esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Performance Evaluation
### Handoff Overhead  
The handoff overhead is the signaling overhead that is incurred when the MN breaks off its association with its previous SN and attaches with a next SN. This includes the signaling cost for the handoff, like sending _new_node_ and LU messages. In order to derive the handoff overhead through analytical modeling, we consider Figure 22. The circular dotted line is the transmission range of the SNs, whereas the SNs are organized in a grid. The squares in the Figure represent the Association Range (AR) of a SN. In other words, when the MN is within the square then it is associated with one particular SN. When it moves out of the square, the signal strength between the MN and its current SN decreases and it associates with the next SN. Also, there are 4 MSPs located in the PAN. In order to derive the handoff signaling overhead, we determine the average number of SNs with which a MN associates, when fluid flow mobility model is used.  
For evaluation purposes, we assume the following:  
1. A domain is composed of \(N\) equal squares that are equal in size and form a contiguous area.
2. The MNs are moving with an average speed of \(v\), and the direction of their movement is between \(0\) to _2_\(\pi\).
3. MN moves out of the PAN within \(K\) finite movements  
In [33] the authors proved that _\(\mu\)(k)_ is the average number of SNs with which a MN associates with, and it is rarely sensitive to change of \(K\), unless \(K\) is much smaller than \(N\). Thus we assume \(K\) equals \(N\).  
Figure 22: Association Region of SNs in the network topology.  
The signaling cost in the LoWMob scheme can be broken down to:  
1. The cost incurred in sending LU messages to the GW, and the cost of sending _new_node_ messages when a MN associates with a new SN.
2. The cost incurred in tunneling data packets from the GW to the SNs, for the destination MN. The default parameter values for calculation of LU and tunneling cost are given in Table 2.  
In Table 2, the LU message cost for one hop is 48 bits. Moreover, in order to deliver the data packet to the MN, extra 16 bits are appended that represent the SN's short address.
# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 2 Methods
### Fetal R-Peak Detection  
In the testing R-peak detection stage, constructed inputs from test signals are applied to the model constructed in the training stage. The output from the model then undergoes additional post-processing steps, aimed at identifying and eliminating false positives, thereby enhancing the accuracy of R-peak detection.  
The output map segments are restored to a full length through an overlap-add process, and additional processing steps are executed to finalize the peak screening process. Firstly, a filtering criteria removes potential false-positive predictions where amplitudes of the output pulse train are weak and below the first threshold (\(Thr_{1}\)). Currently, the threshold \(Thr_{1}\) is set to a fixed value of 0.3. This means that predicted values below \(Thr_{1}\) will be converted to 0, and the remaining values will be converted to 1.  
The middle value of each pulse is then taken to generate a fiducial R-peak series, corresponding to predicted R-peak locations. Note that where a predicted pulse was found to have a width of one (compared to width of 15 samples associated with typical QRS-complex), that pulse is excluded, and no pulse is predicted in that location.  
Finally, a second threshold (\(Thr_{2}\)) is applied to eliminate closely spaced potential beats. As specified in Section 2.1, fetal heart rates are typically within the range of 110-160 bpm [24]. A threshold \(Thr_{2}\) of 81 sampling points is employed to filter out potential R-peaks that are too close to be deemed physically possible. When two beats are found to be closer than the defined threshold \(Thr_{2}\), the beat further from the midpoint of preceding and subsequent beats is selectively removed. The result is an output signal where each pulse corresponds to the location of a predicted R-peak within the original FECG signal.
# Integrity Monitoring of GNSS Constellations with Only LEO-PNT Satellites +
## 3 User-Based Integrity with LEO-PNT Formulation (Option 2)  
### Integrity Concept  
In this section, the integrity monitor is discussed in the context of a broader integrity concept. We use Ithaca [2] as a convenient source of a reference for the \"Integrity Concept for EGNSS high accuracy\", which explores different users' requirements and system configurations. We select the road use case presented in Table 2.
## 3 Related Theories and Technologies
### Cuckoo Filter  
Determining whether a particular element belongs to a given set is a common problem in computer science, with widespread applications in bioinformatics, machine learning, computer networks, the Internet of Things, and database systems [39]. Filter data structures such as Bloom filters and Cuckoo filters can approximately determine if an element is part of a specified set and have been extensively applied in network routing [40], information retrieval, file merging [41], spam detection [42], and distributed systems [43].  
Filter data structures are used to approximately ascertain if an element belongs to a specific set. In essence, for a given set \(S\) and a query element \(x\), the filter can approximately inform the query whether \"x is in S\". \"Approximately\" here implies that if \(x\) is actually not in \(S\), the filter has a small error probability \(p\) of wrongly indicating that \"x is in S\"; however, if \(x\) is indeed in \(S\), the filter will always correctly return that \"x is in S\". Filter data structures sacrifice some query accuracy to enhance space and time efficiency. Unlike data structures that require storing the complete information of each element for precise queries, filters approximate the presence of an element solely through partial information such as hash values or \"fingerprints\". Based on this principle, existing filter data structures are mainly categorized into two types: one type uses bit arrays as in Bloom filters; the other type, exemplified by Cuckoo filters, is based on element \"fingerprints\".  
The Cuckoo filter [44] is an advanced retrieval structure made up of multiple buckets, each capable of containing several bits. Compared to Bloom filters, Cuckoo filters offer the significant advantage of supporting deletion of elements and having higher space efficiency. With equal storage space, Cuckoo filters can achieve more accurate search results and shorter search times. When querying an element, the time complexity for Cuckoo filters is \(O(1)\), meaning constant time complexity. This indicates that the execution time for query operations does not increase with the number of elements in the filter, an important performance feature of the Cuckoo filter design. In this paper, Cuckoo filters are used to store data at a large medical institution.
## 3 Results  
### Flight Proximity and Nap-of-the-Object Photogrammetry Accuracy  
#### 3.1.1 Positioning Accuracy Analysis  
The analysis centered on the results obtained at four different flight proximities, as depicted in Table 2. All the images were calibrated with a success rate of 100%. The number of GCPs for each set of flight proximities was nine. The ground resolution value increased from 0.4 cm to 1.2 cm as the flight proximity increased. The mean reprojection error obtained from the four sets of processed data did not differ significantly, remaining stable at approximately 0.012 pixels.  
Four groups of GCP positioning errors obtained at different flight proximities were analyzed as a measure of positioning accuracy. From the results shown in Table 3, it can be seen that the average errors of the GCP coordinates in the X-, Y-, and Z-directions are close to each other with the increase in flight proximity, except for the case of the distance of 30 m. At a flight proximity of 30 m, the average error of the Z-coordinate reaches \(-\)0.21 mm. The maximum mean absolute error value, 8.78, occurs in the horizontal direction (x-y coordinates) when the flight proximity is 30 m. In the vertical direction (Z-coordinate), the maximum average error is 7.59 mm when the flight proximity distance is 25 m. In other words, there is not an excessive difference in the average absolute coordinate errors appearing in the three directions.  
The RMSE of the Z-coordinate decreases and then increases as the flight proximity increases, with a turning point at the flight proximity of 20 m. At a flight proximity of 30 m, the RMSE of the Z-coordinate is the largest, 8.89 mm, and the X-coordinate error value changes the most significantly, reaching a maximum of 10.4 mm. To summarize, the GCP errors are more stable when the flight proximity is 15-20 m.
# Registration of Dental Tomographic Volume Data and Scan Surface Data Using Dynamic Segmentation
## 2 Proposed Method: MICP  
### Overview  
The overall process of the algorithm can be written in pseudo-code as follows:  
```
Data: V, P Result: \(T_{f}\) while(e < ending criteria) \(\{V^{\prime}\}\leftarrow\)interpolation(V) \(\{E\}\leftarrow\)step_edge(V) \(\{M\}\leftarrow\)match(E,P\({}^{\prime}\),N) \(\{T_{init}\}\leftarrow\)minimizing point-to-plane distance metric \(\{P^{\prime}\}\leftarrow\{T_{init}\text{P}\}\) end \(\left\{T_{f}\right\}\gets ICP(T_{init}\cdotp_{s},\;P_{t})\)
```  
To align the volume data (V) and the scan data (P), the two were initially manually placed proximally. For a single point and normal vector from the surface data, an intensity profile can be defined in the volume data. The intensity profile has several new points aligned with the normal vector to the surface data. These points are defined with uniform intervals and new intensity values are given to these points by 3D interpolation (V\({}^{\prime}\)). Because the volume data and surface data are initially positioned well, a single intensity step edge is apparent in the intensity profile (Figure 2).  
The step edge represents the boundary of the teeth in the volume data and provides valuable information for both segmentation and registration. For most existing segmentation algorithms that extract surface data from volume data, points of the segmented surface must be positioned in the step edge(E). In other words, an edge point on an intensity profile should relate directly to a 3D segmented point. From the registration aspect, if the volume data and scan data are aligned properly, this step edge point must converge to the origin (x = 0).  
The intensity profile is calculated for every point so the step edge can be determined. If the step edge exists in the intensity profile, the interpolated edge point in the 3D vertex can be obtained and the edge point becomes a segmented point. These points are the corresponding points used for the ICP algorithm (M). The rigid transformation matrix can be calculated by minimizing the distance between  
Figure 2: Intensity profile and step edge point.  
the matching points. If the average distance between the two data sets becomes less than the ending criteria, the dynamic segmentation process terminates.  
Unlike the previous methods that use only volume data for segmentation, we use both volume data and surface data. The proposed registration procedure does not need any human input except for initial positioning and works automatically. In addition, the proposed method uses the edge points of the teeth. Therefore, it is robust to the artifact problem. The overall flow chart of the proposed registration procedure is shown in Figure 3.
## 3 LoRa and Localization Algorithms
### Overview of LoRa
#### 3.1.3 Link Budget  
The link budget cab be determined from transmitted power and node sensitivity, and is expressed as:  
\[\mathit{Link\ Budget}_{(dBm)}=\left(\mathit{Antenna\ transmitted\ power}\ \ \ (\mathit{dB})\ \ \right)-\left(\mathit{Node\ sensitivity}\ \ \ (\mathit{dB})\ \ \right) \tag{2}\]
## 3 Proposed Deep PUF  
Our proposed method substantially focuses on an authentication technique which is suitable for device identification with no extra overheads for resource-constraint nodes in an IoT network. In this work, we first generate DRAM PUF responses for different challenges over multiple measurements under various temperature conditions. We create a CRP database including each challenge with its corresponding responses (Section 4). Next, we take the advantages of CNNs to extract the shared features of generated responses as well as failure patterns. In such a way, the developed CNN learns the shared features of several responses generated for each challenge. Then, it will be able to recognize corresponding responses produced in all operating conditions. This method will help us to address the error-reduction issues and confront reliability requirements to organize an authentication technique. Deep PUF can be used as a standalone security mechanism or as a part of multi-factor authentication (MFA). The proposed authentication process is generally divided into two major stages: (i) an enrollment phase and (ii) an authentication phase.
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 3 System Model and Problem Formulation
### Problem Statement
As shown in Figure 2, reactive jamming at the same frequency in one time slot may shift to a different frequency jamming in the next, potentially causing frequency-detection errors. Due to the dynamic and unpredictable nature of reactive jamming signals, understanding their jamming rule is crucial. To address this, we designed the IMSFH framework, which leverages neural  
Figure 2: Spectrum waterfall diagram of complete communication flow in complex jamming environment. networks to learn the rule of reactive jamming within the spectrogram waterfall, thereby improving the performance of frequency detection. The IMSFH communication framework designed in this paper is illustrated in Figure 3, and its structure is similar to the MSFH communication framework introduced in [25]. At the transmitter part, we select channels based on the bit information to synthesize a multi-sequence frequency hopping sequence. This sequence controls the frequency synthesizer to generate the transmission carrier frequency, and the end of the RF part produces the multi-sequence frequency-hopping signal. At the receiver part, we replace the filtering and non-coherent detection components from reference [25] with a frequency-detection network that has frequency-detection and intelligent learning capabilities. Finally, the communication frequencies identified by the frequency-detection network are assembled into a frequency sequence, which is then compared with the corresponding frequency positions in channel 0 and channel 1 to demodulate bit information.
###### Abstract  
Low-cost dual-frequency receivers and antennas have created opportunities for a wide range of new applications, in regions and disciplines where traditional GNSS equipment is unaffordable. However, the major drawback of using low-cost antenna equipment is that antenna phase patterns are typically poorly defined. Therefore, the noise in tropospheric zenith delay and coordinate time series is increased and systematic errors may occur. Here, we present a field calibration method that fully relies on low-cost solutions. It does not require costly software, uses low-cost equipment (\(\sim\)500 Euros), requires limited specialist expertise, and takes complex processing steps into the cloud. The application is more than just a relative antenna calibration: it is also a means to assess the quality and performance of the antenna, whether this is at a calibration site or directly in the field. We cover PCV calibrations, important for deformation monitoring, GNSS meteorology and positioning, and the computation of PCOs when the absolute position is of interest. The method is made available as an online web service. The performance of the calibration method is presented for a range of antennas of different quality and price in combination with a low-cost dual-frequency receiver. Carrier phase residuals of the low-cost antennas are reduced by 11-34% on \(L_{1}\) and 19-39% on \(L_{2}\), depending on the antenna type and ground plane used. For the cheapest antenna, when using a circular ground plane, the \(L_{1}\) residual is reduced from 3.85 mm before to 3.41 mm after calibration, and for \(L_{2}\) from 5.34 mm to 4.3 mm. The calibration reduces the Median Absolute Deviations (MADs) of the low-cost antennas in the vertical direction using Post Processed Kinematic (PPK) by 20-24%. For the cheapest antenna, the MAD is reduced from 5.6 to 3.8 mm, comparable to a geodetic-grade antenna (3.5 mm MAD). The calibration also has a positive impact on the Precise Point Positioning (PPP) results, delivering more precise results and reducing height biases.  
GNSS; receiver antenna calibration; Phase Center Variation (PCV); ZTD; positioning; U-Blox; ZED-F9P; low-cost +
Footnote †: journal: _Sensors_
# An Authentication Method for AMBTC Compressed Images Using Dual Embedding Strategies
## 3 The Proposed Method
### The Embedment Algorithm of Complex Blocks  
For a compressed code \(C_{i}=(a_{i},\ b_{i},\ B_{i})\), if \((b_{i}-a_{i})\geq T\), the block \(I_{i}\) is a complex one and embed the authentication code \(ac_{i}\) into the quantized values \((a_{i},\ b_{i})\) using APPM with the following algorithm.  
* Step 1: Use the following equation to construct the reference table \(R_{64}^{\parallel_{i}}\): \[R_{64}^{\parallel_{i}}(a_{i},\ b_{i})=((c_{64}\times a_{i}+b_{i})+\parallel_{ i})\text{ mod 64},\] (4) where \(\parallel_{i}\) is an random integer generated by a key \(\mathcal{K}\), and \(c_{64}=14\).  
* Step 2: Use the bitmap \(B_{i}\) and position information \(i\) to generate the 6-bit \(ac_{i}\) by \[ac_{i}=\text{hash}_{6}(B_{i},i).\] (5)
* Step 3: Once \(ac_{i}\) is obtained, \(\left(ac_{i}\right)_{10}\) of base 64 is embedded into the quantized values \(\left(a_{i},\ b_{i}\right)\) using APPM to obtain the marked quantized values \(\hat{a}_{i}\) and \(\hat{b}_{i}\), where \(\left(ac_{i}\right)_{10}\) is the decimal value of \(ac_{i}\). In comparison to Equation (1), Equation (4) adds an additional integer \(\parallel_{i}\) to generate the reference table. Actually, the image quality obtained by referring to \(R_{64}^{\parallel_{i}}\) is equal to that of \(R_{64}\). However, if \(ac_{i}\) is embedded based on \(R_{64}\), it can be extracted publicly by Equation (1). Therefore, one can tamper with \(\left(\hat{a}_{i},\ b_{i}\right)\) by finding an alternative pixel pair \(\left(\hat{a}_{i},\ \hat{b}_{i}\right)\) that satisfies \(\left(c_{64}\times\hat{a}_{i}+\hat{b}_{i}\right)\text{mod}\ 64=\left(c_{64}\times \hat{a}_{i}+\hat{b}_{i}\right)\text{mod}\ 64=ac_{i}\) to escape detection. In contrast, \(ac_{i}\) in Equation (4) can be obtained only if \(\parallel_{i}\) is known. Thus, the embedment using \(R_{64}^{\parallel_{i}}\) is not only more secure than just using \(R_{64}\), but also maintains the same image quality.
# Leveraging Expert Knowledge for Label Noise Mitigation in Machine Learning
## 2 Materials & Methods  
### Methodology Overview  
We propose a methodology derived from the filter framework consisting of label noise detection part and label noise processing part, as shown in Figure 1. Unlike the aforementioned traditional approaches, where only training data are used to train the model, rules created by experts are added to this framework to first detect the label noise samples. The label noise detector utilizes the rules, the non-expert training set, and the learning model created by the training set. The detected data samples are used as input of the label noise processor to train a better model than when the only non-expert training set is available.  
The detail of the proposed label noise detector is shown in Figure 2a. First, a machine learning model is trained from the non-expert training data, and then that model is used to predict the non-expert training data. An assumption here is that this model still has good enough performance, despite suffering from the label noise. The main reason people wrongly assign labels is that data samples are quite similar to other data in the different class. Consequently, these label noise samples are misclassified with high probability. Hence, based on the rules derived from expert knowledge, a Logic Inference Machine detects a set of data with a predicted output that violates the rules and differs from the label. Most of the label noise is included in this set, and this set is considered the detected label noise set. Unlike traditional methods [28; 29], the rules derived from expert knowledge are added to detect the label noise more precisely. A data sample is considered as label noise not only its predicted output differs from the label, but also violates the rules. This reduces the number of normal data samples that are wrongly detected as label noise.  
The label noise processing part can be any algorithms to treat with detected label noise to train a better model. Consequently, the output model suffers less from label noise, and the effect of label noise in the output training set is mitigated.  
Figure 1: Filter framework diagram.  
Based on that method, two algorithms are proposed: Rule-Remove and Rule-Weight. The label noise processing part of Rule-Remove completely remove the detected label noise data samples, and train the model from the removed label noise data set. On the other hand, the label noise processing part of Rule-Weight weight the detected label noise data samples, and train the model from the weighted data set. Rule-Remove algorithm can be considered as a special case of Rule-Weight when weight is set to zero at the detected label noise samples. Therefore, only the Rule-Weight algorithm is described in detail in this section.
# Design and Trajectory Tracking Control of a Magnetorheological Prosthetic Knee Joint
## 3 Design of MR Damper Used in the MR Prosthetic Knee Joint  
### Structure Principle of the Proposed MR Damper  
According to the working principle of the MR damper, the designed MR damper is shown in Figure 6. As can be seen from the figure, the MR damper is mainly composed of lifting lugs, piston rods A and B, an end cover, a cylinder, a coil winding frame and an excitation coil. Among them, piston rods A and B are connected to each other through threads and play the role of fixing the coil winding frame. The excitation coil is evenly wound on the coil winding frame; both of them form a piston head, and there is an annular gap between the piston head and the MR damper cylinder. The MR fluid flows into the cylinder of the MR damper to make the gap between the piston head and the cylinder form an effective damping channel. The piston rod moves up and down due to external vibration, so that the MR fluid in the upper and lower chambers flows back and forth at the effective damping gap. The excitation coil is powered on, and a variable magnetic field is generated. The rheological characteristics of the MR fluid change under the action of the magnetic field. The viscosity of the MR fluid increases as the increase of current, that is, the pressure difference between the two ends of the effective damping gap, increases. Therefore, within a certain range, the output damping force of the MR damper can be adjusted by controlling the applied current of the excitation coil.  
Figure 6: Schematic diagram of MR damper. (1) Upper lifting lug, (2) piston rod A, (3) end cover, (4) cylinder body, (5) coil winding frame (6) excitation coil, (7) piston rod B, (8) lower lifting lug.