esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Network bandwidth is a scarce resource in big data environments, so data locality is a fundamental problem for data-parallel frameworks such as Hadoop and Spark. This problem is exacerbated in multicore server-based clusters, where multiple tasks running on the same server compete for the server's network bandwidth. Existing approaches solve this problem by scheduling computational tasks near the input data and considering the server's free time, data placements, and data transfer costs. However, such approaches usually set identical values for data transfer costs, even though a multicore server's data transfer cost increases with the number of data-remote tasks. Eventually, this hampers data-processing time, by minimizing it ineffectively. As a solution, we propose DynDL (Dynamic Data Locality), a novel data-locality-aware task-scheduling model that handles dynamic data transfer costs for multicore servers. DynDL offers greater flexibility than existing approaches by using a set of non-decreasing functions to evaluate dynamic data transfer costs. We also propose online and offline algorithms (based on DynDL) that minimize data-processing time and adaptively adjust data locality. Although DynDL is NP-complete (nondeterministic polynomial-complete), we prove that the offline algorithm runs in quadratic time and generates optimal results for DynDL's specific uses. Using a series of simulations and real-world executions, we show that our algorithms are 30% better than algorithms that do not consider dynamic data transfer costs in terms of data-processing time. Moreover, they can adaptively adjust data localities based on the server's free time, data placement, and network bandwidth, and schedule tens of thousands of tasks within subseconds or seconds.  
data locality; task scheduling; multicore server; load balancing; bipartite graph; big data; cloud computing +
Footnote †: journal: Applied
# Sustainable Operation of Active Distribution Networks
## 3 Agents' Modeling
### Cluster Agents  
2.1 Cluster of Building Thermal Loads Agent (CBTL/A), Cluster of Building Electrical Loads Agent (CBEL/A)  
The primary goal of CBTL/As is to aggregate the responses they receive from the BTL/As being in their control areas. The flexibility of a cluster of buildings to increase (\(FL_{CB}^{\uparrow}\)) or decrease (\(FL_{CB}^{\downarrow}\)) its power is the respective weighted average of the flexibilities of the buildings it supervises, as described in Equations (17) and (18). \(V_{b}\) is the air volume of the bth building.  
\[FL_{CB}^{\uparrow}(t)=\frac{\sum_{b}FL_{b}^{\uparrow}(t)\cdot V_{b}}{\sum_{b}V _{b}} \tag{17}\]  
\[FL_{CB}^{\downarrow}(t)=\frac{\sum_{b}FL_{b}^{\downarrow}(t)\cdot V_{b}}{\sum_{b }V_{b}} \tag{18}\]  
Moreover, CBTL/As define suitable flexibilities (\(FL_{CB,fz}\)) by exploiting fuzzy logic based on information regarding the respective node voltage and their primary flexibility to adjust their power, as shown in Figure 3. Initially, the inputs and outputs were mapped into fuzzy sets using the respective membership functions. Both inputs were fuzzified using five sets with linguistic variables for the node voltage deviation from its upper or lower bound and the flexibility of the cluster of buildings to change its power. These variables were defined as very small (VS), small (S), medium (M), large (L), and very large (VL). The output of the fuzzy logic was determined by defining the rules indicated in Table 1.  
BEL/As aggregate the responses they receive from the BEL/As they control in order to model the electrical load profiles of the clusters of residential buildings.
###### Abstract  
This paper investigates the performance of integrated wireless sensor and multibeam satellite networks (IWSMSNs) under terrestrial interference. The IWMSNNs constitute sensor nodes (SNs), satellite sinks (SSs), multibeam satellite and remote monitoring hosts (RMHs). The multibeam satellite covers multiple beams and multiple SSs in each beam. The SSs can be directly used as SNs to transmit sensing data to RMHs via the satellite, and they can also be used to collect the sensing data from other SNs to transmit to the RMHs. We propose the hybrid one-dimensional (1D) and 2D beam models including the equivalent intra-beam interference factor \(\beta\) from terrestrial communication networks (TCNs) and the equivalent inter-beam interference factor \(\alpha\) from adjacent beams. The terrestrial interference is possibly due to the signals from the TCNs or the signals of sinks being transmitted to other satellite networks. The closed-form approximations of capacity per beam are derived for the return link of IWMSNNs under terrestrial interference by using the Haar approximations where the IWMSNNs experience the Rician fading channel. The optimal joint decoding capacity can be considered as the upper bound where all of the SSs' signals can be jointly decoded by a super-receiver on board the multibeam satellite or a gateway station that knows all of the code books. While the linear minimum mean square error (MMSE) capacity is where all of the signals of SSs are decoded singularly by a multibeam satellite or a gateway station. The simulations show that the optimal capacities are obviously higher than the MMSE capacities under the same conditions, while the capacities are lowered by Rician fading and converge as the Rician factor increases. \(\alpha\) and \(\beta\) jointly affect the performance of hybrid 1D and 2D beam models, and the number of SSs also contributes different effects on the optimal capacity and MMSE capacity of the IWMSMSNs.  
integrated wireless sensor and multibeam satellite networks; terrestrial interference; optimal capacity; minimum mean square error; Haar approximation; Rician fading +
Footnote †: journal:
## 4 Results  
### Camera Fixation  
The experimental environment was an outdoor open place, and the wall was regarded as the monitoring object. Upon fixing the digital camera, the landmark was artificially controlled to move 10 mm on the \(X\) and \(Z\) axes, and the camera was controlled to capture continuous images of the landmark. The displacement of the landmark was calculated using the above method, and images were captured every 5 min for a total of 150 min. Over time, the calculated \(X\) and \(Z\) axis displacements fluctuated around the true value of 10 mm, as shown in Figure 8a. The landmark was artificially controlled to move 100 mm on the \(Y\) axis and imaged every 5 minutes for a total of 150 min. The results calculated by this method are shown in Figure 8b. Subsequently, the landmark was artificially controlled to move 20 mm on the \(X\) and \(Z\) axes and 200 mm on the \(Y\) axis for the same operation, and the results are shown in Figure 8c,d.  
The root mean square error of the displacement calculated by this method was obtained by subtracting the calculated displacement from the corresponding true value:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}{(S-s)}^{2}}{n}} \tag{21}\]  
where \(S\) represents the true value and \(s\) represents the calculated displacement. Results show that when the true values of the landmarks on the \(X\) and \(Z\) axes were 10 mm, the RMSE in the \(X\) axis direction was 1.11 mm, whereas that on the \(Z\) axis was 1.06 mm. When the true value on the \(Y\) axis was 100 mm, the corresponding RMSE was 7.55 mm.  
When the true values on the \(X\) and \(Z\) axis were 20 mm, the RMSE in the \(X\) direction was 0.91 mm and that in the \(Z\) direction was 1.91 mm. When the true value on the \(Y\) axis was 200 mm, the corresponding root mean square error was 9.82 mm.
## 5 Experimental Results and Discussion
### Conventional Machine Learning Experimental Results
#### 5.2.1. SFP XP-TDD Data Set Experimental Results  
According to the experiments, no significant increase or decrease was seen in the Naive Bayes and KSTAR algorithms. ACC values of the remaining algorithms has shown an increase. As seen in Table 3, the average eACC value of the five base classifiers is 94.30%, and the average ACC is 93.61%. The Diff value that shows the difference between two primary metrics indicates a positive increase of 0.69%. Similarly, the average AUC increased from 0.772 to 0.825 and the average FM increased from 0.929 to 0.936.  
Table 7 clearly shows that the SVM classifier and Random Tree ensembles classifiers have a higher ACC compared to the other classifiers. They have a better ACC value in 94.92% of the five algorithms in question.
## 3 Materials and Methods
### Sample Preparation  
Hydrogen sulfide standards were prepared using equivalents of sodium sulfide (Na\({}_{2}\)S) dissolved in distilled water and further diluted with cold (\(-15\)\({}^{\circ}\)C) methanol. MeSH standards were prepared by slowly bubbling the pure gas over cold methanol and recording the gained mass. All other standards were prepared by dilution with cold methanol. A standard mixture (mix 1) was prepared, containing DMS (3000 \(\upmu\)g/L), MeSOAc (1285 \(\upmu\)g/L), DMDS (218 \(\upmu\)g/L), EtSOAc (564 \(\upmu\)g/L), DEDS (55 \(\upmu\)g/L), and DMTS (47 \(\upmu\)g/L). Because MeSH readily oxidizes to DMDS, and the higher affinity for DMDS on the SPME fiber causes much greater peak responses, the two compounds were not analyzed simultaneously. A separate mixture (mix 2) was thus prepared, containing MeSH (36.9 mg/L) and H\({}_{2}\)S (31.25 \(\upmu\)g/L). Finally, a mixture of internal standards (IS mix) was prepared containing EMS (5 mg/L), DES (1 mg/L), MIS (1.5 mg/L), EIS (1 mg/L), and DIDS (25.9 \(\upmu\)g/L).  
Samples were prepared in 20 mL deactivated screw-cap glass vials with Teflon-faced silicone septa. Synthetic wine was prepared using a 3.5 g/L tartaric acid solution in 12% ethanol, and 2 mL of synthetic wine was used for analysis. Ethanol was added corresponding to target concentrations of 0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, and 6.0%. Saturated salt water was then added to a total volume of 10 mL in order to drive more volatiles into the headspace [9]. The vials were flushed gently with argon under a low flow rate (barely disturbing the surface of the sample liquid) to avoid turbulence. All samples received 20 \(\upmu\)L of the standard mixture (mix 1 or 2) and 10 \(\upmu\)L of the IS mix (thus 30 \(\upmu\)L of methanolic solutions added in total). In the case of mix 2, all standards were introduced via a syringe through the sample-vial septum to avoid oxygen contact. All standards were stored in a freezer (\(-15\)\({}^{\circ}\)C).
# Special Issue on HVDC for Grid Services in Electric Power Systems
## 2 Improved HVDC Operation and Control Strategy for System Stability  
### Analysis of Six Active Power Control Strategies of Interconnected Grids with VSC-HVDC [1]  
In this paper, the generator angle stability of several active power control schemes of VSC-based HVDC is evaluated for interconnected two AC systems. Furthermore, in order to effectively evaluate angle stability, the Generators-VSC Interaction Factor index is newly implemented to distinguish the participating generators group which reacts to the converter power change.  
A Virtual Impedance Control Strategy for Improving the Stability and Dynamic Performance of VSC-HVDC Operation in Bidirectional Power Flow Mode [2]  
This paper adds the control loop to improve the performance and eliminate the steady-state error in the existing virtual impedance control of VSC HVDC. This paper eliminates the steady state with an additional control loop and verifies it by experiment and modeling.
## 8 Conclusions and Future Research Directions  
The performance of the entropy-based detection approach shows significant results based on the resulting DR and FPR in detecting high-rate and low-rate DDoS attacks against the SDN controller, which triggers a single host's or multiple hosts' attacks irrespective of whether a target is a single or multiple victims. The simulation scenarios were used to assess the performance of the entropy-based detection technique in spotting different DDoS attack traffic rates in terms of DR and FPR.  
The DRs and FPRs of the low-rate DDoS attack traffic are evaluated and compared with the high-rate DDoS attack traffic by adopting an entropy-based detection technique in terms  
Figure 7: Enhancement of the proportion of detecting low-rate DDoS attacks to high-rate DDoS attacks by the entropy-based detection approach using simulation scenarios.  
of average DR and average FPR. In addition, the results obtained from the comparison of low-rate DDoS attack traffic and high-rate DDoS attack traffic demonstrated that the entropy-based detection approach detects high DDoS attack traffic rates with a high DR and a low FPR in all simulation scenarios. Future research could add security layers through the infrastructure layer to increase the network traffic analysis granularity.  
M.A.A., T.A.A.-A. and D.R.I.; Methodology, M.A.A., M.A., A.A.B. and D.R.I.; Software, M.A.A., M.A., J.H., I.H.H., A.A.B. and D.R.I.; Validation, M.A.A., M.A., I.H.H. and T.A.A.-A.; Formal analysis, M.A.A., M.A., I.H.H. and A.A.B.; Investigation, M.A.A., M.A., J.H.H., A.A.B., T.A.A.A. and D.R.I.; Resources, M.A.A., M.A., A.J.H., I.H.H., A.A.B., T.A.A.-A. and D.R.I.; Data curation, M.A., M.A., J.H. and T.A.A.-A.; Writing--original draft, M.A.A., M.A. and I.H.H.; Writing--review & editing, M.A.A., M.A., J.H. and I.H.H.; Visualization, M.A.A. and M.A.; Supervision, M.A.A.; Project administration, M.A.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Universiti Sains Malaysia under an external grant (Grant Number 304/PNAV/650958/U154).  
Not applicable.
## 3 Experiments
### Experimental Results  
#### Overall Performance  
Experimental results listed in Table 2 show that our proposed model (SDNN) performs competitively on MR and SST datasets. We can draw the following observations from Table 2.  
First, we can see that the Feature-SVM outperformed the SVM on two datasets by roughly 0.9% on accuracy and 0.8% on F1. It shows that the SVM equipped with sentiment linguistic knowledge can better learn the representation of text. It also means that the sentiment linguistic knowledge is important to text sentiment classification tasks. Obviously, compared with Feature-SVM on two datasets, our SDNN model improved the accuracy by roughly 8.0% and F1 by roughly 7.3%. The main reason is that traditional machine learning-based methods pay more attention to word frequency features while ignore the context structure information.  
Second, the conventional deep learning models (i.e., LSTM, Bi-LSTM, and CNN) outperformed Feature-SVM by a large margin on the MR and SST datasets. Although the performance of LSTM on the MR dataset was similar to that of the Feature-SVM, the accuracy and F1 of the LSTM on the SST dataset were improved by 4.9% and 4.3%, respectively. The LSTM was the worst performer of the conventional deep learning models. Undoubtedly, compared with the traditional deep learning models (i.e., LSTM, Bi-LSTM, and CNN), BLSTM-C had better results by effectively combining the CNN and LSTM networks, because it can simultaneously learn the sequence structure information of the text and capture the local features of the text, which helps to better understand the text structure information.  
Further, the deep learning models with parsing structures (i.e., Tree-LSTM and LR-Bi-LSTM) outperformed the LSTM model on the two datasets by roughly 4.0% on accuracy and 3.8% on F1. These examples demonstrate that integrating external knowledge into deep neural networks can have a better understanding of the input text for sentiment analysis. As we expected, the SDNN achieved the best performance among all the strong competitors on the MR and SST datasets. Compared with the BLSTM-C, which was the best performer of all of the baseline methods on the same datasets, our SDNN model upgraded the results by about 1.2% on accuracy and 1.0% on F1. The results confirm our main idea that integrating sentiment linguistic knowledge into the deep neural network can enhance the quality of text representation learning for sentiment classification.  
In order to analyze the effectiveness of the sentiment attention mechanism of SDNN, we also designed the ablation test in terms of discarding the sentiment attention mechanism (denoted as SDNN w/o sentiment attention). It can be clearly seen from the experimental results that the accuracy and F1 of the SDNN decreased sharply without regard to the sentiment attention mechanism. This proves our intuition that the proposed sentiment attention mechanism plays a crucial role in SDNN for text sentiment classification.
## 2 Theoretical Background
### The Recovery of the Nonstationary Signal and Related Signal Processing
If the number of zero crossings is odd, then the signal consists of an integral number of cycles; otherwise, the samples from the second-last zero-crossing until the end should be discarded;
* The band-stop IIR filter is then tuned to suppress the fundamental component;
* The recovered signal shows good spectral legibility both for frequency (steady state) and time-frequency (transient) analysis.
# Dynamics of Mechanisms with Superior Couplings
## 2 Materials and Methods
### Geometric Synthesis
In order to determine the total mechanical efficiency, it is necessary to integrate on the whole working portion, ascent, and return. \[F_{m}=F_{c}\cdot\sin\tau \tag{4}\]  
\[v_{2}=v_{1}\cdot\sin\tau \tag{5}\]  
\[P_{u}=F_{m}\cdot v_{2}=F_{c}\cdot v_{1}\cdot\sin^{2}\tau \tag{6}\]  
\[P_{c}=F_{c}\cdot v_{1} \tag{7}\]  
\[\eta_{i}=\frac{P_{u}}{P_{c}}=\frac{F_{c}\cdot v_{1}\cdot\sin^{2}\tau}{F_{c} \cdot v_{1}}=\sin^{2}\tau=\cos^{2}\delta \tag{8}\]  
\[\sin^{2}\tau=\frac{s\prime^{2}}{r_{A}^{2}}=\frac{s\prime^{2}}{\left(r_{0}+s \right)^{2}+s\prime^{2}} \tag{9}\]  
Figure 3: Forces and speeds at the translated dept with the sole. \[F_{\psi}=F_{c}\cdot\cos\tau \tag{10}\]  
\[v_{12}=v_{1}\cdot\cos\tau \tag{11}\]  
\[P_{\psi}=F_{\psi}\cdot v_{12}=F_{c}\cdot v_{1}\cdot\cos^{2}\tau \tag{12}\]  
\[v_{2}=v_{1}\cdot\sin\tau \tag{5}\]0  
It can be easily verified that the instantaneous loss added with instantaneous efficiency permanently gives the total value 1. The way in which the dynamic coefficient \(D\) is expressed and determined is represented exactly (in more detail) in the paper [1], here being only exposed the relation (14) from which it is made explicit. The dynamic coefficient of this type of mechanism or its transmission function, \(D\), is determined by the relations (14), where \(D^{\prime}\) is the \(D\) derivative depending on the angle FI [1]. \[\left\{ \begin{array}{l}D=\tau^{\prime}+1;\tau^{\prime}=\frac{g^{\prime \prime}\cdot(\tau_{0}+s)-s^{\prime 2}}{(\tau_{0}+s)^{2}+s^{\prime 2}}\\ D^{\prime}=\tau^{\prime};\tau^{\prime\prime}=\frac{[s^{\prime\prime}\cdot(\tau _{0}+s)-s^{\prime}\cdot s^{\prime\prime}]\cdot[(\tau_{0}+s)^{2}+s^{\prime 2}]-2 \cdot s^{\prime}\cdot[s^{\prime\prime}\cdot(\tau_{0}+s)-s^{2}]\cdot[\tau_{0}+s +s^{\prime\prime}]}{[(\tau_{0}+s)^{2}+s^{\prime 2}]^{2}}\end{array} \right. \tag{14}\]  
The speed based on the dynamic coefficient is expressed with the relation (15) and the corresponding acceleration is determined with the Equation (16), Figure 4 [1]. \[\dot{y}\equiv v^{2}=s^{\prime}\cdot D\cdot\omega \tag{15}\]  
\[\ddot{y}\equiv a_{2}=(s^{\prime\prime}\cdot D+s^{\prime}\cdot D^{\prime}) \cdot\omega^{2} \tag{16}\]  
Figure 4 shows the differences between known classical kinematics and precision kinematics introduced, for the first time, by the author in the paper [1]. The precision kinematics influenced by the dynamic coefficient, presents a slight dynamic aspect, due to the influence of the way in which the forces and speeds are transmitted within the mechanical coupling.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 2 DTNbox
### BP Settings and Retransmission Features
#### 2.4.2 DTNbox Acknowledgments and Retransmissions  
In a DTN network, the end-to-end path to destination is divided into multiple DTN hops and hop-by-hop reliability is enforced by BP using a reliable transport protocol on each hop, such as TCP or LTP \"red\" [18; 19]. However, proper end-to-end reliability requires acknowledgments from  
Figure 3: Multilateral synchronizations: complex synchronization trees.  
destination. Therefore, on the destination node DTNbox acknowledges all file updates and a few selected important commands. If these acknowledgments are not received in time, DTNbox retransmits the unacknowledged data. The retransmission timeout is automatically set to twice the bundle lifetime, as after this time it is impossible to receive any acknowledgment (one lifetime for the original data, another for the acknowledgment). Multiple retransmission can be carried out, as usual, until a threshold is reached. The total number of transmissions can also be set according to the destination. By not allowing any retransmission, the user can disable this feature to rely only on BP mechanisms.
# Unique Features of Nonlocally Nonlinear Systems with Oscillatory Responses
## 4 Characteristics of Solitons
### In-Phase and Out-of-Phase Bound-State Solitons  
In the case of \(s=1\), the nonlinear system exhibits self-focusing nonlinearity in lower ranges of degree of nonlocality, that is, \(\sigma_{0}<\sigma_{c}\). In these ranges, we obtained bright solitons with complicated structures, which are the in-phase bound-state solitons when \(\sigma_{0}\in[0.28,0.78]\) and the out-of-phase bound-state solitons when \(\sigma_{0}\in[0.38,0.78]\)[29]. The in-phase bound-state soliton, shown in Figure 9, owns the symmetrical profile and the nonzero central value. On the other hand, the out-of-phase bound-state soliton, shown in Figure 10, owns the antisymmetrical profile and the zero central value. When the degree of nonlocality decreases, the in-phase bound-state soliton approaches the sech profile shown in Figure 9, and the out-of-phase bound-state soliton tends toward the first-order Hermite-Gaussian profile shown in Figure 10. The above-mentioned abnormal properties in the previous section, that is, the negative propagation constants and the negative slope of the power-propagation constant (the so-called anti-Vakhitov-Kolokolov stability criterion), also exist for bound-state solitons, which is shown in Figure 11a. Furthermore, it can be found from Figure 11b that the power-propagation constant diagrams are the same for both the in-phase and the out-of-phase bound-state solitons. In other words, we can say that two forms of bound-state solitons form degenerate modes.  
Figure 8: Critical power \(P_{c}\) and the soliton propagation constant \(\lambda\) versus the degree of nonlocality \(\sigma\). The inset shows \(P_{c}\) versus \(\lambda\). All plots are for \(D=1\). The obtained solitons are stable (After Ref. [27]).  
Figure 11: (**a**) Power \(P\) and propagation constant \(\lambda\) vs. the degree of nonlocality \(\sigma\); (**b**) power \(P\) vs. propagation constant \(\lambda\) of the in-phase and out-of-phase bound-state solitons for the case that \(s=1\). The obtained solitons are stable (After Ref. [29]).  
Figure 10: Profile of out-of-phase bound-state solitons (solid red lines) and the induced NRI (dashed black lines) for the case that \(s=1\). (**a**) \(\sigma_{0}=0.453\), (**b**) \(\sigma_{0}=0.731\), (**c**) \(\sigma_{0}=0.777\) and (**d**) \(\sigma_{0}=0.778\). The obtained solitons are stable (After Ref. [29]).  
Figure 9: Profiles of in-phase bound-state solitons (solid red lines) and their induced NRI (dashed black lines) for the case that \(s=1\). (**a**) \(\sigma_{0}=0.472\), (**b**) \(\sigma_{0}=0.700\), (**c**) \(\sigma_{0}=0.773\) and (**d**) \(\sigma_{0}=0.778\). The obtained solitons are stable (After Ref. [29]).