esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
Descriptive statistics of the ANN were conducted to provide insights into the average sample size percentages of the training and testing modules and the average number of units within the hidden layer. The root mean squared error (RMSE) and relative root mean squared error (rRMSE) for each ANN were calculated and used to assess the accuracy of each model in the training and testing modules. Pearson's correlation coefficient (\(r\)) was used to measure the agreement between ANN estimated output variables, categorized as weak (\(r\leq\) 0.35), moderate (0.35 \(<r\leq\) 0.67), strong (0.67 \(<r\leq\) 0.90), and excellent (\(\geq\)0.90) [6]. Sensitivity analysis of the input variables was performed to determine the individual importance of the predictors in determining the neural network and to identify the most important input variable (MIIV). All statistical analyses were performed using SPSS software (IBM Corporation, Armonk, NY, USA).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Segment** & **Plane of Motion** & **Type of Velocity** & **Foot Stance Subphases** \\ \hline \multirow{4}{*}{Thorax} & Vertical & Linear & Impact \& Propulsion \\ \cline{2-4}  & Horizontal & Linear & Impact \\ \cline{2-4}  & Lateral & Linear & Propulsion \\ \hline \multirow{4}{*}{Pelvis} & Horizontal & Linear & Impact \& Propulsion \\ \cline{2-4}  & Internal/External Rotation & Angular & Impact \\ \cline{2-4}  & Flexion/Extension, & & \\ \cline{2-4}  & Adduction/Abduction \& Internal/External Rotation & Angular & Propulsion \\ \cline{2-4}  & Lateral & Linear & Propulsion \\ \hline \multirow{4}{*}{Thigh} & Vertical & Linear & Impact \& Propulsion \\ \cline{2-4}  & Internal/External Rotation & Angular & Impact \\ \cline{2-4}  & Flexion/Extension \& & \\ \cline{2-4}  & Internal/External Rotation & Angular & Propulsion \\ \hline \multirow{2}{*}{Shank} & Adduction/Abduction & Angular & Impact \& Propulsion \\ \cline{2-4}  & Internal/External Rotation & Angular & Propulsion \\ \hline \multirow{4}{*}{Foot} & Adduction/Abduction & Angular & Impact \\ \cline{2-4}  & Internal/External Rotation & Angular & Impact \& Propulsion \\ \cline{1-1} \cline{2-4}  & Horizontal \& Lateral & Linear & Propulsion \\ \hline \hline \end{tabular}
\end{table}
Table 2: Peak segment velocities selected as output variables in the ANNs.
# Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database +
The primary system, which reached the second position in the competition, was trained with the Kaldi toolkit and included a hybrid CNN-TDNN-F acoustic model, whilst the third contrastive system was based on a lighter Quartznet acoustic model [6]. The comparison presented in this work demonstrated not only an evolution and improvement of similar architectures, such as the DNN-HMM or Quartznet systems, but also the inclusion of a new architecture based on self-supervised learning methods evaluated over the same evaluation set. All the systems were built by using the same speech labelled data to train and/or fine-tune the corresponding acoustic models. Likewise, we used the same text data to construct the different language models to perform the decoding and rescoring processes of the initial hypothesis. The remainder of this paper is organised as follows: Section 2 introduces recent advances in speech recognition technology. Section 3 describes the corpora used to train and evaluate the systems, whilst in Section 4 we describe the different speech transcription systems, including the three novel architectures and the two systems previously submitted to the challenge. Section 5 presents the results of the systems on the same evaluation set of the RTVE2020 database, in addition to the number of resources and processing time neededper system to process the whole test set. Finally, Section 6 draws the main conclusions and the lines of future work.
## 2 Related Works
Furthermore, it only chooses the least temperature route which may not be the least delay path or reliable path. QoS-aware routing protocols in the context of WBAN have not gained much attention so far. A QoS-aware routing framework for biomedical sensor network has been proposed by [PERSON] _et al_. [11] that provides prioritized routing service and user specific QoS support exploiting cross-layer functionalities. This work considers user specific QoS requirements, wireless channel status, packet priority level, and sensor node's willingness to be a router in route selection. ZEQoS [12] presents an integrated energy and QoS-aware routing protocol following modular approach. It provides service differentiation through classifying traffic into three categories, and determines an end-to-end route that ensures the satisfying of QoS parameters based on respective traffic type. Exploiting the geographic location, DMQoS [13] has been proposed for biomedical wireless sensor networks. This protocol also provides differentiated service for four classes of traffic, and focuses on integrating energy-efficiency with QoS provisioning. All the existing QoS-aware protocols are mainly designed for inter-BAN communication, and ignore the thermal effects that could cause in the context of intra-BAN routing. Lately, [PERSON] _et al_. [14] proposed a thermal-aware multi-constrained intra-body QoS-routing for WBAN that integrates the QoS provisioning issue with thermal-awareness in route selection for _in-vivo_ sensor nodes. However, TMQoS is not well-suited for highly dynamic environment due to its dependency on global network information as we discussed in the previous section. The lack of an efficient routing protocol integrating thermal-awareness with QoS provisioning for diverse traffic types thus motivates us to develop a localized routing solution for WBAN with _in-vivo_ nodes that in one hand will address the highly dynamic nature of intra-BAN environment, and in other hand, will provide QoS-provisioning for heterogeneous traffic, minimizing the temperature rise along the network, also avoiding hotspot(s).
## 5 Experimental Setup
### Training Setup  
We train the same architecture with several window sizes, in order to test the effect of temporal context size. The sizes included in the current experiment are 10/10, 20/20, 40/40, 10/20, 10/40, 20/40, 10/640/, 20/640, and 40/640, where \(a/b\) denotes a compressed window size of \(a\) time steps (see Section 4.3), and an expanded size of \(b\) time steps, with a hop time of 0.01 s between timesteps (see Section 4.1). Thus, the compressed window sizes range from 0.1 s to 0.4 s, and the expanded window sizes range from 0.2 s to 6.4 s. All of the models use encoders/decoders consisting of 5 stacked dilated residual blocks (Section 4.4.1), each with 128 feature maps. The dilation of the convolutions is chosen such that the receptive field of the highest block is at least as large as the input both in the time and frequency dimension. The neighborhood size for selecting pairs of instances during training (see Section 4.4.2) is set to 5 s. All of the datasets are split into a training set and validation set according to the proportion 90%/10%. Although we use the latter set for reporting results we do not refer to it as a test set, because it is used for early stopping the training process (see below).  
Each model is trained by stochastic optimization of \(\mathcal{L}_{train}\) (Equation (9)), while using the Adam method [68] with a batch size of 20 and a learning rate of 0.0001. For the pop/rock dataset, we use on-the-fly random mixing as a form of data augmentation. This means that, for each data instance, the stems of the mix are combined into a mono mix with random mixing coefficients between 0.5 and 1.0, before computing the CQT on the normalized mono mix. After training on a mini epoch of 1000 instances, the validation loss is computed. Training is terminated when the validation loss has not improved for more than 1000 mini epochs for the artificial datasets and 3000 mini epochs for the pop/rock dataset.
## 2 Suggested Topology and Its Operation  
Figure 2 presents the schematic of the developed topology which is structurally quite similar to the one presented by the same authors in [22]. However, the proposed topology is being used for both low and high-frequency outputs here. It is also being employed for the output voltage regulation by using various voltage control schemes. The unwanted components or fluctuation in the supplied voltage are tackled with the help of input capacitor \"\(C_{in}\).\" The role of the inductor \"\(L\)\" at the intermediate stage is to tackle the possible shoot-through challenge once controlled switching units operating in complementary mode remain due to practical constraints. Six MOSFETs are selected as switching units to have the advantage of their high switching characteristics. The series-connected diodes with the controlled switching units \"\(Q_{1}\)\" to \"\(Q_{4}\)\" can tackle the reverse recovery issue of their body diodes by blocking their currents. These diodes also help  
Figure 1: Compact power converter reported in [24].  
to simplify the switching mechanism and facilitates to increase boost factor in output frequency boost operation. There is no such issue for the controlled switching units \"Q5\" and \"Q6\" connected to the output side. So, there is no need to employ series diodes. The diodes \"_D5\"_ and \"_D6\"_ connected toward the input side, detect the polarity of the input voltage and becomes forward biased during the positive and negative input voltage respectively. The detail about the governance of the issue of shoot-through is discussed in the next section.
## 3 Test Results and Analysis
### Unconfined Compressive Strength  
The UCS test revealed significant variations in failure forms of soil samples with different strengthening methods, as depicted in Figure 11. The group JR exhibited a typical tensile failure, with cracks running through the whole sample column from top to bottom, similar to the uniaxial compression failure mode of brittle materials [32]. In group JK, the soil bottom was partially broken, and an obvious weak strengthening area was observed. Groups JRY and JKY showed multiple fracture surfaces, indicating the presence of several weak points in the test piece. Group JPY resulted in an overall fracture, with the internal soil not consolidated together, except for the massive exfoliated soil on the surface of the test piece.  
The results presented in Figure 12 indicate that the UCS of group JR reached a maximum of 1.663 MPa, which is 513.65% higher than the uncured group (0.271 MPa). On the other hand, group JRY only achieved a strength of 0.484 MPa, which is significantly lower than group JR, which underwent multiple injections of bacteria, confirming the shear test phenomenon. The air compressor grouting mode showed the second-best strengthening effect, while the JK group with multiple grouting also demonstrated a better effect, with a strength increase of 331.37%. Despite multiple attempts at grouting, the strengthening effect of the bottom soil has been poor due to the sinking of fine particles caused by air pressure. This destroyed the bottom soil sample before the upper soil reached the ultimate  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Curing Method** & **Test Group** & **Cohesion (kPa)** & **Internal Friction Angle (\({}^{\circ}\))** \\ \hline / & Uncured & 42.11 & 26.84 \\ \hline \multirow{2}{*}{Peristaltic pump grouting} & JR & 167.59 & 54.15 \\  & JRY & 108.1 & 37.38 \\ \hline \multirow{2}{*}{Air compressor grouting} & JK & 136.95 & 51.32 \\  & JKY & 101.6 & 33.38 \\ \hline \multirow{2}{*}{Soaking method} & JPY & 93.7 & 30.71 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cohesion and internal friction angle.  
Figure 10: Relationship between shear strength and vertical stress.  
bearing capacity. The strengthening of group JPY only affected the surface soil, resulting in a weak UCS of only 0.351 MPa, with the lowest increase.
## 3 The Clustering Protocol
### The Dijkstra Algorithm  
The Dijkstra algorithm is a routing algorithm proposed by the Dutch computer scientist [PERSON] in 1959 to solve the shortest path optimization problem between the source point and the remaining points in the directed graph. The algorithm takes a greedy strategy, and its main steps are as follows:  
(1) Construction of an initial weight matrix  
(2) Updating the weight  
Suppose there are \(n\) vertices in the region, and the set of vertices is \(T=\{1,2,\ldots,n\}\). In Step 1, we have \(n^{2}\) initial weights, and a two-dimensional matrix \(W\) is used to store the \(n^{2}\) initial weights, where \(W\)(_i,j_) represents the weight of point \(i\) to point \(j\). It is only if point \(i\) and point \(j\) can directly convey information to one another that \(W\)(_i,j_) = \(W\)(_j,i_). Otherwise, only point \(i\) can transmit information directly to point \(j\), and point \(j\) cannot directly transmit information to point \(i\), so \(W\)(_i,j_) is a finite positive number, and \(W\)(_j,i_) has a value of positive infinity. For \(i=1,2,\ldots,n\), \(W\)(_i,j_) = 0.  
In Step 2, we first determine the source point \(s\), then establish the vertex set \(P\) of the known shortest paths and the vertex set \(Q\) of the unknown shortest paths. Now \(P=\{s\}\), and \(Q=T-P\). Here, we declare an array _dis_ to store the initial weight of the source point \(s\) to the remaining points. The representation of _dis_ is shown in Figure 9.  
Then, we can find the vertex \(x\) with the smallest weight from itself to s from \(Q\), then remove \(x\) from \(Q\), add it to \(P\), and compare whether the sum of the weights from point \(s\) to point \(x\) to one of the other points \(i\) is smaller than the weight of point \(s\) to point \(i\) directly. If yes, then \(W(s,i)=W(s,x)+W(x,i)\), and we replace the corresponding value \(W(s,i)\) in _dis_. Then, we can find the vertex \(y\) with the smallest weight from itself to \(s\) from \(Q\), remove \(y\) from \(Q\), and add it to \(P\), repeating the previous steps until \(P\) contains all the vertexes in the graph.  
The detail procedure of the Dijkstra algorithm is given by the pseudocode in Algorithm 1.  
```
1:\((1)\)\(n^{2}\) weights
2:\((2)\) vertex set, \(T\)
3:an array of weights from source point to remaining points, _dis_
4:for \(i=1\to n\)
5:for \(j=1\to n\)
6:\(W(i,j)=\) weight from Point \(i\) to Point \(j\);
7:end for
8:end for
9:determine the source point in the region, \(s\);
10:\(P=\{s\}\), \(Q=T-P\);
11:\(dis=[W(s,1)\), \(W(s,2)\),..., \(W(s,\,n)]\);
12:while (\(Q\) -= \(\emptyset\))
13: find the vertex \(x\) with the smallest weight from the vertex \(s\) from \(Q\);
14:\(P=P+\{x\}\), \(Q=Q\) - \(\{x\}\);
15:for \(i=1\to n\)
16:if \(i\) is in \(Q\)
17:if \(W(s,i)>W(s,x)+W(x,i)\)
18:\(W(s,i)=W(s,x)+W(x,i)\);
19: update the corresponding value, \(W(s,i)\) in _dis_;
20:endif
21:end if
22:end for
23:end while
```  
**Algorithm 1** Dijkstra Algorithm
## 2 Taxonomy and Design Drivers
### Range versus Point Queries  
Range query is another challenge for DCS schemes. For example, a user may be interested in a range rather than a specific point value. For example, air pollution may occur if the level of carbon monoxide is in the range of 30 L/mol to 90 L/mol. A possible query is to find all of the sensing points where the level of carbon monoxide falls into the possible air pollution range. With range queries, users can drill down to improve their search efficiency for the events of interest. The example query presented above illustrates this and may be used by an environmental scientist carrying out a study in a particular forest to identify if carbon monoxide levels cause air pollution and perhaps also to map the results across the region under study with other parameters to identify causal links or to draw a conclusion [13].
## 2 Methodology
### Experimental and Diagnostic Approach
#### 2.1.1 Probe Diagnostics  
For the determination of specific plasma quantities including \(\phi_{\text{plasma}}\), \(\phi_{\text{float}}\), electron number density \(n_{e}\) and temperature \(T_{e}\), a combination of Langmuir and emissive probe diagnostics was employed. Langmuir probes are used for the determination of \(n_{e}\) from the ion part of the voltage current characteristics (VCCs), and the floating potential is determined from zero-current condition. The plasma potential is determined as a floating potential of the emissive probe at the limit of high emission [5]. The electron temperature \(T_{e}\) is estimated from the difference between the measured floating and plasma potentials \(T_{e}\approx(V_{\text{plasma}}-V_{\text{float}})/5.28\).  
In the expected range of \(n_{e}\approx 1\times 10^{12}\) to \(1\times 10^{15}\,\text{m}^{-3}\) at \(T_{e}\approx 2\) to 10 eV, the Debye length is located in the range of \(\lambda_{D}\approx 0.3\) to 3 mm. The ratio of \(\lambda_{D}\) to the Langmuir probe diameter \(\lambda_{D}/R\geq 3\), and thus, the orbital motion limited (OML) theory of the ion current applies to the probe according to [4]. For cylindrical probes, this theory assumes that \(I_{\text{probe}}\propto\sqrt{\phi_{\text{probe}}}\) and \(I_{\text{probe}}\
ot\propto T_{e}\). The squared values of the ion current should yield a straight line when plotted against the probe potential. The slope \(S\) of such a line is given by  
\[S=\frac{2A^{2}n_{e}^{2}e^{3}}{m_{\text{Xe}^{+}}}\,, \tag{1}\]  
where \(A\), \(e\) and \(m_{\text{Xe}^{+}}\) denote the probe area, elementary charge and ion mass, respectively. From the experimentally measured slope, \(n_{e}\) can be determined, and the applicability of the OML theory evaluated upon the appearance of the squared ion current values is a straight line. An example of such an evaluation is given in Figure 4. Based on the obtained data, it  
Figure 3: Schematic representation of the electrical connections and circuitry used in the experiment. Only two of the four installed probes are indicated. The emissive probe (E) is heated by the isolated DC power supply and connected to the SMU. The Langmuir probe (L) is directly connected to the SMU. The fixed thermionic cathode (TC) can be heated by isolated DC power supply and is connected to the SMU. All electrodes are capable of floating operation (no net electric current flowing to the plasma). The thruster is placed on the isolated platform (SSG), which can be connected to the facility ground GND either directly or via a [PERSON] multimeter. During the measurement, all probes are referenced to the SSG.  
is manifested that over a wide range of electron densities the squared ion current represents a straight line.
## 3 The Proposed Exact Scheme
### Service Requests  
Given that traditional network functions can be mapped to one VNF or VNFs chain, where those VNFs might be implemented with different virtualization technique types. As well, it is possible to compose several combinations of the service chains. In addition, the service request might contain further policy-based constraints, such as the life time of the service or affinity and anti-affinity constraints. Therefore, the service request is denoted by \(S\), which can be expressed as in Equation (6) below:  
\[S=\{r_{id},s_{dc},\Psi\}, \tag{6}\]  
where \(r_{id}\) is the service request identification, \(s_{dc}\) is the set of possible service decompositions, \(s_{dc}=(dc_{1},dc_{2},\ldots,dc_{x})\), \(x\in\mathbb{N}\), and \(\Psi\) is a set of policy constraints. In this work, we implemented one constraint, which is the life time of the service request denoted by \(\tau\).  
However, each service decomposition is a VNF-FG for a service chain. The service chain is modeled as a directed graph \(G_{s}=(F,E)\), where \(F\) is the set of virtualized network functions and \(E\) is the set of links between them. Each network function \(\{f|f\in F\}\) has a resource set denoted by \(\{R_{f}^{I,k}|k\in K\) and \(t\in T\}\), where \(K\) is a resource set and the function \(f\) is implemented by a virtualization technique type \(t\). Each virtual link between two functions \(i\) and \(j\) is denoted by \(e_{ij}\in E\) and \(i,j\in F\). The \(e_{ij}\) requires a bandwidth resource, which denoted by \(B_{e_{ij}}\) and it has a maximum allowed delay that denoted by \(D_{e_{ij}}\).  
The service chain may contain one end-to-end path or more. Each end-to-end path starts at a source function and ends at a destination one. End-to-end virtual path is denoted by \(p\) and the set of all end-to-end paths in the service chains is denoted by \(P_{e2e}\). A network function in \(p\) is denoted by \(\{f_{n}^{p}|n\in m\}\) and \(m\) is the number of network functions in \(p\). The virtualization technique type of the \(f_{n}^{p}\) is denoted by \(t_{f_{n}^{p}}\). All paths of the service is identified by an ordered set of types of functions in paths. Path identification was denoted by \(I_{p}=(t_{f_{n}^{p}},t_{f_{n}^{p}},\ldots,t_{f_{n}^{p}})\). \(I_{p}\) is a unique identification and it is possible to address more than one end-to-end virtual paths that have the same length and order of virtualization type.  
Figure 2: An example of building path identity and catalogue of physical paths.
## 4 Results
### SSC Mapping from GOCI and OSU  
By using the MCC method and OSU tidal current model, we can obtain seven SSC maps each day. Figures 11 and 12 show the SSCs in the Yellow Sea obtained by the GOCI inversion (blue arrows) and the OSU model (red arrows) on 5 August 2012. The current field vectors obtained by the OSU model are relatively regular. There are spurious vectors in the current field retrieved by the GOCI inversion. The blank areas in the current field are due to the change in cloud amount or tracer concentration. The average speed obtained by the GOCI inversion is 0.63 m/s, and that obtained by the OSU model is 0.65 m/s. Comparing the current field maps obtained by the two methods, we find that the vector velocity of the current field decreases in central the Yellow Sea, presenting the characteristics of a weak current region; the current direction presents the characteristics of a north-south reversing current [42]. The currents derived by the MCC algorithm are compared with those in [PERSON] et al. [43] and [PERSON] et al. [5]; the currents calculated by the OSU tidal model are compared with those in [PERSON] et al. [32] and [PERSON] et al. [44] The current field information is similar, and the current field is greatly affected by the counterclockwise tidal wave system in the Yellow Sea [45,46].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Buoy Number** & \begin{tabular}{c} **Number of** \\ **Sites** \\ \end{tabular} & \
\begin{tabular}{c} **BUOY-ACS** \\ **(m/s)** \\ \end{tabular} &
\begin{tabular}{c} **OSU-ACS** \\ **(m/s)** \\ \end{tabular} & **AAE (\({}^{\circ}\))** \\ \hline
1132711 & 1759 & 0.43 & 0.41 & 44.16 \\
1131901 & 1787 & 0.28 & 0.34 & 49.90 \\
1227890 & 320 & 0.45 & 0.38 & 37.82 \\ Average & 1289 & 0.39 & 0.38 & 43.96 \\ \hline \hline \end{tabular} Note: AAE: average angular error; ACV: average current speed.  
\end{table}
Table 2: Comparison of OSU mode calculation results and actual measured values.  
Figure 10: (**a**) Comparison results of partial continuous measured data and OSU model data in this study. (**b**) Comparison of buoy vectors and OSU vectors for some case data during the GOCI active period, which is 8:30–15:30 local time.  
Figure 11: GOCI-derived SSC field in the Yellow Sea at seven intervals on 5 August 2012.