esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Assimilation Method and Experiment Configuration
### Experiment Configuration  
To investigate the impact of simultaneous assimilating satellite and radar data with POD-4 DEnVar method, a series of analysis and forecasts are made from 0000 UTC 27 June 2016 to 0000 UTC 7 July 2016 and 0000 UTC 28 June 2017 to 0000 UTC 8 July 2017, during which obvious summer precipitation in China took place.  
Two experiments are conducted in this study. A baseline experiment (CON) is carried out without any assimilation. The second experiment (POD) assimilates satellite radiance and radar radial velocity and reflectivity observations with POD-4 DEnVar method, with the analysis time at 0000 UTC for each day. The POD experiment assimilates channel 5-9 (central frequencies from 53.596 to 57.290 GHz, respectively) radiance observations of advanced microwave Sounding Unit-A (AMSU-A) from METOP-2, NOAA-15, NOAA-16, NOAA-18 and NOAA-19. The assimilation time window for the satellite is 6 h, consist of seven sub-windows every 1 h. Before assimilation, satellite observations are thinned to 120 km, after which the quality control procedure is necessary. Doppler radar radial velocity and reflectivity are also assimilated in POD. The assimilation time window of radar is 1 h, with 11 sub-windows every 6 min.  
Assimilation is carried out at 0000 UTC each day. The samples are prepared before assimilation, the number of which is 62 for satellite and 142 for radar according to the number of sub-windows.  
After the data assimilation, 48-h deterministic forecast is run initiated at 0000 UTC each day. The ensemble forecast and final deterministic forecast are made in the WRF model. The WRF model is configured with a two-domain nested grid system, shown in Figure 4. The horizontal resolution of the outer domain is 9 km, with the grid points \(400\times 400\) for satellite data assimilation. The horizontal resolution of the inner domain is 3 km, with the grid points \(400\times 376\) for radar data assimilation. Both domains have 35 vertical layers, with the model top at 50 hPa. The boundary conditions are updated every 6 h from the \(1\times 1^{\circ}\) National Centers for Environmental Prediction (NCEP) Final Operational Model Global Tropospheric Analysis. Overall, 22 analysis and forecast results are obtained finally, which will be used to evaluate the statistical impact of POD-4 DEnVar assimilation.  
The main physical parameterization schemes in ensemble forecast and deterministic forecast are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline Microphysics & WSM 6-Class Graupel Scheme \\ Cumulus parameterization & Kain-Fritsch (new Eta) scheme (none in the inner domain) \\ Planetary boundary layer & YSU (Yonsei University) \\ Surface layer & Revised MM5 Monin-Obukhov scheme \\ Longwave radiation & Rapid Radiative Transfer Model for GCMs \\ Shortwave radiation & Dudthia scheme \\ \hline \end{tabular}
\end{table}
Table 1: Physical parameterization schemes.  
Figure 4: Nested Weather Research and Forecast (WRF) model domain configuration. See text for details.
## 2 Experimental Materials and Methods
### Preparation of the Sprayed UHPC and Cast UHPC Specimens  
The mixture proportion of the sprayed UHPC and cast UHPC matrix, based on the dense packing theory [24], was provided in Table 2. For both the spray and cast processes, the water-binder ratio was fixed at 0.18. The SP dosage was set at 2% by the mass of cementitious materials. Based on prior studies and preliminary experimental results, the optimal PP fiber content was determined as 2% by volume (equivalent to 18.2 kg/m\({}^{3}\)) [25]. The UHPC slurry was prepared using a 0.8 m\({}^{3}\) horizontal forced action mixer, and the spraying machine used was the SPL6 wet-mix concrete sprayer, which was produced by the Henan Coal Science Research Institute, with a maximum pumping capacity of 6 m\({}^{3}\)/h. The spraying procedure is illustrated in Figure 2. The procedure included three stages:  
1. Dry mixing of the PC, SF, WRT, and MS took place for 1-2 min at a speed of 65 rpm; the subsequent addition of water and SP with continued mixing for 6-8 min to form a homogeneous slurry; and a final incorporation of PP fibers with additional mixing for 2-3 min to ensure uniform dispersion occurred. After mixing, a flow expansion test was carried out to evaluate the concrete's flowability. The total mixing time was controlled within 15 min, and the fresh slurry was required to be sprayed within 40 min.
2. Before commencing the spraying operation, the air compressor should be activated to maintain a constant pressure of 0.6 MPa in the air receiver tank. Subsequently, the UHPC slurry was poured into the hopper of the wet-spray machine,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**WRT** & **Primary Technical Specifications** \\ \hline Chloride ion content & \(\leq\)0.6\% \\ Alkali content & \(\leq\)1.0\% \\ Compressive strength ratio & \(\geq\)100\% \\ Fineness (85 \(\upmu\)m Residue) & \(\leq\)10\% \\ Setting Time Difference & \(-\)90\(-\)+120 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primary technical specifications of WRT.  
Figure 1: Properties of MS.  
with the slurry pumping speed maintained at 3-5 m\({}^{3}\)/h. To ensure operational safety, the procedure requires that the high-pressure air be turned on before the UHPC slurry is pumped. Referencing the Chinese standard GB 50086-2015 [26], with the assistance of high-speed air, the UHPC slurry was sprayed into the mold (\(450\,\mathrm{mm}\times 450\,\mathrm{mm}\times 120\,\mathrm{mm}\)). During the spraying process, the spray nozzle should be perpendicular to the mold and maintain a distance of 1.3-1.7 m.
3. Following the completion of the spraying process, the specimens were demolded after 24 h, cut into the designated dimensions as shown in Figure 2, and placed in a standard curing room at a temperature of 20 \(\pm\) 2 \({}^{\circ}\)C and a humidity of 95% until 28 days and 90 days had passed [27].  
Figure 2: **(A)**. Experimental procedure and mechanical testing of sprayed UHPC. **(B)** Sprayed UHPC specimens used for MIP and SEM tests.
## An Efficient Medium Access Control Protocol with Parallel Transmission for Wireless Sensor Networks  
**[PERSON] * and [PERSON]**  
Graduate School of Global Information and Telecommunication Studies, Waseda University,  
Tokyo 169-0051, Japan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: arif(@fuji.waseda.jp or [EMAIL_ADDRESS]; Tel./Fax: +81-3-5292-7897.  
_Received: 20 June 2012; in revised form: 15 August 2012 / Accepted: 22 August 2012 / Published: 27 August 2012_  
**Abstract:** In this paper, we present a novel low power medium access control protocol for wireless sensor networks (WSNs). The proposed protocol, EP-MAC (Efficient MAC with Parallel Transmission) achieves high energy efficiency and high packet delivery ratio under different traffic load. EP-MAC protocol is basically based on the Time Division Multiple Access (TDMA) approach. The power of Carrier Sense Multiple Access (CSMA) is used in order to offset the fundamental problems that the stand-alone TDMA method suffers from, _i.e._, problems such as lack of scalability, adaptability to varying situations, _etc_. The novel idea behind the EP-MAC is that it uses the parallel transmission concept with the TDMA link scheduling. EP-MAC uses the methods for the transmission power adjustment, _i.e._, uses the minimum level power necessary to reach the intended neighbor within a specified bit error rate [BER] target. This reduces energy consumption, as well as further enhances the scope of parallel transmission of the protocol. The simulation studies support the theoretical results, and validate the efficiency of our proposed EP-MAC protocol.  
**Keywords:** medium access control; energy efficiency; parallel transmission; wireless sensor network; carrier sense multiple access
## 2 Terminologies in EMI Shielding
### Shielding by Absorption (Se\({}_{A}\))  
When an EM wave passes through any medium, its intensity becomes exponentially reduced. The critical thickness at which the EM field strength reduces to 1/e of the incident field is known as the skin depth (8). This decay occurs as currents induced in the material  
Figure 2: Plausible EMI shielding mechanisms associated with a shield material.  
produce ohmic losses, which also heats up the material. The absorption losses within a shield can be expressed by the following equation [89]:  
\[\mathrm{SE_{A}}=-20\frac{\mathrm{t}}{\delta}\ \log_{10}\mathrm{e}=-8.68\left(\frac{ \mathrm{t}}{\delta}\right)=-8.68\,\mathrm{t}\left(\frac{\mathrm{\sigma_{T}} \mathrm{\mu_{r}}\mathrm{\omega}}{2}\right)^{\frac{1}{2}} \tag{2}\]  
where t is the thickness of the shield in inches, \(\delta\) is the skin depth and f is the frequency in Hz. It is also evident from the above expression that \(\mathrm{SE_{A}}\) is directly proportional to the square root of the product of permeability (\(\mathrm{\mu_{r}}\)) and conductivity (\(\mathrm{\sigma_{T}}\)). In addition, the \(\mathrm{SE_{A}}\) of a given material also increases with increasing frequency. The skin depth (\(\delta\)) of a shield can be expressed as the following equation [89]:  
\[\delta=\sqrt{\frac{2}{\mathrm{f}\mathrm{\mu}\mathrm{\sigma}}}=-8.68\times \frac{\mathrm{t}}{\mathrm{SE_{A}}} \tag{3}\]  
where t is the thickness of the shield, f is the frequency and \(\mathrm{\sigma}\) is the electrical conductivity, respectively. However, it is evident that the skin depth of the shielding material has a direct relation with the thickness of the shield. In such case, two situations are possible.  
When (t << \(\delta\)): The material is called electrically thin when the thickness of the shield is less than the skin depth, and this generally occurs at lower frequencies. In this situation, the shielding due to absorption can be ignored and attenuation occurs mostly through the reflection mechanism. Then, the total shielding becomes independent of frequency and can be expressed in terms of free space impedance, as follows [89]:  
\[\mathrm{SE\ (dB)}=-20\log_{10}\left(1+\frac{Z_{0}}{2}\ \mathrm{t}\mathrm{ \sigma_{T}}\right) \tag{4}\]  
where \(Z_{0}\) is the impedance of the free space (=377 \(\Omega\)).  
When (t >> \(\delta\)): The material is called electrically thick when the skin depth is much lower compared to the thickness of the shield. Here, the total SE can be calculated after making a good conductor approximation.
## 3 Data and Methods
### Method
#### 3.2.2 Pot-Sbas  
Traditional POT technology is generally limited to processing with two images. It is often difficult to obtain the temporal characteristics of glacier motion and unexpected conditions, such as glacier surges. With the POT-SBAS technique and monitoring points with better stability on the glacier surface, the frequency of motion monitoring can be increased to better reflect the spatial and temporal evolutionary characteristics of glacier motion [26]. The monitoring of the spatiotemporal evolution of glacier motion with the help of the POT-SBAS technique relies on the optimal baseline combination of the SBAS method to add redundant observations. Then, with the help of singular value decomposition (SVD) and other methods, the effect of noise can be further reduced [27]. Assuming that the range direction displacements of \(M\) image pairs corresponding to \(N+1\) SAR acquisition moments are acquired, the following observations are available:  
\[\delta N=\left[\delta d_{N1}...\delta d_{NM}\right]^{T} \tag{3}\]  
\(\delta d_{Ni}(i=1,...,M)\) is the displacement value corresponding to any set of image pairs in the upward range direction. The time series corresponding to the master and slave images are  
\[D_{m}=\left[D_{m1},...,D_{m1M}\right] \tag{4}\]  
\[D_{s}=\left[D_{s1},...,D_{s1M}\right] \tag{5}\]  
Arranged in time order, \(D_{mi}\) is earlier than \(D_{si}(i=1,...,M)\), and the time interval of the \(M\) image pairs is expressed as  
\[\delta N_{j}=d(t_{Dmj})-d(t_{Djs})(j=1,...,M) \tag{6}\]The equation is an \(M\) equation with \(N\) unknowns; written in matrix form, it appears as follows:  
\[\delta N=Ad \tag{7}\]  
Each row of the \(A\) matrix corresponds to an image pair, and each column corresponds to one SAR image. The displacement value d for each time in the line of sight (LOS) can be obtained by using SVD. Based on the POT-SBAS technique, this paper obtains the offset tracking results for the corresponding combinations through the combination of spatiotemporal baselines formed by two baseline constraints in time and space, and on this basis, the time-series information for the glacier surface motion is obtained by inversion.
## 5 Evaluation
### Cold Starts
#### 5.3.2 Measurement Analysis  
Given the nature of cold starts, not all 300 records per function definition were considered when determining the average execution time. Each function bar, illustrated in Figure 13, only takes into account the best 75% of the total measured records to perform the average calculation. This reduces the probability of presenting misleading results, known as outliers, that occur frequently in cold starts due to latency issues.  
From the results illustrated above, we can conclude that the cloud-agnostic function definition had an increase in execution time of 3.2% in Google Cloud Platform and 3.8% in Microsoft Azure, when compared to the cloud-non-agnostic one. The slight increase in execution times was inevitable for cold starts, given that template functions start by reading the configurations JSON file, ending up causing some overhead due to being a file I/O operation. The only configuration specified by the _search blobs_ use case is the bucket's access key when deployed to MsAzure, which is a requirement for accessing the storage service. The same access key is _hardcoded_ in the respective cloud-non-agnostic definition, so that we can have a clear perception of the impact on execution time when performing the file I/O operation. For the time being, the configurations file is always read in cold starts, regardless of whether the JSON properties are used or not. As for GCP, the execution time could be improved by not reading the file, since no storage access key is needed.  
We can also point out that around 17% of the total number of cold start execution times measured in MsAzure (cloud-agnostic and cloud-non-agnostic), with no exclusions, were above three seconds. While in Google Cloud Platform, no record reached the two-second mark. This can be attributed to different hardware, but also to the underlying operating system and virtualization technology [70]. Perhaps Ubuntu containers, from GCP, have faster start-up times on average when compared to Windows containers from MsAzure [71].  
As for the memory usage, represented in Figure 14, no records were ignored when determining the average memory consumption.  
The difference in memory usage between the two function definitions is practically non-existent in both providers. This difference was expected to be close to zero, since there are no extra heavy allocated objects or operations executed by cloud-agnostic libraries that could cause the memory usage to increase drastically.  
Figure 13: Cold start execution time.
## 3 Forensic Human Identification through the Analysis of Biomedical Images
### AI-Based Approaches for Craniofacial Superimposition
#### 3.4.4 3D-3D Computer-Aided Approaches for Crani Superimposition  
Over the recent years, some authors have proposed a 3D skull-3D face approach for CFS unlike the existing traditional (image and video) and computer-aided 2D-3D approaches mentioned above.  
[PERSON] et al. [176] proposed a novel ID method based on the correlation measure between 3D skull and 3D face in morphology. The mapping between skull and face is obtained using canonical correlation analysis. Unlike existing techniques, this method does not need the accurate relationship between skull and face, and only measures the correlation between them. In order to measure the correlation between skull and face more reliably and improve the identification capability, a region fusion strategy is adopted. Experimental results validate the proposed method, and show that the region based method significantly boost the matching accuracy. The correct recognition rate reaches 100% using a CT dataset.  
In [186] another 3D-3D superimposition approach was presented in an effort to contribute to computer-aided CFS. The proposed method emphasizes adherence to two important parameters: (1) maintaining the life-size of the face image in relation to the size of the skull, and (2) orienting the skull on an anthropological basis using selected feature points. The proposed method commences by reconstructing the 3D face model from a given 2D face image using a mean simplified generic elastic model, followed by registering the face model to a 3D skull along the jaw line using the analytical curvature B-spline (AC B-spline). The accuracy index of the registration is, then, evaluated to suggest the degree to which the face image corresponds to a skull. The superimpositions of positive and negative cases were conducted on a set of 3D skulls versus a set of 2D face images. The accuracy indices of the registration results suggest that the AC B-spline is more robust in 3D-3D superimposition compared to the other existing methods. The full experimental results have demonstrated the potential of the proposed method as an assistive tool to the forensic scientists for craniofacial identification.  
As conclusion, CFS is arguably the SFI approach that has been more benefited from the use of AI techniques in the last years. However, the main limitation it currently presents is the lack of massive empirical evidence in favor of its use as forensic ID method. This prevents many forensic experts and institutions from using it for ID.
## 6 Conclusions  
In this work, we explored the DUDA scheme for NGEO MSS, aiming to enhance the transmission performance while addressing in-line interference issues. We introduced a spherical surface model based on geocentric angle to succinctly capture the interplay between distance and antenna gain within NGEO MSS. Leveraging this model, we devised the DUDA scheme, incorporating strategies to mitigate in-line interference. Our results illustrate the substantial superiority of the proposed DUDA scheme over conventional DUCA, particularly in scenarios involving in-line interference. Notably, the uplink transmission rate experiences significant improvements without the need for additional physical resources. We also ascertain that if the density of lower orbit satellite is substantial, the decoupled access probability will diminish.  
These results underscore the potential deployment of the DUDA scheme for centralized NGEO MSS. In future work, the DUDA protocol for distributed satellite networks and the decoupled criterion grounded in service demand are worthy of exploration.  
**Author Contributions:** Methodology, [PERSON] ([PERSON]) and Y.L. ([PERSON]); software, [PERSON] ([PERSON]) and validation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); investigation, X.K.; resources, X.K.; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, and X.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper was funded by the National Key R&D Program of China under Grant 2021 YFB290040, the National Key Laboratory of Wireless Communications Foundation under Grant 6142102222102 and IFN20230203, and the National Natural Science Foundation of China under Grant 62371098.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 8: Throughout capacity of DUDA and DUCA, \(\rho_{L}/\rho_{H}=2\).
# Reconstruction of the Subsurface of Al-Hassa Oasis Using Gravity Geophysical Data
## 6 Conclusions  
An extensive gravity survey was conducted in the Al-Hassa Oasis area to develop a detailed and intricate subsurface model of the area being studied. The acquired gravity data indicated that the subsurface geophysical structures in the research area are complex and three-dimensional (3D), characterized by linear anomalies/features with low density. The 3D gravity inversion revealed an intricate subsurface configuration characterized by linear discontinuities oriented in the NNW-SSE and W-E directions. Given the anticipated stress distribution in the wider research region, these discontinuities can be interpreted as fracture zones. Furthermore, the extensive analysis of density variations aligns with the structural characteristics of a salt dome, as explained in the discussion section. Based on the predicted data, it has been concluded that gravity can provide images of anomalies with a relatively minor contrast variation. Geological, geochemical, or other geophysical data, particularly seismic data, could be valuable for validating the generated 3D model for the research region and refining the final conceptual model for the Al-Hassa Oasis area.  
Conceptualization, A.S. and P.S.; methodology, A.S. and P.S.; software, K.C. and A.S.; validation, A.S. and P.S.; formal analysis, A.K., K.C., P.K. and P.S.; investigation, A.K., K.C., P.K. and P.S.; resources, P.S.; data curation, K.C. and A.S.; writing--original draft preparation, A.K., K.C., P.K., A.S. and P.S.; writing--review and editing, A.K., K.C., P.K., A.S. and P.S.; visualization, A.K., K.C. and P.K.; supervision, A.S. and P.S.; project administration, P.S.; funding acquisition, P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 3 Results and Discussion
### Consistency Index of the Co-Fermented Milk  
The milk co-fermented by _B. subtilis_ and _L. plantarum_ was fortified with probiotics, peptides, and \(\gamma\)-PGA. \(\gamma\)-PGA may act as a prebiotic according to a study on its effects on the gut microbiota of mice [26]. In addition, fermentation of milk with _B. subtilis_ improved the consistency of the fermented milk. Consistency is an important rheological property because it affects taste, mouthfeel, and whey separation. Generally, hydrocolloids such as gums are applied to fermented milk to enhance its consistency. The addition of prebiotics such as galactooligosaccharide and inulin to Greek yogurt has resulted in enhanced rheological properties, providing a more consistent, elastic, viscous, and firm product [27].  
Thus, alkaline fermentation of milk with _B. subtilis_ in the presence of MSG could be a potential means to naturally enhance the consistency of fermented products. As shown in Figure 7, the milk fermented with _B. subtilis_ for 1 day showed a consistency index of 1.66 Pa-S\({}^{n}\), but serial co-fermentation with _L. plantarum_ for 1 day decreased to 0.38 Pa-S\({}^{n}\). After 5 days of co-fermentation, the consistency index decreased to 0.06 Pa-S\({}^{n}\) (Figure 7), indicating that the consistency of mucilage was greatly dependent upon the acidic environment, and increased acidity resulted in lowered consistency. \(\gamma\)-PGA is a water-soluble anionic polypeptide, which undergoes structural changes related to the degree of ionization of its \(\gamma\)-carboxyl group. For these reasons, the consistency and molecular weight of \(\gamma\)-PGA can greatly decrease at a low pH [28]. Although the co-fermented milk had the lowest consistency, it was fortified with bioactive compounds, which may be considered a trade-off.  
Figure 6: SDS-PAGE pattern of co-fermented milk. SDS-PAGE was performed on a gradient polyacrylamide gel (4–15%). Lane a, standard protein molecular weight markers: 10, 15, 25, 35, 40, 55, 70, 100, 130, 170 kD a; lane b, 0.1% skim milk solution; lane c–d, _B. subtilis_ fermentation (0, 1 day); lane e–h, co-fermentation (0, 1, 3, 5 days).  
Figure 7: Consistency index of milk co-fermented by _B. subtilis_ HA and _L. plantarum_ EJ2014.