esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Privacy-Preserving Parallel k-Means Clustering Algorithm  
In this section, we propose a privacy-preserving parallel k-means clustering algorithm. To perform the parallel processing of the k-means clustering algorithm, we utilize a thread pool to prevent data bottlenecks and parallelize encryption operation protocols for efficient support of k-means clustering. However, when parallelizing homomorphic encryption techniques, there is the issue of the increased operational overhead per core, leading to bottlenecks and performance degradation. To mitigate this bottleneck in parallel processing, we reduce the computational cost during k-means clustering by preprocessing operations that generate random values for hiding data and encrypting them in the encryption operation protocol. This random value pool generates random numbers (i.e., integers) in the form of plaintext and ciphertext pairs <r, E(r)> before starting k-means clustering, storing them in a queue-like memory space. When needed, the data are extracted and used in a first-in-first-out (FIFO) manner.  
The Paillier encryption system used in this paper consists of computationally expensive operations such as the exponentiation function (i.e., exp) and modular function (i.e., mod). These operations impose a significant computational overhead on the CPU, leading to CPU bottlenecks and performance degradation. This problem is exacerbated when memory access is frequent in parallel processing algorithms. In [17], the SM protocol generates and encrypts random numbers before performing homomorphic addition operations. Encrypting random numbers incurs a cost of two encryption operations, utilizing the CPU's computational resources. Additionally, the SM protocol, which performs multiplication as an encryption operation, is used within the ASMIN encryption operation protocol. Generating and encrypting random numbers during k-means clustering leads to high computational costs. Therefore, this paper addresses this issue by storing the generated random numbers (i.e., r) and their encrypted counterparts (i.e., E(r)) in the random value pool's memory space. When needed, the cryptographic authority (CA) can select <r, E(r)> pairs from the random value pool, enabling their use at a lower memory load cost. The reduced computational cost for each protocol using the random value pool is summarized in Table 2.  
The proposed parallel k-means clustering algorithm is executed in two phases: parallel preprocessing and parallel k-means clustering.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Secure Protocol** & \
\begin{tabular}{c} **Computational Cost without** \\ **a Random Value Pool** \\ \end{tabular} &
\begin{tabular}{c} **Computational Cost with** \\ **a Random Value Pool** \\ \end{tabular} \\ \hline SM protocol & \(3\times\rm{E}\) & \(1\times\rm{E}\) \\ ASMIN protocol & \(10\times\rm{E}\) & \(4\times\rm{E}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational cost without/with a random value pool.
## 3 Results  
### FXS vs. CS  
The classification accuracy for classification between FXS and CS in all the analysed supervised algorithms is summarized in Table 1.  
Figure 2: Diagram with the overview of the gait classification using seven supervised learning algorithms.  
Overall, all the algorithms were generally able to classify gait characteristics from different input vectors. The average predictive accuracy for new data was 95.0% for S1, 98.1% for S2 and 92.3% for S3. The best accuracy for S1 was achieved using random forest (98.3%); for S2 using k-NN (100%) and for S3 using SVM (98.4%). The worst accuracy for all analysed vector sets was obtained using naive Bayes.  
In terms of input parameters, the best classification was obtained using S2 (kinematic parameters). This subset of parameters was then further investigated in order to verify the presence of statistically significant differences between the FXS and CS populations of subjects, and statistically significant differences (\(p\) < 0.05) were found in maximum hip, knee and ankle angles; minimum hip and knee angles; and hip and knee range of motion (see Figure 3).
## 4 Integrative Example  
To provide an example that demonstrates a realistic use case for our BBCP, we analyzed an existing application from the perspective of a black-box service-oriented architecture (SOA), and created a couple of BBCPs to simulate its behavior. The application in question, GeForce Now, is a cloud gaming service developed by NVIDIA [35], which we chose due to the popularity of streaming platforms and, more specifically, the human-computer interaction involved in cloud gaming. In cloud gaming, the hardware responsible for running the game and managing inputs and outputs from and to the consumer is in the cloud (a computer cluster in the network), making use of the software-as-a-service delivery model (SaaS) based on subscriptions.  
In contrast to other streaming services such as video streaming, the user provides intermittent hardware peripheral input such as mouse movement and keyboard strokes to the cloud over the internet. The user's input is then processed and reacted upon by the game in the cloud, resulting in subsequent video and audio sent over the internet to the user. From a SOA perspective, there are two self-contained functions (services) that we have profiles: the Catalog Service and the Game Stream Service.  
The Catalog Service is responsible for offering the user a menu of the games available to play, usually by employing recommendation algorithms that cater the content to the preferences of the user. Once a game is selected from the menu, the Game Stream Service is responsible for the rest of the I/O operations between the cloud and the user, until the user decides to stop playing the game. The objective of this example is to obtain an estimation of the energy consumption of the Game Stream Service using BBCP in combination with the time constraints that the subscription service employs to limit the play time of a user demoing it.
# Seismic Periodic Noise Attenuation Based on Sparse Representation Using a Noise Dictionary
## 5 Discussions  
The proposed de-noising method is based on sparse representation of periodic noise. The key to our method is the construction of the noise dictionary. Because ambient noise contains no seismic waves but dominant periodic noise and other random noise, periodic noise can be estimated without the influence of seismic reflections. Therefore, our method is useful regardless of whether the seismic waves are strong or weak. A scanning method is used to estimate the noise period. The accuracy of the noise period largely influences our de-noising result. To obtain an accurate noise waveform, the waveforms in the time domain and the space domain are stacked shown as the Equations (5) and (6), respectively. It must be emphasized that our de-noising method is only applicable to stationary noise with a constant period, waveform and amplitude. It can be used to attenuate power line harmonic noise, pump jack noise and engine operation noise in land or oceanic seismic exploration.  
Based on the proposed method, ambient noise detection is urged for noise estimation. However, ambient noise has not specifically been detected in oil exploration. Therefore, we suggest that ambient noise should be acquired for one second before source excitation. Long-term ambient noise will be helpful for building a perfect noise dictionary.  
Wind- or water-induced noise is not strictly stationary, and the stationarity becomes weak with increasing recording time [22; 23]. Our method cannot be used to attenuate this kind of noise. To broaden the method for non-stationary noise, higher-order statistics [24] need to be considered further. In addition, methods of noise feature extraction from ambient noise based on machine learning [25] will draw increased attention in the future.
## 3 Error Correction Framework  
The system proposed in this thesis was developed and realised using an experimental approach. The framework is developed to handle the problem of realtime (dynamic) error correction and resource sharing. The system is developed in two stages:  
1. Static error correction;
2. Realtime (dynamic) error correction.
# LEO Constellation-Augmented Multi-GNSS for 3D Water Vapor Tomography
## 4 Discussion  
By comparing five different constellation combinations, LEO has improved the time resolution, tomographic accuracy, and horizontal resolution of water vapor tomography. Especially after excluding the repeated observation rays, the tomographic scheme with LEO has great potential in shortening the time of tomographic modeling. Within the same observation period, the number of non-repetitive observations is also doubled. This is an effect that cannot be achieved by increasing the number of GNSS satellites. In addition, the addition of the LEO satellite tomography scheme can also reduce the proportion of voxels without observations in the tomography, and increase the proportion of voxels that are penetrated by non-parallel crossing rays. In terms of tomography accuracy, the scheme with LEO has improved the tomography accuracy, but the improvement of water vapor tomography accuracy is not as large as expected. This conclusion also echoes some previous studies [18; 19]. The reason for this is because of the 30-min observation, so there are already many observation rays in the tomographic area. Another reason that affects the accuracy of tomography is that even if observation satellites are added, there are still voxels without observations in the bottom layer of the tomography. The loss of accuracy in this area affects the overall tomographic accuracy. However, it can also be seen from our experiments that when the observation time is shortened to 10 min, the accuracy of the scheme with added LEO satellites is higher. From the perspective of horizontal resolution, when the horizontal grid is more densely divided, the scheme of adding LEO satellites can effectively suppress the increase in the proportion of empty voxels in the tomographic area. Of course, the biggest key to LEO adding water vapor tomography is the accuracy of the LEO calculation of SWV. Since there are no real data at present, many studies still use simulation data. However, the current studies have given more positive results. The main purpose of this paper is also to quantitatively analyze the changes brought about by the addition of LEO to water vapor tomography. In the future, obtaining the SWV value of LEO inversion with high precision is still a problem that needs to be paid attention to and solved in the future.
## 4 Numerical Simulation Analysis of the Hole-by-Hole blasting and Three-Hole Simultaneous Blasting
### Methodology for Numerical Simulation
#### 4.1.2 ALE Method  
The ALE algorithm combines the advantages of the Lagrangian algorithm and the Euler algorithm. The grid is not completely fixed on the medium like the Lagrangian algorithm, nor is it completely fixed in space like the Euler algorithm. The mesh of the ALE algorithm can move freely. The ALE algorithm allows explosives, rocks, and air to freely shuttle between each other. The process of the explosion can be analyzed through the flow of substances. It can directly simulate the detonation process of explosives. It can effectively avoid the problem of calculation interruption caused by excessive mesh deformation in calculations [27].  
The core algorithm of the ALE calculation process is the convection algorithm. It is a mapping relationship that controls the material transfer between the new and old grids. When the mesh unit reaches a certain amount of deformation, it needs to be self-repurchased using a certain smoothing algorithm. This article adopts a half index shift algorithm and donor-cell algorithm. Assuming the name of the substance to be transferred in the convection algorithm is \(f\), the amount of transportation required can be calculated using the donor-cell algorithm  
\[f_{j}^{\varphi}=\frac{a_{j}}{2}(\varphi_{j+\frac{1}{2}}^{n}+\varphi_{j-\frac{ 1}{2}}^{n})+\frac{\left|a_{j}\right|}{2}(\varphi_{j+\frac{1}{2}}^{n}+\varphi_{ j-\frac{1}{2}}^{n}) \tag{18}\]  
where \(j\) is the number of points in which section (\(j=1,2,3\cdots\)); \(n\) is the number of iterations of the algorithm; \(a_{j}\) is the speed of two discontinuous contacts; \(\varphi_{j+\frac{1}{2}}^{n}\) and \(\varphi_{j-\frac{1}{2}}^{n}\) are the values on both sides of node \(j\) at the beginning of the \(n\) transportation. It can be expressed as:  
\[\varphi_{j+\frac{1}{2}}^{n+1}=\varphi_{j+\frac{1}{2}}^{n}+\frac{\Delta t}{ \Delta x}(f_{j}^{\varphi}-f_{j+1}^{\varphi}) \tag{19}\]  
where \(\Delta t\) is the time step size and \(\Delta x\) is the unit feature length. If it is necessary to solve the three-dimensional flow rate, then it is necessary to use a first-order accuracy half index shift algorithm.  
\[\left[\begin{array}{c}f_{1j+\frac{1}{2}}^{-}\\ f_{2j+\frac{1}{2}}^{-}\end{array}\right]=\begin{bmatrix}a&b\\ c&d\end{bmatrix}\left[\begin{array}{c}\varphi_{j}^{-}\\ \varphi_{j+1}^{-}\end{array}\right] \tag{20}\]  
\[\left[\begin{array}{c}\varphi_{j}^{+}\\ \varphi_{j+1}^{+}\end{array}\right]=\frac{1}{ad-bc}\begin{bmatrix}d&-b\\ -c&a\end{bmatrix}\left[\begin{array}{c}f_{1j+\frac{1}{2}}^{-}\\ f_{2j+\frac{1}{2}}^{-}\end{array}\right] \tag{21}\]  
where \(a,b,c\), and \(d\) are matrix constants; \(f\) is the name of the substance that needs to be transported; \(\varphi\) is the initial quantity of a certain node; and \(j\) is the number of nodes.
# A Guaranteed Approximation Algorithm for QoS Anypath Routing in WMNs
## 3 An An Anypath Routing Approximation Algorithm for QoS
### Design of HMAA Algorithm
#### 3.1.1 HMAA Algorithm Implementation Steps  
**Step 1:** Initialize all nodes \(v\in\mathbf{V}\) in the graph \(\mathbf{G}(\mathbf{V},\mathbf{E},\overrightarrow{\mathbf{W}},\overrightarrow {L},\overrightarrow{p})\) and give the currently calculated value \(\hat{w}_{m}(v)\) and \(\hat{\mathbf{F}}(v,P)\) the initial value;  
**Step 2:** If \(Q\
eq\varnothing\) and \(w_{1}(s,P)\leq L_{1}\), all of the nodes in the link matrix \(Q\gets Q-\{u\}\) are traversed: the node \(\mathrm{u}\) with the smallest AAW is added to the current anypath \(\mathrm{P}\), then the AAW value of the current node \(\mathrm{u}\) is calculated; otherwise, skip to Step 5.  
**Step 3:** The neighbor node of the minimum AAW node is traversed, the value \(\hat{w}_{m^{\prime}}(v)\) of the neighbor node is calculated by the Equation (9), then the K-1 value \(\hat{w}_{i}(v)\) of the node is calculated.  
**Step 4:** If \(j>\) K, skip to Step 5; otherwise, make \(j=j+1\), and repeat Step 2.  
**Step 5:** Check the first weight of the current anypath P. If \(w_{1}(s,P)\leq L_{1}\), output anypath \({P_{s}}^{*}=P\) found by HMAA; otherwise, output \"NO Feasible Anypath\".
# Performance Analysis of Hybrid Optical-Acoustic AUV Swarms for Marine Monitoring
## 2 Related Work
### Experimental Setups and Real Prototypes  
The first prototype of a hybrid acoustic/optical underwater system was presented in [7]: the presented system included static sensors (Aquafleck), vessels for sensors deployment (Amour AUV) and mobile nodes for data collection (Starbug AUV). The acoustic communication was used mainly for signaling among the devices. Mobile AUVs operate as data mules and use the optical system for collecting then data from the sensors.  
In [8], the authors evaluated the effectiveness of hybrid acoustic/optical solution for real time video streaming applications. In the presented system, real-time video collection is performed using a multi hop network of optical links and the acoustic channel is used for network coordination and signaling. However, if optical link fails, a backup channel can be activated with an ultra compressed video streamed by the acoustical channel. The paper presents a real experiment of compressed video transmission by the AquaSeNT acoustic modems.
# Stability-Aware Geographic Routing in Energy Harvesting Wireless Sensor Networks
## 4 Stability-Aware Geographic Routing in EH-WSNs (SAGEEH)  
### Energy Harvesting Wastage-Aware Protocol (EHWA)  
Despite significant development in WSNs, battery capacity remains a major limitation to WSN applications. In former energy-aware routing protocols, sensors were powered by batteries with limited capacities, and routing decisions were made based on the energy consumption of receiving or transmitting packets or residual energy at each node. The objective of these protocols was to maximize network lifetime, which measures the amount of time elapsed before the first sensor (or a fraction of sensors) is dead. Even with state-of-the-art energy harvesting technologies, the rate of harvested energy may be lower than the typical power consumption level. Consequently, the recent energy-harvesting routing algorithms still focus on the network lifetime problem. This study used the EHWA to represent energy-harvesting routes to maximize network lifetime. Specifically, the EHWA selected the routing path that results in the maximum total residual energy network energy after routing realization, which is expressed as [28]  
\[\begin{array}{c}\underset{p_{k}}{\arg\max}\ \left\{\sum\limits_{\forall i}RE(p_{k})\right\} \\ \underset{s.t.}{\text{\emph{p}}_{k}}\in P\\ \underset{i\in N}{\text{\emph{N}}}\end{array} \tag{12}\]  
where \(P\) is a set of all available routing paths \(p_{k}\), \(i\) represents node \(v_{i}\), which belongs to path \(p_{k}\).  
The EHWA tends to maximize the total residual energy, or the total consumption along the route needs to be minimized, which is intuitive. Moreover, authors propose a route selection scheme that takes into account the predicted wastage energy due to overcharge of finite battery. However, they do not consider the influence of link quality on the network performance. To solve this problem, we propose a SAGREH routing protocol which is aware of the reliability of packet sending from source to sink by selecting the most stable path among available ones.
## 3 Underwater Channel Characterization
### EM Propagation in Seawater  
Sewater is considered a high-loss medium due to the fact that it is highly conductive. The conductivity of seawater is almost 400 times greater compared to freshwater [4; 25].
# Optimization of Glulam Regular Double-Tapered Beams for Agroforestry Constructions
## 3 Results and Discussion
### Relationship between the Purlin Height and Spacing, Snow Load and Span  
The relationship between Snow, Span and Sp has only been studied for Hp = 160 mm, since when purlin height is 200 mm the separation between them reaches the maximum allowed (1.25 m) and does not vary as a function of the snow load or the span. As the correlation coefficient R has a value of 0.986, it indicates a strong linear dependence between Sp, Snow and Span (Table 8). Therefore, the proposed multiple linear regression model can be used to make reliable predictions of purlin spacing for given values of the snow load and span. The coefficient of determination R\({}^{2}\) = 0.972 indicates that 97.2% of the variability in the optimal cost can be explained by the variability in Snow and Span, since a value approaching unity indicates a good linear fit between the three variables.  
Table 9 shows the values of the linear correlation coefficients and coefficients of determination, the test value and the _p_-value that confirm the strong linear fit between the three variables. The data did not deviate significantly from the normal distribution (Shapiro-Wilk 0.981, \(p\) > 0.05). Furthermore, the slope of the plane is significantly different from zero, as indicated by the _t_-test for Snow and Span, which are 62.219 (_p_ < 0.001) and 4.209 (_p_ < 0.001), respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**R**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multicolumn{5}{c}{**General Model Test**} \\ \cline{3-6}  & & **F** & **df1** & **df2** & \(p\) \\ \hline
0.986 & 0.972 & 1944.478 & 2 & 114 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Correlation, determination and test coefficients.  
Figure 7: Purlin height depending on the snow load ranges.  
Based on the coefficients of the multiple regression shown in Table 9, Equation (24) is proposed to determine the purlin spacing depending on the specific height of the structural element:  
\[\begin{split}\text{For}\ \text{Hp}=160\ \text{mm}\rightarrow\text{Sp} =\frac{\text{Snow}}{\left(0.736+0.209\times Snow+0.001\times Span\right)^{5}},\\ \text{For}\ \text{Hp}=200\ \text{mm}\rightarrow\text{Sp}=1.25 \end{split} \tag{24}\]  
where Hp is the height of the purlin (mm), Sp is the purlin spacing (m), Snow is the applicable snow load (kN/m\({}^{2}\)) and Span is the full dimension of the regular double-tapered beam (m).  
To the authors' best knowledge, only one previous investigation [29] has considered the purlin in the optimization of a timber roof structure. This limited body of research highlights the relevance of the present investigation but significantly limits the discussion of the findings obtained in this research work. For purlin with a height of 160 mm, a value between 1.0 and 1.25 m is generally observed as optimal. Conversely, for purlin with a height of 200 mm, the optimum spacing is 1.25 m. These findings align with those reported by [PERSON] et al. [29], who reported the optimal purlin spacing at the maximum allowable spacing.
# Retrieval of High Spatial Resolution Aerosol Optical Depth from HJ-1 A/B CCD Data
## 2 Data and Methods
### Overall Framework of the AOD Retrieval Algorithm  
By assuming that a significant correlation exists between the visible bands, AOD could be retrieved based on the model simulated aerosol lookup table. However, this correlation varies with the solar/view angle and land cover type, so it was necessary to establish an interband regression coefficient lookup table based on the Bidirectional Reflectance Distribution Function (BRDF) dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Band** & **Spectral Region (\(\upmu\)m)** & **Spatial Resolution** & **Breadth (km)** & **Revisiting Period** \\ \hline
1 & 0.43–0.52 (blue) & & 360 \\
2 & 0.52–0.60 (green) & & (single sensor) & 4 days (single sensor) \\
3 & 0.63–0.69 (red) & & 700 & 2 days (2 sensor) \\
4 & 0.76–0.90 (near-infrared) & & (2 sensors) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the Huanjing-1 (HJ-1) A/B charge-coupled diode (CCD) satellites.  
In this study, we first determined the correlation between the visible bands based on the U.S. Geological Survey (USGS) spectral library and selected the optical bands with the highest correlation to establish a linear regression relationship. Then, the POLarization and Directionality of the Earth's Reflectances (POLDER) BRDF dataset was used to obtain the regression coefficients for different solar/view angles and land cover types. Finally, high-spatial resolution AODs could be retrieved based on a model simulated aerosol lookup table. In this procedure, the original HJ-1 A/B CCD data with a spatial resolution of 30 m were resampled to 300 m to minimize the effects of sensor noises. Because the inversion results were sensitive to the regression coefficients, an aerosol correction index (ACI), a seasonal variation of normalized differential vegetation index (NDVI), and AERONET measurements were employed as constraints to obtain optimal estimation. The flowchart of our method is shown in Figure 1.