esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Computational Results
### Small VRF Instances  
#### 5.1.1 MIP Versus CP  
Computational results are given in Table 3 for each combination, giving a total of 240 small VRF instances. For each combination, the table summarizes the number of optimal solutions (\(n\)Opt) found for ten instances of each job-machine combination (\(n\times m\)), the average relative percent deviation (ARPD%) from the upper bounds given in [9], the average CPU time for its ten instances, and the optimality gap percentage (GAP%) on termination, which means the gap between best lower and best upper bound. The maximum CPU time is restricted to an hour (3600 s). The result of CP and MIP models are compared for job sizes 10 and 20. While MIP model can find solutions for very small sized instances (10 jobs) in a shorter time than CP model, it becomes hard for MIP to solve large sized problems (20 jobs and more). Both models cannot always find optimal solutions when the machine size becomes greater than 5, but the MIP model has larger gaps than the CP model. The results show that CP is more efficient than MIP on PFSP, except for very small-sized instances. The results of the remaining instances are obtained only from the CP model because of very large gaps by MIP model. CP model always captures optimal solutions when the machine number is five regardless of the number of jobs. Besides, CP can find optimal solutions in some of the instances when the machine size is 10. Overall, within the time limit, the CP model verifies optimality for 108 out of 240 instances.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CP**} & \multicolumn{3}{c}{**MIP**} \\ \cline{2-10} \(n\times m\) & \(n\)**Opt** & **ARPD** & **CPU** & **GAP** & **nOpt** & **RPD** & **CPU** & **GAP** \\ \hline \(10\times 5\) & 10 & 0 & 14.03 & 0 & 10 & 0 & 2.68 & 0 \\ \(10\times 10\) & 10 & 0 & 102.13 & 0 & 10 & 0 & 4.35 & 0 \\ \(10\times 15\) & 10 & 0 & 256.45 & 0 & 10 & 0 & 5.68
## 3 Results and Discussion
### Determination of the Form of Solid State Samples in the Co-Crystal and Salt Category
The co-crystallization which took place between the CFA and PCA as well as the NA and INA species did not induce proton transfer from the acid to the base because the transfer of acidic protons was responsible for the formation of caffeine and protocatechuate anions comprising the deprotonated COO\({}^{-}\) group, which created the specific pair of signals of asymmetric and symmetric stretching vibrations located near 1550 cm\({}^{-1}\) and 1400 cm\({}^{-1}\)[28]. The lack of signals derived from the carboxylate group in the infrared spectra of phases **1-4** ultimately proves that the obtained complexation products existed in the form of co-crystals and not salts. The observed shifts in the positions of the signals occurred in the spectral profiles of compounds **1-4** compared with their positions seen in the spectra of pure components, which were associated with the most diagnostic stretching vibrations of acidic OH, phenolic OH, carbonyl fragment of carboxylic group as well as the carbonyl and amine parts of the amide functional being susceptible to hydrogen bonding, defined that the obtained solids were actually supramolecular frameworks. Additionally, the co-crystal form of the caffeic-nicotinamide complex coincided with the CFANAM-H2O crystal structure [27], in which the carbonyl parts of the amide group of NA and protonated acidic OH group were those involved in the formation of the shortest and, the same time, the strongest hydrogen bonds (donor\(\cdots\)acceptor distance was 2.548(2) A) amongst all that existed in its crystal lattice.
# Review on Quality Control Methods in Metal Additive Manufacturing
## 4 Experimental Quality Monitoring Methods in Metal AM and Welding  
This section reviews the experimental quality monitoring methods for metal AM and welding. Experimental quality monitoring methods have been used to identify anomalies during the process and to predict the final product quality [51]. Experimental quality monitoring can be conducted in a destructive or nondestructive way. For example, X-ray computed tomography (X-ray CT) has attracted attention as a nondestructive quality monitoring method for metal AM [52; 53; 54]. X-ray CT forms a 3D model of the product by capturing and reconstructing a number of X-ray images around the axis of rotation [55]. The 3D CT model can be used for the visualization, geometry or porosity measurement, and reverse engineering of metal AM products [56]. Recently, experimental quality monitoring has often been conducted using nondestructive means by implementing in situ monitoring. In situ monitoring allows the nondestructive identification of anomalies in advance and helps in decision making on interventions for quality issues in a short time. As a representative anomaly during deposition, melt pool behavior is related to both metal AM and welding. The melt pool behavior varied according to the process parameter settings. Melt pool monitoring allows the process engineer to adjust the values of the process parameters to control the melt pool behavior [57; 58].
## 4 Experimental Components
### Description of Benchmark Functions  
To validate the SDSGA's suitability for optimizing the hyperparameters of ML models, its performance was compared to that of some well-known metaheuristic optimization algorithms. The proposed SDSGA was tested on 17 benchmark functions that included both unimodal and multimodal functions. The chosen functions have been widely used in the literature as alternative functions in place of real-world simulations. They also present various challenges to optimization algorithms, making them excellent tools for comparing and validating different metaheuristic algorithms. The mathematical expressions of some of these functions are described in [35].  
A dimension of 10 was used for all the benchmark functions with a population size of 200 and iteration of 100, which is within the limit of the maximum number of fitness evaluations as noted in [36]. The use of dimension 10 generally introduces some level of complexity and difficulty into the optimization process with difficulty increasing as dimension increases. The initial population was generated randomly only once for every run and passed to each of the optimization algorithms. This is done to make sure that they all start under the same initial condition.  
The optimization algorithms were run five times for each benchmark function and a pivot table was created. The mean and standard deviation of the runs were computed. These were used to compare the performance of the optimization algorithms.
# Gold Nanoparticles and Nanorods in Nuclear Medicine: A Mini Review
## 4 Gold Nanoparticle and Nanorods: Clinical Aspects
### Monoclonal Antibody-Labeled AuNPs  
Monoclonal antibody-labeled AuNPs were considered promising agents for cancer imaging and therapy. To functionalize AuNPs, a good candidate was cetuximab: a mouse-human chimeric monoclonal antibody that binds competitively to the epidermal growth factor receptor (EGFR), which is a tyrosine kinase receptor. In view of EGFR overexpression in many epithelial solid tumors, anticancer therapy against EGFR was already widely studied and applied [96].
## 4 Results and Discussion  
The proposed models were implemented on a computer running a 64-bit Windows 11 Pro OS, featuring an i5-11400H processor with a 2.70 GHz clock speed, 32 GB of RAM, and a GTX 1650 GPU. The classification accuracy and training parameters of all models are given Table 3. Stochastic gradient descent with momentum (SGDM) and adaptive moment  
Figure 3: Network architecture of proposed MSU-CNN model.  
estimation (Adam) are optimization algorithms used in deep learning. These algorithms are used to update the network's learnable parameters in a custom training loop. The learning rate for all algorithms was configured to be 0.0001.  
The models, including AlexNet, GoogLeNet, the proposed MSU-CNN, VGG16, EfficientNet-b0, ResNet-18, Inception-v3, and ResNet-50, attained mean classification accuracies of 94.88%, 96.79%, 96.79%, 97.90%, 98.88%, 99.02%, 99.21%, and 99.63%, respectively, during 5-fold cross-validation. Among these, ResNet-50 attained the highest accuracy. The proposed MSU-CNN model stands out for its notable competitiveness when compared to the pre-trained models, and it demonstrates a good level of performance in accurately classifying the types of pistachios. In the 5-fold cross-validation, where the dataset is divided into 80% for training and 20% for testing, the models achieved the following highest classification accuracies from each fold: AlexNet, GoogLeNet, the proposed MSU-CNN, VGG16, EfficientNet-b0, ResNet-18, Inception-v3, and ResNet-50 attained mean classification accuracies of 98.84%, 98.37%, 97.21%, 99.07%, 99.53%, 99.53%, 99.77%, and 100%, respectively.
## 3 Analysis of the Time Fluctuation of Underwater Acoustic Communication Signals  
The time window of a UAC system is smaller than other systems. Therefore, this study mainly analyzed the impact of small-scale spatial-temporal fluctuations caused by environmental parameters, such as wind and waves, on underwater acoustic communication. Moreover, environmental factors, such as ocean currents, tidal waves, and internal waves, with large spatial-temporal fluctuations were not considered.
# Cloud Storage Data Verification Using Signcryption Scheme
## 4 Discussion and Security Analysis
### Unforgeability  
We present the unforgeability of two cases in our scheme, a malicious CSP cannot forge proof \(\delta\) to deceive the verifier, and a malicious verifier/TPA cannot forge the verification results.  
* Malicious CSP. A malicious CSP cannot forge proof \(\delta\) because every time the TPA sends a challenge, a random value \(f\) will be given in the _chal_ variable. The CSP will generate proof \(\delta\) based on \(f\) from the TPA as shown in Equation (3) and value \(f\) is different for each data shard. Even if we further assume that the malicious CSP forges \(\delta\), namely \(\delta\
eq\delta^{\prime}\), the verification process done by TPA in Equation (5) shows that the TPA must validate \(\delta^{\prime}\) with two other variables, the sender's public key \(PK_{S}\) and proof \(\omega\) generated by TPA, which also consist of \(f\) value. Therefore, the \(\delta^{\prime}\) cannot make Equation (5) hold. Another case is when a malicious CSP tries to deceive the verifier by replacing challenged data block \(m_{j}\) with another data block \(m_{k}\) when the former data block is broken. Accordingly, the proof \(\delta^{\prime}\) becomes \[\delta^{\prime}=\sum_{i\in I,i\
eq j}f_{i}Hash_{1}(m_{i})P+f_{j}Hash_{1}(m_{k})P\] (8) So, the Equation (5) can be represented as \[e(\delta^{\prime}+PK_{S},\omega)=e(\delta+PK_{S},\omega)=e(P,P)\] (9) Hence, we have \(Hash_{1}(m_{k})=Hash_{1}(m_{j})\). However, \(Hash_{1}(m_{k})\) cannot be equal to \(Hash_{1}(m_{j})\) due to the anti-collision property of the hash function. Therefore, it is infeasible to make Equation (9) hold, and the proof from CSP cannot pass the verification process.
* Malicious Verifier.  
A malicious verifier/TPA cannot forge verification results because verifier/TPA must generate proof \(\omega\) that required \(V\), which is a signature generated by the user as shown in Equation (4). We further assume that the malicious verifier forges proof \(\omega\). To generate variable \(V\), the malicious verifier needs the user's private key \(SK_{S}\) and original message \(m_{i}\). Our proposed scheme provides privacy-preserving and blockless verification properties, meaning the verifier capable of verifying the data without receiving the original message \(m_{i}\) from the user or CSP. So, without the possession of those two variables, \(SK_{S}\) and \(m_{i}\), it is impossible to generate a forged \(m^{\prime}_{i}\) and make equation \(\left(Hash_{1}(m^{\prime}_{i})+SK_{S}\right)^{-1}P=\left(Hash_{1}(m_{i})+SK_{S }\right)^{-1}P\) hold. Both malicious CSP and verifier cannot calculate the user's private key from the public key under the Inv-CDHP assumption. Furthermore, in the verification process, the verifier needs to validate \(\omega\) with two other variables, the sender's public key \(PK_{S}\) and proof \(\delta\) generated by CSP as shown in Equation (5). Therefore, it is infeasible to make equation \(e(\delta+PK_{S},\omega^{\prime})=e(P,P)\) hold, where \(\omega^{\prime}\
eq\omega\).
# DAEOM: A Deep Attentional Embedding Approach for Biomedical Ontology Matching
## 2 Method
### Overall Framework  
Existing work [17] designs a negative sampling skill tailored for structural relations to obtain better vector representations of concepts, which only leverages the structural relations for embedding learning indirectly, and can not effectively preserve the network structure information into vectors directly. Graph attention networks (GATs) [28] can assign nodes in a network to low-dimensional representations and effectively preserves the network structure. It is highly effective in network analysis tasks, such as link prediction [29] and node classification [30]. Until recently, this method did not get enough interest in ontology matching. Inspired by their works, we develop an ontology attentional encoder which effectively integrates both structure and terminological description to learn a latent representation. This model uses both direct and indirect methods to solve the sparsity problem of structural relations for representation learning. With jointly encoding terminological description and network structure to continuous vector representation, we can obtain more similar concepts and discover more potential mappings among ontologies. Moreover, there is no need to select and aggregate different single similarities in the similarity computation as described in [17].  
Our framework is shown in Figure 1 and consists of two parts: An ontology attentional encoder and a mapping selection module.  
* **Ontology attentional encoder.** Our encoder takes the terminological description and network structure as input, and learns the latent embedding by minimizing the reconstruction loss, and designs an inter-intra negative sampling skill to indirectly alleviate the sparsity problem for embedding learning;
* **Mapping Selection.** The mapping selection module performs ontology matching based on the learned representation. It includes two phrases: Candidate selection and final alignment. To reduce the running time of mapping selection, we improve the Stable Marriage algorithm's ability to choose the 1:1 mappings from two different ontologies and introduce an automatic adjustment of the threshold method by using the highest correspondences.
# SMS: A Secure Healthcare Model for Smart Cities
## 4 Proposed SMS Framework
### Phase 3-Data Storage  
Once the patient's data is encrypted and standardized, these data and information are passed to the pre-authenticated account of the patients on the cloud for storage. The proposed SMS uses \"OAuth 2.0\" for performing the \"authentication\" and \"authorization\" process [61; 62]. The OAuth protocol is a widely used industry-level authorization and authentication protocol that usually provides predefined threshold  
Figure 3: Proposed SMS architecture.  
value-based access to the users. This may include time-based access, level-based access, or complete access. Once the threshold value expires, the access is revoked [61; 62]. This mechanism is useful for maintaining consistency of the data along with authorization and authentication. The encrypted records from the cloud storage can be made available for any medical assistance and case-studies by doctors, students, and researchers on mutual agreements with the hospitals and patients. In the final phase, the doctors and the hospitals access this data in real time by logging into the central monitoring system and requesting access to the \"data and information\" of the patients. The proposed SMS framework is a generic concept that can be modelled into any specific medical domain and use-case. The presence of OAuth 2.0, Twofish, and SDN makes it a secured, dynamic, privacy-preserved, intelligent, and dependable framework for the healthcare data management.  
The data collected from various sources (wearable devices, fixed equipment, test data, etc.) is initially stored in the local storage units and later transferred to permanent storage hubs. While the data resides in local storage, it is preprocessed and refined to extract useful data for further processing. Once the process of extraction is completed, the extracted data is fed into the data analytics models for obtaining insights about the conditions of the patients and perform predictive and preventive analysis. Some of these analyses (which are urgently required) are done at the edge nodes while detailed analysis is done at the central control units. This serves two purposes, the immediate requirements and firsthand analysis are available in near-real time so that informed decisions can be taken in cases of emergency, while for regular cases, a detailed analysis is performed at the central hubs. This improves the overall performance of the system by minimizing latency, network bandwidth consumptions, and increasing the throughput.
# Statistical Pass-By for Unattended Road Traffic Noise Measurement in an Urban Environment
## 4 U-SPB Procedure
### Measurement Setup  
The monitoring station includes a microwave traffic counter system, a sound level meter, a power system and a weather station to exclude periods with a wind speed higher than 5 m/s or rainy periods. The traffic counter acquires single-vehicle passage data, such as vehicle length, velocity, transit time and time distance from the previous vehicle passage. The devices should be carefully synchronized in time in order to simplify analysis. The sound level meter should fulfill the ISO 1996-2 requirements and should be positioned preferably 7.5 m away from the centerline. If impossible, the distance from the roadside should not be less than 3 m in order to avoid source directivity problems and be no more than 15 m away in order to avoid ground and spurious reflection issues. In this way, the sight angle of the road is maximized without going too far from centerline and avoiding measuring a lower sound signal.  
The sound meter level should be placed at 4 m height and should be set to acquire:  
* \(\text{L}_{\text{Aeq}}\) in fast 50 ms;
* Third-octave band spectrum.  
The microwave traffic counter should be installed at the roadside, positioned in order to acquire traffic data of all lanes, and should be set to acquire vehicle length accurately in order to identify the different vehicle categories.  
The sound level meter and the traffic counter should be positioned as close to each other as possible in order to avoid speed differences between the acquisitions.  
A brief on-site observation is suggested at the beginning of the setup to fix category length limits for both lanes, thus, minimizing category mismatching.  
A fixed length for the measurement is not fixed and depends on the flow, but a two-night duration is generally enough to obtain a sufficient number of transits.
## 6 Conclusions  
In this study, a ubiquitous near real-time remote sensing (NRRS) architecture based on LEO satellite Internet was proposed to provide the access capability for remote sensing demand collection and data distribution anytime and anywhere. Several NRRS experiments were carried out based on a test constellation consisting of seven low earth orbit (LEO) satellites. The results show that the LEO broadband Satcom on-the-move (STOM) terminal has the capability of high-speed access and broadband communication, and can be applied to NRRS scenarios with low latency and large bandwidth. It takes less than 25 min from the remote sensing task application to achieve image acquisition, which shows the satisfactory timeliness of the NRRS system. With the construction and application of the satellite Internet, it is expected to fully realize near real-time remote sensing within several minutes.  
The future work will attempt to address several practical issues, such as quantitative research on data latency, data storage versus offloading capacity, onboard processing capability, etc.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Time** \\ \hline Task submit & 2022-11-07 13:10:00 \\ Command upload & 2022-11-07 13:12:42 \\ Data transmission start & 2022-11-07 13:22:59 \\ Shooting start & 2022-11-07 13:23:22 \\ Shooting end & 2022-11-07 13:25:22 \\ Data transmission end & 2022-11-07 13:25:31 \\ Image files upload to the ICRS system & 2022-11-07 13:27:46 \\ File analysis completed & 2022-11-07 13:31:19 \\ Images obtained & 2022-11-07 13:33:44 \\ \hline \end{tabular}
\end{table}
Table 4: Time notes in the NRRS experiment.  
**Author Contributions:** Conceptualization, P.Z., S.Z. and X.Z.; methodology, P.Z. and Q.Q.; software, P.Z. and W.W.; validation, P.Z., X.Y. and W.W.; formal analysis, H.Z.; investigation, P.Z. and X.Z.; writing--original draft preparation, P.Z. and X.Z.; writing--review and editing, Q.Q., S.Z., X.Y., W.W. and H.Z.; visualization, X.Y. and P.Z.; supervision, S.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by China Postdoctoral Science Foundation (Grant number: 2023M730337).  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding authors.  
**Acknowledgments:** The authors would like to thank [PERSON] and [PERSON] for their contribution to NRRS task planning system; [PERSON] and [PERSON] for the architectural description; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for assistance with the NRRS experiments; and [PERSON] for image processing.  
**Conflicts of Interest:** [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] were employed by Yinhe Hangtian (Beijing) Communication Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Analysis and Reduction of Nonlinear Effects in Optical Fiber Frequency Transfer
## 4 Experimental Verification
### Raman Scattering
[39] examine the threshold power for generating Raman scattering as a function of doped-fiber length (specifically, lengths lower  
Figure 9: Comparison of calculated and experimental values of the threshold power for generating the Raman scattering. The attenuation coefficient \(\alpha\) and the effective mode area \(A_{eff}\) are 0.2 dB/km and 1.0 \(\times\) 10\({}^{-10}\) m\({}^{2}\), respectively. The gain coefficient \(\gamma_{R}\) of the Brillouin scattering is 4.2 \(\times\) 10\({}^{-13}\) m/W. (**a**) Comparison of calculated and experimental values of the threshold power for generating the forward Raman scattering as a function of the optical fiber length \(L\). (**b**) Comparison of calculated and experimental values of the threshold power for generating the backward Raman scattering as a function of the optical fiber length \(L\). than approximately 1.3 m). Their experimental results demonstrate good agreement between the measured and calculated threshold powers for both backward and forward Raman scattering.