esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Antioxidants and antioxidant activity
#### 3.3.2 Total Polyphenols  
The dried leaves of _S. rebaudiana_ had similar total phenolic contents: 89.96 mg GA/g DM and 99.71 mg CE/g DM (Polish); 89.31 mg GEA/g DM and 98.98 mg CE/g DM (Paraguayan); and 90.95 mg GAE/g DM and 100.8 CE/g DM (Brazilian) (Table 3).  
The results obtained are higher than those given in the literature. According to [PERSON] et al. [14], who applied the same Folin-Ciocalteu method to determine the total content of polyphenols and flavonoids in methanol extracts of _S. rebaudiana_, the level of polyphenols and flavonoids was 25.2 and 21.7 mg/g DM, respectively. These values are very close to those given by [PERSON] et al. [58], who used both methanol and aqueous extracts. In turn, total polyphenol content (expressed as catechin equivalent per weight unit of the sample), reported by [PERSON] et al. [57] in an aqueous extract, was 130.7 mg CE/g DM. The value determined by the same method by [PERSON] et al. [59], but in ethanol extract of _Stevia_ leaves, was 61.50 mg/g DM. On the other hand, [PERSON] et al. [52] used the liquid chromatography method (HPLC) to establish total polyphenol and flavonoid content, which were 91 and 23 mg/g DM, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Ingredient**} & \multicolumn{3}{c}{**_Stevia rebaudiana_**} \\ \cline{2-4}  & **Polish** & **Paraguayan** & **Brazilian** \\ \hline Vitamin C (mg/100 g DM) & 29.9 \(\pm\) 0.53 \({}^{\text{c}}\) & 4.55 \(\pm\) 0.01 \({}^{\text{a}}\) & 7.58 \(\pm\) 0.21 \({}^{\text{b}}\) \\ Total polyphenols (mg GAE */g DM) & 89.96 \(\pm\) 1.60 \({}^{\text{a}}\) & 89.31 \(\pm\) 1.65 \({}^{\text{a}}\) & 90.95 \(\pm\) 6.93 \({}^{\text{a}}\) \\ Total polyphenols (mg CE **/g DM) & 99.71 \(\pm\) 1.77 \({}^{\text{a}}\) & 98.98 \(\pm\) 1.83 \({}^{\text{a}}\) & 100.8 \(\pm\) 7.68 \({}^{\text{a}}\) \\ Antioxidant activity (\(\mu\)mol Trolox/g DM) & 152.4 \(\pm\) 2.36 \({}^{\text{a}}\) & 163.7 \(\pm\) 2.00 \({}^{\text{b}}\) & 160.3 \(\pm\) 1.56 \({}^{\text{b}}\) \\ \hline \hline \end{tabular}  
* Letters \({}^{\text{a,b}}\) and \({}^{\text{c}}\) indicate statistically significant differences (\(p\leq 0.05\)); data are shown as the mean \(\pm\)SD (standard deviation);
* gallic acid equivalent; *
* CE expressed as catechins equivalent.  
\end{table}
Table 3: Concentration of selected antioxidants in dried leaves of _Stevia rebaudiana_ [PERSON] depending on the country of origin.  
Of the _Stevia_-derived polyphenols, the following should be mentioned: phenolic acids (coumaric, sinapic and cinnamic acids), chlorogenic acids, flavonoids (quercetin and kaempfer) and flavones [8].
# A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks
###### Abstract  
In this paper, the concept of SDN (_Software Defined Networking_) is extended to be applied to wireless networks. Traditionally, in a wired SDN environment, the OpenFlow protocol is the communication protocol used to configure the flow table of forwarding elements (i.e., switches and Access Points). However, although in IEEE 802.11 networks there is no concept of forwarding, the SDN paradigm could also be applied to set up the wireless network dynamically, in order to improve the performance. In this case, not only the network elements, that is the Access Points, but also the mobile elements should configure their link and physical layers parameters following the guidelines of a centralized SDN controller. In particular, we propose a mechanism called DEDCA (_Dynamic Enhanced Distributed Channel Access_) to manage the channel access in wireless networks, and a framework that enables its implementation in 802.11-based wireless networks using SDN technology. The key aspect of this alternative solution is the control over the contention window size of the wireless terminals. Thus, an adequate response to dynamic and short-term Quality of Service (QoS) requirements can be offered to services running on these networks. DEDCA mechanism relies upon the use of a scalar parameter called _gain_. The mathematical model which has allowed us to obtain this parameter is presented and evaluated in this paper. Finally, the usefulness of the proposed solutions have been evaluated by means of their implementation in an example case.  
wireless networks; SDN; QoS; mathematical modeling 11 June 2018; Accepted: 10 July 2018; Published: 12 July 2018
# A Study on the Properties of Recycled Aggregate Concrete and Its Production Facilities
## 3 Development of the Technology to Improve the Recycled Fine Aggregate Quality
### Principle of Technology
#### 3.3.2 Absorption Rate  
The recycled aggregate had significant amount of mortar attached to the aggregate surface, compared to the natural aggregate. Thus, the recycled aggregate had a greater absorption rate than that of the aggregate used for general concrete. If the recycled aggregate that had been air- or oven-dried was used, the water content of the aggregate was increased, to achieve more loss of slump in the unsolidified concrete, and the pumpability was worsened. Therefore, the KS required an absorption rate of 5% or less. This was measured following the KS F 2504 \"Method to Test Density and Absorption Rate of Fine Aggregate.\" Before and after penetration, the absorption rate decreased by an average of approximately 0.70%. The absorption rate decreased from 5.42% \(\rightarrow\) 4.62% in the first test, 5.35% \(\rightarrow\) 4.68% in the second test, and 5.38% \(\rightarrow\) 4.75% in the third test.
# Estimation of the Measurement Accuracy of Wireless Passive Resonance Sensors
## Appendix B Maximum Likelihood Estimations of the Signal Parameters and Noise
### Maximum Likelihood Estimation for \(\tilde{\varphi}\)  
From Equation (113), we obtain for \(\tilde{\varphi}\)  
\[\frac{\partial}{\partial\tilde{\varphi}}\!\left[-2\tilde{b}\cdot \Re\!\left\{e^{-j\hat{\varphi}}\sum_{n=0}^{N-1}z_{n}e^{-\alpha nT}e^{-j\hat{ \omega}nT}\right\}+\tilde{b}^{2}\frac{1-e^{-2N\alpha T}}{1-e^{-2\alpha T}}\right] = 0 \tag{114}\] \[\frac{\partial}{\partial\tilde{\varphi}}\!\left[\Re\!\left\{e^{- j\hat{\varphi}}\sum_{n=0}^{N-1}z_{n}e^{-\alpha nT}e^{-j\hat{\omega}nT}\right\}\right] = 0\]To solve this, we separate the sum into magnitude and phase \(\lambda\)  
\[\sum_{n=0}^{N-1}z_{n}e^{-\alpha nT}e^{-j\omega nT} = \big{|}\sum_{n=0}^{N-1}z_{n}e^{-\alpha nT}e^{-j\omega nT}\big{|}e^ {j\lambda}\] \[\frac{\partial}{\partial\bar{\varphi}}\Bigg{[}\Re\Bigg{\{}e^{-j \bar{\varphi}}\Bigg{[}\sum_{n=0}^{N-1}z_{n}e^{-\alpha nT}e^{-j\omega nT} \big{|}e^{j\lambda}\Bigg{\}}\Bigg{]} = 0\] \[\frac{\partial}{\partial\bar{\varphi}}\left[\cos\lambda-\bar{ \varphi}\right] = 0\] \[\arg\sum_{n=0}^{N-1}z_{n}e^{-\alpha nT}e^{-j\omega nT} = \bar{\varphi} \tag{107}\]  
If \(\omega\) and \(\alpha\) are known, the best estimate for the initial phase \(\bar{\varphi}\) is the phase angle of the weighted sum of the measurement points multiplied by the associated amplitude of the decaying resonator \(e^{-\alpha}\)_diphant_T_ and multiplied by the phase factor \(e^{-j\omega nT}\), which rotates out the carrier frequency.
## 3 Soft Computing Methodologies
### Support Vector Machine  
The basic idea of SVM is to map the nonlinear problem from low-dimensional to high-dimensional spaces by using the kernel function, thereby solving the nonlinear problem by the linear method [38]. Given \(N\) sample datasets \(\{(\mathbf{x}_{i},y_{i}),\ i=1,2,\cdots,N\}\), \(\mathbf{x}_{i}=[x_{1i},x_{2i},\cdots,x_{ni}]\in\mathbb{R}^{n_{l}}\) is the input data, and \(y_{i}\in\mathbb{R}^{n_{h}}\) is the output data corresponding to \(\mathbf{x}_{i}\). A nonlinear mapping \(\varphi(\mathbf{x}):\mathbb{R}^{n_{l}}\rightarrow\mathbb{R}^{n_{h}}\) is used to map the input data \(\mathbf{x}_{i}\) into a high-dimensional feature space \(\mathbb{R}^{n_{h}}\), where a linear function \(f(\mathbf{x})\) exists to describe the nonlinear relationship between input and output. The linear function \(f(\mathbf{x})=\mathbf{\omega}^{\mathsf{T}}\varphi(\mathbf{x})+b\) is exactly the regression function of SVM, where \(\mathbf{\omega}\) is the weight vector, and \(b\) is the offset.  
To reduce the error between training data and \(\varepsilon\)-insensitive loss function, SVM minimizes the structural risk. The formula is as follows:  
\[\min_{\mathbf{\omega},b}\frac{1}{2}\|\mathbf{\omega}\|^{2}+C\sum\limits_{i=1}^{N}\max \bigl{(}0,\bigl{|}y_{i}-\mathbf{\omega}^{\mathsf{T}}\varphi(\mathbf{x}_{i})-b\bigr{|} -\varepsilon\bigr{)}, \tag{5}\]  
where \(\varepsilon\) is the insensitive loss coefficient, and \(C,\ C>0\) is the penalty factor.  
After introducing the slack variables \(\xi_{i}\) and \(\xi_{i}^{*}\), Equation (5) is transformed into the following optimization problem with constraints.  
\[\begin{split}&\min_{\mathbf{\omega},b}\biggl{[}\frac{1}{2}\|\mathbf{ \omega}\|^{2}+C\sum\limits_{i=1}^{N}\,(\xi_{i}+\xi_{i}^{*})\biggr{]}\\ & s.t.\left\{\begin{array}{l}y_{i}-[\mathbf{\omega}^{\mathsf{T}} \varphi(\mathbf{x}_{i})+b]\leq\varepsilon+\xi_{i}\\ \mathbf{\omega}^{\mathsf{T}}\varphi(\mathbf{x}_{i})+b-y_{i}\leq\varepsilon+\xi_{i}^{*} \\ \xi_{i},\xi_{i}^{*}\geq 0,\ i=1,2,\cdots,N\end{array}\right.\end{split} \tag{6}\]  
By establishing the Lagrangian function and satisfying the Karush-Kuhn-Tucker conditions, Equation (6) is regarded as a quadratic programming problem [39].  
\[\begin{split}\min\biggl{\{}\frac{1}{2}\sum\limits_{i=1}^{N}\,( \alpha_{i}-\alpha_{i}^{*})(\alpha_{j}-\alpha_{j}^{*})\kappa(\mathbf{x}_{i},\mathbf{x}_ {j})+\sum\limits_{i=1}^{N}\alpha_{i}(\varepsilon-y_{i})+\sum\limits_{i=1}^{N} \alpha_{i}^{*}(\varepsilon+y_{i})\biggr{\}
# OCT Capillary Depth Measurement in Copper Micro Welding Using Green Lasers
## 3 Results
### Capillary Depth Measurement Using OCT
#### 3.2.4. Inline Measuring of the Melt Pool Width Using the OCT
To determine the melt pool width, the change between the base material and the melt pool must be detected. This change is not necessarily accompanied by a change in the measured depth, which is why the change in signal intensity must be taken into account. In Figure 12a, the scanned OCT beam path along the welding process is displayed in an x-y-plane for an exemplary experiment, where x defines the welding direction. The signal intensity in each measuring point is indicated by the color scale. When evaluating the signal intensity, one can observe that, at maximum distance to the center axis, the signal intensity is significantly higher than towards the symmetry axis. This is due to the sample reflecting the OCT beam without deflections. As soon as the beam is reflected at the molten surface, the signal intensity decreases due to a possible change in the reflective properties and the movement of the molten material. Figure 10: (**a**) Weld seam image overlaid with a sinusoidal OCT beam movement path perpendicular to the welding process; (**b**) possible extractable process information when using the scanned OCT approach. Figure 12b shows an example weld in an x-y plane, where x defines the welding direction. The weld seam is overlaid with the corresponding OCT Signal intensity for the given path in Figure 10a. By applying a threshold value to the signal intensity, a good correlation between the weld seam width and the width, determined by thresholding then signal intensity, can be determined. However, some measuring points show the same signal intensity inside and outside the weld. This leads to the result that no clear edge can be defined. A possible explanation for this could be the melt pool dynamics during the process, as well as the fact that the comparison is only carried out on the static image of the weld seam after solidification of the material. An improvement of this result could possibly be achieved by adding another measurement information, such as the depth information. In conclusion, by spacially modulating the OCT Beam perpendicular to the welding direction during the welding process, multiple information can be extracted from the OCT Signal. In the presented example, the weld seam width was determined by evaluation the positional information of the OCT beam, as well as the signal intensity. By applying a threshold value to the signal intensity, a good correlation was shown between the weld seam width and the signal width could be determined. However, no clear edge could be defined. This could possibly be improved by adding another measurement information, such as the depth information.
## 5 Experimental Results
### Comparisons
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Level-1 (L)**} & \multicolumn{4}{c}{**Level-2 (L2)**} & \multicolumn{4}{c}{**Level-3 (L3)**} & \multicolumn{4}{c}{**Level-4 (L4)**} \\  & **R@1** & **R@5** & **R@10** & **mAP** & **R@1** & **R@5** & **R@10** & **mAP** & **R@1** & **R@5** & **R@10** & **mAP** & **R@1** & **R@5** & **R@10** & **mAP** \\ \hline \multicolumn{11}{c}{Market-1501 Dataset} \\ \hline ResNet50 & 87.65 & 94.69 & 96.85 & 71.15 & & Market-1501 Dataset & & & & & & & & & \\ \hline \(M_{1}\) & 90.86 & 96.44 & 97.92 & 74.99 & 90.77 & 96.59 & 97.92 & 75.61 & 90.38 & 95.96 & 97.71 & 74.80 & **91.06** & 96.59 & 97.92 & **76.92** \\ \hline \(M_{2}\) & 90.08 & 96.29 & 97.63 & 74.78 & 90.97 & 96.44 & 97.83 & 76.63 & 90.20 & 96.35 & 97.95 & 76.00 & - & - & - & - \\ \(M_{3}\) & 90.26 & 96.41 & 97.65 & 75.14 & 90.26 & 96.23 & 97.45 & 75.59 & 90.53 & 96.08 & 97.42 & 74.87 & 89.85 & 96.20 & 97.74 & 74.69 \\ \hline \(M_{4}\) & 89.70 & 96.20 & 97.48 & 74.21 & 89.40 & 96.50 & 97.77 & **74.56** & 89.67 & 96.41 & 97.95 & **74.74** & - & - & - & - \\ \hline \(M_{5}\) & 90.20 & 96.32 & 97.92 & 74.42 & 90.50 & 96.50 & 97.68 & 75.02 & 90.17 & 96.38 & 97.92 & 74.36 & 89.61 & 96.11 & 97.83 & 74.44 \\ \hline \multicolumn{11}{c}{DukeMTMC Dataset} \\ \hline ResNet50 & 77.74 & 87.84 & 91.34 & 60.65 & & & & & & & & & & & \\ \hline \(M_{1}\) & 81.33 & 90.62 & 93.04 & 64.35 & 81.46 & 90.66 & 93.04 & 65.10 & 81.82 & 90.62 & 93.36 & 65.51 & **82.81** & **91.20** & **93.36** & **66.34** \\ \hline \(M_{2}\) & 81.19 & 91.29 & 93.90 & **64.68** & 81.60 & 90.98 & 93.58 & **64.56** & 82.41 & 90.93 & 93.18 & 65.70 & - & - & - & - \\ \(M_{3}\) & 80.75 & 90.75 & 93.45 & 64.54 & 81.64 & 91.11 & 93.94 & 65.20 & 81.55 & 91.07 & 93.54 & 65.82 & - & - & - & - \\ \(M_{4}\) & 81.37 & 90.66 & 93.09 & 65.27 & 82.14 & 90.98 & 93.13 & 65.67 & 81.87 & 90.35 & 93.27 & 65.50 & 81.19 & 90.17 & 93.40 & 65.80 \\ \(M_{5}\) & 80.88 & 90.53 & 92.86 & 64.86 & 81.15 & 89.81 & 92.64 & 64.20 & 81.51 & 90.40 & 93.36 & 65.08 & 81.78 & 90.80 & 93.81 & 65.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Residual Transformers (RTr) performance against baseline (ResNet50) trained and evaluated on Market-1501 [39] and DukeMTMC [40] datasets. The best results are in bold, and “-” represents that the network did not converge in the given number of epochs. The “Levels” represent the presence of transformers after that block, while the subscript of “M” means the number of transformers after each block. The number of heads “h” is four throughout the experiments.
# Local Community Detection Based on Small Cliques
## 3 Local Community Detection Algorithms Based on Small Cliques
### Local \(T\)  
The local T algorithm [19] optimizes their T-measure which is based on internal and external triangles of the community. They define \(T_{in}\) as the number of triangles where all three nodes are in \(C\). The number of external triangles \(T_{ex}\) is then the number of triangles with exactly one node in \(C\). Note that triangles with exactly two nodes in \(C\) are neither internal or external according to this definition. The T metric is then defined as follows:  
\[T:=T_{in}\cdot\begin{cases}T_{in}-T_{ex}&\text{if }T_{in}\geq T_{ex}\\ 0&\text{otherwise}\end{cases}\]  
Their approach is also iterative, in each iteration the node that maximizes the T-measure is added to the community. If there are ties, the node with the lowest \(T_{ex}\) score is preferred. Additionally, they detect outliers, i.e., nodes only weakly connected to a community, and hubs. We do not evaluate these steps here as identifying outliers or hubs is not the goal of this work.  
Implementation Details  
Similar to the data structure for LTE, we also maintain a local graph for Local T. The main algorithmic difference is that no priority queue is used but instead for every node to be added to the community, the whole shell is scanned. Apart from that the algorithm is very similar, we list triangles and update scores at exactly the same places. Therefore, the total running time is \(\mathcal{O}(\text{vol}(C\cup S(C)\cup(S(S(C))))+m_{H}\cdot\alpha(H)+|C|\cdot|S|)\), where \(|S|\) is the maximum size of the shell which is in \(\mathcal{O}(\text{vol}(C))\).  
Note that for LocalT each triangle needs to be considered in the context of the community and therefore no scores can be pre-computed to make the algorithm faster.
## 6 Case-Study: Parallel Workflows
### Experiment 2: Analyzing Network Behavior of Parallel Workflows  
In Figure 14 we observe that, when two capped workflows are started simultaneously, their performance patterns align perfectly, resulting in synchronized peaks and valleys. However, the synchronization of communication phases (valleys) doubles the requirements for network bandwidth, which is ironic given that the network remains largely idle for most of the training time. Unfortunately, dividing network bandwidth is more challenging than dividing CPUs or memory because network operations are influenced by external factors such as congestion and latency.  
In this experiment, we investigate the possibility of reducing peak demand for bandwidth by spreading out the network operations over time. To do so, we examine the behavior of parallel workflows in two scenarios: (i) simultaneous execution, denoted as _in-sync_; and (ii) the execution with a time offset, denoted as _shifted_. These scenarios are compared against a baseline where workflows are started simultaneously without any capping (_native_). We note that to determine an optimal time offset, we leverage the predictable performance profile of capped workflows and attempt to find an offset that minimizes overlap during communication phases. In our setup, a time offset of 3 min proved to be near-optimal.  
Predicting Workload Shifts for Reduced Bandwidth  
Figure 15 demonstrates the highly predictable performance profiles of capped workflows, with actual CPU utilization closely matching the theoretical estimation. Furthermore, Figure 16 reveals that by carefully scheduling the start times of workflows, we can achieve the same training time while utilizing only half of the network bandwidth compared to the in-sync execution. Though somewhat arbitrary, the 3 min offset between workflows effectively reduces bandwidth requirements by minimizing overlap during communication phases. Consequently, _an important area for future investigation involves exploring techniques to determine the optimal shift between workflows, aiming to further minimize communication phase overlaps._  
Figure 15: Performance prediction and performance upon actual deployments. Federated learning workloads can be highly predictable.
# Guidelines towards Information-Driven Mobility Management
## Moving towards Content-Centric Mobility Management  
The benefits provided by the ICN architectural design in regards to mobility support are the basis to rethink mobility management widely, having in mind a data-centric (and not host-centric) goal.  
ICN de-centralised, asynchronous and pull-based model removes the need for a functional centralised or de-centralised MAP. Its architecture can support consumer mobility naturally; however, there is still the need to understand performance impact derived from the type of movement as well as from the types of underlying topologies. In what concerns consumer mobility, the pull-based nature of ICN gives the means to prevent serious packet loss; nevertheless, latency impact is still not clear, and requires future work on performance aspects under highly variable scenarios.  
Producer mobility, on the other hand, is still a challenge to be overcome. Related work argues that producer mobility is a small subset of mobility. That has been the case up until recently. With the advent of IoT and with the growth of environments involving autonomous vehicles, producer mobility becomes as relevant as consumer mobility.
# Air Traffic Simulation Framework for Testing Automated Air Traffic Control Solutions +
## 2 Proposed Architecture Framework
### Operational Modes
#### 2.3.3 Control Mode  
In the most advanced utilization scenario, the influence of ATCO behavior is modeled. To support this, an interface is implemented that enables the traffic simulation to perform the following:  
* Generate pilot requests;
* Respond to received ATCO instructions, incorporating the possibility of human error on the part of the flight crew.  
These functionalities are governed by stochastic properties, with adjustable parameters. Such parameters include the following:  
* The proportion of aircraft within a sector that generate randomized pilot requests;
* The probability of pilot error in executing ATC instructions.  
Figure 2: Simple traffic situation estimation output.  
This configuration enables a controlled traffic flow simulation, illustrated in Figure 3. Additional notations from those detailed at Figure 2 are as follows:  
* \(t_{C}\): duration of controller-pilot communication and action implementation (traffic management);
* \(S_{C_{i}}\): dataset containing the controlled traffic scenario;
* \(C\): dataset containing the pilot-controller communication;
* \(ctr\): control function (manipulation of the traffic scenario as traffic management).  
Pilot request generation is handled within the scope of the aircraft agent, but the simulation environment contains a simplified function for this purpose. For a predefined percentage of aircraft in the simulated traffic state \(S_{i}\), randomized pilot requests are generated. These requests can be bounded, according to the current implementation as follows:  
* A defined set of discrete values for requested changes in flight level;
* Specified percentage ranges for requested speed adjustments.  
Although the current application employs randomized pilot requests, the logic for request generation resides with the aircraft agent. This architecture also allows for the inclusion of en-route navigation features, such as waypoint-based routing.  
The generated pilot requests are appended to the structure describing the traffic situation. This augmented structure, denoted as \(C^{*}\), is then further extended to include controller responses--specifically, approvals or denials--and corresponding controller intentions, resulting in the complete structure \(C\). The process of extending of the traffic state structure \(S_{i}\) is described in detail in Section 4.  
Following this, the traffic management process generates a controlled traffic situation \(S_{C_{i}}\) via the control function \(ctr\). This controlled situation is assigned a dedicated time interval \(t_{C}\) before the next simulation loop begins, during which the approved maneuvers are executed or initiated. Consequently, the simulation time of the controlled traffic situation is updated as \(t(S_{C_{i}})=t_{0}+t_{E}+i\cdot(t_{R}+t_{C})\), assuming a constant control interval \(t_{C}\) across iterations such that \(t_{C}=t_{C_{i}}\forall\ i\).