esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Test-Result Analysis
### Influence of Key Design Parameters on the Force-Transmission Mechanism
#### 6.1.2 The Influence of Plate-Length Design  
The designed length of the weld seam between the built-in double-fixed steel anchor cable-girder box and the web of the main beam is one of the most important parameters affecting the structural force characteristics of the steel anchor box. By varying these parameters while keeping the other parameters constant, the sensitivity of the force characteristics of the steel anchor box and its force-transmission mechanism to the designed length of the plates, i.e., the weld length, was investigated, and the values of the plate length were taken, as shown in Table 6. The results of the force-transmission ratio and the total force-transmission ratio for the different design length of the support plates N1 and N2 are shown in Figure 18.  
Figure 17: Influence of plate-thickness design on the force characteristics of the anchor-box structure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Weld Name**} & **Original Design** & **Thickness Values** \\  & & **Thickness (mm)** & **(mm)** \\ \hline
1 & Support plate (N1) & 32 & 30, 32, 34 \\
2 & Support plate (N2) & 32 & 30, 32, 34 \\
3 & Pressure plate (N7) & 40 & 38, 40, 42, 44, 46 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Designed thickness value of each plate.  
The results of the study showed the following: 1 the force-transmission ratio of each main transmission weld in the built-in double-fixed steel anchor cable-girder box increased with the increasing weld length; 2 the total force-transmission ratio of the built-in double-fixed steel anchor cable-girder box gradually increased with the increasing weld length; 3 the influence of the designed lengths of the plates on the mechanical properties of the steel anchor box was greater in the range of 1600-1800 mm than in the range of 1800-2000 mm.
# A Dual-Polarized and Broadband Multiple-Antenna System for 5G Cellular Communications
## 2 Characteristics of the Dual-Polarized CPW-Fed Antenna
The antenna demonstrates impressive efficiency, achieving over 80% efficiencies across a frequency range from 3 to 6 GHz. This high efficiency indicates that a substantial fraction of the input power is emitted as electromagnetic radiation, making the antenna an effective and capable performer within the specified frequency band. Such efficiency levels reflect the antenna's strong potential for practical applications in modern systems. Figure 3: (**a**) \(\mathrm{S_{mn}}\) and (**b**) \(\mathrm{S_{mn}}\) results for different values of \(\mathrm{L_{1}}\). In Figure 6a, the linear-scaled radiation patterns (Phi) for the various antenna ports are depicted. The figure reveals that the dual-polarized antenna exhibits identical radiation patterns from these ports. In Figure 6b, similar 2D radiation performances are clearly observed, with a 90\({}^{\circ}\) phase difference and polarization diversity resulting from the different arrangements of the ports. The antenna's design allows it to maintain consistent radiation characteristics across its dual-polarized nature, providing reliable and balanced performance regardless of the feeding port arrangement. Figure 7 demonstrates the 3D radiation patterns of the antenna at 3.5 and 5.5 GHz. It is evident that the antenna exhibits similar radiation patterns with distinct orthogonal polarizations. Additionally, the antenna gain, which refers to the actual power gain realized by the antenna in a specific direction, exceeds 3 dB. This indicates that the antenna achieves good directional performance and efficiently radiates electromagnetic waves when fed from both Port 1 and Port 2, offering a balanced and effective radiation pattern for diverse applications [43; 44]. It is worth noting that the antenna's 3-4 dBi gain meets the typical smartphone requirements, prioritizing omnidirectional coverage over high gain. For sub-6 GHz applications requiring higher gain, directional antennas such as patch or end-fire designs (e.g., Yagi or Vivaldi) are potential options. However, their larger size and limited coverage make them less suitable for compact smartphone designs. Array antennas can also enhance gain but are more commonly  
Figure 4: Current densities at (a) 3.2, (b) 4.7, and (c) 5.5 GHz. Figure 5: Efficiency results across the antenna’s broad bandwidth. employed in higher frequency mmWave smartphone systems, where smaller element sizes are practical. This is discussed further in Section 6.
###### Abstract  
There is currently increased interest in the use of alternatives to autoclaved culture media, in order to maintain the properties of the media, while saving energy and time. In this study, we assess a new system for culture media preparation, using a conventional microwave with a water bath and a glass bottle with a rubber cap that allows depressurization. Sterilization, using the proposed system (1000 W, 3 to 20 min), was compared with autoclaving for the preparation of tryptone soy agar (TSA), tryptone soy broth (TSB), Sabouraud 4% dextrose agar (SDA), and violet red bile glucose agar (VRBG). Microwave exposure for 7 min yielded sterile TSA plates. The productivity of both sterilization methods was assessed using the pour plate method, and significant increases in the growth of certain micro-organisms after using a microwave were observed for every culture medium, especially those that were sterilized by boiling (VRBG). The kinetics of microbial destruction showed that _Escherichia coli_ and _Bacillus subtilis_ spores were destroyed after 3 and 7 min in a microwave, respectively, while three decimal reductions were obtained for _Geobacillus starothermophilus_ spores after 15 min in an autoclave. This new sterilization method could be a feasible, rapid, and economical method to prepare microbiological media, with a quality similar to that obtained through autoclaving.  
microwave; autoclave; sterilization; rubber perforable cap; productivity; microbial destruction; microbiological culture media +
Footnote †: journal: _Sciences_
## 4 Results
### Spatiotemporal Variation in Grassland AGB during 1982-2018 in the TRHR
(**a**) MAM (March, April, and May); (**b**) JJA (June, July, and August); (**c**) SON (September, October, and November); (**d**) DJF (December, January, and February). Figure 9: (**a**) Sequential Mann-Kendall test rank statistics for annual AGB; (**b**) interannual variability in AGB, NDVI, and P in the TRHR during 1982-2018. The black dotted line is the turning point of AGB temporal variation found by the sequential Mann-Kendall test. Figure 11: Spatial pattern of NDVI trends from (**a**) 1982–2018, (**b**) 1982–1998, and (**c**) 1998–2018 in the TRHR. The black solid dots refer to grids with 95% confidence. Figure 10: Spatial pattern of AGB trends from (**a**) 1982–2018, (**b**) 1982–1998, and (**c**) 1998–2018 in the TRHR. The black solid dots refer to grids with 95% confidence. We also analyzed changes in the annual AGB trends before and after 1998. Figure 10b shows that the AGB has the main distribution with a significantly increasing trend in the eastern, southern, and central parts of the study area (\(\sim\)43.9%, \(p<0.05\)) between 1982 and 1998, which is relative to the regional vegetation (mainly grass) greening with the NDVI increase (Figure 11b). Figure 10c illustrates that the main regions of the TRHR show a significantly increasing AGB trend (\(\sim\)60.5%, \(p<0.05\)), which is mainly attributed to the upward trend of P occurring in most areas of the TRHR during 1998-2018 (Figure 12c).
# Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm
## Implementation Details
### Local Minima Criterion  
The actual trajectory of the robot is stored in \(T\) and it keeps on updating itself according to the robot's current location \(q\left(t\right)\). It is assumed that the robot is being trapped in the local minimum if no movement is sensed for a defined time, i.e., \(\sum\limits_{k}\left|q(t)-q(t+kdt)\right|<S\). \(dt\) is the time step, \(k\) is used to define the number of observations, and \(S\) is chosen as a minimum distance that the robot covers in one time step. Once it is confirmed that a local minimum has been encountered, then a virtual obstacle is generated at the same location. From the stored trajectory, a point is chosen based upon \(dis\). An additional repulsive Gaussian cost function is created for the virtual obstacle:  
\[J_{add}=H_{add}exp^{-W_{add}\|q(t)-T(j)\|^{2}} \tag{15}\]  
where \(H_{add}\) and \(W_{add}\) are used to define the height and width of the virtual obstacle. This cost function is only effective when the robot falls into a local minimum; moreover, its effect keeps on decreasing as it approaches the desired point on the trajectory. In order to generate a cumulative effect, \(J_{add}\) is added to the cost functions in Equation (12). The flow chart of the entire process is depicted in Figure 1.  
Figure 1: Flowchart of the proposed RBF algorithm.
## 4 Verification of the Proposed Algorithm  
The present study investigated the effectiveness of the proposed algorithm by comparing the results of the proposed algorithm with those of [PERSON] [24] and [PERSON] [32]. The proposed algorithm was applied to a circular tunnel with an excavation radius \(r_{0}=3\ m\). The input data were E = 10 GPa, v = 0.25, \(\sigma_{0}=20\) MPa, \(\varphi_{p}=30\), \(\varphi_{r}=22\), \(c_{p}=1\) MPa, \(c_{r}=0.7\) MPa, \(\phi_{p}=3.75^{\circ}\), \(\phi_{r}=3.75^{\circ}\), and \(\eta^{*}=0.008\).  
(1) Figure 4 shows the results of the three approaches, including the stress components, the strain components, and displacements.  
Figure 4: _Cont._  
As shown in Figure 4a, the tangential stress curve first gradually increased and then decreased with the increase of \(\frac{r}{r_{0}}\), and the tangential stress reached a maximum at the elasto-plastic boundary. Figure 4b,c show that the maximum strain and displacement occurred at the surface of the excavated tunnel. The three criteria could be ranked by strain and displacement as D-P criterion > M-C Coulomb criterion > unified strength criterion.  
(2) An analysis of the distribution of stress, strain, and displacement curves showed that the results obtained by the D-P and M-C criteria were similar. The D-P criterion obtained the largest range of the plastic zone, followed by the M-C Coulomb criterion, with the unified strength criterion obtaining the smallest range. The stress, strain, and displacement calculated by the unified strength criterion were obviously more conservative than those by the D-P criterion. The analysis showed that the Unified Strength Theory may underestimate the plastic radius and the radial displacement, resulting in the calculated support strength underestimating the pressure experienced in a real-world tunnel engineering project.  
Figure 4: Distributions of stress, strain, and radial displacement under different algorithms: (**a**) distribution of stress; (**b**) distribution of strain; (**c**) distribution of radial displacement.
# Adaptive Deployable Thin Spherical Shell Reflectors
## 2 Electrostrictive PVDF-TrFE Film  
The behavior of the electrostrictive polymer was investigated in an earlier phase of the project [17], in the configuration described in Figure 2a. The active material consists of PVDF-TrFE (PVDF-TRFE FC25 from Piezotech, 0.12 g/mL in Acetone:Dimethylacetamide 95:5), annealed at 140 \({}^{\circ}\)C for 2 h. The material was applied by spin coating in [6] and by spray coating in [7] (spray coating can be easily scaled up to large sizes).  
The behavior of the thin film of PVDF-TrFE can be modeled by a linear dielectric model:  
\[D_{3}=P_{s}+\epsilon_{1}E_{3} \tag{3}\]  
and the quadratic unidirectional material model of the strain \(S_{3}\) along the polarization direction:  
\[S_{3}=Q_{33}D_{3}^{2}=Q_{33}P_{s}^{2}+2\epsilon_{1}P_{s}Q_{33}E_{3}+\epsilon_{ 1}^{2}Q_{33}E_{3}^{2} \tag{4}\]  
where \(D_{3}\) is the electric displacement, \(P_{s}\) is the spontaneous polarization, \(\epsilon_{1}=\epsilon_{0}\epsilon_{r}\) is the dielectric constant (\(\epsilon_{0}=8.85\times 10^{-12}\) F/m) and \(E_{3}\) the electric field. \(Q_{33}\) is the electrostrictive coefficient, \(Q_{33}P_{s}^{2}\) is the poling strain which takes place during the polarization. \(Q_{33}\) and \(P_{s}\) are material properties that are determined experimentally. The isotropic in-plane strain (normal to the poling direction) is related to \(S_{3}\) by the [PERSON]'s ratio \(\
u\)  
\[S_{1}=-\
u S_{3}=-\
u(Q_{33}P_{s}^{2}+2\epsilon_{1}P_{s}Q_{33}E_{3}+\epsilon_{ 1}^{2}Q_{33}E_{3}^{2}) \tag{5}\]  
It follows that the piezoelectric coefficient \(d_{31}\) is given by  
\[d_{31}=\frac{\partial S_{1}}{\partial E_{3}}=-2\
u(\epsilon_{1}P_{s}Q_{33}+ \epsilon_{1}^{2}Q_{33}E_{3}) \tag{6}\]  
Thus, after polarization, the PVDF-TrFE behavior is essentially piezoelectric, with a piezoelectric constant \(d_{31}\) increasing slightly with the bias electric field \(E_{3}\) (Figure 3). This equation confirms that an electrostrictive material can be made piezoelectric by applying a bias electric field [18]. The method for determining the material properties (\(\epsilon_{1},d_{31},Q_{33},P_{s}\)) has been described in [6; 7]; they are reported in Table 1; the two values for \(P_{s}\) refer to two independent methods of estimation. Notice that the value of the dielectric constant \(\epsilon_{r}\) has been found consistently smaller for the spray-coated samples than for the spin coated ones.  
According to Equation (5), the poling strain is given by  
\[S_{p}=-\
u Q_{33}P_{s}^{2} \tag{7}\]  
This was confirmed by direct measurement of the curvature during the poling process of a thin glass plate covered with PVDF-TrFE. The details of the experiment leading to Figure 3 can be found in [6].  
Figure 2: Layer configurations: (**a**) unimorph configuration used in earlier studies; (**b**) with a thermal balancing layer and low CTE substrate.
## 1 Introduction
Thus, the CI methods are sensitive to the quality of load data, namely the noise factors might result in redundant uncertainty due to the fluctuations in interval size. However, for the short-term load forecasting (STLF) in an hour-ahead situation, the procedure of LF must be effective so that market operators and participants can react in time [31]. Hence, the massive computation and time cost of quantile methods are unbearable. Although interval methods with the application of ELM can overcome these limitations by removing training steps, the accuracy of output highly depends on its random given parameters [32]. In addition, the fixed intervals of PI methods and the redundant uncertainty of CI methods are adverse. Meanwhile, the impacts of load data noise on the PLF can not be neglected [33]. Variable selection and feature extraction are the critical steps for better performance in PLF. [PERSON] [34] and [PERSON] [35] summarized the frequently-used tools in load data processing, among which correlation analysis and wavelet transform were the most typical and efficient ones to be integrated into load forecasting ensembles [36]. In this paper, an integration scheme for PLF is proposed in order to strike a trade-off among the advanced techniques mentioned above. The proposed scheme is a CI method that integrates the techniques of wavelet packet transform, correlation analysis, Gaussian process and improved weighted ELM. More specifically, the load data in time series form are selected and extracted by the cooperation of wavelet packet transform and correlation analysis. The improved weighted ELM with a computation reducing switch algorithm is the core of the forecasting model, while the Gaussian process provides probabilistic intervals. Furthermore, the load data are separated into principal parts and noise. The principal parts are utilized to load forecasting, while the noise, as a part of total forecasting uncertainty, is merged with the model uncertainty. The contributions of this paper are summarized as follows:  
1. The innovative scheme of combining wavelet packet transform and correlation analysis methods is proposed to remove noise from raw data. 2. The improved weighted ELM is applied for forecasting, and a computation reducing switch algorithm is presented to obtain stable parameters for this ELM. 3. Considering the forecasting uncertainty, the Gaussian process is creatively used to deal with the noise uncertainty and generate CIs for PLF. 4. Newly raised criteria and an indicator are adopted for efficiency validation and contrast between different PLF methods involved in comparisons. The remainder of the paper is organized as follows. In Section 2, the techniques involved in the integration scheme are elaborated sequentially. Section 3 explains the whole procedure for implementing load forecasting in detail. Validation and case study with real data are demonstrated in Section 4. Section 5 provides analysis and discussion of the results, while Section 6 concludes the whole paper.
# An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application
## 4 Results and Discussion
### Recognizing the Existing Obstacles in the Development of SCs
#### 4.1.2 Public Transport  
Considering the safety and efficiency of citizens of SCs is crucial, especially on roads [31]. Therefore, municipalities are attempting to implement smart traffic using IoT development solutions [32]. In this regard, the IoT will play a crucial role in traffic management. For instance, data from various types of sensors and GPS systems are sent by drivers' smartphones in order to determine the speed, number, and locations of vehicles on a particular road. Subsequently, smart traffic lights are immediately connected to a cloud-management platform and provide timing information to automatically and accurately monitor green lights, thereby preventing traffic congestion. Additionally, these methods can predict traffic in the future and offer prevention plans, with which the transport administration department is able to detect potentially dangerous situations in time and take required actions to prevent traffic congestion [33]. Therefore, considering the obvious importance of transportation systems in SCs, specific and accurate planning to control these systems is necessary [34]. According to [35], transport technological development with the IoT will have a big revolution between 2020 and 2030, that will have direct impact on toll operators and highways and provide safe and secure networks [35]. In addition, traffic data from multiple sources, such as traffic information and ticket sales, can be used to perform sophisticated analyses and achieve better results, and train operators can maximize the capacity of tracks and easily prevent train delays [36]. Fortunately, many countries around the world, especially developing countries, are now trying to make use of new systems connected to the IoT for controlling their transportation systems [37].
# Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform
## 3 Methods
### Vehicle Detection
\begin{split}&\frac{\lambda_{bbox}}{N_{obj}}\sum_{i=1}^{W}\sum_{j=1 }^{H}\sum_{k=1}^{K}I_{ijk}[(\delta x_{ijk}-\delta x_{ijk}^{G})^{2}]+(\delta y_{ ijk}-\delta y_{ijk}^{G})^{2}\\ &+(\delta w_{ijk}-\delta w_{ijk}^{G})^{2}+(\delta h_{ijk}-\delta h _{ijk}^{G})^{2}]+\\ &\sum_{i=1}^{W}\sum_{j=1}^{H}\sum_{k=1}^{K}I_{ijk}\frac{\lambda_{ conf}^{+}}{N_{obj}}I_{ijk}(\gamma_{ijk}-\gamma_{ijk}^{G})^{2}+\frac{\lambda_{ conf}^{-}}{WHK-N_{obj}}I_{ijk}\gamma_{ijk}^{2}\\ &+\frac{1}{N_{obj}}\sum_{i=1}^{W}\sum_{j=1}^{H}\sum_{k=1}^{K} \sum_{k=1}^{C}I_{ijk}l_{c}^{G}log(p_{c})\end{split}  \tag{2}\]  
where \((\delta x_{ijk}^{G},\delta y_{ijk}^{G},\delta w_{ijk}^{G},\delta h_{ijk}^{G})\) are the ground truth bounding boxes. The first part of the loss function is the bounding box regression that assures that _ijk_-anchor, which is the closest to the ground truth box returns \(\delta x_{ijk}\), \(\delta y_{ijk}\), \(\delta w_{ijk}\), \(\delta h_{ijk}\) parameters that will fit ground truth bounding box parameters. During the training the ground truth boxes are compared with all anchors and assigned to the anchor box with the highest overlap (IOU) with ground truth. This means that only the closest anchor is accountable for detecting a given object. This operation is achieved by \(I_{ijk}\) which evaluates to 1 if _ijk_-anchor has the highest IOU with the ground truth box, 0 otherwise. Finally, because there might be multiple objects in the image, the value is divided by the number of objects \(N_{obj}\). The second part in the loss function is the confidence score regression. The goal here is twofold: assure that the closest anchor confidently detects an object, and to penalise the other anchors for false positive detections. \(\gamma_{ijk}\) is the confidence score from _ijk_-anchor of how likely it contains an object. \(\gamma_{ijk}^{G}\) is the IOU between accountable _ijk_-anchor and the ground truth. At the same time, the confidence scores of all other boxes are penalised with \(I_{ijk}\gamma_{ijk}^{2}\) where \(I_{ijk}=1-I_{ijk}\) and normalised by the number of anchors (_WHK_) minus the number of objects in the image. The last part of the loss function is a cross-entropy loss for the classification of the object. The loss function defines three hyperparameters: \(\lambda_{bbox}\), \(\lambda_{conf}^{+}\), and \(\lambda_{conf}^{-}\), which adjust the weights of the loss components. In our training, the same values were used as in the original paper [11], \(\lambda_{bbox}=5\), \(\lambda_{conf}^{+}=75\), \(\lambda_{conf}^{-}=100\). The loss function is optimized directly through back-propagation, which updates the weights of the neural network.
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 8 Conclusions
### Agenda for Future Research
**Abbreviations**  
AI Artificial intelligence  
ANM Autonomic Network Management  
ANN Artificial Neural Network  
AP Access Point  
ARoF Analog Radio-over-Fiber  
BBU Baseband Unit  
C-RAN Cloud Radio Access Network  
DBA Dynamic Bandwidth Allocation  
EE Energy Efficiency  
eMBB Enhanced Mobile Broadband  
FCM Fuzzy C-Means  
FiWi Fiber Wireless  
InP Infrastructure Provider  
IoT Internet of Things  
KPI Key Performance Indicator  
LTE Long Term Evolution  
MEC Multi-access Edge Computing  
MIMO Multiple Input Multiple Output  
ML Machine Learning  
mmWave Millimetre Wave  
NF Network Function Virtualization  
NP-hard Non-deterministic Polynomial Time Hard  
OLT Optical Line Terminal  
ONU Optical Network Unit  
PON Passive Optical Network  
QoE Quality of Experience  
QoS Quality of Service  
RAN Radio Access Network  
RoF Radio-over-fiber  
RRH Remote Radio Head  
SDN Software-Defined Networking  
SE Spectral Efficiency  
TDMA Time Division Multiple Access  
UDN Ultra-Dense Networks  
URLLC Ultra-Reliable Low Latency Communication  
VN Virtual Network  
VNF Virtual Network Function