esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In conclusion, establishing LCs is crucial for efficiently functioning supply chain networks. Careful thought must be given to the complications of defining and rating these centers, particularly in light of developing an inclusive LC system in China and Pakistan. To overcome the difficulties in location selection, this study has presented a decision-making framework that integrates fuzzy logic and TOPSIS. The research has identified important logistics hubs in China and Pakistan. It has produced a ranking list of the top 10 logistics center possibilities by considering factors like port accessibility and transportation costs. Furthermore, a comparison has been drawn between Taiwan and the CPEC, which clarifies location, cost, security, and administration issues. The suggested method improves the process of selecting LCs by doing away with uncertainty and accounting for various factors. This enhances the process and results in more intelligent and effective logistical plans. It is imperative to acknowledge the constraints, such as the absence of experts, potential bias, and extraneous elements that may distort the outcomes. To increase the study's relevance, real-time data integration, complex AI models, and dynamic standards for changing CPEC logistics environments should be considered for continual optimization. It is also critical to remember that the findings mostly relate to developing nations like China and Pakistan. Future studies should be conducted in developed nations with developed transportation infrastructure to replicate the findings.  
Further research should investigate incorporating logistics center size as a factor. This study offers insightful information on the choice of logistics hubs and establishes the foundation for additional investigation and improvement in this crucial area of supply chain management.  
Conceptualization, M.I., Z.J. and I.U.; Methodology, M.I. and I.U.; Software, M.I. and Z.J.; Formal analysis, I.U.; Writing--original draft M.I. and I.U.; Writing--review and editing, Z.J.; Supervision, Z.J.; Funding acquisition, Z.J. All authors have read and agreed to the published version of the manuscript.  
This research is partially supported by the Belt and Road Program of China Association for Science and Technology (2020 ZZGB072032), the Joint Program of Liaoning Provincial Natural Science Foundation of China (2020 HYLH49), the Leading Talents Support Program of Dalian Municipal Government (2018-573) and Fundamental Research Funds for the Central Universities (3132019301), the National Natural Science Foundation of China (71572023), European Commission Horizon 2020 (MSCA-RISE-777742-56); the Leading Talents Support Program of Dalian (2018-573), and Fundamental Research Funds for the Central Universities (3132020301),Liaoning Provincial Education Department 2021 Scientific Research Funding Program (IJKR0020).  
Not applicable.  
Data availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# GNSS Based Low-Cost Magnetometer Calibration
## 1 Introduction
In our experiments, a carmounted system is utilized as a test platform. In the case of using a ship or an airplane, we would have to consider the rotation of the vehicle with respect to the direction of the velocity vector due to the influence of sea currents and wind. With ground vehicles like cars, this problem is minimized.
# Modeling and Analysis of Wearable Antennas
## 2 Sheet-Like Transmission Line
### Power Transmission by the Sheet-Like Transmission Line  
Although the characteristics of the sheet-like waveguide have been explained focusing on information transmission so far, sheet-like waveguides can also be used for power transmission simultaneously with information. First, we show an example in which a model car powered by a rectenna (directly connected to a rectifier circuit) is used as a rail for an electric power supply with a one-dimensional structure line. As shown in Figure 6, a signal source of 2.4 GHz is used as a power source, and electric power is supplied on the one-dimensional line. A rectenna is installed that integrates the antenna and the rectifier circuit 5 mm above the track and the motor of the car is turned with the received electric power. Since it is not possible to receive all the supplied power by the rectenna, it is desirable to install a power recovery device at the end of the line.  
In order to efficiently perform power transmission, a patch antenna is used as a power receiving antenna and a full-wave rectifying circuit is incorporated as a rectenna. Although the electric field component in the vicinity of the line is mainly an electric field component parallel to the line, a circularly polarized wave element is used for the power receiving antenna so as to correspond to an arbitrary polarization plane. At this time, the coupling to the rectenna is greatly improved compared with \(-5\) dB compared with the dipole. In the configuration of Figure 6 in which the rectifying circuit is connected to this patch antenna, at the input power of 40 dBm at 2.4 GHz, the output voltage becomes 5.1 V with respect to the load resistance of 3.8 \(\Omega\) of the motor, and the power transmission efficiency in this system is about 70%. Further improvement in efficiency can be expected by optimizing the rectenna parameters. As described above, it was shown that power transmission by one-dimensional sheet-like transmission line is possible. It is to be noted that electric power transmission using a two-dimensional sheet can be realized in the same manner. In this manner, a method using a sheet-like transmission  
Figure 5: Transmission line for the dual-frequency band.  
line that can be used only for devices that are closely coupled is effective. An example of this is in applications such as being able to charge from the access area while riding in an electric car. It is possible to achieve this when transmission line is laid on the road. It is also possible to charge the battery while parking without interfering with the next car by laying mats one by one on the floor of the parking lot.
###### Abstract  
The article presents a study of the accuracy of estimating the position coordinates of BAUV (Biomimetic Autonomous Underwater Vehicle) by the extended Kalman filter (EKF) method. The fusion of movement parameters measurements and position coordinates fixes was applied. The movement parameters measurements are carried out by on-board navigation devices, while the position coordinates fixes are done by the USBL (Ultra Short Base Line) system. The problem of underwater positioning and the conceptual design of the BAUV navigation system constructed at the Naval Academy (Polish Naval Academy--PNA) are presented in the first part of the paper. The second part consists of description of the evaluation results of positioning accuracy, the genesis of the problem of selecting method for underwater positioning, and the mathematical description of the method of estimating the position coordinates using the EKF method by the fusion of measurements with on-board navigation and measurements obtained with the USBL system. The main part contains a description of experimental research. It consists of a simulation program of navigational parameter measurements carried out during the BAUV passage along the test section. Next, the article covers the determination of position coordinates on the basis of simulated parameters, using EKF and DR methods and the USBL system, which are then subjected to a comparative analysis of accuracy. The final part contains systemic conclusions justifying the desirability of applying the proposed fusion method of navigation parameters for the BAUV positioning.  
underwater positioning; estimation of coordinates; autonomous biomimetic underwater vehicle +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Model Spirit Solution
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Chemical Class** & **Compound** & **CAS** & \
\begin{tabular}{l} **Concentration** \\ **(mg/L)** \\ \end{tabular} &
\begin{tabular}{l} **Mean Odor Threshold in** \\ **Water (mg/L)** \\ \end{tabular} \\ \hline Alcohol & 2-phenylethanol & 60-12-8 & 7.31 & 1.331 \\ \cline{2-5}  & decanol & 112-30-1 & 0.114 & 0.047 \\ \hline \multirow{4}{*}{Ester} & ethyl hexanoate & 123-66-0 & 17.4 & 0.00186 \\ \cline{2-5}  & diethyl succinate & 123-25-1 & 3.78 & 353 b \\ \cline{2-5}  & 2-phenylethyl acetate & 103-45-7 & 0.457 & 0.249 \\ \cline{2-5}  & ethyl decanoate & 110-38-3 & 7.45 & 1.299 \\ \hline \multirow{4}{*}{Lactone} & sotolon & 28664-35-9 & 0.000938 & 0.00565 \\ \cline{2-5}  & (Z)-whiskey lactone & 80041-00-5 & 0.69 & 0.035 c \\ \cline{2-5}  & (E)-whiskey lactone & 39638-67-0 & 0.31 & 0.05 d \\ \hline \multirow{4}{*}{Carbonyl} & hexanal & 66-25-1 & 0.132 & 0.0501 \\ \cline{2-5}  & benzaldehyde & 100-52-7 & 7.98 & 1.72 \\ \cline{2-5}  & 2-octanone & 111-13-7 & 0.261 & 0.0418 \\ \cline{2-5}  & (E)-\(\beta\)-damascenone & 23726-93-4 & 0.0469 & 0.00125 \\ \hline Acetal & 1,1,3-triethoxipropane & 7789-92-6 & 0.103 & 3.70 b \\ \hline Acid & decanoic acid & 334-48-5 & 5.56 & 5.464 \\ \hline Phenol & vanillin & 121-33-5 & 2.86 & 0.301 \\ \hline Furan & furfural & 98-01-1 & 18.2 & 9.602 \\ \hline Pyrazine & 2,3,5-trimethylpyrazine & 14667-55-1 & 0.154 & 0.216 \\ \hline Sulfur compound & dimethyl trisulfide & 3658-80-8 & 0.0595 & 0.00003 \\ \hline Aromatic & o-xylene & 95-47-6 & 0.313 & 0.45 \\ \hline Monoterpene & \(\alpha\)-pinene & 80-56-8 & 4.02 & 0.296 \\ \hline Oxygenated monoterpene & linalool & 78-70-6 & 5.28 & 0.0108 \\ \hline Sesquiterpene & \(\beta\)-caryophyllene & 87-44-5 & 0.679 & 0.461 \\ \hline Oxygenated sesquiterpene & (Z)-nerolidol & 3790-78-1 & 0.0387 & 0.1 \\ \hline Total & 24 compounds & & & \\ \hline \hline \end{tabular}  
* from [22] b in a 46% (\(v/v\)) ethanol solution, from [23] c in a 12% (\(v/v\)) ethanol solution d in a 9.4% (\(v/v\)) grain spirit.  
\end{table} 
Table 1: Composition of the model mixture.
## 3 Results and Discussion
### Stability, Repeatability, and Reproducibility  
The long-term stability test of the PPy NTs/GSPE using DPV was performed at room temperature. The results exhibited that the peak current of 50.0 uM hydroxylamine on the PPy NTs/GSPE remained at 92.7% of its primary current after seven days, 91.9% after 14 days, and 88.7% after 21 days, indicating the superior long-term stability of the proposed sensor.  
Figure 9: DPVs recorded for the PPy NTs/GSPE in 0.1 M (pH 7.0) with variable hydroxylamine concentrations. Nos. a–I: 0.005, 0.07, 1.0, 5.0, 15.0, 30.0, 75.0, 100.0, 150.0, 200.0, 250.0, and 290.0 uM of hydroxylamine. Inset: Plot of the electrocatalytic peak current as a function of the variable hydroxylamine concentrations (0.005–290.0 uM).  
The oxidation of hydroxylamine (50.0 \(\upmu\)M) on the same PPy NTs/GSPE electrode was performed using ten repeated voltammetric measurements, the results of which confirmed the superior repeatability of the fabricated sensor, with a relative standard deviation (RSD) of 3.0%.  
Similar conditions were provided for the measurement of the response currents of hydroxylamine (50.0 \(\upmu\)M) on five PPy NTs/GSPEs, the results of which confirmed the excellent reproducibility of the fabricated sensor, with an RSD value of 3.7%.
## 3 Results and Discussion
### Comparison of Accuracy before and after Data Balancing  
We also compared FMS performance using the original unbalanced feature data (Figure 4a) and balanced feature data after oversampling pre-processing (Figure 4b) in this research. As shown in Table 2, the average accuracy of balanced distribution of feature data is 0.8, while the average accuracy of unbalanced distribution of feature data is only 0.62, which indicates that the balanced features perform better in the FMS assessment. The scoring accuracy of the balanced movement features is greatly improved compared with the unbalanced movement features. This imbalance will cause the performance of the classifier to be biased towards the majority samples due to the unbalanced sample size. Using sampling processing can improve the balance of the training data by increasing the number of samples, thus effectively avoiding this bias. The balanced features after oversampling pre-processing not only reflect the FMS movement quality more comprehensively, but also the accuracy of the classifier is significantly improved.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Accuracy** & **maF1** & **Weighted-maF1** & **Kappa** &
\begin{tabular}{c} **Level of** \\ **Agreement** \\ \end{tabular} \\ \hline Naive Bayes & 0.75 & 0.75 & 0.71 & 0.6 & Moderate \\ \hline AdaBoost.M1 & 0.72 & 0.7 & 0.71 & 0.55 & Moderate \\ \hline Traditional & 0.38 & 0.34 & 0.35 & 0.1 & Poor \\ GMM & 0.8 & 0.77 & 0.79 & 0.67 & Good \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall comparisons of different methods in FMS assessment.  
Figure 5: Confusion matrix for per-level assessment in FMS assessment.
# Comparison of Diagnostic Accuracy of Physical Examination and MRI in the Most Common Knee Injuries
## 3 Results
### ACL Tears Diagnostic Accuracy of Physical Examination and MRI  
Out of 32 total ACL ruptures, four were not diagnosed with a physical examination. The most sensitive test for ACL tear was the Lachman test, which enabled the diagnosis of ACL tears in 27 patients. The sensitivity of the Lachman test was as high as 84%, with a high specificity of 92% as well. The anterior drawer test showed a sensitivity of 69% with a specificity of 93%. The least sensitive test in this study was the pivot shift, which showed only 43% sensitivity; however, it showed 98% specificity. Diagnostic accuracy for all tests was evaluated with regard to ROM, but no statistically significant influence was found for any of the tests. The results are shown in Table 2 and Figure 5.  
Figure 4: ROC curve describing the relation of sensitivity to specificity in clinical tests of the logistics model and MRI for the lateral meniscus. Figure 3: ROC curve describing the relation of sensitivity to specificity in the clinical tests of the logistics model and MRI for the medial meniscus. The sensitivity of MRI in detecting ACL ruptures was estimated to be 75% and was statistically inferior in comparison with the physical examination. There were no statistically significant differences between the specificity of the MRI and physical examination in regard to ACL ruptures. The combined diagnostic accuracy of physical examination and MRI was also calculated. Introducing MRI into the diagnostic routine had a significant impact on diagnostic accuracy in regard to MM (\(p<0.001\)) and LM (\(p=0.003\)). Introducing MRI also significantly (\(p<0.001\)) increased the diagnostic accuracy of the physical examination of the ACL. The results are presented in Table 2 and Figure 6.  
Figure 5: ROC curve describing the relation of sensitivity to specificity in diagnostic tests for the anterior cruciate ligament. Figure 6: ROC curve describing the relation of sensitivity to specificity in clinical tests of the logistics model and MRI for anterior cruciate ligament.
# Real-Time Compensation for Thermal Errors of the Milling Machine
## 3 Experiment Approach
### Application of Compensation Module  
Following the above experiments, the thermal model was created based on the major temperature fields measured at the major points and the displacement variations of the working table. Then, the hardware module for compensation of thermal induced error was established, as shown in Figure 9, which mainly consists of the 8-bit microprocessor PIC18F6520 with 32 KB flash rom. The interactive communication and calculation for compensation protocol between data sensors and microprocessor was shown in Figure 10.  
To verify the feasibility of the proposed thermal error compensation module in the practice of machining, this system was applied to investigate the positioning precision of the milling machine. The testing procedures are described briefly as follows:  
1. Initial running of spindle for 48 h to ensure the measuring points on the machine tool, which can satisfy the desired temperature fields of thermal model.
2. Operating the spindle at 60% of full speed for 400 h--then, turning off the spindle and ensuring the \(x\)-\(y\) table and the spindle nose return to their initial positions.
3. Performing the test by running the spindle at 90% of full speed for 400 h, in the meantime, recording the displacements of the table on the \(x\)- and \(y\)-axes and spindle nose on the \(z\)-axis.
4. Activating the compensation module to remove the displacement error due to temperature rise.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Temperature Sensor** & **Locations** \\ \hline T1 & Nearby rear bearings of spindle \\ \hline T2 & Heat source of belt transmission in spindle head \\ T3 & Nearby spindle motor (directly) \\ T4 & Machine base, temperature of the environment \\ \hline \hline \end{tabular}
\end{table}
Table 1: Locations of the temperature sensors.  
Figure 9: Thermal compensation module.
# Contemporary Approaches in Evolving Language Models
## 3 Neural Network Models
### Word Representation
#### 3.1.2 GloVe  
Global co-occurrence-based models provide an alternative to predictive methods, like the local context window approach employed by word2 vec. Co-occurrence techniques are often high-dimensional and memory-intensive. Dimensionality reduction methods such as latent semantic analyses are applied, but they typically fail to capture the nuanced semantic relationships of words and are dominated by frequently occurring terms. On the other hand, predictive methods like word2 vec, which rely on local context, often struggle to gather comprehensive corpus statistics.  
In 2014, [PERSON] and his colleagues introduced a logarithmic bilinear model that merges the strengths of global co-occurrence methods with shallow window techniques [22]. They named this model GloVe, a fusion of the words \"**Global**\" and \"**Vector**\". GloVe is trained using a least squares optimization with a cost function (6):  
\[J=\sum_{i=1,j=1}^{V}f\big{(}X_{ij}\big{)}\left(u_{i}^{T}v_{j}-log\big{(}X_{ij} \big{)}\right)^{2} \tag{6}\]  
Figure 4: Skip-gram model [21].  
where \(V\) represents the size of the vocabulary, \(X_{ij}\) indicates the number of times words \(i\) and \(j\) co-occur in the corpus,\(f\) is a weighting function that diminishes the influence of frequent occurrences, and \(u_{i}\) and \(v_{j}\) are the vector representations of the words. Typically, a truncated power form is used for the function \(f\) (7):  
\[f\big{(}X_{ij}\big{)}=f(x)=\left\{\left(\frac{X_{ij}}{X_{max}}\right)^{2}, \quad if\ X_{ij}<X_{max}\right. \tag{7}\]  
The parameter \(X_{max}\) is determined during the training process based on the data available in the corpus. It is noteworthy that the model trains the context vectors \(U\) and word vectors \(V\) independently of each other, and the vector embeddings created by the GloVe method are formed by adding these two vector representations together (\(U+V\)). Analogous to word2 vec, GloVe's vector embeddings are capable of expressing semantic and syntactic relationships through vector addition and subtraction operations. Furthermore, the word embeddings obtained using GloVe surpass word2 vec in many natural language processing tasks, especially in scenarios in which global context is crucial, such as in named-entity recognition tasks.  
GloVe has an advantage over word2 vec when working with smaller datasets or when there are not enough data to accurately reflect local contextual dependencies. However, word-embedding models have some well-known limitations, like the inability to handle out-of-vocabulary words and a limited capacity to account for antonyms, polysemy (words with multiple meanings), and biases.
# Future Generation 5G Wireless Networks for Smart Grid: A Comprehensive Review
## 3 5G Networks--Outlook
### Applications of 5G Networks in General  
There are several applications where 5G networks provide a tremendous advantage, such as in IoT, healthcare systems, energy sectors, financial technology, and many more. IoT [60] is presently gaining vast attention in different domains because of its ability to connect many devices at any time and in any place. This involves connectivity between billions of distinct devices with various functionalities, enabling mutual interactions between them. The wide area coverage between these distributed and heterogeneous devices can be done with 5G networks with high throughput and low latency. Another important area is Internet of vehicles, which involves interconnection between various vehicles where reduction and congestion of traffic probabilities should be made easier. Intelligent and smart vehicular communication can be implemented by utilizing the ubiquitous availability [61; 62; 63; 64; 65]. It is used for considering very large bandwidth and low latency for these promising 5G networks. The demand for better and easily accessible medical facilities is always increasing. The advent of wearable technology has boosted the health monitoring sector. In these medical applications, continuous monitoring of real time data is to be maintained in a continuous fashion. The 5G network offers large extensive bandwidth, low latency, and improved security, which facilitates reliable real time health monitoring.
# Random Frequency Division Multiplexing
###### Abstract  
In this paper, we propose a random frequency division multiplexing (RFDM) method for multicarrier modulation in mobile time-varying channels. Inspired by compressed sensing (CS) technology which use a sensing matrix (with far fewer rows than columns) to sample and compress the original sparse signal simultaneously, while there are many reconstruction algorithms that can recover the original high-dimensional signal from a small number of measurements at the receiver. The approach choose the classic sensing matrix of CS-Gaussian random matrix to compress the signal. However, the signal is not sparse which makes the reconstruction algorithms ineffective. We take full account of the great power of deep neural networks (DNN) to detect the signal as it is an underdetermined equation. The proposed RFDM establishes a novel signal modulation and detection method to target better transmission efficiency, and the simulation results show that the proposed method can achieve good BER, offering a new research paradigm to improve the spectrum efficiency of a multi-subcarrier, multi-antenna, multi-user system.  
RFDM; random matrix; DNN; MIMO 2025