esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methodology
### Nightlife Areas Identification Process
From a visual analysis and considering previous knowledge of the city's nightlife, the results are positive: the majority of the grids identified are in known nightlife areas in downtown Lisbon, and the few scattered around the rest of the city are expected, as nightlife is not exclusively located downtown. Another positive aspect of the results is the lack of hospitals in the identified areas, as the large amount of movement in these places could be incorrectly attributed to nightlife activities. The quality of the clusters obtained can be evaluated quantitatively. Upon investigating how the algorithm could be improved, a method that optimized the parameters by minimizing the quantization error (QE) [20] was used. Due to the data's distinct structure, a function that calculates the QE was developed from the ground up in this work, using theoretical knowledge [19] to check if the two methods gave the same results. After verifying both techniques, the QE value of the model was determined to be 3.192, which was proven to be a good quality measure, taking into account the QE values in [PERSON] et al. [21].
# A MIMO Antenna with High Gain and Enhanced Isolation for WLAN Applications
## 2 Antenna Structure and Design
This confirms that, in Figure 2b, after adding L-shaped strips, the isolation of low-frequency band is significantly improved. With the defected ground structure, the current distribution in the working frequency band is mainly concentrated on the two antenna elements. The neutralization line structure promotes the cancellation of the coupled induced current between the two elements and guides the radiation field to gather along the diagonal direction of the neutralization line. In Figure 6a,b, the photographs of the top and bottom views of the fabricated antenna are presented. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & \(W_{1}\) & \(W_{2}\) & \(W_{3}\) & \(W_{4}\) & \(W_{5}\) & \(W_{6}\) \\
**Value** & 11 & 4.4 & 18 & 13 & 7.2 & 1 \\
**Parameter** & \(W_{7}\) & \(W_{8}\) & \(W_{9}\) & \(W_{10}\) & \(W_{11}\) & \(W_{s}\) \\
**Value** & 1 & 10 & 4.5 & 5.6 & 4.1 & 50 \\
**Parameter** & L\({}_{1}\) & L\({}_{2}\) & L\({}_{3}\) & L\({}_{4}\) & L\({}_{5}\) & L\({}_{6}\) \\
**Value** & 2 & 4 & 1 & 1 & 2 & 3 \\
**Parameter** & L\({}_{7}\) & L\({}_{8}\) & L\({}_{9}\) & L\({}_{10}\) & L\({}_{11}\) & L\({}_{8}\) \\
**Value** & 11 & 6.5 & 33.5 & 41.5 & 2 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the presented antenna (unit: mm). Figure 5: Simulated surface current directions. (a) 2.45 GHz, (b) 5.5 GHz. Figure 6: The photographs of the fabricated antenna. (a) top view, (b) bottom view.
## 4 Simulation Results
### Case Study 3: Plug and Play Test  
In this study case, the focus is on the plug and play adaptability, which is one of the most important advantages of the future CHP system. The initial conditions of are the same as in case 1. The electrical and heat load demands maintain unchanged during the simulation. At time step \(t=400\), CGA2 is removed from the test system. It is not very difficult to verify the remaining subgraphs \(G_{EC}\) and \(G_{HC}\) are also strongly connected. The electrical (heat) loads supplied by CG2 are sent fifty-fifty to its out-neighbors in \(G_{EC}\) (\(G_{HC}\)). From the results in Figure 7, both the electrical and heat incremental cost increase and converge to their new optimal values, meanwhile the electrical and heat power mismatch go to zeros. Of course, the remaining agents have to supply more electrical and heat power to compensate for the electrical and heat power previously supplied by the disconnected CGA2. Moreover, at time step \(t=800\), the CGA2 is plugged again in the test system, and the output of CGA2 is set to \(x_{11}^{pq}(800)=44\) (p.u.), \(x_{11}^{qp}(800)=75\) (p.u.), \(y_{11}^{pq}(800)=-44\) (p.u.), \(y_{11}^{qp}(800)=-75\) (p.u.), \(\lambda_{11}^{pq}(800)=\lambda_{11}^{qp}(800)=0\). Then, the system again converges to the new solutions responding to the new topological change. In order to accommodate CGA2, the other agents have to reduce or not change (if the ones have been running on the upper bounders) their electrical and heat power outputs, while CGA2 increases its corresponding outputs. Thus, the electrical and heat incremental cost drop due to the lower average electrical and heat power outputs. In addition, the finally stable values are listed in Table 2. It can be seen that they are very close to the solution prior to disconnection. Therefore, this case study can verify the proposed method performs good plug and play capability.  
Figure 6: Case study 2, time-varying demand: (**a**) electrical and heat power load demands; (**b**) estimated electrical and heat incremental cost; (**c**) estimated electrical and heat power mismatch; and (**d**) electrical and heat power outputs.
# Process of Formaldehyde and Volatile Organic Compounds' Removal from Waste Gases
## 2 Production Processes of Formaldehyde
### Formaldehyde Production Using Silver Catalyst Processes  
The first industrial formaldehyde production processes used silver as catalyst [7]. This process burdens the environment with gas emissions into the air, and with the generation of excess steam condensate containing up to 0.2% of formaldehyde, which cannot be recycled into the process.  
The silver catalyst process partially converts methanol to formaldehyde with efficiency of up to 90%. The ternary mixture of formaldehyde/water/methanol must be separated via steam distillation. Afterwards, the steam condensate is contaminated with formaldehyde (up to 0.2%), creating extra costs for disposal or further treatment.  
The synthesis is carried out using catalytic oxidation and dehydration of methanol in contact with a silver catalyst. Several reactions take place during the conversion process:  
\[\mathrm{CH_{3}OH+1/2\;O_{2}\to CH_{2}O+H_{2}O\;dH=-159\ kJ/mol} \tag{2}\]  
alternatively:  
\[\mathrm{CH_{3}OH\to CH_{2}O+H_{2}\;dH=+84\ kJ/mol} \tag{3}\]  
\[\mathrm{H_{2}+1/2\;O_{2}\to H_{2}O\;dH=-243\ kJ/mol} \tag{4}\]  
Reaction (4) is exothermic. The thermal energy released in this reaction maintains the temperature of the reactor and, at the same time, facilitates in reaction (3) the shift of the equilibrium to the right side. This increases the formation of formaldehyde. Parallel to the main reactions (2)-(4), some side reactions are carried out, reducing the efficiency of the process. The important side reactions are:  
\[\mathrm{CH_{2}O\to CO+H_{2}} \tag{5}\]\[\text{CH}_{2}\text{O}+1/2\ \text{O}_{2}\to\text{HCOOH}\ \text{and}\ \text{CO}+\text{H}_{2}\text{O} \tag{6}\]  
\[\text{CH}_{2}\text{O}+\text{O}_{2}\to\text{CO}_{2}+\text{H}_{2}\text{O} \tag{7}\]  
In this basic concept, no use of off-gases is included. Natural gas is purchased for the generation of steam, and external electric energy is required for the methanol conversion process and the connected UF and PF resin production.  
In total, 89 kg of CO\({}_{2}\) are created and emitted per ton of products manufactured (formaldehyde in 37% concentration, PF resin and UF resin).
## 3 MG Overview and Experimental Test Bench
### Experimental Setup for the SSWT  
The WT rotor converts the kinetic energy of wind into mechanical energy. The aim of this study is to integrate an SSWT in the DC MG already developed in a research testing platform in the AVENUES laboratory of the Universite de Technologie Compiegne, France. The experiment was conducted in a wind tunnel, as shown in Figure 2a.  
The SSWT is installed in a ventilation tunnel with an axial fan. It is placed downstream of the WT to reduce aeraulic disturbance. This wind tunnel is also equipped with two differential pressure transmitters (KIMO CP210) (red lines in Figure 2b). One of them is combined with a Pitot tube (blue lines in Figure 2b) that is used as a flow transmitter. This Pitot tube measures the dynamic pressure of the air produced by the fan in the wind tunnel and then can deduct the wind speed in m/s.  
Figure 1: The microgrid (MG) overview.  
In this study, the SSWT used is a horizontal axis WT produced by ATMB Marine. It is constructed of five blades and the diameter of the turbine is 1.1 m. This SSWT is designed by the manufacturer to operate at a rated power of about 400 W at 11 m/s while the maximum power is 600 W. The maximal rotational speed is estimated to be 1250 rpm at 11.5 m/s.  
The SWECS investigated in this study is based on a three-phase diode rectifier to convert AC voltage obtained from the PMSG to DC voltage (Figure 3). A smoothing capacitor \(C_{WT}\) is used to constitute the DC bus of the SWECS. Then, a controllable DC/DC converter with a controller bloc, where the algorithms are implemented and which is driven by dSPACE, is designed to adjust the desired power and reach the operating point. Finally, to emulate the MG load demand, a programmable DC electronic load is used. Thus, the power required by the MG load or the user, \(P_{\text{lim}}\), and the DC bus voltage, \(v_{WT}\), are used as parameters for algorithms which aim to manage the electrical power available in the DC bus (\(p_{WT}\)). The result provided by the controller is \(v_{WT\_REF}\).  
Prior to investigating different power control strategies, several tests are carried out within the wind tunnel by changing the load and the wind speed. The voltage, the current and the electrical power  
Figure 3: Electrical scheme and components of the studied small wind energy conversion system (SWECS).  
Figure 2: (**a**) Wind tunnel; (**b**) pressure transmitters and Pitot tube.  
in the DC bus are then measured. In addition, the mechanical speed (\(\Omega\)) of the PMSG is observed and measured in order to not exceed the maximum value recommended for this WT. Collected experimental data are given in Figure 4.  
As can be seen in Figure 4,c, all power curves reach a maximum point and then decrease as \(v_{WT}\) and \(\Omega\) rise. Additionally, it is noticed that each value of \(v_{WT}\) corresponds to one value of \(p_{WT}\). However, one value of \(i_{WT}\) is attributed to two values of \(p_{WT}\) (Figure 4). Thus, \(v_{WT}\) is used as the controlled parameter.
## 7 Cooperative UAV-UGV Docking with Task Prioritization
### Rover Dynamics  
The dynamics of the rover are calculated with mass \(m_{r}\) and moment of inertia \(\mathbf{J}_{r}\), assuming that it runs on a flat surface and provided in (61). Based on this assumption, model states are denoted as \(\mathbf{x}_{r}\ =\ \left[\begin{array}{cc}p_{r,x}&p_{r,y}&\Psi_{r,x}&w_{r,z} \end{array}\right]^{T}\), where \(p_{r,x}\) and \(p_{r,y}\) represent inertial position on the \(x-y\) plane, \(\Psi_{r}\) is the Euler angle about the \(z\) axis, \({}_{B}\mathbf{v}_{r,x}\ =\ v_{r,x}\) is the horizontal velocity of the rover, and \({}_{B}\mathbf{w}_{r,z}\ =\ w_{r,z}\) is the angular velocity of the rover. The composite rotation matrix for the rover is denoted as \({}_{t}\mathbf{C}_{r}\ =\ _{t}\mathbf{C}_{r}(\Psi_{r})\). Inputs to the system are denoted as \(\mathbf{u}_{r}\ =\ \left[\begin{array}{cc}f_{r,1}&f_{r,2}\end{array}\right]^{T}\), which represent the traction forces applied on the surface by a set of wheels on the right- and left-hand sides of the rover, respectively. \(m_{r}\) is taken as \(1\) kg, and \(\mathbf{J}_{r}=diag\{0.1\}\) kg\(\cdot\)m\({}^{2}\).  
\[\dot{\mathbf{x}}_{r} = \left[\begin{array}{c}\dot{\mathbf{p}}_{r,x}\\ \dot{\mathbf{p}}_{r,y}\\ \dot{\mathbf{v}}_{r}\\ \dot{w}_{r,z}\end{array}\right]\ =\ \mathbf{f}(\mathbf{x}_{r},\mathbf{u}_{r}) \tag{61}\] \[= \left[\begin{array}{c}\mathbf{u}_{1}^{T}\mathbf{C}_{r}\mathbf{v}_{r,x}\mathbf{u }_{1}\\ \mathbf{u}_{2}^{T}\mathbf{C}_{r}\mathbf{v}_{r,x}\mathbf{u}_{1}\\ w_{r,z}\\ (1/m_{r})(f_{r,1}+f_{r,2})\\ \mathbf{J}^{-1}(\mathbf{r}_{1}\times f_{r,1}+\mathbf{r}_{2}\times f_{r,2})\end{array}\right]\]
## 4 Discussion
[36] reported that the apical transportation and canal straightening was comparable between HyFlex CM and iRaCe (FKG Dentaire SA, La Chaux-de-Fonds, Switzerland), a conventional superelastic NiTi file, and both files performed efficiently and kept the original canal curvature well. Further studies about the influence of heat-treated orifice preflaring NiTi files on the torque generation to dentin is required. When NiTi files cut dentin, proper hardness and stiffness are essential. During preflaring preparation, the files are subjected to lateral force. A file which withstands more lateral force would be advantageous. [PERSON] et al. [20] argued that the lower stiffness and higher cyclic fatigue resistance of OF would be advantageous in the preparation of coronal root dentin with curvature. Nevertheless, the coronal curvature of the root canal could be overcome by tilting the hand-piece as far as possible in the direction of the curvature. According to [PERSON] et al. [37], an inclined insertion enhanced the cutting ability of three different glide path NiTi files due to the increased contact surface area between the file and the substrate. [PERSON] et al. [11] reported that a martensitic coronal flaring file removed more bovine dentin compared to austenitic coronal flaring files. In the present study, HyFlex EDM Orifice Opener and HyFlex CM #25/.08 were more martensitic compared to OneFlare. Further studies are needed regarding the cutting efficiency of the tested files, and the relationship between the cutting efficiency and mechanical properties. The limitation of this study is the comparison of three NiTi files with different cross-sectional designs, instrument lengths, tapers and heat treatments. If the design, instrument length and taper are all identical and heat-treatment is the only variable factor, it will be easier to determine whether the bending the cyclic fatigue resistance are determined by the heat treatment. The effect of bending resistance and cyclic fatigue resistance on the clinical performance of orifice preflaring NiTi files, such as the cutting efficiency and torque generation, need to be investigated.
# Deep-Feature-Based Approach to Marine Debris Classification
## 4 Results
### Deep Feature Classification with Conventional ML Classifiers
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Classifier** & **Accuracy (\(\%\))** & **F1-Score (\(\%\))** & **Kappa** \\ \hline \multirow{6}{*}{**VGG19** (FF + FT)} & NN & **86.16** & **86.15** & **0.83** \\  & RF & 80.08 & 78.84 & 0.75 \\  & SVM & 84.28 & 83.57 & 0.80 \\  & NB & 68.97 & 67.04 & 0.61 \\  & LR & 80.71 & 79.95 & 0.76 \\  & KNN & 73.79 & 72.53 & 0.67 \\ \hline \multirow{6}{*}{**InceptionV3** (FT)} & NN & 90.57 & 91.07 & **0.88** \\  & RF & 88.47 & 88.42 & 0.86 \\  & SVM & 90.57 & 90.89 & **0.88** \\  & NB & 88.47 & 88.60 & 0.86 \\  & LR & **90.78** & **91.28** & **0.88** \\  & KNN & 89.52 & 90.13 & 0.87 \\ \hline \multirow{6}{*}{**ResNet50** (FF + FT)} & NN & 85.53 & 86.51 & 0.82 \\  & RF & 83.02 & 82.83 & 0.79 \\  & SVM & **86.16** & **87.07** & **0.83** \\  & NB & 81.55 & 82.03 & 0.77 \\  & LR & 85.95 & 86.97 & 0.82 \\  & KNN & 79.66 & 79.47 & 0.74 \\ \hline \multirow{6}{*}{**Inception-ResNetV2** (FT)} & NN & 91.40 & 92.08 & 0.89 \\  & RF & 90.99 & 91.80 & 0.89 \\  & SVM & **91.61** & **92.27** & **0.90** \\  & NB & 90.99 & 91.75 & 0.89 \\  & LR & 90.99 & 91.60 & 0.89 \\  & KNN & 90.15 & 90.94 & 0.88 \\ \hline \multirow{6}{*}{**DenseNet121** (FT)} & NN & **88.05** & 88.26 & **0.85** \\  & RF & 85.74 & 85.52 & 0.82 \\ \cline{1-1}  & SVM & **88.05** & **88.53** & **0.85** \\ \cline{1-1}  & NB & 83.02 & 83.52 & 0.79 \\ \cline{1-1}  & LR & 86.79 & 86.58 & 0.83 \\ \cline{1-1}  & KNN & 84.28 & 84.91 & 0.80 \\ \hline \multirow{6}{*}{**MobileNetV2** (FT)} & NN & 82.60 & 82.61 & 0.78 \\ \cline{1-1}  & RF & 84.07 & 83.56 & 0.80 \\ \cline{1-1}  & SVM & **85.32** & **85.62** & **0.82** \\ \cline{1-1}  & NB & 84.49 & 84.40 & 0.81 \\ \cline{1-1}  & LR & 83.02 & 83.47 & 0.79 \\ \cline{1-1}  & KNN & 82.39 & 83.14 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of machine learning classifiers on the test set.
# On the Use of Data Envelopment Analysis for Multi-Criteria Decision Analysis
## 2 Materials and Methods
### Data and Analyses  
The analysis utilized two different data sets to test the implications of model choice given the structure of the data used and the importance of hierarchical structures. First, a hypothetical data set was generated to ensure a wide spread of criterion outcomes. The 'efficiency' of each set of outcomes was compared given different hierarchical structures and model type (i.e., radial or slacks-based) to assess how these impact the overall scores.  
Second, a waste management data set from a previous MCDA application was re-examined using the two different DEA methods, with the DEA outcomes compared with the original MCDA analysis.  
The two data sets differ in terms of the relationship between the variables. The variables, representing outcome indicators, within the hypothetical data set are relatively uncorrelated. In contrast, the outcome variables in the waste management data set were more correlated.  
A hierarchical structure was also applied to both data sets to test the effects of introducing such a structure on the overall management alternative efficiency score. In the case of the hypothetical data set, this structure was varied to test the effects of different number of indicators/sub-objectives/criteria on the estimated efficiency score and overall estimate of performance. For the waste management data set, the indicators naturally grouped into environmental, technological and economic higher order groups.
# A Cognitive Anycast Routing Method for Delay-Tolerant Networks
## 3 Anycast Routing
### CSG Routing Method
#### 3.1.3 CNC Learning  
After a routing decision \(a\) has been made, the CNC computes the estimated cost (or negative reward) of that choice. In this paper, the interest is in optimizing response times, and thus the routing cost \(C\) is computed in terms of the expected delivery time of thebundle (at the destination) via the selected outbound link. The average observed cost can be iteratively calculated using the expression: \(G\leftarrow\alpha C+(1-\alpha)G\), where \(0\leq\alpha\leq 1\) is a hyperparameter. The gradient \(\delta=G-C\) is used to update the SNN weights using the scaling factor \(\eta>0\) (the learning rate):  
\[\begin{array}{ll}w(N_{j},N_{a})&\gets w(N_{j},N_{a})+\eta\ \delta\ \ ;j=1,\ldots,K;\ a\
eq j\\ w(M_{k},N_{a})&\gets w^{k}(M_{a},N_{a})-\eta\ \delta\ \ ;k=1,\ldots,K-2 \end{array} \tag{1}\]  
As routing decisions are made and the SNN's weights are changed, the range of weight values is observed. When the range exceeds a predetermined threshold, a weight regulation process is applied to shift and scale the weights to a selected range. This is done to prevent synapse connections from becoming too large or too small, which may impact the emission of spikes. The learning algorithm has the linear complexity \(O(n)\), where \(n\) is the number of decisions.
# A Brief Review of Multipath TCP for Vehicular Networks
## 6 Multipath Routing in Vehicular Networks
### Cross-Layer Multipath Routing  
A cross-layer position-based delay-aware communication protocol called PROMPT is proposed in [127]. PROMPT adopts a position-based routing approach to deal with the mobility problem in vehicular environments. The vehicles exchange information with each other by sending beacons to estimate the delay and select the minimum delay path. In PROMPT, the routes are expressed as a sequence of street and direction. The relay node is chosen based on the receiver-based MAC channel contention approach, which is related to the distance and direction of the relay nodes from the transmitter. In [128], [PERSON] et al. proposed a cross layer routing scheme for VANETs. This scheme gathers queuing information of all neighboring nodes and channel information to make optimal routing decision. The method of selecting neighboring nodes set is also discussed in this work. A cross-layer multi-path routing (CLMR) protocol among application, network, media access control (MAC), and the physical layer was proposed [129]. Redundant array inexpensive disks (RAID) technology is utilized to minimize the retransmissions. [PERSON] et al. [130] proposed R-S-AOMDV based on an improvement of AOMDV. In addition to the hop count, R-S-AOMDV considers the link quality and vehicle movement in the route selection.
# Impact of CoAP and MQTT on NB-IoT System Performance +
## 3 Results
### Coverage  
Finally, we look at impact of the stack on coverage. This is depicted in Figure 4, where we show the served devices with the lowest reachable path gain. Two observations can be made from the figure: first, the lowest reachable path gains for MQTT are on average higher than the lowest reachable path gains for CoAP. Also, enabling CoAP confirm helps to reach devices in deep coverage better. This basically hints that CoAP service has a better coverage than MQTT service, especially when confirm is used. Second, as load increases, the lowest reachable path gain also increases. It seems that as the increased number of devices in the system demand more system resources, the devices in deep coverage are left without. Thus, it seems that in high load, the system is not able to allocate enough resources for users in bad coverage.
## 2 Analysis of Two Basic Forms of Reinforcing Ribs
### At the Structural Discontinuity Region  
Structural discontinuities inevitably exist in engineering structures, and in the structural discontinuity regions often exist high stress and so they need to be reinforced. How do we design reinforcing ribs rationally? As a demonstration, Figure 4 shows a thin-walled structure composed of two flat plates. Limit load analysis was performed under the lateral pressure load on the surface of the plate, which was constrained at both ends. As shown in Figure 4, there is a long yield zone at the connection region of the flat plates. In order to increase the limit load, two forms of reinforcing ribs are proposed here. Rib form 1: as shown in Figure 5a, the same ribs with a thickness of 20 mm and a height of 390 mm are set up in the structures of the two plates and connected at the structural connection region. Figure 5b shows the von Mises stress distribution after reinforcement. Obviously, the ribs play a significant role in strength reinforcement. Rib form 2: as shown in Figure 6a, based on rib form 1, a triangular plate was added to the connection area of the two ribs having a decreased height of 300 mm compared with the ribs in Figure 5a in order to keep the weight of the two forms of ribs the same. Figure 6b shows the von Mises stress distribution after reinforcement. We kept rib form 1 and rib form 2 at the same weight and performed finite element limit load analysis, and the results of the limit loads are listed in Table 2. It can be seen that the limit load by using rib form 2 is much larger than that using rib form 1. Therefore, in the discontinuity region, such as the connection of a tube and shell, reinforcing ribs with triangular plates are more effective to increase the load-carrying capacity of the structure.  
Figure 3: Illustration of the reinforcing ribs along the short side.  
Figure 2: Illustration of the reinforcing ribs along the long side.  
Figure 4: Contours of the von Mises stress distribution of connected plates without ribs.  
Figure 5: Contours of the von Mises stress distribution of connected plates reinforced with rib form 1: (**a**) rib form 1; (**b**) contours of the von Mises stress distribution.  
Figure 6: Contours of the von Mises stress distribution of connected plates reinforced with rib form 2: (**a**) rib form 2; (**b**) contours of the von Mises stress distribution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structures** & **Rib Form 1** & **Rib Form 2** & **Rib Form 2 over Rib Form 1** \\ \hline Limit load value (Mpa) & 0.198 & 0.380 & Increased by 91.91\% \\ Rib volume (m\({}^{3}\)) & 0.395 & 0.394 & Reduced by 0.25\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of reinforcement effects for the different ribs at structural discontinuity region.