esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
When fermentation is first started, the osmotic pressure changes due to the high concentration of solutes in the medium, which may lead to cell exposition causing water efflux from the cell, result in inactivation of the critical enzymes of microbial metabolism [13]. Later, during the fermentation process, pH and temperature change due to acid products and incubation temperatures. For yoghurt especially, the culture concentration and temperature have a significant effect on the physicochemical properties of the end products [14]. Based on the limitations on yoghurt production, obstacles such as maintaining its viability during the processing and storage, cost efficacy and resistance to the physicochemical processing must be considered [15]. These limitations can be improved by genetic engineering of the strains. However, careful consideration of using is required to develop strains that are suitable for the desired purpose, and safe for consumption. Various studies have used adaptive laboratory evolution (ALE) to control evolution and create strains with unique characteristics. ALE is a method to employ the fundamental mechanisms of molecular evolution and adaptive changes that are generated in microbial populations during long term selection under specific growth conditions [16]. ALE is believed to be methodologically straightforward to investigate the effect of evolutionary forces and develop unique strains with desirable phenotypes, performance, and stability [17]. In order to perform ALE on a micro-organism, a single strain culture is exposed to continuous propagation under stress in order for the organism to evolve, survive and grow under the conditions of the desired stress factor. This new phenotype is acquired through mutations in key metabolic genes [18] as well as genome and transcriptome reorganization [19]. Applications of ALE have been made to create new brewing yeast for ale and lager to improve the brewing efficiency and beer quality; _E. coli_ for developing freeze-thaw stability for baker's yeast and _Lactobacillus rhamnosus_ through freeze-thaw cycles to select tolerant strains [20; 21; 22]. Applying ALE to _L. bulgaricus_ in yoghurt production is a novel approach. The result of previous studies on ALE has given promising results in reducing obstacles and to maximise productivity and quality of different products. Therefore, ALE could be used to modify starter cultures to create yoghurt with unique nutritional, taste and aroma characteristics. Hence, the aim of this study was to apply ALE to _L. bulgarcius_ in order to develop a thermo-tolerant strain using a gradual increase in growth temperature. The study also aimed to inoculate milk with the mutated thermo-tolerant strain and investigate subsequent changes observed in microbiological, physicochemical, and nutritional characteristics.
## 3 Framework
### Overhead Reduction and Properties of SERENA
#### 3.5.2 Properties
_Property P5: SERENA tree coloring provides a valid coloring for data gathering applications, assuming an ideal environment._  
_Proof:_ For SERENA tree coloring, the set \(\mathcal{N}(N)\) contains by construction, all nodes up to 2-hop from \(N\), and all nodes \(M\) meeting one of the four cases depicted in Figure 3. Assuming an ideal environment, SERENA ensures that no other node in \(\mathcal{N}(N)\) can take the same color as \(N\). Furthermore, assuming an ideal environment, nodes that do not belong to \(\mathcal{N}(N)\) cannot create a collision with data sent by \(N\) or acknowledgement sent to \(N\), as shown by property P4. Hence, SERENA does not create collision.
## 2 Materials and Methods
### Implementation of the DBIM-FISTA with a GPU-Based FDTD Forward Solver
In our simulations, the iteration number ranges between 1000-2000. To compare computational burden vs. grid resolution, we calculated run times for a two antenna system using 2 mm, 1.5 mm and 1 mm size cubic voxels. These are acceptable resolutions for a grid with physical volume of about 300 mm \(\times\) 300 mm \(\times\) 200 mm, which is required for our inverse problem with the numerical head phantom. Resolutions of 0.5 mm or lower exceed the GPU's memory requirements, and they also increase errors and instabilities for the inverse model [51]. The size and run times corresponding to these different grids when 16 antennas are used as transmitters and receivers are shown in Table 1.  
Figure 1: Flowchart of the GPU-based FDTD algoritm. As this implementation is specific for the inverse problem at hand, it is more than 30% faster than our previously used codes implemented with commercial software package Acceleware, which is designed for general GPU-based FDTD simulations. Moreover, the mean absolute error of the fields received by each antenna calculated by Acceleware and our new code is less than \(10^{-8}\), which shows our implementation is as accurate as Acceleware's. The running time for FISTA for the above-mentioned case with 2 mm resolution where the size of \(A\) is 240 \(\times\) 449,949 at each DBIM iteration, is around 5-15 s depending on the number of FISTA iterations. This number typically ranges from 20-100, i.e., for each iteration the average running time is around 0.2 s.  
As noted earlier, we have also employed the 2-D DBIM-FISTA algorithm [40] in this paper to compare performance with the 3-D implementation. The transverse magnetic (TM) mode of the EM wave is considered for the 2-D problem. The 2-D code is implemented in MATLAB without the need of GPU acceleration for the FDTD forward model, which requires approximately 1 s for each of the eight transmitting antennas and a total of 10-20 s for each DBIM iteration.
## 4 Experimental Results and Analysis
### Signal Reconstruction with Gradient Inversion Attack Versus Deep Learning Level  
The recovery of private data can be viewed as an iterative optimisation process utilising gradient descent.  
Figure 5 shows an iterative optimisation, where each iteration k refines the estimate of the signal as a function of the error magnitude of the recovery iterations k, in a process called iterative randomised signal updating. (a) The blue curve shows the error magnitude when no noise is added to the signal and serves as a baseline. (b, c) The green and orange curves represent scenarios where noise is added at two different levels. In the first method, the MSE error reaches saturation at 0.05 \(\times\) 10\({}^{-5}\) without DP and saturation at 0.15 \(\times\) 10\({}^{-5}\) MSE with DP. In the second method, the saturation is 1 \(\times\) 10\({}^{-10}\) without DP and 0.1 \(\times\) 10\({}^{-8}\) with DP. In all cases, the error magnitude decreases rapidly during the first few recovery iterations. However, the final error magnitude is higher for the curves with DP noise (b, c) and increases with increasing noise level. The moderate noise level of 1.1 that we chose (b) leads to a slightly higher error after convergence. In contrast, the higher noise level 1.15 (c) leads to more distortion and thus a larger final error magnitude. In the absence of any noise (a), the recovery process perfectly matches the true values. It is summarised in Table 1.  
As can be seen in Figure 6, the original signal (a, blue line) and the recovered signal (b, green line) represent the output after some processing. Initially, the blue line matches the original signal, but there is a sudden deviation around time step 60, followed by a gradual recovery. As time progresses, however, the green line deviates from the original signal. The recovery signal follows the recovery signal very closely at the beginning, which shows that the recovery technique works well for early time steps. Beyond time step 60,  
\begin{table}
\begin{tabular}{c c c} \hline
**DP Levels** & **GIA with Random Updates** & **GIA with Probe Set** \\ \hline No DP & \(\sim\)0.05 \(\times\) 10\({}^{-5}\) & \(\sim\)1 \(\times\) 10\({}^{-10}\) \\ \hline DP 1.1 & \(\sim\)0.08 \(\times\) 10\({}^{-5}\) & \(\sim\)0.02 \(\times\) 10\({}^{-8}\) \\ \hline DP 1.15 & \(\sim\)0.15 \(\times\) 10\({}^{-5}\) & \(\sim\)0.1 \(\times\) 10\({}^{-8}\) \\ \hline \end{tabular}
\end{table}
Table 1: GIA saturation MSE values.  
Figure 5: Iterative randomised signal update. (a) Signal recovery with random signal updates, (b) signal recovery with a discrete probe set.  
the recovered signal continues to grow but does not exactly match the original signal. The recovery process is partially successful, but cannot maintain accuracy across all time steps, especially after a sudden drop (e.g., time step = 60). The significant deviations around time step 60 indicate either noise, a loss of information or an error in the recovery at this point.
## 2 Experimental Setup and Control
The laser beam is centered onto the OAP reflective surface and the mirror is tilted vertically until the focal spot lies in the same horizontal plane as the axis of the beam inside the interaction chamber. Subsequently, the OAP is tilted in the horizontal plane to bring the focus close to the reference tip. As soon as the magnified focus becomes visible through the objective, the OAP optimization can start. The monitoring system is moved towards and outwards of the incoming beam to evaluate the size and shape of the beam close to the focal point. One can achieve a small focal spot size with a large value of encircled energy (i.e., minimal wave front distortion and high laser intensity) on target only when the focusing OAP is properly optimized, which is evident from the circular shape of the converging and diverging beam in the proximity of the laser focus. If the focal spot is not circular, the OAP alignment shall be optimized. The tip and tilt of the OAP are used while scanning the focal spot in the longitudinal direction. During this optimization, the focal spot moves away from the tip. This must be simultaneously compensated using the movement of the OAP holder since the focal spot follows the X, Y, Z movement of the OAP. This procedure is normally done manually step by step until the desired result is reached. As soon as a circular focal spot is achieved in front of and behind the focal plane, the transversal optimization ends. The monitoring system is then pointed back on the reference tip and the OAP is finally moved to place the focus (now a very small circle) onto the top of the reference tip. This ensures that the optimized focal spot sits in the interaction point. As the last step, the target is moved into the same position.
## 3 Results and Discussion
### Experiments on the Self-Built Actual Inspection Dataset  
Since there is no publicly standardized transformer substation intelligent inspection dataset available, and to more convincingly evaluate the performance of the proposed method, we gathered videos of real inspection scenarios and compiled a small dataset for the experiment. The self-built dataset contains scenes of electrical equipment such as instruments, insulators, and liquidometers taken by inspection robots and UAVs. The same evaluation metric described above for the benchmark dataset is utilized to evaluate the overall performance of the proposed approach.  
In the comparison experiment, classic tracking networks are selected, including TADT [32], SiamRPN [39], SiamMASK [40], SiamCAR [44], and Ocean [46]. The tracking effect is shown in Figure 7. Our approach ranks first in success and precision rate, with a precision score of 89.3% and a success rate AUC score of 74.9%, respectively. Also, the proposed method achieves a processing speed of up to 33 FPS, which exceeds the minimum requirement of 30 FPS for real-time segmentation, and real-time tracking of substation detection is realized.  
Figure 8 shows a qualitative comparison of our algorithm and baseline on the real inspection scene videos. Our algorithm demonstrates great advantages in segmentation accuracy in four substation inspection scenarios. The method has smoother segmented contours than the baseline segmentation tracking algorithm, which improves the tracking accuracy and is an effective network that can be used for real-time segmentation and tracking. The green bounding box is the ground truth, and the segmentation and tracking results are shown with the pink mask and the yellow bounding box.  
In order to compare and analyze the tracking ability of the proposed methods even further, the tracking results of the different methods are considered to be demonstrated in four real scenarios, as shown in Figure 9. It can be seen that the algorithm in this paper is  
Figure 6: Success and precision plots on UAV123.  
able to obtain better tracking results with strong robustness and real-time performance for tracking power equipment in substation scenarios.  
Figure 8: Segmentation and tracking results of our method and baseline on real inspection scene videos. The green bounding box is the ground truth, and the segmentation and tracking results are shown with the pink mask and the yellow bounding box.  
Figure 7: Success and precision plots on the self-built actual inspection dataset.
## 1 Introduction
They found that different FAPAR products could result in different model performances and in reducing uncertainties of different parameters. The combination with other datasets (e.g., eddy covariance fluxes) would improve the fit to independent observations. Remotely sensed soil moisture has been widely used for assimilation in hydrological models [24; 25; 26], but also for assimilation in the context of updating gross carbon fluxes on a regional scale [27; 28]. Recently, the assimilation work has shown that remotely sensed soil moisture is able to provide, as a complementary dataset, an additional constraint on hydrological as well as carbon cycle processes in a terrestrial biosphere model. Soil moisture, especially plant-available soil moisture, plays a key role in controlling photosynthesis and transpiration in plants and therefore regulates CO\({}_{2}\) uptake through leaf stomata. Moreover, drought, freeze/thaw and, again, surface soil moisture control plant carbon cycling [29]. Nowadays, surface soil moisture can be observed by microwave sensors and is therefore independent of cloud cover. Such data sets have the potential to improve the simulation of hydrological processes in terrestrial biosphere models, thus resulting in improved terrestrial carbon cycle simulations. Soil moisture from the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission provides such a high-quality dataset for assimilation in hydrological and terrestrial biosphere models [19; 30; 31; 32]. Assimilation of SMOS L3 soil moisture data together with atmospheric CO\({}_{2}\) concentration data into the BETHY model to constrain the terrestrial carbon cycle on a global scale was done [19]. Assimilating SMOS soil moisture data into BETHY improved several aspects of the hydrological and carbon cycle [19]: (1) The agreement between both modeled and independently observed surface soil moisture (such as in-situ measurements of soil moisture from the International Soil Moisture Network (ISMN) [33]) improved significantly, and (2) the agreement between modeled and observed CO\({}_{2}\) concentrations improved. A combined assimilation of FAPAR and soil moisture has been proposed by many studies as a potential improvement of the modeled carbon cycle [16; 18; 19]. In this study, we investigate the potential of such a simultaneous assimilation of remotely sensed surface soil moisture and FAPAR data to constrain the terrestrial carbon cycle both in single-site and in multi-site setups. The questions addressed are: What are the advantages of combining soil moisture and FAPAR data in constraining parameters of BETHY in a multiple sites approach? What do the posterior parameter values obtained by the assimilation experiments tell us about the mechanistic processes represented in the model? We also address the question whether an optimal parameter set obtained from site scale assimilation experiments improves global scale simulations.
# Wide Scanning Angle Millimetre Wave 1 \(\times\) 4 Planar Antenna Array on InP at 300 GHz
## 3 Conclusions  
A linear 1 \(\times\) 4 array is realised on InP, which is interfaced with a high-speed unit-travelling carrier photodiode. The designed array structure can provide a maximum simulated gain of 10 dBi with an SLL of better than 8 dB for a 100\({}^{\circ}\) scan angle. The array fabrication does not need via holes within InP, and thus overcomes the fabrication challenges of InP. For the first time, a modified ground plane is proposed for InP-based sub-THz antenna arrays. By utilizing a modified MSWIGP structure in the ground plane, the gain variation across the bandwidth was decreased to 3 dB with an improvement of 2 dB for side lobe levels. Although the peak gain of the array has decreased by 0.85 dB, the array is able to provide an acceptable performance over the whole band. It is worth noting that the introduction of MSWIGP does not require any additional fabrication steps nor a change in the layer stack-up. The introduction of MSWIGP also does not increase the back lobe as the slots are in the order of few micrometres. Using similar design techniques, it is possible to realise a 2D array for improved gain and beamwidth. The SLLs can be further improved by using amplitude tapering. The presented array demonstrates that it is possible to realise a UTC-PD conjugately matched phased array radiating structure with acceptable performance parameters on InP for the sub-THz frequency range.  
Conceptualization, H.M.S. and L.M.P.; methodology, L.M.P. and B.H.; software, B.H.; validation, B.H., L.M.P.; formal analysis, B.H.; investigation, L.M.P.; resources, L.M.P.; data curation, B.H.; writing--original draft preparation, B.H.; writing--review and editing, L.M.P. and H.M.S.; visualization, L.M.P. and H.M.S.; supervision, H.M.S.; project administration, L.M.P.; funding acquisition, L.M.P. All authors have read and agreed to the published version of the manuscript.  
This research has received funding by European Commission under project TERAPOD (Grant Number: 761579).  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
In this paper, we demonstrate how UHS-based seismic microzonation can be applied in low-to-medium seismicity areas with deep local soil and deep geological deposits under the local soil. The case study area surrounds the city of Osijek, Croatia, which is in the south-central region of the Pannonian Basin. New frequency-dependent scaling equations are derived, and the empirical response spectra are compared to the spectra of real strong motions in the surrounding region. Empirical calculations for deep soil atop deep geological strata show a 37% reduction in short-period spectral amplitudes when compared to rock locations. This demonstrates that local soil amplification is mitigated by energy dissipation in deep soils. For vibration periods longer than 0.3 s, spectral amplitudes are being amplified. This amplification goes up to 2.37 times for vibration periods around 0.5 s. UHS spectra for Osijek are computed using regional seismicity estimates, data on local soil and deeper geological surroundings, and newly created regional empirical equations for scaling various spectral amplitudes. UHS amplitudes for Osijek are also compared to the Eurocode 8 spectra for ground type C. The results show that ratios of the maximum UHS amplitudes to PGA values are up to 46% larger than the corresponding 2.5 factor that is recommended by Eurocode 8 for horizontal spectra. The UHS results might be viewed as preliminary for Osijek and regions with similar seismicity and local soil and deep geology conditions. When the number of regional strong-motion records grows many times beyond what it is currently, it will be feasible to properly calibrate the scaling equations, resulting in more reliable and long-term UHS estimations for the area under consideration.  
UHS; microzonation; scaling equations; deep local soil; deep geological deposits; Eurocode 8 +
Footnote †: journal: Applied Sciences
# Energy Efficient Determinism in WSN through Reverse Packet Elimination
## 7 Discussions  
Our analysis follows the methods used by LFC, and as LFC we investigate only a section of a WSN. A complete WSN with several paths puts requirements on the network design as two paths must be available for critical traffic flows. Our RPE method imposes only the requirement that the two paths are disjoint. Paths may hold a different number of nodes; this difference will alter the performance  
Figure 14: Funneling effect at A1 and B1. Represented with node lifetime in days. (Note: y-axis starts at 1500 days).  
Figure 15: Average current consumption.  
in terms of finding the optimal delay of the replication packet. Clearly, the method depends on the requirements imposed on the WSN in terms of delay tolerances and the performance of the WSN. Our results show optimal delay equals the time delay from the source to the sink and back along the second path. In our network, this was a delay of eight slots. The reason is that in an environment with no packet losses, the tracks allow a packet to loopback and stop the source from sending the replicated packet. Based on the results with 80% link quality, \(\tau=8\) achieves the best traits of all replication delays. Comparing our RPE with scenarios Dual Path and Single Path with PRR = 80%, the lowest node lifetime increased with 1.74%. Next, the average latency is reduced with 39.1% and reliability increases from 97.7% to 99.95%, when comparing our RPE with Single Path. A natural disadvantage with the RPE proposal is the increase in average current consumption compared to using a Single Path. This is due to extra tracks and packets traversing disjoint paths. However, packet replication is recommended by DetNet architecture and RPE decreases the average current consumption compared to Dual Path, increases with 18% compared to Single Path. Finally, if low average latency is critical, it is recommended to utilize Overprovisioning. Overprovisioning has a 71.2% lower average latency than \(\tau=8\). However, this comes with an 18.34% reduction in the lowest node lifetime.
## 4 THz Surface Plasmon Resonance Sensing and Devices Based on ATR
### SPR Devices Based on Otto Geometry  
[PERSON] et al. [42] proposed an SPR-based gas sensor in THz frequency with Otto configuration using free standing doped monolayer graphene (Figure 3b). The proposed sensor could achieve an ultrahigh figure of merit about 1150 RIU\({}^{-1}\) (refractive index unit). It was found that the gap distance between the prism and graphene plays an important role in the coupling efficiency, which further determines the sensitivity and accuracy of the proposed sensor. However, the sample fabricating becomes a challenge since a tiny air gap needs to be maintained in this Otto geometry. To solve this problem, another SPR based gas sensor was proposed by using the different dielectric polymer spacing layer instead of the air gap with the similar structure [43]. This structure achieves the FOM of 741 RIU\({}^{-1}\) and is more suitable and convenient for practical sensing. [PERSON] et al. [44] further modified this structure and realized an active control SPR sensor through the magnetic field. The dielectric properties of graphene could be changed through the magnetic field, thus the sensitivity of the sensor could be optimized by properly adjusting the magnetic field. Another hybrid structure consisting of a double-layer graphene, a thin dielectric film, and planar waveguide was proposed, which can be used to support a surface plasmon polariton mode in long range [45]. This hybrid sensor structure could achieve sensitivity of 292 RIU\({}^{-1}\). The SPR sensors achieved the matching of the SPR mode between ATR geometry and graphene, and further confine the THz electric field in the vicinity of the surface.