esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets
## 3 Results and Validation
### Wind Speed Retrieval Based on ERA5 and GDAS
The dependence of retrieval error on the incidence angle is shown in Figure 7c, where the highest density of retrieval errors is generally distributed near zero error. There is a pyramid distribution between the retrieval error and RCG as shown in Figure 7d; since RCG represents the received signal strength, larger RCGs can mean better-received signal quality. Figure 8 shows the average deviation and RMS of the inversed wind speed at different wind speed bins. The maximum matching reference wind speed of the test samples was 23 m/s, and the bias and RMS had large  
Figure 5: Delay-Doppler Map Average (**a**) and Leading Edge Slope (**b**) geophysical model function using Numerical Weather Prediction winds as the ground truth wind speed. Figure 6: Delay-Doppler Map Average (**a**) and Leading Edge Slope (**b**) geophysical model function at specific incidence angles using Numerical Weather Prediction winds as the ground truth wind speed. fluctuations around 20 m/s. The main reason is that there are fewer test samples under this wind speed range, which caused large errors.
## 9 Discussion, Contributions, and Future Directions
### Implications for Industry 4.0  
The findings emphasize the centrality of interoperable, real-time data architectures within Industry 4.0 contexts. By facilitating native protocol integration, manufacturers can prolong the service life of existing production lines while incrementally introducing advanced digital technologies. Additionally, the architecture's capability to dynamically scale data processing--from lightweight filtering at the edge to extensive analytics in the cloud--positions organizations to adapt to fluctuating production loads, seasonal variations, and evolving regulatory demands.  
A further implication is the potential for cross-company collaboration. As modern supply chains increasingly span multiple facilities and organizational boundaries, a se cure, standardized data exchange framework can accelerate joint process optimization, innovation, and transparency across stakeholders.
## 3 Results  
### Effect of Hearing Loss  
As the main focus of this study is the association between HTs and DPOAEs, the following analyses deal with ears rather than participants. Figure 2 presents average HTs for the three groups divided according to HL: G16, with HTs equal to or better than 25 dB HL over the whole frequency range up to 16 kHz (74 ears, age 29 \(\pm\)7); G8, with HTs equal  
Figure 1: Flowchart of examinations performed.  
to or better than 25 dB HL up to 8 kHz (69 ears, age 47 \(\pm\) 10); and Gim, with at least one HT worse than 25 dB HL at frequencies below 8 kHz (29 ears, age 51 \(\pm\) 11). As standard tests of hearing cover the range up to 8 kHz, groups G16 and G8 can be considered as having normal hearing, with the G16 group having 'exceptionally good' hearing. Gim, on the other hand, includes ears that have some elevation in threshold, signifying a degree of hearing impairment. Pairwise comparisons for HTs showed significant differences between G16 and G8 for frequencies of 1.5-16 kHz, between G16 and Gim at all tested frequencies from 1 to 16 kHz, and between G8 and Gim for all frequencies (marked by asterisks in Figure 2).  
Figure 3 presents average DPOAE response levels for the three groups. Pairwise comparisons for response levels at different frequencies showed significant differences between G16 and G8 for frequencies of 3-16 kHz, between G16 and Gim at all tested frequencies 1-16 kHz, and between G8 and Gim for frequencies of 1-8 kHz. All of these are marked by asterisks in Figure 3. It can be seen that DPOAE amplitudes for G16 surpassed the noise floor for all frequencies. In the case of G8, amplitudes in the 8-16 kHz range approached the noise floor. For group Gim, DPOAE levels were still above the noise over the 1.5-4 kHz range. Somewhat curiously, however, there was a sharp rise in DPOAE level at 12 kHz, a frequency where HTs were in fact worse, and this apparent contradiction suggests caution in interpreting data at this frequency. In general, the decrease in DPOAE levels with hearing loss in the EHF range is smaller than might be expected (especially in comparison with the much more evident decrease in DPOAEs in the SF range).  
Figure 3: Average DPOAE response levels for all tested frequencies for the three groups—G16, G8, and Gim (as described in Figure 2). Dashed lines represent average noise levels for each group. Whiskers indicate standard deviations. Asterisks mark significant differences between groups.  
Figure 2: Average pure tone hearing thresholds (HTs) in test subjects divided into three groups according to hearing acuity: G16—thresholds equal to or better than 25 dB HL for all frequencies up to 16 kHz (green); G8—thresholds equal to or better than 25 dB HL up to 8 kHz (blue); and Gim—impaired, thresholds worse than 25 dB HL at one or more frequencies (red). Whiskers indicate standard deviations. Asterisks mark significant differences between groups.
## 4 Temperature Dependence
### Temperature Dependence of Slow Dynamic Parameters  
When considering the strain dependence of the conditioned velocity (see Figure 3), two properties of the curves are related to slow dynamic effects: the area of the loop and the residual velocity variation when the amplitude returns to zero in the unloading phase (intersection with the y-axes). The two parameters are shown against temperature in Figure 7. Stronger effects due to slow dynamics in the concrete sample compared to Berea can be appreciated (see the scales on the axes).  
The situation for what dictates the slow dynamic parameters' dependence on temperature is different.A clear trend with temperature is observed for Berea, exhibiting a reduction in slow dynamic effects, which, despite the large error bars, seems to be reliable. For concrete, no clear trend could be identified, since, especially for residuals data, slow dynamics seem to be unaffected by temperature variations.  
Figure 5: Nonlinear fast velocity variations vs. strain at different temperatures. Here, the x-axes (strain) correspond to the output amplitude (in volts) defined by Equation (3), when \(A_{\text{drive}}=A_{\text{end}}\).  
The strongest conditioning observed in the concrete, corresponding to the fact that it is temperature-independent, may be due to several causes. In our opinion, it is possible that the observation is due to the different roles of water in the two materials. A higher water mobility might indeed result in a stronger dependence of the strength of slow dynamics' contribution to external parameters like the temperature. While, in a more chemically active system, like concrete, the presence of water might produce stronger non-equilibrium effects.  
Figure 6: Nonlinear fast elastic parameters vs. temperature. Data are averaged over repetitions of the experiment and the errorbar is given by the semidispersion. Data refer to the Berea sandstone sample.  
Figure 7: Slow dynamic parameters vs. temperature. Data are averaged over repetitions of the experiment and the errorbar is given by the semidispersion. Note that the area of the loops has the dimension of volts.
# Rapid In-Field Volatile Sampling for Detection of _Botrytis cinerea_ Infection in Wine Grapes
## 3 Materials and Methods
### VOCs Extraction Using ZIF-8  
#### 3.3.1 Volatile Extraction in a Closed Environment  
Volatile metabolites were extracted from the headspace of whole bunches of the same grapes harvested from the in-field sampling experiment, using an enclosed glass container. Ten _Chardonnay_ and ten _Riesling_ grape bunches were collected from a local vineyard in Tumbarumba, NSW, Australia, and _B. cinerea_ infection severities were measured using a lateral flow device (Section 3.5).  
Sampling for volatiles from a standard mix of target volatile compounds and from naturally infected bunches of grapes, assessed for visible signs of grey mould, was undertaken using a 2.5 L glass container to control the temperature and air movement. Synthetic grape juice (100 mL, as described previously) spiked with 50 \(\upmu\)L mixed standard in methanol to a final concentration of 7.2 \(\upmu\)g/L 1-octen-3-one, 93.6 7.2 \(\upmu\)g/L 3-octanol, 32.8 \(\upmu\)g/L 1-octen-3-ol, 2.39 \(\upmu\)g/L 1,5-dimethyltetralin, 1690 \(\upmu\)g/L benzyl alcohol, 247 \(\upmu\)g/L phenylethyll alcohol, and 3.39 \(\upmu\)g/L 1,5-dimethyllnphthalene in the container, or individual grape bunches were placed into the container prior to insertion of the IS. An IS comprising 10 \(\upmu\)L 40 mg/L 2-methyl-4-pentanol in methanol was applied on a cut filter paper disc (200 mm, Whatman #1, Sigma-Aldrich, Sydney, NSW, Australia) fixed onto a copper wire inside the glass container (Figure 5). The paper discs were heated at 120 \({}^{\circ}\)C for 2 h before they were used to remove any impregnated volatiles via volatilization. Prior to sampling, the glass container was placed in a water bath for pre-heating at 30 \({}^{\circ}\)C for 5 min to simulate the field environment. During the headspace sampling process, a ZIF-8 tube, and a Tenax(r)-TA sorbent tube were connected in parallel for simultaneous sampling. As shown in Figure 5, a SKC pump was connected to the absorbent tube for a 15 min extraction at 100 mL/min, and nitrogen gas was applied at 200 mL/min for pressure equivalent.
# A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service
## 4 Our Proposed Scheme  
We propose a key aggregate scheme for multi-delegation and authentication without TTP in this section. The proposed scheme consists of six phases, namely setup phase, data upload phase, aggregation key generation phase, trapdoor generation and retrieve phase, authentication for delegation, and group identity revocation phase.  
\begin{table}
\begin{tabular}{l l} \hline
**Notations** & **Meanings** \\ \hline \(DU_{j},ID_{j}\) & \(j\)th data user and their identity, respectively, \\ \(DO\) & Data owner \\ \(CS\) & Cloud server \\ \(HID_{j}\) & The hidden identity of \(j\)th data user \\ \(G_{1},G_{2}\) & Bilinear groups \\ \(PK_{do}\) & The \(DO\)’s public key for encrypt data \\ \(DPK_{do}\) & The \(DO\)’s public key for authentication \\ \(r_{do},\rho_{do}\) & Master secret key and secret key of \(DO\) \\ \(PK_{j},PK_{cs}\) & The public key of data user and cloud server, respectively \\ \(GID_{l},HGID_{l}\) & The group identity defined by data owner and its hidden identity \\ \(T_{1},T_{2},T_{3}\) & Current timestamps \\ \(\Delta T\) & Maximum transmission delay \\ \(R_{du},R_{cs},r_{A},r_{B}\) & Random nonces \\ \(W\) & The keyword \\ \(CK\) & The encrypted keyword \\ \(Tr\) & The trapdoor \\ \(||\) & Data concatenation operator \\ \(h_{1},h_{2}\) & The hash function \(\{0,1\}^{*}\to Z_{q}\) \\ \(h_{2}\) & The map-to-point hash function \(\{0,1\}^{*}\to G_{1}\), \\ \(\oplus\) & Bitwise exclusive-or operator \\ \hline \end{tabular}
\end{table}
Table 1: Notations used in this paper.
# Provision of Energy- and Wavelength-Efficient Traffic
## 4 Simulation and Performance Evaluation  
In this section, the simulation results and analysis are presented to demonstrate the effectiveness of the proposed TPTG-MA. The simulation is conducted based on the MATLAB 2020 (a), where the topology generation and algorithm implementation are performed. The DSCNs are composed of DSCs with the star topology, and the DSCs communicate with each other through ISLs established between boundary nodes, as shown in Figure 2. The number in each link indicates the physical distance of the link in km, which is a critical parameter for routing selection. The test network topology is randomly generated, where different network sizes, such as 6 nodes, (e.g., DSC1), 12 nodes, (e.g., DSC1 and DSC4), and 22 nodes, can be obtained for further performance evaluation. It is assumed that the satellite beam switching does not happen during the simulation time. Therefore, the traffic grooming in this scenario is considered to be static.  
There is no queuing mechanism, and once the capacity of the lightpath is insufficient, the traffic request will be blocked. The preference parameters \(\rho_{1}\) and \(\rho_{2}\) are both set to be 0.5. Other major simulation parameters are listed in Table 1. To evaluate the performance of the proposed TPTG_MA, the DLG heuristic and the GA [14,15] are simulated for comparison with respect to the AWUR, the ECS, the number of hops, and the blocking probability under the condition of different traffic intensity. Here, the blocking probability is the ratio of the number of traffic request failures to the number of total traffic requests, and the traffic intensity represents the product of the number of traffic requests per time unit and the duration of this simulation. Traffic requests are randomly generated within the bandwidth ranges. To determine the number of traffic requests between any node pair, we first construct a random matrix and make sure that the sum of all the elements in the matrix is 1. Then, the total number of traffic requests are distributed according to the weight of the matrix elements.  
Figure 2: Test network topology in simulation.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 4 Flexible Antennas for a Sub-6 GHz 5G Band
### Flexible MIMO Antennas for Sub-6 GHz 5G Band  
A Multiple-Input Multiple-Output system is a promising technology that can be used for the recent generation of communication systems since MIMO contributes to a higher spectral efficiency, a larger coverage area, and an increased system capacity. By utilizing numerous antennas at the transmitter and receiver, which allow different signal paths to deliver the same information, the channel's capacity can be improved. The MIMO technology makes use of multi-path environments providing the receiver with multiple versions of the same signal. Over the past few years, MIMO techniques for wireless communication have been researched to achieve the considerable capacity improvements required to enable a high-rate communication. Figure 15 shows the basic structure of the MIMO system [67]. The single patch element antennas discussed in the previous section are still not sufficient to meet the channel capacity. Therefore, the MIMO technology is a suitable candidate to be utilized for the enhancement of channel capacity in the sub-6 GHz 5G band.  
Figure 14: Transparent array antenna for 5G communication: (**a**) Fabricated GZO array (**left**) and single element (**right**); (**b**) Simulated and measured results of all of the fabricated antennas. Reprinted from ref. [66].  
In MIMO antennas, multiple antenna elements are oriented with an acceptable isolation between the antenna elements and each input antenna is fed to a separate port and also, the ground planes of the individual elements are connected together. When multiple antennas are placed at a distance less than the quarter wavelength, then there is an interference between the elements of the antenna, that affects the efficiency of the MIMO system. This is termed mutual coupling. Closely oriented antenna elements will lead to a high coupling between the antenna elements that have to be reduced by the proper orientation of the antenna after a very proper analysis on how exactly the antenna elements could be placed. To provide better isolation between the individual elements in a MIMO system and to provide better isolation, there are various decoupling structures used, such as parasitic elements [68], a defected ground plane structure (DGS) [69], a neutralization line (NL) [70], metamaterial loading (MTM) [71] and so on.  
To ensure the performance of the MIMO antenna, several performance metrics have to be analyzed. The key performance metrics in the analysis of the MIMO performance are the envelope correlation coefficient (ECC), the diversity gain (DG), the mean effective gain (MEG), the total active reflection coefficient (TARC), and the channel capacity losses (CCL). All of these parameters must be within the threshold level to ensure a good MIMO performance [72]. The threshold values of the abovesaid diversity parameters are as shown in Figure 16.  
Figure 16: Threshold values of the different MIMO diversity parameters. Reprinted with permission from ref. [72]. Copyright 2021 Elsevier.  
Figure 15: The basic structure of MIMO. Reprinted from ref. [67].
## 2 Study Area  
The Lowari Pass in Khyber Pakhtunkhwa, Pakistan, is situated at an altitude of 3100 m and is characterized by heavy snowfall, landslides, rockfalls, and snow avalanches, which make road construction and maintenance challenging and costly. To overcome these challenges, an approximately 8.5 km long and 87.2 m\({}^{2}\) cross-sectional area road tunnel was constructed, crossing through various geological formations, such as granite, granite biotite, metasediments, meta-igneous, meta-volcanic, and shear zones. Tunnel construction began in 1975, paused in 1977, and resumed in 2006, with a breakthrough in 2009, and operation commencing in 2017. The south and north portals of the tunnel are located at 35\({}^{\circ}\)18\({}^{\prime}\)59.39\({}^{\prime\prime}\) N and 71\({}^{\circ}\)50\({}^{\prime}\)08.17\({}^{\prime\prime}\) E, and 35\({}^{\circ}\)22\({}^{\prime}\)41.11\({}^{\prime\prime}\) N and 71\({}^{\circ}\)47\({}^{\prime\prime}\)05.19\({}^{\prime\prime}\) E, respectively. The tunnel has a south-to-north slope of 1.8% and a maximum overburden of 1100 m, and experiences significant water infiltration during the snowmelt period between April and September, as well as due to excessive rainfall, localized flooding, spring divergence, and disturbance of underground aquifers [43]. The drainage pattern of the Lowari Tunnel and surrounding area is illustrated in Figure 1. The Dare and Daro streams pass over the Lowari tunnel, resulting in increased water ingress inside the tunnel when sheared zones, minor faults, fractures, and jointed rock strata are present. In the vicinity of the Lowari Tunnel's south portal, small tributaries join the main Lowari Stream, which flows southwards toward Dir. At the north portal area, the Sharai, Tanger, and Gatus streams converge to form the Ashret stream, which joins the Kunar River before flowing northwest toward Chitral.  
Figure 1: The drainage pattern of the Lowari Tunnel and surrounding area.
## 3 Results  
### Numerical Simulation Experiment Results  
In the numerical models and the corresponding failure results, the models with different dip bedding show different crack distribution states (Figure 5). The bedding plane is shown as a light blue dotted line, the tensile cracks are represented by TF(Tensile Failure), and the shear-slip along the bedding plane is represented by SP (Shear-Slip). The model with a bedding dip of 0\({}^{\circ}\) almost only produces tensile cracks (TF-0A and TF-0B in Figure 5a), which are distributed between the bedding planes and do not penetrate them. When the bedding dip is 30\({}^{\circ}\), the model mainly undergoes shear-slip along the bedding plane (SP-30 in Figure 5b), and secondary tensile cracks (TF-30A and TF-30B in Figure 5b) develop during the slip process. When the bedding inclination increases to 60\({}^{\circ}\), the model  
Figure 4: Rock specimens with different bedding dips.  
basically only generates two major cracks, and a slip occurs (SP-60 in Figure 5c). The model with a bedding inclination dip of 90\({}^{\circ}\) produces tensile cracks along the vertical bedding plane (TF-90A and TF-90B in Figure 5d). The cracks generated by the models with bedding inclination dips of 120\({}^{\circ}\) and bedding inclination dips of 150\({}^{\circ}\) are symmetrical with the models with bedding inclination dips of 60\({}^{\circ}\) and 30\({}^{\circ}\), respectively, but basically only the shear-slip surfaces SP-120 and SP-150 are generated, and there are almost no obvious tensile cracks (Figure 5e,f).  
With the increase in the dip of the bedding, the failure mode of the numerical model undergoes a process of \"tensile failure \(\rightarrow\) compression-shear failure \(\rightarrow\) shear failure \(\rightarrow\) tensile shear failure \(\rightarrow\) tensile failure\". The slip phenomenon along the bedding plane can be observed in the numerical models with bedding inclination dips of 30\({}^{\circ}\), 60\({}^{\circ}\), 120\({}^{\circ}\), and 150\({}^{\circ}\), among which the slip phenomenon is most obvious in the models with bedding dips of 60\({}^{\circ}\) and 120\({}^{\circ}\), which is consistent with the results derived from the single weak surface principle in Chapter 2.1.2. When the bedding plane is perpendicular or parallel to the loading direction, the model is dominated by tensile failure, and the bedding plane is obliquely intersected with the loading direction. The model is dominated by shear-slip failure, which is consistent with the results of [12].
## 5 Experimental Results and Discussion
### Experiment 1  
This section validates the effectiveness of the proposed method. In this experiment, the dataset was provided by the bearing data center of the Case Western Reserve University (CWRU). In addition, the robustness experiment was carried out by adding the noise artificially.
# Deep Instance Segmentation of Laboratory Animals in Thermal Images
###### Abstract  
In this paper we focus on the role of deep instance segmentation of laboratory rodents in thermal images. Thermal imaging is very suitable to observe the behaviour of laboratory animals, especially in low light conditions. It is an non-intrusive method allowing to monitor the activity of animals and potentially observe some physiological changes expressed in dynamic thermal patterns. The analysis of the recorded sequence of thermal images requires smart algorithms for automatic processing of millions of thermal frames. Instance image segmentation allows to extract each animal from a frame and track its activity and thermal patterns. In this work, we adopted two instance segmentation algorithms, i.e., Mask R-CNN and TensorMask. Both methods in different configurations were applied to a set of thermal sequences, and both achieved high results. The best results were obtained for the TensorMask model, initially pre-trained on visible light images and finally trained on thermal images of rodents. The achieved mean average precision was above 90 percent, which proves that model pre-training on visible images can improve results of thermal image segmentation.  
instance segmentation; deep learning; computer vision +
Footnote †: journal: Applied Sciences