esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 6 Service Management Framework
### Cost Analytical Model
#### 6.2.1 Communication Penalty Cost for Bandwidth Allocation \(\bar{\
u}\) in Fog Environment  
Each fog job \(J_{i}\) demands a pre-defined bandwidth requirement denoted by \(\bar{\
u}\) allocated to communicate data between fog nodes and the cloud tier. A cost \(\Lambda_{i}\) of bandwidth usage is incurred per data unit of a fog job \(J_{i}\), modeled by an exponential distribution of bandwidth penalty mean \(\mu_{\Gamma}\) as follows:  
\[\Lambda_{i}=\exp(\mu_{\Gamma}) \tag{13}\]  
The communication bandwidth \(\Gamma_{i}\) allocated for a fog job \(J_{i}\) in the fog tier is subject to an SLA that stipulates an exponential bandwidth penalty cost curve modeled by \(\rho_{i}^{\Gamma}\), formulating the total penalty cost of bandwidth usage per time unit of data as follows:  
\[\rho_{i}^{\Gamma}=\kappa_{\Gamma}*(1-\mathrm{e}^{-\
u\ \Lambda_{i}\ \bar{\
u}}) \tag{14}\]  
where \(\kappa_{\Gamma}\) is a monetary cost factor for the bandwidth allocation penalty and \(\
u\) is an arbitrary scaling factor.  
.2.2 QoS Penalty Cost for Queue Waiting \(t\omega_{i}^{\beta}\) across the Cloud-Fog Environment  
For each fog job \(J_{i}\) waiting in resource queues \(\mathbf{Q}\) of the cloud tier to receive service, there exists a waiting cost denoted by \(\psi_{i}\) for each time unit of waiting \(t\omega_{i}^{\beta}\), modeled by an exponential distribution with a waiting penalty mean \(\mu_{\omega}\) as follows:  
\[\psi_{i}=\exp(\mu_{\omega}) \tag{15}\]  
As explained in 8, the waiting \(f\omega_{i}^{\beta}\) of a fog job \(J_{i}\) in the fog environment and its waiting \(c\omega_{i}^{\beta}\) in the cloud environment compose a total waiting \(t\omega_{i}^{\beta}\). Thus, the waiting \(t\omega_{i}^{\beta}\) of a fog job \(J_{i}\) reaching the cloud tier is subject to an SLA that stipulates an exponential waiting penalty cost curve modeled by \(\rho_{i}^{\omega}\), formulating the penalty cost for each time unit of waiting \(t\omega_{i}^{\beta}\) as follows:  
\[\rho_{i}^{\omega}=\kappa_{\omega}*(1-\mathrm{e}^{-\
u\ \bar{\psi}_{i}\ (f \omega_{i}^{\beta}+c\omega_{i}^{\beta})}) \tag{16}\]  
where \(\kappa_{\omega}\) is a monetary cost factor for the waiting penalty.
# Influence of Surface State in Micro-Welding of Copper by Nd:YAG Laser
## 1 Introduction
Moreover, surface roughness of workpiece would affect absorption phenomenon of laser beam. There is a minimum depth required to ensure multi-reflections effect within the keyhole. A shallow keyhole means that the reflection will be directed to the surrounding as shown in Figure 1. By considering Fresnel absorption, part of the incident laser energy is reflected by keyhole walls, while part is absorbed within the keyhole walls. The angle of incidence of laser beam is equal to the angle of reflection at the interface of liquid and vapor. In actual phenomenon, some of the energy will be lost within the keyhole by plasma absorption [14; 15]. [PERSON] et al. employed ray tracing technique to study Nd:YAG laser absorption behavior on both smooth and rough surfaces. They showed that for light which is normally incident to the surface, the absorption rate increases with increase in roughness, but for tangentially incident light, the relationship between absorption rate and surface roughness is intricate [16]. However, these effects on welding characteristics of copper have not been fully clarified yet. Therefore, effects of surface shape and surface roughness on absorption characteristics were experimentally investigated using 1064 nm Nd:YAG laser in order to improve the absorption rate for stable welding of copper. Figure 1: Schematic illustration of multi-reflections effect inside the keyhole.
## 4 Results of Finite Element Analysis
### Inductances  
Inductances along d and q-axis, respectively, were evaluated while either of d or q-axis current is set during rotation at 6000 r/min. For instance, when d-axis current is set, q-axis current is 0 to prevent the saturation on core by q-axis current. Now that these inductances are to be compared with the expectation from equations in LPM, the same condition must be satisfied.  
D and q-axis inductance in Figure 10a both tend to be higher across the overall range as the angle becomes wider. Note that d-axis inductance grows to a certain degree and afterwards becomes constant. The deviation between min and max is observed 28% at 70 degrees and gradually diminished to 4.9% at 110 degrees as getting the angle wider. Min values among the models range from 2.126 to 2.9 mH, which increase by 36%. On the other hand, max value increases by 3.31%. Declines in q-axis inductance are similar in all the models, which are caused by saturation in core. Maximum at 110 degrees is higher by 12.8% than one at 70 degrees, and it is 10.4% in case of minimum. Assuming that an inverter has 100 V\({}_{\text{dc}}\) and 14 A\({}_{\text{rms}}\) and d-axis inductance is constant at the maximum.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Angle** & **Magnet Volumes** & **PM Flux Linkage** & **Back-Emf** \\
**[degree]** & **[cm\({}^{3}\)]** & **[mWb\({}_{\text{rms}}\)]** & **THD [\%]** \\ \hline
70 & 15.98 & 52.3 & 14.42 \\
80 & 14.31 & 49.67 & 10.02 \\
90 & 13.16 & 48.34 & 8.74 \\
100 & 12.53 & 46.49 & 7.01 \\
110 & 11.80 & 45.06 & 6.65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PM flux linkage and THD in back-emf.  
Figure 8: Back-EMF at 6000 r/min: (**a**) waveforms of back-EMF (**b**) flux density distribution in FEA.  
Figure 9: Magnitudes of harmonic components in back-EMF.
## 3 Sliding Mode Controller
### Stability Analysis  
To assess the stability of the introduced controller, consider the function \(V(t)=\frac{1}{2}\gamma^{2}(t)\) as a potential Lyapunov candidate. Differentiating \(V(t)\) with respect to time and incorporating the relationship given in Equation (6), we obtain  
\[\begin{split}\dot{V}(t)&=\gamma(t)\dot{\gamma}(t)\\ &=\gamma(t)(\tilde{\epsilon}_{d}+k_{1}\dot{\epsilon}_{d})\\ &=-\gamma(t)(Q\text{ sgn}(\gamma(t)))\\ &=-Q\frac{\gamma^{2}(t)}{|\gamma(t)|}\end{split} \tag{9}\]  
When \(Q>0\), \(V(t)\) is determined to be negative semi-definite. Consequently, a value of \(\gamma(t)=0\) will be attained in finite time.  
For the practical implementation of the controller, specifically tailored to this context, namely a low-velocity parking task, the presence of speed terms in the denominator can lead to undesirable steering commands. Hence, the design of the gain coefficients \(Q\) and \(k_{1}\) is as follows:  
\[Q=\hat{Q}v^{2}l\cos{(\psi)}/L_{2}, \tag{10}\]  
\[k_{1}=\hat{K}_{1}lv\cos{(\psi)}/L_{2}. \tag{11}\]  
Therefore, the expression for steering input can be reformulated as  
\[\varphi_{d}^{SMC}=\tan^{-1}\biggl{(}-\frac{\tan(\psi)}{l}+\hat{k}_{1}\tan{ \epsilon_{\theta}}+\hat{Q}\tan^{-1}\gamma(t)\biggr{)}. \tag{12}\]
## 3 Results and Discussion  
### Material Thermo-Physical Characterization  
The DSC hysteresis curves for the three PCMs, namely salt hydrate, paraffin wax and milk fat (refer to Appendix A) are interpreted to obtain PCM thermo-physical properties. The thermo-physical properties of interest for each material are summarized in Table 4. The table shows that the salt hydrate has the highest latent heat of fusion (\(L\)) with narrow melting range (desired), whereas milk fat has the lowest \(L\) with wider melting range (undesired). It is very obvious that salt hydrate has superior thermal energy storage compared to the other two PCMs, however the availability, lower cost, lower corrosion rates and human friendliness of the paraffin and milk fat can justify their use as PCMs compared with salt hydrates. Milk fat shows the least heat of fusion which is mainly because the heat of fusion in the melting peak between 18 and 28 \({}^{\circ}\)C is considered by the DSC calculation while milk fat melts over a wider range of 10-42 \({}^{\circ}\)C. In case the thermal energy storage by milk fat over the whole melting range is considered, it may compare with that of paraffin wax.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & **Salt Hydrate** & **Paraffin** & **Milk Fat** \\ \hline Thermal conductivity (W/m\({}^{\circ}\)C) & 0.60 [48] & 0.20 [49] & 0.29 [34] \\ Specific heat capacity (kJ/kg\({}^{\circ}\)C) & 2.0 [48] & 2.0 [49] & 2.3 [35] \\ Density (kg/m\({}^{3}\)) & 1500 [48] & 802 [49] & 911 [50] \\ Viscosity (kg/m-s) & 0.00184 [48] & 0.003 [49] & 0.045 [51] \\ \hline \multirow{2}{*}{Solidus temperature (\({}^{\circ}\)C)} & Catalogue & 27 [48] & 38 [49] & 10 [35] \\  & DSC & 28 & 38 & 10 \\ \hline \multirow{2}{*}{Liquidus temperature (\({}^{\circ}\)C)} & Catalogue & 32 [48] & 43 [49] & 40 [35] \\  & DSC & 32 & 43 & 40 \\ \hline \multirow{2}{*}{Latent heat of fusion (kJ/kg)} & Catalogue & 200 [48] & 140 [49] & - \\  & DSC & 214 & 142 & 60 \\ \hline \multirow{2}{*}{Material source} & \multicolumn{3}{c}{Rubitherm (SP-29)} & \multicolumn{1}{c}{Rubitherm (RT-42)} & \multicolumn{1}{c}{Kerry Gold} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermo-Physical properties of phase change materials (PCMs) characterized by differential scanning calorimetry (DSC).
## 3 Results and Discussion
### Pathogenesis-Related (PR) Proteins  
Pathogenesis-related (PR) proteins, especially thaumatin-like proteins (TLPs) and chitinases, occur naturally in grapes and mostly remain stable under fermentation conditions. These proteins are the primary contributors to protein instability and unwanted haze in white wines. To address this issue, white wines undergo protein stabilization through bentonite firing, which effectively removes excess proteins before bottling [18].  
In 2021, no significant differences in the concentrations of PR proteins were observed between the wines studied (Table 2). In 2022, SPA-22 wine contained a higher concentration of TLP-2 and lower concentrations of chitinases compared to control SCE-22 wine. This trend was supported by the results of two-way ANOVA. Previous studies have demonstrated that chitin and its deacetylated derivative, chitosan--substrates for PR chitinases--can effectively remove class IV chitinases from wine and enhance wine protein stability [35; 36]. Certain strains of _S. paradoxus_ have been reported to possess elevated levels of yeast cell wall chitin. This feature allowed them to bind more grape chitinases than _S. cerevisiae_ and to more effectively remove them from wine model solutions, as shown earlier [10]. This was further supported by [PERSON] and [PERSON] [11], who noted improved protein stability of wines from certain grape cultivars treated with _S. paradoxus_ yeast hulls in laboratory tests, including the strains P01-161 and P01-167.  
In this study, SPA fermentation with _S. paradoxus_ did reduce the concentration of certain chitinases but did not improve protein stability compared to control SCE treatment. The wines produced by the two investigated yeasts required almost equal bentonite doses for complete protein stabilization (Table 2). The difference between this result and previous findings [35; 36] might be due to yeast and PR proteins interacting differently during real fermentation compared to laboratory conditions. Additionally, [PERSON] and [PERSON] [11]observed variability in the effectiveness of _S. paradoxus_ yeast hull treatments across different wine cultivars and styles. This implies that the specific PR protein profile of Malvazija istarska grape juice and wine could have contributed to the reduced effect observed in this study. A significant impact of harvest year was observed, with wines from 2022 containing higher concentrations of PR proteins than those from 2021 (Table 2).
# PM2.5 Concentration Prediction Based on CNN-BiLSTM and Attention Mechanism
## 3 Experiment
### Results and Discussion
#### 3.3.2 Long-Term Forecast with Multi-Source Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **24** & **48** & **72** & **96** & **120** & **144** \\ \hline CNN-BiLSTM-Attention & 5.295 & 3.095 & 4.834 & 6.467 & 8.768 & 8.974 \\ CNN-BiLSTM & 6.215 & 5.333 & 8.219 & 7.704 & 9.063 & 10.579 \\ CNN-LSTM & 5.817 & 7.032 & 10.000 & 10.185 & 10.671 & 13.169 \\ XGBOOST & 6.041 & 8.541 & 13.290 & 15.346 & 16.800 & 16.714 \\ SVR & 8.123 & 8.549 & 11.484 & 11.945 & 12.154 & 12.884 \\ Ridge Regression & 7.961 & 9.032 & 12.722 & 13.852 & 14.254 & 15.482 \\ Lasso Regression & 8.234 & 9.781 & 12.633 & 12.975 & 13.551 & 16.394 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: RMAE for long-term prediction of each model (Unit: \(\text{\mu g}/\text{m}^{3}\)). factors, which we use mean interpolation to fill in, and the min-max method is used to normalize the data to make the model more stable. 2. Secondly, the role of hyperparameters in a model is very important. We determine the values of the hyperparameters through parameter tuning tools and experimental results, and then ensure the input and output feature sizes through a large number of experiments. 3. Finally, the proposed model is used to predict the PM2.5 concentration. To prove the better performance of the proposed model, six comparison models are set up. Additionally, we verify the effectiveness of the model from both short-term and long-term aspects. In terms of short-term forecasts, the results show that the proposed model not only has a smaller error, but also holds a faster convergence speed. In terms of long-term forecasts, although the error value of prediction will become larger and larger, the results are also better than other comparison models.
## 3 Basic Concepts
### Location Attacks
#### 3.4.2 Visit Attack  
In this attack, introduced in [3; 9; 24; 25], an an adversary is aware of a subset of the locations that the individual has visited and the time the individual visited these locations. Let \(k\) be the number of visits \(vs.\) of an individual \(s\) known by the adversary. The visit background knowledge consists of configurations derived from \(k\) visits, formally represented as \(B_{k}=T_{s}[k]\), where \(T_{s}[k]\) indicates the set of all possible \(k\)-length subsequences within the trajectory \(T_{s}\).  
Each instance \(b\in B_{k}\) represents a spatio-temporal subsequence \(X_{s}\) of length \(k\). The subsequence \(X_{s}\) positively matches a given trajectory if the trajectory aligns with \(b\) in both spatial and temporal aspects. Formally, given a record \(d\) in the dataset \(D\), the matching function is defined as  
\[evaluate(T,b)=\begin{cases}1,&\forall(l_{i},t_{i})\in b,\exists(l_{d_{i}},t_{d_{i} })\in d\text{ such that }l_{i}=l_{d_{i}}\wedge t_{i}=t_{d_{i}}\\ 0,&otherwise\end{cases} \tag{2}\]  
In this attack, the attacker knows that, for instance, a person went, first, to a supermarket at 1 p.m. and after work at 2 p.m. They know the time when the person visited the place.  
In the next subsection, we will introduce the _combinatorial problem_.
## 3 Model Architecture
### ASTGC-LSTM Network
#### 3.3.2 Temporal Hierarchical Architecture  
After the LSTM layer, the incremental features of the sequence \(\{E_{1},E_{2},\cdots,E_{T}\}\) were input into the STGC-LSTM layer as node features. Three ASTGC-LSTM layers were stacked throughout the process to learn the spatial configuration and temporal dynamics. Inspired by the space pooling in CNN, the ASTGC-LSTM also adopts a temporal hierarchical architecture and average pooling to increase the temporal receptive field. Through the temporal hierarchical architecture, the temporal receptive field of each time input at the top ASTGC-LSTM layer becomes a short-term clip from a frame, which can be more sensitive to the perception of the temporal dynamics. Moreover, the performance of the algorithm is improved, and the computational complexity is greatly reduced.
###### Abstract  
The article mainly presents that a simple antenna structure with only two branches can provide the characteristics of dual-band and wide bandwidths. The recommended antenna design is composed of a clockwise spiral shape, and the design has a gradual impedance change. Thus, this antenna is ideal for applications also recommended in these wireless standards, including 5G, B5G, 4G, V2X, ISM band of WLAN, Bluetooth, WiFi 6 band, WiMAX, and Sirius/XM Radio for in-vehicle infotainment systems. The proposed antenna with a dimension of \(10\times 5\) mm is simple and easy to make and has a lot of copy production. The operating frequency is covered with a dual-band from 2000 to 2742 MHz and from 4062 to beyond 8000 MHz and, it is also demonstrated that the measured performance results of return loss, radiation, and gain are in good agreement with simulations. The radiation efficiency can reach 91% and 93% at the lower and higher bands. Moreover, the antenna gain can achieve 2.7 and 6.75 dBi at the lower and higher bands, respectively. This antenna design has a low profile, low cost, and small size features that may be implemented in autonomous vehicles and mobile IoT communication system devices.  
Keywords:dual-band; mobile IoT; autonomous vehicles; B5G; 5G; V2X; DSRC; WiFi 6 band; Sirius/XM Radio; ISM band +
Footnote †: journal: 2021  
_Acticle_  
Minaturized Broadband-Multiband  
Planar Monopole Antenna in Autonomous Vehicles Communication System Device.  
_Electronics_**2021**, 10, 2715. [[https://doi.org/10.3390/electronics10212715](https://doi.org/10.3390/electronics10212715)]([https://doi.org/10.3390/electronics10212715](https://doi.org/10.3390/electronics10212715))
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 5 Results and Discussion
### Simulation 3: Performance Evaluation under High-Density Nodes Scenario (100 Nodes)
#### 5.3.2 Delay Performance Analysis  
The results of the scenarios jin Figure 19 show a high delay margin for the malicious scenario as compared to the framework scenario. In the sinkhole scenario, the highest recorded delay value was 6,152,112.929 microseconds for node 63 as it was the first computed node, such as nodes 29 and 53. These levels are too high as compared to the framework scenario. The framework scenario at node 1 had a delay of 581,591.68 microseconds. All the other nodes produce a constant delay of 429,470.72 microseconds. This was a significant drop in delay compared to the other scenario. The effectiveness of the framework is clearly shown in the result presented in Figure 19 where the delay was reduced to about four times as compared to that of the sinkhole scenario. The framework aimed to maximize QoS offering and this is evident as it outperforms the normal scenario as well. It is important to note that the delay caused by the malicious nodes was improved significantly by the framework implementing the modified OLSR protocol.
## 7 Conclusions and Future Work  
We considered the moving object coverage problem in a WSN with a limited number of mobile sensors. We required full coverage of a moving object on a predetermined trajectory. To conserve energy, we determined a movement repertoire where the total traveled distance was minimized and where the residual energy of the mobile sensors was as balanced as possible. Our results revealed that a very long time is required to determine the optimal solution to the moving object coverage problem when the predetermined trajectory is expansive. We employed two nondeterministic algorithms, the GA, and the DPSO algorithm, to manage this complexity and produce solutions at a relatively short computation time. The algorithms generated effective solutions to the moving object coverage problem efficiently. Our algorithms quickly determined quasi-optimal trajectories for mobile sensors and were determined to be applicable to larger predetermined trajectories. Under the same encoding, the GA produced trajectories significantly superior to those produced by the DPSO algorithm.  
In the future, the proposed algorithms can be applied to tracking multiple objects in the same area, each with a different predetermined path. Developing a hybrid of the GA and DPSO algorithms for moving object coverage problems may also be an area of interest. It is also interesting to study specific trajectories, such as the Hilbert space-filling curve. Furthermore, regarding the practical applications of sensors, future studies should evaluate the effects of different environmental parameters, such as wind speed, altitude, flying speed, and changes in the communication environment.  
**Author Contributions:** Conceptualization, C.-K.L.; methodology, C.-K.L.; software, H.-W.C.; investigation, H.-W.C.; validation, C.-K.L.; writing--original draft preparation, H.-W.C. and C.-K.L.; writing--review and editing, C.-K.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Capacity Evaluation Analysis
### Model Establishment
#### 5.1.2 Fitting Model Selection  
According to the analysis results in Section 4 and capacity definition, this paper further studied the potential relationship between FD or FLR and traffic capacity through model fitting, so as to verify the effectiveness of their application in traffic capacity analysis.  
First, the relationship between FD and capacity is established.  
Through data processing and analysis of the above ten simulation scenarios, it is found that dividing the length of failure duration (FD) by traffic flow (Q) can better reflect the impact of failure phenomena on traffic conditions around bus stops. Using this index as an independent variable for fitting can make the dispersion degree lower and the fitting accuracy higher.  
In Section 4, a positive correlation between FD and flow is obtained. On this basis, several common models are used for fitting and fitting effects are compared. This paper used the statistics of each of the ten simulation schemes under different CAV penetration scenarios (0%, 50%, 100%) for the fitting model.  
By analyzing the relationship between FD and capacity, it can be found that the correlation between FD and the capacity of curbside stops is poorer than that of other stops (see the circled points in Figure 11a-c). This is mainly because the curbside stop blocks the traffic flow when a transit is parked at the stop and, thus, affects the traffic capacity.  
Then the point of the curbside stop is removed. As a result, the linear fitting between the failure service index and the capacity of the other nine schemes is good. It is further verified that the index can better reflect its impact on the capacity of the road near the bus stop. Tables 7-9 shows the fitting functions used to fit the model of nine schemes and their fitting effects. It can be found that the quadratic polynomial model has the highest fitting accuracy in most cases, so this model is selected for fitting. The fitting image is shown in Figure 11.  
Figure 11: The fitting curve of the relationship between FD and traffic capacity for simulation schemes under different rates of Ra. **(a)** Fitting curve for ten schemes when Ra is 0%; **(b)** fitting curve for ten schemes when Ra is 50%; **(c)** fitting curve for ten schemes when Ra is 100%; **(d)** fitting curves for nine schemes when Ra is 0%; **(e)** fitting curves for nine schemes when Ra is 50%; **(f)** fitting curves for nine schemes when Ra is 100%.