esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### System Setup  
A Vantage 64 LE HF research ultrasound platform (Verasonics Inc., Redmond, WA, USA) equipped with a customized linear array transducer (Vermon Inc., Tours, France, center frequency: 15 MHz, pitch: 0.11 mm, \(-6\) dB bandwidth: 61%) was employed for H-Doppler in this study. To obtain the best performance of tissue scattering classification for H-scan, an arbitrary waveform generator module was adopted in the experiment, as shown in Figure 4a. The ultrasonic transmitted signal is a 15-MHz Gaussian window weighted sinusoidal signal established through the Vantage arbitrary waveform generator toolbox. Figure 4b shows the theoretical waveform of the G\(H_{4}\) kernel and the transmitted waveform received by the hydrophone (NH1000, PA, Dorchester, UK) in our experiments. RF signals were acquired from a coherent plane wave approach compounding with 7 angles ranging from \(-11^{\circ}\) to \(11^{\circ}\) at a sampling frequency of 60 MHz. The delay between each transmission was set to 160 microseconds. Since the Vantage 64 LE HF system can only receive 64 channels in each transmission, it takes two transmissions to get a full frame. Therefore, the nominal pulse repetition frequency (PRF) was 3.125 kHz. The raw RF data from the 128 elements transducer was beamformed using a delay-and-sum method [32].  
Figure 3: **(a)** Schematic diagram of the block-wise local (BWL)-SVD filtering approach. The size and overlap of each block are \(128\times 24\) and \(120\times 24\), respectively, and the overlap percentage is 78.125%. **(b)** Schematic diagram of the H-Doppler flow imaging with BWL-SVD filtering. The local SVD clutter filtering is separately applied to R and B channel dataset in block \(k\). Then, the clutter-filtered results are performed with a subtraction operation to obtain the final H-Doppler image of block \(k\).
# Enhancing Compressive Strength of Reticulated Porous Alumina by Optimizing Processing Conditions
## 5 Conclusions  
Reticulated porous alumina specimens were prepared by a conventional replica method using sacrificial polymer templates. The effects of the parameters of the alumina slurry (average particle size, binder, solid loading, and dispersant) on the densities, pore size distributions, and the consequent compressive strengths of the reticulated porous alumina were studied. The experimental results explained the relationships between the properties of the reticulated porous alumina and the processing conditions as follows.  
The intermediate alumina particles, 63.5 wt. % of solid loading, and 10 wt. % of PVA binder contributed to the higher density of 0.60 g/cm\({}^{3}\) from a reticulated porous alumina specimen. Accordingly, increased compressive strength of 0.78 MPa can be obtained. These partially optimized processing conditions are not sufficient to ensure the highest density and the thickest coating of the strut walls without macrovoids within the reticulated porous alumina specimen, and a further increase can be expected with the aid of a dispersant. Although the addition of a dispersant to an alumina slurry may help reduce the viscosity of the slurry, it does not always result in the highest density of the reticulated porous alumina. Only the appropriate type and amount of dispersant will increase the overall compressive strength with uniform and defect-free surfaces of the strut walls of the reticulated porous alumina.  
In this study, the novel optimized processing conditions consisted of a slurry composition (average particle size of 0.26 \(\mu\)m, 63.5 wt. % of solid loading, 10 wt. % of PVA binder, 1 wt. % of Dolapix CE 64 dispersant), and sintering conditions (45 PPI and 1600 \({}^{\circ}\)C) that can be used to obtain the highest density (0.85 g/cm\({}^{3}\)) and a compressive strength of 2.72 MPa from a reticulated porous alumina specimen.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Program of the Korean Institute of Materials Science (KIMS), Grant No. PNK7420 and by the Technology Innovation Program (20003782) of the Ministry of Trade, Industry, and Energy.  
The authors declare no conflict of interest.
###### Abstract  
Effective sales prediction for e-commerce would assist retailers in developing accurate production and inventory control plans, which would further help them to reduce inventory costs and overdue losses. This paper develops a systematic method for e-commerce sales prediction, with a particular focus on predicting the sales of products with short shelf lives. The short-shelf-life product sales prediction problem is poorly addressed in the existing literature. Unlike products with long shelf lives, short-shelf-life products such as fresh milk exhibit significant fluctuations in sales volume and incur high inventory costs. Therefore, accurate prediction is crucial for short-shelf-life products. To solve these issues, a stacking method for prediction is developed based on the integration of GRU and LightGBM. The proposed method not only inherits the ability of the GRU model to capture timing features accurately but also acquires the ability of LightGBM to solve multivariable problems. A case study is applied to examine the accuracy and efficiency of the GRU-LightGBM model. Comparisons among other sales prediction methods such as ARIMA and SVR are also presented. The comparative results show that the GRU-LightGBM model is able to predict the sales of short-shelf-life products with higher accuracy and efficiency. The selected features of the GRU-LightGBM model are also useful due to their interpretability while developing sales strategies.  
sales prediction; e-commerce; GRU; LightGBM; short-shelf-life product
## 2 Oil Shale Properties
### Oil Shale Geology
Conversely, the warm alkaline lake waters in the Green River Formation primarily supported the extensive growth of blue-green algae, which served as the main contributor to the organic content in the oil shale deposits [7]. Through geochemical assessments and microscopic studies, [PERSON] et al., found that the primary source of organic matter in the oil shale was aquatic organisms, substantial deposits of dark mudstone and oil shale in the Sangonghe area situated at the northern foothills of the Bogeda Mountains within the Junggar Basin. These deposits were formed under anoxic conditions in a deep lake setting [12]. The lake productivity had a more significant impact on the oil shale quality than the preservation state of the organisms. Multiple factors, including the ancient climate, source rock chemical alterations, and lake conditions, contributed to the formation of high-quality oil shale. Warm and moist conditions led to the erosion of rocks and the release of nutrient elements into the prehistoric lake. Elevated paleotemperatures, alkaline waters, strong anoxic conditions, and particular hydrodynamic circumstances further aided in the release of nutrients from sediments at the lake's bottom, thereby enhancing lake productivity and enabling the development of high-quality oil shale [12]. [PERSON] et al. (2022) describe a study on the Qingshankou Formation and Nenjiang Formation in the Songliao Basin, which aimed to reveal differences in hydrocarbon generation processes and controlling factors of oil shales of different qualities. They showed that oil shales abundant in algae possess significant hydrocarbon potential, making the investigation of hydrocarbon generation processes in oil shales of varying quality crucial for shale oil resource discovery. They found that oil yields (O.Y.s) possess a linear relationship with TOC concentration at varying depths. A threshold thermal maturity of 1.05% is found beyond which oil shale starts to produce illite due to the enhanced catalytic activity of smectite with the presence of bulk telalginite, which is beneficial for hydrocarbon generation [13].
## 5 General Discussion and Conclusions
In fact, from polymerization methods, (i) polymer nanorods/nanofibers nanostructures with different morphologies and nano-microscopic sizes (pore diameter ranging from 35 to 350 nm and pore length ranging from 700 nm to 100 \(\upmu\)m) can be synthesized directly inside the AAO nanoreactors and then extracted from the nanocavities; (ii) the entire library of polymers can be synthesized from the precursor monomers by polymerization reactions inside AAO nanocavities. This includes thermoset polymers. Moreover, dual-sized polymer nanowires mimicking the design of gecko toe pad can be obtained from branched AAO templates. AAO nanoreactors have been employed as precursor of applications. The construction of electrochemical biosensors that show good operational stability, polymer scaffolds for cell adhesion or \"patterned\" surfaces, are only a few examples of intricate polymer nanostructures that have been obtained by AAO templating, microlithography or a combination of both. The aforementioned results support the idea that nanoporous materials are ideal systems to accomplish polymerization reactions in nanoconfinement. However, they also highlight the necessity of further studies, especially on the influence of particular geometrical constraints in each type of polymerization reaction and for different monomer families. Further effort should be also devoted to developing theoretical models that describe these polymerization reactions. Although these fundamental studies are still lacking, the huge progress that has been made in recent years gives promise for the targeting of the new applications for these materials, particularly in systems that can take advantage of the well-defined multilevel polymer architecture that can be achieved via the polymerization of monomers in nanopores. Both authors have contributed to the literature survey, preparation, review and editing of manuscript. All authors have read and agreed to the published version of the manuscript. The authors acknowledge the Spanish Ministry of Science, Innovation and Universities (MINECO) for financial support in the form of grants MAT2020-83014-C2-2-P and PGC2018-094620-A-I00; [PERSON] thanks MCIU for the Ramon y Cajal and Ikerbasque Foundation for the Ikerbasque Research Fellow program. Not applicable. The data used to support the findings of this study are available from the literature and the corresponding author upon request. The authors declare no conflict of interest.
## 2 Materials and Methods
### Performance Indicators  
The performance of three hybrid ML models was evaluated by three statistical criteria, namely RMSE, R, and MAE [7; 75; 76; 77]. The R-value indicates the correlation between the predicted values, given by the ML models, and the actual values [78]. RMSE is the average squared difference between the predicted and actual values [19; 79; 80]. MAE measures the average of absolute difference between outputs (predicted values) and targets (actual values) [81; 82; 83]. On the one hand, higher R indicates a better model [84]. On the other hand, lower RMSE and MAE indicate a better model [85; 86]. The formulas are defined as follow [87; 88; 89; 90]:  
\[\text{MAE}=\frac{\sum_{i=1}^{n}\left|p_{i}-v_{i}\right|}{n} \tag{2}\]  
\[\text{RMSE}=\sqrt{\sum_{i=1}^{n}\frac{\left(p_{i}-v_{i}\right)^{2}}{n}} \tag{3}\]  
\[\text{R}=\sqrt{\frac{\sum_{i=1}^{n}\left(p_{i}-q\right)\left(v_{i}-\underline {v}\right)}{\sqrt{\sum_{i=1}^{n}\left(p_{i}-q\right)^{2}}\sum_{i=1}^{n}\left(v _{i}-\underline{v}\right)^{2}}} \tag{4}\]  
where \(n\) refers to the number of samples in the dataset; \(p_{i}\) and \(q\) are the values and means of the predicted critical buckling load, respectively; and \(v_{i}\) and \(\underline{v}\) are the individual and mean values of the actual critical buckling load, respectively.
## 3 Blockchain-Enhanced Security and Operational Efficiency in VANETs
### Invalid or Fraudulent Data Management
#### 3.5.1 Special Cases: Node Disconnection and Re-Entry  
Any vehicular node that either autonomously disconnects or is identified as malicious and consequently ejected from the network will have its status updated by the RSU to the TA. The TA will revoke the node's cryptographic keys and digital certificates, thereby disallowing any further participation in network activities. To re-enter the network, a complete reregistration process with the TA is obligatory. All pertinent information related to the vehicular node will be eternally archived in the blockchain's reputation chain.
## 2 Materials and Methods
### Determination of Polyphenols by Spectrophotometric Methods  
#### 2.4.1 Total Phenolic Compounds (TPC)  
The total phenolic content (TPC) was determined using the Folin-Ciocalteau method, with light modification from the literature [32]. A 150 \(\mu\)L sample was mixed with 2400 \(\mu\)L of distilled water and 150 \(\mu\)L of Folin-Ciocalteau reagent (0.25 N), followed by homogenization using a vortex homogenizer. Subsequently, 300 \(\mu\)L of 1 N sodium carbonate was added, and the mixture was homogenized again using the vortex homogenizer. The resulting mixture was incubated in the dark at room temperature for 120 min. The absorbance was measured at 725 nm using a UV-2601 spectrophotometer (RayLeigh, Beijing, China). The calibration curve was generated using gallic acid as the standard (y = 0.0052x + 0.0579; R\({}^{2}\) = 0.9986). Total phenolic content values were expressed as milligrams of gallic acid equivalents per 100 g of extract (mg GAE/100 g).
## 2 Experimental Set-Up
### Selection of Platforms
#### 2.2.4 Apache Kafka Streaming Platform  
Apache Kafka is a distributed streaming platform first introduced by LinkedIn engineers and later open-sourced [7]. Apache Kafka consists of three key capabilities: (i) to publish and subscribe to streams of records; (ii) to store streams of records in a fault-tolerant durable way; (iii) to process stream records in the order they occur. The Apache Kafka architecture has three main components, specifically; producers, brokers and consumers. The producer creates data and sends them to a broker who receives, categorises and stores data before the consumer pulls data from the broker. The categorisation of data is processed through topics, and one or more topics are stored in partitions on the broker(s). Records written to the partitions are in the form of (key, value, timestamp) triples which are: immutable, persistent and added to an append-only list preserving message order [30]. The consumers maintain their own state and poll for new data when needed. This allows Apache Kafka to persist a single message independently from the number of consumers, resulting in high-throughput for read and write operations [31].
# A Nonlinear Convolutional Neural Network-Based Equalizer for Holographic Data Storage Systems
## 3 Proposed Model
### Proposed Nonlinear Equalizer
However, we find out that the BER performance is a little bit improved when applying the CNN structure. The reason for this problem is that when applying straight structures (Figure 5a), the weights in early layers are not updated completely. The final layers of the CNN model are trained and updated with the parameters. This is called the vanishing gradient. To solve the above problem, the skipping branch (Figure 5b) is introduced to update the parameters in early layers. Instead of the update parameters going through many layers from the final to the beginning layers, they can go directly to the early layers by skipping branches.
## 4 Design of Proposed Scheme: FAPR
### Reactive Part
#### 4.3.4 Flow Analysis Module  
The flow analysis module is the most crucial module of FAPR's reactive part, and its role is to filtrate the flows that are affected by the link failure, and analyze their characteristics to allocate suitable backup paths. In a real network, the vast majority of flows are generated by an application at the application layer. Moreover, each flow corresponds to a pair of port numbers to identify different types of applications, and uses either a transport control protocol (TCP) or a user datagram protocol (UDP) as the transport layer protocol. As a result, FAPR distinguishes different types of network services by analyzing transport layer protocols and port numbers. After identifying the types of network services, FAPR will allocate the most suitable backup paths for each flow. For example, file transfer applications have a higher demand for bandwidth, while remote desktop applications, real-time chat programs, video conferences, and online games are generally more sensitive to latency.  
In Section 4.3.3, we mention that the failure detection module transmits the standardized information of the failed link and other data to the flow analysis module, which contains the information of flows affected by the failure (in the form of flow table entries), the original paths of these flows, and so on. The flow analysis module extracts important tuples from these flow table entries, including the transport layer protocol and port num  
Figure 6: Standardized link information.  
bers of each flow, and determines the type of network service. Moreover, the application type and original path of each flow, along with other information, will be sent to the backup path allocation module for processing.
# Satellite Quantum Communications When Man-in-the-Middle Attacks Are Excluded
## 2 Results  
Table 1 shows a summary of the hardware types and corresponding QKD and PKD methods considered. Figure 1 shows a comparison of the quantum bit error rate (QBER) and bits per pulse achieved with these methods at increasing distances (losses). In this way the results can be presented independently of the pulse rate of any particular hardware setup. Although we use the term 'pulse' here for straightforwardness of comparison, the spontaneous parametric down conversion (SPDC) sources we consider are operated by a continuous-wave pump, i.e., not pulsed, and produce photon pairs on a stochastic basis, so 'detection events' would be the more appropriate term. Figure 1a,b show that for QKD protocols (BB84, BBM92) there is a sharp drop off in key rate as losses increase. At high losses more key has to be discarded in privacy amplification than is available so the secure key rate  
drops to zero. This does not happen in the PKD equivalents as it is assumed that there are no active man-in-the-middle attacks, and so privacy amplification is only required for multi-photon pulses.
###### Abstract  
The objective of this investigation was to fabricate a photothermally responsive composite bone scaffold aimed at facilitating bone tissue regeneration and remedying bone defects via mild thermal stimulation. The photothermal-sensitive nanomaterial SiO2 coated Fe3O4 (SiO2@Fe3O4), synthesized through the hydrolysis-condensation process of tetraethyl orthosilicate (TEOS), displayed a uniform distribution of SiO2 coating, effectively preventing the aggregation of Fe3O4 particles within the scaffold matrix. The composite scaffold containing 5% mass fraction of photothermal-sensitive nanoparticles exhibited evenly dispersed microstructural porosity, a compressive strength of 5.722 MPa, and a water contact angle of 58.3 deg, satisfying the mechanical property requisites of cancellous bone while demonstrating notable hydrophilic characteristics. Upon exposure to near-infrared light at ambient temperature, the 5% composite scaffold underwent a temperature elevation of 3-6 degC within 40-45 s, attaining a temperature range (40-43 degC) conducive to fostering osteogenic differentiation. Experimental findings validated that the SiO2@Fe3O4/polyvinyl alcohol (PVA)/hydroxyapatite (HA)/polycaprolactone (PCL)/ \(\beta\)-tricalcium phosphate (\(\beta\)-TCP) bone scaffold showcased outstanding mechanical and photothermal attributes, thereby presenting a pioneering avenue for advancing bone tissue cell proliferation and addressing bone defect rehabilitation.  
bone tissue engineering; photothermal composite bone scaffold; photothermal-sensitive nanoparticles; mechanical properties; photothermal performance
# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## Materials and Methods
### Datasets
#### 2.1.1 Baseline NUCAPS Soundings  
In 2019, four NUCAPS-derived products were tested in the HWT: baseline, modified, gridded, and forecast. Baseline NUCAPS is the NOAA operational product, which is currently available at all NWS forecast offices through AWIPS.  
When baseline NUCAPS was first demonstrated in the 2015 HWT, all retrievals were displayed without any indication of whether they passed quality control or not. This meant that forecaster had no way of knowing whether a retrieval accurately characterized the atmospheric structure or not. One of the first requests forecasters made, therefore, was that information on retrieval quality be readily available and visually communicated [20]. Now we see baseline NUCAPS in AWIPS displayed as three colors corresponding to different stages of success (Figure 2a). A green flag indicates a retrieval that successfully passed all quality control flags. It signals to forecasters that they can have confidence in the NUCAPS sounding. Yellow indicates MW-only was successful but one or both of the IR + MW regressions failed (Figure 1, steps B and D, respectively). Red signals that the MW-only retrievals, cloud clearing, and/or the IR + MW physical retrievals failed (Figure 1, steps A, C, and/or E, respectively). Retrievals that are flagged yellow or red may still be useful to the forecaster but should be interpreted with caution as they will contain greater uncertainty due to clouds.  
Figure 2: The four NUCAPS products demonstrated in the 2019 Hazardous Weather Testbed Experimental Forecast Program: Baseline NUCAPS soundings in **(a)** plan view with quality flags. The NSHARP display of **(b)** baseline NUCAPS soundings and **(c)** modified soundings northeast of Bismarck, ND on May 15, 2019 ahead of a low-level moisture gradient; **(d)** gridded NUCAPS showing 2 FHAG Temperature on June 3, 2019; and **(e)** NUCAPS-Forecast on May 10, 2019 showing CAPE gradients five hours past initialization.  
When displayed in AWIPS, baseline NUCAPS appears in the separate display as a Skew-T, which is the same tool that displays radiosondes (Figure 2b). While the profiles look very similar, some differences between NUCAPS and radiosonde Skew-T's were noticeable to forecasters. These differences occur because (1) sounder moisture measurements are layers and whereas radiosondes take point observations at a specific pressure levels, (2) sounders typically have a 2.7 km vertical resolution, which is much lower than the radiosonde resolution of 20 m, and (3) NUCAPS utilizes smoothing operators within the retrieval itself [21]. In the HWT, these differences are typically explained in training prior to the beginning of the experiment, or with face-to-face conversation while forecasters evaluate the product.
# Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning
## 3 Methods
### Proposed Framework  
As input, we use Blockchain transaction addresses which are preprocessed and transformed into the numerical domain using word embedding. The classification uses three types of neural network models (LSTM, BI-LSTM, and CNN-LSTM). We have selected Bi-LSTM over other alternatives such as GRU (Gated Recurrent Unit) because Bi-LSTM is generally considered better than GRU in tasks where modeling long-term dependencies is important. This is because Bi-LSTM has a more complex architecture that allows it to remember information from earlier in the sequence for longer periods. Additionally, the bidirectional aspect of Bi-LSTM allows it to capture dependencies in both directions, which can be especially useful in tasks where the ordering of the data is important. Finally, the results are aggregated using ensemble voting to produce the final prediction (malicious or benign transaction).