esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Changes in pH, TBARS, VBN, and TMC during Storage of Tteokgalbi
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Storage Days**} & \multicolumn{4}{c}{**Treatments 1**} \\ \cline{2-6}  & **CON** & **REF** & **B1** & **B2** & **B3** \\ \hline \multirow{3}{*}{pH} & 0 & 6.07 \(\pm\) 0.02 \({}^{\text{2.5 MA}}\) & 5.97 \(\pm\) 0.03 \({}^{\text{BA}}\) & 6.05 \(\pm\) 0.01 \({}^{\text{AA}}\) & 6.06 \(\pm\) 0.02 \({}^{\text{AA}}\) & 6.08 \(\pm\) 0.02 \({}^{\text{AA}}\) \\  & 7 & 5.15 \(\pm\) 0.03 \({}^{\text{AB}}\) & 5.16 \(\pm\) 0.01 \({}^{\text{AB}}\) & 5.22 \(\pm\) 0.02 \({}^{\text{GB}}\) & 5.48 \(\pm\) 0.02 \({}^{\text{BB}}\) & 5.56 \(\pm\) 0.02 \({}^{\text{AB}}\) \\  & 14 & 5.01 \(\pm\) 0.02 \({}^{\text{AC}}\) & 5.04 \(\pm\) 0.01 \({}^{\text{BC}}\) & 5.05 \(\pm\) 0.01 \({}^{\text{BC}}\) & 5.11 \(\pm\) 0.02 \({}^{\text{AC}}\) & 5.12 \(\pm\) 0.02 \({}^{\text{AC}}\) \\ \hline TBA \({}^{\text{5}}\) (mg malon-adehye/kg) & 0 & 0.30 \(\pm\) 0.00 \({}^{\text{AC}}\) & 0.20 \(\pm\) 0.00 \({}^{\text{BC}}\) & 0.21 \(\pm\) 0.03 \({}^{\text{BC}}\) & 0.20 \(\pm\) 0.00 \({}^{\text{BC}}\) & 0.23 \(\pm\) 0.00 \({}^{\text{BC}}\) \\  & 7 & 0.49 \(\pm\) 0.01 \({}^{\text{AB}}\) & 0.41 \(\pm\) 0.00 \({}^{\text{AB}}\) & 0.46 \(\pm\) 0.01 \({}^{\text{BB}}\) & 0.44 \(\pm\) 0.00 \({}^{\text{AB}}\) & 0.41 \(\pm\) 0.00 \({}^{\text{AB}}\) \\  & 14 & 0.55 \(\pm\) 0.00 \({}^{\text{AA}}\) & 0.50 \(\pm\) 0.00 \({}^{\text{AA}}\) & 0.54 \(\pm\) 0.02 \({}^{\text{AA}}\) & 0.50 \(\pm\) 0.01 \({}^{\text{AA}}\) & 0.45 \(\pm\) 0.01 \({}^{\text{AA}}\) \\ \hline \multirow{3}{*}{VBN \({}^{\text{6}}\) (mg/100 g)} & 0 & 6.75 \(\pm\) 0.56 \({}^{\text{NNC}}\) & 6.58 \(\pm\) 0.09 \({}^{\text{C}}\) & 6.78 \(\pm\) 0.99 \({}^{\text{C}}\) & 6.82 \(\pm\) 0.56 \({}^{\text{C}}\) & 6.58 \(\pm\) 0.27 \({}^{\text{C}}\) \\  & 7 & 10.42 \(\pm\) 0.14 \({}^{\text{AB}}\) & 8.19 \(\pm\) 1.02 \({}^{\text{AB}}\) & 10.41 \(\pm\) 0.84 \({}^{\text{ABpattern as VBN. Before cold storage, there was no significant difference in the TMC between all Tteokgalbi groups; however, the TMC increased significantly with longer periods of cold storage in all Tteokgalbi groups in a time-dependent manner. Compared to CON, the TMC was significantly lower (\(p<0.05\)) in \(>1.0\%\) (B2 and B3) and \(1.5\%\) (B3) of the BP-treated Tteokgalbi after cold storage for 7 and 14 days, respectively. _Boosenbergia pandurata_ (Roxb.) is widely used as a natural antibacterial resource to suppress _Staphylococcus aureus_, and _Bacillus cereus_ grows more efficiently than ginger [8]. Lower microbial growth in meat products is closely related to increasing WHC and pH maintenance [37]. In our study, BP increased WHC and prevented the reduction in pH and TMC, as was also reported in a report on garlic powder addition in rabbit burger [38] and as a mixture of organic compounds [39]. The results of this study clearly demonstrate that BP treatment decreases the VBN content and microbial growth during cold storage in Tteokgalbi, probably due to the abundance of polyphenols and flavonoids in BP.
## Appendix A  
The estimation of the MAP parameter set \(\mathbf{\theta}=\{\lambda_{i},q_{ij}\}\) is based on the maximization of its likelihood function, \(L(\mathbf{\theta}|T)=\mathbf{\pi}_{arr}^{T}e^{\mathbf{D}_{0}\tau_{1}}\mathbf{D}_{1} \ldots e^{\mathbf{D}_{0}\tau_{N}}\mathbf{D}_{1}\mathbf{1}_{K}\), with interevent times, \(\tau_{i}=t_{i+1}-t_{i},i=1,\ldots,N\), comprising the trace \(T=\{\tau_{1},\ldots,\tau_{N}\}\) of the data, \(K\) hidden states and \(N+1\) number of events. The EM algorithm [73] is used for the optimization of the likelihood function, which is a common procedure for applications with hidden data, i.e., the change points of seismicity rate. At each iteration of the algorithm, the log-likelihood (LL) function is computed through the forward and backward vectors, which describe the evolution of the process recursively. The \(i\)-th element of the forward vector \(\mathbf{f}[k]=\{f_{i}(k),i=1,\ldots,K\}\) gives the probability to be in state \(i\) by taking into account the history of occurrences up to time \(t_{k+1}\). Their values are obtained recursively through \(\mathbf{f}[k]_{j}=\sum_{i=1}^{K}\mathbf{f}[k-1]_{i}e^{-\lambda_{i}\tau_{i}}q_ {ij}(1)\), with \(\mathbf{f}[0]=\mathbf{\pi}_{arr}\). Similarly, the backward vectors \(\mathbf{b}[k]=\{\mathbf{b}_{i}(k),i=1,\ldots,K\}\) are defined giving the likelihood function \(L(\mathbf{\theta}|T)=\mathbf{f}[k]\mathbf{b}[k+1]\). Additionally, the forward and backward equations are used for the evaluation of the transitions among the states of the Markov process \(J_{t}\). This is crucial for the implementation of our method, since it allows the detection of changes in the seismicity rate. The state probabilities of the hidden process \(J_{t}\) at a given time \(t_{k}\) are obtained by  
\[p_{t_{k}}(i)=P(J_{t_{k}}=i)=\frac{p(\tau_{1},\ldots,\tau_{N},J_{t_{k}}=i)}{p( \tau_{1},\ldots,\tau_{N})}=\frac{\mathbf{f}[k-1]_{i}\mathbf{b}[k]_{i}}{L(\mathbf{ \theta}|T)}, \tag{10}\]  
For the determination of the hidden states number that is appropriate for capturing the seismicity rate changes, the Bayesian Information Criterion (BIC) [74] is used, which is a metric based on the maximum log-likelihood of each model. It is expressed through \(BIC=-2\times LL+log(N^{*})\times k\), where \(k\) is the number of estimated parameters and \(N^{*}\) corresponds to the number of observations.
# Three-Dimensional Imaging of Metallic Grain by Stacking the Microscopic Images
## 2 Specim and Layer Capture  
In this study, carbon steel SA106 was targeted. The sample was cut and cropped to a small volume of 25 \(\times\) 9 \(\times\) 25 mm (width \(\times\) length \(\times\) height), respectively, and mounted on a cylindrical resin with a diameter of 35 mm and a height of 27 mm. Then, the observation side and the backside of the mounted specimen were polished to be exposed outside the resin. This allows the \"polished\" height to be measured more accurately when viewed under a microscope. In other words, when measuring the height of the sample with a micrometer after polishing, errors likely to be caused by compression of the resin can be avoided when the resin is used as a measurement reference and not the surface of the sample. As shown in Figure 1a, the side surface was polished to deflect by 1.84 mm. This deflected side surface (hereafter \"key-hole\") was used as a reference surface for microscopic observation. Figure 1b is a jig for uniform grinding of the key-hole, and Figure 1c is a template (state) in which the key-hole is polished in the sample mounted in the jig. Figure 1d illustrates these mounted samples; they are inserted into the X-Y stage holder under the microscope and are fixed by a key (expressed in black in the figure). Due to this structure, the area observed by the microscope can fix the measurement surface with a repeatability of the order of 10 to 20 \(\upmu\)m.  
Figure 1: Specim adjustment to reduce positioning error. (**a**) Mounted specimen; (**b**) Jig for key-hole; (**c**) After grinding Mounted specimen; and (**d**) Set specimen on the XY-stage using the key and key-hole.  
The observation surface was polished using SiC polishing pads FEPA P200, P400, P800, P1200, and P2000. The last stage, P2000, has a grid size of 10.3 \(\upmu\)m. Finally, fine polishing was carried out for 20 s using polishing cloths and polycrystalline diamond paste having a grid size of 1 \(\upmu\)m. Once the micro-polishing was completed, an etchant solution obtained by diluting with nitric acid 3 g in 65 g ethanol was exposed onto the observation surface for 5 s, followed by ultrasonic cleaning with distilled water for 15 s and then cleaning with ethanol for 10 s. This chemical etching was performed a total of three times. The height of the four sides of the test sample was measured with a micrometer having an accuracy of \(\pm\)0.01 \(\upmu\)m, visualized through the microscope (500\(\times\)), and recorded on the laptop. The observation by the microscope was repeated while repeating fine polishing, chemical etching, as well as the height measurement and can be directly linked to the 3D image processing. A total of
# BRNN-LSTM for Initial Access in Millimeter Wave Communications
## 4 Simulation Results and Performance Evaluation
### Access Times  
The initial beam access time at the MS T\({}_{\mathrm{acc}}^{\mathrm{MS}}\) (likewise at BS) is defined as the duration required to determine the best beam index that returns the highest signal level, i.e.,  
\[\mathrm{T}_{\mathrm{acc}}^{\mathrm{MS}}=\ \tau_{\mathrm{acc}}\ \mathrm{D}_{ \mathrm{RS}}/\mathrm{R}_{\mathrm{MS}} \tag{13}\]  
where \(\tau_{\mathrm{acc}}\), \(\mathrm{D}_{\mathrm{RS}}\) and \(\mathrm{R}_{\mathrm{MS}}\) are the number of time slots occupied during control signals exchanged between MS and BS, the reference signal duration, and number of RF chains at the MS. Figure 6 shows that the proposed scheme yields significant reduction in access times versus existing schemes. Namely, 0.2 ms are required to acquire a pencil beam (5.5\({}^{\circ}\)) when using 64 beamforming vectors. This is compared to 0.8 ms for the grating lobes, 4.8 ms for the sidelobes, 7.6 ms for the metaheuristics, 9 ms for the iterative search, 12.8 ms for the subarray and GPS, and 25 ms DL-UL beam-training schemes, respectively.
# Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing
## 1 Introduction
The latter may encounter a well-known phenomenon known as catastrophic forgetting [11], which brings about a broken loop in the knowledge learned during previous training. Incremental learning [12] is considered a strategy for overcoming the problems of excessive storage and catastrophic forgetting while fully considering the data flow scenarios where new spoof algorithms continue to appear. This study proposes a voice spoof detection and recognition system that includes two modules: a voice spoof detection module and a voice spoof algorithm recognition module. The first module focuses on the generalizability of spoof detection. In the second module, a method capable of classifying the voice generated by new spoof algorithms is proposed without degrading the recognition accuracy of previous spoof algorithms. The contributions of this paper can be summarized as follows. 1. The generalizability of the spoof detection model is discussed from the perspective of the embedding space and decision boundaries. The embedding space of real voices is compactly distributed, whereas that of the spoofing voices is more dispersed. A highly generalized decision boundary is learned to maintain them at a certain distance from each other. 2. A method for voice spoof algorithm recognition based on incremental learning is presented, which can adapt to the feature distribution of the new algorithms for spoofing voices without having an excessive impact on the feature distribution of the old algorithms. 3. A system combining spoof detection and algorithm recognition is first proposed, which facilitates the traceability of spoofing voices by implementing spoof algorithm recognition following detection. The experimental results demonstrate the effectiveness of the proposed system.
## 2 Materials and Methods
### Trueness and Precision  
For the trueness evaluation, all of the STL files for each group acquired by IOSs were imported into the reverse-engineering software (Geomagic Control X) as measurement data and superimposed onto the reference data selected from the eight scans acquired by the T500 scanner. The working process was as follows. First, two models were roughly aligned using the \"initial alignment\" function, in which the software detected features that were identical in the IOS and reference models and roughly aligned them. Second, the \"best fit\" function was applied. Since all of the models had been trimmed uniformly, it was not necessary to select a specific area to use in the alignment process. The distances (quantified as mean \(\pm\) standard deviation (SD)) between the reference and measurement models were automatically calculated by the software. Finally, the \"3D compare\" function was used to display the 3D distance between the two models within a tolerance of 100 \(\upmu\)m. The root mean square (RMS) was then acquired. The overall RMS values were calculated using the following formula:  
\[RMS\ =\ \frac{1}{\sqrt{n}}\cdot\sqrt{\sum_{i=1}^{n}\left(x_{ref}-x_{i} \right)^{2}}\,\]  
where \(x_{ref}\) is the measurement made on the reference scan, \(x_{i}\) is the measurement of the test model scan being compared with the reference scan, and \(n\) is the total number of measurements.  
All the values were recorded in micrometers. The process of precision evaluation was identical. Pairwise comparisons were performed for all scans in each group, with each scan serving as the reference model. The scans in each group were overlapped and calculated using the software.
## 3 Results  
### Artificial Intelligence Accurately Predicted In Vitro Shoot Regeneration  
In this study, GRNN was implemented for modeling in vitro shoot regeneration of wheat as an output based on ten input variables (genotypes, explants, BAP, Kin, 2,4-D, IAA, IBA, NAA, Zeatin, and CuSO\({}_{4}\)).  
The assessment of predicted and observed data on training and validation sets was used for describing the efficiency of the GRNN model. As can be seen in Table 1, the GRNN model was successful in predicting in vitro shoot regeneration outcomes of wheat in training (R\({}^{2}\) > 0.88, RMSE = 14.12, and MBE = \(-\)0.33) and validation (R\({}^{2}\) > 0.78, RMSE = 14.76, and MBE = \(-\)0.89) processes with correlations between observed and predicted data demonstrating a good fit (Figure 3).  
Determining an Optimized Solution for Regeneration by Using Generalized Regression Neural Network (GRNN)-GA  
We further the investigation and optimized the GRNN by using GA in order to present and avail a precise condition for wheat in vitro shoot regeneration based on the concentrations of phytohormones, types of explants, and genotypes. We would caution that the GRNN gives appropriate accuracy for interpolation but not for extrapolation. Thus, the upper and lower bounds of input data (Table S1) were set as constraints. According to GRNN-GA (Table 2), our model predicted that the highest shoot  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Measure** & **Training** & **Validation** \\ \hline R\({}^{2}\) & 0.88 & 0.78 \\ RMSE & 14.12 & 14.76 \\ MBE & \(-\)0.33 & \(-\)0.89 \\ \hline \hline \end{tabular} R\({}^{2}\): coefficient of determination; RMSE: root mean square error; MBE: mean bias error.  
\end{table}
Table 1: Performance criteria of the generalized regression neural network (GRNN) model for in vitro shoot regeneration of wheat in training and validation processes.  
Figure 3: Experimental observed data against GRNN predicted outcomes of wheat in vitro shoot regeneration in training set (**A**) and in validation set (**B**).  
-generation frequency (97.63%) can be obtained from immature embryo explant on medium containing 0.15 mg/L BAP, 0.73 mg/L Kin, 0.17 mg/L 2,4-D, 0.37 mg/L IAA, 0.04 mg/L IBA, 0.01 mg/L NAA, 4.51 mg/L Zeatin, and 13.08 mg/L CuSO4. It is worth to say that shoot regeneration frequencies > 90% were obtained when we replaced the best genotype with any other genotype in the model (Table S2).
## 2 Preliminary Knowledge
### TLS (Transport Layer Security) Protocol  
For large amounts of information transfer in network communications, the TLS protocol can be used to secure the data transfer. The TLS protocol can combine symmetric and asymmetric encryption techniques to ensure the confidentiality and integrity of the communication data. Cyber crooks use different techniques to steal website resources or customers' sensitive information. They then use this data to steal money from the target accounts or perform any other malicious activities for their benefit. To be protected from these threats, client certificates and server certificates (i.e., SSL certificates) are used. These certificates ensure that the information exchanged between the browser and the website is encoded and is not restricted by third parties.  
The TLS protocol connection flow is shown in Figure 2.  
Step 1: During the TLS handshake, the client first sends a Client Hello message to the server, which contains information about supported TLS versions, cryptographic algorithms (e.g., AES, RSA, etc.), hashing algorithms, and the Diffie-Hellman parameter used to generate the key. Upon receiving the Client Hello, the server selects the best encryption algorithm supported by both parties and replies with a Server Hello message to the client confirming the encryption algorithm, hash algorithm, session ID, and other information. The purpose of this step is to establish a preliminary encryption protocol for the communicating parties and negotiate a set of encryption parameters.  
Figure 1: Multi-threaded C/S architecture diagram.  
Step 2: The server sends a digital certificate to the client to prove its identity. The digital certificate contains the public key of the server and the signature of the Certificate Authority (CA). Clients verify the validity of the server certificate against the pre-stored CA root certificate. After successful certificate validation, the client confirms the server identity and sends the client certificate to the server as needed (if requested by the server) for mutual authentication.  
Step 3: The client generates a random key and encrypts the key through the server's public key, then sends the encrypted key to the server. After the server decrypts the key using its own private key, the two parties negotiate a shared key that will be used for subsequent encrypted communication. The client also sends its digital certificate and other authentication information to the server.  
Step 4: During the handshake, both parties may choose a more secure encryption algorithm or expand the key size to provide a stronger defense against attacks. The purpose of this step is to ensure the security and adaptability of encrypted communication. At the end of the communication, one party sends a close connection message, and the other party confirms and closes the connection, thus safely ending the session.
## 3 Experimental Results and Discussions
### Discussions  
In our present experiments, the KNN classifiers were trained with two separate datasets. One dataset consists of mass cases healthy cases to train and test a mass classifier, while the other consists of calcification cases healthy cases to train and test a calcification classifier. The normal cases were from the same group of healthy subjects (digitized by the \"Lumisys\" scanner). Overall, the performances of the presented GCD algorithm in both mass detection and calcification detection are very good in contrast with the results reported in literature [27].  
For real CAD applications (in fields), the following solutions may be considered. (i) Run two trained classifiers (say a mass classifier and a calcification classifier) with a given mammogram. This way can distinguish mass annotations from calcification annotations but may put more false marks on the analyzing mammogram; (ii) Run one trained classifier trained with one mixed database including cancerous cases (of mass, calcification, or both) and healthy cases. This way may reduce false positives but cannot tell if the marked segment is mass or calcification; (iii) Run a multiple-class classifier trained with the mixed database, which may tell the lesion type (mass or calcification) and give less false positives. However, finding a set of suitable features for a multiple-class classifier will be more challenging. More extensive research and experiments will be conducted regarding how to classify multiple lesion types in breast cancer detection.  
Our future efforts will also focus on extracting and integrating new features (such as wavelet features), analyzing multiple views (such as the asymmetry between left and right beast, and the  
Figure 12: GCD results of Case “3674_Right” (healthy): (a) Original mammogram; (b) Preprocessed image with one annotation—one false alarm (\(P_{\mathrm{C}}=0.66\)) was annotated; (c) 8 alarm segments (\(P_{\mathrm{C}}=0.66\), 0.43, 0.35, 0.30, 0.29, 0.29, 0.28, 0.27).  
correspondence between CC view and MLO view), and fusing multiple classifiers (such as support vector machine).
## 2 Related Work
This algorithm combines the location information with the intelligent ant colony algorithm to update the routing table efficiently to improve the node's linkages. The protocol updates the dynamic routing tables by consuming less setup time to improve the network's response time. The overhead increases in the case of high mobility because of the maintenance of tables [41]. In this paper [42], the path planning scheme for mobile robots has been introduced to find the path in a dynamic environment. The first feasible path is found in the initial phase, and then the optimal path is found for the robots in a dynamic environment using the proposed algorithm. The overhead and response time increases in managing path planning during dense traffic [42]. In this paper [43], the unmanned aerial vehicle is used to improve the network performance by reducing the latency and signaling overhead and improving the packet delivery ratio by proposing a relay selection algorithm with low complexity. The aerial vehicles communicate with the ground vehicles by using the carry-and-forward scheme [43]. In the protocol proposed in [44], the UAVs are used to increase the efficiency of message dissemination among the UGVs. The main purpose of the scheme is to perform search-and-rescue operations. The protocol uses maps and GPS locations to establish routes and suggest the fastest route for rescue operations [44]. Similarly, the rest of the reviewed multicast protocols [33,45,46] in [16] are also found to be unsuitable for the dissemination of MDMs in real time because of their dependence upon the position information because these are based upon the geocast routing scheme. Hence, the delay incurred while accessing the location information can cause high destruction. The routing schemes of [41; 47] are developed for military vehicle communication without considering their stable IVC links, which is the foremost requirement of MDM dissemination. In ref. [42], the procedure of path planning has been developed, but it cannot produce any central coordination system for creating stable IVC links. Similarly, [48] is also based on the geographical position information for routing, which is inappropriate for our desired application. The protocols [49; 50; 51] are also unsuitable for MDM dissemination because of their aim and focus on path planning only for routing messages. The protocols [52; 53; 54] are also reviewed and analyzed for message dissemination in VANET and found inappropriate because of the increase in overhead within dense and congested traffic. The analysis of the literature review has been summarized in Table 1: