esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Ablation Studies
#### 3.5.3 Policy Inputs  
Lastly, we evaluated the choices of inputs for the policy. We compared our proposed policy architecture with all inputs, as defined in Section 2.2.2, with two variants. First, we considered the policy without the inclusion of prior action \(\mathbf{a}_{t-1}\). Second, we considered the policy without knowledge of desired insertion force \(F_{g}\). The training environment was the cuboid-peg insertion task in the  
Figure 11: Comparison between policy architectures: learning curve for the cuboid-peg insertion task with random initial positions.  
Figure 10: Comparison between learning from scratch and learning from a policy learned in the simulation: learning curve for the 3D-printed cuboid-peg insertion task in a real robot with random initial positions.  
simulation with a random initial position and random desired insertion force. In the case of the policy that did not have \(F_{g}\) as input, the cost function still accounted for the desired insertion force.  
Figure 12 shows the comparison of the learning curves. Most notable is the poor performance of the policy that lacked the knowledge of prior action \(\mathbf{a}_{t-1}\). Prior-action information is critical for the agent to more quickly converge to an optimal policy. Additionally, knowledge of \(F_{g}\) enables the agent to find policies that yield higher cumulative rewards, and to learn faster.
## 2 Materials and Methods
### Model Scenarios
#### 2.2.3 Flash Calculations and the Rachford-Rice Equation  
To determine the phase compositions at equilibrium, flash calculations were performed. The Rachford-Rice equation was used to compute the vapor fraction \(n^{V}\) by solving the following:  
\[f\Big{(}n^{V}\Big{)}=\ \sum_{i=1}^{N}\left(y_{i}-x_{i}\right)=\sum_{i=1}^{N}\frac{ z_{i}(K_{i}-1)}{1+(K_{i}-1)n^{V}}=0 \tag{1}\]  
where \(z_{i}\) is the overall mole fraction of the component, \(K_{i}\) is the equilibrium ratio, \(n^{V}\) is the vapor fraction, \(x_{i}\) is the liquid composition, and \(y_{i}\) is the vapor composition. The first guess of \(n^{V}\) is set to the mean of \(\left(\frac{1}{1-Kmax},\frac{1}{1-Kmin}\right)\). Furthermore, we solved the K-values and \(n^{V}\) iteratively using both the Newton-Raphson method and successive substitution, starting with updating \(n^{V}\) at the fixed K-value followed by further updating the K-values. Function \(f\big{(}n^{V}\big{)}\) decreases monotonically with asymptotes \(1/(1-K_{i}\ )\). Final convergence was achieved when the K-value error was in the range of the accepted tolerance.
## 4 Simulation Validation
### Merging of the Platoon  
The platoon can be applied in the direction of commercial vehicles for medium and long-distance cargo transportation and common individual vehicles seeking collaborators to save fuel and reduce driver workload. When there are enough vehicles gathered for the same or similar purposes and ready for coordinated control, each vehicle needs to form the geometry that is required by the platoon. When the market share of cars with multi-vehicle coordination ability is low, the ratio of intelligent cooperative vehicles in a local area can be increased by this way, so that the advantages of queue coordination control become more obvious [25]. The aggregation of the platoon requires that, when the leading vehicle issues a platoon merging command, the potential following vehicle can arrive at a regular lane or a dedicated lane according to a particular order, and there is no additional acceleration, braking, or lane change in the process.  
It is assumed that both the leading vehicle and the following vehicle have communication capabilities so that the balanced point of the leading vehicle setting can be known. To strike a balanced between accuracy and conciseness, it is assumed that: constant spacing control strategy is used in control, which means that the distance between the desired balanced point of the following vehicle and the leading vehicle is constant. In the platoon merging, different FWIDSVs in different positions determine their own balanced points under the coordination of the pilot vehicles, and coordinate and merge. The position and spacing information of the pilot vehicle and the FWIDSV as the following vehicle are shown in the Table 4.  
Figure 14: Tracking performance in curved road: (**a**) The path of the vehicles; and, (**b**) The longitudinal following error.  
Through the Figure 15, it can be seen that FWIDSVs located at different initial positions track the path and stabilize at their own balanced point after obtaining the coordinated command of the leading vehicle. The following vehicles at different locations can operate normally, which shows that the controller has good control performance under a longitudinal and lateral coupling situation.
###### Abstract  
A single Radio-Frequency Interference (RFI) is a disturbance source of modern wireless systems depending on Global Navigation Satellite Systems (GNSS) and Satellite Communication (SatCom). In particular, significant applications such as aeronautics and satellite communication can be severely affected by intentional and unintentional interference, which are unmitigated. The matter requires finding a radical and effective solution to overcome this problem. The methods used for overcoming the RFI include interference detection, interference classification, interference geolocation, tracking and interference mitigation. RFI source geolocation and tracking methodology gained universal attention from numerous researchers, specialists, and scientists. In the last decade, various conventional techniques and algorithms have been adopted in geolocation and target tracking in civil and military operations. Previous conventional techniques did not address the challenges and demand for novel algorithms. Hence there is a necessity for focusing on the issues associated with this. This survey introduces a review of various conventional geolocation techniques, current orientations, and state-of-the-art techniques and highlights some approaches and algorithms employed in wireless and satellite systems for geolocation and target tracking that may be extremely beneficial. In addition, a comparison between different conventional geolocation techniques has been revealed, and the comparisons between various approaches and algorithms of geolocation and target tracking have been addressed, including \(H_{\infty}\) and Kalman Filtering versions that have been implemented and investigated by authors.  
Radio-Frequency Interference; localization; geolocation; TDOA; FDOA; target tracking-optimization approaches; uncertainty; \(H_{\infty}\) and Kalman filtering
## 3 Results
### Segmentation Results
#### 3.1.3 Results  
In Figure 4, the results applying the three segmentation methods for four example images are shown.  
Figure 3: ArgMax ensemble configuration for the skin lesion classification.  
Table 1 presents the performance parameters for each method, as follows: Dice coefficient (DICE), Jaccard index (JACC), Sensitivity (Se), Specificity (Sp) and Accuracy (Acc). This table presents the results of applying the three methods to the test set of the database, ISIC 2017 (600 images). The best performance is achieved by the SegNet neural network in four out of five parameters.  
Figure 4: Segmented Images. For images (**a–d**): first row shows the original image with its ground truth mask and its corresponding segmentation result; second row shows the results of applying semantic segmentation with SegNet; third row shows the result of energy minimization algorithm via convex optimization; fourth row shows the result of segmentation based on VGG16 feature images.  
Beyond the evaluation parameters, it is relevant to discuss the advantages and drawbacks of each methodology. Although the SegNet neural network obtains the highest performance, the accuracy obtained by the energy minimization algorithm is acceptable, taking into account the resources needed. Nevertheless, SegNet requires a high computational effort for the training but, once the training process has been done, the computational cost of the segmentation process is comparable to the cost required by the other two methods. On the other hand, semantic segmentation via SegNet is an automatic process, which allows us to obtain the segmented lesion without human supervision, as well as the segmentation based on feature images from VGG16. In contrast, energy minimization segmentation requires human intervention, slowing down the segmentation process or making it difficult to use, in the case of large databases.  
In Table 2, a comparison with other methods published in the literature is presented. This table shows that the segmentation with SegNet attains competitive results. This justifies that this technique can be chosen as a good segmentation method, in order to evaluate the convenience of including a segmentation step before the classification of the lesions.
## 4 Numerical Computational Method for Sealing Performance
### Method for Calculating the Bearing Capacity of Gas Film  
#### 4.1.1 Gas Film Thickness  
The film thickness equation for the seal between shafts is as follows [10]:  
\[h=C(1+\varepsilon\cos\theta) \tag{9}\]  
where \(C\) is the gap between the sealing ring and the inner rotor radius, and \(\varepsilon\) is eccentricity, which changes with the pressure in the fluid (as shown in Figure 3). We must consider the centrifugal expansion effect of the rotor and the sealing ring. Since the sealing ring rotates with the outer rotor, we only considered the centrifugal expansion effects of the sealing ring and inner rotor radius \(C\). The gap between the sealing ring and the inner rotor radius \(C\) is as follows [9]:  
\[C=R_{1}+u_{s}(R_{1})-r_{2}-u_{r}(r_{2}) \tag{10}\]where \(u_{s}\) and \(u_{r}\) are the centrifugal elastic deformation of the sealing ring and the inner rotor, and can be calculated using the elastic mechanics method [9].  
\[\begin{array}{rl}u_{s}(R_{1})=&-\frac{(1-v_{1}^{2})\rho_{2}^{2}m_{2}^{2}R_{1}^{ 3}}{8E_{2}}+\left[\frac{p(1-v_{2})R_{2}^{2}}{E_{2}(R_{2}^{2}-R_{1}^{2})}+\frac {(1-v_{2})(3+v_{2})\rho_{2}m_{2}^{2}(R_{2}^{2}+R_{1}^{2})}{8E_{2}}\right]R_{1} \\ &+\left[\frac{p(1+v_{2})R_{2}^{2}R_{1}^{2}}{E_{2}(R_{2}^{2}-R_{1}^{2})}+\frac {(1+v_{2})(3+v_{2})\rho_{2}m_{2}^{2}R_{2}^{2}R_{1}^{2}}{8E_{2}}\right]\frac{1} {R_{1}}\end{array} \tag{11}\]  
\[\begin{array}{rl}u_{r}(r_{2})=\frac{(1+v_{1})(3-2v_{1})}{8E_{1}(1-v_{1})} \rho_{1}n_{1}^{2}r_{2}[(1-2v_{1})(r_{1}^{2}+r_{2}^{2})+\frac{r_{1}^{2}r_{2}^{2 }}{r_{2}}]-\frac{(1+v_{1})(1-2v_{1})}{8E_{1}(1-v_{1})}\rho_{1}n_{1}^{2}r_{2}^{ 3}\end{array} \tag{12}\]  
Figure 3: Calculation flow chart.  
where \(E_{1}\) is the elasticity modulus of the inner rotor, \(E_{2}\) is the elasticity modulus of the sealing ring, \(\
u_{1}\) is the Poisson's ratio of the inner rotor, \(\
u_{2}\) is the Poisson's ratio of the sealing ring, \(\rho_{1}\) is the density of the inner rotor and \(\rho_{2}\) is the density of the sealing ring.  
Generally, the sealing ring is made of graphite. The centrifugal expansion effect on the seal gap at high speeds cannot be ignored. Performance parameters for the inner rotor and sealing ring in the sealing structure are listed in Table 2[20].
## 4 Design of TLQoS
### Routing Potentials
#### 4.3.3 Reliability Potential Field  
The reliability potential field, \(P_{n_{i}}^{r}\) on a node \(n_{i}\) is defined as  
\[P_{n_{i}}^{r}=R(n_{i}) \tag{7}\]  
Here, \(R(n_{i})\) denotes the average packet loss ratio at node \(n_{i}\). Hence, the lower the ratio, the higher the reliability of the node.  
The reliability force, \(F_{r}(n_{i},n_{j})\) acting on node \(n_{i}\) to \(n_{j}\in NB(n_{i})\) is given by  
\[F_{r}(n_{i},n_{j})=\frac{P_{n_{i}}^{r}-P_{n_{j}}^{r}}{c_{n_{i},n_{j}}} \tag{8}\]  
Thus, driven by this potential, a packet moves along the nodes having least packet loss ratio, _i.e_., reliable nodes on its path to the \(BC\). This potential is also time-variant and is not loop free. However, combining with hop-count potential would result a loop free path as will be discussed later.
## 4 Methods
### Analysis of GPS TEC Anomalies  
Usually, for the full constellation, a ground-based receiver can simultaneously record signals from more than 10 GPS satellites [56]. Therefore, for a network of n ground-based GPS receivers, an image over the network area with about 10n TEC points can be constructed every 30 s. Similarly, based on measurements of worldwide limited hundreds of ground-based receivers of the International GNSS Service (IGS), the global ionosphere map (GIM) of the TEC has been daily and routinely published by the Center for Orbit Determination in Europe (CODE), European Space Operations Centre (ESOC), University of Warmria and Mazury (UWM), NASA's Jet Propulsion Laboratory (JPL), and Technical University of Catalonia (UPC) ([[ftp://cddis.gsfc.nasa.gov/gps/products/ionex/](ftp://cddis.gsfc.nasa.gov/gps/products/ionex/)]([ftp://cddis.gsfc.nasa.gov/gps/products/ionex/](ftp://cddis.gsfc.nasa.gov/gps/products/ionex/))) to estimate ionospheric effects on radio propagation geodesy applications since 1998 [74]. Currently, the GIM is routinely published in a 1- or 2-h time interval with 1-2 days delay. The spatial resolutions of the GIM on the \(\pm 87.5^{\circ}\)N latitude and \(\pm 180^{\circ}\)E longitude are \(2.5^{\circ}\) and \(5^{\circ}\), respectively. Thus, a local network of ground-based receivers is useful to monitor the fine structure of the TEC over the network area (a small region, such as that affected by a volcanic eruption), while the GIM is suitable to observe the relatively large structure of the TEC (a larger area, such as that affected by a dust event). Specific methodologies were developed to detect anomalies of GPS TEC signals. They were applied here to help in interpreting DEMETER observations.
## 3 Results and Discussion
### Battery Pack Model Development  
In this section, two model development techniques are applied to the data set applied and obtained from the experiments in this study. Raw input data as described in Table 6 and maximum temperature data obtained from the 16 monitored cells are used as input and output training data for an artificial neural network (ANN) algorithm to develop a model for the battery pack investigated in this study. All 12 unique current rate and air flow rate combination experiments developed by the full factorial DOE method are used as input data (X,Y) with the averaged absolute maximum temperature of each unique experiment used as an output (Z) to develop a surface regression model.
# Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events
## 3 Proposed Method
### Deep Clustering Network  
Considering \(n\) user multi-source behavior event sequences \(\left\{s_{q}\in S\right\}_{q=1}^{n}\), the objective of our proposed deep clustering network is to simultaneously learn the feature representation and class labels of user multi-source behavior event sequences. This process consists of two parts: user multi-source behavior sequence feature representation learning and deep clustering. We first pretrain the network to initialize the user multi-source behavior sequence feature space and cluster centers, then train the network to optimize the feature space and cluster centers.  
The network structure, shown in Figure 1, consists of an encoder-decoder model and the deep clustering part. We first introduce the training of the encoder-decoder model to learn the feature space of user multi-source behavior event sequences, which is the first stage of learning in the deep clustering network.
# Quasi-Yagi Slotted Array Antenna with Fan-Beam Characteristics for 28 GHz 5G Mobile Terminals
## 2 Antenna Design and Performance
### 1 \(\times\) 8 Array Antenna Design and Performance  
Figure 13 shows the structures and simulated results of the \(1\times 8\) array antenna with and without directors. The array antenna is located at the top side of the ground plane with dimensions of \(150\times 70\) mm, to verify the antenna performance on a typical mobile terminal. The substrate material and detailed antenna structures are identical to those of the single and \(1\times 2\) array cases, except for the size of the ground plane. Figure 13c shows the simulated S-parameters of the array antenna with the directors in free space. The simulated \(-10\) dB reflection coefficients (S\({}_{11}\)-S\({}_{88}\)) cover the frequency band from 27.03 to 28.82 GHz. The proposed antenna has a low mutual coupling characteristic of lower than \(-25.02\) dB within the target band due to the inserted slits. The simulated radiation patterns at 28 GHz are illustrated in Figure 13d. The simulated peak gain of the antenna without the directors is formed around from \(-90^{\circ}\) to \(-120^{\circ}\) and from \(+90^{\circ}\) to \(+120^{\circ}\) in the \(xz\) plane since the electromagnetic wave is radiated perpendicular to the slotted aperture. Therefore, the directors are added to compensate for the reduced gain in the \(+z\) direction. The simulated gains of the array antenna with and without the  
Figure 11: The \(1\times 2\) array antenna with Slit 1 and Slit 2: (**a**) top view, (**b**) simulated surface current distribution at 28 GHz, and (**c**) simulated E-field distribution in the E-plane at 28 GHz.  
Figure 12: Simulated performances of the \(1\times 2\) array antenna with directors considering slits: (**a**) with S-parameters, and (**b**) showing the radiation patterns in the \(xz\) plane at 28 GHz.  
directors in the \(+z\) direction are 11.16 and 7.09 dBi, respectively. The proposed array antenna with the directors has a very wide HPBW of 256.72\({}^{\circ}\) in the \(xz\) plane and a side-lobe level (SLL) of \(-13.06\) dB in the \(yz\) plane. The simulated front-to-back ratio (FBR) of the array antenna with the directors is 12.71 dB. Figure 13e shows the simulated beam steering performance of the \(1\times 8\) array antenna with the directors in the \(yz\) plane. At 28 GHz, the proposed array antenna covers up to \(\pm 48^{\circ}\) by using the linear phase progression with uniform amplitude between elements. The reference to define the maximum beam steering angle is the SLL of \(-10\) dB [15]. In this range, all of the simulated peak gains are higher than 11.09 dBi.
## 4 Experiments and Results
### Error Discussion
#### 4.5.2 Label Errors  
The datasets used in the study were annotated by a crowdsourcing platform. Some pixels were mislabeled in the ground truth, which would lead to incorrect predictions of the model. An example from the dataset is shown in Figure 12, where the background marked by a red circle is mislabeled as the cloud.  
Figure 11: Misdetection example.  
Figure 12: Label error example.
# A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification
###### Abstract  
Drowsiness detection is an important task in road safety and other areas that require sustained attention. In this article, an approach to detect drowsiness in drivers is presented, focusing on the eye region, since eye fatigue is one of the first symptoms of drowsiness. The method used for the extraction of the eye region is Mediapipe, chosen for its high accuracy and robustness. Three neural networks were analyzed based on InceptionV3, VGG16 and ResNet50V2, which implement deep learning. The database used is NITYMED, which contains videos of drivers with different levels of drowsiness. The three networks were evaluated in terms of accuracy, precision and recall in detecting drowsiness in the eye region. The results of the study show that all three convolutional neural networks have high accuracy in detecting drowsiness in the eye region. In particular, the Resnet50V2 network achieved the highest accuracy, with a rate of 99.71% on average. For better visualization of the data, the Grad-CAM technique is used, with which we obtain a better understanding of the performance of the algorithms in the classification process.  
driver monitoring system; drowsiness detection; convolutional neural network; Grad-CAM visualization +
Footnote †: journal: _aciences_
# Protecting Private Communications in Cyber-Physical Systems through Physical Unclonable Functions
## 3 Proposed Encryption Scheme
### Key Generator
#### 3.3.3 Global Behavior and Seed Obtention
However, for this initial work presenting the solution, we are considering as challenge a simple composition of \(N_{c}\) harmonics; and the seed calculation was based on a sampling scheme (a sigma-delta modulator) to extract from the analog signal three digital words with the appropriate length. Besides, in order to remove transitory responses and other exogenous effects affecting the seed calculation, the post-processing module was configured to only respond to long-term changes in PUF response. Moving average filters are implemented to perform this function. This technological feature might limit the randomness of the generated keys, but the use of PRNG prevents this problem.
# In Situ Measurement of Alkali Metals in an MSW Incinerator Using a Spontaneous Emission Spectrum
## 2 Experimental Setup  
Due to the high moisture and low heat value characteristics of Chinese MSW, the preferred furnace type for MSW incineration is a mechanical grate incinerator; the reciprocating grate incinerator is a particularly common choice, because it can decrease the pollutant emissions by means of a forward and backward pushing two-stage grate. Therefore, our experiment was conducted in a two-stage reciprocating grate incinerator of the GCL Renewable Energy Power Generation Co., Ltd., which is located in Xuzhou, China. The incinerator can dispose of MSW at a rate of 400 tons per day. The rated evaporative capacity of the incinerator is 35 ton/h. The MSW studied was a mixture of household trash (70%), street waste (20%), and office waste (10%). During the experiment, the MSW was sampled and analyzed three times. Table 1 lists the proximate and ultimate analysis of the MSW used in the experiment. The composition of the incinerated MSW incineration changed only slightly.  
As shown in Figure 1, the L-shaped furnace was adapted by the two-stage reciprocating grate waste incinerator to prolong the time that the flue gas would reside in the furnace and to prevent a flue gas blockade. The total length and width of the grate were 9.7 m and 7.07 m, respectively. The furnace outlet was located above the grate center, and the waste underwent drying, pyrolysis, and combustion progressively along the grate after entering the incinerator. To achieve the staged combustion process, the total air supply was divided into primary and secondary air. The primary air used for assisting combustion was heated to 230 \({}^{\circ}\)C in two stages of preheating by steam and flue gas, respectively. The residence time of the flue gas in the furnace was \(>\) 2 s at a temperatures \(>\) 850 \({}^{\circ}\)C after the secondary air supply was provided.  
As shown in Figure 1, a spectrometer system, consisting of a portable spectrometer (AvaSpec-USB2048, Avantes, Apeldoorn, The Netherlands) with 2048 pixels, a fiber with a collimating lens, and a laptop was placed at the rear section of the combustion chamber. The measurement wavelength range of the spectrometer was 200 nm to 1100 nm, and the spectral resolution was 0.8 nm. In the process of spectral analysis, a smoothing function was introduced to decrease noise, each pixel will be averaged with three left and three right neighboring pixels, in this case, and the resolution of spectrum data is about 1.71 nm. The spectra of the MSW flames were collected by the fiber and then converted into digital signals by the spectrometer; finally, they were sent to the laptop via a USB cable. Dedicated application software developed by the authors of this study was used to obtain and process the MSW flame emission spectra in situ.