esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact Planar Wi-Fi Antenna with Optimized Radiation Patterns for Small UAV Applications
## 3 Parametric Studies and Full-Wave Simulations  
The proposed antenna was simulated by the ANSYS High-Frequency Structure Simulator (HFSS) [19]. From (1), f\({}_{H}\) decreases as \(\text{L}_{2}\) increases, \(\text{L}_{2}\) can be derived from (7), and \(\text{L}_{2}\) increases as \(\text{T}_{h}\) increases. Hence, f\({}_{H}\) decreases as \(\text{T}_{h}\) increases.  
\[\text{L}_{2}=\sqrt{\text{T}_{h}{}^{2}+\left(\frac{\text{T}_{w}}{2}\right)^{2}} =\sqrt{5.7^{2}+\left(\frac{1.5}{2}\right)^{2}}=5.75, \tag{7}\]  
Figure 8 shows the simulated reflection coefficients for different values of \(\text{T}_{h}\). The simulated data agree with (1) and (7), f\({}_{H}\) decreases as \(\text{T}_{h}\) increases, and f\({}_{H}\) is about 5.63 GHz when \(\text{T}_{h}\) is 5.7 mm.  
Figure 8: The simulated reflection coefficients for different values of \(\text{T}_{h}\).  
Figure 7: The simulated surface current density at 2.4 GHz.  
From (6), \(f_{\rm L}\) decreases as \(W_{1}\) increases, and Figure 9 shows the simulated reflection coefficients for different values of \(W_{1}\). The simulated data agree with (6), \(f_{\rm L}\) decreases as \(W_{1}\) increases.  
From (2) and (5), the normalized total field at 5.8 GHz changes as \(\beta_{\rm d}\) changes, and from (3), \(\beta_{\rm d}\) gets closer to \(-100.92^{\circ}\) as \(T_{\rm w}\) decreases closer to zero. Hence, the end-fire radiation becomes more significant as \(T_{\rm w}\) decreases. Figure 10 shows the simulated vertically polarized radiation patterns at 5.8 GHz in the y-z plane for different values of \(T_{\rm w}\). The simulated data are in agreement with (2), (3), and (5), and the end-fire radiation becomes more significant as \(T_{\rm w}\) decreases.  
Figure 10: The simulated vertically polarized radiation patterns at 5.8 GHz in the y–z plane for different values of \(T_{\rm w}\).  
Figure 9: The simulated reflection coefficients for different values of \(W_{1}\).
# Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model
## 3 Results
### Comparison Experiment  
The calibration method based on the general imaging model proposed in this paper and the calibration method based on the pinhole imaging model are applied to the orthogonally splitting imaging pose sensor, and the results of error valuation are compared.  
The calibration method based on the general imaging model selects the MQ function and its shape parameter is selected according to the parameter experiment.  
Since the pinhole imaging model is an ideal model and the pose sensor has a large distortion, the distortion correction is performed first, and then the Tsai two-step method is used for calibration. The first step uses a linear iterative optimization algorithm to calculate the initial values of the external parameters of the camera. The second step uses the optimization method to solve the internal parameters nonlinearly and further optimizes the external parameters. At last solve the world coordinate using parameters and calculate the RMS  
The experimental results are shown in Table 3. It can be seen that the calibration accuracy based on the general imaging model is higher than the calibration accuracy based on the pinhole imaging model.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 5 Challenges in Designing a Flexible Antenna
### Miniaturization of an Antenna  
The increase in the size of the flexible antenna may cause discomfort in the user wearing the antenna. So, a compact flexible antenna is needed especially in medical applications, and still maintaining the expected radiation characteristics is a challenge. Miniaturization techniques including applying slots, using substrates with higher dielectric constants, using fractal shapes, etc., can be used to reduce the size of the antenna and thus make it useful for wearable applications.
## 3 Results and Discussion
### Free Volatile Aroma Compounds in Fresh and Dehydrated Grapes  
Free volatile compounds in the fresh grapes and in the grapes after dehydration are shown in Table 2 grouped in the chemical classes. A total of 36 volatile compounds were identified in the fresh grapes, including alcohols (8), esters (8), terpenes (13), ketones (2), aldehydes (3), C-13 norisoprenoids (1), and lactone (1). Regarding free volatile compounds, 1-hexanol, 2-hexen-1-ol, and 3-methyl-1-butanol occurred in the highest concentrations in fresh grapes, followed by ethyl acetate and 2-hexanal. Linalool and linalool oxide were present in the highest concentration among terpenols, but their concentration was quite low, 3.81 and 3.04 \(\mu\)g/L, respectively. Other identified terpenes, such as \(\alpha\)-terpineol, myrcene, o-cymene, terpinen-4-ol, and geraniol, were found in concentrations between 0.25 and 0.38 \(\mu\)g/L, whereas citronellool, nerol, linalool acetate, and geranyl acetone, were found in concentrations between 0.03 and 0.08 \(\mu\)g/L. Both aromatic alcohols (2-phenylethanol and benzyl alcohol) were also detected in the free fraction, although in very low concentrations. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Fresh Grapes** & **G-D Grapes** & **C-D Grapes** \\ \hline \({}^{\circ}\)**Brix** & 21.2 \(\pm\) 1.2 \({}^{\text{a}}\) & 36.2 \(\pm\) 0.1 \({}^{\text{b}}\) & 35.6 \(\pm\) 5.9 \({}^{\text{b}}\) \\
**pH** & 3.6 \(\pm\) 0.2 \({}^{\text{a}}\) & 4.0 \(\pm\) 0.0 \({}^{\text{b},\#}\) & 4.3 \(\pm\) 0.1 \({}^{\text{c}}\)\# \\
**total acidity** (g/L) & 5.5 \(\pm\) 0.4 \({}^{\text{a}}\) & 4.2 \(\pm\) 0.1 \({}^{\text{b},\#}\) & 6.4 \(\pm\) 0.6 \({}^{\text{\#},c}\) \\
**total phenols** (mg/L) & 335 \(\pm\) 157 \({}^{\text{a}}\) & 631 \(\pm\) 22 \({}^{\text{b}}\) & 783 \(\pm\) 137 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Different letters within the row (a, b, c)—indicate significant differences, \(p<0.05\). # statistically significant differences in juice must properties between the grape drying methods, \(p<0.05\).  
\end{table} 
Table 1: Basic parameters of juices from ‘Marastina’ fresh grapes, greenhouse-dried (G-D) grapes, and chamber-dried (C-D) grapes at 50 \({}^{\circ}\)C (average \(\pm\) standard deviation).
## 2 Study Area and Data Processing
### Satellite Precipitation Data  
This study evaluated eight SPPs from 1 April 2014 to 31 December 2016. A brief description of these SPPs is given as follows.
# Deep Learning-Powered System for Real-Time Digital Meter Reading on Edge Devices
## 3 Results and Discussion  
### Screen Detection  
Regarding the proposed methodology for screen detection, as performance metrics, the mean average precision (mAP) is used for comparison of the predicted bounding box and the corresponding ground truth, as it is the COCO [10] evaluation metric, and intersection over union (IoU) and Euclidean distance (ED) for assessment of the keypoint detection [1; 5]. We compare the performance of an equipment-agnostic screen bounding box detection model, denoting a single model capable of identifying the screen in the UC and FM devices, with specific models trained for each.  
Figure 5: Screenshots of the mobile application when using both devices (FM on the left and UC in the right): (**a**,**c**) images corresponding to the first stage where automatic screen detection is run in real-time and (**b**,**d**) outputs the image acquired, and the reading obtained.  
Analyzing the results for screen bounding box detection shown in Table 2, the FM-specific model can detect the screen of these devices, despite noticeable differences in the appearance of its two subtypes, more precisely than the model for UC. Nonetheless, the equipment-agnostic approach proves to be superior, with an impressive jump of 56% in [EMAIL_ADDRESS] for UC and 16% for FM.  
The results concerning screen keypoint detection are presented in Table 3. A big overlap between the predicted and ground truth polygons for both devices is demonstrated, with IoU values above 90%. Furthermore, the ED is around 7-8 pixels, which correspond to an error of approximately 3.5%. The good performance of this model is very important for competent perspective warp, allowing to correct and reduce distortion caused by camera misalignment.
## 4 Security Threats
### Negotiation
#### 4.2.4 Client Feedback Falsification  
Almost any approach being proposed in the literature exploits information pertaining to operational parameters of the nodes, as well as information about the environment they operate in. Such details are taken into account during the decision making phase of the (optimal) spectrum sharing procedure and are carefully combined with the received spectrum requests. All nodes should feed the sharing mechanism with their real data and measurements. Unfortunately, this might not always be the case. An attacker may either deny submitting the required information to the sharing mechanism, or may send information not representing the reality [28, 29]. Such behavior is based on the fact that both collecting the required details, as well as sending them back to the respective collector, is not only time, but also resource, consuming (e.g., energy to be consumed may be critical for small mobile devices).  
In addition, an attacker may also attempt to modify the packets carrying the required information being sent by his legitimate competitors, or inject fake ones in the network. In a more extreme scenario, an attacker may even try to influence the environment his competitors operate in (temporarily, but at critical points in time), in order to mislead them towards reporting unrepresentative details regarding their operating conditions.  
If any of the aforementioned attacks is carried out successfully, the sharing mechanisms operating in a DSA network might be rendered unable to get a real view, which will unavoidably lead to suboptimal and inappropriate transmission opportunities allocation. Additionally, in the case of the last attack described (temporarily influencing the environment), any instantaneous cross-checking mechanism would also fail to detect a different (\"correct\") view, extending the validity of the attack. To make matters worse, two or more misbehaving nodes could collude to make their attacks more credible and, consequently, even more difficult to detect. These observations are sufficient to reveal the need for applying novel techniques in DSA networks for efficiently cross-checking received information, filtering out incorrect and misleading information and blacklisting offending (or possibly, even suspicious) nodes.
## 3 Test Results and Analysis
### Test Analysis  
#### 3.2.1 Crack Resistance Analysis  
As shown in Figure 6, compared with the BFRP bar concrete beam L-0-0, the addition of bamboo fiber and steel wire mesh can increase the cracking load of the BFRP bar concrete beam to a certain extent. The lifting effect of the two materials alone is obvious. Among them, beam L-0-30 with only bamboo fiber increased by 52%, and beam L-1-0 with only steel wire mesh increased by 68%. However, the lifting effect under the composite action of bamboo fiber and steel wire mesh was not as good as the lifting effect under their separate actions. This is because the fiber length was greater than the size of the steel wire mesh, causing the steel wire mesh to obstruct the passage of bamboo fibers to a certain extent, so that the fiber stuck to the steel wire mesh in the concrete. On the one hand, the fiber is not uniformly distributed in the concrete, which made the tensile performance of the fiber-reinforced concrete specimens unsatisfactory. On the other hand, the sticking of the bamboo fiber also obstructed the enhancement of the tensile performance of the concrete specimen by the steel wire mesh, so the cracking load was reduced in the composite.  
Figure 5: Beam L-1-30 experimental phenomena. (**a**) Development of cracks on the front of beam L-1-30; (**b**) the concrete on the upper part of the beam L-1-30 is crushed; (**c**) a through crack at the bottom of beam L-1-30; (**d**) development of cracks on the back of beam L-1-30.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Specimen** & \(f_{cu}\) **(MPa)** & \(f_{t}\) **(MPa)** & **Cracking Load (MPa)** & **Ultimate Load (MPa)** \\ \hline L-0-0 & 45.1 & 3.06 & 25 & 340 \\ L-0-30 & 43.2 & 3.23 & 38 & 329 \\ L-1/2-30 & 43.2 & 3.23 & 32 & 284 \\ L-1-0 & 45.1 & 3.06 & 42 & 397 \\ L-1-30 & 43.2 & 3.23 & 30 & 340 \\ L-1-45 & 44.5 & 3.94 & 28 & 343 \\ \hline \end{tabular}
\end{table}
Table 5: Concrete mechanical properties, cracking load, and ultimate load of specimens.  
Comparing specimens L-1-0, L-1-30, and L-1-45, the cracking load of the test beam decreased with the increase of fiber length. This is because the 45 mm bamboo fiber is longer than the 30 mm bamboo fiber, and the steel wire mesh has a greater obstructing effect on it, so that the effect of improving the tensile performance of the concrete specimen is not ideal and the cracking load is reduced. Comparing specimens L-0-30, L-1/2-30, and L-1-30, the layout of the steel wire mesh in the whole beam section reduced the cracking load of the BFRP bars beam to a certain extent. This is because the concrete at the bottom of the pure bend section in the middle span of the beam undergoes greater tensile stress during bending. When the steel wire mesh is in the whole beam section, the partial tensile stress of the impure bending section is dispersed to the pure bending section, which causes the tensile stress of the pure bending section to further increase under the same load, so the cracking load is reduced.
# Nonlinear Optimization and Adaptive Heuristics for Solving Irregular Object Packing Problems
## 2 Continuous Nonlinear Optimization  
### Problem Statement  
The object configuration problems discussed in this article can be formulated by applying the following nonlinear programming (NLP) model framework.  
1. Minimize \(f(x)\) under the conditions \(x\in D\). The feasible set \(D\) is defined as
2. \(D:=\big{\{}x:l_{i}\leq x_{i}\leq u_{i}\text{ for }i=1,\dots,n,g_{j}(x)\leq 0\text{ for }j=1,\dots,m\big{\}}\).  
Here, \(x=(x_{1},\dots,\ x_{n})\in R^{n}\) denotes the \(n\)-vector of decision variables; we assume that the objective function \(f:R^{n}\to R\) is continuous, without further specifications at this point. We also assume that the set of feasible solutions \(D\) is non-empty, closed, and bounded: \(D\) is defined by \(n\) pairs of finite box constraints \(l_{i}\leq x_{i}\leq u_{i}\text{ for }i=1,\dots,\ n\), and \(m\) general functional constraints \(g=(g_{1},\dots,\ g_{m})\). The vectors \(l\in R^{n}\), \(u\in R^{n}\) define the component-wise lower and upper bounds of \(x\); \(g:R^{n}\to R^{m}\) is the \(m\)-vector of continuous function constraints. In the context of our discussion, we can assume that \(f\) and/or some components \(g_{j}\) of \(g\) are nonlinear functions. Note also that while in purely box-constrained optimization problems \(m=0\), in OP problems typically \(m>0\).  
For completeness, we remark that binary variables \(y\in\{0,1\}\) can be formally handled by models with continuous variables, e.g., by adding the constraints \(0\leq y\leq 1\) and \(y(1-y)\leq 0\). The added nonlinear constraint is nonconvex, thereby numerically inconvenient: here, we only wish to point out the formal handling of a binary variable by a continuous variable. Similarly, more general integer variables can be deduced into continuous variables following their binary decomposition. Therefore, continuous nonlinear optimization models formally cover the entire class of mixed-integer nonlinear programming models.
## 2 Materials and Methods
### Classification Methods
The start symbol of the grammar serves as the starting point for the production procedure, which gradually produces the program string by substituting nonterminal symbols with the right hand of the chosen production rule. Figure 4 shows the GenClass flowchart. The main advantage is that it does not require any additional information, such as the derivatives of the objective problem, which cost in time and memory. Specifically, it generates a series of classification rules in a C-like language that can be easily programmed and used in real C programs without many modifications. The generated rules are constructed with the use of if-else conditions, and the variables represent the corresponding features. The source code of the method can be found in [[https://github.com/itsoulos/GenClass](https://github.com/itsoulos/GenClass)]([https://github.com/itsoulos/GenClass](https://github.com/itsoulos/GenClass)) (accessed on 30 December 2022). The application details of the utilized classifiers are depicted. The following techniques were used to successfully identify the categories in the 3 datasets: RBF with 10 processing neurons [79], DNN approaches described thereafter, and GenClass [70]. The number of chromosomes used in GenClass were 500 and a maximum of 2000 generations were allowed. Experimental settings parameters are shown in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{3}{c}{**Parameters**} \\ \cline{2-3}
**Architecture** & **Name** & **Value** \\ \hline GenClass & Chromosomes & 500 \\ GenClass & Generations & 2000 \\ RBF & Processing Neurons & 10 \\ DNN & Layers & 1–3 \\ DNN & Input layer & 16, 17, 31 \\ DNN & Layer 5 (Activation Function: Sigmoid) & 256 \\ DNN & Layer 4 (Activation Function: Sigmoid) & 128 \\ DNN & Layer 3 (Activation Function: Sigmoid) & 64 \\ DNN & Layer 2 (Activation Function: Sigmoid) & 32 \\ DNN & Layer 1 (Activation Function: Softmax) & 16 \\ DNN & Output Layer & 3 \\ DNN & Optimizer & Nadam \\ DNN & epochs & 1000 \\ DNN & Batch Size & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental settings parameters. The provided DNN approaches were implemented using Python language and Keras library. Three different approaches were considered for the comparisons with different fully connected layers. The approaches were named according to the corresponding adopted layers, with the names DNN-3, DNN-4, and DNN-5, accordingly. The architecture of DNN-3 consists of three fully connected layers with 64, 32, and 16 neurons, respectively, and a final output layer with three neurons. The neurons used the sigmoid activation function [90], while the final output neurons used the softmax activation. The model is compiled with the Nadam optimizer and categorical cross-entropy loss function and trained over 1000 epochs with a batch size of 8. Accordingly, for DNN-4, the extra added layer has 128 neurons, and for DNN-5, 256.
## 4 Results
### Results of Individual Studies and Synthesis
#### 4.1.3 Resource Allocation and Scheduling Strategies
The study emphasises the importance of prioritising resource allocation in multi-layered IoT architectures to maintain QoS standards and improve energy performance.
## 3 Results
### Effects of Various Soil Properties on Displacements
#### 3.1.2 Particle Size  
Figure 6 illustrates the effects of various soil particle sizes on tunnel displacements induced by both normal and reverse faults. Soil particle sizes of 0.2, 0.4, and 0.6 mm were labeled as P0.2R, P0.4R, and P0.6R respectively, for reverse faults, and P0.2N, P0.4N, and P0.6N for normal faults. Overall, the curves of reverse faults are sharper than those in normal faults. The results revealed that soil particle size of 0.6 mm had less displacements than the others. For instance, the results showed an almost 39% difference in displacements between the finest (P0.2R) and the largest (P0.6R) soil particle size. The most relevant study from the literature is by [PERSON], which revealed that coarse sand with clay has a higher soil strength and can tolerate more stress in soil [34]. Notably, normal faults with particle sizes of 0.4 (P0.4N) and 0.6 mm (P0.6N) started from the same displacements (6 mm), but in a reverse fault, they had different starting displacements (8 mm for P0.4R and 10 mm for P0.6R). The results agreed with those of other studies, which showed that fine soil has less tolerance under force [35; 36].  
Figure 5: The effect of water content in reverse and normal faults.
## 5 Results
### SN Communication Range Experiments  
The communication range between an SN node and a DM was evaluated under line-of-sight conditions without obstructions. To determine the distance between DM 1 (UAV) and a location with known GPS coordinates, which matched the SN node's location, geopositioning information provided by the UAV's remote control was used, with 1 m of error.  
During the range test, the UAV was maintained at an altitude of 30 m as specified. After reaching this altitude, the UAV was moved horizontally northward, increasing the distance between DM and the fixed SN node until there was no communication between them for a period equal to or greater than 5 s.  
Figure 12: Location of different architecture entities and trajectory of DM 1.  
Transmission range tests were conducted using 3-byte advertising messages sent every second. The range was evaluated using different transmission powers of the SN. Although the SN typically operates with a transmission power of 0 dBm, its performance was assessed at various levels of BLE radio power, as shown in Figure 13.
## 3 Results and Discussion
### Surface Structure Modification as a Result of Ion Irradiation
On the other hand, the curved fibers inside the honeycomb wall structure were expected to be Mg in the first approximation. Based on the profile of the Al\({}_{3}\)Mg\({}_{2}\) phase's chemical composition (profile 2 in Figure 5), the protruding walls on the SEM image corresponded to an increase in the Al concentration relative to the general profile of the Mg, whereas fibrous structures corresponded to an increase in the Mg concentration relative to the Al concentration. It should be noted that a layer on the interlayer surface corresponding to the Al\({}_{12}\)Mg\({}_{17}\) phase may also consist of Al. The morphology of the Al\({}_{12}\)Mg\({}_{17}\) interlayer surface, the walls of the cellular wall structure of the Al\({}_{3}\)Mg\({}_{2}\) phase interlayer surface and the Al surface were generally similar. It should also be noted that, at all interphase boundaries between the Al and Mg in the Al-Mg sample, the interlayer of the Al\({}_{3}\)Mg\({}_{2}\) intermetallic phase was wider than the interlayer of the Al\({}_{12}\)Mg  
Figure 4: SEM images (SE mode) of the Al-Mg nanostructured composite surface after ion irradiation. Tilt angle is 45\({}^{\circ}\). phase. These data were in good agreement with other results [46; 47], where more detailed studies of the microstructure of these intermetallic phases were presented. In the current study, the width of the interlayer of the Al\({}_{3}\)Mg\({}_{2}\) phase was \(\approx\)10 \(\upmu\)m, and the width of the Al\({}_{12}\)Mg\({}_{17}\) phase interlayer did not exceed 3 \(\upmu\)m.  
Figure 5: SEM image combined with an elemental map of the Al (in red) and Mg (in green). The element composition is shown along lines 1–5 at the top of the panel in the form of a relative intensity plot in relation to the scanning distance of the Al and Mg, marked in red and blue, respectively.