esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm
## 4 Simulation Experiment  
To verify the effectiveness, fault tolerance, and real-time performance of the proposed improved AFKF for INS/GNSS/VNS integrated navigation, we conducted simulation experiments and compared them with a classic FKF. In the simulation experiments, we set different index parameters for the accelerometer, gyroscope, GNSS, and VNS, as shown in Table 1. At the same time, we also considered the complexity of the real environment in unmanned vehicle navigation and reasonably set the failure scenarios for GNSS and VNS. In the different scenarios, we compared and analyzed the performance of the different algorithms, and the simulation trajectories are shown in Figure 2. The running platform for this simulation experiment was an INTEL(R) Core (YM) I5-12500H CPU with 16.00 GB of memory, and the simulation software used was the PSINS toolbox under matlab2022a, which was developed by Professor [PERSON] from Northwestern Polytechnical University, which can be used for simulation experiments of integrated navigation systems.  
The initial position (local coordinates) was set to \([0\;\mathrm{m},0\;\mathrm{m},0\;\mathrm{m}]\), the initial attitude (pitch, roll, yaw) was set to \([0^{\circ},0^{\circ},0^{\circ}]\), and the initial velocity (local coordinates) was set to \([0\;\mathrm{m}/\mathrm{s},\;0\;\mathrm{m}/\mathrm{s}]\). This corresponded to a simulation of an unmanned vehicle going downhill, uphill, and around corners in an unmanned vehicle trajectory.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Characteristic** & **Value** & **Frequency** \\ \hline Gyroscope & \begin{tabular}{c} Constant drift \\ White noise \\ Zero bias \\ White noise \\ Position error \\ Speed error \\ VNS \\ \end{tabular} & \begin{tabular}{c} 0.5\({}^{\circ}/\mathrm{h}\) \\ Zero bias \\ White noise \\ Position error \\ Speed error \\ Attitude error \\ \end{tabular} & \
\begin{tabular}{c} 0.5\({}^{\circ}/\mathrm{h}\) \\ 0.5\({}^{\circ}/\mathrm{h}\) \\ 0.5\({}^{\circ}/\mathrm{h}\) \\ 1 mg \\ 0.1 mg \\ \end{tabular} &
\begin{tabular}{c} 100 HZ \\ 100 HZ \\ 1 Hz \\ 1 Hz \\ 1 Hz \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters in simulations.  
Figure 2: Simulation trajectory.  
In order to simulate the complex environment that unmanned vehicles may encounter in obstructed spaces, we simulated two scenarios. Scenario 1 involved a situation where only the GNSS signal was interfered with or obscured. Scenario 2 involved a situation where both GNSS and VNS were interfered with.
# 5G-Compliant Authentication Protocol for RFID
## 3 Proposed Protocol
### 5G-Compliant RFID Authentication Protocol  
Once the initialization process is completed, the reader is capable of accessing a service network in its proximity under the tag's subscriber identity. Thus, when the tag wants to authenticate itself to the 5G network, the authentication protocol, described in Figure 5, is performed. Interaction between SN and HN are exactly the same as described in the previous section and therefore have not been included in the figure. The protocol starts with the tag generating a session nonce and sending the reader an authentication request. This authentication request is masked with the value \(AK1t\) (computed using \(h1\)), and sent along with the message authentication code \(AUTNt\) (computed using \(h6\)), to guarantee its authenticity and integrity. Likewise in the initialization protocol, the reader looks up in its DB for a key \(k_{t}\) that matches with \(AUTNt\); if it is not found, the process is aborted, otherwise the tag gets identified as \(id_{t}\) and the reader initiates the communication with the SN in its vicinity, whose identifier is \(SN_{name}\). If the reader has a previous valid \(GUTI\) for that \(id_{t}\), then it sends it to SN (\(IDENT=GUTI\)), otherwise it computes and sends the \(SUCI\) (\(IDENT=SUCI\)). If \(GUTI\) was sent but an \"Identifier Request Message was received\", then the Reader computes and sends the \(SUCI\) (\(IDENT^{*}=SUCI\)). After the tag is identified as a 5G subscriber by HN, the reader receives the challenge and forwards it to the tag. The tag acts then as in the 5G-AKA protocol; it checks the  
Figure 4: 5G-compliant RFID Initialization Protocol.  
authenticity of the challenge, using \(AUTN\), and the freshness, verifying \(SQ_{UE}\). If they are correct, then the tag computes \(RES^{*}\) and the keys \(CK\) and \(IK\), and masks them computing the values \(AK2_{t}\), \(AK3t\) and \(AK4t\) (computed using \(h2\), \(h3\) and \(h4\)), to obtain \(RESt\), \(CKt\) and \(IKt\). Next, the tag sends these to the reader along with a message authentication code \(INTRESt\) (computed using \(h5\)). Upon receiving these values, the reader checks that \(INTRESt\) is correct and if so, computes the masking values and retrieves \(RES^{*}\), and the keys \(CK\) and \(IK\). Now, the reader, acting as the ME of the UE, computes and forwards \(XRES^{*}\) to SN, and derives the anchor key \(K_{SEAF}\). If HN accepts the response, the tag is authenticated as a 5G subscriber. From that point on, the communication between the tag and the reader, which is expected to consist of not very frequent small-data, is protected using \(k_{t}\), while the 5G communication (implementing the different layers of the communication protocol) is carried out by the reader.  
Figure 5: 5G-compliant RFID Authentication Protocol.
# Internal Force Analysis of Buried-boring Piles in the Yuanzishan Landslide
## 5 Discussion
### Effect of the Pile Diameter on the Lateral Reaction of the Soil below the Slip Surface  
The lateral reaction of the soil below the slip surface is a critical factor relating to the failure of anti-slide piles [37; 42].  
When the pile head depth increased from 0 to 7.5 m at a pile diameter of 0.5 m, the maximum lateral reaction of the soil decreased by 83.5% from 994 kPa to 164 kPa at the slip surface (Figure 6c).  
Figure 9: Relationship between the maximum internal force for a varying pile length and the sliding depth ratio to diameter ratio: (**a**) the relationship between the maximum bending moment and the sliding depth ratio; and (**b**) relationship between the maximum soil reaction force and the sliding depth ratio.  
The maximum lateral reaction of the soil increased as the buried depth ratio of the pile increased (Figure 7b), following a power law relationship. However, when the pile head depth increased from 0 to 7.5 m with a varying pile diameter, the maximum lateral reaction of the soil increased by 63.2% from 345 kPa to 563 kPa at the slip surface (Figure 8c). In this case, the maximum lateral reaction of the soil decreased as the buried depth ratio of the pile increased (Figure 9b), following a negative power law. An increasing pile diameter decreased the maximum lateral reaction of soil.  
During the design of bored piles, the lateral reaction of the soil must be less than the maximum soil strength, \(\mathrm{P_{lim}}\). For bedrock [29], \(\mathrm{P_{lim}}=0.79\)\(\mathrm{K_{L}C_{L}R}\), where \(K_{L}\) is the bearing capacity coefficient of the rock layer (\(0.5\leq\mathrm{K_{L}}\leq 1\)), \(C_{L}\) is the coefficient of rock fissure and weathering (\(0.3\leq\mathrm{C_{L}}\leq 0.5\)), and \(R\) is the uniaxial, ultimate compressive strength of the rock. In the Yanzishan landslide (Table 2; Figure 4), \(\mathrm{K_{L}}=0.5\), \(\mathrm{C_{L}}=0.3\), and \(\mathrm{R}=5000\) kPa, such that the maximum strength of the soil (\(\mathrm{P_{lim}}=592.5\) kPa) was greater than the maximum lateral stress of the soil (\(\mathrm{S_{max}}=563\) kPa, Figure 9b). If the foundation beneath the slip surface is rock, an \(h/D\) ratio of 10 is sufficient to meet the strength requirements of the rock. [PERSON] [2] proposed a calculation method for the maximum soil strength (\(\mathrm{P_{lim}}=95\)\(\mathrm{U}\)D, where \(S_{U}\) is the undrained shear strength). If D = 0.5 m, the maximum strength of the soil meets the strength requirements of the soil (Figure 7b).  
Buried-boring piles can be used on their own for landslide prevention, but can also be used in combination with large sections of anti-slide piles [27]. The material of the pile is generally reinforced concrete, and waste steel rails can also be used instead of steel bars. This material substitution can improve the shear resistance of the pile. The combined structural stresses of this approach need to be further studied. The potential sliding line position of the soil behind the pile also needs further research.
## 3 Use Case: UMTS Networks  
In this section, we will develop a use case that is based on UMTS technology, similar to the one shown in [20]. Although we are currently immersed in the race towards Release 17 of 5G, this choice is motivated by the maturity of the technology and the simulation and availability of KPIs [26]. Note that, as pointed out in [16], the exact list of KPIs and their values in 4G or 5G are not usually available, due to their sensitive and vendor-related nature. Some signal or channel related open data sets can be found, for example, in [29; 30; 31]. Moreover, recent reports show that Third generation (3G) technologies will hold around an 18% share of global mobile connections in 2025, excluding cellular IoT [32]. In regions, such as Sub Saharan Africa, this percentage increases up to 58%. Therefore, solutions adopting 3G technologies in advanced scenarios, such as ultra-dense networks, are justified, especially when targeting sustainable infrastructures, since 3G technologies will remain active in the next decade in large regions of the world. However, the majority of selected KPIs can also be found in 4G technology for the sake of generality.  
In the remainder of the section, steps 1 to 9, as presented in Section 2, will be particularized while assuming a UMTS-based network. Visualization (step 10) and a discussion of the results will be provided in Section 4.
# Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
## 2 Materials and Methods
### Determination of Chlorophyll Content  
Chlorophyll content was measured with a portable chlorophyll meter, and three samples with uniform growth representing the growth of the whole plot were selected from each experimental area. The chlorophyll content in the top leaves for each sample was estimated by averaging values over the three samples. In this study, SPSS25 is mainly used for data analysis. Under the same study conditions, Table 1 shows the chlorophyll content at different growth stages. It is clear to note that the statistic value gradually decreased from the flagging stage to the maturity stage in terms of minimum, maximum, and mean values. However, the variance shows an inverse trend during the whole growing cycle.
## 3 Experimental Results
### Steady-State Performance  
The experimental behaviors of the PV module and motor pumping system have been tested under steady-state condition of irradiance level 1000 W/m\({}^{2}\). The proposed MPPT design technique is working effectively and tracks optimal power from PV module with unity duty cycle at 1000 W/m\({}^{2}\) solar insolation level depicted in Figure 9. Practical results obtained for the BLDC-driven Luo converter-employed PV pumping are described in Figure 9a PVG at 1000 W/m\({}^{2}\). (Figure 9b) BLDC performance at 1000 W/m\({}^{2}\). (Figure 9c) generated hall sensor pulses at 1000 W/m\({}^{2}\) (Figure 9d) switched and hall pulses at 1000 W/m\({}^{2}\) (Figure 9e) BLDC performance at 300 W/m\({}^{2}\) (Figure 9f) switched and hall pulses at 300 W/m\({}^{2}\).The corresponding BLDC motor and torque (1500 rpm) has been demonstrated in Figure 9d presents the obtained hall sensor pulses with motor torque. The performance of the BLDC motor-pumping system has been evaluated with 300 W/m\({}^{2}\) solar irradiance. The motor torque  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Angle**} & \multicolumn{3}{c}{**Hall Signals**} & \multicolumn{3}{c}{**Switching States**} \\ \cline{2-9}  & **H\({}_{1}\)** & **H\({}_{2}\)** & **H\({}_{3}\)** & **\(S_{1}\)** & **\(S_{2}\)** & **\(S_{3}\)** & **\(S_{4}\)** & **\(S_{5}\)** & **\(S_{6}\)** \\ \hline
0  
is experimentally obtained, which is sufficient to operate PV water pumping. Based on duty cycle generation using the MPPT algorithm, the corresponding hall signals have been generated to trigger six switches of the inverter.  
Figure 9: BLDC-driven Luo converter-employed PV pumping (**a**) PVG at 1000 W/m\({}^{2}\);(**b**) BLDC performance at 1000 W/m\({}^{2}\); (**c**) generated hall sensor pulses at 1000 W/m\({}^{2}\); (**d**) switched and hall pulses at 1000 W/m\({}^{2}\); (**e**) BLDC performance at 300 W/m\({}^{2}\); (**f**) switched and hall pulses at 300 W/m\({}^{2}\).
# Vertical GaN MOSFET Power Devices
## 4 Techniques to Boost the Breakdown Voltage
### Edge Termination
However, it is worth noting that this intricate process can increase the fabrication costs of the device. The choice of the optimal method for improving \(V_{BD}\) depends on specific requirements, necessitating careful consideration of associated trade-offs. The advantages and trade-offs of the abovementioned methodologies are as follows. (1) Modifying doping concentration in the drift region: decreasing doping concentration increases \(V_{BD}\); however, \(R_{ON}\) increases. Conversely, increasing doping concentration improves \(R_{ON}\) but reduces \(V_{BD}\). (2) Drift region scaling: increasing drift thickness enhances \(V_{BD}\) but simultaneously increases device \(R_{ON}\), thus reducing the switching speed of the device. (3) Gate oxide engineering: optimizing gate oxide thickness and material properties can influence GaN MOSFETs' voltage-blocking capabilities. Thicker gate oxides enhance \(V_{BD}\) but elevate gate capacitance, impacting overall performance. (4) Field plate termination: field plate termination is a widely adopted method for improving \(V_{BD}\). It facilitates even distribution of the electric field, reducing electric field crowding at the gate-edge region, thus improving voltage handling capabilities and device reliability. However, the efficacy of field plate termination diminishes as device size decreases. (5) Edge termination techniques: edge termination techniques aim to enhance \(V_{BD}\) at the device's edges, especially beneficial for high-voltage applications. These techniques effectively mitigate the risk of edge breakdown, offering simplicity and scalability. Nevertheless, the dry-etched sidewalls in edge termination introduce a leakage path that significantly impacts the off-state drain current [111]. Ion implantation has demonstrated significant improvements in Si and SiC devices by protecting the dielectric at the trench bottom and serving as a body contact. In GaN, limitations exist for p-ion implantation and selective area doping. An alternative approach involves creating a buried shield under the trench using etching and regrowth methods. Ion implantation has shown significant improvement in Si and SiC-based devices to protect the dielectric at the bottom of the trench and to serve as body contact. However, due to the limitation in p-ion implantation and selective area doping in GaN [112], a buried shield is formed under the trench by the etching and regrowth method [113].
# Tunable non-Hermiticity in Coupled Photonic Crystal Cavities with Asymmetric Optical Gain
## 2 Numerical Calculation for Coupled PhC Cavities  
To explore non-Hermiticity in a coupled optical system, we performed three-dimensional (3D) numerical simulations using the finite element method (FEM; COMSOL Multiphysics 5.4, RF module, COMSOL Inc, Stockholm Sweden). The FEM solves the 3D vector Helmholtz equation, which is based on [PERSON]'s equations, for a given optical structure, and it provides complex eigenfrequencies and field profiles of resonant optical modes in the structure. We employed the modified L3 defect PhC cavity, which was constructed by using three missing airholes and two reduced and shifted nearest-neighbor airholes in a triangular lattice PhC slab structure. The lattice constant of the PhC was 420 nm with a regular airhole diameter of 265 nm. At the cavity boundary, the reduced airholes with a diameter of 140 nm were outwardly shifted by 63 nm from their original lattice positions. The thickness of the slab was 250 nm. The complex index of refraction, \(N_{\text{slab}}(\mathbf{x})+iK_{\text{slab}}(\mathbf{x})\), defined the slab PhC structure and the cavity. The initial refractive index (\(N_{\text{slab}}\)) and extinction coefficient (\(K_{\text{slab}}\)) of the slab were 3.3 and 0.01, respectively. To introduce optical gain in the cavity, we changed \(K_{\text{slab}}\) in the cavity region with a volume of \(1.46\times 2.52\times 0.25\)\(\upmu\)m\({}^{3}\) centered at the middle of the cavity: \(K_{\text{slab}}\) was varied from 0.01 to \(-0.01\) in the cavity, which led to an optical gain of 0-1.106 THz. An intrinsic optical loss of 0.573 THz was fixed. \(N_{\text{slab}}\) and \(K_{\text{slab}}\) in the airholes were set to one and zero, respectively. In this study, we considered the coupling of the fundamental mode in each single cavity. The simulation domain was terminated by the scattering boundary condition to eliminate unwanted reflections from the domain boundaries.
### Related Works
#### 1.2.1 Tag-Based Tracking  
Registering fiducial markers to the tool head's geometry is a popular approach that has yielded robust and accurate results in the past. This approach has been demonstrated in conjunction with head-mounted displays (HMD) for high-precision drilling operations [4; 9] and tool placement [10]. Multiple fiducial markers are often employed to reinforce the accuracy of detection [11], even using smartphones as the main interface [12; 13]. Small-scale artificial patterns for motion tracking are a valid alternative to traditional ArUco or QR codes for close-range deployments [5; 14; 15]. Using fiducial landmarks with monocular cameras has a major advantage: the camera can easily determine its scaled position around the calibrated object, even without depth information. Further, it is simple to replicate and integrate. In contrast, establishing tag positions on a model requires the careful calibration of physical-digital referencing. This process can be disrupted by shocks and vibrations during its utilization, making it brittle and unfit for unstructured environments.
## 3 Results
### Applications Across Industrial Domains
#### 3.3.7 Semiconductor Manufacturing  
ML plays a critical role in supporting wafer defect detection and enhancing production reliability. Logistic regression with false discovery rate correction achieved 94.64% accuracy on the SECOM dataset, enhanced by synthetic minority oversampling technique (SMOTE), PCA, and latent Dirichlet allocation (LDA) preprocessing [74]. In [75], RF achieved 93.62% accuracy in wafer failure prediction, aiding maintenance scheduling and minimizing interventions.
## 3 Gray Value Compression
### Lz-Family Coding
#### 3.4.2 LZ78  
The LZ78 coding algorithm is also a _dictionary-based coding algorithm_. Instead of searching for the largest substring within a constant number of symbols as the LZ77 algorithm does, the LZ78 algorithm searches for the largest substring within all symbols that have appeared and represent the prefix to be compressed. In the case of an infinitely large input string, the location of the largest substring is unbound and complicates the encoding. To avoid this situation, the input string is divided into large blocks of length \(n\) so that the largest strings can only begin at certain locations and that the location determines the length of the substring.
## 2 System Model
### Energy Efficiency  
The performance metric used in this paper is energy efficiency. Energy efficiency is defined as the ratio of the corresponding rate to the total power consumption, as follows:  
\[EE=\frac{B\log_{2}(1+\gamma)}{P_{tot}}, \tag{33}\]  
where \(B\) is the system bandwidth, \(\gamma\) is the corresponding SNR of each relaying scheme, and \(P_{tot}\) is the total power consumption. In this paper, we assume the total power budget for all the RIS-UAVs and UAV relaying schemes is the same for fair comparison. Hence, the transmit power available for active RIS-UAV relaying scheme, \(P^{act}\); hybrid RIS-UAV relaying scheme, \(P^{phy}\); passive RIS-UAV relaying scheme, \(P^{pss}\); half-duplex UAV relaying scheme, \(P^{bd}\); and full-duplex UAV relaying scheme, \(P^{fd}\), can be computed as [12; 26]:  
\[P_{bs}^{act}+P_{ris}^{act}=P_{tot}-N(P_{sw}+P_{dc})-P_{s}-P_{d}, \tag{34}\]  
\[P_{bs}^{hyb}+P_{ris}^{hyb}=P_{tot}-NP_{sw}-LP_{dc}-P_{s}-P_{d}, \tag{35}\]  
\[P_{bs}^{pas}=P_{tot}-NP_{sw}-P_{s}-P_{d}, \tag{36}\]  
\[P_{bs}^{hd}+P_{r}^{hd}=P_{tot}-P_{relay}-\frac{1}{2}P_{s}-P_{d}, \tag{37}\]  
\[P_{bs}^{fd}+P_{r}^{fd}=P_{tot}-P_{relay}-P_{s}-P_{d}, \tag{38}\]  
respectively, where \(P_{sw}\) is the power consumption of the control circuit and phase shift switch at each RIS element, \(P_{dc}\) is the power consumption of DC biasing at each active RIS element, while \(P_{s}\), \(P_{d}\), and \(P_{relay}\) are the hardware-dissipated power at the BS, UE, and UAV relay, respectively. The BS of the half-duplex relaying scheme is only active half of the time; hence, its hardware-dissipated power at the BS is only half of the other relaying schemes. In addition, fixed power allocation is assumed for each relaying scheme, where equal power is allocated to the BS and RIS-UAV or UAV relays to transmit the signals.
## 3 New Generation Sensor Web Enablement
### Web Notification Service
#### 3.3.5 Discovery  
The SWE framework supports the flexible integration of all kinds of sensor data sources into applications. However, the availability of interoperable sensor data sources must be complemented by discovery solutions that allow users to find the data they need for solving their questions and tasks [93]. In conventional SDIs consisting of servers providing maps, coverages or geometric features, this is usually covered by the OGC Catalogue [94]. Due to the specifics of sensor networks, this approach is not directly applicable for the Sensor Web [95]. On the one hand, there are different metadata models: SensorML within the Sensor Web and for example ebRIM [96] used by catalogues. On the other hand, the often very dynamic structure of sensor networks creates further challenges that are not reflected by the OGC Catalogue interface. Further questions comprise the automatic collection of sensor metadata and semantic problems such as the description of the phenomena a sensor is observing. In order to address these challenges, the approaches described in Table 6 have been developed.  
The enhancements of the SWE framework in order to enable sensor discovery address both the information model (Section 3.2) as well as the interface model (Section 3.3). Within the SWE information model the discovery enhancements mainly address the provision of sufficient SensorML based sensor metadata and the mapping of those metadata elements to catalogue information models such as ebRIM. For extending the interface model two kinds of web services are discussed. The Sensor Instance Registry (SIR) for harvesting, managing and transforming sensor metadata as well as the Sensor Observable Registry (SOR) for managing the semantics of the phenomena observed by sensors.