esa-sceva/satcom-chunk-collection huggingface dataset:
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 3 Proposed AVSR Method Based on DCM Attention
### Training and Decoding with a Hybrid CTC/Attention Architecture  
Similar to [19], a CTC loss is combined with an objective for our attention-based model to force a monotonic alignment required for speech recognition during training. The resulting loss function is a weighted sum of the CTC and attention objectives, which is computed as follows:  
\[\mathcal{L}=\text{a}\text{log }p_{\text{ctc}}(\mathbf{y}\mathbf{x})+(1-\alpha) \text{log }p_{\text{att}}(\mathbf{y}\mathbf{x}), \tag{18}\]  
where \(\mathbf{y}=(y_{1},\cdots,y_{I})\), \(\mathbf{x}\), and \(\alpha\) denote a decoded output character sequence, input feature sequences from both modalities, and a relative weight for the loss function, respectively. A decoded output  
Figure 4: Decoder structure. Similar to the transformer sequence-to-sequence (TM-seq2 seq) model, the transformer with DCM attention (TM-DCM) model receives the audio query encoder output (AV) and video query encoder output (VA) and returns character probabilities. Context vectors (AVc and VAc) computed by multi-head encoder–decoder attentions are concatenated channel-wise and fed to feed-forward layers.  
character may include an extra end-of-sentence label. In the training phase, a ground truth character sequence is used as a target label, and the detailed method is shown in Algorithm 1.  
To calculate the CTC loss, we need to fuse the audio and video encoder results as shown in Figure 5. Therefore, the video encoder output is upsampled to have a sequence with the same length as the audio encoder output. After that, the two encoder outputs are concatenated channel-wise and fed to a feed-forward layer.  
```
\(y_{\mathrm{0}},\hat{y}_{\mathrm{0}}\leftarrow\{<\text{sos}>\}\) \(\mathbf{y}\leftarrow\emptyset\) \(\mathbf{\hat{y}}\leftarrow\text{ground truth sentence}\) whilethe termination condition is not metdo for\(i=1\ldots I\)do \(\mathbf{y}\gets model(\mathbf{x},\hat{y}_{i-1})\)  end for \(\mathbf{y}\leftarrow\{<\text{eos}>\}\) \(loss\leftarrow\text{alog}\ p_{\text{ctc}}(\mathbf{y}\mathbf{x})+(1-\alpha) \text{log}\ p_{\text{att}}(\mathbf{y}\mathbf{x})\)  update model with regard to loss  end for
```  
**Algorithm 1**Hybrid CTC/attention training  
In the decoding phase, we use a joint CTC/attention approach. We calculate a joint score based on CTC and attention decoder probabilities for decoded output character sequences. A hypothesis character output sequence \(\mathbf{\hat{y}}\) is computed as  
\[\mathbf{\hat{y}}=\operatorname*{arg\,max}_{\mathbf{y}}\{\lambda\text{log}\ p_{ \text{ctc}}(\mathbf{y}\mathbf{x})+(1-\lambda)\text{log}\ p_{\text{att}}( \mathbf{y}\mathbf{x})\}, \tag{19}\]  
where \(\lambda\) is a relative weight in the decoding phase. \(\alpha\) and \(\lambda\) are respectively set to 0.2 and 0.1 that are the same as in [19].  
Figure 5: Procedure to compute the loss function for the TM-DCM model. The TM-DCM model is trained by using the hybrid CTC/attention architecture to force monotonic alignments required for speech recognition.
# A Real-Time Data Delivery for Mobile Sinks Group on Mobile Cyber-Physical Systems
## 1 Introduction
However, flooding-based dissemination has still a problem regarding application into the spatiotemporal approach for real-time data. The data flooding could not define the final destination for a mobile sink group. Without the final destination, the source node could not calculate the delivery distance and the required delivery speed. Furthermore, each node on the delivery path is not able to calculate the progress to the destination via its neighbor node. Moreover, the passive data dissemination also could not define the end-to-end distance and the progress. VTS [12] exploits a virtual tube storage to deliver data to mobile sink group. As this scheme requires the process of storing data and acquiring data through queries in the sink, it is difficult to achieve real-time data transmission. To overcome the problem, we propose a real-time data delivery mechanism for supporting a mobile sink group. First, the proposed scheme calculates the movable area of mobile sink group based on the virtual grid structure. Based on the structure, the maximum (farthest) distance for the real-time communication is calculated to find the minimum speed that should be satisfied in the process of data transmission. Finally, the proposed scheme could transfer data to all sinks in the group in a valid time by performing main forwarding and branch forwarding process which along nodes that meet the previously calculated minimum speed. The simulation results verify that the proposed mechanism achieves better performance than the existing ones to support real-time communication for mobile sink groups. The remainder of this paper is organized as follows. In Section 2, we explain the real-time data delivery for mobile sink groups. The performance evaluation results are provided in Section 3. Finally, the proposed scheme and simulation results are summarized in Section 4.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Renewable Energy Sources
#### 4.6.1 Eco-Life  
The \"ECO-LIFE\" project, which involved participants from four countries, dealt with renewable energy production in houses and addresses the issue of balancing energy demand and supply at all times. For this purpose, low energy houses have been built and renewable energy sources have been inserted [112].
###### Abstract  
A narrow coal pillar in double-roadway excavation can solve the problem of working face connection and improve the resource recovery rate, but narrow coal pillars are affected by the full mining stress. Taking the 2109 double-roadway excavation of Qingwa Coal Mine as the engineering background, the roof mechanical structure model of a narrow coal pillar in a double-roadway excavation layout was established, and the bearing characteristics of different coal pillar widths under the influence of full dynamic pressure were studied. The narrow coal pillar retention width was obtained and tested through field industrial experiments. The main research results were as follows: (1) The relationship between the coal pillar bearing load and the immediate roof length was deduced, and the bearing stress of the coal pillar was divided into the steep decline stage, the transition stage, and the stabilization stage. The coal pillar within the width of the stabilization stage has a certain strength surplus capacity. (2) Under the influence of full dynamic pressure, the 5-7 m coal pillar yielded to failure, and the coal pillar of 8 m and above had a certain residual bearing capacity, compared with the first mining. After the second mining, the elastic zone in the coal pillar of each width was significantly reduced; there was no elastic grid in the coal pillar of 5 m and 6 m in width, and the grid area and proportion of the elastic zone of the coal pillars with widths of 7 m and above were very low. The optimal retention width of the narrow coal pillar was determined to be 8 m. (3) Under the influence of repeated mining, the impact of first mining on the roadway displacement of the roof and floor plate was greater, followed by the solid coal side, which had less impact on the coal pillar side. The secondary mining had a greater impact on the floor, followed by the coal pillar side and the solid coal side, which had little impact on the roadway roof. This paper also provides a significant reference for the retention of narrow coal pillars in double-roadway excavation.  
State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology,  
Xuzhou 21116, China  
Correspondence: [EMAIL_ADDRESS] (H.Z.); [EMAIL_ADDRESS] (D.X.)  
double-roadway excavation; small coal pillar; load-bearing characteristics; numerical simulation
# Characterization of a Modified Clinical Linear Accelerator for Ultra-High Dose Rate Beam Delivery
## 2 Materials and Methods
### Pulse Characterization with Silicon Sensors  
The temporal structure and the consistency of the output pulses after the LINAC modification were measured with a silicon sensor previously tested for response linearity up to doses of 10 Gy/pulse at the SIT ElectronFlash accelerator (9 MeV UHDR electron beam) at Centro Pisano FLASH Radiotherapy (CPFR, Pisa) [17]. The sensor was manufactured by the Fondazione Bruno Kessler (FBK) and consists of an epitaxial substrate grown on a low-resistivity silicon layer. This device is a square with a 4.5 mm edge, an active thickness of 45 \(\upmu\)m and a support layer of 570 \(\upmu\)m. It is segmented in eight pads with different active areas (0.03 mm\({}^{2}\)-2.3 mm\({}^{2}\)) isolated by guard rings. For the measurements, the device was mounted on a high voltage (HV) distribution PCB (Figure 2a) designed to polarize the sensor from the back via a copper layer with a window behind the sensor so that only a 2 mm thick layer of FR4 resin is behind the sensor. Bonding wires (Al 1% Si) with a diameter of 25 \(\upmu\)m, allowing a maximum rated current of 500 mA, were used to connect the 2 mm\({}^{2}\) pad to an output channel of the board and to ground the guard ring.  
Figure 2: (**a**) PCB with a bonded sensor in the center. (**b**) Schematic of the setup with the silicon sensor at the crosshair foil position (iii).  
The multipad sensor was reverse biased at \(-200\) V and the stability of the HV supply was monitored during the acquisitions to guarantee the stability of the applied voltage. The sensor was manually positioned at the center of the field at the crosshair foil position (iii) with an uncertainty of 5 mm in the transverse plane.  
The output channel was connected via a coaxial cable to a Keysight Infiniium S-Series oscilloscope (2.5 GHz, 20 GSa/s, Model: DSOS254A) to read and store the output pulse signals delivered by the LINAC (Figure 2b). The produced charge per pulse was determined by dividing the signal area by the input impedance of the oscilloscope (50 \(\Omega\)). Signal processing involved only the voltage offset removal from the signal calculated as the average of the first 10,000 off-pulse samples in each collected waveform.  
The stability and reproducibility of the delivered pulses were then studied with repeated measurements of the charge per pulse generated under consecutive pulses and on different days.
## 4 Experimental Results
### Desulfurization Tests

Table 2: Sulfur capacity of the Cu-based desulfurization sorbents according to the CuO content. Figure 9: COS breakthrough curves by the desulfurization tests over (**a**) powder type and (**b**) pellet type of 15 wt % Cu-based desulfurization sorbents; CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) and CuO/macro-porous Al\({}_{2}\)O\({}_{3}\). to the dissociation reaction of COS that keeps occurring after the breakthrough curve. The generation of CO by the dissociation reaction of COS means that the Cu-based absorbent is not simply an absorbent but potentially has catalytic activity to produce CO from COS(g). DFT calculations of the desulfurization kinetics for COS on a CuO surface were performed to unveil the origin of CO production and the desulfurization mechanism.
# First-Principles Study of the Contact Resistance at 2D Metal/2D Semiconductor Heterojunctions
###### Abstract  
The high contact resistance at metal/two-dimensional (2D) semiconductor junctions is a major issue for the integration of 2D materials in nanoelectronic devices. We review here recent theoretical results on the contact resistance at lateral heterojunctions between graphene or 1T-MoS\({}_{2}\) with 2H-MoS\({}_{2}\) monolayers. The transport properties at these junctions are computed using density functional theory and the non-equilibrium Green's function method. The contact resistance is found to strongly depend on the edge contact symmetry/termination at graphene/2H-MoS\({}_{2}\) contacts, varying between about \(2\times 10^{2}\) and \(2\times 10^{4}\)\(\Omega\cdot\upmu\)m. This large variation is correlated to the presence or absence of dangling bond defects and/or polar bonds at the interface. On the other hand, the large computed contact resistance at pristine 1T/2H-MoS\({}_{2}\) junctions, in the range of 3-4 \(\times\) 10\({}^{4}\)\(\Omega\cdot\upmu\)m, is related to the large electron energy barrier (about 0.8 eV) at the interface. The functionalization of the metallic 1T-MoS\({}_{2}\) contact by various adsorbates is predicted to decrease the contact resistance by about two orders of magnitude, being very promising for device applications.  
2D materials; density functional theory; non-equilibrium Green's function method; electronic transport simulations; contact resistance +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Optimal Configurations Analysis Using Clustering  
The achieved optimal design variables' configurations are subsequently analyzed to identify if it is possible to establish groups of optimal configurations with similar types of characteristics and mechanical responses and how they will be constituted. To this purpose, an unsupervised machine learning method, the K-means method, is used [38; 39]. K-means partitions a dataset into a number of non-overlapping clusters, assigning each data point to a certain cluster. In the present study, we have used the Silhouette method which checks the consistency of clusters' fit. The procedure dataflow of this method is schematically represented in Algorithm 2, where \(d(i,j)\) is the Euclidean distance between two points and \(\mu\) and \(\sigma^{2}\) stand for a feature mean value and variance.  
```
Input: Optimal solutions dataset Output: Silhouette scores Center and Scale Design Variables to \(\mu=0\) and \(\sigma^{2}=1\) For each number of clusters For each point in cluster \(k\) # Compute Intra-cluster distance \(a(i)\) \(a(i)\leftarrow\frac{1}{N_{k}-1}\sum_{j\in C_{k},j\
eq i}d(i,j)\) # Compute Inter-cluster distance \(b(i)\) \(b(i)\leftarrow\min\limits_{C_{m}\
eq C_{k}}\Bigl{(}\frac{1}{|C_{m}|}\sum_{j\in C _{m}}d(i,j)\Bigr{)}\) # Compute point Silhouette score \(s(i)\leftarrow\frac{b(i)-a(i)}{max\{a(i)\}\mathcal{B}(i)\}\)  Compute average Silhouette score within cluster \(k\) (\(C_{k}\))  Compute global average Silhouette score End For End For
```  
**Algorithm 2** Silhouette method. Schematic representation.  
The Silhouette score, which varies between \(-1\) and \(+1\), allows us to verify the points' cohesion within their cluster and how distinctly separated the clusters are from each other. The robustness of the clustering structure increases as the score approximates \(+1\). The K-means method is schematically represented in Algorithm 3.  
The initial calculation of the centroids was performed using K-means++ initialization to have better initial centroids and subsequent convergence. In the present work, the datasets for each case study are matrices constituted by a number of lines equal to the number of runs (10) and with a number of features (columns) corresponding to the number of design variables (\(b1\), \(b2\), \(b3\)). Each line contains the values each design variable assumes for the best solution found in each optimization run.  
```
Input: Optimal solutions dataset Output Clusters, Centroids Center and Scale Design Variables to \(\mu=0\) and \(\sigma^{2}=1\) Select \(K\) Clusters and Initialize their Centroids While not converged do  For each point do  Compute Euclidean Distance from Point to Clusters' Centroids  Assign Point i to Cluster with Nearest Centroid End do  For each cluster do # Update Centroids \(\mu_{k}\leftarrow\frac{1}{N_{k}}\sum_{i=1}^{N_{k}}x(i)\) # Compute the Euclidean Norm of the Centroids Shifts \(shift=\sum_{k=1}^{N_{k}}\|\mu_{k}^{t}-\mu_{k}^{t-1}\|\) End For # Check Convergence Based on Centroids Shifts \(converged=(shift<tolerance)\) End While
```  
**Algorithm 3** K-means method. Schematic representation.
## 5 Some Applications of Antiresonance Assignment through Active Control
### Antiresonance Assignment in Helicopters  
Antiresonance assignment is popular in the avionic field to absorb the vibrations induced by the rotor in helicopters; some of these works are reported in this Section.  
In [81] it is proposed an active control strategy to absorb the vibrations of the Agusta A129 helicopter. The strategy is based on a harmonic cancellation controller that rejects one or more disturbance harmonics by performing the active antiresonance assignment.  
In [82] the modeling and the simulation of a vibration absorber in helicopters is developed by exploiting bond graphs. The system is simulated exploiting the differential algebraic equations and the benefits of the antiresonance assignment are shown.  
In [83,84] an active suspension system is developed to enhance the performances of the dynamic antiresonant vibration absorber traditionally employed in helicopters [85]. In particular, the main advantage of the passive absorber combined with an active part is the power reduction. Furthermore, the authors studied the effectiveness in the bandwidth of \(\pm 10\%\) of the assigned antiresonance.  
In [86] the authors showed that open-loop antiresonance frequencies determine the optimal condition to tune the parameters for the closed-loop controllers, to suppress the transonic buffet, i.e., the aerodynamic instability phenomena which occurs at certain flight parameters.
## 5 Simulation Validation
### Covariance Matrices  
The covariance matrices \(\mathbf{Q},\mathbf{R}\), need to be adjusted accordingly for different situations. Since the \(\mathbf{Q}\) represents the internal property of the dynamic model, it has little relationship with the measurement. Thus, \(\mathbf{Q}\) and \(\mathbf{R}\) can be tuned separately.  
For \(\mathbf{Q}\), when there is no gas mass uncertainties, it is tuned as,  
\[\mathbf{Q}=\begin{bmatrix}0.01^{2}&0&0\\ 0&0.01^{2}&0\\ 0&0&0.01^{2}\end{bmatrix} \tag{25}\]  
If the volumetric efficiency is inaccurate, then it will affect the correlation between the \(\Phi_{int}(k)\) and \(\Phi_{cyl}(k)\), as shown in Equation (14). Taking the error of the volumetric efficiency (i.e.0.1) into Equation (14), \(\mathbf{Q}\) is finally tuned as,  
\[\mathbf{Q}=\begin{bmatrix}0.02^{2}&0&0.01^{2}\\ 0&0.01^{2}&0\\ 0.01^{2}&0&0.02^{2}\end{bmatrix} \tag{26}\]  
For \(\mathbf{R}\), assuming physical sensors are independent, if the virtual measurement is not applied, it is tuned as,  
\[\mathbf{R}=\begin{bmatrix}0.01^{2}&0\\ 0&0.01^{2}\end{bmatrix} \tag{27}\]  
If the virtual measurement is added on the system, since the virtual measurement is obtained from the virtual sensor (the inverse of the ignition delay model, Equation (17)), it is reasonable to assume it is independent on the physical oxygen sensors. For the virtual sensor with a error of 10% drift, the \(\mathbf{R}\) turns to be,  
\[\mathbf{R}=\begin{bmatrix}0.01^{2}&0&0\\ 0&0.01^{2}&0\\ 0&0&0.1^{2}\end{bmatrix} \tag{28}\]  
Applying different \(\mathbf{Q}\) and \(\mathbf{R}\), different simulation scenarios can be implemented as follows.
## 5 Case Study: Experimental Results
### User Clustering
#### 5.2.2 Clustering Coefficient  
In order to demonstrate that user-topic modeling can be used in clustering users on user replying networks, we evaluate the proposed method, which assigns users to clusters according to their dominant topics, and compare it with other baseline methods. We used two baseline methods: (i) spectral clustering and (ii) random assignment. Spectral clustering is a popular clustering method for graph data. Using the adjacency matrix of the input graph, it decomposes the vertices into several groups to separate them as much as possible. Random assignment is used for comparison, to show how well both the proposed method and spectral clustering performed the clustering task. As evaluation measure we used the modularity on the user interaction graph, where edges having a weight less than \(\theta\) were removed. The modularity of cluster \(C=\{C_{i}\subset V(G)\}\) on graph \(G\) is a measure of the probability of two vertices within the same cluster having an edge between them, compared to random chance, and is defined as follows:  
\[\texttt{modularity}(G,C)=\frac{1}{|E(G)|}\sum_{j=1}^{k}(\Psi_{C}(j)-\Delta_{C} (j)^{2}) \tag{8}\]  
where \(\Psi_{C}(j)\) is the number of vertex pairs \((v,w)\in C_{j}\times C_{j}\) such that \((v,w)\in E(G)\), and \(\Delta_{C}(j)\) is the sum of the degrees of vertices \(v\in C_{j}\).  
Table 6 shows the modularity obtained using the above three methods. For a small number of topics the user-topic method tends to outperform the spectral clustering method. If we choose a higher \"threshold\" \(\theta\), which makes the interaction graph more sparse, and each edge likely to represent more intense interaction, the difference in clustering quality becomes clearer, especially for a small number of topics.  
Performing better for a small number of topics is an advantage in many qualitative assessments from which human analysts use the results. Clearly, interpreting results for more than 100 topics would be difficult compared to dealing with just 10 topics. We point out that the spectral clustering method is specialized for improving clustering measures, while our method is not dedicated to this particular task. Nevertheless, the user-topic method performs better than spectral clustering under certain conditions.  
In addition, spectral clustering is not robust in some cases, while our method performs consistently. Spectral clustering sometimes fails, especially when the user interaction graph has very small clusters that are weakly connected to the remainder of the graph, resulting in a giant component and isolated small vertex groups after clustering.