esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 2 Principles and Methods
### Remote Sensing of Snow
#### 2.2.2 Snow Water Equivalent  
Snow water equivalent (SWE) describes the amount of water contained in the snowpack when completely melted. To estimate SWE from satellite microwave observations, the scattering and emission contributions from intervening atmosphere, snow surface, snowpack, and underlying soil and snow-soil interactions need to be distinguished and accounted for, which can be done by exploiting the frequency-dependent sensitivity of microwaves to land surface components [129; 130] (Figure 1).  
Satellite passive microwave SWE estimation relies on multi-frequency observations from SMMR (Scanning Multichannel Microwave Radiometer), SSM/I (Special Sensor Microwave/Imager), SSMIS (Special Sensor Microwave Imager Sounder), AMSR-E/2, and FY-3 MWRI (Microwave Radiation Imager) [125] sensors. The associated SWE algorithms include (a) static [130] and dynamic semi-empirical algorithms [131; 132]; (b) iterative algorithms [133]; (c) physically based statistical algorithms [134]; (d) probabilistic approaches [135]; (e) machine learning methods [136; 137]; and (f) data assimilation methods [138; 139].  
The satellite SAR SWE retrieval algorithms can be grouped into two categories: (a) physical inversion algorithms and (b) interferometry methods. By utilizing the frequency-dependent sensitivity to snow and underlying soil properties, combined multi-frequency SAR observations (e.g., X- and Ku-band) are capable of SWE retrievals as demonstrated by model simulations and field experiments [140; 141; 142; 143]. The uncertainties related to snow density, ice microstructure, snow layer stratification, vegetation, and terrain effects are the main issues affecting the performance of both passive and active microwave snow retrieval algorithms [144; 145; 146]. Alternatively, SAR interferometry techniques show promise in overcoming many of the above difficulties by utilizing interferometry phase difference information for SWE estimation [147; 148].
# Women's Singles Tennis Match Analysis and Probability of Winning a Point
## 4 Discussion
### Practical Implications Related to Service  
Coaches should consider that serving with a first serve not only increases the chances of winning the point, but that putting the ball in play on the first serve also enhances confidence and reduces pressure on the second serve. The need to secure the second serve can increase stress and the probability of double faults, which can be crucial at decisive moments in the match. A powerful serve can dominate the game, putting the receiver in a defensive position. This is particularly relevant on fast surfaces, such as grass or hard courts, where the first serve can generate a direct point or facilitate a winner later on, which could be executed on the third shot. If the point starts with a second serve, the returner will position herself close to the baseline or even inside the court, allowing her to be more aggressive on the return and thus apply more pressure on the server and potentially make her lose the initiative.  
It is essential to spend time perfecting serving technique through specific drills that improve accuracy and power. This includes working on posture, ball launch, and arm movement. In addition, applying biomechanical principles to optimise the service motion and reduce the risk of injury is key.  
It is also important to train the physical component. On the one hand, it is necessary to focus on strengthening the upper body, including the shoulders, arms, and core, as a powerful serve requires an efficient transfer of energy from the legs to the racquet. On the other hand, it is crucial to implement flexibility and mobility exercises to ensure a full range of motion, which is essential for an effective serve and to avoid injuries.  
On a tactical level, it is very important to diversify the serve. Practising different types of serves (flat, spin, and slice) and directions will make it difficult for the opponent to read the serve and thus limit the effectiveness of the return. Serving strategies adapted to different opponents and match situations should also be developed, including deciding when to risk more and when to prioritise consistency.  
In addition, establishing a consistent routine before each service can help concentration and reduce anxiety. This routine can include deep breathing, visualisation of the service, and a consistent physical sequence. Including mindfulness practices and other stress management techniques can also improve focus and calmness under pressure, which are crucial during service.  
To improve first service, video technology can be used to analyse the service and make adjustments based on objective data. Tools such as slow-motion video analysis can reveal details that may go unnoticed by the naked eye. It is also useful to use simulations and training software to practice different scenarios and receive instant feedback on service performance.
# Iterative Receiver Design for the Estimation of Gaussian Samples in Impulsive Noise +
## 5 Results
These strategies exploit soft decisions on the impulsive noise states and show a superior performance, compared to the PIS algorithm, which is based on hard decisions on \(i_{k}\). As can be seen in Figure 5, the performance of PIS is degraded especially around SNR values where the signal and noise power are balanced. At these SNR levels (0-10 dB), signal estimation is neither dominated by noise, nor is it similar to a noiseless scenario. On the contrary, the performance of both EP and TP is close to the  
Figure 4: Simulation results for signal estimation, based on the hard decisions with: (**a**) \(M=2\), (**b**) \(M=4\), and (**c**) \(M=16\) impulsive noise states. The signal parameters are \(a_{1}=0.9\) and \(\sigma_{s}^{2}=1\); the noise parameters are \(x=0.9\), \(\Gamma=0.01\), and \(A=0.2\) or \(1\). We transmitted \(100\) frames of \(1000\) samples each. GAKS stands for genie aided Kalman smoother and PIS for the parallel iterative schedule of Section 4.3.  
lower bound (GAKS), meaning that these algorithms are practically optimal. If we do not consider the issue of convergence, it would thus be irrelevant to choose either of the two. However, we recall that the EP algorithm requires the implementation of an extra improper message rejection strategy. In contrast, the TP algorithm is inherently stable and requires less computations, and it is thus the preferable choice, for this problem. A comparison between Figure 5a,b further proves that the performance of the estimator does not strongly depend on the value of \(\Gamma\), as one could expect. To complete the picture, Figure 6 reports simulation results obtained at other different impulsive index values (\(A=0.1,0.5,1.2\)). The MSE values in Figures 5a and 6 definitely show that it is the impulsive index \(A\) that dictates the system performance. The two values \(A=0.2\) and \(0.8\) considered in Figure 5a are associated with curves that fit in between those in Figure 6, which are associated with the other three values of \(A\). For each value of \(A\), the EP and TP algorithms provide results almost coinciding with the lower bound (GAKS), while the PIS algorithm entails a consistent loss in performance, especially at intermediate SNR values (5-10 dB). The iterative algorithms that were analyzed (PIS, EP, TP) showed a fast convergence in all cases, so that simulations were carried out in four iterations; we verified that convergence is practically reached in three iterations and that no further modification of results occurs after the fourth iteration (as we tested, until the 10 th iteration). Figure 5: Signal estimation, based on the hard (PIS) or soft (EP and TP) decisions. \(M=4\) noise states with \(A=0.2\) or \(0.8\): (**a**) \(\Gamma=0.01\), (**b**) \(\Gamma=0.001\).
## 3 Results and Discussion
### Analysis of Numerical Simulation Results
#### 3.2.2 Energy Analysis
During the 73 rd to 80 th freeze-thaw cycles, the value of the Contact Force increased from 7500 Pa to 10,000 Pa, which is a significant amplification. In addition, the Contact Force also fluctuated wildly during the 63 rd, 72 nd, and 73 rd freeze-thaw cycles, indicating that the energy release rate was accelerated. This phenomenon indicates that the energy inside the model is in a state of continuous growth. The energy release rate becomes faster and faster as the internal cracks gather and penetrate through each other. The variation of the model's Contact Force map provides visual evidence for the above analysis. As shown in Figure 17, the distribution and change of Contact Force show a certain regularity with the increase in freeze-thaw cycles. At the 20 th freeze-thaw cycle, regions of high Contact Force appeared sporadically inside the specimen, signaling the beginning of energy accumulation inside the model. When the freeze-thaw cycle reached 40 times, a large area of high Contact Force appeared in the top part of the model. A small area of high Contact Force formed around the prefabricated crack's perimeter. This indicates that energy has been accumulated to a certain extent inside the model during the  
Figure 16: Variations in Porosity and Contact Force. 40 th freeze-thaw cycle. When the freeze-thaw cycle reaches 60 times, the number of high Contact Force areas decreases, and they are mainly concentrated on the left and right sides of the model. It reveals that energy release occurred in the model during the freeze-thaw cycles from 40 to 60 times. Eventually, at the 80 th freeze-thaw cycle, the number of high Contact Force areas continued to decrease, and the Contact Force was evenly distributed over most of the specimen. This indicates that the energy is stored uniformly in different locations.
## 6 Conclusions  
When designing an efficient sensor application, we must consider the resource constraints of sensor nodes and their scalability. WSN users are concerned about information quality and user requirements for real-time features are also increasing. Moreover, sensor applications are expanding into harsher and more dangerous environments. Therefore, fault tolerant schemes have emerged as important issues in WSNs.  
Clustering routing protocols such as LEACH, PEGASIS and TEEN were designed to improve both energy efficiency and scalability. These protocols compose clusters and elect a cluster head in each cluster. The cluster heads aggregate data from its member nodes and reduce the amount of messages sent by member nodes to the BS directly. In clustering routing protocols, cluster head management is needed because the role of the cluster head is more important than one of member nodes.  
In this paper, we proposed a checkpointing scheme for clustering routing protocols. Our scheme can reduce energy consumption and recovery latency when a cluster head fails transiently. In addition, our checkpointing scheme is easy to implement. The simulation and real-world testbed results show energy consumption and recovery latency efficiencies when our checkpointing scheme is implemented.
# Carousel Greedy Algorithms for Feature Selection in Linear Regression
## 4 Generalized Feature Selection
Following this idea, by using CG, we solve a sequence of smaller size unconstrained linear regression problems where the cardinality constraint is no longer in any subproblem. For generalized feature selection, CG has the same framework. The differences are that the candidate variables to be added are limited by the notion of correlated sets for Case A, and the sequence of smaller size linear regression problems become constrained, as in (21), for Case B. The term we designate for the new process of adding a variable is the \"generalized forward selection criterion.\" Let us start with Case A. **Case A**: The addition of one variable is almost the same as for Algorithms 1-3, but the candidate variables should be those not highly correlated with any current variables, instead of any \(l\
otin S_{d}\). As an example, suppose variables \(i\), \(j\), and \(k\) are highly correlated. (For the sake of clarity, we point out that the data for variable \(i\) is contained in \(X_{i}\).) We want no more than one of these in our solution. At the \(l\)-th carousel step before adding a variable, we check whether one of these three is in \(S_{d}\) or not. If \(i\) is in \(S_{d}\), we cannot add \(i\) or \(k\). If \(i\) is not in \(S_{d}\), we can add \(i\), \(j\), \(k\) or any other variable. **Case B**: We can add variables as usual, but the coefficients corresponding to some of these variables are now bounded. We will need to solve a sequence of bounded variable least squares (BVLS) problems of the form  
\[\operatorname*{argmin}_{l\
otin S_{d}}f(l,S_{d}). \tag{24}\]  
Here, \(f(l,S_{d})\) can be obtained from the following problem:\[f(l,S_{d})=\min_{\theta}\sum_{i=1}^{n}(\sum_{j\in S_{d}\cup\{l\}}X_{ ij}\theta_{j}-y_{i})^{2},\] (25) subject to \[l_{i}\leq\theta_{i}\leq u_{i},\quad\forall i\in S_{d}\cup\{l\}. \tag{26}\]  
We extract the general subproblem of BVLS from the above:  
\[\min_{\theta}\sum_{i=1}^{n}(\sum_{j\in S}X_{ij}\theta_{j}-y_{i})^{2},\] (27) subject to \[z_{i}+z_{j}+z_{k}\leq 1. \tag{19}\]0  
This is a quadratic (convex) optimization problem within a bounded and convex region (a \(p\)-dim box), which can be solved efficiently. There are several algorithms available without the need for any commercial solver. For example, a free and open-source Python library \"Scipy\" can solve it efficiently. The pseudo-codes for the application of CG and MIP to generalized feature selection can be found in Appendix A.
# Generalized Sketches for Streaming Sets
## 6 Evaluation
### Accuracy
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Jaccard Similarity Estimation**} & \multicolumn{6}{c}{**Top 10 Nearest Neighbor Search**} \\ \cline{2-10}  & \multicolumn{3}{c}{**NRMSE**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**the Number of Retrieved Users**} \\ \cline{2-10}  & **SimCar** & **MinHash** & **OPH** & **SimCar** & **MinHash** & **OPH** & **SimCar** & **MinHash** & **OPH** \\ \hline Reuters & 0.177 & 0.175 & 0.176 & 0.732 & 0.735 & 0.732 & 151 & 156 & 151 \\ \hline Movie & 0.072 & 0.072 & 0.073 & 0.800 & 0.800 & 0.800 & 599 & 597 & 598 \\ \hline Wikipedia & 0.085 & 0.085 & 0.085 & 0.799 & 0.800 & 0.800 & 2395 & 2398 & 2395 \\ \hline Tropes & 0.168 & 0.171 & 0.170 & 0.555 & 0.561 & 0.556 & 220 & 222 & 220 \\ \hline Flickr & 0.172 & 0.175 & 0.174 & 0.765 & 0.764 & 0.764 & 299 & 301 & 299 \\ \hline Actor & 0.240 & 0.235 & 0.239 & 0.740 & 0.734 & 0.739 & 174 & 175 & 175 \\ \hline TREC & 0.076 & 0.077 & 0.077 & 0.894 & 0.896 & 0.894 & 219 & 227 & 220 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of our framework, SimCar, compared with MinHash and OPH for Jaccard similarity estimation and nearest neighbor search with \(k=2^{9}\), \(c=2\). Figure 5: User cardinality estimation errors of our framework, SimCar, compared with HLL and CORE. Figure 6: Average NRMSEs of SimCar compared with MinHash and HLL and OPH and HLL for estimating common-interest counts.
# An Energy Efficient Modulation Scheme for Body-Centric Terahertz (THz) Nanonetworks +
## 1 Introduction
### Contribution  
In this paper, we introduce a novel modulation scheme of high practical interest, namely single-pulse variable duration (SPVD), which puts emphasis on the high energy efficiency rather than the data rate, thus having the potential to be utilized in in-vivo body-centric THz applications. The proposed scheme is a combination of the TS-OOK, which has already been proposed for these applications, and the pulse position modulation (PPM). Specifically, it utilizes only one pulse per symbol, which can significantly decrease the energy consumption of the nanodevice. Furthermore, taking advantage of the variable waveform's duration, the proposed modulation scheme is scalable, i.e., the modulation order can be chosen to adjust to each application, depending on the energy conservation and data rate requirements. We show that with a minor cost to the achievable data rate (compared to the TS-OOK), we can obtain significant energy savings. Thus, a trade-off between energy consumption and data rate is established. Compared to the existing TS-OOK scheme, the proposed modulation is proven to increase the energy efficiency, which makes it particularly attractive for battery limited applications. Finally, the new scheme is designed to minimize the effect of the noise in THz body-centric communications systems, without increasing the system's complexity.
# Experimental Study on Vibration Control of
## 5 Experiment of Piping System Vibration Control
### Control Performance of Free Vibration  
#### 5.1.1 Free Vibration with TMD  
When the structure is controlled by TMD, the displacement attenuation of the structure becomes slower than the case without control under free vibration, as shown in Figure 7. The initial displacement of the free vibration is 20 mm. When there is no control on the piping system, the displacement attenuates to 10% of the initial value after 26.5 s. However, it takes 48.8 s to settle when the piping system is controlled through TMD.  
Figure 6: Experimental setup: (**a**) Forced excitation system with motor; (**b**) Data acquisition system.  
Figure 7: Experiment results of free vibration with TMD.  
This phenomenon can be predicted by the [PERSON] closed-form equation [87] for the optimal damping:  
\[\zeta_{opt}=\sqrt{\frac{3a}{8{(1+a)}^{3}}} \tag{1}\]  
\[a=\frac{m_{a}}{m} \tag{2}\]  
where \(\zeta_{opt}\) is the optimal damping ratio of the TMD, and \(a\) is the mass ratio (\(m_{a}\) is the mass of the TMD, and \(m\) is the mass of the structure). According to Equation (1), for a mass ratio of 0.433%, the optimal damping ratio is 11.96%. Since the TMD damping ratio is 0.06%, the addition of the TMD did not improve the vibration suppression, and in this case, it even amplified the vibration. The beating observed in Figure 7 was included in the settling time (48.8 s) of the vibrations.  
To further understand the reason causing the low rate of attenuation, a numerical simulation was performed. Figure 8 illustrates the numerical study results of free vibration with TMD in three different cases: \(\zeta_{pipping}>\zeta_{TMD}\), \(\zeta_{pipping}=\zeta_{TMD}\), \(\zeta_{pipping}<\zeta_{TMD}\), where \(\zeta_{pipping}\) and \(\zeta_{TMD}\) are the damping ratio of the piping system and the TMD. In the numerical study; the damping ratio of the piping system is 0.58%, and the damping ratios of the TMD under three cases are 0.06%, 0.58%, and 1.10%, respectively. When \(\zeta_{pipping}>\zeta_{TMD}\), as shown in Figure 8a, the displacement attenuation rate of the superstructure becomes slower, which is consistent with the experimental results. When \(\zeta_{pipping}=\zeta_{TMD}\), the envelope of displacement attenuation is almost identical, as shown in Figure 8b. When \(\zeta_{pipping}<\zeta_{TMD}\), TMD can promote the suppression of vibration, as shown in Figure 8c.
# Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification
## 5 Analysis and Evaluation
### Data Privacy
#### 5.1.1 Class-I  
In Algorithm 1, the user utilizes the random matrix \(A\) to mask his samples \(\mathbf{x}^{(i)}\), and then sends \(Ax^{(i)}\) to the \(\mathcal{S}_{\mathcal{AU}}\). In this way, the \(\mathcal{S}_{\mathcal{AU}}\) never gets to know any sample included in the training set since \(A\) is randomly selected by the user. In the privacy-preserving training process, although the \(\mathcal{S}_{\mathcal{AU}}\) can obtain the inner-product \(\mathbf{w}_{1}^{(h)}\cdot\mathbf{x},\ldots,\mathbf{w}_{b}^{(h)}\cdot\mathbf{x}\), but it cannot infer the user data without knowing the weight vectors \(\mathbf{w}_{1}^{(h)},\ldots,\mathbf{w}_{b}^{(h)}\).
# Research on Named Entity Recognition Methods in Chinese Forest Disease Texts
## 3 Materials and Methods
### The Proposed Approach
#### 3.2.1 Multi-Feature Embedding Layer  
As the entities in forest disease texts have the characteristics of nested relationships, a large number of rarely used Chinese characters, and a lack of labels, we selected word vectors as the input for the named entity recognition model. Common sentence vector representation methods include one-hot coding and Word2 Vec. The vector sparsity of one-hot coding is very high, while the Word2 Vec model can use continuous and dense vectors to describe the characteristics of words. Therefore, the Word2 Vec model was selected as the word-embedding model.  
The named entity recognition task should fully discover and make use of the context and internal features of named entities. We combined two granularity features, at the characteristic level and word level, to further improve the recognition ability of the model. Through feature analysis of forest disease texts, the following features were selected:  
1. The radical features of Chinese characters. By analyzing texts of forest diseases, it was found that many diseases and fungicides have specific radicals. For example, the names of the diseases contain partial radicals such as \"\(\square\)\", \"\(\square\)\", and \"\(\square\)\", while the control agents typically contain partial radicals such as \"\(\square\)\", \"\(\square\)\", and \"\(\square\)\". Thus, the radical of a character was regarded as a basic feature set.
2. Word boundary features. The place names and organization names contained in the general domain data set contained obvious word boundaries. For example, most of the place names contained obvious boundary words such as \"\(\square\)\" and \"\(\square\)\". In the texts on forest diseases, a large number of entities do not have obvious boundary characteristics, such as \"\(\square\)\", and \"\(\square\)\". Therefore, the word boundary was introduced as a feature. The sentence is automatically labeled with a word boundary.
3. Part-of-speech features. Parts of speech contain the deep information of words, which is a common feature in Chinese natural language processing. By analyzing forest disease texts, it is found that there are certain rules in the part-of-speech distribution of some entities; for example, some disease entities are connected by multiple nouns, while control agent entities usually appear after verbs. This we the result of automatic part-of-speech tagging as a basic feature. Parts of speech include more than 30 kinds of nouns, verbs, prepositions, and adverbs.  
By extracting word boundary features, partial radical features, and part-of-speech features, three kinds of artificial features and word vector splicing were used as the input for the transformer layer.
# Seismic Fragility Analysis of the Transmission Tower-Line System Considering Bol Slippage
## 4 Seismic Fragility Analysis of the TTLS with Fixed Foundation
To further analyze the weak positions of the transmission tower under seismic loads, the tower is divided into eight sections, as illustrated in Figure 12, and the intersegment displacement ratio (\(ISDR\)) for each section is calculated using the following:  
\[ISDR(i)=max\bigg{[}\frac{u_{i}(t)-u_{i-1}(t)}{h_{i}-h_{i-1}}-\theta_{i-1}(t) \bigg{]} \tag{7}\]  
where \(i\) is the number of sections; \(ISDR\)(i) is the intersegment displacement ratio of section \(i\); \(t\) is time; \(u_{i}(t)\) is the horizontal displacement time history of the \(i\)th section; \(h_{i}\) is the height of section \(i\); \(\theta_{i-1}(t)\) is the torsional displacement time history at the bottom of the \(i\)th section. Figure 13 shows the maximum intersegment displacement ratios of JM and RM under 15 seismic waves. The maximum \(ISDR\) values for RM are primarily concentrated in sections B2 and B3, whereas the maximum \(ISDR\) values for JM are predominantly found in sections B3 and B4. This observation indicates that when the bolt slippage behavior is considered, the weak position of the tower also changes. Figure 11: Seismic fragility curves of the TTLS with fixed foundation. Figure 10: Calculation models of seismic demand index: (**a**) RM; (**b**) JM.