esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design Procedure and Application Example
### Summary of the Design and Control Processes
```
1:\(v_{R}\)
2:loop
3:measure\(i_{o}\), \(v_{C2}\), \(v_{b}\), \(i_{L1}\), \(i_{L2}\)
4:\(i_{R}\leftarrow\left\{G_{hp}\cdot i_{o}\right\}\)\(\triangleright\) Filter the load current to detect the fast transients
5:\(k_{c}\leftarrow\left\{\frac{v_{b}}{v_{C2}}\right\}\)\(\triangleright\) Calculate the adaptive current gain
6:\(k_{v}\leftarrow\left\{-\frac{3.9\cdot C_{2}}{k_{c}\cdot i_{C2}}\right\}\)\(\triangleright\) Calculate the adaptive voltage gain
7:\(\Psi\leftarrow\left\{i_{R}+k_{v}\cdot(v_{R}-v_{C2})+(k_{c}-1)\cdot i_{L2}-i_{L1}\right\}\)\(\triangleright\) Calculate \(\Psi\)
8:if\(\Psi\leq-\Delta\Psi\)then\(\triangleright\) Apply the control law
9:\(u\gets 0\)
10:\(\overline{u}\gets 1\)
11:elseif\(\Psi\geq+\Delta\Psi\)then
12:\(u\gets 1\)
13:\(\overline{u}\gets 0\)
14:else
15:\(u\wedge\overline{u}\) do not change
16:endif
17:return\(u\), \(\overline{u}\)\(\triangleright\)\(u\) and \(\overline{u}\) are provided to the MOSFETs
18:endloop
```  
**Algorithm 2** Control process  
Table 3 summarizes the circuital parameters calculated for this application example. This table also presents the energy stored in the elements of the Zeta converter needed to support the high-frequency transients of the load current. The table shows that the energy stored in the capacitors \(C_{1}\) and \(C_{2}\) is very small. In the case of \(L_{2}\), since \(i_{L2}\) is defined by the high-pass filter given in (47), the steady-state value of \(i_{L2}\) is 0 A, and from Equation (20), it is obtained that the steady-state value of \(i_{L1}\) is also 0 A; thus, the inductors do not store energy in the steady state, and they operate only as energy buffers during the transient conditions. Therefore, the total energy stored in the Zeta converter is 0.30 mWh or 0.006 mAh. It must be highlighted that the design of the proposed supporting device (formed by both the Zeta converter and SMC) does not depend on the battery capacity; instead, it only depends on the load transients that must be supported. The battery capacity is related to the load or microgrid operation cycle, which has a low-frequency profile. In contrast, the supporting device capacity only depends on the system's high-frequency transients.
# Utilizing Multi-Dimensional MmWave MIMO Channel Features for Location Verification
## 6 Conclusions  
By utilizing multi-dimensional mmWave MIMO channel features, this paper developed a location verification scheme for mmWave MIMO communication systems. Furthermore, we modeled the three-dimensional mmWave channel features (i.e., AAoA, EAoA and path gain) and extracted these feature parameters through maximum-likelihood estimator. We also derived the analytical expressions for the typical performance metrics. Numerical results indicate that the proposed authentication scheme can benefit from using multi-dimensional mmWave channel features. The results in this paper reveal that the proposed scheme is general in nature and can be applied to more communication systems, and we expect the methodology developed in this paper to be valuable for devising new physical layer authentication schemes in other network scenarios.  
Conceptualization, Y.L. and P.Z.; methodology, Y.L.; validation, J.H., Y.L. and P.Z.; investigation, P.Z.; writing--original draft preparation, P.Z.; writing--review and editing, J.H.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of China (NSFC) Grant (62272241), Japan Society for the Promotion of Science (JSPS) Grant (18H03235), and Nanjing University of Posts and Telecommunications Scientific Research Foundation Grant (NY221122).  
Not applicable.  
The authors declare no conflict of interest.
# Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
## 5 Dataset  
### PHEME Dataset  
In this work, we used the PHEME [35] dataset of rumors and non-rumors, which consists of Twitter conversations for nine different newsworthy events. The distribution of the dataset is shown in Table 2. The basic structure of conversation samples is shown in Figure 3. Each conversation sample has a source-tweet and a set of reactions along time, where reactions express their opinions towards the claim contained in the source-tweet.  
As shown in Table 2, this dataset exhibits a severe event-wise and class-wise unbalanced nature. For example, the Charlie Hebdo event is dominant over all other events present in the dataset in terms of number of samples causing event-wise unbalance. In general, the number of non-rumor class samples are way more than the number of rumor class samples, which is a class-wise imbalance in the dataset.  
Figure 2: Ensemble models designed using three different implementations.  
In our analysis, we removed events Prince Toronto and Ebola Essien as they have extremely unbalanced proportions of rumors and non-rumors, and trimmed down the dataset to seven events. For example, the Ebola Essien event has zero number of non-rumor class samples. The basic statistics of the PHEME dataset with seven events are shown in Table 3. Overall, the PHEME seven events dataset has 6178 data samples, in which non-rumor class samples are almost double the number of rumor class samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Event** & **Rumors** & **Non-Rumors** & **Total** \\ \hline Charlie Hebdo & 458 & 1621 & 2079 \\ \hline [PERSON] & 284 & 859 & 1143 \\ \hline Germanwings crash & 238 & 231 & 469 \\ \hline Ottawa shooting & 470 & 420 & 890 \\ \hline Sydney siege & 522 & 699 & 1221 \\ \hline [PERSON] & 61 & 77 & 138 \\ \hline [PERSON] missing & 126 & 112 & 238 \\ \hline Prince Toronto & 229 & 4 & 233 \\ \hline Ebola Essien & 14 & 0 & 14 \\ \hline
**Total** & 2402 & 4023 & 6425 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The PHEME dataset with nine events.  
Figure 3: Structure of a Twitter conversation sample.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Event** & **Rumors** & **Non-Rumors** & **Total** \\ \hline Charlie Hebdo & 458 (22.03\%) & 1621 (77.97\%) & 2079 \\ \hline [PERSON] & 284 (24.85\%) & 859 (75.15\%) & 1143 \\ \hline Germanwings crash & 238 (50.75\%) & 231 (49.25\%) & 469 \\ \hline [PERSON] & 61 (44.20\%) & 77 (55.80\%) & 138 \\ \hline Ottawa shooting & 470 (52.81\%) & 420 (47.19\%) & 890 \\ \hline [PERSON] missing & 126 (52.94\%) & 112 (47.06\%) & 238 \\ \hline Sydney Siege & 522 (42.75\%) & 699 (57.25\%) & 1221 \\ \hline
**Total** & 2159 (34.95\%) & 4019 (65.05\%) & 6178 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distribution of the PHEME dataset with seven events.
# EC-Kad: An Efficient Data Redundancy Scheme for Cloud Storage
## 3 Cloud Storage System Model Based on Erasure Code
### Data Encoding Storage and Recovery
#### 3.2.3 The Encoding Process of the Generative Matrix \(G\)  
Under the action of generative matrices, redundant terms are generated for the original data. Suppose that the file to be stored is \(F\), which consists of \(k\) data blocks of the same size \((F_{1},F_{2},F_{3},\ldots,F_{k})\), with the last block being padded if it is not a complete block. After passing through the encoder's generator matrix \(G\), redundant data blocks \((D_{1},D_{2},D_{3},\ldots,D_{m})\) are generated. Thus, \((F_{1},F_{2},F_{3},\ldots,F_{k},D_{1},D_{2},D_{3},\ldots,D_{m})\) forms a new redundant storage vector group. The encoding process can be represented as matrix multiplication, satisfying Equation (4):  
\[(F_{1},F_{2},\ldots F_{k},D_{1},D_{2},\ldots,D_{m})^{T}=G\times(F_{1},F_{2}, \ldots,F_{k})^{T} \tag{4}\]  
Specifically, each redundant data block \(D_{j}\) can be calculated using Equation (5):  
\[D_{j}=\sum_{i=1}^{k}(G_{j,i}\times F_{i}) \tag{5}\]In Equation (5), \(G_{j,j}\) is the element in row \(j\) and column \(i\) of \(G\).  
When modifying data within a small range, we can take advantage of the properties of the Cauchy matrix to individually mark and calculate the modified data block. Eventually, we can distribute the generated corresponding redundant data blocks and the source data blocks separately to the storage nodes. There is no need to destroy all data blocks, recalculate them, and then redistribute and redeploy them to the storage nodes.  
Suppose that we need to modify the data block \(F_{i}\). The redundant data blocks can be updated through the following steps: 1 Calculate the modified data block \(F_{i}^{\prime}\). 2 Calculate new redundant data blocks \(D_{i}^{\prime}\) based on the generated matrix \(G\). 3 Finally, distribute \(F_{i}^{\prime}\) and \(D_{i}^{\prime}\) to storage nodes. Through this method, we can efficiently update data blocks in cloud storage without the need to re-encode the entire file.
# Optimization of Optical Trapping and Laser Interferometry in Biological Cells
## 3 Materials and Methods
### Numerical Calculation of LI Sensitivity  
Numerical calculations based on Lorenz-Mie theory were conducted in order to predict \(z\) dependence of LI sensitivity. Using Mieplot software [32], calculations were conducted using parameters relevant for our experimental conditions (\(a\), \(\lambda\), \(w_{0}\), n, and \(n_{\mathrm{p}}\) are 0.5 \(\upmu\)m, 1064 nm, 0.55 \(\upmu\)m, 1.38, and 1.68, respectevely). For each vertical offset z, we calculated the laser scattering caused by small offsets (\(x\), \(y\)), and then obtained the sensitivity \(S_{\mathrm{th}}(z)\). Out-going laser lights were collected in the far field within the scattered angle \(<46.4^{\circ}\) (relevant to the numerical aperture of condenser lens, NA = 1.0). The normalized LI sensitivity \(A_{\mathrm{th}}(z)\) was then obtained as \(A_{\mathrm{th}}(z)\equiv S_{\mathrm{th}}\) (\(z\)) \(S(0)/S_{\mathrm{th}}\) (0).  
Figure 3: Procedure for measuring LI sensitivity in agarose gel and in living cells. (**a**) The sensitivity \(S\) is given by the ratio of sinusoidal output of QPD \(V(t)\) to the displacement \(x(t)\) of the probe as \(S\) = (amplitude of \(V\))/(amplitude of \(x\)). (**b**) \(V(t)\) and \(x(t)\) in agarose gel (see Section 3.4). Piezo sample stage was directly oscillated by a sinusoidal signal generated in a function generator. Similar data [sinusoidal \(V(t)\) and \(x(t)\)] were collected to obtain \(S\) in living cells while sinusoidal oscillation of a probe was made by the feedback which is provide a sinusoidal signal to setpoint of PID controller to control piezo stage in \(x\) or \(y\) directions.
# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
## 4 Experimental Results and Analysis  
### Experimental Data  
This paper conducts experimental analysis using two datasets from the medical field and the construction safety field.  
The IMCS21 dataset [32] is a benchmark dataset designed for automatic medical consultation systems, consisting of a total of 4116 physician-patient dialogue case samples. It includes 2472 dialogues in the training set, 833 dialogues in one validation set, and 811 dialogues in another validation set. The IMCS21 dataset covers 10 pediatric diseases and encompasses five types of named entities and 16 dialogue intents. The complexity of the IMCS21 dataset lies primarily in its normalization of entities such as symptoms to address synonyms or different expressions. More than 1900 common symptoms have been standardized into 444 canonical names, facilitating the model's ability to understand similar expressions and reducing ambiguities. To ensure data quality, some samples with incomplete information or too few dialogue turns were excluded from the original data in this paper. MedicalKG is a knowledge graph in the medical domain constructed by [PERSON] al. [29]. It contains four types of named entities, namely (symptoms, diseases, parts, and treatments) and comprises 13,864 triples.  
The dataset used in the paper for the field of construction safety is self-constructed. The construction safety accident knowledge graph comprises a total of 1156 entities, 2200 relations, and 45,772 knowledge triples. In this knowledge graph, there are six categories of entities and five types of relationships. Based on the professional knowledge stored in the construction safety knowledge graph and the practical application scenarios provided by practitioners, a total of 2648 utterances related to construction safety were constructed under the guidance of experts. This utterance dataset included a total of five categories of intents.
# Synchronous Homopolar Generator without Permanent Magnets for Railway Passenger Cars
## 2 General Description of the SHG Design  
An SHG consists of two or more SRSCs connected to each other in the axial direction by ferromagnetic structural elements. The excitation current is provided by the excitation winding located on the stator. The magnetic flux of the excitation winding is closed through the SRSCs by means of the stator housing and the sleeve on the rotor shaft [30,32]. This magnetic flux is modulated by the rotor teeth, which allows it to interact with the poles of the stator winding. In this case, half of the poles are involved. The addition of ferrite magnets to the SHG makes it possible to use all poles of the stator winding  
Figure 1 shows the considered SHG design. The SHG has two stacks on the stator and two stacks on the rotor made of laminated steel. Each stack of rotor and stator is located in front of each other and forms the stator-rotor stack combination (SRSC). The stator lamination has 54 slots and 12-pole winding with a number of slots per pole and phase \(q=54/(12\cdot 3)=1.5\).  
An excitation winding is located between the SRSCs and is attached to the nonmagnetic supporting core. The nonmagnetic supporting core has slots of the same shape as the laminated stator cores through which the armature winding coils pass. Figure 1b shows the configuration of one armature winding coil. The pitch of the armature coils is four slots. Such a distributed winding is conventional for an SHG design and is widely used in practice.  
The rotor is salient-pole and has no windings. Each rotor stack has six teeth, and the teeth of the rotor stacks are shifted by 30 mechanical degrees. The SHG housing and the rotor sleeve carry the stator lamination and the rotor lamination, respectively. They are made of a solid (not laminated) steel and provide the link to the flux produced by excitation winding.  
It is assumed that the generator delivers power to the DC link through a controlled rectifier with transistor switches, as shown in the diagram in Figure 2. The required DC voltage (corresponding to the amplitude line voltage of the electric machine) should not exceed 116 V. At preliminary calculations, it was found that without parallel branches, each layer of the power winding should contain only one turn. This leads to large eddy losses. Therefore, the number of parallel branches is chosen equal to the number of pole pairs six.  
Figure 1: Sketch of the SHG geometry: (**a**) Stator cross-section and winding pattern (1/4 of the machine is shown). The uppercase letters indicate the location and direction of the sides of the coils of phases A, B, C in the slots. (**b**) General view. Only one coil of the armature winding is shown. The rest of the coils are not shown. The two-layer armature winding has 54 coils in total.
# Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review
## 5 Future Directions and Opportunities
### Role of Explainable AI (XAI) in Medical Applications  
Explainable AI is becoming increasingly important in medical applications, as it addresses the need for transparency and trust in ML models [107]. By providing clear and interpretable insights into how predictions are made, XAI can help clinicians understand the rationale behind recommendations, fostering confidence in AI-driven decision-making [108].  
\begin{table}
\begin{tabular}{p{71.1 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Challenge** & **Description** & **References** & **Potential Solutions** \\ \hline QC Challenges & Scalability, high error rates, and accessibility of quantum hardware. & [86; 87; 88; 89; 90; 91] & Advancements in qubit stability, error correction, fault-tolerant architectures, and increased accessibility. \\ \hline ML Challenges & Data quality, patient privacy, bias, explainability, and regulatory compliance. & [96; 97; 98; 99; 100] & Improved data curation, anonymization techniques, bias mitigation, explainable AI, and adherence to regulations. \\ \hline Integration Challenges & Bridging QC and ML, rigid clinical workflows, interoperability, and high costs. & [101; 102; 103; 104] & Specialized expertise, flexible workflows, interoperability standards, and investment in infrastructure. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of challenges and limitations in medical decision-making and potential solutions.  
Integrating XAI with quantum-enhanced models further enhances its applicability by providing robust, interpretable solutions to intricate medical challenges [109; 110].
## 5 Conclusions  
Acoustic emission data, recorded during constant-rate uniaxial compressive loading as well as during uniaxial compressive loading using stepped-stress increments until the fracture of prismatic marble specimens, were analyzed under non-extensive statistical  
Figure 10: Regarding the UC loading protocol: **(a)** The variability of the values \(F(q)\) and \(Q(q)\) as functions of the index \(q\). **(b)** The linear relationship of the index \(q\) and the parameter \(\beta_{q}\) based on the relationship \(1/\beta_{q}^{*}=Q(q)/F\). **(c)** Comparison between the variability of \(1/\beta_{q}^{*}\) and \(1/\beta_{q}\) as functions of the index \(q\). **(d)** The CDFs and the corresponding fitting for AE hit group 3.  
mechanics (NESM). The AE hit time intervals were studied in terms of a NESM analysis by determining the entropic index \(q\) and the parameter \(\beta_{q}\) at different loading levels of the specimens. The main conclusions are summarized as follows.  
Approaching failure, the entropic index \(q\) progressively increased, reaching maximum values close to 1.5, which is seemingly related to the development of a fairly organized system of microcracks.  
In the last stage, and when the final fracture was imminent, the values of \(q\) showed a decrease, suggesting that the processes of microcracks coalescing into macroracks and, subsequently, to failure are not characterized by mutual interactions. In contrast, the frequency with which the AEs are recorded, as well as the parameter \(\beta_{q}\), were the values that increased the most.  
At pre-failure loading levels, specifically in the range from 70% to 96% of the specimens' strength, a linear correlation was observed between the entropic index \(q\) and the parameter \(1/\beta_{q}\). This behavior can be considered an indication of preliminary damage, providing a warning of entry into the critical stage of impending failure.  
When the test specimens were subjected to loading near their ultimate strength, the linear relationship between \(q\) and \(1/\beta_{q}\) was reversed due to the decreasing values of \(q\), which signaled imminent failure.  
In conclusion, the ability of NESM to describe the transition of dynamical systems from an equilibrium state to a non-equilibrium state has been studied in a series of publications (e.g., see References [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]) which touch on various scientific fields, including seismicity and fracture experiments. Parallels can be drawn between the application of NESM in fracture experiment applications and NESM findings in the field of seismicity. Moreover, a further experimental study is needed to transition from small laboratory-scaled specimens to larger and more complicated in situ structures.  
Conceptualization, D.T.; methodology, D.T.; writing--original draft preparation, A.L.; writing--review and editing, I.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Methods  
In order to evaluate the navigation risk in Bohai Sea and its surrounding waters, first, a sample database based on AHI data and CALIPSO vertical feature mask (CALIPSO-VFM) data is established, and a sea fog identification method based on Multilayer Perceptron (MLP) method is developed. Second, the method is used to obtain a large historical sea fog data set of the study area, which is combined with other evaluation factors to calculate the occurrence frequency of severe sea conditions. Then, the criteria importance through intercriteria correlation (CRITIC) objective weighting method is used to determine the weights of the evaluation criteria, and obtain navigation risk maps of the area via global information system (GIS) mapping technology. In order to further analyze the risks, the Delaunay triangulation method is used to divide the sea area into different waterway regions, and the risk values for the specific waterway areas are calculated. Finally, two validation methods are used to validate the sea fog identification method and navigation risk estimation model, and a sensitivity analysis of the model is carried out. A method flow chart of this process is shown in Figure 2.