esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design Concepts
### Design Considerations for Antenna Components
#### 2.1.2 Primary Radiator  
To achieve a wide impedance bandwidth, a hybrid design with multiple radiators was developed. To simplify the design, the feed structure was configured to serve as the extra radiator. Together with the primary radiator, they produce multiple adjacent resonances, which can be merged to form a wide bandwidth. The overall impedance bandwidth due to the resonance merging depends on the bandwidth of each individual radiator. Therefore, individual radiators having a wide bandwidth is advantageous to the overall bandwidth enhancement. To keep the design process simple, this work mainly focused on deriving a wideband primary radiator.  
For a compact antenna, the primary radiator needs to be compact while simultaneously providing sufficiently long current paths on the feedlines, thereby permitting them to be efficient radiators. This can be achieved using a 3D radiator instead of a planar one. Due to its folded sides, a 3D radiator has a smaller footprint but supports longer current paths. Since the primary radiator is backed by a ground cavity with sidewalls, the use of a 3D radiator does not necessarily lead to a bigger overall antenna profile (i.e., it does not increase the overall height of the antenna). To support wideband or multiband operation, multiple current paths of different lengths should be allowed on the primary radiator [25]. This requires the 3D radiator to be sufficiently large along the \(Z\)-axis. Combining these design constraints, we proposed a hollow metallic open prism with no bottom face, as shown in Figure 2, to be the primary radiator. Dual-polarization was achieved by orthogonally exciting the open prism along the X- and Y-axes, i.e., the two orthogonal polarizations of interest. To ensure identical performance in the two polarizations and suppress cross-polarization, the open prism has an equal length along the X and Y-axes.
## 3 Methods
### Cognitive Model  
In ACT-R, declarative knowledge is represented in the form of chunks, i.e., representations of individual properties, each of which can be accessed via a labeled slot. The cognitive model programmed in LISP for our test scenarios should receive chunks with keywords from the robot application and checking whether chunks for these keywords are already stored in the declarative memory of the model in a suitable combination. We assumed the transfer of three keywords. The programmed productions of the procedural memory checked all combinations of the sequence of keywords for a match with memory content and generated a hit for two out of three or for all three matching keywords. In this case, the associated memory content was called up and the content of a special slot of the memory content chunk from this recollection was returned to the robot application.  
Our LISP code defined a chunk type for memory content in declarative memory as follows: chunk-type keyword, asso-one, asso-two, asso-three, phrase.  
Figure 2: [PERSON] showing a picture of what it sees in dialog with a human.  
This chunk type 'keyword' featured three labeled slots 'asso-one', 'asso-two', and 'asso-three' as well as a 'phrase' slot, which stored the memory content we wanted to retrieve. For example, this phrase could be a remembered fact used to constrain a system prompt for the LLM that the robot used to talk to humans. Each chunk stored in the declarative memory also has an arbitrary name. Figure 3 shows the process of searching for a matching memory chunk. The cognitive model searches the declarative memory for suitable keywords from the chunks stored there. First, it checks whether there are chunks in which all three keywords match; otherwise, it checks whether two keywords match. The order of the keywords as created by the LLM does not have to match the keyword order in the chunks in the memory. Having found a corresponding memory chunk, the cognitive model returned the sentence stored in the 'phrase' slot of this chunk as a fact phrase.  
Workflow for Retrieving Memory Facts Based on Keywords from the LLM  
* The LLM creates chunks with three keywords related to the task at hand in response to what a person has said in the interaction.
* These keyword chunks are transferred to the ACT-R model.
* The cognitive model searches for associated chunks in the declarative memory.
* Having found a corresponding memory chunk, the cognitive model returns the content of the 'phrase' slot of this chunk as a fact phrase.
* This fact phrase is transferred back to the robot application and used for the prompt augmentation of the LLM.
* The LLM provides an answer that includes the remembered fact.  
The following is an example of the possible content contained in such a memory chunk for a train station scenario assumed in one of our tests as follows: asso-one platform-1, asso-two location, asso-three direction-assistance, and phrase Platform 1 is the neighboring platform right here.
# Non-Contact Heart Rate Detection Based on Hand Vein Transillumination Imaging
## 3 Results and Discussion  
In this study, we obtained heart rate information through hand vein transmission images, introduced living body information to vein authentication, and further improved the anti-counterfeiting effect of the vein authentication system. If the purpose is only to determine whether the body is alive, then it is only necessary to confirm whether the heart rate can be detected; however, we plan to further use heart rate information to assess the emotional state of the subject. If the heart rate value is not within the normal range, the individual may be under duress or experiencing extreme anger, in which case, the user is be unable to perform normal authentication, which can further ensure the security of the system. Based on the above considerations, we also have certain requirements for the accuracy of the heart rate obtained through the vein transmission image described in this article. Therefore, in this section, we further analyze the anti-interference ability and accuracy of the proposed heart rate detection method. Accuracy is the closeness of the measurement result of the heart rate detection algorithm to the heart rate reference value. Robustness refers to the stability of the heart rate detection performance under different interference conditions, that is, the anti-interference ability.  
First, we analyzed the results theoretically. In the image acquisition process, we used a near-infrared light source for active illumination and collected hand vein images through transmission, so a high signal-to-noise ratio and information-rich transmission image can be obtained. Before obtaining the one-dimensional pulse wave signal, we used spatial superposition averaging. When obtaining the single-channel image, the three-channelimage superimposition average was used; when the one-dimensional pulse wave signal was obtained, all of the pixels in the image frame were used. Spatial superposition and averaging were performed, thereby largely suppressing the interference of random noise. In pulse wave preprocessing, the morphological filtering method was used to reduce baseline drift. In summary, the aim is to obtain pulse wave signals with a high signal-to-noise ratio and high stability from multiple perspectives of image acquisition and processing.  
Secondly, we verified the anti-interference ability and accuracy of the heart rate monitoring method through further data analysis. The results are analyzed and explained from two perspectives: qualitative and quantitative indicators.
## 3 Mapping
### Pose Optimization  
Calculation errors and measurement errors from depth sensors can lead to the gradual accumulation of errors as the user moves, eventually resulting in environment modeling errors. Thus, we adopted loop closure [35] to enhance system stability by adjusting the camera postures and optimizing the movement trajectories. Suppose that there are \(n\) 3D points and \(m\) images in a given space. Let the intrinsic and extrinsic camera parameters of image \(j\) be vector \(\mathbf{a}_{j}\), and 3D point \(i\) be vector \(\mathbf{b}_{i}\); the point \(i\) seen in image \(j\) is \(\mathbf{x}_{ij}\), \(Q(\mathbf{a}_{j}\), \(\mathbf{b}_{i})\) denotes the projection point of object point \(\mathbf{b}_{i}\) under camera \(\mathbf{a}_{j}\). Thus, reprojection error \(E\) can be defined as follows:  
\[\mathrm{E}=\sum_{i=1}^{n}\sum_{j=1}^{m}\left|Q(\mathbf{a}_{j}\,\mathbf{b}_{i})- \mathbf{x}_{ij}\right|^{2} \tag{6}\]  
We then solved the optimal intrinsic and extrinsic camera parameters \(\mathbf{a}_{j}\) and the 3D feature coordinate \(\mathbf{b}_{i}\) and minimized \(E\) in posture optimization as a non-linear least squares problem.  
We developed a scheme to enable loop closure in a large-scale environment, while taking into account continuity in the subsequent segmentation of objects. We first established two chunks of data in which were stored the optimization parameters required for local loop closure and global loop closure.  
The chunk for local loop closure stores \(n\) pieces of image data. The first-in-first-out strategy was used to obtain the new frame to ensure that the oldest frame is extracted. The information obtained from the chunk was used in subsequent processing, such as image segmentation, and was not altered after extraction. This process is illustrated in Figure 3.  
Figure 3: Flow chart showing the process of local loop closure.  
The global loop closure chunk stores information from the first few images for use in optimizing the entire environment, i.e., the final step in environment modeling.
## 3 Material and Methods
### Simplified VGG-16 Convolutional Neural Networks  
Convolutional neural networks (CNNs) are a class of deep neural networks that have been widely applied in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain-computer interfaces, and financial time series. A CNN consists of an input layer, hidden layers, and an output layer. In any feed-forward neural network, middle layers are called \"hidden\" because their inputs and outputs are masked by the activation function and by the final convolution. In a CNN, these hidden layers include layers that perform convolutions. Typically, this includes a layer that does multiplication or finds a dot product, and its activation function is commonly referred to as a rectified linear unit (ReLU). This layer is followed by others, pooling layers, fully connected layers, and normalization layers.  
VGG is a popular CNN model containing 13 convolutional layers and three fully connected layers, commonly applied to image classification and pattern recognition. However, it is not directly suitable for building our surrogate model. In this paper, we instead use a simplified VGG model for this task. As the traditional VGG-16 model has a 2D layer structure consisting of many convolution layers, max-pooling layers, and fully connected layers, it is difficult to directly apply it to our steelmaking process-optimization problem with its 1D mapping. Therefore, we modified this VGG-16 model into a 1D structure with 10 layers (seven for 1D convolutional layers and three for fully connected layers). The seven convolutional layers generate powerful features, and three fully connected layers use the extracted features for regression.  
More precisely, the differences between the traditional VGG-16 model and our simplified VGG model are shown in Figure 2. The simplified VGG model deletes the final soft-max procedure and maps the process parameters to a single product attribute. It is a one-dimensional CNN model composed of seven convolutional layers and three fully connected layers. The convolutional layers extract the powerful features, and the fully connected layers match the product attributes used when establishing the surrogate model. Our trained simplified VGG first uses three blocks in which two or three convolutional procedures are followed by max-pooling to obtain feature maps with sizes of 3 \(\times\) 32; they are then straightened into one-dimensional form. The sequential fully connected layers are applied in order to regress the product attribute of \(Y_{1}\), \(Y_{2}\), or \(Y_{3}\).  
In this method, the [PERSON] algorithm [42] is used as an optimizer with a loss function equal to the mean square error (MSE) of the true outputs \(\varphi(x_{l})\) of the surrogate model and the desired outputs \(y_{l}\), as defined in Equation (21):  
\[LossFunction=\frac{1}{M}\sum_{l=1}^{M}(y_{l}-\varphi(x_{l}))^{2} \tag{21}\]
## 4 Results and Discussion  
Figure 7 shows the average SNR plots for MRD for three factors and five levels. It can be seen from Figure 7 that the maximum removal depth of blade material was obtained when the speed of the sand belt was 550 m/min (level 5), the grinding force was 40 N (level 5) and the mesh number of the abrasive belt was 120 (level 1). As shown, the material removal ratio increased with the speed and grinding force of the belt, but decreased with the mesh number. In particular, when the belt speed rose, the area of contact between the abrasive belt and the workpiece expanded, increasing the amount of grinding [30]. As the grinding force increased, the friction between the contact wheel and the grinding area of the workpiece also increased. As a result, the workpiece and abrasive particles on the abrasive belt ground more vigorously, increasing MRD. In addition, the higher the mesh number, the smaller the abrasive particles in the sand belt and the smaller the grinding scratches caused to the workpiece during grinding, so the amount of grinding decreased as the mesh number of the sand belt increased.
# Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study
## 3 Overview of the Encryption Algorithms under Study
### Genetic Algorithm (GA)  
The genetic algorithm encryption follows natural reproduction of genetics of humans or animals. In this, images are encrypted in three levels: first stage--reproduction, second stage--crossover, and third stage--mutation.  
Reproduction: The production of new solution/encrypted image is obtained by fusion of two images (one original and another reference image); sometimes, it is called offspring and the algorithm security depends on the best selection of offspring/reference image. [22].  
Crossover: The images to be encrypted are represented with eight bits per pixel; these eight bits of each pixel are equally portioned. For crossover operation, select any two pixels and its equivalent binary bits; now, swap the first four bits of one pixel with the last four bits of another pixel. The swapped pixel and its equivalent intensity generate a new value. This process is repeated for all the pixels in an image to get an encrypted image.  
Mutation: This stage is not mandatory in general, but, for some reason, we perform this stage. It is simply a complement/invert (making 1 to 0 or 0 to 1) of any bit of any pixel, and its key depends on the position of the selected bit for complement.
# An Integrity Monitoring Method for Navigation Satellites Based on Multi-Source Observation Links
## 3 Design of Integrity Monitoring System with Multi-Source Observation Links
### System Composition  
This study proposes an integrity monitoring system with the support of multi-source observation links, which consist of satellite-to-ground, inter-satellite, and satellite-based observation links. As shown in Figure 2, the system can make comprehensive use of satellite-to-ground, inter-satellite, and satellite-based observation data, basic navigation messages, satellite-based augmentation messages, autonomous navigation messages, and other integrity-related monitoring information.  
Satellite-to-ground observation links are mainly used to gain observation data from satellites and broadcast basic and satellite-based augmentation messages to satellites. The corresponding stations consist of the main control station, upload stations, and monitoring stations. All observation data and returned messages should be collected and gathered in the main control station. Then, basic and augmentation integrity parameters can be generated and broadcast through satellite-to-ground and inter-satellite links.  
Inter-satellite observation links are mainly used to gain pseudo-ranging data and autonomous navigation messages. The pseudo-ranging data can not only be integrated with other observations to support the calculation of integrity parameters but also be used to estimate the state of chained signals. Results of autonomous orbit determination and clock accuracy can be used to estimate the operating state of the satellite.  
Satellite-based monitoring links are mainly used to gain observation data and basic integrity monitoring messages of navigation satellites to the main control station.
## 4 Feature Description
### Components and Interactions of [PERSON] et al. [28] Architecture  
In this subsection, components and interactions are summarized from [28].  
1. **ECU:** The ECU receives sensor data from various sensors (temperature, sound, pulse, gyroscope, accelerometer) placed within the car. Then, it compares the received sensor values with predefined thresholds. If any of the sensor values cross their respective thresholds, the ECU triggers an alert.
2. **Head unit:** The head unit receives alerts from the ECU when sensor values cross the thresholds. If a sensor reading is unusual, a message appears on the car's screen, asking if the occupants are okay.
3. **User interaction:** The user responds to the alert (either confirming they are okay or not responding).
4. **Crash detection:** If the user does not respond to the alert, the ECU interprets it as a crash. The ECU then activates the camera and GPS unit.
5. **Camera and GPS:** The camera captures visual data of the surroundings, while the GPS unit accurately determines the precise location of the vehicle.
6. **Telematics control unit (TCU):** The ECU sends the collected sensor data, images, and GPS location to the TCU. The TCU then aggregates and packages these data.
7. **Control room:** The TCU sends the combined data to the control room. Experts in the control room process and analyze the data.
8. **Emergency services dispatch:** Based on the analysis, appropriate emergency services (paramedics, law enforcement, fire personnel) are informed and dispatched to the accident scene.
9. **Cloud platform:** The crash data are securely saved in a cloud platform. This repository of crash data will be invaluable in refining the system, ensuring more effective responses in future accidents.  
In summary, as shown in Figure 2, the architecture involves sensors within the car, an ECU for processing sensor data and generating alerts, a head unit for user interaction, a TCU for collecting and transmitting data, a control room for analysis and emergency service dispatch, and a cloud platform for data storage. This system aims to enhance safety by detecting and responding to potential accidents while also collecting data for future improvements.
## 3 Attacks in Blockchain
### Detecting and Mitigating Malicious Blockchain Attacks
#### 3.3.6 Eclipse Attack  
Eclipse attacks have been addressed in various studies, with several detection approaches proposed. One method involves analyzing the timestamps of questionable blocks. If the time interval between newly created blocks is abnormally high, it may indicate an eclipse attack, with detection typically requiring 2-3 h [66]. Another technique is the ubiquitous gossip protocol, which allows users to connect with protocol-dominated servers through gossip messages without requiring changes to the Bitcoin protocol or the peer-to-peer network. This approach minimizes dependency on web servers for connecting withBitcoin block headers [98]. Additionally, redundancy can be introduced by replicating keys across multiple nodes and conducting routing failure tests to identify compromised nodes.  
To mitigate eclipse attacks, several countermeasures are available. Elimination of Far Successors is a technique where nodes autonomously calculate the distances between their successors, removing any malicious entries immediately [99]. Bitcoin clients also use a classification system to segregate new peers and previously established connections, preventing attackers from exploiting these distinctions [43]. Expanding the network size further complicates an attacker's ability to control a significant portion of the network. Peer-discovery mechanisms allow nodes to connect with a diverse set of peers, thereby reducing the likelihood of isolation and increasing resilience against eclipse attacks.
# Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation
## 3 MKID Applications in Observational Astronomy
### Observational Astronomy: Infrared to Microwaves  
Especially for low energy, high wavelengths photons, MKIDs are a very capable detector. Their small superconductor band gap allows them to be sensitive down to very small photon energies, they are simple to fabricate compared to alternatives like semiconducting bolometers or transition edge sensors and their unrivaled multiplexability facilitates large detector arrays as required by many modern scientific instruments.  
In this lower energy part of the electromagnetic spectrum single photons striking an MKID do not cause a detectable signal on their own. Instead, to generate a signal a sufficiently high photon flux is required to constantly break enough Cooper pairs. A steady flux results in a constant quasi-particle generation rate which will reach an equilibrium with QP recombination resulting in a constant QP density, depending on the number of photons per second and their energies. In this regime a single MKID pixel cannot distinguish between photon flux and wavelength and filters are often used in front of the detector to break this degeneracy. However, it is still possible to achieve energy resolution with MKIDs even for these low energies by using more than one detector in an on-chip spectrometer.
## 2 System Description
### Experimental Procedure for the System Definition  
In Section 2, it was presented how the system that will be implemented in Matlab/Simulink was dimensioned. At the beginning, it was justified that the inverter and the solar PV panels, were chosen to enable an office building with a medium occupancy of four people and three phase power, to produce and consume renewable energy equivalent to more than 50% of the building's energy consumption without batteries. It was also presented the diagrams that describing the variation of the parameters, that will impact the performance of the solar PV system, namely the solar irradiation and the ambient temperature values, that will be used in the dynamic simulation. Using the three phase inverter as a basis, the parameters of the inductors, the capacitors, the IGBT and the schottky diodes were determined, considering a target value of 0.40 for the coefficient \(r\), a voltage ripple  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(V_{in}\) & 160 V \\ \(V_{out}\) & 800 V \\ \(V_{SW}\) & 1.90 V \\ \(V_{D}\) & 1.96 V \\ \(D\) & 0.80 \\ \(f\) & 20 kHz \\ \(I_{out}\) & 3.94 A \\ \(r\) & 0.400 \\ \(\Delta V\) & 5 V \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial boost converter parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Calculated Value** & **Chosen Value** \\ \hline \(L\) & 0.81 mH & 0.85 mH \\ \(r\) & & 0.382 \\ \(C_{in}\) & 9.85 μF & 28 μF \\ \(C_{out}\) & 7.88 μF & 110 μF \\ \hline \hline \end{tabular}
\end{table}
Table 3: Obtained values after the calculations.  
of 5 V and a switching frequency of 20 kHz. The experimental procedure is resumed in Figure 8.  
As it can be seen in the figure above, it was already presented a detailed study of steps 2, 3 and 4. In the next section, the MPPT control algorithms that were chosen for this study, will be presented as well as the system's expected behaviour for both algorithms and the last steps will be discussed and thoroughly analysed in Section 4.