esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The monitoring of nearshore Chl-a changes is a critical ecological issue. The purpose of the current study was to design a proper model for remote sensing Chl-a data fusion for coastal environments. Our contributions are as follows:  
(1) A modified ESTARFM was proposed to make use of the characteristics of Chl-a data and applied to the northern coast of the Yellow Sea. The spatial resolution of GOCI Chl-a was downscaled to 30 m. The methods were tested with actual satellite data. Both fusion models could obtain reasonable coastal Chl-a.  
(2) The predicted results were evaluated visually and statistically. The new model improves the applicability of the spatiotemporal fusion model to the rapidly changing Chl-a data. Compared with the ESTARFM results, the new model results have a more stable indicator performance and better spatial consistency. This shows that the method of segmented fitting and numerical conversion is more suitable for data with rapid changes in spatial distribution.  
(3) In the case input data are limited, numerical conversion reduces the impact of time intervals on the model. The results of this study prove that the new model is more stable and robust than the original model. So, the new model has more potential in practical application.  
Conceptualization, Y.F. and J.S.; methodology, Q.M.; software, Q.M.; validation, Y.C.; formal analysis, J.G. and M.L.; writing--original draft preparation, Q.M.; writing--review and editing, Y.F. and X.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Dalian Science and Technology Program for Innovation Talents of Dalian (2022 RJ06); the Science and Technology Program of Liaoning Province (2021-BS-239, 2022)H2/10130022, 2022)H2/101300183); the Scientific Research Project of Education Department of Liaoning Province (DL202001, JL202006, QL202006, IJKZ0709); the Open Fund Project of the Key Laboratory of Marine Environmental Information Technology, MNR (Yanzhao Fu); and the Dalian Science and Technology Innovation Fund (2020 JJ27 SN100).  
Figure 13: Comparison of Landsat actual reflectance image, ESTARFM_p, and ESTARFM results.  
Data Availability Statement: The data presented in this study are available upon request to the corresponding author.  
The authors appreciate USGS for providing the Landsat data and KOSC for the GOCI data. We thank the data support from the National Marine Scientific Data Center (Dalian) and the National Science and Technology Infrastructure of China ([[http://odc.dlou.edu.cn/](http://odc.dlou.edu.cn/)]([http://odc.dlou.edu.cn/](http://odc.dlou.edu.cn/)) (accessed on 2 September 2023)) for providing valuable data and information.  
The authors declare no conflict of interest.
## 2 Results and Discussion
### Enumeration Cell Death Over Time by Time-Kill Curve

Table 2: The minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) values of EOEP against foodborne microorganisms determined by broth dilution method. Though reports on the antimicrobial activity of EOEP are limited, its inhibitory activity against test bacteria could be attributed to the presence of bioactive compounds, mainly _trans_-anethole and methyl chavicol (estragole), as previously reported [28; 29; 11; 21; 27; 20]. _Trans_-Anethole at a concentration of 75 \(\upmu\)g/mL showed clear inhibition of the growth of _S. aureus_ at inoculum level of 10\({}^{5}\) cells/plate [28]. Estragole had a strong inhibitory effect against _Pseudomonas syringae_ in the liquid culture assay throughout 24 h. The effectiveness of estragole was stronger than eugenol > methyl- eugenol > cinnamaldehyde, respectively [29]. Moreover, the antibacterial activity of estragole is comparatively higher than that of _trans_-anethole [30]. The estragole-rich oil exhibited MICs of 3.7, 6.7 and 13.2 mg/mL against  
Figure 3: Killing curves for various concentrations of EOEP against _B. cereus_ (**A**), _S. aureus_ (**B**) and _L. monocytogenes_ Scott A (**C**). C. albican, S. aureus and E. coli, respectively, while trans-anethole-rich oil showed a MIC of 1.8 mg/mL against C. albican. In contrast, the antioxidative activity of estragole is comparatively less than trans-anethole [31]. Nonetheless, it has been shown to carry antioxidative activity. Both trans-anethole and methyl chavicol (estragole) have shown both antioxidant and antimicrobial activities, but very little is known about their modes of action against target microorganisms.
## 2 Materials and Methods
### Test Methods  
Compressive strength tests were performed according to EN 12390-3 [18] standard. In these tests, cubic specimens with dimensions of 150 \(\times\) 150 \(\times\) 150 mm were used. They were brought to destruction directly between the flat steel plates of the testing machine.  
Crack Mouth Opening Displacement (CMOD) tests were conducted as per the requirements of the EN 14651 [19] standard. Specimens in the form of a prism (beam) were used to determine the relationship of force--CMOD. The ratio of height to width (diameter) of specimens was assumed to be 4. The requirements for preparing the specimens have to comply with the BS EN 12390-1 [20] standard. The value of CMOD is the movement of the external faces of the incision made in the center of specimen when it deflects from the loading. The width of the slot on the specimen should not exceed 5 mm and its depth should be 25 mm \(\pm\) 1 mm.  
For laboratory research, the \"Toni Technik ToniPRAX\" testing machine (Toni Technik GmbH, Berlin, Germany) was used, which implements uniaxial compression of the specimens with a maximum force of 3000 kN and three-point bending of the specimen with a maximum force of 100 kN. The testing machine must operate in a loading-controlled mode during compressive strength tests and in a displacement-controlled mode during CMOD tests.  
For measuring the crack opening during CMOD tests, the high precision displacement transducer \"Controls Group P0331/E\" (Controls S.P.A., Milan, Italy) was used. This displacement transducer has a measuring capacity of 5 mm and sensitivity of 10\({}^{-3}\) strain/mm.  
The CMOD tests requires the fatigue pre-cracking stage. During this stage, the flexural loading is applied to the specimen at a constant rate of 0.02 mm/s until the Crack Opening Displacement (COD) reached 0.3 mm, after which specimens were subjected to approximately one million cycles of loading. The loading was applied between 10% and 90% of the elastic limit. Frequency was 5 Hz. After the fatigue pre-cracking stage, the specimen was fractured with a constant 0.02 mm/s rate.  
Details showing a specimen, supporting and loading system as well as displacement transducer are illustrated in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Young’s Modulus, GPa** & **Compressive Strength, MPa** & **Tensile Strength, MPa** \\ \hline B15 concrete strength class & 24 & 8.5 & 0.75 \\ B25 concrete strength class & 30 & 14.5 & 1.05 \\ BarChip 54 macrofiber & 12 & 640 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic mechanical properties of the studied materials.
## 3 Results and Discussion
### Effect of Raster Angle on Flexural Properties
#### 3.1.1 In-Plane Testing  
Figure 8 and Table 2 summarize the results for the effect of raster angle on the in-plane flexural properties of the specimens. It can be observed from Figure 8 that samples printed at 0\({}^{\circ}\) raster angle outperformed all other raster angles for in-plane testing. The average maximum flexural load and flexural modulus obtained for the 0\({}^{\circ}\) raster angle samples came out to be 138.42 \(\pm\) 3.19 and 3.2 \(\pm\) 0.01 GPa, respectively. In comparison, the lowest values for these flexural properties were found for samples printed at 90\({}^{\circ}\) raster angle. Furthermore, it can be noticed from Table 2 that there was an increase of 188.2% and  
Figure 8 shows a change of fracture behavior from ductile to brittle as the raster angle changed from 0\({}^{\circ}\) to 90\({}^{\circ}\). Figure 8 also indicates that the fracture did not occur for 0\({}^{\circ}\) raster angle even at the very high value of strains, whereas 90\({}^{\circ}\) samples failed abruptly on the onset of plastic deformation, thus indicating very low values of fracture strains. A decreasing trend for the fracture strains was observed from 0\({}^{\circ}\) to 90\({}^{\circ}\) raster angle. As the 3D printed specimens act as laminated structures rather than homogeneous and uniform structures, the maximum value of force that can be endured depends upon the raster angle or simply the infill direction and the loading direction. Therefore, in the case of 0\({}^{\circ}\) samples, all the layers were perpendicular to the loading and provided greater resistance to deformation. Here, each raster bears the load and acts as a single beam carrying the load. The strength of the 0\({}^{\circ}\) specimen (Figure 8c) was the highest for the same reason; however, as the angle approached 90\({}^{\circ}\), the loading axis became parallel to the layers, hence the failure occurred at a lower value of loads. This could be attributed to the ease of shear deformation because, at 90\({}^{\circ}\) raster angle, the loading bearing surface is the interface between the rasters, hence the specimens failed along the direction of load.
# BSM-Data Reuse Model Based on In-Vehicular Computing
## 4 BSM Reuse Model: Our Proposal
### Basic Concepts
#### 4.2.1 Data Capture  
The data capture stage refers to two different parts, the generation and the acquisition of data. The data acquisition serves as the collection of extra-vehicular data in the form of BSM as well as data generation represents the creation of intra-vehicular data through local sensor observations. An additional real-time pre-processing task is also to be conducted in order to classify and filter captured data.
# Present and Future Marine Microwave Satellite Missions in China
## 2 Development of Marine Satellites in China
### Progress History of China's Marine Surveillance Satellites  
The GF-3 satellite in China's high-resolution Earth observation system is China's first marine surveillance satellite. On 10 August 2016, China successfully launched the GF-3 satellite with the Long March-4C rocket at the Taiyuan Satellite Launch Center. The GF-3 satellite is China's first C-band multi-polarized synthetic aperture radar (SAR) satellite with a spatial resolution of 1 m [10]. The missions and purposes of the GF-3 satellite are different when compared with the previously developed satellites. For example, a new system and multi-polarization design were adopted for the purpose of collecting as much information as possible and transmit the data to the ground, thereby providing a basis for obtaining backscatter with four polarizations (VV, VH, HH and HV) from the Earth's surface. The GF-3 satellite is currently the highest-resolution C-band and multi-polarized satellite in the world. Moreover, the microwave images acquired by the GF-3 satellite have high performance and can be used for not only geometric information of specific targets, but also quantitative inversion processes.  
The GF-3 satellite has 12 imaging modes (see Figure 3) which include a traditional strip imaging mode and a scanning imaging mode, as well as a wave imaging mode and a global observation imaging mode for marine applications. At the present time, it is the SAR satellite with the most imaging modes in the world. It has been found that with its wide imaging width and high spatial resolution, it can provide large-scale surveying processes as well as survey specific areas in detail [31]. Therefore, the GF-3 satellite has been able to meet the imaging needs of different users for different targets.  
In addition, the GF-3 satellite has significantly improved the remote sensing earth observational abilities. It is expected that the reliable and stable high-resolution microwave image data provided by the GF-3 satellite will effectively supply the needs of the users for high-resolution civil microwave remote sensing satellite data. At the same time, the GF-3 satellite has successfully obtained high-resolution multi polarization or full polarization microwave images [32], providing high-quality and high-precision earth observation data for users in global marine scientific research, land change monitoring, water conservancy  
Figure 2: Observation efficiency of radar altimeter constellation.  
applications and other fields. It is widely used in marine fields, such as marine disaster, oil spill, green tide, marine wave inversion, marine target ship, sea ice identification and so on [33; 34; 35].
## 2 Materials and Methods  
### Area of Study  
Four case-studies (Figure 1) are chosen to assess the developed LST algorithm.  
The first site area comprises several case-studies analyzed between the years 2017 and 2018 over a track of the Pedemontana motorway in the North-West of Italy at three station points: SP2, Ponte Olona and Gorla (Figure 1a). In this area, observed LST data are measured by Lufft, IRS31 Pro-UMB, which provides every 10 min an average temperature of the first centimeters of roadway asphalt with an accuracy of \(\pm\)0.1 \({}^{\circ}\)C for values between \(-\)20 \({}^{\circ}\)C and \(+\)20 \({}^{\circ}\)C, else \(\pm\)0.2 \({}^{\circ}\)C. The same sensor can also measure water film height up to 4 mm, freezing temperature for different de-icing materials (NaCl, MgCl\({}_{2}\), and CaCl\({}_{2}\)), road conditions (dry/damp/wet/ice or snow/moist with salt/wet with salt), friction, and ice percentage.  
In addition to the road surface sensor, Ponte Olona and Gorla station points are also equipped with a weather station (LUFFT WS600-UMB); hence, the MEC algorithm can be fed with observedmeteorological values which provide the following quantities every 10 min: air temperature and relative humidity, precipitation intensity, type and quantity, air pressure, wind direction, and wind speed.  
The second site is inside the Milano Linate airport near the runway (Figure 1b) where a dataset of four years (2015-2018) of measurements was available. Observed LST values are semi-hourly measured by contactless sensor sensitive to infrared radiation, the Apogee radiometers SI-111 which has an accuracy of \(\pm\)0.20 \({}^{\circ}\)C for temperatures between \(-\)10 \({}^{\circ}\)C to 65 \({}^{\circ}\)C. In addition, a thermo-hygrometer probe (by Vaisala) is mounted for measuring air temperature and relative humidity, whose values are stored every 30 min as well.  
Finally, the LST algorithm performance has also been tested on two non-asphalt surfaces: a maize vegetated area (Figure 1c) in the Chiese river basin (Brescia district, northern Italy), and a tomato field (Figure 1d) in the Capitanata Consortium (Foggia district, southern Italy). In these two latter areas soil monitoring surveys have been undertaken by the research group of the Politecnico di Milano during the growth season of 2016, 2017, and 2018 in the framework of SIM project, Smart Irrigation Management [21], www.sim.polimi.it. Of course, for the two vegetated areas, the analyses have been performed to assess the reliability of the LST prediction in different climate regimes and over cultivated fields, while the prediction of ice risk has been omitted, since data collection are available during growing seasons only. In these last two measurement areas (Chiese and Capitanata consortia), all values are semi-hourly archived trough an eddy-covariance tower, fully equipped both with air and soil instruments (Figure 1c,d); in particular, air temperature, relative humidity, and LST values are monitored with the same sensors used in the Linate airport site.
## 5 Methodology and Experiments
### MLP Model  
The first model is an MLP model consisting of dense layer units of Keras API. The first model is shown in Figure 5. Dense layers are the basis of the deep learning process, and they rely on gradient descent [41]. The theory behind gradient descent can be defined as: for each epoch, the algorithm first makes a prediction and measures the error, then goes through each layer in reverse to measure the error contribution from each connection, and finally slightly tweaks the connection weights to reduce the error.  
The Huber loss function of the tensorflow.keras API was used. To mitigate the overfitting effect, the \"EarlyStopping\" callback parameter was also used. To determine the optimal learning rate, the model was trained for 100 epochs, with different learning rates in each epoch. The change in the loss value against different learning rates is shown in Figure 6. Somewhere between 0.0001 and 0.00001 is suitable for the optimal learning rate value.  
The first layer accepts 30 samples as input, and all 30 tensors are fully connected with the first layer's 10 neuron units. The second layer accepts the first layer's output, which is 10 tensors, and it connects those tensors with its 10 units. The last layer accepts inputs from the previous second layer and outputs only one result as the predicted number of asked questions for the next sample. The predictions of the dense model are shown with an  
Figure 5: Dense model representation.  
Figure 6: Loss value versus learning rate.  
orange line in Figure 7, and the blue line represents the real numbers of the asked questions. The dense model achieved a forecasting performance of 5.572 MAE.
Iterative Equalization and Decoding over an Additive White Gaussian Noise Channel with ISI Using Low-Density Parity-Check Codes  
[PERSON] *  
Faculty of Electrical Engineering and Information Technology, University of Oradea, 410087 Oradea, Romania; [PERSON]ADDRESS] (F.L.M.); [EMAIL_ADDRESS] (A.-M.G.)  
[PERSON]  
Faculty of Electrical Engineering and Information Technology, University of Oradea, 410087 Oradea, Romania; [PERSON]ADDRESS] (F.L.M.); [EMAIL_ADDRESS] (A.-M.G.)  
[PERSON]  
Faculty of Electrical Engineering and Information Technology, University of Oradea, 410087 Oradea, Romania; [PERSON]ADDRESS] (F.L.M.); [EMAIL_ADDRESS] (A.-M.G.)  
[PERSON]  
During the iterations, the SISO (soft-in soft-out) equalizer and decoder exchange information, improving symbol estimation [6; 7; 8; 9]. In Section 4.1, this paper presents turbo-equalization based on the Log-MAP criterion [10; 11; 12].  
The current work is a continuation of the previous papers in which a performance comparison was made between the zero forcing (ZF) or MMSE equalization preceded the decoding process using different codes [13; 14], intending to evaluate the performance of the equalization that takes place inside the decoding process as in the case of turbo equalization. In this respect, the contributions of this article can be summarized as follows:  
* We proposed a system, respecting the classic turbo equalization scheme, to fix the errors introduced by ISI over an AWGN channel. However, for transmission, we utilized LDPC coding. At reception, we used a system consisting of a Log-MAP equalizer and min-sum LDPC decoding, which differs from the existing systems;
* Then, the functional analysis of the proposed system was realized, depending on the number of iterations within the iterative process of equalization and decoding or the number of iterations within the LDPC decoder. The proposed system demonstrated the effectiveness of the equalizer in terms of BER vs. SNR;
* Then, taking three impulse responses' h functions as mentioned in Sections 4.3 and 5, the performances in terms of BER vs. SNR of the proposed system were compared. These performances differed depending on the h function that was used;
* The performances in terms of BER vs. SNR of the proposed system were also correlated in comparison with other decoding and equalization systems described in the literature [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13].  
The process of LDPC decoding is iterative. Each attempt to decode the input message notifies the next until a valid codeword is obtained. LDPC codes have been adopted by several standards, such as WiFi, WiMax, WiGig, DVB-S2, or 10 GbaseT [15]. We should also include the enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC), and massive machine-type communications (mMTC) applications of 5G according to [15].
# Secure Service Proxy: A CoAP(s) Intermediary for a Securer and Smarter Web of Things
## 4 The Secure Service Proxy
### Secure Service Proxy: Implementation
#### 4.3.2 Implemented Application Layer Adapters  
In terms of application layer adapters, our proxy currently implements the adapters listed in Table 1. This section describes each of the adapter types in more detail. The access control adapter applies Access Control List (ACL) rules to the CoAP(s) requests it processes. ACL rules are parsed as JSON objects that assign allow and deny rules to either a username or a role of users. An allow and deny rule consists of a regular expression, which is applied to the request URI, and a list of request methods. In case no matching ACL rule is found, then the default policy of the adapter instance (either accept or deny) is applied. The following JSON serialization of an example ACL rule gives user \"bob\" full access to the deviencame resource, while access to the lock resource is restricted to read only. (\"username\": \"bob\", \"allow\": [(\"uri-reger\":\"devicename\", \"methoda\": [\"GET\", \"PUT\", \"POST\", \"DELETE\"]), \"[uri-reger\":\"lock\", \"methoda\": [\"GET\"])], \"dany\": [])  
Hosting a virtual resource on a virtual device is the task of the static resource adapter. In order to allow arbitrary content types of the payload, the value of the virtual resource is encoded in base 64 in the configuration of the adapter. An example is shown in the next section. The cache adapter serves and caches responses for requests to virtual devices. The cache adapter calculates a cache key for every CoAP request it handles. When a fresh response matching the cache-key is found, the adapter chain's execution is halted, and the cached response traverses the adapter chain in reverse. Responses processed by the cache adapter are handled in accordance with Section 5.9 of the CoAP RFC [9]. This means that, e.g., a \"2.05 Content\" response will be cached, while a \"2.04 Changed\" response will mark any stored response as not fresh. Cached responses are removed when they expire after their Max-Age option. Note that the cache adapter does not implement the \"Validation Model\" specified in Section 5.6.2 of the CoAP RFC [9]. When used in conjunction with access control, it is important that all ACL rules are applied before hitting the cache, as the execution of the request leg of the adapter chain will stop when a cache hit is found. The underlying implementation caches responses in memory via a memcached instance. The congestion control adapter in its current form applies traffic shaping on a per host basis. Currently, it is possible to limit the number of open requests between a client and a specific virtual device and between a client and a group of virtual devices. This group encompasses all virtual devices with an adapter chain that shares the same congestion control adapter instance. Open requests are