esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Optimization
### Weight Clustering  
Weight clustering or weight sharing is an optimization technique that consists of determining a representative value for a cluster or group of weights, thus decreasing the  
Figure 3: Dynamic range quantization.  
Figure 2: FP32, FP16, FP8, and INT8 representations.  
number of different values to be stored in the network. It means that many different neurons in a network share the same weight [48].  
Figure 4 illustrates a basic weight clustering example with sixteen weights being clustered in four groups represented by a color. In this case, four reference values correspond to the mean of the values of each cluster, and each weight is quantized to one of the reference values mentioned above. The green cluster, for example, has five elements with distinct values. The mean of these elements is 0.15, which is the reference value for this cluster. After applying weight sharing, all elements represented in green now have 0.15 as the weight value. For this specific cluster, the distinct parameters are replaced with only one FP32 value and the reference index, which is an integer. Considering all clusters in the example, instead of having sixteen different FP32 values, the network after weight clustering replaces these unique values with four FP32 values, thus optimizing the memory storage.  
The number of clusters chosen affects directly the performance and the memory requirements of a model. An important parameter to be considered is the centroid initialization. The strategy of weight clustering has been assessed for deep learning problems in recent works [49; 50; 51; 52].
# Cooperative Admission Control with Network Coding in 5G Underlying D2D-Satellite Communication
## 1 Introduction
### Article Structure  
The structure of the rest of the paper is as follows. Section 2 defines the network model, which includes both the system model and the packet coding framework. Section 3 defines the problem formulation for admission control, multiple access network coding, followed by a bi-directional matching algorithm for joint admission with network coding cooperation. First, the algorithm is formulated for a lossless channel followed by lossy channel conditions. Moreover, the complexity analysis is included in this section. Section 4 defines the simulation and results in terms of capacity, energy efficiency, and packet completion time.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 5 Theoretical and Experimental Evaluation
### Complexity of Algorithm 3: Adding Redundancy to a DODAG
#### 5.3.2 Edge Feasibility Check (Line 8)  
Checking whether a new edge can be added is a constant-time operation \(O(1)\).  
_Final complexity of Algorithm 3:_  
\[O(|V|^{2}) \tag{4}\]  
The quadratic complexity results from the nested loops iterating over nodes and their adjacent neighbors.
## 4 Experiment and Analysis
### Case Study  
The case study is to show the performance of our proposed method on the dataset CMU-MOSI in a visual form. We aim to illustrate in an intuitive way that the single-modal label generation module makes the model more attentive to the differences between unimodal and multimodal labels. In this section, a case study of the unimodal label generation module is performed, and three typical samples from the CMU-MOSI are selected for visualization, and the samples are positive, neutral, and negative. Section 4.4 describes in detail the comparison of our proposed model with the baselines. The case study of the label generation module is shown in Figure 5. The acoustic, visual, and language label values in the figure are the calculated sentiment values from the unimodal label generation module, and the raw labels are the sentiment annotated values from the multimodal dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Design** & **Acc-7 (\(\uparrow\))** & **Acc-2 (\(\uparrow\))** & **F1 (\(\uparrow\))** & **Corr (\(\uparrow\))** & **MAE (\(\downarrow\))** \\ \hline CDML (full model) & **52.9** & **84.3** & **84.3** & **0.761** & **0.553** \\ (\(\cdot\)) multitask learning & 52.6 & 83.7 & 83.6 & 0.751 & 0.573 \\ (\(\cdot\)) adversarial training & 52.2 & 83.5 & 83.4 & 0.747 & 0.578 \\ (\(\cdot\)) attention mechanism & 51.6 & 82.9 & 82.6 & 0.742 & 0.581 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of enhancement algorithm ablation experiments on CMU-MOSEI. In each row, the corresponding component is progressively removed from the CDML.  
Figure 5: Case study on CMU-MOSI.  
The sentiment state in the first case tends to be consistent with that of the label generation values for each unimodal state. In the third case, the sentiment prediction of the audio modal label generation values is shifted concerning the multimodal labels. The predictions obtained from both the textual and visual modalities are negative, but the differences in the audio modality may give a small adjustment to the overall prediction, which is more comprehensive and accurate. That is, the label generation module can make the model pay more attention to the difference between the unimodal labels and the multimodal labels so that the CDML can obtain not only consistency between the modalities during the learning process but also pay more attention to the difference in the sentiment representations.
# Simplified Calculation Model for Contact Resistance Based on Fractal Rough Surfaces Method
## 1 Introduction
Nevertheless, the M-B model makes many ideal assumptions, such as no transition state for the elastic-plastic deformation of the asperity, the asperity base being rigid, and adjacent asperities being independent of each other. Subsequent researchers revised and supplemented the M-B contact model. [PERSON] constructed a multiscale contact model, which describes the process of several contact patches merging into a larger contact area as the compression increases [15]. The process can be described by a diffusion equation that characterizes the real contact area versus contact pressure. The M-E model proposed by [PERSON] and [PERSON] solves the problem of contradicting the order of asperity deformation with the classical Hertz theory [16]. In addition, [PERSON] built a scale-dependent elastic-plastic contact ME correction model. They pointed out that the mechanical properties of a single asperity are related to its frequency index, and extended the conclusions to the entire surface [17]. [PERSON] [18] and [PERSON] [19] studied the asperity dislocation contact (contact angle not 0) from two perspectives: the molecular dynamics model and finite element simulation, respectively. The results show that there is a significant difference between the friction coefficient of dislocation contact and forward contact. Although the contact models constructed in the above literature are experimentally verified with certain accuracy, it is difficult to construct a suitable contact resistance network for the solution of ECR. The reason is that the actual surface has complex structure and strong coupling between adjacent contact spots. Most scholars use finite element and experimental methods to study contact resistance and analyze the working state of contact resistance under different physical factors [20; 21; 22]. However, from the point of view of the mathematical model construction of contact resistance, the contact surface must be simplified and the key contact properties of the rough surface must be preserved as much as possible. The aim of the research is to develop a simplified calculation model for the contact resistance of rough surfaces. The rough surface is constructed based on fractal theory and the Monte Carlo method to simulate Gaussian and non-Gaussian isotropic surfaces. The sinusoidal asperities are used as the basic contact unit of the rough surface which can quantitatively analyze the contact load with respect to the real contact area and contact resistance. Then, the effects of various morphological parameters on the contact characteristics are compared and analyzed. Finally, the finite element contact model is built based on the reverse modeling technique, which verifies the accuracy of the proposed computational model under small contact loads. The research in the paper can provide a useful reference for the reliability verification and optimization design of electrical contacts.
# The XMUSPEECH System for Accented English Automatic Speech Recognition
## 2 System Structure
### Multistream CNN Architecture  
Multistream CNNs have shown their superiority in robust speech recognition since their diversity in temporal resolutions across multiple parallel streams would achieve stronger robustness.  
As shown in Figure 1, the input speech frames were first processed by a few initial single stream CNN layers, which could be TDNN-F or 2D-CNN, and then entered multiple specific branches that were stacked by TDNN-F layers. To achieve the diversity of temporal resolution, every branched stream has a unique dilation rate, which corresponds to the time stride in the TDNNs. Each dilation rate was chosen from the default subsampling rate (three frames) to make the TDNN-Fs better streamlined with the training and decoding process when given input speech frames are subsampled. In our multistream CNN network, the log-mel spectrogram was first randomly masked in both frequency and time by a SpecAugment layer, then five layers of 2D-CNN were positioned to better accommodate the features. We used 3 \(\times\) 3 kernels for the 2D-CNN layers; the filter size of the first two layers was 64, the third and the fourth were 128, and the last was 256. For every other 2D-CNN layer, we applied frequency band subsampling with a rate of two. In the multi-stream part, in each branch, we stacked 11 layers of TDNN-F with 512 nodes and 128 bottleneck nodes. We employed three streams with the 6-9-12 dilation rate configuration, which means the TDNN-Fs of each stream had 6, 9, and 12 time-stride, respectively. The output embeddings of multiple streams were then concatenated followed by ReLu, batch normalization, and a dropout layer.
## 1 Introduction
### Related Works  
The proposition for synchronizing the shutter timings of multiple cameras in a vision network system has been discussed. In a typical 30 frames per second (fps) camera network system, [PERSON] et al. suggested a clock-synchronization-based method using the network time protocol (NPT) [5]. However, the accuracy of the synchronization by NPT was approximately in the millisecond order, which is not acceptable for high sampling rates over intervals of millisecond or submillisecond order. In particular, this method cannot be applied to feedback control systems that require real-time response. [PERSON] et al. proposed an illumination-based method for the real-time contactless synchronization of high-speed vision systems [6]; however, the system is not sufficiently flexible for sensor network systems. Further, the method is considered applicable to only the vision network system. Namely, a slight lack of the general-purpose properties exists. Other methods for data synchronization in multisensor network systems were proposed in [7; 8; 9].  
To summarize, conventional sensor network systems do not possess a higher sampling rate, a highly accurate synchronization of data acquisition, and general-purpose properties. Our objectivesare to solve these problems and develop a high-speed sensor network system with these properties using our proposed method.
###### Abstract
The plasma treatment of tissue can be painful depending on the type of plasma device used. Some plasma can cause holes in the stratum comeum, and this treatment can be potentially painful, as reported in the study of [PERSON] et al. [7]. On the other hand, microplasma used in the drug delivery is painless. This type of plasma can cause etching of the most upper part of the skin--the stratum comeum--up to several micrometers [8]. Intestinal epithelial cells are often used in microplasma-based drug delivery studies as they offer greater permeability than skin. Drug permeation through the intercellular pathway of the skin is limited to 500 Da, while intestinal tissue allows permeability of molecules with molecular weights of 70 kDa without any enhancers [9; 10]. Recently, we showed the capability of microplasma irradiation to deliver high-weight molecules with molecular weights of up to 2000 kDa into rat intestinal epithelial cells [11; 12]. The interactions between CAP and skin or cells are largely unexplored. It has been shown that the reactive species can form nanopores in artificial cell membranes [13]. During the transdermal application of plasma, the ROS and RNS can penetrate 150 \(\upmu\)m-1.5 mm under the skin's surface [14], suggesting interactions with both superficial (i.e., the stratum corneum layer) and deeper skin layers (i.e., epidermal and dermal cells). Cell membranes of both humans and animals mainly comprise diverse lipid species, arranged in a lipid bilayer [15] that plays a crucial role in determining the permeability of the membranes [16]. Microplasma irradiation has the potential to induce changes in the lipid composition of the cell membrane, affecting its fluidity, permeability, and overall structural integrity. Understanding the changes in these properties is crucial for evaluating the safety and long-term effects of plasma technology in biomedical applications, including transdermal drug delivery. In a recent study, we optimized the microplasma irradiation conditions for the delivery of high-molecular-weight molecules, fluorescein isothicyanate (FD-150 and FD-2000), into rat intestinal epithelial cells. Further, we investigated a crucial physical characteristic of cell membranes, the membrane lipid order, after microplasma treatment at the optimized conditions. While the untreated cells exhibited an exclusive liquid-ordered phase, the microplasma-irradiated cells displayed both liquid-ordered and liquid-disordered phases [12]. The study suggested that the altered membrane lipid order facilitates the transport of molecules through the cell membrane by modulating the fluidity and permeability of the membrane. These findings led us to conduct comprehensive lipidomics of cells after microplasma irradiation in order to identify the specific lipid species that contribute to membrane fluidity and permeability, shedding light on the dynamic nature of the cell membrane in response to microplasma irradiation.
# On an Optimal Quadrature Formula in a Hilbert Space of Periodic Functions
## 2 Main Results  
The central results of the present paper are the following.  
Theorem 1: _The extremal function of the periodic error functional \(\ell\) for the quadrature formula (4) has the form_  
\[\psi_{\ell}(x)=\frac{\exp(-\mathrm{i}\omega x)}{\omega^{2m}}-\sum \limits_{k=1}^{N}\overline{C}_{k}\sum\limits_{\beta\
eq 0}\frac{\exp(\mathrm{i} \beta\left(x-\frac{2\pi k}{N}\right))}{2\pi\beta^{2m}}+\
u. \tag{9}\]  
_Here, \(\overline{C}_{k}\) are complex conjugate to the coefficients \(C_{k}\) of the quadrature formula (4), and \(\
u\) is an unknown number._  
Theorem 2: _If \(\varphi(x)\in\widetilde{H}_{2}^{(m)}\), then, for the optimal coefficients of the quadrature formula (4) with the error functional (6), the following formula holds_  
\[\mathcal{C}_{k}=\frac{2\pi}{N}\left(\frac{\sin\frac{\pi\omega}{N} }{\frac{\pi\omega}{N}}\right)^{2m}\frac{(2m-1)!\cdot\exp(\frac{2\pi\mathrm{i} \omega k}{N})}{2\sum\limits_{n=0}^{m-2}a_{n}^{(2m-2)}\cos(2\pi(m-1-n)\frac{ \omega}{N})+a_{m-1}^{(2m-2)}}, \tag{10}\] \[k=1,2,\ldots,N.\]  
_In the last formula, the \(a_{n}^{(2m-2)}\) values are the coefficients of the Euler-Frobenius polynomial \(E_{2m-2}(x)\) of degree \((2m-2)\) and are defined as follows:_  
\[a_{n}^{(2m-2)} = \sum\limits_{j=0}^{n}(-1)^{j}\binom{2m}{j}(n+1-j)^{2m-1},\] \[\binom{2m}{j} = \frac{(2m)!}{(2m-j)!\;j!}.\]  
Theorem 3: _In the space \(\widetilde{H}_{2}^{(m)}\), the square of the norm of the error functional \(\ell(x)\) for the optimal quadrature formula of the form (4) has the following expression:_  
\[\left\|\frac{\epsilon}{\ell}|\widetilde{H}_{2}^{(m)*}\right\|^{2}=\frac{2\pi} {\omega^{2m}}\left[1-\left(\frac{\sin\frac{\pi\omega}{N}}{\frac{2\pi\omega}{ N}}\right)^{2m}\frac{(2m-1)!}{2\sum\limits_{n=0}^{m-2}a_{n}^{(2m-2)}\cos(m-1-n) \frac{\omega}{N}+a_{m-1}^{(2m-2)}}\right]. \tag{11}\]  
Hence, in particular, we obtain the following.  
Remark 1: _For \(\omega=0\), we have the well-known optimal quadrature formula, which is the following rectangular formula_  
\[\int_{0}^{2\pi}\varphi(x)dx\cong\frac{2\pi}{N}\sum\limits_{k=1}^{N}\varphi \left(\frac{2\pi k}{N}\right).\]_Moreover, for the norm of the error function, we obtain_  
\[\left\|\tilde{\ell}|\widetilde{H}_{2}^{(m)*}\right\|^{2} = \frac{\left(\frac{2\pi}{N}\right)^{2m}|B_{2m}|}{(2m)!},\]  
_where \(B_{2m}\) is the Bernoulli number._  
**Remark 2**.: _If \(\omega\) is a multiple for the number \(N\) of the nodes of the quadrature formula, i.e., \(\omega=Np\), \(p=\pm 1,\pm 2,\ldots\), then_  
\[\hat{\mathcal{C}}_{k}=0,\quad k=1,2,\ldots,N\]  
_and_  
\[\left\|\tilde{\ell}|\widetilde{H}_{2}^{(m)*}\right\|^{2}=\frac{2\pi}{\omega^{ 2m}}.\]  
**Remark 3**.: _Calculations show that_  
\[\sum_{n=0}^{2m-2}a_{n}^{(2m-2)}=(2m-1)!,\]  
_where \(a_{n}^{(2m-2)}\) values are the coefficients of the Euler-Frobenius polynomial of degree \((2m-2)\)._
## 4 Conclusions  
The three most commonly used types of screeds (bitumen, polymer and silicate) on the four types of the most commonly used building structures (ceramics, concrete, lime sand and marl) were chosen for the study. The waterproofing ability of used screeds was evaluated in the experimental program in terms modified water permeability test. The cohesion was studied after the freeze-thaw exposure to take into consideration the durability aspects. The main conclusions could be summarised as follows:  
* The suitable waterproofing ability of all types of the tested screeds applied on building materials was confirmed.
* All studied screeds resisted the water pressure of 0.12 MPa, however the mineral screed exhibited a seepage into the substrate material;
* The permeability testing performed on the \"whole masonry\" blocks declared no negative influence of the joints on the tightness of the studied screeds;
* The best cohesion exhibited the polymer screed, however concurrently achieved the highest decay of the cohesion during freeze-thaw cycling;
* The cohesion of the bitumen and mineral screed were very similar. While, the decay of mechanical properties of mineral screed was proportional to passed freeze-thaw cycles, the bitumen screed exhibited significant drop of the mechanical performance after 15 cycles, however following freeze-thaw exposure caused negligible deterioration.  
The performed tests were carried out on masonry from new materials. The next question arises, whether the same results would be achieved in the case of mature masonry from a real structure. In the case of mature masonry, different degrees of chemical salinity due to the penetration of water over time can be observed. The action of chemically impure water (leakage through the masonry) can decrease the mechanical and physical properties of the screed. This will be the subject of a further study conducted in the future.  
Conceptualisation, M.N. and J.P.; methodology, M.N.; investigation, M.N., J.P. and P.R.; writing--original draft preparation, M.N.; writing--review and editing, P.R.; supervision, J.P. and P.R.; project administration, J.P. All authors have read and agreed to the published version of the manuscript.  
Figure 18: (**a**) Average adhesion strength without the influence of the substrate; (**b**) average residual strength after 30 freezing cycles without the influence of the substrate [%].  
**Funding:** This research was funded by CTU in Prague, grant number SGS19/145/OHK1/3T/11.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# PSO-Based Voltage Control Strategy for Loadability Enhancement in Smart Power Grids
## 4 Problem Statement
### Objective Function  
In this research, the worst-case design is employed to determine the optimal control gain matrix \(K^{*}\) in Equation (13). Thus, the problem is to minimize \(\|\Delta\mathbf{V_{L}}\|_{\infty}\). Suppose that \(\mathbf{q}\) is within a certain limit \(\gamma\) in a practical power system; i.e.,  
\[\|\mathbf{q}\|_{\infty}\leq\gamma \tag{14}\]  
Thus, the objective function is expressed as  
\[\min_{K^{*}}\max_{\|\mathbf{q}\|_{\infty}\leq\gamma}\left\|\left(J_{1}-J_{2}K ^{*}J_{p}\right)\mathbf{q}\right\|_{\infty} \tag{15}\]  
Due to the fact that  
\[\begin{array}{ll}\|\left(J_{1}-J_{2}K^{*}J_{p}\right)\mathbf{q}\|_{\infty} &\leq\|\left(J_{1}-J_{2}K^{*}J_{p}\right)\|_{\infty}\|\mathbf{q}\|_{\infty}\\ &\leq\gamma\|\left(J_{1}-J_{2}K^{*}J_{p}\right)\|_{\infty}\end{array} \tag{16}\]  
The minimization problem in Equation (15) is also equivalent to  
\[\min_{K^{*}}\|J_{1}-J_{2}K^{*}J_{p}\|_{\infty} \tag{17}\]  
From the preceding equation, one can see that the optimal robust gain matrix \(K^{*}\) depends entirely on system configurations on pilot-point locations. In other words, \(K^{*}\) remains the same regardless of any unexpected load disturbance. Once \(K^{*}\) is obtained, the optimal control action \(\mathbf{u}\) can be computed by \(\mathbf{u}^{*}=K^{*}\Delta\mathbf{V_{p}}\). Note that it must be within the bounds of control variables.