esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Finite Element Analyses  
As mentioned above, finite element (FE) stress analyses are needed for completing the calibration of the critical stress and the critical distances and, ultimately, for predicting the LCCs of the keyhole notched polymeric samples studied in this paper. In the present investigation, two-dimensional (2D) plane-stress FE models were created and analyzed by employing commercial code ABAQUS/CAE 6.10 (Dassault Systemes Simulia Corp., Johnston, RI, USA). As shown in Figure 4, the bottom line of the model is entirely fixed by constraining all its nodes. In addition, in order to conduct the simulation of the loading conditions accurately, the average critical load for each combination of material and notch radius (see experimental results below) was applied to the top line of the specimen model as a uniformly distributed compressive load (see Figure 4).  
About 160,000 eight-node plane-stress quadratic elements were used with the medial axis mesh algorithm. Since the stress gradient is significant near the notch tip, refined elements with the size of 0.02 mm were defined in this region (see Figure 5). The mesh pattern used in the FE analyses is based on the authors' large experience in the numerical analysis of notched components. The analysis of numerous two-dimensional (2D) notched specimens by the authors (e.g., [31; 32; 33; 34]) has demonstrated that optimal results, with excellent convergence, are obtained when the size of the meshes at the notch tip is about 0.02 mm. Here, it is important to notice that unlike in general FE analyses performed in the context of solid mechanics, for which the entire mesh pattern may contribute to the mesh-sensitivity analysis, in the field of notch fracture mechanics (NFM), the mesh pattern far from the notch tip does not affect the numerical results.  
Figure 4: Loading and boundary conditions for the keyhole notched specimens subjected to compressive loading. BC: boundary conditions.  
It should be highlighted that, based on the experimental observations, in which obvious contact occurred between the horizontal edges of the key-hole notches, frictionless contact was defined between such edges.
## 4 Application: Case Study
### Evaluation and Validation  
In the evaluation stage of the LBA, we used a manual method similar to that proposed by [PERSON] et al. [51], which consists of randomly selecting and labeling training comments. The difference is that in this work, we involved members of the SP group to classify the comments as positive, negative, or neutral. The test set consisted of labeling all comments from the primary dataset. For LBA, Figure 5a shows a multi-class confusion matrix containing the classification results for the test dataset. The results were obtained using Python programming language. The entries are shaded according to the number of observations in each category, with the largest number of observations is represented by the darker shade. It can be determined that the VADER model was \"confounded\" mainly by negative and neutral observations.  
Figure 4: Proportion of opinion polarity by approach.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Instance IDs** & \(\mathbf{T}k_{1}\) & \(\mathbf{T}k_{2}\) & \(\mathbf{T}k_{3}\) & \(\mathbf{T}k_{4}\) & \(\mathbf{T}k_{5,\dots}\) & \(\mathbf{T}k_{2620}\) \\ \hline \(T_{i1}\) & 0 & 0 & 0 & 1 & 0 & 0 \\ \(T_{i2}\) & 0 & 0 & 0 & 0 & 0 & 0 \\ \(T_{i3}\) & 0 & 0 & 0 & 0 & 0 & 1 \\ \(T_{i4}\) & 1 & 0 & 0 & 0 & 0 & 0 \\ \(T_{i5}\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) \\ \(T_{i685}\dots\) & 0 & 0 & 1 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Frequent itemset matrix.  
Figure 5b,c show the representations used to evaluate SVM and NB within the MLA. The dataset was divided into training and testing sets using 5-Fold cross-validation. The GridSearchCV method was used to determine suitable SVM parameters. This tuning technique attempts to calculate the optimal values of the hyperparameters and is called tuning. The results showed C: 14.0 and gamma: 0.0625. It can be seen that the results of the evaluation metrics were better for VADER, SVM, and then NB. [PERSON] and [PERSON] [91] found that VADER performed better for specific datasets. The representation of the normalized matrix permits the visualization of models that perform better with the metrics used [60].  
Figure 5: Confusion matrices and evaluation metrics of the VADER, SVM, and NB methods.  
Finally, the planning group was followed up. This model was deemed more reliable and robust because it considered expertise.
## 2 Results and Discussion
### Optimum Extraction Yield and Total Phenolic Content
The comparatively minor influence of water quantity in subcritical water processes (extraction and degradation) has been well-established, provided there are enough water molecules to penetrate the solid matrix and carry the solutes of interest to the bulk aqueous phase. Figure 1f  
Figure 1: The 3D response surfaces of the extraction yield for the aboveground part (**a–c**) and roots (**d–f**) of _O. mutabilis_. In (**a**,**d**), the water/plant ratio was kept constant at 0.25. In (**b**,**e**), the extraction time was kept constant at 105 min. In (**c**,**f**), the temperature was kept constant at 150 \({}^{\circ}\)C.  
shows the interaction between water/plant ratio and extraction time at a constant temperature of 150 \({}^{\circ}\)C. At the lower water/plant ratio of 0.1, raising the extraction time from 30 to 180 min led to a \(\sim\)15% increase in the extraction yield, whereas at a ratio of 0.4 the increase was only \(\sim\)5%. The reason for this difference is probably the more prominent role of degradation side-reactions at higher water/plant ratios. This hypothesis is further supported by the curvature of the 3D surface at the longest extraction time of 180 min. It can be seen that up to the water/plant ratio of 0.25, the yield increased; however, beyond that value, excess water molecules participated in degradation reactions, thus reducing the yield.
# Special Issue: New Trends in Recycled Aggregate Concrete
## 6 Reliability of Recycled Aggregate Concrete Structures  
[PERSON] et al. [11] used artificial neural networks (ANNs) to determine the significance of aggregate characteristics on the mechanical properties of recycled aggregate concrete (RAC). The results show that water absorption has the most important effect on aggregate characteristics, further affecting the compressive strength of RAC, and that combined factors including concrete mixes, curing age, specific gravity, water absorption, and impurity content can reduce the prediction error of ANNs to 5.43%. Moreover, for elastic modulus, water absorption, and specific gravity, they are the most influential, and the network error with a combination of mixes, curing age, specific gravity, and water absorption is only 3.89%.
## 4 Implementation Details
### FIR Filter, Decimator and Demodulator
#### 4.3.2. Decimator, Demodulator and Detector  
Decimator in the data-path is an adaptive decimator. It decimates incoming samples depending on the sampling frequency. The demodulator is the 16-ary quasi orthogonal correlation demodulator. It correlates the incoming samples with the stored modulation symbols. Output of demodulator is 16 correlation values. The detector finds the maximum of these correlation values and declares it as the symbol arrived.
## 3 Design of a Wireless Sensor Network for a Power Quality Monitoring System  
### Design of Communication Infrastructure  
In this subsection, we design a communication infrastructure for EDS power quality delivery as seen in Figure 2. The communication infrastructure consists of two parts; wired for the relay subsystem and wireless for the collection subsystem. The relay subsystem forwards data from substations to the monitoring subsystem via wired infrastructure due to the long distances between the relay subsystems and the monitoring subsystems. In the collection subsystem, pole transformers have been deployed sparsely at distances of hundreds of meters. We employ a WSN to construct the collection subsystem, in order to reduce the deployment and management cost. Since substations in the relay subsystem are connected to the monitoring center through a high-speed wired network, the communication between them is highly reliable. Thus the problem of data delivery in EDS is the same as the data delivery problem at the collection subsystem.  
The collection subsystem can be implemented by using the IEEE 802.15.4 [17] or IEEE 802.11b standards [18]. A wireless personal area network (WPAN) based on the IEEE 802.15.4 standard has been used due to the short communication range (e.g., dozens of meters) and the extra low power consumption of a communication devices, in spite of its low data rate. However, in EDS, pole transformer communication devices have been deployed at distances of hundreds of meters. Energy issues do not arise because the EDS is assumed to have the support of power lines. We use Wi-Fi (IEEE 802.11b) radio modules. The advantages of these modules are high data throughput (e.g., 11 Mbps) and the ability to abstract wireless communication details including collision detection and avoidance and error detection.  
Figure 3: Wireless sensor node design on a pole transformer.
## 2 Related Works
### Deep Domain Adaptation Approaches
#### 2.2.2 Adversarial Deep Domain Adaptation  
Adversarial domain adaptation, a recent technique, has made significant strides in addressing domain-adaptation challenges, demonstrating notable progress in demanding tasks [37; 38; 39]. Many researchers have utilized adversarial learning as a powerful domain-invariant feature extractor for domain adaptation due to its popularity [37; 40; 41; 42; 43]. The Generative Adversarial Networks (GANs) [44], are the significant motivation for adversarial domain adaptation. GANs attempt to decrease the cross-domain disparity by learning a deep invariant representation across domains through mini-max optimization. While the original GAN framework is inherently unsupervised, modifications and extensions allow GANs to be applied in various supervised and semi-supervised scenarios. Building upon this concept, ref. [45] proposed Domain-Adversarial Neural Networks (DANNs), a deep adversarial-based DA approach. Several variants have emerged to capitalize on adversarial learning in domain adaptation [16; 43; 46; 47]. For instance, ref. [16] propose Adversarial Discriminative Domain Adaptation (ADDA), which aims to reduce classification, soft label, domain classifier, and domain confusion losses to align cross-domains distributions.  
Adversarial learning in unsupervised domain adaptation has proven effective in scenarios with closely related source and target domains. However, challenges arise when significant dissimilarities exist between the two domains, leading to potential instability during shared-feature learning [48; 49]. In this case, adversarial learning approaches do not guarantee comprehensive coverage of relevant patterns and information present in the target domain. To address this limitation, we propose a Weighted Sequential Unsupervised Domain Adaptation (WS-UDA) framework that incorporates weight initialization to enhance model stability and adaptability.
## 3 Deep Feature-Based Adaptive Level Set Model (DFALS)  
To overcome the auroral oval segmentation problems brought by the image intensity inhomogeneity, the high noise level and the level set initialization, this work proposes a deep feature-based adaptive level set (DFALS) model with the aid of the convolutional neural network.
# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 2 Study Area and Data Sets
### Gpm Imerg  
Since the launch of TRMM in 1997, many different quasi-global satellite precipitation products have been developed [52; 53; 44]. As the successor of TRMM, GPM offers several advantages. The GPM Core Observatory carries a dual-frequency precipitation radar (DPR, the Ku-band at 13.6 GHz and Ka-band at 35.5 GHz) and a conical-scanning multichannel GPM Microwave Imager (GMI, frequency ranging from 10 to 183 GHz). The two instruments (DPR and GMI) are more advanced versions than the precipitation radar (PR) and the TRMM microwave imager (TMI) onboard the TRMM satellite. The observation data generated from PMW and IR platforms have been calibrated with gauge analysis of GPCC to produce IMERG products. The IMERG processing steps include (1) the CMORPH-Kalman Filter for quality-weighted time interpolation (\"morphing\") of PMW estimates following cloud motion vectors [54; 44], (2) the PERSIANN-CCS for retrieving PMW calibrated IR estimates [55; 56], and (3) the TMPA for inter-satellite calibration and monthly gauge adjustment [51]. These contribute to the improved ability of IMERG products to detect light rain and snow [2; 26]. IMERG provides three types of products, all at 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) spatial and half-hour temporal resolutions, including early and late runs with about 4-hour and 12-hour latencies after observation respectively, and the final run with about 2-month-latency after the observation [57]. Early run only uses forward morphing schemes to propagate the instantaneous PMW precipitation estimation, while the late and final runs use both forward and backward morphing schemes. Only final runs are adjusted by gauge measurements, which is conducted in step (3). In this study only the early run product is used. For convenience, GPM IMERG early will be simply referred to as GPM hereafter. The GPM data can be downloaded from the Precipitation Measurement Missions (PMM) website ([[https://pmm.nasa.gov/data-access/downloads/gpm](https://pmm.nasa.gov/data-access/downloads/gpm)]([https://pmm.nasa.gov/data-access/downloads/gpm](https://pmm.nasa.gov/data-access/downloads/gpm))).  
Figure 1 shows the mean daily precipitation over China for May to September in 2017 from AMPA and GPM IMERG Early run. Although they both capture the large-scale spatial pattern of mean precipitation, dramatic differences can be found in many regions, especially over southeast China. Corrections to the GPM precipitation estimates are necessary in order to derive realistic real-time products.
# Fabrication of Eco-Friendly Graphene Nanoplatelet Electrode for Electropolishing and Its Properties
## 1 Introduction  
Electropolishing is one of the most widely applied metal polishing processes, used to passivate and deburr metal parts. Materials to be polished are located as anodes, and counterex electrodes such as Cu, Pb, and Fe are located as cathodes. A direct electric current (DC) passes through the positive terminal (anode) to the electrolyte and the negative terminal (cathode). Electropolishing is the reverse process of electroplating, in which electric current passes from the anode to cathode, resulting in oxidation of the material surface. The main purposes of the electropolishing process are the removal of impurities on the material surface, establishment of uniform surface roughness, and selective control of surface element composition ratio. Common electrolytes for electropolishing are concentrated acidic solutions based on sulfuric and phosphoric acid. Therefore, elution of the cathode electrode inevitably occurs under this electropolishing process environment.  
Elution from cathode electrodes, such as high concentration Cu\({}^{2+}\), is toxic and affects the metabolic processes of marine organisms. Although, naturally, the marine environment contains low concentrations of Cu\({}^{2+}\), a concentration of Cu\({}^{2+}\) > 0.08 uM very negatively affects the completion of different life stages of brown macroalgae. Cu\({}^{2+}\) usually forms inorganic and organic complexes with ligands such as thiols and humic substances [1]. Often, the production of high concentration Cu\({}^{2+}\) with organic complexes can counteract critical toxic effects to humans and related organisms, including photosynthetic organisms [2]. Moreover, accumulated Cu in polluted plants can be horizontally transported to other plants directly in soil via connected stolons [3]. Therefore, a highly conductive graphene-based carbon material could be an alternative for cathode electrodes. Graphene has excellent chemical resistance in acidic environments, with high levels of electrical conductivity, mechanical strength, surface area, and flexibility [4; 5]. Moreover, graphene with \(\pi\)-\(\pi\)-interacted functional molecules provides homogenous dispersion of graphene which enable solution-based process for conductive electrode applications [6; 7].  
Herein, we demonstrated an eco-friendly GNP electrode for electropolishing by non-covalently melamine-functionalized GNP electrodes. These functionalized GNP electrodes were fabricated from highly oriented liquid crystals and shear-induced alignment. The electropolishing and elution properties of the GNP electrodes were compared with those of conventional Cu electrodes. The surface morphologies and compositions of the polished 304 stainless steels were studied to assess the electropolishing quality of GNP electrodes.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 4 Experimental Setup
### Preprocessing
#### 4.1.2 Binning  
In Big Data, since analysis can become computationally intensive and expensive, binning was used to group the variables. In particular, binning was used to group the network ports, connection state, and network protocols.  
Dest_port_zeek and src_port_zeek represent the ports involved in network communication. As per [6; 25; 29], to simplify the range of potential port numbers, the ports were categorized into:  
\begin{tabular}{l l} \(\bullet\) & Bin 0: Null or missing values \\ \(\bullet\) & Bin 1: System Ports (0-1023) \\ \(\bullet\) & Bin 2: User/Registered Ports (1024-49,151) \\ \(\bullet\) & Bin 3: Dynamic/Private Ports (49,152-65,535) \\ \(\bullet\) & Bin 4: Out-of-range or unclassified ports \\ \end{tabular}  
The connection state, conn_state, feature describes the state of a network connection. The various connection states were grouped into distinct bins, as follows:  
\begin{tabular}{l l} \(\bullet\) & Bin 0: RSTH (Reset with acknowledgment) \\ \(\bullet\) & Bin 1: SF (Normal establishment and termination) \\ \(\bullet\) & Bin 2: S0 (Connection attempt with no reply) \\ \(\bullet\) & Bin 3: OTH (Other conditions) \\ \(\bullet\) & Bin 4: REJ (Connection attempt rejected) \\ \(\bullet\) & Bin 5: RSTO (Reset received with no acknowledgment) \\ \(\bullet\) & Bin 6: RSTR (Reset sent in response to a connection attempt) \\ \(\bullet\) & Bin 7: SH (Synchronized half-open connection) \\ \(\bullet\) & Bin 8: S2 (Partially established connection) \\ \(\bullet\) & Bin 9: S1 (Connection attempt with no reply) \\ \(\bullet\) & Bin 10: SHR (Synchronized half-open connection response) \\ \end{tabular}  
Protocol  
To categorize the different network protocols in the network traffic data, the protocol (proto) column was used. This column was separated into four separate bins based on the type of their network protocol, as shown below:  
\begin{tabular}{l l} \(\bullet\) & Bin 0: TCP (Transmission Control Protocol) \\ \(\bullet\) & Bin 1: UDP (User Datagram Protocol) \\ \(\bullet\) & Bin 2: ICMP (Internet Control Message Protocol) \\ \(\bullet\) & Bin 3: All other protocols \\ \end{tabular}
# Pore Solution Chemistry of Calcium Sulfoaluminate Cement and Its Effects on Steel Passivation
## 4 Conclusions  
The chemistry of pore solutions is an important factor in influencing steel passivation in concrete. The experiments included in this research reveal the differences between OPC and CSA with regards to pore solution composition, based on which an SPS system was designed to study the effect of CSA pore solution chemistry on steel passivation. The conclusions can be summarized as follows:  
1. The cations in CSA and OPC are similar, both mainly containing Na\({}^{+}\), K\({}^{+}\), and little Ca\({}^{2+}\). However, the composition of anions varies greatly.
2. The concentration of OH\({}^{-}\) in the CSA pore solution is only 55.34 mmol/L at 28 days, while the value of OPC is 451.3 mmol/L. The corrosion potential development in SPSs indicates that alkalinity is the most significant factor for steel passivation. When the OH\({}^{-}\) is below 0.001 M, the steel cannot passivate in any situations. Thus, the steel passive film formed in the environment of CSA is less stable than that in OPC.
3. A high concentration of Al(OH)\({}_{4}\)\({}^{-}\) was detected in CSA pore solution, and its effect on steel passivation was studied. High content of Al(OH)\({}_{4}\)\({}^{-}\) can release enough OH\({}^{-}\) to offer a buffering capacity for the solution alkalinity, hence improving the stability of the passive film.
4. The sulfate ions in CSA can remain at a level of about 50 mmol/L, while their concentration in the OPC was negligible after one day of hydration. According to its potential evolution, it was concluded that SO\({}_{4}\)\({}^{2-}\) is adverse to the stability of passive film, but the effect was only obvious in the case of low alkalinity (C\({}_{\text{(OH)}}\)\(\leq\) 0.01 M), and the negative impact can be reduced with Al(OH)\({}_{4}\)\({}^{-}\).  
Conceptualization, L.W. and S.Z.; methodology, L.W.; software, L.W.; validation, L.W., S.Z. and X.T.; formal analysis, L.W.; investigation, L.W. and X.T; resources, Q.X. and K.Q.; data curation, L.W.; writing--original draft preparation, L.W.; writing--review and editing, L.W. and S.Z.; visualization, L.W.; supervision, S.Z.; project administration, L.W.; funding acquisition, S.Z. This research was funded by National Key R&D Program of China, grant number 2016 YFB0303505-04.  
The authors declare no conflict of interest.
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 3 MAC Address Randomization  
### IEEE 802.11 MAC Address  
IEEE 802.11 devices have a unique Layer-2 identifier known as the MAC address. This address identifies the device as the source of a transmitted frame and allows for proper frame processing upon arrival. A MAC address comprises 6 bytes, and its uniqueness is ensured through a hierarchical structure. At the top level, the responsibility of MAC address registration lies with the IEEE Registration Authority (IEEE RA) [17]. The IEEE RA assigns a block of addresses, MAC-Address Block Large (MA-L) or Organizationally Unique Identifiers (OUIs), consisting of 3 bytes. These blocks are then sold to specific manufacturers of IEEE 802.11 devices. Each manufacturer subsequently assigns the remaining 3 bytes to ensure global uniqueness for every network interface controller (NIC) they produce. To optimize the utilization of the available OUI pool, the IEEE Registration Authority (IEEE RA) provides manufacturers with the option to acquire a 3-byte company identifier (CID) for certain connections where a globally unique MAC address is not essential, such as in the case of active scanning. The MAC address format is structured as follows: the first 3 bytes represent the organizationally unique identifier (OUI), while the last 3 bytes denote the network interface controller (NIC) as illustrated in Figure 1. Unicast and multicast addresses can be differentiated based on the value of the least significant bit (LSB) in the first octet, where a clear bit indicates a unicast address. The distinction between global (OUI) and local (CID) MAC addresses is determined by the state of the second LSB in the first octet, with a set bit indicating a globally unique address.