esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fermentation Properties of Wheat Flour  
The use of a laser fermentograph allowed the effect of replacing water with a GH infusion of different concentrations on the ability to accumulate CO\({}_{2}\) produced by yeast during the optimal time of dough fermentation in a piece of the tested wheat dough to be assessed. In addition, the potential volume of a single loaf of bread was determined (the volume of CO\({}_{2}\) retained in the dough during fermentation). Figure 1 and Table 2 present the results of fermentographic analysis of wheat flour doughs with the addition of ground ivy infusions at concentrations of 1, 3 and 5%. The course of the fermentographic curves (Figure 1) reflects the volume of carbon dioxide produced, retained and lost during dough fermentation. Statistically significant differences were found between the samples for both the optimal dough fermentation time and the amount of released carbon dioxide (\(p\) < 0.05) (Table 2). \begin{table}
\begin{tabular}{l c} \hline
**Flour Quality Parameters** & **Wheat Flour (Type 650)** \\ \hline Moisture content [\%] & 14.40 \(\pm\) 0.03 \\ Falling number [s] & 438 \(\pm\) 10 \\ Wet gluten content [\%] & 34.87 \(\pm\) 0.14 \\ Zeleny sedimentation indicator [cm\({}^{3}\)] & 33 \(\pm\) 2 \\ Protein content [\% d.w.] & 13.67 \(\pm\) 0.12 \\ Fat acidity [mg KOH/100 g d.w.] & 116.21 \(\pm\) 9.55 \\ Ash content [\% d.w.] & 0.74 \(\pm\) 0.01 \\ \hline \end{tabular} Mean values from three repetitions \(\pm\) SD.  
\end{table} 
Table 1: Proximate composition of the flour used for bread baking. The use of infusions with a concentration of 3 or 5%, respectively, for the preparation of dough resulted in a reduction in the optimal dough fermentation time by 10 and 15 min compared to the time determined for the control sample (55.0 min). For these samples, the fermentation capacity of the flour (total volume of CO2 released during dough fermentation) was also lower in the recorded fermentation time. In the case of the sample with 3% infusion, the volume was 65 cm3 smaller, and for the sample with 5% infusion, the total volume of CO2 released was 100 cm3 smaller compared to the control sample, for which the value of this parameter was 254.5 cm3. This proves the significant, unfavorable effect of increasing the concentration of bioactive substances contained in the _G. hederacea_ L. infusions used to prepare the dough on the activity of yeast during dough fermentation. [PERSON] et al. [41] showed that the addition of yerba mate leaves affects CO2 production during dough fermentation. The amount of gas released during the fermentation process in the samples decreased (\(p<0.05\)) with the increase in the share of yerba mate leaves addition. Based on the studies of the cited authors and the results of studies by other authors [42], it can be assumed that the presence of phytochemicals from _G. hederacea_ L infusions in the tested wheat dough could have led to the formation of complexes with the yeast structure and, consequently, to a decrease in yeast activity, which was manifested
# Collaborative Multi-Robot Formation Control and Global Path Optimization
## 3 Improved Global Path Planning with Ant Colony Algorithm
### Improved Pheromone Concentration Update Rules  
When an ant passes a node, the pheromone change rule at that point is as follows:  
\[\tau_{ij}(t)=(1-\lambda)\tau_{ij}(t)+\lambda\tau_{0} \tag{16}\]  
where \(\lambda\) is the local pheromone volatility coefficient, \(\lambda\in(0,1)\); \(\tau_{0}\) is a very small constant; and by making \(\tau_{ij}(t)\) equal to the limit value, the algorithm will avoid stagnation when \(\tau_{ij}(t)\) exceeds the limit value.  
The update rules for pheromone concentration according to the quality ant update is as Equation (17):  
\[\tau_{ij}(t+1)=(1-\rho)\tau_{ij}(t)-\varepsilon\frac{L_{worst}}{L_{best}} \tag{17}\]  
where \(\varepsilon\) is the introduced parameter, \(\varepsilon\in(0,1)\), and \(L_{worst}\) and \(L_{best}\) are the lengths of the paths taken by the worst and best ants, respectively.  
If the algorithm falls into a local optimum for a consecutive iteration \(T\), it is automatically adjusted by the volatility factor \(\rho\), as follows:  
\[\rho(t+1)=\left\{\begin{array}{l}\gamma\cdot\rho(t),\ \rho(t)\geq\rho_{\min} \\ \rho_{\min},\ otherwise\end{array}\right. \tag{18}\]  
where \(\gamma\) is the global pheromone decrement factor, \(\gamma\in(0,1)\), and \(\rho_{\min}\) is the minimum pheromone volatility factor.
## 3 Algorithm and Platform
### Basic Algorithms
previous output of the ESN. To increase stability, as mentioned in Section 2.1, before computing the transformation \(f_{res}(\cdot)\), we add a small uniform noise term to the state update. (2) The ESN's current output is computed by:  
\[\mathbf{y}[n]=f_{out}(\mathbf{W}_{i}^{o}\mathbf{x}[n]+\mathbf{W}_{r}^{o} \mathbf{h}[n]) \tag{2}\]  
where \(\mathbf{W}_{i}^{o}\in\mathbb{R}^{N_{o}\times N_{i}}\) and \(\mathbf{W}_{r}^{o}\in\mathbb{R}^{N_{o}\times N_{r}}\) are objects to be optimized and determined by the training datasets, and \(f_{out}(\cdot)\) in Equation (2) is an invertible non-linear function. To optimize the weight matrices \(\mathbf{W}_{i}^{o}\) and \(\mathbf{W}_{r}^{o}\) in the output layer, suppose a sequence of \(P\) desired input-output pairs are given by:  
\[(\mathbf{x}[1],\mathbf{d}[1]),\cdots,(\mathbf{x}[P],\mathbf{d}[P]) \tag{3}\]  
At the first phase of training, after the \(P\) inputs are fed to the reservoir, a sequence of internal states \(\mathbf{h}[1],\cdots,\mathbf{h}[P]\) is generated according to Equation (1). Since the outputs of the ESN are not available for feedback, we use the desired output \(\mathbf{d}[1],\cdots,\mathbf{d}[P]\) to replace \(\mathbf{y}\) in Equation (2) in the training process. The input and internal states are stacked in a matrix \(\mathbf{H}\in\mathbb{R}^{P\times(N_{i}+N_{r})}\) and the desired outputs in a vector \(\mathbf{d}\in\mathbb{R}^{P\times N_{o}}\):  
\[\mathbf{H}=\left[ \begin{array}{c}\mathbf{x}^{T}[1],\mathbf{h}^{T}[1]\\ \vdots\\ \mathbf{x}^{T}[P],\mathbf{h}^{T}[P]\end{array} \right] \tag{4}\]  
\[\mathbf{d}=\left[ \begin{array}{c}f_{out}^{-1}(\mathbf{d}[1])\\ \vdots\\ f_{out}^{-1}(\mathbf{d}[P]\end{array} \right] \tag{5}\]  
Based on this, the training issue of the weight matrices \(\mathbf{W}_{i}^{o}\) and \(\mathbf{W}_{r}^{o}\) turns out to be a standard linear regression, which can be solved easily, i.e., by solving the following regularized least-square problem:  
\[\mathbf{w}^{*}=\underset{\mathbf{w}\in\mathbb{R}^{N_{i}+N_{r}}}{\text{argmin}} \frac{1}{2}\|\mathbf{H}\mathbf{w}-\mathbf{d}\|_{2}^{2}+\frac{\lambda}{2}\| \mathbf{w}\|_{2}^{2} \tag{6}\]  
where \(\mathbf{w}=\left[\mathbf{W}_{i}^{o},\mathbf{W}_{r}^{o}\right]^{T}\) and \(\lambda\in\mathbb{R}^{+}\) is the regularization factor. Additionally, the first \(D\) rows from Equations (4) and (5) should be discarded when solving Equation (6), since they refer to a transient phase in the ESN's behavior, also denoted as the dropout elements [39].
## 2 Materials and Methods
### Experimental Setup
PCM in the solid state a 9:00 h, and it is in the liquid state at 11:00 h. Figure 5 also shows general pictures of the experimental setup and the installed sensors. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensor** & **Measurement** & **Model** & **Company** & **Quantity** & **Accuracy** \\ \hline Current/voltage sensor & HVAC power & Multivoies & OmégaWatt & 1 & \(\pm\)2\% \\ Thermoouples & Surface temperature & Type T & Pelican Wire & 20 & \(\pm\)5\% \\ Heat flux sensor & Heat flux & HFP01 & Husleflux & 4 & \(\pm\)5\% \\ Pyranometer & Transmitted solar radiation & CMP11 & Kipp \& Zonen & 1 & \(\pm\)1.35\% \\ Pyragoometer & Longwave radiation & CGR3 & Kipp \& Zonen & 1 & \(\pm\)5.7\% \\ Humidity and & Relative humidity and & HMP60 & Vaisala & 1 & \(\pm\)3\% (temp) \& \(\pm\)5\% \\ temperature probe & temperature & & & (RH) \\ Anemometer & Air velocity & SensoData 5500 & Sensor Electronic & 1 & \(\pm\)0.02 m/s \\ Thermal comfort & & Anemo & & & \\ sensor & PMV & SensoData 5500 MK & Sensor Electronic & 1 & \(\_^{1}\) \\ Globe thermometer & Mean radiant temperature & Testo 0602 0743 & Testo & 1 & \(\pm\)1\% \\ CMOS sensor & Luminance distribution & IMX 219 & Sony & 1 & \(\pm\)3\% to \(\pm\)17.5\% \\ Raspberry Pi 3 & Data acquisition & Model B & Rosenlyr & 1 & - \\ Data Acquisition System & Data acquisition & 34972A & Keysight & 1 & - \\ \hline \hline \end{tabular}  
* Thermal comfort sensor is equipped with a natural wet, air and globe thermometer with an accuracy of \(\pm\)0.1 \({}^{\circ}\)C, a humidity probe with an accuracy of \(\pm\)2%, and an air speed probe with an accuracy of \(\pm\)0.02 m/s.  
\end{table} 
Table 5: List of sensors for each room. Figure 3: Sensor’s position. Dimensions in mm. TI and TE refer to thermocouples in the interior and exterior surface of the window, respectively. HF refers to heat flux sensors. ANEM refers to the anemometer. PYR and PYRA refer to the pyragoometer and pyranometer, respectively.
# Optical Technology for NFV Converged Networks
## 2 Reference Network Scenario and Main Requirements
### Ring Networks  
As shown in Figure 4, CO and antenna sites can be connected by a WDM ring. In this kind of network, to ensure the compatibility of well-established protection mechanisms and the optical components widely used in metro networks (transceivers, wavelength multiplexers, and demultiplexers, optical amplifiers, etc.), the use of two fibers, one for each propagation direction, is common [16].  
Also, in this Xhaul case, different radio split options must be supported concurrently. Since this scenario is expected to be used for centralized radio access network deployments, where based-band processing is pooled among geographically distant Remote Units (RU), the distance between the CO and the antenna site is usually 10-20 km, compatibly with the latency requirements of the fronthaul interfaces.  
In such centralized scenario, 25 Gbit/s channels from the antenna sites may be aggregated (using a packet or circuit switch) in 100 Gbit/s WDM channels. With Dual-Polarization Quadrature Phase Shift Keying (DP-QPSK) this requires a bandwidth (BW) of at least 37 GHz to the ROADMs in the ring. In Figure 4, only the ROADMs are shown;  
Figure 3: TRX functional architectures. (**a**) tunable TRX transmission only [15; 16]; (**b**) Full tunable TRX.  
the electrical switch is out of the scope of this analysis, more details about it can be found in [7].  
ROADMs are unusual in traditional access networks but here they are essential to assure the level of automation in network operation that SDN and NFV technology require. De facto the ROADMs allow to dynamically and automatically configure functions of wavelength bypass for traffic not to be processed locally, wavelength add or drop for local traffic and wavelength routing toward adjacent rings, e.g., used as further stage of aggregation. Up now, ROADMs are used in aggregation and long-haul networks, to deal with high amounts of aggregated traffic and long distances.  
Hence, the ROADMs used in current optical metro long-haul networks are based on high performance but expensive Wavelength Selective Switches (WSS) [17]. Alternative, less expensive and power efficient technologies are required to address the use case of the access network. Integrated silicon photonics is a promising technology to realize small size, low cost ROADMs that operate over a distance much shorter than long-haul. In Sections 3 and 5 details about the use of silicon photonics to realize ROADMs for radio-access segment are disclosed.
## 4 Ad Hoc Routing: Configuration, Scenarios, and Results
### Emergency Response System: Real-Time Statistics  
In each aforementioned scenario, queries were generated in order to check the statistics displayed on the server side at the control room. Figure 5 presents the user interface at the server side that shows the disaster statistics, activity logs, and resource entities available in the inventory, to yield the facilitation to the affected people. After generating 200 queries, the system presented real-time statistics against the available resources in the inventory and the disaster statistics for the investigated scenario. It is assumed that the disaster occurred due to an earthquake, which has destroyed or demolished the already deployed communication infrastructure like LTE, or 3G/4G. Additionally,  
Figure 14: The same next-hop for all five available users.  
Figure 15: Dynamism of BATMAN-ADV Protocol.  
the proposed system was tested with the presumption that the volunteers are dispersed in the affected area. Since the system is designed to support the rescue operation for post-disasters, it is thus assumed that the rescue operation is well-prepared with basic entities to handle critical life threats. Hence, it is equally important to have information of available and allocated resources in the inventory. Based on this assumption, the proposed system gives real-time statistics about the available and allocated/busy resources on the user-interface at the server side in the control room. Figure 16 depicts the status of the inventory that is maintained to cope with the challenges of post-disaster management. This chart shows the quantity of resources on an \(x\)-axis and available entities on the \(y\)-axis. It provides the information of total resources indicated with the blue bar, available resources with the red bar, and allocated/busy resources with the grey bar. This may vary in different scenarios depending on the need and the level of disaster in a particular area. Figure 17 presents the incident reported via emergency messages to the control room. These incidents include collapsed lands, broken bridges, collapsed buildings, and blocked roads. The increased number of reported events indicates the level of disaster.  
Figure 16: Emergency response system—the status of the inventory after receiving 200 queries from the disaster area.  
Figure 17: Emergency response system—the level of disaster in a particular area in terms of destruction.  
The results in the following section show that the system performed all the above-mentioned tasks. The performance of the system was analyzed in terms of response time, successful data transmission, and delay. However, the implemented system was tested in a lab under the constraints of a limited availability of hardware and testing area.
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 6 Conclusions and Recommendations
### Battery Sizing  
Sizing batteries is a critical design decision for turboelectric power systems. A serious risk was posed to the test aircraft in this study because the rear motor pulled more power than expected in flight. Thus, battery systems should be large enough to fully power the early flight test aircraft in takeoff and flight. Battery sizing can be refined after acquiring measured in-flight aircraft and turboelectric system performance. Sizing the engine for cruise is suggested to maximize the advantages of a hybrid gas-electric power system, but this study demonstrated sizing the batteries should be done incrementally. Future hybrid gas-electric power system designs should also include a second current sensor on either the batteries or engine, to determine the contributions of each to total power generation.
## 4 Methodology
### Evaluation Metrics  
One key step in assessing the quality and reliability of a model is to evaluate its performance using appropriate evaluation metrics. Accordingly, the ground truth annotated buildings were used for comparison and statistical accuracy assessment of our proposed method. For this purpose, true building classes in reality, which indicate trueness or falseness, were compared to predictions of the model in the structure of a confusion matrix. Figure 11 simplifies the understanding of how a confusion matrix can be interpreted in a binary classification scenario. True positive (TP), false positive (FP), true negative (TN), and false negative (FN) values were extracted from the confusion matrix by looking at their exact definitions. After defining the basic parameters, according to the confusion matrix, four other dependent metrics, i.e., precision, recall, F1-score (i.e., Dice coefficient), and Intersection-over-Union (IoU, i.e., Jaccard index), could be defined by Equations (1)-(4) [69]:  
\[\mathrm{Precision}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \tag{1}\]  
\[\mathrm{Recall}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}} \tag{2}\]  
\[\mathrm{F1_{score}}=2\times\frac{\mathrm{Precision}\times\mathrm{Recall}}{ \mathrm{Precision}+\mathrm{Recall}}=\frac{2\times\mathrm{TP}}{2\times\mathrm{ TP}+\mathrm{FP}+\mathrm{FN}} \tag{3}\]  
Figure 10: Diagram of the Selective Kernel Module which shows its three stages of Split, Fuse, and Select, for a sample two-branch selective kernel.  
Precision is the ratio of correctly classified buildings to the total number of samples that were assigned to the building class, as in the localization stage. Recall is the ratio between the number of correctly classified buildings and the total number of buildings that really exist. Higher precision relates to a lower false positive rate, i.e., lower false alarms, and higher recall is related to lower false negative rates, i.e., lower algorithm misses. It should be noted that in the building damage assessment stage, a four-class problem (i.e., damaged, no-damaged, minor damaged, major damaged) was considered for performance evaluation. In localization and classification problems, higher values for both of these metrics are desired. For this reason, the F1-score, the harmonic mean between precision and recall, is defined to understand the overall performance of a model with one measure. The other metric that is capable of estimating the performance of a model is Overall Accuracy, defined as the number of all correctly classified buildings divided by the number of all existing buildings in the scene. Finally, in order to enhance the assessment procedure, the generated building damage assessment maps were visually evaluated by comparing the results with pre- and post-disaster satellite images.
# A Rectangular Notch-Band UWB Antenna with Controllable Notched Bandwidth and Centre Frequency
## 2 Antenna Design
### Design of the UWB Antenna with the Rectangular Notch  
The conventional notch using one EBG described in the previous section had high selectivity at lower frequencies but low selectivity at higher frequencies. Therefore, another EBG was added to Antenna-3 and optimized to obtain high selectivity in the entire rejection band by merging the individual notch bands of EBG1 and EBG2. Figure 7 shows the geometry of the rectangular notch-band antenna with EBG1 and EBG2 (i.e., Antenna-4). The \(\mid\)S\({}_{11}\mid\) characteristics of Antenna-3 and Antenna-4 are shown in Figure 8. The conventional notch of Antenna-3 (with one EBG) was changed to a rectangular notch band with high selectivity in Antenna-4 (with two EBGs). The important parameters that affected the notch-band characteristics are discussed later in this paper.  
The design summary of the rectangular notch UWB antenna is illustrated in the detailed flowchart shown in Figure 9. First, a simple rectangular patch that is fed with a microstrip line with the partial ground plan is designed. The antenna is then optimized by varying the parameters of the antenna. The lower ends of the patch are then truncated in the semicircle \(R\) and tuned to attain UWB characteristics. A rectangular EBG is introduced, and its parameters (\(l_{1}\), \(w_{1}\), and \(s\)) are optimized to realize a conventional notched band. Finally, another EBG is designed and its parameters (\(l_{2}\), \(w_{2}\), and \(s_{1}\)) are tuned to achieve a rectangular notch band in the UWB antenna.  
Figure 8: \(\mid\)S\({}_{11}\mid\) characteristics of Antenna-3 and Antenna-4.  
Figure 7: Characteristics of Antenna-2 and antenna design procedure to achieve conventional notched UWB patch antenna.
## 2 System Model
### The Receiver
\begin{split}\hat{s}_{j,R}^{i}&=\mathcal{R}\{z \left(\hat{k}_{I,j}\right)\},\quad\ j=1,\ldots,J\\ \hat{s}_{j,I}^{i}&=\mathcal{F}\{z\left(\hat{k}_{Q,j} \right)\},\quad j=1,\ldots,J.\end{split}  \tag{8}\]  
Combining \(\hat{s}_{j,R}^{i}\) and \(\hat{s}_{j,I}^{i}\) into \(\hat{s}_{j}^{i}=\hat{s}_{j,I}^{i}+\sqrt{-1}\hat{s}_{j,I}^{i}\), the estimated modulated symbols are then obtained. At last, the estimated symbols are sent to the constellation de-mapping block. The modulated bits \(\mathbf{b}\) are recovered. ```
Input:\(J\) and \(\mathbf{\Omega}\)
1:for\(i=1:J\)
2:\(\kappa_{i}=\operatorname*{arg\,max}_{r=1\cdots,j_{max-1}}(\mathbf{\Omega}(p))\)
3:\(\mathbf{\Omega}(\kappa_{i})=0\)
4:endfor
5:\(\mathrm{Y}=\{\kappa_{1},\kappa_{2},\cdots,\kappa_{I}\}\)
6:\(\left[\hat{k}_{g,I},\ldots,\hat{k}_{g,1}\right]=sort(\mathrm{Y})\) Output:\(\left[\hat{k}_{g,I},\ldots,\hat{k}_{g,1}\right]\)
```  
**Algorithm 1** To Estimate the Indexes \(\left\{\hat{k}_{g,j}\right\}_{j=1}^{J}\), \(\xi\in\{I,Q\}\).
# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## 2 Materials and Methods
### Classification  
The primary goal of the study was prediction of ground surface or terrain using hand-crafted features. For this purpose, we used the Scikit-learn library (version 0.19.2), which is an open source machine learning library for Python [46]. We used two supervised learning algorithms i.e.,Random Forest (RF) and Support Vector Machine (SVM), and trained them using the features set discussed in the previous section. We used grid search and found the best parameters for classification of terrains. For RF, the model was trained using the following parameters: number_of_trees = 500, criterion = \"gini\", max_features = \"auto\", and max_depth = \"none\". For SVM, the following parameters were used for training the model: kernel = \"RBF\", C = 10, and gamma = 0.01. Since the sensors were placed on two different locations of the subjects' bodies, each model was trained and validated with a features set computed from the data collected through the respective sensor. Furthermore, five different sets of features for each stride against each dataset were computed to test the prediction accuracy of each model under variable feature sets. The number of features computed for each stride under five different feature sets are as follows: (1) all features (tempo-spectral): 194 features; (2) temporal features: 110 features; (3) spectral features: 84 features; (4) 3D accelerations (tempo-spectral): 97 features; and (5) 3D angular velocities (tempo-spectral): 97 features. The _k-fold_ cross validation model [47], which is a well-known model for cross validation, was used to measure the performance of each predictor. The chosen value of \(k\) was 10.