esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
## 2 Subspace Identification
### General Instruction for User-Defined Parameters
#### 2.2.3 Criterion of Assured Modes  
In field applications, the modes identified by subspace methods can be affected by many factors. For instance, uncertainties, environmental effects, process noise, and measurement noise can lead to unstable and spurious poles. Differentiating assured modes from others is crucial, after applying Equations (8) and (9), to realizing structural systems. A simple way to sieve out these assured modes is to select stable (or invariable) identified parameters regarding the dynamic characteristics in a stability diagram. One of the easiest methods is proposing thresholds to limit variations of modal frequencies, damping ratios, and mode shapes [48]. However, a nonlinear or time-variant structural system naturally generates variable modal parameters, which, though minor, violate the invariable identification results.  
A substitute is to compare identified mode shapes using MPC, modal assurance criterion (MAC), modal complexity factor (MCF), and so on. Regardless, [PERSON] et al. had evidenced that MPC is nothing more than MAC evaluated using identified mode shapes and their conjugate shapes [49]. Additionally, a few derivations can demonstrate the apparent similarity between MPC and MCF, as shown below  
\[S_{xx}=\mathbf{\Phi}_{r}^{T}\mathbf{\Phi}_{r},\ S_{yy}=\mathbf{\Phi}_{i}^{T}\mathbf{\Phi}_{i}, \ S_{xy}=\mathbf{\Phi}_{r}^{T}\mathbf{\Phi}_{i}, \tag{11}\]  
\[MPC(\mathbf{\Phi})=\frac{(S_{yy}-S_{xx})^{2}+4S_{xy}^{2}}{(S_{yy}+S_{xx})^{2}}=1- MCF(\mathbf{\Phi}) \tag{12}\]  
where \(\mathbf{\Phi}\) is the identified mode shapes, and the subscripts \(r\) and \(i\) denote the real and imaginary parts of the complex vectors, respectively. Therefore, MAC is recommended in this study to differentiate assured modes from others.
## 4 Discussion
### Limitations  
This study focused mainly on the muscle forces acting on the mandibular symphysis fracture site and the force of 2 HP in the inferior border to determine the force. However, the force acting on the molar teeth requires further investigation. There are seldom literatures mentioned about the scenario of the occlusal force after the fracture treatment. This study applied normal occlusal loadings which might be greater than that of posttreatment. Therefore, the stress level might be overrated since the occlusal function decreases after surgery. Moreover, the major concern of this study focused on the stress distribution around the fixation, and no force was directly applied to the teeth. Nevertheless, the simplification of the teeth in this finite element model was acceptable. However, the biomechanical effect of the dentition needs further investigation. Only static forces were applied in this study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Muscles Actions** & **Location** & **S4 HP** & **Per4H2 HP** & **PerD2 HP** \\ \hline \multirow{4}{*}{Anterior lower border} & \multirow{2}{*}{Right} & Outer hole & 32.897 & 111.18 & \multirow{4}{*}{140.4} \\ \cline{3-5}  & & Medial hole & 107.66 & 155.79 & \\ \cline{3-5}  & & & Outer hole & 114.35 & 310.73 & \\ \cline{3-5}  & & & Medial hole & 88.145 & 98.197 & \\ \hline \multirow{2}{*}{Inferior border} & \multirow{2}{*}{Right} & \multirow{2}{*}{} & \multirow{2}{*}{185.45} & \multirow{2}{*}{442.19 (+138.44\%)} \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & Left & & 259.18 & 107.05 (\(-\)142.11\%) \\ \hline \hline \end{tabular} \% denotes comparison of the stress of the screws between the Per4H2 HP and PerD2 HP.  
\end{table}
Table 4: The MaxVMS of screws in three fixation groups with mandibular symphysis fracture (Unit: MPa).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Muscles Actions** & **Location** & **S4 HP** & **Per4H2 HP** & **PerD2 HP** \\ \hline \multirow{4}{*}{Anterior lower border} & \multirow{2}{*}{Right} & Outer hole & 13.145 & 23.278 & \multirow{4}{*}{80.758} \\ \cline{3-5}  & & Medial hole & 10.554 & 24.611 & \\ \cline{3-5}  & & & Outer hole & 14.16 & 35.14 & \\ \cline{3-5}  & & & Medial hole & 32.68 & 64.483 & \\ \hline \multirow{2}{*}{Inferior border} & \multirow{2}{*}{Right} & \multirow{2}{*}{} & \multirow{2}{*}{58.582} & \multirow{2}{*}{132.03 (+125.38\%)} \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & Left & & 98.035 & 39.189 (\(-\)60.02\%) \\ \hline \hline \end{tabular} \% denotes comparison of the stress of the screws between the Per4H2 HP and PerD2 HP.  
\end{table}
Table 5: The MaxVMS of screws in three fixation groups with mandibular symphysis fracture (Unit: MPa).  
Dynamic force with added force on the molar teeth or mandible angle might be needed to further confirm the stability of PerD2 HP.
# An Emergency Alert Broadcast Based on the Convergence of 5G and ATSC 3.0
## 3 The Novel Emergency Alert Broadcast Approach Based on the Convergence
### The Convergence Architecture and the Scenario
The 5G-AP includes the CBS message in Figure 7.
2. The N3 IWF acts as a relay between the AMF and the ATSC 3.0 core network. For the connection to the AMF, the N3 IWF adopts the N2 protocol stack. For the connection to the ATSC 3.0 core network, the N3 IWF relies on the ATSC 3.0 protocol stack, which consists of ATSC 3.0 application protocol (ATSC3.0-AP), UDP, IP, ATSC 3.0 link layer protocol (ALP), and ATSC 3.0 PHY. The N3 IWF extracts the CBS ID from the CBS message, and sends the CBS ID to the multiplexer in the ATSC 3.0 core network. The ATSC3.0-AP includes the extracted CBS ID in Figure 7. \begin{table}
\begin{tabular}{c c} \hline \hline
**Elements** & **Description** \\ \hline Header & \\ \textless{}CBS flag\textgreater{} & A true/false value for whether the AEAAT is \\ \textless{}CBS ID\textgreater{} & The identifier of the associated CBS message \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The additional elements of the modified AEAAT. In the ATSC 3.0 core network, the multiplexer uses the CBS ID as the trigger information. If the received CBS ID is matched to the Header\(<\)CBS ID\(>\) of the modified AEAT, the multiplexer multiplexes the AEAT and the NRT components into the AEA message. Otherwise, the multiplexer suspends the multiplexing operation until the received trigger information is matched. Therefore, the CBS ID can be used as the trigger information for a synchronization between the CBS paging signal and the AEA message. 4. In the ATSC 3.0 core network, the ATSC 3.0 gateway transforms the AEA message into a frame unit on the physical layer. Then, the ATSC 3.0 gateway transmits the frame to the ATSC 3.0 modulator. 5. The ATSC 3.0 modulator converts the physical-layer frame to the waveform signal for broadcast. 6. The ATSC 3.0 broadcast station broadcasts the waveform signal for the AEA message. Some alternate architectures to the proposed approach were suggested in [30] and [31]. However, the architectures do not include some specific frameworks for emergency alert broadcasts as stated earlier.
## 3 Obstacle Avoidance Motion Planning Framework
### Violation Cost Potential Field  
The violation cost potential field aims to reduce the violation cost during obstacle avoidance under necessary violations. The compliance-oriented potential field of the corresponding traffic law is truncated according to the PVCI, forming the violation cost potential field, which is defined as:  
\[U_{\textit{v}laneline}=min(\text{PVCI}_{\textit{laneline}},U_{\textit{laneline}}) \tag{29}\]  
\[U_{\textit{t}stopline}=min(\text{PVCI}_{\textit{stopline}},U_{\textit{stopline}}) \tag{30}\]  
\[U_{\textit{t}sped}=min(\text{PVCI}_{\textit{spred}},U_{\textit{spred}}) \tag{31}\]  
\[U_{\textit{vio}}=\sum_{i}U_{\textit{claneline}_{i}}+\sum_{j}U_{\textit{ postopline}_{i}}+\sum_{m}U_{\textit{t}sped_{m}} \tag{32}\]  
where \(\text{PVCI}_{\textit{laneline}}\), \(\text{PVCI}_{\textit{stopline}}\), and \(\text{PVCI}_{\textit{spred}}\) are the calculated PVCI values for the crossing lane line, crossing stop line, and speed violation; \(U_{\textit{t}smenting}\), \(U_{\textit{stopline}}\), and \(U_{\textit{t}sped}\) are the violation cost potential field of the lane line, stop line, and vehicle speed; \(i\), \(j\), and \(m\) denote the index of the \(U_{\text{taneline}}\), \(U_{\text{t}stopline}\), and \(U_{\text{t}sped}\), respectively; \(U_{\text{vio}}\) is the overall violation cost potential field. In this way, the non-crossable compliance-oriented potential field is changed to a crossable violation cost potential field, allowing the vehicle to make effective obstacle avoidance motion planning in unanticipated obstacle scenarios.  
Figure 6: Obstacle vehicle potential field.
## 3 Scheme of the Proposed Method  
### Hardware of the Vision System  
Considering that the minimal diameter of an MCB component is approximately 1 mm, the precision of the stereo camera must be higher than 0.1 mm. In addition, to scan all components in the camera's field of view, the dimensions of the view field must be larger than 90 \(\times\) 70 mm. Therefore, we employ a Phoxi 3D Scanner XS stereo camera [22] whose scanning precision is 0.055 mm, selected to meet the requirements. The specifications of the stereo camera are shown in Table 1.  
The vision system is shown in Figure 5. The two yellow lines illustrates the range of the generated structured light, and the range beween the two blue lines represents the FOV of the camera lense.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Brand & Photoneo \\ Model & PhoXi 3D Scanner XS \\ Imaging method & Structured light \\ Point-to-point distance (sweet spot) & 0.055 mm \\ Scanning range & 161–205 mm \\ Optimal scanning distance (sweet spot) & 181 mm \\ Scanning area (sweet spot) & \(118\times 78\) mm \\
3D points throughput & 16 Million points per second \\ GPU & NVIDIA Pascal Architecture with 256 CUDA cores \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the stereo camera.  
Figure 5: Hardware design of the stereo vision system.  
The stereo camera collects the point clouds of the components on the loading carrier and sends the data to a computer. The heights of the components are between 1.5 and 15 mm, and the distance between the stereo camera and the upper surface of the loading carrier is 195 mm, so the object distances are within [180; 193.5], which is around the optimal scanning distance of the camera. The side length of the field of view (FOV) is 127 mm, which can contain all components of a single-pole MCB.  
Figure 6 shows how the vision system is set up. The stereo camera is fastened on a frame by a support and two clamps. The parallelism between the camera and the loading carrier can be adjusted by the flexible support and clamps and verified by the imaging outputs of the camera so that the error of alignment can be minimized. A specified Ethernet cable is connected to a Power-over-Ethernet (PoE) adapter for both power supply and data transmission to the computer.
## 4 Performance Evaluation of Offshore Communications
### Channel Capacity  
Channel capacity is the minimum upper bound of the achievable rate when information can be reliably transmitted in a channel. According to the description of the Shannon-Hartley theorem [34], the relationship of channel capacity, signal-to-noise ratio and bandwidth of a wireless channel can be summarized by the Equation (5).  
\[C=B_{w}\cdot\log_{2}\left(1+10^{sm/10}\right) \tag{5}\]  
where \(C\) represents the capacity of the channel on bit per second (b/s), \(B_{w}\) is the bandwidth on Hz and \(smt\) is the signal-to-noise ratio on dB.  
Based on the collected measurement data, we obtained the channel capacity of the two scenarios. The result indicates that the channel capacity of the S2I and the S2S measurement is 0.47-2.51 Gb/s with an average value of 1.17 Gb/s and 1.21-2.35 Gb/s with an average value of 1.72 Gb/s, as shown in Table 4. The average channel capacity of S2S measurement is bigger than that of S2I measurement due to the short Tx-Rx distance. This average channel capacity is big enough to meet the demand of intelligent shipping communication and support the general data transmission, such as video, monitoring, etc.  
According to our description above, the factors that affect the distribution of power, delay spread, and propagation path loss have been analyzed. We find that the main influencing factor for the sea propagation environment is the reflection from the water surface.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **Channel Capacity (Gb/s)** & **Average Value (Gb/s)** \\ \hline S2I & 0.47–2.51 & 1.17 \\ S2S & 1.21–2.35 & 1.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the channe capacity.  
Figure 7: The measured coverage range of S2I communication.  
Meanwhile, the earth curvature effect is also not neglected for long-distance communication. Therefore, in the planning of the offshore wireless communication network, we should select an appropriate carrier frequency and bandwidth. In addition, the reflection effect from the water surface, the influence of ship motion on energy distribution, the multi-path effect and shadowing from the passing huge ships and buildings on the shore should also be considered.
## 2 ZnO-Based Nanostructures Preparation
### Hydrothermal Method  
The hydrothermal method is a convenient and highly efficient method, which requires a lower reaction temperature and saves costs [70]. In addition, by adjusting the duration, density, and reaction temperature of the contained substances, the morphology and size of the particles can be controlled [71]. As shown in Figure 5a-c, TEM images of synthesized ZnO particles at precursor concentrations of 5, 10, and 20 mM are revealed. The morphological features of the poorly dispersed nano-ZnO crystals are clearly demonstrated by the TEM images in all cases. At precursor concentrations of 5, 10, and 20 mM, ZnO nanoparticles were observed to have diameters of approximately 4.5, 6, and 8-9 nm, respectively. Furthermore, it was also observed from the images that the size distribution of the nanoparticles was fairly uniform [72]. As shown in the XRD pattern (Figure 6), the crystalline structure of the synthesized ZnO particles after hydrothermal treatment was confirmed. Simultaneously, no impurity peaks were detected from the XRD pattern, indicating that the target substance with a higher purity was successfully synthesized [72]. Furthermore, the preferred orientation of the ZnO particle samples was not seen from XRD patterns, suggesting that ZnO crystals may have the most shapes other than rods or sheets. Based on the above conclusions, the hydrothermal method is a suitable method to prepare ZnO-based nanoparticles with different shapes. In addition, the antibacterial effect of ZnO-based nanomaterials prepared by hydrothermal method is affected by several factors, such as pH, reaction temperature, and dosage ratio [73,74]. The reaction temperature directly affects the structure of the material and changes the antibacterial ability, while the pH changes the surface properties and shape of the material to affect the antibacterial ability [75].  
Figure 4: Typical XRD patterns of ZnO nanostructures synthesized with different reaction pH ((**a**) NaOH and (**b**) KOH as pH controlling agents, 7, 8, 10, 12 correspond to their respective concentrations). Reprinted/adapted with permission from Ref. [65]. Copyright © 2022, Elsevier Ltd. and Techna Group S.r.l.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 2 Mission
### Scientific and Technical Goals and Payloads
#### 2.4.4 Radio Communication Protocol  
The integrated radio hardware in the EFR32 FG12 microcontroller handles Open Systems Interconnection model (OSI) Layer 1 by itself. Further the hardware handles framing and checksum of Layer 2. The software, therefore, must handle radio packet collision avoidance and addressing.  
To keep things simple and deterministic there is one designated master node, which controls all communication as well as the power unit. To avoid that this master is a single point of failure it is dual modular redundant. The master sends synchronization frames in regular intervals. The structure of the frames send on the radio link is outlined in Figure 4. The sync frames contain a list of relative times to the synchronization frame. These mark the time slots at which each other node may transmit. The end of the transmission slot of one node is marked by the beginning of the next. The slot length can be adjusted to account for different bandwidth requirements of the nodes. After the slot-list the master sends its payload, if any, like any other node. This can ensure a minimum bandwidth for every node. The maximum latency is depended on the maximum length of the cycle. The frame of the non-master nodes is the same except that the time-slot list is missing.  
Figure 3: Overview of the redundancy management.  
The payload contains the data of the higher level Rodos (real-time on-board dependable operating system) middleware protocol, which transmits the published topic data to all nodes.  
The Rodos middleware frames which contain the topic data are transmitted in the payload. One radio frame can contain multiple Rodos middleware frames to allow full usage of the slot if there are only small frames available. Addressing is then handled inside the Rodos middleware frames with its publisher/subscriber architecture and topic IDs. Each node processes all frames and forwards the topics it is subscribed to, to its local subscribers. The middleware and topics are further described in Section 3.5.5.
# An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework
## 2 Related Works
### Sparse Representation and Dictionary Learning
Since a small local dictionary as a subset of the global dictionary can be used to describe patch images, [PERSON] [46] proposed a clustering-based denoising method with locally learned dictionaries. A dictionary is iteratively trained by obtaining principal components of each cluster that reflect representative local features of source image. [PERSON] [19] proposed an online dictionary learning method based on stochastic approximations to minimize the expectation with respect to the unknown probability distribution of input data. It uses a stochastic version of the majori-zation-minimization principle to do the calculation [47]. A quadratic surrogate of the expected cost is sequentially built and minimized at every iteration. Stochastic gradient descent algorithm, as an effective way of dictionary learning, was proposed by [PERSON] and [PERSON] in 1996 [39]. It described gradient steps with a fixed step size computed on mini-batches of the training set and rescaled heuristic gradient to prevent the norm of the columns to grow out of bounds by taking the implicit constraint into account. However, it is actually difficult to choose the suitable step sizes in a data-independent manner. One classical approach of dictionary learning is the alternate minimization scheme optimizes dictionary learning process [41; 48]. It alternates two minimization steps: one with respect to dictionary D with the fixed sparse code A, and one with respect to A with fixed D. Although it is not as fast as a well-tuned stochastic gradient descent algorithm in practice, it is parameter-free. Learning a compact and representative dictionary is very important for sparse representation. [PERSON] [49] adopted the rule of maximization of mutual information to learn a compact and discriminative dictionary for action attributes. [PERSON] [50] learned a context aware dictionary to improve the recognition and localization of multiple objects in images. Other methods improve the dictionary performance by separating the particularity and commonality [51] and adding discrimination constraints [52; 53]. Most of these methods consider the discrimination between classes, while seldom involving relationships between samples or the local information in the data space.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
### Why Is Cognition Crucial to the Hybrid Route Selection Scheme in 5G Networks?  
Due to the need for interaction with the operating environment, the majority of testbeds using USRP have adopted Q-learning to adapt to the environment. Q-learning is the preferred approach because it does not need datasets required in supervised machine learning approaches and does not explore the underlying pattern or relevancy. A clear justification is needed (e.g., utilized or unutilized), which is why an unsupervised ML is not preferred [24; 25; 26]. For more details, refer to Section 3.1.  
Due to the dynamicity of the operating environment, it is necessary to practice constant learning to achieve optimal network performance as time passes. As different types of network cells have different characteristics, the dynamicity level varies across the macrocell and small-cell layers. For example, the dynamicity of channel availability is lower in the macrocell layer as channels with a higher number of white spaces have been assigned to the layer. Therefore, a single set of rules or policies would less likely be optimal when applied across the entire network. In CenTri, BSs and nodes embrace a popular cognition approach called reinforcement learning (RL).
## 3 Electrodeposition  
The electrodeposition of alloys is a significantly more complex process compared to the deposition of individual metals. This complexity arises not only from the selection of electrolyte composition and pH but also from the current-potential conditions required to ensure the simultaneous reduction of metal ions [18]. Metal ions may be present in the bath as simple cations, complex ions, or oxyanions. The chemical form of the metal ions determines not only their reduction potentials but also the cathodic overpotentials of the individual components. By controlling the kinetic and thermodynamic parameters of metal ion reduction, alloy formation can proceed through different mechanisms, typically classified as normal (regular, irregular, or equilibrium) or abnormal (anomalous or induced) codeposition from aqueous baths [19]. In the case of normal deposition, more noble metals are reduced and deposited preferentially. In contrast, under abnormal conditions, less noble metals may deposit preferentially (anomalous deposition), or metals that cannot be electrodeposited individually from aqueous solutions may be reduced in the presence of other metal ions and incorporated into the alloy (induced codeposition). The mode of codeposition influences the final composition of the alloy and the relative proportion of its individual constituents (Figure 2).  
Figure 2: Codeposition modes of alloys: (**a**) normal codeposition; (**b**) abnormal codeposition. Dashed line represents equal same metal percentages in both the electrolyte and alloy. Based on [PERSON]’s classification [19].  
Abnormal codeposition behavior appears to be the most commonly exploited phenomenon in the electrodeposition of high-entropy alloys based on iron-group metals (Fe, Ni, Co) [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57]. Metals from this group not only tend to codeposit anomalously with other metals but they also play a key role in inducing the incorporation of elements that exist in the electrolyte as oxyanions, which are otherwise not reducible on the cathode from aqueous solutions on their own (W, V, Mo, Re) [21; 22; 25; 26; 27; 28; 29; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 53; 54; 57].