esa-sceva/satcom-chunk-collection huggingface dataset:
# Service for Deploying Digital Twins of QKD Networks
## 4 Implementation
### QKD Module Software
The other component of the Key Synchronization layer is the Key Synchronization asynchronous interface which, as well as in the case of the Key Manager layer, enables the horizontal communication between Key Synchronization layers of different sites, a crucial feature in order to settle cryptographic quantum keys between two sites of a network. Underneath the Key Synchronization layer, the Quantum Backend layer is formed by two other components. The first one, the QKD dispatcher, is responsible for choosing the thread in which the QKD protocol for a specific key will run. This means that the QKD module can simultaneously create several quantum keys, depending on the characteristics of the machine on which the module is located. Below the QKD dispatcher is an element called SimulaQron mux-demux that adds a layer between the quantum emulator and the other components to perform a little information processing and send it properly to the quantum emulator. It also helps to manage the various threads produced by the QKD dispatcher. Figure 5: Overview of the software architecture of a quantum node. Layers can be identified by sections and colours. Lastly, the emulated quantum component layer has as many components as there are QKD links on the site. For each QKD link, we use the quantum emulator SimulaQron to create the required keys following a QKD protocol. In the first version of the service, a variant of the E91 protocol is implemented to form the quantum keys. It is worth noting that SimulaQron allows emulating quantum and classical channels, necessary to complete QKD protocols between different machines, as well as performing local operations on qubits and managing the entanglement of qubits in different machines. This allows the creation of distributed QKD modules, making the digital twin environment much more similar to a real QKD network. Two other components, which do not correspond to a specific layer, are also implemented. The first one is an Internal Database, which stores information related to the key streams and their buffered keys, such as the destination and the source of the streams, the KSID, or the sizes of the keys. It also notifies other components when a free space is left in the buffer. Both the Key Manager layer and the Key Synchronization layer have access to this database to correctly perform their corresponding tasks. The last component implemented in the QKD module is a SimulaQron configurator, which uses the SimulaQron API to transparently create a QKD network from the provisioning file and start tracking its execution. All these components that form the QKD modules have been implemented in a single Python package that can be freely installed. In principle, the user of the service does not need to worry about the functioning of this package, since to write his or her applications and perform experiments over the QKD network digital twin environment, a QKD network client package has been developed specifically for this purpose, with the aim of making the service as accessible as possible.
## 3 Implementation
### Hardware  
Figure 4 illustrates a schematic diagram of the real-time communication protocol prototype, which consists of two embedded system boards using Arduino with SIM900 shields. These boards maintain voice calls for transmitting the DTCs.  
Error codes are collected using a commercial OBDII scanner (see Figure 4), which is physically connected to the vehicle. In most vehicles, the connector is typically located near the fuse box.
## 3 Azimuth Deramping Pre-Processing for GTLR-BiSAR
\begin{split} S_{dara}(f_{\tau},t_{a}^{\prime})&=S_ {echo}(f_{\tau},t_{a}^{\prime})\ \otimes_{Azi}\ S_{ref}(t_{a}^{\prime})\\ &=exp(\xi j\pi\frac{V_{eff}^{2}}{\lambda R_{ref}}t_{a}^{\prime 2}) \int_{t_{a}}S_{echo}(f_{\tau},t_{a})exp(\xi j\pi\frac{V_{eff}^{2}}{\lambda R_{ ref}}t_{a}^{2})exp(-\xi j2\pi\frac{V_{eff}^{2}t_{a}^{\prime}}{\lambda R_{ref}}t_{a} )\ dt_{a}\end{split} \] (25) where \(\otimes_{Azi}\) denotes the azimuth convolution, and \(t_{a}^{\prime}\) is the new azimuth time corresponding to the new azimuth frequency \(f_{a}^{\prime}\). It should be noted that \(\xi\) is a switch factor with the values of \(\pm 1\). When \(\xi=1/-1\), the convolution operation is utilized to remove the Doppler aliasing of SS/TOPS mode. The convolution operation can be divided into two parts: the integration part and the phase compensation part. After the removal of azimuth aliasing, the azimuth time and frequency are updated so that we can process the echo data in the azimuth frequency domain efficiently. The detailed explanations of these new parameters and the convolution operation steps can be referred in [21], so we will not go into much detail in our paper. It should be noted that we still utilize \(t_{a}\) and \(f_{a}\) to express the updated azimuth time and frequency in order to be consistent with the previous and subsequent derivations in Sections 2, 4 and 5. We focus on the correction of spatial variance for GTLR-BiSAR system in the next section.
## 2 Experimental Methods
### Evaluation Databases
#### 2.2.2 AUDAX Private Database  
A private database was constructed during an industrial project between our research group and the Slovenian company AUDAX ([[https://audaxceph.com](https://audaxceph.com)]([https://audaxceph.com](https://audaxceph.com)) (accessed on 13 April 2022)), which is specialized for the development of orthodontic software. This database consists of 4695 unique skull X-ray images. We assumed that each radiograph belongs to a different subject. Information about the image spatial resolution and about the subject in the image (e.g., gender, age, health status) was not provided by AUDAX. The size of images ranged from \(355\times 480\) pixels (min size) to \(4417\times 5963\) pixels (max size). There are 287 unique image sizes in this database. On this basis, we concluded that the images were captured with just as many different X-ray devices. The five most common sizes of radiographs were as follows: \(2808\times 2148\) pixels (1598 images), \(1000\times 900\) pixels (419), \(2685\times 2232\) pixels (310), \(1804\times 2148\) pixels (309), and \(1000\times 765\) pixels (222). An average image size was \(1740\times 2012\) pixels.  
Seventy-two cephalometric landmarks were annotated on each image by a single experienced orthodontist. A list of all annotated landmarks is gathered in Table 2. Most landmarks are anatomic landmarks, while the rest were constructed relative to anatomic landmarks, or were defined as intersections of particular lines and/or planes, where lines/planes were defined by specific anatomic landmarks or skull structures. An example of a constructed landmark is RT-abo, which is lying on a silhouette, halfway between the landmarks articulare (Ar) and gonion (Go). Based on their expertise, AUDAX classified landmarks into five classes with respect to their importance in cephalometric analyses. The 38 most important landmarks (class 5) are highlighted in Table 2. On the other hand, AUDAX also classified the landmarks into five classes with respect to the difficulty of their determination. The six most difficult to determine landmarks (class 5) are underlined in Table 2. All 72 denoted landmarks were used as the ground truth in our research. Figure 1b depicts a sample image from this private database, with 72 annotated cephalometric landmarks.  
The K-fold validation technique was employed by utilizing data from this database to verify the detection methods. The K parameter was set to 3, thus dividing the private database randomly into 3 folds of the same size (i.e., each fold consists of 1565 unique images).  
Figure 1: Sample annotated cephalograms from (**a**) the ISBI public database [5], with 19 landmarks, and (**b**) the AUDAX private database, with 72 landmarks (white circles).
# Electromagnetic Power Harvester Using Wide-Angle and Polarization-Insensitive Metasurfaces
## 2 Design of the MS Energy Harvester
As the angle of incidence waves increases, the working frequency of the maximum efficiency obtained by the MS will slight shift. The MS have a certain bandwidth. No matter TE or TM oblique incidence from \(0^{\circ}\) to \(60^{\circ}\), the energy harvesting efficiency using the square-ring MS collector is always stable and the collection efficiency can reach 90%. Though it has a certain frequency-shift in the incidence of the large angles, it can be adjusted through backend self-adaptive tuning circuits. The surface current analysis can be used to gain insight into the polarization and the angle stability of the MS. The resonant behavior can be described by using the equivalent inductance (L) and the equivalent capacitance (C) of the MS structure. The small square-ring of each unit and gap between units can be equivalent to LC resonance circuit. The surface currents induced on the front metallic layer operating at work frequency of 2.5 GHz are oscillated along the square ring of each element and transferred to the load through vias. The surface currents in TE and TM polarization of the incident wave at the resonant frequency (2.5 GHz) and non-resonant frequency (2.2 GHz) for the incidence angle \(0^{\circ}\) are shown in Figure 7, respectively. It can be seen that the maximum intensity of the surface induced current to the load is generated on the metallic ring at operating frequency of 2.5 GHz, and the maximum energy harvesting efficiency can be obtained. When offsetting the operating frequency, the intensity of the surface induced current on the metallic ring is reduced, which causes the load absorption decreased. Figure 8 shows the surface current distribution at the working frequency for different incidence angles in TE polarization. Maximum energy harvesting efficiency with \(\theta=30^{\circ}\) and \(60^{\circ}\) of incident wave at 2.56 GHz and 2.67 GHz, respectively. Because the square-ring MS is centrosymmetric, no matter whether the incident wave is TE or  
Figure 5: Surface current distribution on the (**a**) square ring and (**b**) back ground of the MS structure at the resonant frequency (2.5 GHz). TM polarization with large angle range, it can excite surface currents along the metal ring and translate into load by vias for efficient energy harvesting. Figure 6: Power harvesting efficiency of **(a)** transverse electric (TE)-polarized oblique incidence and (b) transverse magnetic (TM)-polarized oblique incidence. Figure 7: Simulated surface current distribution on the square-ring at **(a)** the resonant frequency of 2.5 GHz with TE polarization; **(b)** the non-resonant frequency of 2.2 GHz with TE polarization; **(c)** 2.5 GHz with TM polarization; and **(d)** 2.2 GHz with TM polarization.
## 7 MATLAB Simulations
### Handling Circular Objects  
We first consider a case where multiple objects exists, such that each object has a circular shape. Figure 6 shows the map of \(\alpha\) floor with a static circular object. MapPoints are plotted with yellow points. The boundary of each static object is depicted with black circles. The robot plans the path from \((200,150)\) to the elevator location \((370,120)\). Considering one MC simulation, the robot's location at every \(dt\) seconds is depicted as green diamonds.  
At \(\beta\) floor, we have multiple objects, among which four objects move with velocity \((-0.1,0.7)\) in m/s. Consider the map of \(\beta\) floor with moving circular objects. Considering one MC simulation, Figure 8 indicates the robot's location (green diamonds) at every \(dt\) seconds. The boundary of an object at every \(dt\) seconds is depicted with black circles. The robot plans the path from the elevator position \((370,120)\) to the goal \((350,470)\). Figure 9 is the enlarged figure of Figure 8.  
Considering the scenario in Figure 8, Figure 10a shows the relative distance between the robot and its closest _detectedPoint_ at each sample-step. Since the robot's maximum sensing range is 20 m, the relative distance is bounded above by 20 m. A collision occurs at the moment when the distance between the robot and any _detectedPoint_ is shorter than 1 m. See that no collision happens during the robot maneuver. Recall that \(C(k)\) defines the point on the path \(P_{V}\), which is closest to the robot at sample-step \(k\). Figure 10b indicatesthe relative distance between the robot and \(C(k)\) at each sample-step \(k\). See that the robot keeps following the path \(P_{V}\).  
Figure 7 is the enlarged figure of Figure 6. See that the robot's path is slightly deviated from Voronoi edges (blue line segments), in order to keep away from colliding with an object.  
Figure 6: This figure shows the map of \(\alpha\) floor with static circular objects. The boundary of each static object is depicted with black circles. The robot plans the path from \((200,150)\) to the elevator location \((370,120)\). Considering one MC simulation, the position of the robot at every \(dt\) seconds is depicted as green diamonds. Two red asterisks indicate the robot’s start and end positions, respectively.  
Figure 7: The enlarged figure of Figure 6. We consider the case where multiple objects exists, and each object has a circular shape. The robot’s path is slightly deviated from Voronoi edges (blue line segments), in order to keep away from colliding with an object.
# Delayed Combination of Feature Embedding in Bidirectional LSTM CRF for NER
###### Abstract  
Named Entity Recognition (NER) plays a vital role in natural language processing (NLP). Currently, deep neural network models have achieved significant success in NER. Recent advances in NER systems have introduced various feature selections to identify appropriate representations and handle Out-Of-the-Vocabulary (OOV) words. After selecting the features, they are all concatenated at the embedding layer before being fed into a model to label the input sequences. However, when concatenating the features, information collisions may occur and this would cause the limitation or degradation of the performance. To overcome the information collisions, some works tried to directly connect some features to latter layers, which we call the delayed combination and show its effectiveness by comparing it to the early combination. As feature encodings for input, we selected the character-level Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM) word encoding, the pre-trained word embedding, and the contextual word embedding and additionally designed CNN-based sentence encoding using a dictionary. These feature encodings are combined at early or delayed position of the bidirectional LSTM Conditional Random Field (CRF) model according to each feature's characteristics. We evaluated the performance of this model on the CoNLL 2003 and OntoNotes 5.0 datasets using the F1 score and compared the delayed combination model with our own implementation of the early combination as well as the previous works. This comparison convinces us that our delayed combination is more effective than the early one and also highly competitive.  
delayed combination; CNN dictionary; named entity recognition; deep learning NER; bidirectional LSTM CRF; CoNLL; OntoNotes +
Footnote †: journal: Applied Sciences
# Development of an Autonomous Underwater Helicopter with High Maneuverability
## 5 Experiments and Results
The step response is shown in Figure 13, and the average rotation speed is about 20 deg/s, almost four times as much as BOOMERANG. According to the observation, AUH had no obvious displacement when rotating. Therefore, we recognized that it does have the maneuverability of zero radius rotation. Figure 12: The pitch angle of horizontal motion experiments when F is 3.2 N with vertical propellers. Figure 13: The step response of the yaw angle in the rotation experiment. To demonstrate maneuverability, a quadrangle motion experiment in the horizontal plane was carried out. In the experiment, the AUH was controlled by the program along a rectangular path. As shown in Figure 14, the moving target of rectangular trajectory had been basically completed in the order of (a), (b), (c), (d). The AUH could cruise along a particular curve in a small area. Therefore, the maneuverability of the plane is confirmed. To further demonstrate its maneuverability, an attitude sensor was used to record the attitude angle and the triaxial acceleration of the AUH, so as to estimate and evaluate the motion under the preset path experiment. The recorded attitude angle data could be obtained as shown in Figure 15a after low-pass filtering. The roll and pitch angle were relatively stable. The roll angle was small, generally below 3 degree, with a maximum of 5 degrees. The pitch angle was also controlled at around 10 degree. The trend of the yaw angle was consistent with the preset path, that is, 90 degrees per 60 s. Combined with the recorded three-axis acceleration data, an estimate of the AUH motion track was shown in Figure 15b. In the absence of position correction, AUH could still basically complete the preset path, and it was easy to see that it had excellent steering characteristics, which proves that AUH has the potential for high maneuvering performance in the horizontal plane. The vertical lifting motion experiment was used to demonstrate the landing ability of AUH. As shown in Figure 16, the buoyancy adjusting device was used to adjust the buoyancy and complete the landing process. The AUH roll angle and pitch angle were recorded using the attitude sensor, and the results are shown in Figure 16e. Throughout the whole process, the AUH's roll angle and pitch angle were very stable. Except for the impact caused by landing at about 400 s, they were both maintained near 0 degrees, with a maximum of 2 degrees. The overall landing process was stable, showing the stability of the AUH in the vertical direction. Figure 14: Photo of the quadrangle motion experiment in the horizontal plane: (**a**) the initial state of the quadrangle motion; (**b**) the first turn of quadrilateral motion; (**c**) the second turn of quadrilateral motion; (**d**) the third turn of quadrilateral motion. Figure 15: The quadrangle motion experiment in the horizontal plane. (**a**) The pitch, roll and yaw angle of AUH during the quadrangle motion experiment; (**b**) estimation of the trajectory of AUH.
# Frequency Selective Surfaces: A Review
## 3 Frequency Selective Surfaces (FSSs)
### History and Significant Advances of FSS in Recent Years
In such applications, enhanced field, heat generated by Ohmic-losses, and dangerous emission of electrons, are exceedingly frequent problems that result in the flashover and electric discharge. Quite recently, a miniature element FSS has been encapsulated in a dielectric by means of a high break-down power to minimize the amplitude of the electric field [48]. Peak power of about 25 kW could be handled with this method. From the above illustration, it is evident that FSS is a massively growing area and needs more state-of-the-art methodologies and innovative research to support foreseen novel applications of FSSs.
# Components of the Fracture Response of Alkali-Activated Slag Composites with Steel Microfibers
###### Abstract  
Knowledge of the mechanical and primarily fracture parameters of composites with a brittle matrix is essential for the quantification of their resistance to crack initiation and growth, and also for the specification of material model parameters employed for the simulation of the quasi-brittle behavior of structures made from this type of composite. Therefore, the main target of this paper is to quantify the mechanical fracture parameters of alkali-activated slag composites with steel microfibers and the contribution of the matrix to their fracture response. The first alkali-activated slag composite was a reference version without fibers; the others incorporated steel microfibers amounting to 5, 10, 15 and 20% by weight of the slag. Prism specimens with an initial central edge notch were used to perform the three-point bending fracture tests. Load vs. displacement (deflection at midspan) and load vs. crack mouth opening displacement diagrams were recorded during the fracture tests. The obtained diagrams were employed as inputs for parameter identification, the aim of which was to transfer the fracture test response data to the desired material parameters. Values were also determined for fracture parameters using the effective crack model, work-of-fracture method and double-\(K\) fracture model. All investigated mechanical fracture parameters were improved by the addition of steel microfibers to the alkali-activated matrix. Based on the obtained results, the addition of 10 to 15% of microfibers by weight is optimal from the point of view of the enhancement of the fracture parameters of alkali-activated slag composite.  
alkali-activated slag; steel microfibers; fracture test; identification; work-of-fracture method; double-\(K\) model; crack propagation
# Trusted Composition of Internet of Medical Things over Imperfect Networks
## 8 Verification Conditions
### Thread Verification Conditions  
For threads, a verification condition is generated for each state (except final states) and a verification condition is generated for each transition.  
For each complete state, its assertion must imply the thread invariant. The invariant for Respiration_Monitor_Thread.impl is << RA() and MRP_A() >>. State run has the the same assertion. So, the verification condition for complete state run is a tautology, as shown in Listing 6.  
Figure 5: Respiration Monitor state machine.  
```
[serial1012]:IoMTwin::Respiration_Monitor_Thread.impl P[428]<<RA()andMRP_A()>> S[418]-> Q[418]<<RA()andMRP_A()>> Whycreated:<<(check_echo)>>-><<I>>frominvariantIwhen completestatecheck_echohasAssertion<<M(check_echo)>>initsdefinition
```  
**Listing 6**Verification condition for run complete state.  
For each execution state, its assertion must imply the disjunction of the transition conditions of all transitions for which that state is its source.  
For each transition, a verification condition is generated as  
\[\ll P\wedge b\gg\ S\ll Q\gg\]  
where \(P\) is the assertion of the source state, \(Q\) is the assertion of the destination state, \(b\) is the transition condition, and \(S\) is the action of the transition. The verification condition for transition go is generated as shown in Listing 7.  
```
[serial1014]:IoMTwin::Respiration_Monitor_Thread.impl P[426]<<RA() andMRP_A()>> S[431]-> Q[427]<<RA() andMRP_A()>> Whycreated:<M(start)>>-><<M(run)>>forgo:start-[]->run();
```  
**Listing 7**Verification condition for transition go.  
Numbers in the square brackets are the line numbers in the program. Within IoMTwin-.aadl, line 426 has definition of the start state, and line 427 has definition of the destination state run. The transition go is defined at line 431 without any behavior action.  
Transitions leaving complete states have dispatch conditions which makes the transition condition \(b\) more complex. The dispatch condition holds when dispatched at time now, and dispatch has not happened since the time-of-previous-dispatch, tops. The precondition for the verification condition generated for got_echo (shown in Listing 8) includes RM_Alarm_Echo@now, which means event arrival at the RM_Alarm_Echo port in the present instant. After which, several \"not exists\" terms express that no other transition leaving state check_echo occurred since the thread was last dispatched. In some cases, those additional terms are pertinent; here, they are not.