esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Study Case
### Construction of Prototype System  
The prototype system adopts the most popular Web development technology, and uses Java (JDK 1.8) as the main development language, as shown in Figure 9. In order to improve the scalability and maintainability of the prototype system, the prototype system is implemented using the microservice architecture [33].  
The three components of the proposed framework are, respectively, realized as \"microservices\". The \"Data service container microservice\" mainly realizes the functions of  
Figure 8: The execution process of data-computing tasks.  
service release, access, and execution. The \"Workspace microservice\" mainly realizes the functions of workspace creation, data configuration, and computing-solution generation. The \"Data-computing engine microservice\" mainly realizes the functions of computing-task generation, resource synchronization, and computing-task execution. Each microservice can be deployed and run independently, and microservices can communicate with each other based on message passing, including communication based on HTTP REST [34], gRPC [35], message queue [36], and WebSocket [37].  
This prototype system uses \"Spring Boot\" (2.2.6 version) as the Web backend development framework and \"Spring Cloud\" (2.2.1 version) as the microservice development framework. The former provides a framework for developers to quickly develop Web applications, which can greatly improve development efficiency. \"Spring Cloud\" is based on \"Spring Boot\" and provides a framework for developers to quickly develop distributed system infrastructure.  
Each microservice uses the MySQL (8.4.2 version) database [38] to store application data: data service container (such as service entries, service run records, service input and output data, etc.), workspace (such as workspace instances, data-computing solution, etc.), and data-computing engine (data-computing tasks, task-execution records, etc.). The Redis (3.2.9 version) [39], as an in-memory database, is mainly used to improve system performance and optimize user experience. For example, when the data service is bound to each node of the data-processing workflow in the workspace, the data service queried in the network can be cached into the Redis database, and the cached data can be read directly when the next access is needed, without having to look up from the network again.  
The front-end of this prototype system uses Vue.js (3.8.0 version) as the development framework, and uses \"Element Plus\" library (2.3.9 version) to build front-end pages. Node.js (22.11.0 version) is mainly used to build and manage front-end projects. In this prototype system, the data service container, workspace, and data-computing engine are implemented by corresponding front-end projects.  
Figure 9: The implementation of the prototype system.
## 2 Materials and Methods  
### Subjects  
For model validation, a group of 4 healthy subjects (namely, HS1-HS4) and 3 patients with PD (namely, P1-P3), enlisted from the Movement Disorders Outpatient Service of Sapienza University in Rome, Italy, was recruited. The clinical evaluation of patients with PD involved assessing MDS-UPDRS scores, focusing on primary motor symptoms such as bradykinesia, rigidity, tremor, gait, and balance issues. Cognitive functions were evaluated using the Mini-Mental State Evaluation (MMSE) [27], which is a comprehensive cognitive scale assessing various abilities including orientation, memory, fluency, attention, and visuospatial skills. Moreover, the Hoehn and Yahr scale [28], Beck Depression Inventory-II (BDI-II) [29] and Parkinson's Disease Questionnaire (PDQ-39) [30] were included in patients' assessment. Inclusion criteria for patients with PD included a diagnosis of idiopathic PD according to consensus criteria, H&Y scale \(\leq\) 2.5, and the ability to keep up the right stance and walk independently. Exclusion criteria comprised diagnosis of atypical parkinsonism, severe PD (H&Y \(>\) 2.5), L-Dopa-induced dyskinesia, dementia (MMSE \(<\) 24), and comorbidities affecting gait or arm movements (e.g., orthopedic, or rheumatticolisses, polyneuropathies, and other neurological disorders).  
Patients with PD were undergoing chronic dopaminergic treatment. Arm swing during gait, akin to rigidity, bradykinesia, or tremor, may benefit from levodopa intake. Therefore, PD patients were evaluated in two sessions according to the therapeutic condition: the OFF (i.e., after drug withdrawal for at least 12 h) and ON state of therapy (i.e., one hour after taking the 150% of their usual L-Dopa dose). The L-Dopa equivalent daily dose (LEDD) was calculated according to standardized procedures [31]. Anthropometric data (weight, height, center of mass, and turning radius for both upper arm and forearm) were used to allow for detailed modeling. Tables 1 and 2 summarize participants' demographic and clinical features.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ID** & **Age [Years]** & **Sex** & **Height [cm]** & **Weight [kg]** & **H\&Y OFF** & **DD [Years]** \\ \hline PD1 & 72 & M & 165 & 80 & 2.5 & 3 \\ PD2 & 69 & F & 168 & 68 & 2 & 3 \\ PD3 & 65 & M & 190 & 100 & 2.5 & 7 \\ HS1 & 29 & M & 188 & 72 & - & - \\ HS2 & 63 & M & 180 & 82 & - & - \\ HS3 & 60 & F & 174 & 90 & - & - \\ HS4 & 61 & M & 179 & 80 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic and clinical features of participants. H&Y: Hoehn and Yahr scale; DD: disease duration.
## Materials and Methods
### Analysis of Volatile Metabolic Profiles by HS-SPME/GC-MS
#### 4.6.2 HS-SPME/GC-MS Analysis Conditions  
Tomato volatile compounds were extracted by headspace-solid phase microextraction (HS-SPME) equipped with a 50/30\(\upmu\)m Carboxen/polydimethylsiloxane/divinylbenzene (CAR/PDMS/DVB) fiber (Sigma-Aldrich, St.Louis, MO) by modifying the conditions of our previous work [72]. The samples were incubated and extracted for 2 and 30 min at 60 \({}^{\circ}\)C, respectively. The SPME fiber was desorbed at 225 \({}^{\circ}\)C for 2 min, fiber conditioning was followed for 7 min, by placing it into the injector of gas chromatography equipped with an electron ionization source with a Dual-Stage Quadrupole (DSQ II) mass spectrometer (Thermo Scientific, Austin, TX, USA). Chromatographic separation was achieved with a Zebron ZB-5 MS plus capillary column coated with 5% diphenyl-95% dimethylpolysiloxane (30 m \(\times\) 0.25 mm) (Phenomenex, Inc. Torrance, CA, USA). The conditions applied for the GC-MS was an initial oven temperature of 40 \({}^{\circ}\)C, held for 1 min, then increased to 90 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min, and increased to 175 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min. Finally, it was increased to 230 \({}^{\circ}\)C at a rate of 35 \({}^{\circ}\)C/min, and held for 2 min at the final temperature, with a total run time of 38 min. The electron impact (EI) data from _m/z_ 40 to 450 were acquired at a scanning speed of 11.5 scans per sec and with an ionization voltage of 70 eV. The ion source temperature and mass transfer line temperature were maintained at 280 \({}^{\circ}\)C. The data were recorded and processed using the Xcalibur software (v. 2.0.7., Thermo-Fisher Scientific, San Jose, CA, USA).
## 3 Results  
Table 2 shows the image features selected by the LASSO regression and their coefficients. All abbreviations are explained in Table S1. Eleven image features were selected using the LASSO regression from the 484 image features. Of these, two were texture features, and nine were histogram features. The image features with the highest LASSO regression coefficients were Energy.1 with a coefficient of 1.383, followed by LLH Variance.1 with a coefficient of 0.438 and LLH ZP with a coefficient of 0.239.  
Figure 2: Building and evaluation of a rupture prediction model based on imbalance adjustment strategy.  
Figure 3 shows AUCs, accuracies, sensitivities, and specificities of 10 models with combinations of features selected by LASSO. The upper and lower bars show the results of the training and test models, respectively, for each combination of image features. The details are shown in Table S2. The combination of the top first, second, and third coefficients [Energy.1 (texture/GLCM) + LLH Variance.1 (texture/GLCM) + LLH ZP (texture/GLSZM)] reached a highest AUC of 0.971, an accuracy of 0.948, a sensitivity of 0.700, and a specificity of 0.953.  
Figure 4 depicts the scattered plots between the two features selected from the four image features for the RA and UA training cases. The image features for the RA and UA cases appear to have different distributions, where some of the RA or UA cases could be discriminated, but others overlapped.  
\begin{table}
\begin{tabular}{l l} \hline
**Radiomic Feature** & **Coefficient of LASSO** \\ \hline Energy.1 (texture/GLCM) & 1.383 \\ LLH Variance.1 (texture/GLCM) & 0.438 \\ LLH ZP (texture/GLSZM) & 0.239 \\ LLL MAD (histogram) & 0.189 \\ HLL ZP (texture/GLSZM) & 0.107 \\ LLH Variance (histogram) & 0.0659 \\ Uniformity (histogram) & 0.0485 \\ LLH Autocorrelation (texture/GLCM) & 0.0311 \\ LLH GLV (texture/GLRLM) & 0.0260 \\ HHH ZP (texture/GLSZM) & 0.0153 \\ LLH Contrast (texture/GLCM) & 0.000211 \\ \hline \end{tabular} \({}^{\pi}\)Energy.1\({}^{\pi}\) is the energy for a texture analysis, but “Energy” is that for a histogram. “Variance.1\({}^{\pi}\) is the variance for a texture analysis, but “Variance” is that for a histogram.  
\end{table}
Table 2: Eleven radiomic features with their coefficients selected by LASSO. All abbreviations are explained in Table S1.  
Figure 3: AUCs, accuracies, sensitivities, and specificities of 10 models with combinations of features selected by the top 1 st–4 th coefficients of LASSO.  
Figure 5 shows MRA images of the RA and UA cases. The intensities (mean: 240) around the aneurysm centers for this RA case are lower than that (mean: 456) of aneurysm surfaces shown in Figure 5a, whereas the UA case has higher intensities (mean: 655) around centers shown in Figure 5b.
# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
## 6 Results and Analysis
### RQ1: Overall Performance
The post-processing-based CaliRec model then re-ranks the top-100 items with \(K\) steps (\(K\) stands for the top-K recommendations). At each step, it computes the gains of the candidate items when they are added to the recommendation list. However, our model follows an end-to-end framework only with a sorting stage to select top-K items after the scores of all items are computed. Therefore, our model obtains better performance and requires less time consumption than the CaliRec model for each sequence. **Generalization of DACSR Model** We are also interested in whether our model is also effective when the sequence encoder changes. We incorporated the GRU4 Rec model [1] as the sequence encoder, which is also a widely used sequential recommendation model. The experimental settings were same to the previous section with \(\lambda=0.5\). We use DACSR(G) to denote our DACSR model which takes GRU4 Rec as the sequence encoder, and CaliRec(G) to denote the post-processing-based CaliRec model with candidates provided by the GRU4 Rec model. The performances are listed in Table 4.  
As shown in the table, our model can still achieve calibrated and accurate recommendation lists when we use GRU4 Rec as the sequence encoder. On the MI-1m dataset, the performances of \(C_{KL}\)@20 are 0.6840 and 0.8356 of our DACSR(G) model and the GRU4 Rec model, respectively. On the Tmall dataset, our model also obtains an improvement of 21.95% in terms of calibration. Toward recommendation accuracy, the performances of our model are still better than the original GRU4 Rec model. The improvement is not as great as the DACSR model with the SASRec sequence encoder. We think that this is because the ability to model sequences of GRU4 Rec is worse than that of the SASRec model. The SASRec model with self-attention mechanisms can better find the user's preference and represent the sequence. The CaliRec(G) model also sacrificed the ranking performance to improve the calibration, which is similar to the CaliRec model with the SASRec model. Compared to the CaliRec(G) model, our DACSR(G) model can also achieve better performance in terms of accuracy and calibration, as listed in Table 4. The performance comparisons indicate that our model can be used for other basic sequence encoders, which is not specifically designed for the SASRec model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **Rec@20** & **MRR@20** & \(\mathbf{C_{KL}@20}\) \\ \hline \multirow{4}{*}{Ml-1m} & GRU4 Rec & 0.3460 & 0.1142 & 0.8356 \\ \cline{2-5}  & CaliRec(G) & 0.3454 & 0.1105 & 0.7013 \\ \cline{2-5}  & DACSR(G) & **0.3472** & **0.1161** & **0.6840** \\ \hline \multirow{4}{*}{Tmall} & GRU4 Rec & 0.1724 & 0.0863 & 2.2123 \\ \cline{2-5}  & CaliRec(G) & 0.1732 & 0.0846 & 1.8597 \\ \cline{1-1} \cline{2-5}  & DACSR(G) & **0.1750** & **0.0878** & **1.7266** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performances of our DACSR(G) model with the GRU4 Rec sequence encoder (best performances are marked in bold).
# Analysis of Rewetting Characteristics and Process Parameters in Tobacco Strip Redrying Stage
## 5 Conclusions  
In this study, the conjugate heat and mass transfer model of tobacco rewetting was established by using a numerical simulation method, and the effects of relative humidity and temperature on the moisture content, the moisture humidification rate, the effective moisture diffusivity, and the activation energy of heaped tobacco strips were analyzed.  
1. The rewetting of heaped tobacco strips during the redrying stage includes two stages, the accelerating stage and approximate constant rate stage. The vapor mainly condensates in the accelerated stage, which is characterized by fast growth and a large increment of moisture content. The diffusivity of the liquid phase mainly occurs in the approximate constant rate stage, which is characterized by the slow growth of the moisture content and stable increments.
2. The relative humidity and temperature have a great influence on the rewetting characteristics of heaped tobacco strips. The higher the relative humidity and temperature, the higher the moisture content of heaped tobacco strips, and the greater the increment and the faster the humidification rate. The best conditions for rewetting tobacco strips are a relative humidity of 85% and a temperature of 55 \({}^{\circ}\)C. This maintains the moisture content in the range from 11 to 13 percent.
3. An effective moisture diffusivity is greatly influenced by the relative humidity and temperature, showing a changing law of rapid increase and then stable increase. The higher the relative humidity and temperature, the greater the variation, and the faster the rate of increase. At the same time, the effective moisture diffusivity of the heaped tobacco strips' surface is more sensitive than the interior.  
Conceptualization, H.W. and L.W.; methodology, H.W., L.W. and W.J.; software, W.J., Q.W. and H.W.; validation, H.W., W.J., Q.W. and H.Z.; formal analysis, H.W., W.J. and H.Z.; investigation, H.W.; resources, L.W. and Q.W.; data curation, H.W.; writing--original draft preparation, H.W.; writing--review and editing, L.W. and W.J.; supervision, L.W.; project administration and funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Science and Technology Special Project of Yunnan Province of China, grant number 202002 AD080001.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 13: The effect of temperature on the effective moisture diffusivity on the surface and inside.
# Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing
## 3 Methodology
### Optimization  
The learning process of MGSPU can be divided into two stages: (1) hash code learning stage, and (2) hash function learning stage. During the hash code learning stage, the optimization of the objective function \(\mathcal{L}_{code}\) is performed by minimizing Equations (14)-(16):  
\[\begin{split}\underset{\mathbf{\theta}^{c}}{min}& \mathcal{L}_{code}=\lambda_{1}\mathcal{L}_{rec}+\lambda_{2}\mathcal{L}_{mod}+ \lambda_{3}\mathcal{L}_{sim}.\end{split} \tag{20}\]During the hash function learning stage, the optimization of the objective function \(\mathcal{L}_{hash}\) entails minimizing Equations (17)-(19):  
\[\underset{\mathbf{\theta}^{c},\mathbf{\theta}^{t}}{min}\quad\mathcal{L}_{hash}=\beta_{1} \mathcal{L}_{f_{1}}+\beta_{2}\mathcal{L}_{f_{2}}+\beta_{3}\mathcal{L}_{f_{3}}. \tag{21}\]  
The entire algorithm process of MGSPU is outlined in Algorithm 1, which is implemented by Adam adaptive algorithm [54].  
```
0: Training set size \(n\); number of epochs \(e\); hash code length \(C\); batch size \(m\); learning rate of the network \(lr_{code}\) and \(lr_{hash}\); hyperparameter \(\alpha_{1},\alpha_{2},\eta_{1},\eta_{2},k,\lambda_{1},\lambda_{2},\lambda_{3}, \beta_{1},\beta_{2},\beta_{3}\);
0: Network parameters for the optimal hash function learning part;
1: Initialize network parameters \(\mathbf{\theta}^{c},\mathbf{\theta}^{c},\mathbf{\theta}^{t}\);
2: Construct a unified semantic matrix \(\mathcal{S}^{t}\) using image features \(\mathbf{F}^{v}\) and text features \(\mathbf{F}^{t}\);
3:repeat
4: Iterative training \(e\) times;
5:for\(i=1,2,...\), \(\left\lceil\frac{n}{m}\right\rceil\)do
6: Arbitrarily select \(m\) training data;
7: Generate continuous hash codes \(\mathbf{H}^{v},\mathbf{H}^{t}\) by forward propagation algorithm;
8: Generate the semantic matrix \(\mathcal{S}^{h}\) through the hash code \(\mathbf{H}^{v},\mathbf{H}^{t}\), and update the original semantic matrix \(\mathcal{S}^{t}\) to generate a unified semantic matrix \(\mathcal{S}\);
9: Calculate the loss function Equations (14)-(16) and update the parameters through backpropagation \(\mathbf{\theta}^{c}\);
10:endfor
11:for\(i=1,2,...\), \(\left\lceil\frac{n}{m}\right\rceil\)do
12: Select \(m\) training image-text pairs and hash code \(\mathbf{H}^{v},\mathbf{H}^{t}\);
13: Map the original feature \(\mathbf{F}^{v},\mathbf{F}^{t}\) into a hash code through \(f^{v}\) and \(f^{t}\);
14: Calculate the loss Equations (17)-(19), and update the parameter \(\mathbf{\theta}^{v},\mathbf{\theta}^{t}\) with back-propagation;
15:endfor
16:until Convergence.
```  
**Algorithm 1** Algorithm of MGSPU.
# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 3 Progress of Information Fusion for Multi-Source Material Data
### Algorithms for Material Data Fusion  
Multi-source data fusion is a process of cognizing, synthesizing and judging a variety of data. The data involved in the fusion process often have properties such as multi-source, heterogeneity and incompleteness. Generally, data fusion methods can be categorized as probability-based methods, evidence reasoning methods and knowledge-based methods [33; 34; 35], but another point-of-view is to divide data fusion into data-level fusion, feature-level fusion and decision-level fusion [36; 37], as shown in Figure 3. The data-level fusion focuses on data collection and preprocessing, through which all data sources are transformed, cleaned and integrated into a fused data store. The feature-level fusion mainly consists of two processes, namely feature extraction and feature fusion. The feature extraction process aims to extract features from original data items or sampled data items. The extracted features are inputted into the feature fusion process to form fused features. For example, physical features can be fused with chemical features to produce fused features for materials. The decision-level fusion is regarded as a multi-model classification, where different classifiers are examined on features as well as data items to get the optimal model for decision making, e.g., predicting the performance of a future material.
# Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation
## 4 Proposed Method
### Dynamic Algorithm for Online Label Distribution Feature Selection  
Most feature selection algorithms assume that all candidate features are available to the algorithm before feature selection. In contrast, for streaming features, all the candidates cannot be collected before learning begins because they arrive dynamically and incrementally over time. Therefore, based on Method 1, we incorporate the online streaming feature selection framework [50] and propose an online labeled distributed streaming feature selection algorithm to solve the labeled distributed stream feature selection problem.  
In the labeled distributed stream feature decision system \(LFDS=<U,C\cup L,t>,U=\{x_{1},x_{2},\ldots,X_{n}\}\) represents the set of all non-empty instances, \(C\) represents the candidate feature subset, \(L\) represents the label space, \(t\) represents the time when the feature arrives, \(F_{t}\) represents the new feature of \(t\), and \(S_{t-1}\) represents the current candidate feature subset at time \(t\).
# A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition
## 2 Related Work
### Our Contributions  
Inspired by the above research works, we proposed a novel deep learning model called Heterogeneous Parallel Convolution Bi-LSTM (HPCB) to exploit spatiotemporal information more effectively in SER. HPCB employs novel heterogeneous parallel learning structures to exploit the spatiotemporal information. Furthermore, multi-features are used to unveil the complete emotional details in a more robust and effective way. Moreover, HPCB demonstrates an advantage over the previous methods in literature on the benchmark databases that include EMODB, CASIS, and SAVEE.  
The model with Heterogeneous Parallel achieves improvements over the baselines in most cases. We make the following contributions in this study:  
1. 32D LLD features at the frame-level are extracted, and its HSF features at the sentence-level are computed. Totally, 72D acoustic features are used as the input of the model.
2. The proposed HPCB architecture can be trained with features extracted at the sentence-level (high-level descriptors), or at the frame-level (low-level descriptors) simultaneously.
3. We provide a comprehensive analysis of Heterogeneous Parallel network training for the sake of SER, demonstrate its advantages within corpus evaluations, and observe performance gains.  
The rest of the paper is organized as follows. Section 3 presents the proposed Heterogeneous Parallel architectures. Section 4 gives details on the experimental setup including the databases and features used in this study. Section 5 presents the exhaustive experimental evaluations, showing the benefits of the proposed architecture. Finally, Section 6 provides the concluding remarks, discussing potential areas of improvements.