esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Computational Fluid Dynamics Simulations
#### 3.2.1 Mesh Independence Study  
The results of a CFD model can be altered depending on the mesh imposed onto the geometry. To obtain more accurate results, choosing an appropriate mesh configuration is essential. It has been found that smaller meshes tend to produce better solutions with increased precision [41]. However, this requires utilizing a finer resolution for the simulation, which increases computing power, as more equations must be solved to complete the task. Thus, selecting the appropriate mesh involves balancing accuracy and computational ability. Before running the CFD simulations, a mesh independence test was conducted to determine the most suitable grid size for this numerical study. Three meshes (M1: 3.5 mm; M2: 4.5 mm; and M3: 5.5 mm base size) were utilized and examined to understand how they impacted water droplet diameter distribution, as shown in Figure 6. The results depicted in Figure 6 show that the mesh's base size did not have a significant effect on the water droplet diameter distribution plots, which remained unchanged and steady for the respective case. Taking into account computational efficiency, it was decided that a 4.5 mm base-size polyhedral mesh would be used (with 266,016 mesh cells).
## 5 Conclusions  
This study reported the introduction of TS 1411 to construct a two-plasmid system to modulate the MVA pathway and achieve tricyclene biosynthesis in _E. coli_. By optimizing the culture temperature, increasing the copy number of the TS-coding gene, and truncating the random coil sequences at the N-terminal of TSs, we achieved a remarkable 794.5-fold improvement in the tricyclene titer compared to its wild type, with an increase from 0.060 mg/L to 47.671 mg/L. The results of SDS-PAGE analysis revealed that the solubility of TS was significantly enhanced by reducing the induction temperature and truncating the TSs, indicating a close relationship between tricyclene titer and TS solubility. Furthermore, varying degrees of increases in tricyclene titers were observed upon truncation of other TSs. To the best of our knowledge, this report presents the highest recorded titer of tricyclene and specificity of tricyclene synthase to date.  
**Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/fermentation10030173/s1](https://www.mdpi.com/article/10.3390/fermentation10030173/s1)]([https://www.mdpi.com/article/10.3390/fermentation10030173/s1](https://www.mdpi.com/article/10.3390/fermentation10030173/s1)), Table S1: Strains and plasmids used in this study; Table S2: Primers used in this study. Table S3: Tricyclene synthases derived from different species. Table S4: Tricyclene and by-product titers of different TSs. Figure S1: SDS-PAGE analysis of wild-type 1411 and 1411(44).  
**Author Contributions:** Conceptualization, M.Z., S.B., H.J. and W.Z.; methodology, M.Z., S.B., X.Z. and W.Z.; software, S.B.; validation, M.Z., S.B., J.L., C.C. and P.H.; formal analysis, M.Z., S.B., F.W. and X.W.; investigation, M.Z. and S.B.; resources, G.Y.; data curation, M.Z., S.B., J.L., F.W., G.Y. and X.W.; writing--original draft preparation, M.Z.; writing--review and editing, M.Z., S.B., H.J., X.Z. and W.Z.; visualization, M.Z., S.B., J.L. and P.H.; supervision, H.J., X.Z. and W.Z.; project administration, W.Z.; funding acquisition, H.J., X.Z. and W.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China Grant 32300066.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article and Supplementary Materials.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 3 Results and Discussion
### Evaluation Metrics
#### 3.2.2 Effectiveness Analysis of Multi-Source Fusion Prediction Model
The above experimental results are related to the distribution of the dataset, and the number of positive samples is small. These experimental results prove that the proposed method can effectively combine the characteristics of RGB images and vegetation indices to learn the characteristics of various diseases and pests and accurately identify five common apple diseases and insect pests.
# Dialogue Enhanced Extended Reality:
## 4 Use Cases
### Use Case 2: Industrial Electrical Wiring  
The proposed architecture was also validated for cabling tasks, more specifically the assembling of electric panels. This use case was proposed by industrial partners of the project, since it is a process that they carry out on a daily basis. Voice interaction was achieved by headphones with noise-cancelling, which participants could choose to wear on them or leave on the working table. To handle the visual  
Figure 4: Sequence of steps required to complete the gripper’s maintenance task.  
Figure 3: An operator uncoupling the Universal Robot’s Gripper.  
communication, an Optoma projector with a wide angle lens was installed to handle the projection mapping. Additionally, it could also deliver audio output for users that were not wearing headsets. Figure 5 shows an operator in the described setting during the task. Additionally, a third-party optical reader [45] was added to the Device Control Layer to receive visual input. This reader detects the wires grabbed by the operator, so this information can be used for interaction contextualisation.  
In this scenario, different uses of the projections and voice-based responses were combined to develop a user-centred communication, adjusted to their needs and impairments. For example, for participants with hearing impairments, aural responses were also projected in the form of captions. The completion of this task required the participants to work with both hands, one to manipulate wires and the other to screw the wire connection to the terminal. The best practices for the wiring sequence recommend operators to connect the longest wires first in order to reduce cluttering at early stages. Each wire is identified with a printed label and has its properties, attributes, and process logic stored in the DKL.  
A set of ten participants from an external industrial factory was selectively assigned to the testing group of this use case. The inclusion criteria for this group was the level of worker disability. Only workers with a cognitive or physical disability were selected. Due to the sensitive nature of this particular group of testers, we are not providing further information on their age and gender.
## 1 Introduction  
Gravitational microlensing is an astronomical phenomena. When a lensing body comes in precise alignment with a source star, which is being observed through an optical system, the light rays bend due to the gravitational effects of the lensing system, causing a magnification in the observed light curve. By obtaining these photometric curves and analyzing their characteristics, we can determine the science parameters of a lensing system. We show a compressive sensing (CS) based architecture system for a space flight constellation here, performing photometric measurements. Typically, the galactic bulge is surveyed in order to increase the chances of capturing a microlensing event. A space based microlensing parallax can be obtained using a CS architecture. A CS system will enable capturing and collecting data in SmallSat type instruments by reducing the need for on-board data storage and data downlink bandwidth.  
Compressive sensing is a mathematical theory for sampling at a rate much lower than the Nyquist rate, and yet reconstructing the signal back with little or no loss of information. The signal is reconstructed by solving an underdetermined system. In a CS architecture, to acquire a signal of size \(n\), we collect \(m\) measurements, where \(m<<n\). One measurement sample consists of a collective sum. We solve for Equation (1) to determine \(x\) through the observation \(y\)[1, 2, 3, 4].  
\[y_{mx1}=A_{mxm}x_{mx1} \tag{1}\]  
Using the acquired measurements vector \(y\) and the known measurement matrix \(A\), we can solve for a sparse \(x\) by applying various techniques, including greedy algorithms and optimization algorithms.
# Light-Weighted Password-Based Multi-Group Authenticated Key Agreement for Wireless Sensor Networks
## 3 Proposed Multi-Group Authenticated Key Agreement Protocol for WSNs
### Authentication and Key Agreement Phase  
This phase, as shown in Figure 2, enables sensor nodes \(SN_{i}\) for \(i\) = 1,2,... \(n\) to authenticate each other and to negotiate session keys for each group with the help of \(AS\). First, sensor node \(SN_{i}\) sends its password \(pw_{i}\) to \(AS\), which is encrypted with a secret key of \(SN_{i}\) and \(AS\). After \(AS\) successfully authenticates \(SN_{i}\), \(AS\) assists these sensor nodes in agreeing a common secret key as their group session key. The details are worked as follows.  
Step 1. SN\({}_{i}\)\(\rightarrow\)AS : M\({}_{i,1}\) = \(\{\)[ID\({}_{i}\), X\({}_{i}\), C\({}_{i}\), T\({}_{i}\)\(\}\)  
Each sensor node SN\({}_{i}\) chooses a nonce r\({}_{i}\), computes K\({}_{1}\) = T\({}_{\rm{r_{1}}}\)(pks) mod p, DID\({}_{i}\)=K\({}_{1}\)\(\oplus\)ID\({}_{i}\), X\({}_{i}\) = T\({}_{\rm{r_{1}}}\)(x) mod p, Q\({}_{i}\)=h(ID\({}_{i}\)\(\|\)pw\({}_{i}\)), C\({}_{i}\) = h(DID\({}_{i}\)\(\|\)K\({}_{i}\)\(\|\)K\({}_{i}\)\(\|\)K\({}_{i}\)\(\|\)C\({}_{i}\)\(\|\)T\({}_{i}\)), where T\({}_{i}\) is the current timestamp, and sends M\({}_{i,1}\) = \(\{\)[DID\({}_{i}\), X\({}_{i}\), C\({}_{i}\), T\({}_{i}\)\(\}\) to AS.  
Step 2. AS\(\rightarrow\)SN\({}_{i}\) :M\({}_{i,2}\) = \(\{\)Y\({}_{i-1}\), Y\({}_{i+1}\), HGP\({}_{i,m}\), C\({}_{S}\), T\({}_{S}\)\(\}\)  
After receiving \(M_{i,1}\), \(AS\) checks the validity of \(T_{i}\). If successful, \(AS\) computes \(K_{1}^{{}^{\prime}}\) = \(T_{mk}(X_{1})\) mod p, ID\({}_{i}\) = DID\({}_{i}\)\(\oplus\)K\({}_{1}^{{}^{\prime}}\), HID\({}_{i}\) = \(h(ID_{i}\)\(\|\)m\({}_{k}\)), retrieves (\(Q_{i}\), \(Group_{i}\)) by \(HID_{i}^{{}^{\prime}}\), and checks \(C_{i}\) = \(h(DID_{i}\)\(\|\)Q\({}_{i}\)\(\|\)K\({}_{i}\)\(\|\)X\({}_{i}\)\(\|\)T\({}_{i}\)). If successful, \(AS\) chooses a nonce r\({}_{S}\), computes \(Y_{i}\) = \(T_{rs}(X_{i})\) mod p, constructs a group identity \(GID_{im}\) = \((DID_{1}\), \(DID_{2}\),...,DID\({}_{i}\), \(\ldots\),DID\({}_{j}\),... ) by using sensor nodes' temporal identity \(DID_{i}\) and calculates \(HGP_{i,m}\) = \(h(K_{1}\)\(\|\)G\({}_{im}\)\(\|\)T\({}_{S}\)\(\oplus\)GID\({}_{im}\), CS\({}_{i}\) = \(h(K_{1}\)\(\|\)Q\({}_{i}\)\(\|\)Y\({}_{i,1}\)\(\|\)\(\|\)\(\|\)\(\|\)\  
\(V_{S_{i}}=h(K_{1}\parallel Q_{i}\parallel Q^{{}^{\prime}}{}_{i,nw}\parallel K_{1} \parallel X_{i}\parallel T_{s})\), where \(T_{s}\) is the current timestamp, and sends \(M_{i,2}=\{V_{S_{i}},\ T_{S}\}\) to \(SN_{i}\). Finally, \(SN_{i}\) makes sure that \(AS\) has updated \(SN_{i}\)'s verification data in \(S\)'s database by validating \(T_{s}\) and checking \(V_{S_{i}}=h(K_{1}\parallel Q_{i}\parallel Q_{i,nw}\parallel K_{1}\parallel X _{i}\parallel T_{s})\).  
Figure 2: The proposed multi-group authenticated key agreement protocol for wireless sensor networks (WSNs).
# Curvature Sensing and Control of Soft Continuum Robots Using e-Textile Sensors
## 5 Results and Discussion
### e-Textile Sensor Responsiveness  
This experiment examines the effectiveness of an e-textile sensor array in dynamically sensing the shape of a continuum robot using a 4 \(\times\) 4 matrix of sensing points labeled from \(A_{0}\) to \(A_{15}\). Each point's resistance inversely correlates with the pressure applied due to the robot's deformation in terms of curvature \(\kappa\). As shown in Figure 8, our results highlight the sensor's sensitivity and accuracy in mapping curvature changes, offering valuable insights into the integration of soft robotics with the soft e-textile sensor.  
The data indicate that resistance changes at the sensing points are not uniform but vary in a way that directly mirrors the robot's deformation pattern. This variation implies that the e-textile sensor can not only detect the occurrence of bending but also distinguish its magnitude and directionality.
## 4 FWA Design Considerations, Challenges, and Solutions
### Envisaged 5G Deployment Spectrum  
In general, lower bands implementation comparatively offers lesser data transmission over longer geographic distances. Besides, it consumes less power and exhibits better interference resistance [4]. On the other hand, higher bands present considerably more data transmission, however, over shorter geographic ranges. As aforementioned, the 5G FWA network deployments are anticipated to be on the mm-wave bands that are above 28 GHz. However, some providers such as Sprint and T-Mobile have been considering deployment between 600 MHz and 6 GHz. Furthermore, the cable operators like CableLabs and Arris have been developing a considerable interest in the 3.5 GHz Citizens Broadband Radio Service (CBRS) band [252]. At that band, broadband capability in hundreds of Mbps can be delivered at up to 800 m transmission distances in the NLOS conditions. Additionally, with channel aggregation, the throughput can be further increased to about 10 Gbps.  
Figure 12: End–to–end FWA networking using MIMO and beamforming (adapted from [6; 12; 14; 252]).  
However, with increasing traffic due to various applications and services, lower bands such as 3.5 GHz will not be able to support the network demands efficiently. Consequently, lower bands are envisaged to be employed for backup connections [13]. They can also be employed in applications like machine-to-machine (M2M) connectivity and smart metering when high data rates are inessential for efficient operation [4; 252].  
It is remarkable that for FWA to be an appealing alternative/complementary technology to the current wireline broadband, it has to satisfy the throughput, latency, and capacity demands that are highly comparable with that of FTTx-type broadband connections [249]. This will enable it to deliver broadband services at fiber-like speeds with low latency for the UL and DL transmissions. As previously mentioned, one viable means of enhancing the 5G FWA system performance regarding cell capacity is via the implementation of huge-bandwidth mm-wave frequencies such as 28 GHz, 37 GHz, 39 GHz, 60 GHz, and 64-71 GHz. However, mm-wave employment presents aesthetic, operational, and technical challenges [13]. Therefore, adoption of innovative technology is very essential to address the associated limitations of the scheme [5; 69; 6]. In the following, we expatiate on the key operational and technical challenges of this technological implementation.
## 4 Results  
All the results are available in the data archive. For reasons of space, and as it is the most likely outcome, only results on late arrivals for a scenario with a single one-hour disruption are presented in Figure 7. The baseline and the resilient approaches are on the abscissa, the mean total numbers of late arrivals are on the left, the mean total times of late arrival are on the right, and the results for the handmade schedule are hatched, unlike those for the optimized schedule.  
The approach 0 in red presents the worst performances: 15.8 late arrivals on average (i.e., 11.3% of care visits are late), with a total of 6.7 h of late arrival in the optimized schedule as well as 14.9 late arrivals and 7.2 h in the handmade schedule. Overall, the two other approaches (in yellow and green) are better, with the X-1 sub-approaches yielding the best results. With approach 1-1, the number of late arrivals is reduced by 9% in the optimized schedule (7% in the handmade one), with the total time of late arrival reduced by 18% (16% in the case of the handmade one). With approach 2-1, the number of late arrivals is reduced by 11% in the optimized schedule (10% in the handmade one), with the total time of late arrival reduced by 21% (19% in the case of the handmade one). The 2-1 approach, therefore, improves performance. The X-2 approaches are the worst performing sub-approaches, while the X-3 performs better than the X-1, with regard to the number of late arrivals but not in terms of the total time of late arrival. Finally, there is a slight difference between the two schedule solutions, but the previous remarks are valid for both.  
Figure 6: Percentage deviation of the confidence interval about the mean time of late arrival depending on the number of replications where the red arrow shows the number of replications needed.  
Figure 7: Number of late arrivals (on the **left**) and total time of late arrival (on the **right**) depending on the resilient approach—Scenario with a single one-hour disruption.  
To better understand the effect of the resilient approaches on the route end times, their distribution and cumulative distribution are presented in Figure 8. Since caregivers must finish before 1 p.m., a dashed blue line has been added to the center of each graph.  
The dotted black curve represents the distribution when there is no disruption: in this case, all routes end before 1 p.m. with an average of 12:24 p.m. With approach 0 in red, the distribution splits into two parts, and 20% of the routes finish after 1 p.m. (this corresponds to the disrupted route out of the five) with an average of 12:36 p.m. With approach 1-1 in yellow, the average rises to 12:50 p.m., with almost 21% of the routes ending after 1 p.m. because of time wasted reaching colleagues. Finally, thanks to approach 2-1 in green, only 10% of the routes go beyond 1 p.m. (i.e., twice less), with the average end time dropping to 12:44 p.m.  
Approach 2-1, thus, enables a reduction in the number of late arrivals, the total time of late arrival, and the number of routes ending after 1 p.m.
# Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale
## 2 Satellites Sensors and Data
### Brightness Temperature Datasets
#### 2.1.2 Amsr2  
The GCOM are two series of JAXA satellites (e.g., GCOM-W and GCOM-C) designed to enable continuous global-scale observations for 10-15 years of Earth's environmental changes. The AMSR2 sensor onboard the GCOM-W1 satellite, launched on May 17, 2012, is a conical-scanning PMW radiometer operating at six frequency channels ranging from 7 to 89 GHz [25]. It flies at about 700 km altitude with approximately 98\({}^{\circ}\) inclination on a sun-synchronous orbit as part of the \"A-Train\" satellite constellation. AMSR2 provides TB measurements over 1450 km swath width and has an incidence angle of 55\({}^{\circ}\). The daily fixed acquisition times are about 1:30 and 13:30 local solar hours. Level 1C version 05 product, calibrated against GMI, used in this study was obtained from the NASA website at [[https://disc.gsfc.nasa.gov/](https://disc.gsfc.nasa.gov/)]([https://disc.gsfc.nasa.gov/](https://disc.gsfc.nasa.gov/)) (accessed on 12 January 2021).
## 2 Results and Discussion  
### LVI-GC-MS(SIM) Optimization  
The initial step of this work was establishing the instrumental conditions that better fit the compounds under study. A mix solution of six TCAs was analyzed by GC-MS operating in the full-scan mode acquisition, which allowed to obtain the target ions (base peaks) and quantifier ions, also in accordance with the literature (Table 1) [24; 25; 26; 27]. The obtained chromatogram by LVI-GC-MS(SIM) showed symmetrical peak shapes in less than 20 min of running time. The instrumental analytical thresholds were evaluated through the LODs and LOQs, corresponding to S/N of 3:1 and 10:1, in which 0.50 \(\mu\)g L\({}^{-1}\) and 1.65 \(\mu\)g L\({}^{-1}\) were achieved, respectively. The instrumental linearity was also assessed with eleven concentration levels ranging from 2.4 to 2500.0 \(\mu\)g L\({}^{-1}\). Linear regressions showed plots with good linearity with determination coefficients (\(r^{2}\)) higher than 0.9958.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**TCAs**} & **Ions** & **Retention Time** \\  & **(_ml/z_)** & **(min)** \\ \hline
**AMT** & **58**/202/215 & 12.91 \\ \hline
**MIA** & **193**/220/264 & 13.13 \\ \hline
**TRI** & 58/249/294 & 13.22 \\ \hline
**IMP** & 58/234/280 & 13.28 \\ \hline
**MIR** & **195**/208/265 & 13.77 \\ \hline
**DOT** & 58/202/295 & 15.96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Target ions (base peaks in bold) and quantifier ions for each TCA studied by LVI-GC-MS(SIM), under optimized instrumental conditions.  
Figure 1: Chemical structures of the six TCAs studied in the present work.
## 2 Notes on the Theory, Method and Data
### Data: Remote Sensing and Terrestrial  
#### Gravity and Magnetic Data  
The input data to our analysis are the harmonic geopotential coefficients (Stokes parameters) of the spherical harmonic expansion of the disturbing gravitational (not gravity) potential. A set of these coefficients defines the so-called global static gravitational (gravity) field model.  
We make use of the high quality, of the highest accessible precision worldwide and of the highest resolution combined gravity models, based on satellite and terrestrial data; now, this is still the EIGEN 6C4 (European Improved Gravity model of the Earth by New techniques). This is a global but detailed gravity field model which includes gradiometry data from the whole GOCE mission (Gravity field and steady-state Ocean Circulation Explorer, ESA) [16]. It is expanded to degree and order (\(d/o\)) = 2190. This corresponds to the ground half-wavelength resolution \(5\times 5^{\prime}\) (arc minutes, arcmin) or equivalently ~9 km on the Earth's surface. The remote data (satellite altimetry, gradiometry, other types of satellite measurements) together with terrestrial data (gravity anomalies from gravimeters) are important components of the gravity field models \"mixed\" in them in a specific way.  
We have tested an experimental XGM2019e gravity field model [17], which, however, must produce results similar to those from EIGEN 6C4 owing to the similar database. XGM2019e might be slightly better over the oceans due to the additional measurements likely provided by altimetry satellites. There is a version of XGM2019 to \(d/o\) \(\sim\)2190 (with the ground resolution \(\sim\)9 km), the same as EIGEN 6C4, and another version up to \(d/o\) = 5399 (the resolution \(\sim\)4 km); we used both for a comparison. It is impossible to guarantee that some of the fine features shown by these gravity models are not just artefacts caused by noise [17]. For Egypt, we did not find any observable difference in most of the gravity aspects, but we observed a total failure in the strike angles. The reason is not yet known, but it may be due to altitude difference as the gravity field changes rapidly from the surface sources. Therefore, we continue to work only with the EIGEN 6C4 model; we count with the ground resolution \(\sim\)10 km and estimate of the external accuracy not more than 10 mGal ([PERSON], priv. commun.).  
The magnetic anomalies--the values of magnetic induction (in nanotesla [nT]) as an additional informative source to the gravity data/aspects--are taken from the worldwide model _EMAG 2_[18]. This is a global 2' resolution grid compiled from satellite, marine, aeromagnetic and ground surveys. It has a ground resolution of 5-10 km (depending on the location). We also tested _EMAG 3_ (with a lower resolution) but without effect for Egypt. Some parts of Egypt (central and southern areas) are without any data in the EMAGs.