esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### TMDs Prevalence and Etiology
#### 4.1.4 Genetic Contributions  
Genetic predisposition plays a significant role in TMDs susceptibility. The OPERA studies identified polymorphisms in key genes, such as COMT (catechol-O-methyltransferase), which modulates pain sensitivity and stress response [54; 55]. Variants in serotonergic pathway genes like HTR2A and SLC6A4 have also been implicated, linking genetic factors to psychological resilience and nociceptive processing [55]. Additionally, genes associated with inflammation (PTGS1) and connective tissue integrity (COL1A1, COL5A1) further highlight the genetic complexity of TMDs [55].
## 4 Simulation Analysis  
### Selection of Simulation Model and Parameters  
The earth fault simulation model of the low-voltage distribution network is depicted in Figure 6. The system comprises a 10 kV/0.4 kV distribution station area with five outgoing cables, and the transformer is connected in a \(\Delta\)/Y configuration. The grounding resistance of the neutral point system on the low-voltage side is set at 4 \(\Omega\)[9]. Altering the wiring configuration on the load side results in a variation in the grounding system employed. The residual current protection devices are set in positions A, B and C to structure a closed section.  
Figure 5: Schematic of self-adaptive expansion of closed section.  
The simulation model includes five outgoing lines, with line 1 serving as a test simulation line spanning 500 m. Distributed along the line are five branches, each evenly spaced and varying in length from 50 to 100 m. Branch 3 features two secondary branches within it. The parameters of the lines and transformer are shown in Table 1. Operating at approximately 70% of the load, the total system load corresponds to 70% of the transformer capacity [9]. Line 1 bears a load of 200 kVA, evenly distributed across its branches, with each branch carrying 40 kVA and the secondary branch supporting 20 kVA. The power factor across the system is maintained at 0.95. Lines 2-5 collectively carry a load of 170 kVA, with the same power factor as line 1. The simulation parameters in ATP-EMTP are configured with a step size of 10 us and a sampling frequency of 10 kHz/s.
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.5 Resistance to Sensor Capture Attacks  
Suppose that adversary \(\mathcal{A}\) captures an IoT sensor and extracts secret values from it. To impersonate the sensor or compute the session key, \(\mathcal{A}\) requires \(SI_{j}\), which is masked with the PUF response value \(RE_{j}\). However, based on the properties of PUF presented in Section 3.1, replicating or predicting the PUF response is infeasible for \(\mathcal{A}\). Thus, our scheme is resilient to sensor capture attacks.
# Multi-Condition Constrained Pedestrian Localization Algorithm Based on IMU
## 2 Methods
### Single-Foot Constraint Model  
During pedestrian walking, the projection location of the foot on the ground has a maximum range value between its current moment and the previous zero-velocity moment. This value is related to factors such as the pedestrian's height and leg length. However, due to issues like heading drift in the IMU, there may be a significant deviation between  
Figure 4: Zero-Velocity Interval After Threshold Phase Constraint with GLRT Algorithm.  
the computed location and the actual location. To suppress this error, the maximum step displacement principle can be used to constrain locations that exceed the maximum step length in each step. The model of this constraint method is shown in Figure 5.  
As displayed in Figure 5, the center of the circle represents the location of the single foot at a particular zero-velocity moment, and the radius of the circle corresponds to the maximum distance traveling in a step under ideal conditions.  
For the locations of the foot that exceed the ideal step length range after IMU calculation, this paper applies the maximum distance constraint method to correct them. By analyzing the location of the foot in each gait cycle and constraining the out-of-range values, the method effectively suppresses long-term drift of the IMU. Specifically, the IMU-calculated location of the single foot at any moment must satisfy the following constraint:  
\[\left\|\hat{\mathrm{p}}(\mathrm{i})-\hat{\mathrm{p}}_{\mathrm{ms}}\right\|_{2 }^{2}\leq\gamma_{\mathrm{single}}^{2}. \tag{8}\]  
To obtain the corrected location that satisfies the above constraints, Lagrange multipliers and the least squares method are combined. The effective single-foot location update formula is as follows:  
\[\mathrm{p}(\mathrm{i})=\frac{\left[\hat{\mathrm{d}}(\mathrm{i})-\gamma_{ \mathrm{single}}\right]\cdot\hat{\mathrm{p}}_{\mathrm{ms}}+\left[\hat{\mathrm{ d}}(\mathrm{i})+\gamma_{\mathrm{single}}\right]\cdot\hat{\mathrm{p}}(\mathrm{i})}{2 \cdot\hat{\mathrm{d}}(\mathrm{i})}, \tag{9}\]  
where \(\mathrm{p}(\mathrm{i})\) is the corrected foot location at the i-th moment; \(\hat{\mathrm{d}}(\mathrm{i})\) is the distance between the foot's projection on the ground at the i-th moment and the location at the last zero-velocity moment; \(\hat{\mathrm{p}}_{\mathrm{ms}}\) is the location information at the last zero-velocity moment; \(\hat{\mathrm{p}}(\mathrm{i})\) is the foot projection location at the current i-th moment; and \(\gamma_{\mathrm{single}}\) is the constraint threshold for the single-step distance.
## 6 Conclusions  
Source location privacy protection is a challenging task in underwater acoustic sensor networks. This paper discusses the key challenges of source location privacy protection, designs for the scenario of multiple cooperative attackers, and proposes an underwater source location privacy protection scheme based on game theory. The scheme comprehensively considers privacy protection, delay and energy issues, and it effectively protects the source location privacy by means of virtual coordinate system transformation, relay node selection strategy and fountain code secure data transmission technology. The introduction of game theory as a framework allows strategic interaction between source nodes and malicious nodes, provides a new perspective for source location privacy protection in underwater acoustic sensor networks, and improves network security. The simulation results show that the proposed scheme achieves significant improvements in terms of packet delivery ratio, security time, delay and energy consumption. The packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP, which provides strong support for the security and performance of underwater acoustic sensor networks.  
However, there are some limitations to the proposed scheme. Due to the lack of real marine data, mathematical methods are currently the only means to quantify the benefits and costs of the game between source nodes and attackers. In the future, research needs to delve deeper into the cooperative patterns among multiple attackers to devise more effective response strategies. Considering simulation experiments and actual observational data for validating and optimizing the accuracy and practicality of the model, and further understanding the advantages and limitations of various methods, will be crucial in exploring more effective and reliable source location privacy protection mechanisms to ensure location privacy and data security in underwater acoustic sensor networks.  
Conceptualization, B.W. and X.Y.; methodology, B.W. and Y.L.; software, B.W., X.Y. and Y.L.; validation, K.H., Y.L. and X.Y.; formal analysis, K.H.; investigation, X.Y.; resources, X.Z.; data curation, X.Z.; writing--original draft preparation, X.Y.; writing--review and editing, K.H. and B.W.; visualization, Z.L.; supervision, Z.L.; project administration, K.H.; funding acquisition, K.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Chunhui Cooperation Program of the Ministry of Education, HZKY20220590-202200265; National Natural Science Foundation of China under Grant 61902273.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
###### Abstract  
Impulse radio ultra-wideband (IR-UWB) radar, operating in the low-frequency band, can penetrate walls and utilize its high range resolution to recognize different human activities. Complex deep neural networks have demonstrated significant performance advantages in classifying radar spectrograms of various actions, but at the cost of a substantial computational overhead. In response, this paper proposes a lightweight model named TG2-CAFNet. First, clutter suppression and time-frequency analysis are used to obtain range-time and micro-Doppler feature maps of human activities. Then, leveraging GhostV2 convolution, a lightweight feature extraction module, TG2, suitable for radar spectrograms is constructed. Using a parallel structure, the features of the two spectrograms are extracted separately. Finally, to further explore the correlation between the two spectrograms and enhance the feature representation capabilities, an improved nonlinear fusion method called coordinate attention fusion (CAF) is proposed based on attention feature fusion (AFF). This method extends the adaptive weighting fusion of AFF to a spatial distribution, effectively capturing the subtle spatial relationships between the two radar spectrograms. Experiments showed that the proposed method achieved a high degree of model lightweightness, while also achieving a recognition accuracy of 99.1%.  
UWB radar; human activity recognition; time-frequency analysis; lightweight model; feature fusion; attention mechanism +
Footnote †: journal: Applied Sciences
# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 4 Results and Discussion
### Ablation Studies
The experimental findings indicate that, although there are some similarities in the overall feature response patterns, the detailed information captured by these two processing directions exhibits significant differences. A deeper analysis reveals that top-to-bottom processing is more effective at capturing the sequential features of the upper parts of objects, which is crucial in certain scenarios for understanding object structures and spatial relationships. On the other hand, bottom-to-top processing better captures features from the lower parts of objects, which may contain important contextual information that complements the top-to-bottom features. These differences are reflected in the performance metrics of our object-counting task, where combining both directions leads to more accurate estimations compared to using a single direction alone. Similarly, for width-wise (W-wise) processing, we compared the results of right-to-left and left-to-right processing. The findings indicate that while some feature responses overlap, each direction captures unique aspects of object features along the horizontal dimension. Right-to-left processing highlights specific object features on the right side, which may be related to object orientation or the presence of nearby objects. In contrast, left-to-right processing provides a different perspective on object features from the left side, contributing to a more comprehensive understanding of spatial layout. These ablation studies confirm that separating the height and width dimensions in the spatial similarity extraction method is essential for capturing multi-dimensional spatial information of objects. This enables our model to better adapt to different object orientations and spatial arrangements, ultimately improving performance in the few-shot object counting task.
## 3 Results
\[q_{22}=\frac{2.6679e^{-0.3\text{s}}}{8.8871s^{0.9683}+1} \tag{56}\]  
In this example, the fractional controllers are obtained as follows:  
\[g_{c1}=-2.5765\frac{1}{s^{0.03}}\Big{(}1+\frac{1}{6.6757s^{0.97}}\Big{)} \tag{57}\]  
\[g_{c2}=2.082\frac{1}{s^{0.0317}}\Big{(}1+\frac{1}{8.8871s^{0.9683}}\Big{)} \tag{58}\]  
In this simulation, the performances of the proposed method are compared with those of the simplified decoupling Smith predictor (SDSP [15]) and the centralized inverted decoupling method ([PERSON] et al. [21]). For the sequential unit step changes in the set-points, Figure 10a,b compare the closed-loop time responses afforded by the proposed and other methods. It is clear that in the 1 st loop, the fractional controller shows superior responses over the others in a set-point change. In the 2 nd loop, the obtained performance is almost the same as SDSP one (dash line) and still better than the response of the centralized inverted decoupling proposed by [PERSON] (dot line). The control signals of the two loops are shown in Figure 12a,b. Moreover, the values of the performance indices in Table 4 also confirm the obtained results. Figure 11: The SSV plots of robust stability in Example 3*. Figure 10: (a) Closed-loop responses to the unit step changes in the set-point of loop 1 in Example 3*. (b) Closed-loop responses to the unit step changes in the set-point of loop 2 in Example 3*. (* [PERSON] in all Figures 10–12 is used instead of “The centralized inverted decoupling method proposed by [PERSON] et al. [21]”). The robustness analysis in this example is illustrated in Figure 11 in which the weighted matrix, Wo(s), is the same as previous examples. The figure shows that the proposed method and the SDSP controller have similar robustness. On the other hand, in comparison with [PERSON]'s method, the proposed F-SDSP presents much better robustness performance. The \(\upmu\) values of those methods are also listed in Table 4 in which the fractional controller gives the smallest value.
# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
## 4 Discussion  
### Point-Cloud Downsampling Experiment and Analysis Based on FFN Output Facets  
When processing 3D point clouds with neural networks, it is often necessary to fix the number of input points, whereas the actual number of captured points is usually variable and unevenly distributed. Common downsampling methods include random sampling, voxel-grid filtering, Farthest Point Sampling (FPS), and normal-based sampling. Random sampling is simple and efficient, but may miss the critical structures. Voxel-grid filtering can achieve approximately uniform sampling but tends to lose local details. The FPS maximizes spatial coverage; however, its computational complexity increases rapidly with the number of points and samples. Normal-based strategies preserve geometric variation information but are sensitive to noise and error in measurement. Existing methods struggle to balance output-point-count control, global uniformity, and local geometric fidelity; therefore, a strategy that combines these three advantages is required to improve the performance of subsequent deep-network processing.
# ConDPC: Data Connectivity-Based Density Peak Clustering
###### Abstract  
As a relatively novel density-based clustering algorithm, Density peak clustering (DPC) has been widely studied in recent years. DPC sorts all points in descending order of local density and finds neighbors for each point in turn to assign all points to the appropriate clusters. The algorithm is simple and effective but has some limitations in applicable scenarios. If the density difference between clusters is large or the data distribution is in a nested structure, the clustering effect of this algorithm is poor. This study incorporates the idea of connectivity into the original algorithm and proposes an improved density peak clustering algorithm ConDPC. ConDPC modifies the strategy of obtaining clustering center points and assigning neighbors and improves the clustering accuracy of the original density peak clustering algorithm. In this study, clustering comparison experiments were conducted on synthetic data sets and real-world data sets. The compared algorithms include original DPC, DBSCAN, K-means and two improved algorithms over DPC. The comparison results prove the effectiveness of ConDPC.  
clustering; connectivity; Euclidean distance; neighbor distance; density difference 1  
[PERSON], [PERSON], Z. ConDPC: Data Connectivity-Based Density Peak Clustering. _Appl. Sci._**2022**, 12, 12812. [[https://doi.org/10.3390/app122412812](https://doi.org/10.3390/app122412812)]([https://doi.org/10.3390/app122412812](https://doi.org/10.3390/app122412812))  
Academic Editor.  
[PERSON]  
Received: 3 October 2022  
Accepted: 8 December 2022  
Published: 13 December 2022  
**Published: 13 December 2022**  
This study integrates connectivity into the original DPC algorithm to improve the clustering effect. The primary contributions of this study are as follows.  
1. Constructing connected groups based on the idea of connectivity. Points in the same group are connectable to each other, whereas points in different groups are not.
2. Adjusting the calculation method of neighbor distance based on the idea of connectivity to improve the accuracy of determining the cluster center.
3. Adjusting the neighbor allocation strategy based on the idea of connectivity to improve the accuracy of clustering.  
The rest of this paper is organized as follows. We provide an overview of the DPC algorithm in Section 2. Section 3 explains the detailed steps of the proposed algorithm ConDPC. In Section 4, experiments are conducted on synthetic and real-world datasets. Conclusions are presented last.
## 5 Approaches of Optimal State Estimation Utilizing Filtering
### \(H_{\infty}\) Filter
The distances between emitters and sensors are in the order of kilometres for far-field, while the distances will be shorter in near-field environments, which leads to fewer errors. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Filter** & **Advantages** & **Drawbacks** \\ \hline KF & Simple mathematically, and fast version [127]. & Working only in linear systems [127]. \\  & Less complexity in the mathematical process, & A high initial estimation is required [128]. \\  & and easy to implement [128]. & If the target has high rotational speed, \\  & & EKF [112]. \\  & & Mathematically costly, and more parameters need to \\  & & tune [127]. \\  & & Limited use, just used at specific applications [127]. \\  & & It depends on the number of particles employed. \\  & & When updating equation iteration, it may produce a \\  & & significant weight of particles [129]. \\  & & The achieved result of segmentation by GMM is not \\  & & robust to noise measurement [130; 131]. \\  & & The number of Gaussian phases increases \\  & & exponentially over time [132]. \\  & & Complex mathematically [30]. \\  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between various filters used for optimal state estimation of target tracking. Figure 35: GNSS signals data collection in a Montreal urban environment with high buildings as obstacles. Employing hybrid filters such as GHKF\(/H_{\infty}\), UKF\(/H_{\infty}\), and EKF\(/H_{\infty}\) may achieve high accuracy target tracking and estimation compared to using the individual filter.
## 2 Thematic Review and Analysis
### Control Strategies
#### 2.3.2 Supply Temperature Control  
Supply temperature control aims to maintain an optimal and consistent temperature in the pipe network to efficiently meet building heat demands. This set supply temperature significantly impacts system performance and return temperature. Traditional DH systems either use a fixed supply temperature year-round or a variable supply temperature adjusted according to outdoor conditions via a 'weather compensation control' technique, also known as 'temperature reset control' or 'external temperature compensator'. Figure 8 illustrates various forms of these weather compensation curves, which typically focus on adjusting the supply temperature in response to outdoor conditions (these curves normally do not directly account for variations in return temperature).  
Similarly, substation controllers utilize an outdoor temperature reset approach by adjusting the inlet temperature to the internal heating system based on external conditions. This adjustment is achieved by controlling the flow through the substation heat exchanger in indirect configurations or by mixing the return medium with the supply medium in direct configurations.  
[PERSON] et al. [91] found that weather compensation curves outperform fixed supply temperature methods in terms of performance. However, improper commissioning of weather compensation curves can result in worse performance than fixed supply temperatures [71; 91], the main reason being attributed to the lack of controller feedback on indoor temperature [92]. Additionally, [PERSON] and [PERSON] [93] highlight a lack of information on optimizing weather compensation curves for achieving low return temperatures in residential hydronic systems.  
Figure 8 reveals significant variation among weather compensation curves. This observation is supported by [PERSON] et al. [94], who found that two distinct optimization strategies carried out for the same district produced different temperature curves.  
[PERSON] et al. [91; 95] propose an adaptive control method, termed the \"Inferential Control Scheme,\" as an alternative to weather compensation curve controllers. This method adjusts the supply temperature based on real-time measurements of space heat load. Unlike weather compensation controllers, which can lead to overheating and high return temperatures, especially with outdated thermostatic radiator valves, the Inferential Control Scheme utilizes feedback to precisely meet heat demand without causing overheating or underheating.  
[PERSON] [23] proposes a smart \"delta T\" controller that integrates return temperature monitoring with weather compensation, applicable at both the building and DH levels. This controller is designed to minimize the return temperature while also optimizing the supply temperature when feasible.