esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 DI-distributed Localization (DIL) Algorithm
### Node Deployment Strategy  
In our localization system, normal nodes should receive enough information from the beacon and anchor nodes to calculate their location information correctly. In order to ensure every normal node get enough information, it is necessary to design a proper node deployment strategy so that nodes can be localized efficiently. As per our assumption, normal nodes get angle information from the anchor nodes. Hence, first the anchor nodes are deployed randomly to make sure that the entire monitoring region is fully covered. It is assumed that the size of the monitoring region is \(m~{}\times~{}n\), and communication range of the anchor node is \(R_{c}=2R_{s}\), where \(R_{s}\) is the sensing range of each normal node. It is to be noted that each anchor node knows its location via GPS.  
A small percentage of beacon nodes that is more than the number of the anchor nodes are deployed on the monitoring region randomly after deployment of the anchor nodes. After random deployment of the anchor and beacon nodes, higher percentage of the normal nodes that is more than the number of the beacon nodes are deployed randomly. Hence, in our localization scheme, it is assumed that percentage of _Anchor_ nodes \(<\) percentage of _Beacon_ nodes \(<\) percentage of _Normal_ nodes.
###### Abstract  
The aim of the present investigation was to calculate the stress distribution generated in the root dentine canal during mechanical rotation of five different NiTi endodontic instruments by means of a finite element analysis (FEA). Two conventional alloy NiTi instruments F360 25/04 and F6 Skytaper 25/06, in comparison to three heat treated alloys NiTi Hyflex CM 25/04, Protaper Next 25/06 and One Curve 25/06 were considered and analyzed. The instruments' flexibility (reaction force) and geometrical features (cross section, conicity) were previously investigated. For each instrument, dentine root canals with two different elastic moduli(18 and 42 GPa) were simulated with defined apical ratios. Ten different CAD instrument models were created and their mechanical behaviors were analyzed by a 3D-FEA. Static structural analyses were performed with a non-failure condition, since a linear elastic behavior was assumed for all components. All the instruments generated a stress area concentration in correspondence to the root canal curvature at approx. 7 mm from the apex. The maximum values were found when instruments were analyzed in the highest elastic modulus dentine canal. Strain and von Mises stress patterns showed a higher concentration in the first part of curved radius of all the instruments. Conventional Ni-Ti endodontic instruments demonstrated higher stress magnitudes, regardless of the conicity of 4% and 6%, and they showed the highest von Mises stress values in sound, as well as in mineralized dentine canals. Heat-treated endodontic instruments with higher flexibility values showed a reduced stress concentration map. Hyflex CM 25/04 displayed the lowest von Mises stress values of, respectively, 35.73 and 44.30 GPa for sound and mineralized dentine. The mechanical behavior of all rotary endodontic instruments was influenced by the different elastic moduli and by the dentine canal rigidity.  
computer-aided design; finite element analysis; nickel-titanium alloys; endodontic treatment
# Conformal Test Martingale-Based Change-Point Detection for Geospatial Object Detectors
## 1 Introduction
Therefore, whether they can be used to detect change-point for high-resolution images with complex contents and backgrounds such as geospatial images is worth exploring. Therefore, we introduce CTMs to detect change-point of different domains for geospatial object detectors, such that the domain adaptation algorithm can be called automatically. Moreover, we develop transformations on top of conformal test martingales using momentum [27] to make change-point detection faster. To test the proposed approaches, we propose using clustering analysis on a remote sensing dataset to construct different  
Figure 1: Flowchart of deploying object detectors where the test data comes from different domains. A change-point detector is needed to detect the change point of different domains and decide to call the domain adaptation algorithm to update the object detector. The different domains come from the partitions of the remote sensing dataset with clustering analysis in Section 3.1.  
domains, since there is a lack of datasets, especially for domain adaptive geospatial object detectors. In summary, this work is the first to attempt to address the problem of detecting the change point of different domains for geospatial object detectors, and which employs conformal test martingales and proposes a novel transformation method to accelerate the detection process. The contributions of this paper are as follows:  
* Clustering analysis for partitioning our released large-scale remote sensing dataset for geospatial object detection [28] is proposed for domain shift problems in the field of geospatial object detection. * Conformal test martingales and a novel transformation method are first introduced to detect change-point for object detectors, with few false alarms for the target domain. * Experimental results not only demonstrate the effectiveness and efficiency of our proposed methods for change-point detection, but also verify the partitions of the datasets in a statistical way. The rest of this paper is organized as follows. Section 2 introduces conformal test martingale and change-point detection. Section 3 proposes the methods of constructing different domains, calculation of nonconformity measure, and improving detection efficiency with momentum. In Section 4, the experiments are conducted to verify the partitions and test our proposed algorithm for change-point detection. The conclusions are drawn in Section 5.
# Speeding up Statistical Tolerance Analysis to Real Time
###### Abstract  
Statistical tolerance analysis based on Monte Carlo simulation can be applied to obtain a cost-optimized tolerance specification that satisfies both the cost and quality requirements associated with manufacturing. However, this process requires time-consuming computations. We found that an implementation that uses the graphics processing unit (GPU) for vector-chain-based statistical tolerance analysis scales better with increasing sample size than a similar implementation on the central processing unit (CPU). Furthermore, we identified a significant potential for reducing runtime by using array vectorization with NumPy, the proper selection of row- and column- major order, and the use of single precision floating-point numbers for the GPU implementation. In conclusion, we present open source statistical tolerance analysis and statistical tolerance synthesis approaches with Python that can be used to improve existing workflows to real time on regular desktop computers.  
computation time; statistical tolerance analysis; Monte Carlo simulation; sample size; statistical tolerance synthesis; tolerance engineering; Python +
Footnote †: journal: Applied Sci. 2021, 11, 4207. [[https://doi.org/10.3390/app11094207](https://doi.org/10.3390/app11094207)]([https://doi.org/10.3390/app11094207](https://doi.org/10.3390/app11094207))
# VirtualFilter: A High-Performance Multimodal 3D Object Detection Method with Semantic Filtering
## 3 Method
### 3d-Dgaf  
The role of the 3D-DGAF (3D Distance-based Grid Attentional Fusion) module is to fuse the RoI features coming from the LiDAR branch and the image branch. Since the original point cloud and the virtual points are fundamentally different in the way they are acquired, a more refined RoI fusion strategy is necessary. Our fusion strategy was based on the attention mechanism, which can be calculated as follows:  
\[Attention((K,V),Q)=\frac{exp(s(q,k_{i}))}{\sum_{j=1}^{A}exp(s(q,k_{j}))}v_{i} \tag{4}\]  
where \((K,V)=[(k_{1},v_{1}),(k_{2},v_{2}),...,(k_{P},v_{P})]\) represents the key-value pairs; \(Q\) denotes the query information; \(s(q,k)\) denotes the attention scoring function, \(q\in\mathbb{R}^{Q}\); \(\mathcal{P}\) denotes the number of corresponding key-value pairs. For the design of the attention scoring function \(s(q,k)\), since the lengths of the query and key vectors may differ, we used the additive attention scoring function, as expressed in Equation (5):  
\[s(q,k)=w_{v}^{T}\text{tanh}(W_{q}q+W_{k}k) \tag{5}\]  
where \(W_{q}\in\mathbb{R}^{h\times q}\), \(W_{k}\in\mathbb{R}^{h\times k}\), \(w_{v}\in\mathbb{R}^{h}\) denote learnable parameters; \(h\) denotes the hyperparameter for the hidden unit.  
Previous methods mostly used 2D RoI to obtain image features. Due to the lack of correlation between image RoI features and point cloud RoI features, the two can only be directly concatenated. Thanks to virtual point generation technology, the generated virtual point and point cloud data have a strong correlation. Based on this, we obtained the final RoI feature by weighting the grid features corresponding to the virtual point RoI feature and the point cloud RoI feature. We were inspired by SFD to design this module. SFD uses equally sized grids to partition weighted grids. Unlike the fusion module in SFD, we differentiated the number of grids by distance, and for a single RoI \(r\), the farther the distance, the higher the number of contained grids. We used \(F^{org}\in\mathbb{R}^{N\times\mathbb{C}}\) and \(F^{vir}\in\mathbb{R}^{N\times\mathbb{C}}\) to denote the RoI features of the original point cloud and the semantically filtered virtual point cloud features within \(r\), respectively. Here, \(N\) denotes the total number of grids in a 3D RoI, with values based on distance: \(4\times 4\times 4\), \(6\times 6\times 6\), \(8\times 8\times 8\). \(C\) denotes the number of the grid feature channels. For each pair of 3D RoI grid features \((F_{i}^{org},F_{i}^{vir})\), we concatenated the two with a pair of weights \((w_{i}^{org},w_{i}^{vir})\), where \(w_{i}^{org}\), \(w_{i}^{vir}\) are constants. Then, the spliced RoI grid feature \(F_{i}\) can be obtained as follows:  
\[F_{i}=MLP(CONCAT(W_{i}^{org}F_{i}^{org},W_{i}^{vir}F_{i}^{vir})) \tag{6}\]  
Distinguishing the number of grids within each 3D RoI by distance enables a finer-grained fusion of the two features, and ablation experiments prove the effectiveness of our module.
## 1 Introduction
[25] suggested three main theoretical mechanisms of dust emission from aggregates (Figure 1): (i) a direct aerodynamic lifting of loose microaggregates (<63 \(\upmu\)m); (ii) ejection of dust particles from soil aggregates by the impact of saltating grains; and (iii) ejection of dust by self-breakdown of the saltating aggregates. In many soils, the small dust-sized particles (<63 \(\upmu\)m) are held in aggregates, and thereby are not available for directly lifting by the wind. [PERSON] [25] argues that the emission of fine particulate matter (dust) is produced mainly by the fragmentation of soil aggregates that are transported in horizontal flux (saltation). Aggregates (mainly those <63 \(\upmu\)m) that are moved in saltation can break down at a rate of a 38% along fetch distance of 100 m. An experimental study of [PERSON] & [PERSON] [26] found a significant change in aggregate size distribution during dust emission processes. They showed that the rate of saltation and dust emissions depends on the initial size distribution of the soil aggregates at a given shear velocity. The erosion of the larger aggregates (>500 \(\upmu\)m) increased the content of the saltator-sized aggregates (63-250 \(\upmu\)m), which affects the rate of the dust emission over time. Figure 1: Illustration of the three dust emission mechanisms: (**a**) aerodynamic entrainment, (**b**) ejection of dust aerosols from soil aggregates by impacting saltating particles, and (**c**) ejection of dust aerosols from soil aggregates that are participating in saltation. Adapted from [PERSON] [24]. The ejection of dust from soil aggregates due to saltation has been hypothesized to play a major role in dust emission [24; 25; 26; 27]. Previous empirical studies examined dust emission in relation to the aggregate size distribution of the soil [15; 16; 17; 18; 20; 26]. Yet, information on the role of different aggregate sizes in dust emission is still lacking. The main goal of this study is to explore the dust emission threshold in different aggregate sizes that are subjected to saltation. To this end, we conducted a targeted wind-tunnel experiment on dust emission in a semiarid loess soil. The loess soil-type covers 10% of the Earth's land surface and it is associated worldwide with dust emissions e.g., [28]. In semiarid areas that are subjected to increased land uses and possible climate change of reduced precipitation, changes in soil erosion and dust emission intensities should be considered for the environmental consequences. The results aimed to improve our understanding and capabilities for model parameterization in dust emission schemes.
## 3 Materials and Methods
### System Architecture
#### 3.1.4 Temporal Modeling  
To capture temporal dependencies and context information across the video frames, a BiLSTM network is employed. The fused features from the previous stage are fed into this bidirectional LSTM layer. The LSTM processes the sequence of fused features \(X=(x_{1},\ \dots,\ x_{T})\) in both forward and backward directions, as follows:  
\[h_{t}^{f}=LSTM_{f}\Big{(}x_{t},\ h_{t+1}^{f}\Big{)} \tag{3}\]  
\[h_{t}^{b}=LSTM_{b}\Big{(}x_{t},\ h_{t-1}^{b}\Big{)} \tag{4}\]  
where \(h_{t}^{f}\) and \(h_{t}^{b}\) represent the hidden states at time step \(t\) in the forward and backward directions, respectively. The final output for each time step is obtained by concatenating these forward and backward hidden states:  
\[h_{t}=\Big{[}h_{t}^{f};h_{t}^{b}\Big{]} \tag{5}\]  
By processing the sequence in both forward and backward directions, the model can leverage both past and future context, which is crucial for understanding the temporal dynamics of surgical procedures. This temporal modeling enhances the model's ability to detect and classify tools consistently across consecutive frames, improving overall performance and reducing frame-to-frame fluctuations in predictions.
## 2 Materials and Methods
### The ARIMA Model  
The main part of the ARIMA model concerns the combination of autoregression (AR) and moving-average (MA) polynomials into a complex polynomial, as seen in the Equation (1) [1]. The ARIMA model is applied to all the data points for each cost data object (labour and material).  
\[y_{t}=\mu+\sum_{i=1}^{p}(\sigma y_{t-1})+\sum_{i-1}^{q}(\theta_{i}\epsilon_{t- 1})+\epsilon_{t} \tag{1}\]  
where the notation is as follows:  
\(y_{t}\): the actual data over time;  
\(\mu\): the mean value of the time series data;  
\(p\): the number of autoregressive cut-off lags;  
\(d\): the number of differences calculated with the equation \(\Delta y_{t}=y_{t}-y_{t-1}\);  
\(q\): the number of cut-off lags of the moving average process;  
\(\sigma\): autoregressive coefficients (AR);  
\(\theta\): moving average coefficients (MA);  
\(t\): time \(\{1,\ldots,k\}\);  
\(\epsilon\): the white noise of the time series data.  
The value of the ARIMA parameters \((p,d,q)\) for AR and MA can be obtained from the behaviour of the autocorrelation function (ACF) and the partial autocorrelation function (PACF) [1]. These functions help in estimating parameters that can be used to forecast data by using the ARIMA model.
# Optimal Algorithms for Sorting Permutations with Brooms
## 5 Analysis of Algorithm \(D^{*}\) on Millipede Tree
### Algorithm \(D^{*}\)  
Throughout this section, we employ the terminology used in [1; 24; 29]. _The eccentricity_ of a node \(u\) in \(V_{T}\), denoted by \(ecc(u)\), is equal to the maximum value of the distance between \(u\) and any other node \(v\in V_{T}\). The _Center_ of a tree is a node with minimum eccentricity. The _Diameter_ of \(T\) denoted as \(diam(T)\) is the maximum among all the eccentricities. The set of vertices in \(V_{T}\) with maximum eccentricity is defined as \(S\). \(S\) is a subset of the leaf nodes. Algorithm \(D^{{}^{\prime}}\) deletes a set of leaf nodes \(L\subset V_{T}\), and calculates the upper bound required to home markers destined to the vertices in \(L\). This process is continued till the graph becomes a star. The exact upper bound for a star is defined in [1], which is \(\lfloor 3(n-1)/2\rfloor\). Let \(S\) be divided into \(k\)_clusters_\(C_{1},C_{2},\ldots C_{k}\) such that for any \(u,v\) in \(C_{i}\), \(\forall_{i=1..k}\ d_{T}(u,v)<diam(T)\). Let \(X\) be any one of the \(k\) clusters. When the vertices of \(S\setminus X\) are deleted, then the diameter of the resultant tree decreases by one. The maximum distance a marker need to travel to be homed is the diameter of the tree, \(diam(T)\). Thus, \(D^{{}^{\prime}}\) deletes all the vertices of \(S\), except the largest cluster \(C^{*}\), so that minimum number of markers are homed at the current diameter and then reduces the diameter by one. Since the markers homed to the leaves are not required to move further, algorithm \(D^{{}^{\prime}}\) deletes such leaves from the tree. Two variants of \(D^{{}^{\prime}}\), named \(D^{{}^{\prime}}_{v1}\) and \(D^{{}^{\prime}}_{v2}\) were introduced. \(D^{{}^{\prime}}_{v1}\) removes the entire set \(S\) if \(|C|>\frac{2}{3}|S|\) and \(D^{{}^{\prime}}_{v2}\) removes \(S\) if \(|C|\geq\frac{2}{3}|S|\), where \(C=S\setminus C^{*}\). \(D^{*}\), which calculates the upper bound \(\delta^{*}\)[29] is an improved version of \(D^{{}^{\prime}}\). If \(|S\setminus C^{*}|>|C^{*}|\), then at most \(|C^{*}|\) markers need \(diam(T)\) moves each to be homed. Algorithm \(D^{*}\) works based on this idea. The proof can be seen in [29; 31]. \(D^{*}\) deletes \(|C^{*}|\) nodes in \(diam(T)\) cost and the remaining nodes in the cluster with \((diam(T)-1/2)\) cost. It is observed that the \(\delta^{*}\) is tighter than \(\delta^{\prime}\) when \(|C^{*}|\) is between one-third and half of the total size of set \(|S|\).  
Figure 6: Millipede tree, \(M_{5}\).
## 4 Payload and Subsystem Architectures and Design
### Payload Based on VHF Receiver  
The receiver payload for this mission is essentially a dual-channel VHF receiver that can be tuned to any of the VHF channels in the maritime VHF band between 156.025 and 162.025 MHz and for an autonomous ground station that will use the VHF band between 144 and 146 MHz to send data gathered from a sensor network. The specifications of this type of receiver are presented in the following Table 5.  
The block diagram of the proposed VHF receiver can be seen in Figure 14.  
This proposed communication payload, which is intended to collect measurements from AIS transmitters and the autonomous ground station attached to the sensor network used for fire detection, can be in-house customized and manufactured or provided by space companies such as ISIS or SatLab [42; 43]. For example, the SatLab can provide a space- and flight-tested commercial solution for this AIS application [44]. The CubeSat Space Protocol (CSP) is supported by the receiver provided by SatLab, which can greatly shorten the buy-to-fly time and facilitate integration. Three CSP servers, which are needed to configure the receiver, read back status, download messages, and raw samples, are present in this flight-proven receiver. The Blob Transfer Mechanism (BTP), a remote shell and compact file transfer protocol, is based on CSP. BTP supports file uploading, download, and standard file operations, including list, delete, copy, and move.
## 3 Results and Discussion
### X Ray Diffraction (XRD) Analysis of Composite Photocatalyst  
Figure 2 is an XRD pattern of TiO\({}_{2}\) and TiO\({}_{2}\)-GO composite photocatalyst. As shown in Figure 2, diffraction peaks at 20 = 25.3\({}^{\circ}\), 37.8\({}^{\circ}\), 48.0\({}^{\circ}\), 53.9\({}^{\circ}\), 62.7\({}^{\circ}\), 70.3\({}^{\circ}\), and 75\({}^{\circ}\) belonged to diffraction peaks of the (101), (004), (200), (105), (211), and (204) crystal planes of the anatase. XRD pattern revealed that the prepared TiO\({}_{2}\) is at anatase phase, with strong and sharp diffraction peak, indicating good crystallinity. The comparison of the two images demonstrated that TiO\({}_{2}\) was still present as an anatase phase in composite photocatalyst, but there was no characteristic diffraction peak of GO in the composite photocatalyst, which indicated that the presence of GO does not affect the crystal form and structure of TiO\({}_{2}\). This phenomenon may be explained by low GO content (15%) in the composite photocatalyst and covered by the diffraction peak of TiO\({}_{2}\) at 25.4\({}^{\circ}\)[20].  
Figure 1: Scanning electron microscope (SEM) images of TiO\({}_{2}\) nanoparticle and composite photocatalyst with graphene oxide (GO): (**a,b**) SEM images of TiO\({}_{2}\) nanoparticle at different resolutions; (**c**) SEM images of composite photocatalyst at 3% GO content; (**d**) SEM images of composite photocatalyst at 23% GO content.
## 4 Basic Correlations for Reference Conditions
### Variation Profiles with CR  
Figure 8 presents a typical instance of profiles of the reference minimum-loss incidence and deviation angles with varied CR for a certain parameter combination. It is shown that there is a minimum value for both angles. In addition, when CR is between zero and unit, both angles are lower than those of conventional cascades for which CR equals zero or unit. Any other combinations of different geometry parameters have similar profiles.  
Figure 7: Blade surface pressure distribution for case two (Dashed lines denote the original designs, and solid lines denote the “impact-free entry” designs). (**a**) PP = 0.83; (**b**) PP = 0.50; (**c**) PP = 0.22.  
To model the profiles discussed above, a profile function is employed, which is formulated algebraically by Equations (4) and (5).  
\[\frac{i_{\text{bas}}-i_{\text{min}}}{i_{\text{eq}}-i_{\text{min}}}=[1-\exp(nC^{m} )]_{i} \tag{4}\]  
\[\frac{\delta_{\text{bas}}-\delta_{\text{min}}}{\delta_{\text{eq}}-\delta_{ \text{min}}}=[1-\exp(nC^{m})]_{\delta}. \tag{5}\]  
For the left terms, both incidence and deviation angles are normalized by a non-dimensional parameter group composed of the corresponding minimum and equivalent values of the incidence and deviation angles. The index \"bas\" represents the basic correlations for the reference minimum-loss angles.  
The right terms primarily involve the CR and CR\({}_{\text{min}}\) (denoting the CR at the minimum of incidence or deviation angles) as the basic components of \(C\) and \(m\) shown in Equations (6) and (7):  
\[C=\frac{\text{CR}-\text{CR}_{\text{min}}}{\varepsilon(\text{CR}-\text{CR}_{ \text{min}})-\text{CR}_{\text{min}}} \tag{6}\]  
\[m=m_{\text{L}}+(m_{\text{R}}-m_{\text{L}})\varepsilon(\text{CR}-\text{CR}_{ \text{min}}), \tag{7}\]  
where the expression \(\varepsilon(x)\) is a step function, as expressed in Equation (8). Through the analyses based on the database, CR\({}_{\text{min}}\) can be approximately formulated in a linear manner with CCR and camber, which is shown in Equation (9):  
\[\varepsilon(x)=\left\{\begin{array}{ll}0,&x\leq 0\\ 1,&x>0\end{array}\right. \tag{8}\]  
\[\text{CR}_{\text{min}}=\text{CCR}+a\varphi+b. \tag{9}\]  
All constant coefficients used in the basic correlations are given in Table 4. The coefficients are attained by adequately fitting the basic CFD data so as to reduce the correlation errors.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Coefficients** & \(n\) & \(m_{\text{L}}\) & \(m_{\text{R}}\) & \(a\) & \(b\) & \(k_{\text{cs}}\) & \(k_{\text{eq}}\) & \(k_{\text{j}}\) \\ \hline for \(i\) & \(-6.38\) & \(2.0\) & \(2.67\) & \(-4.89\times 10^{-3}\) & \(9.33\times 10^{-2}\) & & \\ for \(\delta\) & \(-3.36\) & \(4.0\) & \(2.96\) & \(-5.32\times 10^{-4}\) & \(-1.92\times 10^{-1}\) & \(0.5\) & \(0.5\) & \(1.1\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Constant coefficients for the basic correlations.  
Figure 8: Variation profiles of reference minimum-loss (**a**) incidence and (**b**) deviation angles with CR. ES 1.78, camber angle \(38.3^{\circ}\), inlet metal angle \(63.0^{\circ}\).