esa-sceva/satcom-chunk-collection huggingface dataset:
# Stimulation Montage Achieves Balanced Focality and Intensity
## 2 Materials and Methods
### Optimization Model  
With the intent to balance the tradeoff between stimulation focality and intensity, Stimulation with Balanced Focality and Intensity (SBFI) combines both in the cost function. The focality is represented by the total energy of the non-target regions \(\parallel\mathbf{Ds}\parallel^{2}\). Here, \(\mathbf{D}\) is the submatrix of \(\mathbf{K}\) relating the injected current at non-target regions. The intensity can be expressed by \(\mathbf{e}_{0}^{T}\mathbf{Cs}\). The distribution of the desired electric field intensity at the target is \(\mathbf{e}_{0}\). \(\mathbf{C}\), as the submatrix of \(\mathbf{K}\), is the coefficient matrix of current at the target region (s). Thus, the cost function of SBFI can be further written as:  
\[\mathbf{s}=\underset{s}{arg\min}\bigg{(}\frac{n_{tar}}{n_{non}}\parallel\mathbf{Ds} \parallel^{2}-\lambda\mathbf{e}_{0}^{T}\mathbf{Cs}\bigg{)} \tag{1}\]  
where \(\lambda\) is the optimization parameter to balance the first term of focality and the second term of intensity. Higher \(\lambda\) favors intensity, while smaller \(\lambda\) tends to have better focality; in this work, \(\lambda\) is chosen empirically based on parameter sweep simulations. The number of voxels at the target and non-target regions are \(n_{tar}\) and \(n_{non}\), respectively. This convex optimization problem can be solved efficiently by software such as CVX, and the only unknown current pattern \(\mathbf{s}\) is therefore obtained.  
Furthermore, the algorithm can be expanded to fulfil the needs of targeting multiple brain regions in a single stimulation session. For example, different erectile dysfunction (ED) symptoms are mapped to different cortical targets, and stimulating a single target may be insufficient to address multi-dimensional ED pathology [27; 28; 29]. In addition, network-targeted transcranial direct current stimulation (net-tDCS) is able to change the excitability of the sensorimotor network, and show the potential to manipulate network connectivity patterns [30]. Similarly, multi-target stimulation is desirable in the potential application of stopping seizure with the guidance of neural recording techniques [31; 32]. However, stimulation effects at different targets may not be the same if complex brain structure and other factors are considered. Thus, the optimization parameter \(\lambda\) is further refined by \(\mathbf{\lambda}=diag(\lambda_{1},\,\lambda_{1},\dots,\,\lambda_{n_{tar}})\) to balance the stimulation effects of different targets.  
\[\mathbf{s}=\underset{s}{arg\min}\bigg{(}\frac{n_{tar}}{n_{non}}\parallel\mathbf{Ds} \parallel^{2}-\mathbf{e}_{0}^{T}\mathbf{\lambda}^{T}\mathbf{Cs}\bigg{)} \tag{2}\]
# A Rotating Object Detector with Convolutional Dynamic Adaptive Matching
## 4 Experiment
### Ablation Studies
#### 4.4.2 Evaluate DCM  
To further validate the effectiveness of the proposed DCM, Table 3 validated the impact of convolution number n on network performance on the HRSC-2016 dataset. \(n=0\) is the baseline model without the addition of DCM, and as the number of convolution kernels increases, mAP also continuously increases. When \(n=1\), it indicates that only one rotation convolution kernel is added, which increases mAP by 1.14% compared to the baseline model. This indicates that the rotation convolution can better extract the features of rotating targets. When \(n=4\), mAP reaches its maximum value of 90.41%, indicating that using multiple rotation convolution weighted summation is more effective. As the value of n continues to increase, mAP actually decreases. This indicates that when DCM has four rotation angle branches, dynamic rotation convolution has extracted enough directional feature information. The rotational characteristics of the target are limited, and increasing the value of n does not enhance the model's performance. Nevertheless, the number of parameters (Params) will continue to grow, thereby increasing the model's complexity and potentially impeding training.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**With DCM** & **With GAE** & **mAP (\%)** \\ \hline \(\bigstar\) & \(\bigstar\) & 84.35 \\ \(\bigstar\) & \(\bigstar\) & 88.69 \\ \(\bigstar\) & \(\bigstar\) & 87.23 \\ \(\bigstar\) & \(\bigstar\) & **90.14** \\ \hline \hline \end{tabular} DCM: the dynamic convolution module; GAE: the gradient adaptive equalization loss function.  
\end{table}
Table 1: Effects of different modules on the HRSC-2016.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**With DCM** & **With GAE** & **mAP (\%)** \\ \hline \(\bigstar\) & \(\bigstar\) & 86.79 \\ \(\bigstar\) & \(\bigstar\) & 88.14 \\ \(\bigstar\) & \(\bigstar\) & 87.43 \\ \(\bigstar\) & \(\bigstar\) & **90.52** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effects of different modules on the UCAS-AOD.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**n Number** & **mAP (\%)** & **Params (M)** \\ \hline
0 & 84.32 & 73.24 \\
1 & 87.24 & 74.38 \\
2 & 88.46 & 78.42 \\
4 & **90.41** & 79.57 \\
8 & 88.66 & 96.52 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The impact of quantity \(n\) on network performance.
## 3 Data Analysis and Result Interpretation
### Q-Likert Factor Scores  
After factor extraction, the details of the factors and their association with the variables were calculated and expressed through factor loading plots and matrices. Each Q-Likert sort and its elements had a different relation with each identified factor. The significance of factor loading in PM was determined through the standard error (\(SE\)) of factor loadings, where \(N\) = number of statements.  
\[SE=\frac{1}{\sqrt{N}} \tag{1}\]  
Expert sets with loadings greater than 2.58 (\(SE\)) were considered statistically significant at the level of 0.01, which showed a strong relationship between the Q-Likert sort and the identified factors. The loadings greater than 0.31 (ICT-1) and 0.38 (ICT-2) were statistically significant at the 0.01 level.  
Standard error of factor loading for (ICT-1) 71 statements = 2.58(1/\(\sqrt{7}\)1) \(\geq\) 0.31  
Standard error of factor loading for (ICT-2) 45 statements = 2.58(1/\(\sqrt{45}\)) \(\geq\) 0.38  
The weights of each factor were calculated and multiplied by the respective array of statements of experts' responses. It followed the standardization of all arrays of respective experts relating to each factor. For standardizing the array of responses from experts for each factor, first, Q-Likert sort factor loadings (\(f\)) from the PM for each extracted factor were separated. After separation, \(w\) (weight) was calculated through Equation (2).  
\[W=\frac{f}{(1-f^{2})} \tag{2}\]  
The weighted values of each Q-Likert sort were then multiplied with each statement of the array (raw responses from the experts) of that respective Q-Likert sort. Afterward, the sum of each statement score was calculated, for which \(Z\)-scores for each statement were computed by Equation (3), where '\(T\)' is the total of each weighted statement and '\(s\)' is the standard deviation of the Total (\(T\)).  
\[Z=\frac{T-\overline{X}}{S} \tag{3}\]  
The \(Z\)-scores were used to compare and standardize the effect of different statements in different arrays to reflect the effect on each factor. The index used was (\(-\)2 to +2), which was compatible with the Likert scale used during data collection. Each statement was discussed according to its scores. If any statement had a similar score but with reverse signs, then both possibilities about the concourse were provided. In other cases, higher loading was preferred over lower loading to interpret the results.
## 3 Results  
### Dam Break Hydrodynamics  
The hydrodynamics modelling is validated by comparing the numerical time series of the flow depth with the mean experimental one (Section 2.3) measured by [PERSON] et al. (2018). Figure 3 shows the evolution of the normalised flow depth \(h/h_{0}\) over \(t\) at every experimental WG position. The modelled emptying of the reservoir at \(x=-0.10\) m (Figure 3a) follows closely the experimental mean, also reproducing the backward travelling perturbation due to the presence of the step and the gate structures, resulting in an \(\text{nRMSE}_{l}=0.010\) for \(h\).  
The evolution of \(h/h_{0}\) in the remaining two WGs is less accurate with \(\text{nRMSE}_{l}=0.068\) at WG2 (Figure 3b) and \(\text{nRMSE}_{l}=0.031\) at WG3 (Figure 3c). At WG2 (Figure 3b) \(h/h_{0}\) is better modelled for \(t\geq 2.25\) s whereas \(h/h_{0}\) immediately after the arrival of the tip of the dam break flow shows a larger difference, this is due to the 3D flow effects, as mentioned in Section 2.3, not being fully captured by the numerical model. However, the \"step\" present in the experimental data of \(h/h_{0}\), caused by the gate structure protruding inside the flume cross section, is qualitatively reproduced. At the WG3 position, the close match between the experimental and numerical arrival time is essential for a good initialization of the container dynamics. This is because its centre in \(x\)-direction is positioned at the same location of WG3. The close match for \(1.75\) s \(\geq t\geq 2.5\) s is also important as that part of the flow is responsible for the container entrainment in the dam break flow.  
Additionally, the velocity of the tip of the flow is compared with the values calculated by [PERSON] et al. (2018); [PERSON] et al. (2018) by using the dam break arrival time. The first study found this  
Figure 3: Comparison of \(h/h_{0}\) between numerical results and [PERSON] et al. (2018) at (**a**) WG1 (\(nRMSE_{l}=0.010\)), (**b**) WG2 (\(nRMSE_{l}=0.068\)) and (**c**) WG3 (\(nRMSE_{l}=0.031\)).  
velocity being 2.46 m/s when using a single container setup with the second finding it equal to 2.2 m/s but with multiple containers. The numerical flow velocity is calculated by directly using the output particle velocity of the fluid at WG3, resulting in a value of 2.53 m/s. Therefore, when compared to the experimental velocities only a percentage error of 2.8% is found. Finally, if compared to the analytical solution of [PERSON] [58] for which the bore velocity would results in 2.65 m/s only a 4.5% difference is found.
## 2 Data Provenance  
This section first reviews three forms of data provenance. The proposed model aims to pay a query, which is based on the source tuples contributing to the query result. Therefore, it is needed to track the source tuples given a query and its results.  
[PERSON] et al. [15] stated that a relational database has three forms of \"provenance\", which describes the relationship between source data and result data. \"Where-provenance\" is the first form, which has attribute-level granularity, indicating the origin of the attribute value of the source tuple contained in the query result. \"Why-provenance\" is the second form that explains why the result tuple returned is in the query result. \"How-provenance\" [13] is the third form that describes how source tuples form result tuples. The latter two forms have tuple level granularity.  
The following is a sample relational database instance and query through Example 1.  
**Example 1**.: _Let 1 be a database instance, which is made up of relations \(R\) and \(S\). Table 1 presents the contents of relation \(R\), and Table 2 presents the contents of relation \(S\). Relation \(R\) records the course No. and the name of the student. For instance, the course No. is 00602001 for the student [PERSON]. Relation \(S\) records the grade and the credit. For instance, consider Course No. 05110208, whose grade is 75 and whose credit is one. Each record in a relation is called a base tuple, for example relation \(R\) and relation \(S\) each contain four base tuples._  
Suppose that student names need to be retrieved; then, the query (relational calculus) is \(Q=\{<n>|\exists cn,\exists g,\exists c(R(n,cn)\bigwedge S(cn,g,c))\}\). Here, \(Q\) stands for query, \(R\) and \(S\) for relation, \(n\) for name attribute, \(cn\) for course No. attribute, \(g\) for grade attribute, and \(c\) for credit attribute.  
The query result is \(Q(I)=\{<[PERSON]>,<[PERSON]>\}\). Here, \(Q\) represents the query, \(I\) represents the database instance, and \(Q(I)\) represents the query result on database instance \(I\).  
Table 3 records the various forms of provenance of the query results in Example 1. In the discussion below, Table 3 will be referenced.
# Robust Model-Free Control for MMC Inverters in Cold Ironing Systems
## 2 Description and Modeling of the MMC Converter  
To ensure shore power systems meet the high power and voltage demands of ships, selecting a suitable converter for high-power generation and transmission is crucial. Thishas led to the adoption of the MMC converter in such systems. Due to its modularity, the MMC converter is advantageous because it can adapt to varying voltage and power levels, unlike traditional topologies, which are typically limited to three or five levels in medium-voltage applications. Increasing the number of levels in conventional converters was restricted by the complexity of possible topologies [39].
## 4 New Classification of Methods for the Detection of Water Network Operating Conditions
### Software Methods (Indirect)  
#### 4.2.1 Mass/Volume Balance Methods  
The implementation of balance methods in water supply companies results mainly from economic needs [41]. Water losses and related network failure rates are measures of the quality of the water distribution system management. Water losses are commonly understood as the difference between the amount of water injected into the water distribution network and the sum of water used for the system's technological purposes and sold to customers. According to the traditional methodology, water losses are given as an index defining the percentage of the sum of the annual volume of water sold and used out of the annual volume of water injected into the water network. The value of the water loss index, according to the classic method of their estimation, should not be greater than 10%.  
In 1999, the International Water Association (IWA) appointed a task force to develop guidelines for standardizing the methodology of tap water loss control. Since 2002, conferences have been organized by this team to present lessons learned from implemented procedures. The water balance framework defines, among others, the Infrastructure Leakage Index (ILI), the Unavoidable Annual Real Losses (UARL), the Real Loss Basic (RLB), and the Non-Revenue Water Basic (NRWB) volume index [42].  
Balance methods, as common solutions, have already been implemented in many water supply companies but should find wider application. This is possible provided that the calculations are automated, which reduces the time consumption and increases the reliability of the performed balances. [PERSON] et al. [43] developed a water balance system according to IWA standards based on geographic information system GIS) software. [PERSON] et al. [44] showed that significant drawbacks of balance methods for the diagnostic purposes of anomalous distributions are very often too little data and their low reliability. In order to improve the diagnoses based on the balance method, simulations were performed using the Monte Carlo method of generating pseudo-random numbers and the high-density regions (HDR) method of probability distribution. A more advanced solution [45] implemented multi-criteria decision analysis (MCDA) to support water supply network management and anomalous condition detection. The mass and volume methods are widely used in management at the decision-making stage.
# A Terminal-Oriented Distributed Traffic Flow Splitting Strategy for Multi-Service of V2X Networks
## 2 Related Works
### Priority-Based Service Offloading  
The priority-based service offloading scheme has priority control on services, and each service has a corresponding priority relationship and is stored in the service offload algorithm. Based on the current available resources, congestion level, and service characteristics of the network, the connection between the service flow and the network interface is automatically established, thereby replacing the user's shunting decision, which can partially compensate for the deficiencies of the AHP algorithm. This allows services with higher priority to prioritize network resources in the case of heavy network load.  
Service priority-based channel access technology (TP-CAT) using IEEE 802.15.6 protocol is proposed to minimize the transmission delay of critical data packets and to resolve collisions between other priority nodes during the back-off phase [19]. TP-CAT is a hybrid preemptive/non-preemptive recovery priority scheme that can handle the heterogeneity requirements of M2M (Machine-to-Machine) services while integrating into an LTE (Long Term Evolution) network [20]. In the IoT scenario, a class-based dynamic priority (CBDP) algorithm is proposed for delay sensing and scheduling management of uplink M2M services, and has minimal impact on the QoS of uplink H2H (Human-to-Human) services [21]. A constrained utility-optimization formulation of the joint-bandwidth allocation problem for multiple classes of traffic in inter-data center communication is proposed to handle priorities between traffic classes in a soft manner, and explicitly considers the delay requirement of interactive flows [22].
## 4 Results and Discussion
### Inland Waterways Transportation: Present Status
For comparison, infrastructure and external costs in the European Union are as follows, EUR/(1000 tkm): road transport--from 28 to 77; railway transport--from 41 to 211; IWT--from 19 to 38 [37; 38]. IWT has low transport costs. Its average values vary from USD 0.011/tkm to USD 0.027/tkm. The same indicator for road transport is around USD 0.24/tkm. Railways are between them, and their average transport costs are from USD 0.015/tkm to USD 0.018/tkm [36]. In the European Union, transport costs have the same trend. For road freight, this indicator ranges from EUR 0.0572/tkm to EUR 0.142/tkm. It depends on load capacity and distance of transportation. Railway transport has a wider range: from EUR 0.02882/tkm to EUR 0.164/tkm. As to inland shipping, the unit of transport costs depend on the distance of transportation, the vessel draft, and the type of return voyage (empty return or loaded return). For empty return voyage, transport costs are within the range from EUR 0.0241/tkm to EUR 0.0515/tkm. For loaded return voyage, this indicator decreases and ranges from EUR 0.0167/tkm to EUR 0.0344/tkm [10].
## 3 Methodology
### A Centralized Routing Protocol for Lifetime and Energy Optimization Using GA and LSPI
```
0:\(\mathsf{G}(\mathsf{V},\mathsf{E})\), Basic Functions, \(\varphi\), \(\gamma\), \(\epsilon\), Learning round (\(L\)), stopping criterion, \(\epsilon\)
0: Optimal RT(s)
1: Sink executes Algorithm 2 to generate List of RTs
2: Sink collect set of samples, \(\mathcal{D}\) with Algorithm 3
3: Initialize \(s_{0}\) as a random RT
4: Initialize \(\hat{X}^{0}\gets 0\) and \(\hat{y}^{0}\gets 0\)
5: Initialize weight, \(w_{o}\gets 0\)
6:\(\hat{w}\gets w_{o}\)
7:repeat
8:\(w\leftarrow\hat{w}\)
9:for\((s_{i},a_{i},r_{i},\hat{s_{i}})\in\mathcal{D}\)do
10:\(\hat{X}^{i+1}\leftarrow\hat{X}^{i}+\varphi(s_{i},a_{i})\bigg{[}\varphi(s_{i},a_{i})-\gamma\varphi(\hat{s_{i}},\underset{a\in A}{\text{argmin}}(\hat{s_{i} },a))\bigg{]}^{T}\)
11:\(\hat{y}^{i+1}\leftarrow\hat{y}^{i}+\varphi(s_{i},a_{i})r_{i}\)
12:endfor
13:\(\hat{w}=\hat{X}^{-1}\hat{y}\)
14:until\(||w-\hat{w}||<\epsilon\)
15:return\(w\)
16:for\(t=1\) to \(L\)do
17:\(s_{t}=s_{o}\)
18: Sink chooses an RT using Equation (29)
19: Sink receives data from the sensors using the chosen RT. 20: Updates \(s_{o}\) as the current RT. 21:if Any sensor node dies, then
22: Delete the dead sensor node(s) from \(G(V,E)\)
23: Delete edges connected to the dead sensor node(s)
24: Rebuild \(\mathsf{G}(\mathsf{V},\mathsf{E})\)
25:if\(\mathsf{G}(\mathsf{V},\mathsf{E})\) is connected, then
26: Do steps 1 to 20
27:else
28:break
29:endif
30:endif
31:endfor
```  
**Algorithm 4** CRPLEOGALSPI.
# Condition Rating Prediction Using an Interactive Deterioration Model Development Package
## 4 Development of Deterioration Models  
### Deterministic Deterioration Models  
The NBI condition ratings for the bridge elements are determined by converting of the inspection data using the mapping criterion [10]. The deterministic deterioration model uses the current status of the NBI condition ratings by taking their mean according to each age and applying a curve-fitting algorithm to connect them. In the DMDP, a power function is employed to develop the deterioration curve, for which the number of bridges corresponding to each condition rating is used as a weighting factor.
# A New Approach to Characterize Superplastic Materials from Free-Forming Test and Inverse Analysis
## 1 Introduction
The novelty of this work is due to the use of a numerical-experimental approach based on inverse analysis to identify the characteristic parameters of sheets in AZ31 superplastic magnesium alloy that varies with strain. The reference constitutive equation, widely used in the scientific literature, which describes the behaviour of superplastic materials [3; 4; 5; 37; 41; 42; 43; 44], is used in this study. In a previous work, the authors introduced the general procedure for the characterization of magnesium alloy AZ31 [45], the new approach is deeply discussed and compared with the conventional procedure for the characterization of materials (based on the free-forming and inverse analysis). The procedure proposed in this paper allows for a reduction in the number of constants needed to determine the material constitutive equation, thereby requiring low simulation time. It is more suitable for use in the industrial field. Furthermore, the proposed procedure appears equally simple and gives more accurate results than the conventional procedure. The forming times of free-forming processes are predicted more accurately (the maximum achieved error is 8%).