esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Discussion
### Simulation Environment  
The proposed encoding algorithm is implemented in the HM 16.0 reference encoder. The decoding-complexity estimation models presented in [24; 43; 44; 45], the Lagrangian cost function that determines the coding modes and the proposed decoding-complexity, and rate-controlling algorithm, are integrated into the HEVC encoding tool chain. The resultant bit streams are decoded using the openHEVC [54] software decoder. The decoding was performed on an Intel x86 Core i7-6500U system running Ubuntu 16.04 to measure the decoding-complexity performance of the bit streams. The proposed algorithm's performance is compared with three state-of-the-art approaches: the power-aware encoding algorithm proposed by [PERSON] et al. [20]; the rate, distortion, and decoder energy optimized encoding algorithm proposed by [PERSON] et al. [46]; and the tunable HEVC decoder proposed by [PERSON] et al. [34]. The video sequences used in the experiments reported in this section are of HD (1920 \(\times\) 1080) resolution. In this case, \"Kimono\" and \"Parkscene\" sequences are defined in the HEVC common test configurations [55] and the rest are a collection of proprietary sequences. Their sequence categories (i.e., motion and texture complexity levels) are defined in the Table 1. Each video sequence is encoded at 900 kbps, 1 Mbps, 2 Mbps, and 4 Mbps video bit rates using the _random access_ configuration with rate control enabled. Moreover, the bit streams corresponding to the proposed algorithm were generated with two decoding-complexity levels, which are referred to as L1 and L2. The bit streams corresponding to these two levels are encoded such that decoding-complexities are 30% and 40% less (in terms of CPU cycles) as compared to HM encoded bit streams, respectively. The complexity of the decoding process was measured using the instruction level analysis tools callgrind/valgrind [56]. The numbers of CPU cycles identified were assigned as the available decoding-complexity budget for the sequence, when performing the decoding-complexity allocation calculations described in Section 4.1.  
Finally, the decoder's energy consumption was determined by measuring the energy dissipated by the system during the video playback. In this context, a test bed that implements an online video streaming scenario where the openHEVC decoder is used as the playback client was used for this assessment. The encoded bit streams were streamed for a duration of 15 min and the energy capacity reduction of the playback device's battery was measured using the Linux power measurement tools [57]. It should be noted that the measured battery capacity reduction corresponds to the overall energy consumption by the device that includes the energy consumed for the wireless transmission, video decoding, and video presentation. Furthermore, the relative energy consumption performances when using an application-specific DVFS algorithm [10] and the Linux ondemand frequency governor were also analyzed and are compared in the experimental results.
## 5 Sub-Areas of Traffic Prediction Applications Using RNNs
### Traffic Accidents and Congestion Prediction
This approach primarily involves outlier removal and correction of missing values guided by data trends and patterns, followed by using the LSTM model to forecast traffic conditions. The study demonstrates that this method outperforms traditional models by effectively managing time series traffic data and handling missing information. [PERSON] et al. [128] proposed a hybrid neural network model combining the CNN, LSTM, and transpose CNN to predict city-wide traffic congestion. Their approach utilizes the CNN to extract spatial features from traffic images and LSTM to analyze temporal patterns. The model leverages real-time data captured from the Seoul Transportation Operation and Information Service (TOPIS) to address the challenge of forecasting congestion levels across the entire city. While the model effectively learns spatial and temporal relationships, enhancing its ability to handle the real-world complexities of traffic data more effectively remains a challenge. [PERSON] et al. [129] proposed a framework designed specifically for predicting traffic congestion events. Their research integrates the transformer and GCN to effectively capture spatiotemporal dependencies from historical traffic data and road network information. The model utilizes continuous gated recurrent units to handle the spatiotemporal dynamics and evolution of congestion patterns, enabling it to predict not only the timing but also the duration of future congestion events. Additionally, the study emphasizes the challenge of accurately modeling the complex dynamics of road traffic. The utilization of these advanced deep learning techniques shows significant promise in enhancing the accuracy and reliability of traffic prediction systems. Overall, the literature indicates a strong trend toward leveraging deep learning models, especially RNNs, LSTMs, and GRUs, for traffic accidents and congestion prediction, contributing to the advancement of intelligent transportation systems. This literature underscores the importance of traffic
# Risk-Based Virtual Power Plant Implementation Strategy for Smart Energy Communities
## 6 Conclusions  
This paper proposed VPP implementation strategies, considering their risk. It is difficult to balance energy in the implementation stage due to uncertainties in demand and resources. To reduce the difficulty, the VPP implementation was designed using a risk factor that reflected the energy imbalance risk between the demand and a resource. Using the risk factor, it was discovered that the temporal correlation between demand and resources was the dominant factor impacting VPP implementation. Particularly, it was shown that to reduce risk, a resource constituting part of the VPP should have a high correlation coefficient with the demand and a low correlation coefficient with other resources. Based on that, an optimization-based strategy was proposed. Moreover, a simple strategy that could be solved in an iterative way was suggested. Experimental results using the real data sets show that the proposed strategies based on the risk factor are effective means of VPP implementation for commercial and residential SECs. According to the characteristic of demand, PV and wind are suggested to be suitable resources for VPP implementation for commercial SECs and residential SECs, respectively. VPP implementation is critical for VPP operation. This is because the resources constituting the VPP determine the operation room of the VPP. This means that a VPP's operation method can influence the design of its implementation. Moreover, in terms of flexible resources, which increase the operating areas of VPPs, it is difficult to directly apply the correlation-based risk factor suggested in this study. Therefore, co-optimization that considers both the implementation and the operation of VPPs as well as the consideration of flexible resources for VPP implementation are future research directions.  
This research was supported by the National Research Foundation of Korea with a grant funded by the Korean government (Ministry of Science and ICT) (No. 2020R1A2B5B01095044).  
Not applicable.  
The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Strengthening of RC Beams with CFC Panels for Improving Impact Resistance
###### Abstract  
In this study, continuous fiber composite (CFC) panels were used as a strengthening material to improve the impact resistance of reinforced concrete (RC). Both experimental tests and numerical analyses were carried out to investigate the impact resistance of RC beams strengthened with CFC panels. The experiments involved repeated drop-weight impact tests at constant speed. The experimental results confirm that the strengthening of RC beams with CFC panels improves the impact resistance, thereby increasing the number of repeated impacts that can be allowed before a specified residual displacement is reached. In addition, a virtual particle model based on the conventional smoothed particle hydrodynamics (SPH) method, which takes into account the mechanical properties of the adhesive, was introduced as an analytical method to simulate the impact fracture behavior of RC beams strengthened with CFC panels. The analysis results show that the improved SPH method proposed in this study can accurately reproduce the impact behavior of RC beams strengthened with CFC panels and predict the allowable number of repeated impacts. Furthermore, a parametric study was carried out using a validated analytical approach to compare the load-bearing capacity and discuss the impact performance of RC beams with three types of CFC panel reinforcement.  
impact resistance; reinforced concrete beam; smoothed particle hydrodynamics; continuous fiber composite panel; impact test simulation; drop hammer test +
Footnote †: journal: Applied Sciences
## 3 Algorithms
### Quality Factor and Bandwidth Formulation  
The 3 dB bandwidth \(\Delta f\) in [2; 28] relates to the center frequency \(f_{0}\) through the generalized quality factor \(Q\) or the dimensionless coupling coefficient \(k\), expressed in (2) as follows:  
\[\Delta f=\frac{f_{0}}{Q}=kf_{0} \tag{2}\]  
This generalized \(Q\) specifically corresponds to the loaded quality factor \(Q_{L}\) for coupled resonator systems, which combines the following three principal contributions: the unloaded quality factor \(Q_{u}\) of the resonators, the external quality factor \(Q_{e}\) of the feed structure, and the parasitic quality factors \(Q_{\text{parasitic},i}\) from tuning elements and bonding materials. The complete relationship is expressed in (3), as discussed in [2; 28], including the following parasitic contribution:  
\[\frac{1}{Q_{L}}=\frac{1}{Q_{u}}+\frac{1}{Q_{e}}+\sum_{i=1}^{n}\frac{1}{Q_{ \text{parasitic},i}} \tag{3}\]  
where \(Q_{e}\) comprises individual loss contributions from conductors (\(Q_{c}\)), dielectrics (\(Q_{d}\)), and radiation (\(Q_{r}\)). Given that the cavity design incorporates multiple screws to ensure hermetic sealing, the system operates in a loss-dominant regime where radiative losses become negligible (\(Q_{r}\gg Q_{c},Q_{d}\)). Consequently, (4) reduces to the following:  
\[\frac{1}{Q_{u}}=\frac{1}{Q_{c}}+\frac{1}{Q_{d}} \tag{4}\]
# Predicting Swell in Clay-Sand Mixtures Used in Liners
## 4 Discussion
### Prediction of the Swelling Behavior of Clay  
Many factors, including the mineralogical composition, fabric, stress history, placement conditions, and geologic mode of the formation of the clay, influence the swelling behavior of naturally occurring clays. This prediction method assumes that the active constituents of natural clay give similar results as for remolded clays. Material with a similar amount of active clay compacted to variable densities will swell to a maximum level and maintain the same slope of swellability. The actual swell profile of natural clay may indicate a shift or slight displacement. The slope of the clay void ratio versus the vertical effective stress for the natural clay is obtained as a function of the slope predicted for the fine clay constituents using the fall cone method. However, the study of artificial and remolded clay is necessary to understand how these minerals behave when subjected to a moisture increase, which forms a useful guide for predicting the swell behavior of mixtures. Artificial mixtures are more common in the waste containment and liner industries.  
The proposed prediction is based on obtaining the clay void ratio versus the vertical effective stress from the slope of relevant artificial clay. To achieve this, it is necessary to run a single or a few fall cone tests to establish the slope of swellability.  
The clay paste, which included only 40% bentonite by weight, was tested for penetration for the characterization of clay using the fall cone method. The profile of the clay void ratio versus the vertical effective stress was obtained for the same material. The predicted equation for this type of clay (shown in Figure 4) is given by the following:  
\[e_{c}=-0.6656\ ln\ \sigma c^{\prime}+4.824\qquad\mathrm{R}^{2}\ =\ 0.9142 \tag{11}\]
## 3 Results
### Multimodal Sensor Fusion
Indicatively, the records of the magnetometer when fused with those of either accelerometers or gyroscopes provided F1-scores equal to 0.887 or 0.915, respectively, whereas the combination of accelerometers and gyroscopes resulted in the lowest model performance (F1-score equal to 0.774). The highest F1-score of 0.939 was achieved by combining all three sensors, indicating that the complementary information from the accelerometer, gyroscope, and magnetometer is very important for accurate activity classification.
# Review and New Aspects in Combining Multipoint Moulding and Additive Manufacturing
## 3 Possible Additive Manufacturing Methods
### Processes for Other Materials  
In addition to the methods already presented, there are adapted or proprietary processes for many other materials. Binder jetting is also available for sand and other materials as base material. A special application for the sand-based process is the foundry industry, where binder jetting is used to produce cores for moulds quickly and cheaply without an expensive and time-consuming production of a mould for a core shooter (2018; 2020).  
[PERSON] and [PERSON] (2018); [56] also found in a comparative study that the energy balance of binder jetting is significantly better than that of fused filament fabrication. It was found that the energy consumption for building a test prototype in the binder jetting process is significantly lower. The most important factor influencing energy consumption was the construction time.  
The fused filament fabrication has been further developed too. There are many versions for a wide variety of materials. [PERSON] et al. (2018) present a version of fused layer modelling for the production of large curved concrete parts. [PERSON] (2018), on the other hand, is developing a process for the extrusion of ceramic components. [PERSON] et al. (2018) show in an overview of the current state of research that the fused filament fabrication is also interesting for the production of organic components. Current research ranges from the production of replacements for missing skin areas to the extrusion of tissue for entire organs. Medical technology also deals with additive manufacturing. The idea is usually the possibility of individual adaptation of the parts to the patient's needs. For example, [PERSON] et al. (2018) show an approach for the production of a prosthesis of the lower extremities. However, an individually adapted shape is created in a multi-material approach, which can be produced in one production step by additive manufacturing.  
The laminated layer manufacturing can use paper as a raw material. Thereby, the part is built from individual layers of paper, which are glued together and cut out (2018; 2020).  
Summarising the additive manufacturing processes for other materials are mostly variants of processes originally using metals or plastics.
## 6 E-DDTMD Algorithm Based on ADMM
### Problem Decomposition  
For the optimization layer to perform problem-solving computation locally for each SN, the problem needs to be separated. However, the variables \(\mathbf{b}\) and \(\mathbf{d}\) in problem (29) are global variables and cannot be separated. Therefore, to make the problem separable, we introduce local copies of the global variables \(\mathbf{b}\) and \(\mathbf{d}\) so that each SN can use its local copy to compute independently to solve the problem. There will be global variables \(\mathbf{b}\) and \(\mathbf{d}\) in the optimization of the local copy on layer SN i using \(\tilde{\mathbf{b}}^{i}=\left\{\hat{b}_{k,j}^{i}\right\}_{k\in\mathcal{I},j\in \mathcal{J}}\) and \(\hat{\mathbf{d}}^{i}=\left\{\hat{d}_{k,j}^{i}\right\}_{k\in\mathcal{I},j\in \mathcal{J}}\).  
Define the following set as the local variable feasible set on SN i:  
\[\varphi_{i}=\] \[\left\{\begin{matrix}a_{i}\\ \hat{\mathbf{b}}^{i}\\ \hat{\mathbf{d}}^{i}\\ \hat{\mathbf{d}}^{i}\end{matrix}\middle|\begin{matrix}\sum\
olimits_{k=1}^{l}\left( \hat{b}_{k,j}^{i}D_{k}+\hat{d}_{k,j}^{i}D_{k}^{C}\right)\leq Z_{j},\forall j\\ \sum\
olimits_{j=1}^{j}\left(\hat{b}_{k,j}^{i}+\hat{d}_{k,j}^{i}\right)+a_{i} \leq 1,\forall k\\ \sum\
olimits_{j=1}^{l}\left[\hat{b}_{k,j}^{i}\left(T_{i,j}^{b}-T_{i}^{c} \right)+\hat{d}_{k,j}^{i}\left(T_{i,j}^{d}-T_{i}^{c}\right)\right]+a_{i}\left( T_{i}^{a}-T_{i}^{c}\right)+T_{i}^{c}\leq T_{i},\forall k\\ 0\leq a_{i}\leq 1\\ 0\leq\hat{b}_{k,j}^{i}\leq 1\\ 0\leq\hat{d}_{k,j}^{i}\leq 1\end{matrix}\right\},\forall i. \tag{30}\]  
The local utility function on SN i can be denoted as  
\[v_{i}\left(a_{i},\hat{\mathbf{b}}^{i},\hat{\mathbf{d}}^{i}\right)\] \[=\left\{\begin{matrix}\sum\limits_{j=1}^{J}\left[\hat{b}_{k,j}^{i} \left(C_{i,j}^{b}-C_{i}^{c}\right)+\hat{d}_{k,j}^{i}\left(C_{i,j}^{d}-C_{i}^{c }\right)\right]+a_{i}\left(C_{i}^{a}-C_{i}^{c}\right)+C_{i}^{c},when\left(a_{i },\hat{\mathbf{b}}^{i},\hat{\mathbf{d}}^{i}\right)\in\varphi_{i}\\ 0.otherwise\end{matrix}\right. \tag{31}\]  
Then the equivalent formulation of problem (29) can be expressed as [30].  
\[\underset{\{\mathbf{a},\mathbf{b}^{i}\hat{\mathbf{d}}^{i}\}\}{\min}\sum\
olimits_{i\in \mathcal{I}}v_{i}(a_{i},\hat{\mathbf{b}}^{i},\hat{\mathbf{d}}^{i}), \tag{32a}\] \[S.t.\hat{b}_{k,j}^{i}=b_{i,j},\forall i,j,k,\] \[\hat{d}_{k,j}^{i}=d_{i,j},\forall i,j,k. \tag{32b}\]  
Clearly, in problem (32a), the objective function \(v_{i}\) with feasible set \(\varphi_{i}\) is separable with respect to all SNs in the optimization layer, and the separation of the objective function enables each small unit to deal with the subproblems related to it independently. Additionally, constraint (32b) ensures consistency between all local and global variables, which is exactly what we want. In Section 6.3, we will apply a new energy-efficient distributed data transmission mode decision algorithm (E-DDTMD) based on ADMM [31] to solve this problem in a distributed manner.
## 4 Results and Discussion
### Results of the ECG R-Peak Detection Stage  
The implemented R-peak detector is evaluated by comparing it with the annotated information. We computed three quantitative measures from the detection results: true-positive (TP), which indicates that the proposed algorithm correctly identified a R-peak; false-negative (FN), which indicates that the proposed algorithm failed to detect a R-peak; and false-positive (FP), which indicates that the proposed algorithm mistakenly classified a noise spike as a R-peak.  
The evaluation metrics were sensitivity (SE) and positive predictivity (+P), which were defined as follows:  
\[SE=\frac{TP}{TP+FN} \tag{20}\]  
\[+P=\frac{TP}{TP+FP} \tag{21}\]  
The QRS complex detector achieved a sensitivity of SE = 99.83% and a precision of \(p\) = 99.65%. Table 1 summarizes the comparison of the R-peak detection algorithm proposed in this paper with some previous works. As we can see from the table, the performance of the technique is comparable to or better than the state-of-the-art methods and satisfactory for advanced ECG feature extraction.
## Appendix A Nonlinearity Control Theory and Parameter Analysis in Third-Order Systems  
### Some Definitions in Nonlinearity Control Theory  
1. Vector field: Given a subset \(\mathbf{S}\) in \(\mathbf{R}^{n}\), a vector field is represented by a vector-valued function \(\mathbf{V}\): \(\mathbf{S}\rightarrow\mathbf{R}^{n}\) in standard Cartesian coordinates (\(x_{1},\ldots,x_{n}\)). If each component of \(\mathbf{V}\) is continuous, then \(\mathbf{V}\) is a continuous vector field, and more generally \(\mathbf{V}\) is a \(\mathbf{C}^{k}\) vector field if each component of \(\mathbf{V}\) is \(k\) times continuously differentiable. A vector field can be visualized as assigning a vector to individual points within an \(n\)-dimensional space. A vector field defines a unique dynamical system, described as a differential equation: \[\overset{\bullet}{\mathbf{x}}=f(\mathbf{x}),\] (A1)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Section No.**} & \multirow{2}{*}{**Ideal Maximum Power (W)**} & \multicolumn{4}{c}{**Output Power Obtained (W)**} \\ \cline{3-5}  & & **P \& O** & **Improved P \& O** & **ANN + AFL 1** \\ \hline SP1 & 8393.4 & 8282.9 & 8283.1 & 8381.4 \\ SP2 & 7341.3 & 5989.2 & 7225.3 & 7311.2 \\ SP3 & 7811.6 & 7108.4 & 7112.6 & 7798.8 \\ \hline \hline \end{tabular}  
* ANN: artificial neural network; AFL: augmented state feedback precise linearization.  
\end{table}
Table 3: Performances of the different MPPT (maximum power point tracking) methods under different scenarios.  
i.e., a vector field \(f\), with initial condition \(\mathbf{x}_{0}\), determine an integral curve \(t\rightarrow(x_{1}(t),\ldots,x_{n}(t))\), which is a solution to (A1) such that \(\mathbf{x}(0)=\mathbf{x}_{0}\).
2. Lie derivative: If \(f\) is a smooth vector field on U and \(h\) a smooth function on U then \(f(h)\) is the smooth function on U defined by \[f(h(\mathbf{x}))=\sum_{i=1}^{n}f_{i}(\mathbf{x})(\frac{\partial h(\mathbf{x} )}{\partial x_{i}}).\] (A2)  
A vector field may be interpreted as an operator mapping the function \(h\) into the function \(f(h)\). The function \(f(h)\) is called the Lie derivative of the function \(h\) along the vector field \(f\); it is usually denoted as L\(h\), which is a more convenient notation in the case of repeated operations:  
\[\mathrm{L}_{f_{1}}\mathrm{L}_{f_{2}}\mathrm{L}_{f_{3}}\ldots\mathrm{L}_{f_{l} }h=f_{1}(f_{2}(f_{3}(\ldots f_{i}(h)\ldots))).\]  
Repeated Lie derivatives along the same vector field \(f\) are denoted as \(L_{f}^{k}h=\mathrm{L}_{f}(L_{f}^{k-1}h)\), \(\mathrm{L}_{f}^{0}h=h\). The Lie derivative L\(h\) of a smooth function \(h\) along a vector field \(f\) is also denoted by \(\langle dh,f\rangle\).
3. Lie bracket: If \(f\) and \(g\) are a smooth vector field on U and \(h\) a smooth function on U then \([f,g](h)\) is the smooth function on U defined by \[[f,g](h)=f(g(h))-g(f(h))=\mathrm{L}_{f}\mathrm{L}_{g}h-\mathrm{L}_{g}\mathrm{ L}_{f}h.\] (A3)  
The Lie bracket \([f,g]\) of two vector fields \(f\) and \(g\) is also denoted by ad\(\beta\). \([f,g]\) is a vector field. Repeated Lie brackets are denoted as \(ad_{f}^{i}\beta=ad_{f}(ad_{f}^{i-1}g)\), \(ad_{f}^{0}g=g\).