esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The results of these studies are difficult to extrapolate to clinical applications. Hence the interest in in vivo validation based on joint contact forces, measured from instrumented knee prostheses [16; 26] has recently been increased. Although it is not a complete validation--only the contact forces are measured, not the muscular ones--the instrumented prostheses are the source of experimental data in vivo with measurements closest to the reality of the internal reactions in the knee joint. One of the most recent and interesting applications of MSM is their inclusion within the control algorithm of the robotic systems devoted to rehabilitation exercises. The knowledge of the muscular and ligamentous forces during movement allows (i) to increase the patient's safety during the process; (ii) to monitor the rehabilitation process over time, thus quantifying its progress; and (iii) to establish a priori what force levels are to be achieved during the movement in specific muscles [27]. Within the mechanical systems that constitute the core of robotic rehabilitation systems, two main types can be distinguished: open kinematic chains, mainly exoskeletons [28; 29; 30], and closed kinematic chains, mainly parallel robots. One of the main differences between exoskeletons [31] and parallel robots for knee rehabilitation (RPRs) [32] is that the RPRs exert mechanical actions on the distal end of the limb. For this reason, it is necessary to ensure that the forces transmitted by the robot terminal element (mobile platform) to the patient's foot give rise to the desired actions on the knee. As a result, developing a precise and adjustable MSM for the estimation of the relevant forces in the knee, such as muscle, tendon, ligament, and tibial contact forces, while performing the exercise with the robot is of paramount importance for the practical implementation of RPRs [21]. Additionally, if the model is to be integrated into the RPR control scheme, it must be simple enough to be able to operate in real-time. Finally, the MSM must be verified experimentally, since the safety and evolution of the patient's recovery from injury will depend on it. In this research, we propose a simplified MSM lower limb model for the control of an RPR [33]. It is based on a scalable parametric model [12], with some adaptations to reduce the computational cost and thus operate in real-time [11]. The model has been validated by using data from the _\"Third Grand Challenge Competition to Predict in Vivo Knee Loads\"_[26; 34].
## 3 Numerical Simulation Analysis of Deformation Mechanism of Tunnel-Landslide Orthogonal System
### Solid Modeling of Tunnel-Landslide Orthogonal System  
According to the actual engineering situation, the model materials in the orthogonal system of a tunnel-slide in northwest China are mainly divided into the tunnel, common geotechnical body, slide body, slide zone, bedrock (landslide bed), and other parts. Among them, the distribution of soil layers in the tunnel peripheral rock and slip belt is mainly mudstone sandstone and sandstone conglomerate, and the overlying soil layer is loess. The mechanical parameters and properties of different strata are assigned according to the different properties of each layered material. Specific numerical simulation calculation parameters are based on the field engineering geological investigation report and indoor test results and combined with relevant engineering specifications and similar engineering experience, as shown in Table 1 below.  
The numerical model is established according to the main axis section of the tunnel-landslide system II-II', and the geometry is the same as the original section. The model is mainly divided into four parts: slide bed, slide body, slide belt, and tunnel. The total length of the model is about 2076.82 m, and a thickness of 100 m is taken as the research object. The shape of the tunnel is a horseshoe, with a width of about 15 m and a height of about 14 m, as shown in Figure 10. The model meshing is performed by the Griddle module in Rhino 6 software, and the model is divided by tetrahedral mesh. The number of cells in the tunnel-landslide system model is 25,825 and the number of nodes is 51,538; the number of cells in the tunnel model is 5258 and the number of nodes is 1314. The tunnel and the grid around the tunnel are encrypted locally in the model, which can better reflect the deformation and damage of the tunnel with the sliding of the landslide body.  
Figure 9: Numerical simulation II-II’ of simplified layered diagram of landslide section.
## 2 Materials and Methods
### Methods
#### 2.2.3 NDVI Difference Calculation  
The growth cycle of crops can be divided into the \"sowing stage\", \"growing stage\", and \"harvesting stage\", and the NDVI of crops between the growing and non-growing stages are more obvious than those of natural vegetation due to anthropogenic sowing and harvesting activities. In this study, the abandoned farmland data were extracted according to this principle. Therefore, we required a time series dataset that could cover the growing and non-growing stages of major crops in the study area and quantify the NDVI changes through the difference in the NDVI values between the growing and non-growing stages.  
Firstly, the year was divided into four time periods (January-March, April-June, July-October, and November-December) according to the growing and non-growing period patterns of the crops in this study area (Figure 1). These four periods should cover the growing and non-growing stages of the crops and, further, should ensure that each time period was covered by the NDVI data. However, due to the influence of the low temporal resolution of the data, as well as cloudy weather, the use of GF-6 data as a single data source often fails to cover the growing and non-growing stages of the major crops in this area. As a consequence, in this study, STARFM was utilized to fuse the GF-6 and Landsat 8 data to obtain the GF-6 resolution-like NDVI data, which were combined with the GF-6 data to jointly construct the NDVI time series dataset. Then, the NDVI of adjacent time periods were differenced, and the absolute values were taken. If there was multiple NDVI data coverage in a certain time period, an MVC was taken after separate calculations, and the result was used to measure the NDVI changes in the two adjacent time periods. Lastly, the three NDVI differences between the four time periods were composited into the NDVI difference of the year based on the MVC to measure the NDVI changes in that year. The calculation process was as follows:  
\[NDVI\ difference_{season}=max\{NDVI(i,m)-NDVI(j,n)) \tag{7}\]  
\[NDVI\ difference_{year}=max\{NDVI\ difference_{season}\} \tag{8}\]  
where \(NDVI\ difference_{season}\) is the NDVI difference in one season, \(NDVI\ difference_{year}\) is the NDVI difference in one year, \(i\), and \(j\) represent the \(i\)th and \(j\)th adjacent time periods, and \(m\) and \(n\) represent the number of NDVI data in \(i\)th and \(j\)th time period.
# Power Curve Modeling of Wind Turbines through Clustering-Based Outlier Elimination +
## 3 Proposed Procedure
Applying K-means clustering to remaining data points after DBSCAN clustering, clusters located near the usual power curves can now be used for power curve modeling of wind turbines. After identifying and eliminating outliers from the dataset through clustering, the behavior of power curves from the cut-in speed to rated speed is often fitted with differentparametric models such as polynomial, Gompertz, Weibull, and logistic functions [3; 5]. The polynomial regression has often been adopted for power curve modeling [5], which is a generalization of linear regression for nonlinear datasets. The polynomial regression of degree \(k\) can be written as  
\[f(v,\underline{\theta})=\theta_{0}+\theta_{1}v+\theta_{2}v^{2}+\ldots+\theta_{ k}v^{k}+e \tag{2}\]  
where \(f(v,\underline{\theta})\) denotes the polynomial function, \(\underline{\theta}=(\theta_{0},\theta_{1},\ldots,\theta_{k})\) represents the set of coefficients, and the random error term denoted by \(e\) follows the standard normal distribution, i.e., \(e\sim N(0,1)\). Noting that the power curve is S-shaped, other parametric models may also be employed for power curve modeling [3; 4]. The most popular S-shaped models are Gompertz, Weibull, and logistic functions, which are defined by Equations (3)-(5) in the below, respectively. \[f(v,\underline{\theta})=\theta_{2}+(\theta_{3}-\theta_{4})\exp(-\exp(\theta_ {1}(v-\theta_{4})))+e \tag{3}\]  
\[f(v,\underline{\theta})=\theta_{2}+(\theta_{3}-\theta_{2})\exp(-\exp(\theta_ {1}(\log v-\log\theta_{4})))+e \tag{4}\]  
\[f(v,\underline{\theta})=\theta_{4}+\left[\frac{(\theta_{1}-\theta_{4})}{1+(v /\theta_{3})^{\theta_{2}}}\right]+e \tag{5}\]  
The goodness-of-fit of each model can now be compared to determine which fits best the data refined through clustering, using usual performance metrics such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination \(R^{2}\). Denoting the actual and fitted values of power output by \(y_{i}\) and \(\hat{y}_{i}\), RMSE, MAE, and \(R^{2}\) can be written as  
\[\text{RMSE}=\left[\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}\right]^{1/2} \tag{6}\]  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}\left|y_{i}-\hat{y}_{i}\right| \tag{7}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{N}(y_{i}- \overline{y})^{2}} \tag{8}\]  
where \(N\) and \(\overline{y}\) represent the number of data points and the sample mean of power output, respectively. Partitioning potential outliers into three different types, a 3-step power curve modeling procedure can be summarized as shown in Figure 3.  
Figure 3: Modeling Procedure of Wind Turbines Power Curve.
# Encrypted Client Hello Is Coming: A View from Passive Measurements
## 5 Conclusions  
This paper presented the first study of Encrypted Client Hello and HTTPS DNS records using passive measurements. Although these are recent standards (ECH is still an IETF draft by the TLS Working Group), we observe that many client devices already use such features, likely due to popular browsers' support for these privacy-enhancing technologies. Specifically, HTTPS DNS represents 5-10% of all DNS queries, but fewer than 5% of those queries receive a valid response. Not all providers support HTTPS DNS; only Cloudflare and Fastly provide complete and correct answers. ECH deployment is at a later stage from the clients' perspective; 56% of QUIC flows include an ECH, meaning the respective clients already support the standard. Interestingly, adoption on the server side is almost nonexistent. Only Cloudflare partially supports ECH; thus, in almost all cases, the observed ECH is \"GREASE\", meaning it does not include any encrypted domain name but rather verifies possible traffic differentiation at middleboxes.  
As ECH and HTTPS DNS continue to evolve, further research is necessary to fully understand their implications across different segments of the internet ecosystem. It remains uncertain whether major internet providers and platforms will adopt these technologies at scale, given the non-trivial operational and infrastructural costs they entail. Continuous passive monitoring is, therefore, essential to track real-world deployment and usage trends. Additionally, the computational impact of ECH and HTTPS DNS on both clients and servers--particularly in terms of handshake processing and DNS resolution--has yet to be thoroughly evaluated and represents a promising direction for future work. These technologies also shift the balance between privacy and network observability, potentially conflicting with the security and policy enforcement requirements in corporate networks. As a result, their deployment may face resistance or outright blocking in environments relying on firewalls, intrusion detection systems, or parental control middleboxes, raising further questions about compatibility, detectability, and circumvention.  
Conceptualization, M.T. and D.G.; methodology, M.T. and G.M.; investigation, G.M.; resources, M.T.; data curation, M.T. and G.M.; writing--original draft preparation, M.T. and G.M.; writing--review and editing, M.T. and G.M.; supervision, M.T. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results was funded by projects 20228 FT78M \"DREAM\" and 2022M2Z728 \"COMPACT\" under the Italian Ministry of University and Research 2022 PRIN program. This work received funding from Next Generation EU, Mission 4 Component 1, CUP: D53D23001340006.  
The datasets presented in this article are not publicly available because of privacy or ethical restrictions. Requests for access to the datasets should be directed to the authors.  
The authors declare no conflicts of interest.
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
###### Abstract  
With the rapid advancement of network technologies and the widespread adoption of smart devices, the demand for efficient indoor localization and navigation systems has surged. Addressing the navigation challenge without requiring additional hardware is critical for the broad adoption of such technologies. Among various fingerprint-based systems--such as Bluetooth, ZigBee, or FM radio--Wi-Fi-based indoor positioning stands out as a practical solution, due to the pervasiveness of Wi-Fi infrastructure in public indoor spaces. This study introduces an ESP32-based data-collection tool designed to minimize offline training time for Wi-Fi fingerprinting, and it presents a novel dataset incorporating room-to-room transition time, which represents the time taken to move between rooms, alongside Wi-Fi signal strength data. The proposed approach focuses on room-level localization, leveraging Machine Learning (ML) models to predict the most likely room rather than precise (x, y) coordinates. To assess the effectiveness of this feature, three datasets were collected from different residential environments by three different individuals, enabling a comprehensive evaluation across multiple spatial layouts and movement patterns. The experimental results demonstrate that incorporating room-to-room transition time consistently enhanced localization performance across all the datasets, with accuracy improvements ranging from 1.17% to 12.47%, depending on the model and dataset. Notably, the Wide Neural Network model exhibited the highest improvement, achieving an accuracy increase from 82.37% to 94.77%, while the Ensemble-based methods such as Ensemble Bagged Trees also benefited significantly, reaching up to 93.17% accuracy. Despite varying gains across the datasets, the results confirm that integrating room-to-room transition time improves Wi-Fi-based indoor positioning by leveraging temporal movement patterns to enhance classification.  
2020 IEEEexample:BSTcontrolcontrol
## 4 Proposed Algorithm
### Performance Metrics  
In MOPs, the diversity and convergence of the algorithm are the main performance metrics that need to be evaluated. A single metric has difficulty comprehensively measuring the performance of the algorithm. In this paper, two performance metrics, HV [1] and IGD, are adopted to evaluate the performance of the algorithm. These metrics are described below:  
As shown in Equation (53), the HV metric is the hypervolume metric, which is a measure of the diversity of an algorithm by calculating the volume of the target space formed by the set of nondominated solutions and reference points. The larger the HV is, the better the diversity of the algorithm.  
\[HV=Leb(\bigcup_{i=1}^{|S|}v_{i}) \tag{53}\]  
\(Leb\) represents the Lebesgue measure, which is used to measure the volume. \(|S|\) represents the number of nondominated solution sets, and \(v_{i}\) represents the hypervolume formed by the reference point and the ith solution in the solution set.  
As shown in Equation (54), the IGD metric is an inverse generational distance metric: a comprehensive performance metric that evaluates the convergence and distribution performance of the algorithm by calculating the average minimum distance from each point on the true Pareto front (PF) to the set of individuals obtained by the algorithm. The smaller the IGD is, the better the convergence and distribution performance of the algorithm.  
\[IGD\Big{(}P,P^{*}\Big{)}=\frac{\sum_{x\in P^{*}}min_{y\in P}dis(x,y)}{|P^{*}|} \tag{54}\]  
\(P\) is the solution set obtained by the algorithm, \(P^{*}\) is a set of uniformly distributed reference points sampled from the Pareto front, and \(dis(x,y)\) represents the Euclidean distance between the point \(x\) in the reference set \(P^{*}\) and the point \(y\) in the solution set \(P\).  
Because it is difficult to find the true Pareto front in real scenarios, the PF is approximated by considering all nondominated solutions obtained by all algorithms.  
In this experiment, the normalized metrics were used to unify the measures between the different objectives.
# Improved Lightweight Multi-Target Recognition Model for Live Streaming Scenes
## 5 Conclusions and Future Research  
In our research, we aimed to construct an object detection model that can achieve a remarkable balance between precision and lightweight design and applied it to live streaming scenarios. We have enhanced the yolov5s framework by incorporating the MobileNetV3 structure for the optimization of the Backbone and Neck layers. Additionally, we have introduced the CA attention mechanism to create an extremely lightweight model. Through a series of experiments, we have demonstrated that our model achieves significant improvements in terms of parameter reduction and weight while further enhancing accuracy. As a result, our model offers greater flexibility for deployment in various devices and occupies minimal space in this application scenario. These advancements provide robust technical support for a wide range of marketing strategies in live marketing. The selection of MobileNetV3 for this study, in comparison to our previous model enhancements [11], enabled us to achieve advancements in both the Backbone and Neck layers. This choice also provided us with a broader scope for parameter experimentation, and the results have proven this approach to be highly beneficial. The former model significantly contributed to balancing various parameters including accuracy, weight, and speed. Building upon previous work, our refined model further emphasizes the importance of reducing model weight and parameter count, making it exceptionally fitting for our application's live streaming scenario.  
In future research, we aim to further optimize our model to address its detection speed limitations. Additionally, we will explore additional sensory cues, including but not limited to visual and auditory inputs, in live marketing strategies. The integration of multimodality is a research direction that deserves attention. Our goal is to build a more comprehensive object detection network specifically designed for live streaming scenarios. This will provide marketers and researchers in the field of live streaming with reliable technical support that meets diverse requirements and needs.  
Conceptualization, Z.L. ([PERSON]) and Z.L. ([PERSON]); methodology, Z.L. ([PERSON]); software, Y.Z.; validation, J.C., K.Q. and Z.L. ([PERSON]); formal analysis, Z.L. ([PERSON]); investigation, K.Q.; resources, Y.Z.; data curation, J.C.; writing--original draft preparation, K.Q.; writing--review and editing, K.Q.; visualization, K.Q.; supervision, Z.L. ([PERSON]); project administration, Z.L. ([PERSON]). 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, Study on the mechanism and spatial and temporal effects of international learning on the internationalization speed of manufacturing enterprises, grant number 71974130.  
Not Applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
All data are publicly available.  
The authors declare no conflict of interest.
## 2 Principles and Methods  
### Seismic Interferometry  
SI was proposed by [PERSON] in 1968. According to the theory, the results of the cross-correlation of microtremor signals received at two different stations on the Earth's surface can be regarded as a new signal record with one station as the virtual source and another station as the receiver [22; 23]. The schematic diagram of SI is shown in Figure 2. a, b represents the microtremor signals recorded by two detectors at different locations on the surface. This record is similar to the Green's function and can reflect the information of the subsurface medium between two stations.  
The basic method is to perform cross-correlation calculation on microtremor signals recorded by two stations to obtain the Green function and then use the Green function to extract the dispersion curve. The cross-correlation operation can be performed in the time domain or the frequency domain; convolution in the time domain corresponds to the conjugate dot product in the frequency domain. To extract the dispersion curves with the Green function, there are mainly three methods: traditional time-frequency analysis method, Yao method, and fitting the real part of spectrum of SI results with zero-order Bessel function method [24]. This study performed cross-correlation in frequency domain and extracted the dispersion curve by fitting the real part of spectrum of SI results with zero-order Bessel function.  
In the frequency domain, the SI cross-correlation results of the microtremor signals between two stations \(a\) and \(b\) can be written as follows:  
\[\gamma_{a,b}(f)=\frac{\text{Re}[S_{ab}(f)]}{\sqrt{S_{a}(f)S_{b}(f)}} \tag{1}\]  
where \(\gamma_{a,b}(f)\) is correlation coefficient, \(S_{ab}(f)\) is the cross-correlation function of the microtremor signals between station \(a\) and station \(b\), \(S_{a}(f)\) and \(S_{b}(f)\) refer to the autocorrelation function of microtremor signals collected at station \(a\) and station \(b\), respectively.  
According to the theoretical formula derived by [PERSON] in proving the consistency between SI and SPAC [25], the vertical component of microtremor signals between two stations using SI can be cross-correlated as follows:  
\[\gamma_{a,b}(f)=J_{0}(kr_{ab}) \tag{2}\]  
where \(J_{0}\) represents zero-order first-class Bessel function curve, \(r_{ab}\) is the distance of station pair, k means the wavenumber and \(k=2\pi f/v\), \(v\) is the phase velocity, \(f\) stands for the frequency, and Equation (2) can be obtained:  
\[\gamma_{a,b}(f)\frac{\text{Re}[S_{ab}(f)]}{\sqrt{S_{a}(f)S_{b}(f)}}=J_{0}( \frac{2\pi f}{v}r_{ab}) \tag{3}\]  
By traversing the frequency and using the Newton iterative approach to determine the phase velocity corresponding to each value of the correlation function, the dispersion curve can be obtained. The algorithm of SI is shown below as Algorithm 1.  
Figure 2: Schematic diagram of SI.