esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results
### Analytical Test Cases
#### 4.1.2 Problem 2: Borehole Function (8-D)  
The Borehole function is an 8-D benchmark problem that models water flow through a borehole. The extension to multi-fidelity purposes is described by the following equation [48],  
\[f_{hf}(x)=\frac{2\pi T_{u}(H_{u}-H_{l})}{\ln(r/r_{w})\Big{(}1+\frac{2 LT_{u}}{ \ln(r/r_{w})r_{w}^{2}K_{w}}+\frac{T_{u}}{T_{l}}\Big{)}} \tag{29}\]  
Figure 4: Comparison of probability density function (\(N_{hf}=20\), \(N_{lf}=35\)).  
\[f_{lf}(x)=\frac{5T_{u}(H_{u}-H_{l})}{ln(r/r_{w})(1.5+\frac{2 LT_{u}}{ln(r/r_{w})r_{ w}^{2}K_{w}}+\frac{T_{u}}{T_{l}})} \tag{30}\]  
For this uncertainty quantification benchmark problem, parameter statistics are defined as shown in Table 2.  
For the value ranges defined in Table 2, a surrogate model is established for the number of different high-fidelity analyses with the LRA, PCE, MF-PCE and MF-LRA methods. The accuracy metrics of the surrogate models established using different numbers of high-fidelity simulations results are shared in Figure 5.  
As observed in Figure 5, a higher number of high-fidelity simulations results are required to establish an accurate surrogate model with PCE compared to other methods. It is also observed that the multi-fidelity versions of the methods are more accurate than the single-fidelity versions when using the same number of high-fidelity simulations. Remarkably, the surrogate model constructed by LRA with fewer high-fidelity simulation results is more accurate than the surrogate model constructed by MF-PCE. The accuracy values obtained by using only 10 high-fidelity data in the MF-LRA method are achieved by using 50 high-fidelity analyses in the MF-PCE method. When the single fidelity results of the methods are examined, it is observed that the LRA method gives more accurate results with a small number of simulation data as in the first application, while the PCE method produces more accurate results both locally and globally when many high-fidelity simulation data are used.  
For the case where 50 high-fidelity simulations results are used, the PDF value obtained from the surrogate models is demonstrated in Figure 6. In this case, the surrogate model obtained by the PCE method is highly erroneous. The PDF value obtained by the PCE method is therefore quite different from the MC result. However, it is observed that the results obtained with other methods overlap with the results obtained with the MC method.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Statistics** \\ \hline \(r_{w}\) & \(\mathcal{U}\)(0.05, 0.15) \\ \(r\) & \(\mathcal{U}\)(100, 50,000) \\ \(T_{u}\) & \(\mathcal{U}\)(63,070, 115,600) \\ \(H_{u}\) & \(\mathcal{U}\)(990, 1110) \\ \(T_{l}\) & \(\mathcal{U}\)(63.1, 116) \\ \(H_{l}\) & \(\mathcal{U}\)(700, 820) \\ \(L\) & \(\mathcal{U}\)(1120, 1680) \\ \(K_{w}\) & \(\mathcal{U}\)(9855, 12,045) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Borehole function parameter statistics.  
Figure 5: Surrogate model error metrics for Borehole function (\(N_{lf}=200\)).
# An Overview of Transcranial Magnetic Stimulation and Its Application in Multiple Sclerosis
## 5 TMS Measures in the Diagnosis and Evaluation of MS
### rTMS Applications in MS  
This manuscript has focused on the use of single- and pulse-paired TMS as a potential biomarker in MS, but there have also been significant developments in the application of repetitive TMS (rTMS) as a possible therapy. According to a 2021 review by [PERSON] et al., many studies have focused on using rTMS to reduce spasticity, a common symptom of many MS patients [66]. A recent recommendation was made for \"probable efficacy of intermittent theta burst stimulation (iTBS) of the leg area of M1 contralateral to the most affected limb (or both M1) in lower limb spasticity\", as found in the evidence-based guidelines for the therapeutic use of rTMS, established by [PERSON] et al. [67]. In addition to this, the same review by [PERSON] et al. expands on other recent studies that have been conducted on the potential of rTMS in addressing cerebellar dysfunction and gait issues, urinary tract dysfunction, cognitive dysfunction, fatigue, and depression. However, sample sizes and differences in protocols continue to be challenges in this field [66].
## 10 Stability of PGB  
The stability of a point mass rotor (\(M_{r}\)) supported on one PGB is derived from \(\left|\mathbf{H}_{(\omega)}-\,\omega^{2}M_{r}\,\mathbf{I}\,\right|=0\), where \(\mathbf{I}\) is the \(2\times 2\) identity matrix. Following San Andres (2018), the instability threshold occurs at frequency \(\varpi_{\mathrm{s}}\) defined when the equivalent complex stiffness \(H_{e}\) has its imaginary part Im (\(H_{e}\)) = 0 and its real part \(>\) zero. The equivalent complex stiffness (\(H_{e}\)) is defined as  
\[H_{e_{(\omega)}}=\,\frac{1}{2}\Big{(}H_{XX_{(\omega)}}+H_{YY_{(\omega)}}\Big{)} -\bigg{[}\frac{1}{4}\Big{(}H_{XX_{(\omega)}}-H_{YY_{(\omega)}}\Big{)}^{2}+H_{ XY_{(\omega)}}H_{YX_{(\omega)}}\,\bigg{]}^{1/2}. \tag{35}\]  
Recall that at the centered condition (\(e=0\)), \(H_{XX}=H_{YY},H_{XY}=-H_{YX}\). Hence, \(H_{e}\) becomes  
\[H_{e_{(\omega)}}=\,H_{XX_{(\omega)}}-i\,H_{XY_{(\omega)}}=\left(K_{XX}+\omega C _{XY}\right)_{(\omega)}+i(\omega C_{XX}-K_{XY})_{(\omega)}=K_{\mathit{eff}_{( \omega)}}+i\,\omega C_{\mathit{eff}_{(\omega)}} \tag{36}\]  
\[\mathrm{Im}\Big{(}H_{e_{(\omega)}}\Big{)}=0\,\,\rightarrow\,\,C_{\mathit{eff}_ {(\omega)}}=0\,\,;\,\mathrm{Re}\Big{(}H_{e_{(\omega)}}\Big{)}\,\,>0\,\to \,\,M_{cr}=\frac{K_{\mathit{eff}_{(\omega)}}}{\omega^{2}}. \tag{37}\]  
When \(\omega=\frac{1}{2}\Omega\,\,\rightarrow\,C_{\mathit{eff}_{(\omega)}}=0\), and taking the operating speed as being the threshold speed of instability (\(\Omega_{T}=\Omega^{\star}=2\omega\)), then the largest mass the rotating system can hold is \(M_{cr}=\frac{K_{\mathit{eff}_{(\Omega\,T)}}}{\left(\frac{1}{2}\Omega\,\tau \right)^{2}}\). Table 2 lists the critical rotor mass for operation at various supply pressures and at MOS = 25 krpm. \(M_{cr}\) increases from 26.2 kg to 146.1 kg as \(p_{S}=2\) bar \(\rightarrow\) 8 bar. Note the weight of \(M_{cr}\) is much smaller than the PGB load capacity of \(\sim\)(\(K_{\Omega}\times c\)). Here\(K_{\Omega}=\sqrt{K_{XX}^{2}+K_{XY}^{2}}\) is a hybrid mode operation stiffness evaluated at the MOS (\(\Omega^{\star}\)) and a zero whirl frequency (\(\omega=0\)).
## 3 Results
### Adaptability to Specific Scenarios
#### 3.4.2 Emergency Siren Detection  
For this scenario, we used a data set of 400 urban recordings, where 200 were emergency sirens, and 200 were other urban sounds. Table 9 shows the results of using the algorithm to detect emergency sirens. The accuracy obtained is 96.3%, indicating that the model is highly reliable in differentiating between the sound of an emergency siren and other environmental noises. The sensitivity, which is 95.0%, suggests that the system effectively identifies a very high proportion of real sirens, with only a tiny margin of error in cases where it fails to detect them. On the other hand, the specificity, with a value of 97.5%, reveals the algorithm's ability to correctly recognize sounds that are not sirens and avoid false alarms. These results highlight the efficiency of the method in noisy urban environments.
## 3 Results and Discussions
### Future Research Perspectives and Challenges  
SE has proved to be a versatile and efficient pretreatment for different lignocellulosic biomass. It has already reached the industrial phase and currently acts as a reference for the pretreatment of lignocellulosic biomasses [28] for the subsequential AD process. However, one of the challenges that need to be addressed is using biomass with high lignin content such as _L. grandiflora_. In addition, the bioconversion of aquatic biomass is still a problem due to the obstacle in utilising of lignocellulosic content and the subsequent low methane yield.  
This study showed that even after performing SE pretreatment, high lignin content was present in the effluent of AD. As lignin is mainly composed of carbon, it accumulates undigested carbon during AD (Table 3). This undigested carbon led to an imbalance of nutrients which can be improved with the help of co-digestion, as mentioned in the study. Therefore, the co-digestion of steam-exploded _L. grandiflora_ with other easily degradable substrates, such as cow dung, can be studied as a potential topic, which can help achieve higher biogas yields and stability in anaerobic reactors during industrial application.  
In this study, due to the decreasing pH and the biogas yield during phase 1, NaOH addition was performed from operational day 67 to day 98. Even though the addition of NaOH did not show a specific increase in the pH value, it led to an increment in the daily production of biogas. However, the effect of NaOH on the AD process is still unclear. Besides, when an unknown phenomenon affects AD, the determination of microbial population could be used as an indicator to elucidate the changes during the digestion stage. Therefore, microbial analysis of the digestant should be performed to evaluate the optimisation of the reactor.  
Finally, to practically apply this study, the pilot-scale process for an industrial-scale operation, the translation of pilot-scale AD to industrial, commercial reactors is another crucial aspect that needs more research work. Modelling is a good option for these research areas, while more experimental studies are also required to obtain necessary data and verify model assumptions and results. Computational tools, such as computational fluid dynamics (CFD), can be employed in the scale-up and optimisation of AD. In addition, techno-economic analyses are necessary to improve the process efficiency and economic feasibility. The challenges at large scale operations like the easy use of biomass, its availability for lignocellulosic biorefineries and price, which majorly depends on the landowner, seasonal availability, storage loss and transportation cost are significant factors for further development of the process. The SE equipment from Yasujima Co. Ltd. used in this study can treat a large amount of substrate in a single operation, making it suitable for the industrial-scale process.
## 2 Results and Discussion  
### Synthesis of Polymer-Supported Sequestrant  
Dithiol-functionalized polybutadiene **1** was prepared via a green thiol-ene \"click\" reaction [33] using commercially available 1,2-ethanedithiol and alkene-terminated PIB Glissopal 1000 (\(DP_{n}\) = 18) as depicted in Scheme 1. Two equivalents of dithiol were used to avoid formation of undesired dimer **2** that is typical for equimolar reagent ratio (Figure S2). While others have shown that functional thiols can be attached to polyolefin oligomers with thiol-ene thermal chemistry [34], in our hands the photochemical approach described by [PERSON] worked better [35]. Photoinitisation with 365 nm UV light of the PIB-alkene in 8 h afforded a mixture of the desired compound **1** and bis-adduct **2** (Figure S2). The desired oligomer **1** was purified and isolated as a clear viscous liquid in 92% yield and characterized by \({}^{1}\)H and \({}^{13}\)C-NMR spectroscopy (Figures S3 and S4). Based on the \({}^{1}\)H-NMR integral intensities of polyisobutylene backbone and functional chain end (Figures S1 and S3), each polymer chain contains one thiol-thioether unit per chain. SEC (Figure S5) revealed slight increase of molecular weight that is consistent with theoretical expectations, while dispersity and molecular weight distribution remain essentially the same. This allows to estimate the degree of the functionalization on the level of 0.92 mmol/g or 5.9% of sulfur by weight.
## 3 Case Study  
### Study Object and Data Collection  
The study focused on the foundry workshop of an aluminum processing plant located in South China. The workshop is located on the eastern side of the plant and has a somewhat open construction. At present, there are three casting units in operation: Units No.2, No.3, and No.4, as shown in Figure 9a. The collection was collected from archival video footage recorded by AI cameras positioned overhead the casting molds of different components. Figure 9b,c depict the precise locations where the cameras and monitoring screens are installed, respectively. Data collection was conducted between March 2023 and August 2023, encompassing routine casting operations as well as occurrences of anomalies specifically connected to the loss of liquid aluminum.  
Table 2 contains a selection of video data samples. The captured films were divided into individual frames at a frequency of one frame per second to create the first dataset.
## 1 Introduction
The most common compositions of BG are silicate- or borate-based oxides, while in recent decades, new formulations have been proposed [10]. The final structure of bioactive glasses is determined by the presence of network formers and network modifiers. Network formers are mainly oxides such as P\({}_{2}\)O\({}_{5}\), Bi\({}_{2}\)O\({}_{3}\), B\({}_{2}\)O\({}_{5}\), and SiO\({}_{2}\). Network modifiers are usually elements of alkali metals or alkaline earth such as K, Na, Li, Ca, and Mg. Bioactive glasses based on the SiO\({}_{2}\)-Na\({}_{2}\)O-CaO-P\({}_{2}\)O\({}_{5}\) oxide system have been widely studied. However, binary systems such as SiO\({}_{2}\)-CaO glasses also showed bioactivity depending on the glass composition [11]. Scaffolds containing alginate-bioactive glass composites were 3D printed, and their mechanical properties, bioactivity, and cell responses in rat bone mesenchymal stem cells were examined. It was reported that the presence of bioactive glass increased their compressive strength and Young's modulus and, in addition, showed high cytocompatibility and bioactivity properties [12]. Bioactive glass nanoparticles embedded into an alginate/gelatin ink containing bone-related cells allowed their proliferation, while their bioactivity increased [13]. In the first part of the present work, hydrogel inks comprising alginate/methyl cellulose/trimethyl chitosan (TMC) and silicate glasses were fabricated. Methyl cellulose is derived from cellulose after the partial substitution of hydroxyl groups with methoxy groups [14]. It is widely used in cosmetics, pharmaceutics, and the food industry as an emulsifier or as a thickening agent. TMC is a modified derivative of chitosan soluble at physiological pH and at different pH values in contrast with chitosan, which is soluble only in acidic solutions. Trimethyl chitosan also exhibits antibacterial properties with high degradation rates and possesses a positive charge due to quaternary moieties (-N\({}^{+}\)(CH\({}_{3}\))\({}_{3}\)) [15]. Using the above ink formulations, 3D scaffolds were fabricated by the extrusion method. Next, their structural characteristics, swelling properties, and in vitro bioactivity performance were investigated. Moreover, the biological evaluation of the fabricated scaffolds was conducted by the assessment of cell viability, proliferation, adhesion, morphology, alkaline phosphatase (ALP) activity, and total collagen production using the pre-osteoblastic cells MC3T3-E1 to demonstrate the capacity of the composite scaffolds to promote bone tissue regeneration.
## 4 Energy Consumption
### Average Energy Consumed by RN2 during Awake Cycles
#### 4.5.1. Energy Consumption in Type 2A Cycles  
Let \(E_{tx,s}^{aw}[2A]\), \(E_{tx,f}^{aw}[2A]\) and \(E_{oh}^{aw}[2A]\), be the average energy consumed by RN2 in type 1A cycles when RN2 completes a successful PF transmission, a PF from RN2 collides, and RN2 _overhears_ a PF transmission from other C2 SNs, respectively.  
These RN2 energy consumption terms can be determined as,  
\[\begin{split} E_{tx,s}^{aw}[2A]&=\sum_{i=1}^{Q_{2} }\sum_{k=1}^{N_{2}}\pi_{2}(i,k)P_{s,k,2}\Big{[}E_{1}^{aw,2}-(\alpha_{2}\,t_{ DATA}+BT_{s,k,2})P_{rx,2}\Big{]}\,,\\ E_{1}^{aw,2}&=\big{[}T-T_{sc,2}-\big{(}t_{RTS}+t_{CTS }+t_{ACK}+4D_{p}\big{)}\big{]}P_{rx,2}\,,\\ \alpha_{2}&=min(i,F_{2})\,.\end{split} \tag{16}\]  
\[\begin{split} E_{tx,f}^{aw}[2A]&=\sum_{i=1}^{Q_{2} }\sum_{k=1}^{N_{2}}\pi_{2}(i,k)P_{f,k,2}\Big{[}E_{2}^{aw,2}-BT_{f,k,2}P_{rx,2} \Big{]}\,,\\ E_{2}^{aw,2}&=\big{[}T-T_{sc,2}-\big{(}t_{RTS}+2D_{p }\big{)}\big{]}P_{rx,2}\,.\end{split} \tag{17}\]\[E_{\mathit{oh}}^{\mathit{aw}}[2A] =\sum_{i=1}^{Q_{2}}\sum_{k=1}^{N_{2}}\pi_{2}(i,k)kP_{s,k2}\Big{[}E_{3} ^{\mathit{aw}2}-\widehat{f}_{k,2}\,t_{DATA}(P_{rx,2}-P_{sl})\Big{]} \tag{18}\] \[+\sum_{i=1}^{Q_{2}}\sum_{k=2}^{N_{2}}\pi_{2}(i,k)\widehat{P}_{f,k,2}(T-T_{s,2})P_{rx,2}\,,\] \[E_{3}^{\mathit{aw}2} =(T-T_{s,2})P_{rx,2}-\big{(}t_{CTS}+t_{ACK}+3D_{p}\big{)}(P_{rx,2} -P_{sl})\,.\]  
where \(\alpha_{2}\), \(F_{2}\), \(\widehat{P}_{f,k,2}\) and \(\widehat{f}_{k,2}\) have identical meaning and purpose than their C1 counterparts defined in (10).  
Then, the average energy consumed by RN2 in type 1A _awake_ cycles is,  
\[E_{\mathit{aw}}[2A]=E_{\mathit{tx},s}^{\mathit{aw}}[2A]+E_{\mathit{tx},s}^{ \mathit{aw}}[2A]+E_{\mathit{oh}}^{\mathit{aw}}[2A]\,. \tag{19}\]
## 2 Materials and Methods
### Corpus Recording  
The recorded data for this study were obtained from a graphic adventure video game [16] that engages users in activities designed to help improve perception and production of prosodic cues. Users are required to perform these activities in order to progress in the game. These activities target various language functions such as questioning, expressing opinions, and social interaction. Additionally, different prosodic functions, such as chunking or prominence, are employed in different production modes such as reading, elicited speech, or free speech. All oral productions and user interactions were recorded and categorized based on the specific activity and speaker during gameplay. The corpus was compiled from various software testing and user training sessions. It contains 2151 recordings of people with Down syndrome, produced by 42 different speakers. In addition, the corpus contains recordings of the game recorded by 30 individuals without Down syndrome, totaling 1589 utterances. In order to diminish background noise during the recording phase, the performers utilized a headset equipped with an integrated microphone (specifically, the Plantronics USB headset). This headset recorded audio in MS-WAVE PCM format at a frame rate of 44,100 Hz, with 16 bits per sample, and in a mono configuration. The incorrect or highly noisy audio files were removed, but no further processing was conducted on them. Additional information can be found in [19].  
Out of the 966 evaluated recordings (see Section 2.2), 605 belong to the same 5 users, and these users had more than 40 utterances each. These 5 speakers provide enough information about the potential of the proposed methodology and facilitate an individualized analysis of each one. Detailed information regarding the speakers' gender, age, and cognitive abilities can be found in Table 1. To obtain this information about the speakers' profile, a Spanish version PEPS-C test [25] was used. This test aims to evaluate prosodic competence across multiple dimensions. It assesses both the form, which includes perceptual and motor abilities, and the function, which encompasses cognitive comprehension and expressive capabilities. Additionally, it evaluates both receptive (input) and expressive  
Figure 1: Experimental procedure diagram.  
(output) skills. This corpus is referred to as \(\theta_{DS}\). To compare the voices of individuals with Down syndrome to those of typical speakers, we used the subset of the corpus with recordings from typical speakers. We named this corpus \(\theta_{TD}\).
# Similarity Measurement and Classification of Temporal Data Based on Double Mean Representation
## 5 Conclusions  
The SAX-DM algorithm is proposed in this paper, taking into account the strengths and limitations of SAX-TD and SAX-BD. SAX-TD has limitations in handling complex sequences with varying patterns, while SAX-BD requires more symbols for representation. Our novel representation method adds an additional character to the original SAX, allowing for an intuitive representation of the change trend in subsegments. SAX-DM not only uses fewer characters to represent time series but also employs a new boundary distance measure to quantify time series similarity. Furthermore, we demonstrate that our distance measure maintains a lower bound on the Euclidean distance. Using this distance measure significantly reduces classification error rate, improves efficiency, and enhances accuracy in similarity calculations.  
Conceptualization, Z.H.; Methodology, Z.H. and C.Z.; Software, C.Z.; validation, Z.H., C.Z. and Y.C.; formal analysis, Y.C.; investigation, Y.C.; data curation, Y.C.; writing--original draft preparation, C.Z.; Writing--review & editing, Z.H. and C.Z.; visualization, Y.C.; supervision, Z.H.; Project administration, Z.H.; funding acquisition, Z.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2022 YFF0801200,2022 YFF0801203) and the National Natural Science Foundation of China (41972306).  
The data used in this experiment can be accessed through the URL [[https://www.cs.ucr.edu/~eamonn/time_series_data_2018/](https://www.cs.ucr.edu/~eamonn/time_series_data_2018/)]([https://www.cs.ucr.edu/~eamonn/time_series_data_2018/](https://www.cs.ucr.edu/~eamonn/time_series_data_2018/)), accessed on 10 June 2023.  
The work described in this paper was supported by the National Key Research and Development Program of China (2022 YFF0801200,2022 YFF0801203) and the National Natural Science Foundation of China (41972306). Thanks for the reviewers' constructive comments; they really help us improve this paper.  
The authors declare no conflict of interest.  
Figure 10: SAX and its improved method accuracy and compression ratio.
## 7 Topology Control
### Link Constraint Models
#### 7.2.2 On-Off Channel Model  
This model contains independent channels, each of which is either on with probability Pn or off with probability (1-Pn), where Pn is a function of \(n\) for generality. This model requires the channel between two sensors to be on for communication [96; 97; 98; 99].
# Improving Localization of Deep Inclusions in Time-Resolved Diffuse Optical Tomography
## 2 Methods
### Quantitative Evaluation Metrics  
Three different evaluation metrics (localization error, average contrast and relative recovered volume) were used to measure the reconstructions quality from different points of view.  
Localization error is defined as Euclidean distance between the center of mass of simulated inclusion \(\mathbf{x}_{s}\) and the center of mass of the reconstructed inclusion \(\mathbf{x}_{r}\). If the reconstructed optical properties are identical to the truth, the localization error is zero. The recovered inclusion is determined by selecting the reconstructed absorption changes that are greater than a given threshold defined later  
\[\text{Localization error}=\|\mathbf{x}_{s}-\mathbf{x}_{r}\|_{2}. \tag{26}\]  
The average contrast evaluation metric is based on the mean value of the region of interest (i.e., inclusion volume):  
\[\text{Average contrast}=\frac{\sum_{i\in N_{r}}\mu_{i}}{|N_{r}|\hat{\mu}} \tag{27}\]  
where \(\mu_{i}\) denotes the reconstructed absorption at node \(i\), \(N_{r}\) represents the set of nodes at the region of interest, \(|N_{r}|\) denotes the number of nodes within the set and \(\hat{\mu}\) is the truth values of absorption at the region of interest. If the reconstructed absorption is identical to the truth, the average contrast value is one.  
The last evaluation metric is the relative reconstructed volume (\(V_{RRV}\)) which is defined as  
\[V_{RRV}=V_{r}/V_{s}\times 100\% \tag{28}\]  
Figure 11: Reconstruction algorithm flowchart.  
where \(V_{r}\) and \(V_{s}\) are the volume of the reconstructed inclusion and simulated case respectively. The volume of the reconstructed inclusion, \(V_{r}\), is computed by thresholding the recovered absorption changes and computing the volume of those elements. If the reconstructed absorption is identical to the truth, the relative reconstructed volume is one.