esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposed a novel method for measuring vertical displacement in tunnels based on long-gauge strain sensing. Its effectiveness was validated through a series of numerical simulations. The main conclusions of this study can be summarized as follows.  
1. The proposed method effectively addresses the challenge of accurately measuring vertical displacement in the presence of structural coupling deformation. This is achieved by separating the bending strain from the measured strain to estimate the vertical deformation distribution of the tunnel structure.
2. The innovative sensor layout adopted in the monitoring section enables the vertical displacement measurement of the tunnel. This sensor layout not only realizes the decoupling of the measured strain, but also guarantees accurate displacement estimations for the tunnel.
3. Numerical examples successfully confirmed the accurate identification of vertical displacement in a tunnel under different loading conditions, with a maximum error of less than 5%, satisfying the requirements of practical applications. This method also achieves a resolution of 0.1 mm for the identified vertical displacement, with certain units at the mid-span achieving a resolution of 0.01 mm. Fluctuating noise  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Scheme & 1 & 2 & 3 & 4 \\ \hline MAVPE & 5.12 & 7.73 & 4.54 & 6.52 \\ \hline \hline \end{tabular}
\end{table}
Table 7: MAVPEs (%) of identified vertical displacement for different sensor layout schemes.  
Figure 14: REs of identified displacement for different sensor layout schemes: (a) 1; (b) 2; (c) 3; (d) 4.  
levels and various sensor layout schemes were tested to find that they do not degrade the accuracy of the proposed method.  
A series of calculation results validated the suitability of the proposed method, thus offering a valuable reference for the analysis of data collected through field monitoring. In the future, our primary focus will be analyzing a real-world damaged tunnel structure. This is crucial, as the presence of damage in the tunnel structure can potentially impact the proposed displacement monitoring method.  
Conceptualization and methodology, Q.Z.; validation, R.L.; writing--original draft preparation, Q.Z. and H.Y.; writing--review and editing, H.Y. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51908386).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
###### Abstract  
This paper presents an extended Kalman-filter-based sensor fusion approach, which enables path-following control of a holonomic mobile robot with four mecanum wheels. Output measurements of the mobile platform may be sensed at different rates: odometry and orientation data can be obtained at a fast rate, whereas position information may be generated at a slower rate. In addition, as a consequence of possible sensor failures or the use of lossy wireless sensor networks, the presence of the measurements may be nonuniform. These issues may degrade the path-following control performance. The consideration of a nonuniform dual-rate extended Kalman filter (NUDREKF) enables us to estimate fast-rate robot states from nonuniform, slow-rate measurements. Providing these estimations to the motion controller, a fast-rate control signal can be generated, reaching a satisfactory path-following behavior. The proposed NUDREKF is stated to represent any possible sampling pattern by means of a diagonal matrix, which is updated at a fast rate from the current, existing measurements. This fact results in a flexible formulation and a straightforward algorithmic implementation. A modified Pure Pursuit path-tracking algorithm is used, where the reference linear velocity is decomposed into Cartesian components, which are parameterized by a variable gain that depends on the distance to the target point. The proposed solution was evaluated using a realistic simulation model, developed with Simscape Multibody (Matlab/Simulink), of the four-mecium-wheeled mobile platform. This model includes some of the nonlinearities present in a real vehicle, such as dead-zone, saturation, encoder resolution, and wheel sliding, and was validated by comparing real and simulated behavior. Comparison results reveal the superiority of the sensor fusion proposal under the presence of nonuniform, slow-rate measurements.  
mobile robot; sensor fusion; Kalman filter; odometry; mecanum wheels Article
## 4 Interference Analysis
### Mathematical Models for Interference Calculation
#### 4.2.1 Link Budget Calculation  
A link budget meticulously accounts for all the power gains and losses that a signal experiences as it travels from the transmitter to the receiver. This includes the transmitted power, the gains of the transmitting and receiving antennas, various propagation losses (such as free-space loss and atmospheric attenuation), and any other gains or losses within the system. By carefully constructing the link budget for the interfering signal path, we can accurately estimate the interference power level at the victim receiver.
* Uplink Carrier-to-Noise Density Calculations  
The uplink carrier power-to-noise power spectral density ratio at the satellite receiver input, denoted as \(\left[\frac{\mathrm{C}}{\mathrm{N}_{0}}\right]_{\mathrm{up}}\), is a key metric for evaluating the quality of the uplink. This ratio considers the uplink system thermal noise. The calculation of \(\left[\frac{\mathrm{C}}{\mathrm{N}_{0}}\right]_{\mathrm{up}}\) at the S2 receiver, neglecting other potential noise contributions, is given by the Equation (9) [9; 18]:  
\[\left[\frac{\mathrm{C}}{\mathrm{N}_{0}}\right]_{\mathrm{up}}=\mathrm{EIRP}_{ \mathrm{ES}}-\mathrm{FSL}_{\mathrm{up}}-\mathrm{L}_{\mathrm{ABS}}+\frac{ \mathrm{G}_{\mathrm{sat}}}{\mathrm{T}}+\mathrm{k} \tag{9}\]  
\[\mathrm{where}\ \mathrm{FSL}=32.4+20\mathrm{log(f)}+20\mathrm{log(R)} \tag{10}\]  
In Equation (10), the frequency is used in MHz and R in km. These formulas will be used in the Results section to evaluate the signal power levels and the interferences associated.
* Downlink Carrier Power-to-Noise Calculations  
The downlink carrier power-to-noise power spectral density ratio at the ES4 receiver input can be calculated from Equation (11):  
\[\left[\frac{\mathrm{C}}{\mathrm{N}_{0}}\right]_{\mathrm{down}}=\mathrm{EIRP}_ {\mathrm{sat}}-\mathrm{FSL}_{\mathrm{down}}-\mathrm{L}_{\mathrm{ABS}}+\left( \frac{\mathrm{G}}{\mathrm{T}}\right)_{\mathrm{ES}}+\mathrm{k} \tag{11}\]  
In the next subsection, we present the C/ASI calculations in our considered scenario.
## 2 Materials and Methods
### RNA-Seq and Quantitative PCR (qPCR) Expression Analysis  
Three biological replicates of stalk and leaf at the vegetative growth stage, and floret at the commercial stage, were collected from high and low glucoraphanin content pools, respectively. RNA extraction and sequencing were performed by Metware Biotechnology Co., Ltd. (Wuhan, China). Approximately six Gb of transcriptomic data for each segregant pool was available through the Illumina sequencing platform. Clean reads were obtained by filtering low-quality reads from raw reads and then mapped to the _Brassica oleraca_ reference genome. The differentially expressed genes (DEGs) were identified on the basis of |Fold Change| > 2 and \(p\) < 0.05. The significant enrichment threshold for the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was _p_-value < 0.05. The gene functional annotation was aligned with the related species to create a predicted protein interaction network.  
Fresh stalk and leaf at the vegetative growth stage and floret at the commercial stage were collected for qPCR assays. Total RNA was extracted from frozen samples using EasyPure Plant RNA Kit (ER301, Transgene, Beijing, China). Reverse transcription was performed using an ABScript III RT Master Mix for qPCR kit with gDNA Remover (RK20429, ABclonal, Wuhan, China). The qRT-PCR analyses were performed using a 2 \(\times\) Universal SYBR Green Fast qPCRMix (RK21203, ABclonal, Wuhan) kit in a BioRad CFX system following the instructions of the manufacturer. Three biological replicates were performed for each sample. The primer pairs used for qRT-PCR were designed using DNAMAN software. The _Actin_ gene was selected as an internal control. The relative quantification gene expression level was calculated by the 2\({}^{-\Delta\Delta\text{CI}}\) method [32].
## 2 Related Work
When the distance between trajectories is expressed in terms of a likelihood, we speak of probabilistic anomaly detection [28]. In [17, 29, 30, 31], some probabilistic methods for anomaly detection are presented. Many methods tend to first pre-process the trajectories, since commonly used similarity measures, such as the Euclidean distance, require equally spaced and properly aligned trajectories. To overcome these difficulties, some alternative metrics have been proposed, such as the Dynamic Time Warping (DTW) (see, e.g., [14]) which finds the minimum Euclidean distance when the data points of the two trajectories are shifted arbitrarily in time). However, most of the available approaches are thought to work with complete trajectories, _i.e._, they need the points of the whole trajectory before classifying the trajectory as anomalous. That is a problem in areas where positional data are received only intermittently and complete trajectories are not observed. Moreover, when applied for surveillance purposes, the detection of anomalies needs to be performed on-line. In this context, it is crucial to reduce delays between the start of the anomalous behavior and the alarm raised by the monitoring system. Sequential process control techniques aim at shortening the average time required to signal a change in the normal process. In this paper, we apply point-based incremental algorithms both in maritime knowledge discovery and exploitation. The provided example of anomaly detection is performed by using a sliding time window, similarly to video surveillance techniques (see, e.g., [15]). A similar approach is proposed in [32], where sequential motion anomaly detection is performed, assuming that AIS training data are already extracted to form clusters of common paths. In the present paper, the pre-processing, transformation and validation of AIS data is integrated into the functional architecture, which generates the traffic pattern framework.
# Analysis and Design of Functional Device for Vehicular Cloud Computing
## 4 Performance of Two-Hop Relay-Assisted Vehicular Networks
### \(S\to D\) Link  
For wireless vehicular networks, the mutual information capacity \(C_{SD}\) of the \(S\to D\) link channel could be easy to achieve from Equation (3) via the Shannon theory,  
\[C_{SD}=\frac{1}{2}\log_{2}\left(1+\gamma_{0}\right). \tag{16}\]  
The outage probability is defined as the probability of event \(C_{SD}<R\) that the instantaneous channel capacity \(C_{SD}\) falls below this desired rate \(R\), namely  
\[\begin{split} P_{out}^{SD}&=P[C_{SD}<R]\\ &=P[\frac{1}{2}\log_{2}\left(1+\gamma_{0}\right)<R]\\ &=P[\gamma_{0}<2^{2R}-1]\\ &=1-e^{-\frac{2^{2R}-1}{\gamma_{0}}}.\end{split} \tag{17}\]  
There have been a lot of research works in this direct communication case. Therefore, this work mainly focuses on the following \(S\to R_{i}\to D\) link case.
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## 3 Experimental Measurements Conducted in a Cellular Network
### Data Collection  
The data collection process was carried out using a walk test methodology, wherein an operator physically traversed a predefined route while carrying a mobile terminal configured to log network parameters in real time. This approach allows for fine-grained spatial sampling of signal variations experienced by users under mobility. For this study, a 4G-compatible smartphone was employed in conjunction with the NetMonitor application, which enabled the acquisition of measurements specific to the active network and relevant key performance indicators (KPIs), such as the received signal strength indicator (RSSI), GPS position, and time stamps.  
Figure 3 illustrates the general methodology used for data acquisition and analysis. Additionally, Figure 4 presents a flowchart outlining the full measurement process, from data collection to final preprocessing and validation.  
The experimental measurements were conducted in two representative areas of Quito, Ecuador: a densely built-up urban zone and a lower-density suburban zone, both selected to assess the model's performance under different propagation conditions.  
Figure 4: Flowchart of the measurement methodology.  
Figure 3: General methodology for data collection and analysis.
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 4 Results
### Comprehensive Evaluation Results Based on the EWM-TOPSIS Model  
Table 5 is the final evaluation score of the 10 sensors, calculated based on the EWM-TOPSIS model. The three sensors with the highest comprehensive evaluation scores were S7 (0.4232), S4 (0.2369), and S9 (0.091). The above experimental results have proved that the performances of these three sensors (S7, S4, and S9) were excellent for all single evaluation indicators. Therefore, it is reasonable that their comprehensive evaluation scores were also the highest. In contrast, the three sensors that obtained the lowest scores were S6 (0.028), S2 (0.007), and S1 (0.0052). According to the single indicator results, sensor S2 performed poorly in the evaluation of sensitivity and selectivity, and sensor S3 scored lower in the three indicators of sensitivity, selectivity, and repeatability. Although sensor S6 performed well in selectivity, it did not score highly in the repeatability evaluation. Their poor performances in the evaluation of different indicators makes their comprehensive evaluation scores lower. This result also proved the rationality of the as-proposed EWM-TOPSIS model. Therefore, according to the results of the comprehensive evaluation scores, we optimized the E-nose by removing sensors S1, S2, and S6 from the sensor array.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor No.** & **Score** & **Ranking** \\ \hline S1 & 0.005 & 10 \\ S2 & 0.008 & 9 \\ S3 & 0.054 & 5 \\ S4 & 0.236 & 2 \\ S5 & 0.032 & 7 \\ S6 & 0.028 & 8 \\ S7 & 0.423 & 1 \\ S8 & 0.044 & 6 \\ S9 & 0.096 & 3 \\ S10 & 0.076 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation scores of all sensors based on the EWM-TOPSIS model.  
Figure 9: The average values of the sensors’ repeatability evaluation results.
# Error Recovery in the Time-Triggered Paradigm with FTT-CAN
## 3 System Model
### Servers for Aperiodic Messages  
Servers are software entities that act as proxies for associated aperiodic requests, shaping their arrival pattern and allowing for their integration in periodic/sporadic systems. Many server types can be found in the literature [28], being typically characterized by a certain capacity \(C_{S}\) that can be provided over a given interval \(T_{S}\) to serve arriving requests. However, they differ in the rules on how and when their capacity can be used and replenished. A Deferrable Server (DS) [29; 30] replenishes its capacity strictly periodically and allows for consuming its remaining capacity at any point of its period. The server is marked as ready and scheduled whenever it has pending requests to serve and has enough capacity. The capacity is decremented by the exact amount of requested execution time that was actually served. Despite presenting a penalization in terms of the schedulability of lower-priority periodic messages, when compared to other servers, like Polling and Sporadic Servers, the simplicity, small overhead, and responsiveness of DSs make them a good practical option as referred in [31], and so they will be used in this work to handle retransmission requests.
# Pullout Behavior of Nail Reinforcement in Nailed Soil Slope
## 2 Materials and Methods for Soil Nail Reinforcement Pullout Test
### Finite Element Modeling  
The finite element model of nail-reinforced soil slopes was developed to validate the laboratory model results. The nailed soil slope parameters used in the model are given in Table 4. The PLAXIS-2D code was used to develop a finite element modeling of nailed soil slopes considering a plane strain condition. The suitable elements available in PLAXIS were used to model the sand, steel, and Perspex glass material. The 15-node triangular elements available in the PLAXIS code were employed for soil modeling. The 15-node triangle element with 12 nodes on the sides and 3 nodes on the inside of the triangle is a proven element to give a robust solution for large deformation geotechnical problems. The five node plate elements were used for footing modeling. The two node elastic spring elements were used to model nail reinforcements. The interface elements were used to model the interaction between the component of the nailed soil slope. A nonlinear elastic (Hyperbolic) model was used to simulate the sand material. The soil parameters used in the material model are given in Table 5. The discretized nailed soil slope finite element model with boundary conditions is shown in Figure 11. The published literature by the authors [23] presents the validation results obtained by the soil slope finite element models.  
Figure 10: Experimental set-up for pullout test.  
Figure 9: Nail pullout system. 1: 4 Beams of box section; 2: 4 connected angles; 3: 4 plates 280 \(\times\) 160 \(\times\) 25 mm; 4: 2 plates 280 \(\times\) 160 \(\times\) 20 mm; 5: 8 screws 20 mm in diameter; 6: loading frame; 7: electrical gear motor; 8: coupling; 9: gear box; 10: chain; 11: two power screw rods; 12: two big nuts; 13: pulling beam; 14: two limit switch.
## 8 Results
### Search for Optimal L1 and L2 Parameters  
We tried different regularization parameters and recorded the relative correctness of our attacks. The \(\lambda\) parameter was changed by one order of magnitude at a time. The default setting for scikit-learn is \(\lambda\) = 0.001, and for tensorflow, \(\lambda\) = 0.01. Our intuition, based on [17; 18], was that the regularization should be higher than any default value, but because of the discrepancy between the Python packages, we started our investigation for \(\lambda\) = 0.001 and finished with \(\lambda\) = 10. Every attack resulted in 54 key candidates, and we reported only the best-performing key candidates' correctness. We evaluated the power trace (PT) and electromagnetic trace (EMT) of Design_seq and Design_ultra. We used scikit-learn and tensorflow implementations while, in scikit-learn, only L2 regularization is available. We also reported the maximal correctness for the horizontal attack using the difference to the mean as \"initial\" and SVM results from [17] in Table 2. The SVM was not used in this framework but is given for reference. We show the results after only one iteration of ANN training.  
The results from Table 2 show the relative correctness described previously at the end of Section 7 (the iterative framework). The correctness tells us how many bits of the secret key were correctly discovered. The higher the correctness is, the more successful the attack was. The correctness is reported for the best-performing key candidate among 54.  
We observed that the L2 regularization, which worked for the scikit-learn framework, does not work the same way for tensorflow. The optimal values for every framework and regularization type were different. The scikit-learn-based neural network performs best with \(\lambda\) = 10, tensorflow L2 regularization with \(\lambda\) = 1, and L1 regularization with \(\lambda\) = 0.01. The SVM outperformed all other methods for the first iteration. Similar to what was found in [17], attacks against _Design_ultra_ were less successful, showing how synthesis options used during hardware compilation play significant roles in resistance against SCA attacks. The subject was explained in [50].
# An Energy-Saving Forwarding Mechanism Based on Clustering for Opportunistic Networks
## 2 Related Works
An approach used in [21] proposes a protocol based on energy-efficient inactive nodes detection. It is a method to detect nodes in a dead or inactive state in the network and provide an efficient buffer management policy to avoid network congestion. In summary, there is a focus on controlling the transmission of messages destined for dead nodes, eliminating messages destined for them and thus avoiding the consumption of nodes resources. In line with the authors mentioned above, [PERSON] et al. [9] demonstrated clustering methods to improve delivery rates in scenarios with social aspects and message interest profiles. They applied the clustering algorithms EM [22] and k-means [23] to build groups with common interests. They compared the performance of their proposed method (GPDMI) with Bubble Rap [12], which also uses clustering techniques to create social communities. Their results showed that, by introducing interest profiles and social aspects in the OppNet context, clustering nodes by interest profiles is the most suitable choice to improve the dissemination of messages. Since there are more interactions among the nodes with over one common interest topic, message forwarding from the source to the cluster of the topic of interest occurs over the nodes' path with an indirect interest in the message. Although GPDMI performs better than Bubble Rap, we realized a gap in performance evaluation when considering energy constraints. It is essential to emphasize that, though our approach is similar to GPDMI, we added a new clustering layer above to group the nodes with the highest battery level. Our mechanism considers the relationship of users' mobile device usage with battery consumption. The idea is to group them by energy levels, assuming that there is a user behavior correlation to the daily use of their mobile device so that this history can be exploited to optimize the energy resources of the nodes when forwarding messages on the network. [PERSON] et al. (2018) investigated the burden and fairness of forwarding strategies in OppNets. Using mobility traces, they found it difficult to estimate the node's burden determinant factors. The authors claim that resource usage depends on variables unrelated to nodes' contribution in forwarding messages, such as TTL, number of contacts, queuing policy, and routing protocol.
### Purpose and Contributions of the Research  
In this study, we propose a novel defect-adaptive hierarchical structure convolutional neural network (DHS-CNN) model that achieves improved accuracy by modifying the existing InceptionV4 model [23]. The proposed DHS-CNN model, featuring a defect-adaptive hierarchy, can effectively detect and classify defects of various shapes and sizes.  
Figure 1: Various patterns of contact lenses in the final inspection stage (**a,b**) normal lens pattern (**c**) no_lens (**d**) etc_aonormal (**e**) broken (**f**) burr (**g**) bubble defect (**h**) edge defect.  
Figure 2: Image acquisition and channel allocation under varying lighting conditions.  
This capability is critical for inspecting precision products such as contact lenses and addressing different types of abnormalities. Furthermore, this research is expected to enhance the application of AI in the manufacturing sector through the implementation of deep learning technologies, thereby facilitating the broader integration of AI across various industries.