esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Analytical Strain Equations in Cylindrical Coordinate System  
As a matter of convenience, the present study adopts the same analogy for the parameter and coordinate system designations as [PERSON] et al. [16] and [PERSON] et al. [25]. For a transversely isotropic elastic solid, the stresses and strains are related via the generalized Hooke's law [11]. Referring to a local Cartesian \(x^{\prime}y^{\prime}z^{\prime}\) coordinate system, with the \(x^{\prime}y^{\prime}\)-plane coinciding with the material's isotropy plane orientation (see Figure 1), [PERSON]'s law can be written as \[\underbrace{ \begin{Bmatrix}\varepsilon_{x^{\prime}}\\ \varepsilon_{y^{\prime}}\\ \varepsilon_{z^{\prime}}\\ \gamma_{y^{\prime}z^{\prime}}\\ \gamma_{x^{\prime}z^{\prime}}\\ \gamma_{x^{\prime}y^{\prime}}\end{Bmatrix} }_{\varepsilon^{\prime}}= \underbrace{ \begin{bmatrix}1/E&-\
u/E&-\
u^{\prime}/E^{\prime}&0&0&0\\ 1/E&-\
u^{\prime}/E^{\prime}&0&0&0\\ 1/E^{\prime}&0&0&0\\ &1/G^{\prime}&0&0\\ &1/G^{\prime}&0\\ &1/G^{\prime}&0\\ &1/G\end{bmatrix} }_{ \begin{Bmatrix}\sigma_{x^{\prime}}\\ \sigma_{y^{\prime}}\\ \sigma_{z^{\prime}}\\ \tau_{y^{\prime}z^{\prime}}\\ \tau_{x^{\prime}y^{\prime}}\end{Bmatrix} } \begin{Bmatrix}\sigma_{x^{\prime}} \\ \sigma_{y^{\prime}}\\ \sigma_{z^{\prime}}\\ \tau_{y^{\prime}z^{\prime}}\\ \tau_{x^{\prime}y^{\prime}}\end{Bmatrix} }_{\tau_{y^{\prime}z^{\prime}}} \tag{1}\]  
where \(\varepsilon^{\prime}\) and \(\sigma^{\prime}\) are the elastic strain and stress component vectors in Voigt notation and \(S^{\prime}\) is the 6 \(\times\) 6 transversely isotropic compliance matrix. In order to derive the analytical equations for the elastic strain components around transversely isotropic cylindrical samples subjected to uniaxial loading, two further coordinate systems are introduced, i.e., the global Cartesian \(xyz\)-system and the global cylindrical system \(r\theta z\). The general loading configuration \(\sigma=[0,0,\sigma_{z},0,0,0]\) within the test and measurable normal strain components \(\varepsilon_{r}\), \(\varepsilon_{\theta}\) and \(\varepsilon_{z}\) around the cylinder are given within the latter system. Hence, to apply [PERSON]'s law, the compliance matrix \(S^{\prime}\) in Equation (1) needs to be transformed twice (using rotation matrices \(\Omega_{\beta}\) and \(\Omega_{\theta}\) as described in [PERSON] et al. [16] and [PERSON] et al. [25]): first, from \(x^{\prime}y^{\prime}z^{\prime}\) to \(xyz\), corresponding to a rotation around the \(y\)-axis; second, from \(xyz\) to \(r\theta z\).
## Developing a New Wireless Sensor Network Platform and Its Application in Precision Agriculture  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 1 and**  
**[PERSON] 3**  
Footnote 1: Faculty of Telematics, University of Colima, Av. University 333, C. P. 28040, Colima, Col., Mexico; E-Mail: [EMAIL_ADDRESS]  
Footnote 2: Faculty of Electrical and Mechanical Engineering, University of Colima, Coquimatl \(\hat{\mathfrak{a}}\)-Colima, Km.2, Coquimatl \(\hat{\mathfrak{a}}\), Col., Mexico; E-Mail: [EMAIL_ADDRESS]  
Footnote 3: SITELDI Solutions., Canarios 111, C. P. 28017, Colima, Col., Mexico; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-52-312-3132283; Fax: +1-52-312-3139012.  
_Received: 20 December 2010; in revised form: 7 January 2011 / Accepted: 18 January 2011 / Published: 20 January 2011_  
**Abstract:** Wireless sensor networks are gaining greater attention from the research community and industrial professionals because these small pieces of \"smart dust\" offer great advantages due to their small size, low power consumption, easy integration and support for \"green\" applications. Green applications are considered a hot topic in intelligent environments, ubiquitous and pervasive computing. This work evaluates a new wireless sensor network platform and its application in precision agriculture, including its embedded operating system and its routing algorithm. To validate the technological platform and the embedded operating system, two different routing strategies were compared: hierarchical and flat. Both of these routing algorithms were tested in a small-scale network applied to a watermelon field. However, we strongly believe that this technological platform can be also applied to precision agriculture because it incorporates a modified version of LORA-CBF, a wireless location-based routing algorithm that uses cluster-based flooding. Cluster-based flooding addresses the scalability concerns of wireless sensor networks, while the modified LORA-CBF routing algorithm includes a metric to monitor residual battery energy. Furthermore, results show that the modifiedversion of LORA-CBF functions well with both the flat and hierarchical algorithms, although it functions better with the flat algorithm in a small-scale agricultural network.  
**Keywords:** sensor networks; routing algorithm; technological platform; precision agriculture
## 4 Numerical Study: A 10-Story Shear Building
### Comparison of Seismic Control Performance
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Earthquakes** & \(\mathbf{J_{1}}\) & \(\mathbf{J_{2}}\) & \(\mathbf{J_{3}}\) & \(\mathbf{J_{4}}\) & RMSE \\ \hline El Centro & 0.433 & 0.522 & 0.799 & 0.020 & 0.045 \\ Chichi & 0.472 & 0.809 & 0.686 & 0.016 & 0.054 \\ Kobe & 0.527 & 0.576 & 0.632 & 0.030 & 0.049 \\ Meinong & 0.685 & 0.747 & 0.654 & 0.048 & 0.032 \\ Northridge & 0.553 & 0.655 & 0.894 & 0.025 & 0.037 \\ Kumamoto & 0.541 & 0.722 & 0.647 & 0.020 & 0.039 \\ Capemendocino & 0.557 & 0.677 & 0.682 & 0.028 & 0.043 \\ Parkfield & 0.696 & 0.770 & 0.733 & 0.022 & 0.045 \\ Chuetsu Oki & 0.602 & 0.549 & 0.662 & 0.030 & 0.031 \\ Montenegro & 0.779 & 0.809 & 0.696 & 0.029 & 0.040 \\ Imperial Valley & 0.549 & 0.531 & 0.577 & 0.039 & 0.026 \\ Taipei & 0.500 & 0.483 & 0.438 & 0.046 & 0.027 \\ El Mayor & 0.579 & 0.611 & 0.556 & 0.029 & 0.039 \\ Darfield & 0.845 & 0.899 & 0.714 & 0.032 & 0.034 \\
**Average** & **0.594** & **0.669** & **0.669** & **0.030** & **0.039** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Seismic control performance of the ARX (autoregressive with exogenous inputs). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Earthquakes** & \(\mathbf{J_{1}}\) & \(\mathbf{J_{2}}\) & \(\mathbf{J_{3}}\) & \(\mathbf{J_{4}}\) & RMSE \\ \hline El Centro & 0.433 & 0.523 & 0.801 & 0.020 & 0.013 \\ Chichi & 0.471 & 0.813 & 0.690 & 0.016 & 0.015 \\ Kobe & 0.532 & 0.583 & 0.644 & 0.029 & 0.014 \\ Meinong & 0.687 & 0.747 & 0.655 & 0.049 & 0.010 \\ Northridge & 0.553 & 0.655 & 0.891 & 0.025 & 0.008 \\ Kumamoto & 0.543 & 0.720 & 0.646 & 0.020 & 0.011 \\ Capemendocino & 0.559 & 0.677 & 0.687 & 0.028 & 0.013 \\ Parkfield & 0.703 & 0.790 & 0.731 & 0.022 & 0.015 \\ Chuetsu Oki & 0.605 & 0.550 & 0.669 & 0.030 & 0.011 \\ Montenegro & 0.779 & 0.812 & 0.699 & 0.030 & 0.011 \\ Imperial Valley & 0.553 & 0.531 & 0.600 & 0.040 & 0.008 \\ Taipei & 0.501 & 0.487 & 0.453 & 0.047 & 0.007 \\ El Mayor & 0.583 & 0.615 & 0.573 & 0.030 & 0.011 \\ Darfield & 0.847 & 0.897 & 0.736 & 0.033 & 0.009 \\
**Average** & **0.596** & **0.671** & **0.677** & **0.030** & **0.011** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Seismic control performance of the MLP-150.
## 4 Monitoring System Design  
For this segment, the finite element analysis application MIDAS Civil is applied to simulate the whole process of incremental launching, and the stress and deformation of the structure under the most unfavorable conditions in Table 1 are obtained, including the displacement and stress data for the most unfavorable position of the steel box girder. The finite element model of the steel box girder is presented in Figure 8.  
To safeguard the structural integrity of incremental launching, the real-time alignment is adjusted, ensuring the geometric positioning and stress levels of the steel box girder are managed during incremental launching. According to reference [12], the error limits of internal force and deflection in the incremental launching construction process are stipulated to guide the stability of the incremental launching construction structure. In order to analyze the stress for the most adverse scenarios in the incremental launching process and ensure construction safety, the real-time monitoring values of the most unfavorable working conditions are contrasted with the finite element simulation outcomes.  
With the advancement of incremental launching construction progress, the 3-section and 4-section steel box girders are successively welded to the incremental launching steel box girder section. During this process, the stress state of the overall structure is constantly changing. The monitoring system focuses on controlling the alignment of the steel box girder to make it move forward according to the incremental launching trajectory. At the same time, the stress state of the steel box girder, the guide girder, and the incremental launching piers are elucidated in real time to ensure the structural safety and personnel safety of incremental launching construction.
## 3 Analysis of Examples
### Confidence Analysis Based on Blind-Number Theory
According to the current standard, the evaluation outcome indicates that 30 samples are classified as good, while 10 samples are deemed excellent. For instance, considering sample 14, the current standard assigns a PQI evaluation result of 83.64, corresponding to a good evaluation grade. However, when employing the information entropy weight determination calculation for PQI', the result is 73.32, indicating a medium evaluation grade. These evaluation results differ significantly. \begin{table}
\begin{tabular}{c c c c c} \hline
**No. ** & **PQI (Grade)** & **PQI\({}^{*}\) (Grade)** & \(\mathbf{K_{\text{PQI}}}^{*}\) **(90)** & \(\mathbf{K_{\text{PQI}}}^{*}\) **(80)** \\ \hline
1 & 83.64 & 73.31757 & 0.81464 & 0.91647 \\
2 & 83.79 & 76. Analyzing the pavement performance sample data reveals that the evaluation grade for the RDI is poor, while the PCI, RQI, and SRI receive excellent and good evaluation grades, respectively. The current standard assigns weights of 0.35 and 0.4 to the PCI and RQI, respectively, while the weight assigned to the pavement rutting depth index is relatively small [31]. Consequently, the overall pavement evaluation result becomes overly optimistic. In contrast, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [12] adopt the information entropy weight determination method, which considers the difference in evaluation indexes and reflects the contribution rate of these indexes to the system. This approach leads to an improved weight for the road rutting depth index. By comparing the scores provided by different experts, it is observed that the weight determination using information entropy weight aligns more closely with the actual road condition level. Through the proposed confidence model for pavement performance analysis, not only is the evaluation level of pavement performance clearly defined, but the corresponding confidence for each evaluation level is also provided. Moreover, it significantly expands the application scope of pavement performance confidence analysis.
## 2 Direct \(\alpha\)-Halogenation of Aryl Ketones
### Electrophilic Halogenation
#### 2.1.2 I\({}_{2}\) as Halogenation Agent
The I\({}_{2}\)-mediated direct halogenation was carried out at 60 \({}^{\circ}\)C for 3-4 h. With methyl iodide,the reaction time increased to 20 h and the temperature decreased to room temperature. The protocol yielded five \(\alpha\)-iodinated aryl ketones in moderate to excellent yields (61-96%). This route emerged as a relatively mild and green direct halogenation route linked to HTIB, a neutral, highly stable, and relatively safe oxidant. The use of an IL also contributed to simplifying the work-up process by allowing the product to be simply extracted without any further purification needed. [PERSON] reported the selective \(\alpha\)-iodination route mediated by iodine (0.5 equiv.) and Oxone(r) (2 KHOS5 \(\cdot\)KHOS4 \(\cdot\)K2 SO4) (0.1 equiv.) [62]. The reaction proceeded by grinding the reagents and the ketone in a mortar with a pestle. A diverse set of 1,3-dicarbonyls and ketones were halogenated in excellent yields. However, only a limited number of aromatic ketones were tested under these conditions to afford the desired products in excellent yields (91%), after grinding for 3 min. The procedure stands out from other direct iodination routes due to its solvent-free approach, involving a more straightforward work-up. So far, the very limited scope of aryl ketones can be considered as its major weakness. In 2018, [PERSON] and co-workers designed a graphene oxide (GO)-catalysed iodination of arenes and ketones in nitromethane (Scheme 17) [63]. Upon the addition of I2 (1.1 equiv. ), several aryl ketones were efficiently iodinated when stirred for 5 min (min) at 120 \({}^{\circ}\)C. Substrates bearing electron-donating groups (i.e., OMe and Me) performed well under these conditions (96% for both). In comparison, substrates with electron-withdrawing substituents (i.e., F and CN) gave somewhat lower yields (74% and 68%, respectively). The reaction proceeded via a radical pathway, with an iodine radical being formed under the influence of the unpaired electrons of the GO; single electron transfer (SET) from the iodinated substrate back to the GO regenerated the GO catalyst and allowed its re-entry into the catalytic cycle. The authors were able to scale up the reaction to the gram scale in air, thus adding a major benefit to this approach. Despite the efficiency and high applicability of this protocol, the generation of stoichiometric amounts of HI and the high temperature needed are considered major drawbacks. Overall, direct halogenation methods, employing Br\({}_{2}\) or I\({}_{2}\), possess similar intrinsic weaknesses. Bromine is a toxic, corrosive and irritating halogenation agent [46; 47]. Although less harmful than bromine, the use of iodine also poses serious safety and health issues [64; 65]. Hence, the next sections will focus on halogen sources that are generally less harmful to human health and to the environment.
# Tailhook Recognition for Carrier-Based Aircraft Based on YOLO with Bi-Level Routing Attention
## 2 Related Work  
### YOLOv5  
YOLOv5 is a single-stage real-time detection method in the field of object detection, proposed by the Ultralytics team and open-sourced on GitHub [8]. As an iterative version of the YOLO series algorithms, YOLOv5 has significantly improved its detection speed and accuracy through technological innovations and optimizations while retaining its predecessors' fast and efficient characteristics. Compared to the newer YOLO version that currently exists, YOLOv5 has better stability and is more economical in terms of computing resources and memory usage. With its efficient speed and high detection accuracy, YOLOv5 has gained widespread attention in object detection.  
The size and complexity of the YOLOv5 models vary, allowing for a balance between swiftness and precision based on different processing power and real-time processing requirements. By setting hyperparameters to change the network depth and width, different models within the YOLOv5 series are obtained, as shown in Table 1. Among them, the YOLOv5s model is the smallest and swiftest in the original series, suitable for environments where inference speed is crucial and computational resources are limited. The YOLOv5m model compromises size and performance, making it ideal for applications that require a balance between swiftness and precision. The YOLOv5l model provides higher accuracy due to its increased size and complexity but requires more computational power compared to the previous two models. The YOLOv5x model has the highest number of parameters and computational cost, being the largest and most precise, making it suitable for scenarios where the highest performance is needed and computational resources are abundant. Additionally, the YOLOv5n model is designed for extremely resource-constrained applications, optimized specifically for devices with very low computational power.  
A depiction of the YOLOv5 network architecture is presented in Figure 1. The architecture of YOLOv5 inherits the design philosophy of the YOLO series [9; 10; 11; 12], achieving the fast detection of multiple objects in an image through a single forward pass. Its network structure is mainly divided into four parts: the input layer, the backbone network, the neck, and the detection head.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \(\mathbf{mAP^{val}}\) & \(\mathbf{mAP^{val}}\) & **Speed** & **Params** & **FLOPs** \\  & **50–95** & **50** & _V100 b1_ (ms)** & **(M)** & **@640(B)** \\ \hline YOLOv5n & 28.0 & 45.7 & 6.3 & 1.9 & 4.5 \\ YOLOv5s & 37.4 & 56.8 & 6.4 & 7.2 & 16.5 \\ YOLOv5m & 45.4 & 64.1 & 8.2 & 21.2 & 49.0 \\ YOLOv5l & 49.0 & 67.3 & 10.1 & 46.5 & 109.1 \\ YOLOv5x & 50.7 & 68.9 & 12.1 & 86.7 & 205.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different versions of YOLOv5 models (640 \(\times\) 640 resolution).
## 4 Energy Management Systems
### Rule-based Energy Management Systems
#### 4.1.7 Thermostat Energy Management System (THEMS)  
THEMS is widely considered by many authors due to its single-mode control, uncomplicated structure, and minimal control requirements [83]. The core idea behind THEMS is that the energy provided by FC and the storage energy system (battery) can be regulated using battery \(SOC\) and FC power rules. This control strategy is keeping the battery \(SOC\) within a set range and respecting two specified power limits for the FCS. The diagram in Figure 17 displays the operational range of this energy management system. However, the THEMS fails to fulfill the load demand under certain circumstances, when \(P_{fc}>P_{fc,max}\) and \(SOC<SOC_{low}\). Additionally, this approach neglects the important aspect of fuel optimization [84].
## 1 Introduction
### Proposed Approach Contributions  
Traditional energy demand forecasting methods often rely on time-series models or regression-based techniques, but they have limited capacity to capture non-linear patterns and dependencies [30]. In recent years, machine-learning approaches, particularly artificial neural networks (ANNs), have become more prominent due to their ability to model complex relationships in data. Among these, long short-term memory (LSTM) neural networks, a type of recurrent neural networks (RNNs), have demonstrated superior performance in capturing long-term dependencies in sequential data, making them particularly effective for time-series forecasting, such as predicting energy demand [31]. However, the performance of LSTM networks can be further enhanced by integrating clustering techniques with load data, along with a similar-day approach for selecting training data. Clustering allows for the identification of underlying patterns or regimes in historical load data, which can improve the training process for neural networks and is widely utilized in contemporary scientific research [32]. In particular, it has been applied to load demand. For example, the classification of representative load curves based on similar features and site temperatures has been carried out in [33]. Furthermore, similar patterns of electricity usage have been uncovered through clustering analysis in [34], and short-term load forecasting has been implemented for random users in Nanjing by grouping users into clusters with similar consumption patterns and predicting energy consumption using a backpropagation neural network [35]. On the other hand, similar-day methods rely on examining historical days with similarities, including weekday index, weather conditions, coincidences of social events, festivities, disasters, anomalous events, and so on [36; 37]. In this work, we propose an innovative approach that integrates K-means clustering and similar-day criteria with LSTM networks for day-ahead load demand forecasting. By grouping historical load data into clusters and utilizing similar days, the LSTM network is
## 2 Formulations
### Transfer Matrix Method  
Let us consider a waveguide with its rectangular cross section of sides \(a\) and \(b\), and the enclosed multilayer dielectric slab with thickness, \((t_{1},t_{2},t_{1},t_{2},...)\) and dielectric function, \((\varepsilon_{1},\varepsilon_{2},\varepsilon_{1},\varepsilon_{2},...)\), as shown in Figure 1. The TE mode (H mode) and TM mode (E mode) in the rectangular waveguide are characterized by the \(z\) components of the magnetic field and the electric field, \(H_{z}\) and \(E_{z}\), respectively. By definition, these components are never absent in the corresponding modes. The \(z\) components of the [PERSON]'s equations for the inhomogeneous media are  
\[\left\{\varepsilon(z)\vec{\
abla}\times\left[\frac{1}{\varepsilon(z)}\vec{ \
abla}\times\vec{H}\right]\right\}_{z}+\omega^{2}\varepsilon(z)\mu(z)H_{z}=0 \tag{1}\]  
and  
\[\left\{\mu(z)\vec{\
abla}\times\left[\frac{1}{\mu(z)}\vec{\
abla}\times\vec{E }\right]\right\}_{z}+\omega^{2}\varepsilon(z)\mu(z)E_{z}=0. \tag{2}\]  
In these cases, the effect of losses of the medium inside the waveguide is characterized by the complex permittivity \(\varepsilon(z)\) and permeability \(\mu(z)\). Thus, Equations (1) and (2) can be rearranged as:  
\[\left[\frac{\partial^{2}}{\partial x^{2}}+\frac{\partial^{2}}{\partial y^{2}} +\varepsilon(z)\frac{\partial}{\partial z}\frac{1}{\varepsilon(z)}\frac{ \partial}{\partial z}\right]H_{z}+\omega^{2}\varepsilon(z)\mu(z)H_{z}=0 \tag{3}\]  
and  
\[\left[\frac{\partial^{2}}{\partial x^{2}}+\frac{\partial^{2}}{\partial y^{2}} +\mu(z)\frac{\partial}{\partial z}\frac{1}{\mu(z)}\frac{\partial}{\partial z }\right]E_{z}+\omega^{2}\varepsilon(z)\mu(z)E_{z}=0. \tag{4}\]  
By symmetry, using separation of variables, Equations (3) and (4) can be split into transverse and longitudinal parts, and the problem can be simplified as solving the one-dimensional Helmholtz's equation along the \(z\) direction, the longitudinal parts,  
\[\varepsilon(z)\frac{\partial}{\partial z}\frac{1}{\varepsilon(z)}\frac{ \partial}{\partial z}\psi(z)_{TE}+\left[\omega^{2}\varepsilon(z)\mu(z)-k_{c} ^{2}\right]\psi(z)_{TE}=0 \tag{5}\]  
and  
\[\mu(z)\frac{\partial}{\partial z}\frac{1}{\mu(z)}\frac{\partial}{\partial z }\psi(z)_{TM}+\left[\omega^{2}\varepsilon(z)\mu(z)-k_{c}^{2}\right]\psi(z)_{ TM}=0, \tag{6}\]  
Figure 1: (a) 3D schematic of periodic multilayer heterostructure along the \(z\)-direction confined in a rectangular waveguide with a width \(a\) and a height \(b\) and (b) the corresponding dielectric function profile \(\varepsilon(z)\) in the cross-sectional view.
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 2 Background  
This section begins with a briefing of the traditional RAN, the basic concept of O-RAN technology, such as disaggregation, virtualization, RAN intelligent controller (RIC), and open interfaces. Then, this section introduces AI/ML techniques to make optimal or sub-optimal decisions or pattern recognition and classification, and a brief literature review on O-RAN.