esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 6 Conclusions  
In this paper, we propose a novel indoor localization method to improve the localization accuracy and solve the problem that PDR cannot work properly in three-dimensional space by combining vision and inertial sensors. It was proved by experiments that our V-PDR method successfully reduces accumulated errors of PDR and improves the localization accuracy. In addition, it extends application scene of PDR to a three-dimensional space, which can still provide accurate localization service when pedestrians are moving across floors.  
[PERSON] conceived and designed the experiments. [PERSON] and [PERSON] performed the experiments and analyzed the data. [PERSON] contributed the experimental environment and analysis tools. [PERSON] provided critical comments to the paper. [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grants 61903246.  
Jiuchao Qian mainly focuses on indoor localization technologies, video analysis, signal processing, deep learning, ubiquitous computing, and artificial intelligence.
## 2 Related Work
### Communication-Based Validation
Unfortunately, however, the current standards typically allow the adaptation of the transmit (Tx) power for the purpose of congestion control. In particular, the SAE J2945/1 stipulates that a Wireless Access in Vehicular Environment (WAVE) device can transmit at a Tx power ranging from 10 dBm to 23 dBm, depending on the channel congestion condition [24]. Consequently, a factor of 20 power difference obviously wildly affects the communication range of BSM. Moreover, the range can be also highly dependent on the given scenario and buildings in the vicinity, and be anywhere between 100 m and 500 m [13]. Under these circumstances, the position plausibility check based on the estimated maximum communication distance constraint cannot be reliable. Received signal strength indicator (RSSI)-based plausibility check mechanisms are subjectto the same problem. [PERSON] et al. [25] exploits the time of flight to check the distance to the transmitter. But this scheme requires extremely precise time synchronization between vehicles. Lastly, we stress that our paper is not about Sybil attacks. In Sybil attacks, the attacker can forge many false identities [26; 27]. In the current V2X standards, however, the vehicle identity in each message must be proven by the attached security credential based on public key infrastructure (PKI) Ref [1]. Our solution is based on the current standards, so we assume that even the attack should use its certificate to mount the attack. In our threat model, the attacker is not capable of tampering with the PKI. It can only tamper with the sensor input (e.g., Global Positioning System (GPS) coordinates) that is provided to the IEEE 1609.2 security module. In fact, it is the focus of this paper. When an authenticated attacker tries to propagate false information, we can narrow the spatial attack window to a short distance to the potential victims so that the hardware sensors can double-check the received false information. In Section 3, we will introduce a complementary scheme in the third category, based on physical constraints of low-power communication, to defend against remote roadside attackers. Although it does not preclude the possible employment of the other two approaches in combination, an independent check based on the communication constraints has certain advantages over them. First, it can work even when the sensor-based position check does not work, e.g., the in non-line-of-sight (NLoS) condition. This is because most vehicle sensors such as camera, radar, and lidar are LoS devices. Second, it can work when the behavioral analysis may fail. For one, the behavioral analysis cannot be applied to a stopped vehicle, since it does not exhibit any movement behavior. Since V2X is expected to become a regulation-enforced safety feature in the near future [28], we believe the communication-based position verification should be included irrespective of other additional checks. In particular, our scheme can be easily implemented in the WAVE or the cellular V2X (C-V2X) framework.
## 2 Force Analysis and Power Calculation
### Cruise Phase  
The cruise phase can be primarily divided into two sub-phases: the level flight acceleration phase and the steady-speed cruise phase. In the level flight acceleration phase, after the UAV ascends to its cruising altitude, it transitions to level flight and accelerates until reaching the designated cruising speed. The force and power characteristics during this phase are as follows:  
(1) Force analysis:  
\[\left\{\begin{array}{l}L_{cr}=W,\ \textit{Vertical direction}\\ F_{a,cr}=T_{a,cr}-D_{cr},\ \textit{Horizontal direction}\end{array}\right. \tag{9}\]  
(2) Power situation:  
\[P=P_{cr}=T_{a,cr}\cdot V(t) \tag{10}\]  
If there is uniform acceleration, the power can also be expressed as \(P=P_{cr,r}(a_{cr})\), where \(a_{cr}\) is the constant acceleration during the cruise acceleration phase, so the power \(P_{cr,r}\) is also constant at this time.  
During the steady-speed cruise phase, the UAV maintains its cruising speed, which can be categorized into two operational modes: maximum range speed and maximum endurance speed. The maximum range speed is the flight speed at which the UAV minimizes fuel or energy consumption per unit distance, enabling it to cover the greatest distance with the available fuel or energy. Conversely, the maximum endurance speed is the flight speed at which the UAV minimizes fuel or energy consumption per unit time, allowing it to remain airborne for the longest duration at a given altitude. Notably, the speed corresponding to the maximum lift-to-drag ratio is typically the same as the maximum range speed. Since both operational modes involve steady-speed cruise, the aerodynamic forces are balanced, ensuring stable flight dynamics.  
(1) Force analysis:  
\[\left\{\begin{array}{l}L_{cr}=W,\;Vertical\;direction\\ T_{a,cr}=D_{cr},\;Horizontal\;direction\end{array}\right. \tag{11}\]  
(2) Power situation:  
\[P=\left\{\begin{array}{l}P_{cr,t}=T_{a,cr}\cdot V_{t,\max}\\ P_{cr,R}=T_{a,cr}\cdot V_{R,\max}\end{array}\right. \tag{12}\]  
where \(P_{cr,t}\) represents the power corresponding to the maximum endurance speed, and \(P_{cr,R}\) represents the power corresponding to the maximum range speed. \(V_{t,\max}\) and \(V_{R,\max}\) denote the maximum endurance speed and range speed, respectively, defined as \(P_{cr,t}<P_{cr,R}\).  
Under normal conditions, the speed remains constant during the cruise phase. However, in certain scenarios, the UAV may enter a non-equilibrium state while maintaining its cruising altitude. In such cases, the UAV experiences either acceleration or deceleration. The force and power characteristics of the acceleration process have already been discussed; the following provides an analysis of the forces and power requirements during the deceleration process.  
(1) Force analysis:  
\[\left\{\begin{array}{l}L_{cr}=W,\;Vertical\;direction\\ F_{a,cr}=D_{cr}-T_{a,cr},\;Horizontal\;direction\end{array}\right. \tag{13}\]  
(2) Power situation:  
\[P=P_{cr}=T_{a,cr}\cdot V(t) \tag{14}\]
###### Abstract  
In this paper, we report on the use of piezoelectric sensors and actuators for the active suppression of vibrations associated with the motor-driven rotation of thin flexible plate held vertically. Motor-driven flexible structures are multi-input multi-output systems. The design of active vibration-suppression controllers for these systems is far more challenging than for flexible structures with a fixed end, due to the effects of coupling and nonlinear vibration behavior generated in structures with poor damping. To simplify the design of the controller and achieve satisfactory vibration suppression, we treated the coupling of vibrations caused by the rotary motion of the thin flexible plate as external disturbances and system uncertainties. We employed an adaptive fuzzy sliding mode control algorithm in the design of a single-input-single-output controller for the suppression of vibrations using piezoelectric sensors and actuators. We also used a repetitive control system to reduce periodic vibrations associated with the repetitive motions induced by the motor. Experimental results demonstrate that the hybrid intelligent control approach proposed in this study can suppress complex vibrations caused by modal excitation, coupling effects, and periodic external disturbances.  
flexible structure; piezoelectric materials; active vibration control; adaptive fuzzy sliding mode control; repetitive control +
Footnote †: journal: Applied Sciences
# Design of True Time Delay Millimeter Wave Beamformers for 5G Multibeam Phased Arrays
## 2 Beamforming Networks Design
### Tree Type Beamforming Network Architecture
Moving to higher-order stages (e.g., stage 2-\(\
u\)), the power division ratios are evaluated as:  
\[C_{\
u}^{j}=\frac{\sum\limits_{i=\text{ ports }\in\text{ first branch of }i\text{th divider}}P_{i}}{\sum\limits_{i=\text{ ports }\in\text{ second branch of }i\text{th divider}}P_{i}} \tag{7}\]  
while the electrical lengths and the delays of the corresponding delay lines are:  
\[\Delta\phi_{\
u}=\frac{N}{2^{\
u}}\Delta\phi=\omega_{0}\tau_{\
u} \tag{8}\]  
where \(\omega_{0}\) is the center angular frequency. Following this design methodology, any desired amplitude distribution is feasible maintaining the beam at the desired steered angle.
# A Channel Compensation Technique Based on Frequency-Hopping Binary Offset Carrier Modulated Signal
###### Abstract  
Space-Air-GroundIntegrated Network (SAGIN) has been becoming a promising future network construction to enable the integration of terrestrial communications, aerial networks and satellite systems, for achieving high data rate wireless access and seamless coverage. Focusing on the space-to-air propagation, which is requiring transmitted signal of large Doppler shift resilience in dynamic circumstances, the proposed signal as employing I/Q modulation to accommodate frequency-hopping binary offset carrier (FH-BOC) signal and orthogonal frequency division multiplexing (OFDM) signal, and to exploit respective benefits. Finally, numeric results are provided to demonstrate performance superiority on Bit Error Rate (BER) and signal tracking stability. In conclusion, our designed signal requires about 8 dB less energy per bit at the desired BER level than normally compensated OFDM signal.  
S pace-Air-Ground Integrated Network (SAGIN); Unmanned aerial vehicle (UAV); Frequency-Hopping Binary Offset Carrier (FH-BOC); Orthogonal Frequency Division Multiplexing (OFDM); channel compensation 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
202315849. [[https://doi.org/10.3390/rs15071849](https://doi.org/10.3390/rs15071849)]([https://doi.org/10.3390/rs15071849](https://doi.org/10.3390/rs15071849))
# Transmission Efficiency of Cycloid-Pinion System Considering the Assembly Dimensional Chain
## 4 Transmission Efficiency Model  
### Cycloid-Pinion System Mesh Force Analysis Considering Clearance  
When torque is applied, the meshing deformation of the cycloid and the pin tooth is calculated in accordance with Equation (10) [40].  
\[\delta_{i}=\varphi r_{a}\sin\theta_{i}-\Delta_{e} \tag{10}\]  
where \(\theta_{i}=\pi-\theta-\theta_{e}\) represents the angle between the normal of the \(i\)-th profile contact points and the line O\({}_{\text{a}}\)O\({}_{\text{b}}\) extension cord; \(\varphi\) represents the rotation angle of the cycloid subjected to torque; and \(\Delta_{e}\) represents the normal clearance, defined as the contact clearance, which leads to a change in contact force [38; 41], as shown in Figure 5. The mesh force is given in Equation (11).  
\[\left\{\begin{array}{l}F_{i}=4k_{z}T[\sin\theta-P(1-\sin\theta)]/\text{z}_{ \text{a}}\text{r}_{\text{a}}\\ \sin\varphi_{i}-P(1-\sin\theta)\geq 0\end{array}\right. \tag{11}\]  
Key parameters are determined as Equation (12).  
\[\left\{\begin{array}{l}P=k_{z}\pi\Delta_{e}\text{z}_{\text{a}}\text{Ebr}_{ \text{p}}/\text{Co}\\ A=P/(1+P)\\ k_{z}=\pi\Big{[}(1+P)\Big{(}\pi-2\text{asin}A-2A\sqrt{1-A^{2}}\Big{)}\Big{]}^{- 1}\\ Co=8T\big{(}1-\varphi^{2}\Big{)}\left[\ln\frac{\text{z}_{\text{a}}\pi\text{r} \text{Ebr}_{\text{p}}\sqrt{1-\lambda_{c}\lambda^{2}}}{8T(1-\varphi^{2})}+0.815 \right]\end{array}\right. \tag{12}\]  
where \(k_{z}\) indicates the coefficient of increased load in meshing due to clearances; \(P\) indicates the elastic loading parameter in meshing, which is the ratio of the initial clearance in meshing to the maximum deformation; \(C_{o}\) and \(k_{k}\) (\(k_{k}=1\)) are coefficients; \(v\) and E represent [PERSON]'s ratio and the elastic modulus, respectively; b represents the tooth thickness of the cycloid; and \(\lambda_{c}\) represents the shortening factor of the epicycloid, \(\lambda_{c}=r_{b}/R_{b}\).  
Figure 4: Computational efficiency comparison.
# Intersection-Based Routing with Fuzzy Multi-Factor Decision for VANETs
## 3 Protocol Design  
In this section, we describe the routing protocol we proposed in detail. As mentioned earlier, packets can only change direction at intersections due to the limitation of the urban environment. In this article, we focus on the selection of the direction of the packet at the intersection. We exploit three factors for determining the next-hop road segment with the triangular fuzzy number. Moreover, we use a restricted greed forwarding condition to determine the next-hop vehicle on the road segment between two adjacent intersections. So, the proposed routing protocol can be divided into two parts to describe in detail: (1) speed-based restricted greedy forwarding and (2) direction decision at the intersection.
# Feasibility of a Drug-Releasing Radiofrequency Ablation System in a Porcine Liver Model
## 3 Results
### Analysis of Ablation Zone of the Liver  
The mean of the total delivered power according to the ablation duration (per minute) was 1.04 kcal/min. All measurement values of the ablation zone are shown in Table 1. The mean values of D\({}_{\text{max}}\), D\({}_{\text{min}}\), and D\({}_{\text{v}}\) were 3.56 \(\pm\) 0.19, 2.56 \(\pm\) 0.27, and 3.12 \(\pm\) 0.09 cm, respectively. In addition, the AV in the porcine liver and circularity was 15.22 \(\pm\) 2.30 cm\({}^{3}\) and 0.72 \(\pm\) 0.08, respectively.  
The IV and \({}^{18}\)F-FDG measurement values are shown in Table 1. The mean IV around the ablated lesion was 17.21 \(\pm\) 2.85 cm\({}^{3}\) (Figure 5). This was higher than the AV (17.21 \(\pm\) 2.85 vs. 15.22 \(\pm\) 2.30 cm\({}^{3}\)) in the nine ablated lesions. Additionally, a statistically significant difference exists between the AV and IV (\(p\) = 0.008). The mean \({}^{18}\)F-FDG injection dose and the mean-estimated dose from PET/MR images were 7.13 \(\pm\) 0.84 mCi/30 mL and 5.19 \(\pm\) 0.60 mCi/30 mL, respectively. The retention rate ranged between 66.35% and 76.97%, and the mean retention rate percentage was 79.89% \(\pm\) 4.22%. Also, the mean SUV showed 0.44 \(\pm\) 0.05 (range, 0.375-0.541; Table 1).  
Figure 4: The elliptical shape of a radiofrequency ablation (RFA) zone created by the drug-releasing cooled wet electrode. (**a**) A specimen in the electrode insertion axis. D\({}_{\text{max}}\) and D\({}_{\text{min}}\) indicate maximum diameter (black solid arrow) and minimum diameter (black dotted arrow). (**b**) A specimen in the transverse plane perpendicular to the electrode insertion axis. D\({}_{\text{v}}\) indicates vertical axis (white solid arrow).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \(D_{\rm min}\) & \(D_{\rm max}\) & \(D_{\rm v}\) & **AV** & **IV** & **Circularity** & **Injection Dose** & **Estimated Dose** & **Retention** & **SUV** \\  & **(cm)** & **(cm)** & **(cm)** & **(cm)** & **(cm)** & **(cm)** & **(cm)** & **(cm)** & **(cm)** & **Rate (cm)** & **Rate (cm)** & \\ \hline
1.1
## 2 Overview of Study Area, Data, and Methods
### Research Methods  
#### 2.4.1 Analysis of Spatial Distribution Directionality  
Spatial distribution directional analysis refers to the outline and dominant direction of the observed variable in the spatial distribution [43]. The standard deviation ellipse (SDE) is a spatial statistical method used to reveal the spatial distribution characteristics of elements [44]. This method mainly measures the centre of gravity, major axis, minor axis, azimuth angle, and other parameters of the SDE of geographic elements to quantitatively describe the spatial distribution characteristics of the observed variables in the study area [45]. The definition formula is as follows:  
Center of gravity coordinates:  
\[\overline{X}_{w}=\sum_{i=1}^{n}w_{i}x_{i}/\sum_{i=1}^{n}w_{i};\overline{Y}_{w} =\sum_{i=1}^{n}w_{i}y_{i}/\sum_{i=1}^{n}w_{i} \tag{1}\]  
\[\tan\theta=\frac{\left(\sum_{i=1}^{n}w_{i}^{2}\widehat{x}_{i}^{2}-\sum_{i=1}^{ n}w_{i}^{2}\widehat{y}_{i}^{2}\right)+\sqrt{\left(\sum_{i=1}^{n}w_{i}^{2} \widehat{x}_{i}^{2}-\sum_{i=1}^{n}w_{i}^{2}\widehat{y}_{i}^{2}\right)^{2}+4 \sum_{i=1}^{n}w_{i}^{2}\widehat{x}_{i}^{2}\widehat{y}_{i}^{2}}}{2\sum_{i=1}^{n }w_{i}^{2}\widehat{x}_{i}\widehat{y}_{i}} \tag{2}\]  
X-axis standard deviation:  
\[\overline{X}_{w}=\sum_{i=1}^{n}w_{i}x_{i}/\sum_{i=1}^{n}w_{i};\overline{Y}_{w }=\sum_{i=1}^{n}w_{i}y_{i}/\sum_{i=1}^{n}w_{i} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable Code** & **Variable** & **Unit** & **Mean** & **Std. Dev.** & **Min** & **Max** \\ \hline carbonsink & Forest carbon sink & Ton & 5,357,876.00 & 3,167,827.00 & 1,197,632.00 & 15,400,000 \\  & Forest Management & & 6150.67 & 6698.97 & 240.00 & 31,260.00 \\  & Investment & 10 thousand & 79,268.55 & 54,718.58 & 11,291.84 & 354,364.00 \\  & Gross Domestic Product & 10 thousand & 7079.68 & 4884.82 & 398.60 & 21,506.10 \\  & & employees & & & & & \\ workpop & Average number of & & & & & & \\  & employees & & & & & & \\ pre & Precipitation & mm & 639.94 & 109.03 & 398.30 & 934.61 \\  & Temperature & \({}^{\circ}\)C & 20.59 & 0.96 & 16.80 & 23.48 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variable design and descriptive statistics.  
Y-axis standard deviation:  
\[\overline{X}_{w}=\sum_{i=1}^{n}w_{i}x_{i}/\sum_{i=1}^{n}w_{i};\overline{Y}_{w}= \sum_{i=1}^{n}w_{i}y_{i}/\sum_{i=1}^{n}w_{i} \tag{4}\]  
In Formulas (1)-(4), (\(\overline{X}_{w}\), \(\overline{Y}_{w}\)) represents the weighted average centre of each observed variable; (\(x_{i}\), \(y_{i}\)) represents the spatial coordinates of the observed variable; \(w_{i}\) represents the spatial weight; \(\theta\) is the azimuth of the standard deviation ellipse, that is, the main trend direction of the data distribution; \(\sigma_{x}\), \(\sigma_{y}\) respectively represent the standard deviation of the ellipse's x-axis and y-axis; and \(\overline{x}_{i}\), \(\overline{y}_{i}\) respectively represent the coordinate deviation of each observed variable to the weighted average centre.
# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
## 3 Cimkg
### Co-Interactive Module  
The co-interactive module aims to establish a bidirectional connection between intent detection and slot filling and consists of three key components: an intent and slot label attention layer, a co-interactive attention layer, and an extended feed-forward network layer [12].  
First, the intent and slot label attention layers are used to obtain slot and intent representations. Second, the co-interactive attention layer replaces traditional self-attention, explicitly establishing a relationship between intent and slots. Finally, the extended feed-forward network layer implicitly combines intent and slot information. This process captures and integrates intent and slot information through both explicit and implicit methods, thereby establishing a bidirectional connection between intent and slot information.
# Evaluation of Algae-Based Biodiesel Production Topologies via Inherent Safety Index (ISI)
## 2 Materials and Methods
### Process Description
#### 2.1.3 Topology 3. Hydrothermal Liquefaction (HTL)  
Microalgae (1000 kg/h) are fed into the cultivation stage combined with 9000 kg/h of nutrients and subsequently harvested by centrifugation (see Figure 4). The harvested biomass is sent to the next stage where the hydrothermal liquefaction reaction is performed at 200 bar and 350 \({}^{\circ}\)C [42]. Under these conditions, the macromolecules found within the algal biomass (including lipids, proteins, and carbohydrates) suffer depolymerization reactions (fragmentation, hydrolysis, dehydration, deoxygenation, aromatization, and repolymerization) [52], which leads to a three-phase product stream: liquid, solid and gaseous. Then, the mainstream from the HTL reactor enters the solids separation unit where ashes and carbon (CHAR) are separated and discarded. The liquid, gas, and organic phases are then separated, and the bio-crude is obtained. Bio-crude derived from thermochemical biomass conversion is a heavy organic liquid with a relatively high oxygen content, which is transformed into a conventional hydrocarbon fuel by hydrogen treatment in the hydrotreating stage [53]. In the hydrotreating unit, reactions occur at 400 \({}^{\circ}\)C and 100 bar [42], and hydrogen consumption is 13% of the bio-crude mass flow [54]. The hydrocarbon mixture subsequently undergoes the cracking and oligomerization stage where it is fractionated into three boiling points: C4 minus (off-gas) range, naphtha range, and heavy oil range. The latter is subjected to a distillation process to obtain pure diesel-like biofuel (160.9 kg/h).  
Figure 3: Process diagram for biodiesel production from microalgae via in situ transesterification.