esa-sceva/satcom-chunk-collection huggingface dataset:
# Biometrics for Internet-of-Things Security: A Review
## 7 Potential Solutions and Opportunities
### Reducing the Impact of Biometric Uncertainty  
Biometric uncertainty, produced at biometric image acquisition, can degrade the performance of biometric authentication systems in terms of recognition accuracy. To mitigate the negative impact of biometric uncertainty and achieve satisfactory matching performance, apart from multi-modal biometrics discussed in Section 4.1, techniques such as discriminative feature representation and deep/machine learning can act as countermeasures to biometric uncertainty.  
One way of reducing the adverse effect of biometric uncertainty is to use stable and discriminative feature representation. For example, [PERSON] et al. [110] applied finger-to-heart biometric authentication to implantable medical IoT devices. In the proposed system, fingerprints are used to perform authentication for access control over implantable medical IoT devices. To achieve good recognition accuracy, the authors adopted and improved the well-known fingerprint minutia descriptor, Minutia Cylinder-Code (MCC). Only a subset of MCC feature data is chosen to save the limited storage and computing resources of implantable medical IoT devices. Due to MCC's strong discriminative power and stability in combating biometric uncertainty, the proposed system achieves satisfactory recognition accuracy, albeit with only a subset of MCC feature data.  
Another way of tackling biometric uncertainty to attempt good matching performance is the employment of powerful deep learning techniques (e.g., CNN). For example, to decrease noise in ECG signals and improve recognition accuracy, [PERSON] [111] adopted the deep learning technique and proposed a CNN-enabled ECG-based biometric identification framework for IoT applications. In this scheme, features from raw ECG data are learned directly by the CNN without the need of manual feature extraction. The experimental results on the MIT-BIH normal sinus rhythm database show that the system obtains an identification rate of 99%, outperforming other state-of-the-art methods. [PERSON] et al. [112] developed a deep learning-based privacy-preserving finger-vein authentication system for IoT edge devices. By employing the binary decision diagram, the designed template protection algorithm is resource-saving and secures the finger-vein template data, while achieving competitive recognition performance.
## 2 Methods  
### Network Overview  
As shown in Figure 2, the ISTD-DisNet architecture proposed in this article consists of an encoder/mix transformer (MiT) with a hierarchical transformer structure that does not require positional encoding, and a decoder that incorporates a hybrid attention mechanism and a TCUM module. ISTD-DisNet retains the coding structure of SegFormer. The size of the input image is first resized to 512 \(\times\) 512 \(\times\) 3, and the input image then enters into the overlapping patch embedding (OPE) structure for feature extraction and downsampling. The obtained features are then input into the hierarchical transformer block module, where four hierarchical decoding operations decode the input features. After the four hierarchical decoding operations, feature maps of F1 = 128 \(\times\) 128 \(\times\) 64, F2 = 64 \(\times\) 64 \(\times\) 128, F3 = 32 \(\times\) 320, and F4 = 16 \(\times\) 16 \(\times\) 512 are generated sequentially. In the decoding stage, the four feature maps are first upsampled to 1/4 of the input image size using the MLP layer and then fed into the MAM module to capture the most significant semantic information on pavement lesions in different channels and different spatial locations. After performing the Concat operation, the merged feature maps then go through the TCUM module with learning capability to enhance the detail restoration ability of the feature information. Finally, a feature map with the resolution of H \(\times\) W \(\times\) Ncls is obtained, with Ncls = 2, where Ncls represents the number of semantic segmentation categories that contain the background. It is worth noting that the new model replaces the cross-entropy loss function in SegFormer with a mixed loss function to alleviate the adverse effects caused by the imbalance of the pixel share of the lesions in the dataset.
# Estimating Road Segments Using Kernelized Averaging of GPS Trajectories
## 2 From Dynamic Time Warping to Time Elastic Kernels Averaging
### Time Elastic Averaging of a Set of Time Series  
The multiple alignments problem has been widely studied in bioinformatics [18]. It is known to be a NP-complete problem [19; 20]. Due to the \"hardness\" of this problem, heuristics have been proposed to provide centroid estimates in a reasonable time.  
Among others, an iterative heuristic approach was initially introduced by [PERSON] et al. [21] and popularized by [PERSON] et al. [22] who introduced the DTW Barycenter Averaging (DBA) algorithm. The iterative procedure, which integrates three steps, is first initiated by selecting a reference time series \(r\), usually the medoid of the set \(S\) of time series that is to be averaged. The best alignments for all the time series in \(S\) with \(r\) are evaluated during the second step. In the third step, the reference is updated by averaging all the samples that are aligned with the same sample of \(r\). The two last steps are iterated until reaching a local minimum of the summation of the DTW distances between the time series in \(S\) and \(r\).  
The soft-DTW [9], which is fully differentiable in all of its arguments, has also been used to evaluate a centroid estimate for a set \(S\) of time series. Basically, in this case, the direct optimization problem can be solved using a gradient descent approach:  
\[C=argmin_{x}\sum_{i=1}^{N}=\frac{\lambda_{i}}{m_{i}}dtw_{\gamma}(x,y_{i}) \tag{7}\]  
where \(m_{i}\) is the length of time series \(y_{i}\), \(\lambda_{i}\) is a normalized weight associated to \(y_{i}\) (\(\sum_{i}\lambda_{i}=1\)), and \(C\) is the centroid we are seeking for. \(dtw_{\gamma}(.,.)\) is constructed by replacing in \(dtw\) the min operator with a _softmin_ operator that introduces the \(\gamma\) meta parameter:  
\[min^{\gamma}\{a_{1},\ldots,a_{n}\}\coloneqq\begin{cases}\min_{i\leq n}a_{i},& \gamma=0,\\ -\gamma\log\sum_{i=1}^{n}e^{-a_{i}/\gamma},&\gamma>0.\end{cases} \tag{8}\]  
These two time elastic averaging approaches (DBA and soft-DTW) constitute the state of the art in the context of averaging a set of time series.
## 3 Result and Discussion
### Characterization of Adsorbent  
The physical properties of MWCNTs, such as specific surface area, pore size distribution and pore volume, were modified via acidification and summarized in Table 2. The acidification increased the surface area, average pore diameter and pore volume of the MWCNTs. Probably, amorphous carbons were removed from the structure by the acidification to open the pores of MWCNTs. While the S\({}_{\text{BET}}\) of the modified MWCNTs was comparable with those reported by others [43; 44; 45], the pore volume was much higher; 0.81 cm\({}^{3}\)/g was obtained (Table 2). HNO\({}_{3}\) treatment reduced metals on the surface of MWCNTs and created new acidic sites, which dominate the roughness of the MWCNTs.  
Figure 2a gives the SEM (left) and TEM (right) images of raw MWCNTs, respectively. It can be observed that raw MWCNTs shows typical tubular structure morphology, with a length of 5-15 nm. Figure 2b shows the morphology of the modified MWCNTs. The result shows the tubular structure morphology of the modified MWCNTs that has no particle on its surface. The TEM image presents the tubular structure of the raw MWCNTs (right of Figure 2a) and the modified MWCNTs samples (right of Figure 2b). These TEM images confirm the successful removal of metals from the MWCNT surface, which is consistent with the SEM observation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**adsorbent** & \begin{tabular}{c} **S\({}_{\text{BET}}\)** \\ **(m\({}^{2}\)/g)** \\ \end{tabular} & \
\begin{tabular}{c} **Pore Diameter** \\ **(nm)** \\ \end{tabular} &
\begin{tabular}{c} **Pore Volume** \\ **(cm\({}^{3}\)/g)** \\ \end{tabular} & **References** \\ \hline MWCNTs & 122 & 15.1 & 0.44 & \\ Modified MWCNTs (Acidified) & 211 & 15.5 & 0.81 & \\ Acidified MWCNTs & 237 & 15.7 & 0.0042 & [44] \\ Acidified MWCNTs & 121 & & 0.49 & [45] \\ Acidified MWCNTs & 295 & 20–40 & 0.21 & [47] \\ Acidified MWCNTs & 254 & & [48] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific surface area and pore diameter of MWCNTs.  
Figure 2: SEM (left) and TEM (right) images of the MWCNTs **(a)** before and **(b)** after modification.  
Figure 3 shows the zeta potential of the modified MWCNTs as a function of pH which gives the point of zero-charge (ZPC); the ZPC was observed at pH 2.3 (=pH\({}_{\text{zpc}}\)). The zeta potential of the modified MWCNTs was negative in the neutral pH range (Figure 3), indicating that the surface of the MWCNTs was negatively charged. In comparison, the zeta potential of the modified MWCNTs was positive at acidic pHs.
###### Abstract  
The paper describes a numerical study of the combustion of hydrogen enriched methane and biogases containing hydrogen in a Controlled Auto Ignition engine (CAI). A single cylinder CAI engine is modelled with Chemkin to predict engine performance, comparing the fuels in terms of indicated mean effective pressure, engine efficiency, and pollutant emissions. The effects of hydrogen and carbon dioxide on the combustion process are evaluated using the GRI-Mech 3.0 detailed radical chain reactions mechanism. A parametric study, performed by varying the temperature at the start of compression and the equivalence ratio, allows evaluating the temperature requirements for all fuels; moreover, the effect of hydrogen enrichment on the auto-ignition process is investigated. The results show that, at constant initial temperature, hydrogen promotes the ignition, which then occurs earlier, as a consequence of higher chemical reactivity. At a fixed indicated mean effective pressure, hydrogen presence shifts the operating range towards lower initial gas temperature and lower equivalence ratio and reduces NOx emissions. Such reduction, somewhat counter-intuitive if compared with similar studies on spark-ignition engines, is the result of operating the engine at lower initial gas temperatures.  
Keywords:methane; biogas; gaseous fuels; hydrogen; combustion; internal combustion engine; controlled auto ignition; engine efficiency; NOx emissions +
Footnote †: journal: Applied Sciences
## 4 Experimental Results and Analysis
### Ablation Experimental
Additionally, its validation loss curve remains smooth and stable, highlighting the model's good generalization capability on unseen data, thus avoiding significant fluctuations and ensuring reliable detection performance. Compared to other YOLOv8 configurations, LSOD-YOLOv8 shows more consistent performance in \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Model Structure**} & \multicolumn{6}{c}{**Evaluation Index**} \\ \hline
**LAWDS** & **P2-LSCSBD** & **WIMIoU** & **P** & **R** & **mAP50** & **mAP50-95** & **Parameters** & **Detection Speed (s)** \\ \hline \multirow{3}{*}{\(\surd\)} & & & 0.849 & 0.803 & 0.829 & 0.513 & 3,011,043 & 0.55 \\  & & & 0.866 & 0.796 & 0.836 & 0.506 & 2,732,835 & 0.492 \\  & \(\surd\) & & 0.867 & 0.799 & 0.848 & 0.538 & 2,926,692 & 0.508 \\  & & \(\surd\) & 0.877 & 0.799 & 0.841 & 0.516 & 3,011,043 & 0.55 \\ \(\surd\) & \(\surd\) & & 0.856 & 0.798 & 0.851 & 0.526 & 2,141,477 & 0.64 \\ \(\surd\) & & \(\surd\) & 0.869 & 0.81 & 0.847 & 0.513 & 2,367,460 & 0.408 \\  & \(\surd\) & \(\surd\) & 0.851 & 0.82 & 0.856 & 0.535 & 2,419,685 & 0.512 \\ \(\surd\) & \(\surd\) & \(\surd\) & 0.868 & 0.805 & 0.857 & 0.535 & 2,141,477 & 0.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ablation studies. the later stages of training, further confirming its effectiveness in small target detection tasks. These results suggest that the proposed enhancements significantly improve the accuracy and reliability of the small target detection model.
# Shape Optimization of Discontinuous Armature Arrangement PMLSM for Reduction of Thrust Ripple
## 2 Structure of Discontinuous PMLSM and Analysis of Characters
### Analytical Analysis of Detent Force  
The interval distance \(L_{\text{dis}}\) between armatures must satisfy the following equation [20].  
\[L_{\text{dis}}=\left(n+\frac{1}{3}\right)\tau-L_{u}(n=1,2,3\ldots) \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Unit** & **Value** \\ \hline Height of PM, \(P_{h}\) & mm & 10 \\ Width of PM, \(P_{w}\) & mm & 35 \\ Pole pitch, \(\Gamma\) & mm & 40 \\ Back iron height & mm & 10 \\ Back iron length & mm & 137 \\ Turn per phase & - & 300 \\ Slot pitch, \(S_{p}\) & mm & 13 \\ Slot opening, \(S_{o}\) & mm & 2 \\ Height of tooth, \(T_{h}\) & mm & 2 \\ Armature length & mm & 170 \\ Magnetic air gap, \(A_{g}\) & mm & 1 \\ Tooth tip angle, \(T_{a}\) & \(\circ\) & 90 \\ Permanent magnet grade & - & N50 SH \\ Material of the tooth, back iron & - & Sillicon Steel NGO \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the discontinuous armature arrangement PMLSM.  
Figure 1: Discontinuous armature arrangement PMLSM: (a) Schematic diagram of PMLSM; (b) Forces exerted in the PMLSM.  
Figure 2: 2D finite elements analysis model.  
\[L_{dis}=\left(n+\frac{2}{3}\right)\tau-L_{u}(n=1,2,3\ldots) \tag{2}\]  
where, \(L_{u}\) is the length of one stator segment and \(\tau\) is the pole pitch. The electrical degree between the two stators in Equation (1) becomes \(2/3\pi\) and in Equation (2) becomes \(4/3\pi\). Accordingly, if the stators are separated by \(L_{dis}\), the same winding connection as the existing continuous stator can be applied to the discontinuous stator PMLSM. The end effect force of PMLSM is a major component of detent force. The discontinuous armature arrangement PMLSM has more end edges, so the end effect has a greater impact. In the case of the ideal slotless PMLSM, the detent force formula can be expressed as Equation (3) because energy variation occurs [21].  
\[F_{den}=\frac{\pi^{2}L_{ef}}{2\mu_{0}\tau}pA_{ba}\big{(}G_{\mu a}sin\mu kx+G_{ \mu p}cos\mu kx\big{)} \tag{3}\]  
where \(A_{ba}\) is the Fourier coefficient of magnetization vector harmonic component, \(G_{\mu a}\) is the relative permeability in the gap, \(\mu\) is the harmonic order \(k=\pi/\tau\), and \(L_{ef}\) is the length of the lamination stack that provides rigidity to the stator. Using Equations (1) and (2), the detent force can be expressed as Equation (4).  
\[F_{det}(x)=F_{den}(x)+F_{den}\big{(}x+\tfrac{2}{3}\tau\big{)}+F_{den}\big{(}x -\tfrac{2}{3}\tau\big{)}F_{den}(x)sin(n\theta)\big{(}1+2 cos\big{(}\tfrac{2\pi n }{3}\big{)}\big{)} \tag{4}\]  
As shown in Equation (4), when \(\mu\) is (3 m), the 3 m order harmonic components are amplified three times as \(F_{det}\) (\(x\)) = \(3F_{den}\) (\(x\)).
## 3 Transmission Experiment Comparing Analog and Digitized RF-over-Fiber Transport
### Experimental Demonstration  
Shown in Figure 4a is the experimental setup for the uplink broadband wireless OFDM signal transmission over a fiber-wireless link incorporating digitized RF-over-fiber transport [34]. In this demonstration, the baseband (BB) OFDM was programmed in Matlab and physically generated using an arbitrary waveform generated (AWG). To avoid using high-speed I/Q mixers, we make corresponding frequency components conjugate complex to ensure Q equal to zero all the time. The signal bandwidth was limited to 1 GHz and there were 128 carriers with 80 being modulated with 4-QAM data, resulting in a data rate of 1.25 Gb/s. The OFDM signal was upconverted to 10 GHz using a local oscillator (LO) to generate the 10 GHz broadband OFDM wireless signal. In the BS, the OFDM signal at 10 GHz was bandpass sampled using an 8-bit and 1 GSample/s ADC in a digital sampling oscilloscope (DSO). After bandpass sampling, the samples were quantized, serialized and processed offline before the digital data was generated using an AWG. Here the 10 GHz OFDM signal was converted to an 8 Gbps digital data stream. For this demonstration, a DFB laser in conjunction with a Mach-Zehnder (MZM) was used to modulate the digital data stream onto an optical carrier before it was transported over 20 km of standard fiber (SMF). In the CO, the optical signal was detected with a PIN photodetector and the digital data stream was processed offline.  
For comparison, the same 10 GHz OFDM signal was distributed using analog RF-over-fiber transport scheme and the experimental setup is shown in Figure 4b. The upconverted OFDM signal was externally modulated onto the optical carrier using a MZM before transported over 20 km of SMF. Upon reception at the CO, the received 10 GHz OFDM was downconverted to baseband and processed offline. In order to maintain good link linearity, the peak-to-peak driving voltage of the RF signal into the MZM was fixed at one tenth of the MZM switching voltage (Vpi). This corresponds to a modulation depth of 0.16.
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm
### Range Measurement
#### 3.3.2 Range-Based Scheme
UREAL uses RSS information for LOS/NLOS link taxonomy, while the AOA ranging is used in the position estimation. A GPS-free passive localization scheme is presented in [47]. The range data are employed to estimate the position information of the sensor nodes based on the planar trigonometry principles. Localization process with the Seaweb server contains five steps, and, once the node is localized, we classify the whole network field into circular levels and sectors. The aim is to lengthen the lifetime of the sensor node and decrease the traffic complexity. The mesh network is formed inside each of the sectors, which increases the reliability. In [48], the authors propose a 3D underwater target tracking (3 DUT) scheme to collaboratively complete the underwater target accurate tracking with the least energy consumption synergy. Figure 9: System overview. A 3D-UASN formed by randomly deployed dual-hydrophone nodes, which can be used to locate the acoustic source. Each node floats with a buoy [43]. The scheme is based on the TOA algorithm and using the trilateration to obtain the positioning information of the target. Making the most of the positioning information and the calculated velocity of the target, and employing a new target-movement-based duty-cycle mechanism, can achieve the energy-effective target tracking. Due to the continuous surveillance, the energy resources of boundary nodes may deplete rapidly. Considering the problem, the authors employ an adaptive procedure to find, designate, and activate new boundary nodes. The network model is shown in Figure 10. In this scheme, at least three anchor nodes are required. Hence, the sink gathers the information and performs the calculations. The sensor nodes collect and send data to the sink. The projector node may change during the tracking process. In this subsection, we have discussed the range measurement and the difference between range-based schemes and range-free schemes. We use some papers to show the different characteristics of the two schemes. Furthermore, we summarize these papers that use the range measure to achieve the positioning in Table 4. We mainly compare the coverage, localization time, accuracy, computational complexity, and energy consumption. According to the following table, the reader can choose the suitable algorithm for doing some application or for doing some improvement. The comparisons between the localization algorithms referred to in this subsection and other localization algorithms that are introduced in other subsections will be shown in Section 4.
# Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic
## 2 Method of Gaze Guided System
### Overhead Camera System-Robot Tracking  
In Figure 1 (3. robot side), the graphical representation of an overhead camera based robot tracking field is shown. The environment where the robot moves is characterized by static obstacles. Gaze-based collision-free motion control of the robot in such a static environment is important. Environment model is captured by an overhead camera and sent to the NI vision system then the mobile robot on the field is tracked by a standardized vision system. The center of the moving robot area is accepted as (0,0). Horizontal (x) and vertical (y) axes represent the robot direction and wheel velocity. The information of the robot motion control environment is received by the host computer, and the robot tracking algorithm is executed. The robot's position is continuously tracked and updated considering the acquired information from the sequentially captured images. Decision strategy is improved in the host system for plans the robot motion and corresponding velocity commands. The host computer regulates the rotational velocities of wheels and sends these commands to the robot. An experiment image shown in Figure 6 has captured by the overhead camera and this image is sent to the NI vision system and user monitor.  
The robot steering control command is produced by eye gaze movement as explained before. After defining the eye gaze coordinates, the output signal calculated for robot motor speeds by using type-2 fuzzy control sent via Bluetooth.  
Figure 4: Image Analysis Steps.  
Figure 5: Real-image analysis.