esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Operating Principle and Modes
It is clear from Figure 2(b) that there is no change in the control signal \(c_{1}\) and \(c_{3}\) as their role is the same as in non-inverting operation that is to convert the constant sinusoidal input voltage to its controllable absolute value. Here, like a non-inverting operation, the operating states of transistor \(T_{r2}\), \(T_{r6}\) and \(T_{r4}\), \(T_{r5}\) realize the inverting output. This task is obtained by reversing the switching states of the control signals \(c_{72}\), \(c_{76}\) and \(c_{74}\), \(c_{75}\) with respect to their values of non-inverting operation. The role of control signals used for voltage buck operation is changed for non-inverting and inverting voltage boost operation. Still, the generated characteristics remain unaltered, as shown in Figure 3(a),b. Here the PWM control of the transistors \(T_{r1}\) and \(T_{r3}\) is shifted to \(T_{r5}\) and \(T_{r6}\). Now, transistors \(T_{r1}\) and \(T_{r3}\) operate at low input frequency and ensure their on-states for positive and negative input voltage, respectively. For non-inverting and inverting voltage boost operation, the role of the front stage circuit is only to transform the constant voltage sinusoidal input to its absolute form. This feature of the operation may be confirmed from the plots of Figure 3(a),b There is no change in states of the control signals \(c_{1}\) and \(c_{3}\) for the operation in which the output voltage is a non-inverted or inverted form of the input voltage. The inverted and noninverted form of the output depends on switching states of \(c_{2}\), \(c_{4}\) and \(c_{5}\), \(c_{6}\). The switching state of the control signals \(c_{6}\) is swapped with \(c_{5}\) and \(c_{4}\) is swapped with \(c_{2}\) once the output is changed from non-inverting to inverting boost form and vice versa. Figure 3: Generation of control signals with respect to input voltage polarity detector for voltage buck to achieve (**a**) non-inverted form of output and (**b**) inverted form of output.
## 4 Experimental Results  
### Geometric Model Results and Comparison of Different Extraction Methods  
Table 2 presents the geometric model results of the proposed method. The number of GCPs (second column) represents the final number of points that are used in the correction of the RPCs. The number of extracted GCPs mostly depends on the image size, the off-nadir angle, and the acquisition date. It is reduced to the final number after the accuracy check and the gross error removal techniques. In our case, the RANSAC iterations assume that at least 90% of the points do not have gross errors or that the errors are negligible.  
Figure 7: Off-nadir viewing angles and approximate locations of the image acquisitions relative to the areas of interest (AOIs) (marked with a star). The images of Ljubljana AOI (left) are taken from three orbits while the images of Kranj AOI (right) are from four orbits (four stereopairs).  
After the adjustment, the GCPs root mean square errors (RMSEs) are computed from the difference between the extracted coordinates and the coordinates that were obtained with the geometric model. To obtain more meaningful results for each image, 15 evenly distributed manual independent check points (ICPs) were measured through the images. The reference coordinates of the ICPs were manually measured on national aerial orthophotos with 0.5 m resolution. The achieved RMSEs, which were computed from the difference between the measured coordinates and the coordinates that were obtained with the geometric model, are around 1 pixel, and in four cases even reach sub-pixel values.  
To test the accuracy of the proposed extraction method, the results of the geometric model that is based on the previous (original) GCPs extraction and based on the proposed GCPs extraction are compared to the results that were obtained without GCPs (unbiased RPC coefficients). Figure 8 shows the results of this comparison. It can be clearly seen that the smallest RMSE is achieved by the proposed method for the majority of images. As expected, if no GCPs are used, the results are much worse in all cases and they mostly reach values that are also obtained by other studies (e.g., [35]).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}
# Multi-Lidar System Localization and Mapping with Online Calibration
## 4 Real-World Experiment Validation and Result Analysis
### Online Calibration Experiment Results and Analysis
#### 4.1.1 Comparison and Analysis of Online Calibration Results for Different Lidar Layouts
Notably, the yaw angle exhibits greater deviation among the three rotation angles, primarily due to the lidar's relatively low vertical resolution and limited vertical direction constraints.
# Risk Propagation Mechanism and Prediction Model for the Highway Merging Area
## 2 Concept of Accident Risk Propagation  
The merging area of highways is a high-risk area due to the interaction between main road vehicles and ramp vehicles. When a traffic accident occurs, rapid changes in traffic speed and competition for right-of-way among vehicles will change the risk level of upstream sections. Additionally, the driver's non-standard operations, unsuitable road design, and traffic environment interference also increase the accident risks. These risks will accumulate along the traffic flow and spread through congestion and dissipation waves, as shown in Figure 1. After an accident occurs, the following vehicles need to brake to avoid colliding with the accident vehicles. At the same time, the vehicles on the fast lane will also be affected by the lane-changing behavior of the vehicles on the ramp. Therefore, the closer the vehicle is to the accident site, the more likely it is to be affected. Its driving risk increases accordingly. The farther the vehicle is from the accident site, the less affected it is. If the vehicle is far enough away, its driving risk will almost be unchanged.  
However, risk propagation is a dynamic process, as shown in Figure 2. In the early stage of an accident, the risk of collision increases due to the high likelihood of secondary accidents caused by the sudden braking of vehicles. After a period, the traffic flow near the accident position gradually slows down, and the risk between vehicles decreases. At the same time, the risk caused by sudden brakes propagates to the upstream of the traffic flow, resulting in high risk in the upstream area. The collision risk within the traffic flow dissipates when the traffic flow stabilizes.
### Advantages of Using Cerberis3 in a Hub-and-Spoke Network Topology  
The Cerberis3 Quantum Key Distribution (QKD) system from ID Quantique offers several advantages when deployed in a hub-and-spoke network topology:  
1. Centralized management: The system can be managed through a central equipment management system (EMS) that provides status updates on each QKD node, simplifying network management and troubleshooting.
2. Cost-effectiveness: The hub-and-spoke topology reduces the need for redundant resources in each spoke, making it a cost-effective solution compared to mesh or ring topologies.
3. Scalability: Adding new spokes to the network is easy without affecting the central hub, making it a scalable solution for growing networks.
4. Security: The central hub can serve as a firewall, detecting different ports from packets and ensuring secure traffic flow. Logging is also more manageable from a central point, as all network activity passes through the hub.
5. Interoperability: The Cerberis3 system can interface with link encryptors from major vendors, ensuring compatibility with existing encryption systems.
6. Flexibility: The system supports various network configurations, including point-to-point, relay, ring, and hub-and-spoke topologies, allowing for flexible deployment in different network environments.
7. Key management: The Key Management System (KMS) software hosted on the Quantum Node Controller (QNC) blade arbitrates key distribution between QKD blades and key consumers, ensuring secure and efficient key distribution across the network.  
Describing Point-to-Point Architecture That Uses QKD for Key Management and a Monitoring System That Handles Events and Draws Graphs  
Two QKD nodes are directly connected via a dedicated optical fiber link in a point-to-point QKD network. This architecture offers several key benefits:  
1. High bandwidth: The direct connection between the two nodes allows for high-speed data transmission, making it suitable for applications that require large data throughput.
2. Simplified management: With only two nodes to manage, the point-to-point topology simplifies network administration and troubleshooting compared to more complex topologies.
3. Cost-effectiveness: The point-to-point architecture can be a more cost-effective solution for small-scale deployments where only two endpoints need to be connected.
4. Quantum-safe security: The QKD system generates and distributes cryptographic keys using quantum mechanical principles, ensuring that the communication channel is protected against eavesdropping and quantum computing attacks.
# Hardware Update through Digital TV Signals
## 5 The Proposed Hardware Reconfiguration Scheme Based on the DTV Signals
### Hardware Data Multiplexing in Transport Streams
Such a field is used to inform the name of a hardware module (e.g., decoder, multiplexer, or cipher) available for reconfiguration. The next descriptor is named fpga_core_device_info() and is shown in Table 6. A descriptor_tag set identifies it as \"0x03\", and its descriptor_length field presents the size of the fpga_info string field, where each character is also coded with 8 bits. This field informs the name of the FPGA manufacturer, followed by the FPGA family and, lastly, the part number of the FPGA device, for which the core is synthesized. Such values are arranged into the fpga_info character string field and separated by spaces (\"0x32\"): \"fpga-manufacturer-name fpga-family-name fpga-part-number\". This information is used to identify the FPGA device information in the broadcast content. The last descriptor is fpga_section_identifier(), shown in Table 7, which is identified by a descriptor_tag set to \"0x05\". The next field, that being descriptor_length, contains the loop content size in bytes. The remnant_core_pck_pid field informs the PID used for locating packets with the desired hardware content. Aside from that, it is used in association with the remnant_core_sec_tid field (which informs the section TID) for accessing private sections with core content, while remnant_priority informs the \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Data Structure**} & **Bit Rate** & \multirow{2}{*}{**Mnemonic**} & \multirow{2}{*}{**Value**} \\  & **(No. of Bits)** & & \\ \hline fpga\_core\_device\_info() & & & \\ descriptor\_tag & 8 & uimsbf & 0x03 \\ descriptor\_length & 8 & uimsbf & \\ for (i = 0; i \(<\) N; i++) \{ & & & \\ fpga\_info & 8 & uimsbf & \\ \} \\ \} & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The syntax of the descriptor FPGA_CORE_DEVICE_INFO. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Data Structure**} & **Bit Rate** & \multirow{2}{*}{**Mnemonic**} & \multirow{2}{*}{**Value**} \\  & **(No. of Bits)** & & \\ \hline fpga\_core\_module\_name() \{ & & & \\ descriptor\_tag & 8 & uimsbf & 0x01 \\ descriptor\_length & 8 & uimsbf & \\ for(i = 0; i \(<\) N; i++)\{ & & & \\ reserved\_future\_use & 24 & bslbf & \\ descriptor\_core\_length & 8 & uimsbf & \\ for(i = 0; i \(<\) N; i++) \{ & & & \\ fpga\_core\_module\_name & 8 & uimsbf & \\ \} \\ \} \\ \} \\ \} \\ \} \\ \} \\ \} \\ \} \\ \} \\ \} \\ \} \\ \}\end{array}\}\}\}\}\}\}\}\}\}\}\}\\\}  
for each group of 256 sections, represented by its own PID and TID. Such an approach is used if a hardware bitstream needs more than 256*4080 bytes; otherwise, only one group is enough, which is to say it is set to \"0x00\". For the next group represented by other PIDs and TIDs, this value is incremented by one. The receiver system uses the presented information to check the characteristics of the broadcast hardware core and guide the execution of hardware updates if a match is found.
## 3 Nonlinear Robust Control Laws
### Nonlinear Attitude Controller Based on Disturbance Observer  
Consider the subsystem \(\Pi_{2}\), and define \(\xi_{1}=\Theta-\Theta_{d}\) and \(\xi_{2}=\dot{\Theta}-\dot{\Theta}_{d}\), and we have  
\[ \begin{cases}\dot{\xi}_{1}=&\xi_{2}\\ \dot{\xi}_{2}=&-R_{\Theta}J^{-1}S(\dot{\Theta})J\dot{\Theta}+R_{\Theta}J^{-1}LF ^{b}+R_{\Theta}J^{-1}B_{\omega}(\omega^{b})-\ddot{\Theta}_{d}-R_{\Theta}J^{-1} J_{\Delta}(t)R_{\Theta}^{-1}\dot{\Theta}\\ &+R_{\Theta}J^{-1}S(\dot{\Theta})J_{\Delta}(t)\dot{\Theta}+R_{\Theta}J^{-1}L_{ \Delta}(t)F^{b}+R_{\Theta}J^{-1}\Delta_{2}\end{cases}  \tag{37}\]  
Denote \(\xi=[\xi_{1},\xi_{2}]^{T}\). It is assumed that the unknown terms \(R_{\Theta}J^{-1}B_{\omega}(\omega^{b})\) caused by the multi-media dynamics can be rewritten as a combination of \(M\) basis functions \(w_{j}(\xi)\), and we obtain  
\[R_{\Theta}J^{-1}B_{\omega}(\omega^{b})=\Xi^{T}W(\xi)+o_{2}(\xi) \tag{38}\]  
where \(\Xi\in\mathbb{R}^{m\times 3}\) is the unknown constant and \(o_{2}(\xi)\) is the higher-order component, and  
\[W(\xi)=(w_{1}(W(\xi),w_{2}(\xi),w_{3}(\xi),...,w_{M}(\xi)))\in\mathbb{R}^{M} \tag{39}\]  
is the known regression vector. Subsystem \(\Pi_{2}\) can be rewritten as  
\[ \begin{cases}\dot{\xi}_{1}=&\xi_{2}\\ \dot{\xi}_{2}=&-R_{\Theta}J^{-1}S(\dot{\Theta})J\dot{\Theta}+R_{\Theta}J^{-1} LF^{b}+\Xi^{T}W(\xi)-\dot{\Theta}_{d}+\Delta_{\xi}\end{cases}  \tag{40}\]  
where  
\[\Delta_{\xi}=-R_{\Theta}J^{-1}J_{\Delta}(t)R_{\Theta}^{-1}\dot{\Theta}+R_{ \Theta}J^{-1}S(\dot{\Theta})J_{\Delta}(t)\dot{\Theta}+R_{\Theta}J^{-1}L_{ \Delta}(t)F^{b}+R_{\Theta}J^{-1}\Delta_{2}+o_{2}(\xi)\]  
\(\Delta_{\xi}\) indicates the total disturbance. Define \(e_{\xi}=\xi_{2}-\xi_{2}^{*}\) where \(\xi_{2}^{*}\) is the virtual control law. Equation (41) can be given by  
\[ \begin{cases}\dot{\xi}_{1}=&\epsilon_{\xi}+\xi_{2}^{*}\\ \dot{\epsilon}_{\xi}=&-R_{\Theta}J^{-1}S(\dot{\Theta})J\dot{\Theta}+R_{\Theta}J^{ -1}LF^{b}+\Xi^{T}W(\xi)-\dot{\Theta}_{d}-\dot{\xi}_{2}^{*}+\Delta_{\xi}\end{cases}  \tag{41}\]  
Then, we can design the attitude controller and obtain the main results. Assumption 2: Assume \(\Delta_{\xi}\) is bounded, and there exists an unknown constant such that \(\|\Delta_{\xi}\|\leq v\). The observer design method in [19] is introduced to estimate the total uncertainty \(\Delta_{\xi}\) and the adaptive estimate error \(\Xi^{T}W(\xi)-\widehat{\Xi}^{T}W(\xi)\). Denote  
\[\Delta_{\Xi}=\Xi^{T}W(\xi)-\widehat{\Xi}^{T}W(\xi)+\Delta_{\xi} \tag{42}\]  
Assume \(\widehat{\Delta}_{\Xi}\) is the estimate of \(\Delta_{\Xi}\), and the observer is given by  
\[ \begin{cases}\dot{\widehat{\epsilon}}_{\xi}=&-R_{\Theta}J^{-1}S(\dot{\Theta} )J\dot{\Theta}+R_{\Theta}J^{-1}LF^{b}+\widehat{\Xi}^{T}W(\xi)-\dot{\Theta}_{d }-\dot{\xi}_{2}^{*}+\widehat{\Delta}_{\Xi}+A\big{(}\epsilon_{\xi}-\widehat{ \epsilon}_{\xi}\big{)}\\ \dot{\widehat{\Delta}}_{\Xi}=&z_{1}+B\big{(}\epsilon_{\xi}-\widehat{\epsilon }_{\xi}\big{)}\\ \dot{z}_{1}=&z_{2}+C\big{(}\epsilon_{\xi}-\widehat{\epsilon}_{\xi}\big{)}\\ \dot{z}_{2}=&D\big{(}\epsilon_{\xi}-\widehat{\epsilon}_{\xi}\big{)}\end{cases}
## 3 The AgroTRACE Infrastructure
### Event Capturing and IoT Application Platform  
This platform is the location, where the gathering, storage and processing of the data originated from different event capturing applications, IoT applications and networks as well as third-party applications take place. More specifically, in the framework of AgroTRACE, two indicative event capturing and five IoT applications have been developed, which are linked together for supporting particular use cases. The context of these applications is briefly described below.
# AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews
## 5 Discussion  
Embedding is a crucial process in deep learning. In classical LSTM neural network models, the process of word embedding involves randomly initializing vectors, which are then continuously updated during training. In comparison to classical LSTM networks, this study has improved the model by incorporating feature fusion into the word embedding process. The model first uses the multi-head attention strategy of the Transformer model to extract the semantic information of the sentence. The attention mechanism allows the model to focus on specific features related to the target during training and can capture both global and local connections. The multi-head attention mechanism allows the model to concentrate on information from different representation subspaces at different positions, thereby extracting word vectors containing richer feature information. The generated vectors from the multi-head attention mechanism are fused with the word vectors generated during the embedding process to increase the information content of the vectors and avoid the random generation of word vectors in traditional LSTM neural networks. The resulting vectors are then input into the LSTM layer to capture the sequence features of the sentence and perform sentiment analysis.  
The experimental results in the datasets of agricultural product reviews used in this study show that the method proposed in this experiment is about 1-3 percentage points higher than the LSTM model, Bi-LSTM model, and Transformer model, respectively, in _Accuracy_, _Recall_, and _F1-score_, which are scores superior to other models. This model not only takes advantage of the processing ability of LSTM neural networks to sequence information, but also makes full use of the excellent ability of multi-head attention mechanisms to represent semantic information, and it has achieved good results.  
In addition, we also have found that the _Accuracy_ of the two datasets differed by approximately five percentage points. Possible reasons for this may include differences in total data volume or differences in the ratio of positive to negative samples. We will further explore this issue in the future.
## 6 Conclusions  
For reliable operation of OFDM systems, it is important to perform frequency-offset estimation robustly. Addressing this issue, this paper proposed an effective joint CFO and SFO estimation method in the ATSC system using MIMO-OFDM. For this purpose, a pilot subset selection was deployed on a per-subcarrier basis so that the proposed joint LSE was implemented in a decoupled way. To demonstrate the usefulness of the joint LSE approach, the MSE performance was numerically derived in a multipath fading channel considering the MIMO-OFDM context. By deriving a closed-form expression for the MSE of the frequency-offset estimate, it was shown the MIMO-OFDM ATSC system has a rich structural information to synchronize a system. The proposed joint estimation scheme that benefits from the optimal pilot subset was proven to be less affected by the amount of frequency offsets and to be computationally inexpensive, as compared to the conventional estimation scheme. Therefore, the proposed frequency-offset estimation scheme can be used to maximize the benefits of MIMO-OFDM in digital multimedia terrestrial broadcast systems. In future works, the presented theoretical analysis could be straightforwardly applied to other multimedia terrestrial broadcast systems. In addition, the performance of the MIMO-OFDM ATSC system under realistic channel conditions would reinforce the numerical analysis, particularly taking into account scenarios where the channels between transmit-receive antenna couples are correlated.  
[PERSON]-[PERSON] developed the estimation method and conducted computer simulations. [PERSON]-[PERSON] analyzed the performance of the presented detection method and verified numerical results.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07048819).  
The authors declare no conflict of interest.
# Enhanced Radiation Therapy of Gold Nanoparticles in Liver Cancer
## 3 Results and Discussion
### Distribution of PEG-Coated GNPs in Cells  
Surface modification of the GNPs is expected to affect endocytosis and cellular transportation of GNPs in cells. Representative transmission electron micrographs of intracellular uptake of PEG-coated GNPs of 14.4 nm and 30.5 nm diameter by HepG2 and H22 cells during a 24 h treatment are shown in Figure 4. These micrographs gave a microscopic insight of the distribution of nanoparticles. The figure showed that PEG-coated GNPs were distributed in the cytoplasm and observed in the intracellular region. We further found a cellular uptake of 10\({}^{3}\) nanoparticles per cell in HepG2 and H22 cells, as shown in Figure 5. Moreover, there was a 66% cellular uptake for the 30.4 nm and 62% for 14.4 nm GNPs in HepG2 cell, respectively. These findings are consistent with recent reports that 30 nm PEG-coated GNPs presented higher potential of cellular uptake [18; 23; 32; 58; 59]. The transmission electron micrographs obtained for PEG-coated GNPs in H22 and HepG2 cell line are shown in Figure 6a,b, respectively. The particles were wrapped in structure similar to vesicles. In the transmission electron micrographs, the nanoparticles with size of \(\sim\)15 nm and 30-50 nm was observed, most likely because of the formation of a large size corona following the interaction between GNPs and proteins, as illustrated by the schematic in Figure 6c. This observation is in good agreement with that of blood plasma stability in Figure 3.  
Figure 3: Time-dependent surface plasmon resonance (SPR) peak (**a**) and intensity (**b**) of 14.4 and 30.5 nm PEG-coated GNPs on blood plasma.  
We added PEG-coated GNPs to cells and studied their response by TEM. The charges on the surface of GNPs changed from negative to positive because of PEG-SH modifying, which made them easily interact with cells that carried negative charges on the surface, and enhanced the binding between nanoparticles and cells. When nanoparticles carrying positive charges were added to cells, they were electrostatically attracted to the membrane surface carrying negative charges. Endocytosis played a critical role in the interaction of nanoparticles with cells. GNPs modified with different agents would play different roles in endocytosis [60].  
Figure 4: In vitro bio-distribution and TEM images of 14.4 and 30.5 nm PEG-coated GNPs in H22 (**a**,**c**) and HepG2 (**b**,**d**) cell.  
Figure 5: Cellular uptake of 14.4 and 30.5 nm PEG-coated GNPs in H22 and HepG2 cells.