esa-sceva/satcom-chunk-collection huggingface dataset:
the torque transfer capability of the transmission. [PERSON] [11] proposed a magnetic design method for MR clutches by applying nonlinear materials to amend the magnetic axial force direction of a permanent magnet system. [PERSON] et al. [12] performed magnetic field analysis for radial MR clutches with different slot types, summarized the relationship between torque performance, disc radius and magnetic field based on their experimental results, and optimized the design of the working gap. [PERSON] et al. [13] proposed a torque estimation method for disc-type MR devices that considered the centrifugal effects and introduced radial stresses into magnetorheological constitution analysis to improve the accuracy of torque evaluation. [PERSON] et al. [14] presented a computational fluid dynamics simulation model for simulating the behavior of a wet MR clutch and validated the model with experimental data to complement the hardware-based MR clutch design process.  
The above research was focused on discussing the influence of a single factor on the torque of MR device, but rarely analyzed the flowing nature of MRF, and rarely considered the shear-thinning phenomenon of MRF in its working process. In terms of device optimization, the analysis of the influence of multiple factors on the transfer torque is rarely considered. Especially in the question of the optimal arrangement of the magnetic field of MR devices, the magnetic field distribution in the working gap of an MRF is highly uneven, resulting in a low MR transfer torque. In order to solve the above-mentioned problems, the viscoplastic flow of the MRF in a disk-type gap was analyzed, and the torque equation was established considering the shear-thinning phenomenon of MRF. Based on electromagnetic field theory and the finite element method, the distribution of the magnetic field in the disc-type working gap of MRF was analyzed. The optimal magnetic field design scheme was obtained through structural adjustment. This article provides a valuable reference for the design and manufacture of disc-type MR transmission devices.
###### Abstract  
The power network is directly or indirectly exposed to various weather and climate conditions that can positively and negatively influence it. Forest fires and various aerosol contaminations may negatively affect the power network. Surface observations and measurements (e.g., weather stations) help protect against unfavorable influences, but the network is usually poor. That challenge may be solved by including satellite data in the power network planning, operation, and maintenance. This article presents the possibilities of using Sentinel mission's data, at various temporal and spatial levels, for those purposes through examples of protection against forest fires and aerosol contamination. In the case of forest fires, satellite data enable a more-precise calculation of forest fire-spreading danger for areas with no installed weather stations, which may be used in the power system contingency analysis calculation. The analysis showed that aerosol contamination has a correlation with outage appearance in the 25 kV 50 Hz electric traction network. Sentinel 5p data may serve as a tool in a surface air-quality measurement data control to confirm/deny the high concentration of PM\({}_{10}\) particles. Analysis showed that Sentinel data are favorable for power network operations on intra-day and day-ahead operations, as well as for long-term operations, which include planning and maintenance. The spatial resolution of Sentinel data is more than enough to fulfill the surface weather station network. Satellite data also enable the use of additional data that are not measured at ground stations.  
satellite data; Sentinel missions; power network; forest fires; aerosol contamination Article
# Compact N-Band Tree-Shaped Multiplexer-Based Antenna Structures for 5G/IoT Mobile Devices
## 4 Methods and Structures
### Dual-Band Antenna Design  
Figure 4 identifies the main design parameters of the dual-band structure. Two monopoles with dimensions _LMp28_\(\times\)_WMp28_ and _LMp38_\(\times\)_WMp38_ were designed. A 50 \(\Omega\) impedance microstrip line, along with a quarter-wavelength transformer lead the input signal to the connection point referred above. Additionally, in order to maximize the structure's compactness, meander lines were used for the greater stubs.  
From the connection point up to the monopoles, there is a group of microstrip lines which guarantees the adequate matching (at the desired frequency) and the close to infinite impedance at the adjacent operating band.  
The acceptable matching is achieved by the colored identified lines highlighted in Figure 4a. These lines' dimensions and placement were implemented bearing the main concepts of microwave propagation. Starting with the open-ended stub named _Stub 1_, which has a 90\({}^{\circ}\) electrical length of 90\({}^{\circ}\) at 38 GHz, it turns infinite impedance into a short circuit. Naturally, _Line 2_, having the same electrical length at 38 GHz, converts the above-mentioned short circuit once again into a very high impedance at 38 GHz.  
Through this pair of lines, the first requirement at the connection point is guaranteed: an (close to) infinite impedance at 38 GHz. However, these lines surely also influence the impedance in this part of the antenna, at 28 GHz. Therefore, the third line, _Stub 3_, was placed to compensate for this undesired influence, ensuring the impedance matching at 28 GHz.  
Symmetrically, on the other side of the connection point, there is an open-ended stub, immediately followed by another stub, both with an electrical length of 90\({}^{\circ}\) at 28 GHz, enabling the open circuit in the connection point at 28 GHz. Subsequently, a third stub once again compensates the influence in the antenna matching observed at 38 GHz.  
Figure 3: Schemes Proposed for the (**a**) Dual-Band and (**b**) Tri-Band antenna.  
After bearing all theoretical propagation considerations, the dual-band antenna optimized design parameters are presented in Table 1 and the feeding network line width is constant throughout the antenna (0.2 mm), with the exception of the quarter-wavelength transformer (\(Lti\times Wti\)) and the 50 \(\Omega\) input line (\(Lin\times Win\)), connected to the single feeding point.
# IoT Privacy and Security Challenges for Smart Home Environments
## 7 Future Smart Home Security Challenges
### IoT Software and Firmware Updates
As opposed to the system level, the applications and network protocol components run at the component level, and the code at the component level is flexible, which means this code can be updated dynamically instead of replacing the entire firmware. The kernel level is the interface between the system and component levels. It binds dynamic components to one another and to system functions. The authors have demonstrated their approach with the popular open-source IoT operating system Contiki without requiring major modifications to the source code in existing network protocols and applications. Our proposed approach, similar to auto-configuration, relies on two key components. The first is implemented as a web-based service. Either the manufacturer or a trusted third party (as identified during the auto-configuration) maintains the latest versions of the software and firmware, which can be pushed to gateways identified during the auto-configuration process. The web-service can distinguish between vulnerabilities in the operating system or the specific device application code, and can download patches for either. The gateway manages the update process locally. The gateway can auto-schedule these updates at locally convenient times. The gateway can also manage the update of the rollback information if the update installation results in an unexpected loss of functionality when the gateway undertakes automated testing of the updated software. The gateway can also respond automatically to critical vulnerabilities, for example by blocking network access to an insecure device until a patch is available.
## 4 Experimental Setup
### Implementation of Our Method for Experiments  
For the experiments, we implemented the proposed method using the existing tools for Steps 2, 3, and 7.  
To implement Step 2 of our method, we employed an autoencoder network to extract domain features from preprocessed sentences as vectors. [PERSON] et al. [5] used a bidirectional gated recurrent unit (Bi-GRU) as the encoder and decoder unit to capture the forward and backward context information of each sentence. Bi-GRU consists of a forward GRU and a backward GRU. They also train the autoencoder based on 31,155 bug reports. The proposed method uses trained autoencoders based on the method in [5].  
To implement Step 3 of our method, we implemented the Sentence-to-Sentence Cohesion Assessment component and Topic Association Assessment component and applied the existing Sentence Believability Assessment component from [5].  
To implement Step 6 of our method, we utilized the beam search algorithm to select the optimal CS, a candidate summary. It is extremely inefficient to evaluate all possible CS. We can significantly reduce the time and effort of evaluating all possible CS using the beam search algorithm while still ensuring a good quality summary and while maintaining the comprehensiveness of the bug report.  
The beam search algorithm explores all candidate summaries by expanding the top-\(k\) promising choices, where \(k\) means beam size. Figure 4 shows how to generate a summary using beam search algorithms. For example, the bug report contains 4 sentences, and the dimension of the feature vector is 2. Each sentence vector can be denoted as \(S_{1}\) = [0, 1], \(S_{2}\) = [2, 0], \(S_{3}\) = [0, 2], and \(S_{4}\) = [3, 0]. Assuming that all of the sentence vectors have the same sentence assessment scores, which is 1, the full document vector is \(DF\) = [5, 3] based on Equation (3). When \(S_{1}\) is in the CS, the reconstructed full document vector is calculated as \(\widehat{DF}=[0,1]\) based on Equation (4). The reconstruction loss \(\delta\) between two vectors is also calculated as 14.5 based on Equations (5) and (6). If the beam size is 2, only two nodes that have the lowest value of \(\delta\) are expanded. Initially, the CS that contains \(S_{2}\) or \(S_{4}\) is selected. In the next step, similarly, the two candidate summaries, i.e., [\(S_{1}\), \(S_{4}\)] and [\(S_{3}\), \(S_{4}\)], are newly selected. The final CS with the lowest value of \(\delta\) is selected as a summary and denoted as _Selected_Summary_.
## 3 Experimental Setup and Results
### Measurement System
#### 3.1.2 Signal Conditioning Unit  
The output from the envelope detector is fed to the signal-conditioning unit where further amplification is performed via an amplifier, in addition to counting the PD events received. Within the signal-conditioning unit, the envelope-detected signal is integrated as well. The output from the signal conditioning unit is a collected PD activity in the form of metric. The threshold value is set to 3 V. When the output of the integrator reaches the threshold value; the integrator is set to zero. The function of the comparator is to activate the integrator, provided that the PD signal is of the sufficient value. This is vital to ensure that integrator's output voltage is not a result of envelope detected noise signal. Another function of the comparator is to count the number of PD events received. The integrator will stop integrating once the signal strength drops below the threshold. At this point, the output of the integrator is kept at a constant level. The signal then is passed to the microcontroller unit.
# Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning
## 3 Methodology
### Reference Model
#### 3.2.2 Layer 2: Conditional Random Field, CRF  
The second layer, the CRF, performs NER by learning from Mandarin sentence patterns. That is, the CRF examines whether or not the first output of Layer 1 meets the grammar and common usages. Once it realizes that the first output consists of inaccurate or rare entity tags, the model revises the prediction sets until the output is grammatically correct. In other words, the CRF clarifies the primitive output by the unit of the word and examines whether or not the prediction accords with general sentence structures. By screening out abnormal primitive outputs, Layer 2 decreases the prediction errors from the sole use of Layer 1 and results in enhanced NER performance. Figure 4 illustrated the integrated mechanism of the two layers.  
In summary, this module implements Bi-LSTM-CRF for general NER. The sophisticated processes include the following:  
1. Reading of input sentence,
2. Splitting of words into characters,
3. Layer 1: Bi-LSTM, results in a series of entity-tag predictions,
4. Layer 2: CRF, screens and revises the primitive outcome of Layer 1,
5. Comparison of the results of the previous process with tagged entities to minimize losses recursively.  
Figure 4: The integration of two layers.  
At this point, the named entities captured by the reference model are merely general entities. In an attempt to recognize the specific terms of disaster management, the following module, the augmented model, utilizes transfer learning to match the target entity tags from the source tags of the reference model.
## 4 AI-Based Approaches in Smart Buildings
#### 4.1.1 Artificial Neural Networks (ANNs)  
Smart building strategies aim to reduce energy usage and improve client satisfaction and comfort. They are based on the use of intelligent sensors and software to analyze both external and indoor elements to provide comfortable monitoring, as well as safety devices for energy usage management. Artificial neural networks (ANNs) can learn the most important information trends in a multidimensional environment. ANNs have been used in the application of solar energy to estimate building heating needs [54]. ANNs are also being used in ventilation, solar radiation, air-conditioning technologies, power-generation modeling and control, load forecasting, and refrigerators. The random forest model was used to estimate energy consumption in residential structures, and the Bayesian regularized neural network (BRNN) approach is used to predict several building energy demands from an environment input data set [55]. The use of the ANN approach makes it possible to monitor in real-time, for example, an artificial neural network (ANN) can be used to estimate and forecast the temperature of a specific area in the building [56]. Many different scenarios can be simulated using the energy simulation software, Energy Plus, creating an abundance of data that can be used to train an ANN model and calculate energy usage [57].  
Neural networks may not always produce the same results for the same input; neural network-based systems and solutions require extensive training [58]. The flow of input signal analysis to obtain energy estimation is contained in the signal. The outcome of the energy calculation from the input signal is widely used to obtain actions regarding hardware/software-based smart building functionalities. Mobile phones can also be used to acquire voice instructions to regulate the electrical appliances in intelligent buildings [59]. The user can use a mobile phone to enter voice commands, which are then shared with the building's energy management system via Bluetooth and Wi-Fi communication, and then analyzed to decode the necessary actions of electrical appliances.
# Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter
## 5 Discussion  
The estimation of the fault parameter is an attractive approach to fault detection and diagnosis (FDD), because the accurate information about the fault can be beneficial for the design of fault-tolerant control (FTC). This paper presented a parameter estimation based FDD strategy with the adaptive fuzzy fading Kalman filter (AFFKF). The proposed AFFKF can be seen as an extended version of the adaptive Kalman filter (AFFK). Using the fuzzy inference system the AFFKF is able to improve the transient phase of the filter. The effectiveness of the proposed method was evaluated through a set of numerical simulations.  
Conceptualization, D.K.; methodology, D.K.; software, D.K.; writing--original draft preparation, D.K.; writing--review and editing, D.L.; supervision, D.L.; project administration, D.L.; funding acquisition, D.L.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1C1B5076020 and NRF-2017R1A2B4003008).  
The authors declare no conflict of interest.
## 6 Detectivity
In addition, the different dynamic sensitivities lead to significantly different cutoff frequencies at which the initially frequency-independent detectivities increase or deteriorate. These +3 dB cutoff frequencies can be considered as the bandwidths of the sensor system and are given by  
\[f_{\text{cd}}^{\text{AM}}=\frac{f_{\text{res}}\sqrt{2}\,\sqrt{\sqrt{R_{0}^{2}+8 \,R_{0}R_{\text{m}}+10\,R_{\text{m}}^{2}}-R_{\text{m}}-R_{0}}}{4Q\,\sqrt{R_{ \text{m}}}} \tag{59}\]  
in AM mode and by  
\[f_{\text{cd}}^{\text{PM}}=\frac{f_{\text{res}}\sqrt{R_{0}+R_{\text{m}}}}{2Q\, \sqrt{R_{\text{m}}}} \tag{60}\]  
in PM mode, respectively. It can be shown that \(f_{\text{cd}}^{\text{AM}}\leq f_{\text{cd}}^{\text{PM}}\); thus, the bandwidth in terms of the limit of detection can never be higher in AM mode than in PM mode. With regard to the minimum achievable limit of detection inside the bandwidth, a distinction should be made between limitations by the different noise sources. If the detectivity is limited by the thermal electrical noise of the dielectric material, a low-frequency ratio of  
\[\lim_{f_{\text{x}}\to 0}\frac{\text{LOD}_{\text{AM0}}(f_{\text{x}})}{\text{LOD}_{ \text{PM0}}(f_{\text{x}})}=\frac{3\sqrt{3}\,(4\,Q-\sqrt{2})}{8\,Q} \tag{61}\]  
is obtained. For practical \(Q>100\) this relation approaches a value of \(3\sqrt{3}/2\approx 2.5981\). Similarly, if the detectivity is limited by the thermal mechanical noise of the resonator, the ratio is given by  
\[\lim_{f_{\text{x}}\to 0}\frac{\text{LOD}_{\text{AMm}}(f_{\text{x}})}{\text{LOD}_{ \text{PMm}}(f_{\text{x}})}=\frac{3\,\sqrt{8\,Q^{2}-4\sqrt{2}\,Q+1}}{4\,Q} \tag{62}\]  
which, also for quality factors \(Q>100\), approaches a value of \(3\sqrt{2}/2\approx 2.1213\). Thus, independent of the dominant type of noise, the detectivity in PM mode is always more than 6 dB better compared with an operation in AM mode.
## Appendix A  
### Coordinate Mapping  
During operation, the RGB-D camera (Intel RealSense D435i) captures RGB images and depth data of the scene. The YOLOv8-n-segmentation network processes the RGB images and outputs the bounding boxes, and segmentation masks of the detected objects, along with their class labels, confidence scores, surface area and grasping area. The depth data is then used to estimate the 3D coordinates of the object's centroid within the camera frame. A calibrated transformation matrix then converts these coordinates from the camera frame to the robotic arm's frame, ensuring precise grasping. Initially, chessboard calibration was used to obtain (X, Y, Z) position data, but it lacked rotation information, leading to grasping inaccuracies. To improve mapping, an ArUco-based calibration method was implemented, incorporating both position and orientation for better alignment between the camera and the AR4 robotic arm.  
The coordinate transformation follows a two-step process. First, the centroid of the detected bounding box is computed as:  
\[C_{x}=\frac{x_{min}+x_{max}}{2},\ C_{y}=\frac{y_{min}+y_{max}}{2}\]  
The corresponding depth value \(D\left(C_{x},\ C_{y}\right)\) is retrieved from the depth map. Using the camera'  
where \(\left(c_{x},\ c_{y}\right)\) is the principal point, \(\left(f_{x},\ f_{y}\right)\) are the focal lengths, and \(D\left(C_{x},\ C_{y}\right)\) is the depth at the centroid.  
The second step involves transforming the extracted camera frame coordinates into the robot's coordinate system using the extrinsic transformation matrix \(T_{camera\to Robot}\) obtained through ArUco-based calibration:  
\[\begin{bmatrix}X_{r}\\ Y_{r}\\ Z_{r}\\ 1\end{bmatrix}=T_{camera\to Robot}\begin{bmatrix}X_{c}\\ Y_{c}\\ Z_{c}\\ 1\end{bmatrix}\]  
where \(\left(X_{r},\ Y_{r},Z_{r}\right)\) represents the final object position in the robotic coordinate frame. This transformation ensures accurate grasp execution by compensating for the camera's position and orientation relative to the robot. ArUco-based calibration significantly improved the accuracy by including rotation corrections, making object localization more reliable compared to previous methods.
# From Discourse Relations to Network Edges: A Network Theory Approach to Discourse Analysis
## 3 The Datasets
### The STAC Corpus  
There are several discourse-annotated corpora with different theoretical outset but similar concerns and targets, including the Penn Discourse Treebank (PDTB; [42]), the RST Discourse Treebank (RST- DT; [43]), DISCOR [44], ANNODIS [45] and, more recently, the STAC corpus ([27]). The largest of them is RST-DT, which is compiled and annotated based on RST's annotation principles. However, the STAC corpus ([29]) is the first corpus that provides discourse structures for multiparty dialogues situated in a virtual environment, which is the reason why we chose to use it for the comparison of different discourse types.  
The corpus was annotated in two stages leading to two subcorpora: the first subcorpus includes the annotated chat moves of the players during a game session, and the second extends the annotated game sessions of the first subcorpus by adding the annotation of nonlinguistic events that were automatically generated by the same game session. The two subcorpora are offered for direct comparison between situated and nonsituated discourse analysis, as well as for exploring the interaction between the linguistic and the  
Figure 4: The discourse graph of (4).  
nonlinguistic events in the situated subcorpus. In this paper, we follow the convention of [29], and we refer to the annotations of the first subcorpus as the chat-only annotations and the annotations of the second subcorpus as the situated annotations.  
Each game is divided into dialogues, or subsections, of the game that encompass one or more bargaining sessions. As mentioned above, EEUs lack propositional content and, thus, it is already challenging for the interpreters that participate in the dialogue to individuate and conceptualize them, rendering it almost impossible for the annotators to construct and include them in the situated corpus. However, the messages that are automatically generated by the game software and shared between the players in a common virtual environment set the stage for including nonlinguistic events that occur in a _controlled_ and common virtual environment.  
As expected, the subcorpus that includes the situated annotations consists of many more EEUs (31,811) than the chat-only annotated subcorpus (12,588). (All the STAC-annotated data are stored in tabular format--converted as pickle files--and are available upon request from the corresponding author.)