esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research on Lateral Distance Measurements for Tunnel Vehicle Localization
\(a\) represents the distance between a LiDAR point with an elevation angle of \(\theta\) and a horizontal rotation angle of \(\gamma\) and another LiDAR point with an elevation angle of \(\alpha\) and a horizontal rotation angle of \(\gamma.l_{\alpha}\) represents the slope distance between the LiDAR and obstacles measured at an elevation angle of \(\alpha\) and a horizontal rotation angle of \(\gamma\). \(d^{l}\) represents the horizontal distance between the LiDAR and obstacles measured at a horizontal rotation angle \(\gamma\). Calculate the precise horizontal distance between the LiDAR and obstacles. Based on parameter \(d^{l}\) described in step (3), use the following formula:  
\[d=\text{min}d^{l} \tag{6}\]  
In the equation, \(d\) represents the precise distance value between the LiDAR and obstacles, where is the minimum value among \(d^{l}\). Calculate the distance from the LiDAR to the other side of the obstacles. Select the distances \(l_{\theta 1}\) measured between the LiDAR and obstacles at an elevation angle of \(\theta\)  
In the equation, \(a^{\prime}\) represents the distance between the LiDAR point with an elevation angle of \(\theta\) and a horizontal rotation angle of \(\varphi+\pi\) and the LiDAR point with an elevation angle of \(\alpha\) and a horizontal rotation angle of \(\varphi+\pi\), and \(d^{\prime}\) represents the horizontal distance between the LiDAR and obstacles measured at a horizontal rotation angle \(\varphi+\pi\). Correction of lateral distance. In tunnels, emergency stopping zones are provided at regular intervals. Additionally, there are situations where large and high vehicles run parallel to the test vehicle. As shown in Figure 4, under these two special circumstances, the calculated lateral distance values are wrong and require correction. Let \(\mathrm{d}l\) and \(\mathrm{d}r\) represent the horizontal distances from the onboard LiDAR to the left and right obstacles, respectively, and \(\mathrm{d}b\) represents the typical width of a tunnel segment. In normal conditions, horizontal distances of the previous time \(p\) from the LiDAR to the left and right obstacles are represented by \(\mathrm{d}l_{p}\) and \(\mathrm{d}r_{p}\), respectively. Considering that the width of the typical tunnel segment remains unchanged, the correction method for the lateral distance is as follows:  
\[ \begin{cases}\mathrm{d}l=\mathrm{d}l,\mathrm{d}r=\mathrm{d}r\quad\quad\mathrm{ d}l+\mathrm{d}r=\mathrm{d}b\\ \mathrm{d}l=\mathrm{d}l,\mathrm{d}r=\mathrm{d}b-\mathrm{d}l\quad\mathrm{d}l+ \mathrm{d}r\
eq\mathrm{d}b,\ \mathrm{d}l=\mathrm{d}l_{p},\mathrm{d}r\
eq\mathrm{d}r_{p}\\ \mathrm{d}r=\mathrm{d}r,\mathrm{d}l=\mathrm{d}b-\mathrm{d}r\quad\mathrm{d}l+ \mathrm{d}r\
eq\mathrm{d}b,\mathrm{d}r=\mathrm{d}r_{p},\ \mathrm{d}l\
eq\mathrm{d}l_{p}\\ \mathrm{delete}\quad\quad\quad\mathrm{d}l+\mathrm{d}r\
eq\mathrm{d}b,\ \mathrm{d}l\
eq\mathrm{d}l_{p}, \mathrm{d}r\
eq\mathrm{d}r_{p}\end{cases}  \tag{9}\]  
In the equation, within the judgment condition, an error threshold \(\delta\) can be added, for example, by setting \(\delta=5\sim 10\) cm.
## 5 Investigation and Results
### Bit Error Rate (BER) Performance
In contrast, the results obtained with our proposed information bottleneck decoder were quite remarkable compared to the performance of a belief propagation decoder working with double-precision data types. Although only 16 different integers were processed by the information bottleneck decoder, the performance degradation in terms of bit error rate was only 0.15 dB for a BER of \(10^{-3}\) down to \(10^{-5}\). The operations performed during information-bottleneck decoding were only simple look-ups in the offline-generated look-up tables, whereas the belief propagation reference simulation processed double precision channel output values and also performed double precision box-plus operations. The performance degradation was even smaller if the belief propagation decoder with quantized channel LLRs was considered as reference. It suffered from the same information loss introduced by using the channel quantizer as the information-bottleneck decoder. Finally, although the min-sum decoder exchanges double precision LLRs, the performance was significantly worse compared to our proposed information-bottleneck decoder. As a consequence, the performance gain achieved by applying message alignment allows the construction of information bottleneck decoders for irregular LDPC codes which pair coarse quantization, low complexity, simple node operations, and close-to-optimum performance.
## 4 Optimisation Method
### Optimiser and Fitness Function  
In this work, the Strong Pareto Evolutionary Algorithm (SPEA-2) is used, which outperforms other GA algorithms [35] and has the unique feature of storing in a parallel file the outperforming solutions, which can be examined by the DM for the selection of the final solution along with the total population.  
Although SPEA-2 is conceived to solve multi-objective optimization problems where the solution is a field of non-dominated vector solutions known as Pareto optima set, the design process presented in this work sets up the problem as a single objective where the optimal solution is a single vector [36], the strictly minimal solution regarding the fitness function after all generations, while keeping all provided solutions available for further analysis. The intention of this decision is to give the DM the opportunity to analyse the entire solution space, where near-optimal solutions that meet additional requirements can be found.  
Once the parametric model is built, the SPEA-2 can be set up. It is necessary to establish the single-objective fitness function (FF), which is the minimisation of the initial bending stress in the study area. The design vector is given as:  
\[d=\{g_{1},\dots,g_{n}\}, \tag{17}\]  
where \(n=4\) and \(g_{n}\) are the genes described in Section 4.1.  
With the dominant \(\sigma_{\text{m,total,dom}}\) being the highest value at any point \(O\) of the study area, obtained from Equation (16), the FF can be formally stated as:  
\[\text{min}f(d)=\text{min}(\sigma_{\text{m,total,dom}})_{O} \tag{18}\]  
Figure 17: Solution vector composition (chromosome’s genes).  
Finally, the population of each generation and the number of total generations must be defined. This should be done by trial and error to fine-tune the settings for each specific problem. For the PEMADE gridshell, the number of individuals for Step 1 was set to 100 and the maximum number of generations to 10. For Step 2, 20 individuals and 10 generations were established.
## 5 Experimental Verification and Analysis
### UDDS Test  
To evaluate the SOC estimation performance under dynamic loading profiles, an UDDS cycle was performed on the battery cells. According to the actual tolerable currents of the lithium-ion battery cells, the loading currents are scaled down, as shown in Figure 10. The initial SOC value is set to 0.8.  
Figure 8: Experimental terminal voltage results using VB-ADEKF in the pulse current discharge test.  
Figure 9: SOC estimation results using VB-ADEKF and DEKF in the pulse current discharge test.  
The process noise covariances are set as \(Q_{k}^{x}=1\times 10^{-6}I_{2}\) and \(Q_{k}^{\theta}=1\times 10^{-6}I_{3}\). The measurement noise variances used for DEKF are \(\Sigma_{k}^{x}=0.01\) and \(\Sigma_{k}^{\theta}=0.0005\). The initial parameter values of VB-ADEKF are set as \(\alpha_{\theta,0}=100\), \(\beta_{\theta,0}=0.0005\), \(\alpha_{x,0}=10\), \(\beta_{x,0}=0.001\).  
The estimated values of the battery model parameters are presented in Figure 11. It can be seen that the range of parameter values of \(R_{0}\), \(R_{1}\) and \(C_{1}\) are consistent with these in constant current discharge test and pulse current discharge test, but the changing rules are slightly different. Figure 12 presents the the measured terminal voltage and the estimated terminal voltage by VB-ADEKF to verify the effectiveness of the battery parameters identification. The maximum and mean absolute estimation errors are 0.062 V and 0.0011 V, respectively. It is clear that the estimated terminal voltage has a good consistency with the measured voltage. It further shows the battery model parameters are well identified.  
Figure 11: Results of parameters identification using VB-ADEKF in the UDDS test.  
Figure 10: Current profiles in the urban dynamometer driving schedule (UDDS) test.  
Figure 13 presents the SOC estimation results of VB-ADEKF and DEKF. It shows that both VB-ADEKF and DEKF have good estimation accuracy when SOC is between 20% and 90%. But VB-ADEKF still outperforms the traditional DEKF in SOC estimation accuracy and convergence rate. A comparative summary of the two methods is given in Table 2. When SOC is decreased to 20%, the estimation error of both methods begins to increase, but is no larger than 5%. This may be because the polarization effect of the battery is further aggravated at lower SOC level. As a result, the terminal voltage measurement error has increased, thereby reducing the SOC estimation accuracy.
# Autonomous Service Drones for Multimodal Detection and Monitoring of Archaeological Sites
## 4 Validation and Results
### Built Cultural Heritage Detection Algorithm Testing  
We generated 2000 synthetic orthomosaics for training and testing our detection algorithm using our data generation tool and our physical model. 60% of the generated mosaics were used for training, 20% for validating, and 20% for testing. The structure detector model was trained on 15 epochs, with a mini-batch size of 16. The detector was able to recognize 90% of the structures in the testing set. We use Piecewise learning rate scheduling to decay the learning rate over time, set to an initial value of 0.001. A mini-batch RMSE of 0.04 and a mini-batch loss value of \(5.1\times 10^{-3}\) were achieved.
# Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks
###### Abstract  
The current 5G-NR standard includes the transmission of multiple synchronization signal blocks (SSBs) in different directions to be exploited in beamforming techniques. However, choosing a pair of these beams leads to performance degradation, mainly for the cases where the transmit and receive beams are not aligned, because it considers that only few fixed directions among wide beams are established. Therefore, in this article, we design a new 3 GPP-standard- compliant beam pair selection algorithm based on secondary synchronization signal (SSS) angle estimation (BSAE) that makes use of multiple synchronization signal blocks (SSBs) to maximize the reference signal received power (RSRP) value at the receiver. This optimization is performed using the SSSs present in each SSB to perform channel estimation in the digital domain. Afterwards, the combination of those estimations is used to perform equivalent channel propagation matrix estimation without the analog processing effects. Finally, through the estimated channel propagation matrix, the angle that maximizes the RSRP is determined to compute the most suitable beam. The proposed algorithm was evaluated and compared with a conventional beam pair selection algorithm. Ours has better performance results. Furthermore, the proposed algorithm achieved performance close to the optimal performance, where all channel state information (CSI) is available, emphasizing the interest of the proposed approach for practical 5G mmWave mMIMO implementations.  
5G; NR; 3 GPP; massive MIMO; millimeter-wave communications; OFDM; PHY; RAN; beamforming; initial access; beam management Article
## 4 Conclusions  
BTH treatment at the pre-swelling stage significantly increased the 100-berry weight, skin/berry ratio, pH, total phenolic content, and total flavonoid content in ripe Cabernet Gernischt grapes. Additionally, BTH treatment reduced the content of reducing sugars, TSSs, and TA. BTH treatment also delayed the technological ripening of grapes by retarding sugar accumulation. Furthermore, BTH promoted the conversion of C6 aldehydes to C6 alcohols and C6 alcohols to C6 esters by increasing the activities of LOX, ADH, and AAT. As a result, the content of hexyl acetate and Z-3-hexenyl acetate in ripe grapes increased significantly, while the concentration of free E-2-hexenal and C6 compounds decreased. BTH treatment also advanced the changes in the grape aroma by promoting ester production. OPLS-DA and correlation analysis showed that the accumulation of C6 compounds varied greatly after BTH treatment and that the effect of BTH treatment on C6 compounds was progressively strengthened with an increasing grape maturity. BTH treatment had a greater effect on the accumulation of bound Z-3-hexenyl acetate, hexyl acetate, Z-3-hexenol, hexanol, and free hexyl acetate. Also, the activities of LOX and ADH, which are the limiting factors of the LOX pathway, positively regulated the synthesis and accumulation of C6 esters.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods12193710/s1](https://www.mdpi.com/article/10.3390/foods12193710/s1)]([https://www.mdpi.com/article/10.3390/foods12193710/s1](https://www.mdpi.com/article/10.3390/foods12193710/s1)), Table S1: Sampling information during Cabernet Gernischt grapes' ripening; Table S2: Standard curve for quantification of C6 compounds, linolenic and linoleic; Table S3: Concentrations of C6 compounds of BTH-treated (BTH) and CK grapes during ripening; Table S4: Correlation coefficients (rho) and \(p\) values for correlation analysis of linolenic acid, linoleic acid, enzyme activity, and C6 compounds; Figure S1: Changes in temperature and rainfall in Lanzhou City in 2022.  
Conceptualization, J.W. and Y.J.; data curation, X.L.; formal analysis, Y.H.; funding acquisition, Y.J.; investigation, K.W., Y.Z. and M.Z.; methodology, C.C. ([PERSON]) and C.C. ([PERSON]); resources, Y.H.; software, R.G.; supervision, Y.J.; writing--original draft, J.W.; writing--review and editing, J.W., F.E.S. and Y.J. 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, grant number 32060514.  
Data are contained within the article and the Supplementary Materials.  
This work was supported by the Gansu Key Laboratory of Viticulture and Enology, Gansu Wine Industry Technology R&D Center.  
The authors declare no conflict of interest.
## 4 Conclusions  
The study conducted a comprehensive assessment of groundwater quality in the Delhi Region using the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI). The results demonstrated the utility of CCME WQI as a robust tool for evaluating water quality in diverse settings. The assessment covered three districts of Delhi throughout the year 2020, including the pre-monsoon, monsoon, and post-monsoon seasons.  
The findings revealed variations in water quality across monitoring stations, with some locations exhibiting excellent water quality while others displayed fair to poor quality.  
Figure 13: CCME WQI Map of the Nangloi district, Okhla Industrial Area, and the Karol Bagh district of the Delhi Region (author’s creation).  
These variations underscore the importance of effective water quality management and treatment to ensure safe and clean water for various purposes, including drinking, cooking, and industrial applications.  
Key parameters, such as pH level, turbidity, dissolved oxygen, etc., are important and crucial in determining water quality ratings, highlighting their significance in water quality assessment. To enhance these parameters and overall groundwater quality, it is essential to address factors such as industrialization, anthropogenic-geogenic activities, and domestic practices that can impact water quality negatively.  
The study emphasized the pivotal role of CCME WQI as the gold standard for assessing drinking water quality, widely embraced by state authorities, water resource managers, laboratories, scientists, and researchers. Its continued application and vigilant monitoring are essential components of collective efforts to preserve and enhance water quality for present and future generations.
# A Method for Singular Points Detection Based on Faster-RCNN
###### Abstract  
Most methods for singular points detection usually depend on the orientation fields of fingerprints, which cannot achieve reliable and accurate detection of poor quality fingerprints. In this study, a new method for fingerprint singular points detection based on Faster-RCNN (Faster Region-based Convolutional Network method) is proposed, which is a two-step process, and an orientation constraint is added in Faster-RCNN to obtain orientation information of singular points. Besides, we designed a convolutional neural network (ConvNet) for singular points detection according to the characteristics of fingerprint images and the existing works. Specifically, the proposed method could extract singular points directly from raw fingerprint images without traditional preprocessing. Experimental results demonstrate the effectiveness of the proposed method. In comparison with other detection algorithms, our method achieves 96.03% detection rate for core points and 98.33% detection rate for delta points on FVC2002 DB1 dataset while 90.75% for core points and 94.87% on NIST SD4 dataset, which outperform other algorithms.  
Faster-RCNN; orientation constraint; singular points detection 2018
# Interactions in Augmented and Mixed Reality: An Overview
## 3 Research Results
### 3.3. Haptic-Based Modality
#### 3.3.4 Feedback-Based  
Besides input functionality, the haptic-based modality also includes a feedback-based context of interaction. An exceptional review was published by [PERSON] et al. [156]. The authors present a study on elastomeric haptic devices and stress that in haptics we have seen much slower technological advances than visual or auditory technologies because the skin is densely packed with mechanoreceptors distributed over a large area with complex topography. Their review includes haptic wearables, haptic input devices (dataglove, VR tracker, VR controller), haptic feedback output methods (direct force feedback, force substitution-skin deformation, shape morphing-surface, virtual shape rendering with lateral force control), and haptic perception. [PERSON] et al. [157] explored the effect of force feedback or _direct force feedback_ on the performance of knot tightening in robotics-assisted surgery. They stressed that presenting haptic force feedback to the user performing such tasks increased the effectiveness, although this is a debatable subject [158]. Another example of the use of the _direct force feedback_ method is PneumVolley [159]. The authors, [PERSON] et al., presented a wearable cap prototype that is capable of providing pressure feedback to simulate the interaction between a virtual ball and the user's head through pneumatic actuation. [PERSON] and [PERSON] [160] presented a device wearable on the fingertip, capable of transmitting haptic stimuli exploiting the _skin deformation_ method as a force substitution. In a greater depth, a review of wearable haptics and their application in AR is presented by [PERSON] et al. [161]. [PERSON] et al. [162] used the _shape morphing_ method. They mentioned several shape-morphing surfaces, such as (a) the shape display inFORM,(b) a bio-inspired pneumatic shape-morphing device based on mesostructured polymeric elastomer plates capable of fast and complex transformations, and (c) rheological test results of MR fluid affected by a magnetic field. They stated that morphing devices are not yet mature, but they have explosive potential.
# RGCLN: Relational Graph Convolutional Ladder-Shaped Networks for Signed Network Clustering
## 3 The Proposed Model
### Notations  
Before presenting the details of the framework, we introduce some definitions and notations that will be used in our paper. Let \(\mathcal{G}=(\mathcal{V},\mathcal{E})\) be a signed network, where \(V=\{v_{1},v_{2},\ldots v_{m}\}\) is the set of \(m\) nodes, and \(\varepsilon\subset V\times V\) is the set of links. In particular, any link \(e_{ij}\in\varepsilon\) can be \(1\) or \(-1\), where \(e_{ij}=1\) denotes a positive link between \(v_{i}\) and \(v_{j}\), while \(e_{ij}=-1\) denotes a negative link. We denote the adjacency matrix of the signature network \(G\) by \(\mathbf{A}\in\mathbb{R}^{n\times n}\), where \(A_{ij}=1\) indicates a positive link from \(v_{i}\) to \(v_{j}\), \(A_{ij}=-1\) indicates a negative link, and otherwise \(A_{ij}=0\) indicates no link from \(v_{i}\) to \(v_{j}\). \(D\) is the degree matrix, and the values on the diagonal are the degrees of each point. The task of clustering is to divide \(G\) into \(K\) clusters, where the nodes between clusters are as similar as possible, and the nodes between clusters should remain different. The model in this paper verifies the performance of unsupervised and semi-supervised clustering, where the seed nodes are identified with labels before training. The set of seed nodes is denoted as \(V_{\text{seed}}\subseteq V_{\text{train}}\subset V\), where \(V_{train}\) is the set of all training nodes.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 3 Threats and Challenges in Underwater Wireless Sensor Networks (UWSNs)
### Passive Attacks  
Passive attacks refer to the attempts that are made by malicious nodes to perceive the nature of activities and to obtain data transmitted in the network without disrupting the operation. For example, eavesdropping, interfering, leakage of secret information, impersonation, message replay, and message distortion. As discussed in Section 2.2, the underwater acoustic channel is an open channel. Malicious attackers can easily capture packets transmitted in the channel by using a hydrophone or underwater microphone. Moreover, the attacker may predict the nature of communication by analyzing the packets traffic, observe the exchange of the packets, identify communicating hosts, and determine the location of nodes. Based on these actions, the malicious attacker can launch active attacks to cause more severe damage. Unfortunately, it is difficult to detect these passive attacks, since the network operation is not affected. To prevent this problem, the best solution is encryption mechanisms which make it hard for eavesdroppers to gain any information. Due to the large overhead and high energy consumption, the existing encryption mechanisms that used in WSNs and other wireless networks cannot be directly transplanted into UWSNs. The encryption mechanisms for UWSNs will be described in more detail in the subsequent sections.  
Figure 3: Threats and challenges in UWSNs.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 4 Application of Publish/Subscribe Middleware in ITS
### Transportation Mode
#### 4.2.2 Land Transportation
It is a smartphone application that utilizes the power of the crowd to gather data, share information, and send feedback from many passengers to tell everyone where the buses and trains are and if there are any problems. Its strength is the real-time updates on the precise locations of public vehicles. The weakness of these systems is that they depend on user interaction to detect events, potentially inaccurate information, and reliability issues.