing the non-stationary problem for determining the concentration of particles by the apparatus height in time [24].  
\[C(Z,t)=C_{*}+(C_{in}-C_{*})e^{-\frac{\kappa Z}{W}}\;\;H\!\left(t-\frac{Z}{W} \right)+(C_{0}-C_{*})e^{-Kt}\!\left[1-\;H\!\left(t-\frac{Z}{W}\right)\right]\,, \tag{5}\]  
where \(C_{in}\) and \(C_{0}\) are the inlet and initial concentrations as the boundary and initial conditions, respectively (kg/kg).  
Particularly, in the case of \(C_{in}=C_{0}\), Equation (5) can be rewritten in the following form:  
\[C(Z,t)=C_{*}+(C_{0}-C_{*})\left\{e^{-\frac{\kappa Z}{W}}\;\;H\!\left(t-\frac{Z }{W}\right)+e^{-Kt}\!\left[1-\;H\!\left(t-\frac{Z}{W}\right)\right]\right\}\,. \tag{6}\]  
The mathematical model described by the general solution (Equation (6)) of Equation (4) allows graphically representing the dependence between the concentration of fine particles and vertical coordinate in time (Figure 2).  
The obtained results prove the fact that the concentration of fine particles is reduced by the apparatus height in time. This fact allows ensuring the separation efficiency. However, a small number of fine particles are removed from the apparatus with the gas flow.
## 4 Discussion
To validate the whole optimization process, an inorganic and a waste-based optimized culture media, IOM and WOM, respectively, were established and validated in a final trial. IOM exhibited a much higher growth rate from the first to the second day (1.45 day\({}^{-1}\)) than the other conditions. This behavior might be related to N exhaustion. On the other hand, WOM led to 31.14% lipid content, a 1.4-fold increase compared to the initial conditions. It is worth noting that CSP and glycerol seem to be suitable alternatives to replace the commonly used glutamate and glucose, since they not only allowed a lipid yield increase, but also enhanced DHA production. [PERSON] et al. [43] also reported high-fructose corn syrup as a possible option for _Aurantiochytrium_ sp. DHA fermentation, reaching final concentration values of up to 78.5 g/L DW and 51 g/L lipid yields, with a DHA content of 46.4%. Comparing the _Aurantiochytrium_ strain studied here as a DHA feedstock with other thraustochytrids described in the literature is a complex proposition. Indeed, the genus _Schizochytrium_ was amended leading to the introduction of two more genera, _Aurantiochytrium_ and _Oblongichytrium_[45]. This resulted in the distribution of preexisting commercial strains among the three genera, based on their disparate phylogenetics, morphological features, as well as fatty acid and carotenoid profiles [8; 45]. Currently, _Schizochytrium_ sp. is often chosen for industrial production of DHA, since it has been far more studied and optimized, resulting in higher biomass productivities than _Aurantiochytrium_ and other strains [20; 33; 34]. However, _Aurantiochytrium_ sp. has the potential of accumulating higher amounts of DHA (up to 80%) and further optimization is thus possible in the near future, even with the use of growth media that can be labelled as \"organic\". In addition, _Aurantiochytrium_ is able to produce other high-value pigments, such as astaxanthin and \(\beta\)-carotene, which can further increase the commercial value of the biomass produced [8; 45]. Taken together, the genus _Aurantiochytrium_ will thus be the target of further research to yield further optimization concerning their cultivation in low-cost \"organic\" media. Concerning the FAME profiles obtained in this study, they are in accordance with those previously described in the literature for _Aurantiochytrium_ sp., with particularly high contents of myristic, palmitic, DPA and DHA [11; 42; 46; 47] (Table 3). Interestingly, in the final validation trial, although WOM and IOM led to similar growth performances, higher biomass lipid and DHA contents were obtained in WOM. Similarly, [PERSON] et al. [35] obtained around 55 g/L DW and total FAME around 33% with 40 g/L glucose or glycerol and posterior feeding, while DHA concentration was higher with glycerol, 48.4% of total lipids, than with glucose (43.2%).
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
###### Abstract  
Wireless sensor networks (WSNs) are widely used in various fields, and the reliability and performance of WSNs are critical for their applications. However, WSNs are vulnerable to jamming attacks, and the impact of movable jammers on WSNs' reliability and performance remains largely unexplored. This study aims to investigate the impact of movable jammers on WSNs and propose a comprehensive approach for modeling jammer-affected WSNs, comprising four parts. Firstly, agent-based modeling of sensor nodes, base stations, and jammers has been proposed. Secondly, a jamming-aware routing protocol (JRP) has been proposed to enable sensor nodes to weigh depth and jamming values when selecting relay nodes, thereby bypassing areas affected by jamming. The third and fourth parts involve simulation processes and parameter design for simulations. The simulation results show that the mobility of the jammer significantly affects WSNs' reliability and performance, and JRP effectively bypasses jammed areas and maintains network connectivity. Furthermore, the number and deployment location of jammers has a significant impact on WSNs' reliability and performance. These findings provide insights into the design of reliable and efficient WSNs under jamming attacks.  
wireless sensor networks; movable jammer; jamming-aware; routing protocol +
Footnote †: journal: Sensors  
doi:10.3390/s23083846
## 1 Introduction
These observations suggest that the OLSR protocol, which resorts to the intrinsically redundant MPR technique, could achieve a better delivery ratio than CBL thanks to its redundant MPRs if it was modified so as to organize V2V multihop communications per traffic flow direction. Therefore, this study aimed to evaluate whether redundancy achieved through MPRs in the resulting structure of OLSR is preferable to a backbone-like clustering solution such as CBL in the context of multihop V2V communications. This was achieved by evaluating both solutions as part of one-way traffic scenarios. In particular, the impact of the chosen strategy on packet delivery ratio and latency was studied. The rest of this paper is organized as follows: First, the related work concerning clustering strategies and redundancy is reviewed in Section 2. Then, vehicle network and mobility scenarios as well as the evaluated protocols are presented in Section 3, along with the description of the simulation scenarios. The results are compared and analyzed in Section 4, before our conclusions and the perspectives on our future work are provided, in Section 5.
## 4 Methodologies and Materials
### Model Setup  
For both datasets, the batch size is set as 8 and number of training epochs as 20. The number of head for multi-head attention \(N\) is chosen from \(\{1,2,4,8\}\), max sequence length \(len\) from \(\{128,256\}\), initial learning rate for Adam\(W\ lr\) from \(\{5\times 10^{-6},1\times 10^{-5},2\times 10^{-5}\}\). The combination (\(N=4,len=256,lr=1\times 10^{-5}\)), (\(N=4,len=128,lr=2\times 10^{-5}\)) and (\(N=8,len=128,lr=2\times 10^{-5}\)) give the best results on SemEval, ReLocaR and CMR, respectively.  
The evaluation for experiments are accuracy, precision, recall, and F1.  
* **accuracy** The probability of the total sample that predicted correct results;
* **precision** The probability of actually being a positive sample of all the predicted positive samples;
* **recall** The probability of being predicted to be a positive sample in a sample that is actually positive;
* **F1** The F1 score is balanced by taking into account both accuracy and recall. The expression of F1 score is: \[F1=\frac{2*precision*recall}{precision+recall}\] (11)
# River Surface Space-Time Image Velocimetry Based on Dual-Channel Residual Network
## 3 Model Training and Fusion Coefficient Determination
### Determination of Fusion Coefficient  
Multiple sets of experiments are performed on the Panzhihua station dataset to investigate the influence of the fusion coefficient \(\alpha\) on the model's detection accuracy. The coefficient is systematically varied from 0 to 1 in 0.1 increments, with \(\alpha=1\) corresponding to exclusive use of the Gray-Channel and \(\alpha=0\) representing pure Edge-Channel processing.  
\begin{table}
\begin{tabular}{c c c} \hline
**Scenario** & **Training Set (Piece)** & **Test Set (Piece)** \\ \hline normal & 16,800 & 8400 \\ vortex & 8400 & 3360 \\ flare & 8400 & 3360 \\ obstacle & 8400 & 3360 \\ rain & 8400 & 3360 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of Panzhihua dataset.  
Figure 11: MOT by manual labeling.  
Figure 10: STIs in different scenarios.  
The model's detection accuracy is quantified using the Mean Absolute Error (\(MAE\)), which is calculated as follows:  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\left|x_{i}-X_{i}\right| \tag{10}\]  
where \(x_{i}\) is the predicted value estimated by the model, and \(X_{i}\) is the label value. The MAE results corresponding to different fusion coefficients are shown in Figure 12. It can be seen that the MAE with \(\alpha=1\) is less than the corresponding MAE with \(\alpha=0\), indicating that for MOT detection, the contribution of the original STI is greater than the STI after Sobel detection. \(\alpha\) has a small MAE between 0.2 and 0.8. When \(\alpha=0.7\), the curve has the lowest point, indicating that the model can achieve the best detection accuracy, and the fusion of these two features in this ratio can obtain the most effective features. Therefore, the value of \(\alpha\) is set to 0.7.
## 5 The Group-Aware Query Region Tree (GQR-Tree)
### GQR-Tree Manipulations
On the other hand, if \(|g\_N|\leq\theta\) and there exists some query group \(\dot{g}\in\mathcal{G}-\{g\}\) such that \(Conceptual\_Leaf\) stored in \(N\) for \(\dot{g}\) is \(False\), MergeLeaf sets \(Conceptual\_Leaf\) stored in \(N\) for \(g\) to \(True\) and creates a new tuple \(\langle g.bv,partial\_qid\_list\rangle_{new}\) for \(g\) in \(N.partial\_qid\_table\) (Lines 19-21). Next, given the tuple \(\langle g.bv,partial\_qid\_list\rangle\) stored in each \(N\)'s child node \(\dot{N}\), MergeLeaf inserts all the distinct query identifiers contained in \(partial\_qid\_list\) into \(partial\_qid\_list\) of the tuple \(\langle g.bv,partial\_qid\_list\rangle_{new}\) (Lines 22-24). Then, given the tuple \(\langle g.bv,full\_qid\_list\rangle\) maintained in each \(\dot{N}.full\_qid\_table\), for each query \(q\) referred to by each query identifier contained in \(full\_qid\_list\), MergeLeaf checks if \(q\).\(R\) is covered by or partially intersects \(N\). If so, it inserts the query identifier of \(q\) into \(partial\_qid\_list\) of the tuple \(\langle g.bv,partial\_qid\_list\rangle_{new}\) (maintained in \(N.partial\_qid\_table\)) (Lines 25-28). Finally, after deleting the tuples \(\langle g.bv,full\_qid\_list\rangle\) and \(\langle g.bv,partial\_qid\_list\rangle\) from \(\dot{N}\) and \(\dot{N}.full\_qid\_table\), respectively, MergeLeaf invokes MergeNonLeaf with \(N\)'s parent and \(g.bv\) as an input (Lines 29-30). After the insert algorithm (or the delete algorithm) terminates, the server broadcasts the \(InsertQuery\) message (or \(DeleteQuery\) message) to all the moving objects (registered at the server) to notify them of such a change.
## 5 Case Study: SFRC Beam
### Sensitivity Analysis
However, increasing the residual tensile strength after a certain point will not increase the beam's collapse displacement, as other collapse conditions, such as attainment of maximal strain in compression might be achieved before reaching 2% strain in SFRC tension. Alternatively, the ductility can be increased by decreasing the rebar yield strength, decreasing the beam yield displacement and yield strength, without significantly affecting the beam's collapse displacement (Figure 18). Figure 19: Total Sobol’ indices for collapse force. Figure 20: Total Sobol’ indices for ductility.
### Dynamic Behavior Versus Static Behavior  
Historical developments and regulatory changes since the 1972 oil crisis have led to changes in the way thermal envelopes--mainly facades and roofs--are constructed. From massive and supporting systems, they have evolved towards elements composed of overlapping layers, without any supporting function and with the thermal mass of the enclosures significantly reduced. Consequently, thermal capacity (\(k\)), that allows dampening of outside thermal variations in the delimited interior space has also substantially diminished [7]. This reality has not been sufficiently reflected in regulations. The static thermal transmittance (\(U\)) calculation system [8] does not consider the mass of the materials that form the construction systems. The parameter is therefore inaccurate; its inaccuracy becomes greater as the enclosure mass increases [9]. The greater the mass of an enclosure, the further its dynamic behavior moves away from a static calculation. The parameter \"static thermal transmittance\" therefore has limitations when defining an enclosure's characteristics. Considering that the new CTE_DB_HE (2013) [10] does not contemplate the static thermal transmittance value as mandatory and that most calculations performed are based on a dynamic regime, it is necessary to incorporate reference values in the document that take these dynamic behavior variables into account [11].  
According to [PERSON] and [PERSON] [12], the thermal inertia of building enclosures is part of the concept of \"thermal impedance\", a magnitude that allows to quantify and interpret the elements' thermal behavior when subjected to periodic thermal waves. Any incorporation of material in a building's construction solution entails possible heat accumulation, as well as a regulation of temperature fluctuations over time. In the case of buildings with high thermal mass, the envelope's walls reinforce the feeling of comfort through the exchange of radiant energy with the user, providing a natural balance with the ambient air temperature. According to [PERSON], [PERSON] and [PERSON] [13] this balance results from the absence of heat or radiation inputs. As [PERSON] and [PERSON] found, heritage buildings with thermal inertia regulate wall temperatures in their daily evolution, resulting, in the absence of other internal energy inputs, in an almost constant interior temperature, equal to or below the average of the outdoor air temperature [14].  
It is not sufficient, however, for a body to have a large thermal mass for that quality to intervene in the premises' thermal stability. Not all material bodies have the same capacity to accumulate energy. In addition, only a minor part of a construction element's theoretical accumulation capacity is used in practice. [PERSON] and [PERSON] compared experimental and theoretical results regarding transient temperature variation in the different layers of materials that make up some building sections [15]. They concluded that materials such as autoclaved aerated concrete or wood were more suitable than ceramic plating and brick.
# An Application of BOTDR to the Measurement of the Curing of a Bored Pile
## 5 Conclusions  
In this paper, the distributed fiber optic sensing technology based on BOTDR is used to carry out a series of studies on the strain change of the pile in the concrete curing stage; the following conclusions are obtained:  
1. The distributed optical fiber sensing technology based on BOTDR is used, and the accurate data of the pile strain are obtained to study the curing of concrete bored pile deformation; it reflects the advantage of distributed optical fiber sensing technology in the pile foundation monitoring. The hydration heat is released continuously over time, while the temperature of the pile body is confined to the surrounding soil layer. The variation of temperature at different depths is related to the temperature of surrounding soil layer, it affects the strain distribution of the pile.
2. Based on the comparison between the strain distribution of the pile body obtained by BOTDR and the theoretical calculation value, the soil around the pile and the underground constant temperature layer have a significant influence on the pile body strain. The distributed optical fiber can adequately monitor the deformation of the concrete pile during the concrete curing period, especially the pile strain caused by the heat of hydration; it is helpful to the quality control of concrete piles during the curing period.
3. Integrated soil information and monitoring result analysis confirms that the pile deformation in the middle of silty clay layer, powder soil, and silt layer and other hard restriction in soil is larger, and it will affect the quality and bearing capacity of the pile.  
[PERSON] designed the project. [PERSON] and [PERSON] performed the experiment and prepared the manuscript. [PERSON] and [PERSON] improved the English of manuscript. All authors have read and agreed to the published version of the manuscript.  
Figure 9: The last test tested the strain with the calculated free strain.  
**Funding:** This research received the funding from the National Natural Science Foundation of China (Grant No. 52027812), the Fundamental Research Funds for the Central Universities of Hohai University (No. B210202047).  
**Institutional Review Board Statement:** The studies are not involving humans or animals.  
**Informed Consent Statement:** The studies are not involving humans.  
**Data Availability Statement:** Data sharing is not applicable.  
**Acknowledgments:** Financial support coming from the National Natural Science Foundation of China (Grant No. 52027812), the Fundamental Research Funds for the Central Universities of Hohai University (No. B210202047), the Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, and Hohai University (No. GHXN202005) is gratefully appreciated.  
**Conflicts of Interest:** The authors declare no conflict of interest.