esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Method
### Ground Truth for Pedestrian Density and Count Attributes  
The constructed pedestrian detector additionally detects the density and count attributes of pedestrians, which represent the maximum occlusion degree and the count of surrounding occluded pedestrians for each pedestrian. Unfortunately, no specific annotations for the density and count attributes are available in the benchmark datasets for pedestrian detection, and it is expensive to manually annotate a public benchmark dataset. In order to address this challenge, recent studies [13; 42] have proposed methods to generate approximate annotations based on existing annotations. Inspired by these methods, we generate annotations of the density and count attributes using the existing full-body annotations.  
Consistent with [17], we calculate the density attribute of each pedestrian by considering the maximum _IoU_ value between their ground truth and that of the others, which is expressed in Equation (2). Additionally, inspired by Greedy NMS, we account for the occlusion between two pedestrians when their ground truths exhibit an _IoU_ value above the rigid threshold \(N_{t}\). For each pedestrian, the count attribute is determined by summing the number of pedestrians occluded by them. The quantization process is expressed in Equation (13).  
\[c_{i}:=\sum_{b_{j}\in G,i\
eq j}h(iou(M,b_{i})-N_{t}), \tag{13}\]  
\[h(x)=\left\{\begin{array}{l}0,x<0,\\ 1,x\geq 0,\end{array}\right. \tag{14}\]  
where \(c_{i}\) is the count attribute of the \(i\)th pedestrian and represents the count of surrounding pedestrians occluded by them. \(G\) denotes the ground truths and \(b_{i}\) represents the full-body annotation of the \(i\)th pedestrian.
# Robotic Systems and Navigation Techniques in Orthopedics: A Historical Review
## 2 Robotic Systems (RSs)
### Hands-On RS
#### 2.2.13 ExcelisiusGPS  
ExcelisiusGPS (ExcelisiusGPS website (accessed on 28 June 2023): [[https://www.globusmedical.com/musculoskeletal-solutions/excelisiusgps/](https://www.globusmedical.com/musculoskeletal-solutions/excelisiusgps/)]([https://www.globusmedical.com/musculoskeletal-solutions/excelisiusgps/](https://www.globusmedical.com/musculoskeletal-solutions/excelisiusgps/))) (Globus Medical, Audubon, PA, USA), as shown in Figure 11, is a robotic system with real-time image guidance for spine surgery and was approved by the FDA (ExcelisiusGPS FDA 2017 file (accessed on 28 June 2023): [[https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K171651](https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K171651)]([https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K171651](https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K171651))) in 2017 [57]. The robotic arm is mounted on the floor rather than the patient's bone. Preoperative CT scans are not mandatory but optional for screw trajectory planning. Optionally, intraoperative CT or radiographs can also be used for the planning. The ExcelisiusGPS system employs a shock-absorbing dynamic reference base and a separate surveillance marker and associated surveillance software to improve the system navigation integrity [57; 63]. Also, the ExcelisiusGPS system employs an extremely rigid arm for its robotic guidance system, which can achieve an accuracy of less than 1 mm of tool deflection under a lateral disturbance force of 200 N. Additionally, the system alerts the surgeon if any tool deflection is detected by the surveillance software during the surgery [57; 63].  
Figure 10: Mazor systems for spine surgery. (Reproduced with permission from © Medtronic, Dublin, Ireland).
## 3 Results and Discussion
### Health Risks
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Metal(loids)s**} & \multicolumn{2}{c}{**Soil**} & \multicolumn{2}{c}{**Corn**} & \multicolumn{2}{c}{**Wheat**} \\ \cline{2-7}  & **Adult** & **Child** & **Adult** & **Child** & **Adult** & **Child** \\ \hline HQCu & \(6.68\times 10^{-4}\) & \(1.09\times 10^{-3}\) & \(8.50\times 10^{-2}\) & 0.19 & 0.42 & 0.46 \\ HQPb & \(6.43\times 10^{-3}\) & \(1.04\times 10^{-2}\) & \(2.99\times 10^{-2}\) & \(6.76\times 10^{-2}\) & \(5.60\times 10^{-2}\) & \(6.15\times 10^{-2}\) \\ HQZn & \(2.72\times 10^{-4}\) & \(4.42\times 10^{-4}\) & 0.17 & 0.38 & 0.39 & 0.43 \\ HQCd & \(3.43\times 10^{-3}\) & \(5.56\times 10^{-3}\) & \(1.05\times 10^{-2}\) & \(2.34\times 10^{-2}\) & \(3.90\times 10^{-2}\) & \(4.28\times 10^{-2}\) \\ HQCr & \(2.0\times 10^{-5}\) & \(3.25\times 10^{-5}\) & \(4.35\times 10^{-5}\) & \(9.54\times 10^{-5}\) & \(7.93\times 10^{-5}\) & \(8.69\times 10^{-5}\) \\ HQNi & \(3.52\times 10^{-3}\) & \(5.72\times 10^{-3}\) & \(2.69\times 10^{-2}\) & \(5.88\times 10^{-2}\) & \(1.86\times 10^{-2}\) & \(2.04\times 10^{-2}\) \\ HQAs & \(7.04\times 10^{-4}\) & \(1.14\times 10^{-3}\) & 0.11 & 0.25 & 0.34 & 0.37 \\ HQHg & \(2.07\times 10^{-4}\) & \(3.37\times 10^{-4}\) & \(3.22\times 10^{-2}\) & \(7.03\times 10^{-2}\) & \(2.6\times 10^{-2}\) & \(2.85\times 10^{-2}\) \\ THQ & \(1.52\times 10^{-2}\) & \(2.48\times 10^{-2}\) & 0.47 & 1.04 & 1.29 & 1.42 \\ TTHQadult & \(1.78\) & & & & & \\ TTHQchild & \(2.48\) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Health Risk Quotient (HQ) and target hazard quotient (THQ) statistics of three paths (soil, corn, wheat). Figure 5: Comparison of the distribution of HQ and THQ under the three paths. Overall, the results show that the TTHQ values for children were greater than the TTHQ values for adults (Figure 7). Long-term consumption of large amounts of food contaminated with metal(loid)s poses a significant risk to human health (especially for children). The health risk of metal(loid)s in wheat is higher than that of corn. This suggests that a reduction in the intake of staple foods containing wheat will reduce the BCF of Cu, Zn, and As in crops, and reduce the health risks.
## 5 Simulations
### Reference-Tracking Performance in System with Uncertainties  
To verify the robustness of HDFRMS closed-loop system, as shown in Figures 10 and 11, the total uncertainties \(\psi\) in SSS and the uncertainties \(F\) in FFS are chosen as \(\psi=\left[0.001\sin 1.5t+0.0004\text{rand(1)}\ \ 0.0005\cos t+0.0005\text{rand(1)} \ 0.0005\sin 0.5t+0.0005\text{rand(1)}\right]^{\text{T}}\) and \(F=\left[0.001\cos 5t+0.001\text{rand(1)}\ \ 0.001\sin 3.5t+0.001\text{rand(1)} \ \ 0.001\sin 4t+0.001\text{rand(1)}\right]^{\text{T}}\), respectively. Then, both the sine and cosine function are used to express parameter uncertainties in the manipulator system, and the random function represents external disturbances and noises.  
Figure 10 shows the tracking performances of joints and vibration suppression of the third flexible arm in HDFRMS with uncertainties. Compared to the previous cases, the tracking time of joint angle 2, nearly 3.5 s, is the longest response time of the system according to Figure 10a-c. Furthermore, the tracking error fluctuation of joint angle 3 is less than 0.01 rad between 9.2 s and 9.5 s, because of the combined effects of system uncertainties, accumulated errors, full payload and motion coupling. However, the largest error fluctuating ranges can satisfy the requirements of steady state. Figure 10d-f shows that the vibration is also so small and can be suppressed quickly.  
Figure 13: Comparison with the ASMC in SSS.  
The chattering comparisons of control currents with full payload and uncertainties are shown in Figure 11. Clearly, the chattering can be weakened by the saturation function whose boundary layer is 0.01 instead of the sign function.
## 4 Validation and Comparison
### Comparative Testing of Baseline Methods
#### 4.4.1 Path Loss Prediction Method Based on CI Modeling  
The CI model is an essential tool for designing and planning wireless communication systems. The primary purpose of the CI model is to predict the propagation loss of wireless signals in space based on the environmental characteristics and signal propagation conditions to evaluate the distribution of coverage and signal strength. This paper contains three different channels: the base station to the train, through the train window, and multiple reflections in the train. Its channel model is in the form of a cascade. To find the mathematical expression of the channel cascade, relying on the free-space neighboring reference distance model, this paper sets the model as follows:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Weights** & **Value** \\ \hline \(w_{1}\) & 0.3484 \\ \(w_{2}\) & 0.0035 \\ \(w_{3}\) & 0.4153 \\ \(w_{4}\) & 0.2326 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Configuration of neural network weights in the integrated model.  
\[PL_{CI}=10\text{log}_{10}\left(\frac{4\pi d_{0}}{\lambda}\right)^{2}+10n\text{log}_ {10}\left(\frac{d}{d_{0}}\right)+X_{\sigma} \tag{14}\]  
where \(d_{0}\) represents the relative reference distance in free space, usually taken as 1 m. \(\lambda\) represents the wavelength, and \(X_{\sigma}\) represents a Gaussian random variable with a mean of 0 and a standard deviation of \(\sigma\). \(n\) represents the composite factor comprising glass loss, vehicle reflections, etc. In this paper, a CI model is fitted to the seat path loss. After fitting, the value of \(n\) is 4.38.
# Research on Vehicle Trajectory Prediction and Warning Based on Mixed Neural Networks
## 3 Methods and Design
### Trajectory Prediction of a Car
#### 3.3.2 The Structure of the Car Trajectory Prediction Model  
We used LSTM to construct our car trajectory prediction model based on keras in the Python background. Figure 17 shows our model structure. There were six layers, including one LSTM layer, two activation layers, and three dense layers. The first was input layer, and the X and Y coordinates of the cars were the input to the input layer. Next was the LSTM layer. We set the unit number at 256, input shape as (35,2), and return sequences as False. Our activation function was selected to be linear. In addition, we set the number of units in each dense layer to 256, 128, and 35. The essential parameters of the car trajectory prediction model are shown in Table 6. In the LSTM layer, num_units referred to the number of hidden units, input_shape the input shape, and return_sequences indicated whether to return a single hidden state value or the hidden state value of all time steps. False returns were single, and true returns were all. In the Dense layer, num_units represented the number of hidden units. Activation was the activation function to be used, and we used the linear function. In the output layer, it only contained the X or Y coordinates of the cars because we trained the model to predict X and Y separately. The detailed parameter diagram of the model is presented in Figure A1 in the Appendix A.  
Figure 16:
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 2 Literature Review
### 5G Non-Public Networks and Fixed Wireless Access Deployments
#### 2.1.1 FWA Trials  
FWA trials have been taking place in numerous locations around the world since before 2016. For example, Facebook's current Terregraph project [15] connects over 100 homes in and around Mikebuda, Hungary, and provides up to 1 Gbps in the area, for which there is over 99.5% coverage. Before this project, Intel and Ericsson deployed their first 5G FWA network with AT&T in 2017 [16]; since then, many trials have taken place in various contexts, such as the recent Orange trials in Romania, which demonstrated a 5G virtual packet core [17]. Other trials have been conducted recently by vendors such as Samsung, Huawei, and Nokia in addition to operators like Telefonica. In the UK, recent government-funded 5G testbed projects have deployed different networks in over 30 areas [18] based on a wide range of rural [19] or urban [20] scenarios, in addition to considering specific use cases such as transport and industry.  
Accordingly, there is now a growing field of knowledge related to a wide range of 5G FWA deployments that consider a mix of heterogeneous technologies and extend beyond typical MNO-led public wireless networks. However, when we consider the performance optimisation of such networks, much of the research to date has not been underpinned by practical experience or does not directly impact our research. We describe this work in the section below.
# Consensus Guided Multi-View Unsupervised Feature Selection with Hybrid Regularization
## 2 Related Works  
Recent years have witnessed substantial scholarly interest in multi-view learning, particularly in its application to feature selection. This paradigm leverages complementary data representations from multiple perspectives to capture intrinsic structural patterns more comprehensively than conventional single-view approaches, offering distinct analytical advantages.  
Current multi-view unsupervised feature selection methods [7, 20] split into two theoretically grounded frameworks, each employing distinct guidance mechanisms. The first paradigm employs graph-regularized architectures [21, 22] that maintain inter-view correlations through adaptive graph embeddings, effectively preserving manifold structures via topological representations. The second framework utilizes non-negative orthogonal decomposition, implementing matrix factorization with orthogonality constraints to balance discriminative feature selection with interpretable low-dimensional projections. These complementary approaches provide robust theoretical underpinnings and methodological flexibility, collectively advancing the field's technical evolution.  
The discipline has progressed through two synergistic yet distinct research trajectories. Graph-regularized approaches originated with [PERSON] et al. [14], who pioneered adaptive view-weight coordination through joint similarity learning. Subsequent refinements include [PERSON] et al. [23] introducing sparsity-constrained feature weighting via dynamic sample space reconstruction, and [PERSON] et al. [24] developing subspace-embedded collaborative graphs that preserve view-specific local structures while ensuring global consistency. [PERSON] et al. [25] achieved dual-space projection through manifold-aligned graph embeddings, culminating in [PERSON] et al.'s [15] integration of spectral graph theory with clustering-driven selection via adaptive weighting matrices. Concurrently, non-negative decomposition frameworks evolved through [PERSON] et al. [12] embedding sparse feature selection in NMF-based clustering, followed by [PERSON] et al. [26] incorporating cluster-aware optimization via diversity-constrained matrix factorization. [PERSON] et al. [16] advanced weighted NMF formulations with auto-calibrated view importance, ultimately refined by [PERSON] et al. [27] through orthogonalized projection matrices. The two complementary methodological foundations of graph-theoretic regularization and algebraic decomposition have facilitated substantial technical progress through adaptive operationalization strategies.  
Despite these advancements, current methodologies exhibit critical limitations in dynamically distinguishing reliable information from low-quality view-specific data. This deficiency leads to feature redundancy and structural overlap in selected features, substantially constraining their effectiveness when handling complex high-dimensional multi-view datasets with inherent redundancy and noise interference.
## 4 Performance Analysis
### Simplified Methods of Cluster Formation  
The first simplified model, the Mobility and Density Model (MDM), is applied to describe the behavior of the algorithm in the transitional phase without experiencing the warm-up operation. As detailed in Table 1, the basic idea of MDM is to suppose that the probability of each sensor of being a clusterhead, \(p_{i}\), is proportional to the number of the neighboring sensors \(N_{i}\) and the mobility deviation  
\begin{table}
\begin{tabular}{l} \hline a) Assign a probability to sensor \(i\), \(p_{i}\), proportional to the number of the \\ neighboring sensors, \(N_{i}\) and the mobility deviation as shown in (25). \\ b) Let \(B_{i}\) be the set of neighboring sensors of sensor \(i\). \\ \(I\) is the index set of clusterheads. \\ c) \(\mathbf{P}^{(k)}\), \(\mathbf{\widetilde{P}}^{(k)}\), and \(\mathbf{\widetilde{P}}^{(k)}\) are 1 by \(n\) vectors to store the probability distribution \\   at time step \(k\). \\ d) Assign \(k=0\) and \(\mathbf{P}^{(0)}=(p_{1},p_{2},\ldots,p_{n})\). \\
**while** (\(\text{sum}(\mathbf{P}^{(k)})>0\)) \\  (1) Select a clusterhead \\  \(j=\text{arg }\max_{i}\{p_{i}^{(k)}\}\), \(j\in I\). \\ (2) Update the probability distribution \\  \(\tilde{p_{i}}^{(k)}=p_{i}^{(k)}\cdot 1_{\{i\
otin B_{j},B_{i}\cap B_{j}=\varnothing,j=\text{arg }\max_{i}\{p_{i}^{(k)}\}\}}\), \\  \(\tilde{p_{j}}^{(k)}=0\). \\ (3) Normalize the updated probability distribution. \\
**if** (\(\text{sum}(\mathbf{\widetilde{P}}^{(k)})>0\)) \\  \(\tilde{p_{i}}^{(k)}=\tilde{p_{i}}^{(k)}/\text{sum}(\mathbf{\widetilde{P}}^{(k )})\). \\
**else** \\  \(\mathbf{\widetilde{P}}^{(k)}=\mathbf{\widetilde{P}}^{(k)}\). \\
**end** \\  (4) Store the normalized probability distribution. \\  \(\mathbf{P}^{(k)}=\mathbf{\widetilde{P}}^{(k)}\), set \(k=k+1\). \\
**end** \\ \hline \end{tabular}
\end{table}
Table 1: The Mobility and Density Model.  
during the initial deployment. That is,  
\[p_{i}\propto\frac{N_{i}}{\sum_{i=1}^{n}N_{i}}\cdot\left[1-\log_{2}\left(1+\frac{| v_{i}^{(k)}-\frac{1}{2}(v_{\max}+v_{\min})|}{\frac{1}{2}(v_{\max}-v_{\min})} \right)\right]. \tag{25}\]  
If the sensor is not already chosen as a clusterhead and its neighboring sensors are not already in other clusters, then the sensor with the largest \(p_{i}\) is chosen to be a clusterhead and it assigns probability 0 to its neighbors. Moreover, if a sensor is not a member of a cluster and some of its neighbors have already become cluster members, this sensor should choose to wait and join the nearest cluster later. After normalizing the updated probability distribution of sensors, the procedure repeats until all sensors are members of a cluster. The rationale for this choice is that since the performance of the SAMCA protocol highly depends on the connectivity among sensors and the mobility of sensors, the sensors with many neighbors and modest mobility may be good candidates for initiating new clusters. Hence, the model is likely to closely approximate the behavior of the SAMCA in the transitional phase.
# Practical V2I Secure Communication Schemes for Heterogeneous VANETs
## 4 Four Heterogeneous Signcryption Schemes for V2I Communication Scenarios
### Security Proof
We know that  
\[\Pr[E_{1}]\geq(1-\tau)^{q_{k}},\Pr[E_{2}|E_{1}]\geq\varepsilon,\Pr[E_{3}|E_{1 }\wedge E_{2}]\geq\tau(1-\tau)^{n-1}.\]  
So that  
\[\Pr[E_{1}\wedge E_{2}\wedge E_{3}]\geq(1-\tau)^{q_{k}}\varepsilon\tau(1-\tau)^ {n-1}=\varepsilon\tau(1-\tau)^{q_{k}+n-1}.\]  
The computation time of \(C\) comes from \(F\)'s computation time, which contains the time \(C\) responses queries and the time that \(C\) calculates the CDHP example. We can get that 2, 1, 2 scalar multiplications are needed in the \(H_{1}\) query, key-generation query and signcrypt query, respectively. In addition, 1 pairing calculation is needed in the signcrypt query. \(n\)+1 scalar multiplication is needed in \(C\) calculating the CDHP example. Therefore, the CDHP example will be settled within time \(t^{\prime}\leq t+O(2q_{H_{1}}+q_{k}+2q_{s}+n+1)t_{m}+O(q_{s})t_{p}\). The proof process of confidentiality and unforgeability MOHSC-II, OMHSC-I, and OMHSC-II are very similar to **Theorem 1** and **Theorem 2**, respectively. Therefore, here we omit the detailed proof due to the limited space.
# Determining Factors of Fixed Offshore Platform Inspections in Indonesia
## 5 Discussion
### Structural Design  
Structural design factors consist of the type of fixed offshore platform, structural configurations, the number of modifications, and marine growth. There are several types of fixed offshore platforms, such as wells, processes, and services. Fixed offshore platforms are generally designed and constructed for oil and gas production processes [23]. The size, type, and composition of mantle elements significantly affect hydrodynamic forces. Many styles have been proposed, such as Z brackets, X brackets, K brackets, diamond brackets, split brackets, and mixed brackets [24]. Platform changes fixed offshore are equally important. After years of use, a structural integrity check is required to see if the jacket construction is suitable. Maintenance, repair, and retrofitting are ongoing and sustainable processes throughout the lives of fixed offshore platforms [54]. If it is judged that structures do not meet the needs or even do not fulfill them at all, it is necessary to strengthen, repair, or modify them. Fixed offshore platforms are old and their operating lives have been extended, so there are several changes, such as design codes, increased metocean loading, and additional facilities on the upper side [47]. Marine growths are soft and hard particles on the surfaces of structures submerged in seawater. Their growth on the surfaces of structures can increase their diameter and affect the roughness of submerged structures. In addition, marine growth can also increase the unit mass of structural components. This is considered in the design of fixed offshore platforms through appropriate enhancements in the diameter and mass of submerged members [51].  
In the second case study of one of the fixed offshore platforms in Indonesia, Platform Y, which is a service type platform that has four legs and a K-bracing type covered with marine growth of 50.80 mm, the interval for underwater inspections is determined to be 6 years. This was determined because, based on the above factors, the fixed offshore platform was considered to have a medium to high risk.