esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Real-Time and Reliable Transport Layer Protocol for WVSN
For implementing the proposed transport protocol, a simple state-machine is suggested. In order to describe the operations of the transport protocol two waiting states are presented in the state-machine. The conditions of switching between states beside tasks of each transport protocol phase are mentioned on the edges as shown in Figure 7.  
As regards the fact that RTS can be sent to first priority level of M-Frames several times, if it exceeds the threshold -as was mentioned above- transport layer goes to sleep-mode and notifies networks layer in receiver-mode (relay node) or sub-application layer (source node). Also, when the network layer sender assures that no parent is available for transmission, regardless of the number of RTS retransmission, it forces transport layer to be in sleep-mode. Transport layer stays in sleep-mode until hierarchical network topology is reconfigured (see Section 6.1).
## 4 Power Flow Linearization  
A linear approximation is proposed for the power flow Equation (5), taking into account the model of the loads. Equation (5) can be rewritten in matrix form as,  
\[S_{N}^{*}=\text{diag}\bigg{(}V_{N}^{-\frac{n}{2}}\circ\left(V_{N}^{1-\frac{n} {2}}\right)^{*}\bigg{)}(Y_{S}V_{S}+Y_{N}V_{N}). \tag{8}\]  
Now, using the Laurent's series expansion is linearized Equation (8) around the point \(U_{N}=V_{S}\otimes\mathbf{1}_{n-1}\) where \(\mathbf{1}_{n-1}\) represents a vector of size \(n-1\) whose entries are \(1\), and \(\otimes\) is the Kronecker product (see [19] for more details of this linearization approach). Therefore, (8) can be approximated by the following affine compact form [25]:  
\[S_{N}^{*}=A+BV_{N}+CV_{N}^{*}, \tag{9}\]  
where  
\[A =T_{N}^{*}-\left(U_{N}^{1-\alpha/2}\right)^{*}\circ U_{N}^{-\alpha /2}\circ(Y_{N}U_{N})-\left(1-\frac{\alpha}{2}\right)\circ\left(U_{N}^{-\alpha/ 2}\right)^{*}\circ U_{N}^{-\alpha/2}\circ J_{N}\circ(U_{N})^{*}\] \[+\frac{\alpha}{2}\circ\left(U_{N}^{1-\alpha/2}\right)^{*}\circ U _{N}^{-\alpha/2-1}\circ J_{N}\circ U_{N}, \tag{10}\] \[B =\text{diag}\Big{(}\left(U_{N}^{1-\alpha/2}\right)^{*}\circ U_{N }^{-\alpha/2}\Big{)}Y_{N}-\text{diag}\Big{(}\frac{\alpha}{2}\circ\left(U_{N} ^{1-\alpha/2}\right)^{*}\circ U_{N}^{-1-\alpha/2}\circ J_{N}\Big{)},\] (11) \[C =\text{diag}\Big{(}\big{(}1-\frac{\alpha}{2}\big{)}\circ\left(U_{ N}^{-\alpha/2}\right)^{*}\circ U_{N}^{-\alpha/2}\circ J_{N}\Big{)}, \tag{12}\]  
and \(J_{N}\), \(T_{N}\) are auxiliary vectors given by  
\[J_{N} =Y_{S}V_{S}+Y_{N}U_{N}, \tag{13}\] \[T_{N} =\Big{(}U_{N}^{1-\alpha/2}\circ U_{N}^{-\alpha/2}\circ J_{N} \Big{)}^{*}, \tag{14}\]  
in this case, \(\circ\) represents the Hadamard product and \((\cdot)^{*}\) is the complex conjugate. The proposed linearization must be implemented for each of the scenarios to be considered.
## 2 Materials and Methods
### Maintenance Operations Detection
#### 2.4.1 Artificial Neural Network Design and Training  
The first task is to generate, from the dataset created in Section 2.3, a training and a validation dataset. With this training dataset, the feature selection and the structure of the ANN are defined (number of hidden layers and neurons per layer). The validation set is used to make fine adjustments and to improve the credibility of the error estimation. Since only 3.5% of the training samples correspond to detected operations, it was necessary to add a penalty for the incorrect classification of this class. The Scikit-Learn module does not have this feature, therefore these samples were replicated a number of times equal to the penalty value. In this work a value of 8 was used, increasing the percentage of detected operations to 22.4%.  
The feature selection step uses the SelectKBest method (from Scikit-Learn) and the Pearson Correlation over the time-series generated in Section 2.3. The SelectKBest performs a variance analysis and selects the K best features based on a score function. The 10 best features with the highest f_classif score function were selected (this function uses the ANOVA F-Value between label/feature for classification task). Although this analysis chooses the best features, they are selected independently. So, the Pearson Correlation was used to find redundancies and eliminate strong correlated features. After these tasks, multiple sets of features were defined and evaluated. The evaluation consisted of using them to classify the training set and compare their relative errors.  
The ANN structure was defined empirically changing the number of neurons and hidden layers pursuing the lowest classification error. The number of neurons per layer chosen was the value that, after increasing, did not improve the ANN. For the number of layers, due to the vanishing gradients problem, the ANN with one and two hidden layers was tested. The input layer uses one neuron per feature and the output layer is only one neuron, since it is a binary class (detection, no detection). The  
Figure 11: Comparison between computation spectral indices using a pixel and object approach.  
Figure 12: Diagram of the maintenance operation detection stage.  
activation function is the logistic function. The method used for the backpropagation of the error was the stochastic gradient descent, described in [30], with an adaptive learning rate.
## 2 Materials and Methods
### Laboratory Experiments
#### 2.3.2 Wind Tunnel Tests  
Wind tunnel tests were performed at the Institute of Mineral Resources Engineering (RWTH Aachen University, GER) with the wind tunnel set-up used by [PERSON] et al. [15; 16] (Figure 2). After their preparation (day 0), the samples were subjected to five wind tunnel cycles (days 2, 7, 14, 21, and 28). The samples were stored at an ambient temperature (21 \(\pm\) 1 \({}^{\circ}\)C) and humidity (45 \(\pm\) 2.5%) throughout this period. For each wind tunnel cycle, the samples were carefully placed in the test section and exposed for 120 s to a laminar airflow of 13.6 m/s (Figure 2f). Samples were weighed before and after each wind tunnel cycle using a precision scale (KERN PES 4200-2M) to calculate the gravimetric soil loss.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Biopolymer/ Control**} & \multicolumn{4}{c}{**Concentration (wt\%)**} & \multirow{2}{*}{**Application Rate (L/m\({}^{2}\))**} \\ \cline{2-2} \cline{4-6}  & **Medium-Grained Sand** & & & **Fine-Grained Silica Sand** & \\ \hline XG & 0.05 & 0.13 & 0.2 & 0.3 & 0.4 & 0.6 \\ CS & 0.13 & 0.25 & 0.2 & 0.3 & 0.4 & 0.6 \\ CMC & 0.50 & 0.50 & 0.2 & 0.3 & 0.4 & 0.6 \\ FBPC & 0.75 & 1.00 & 0.2 & 0.3 & 0.4 & 0.6 \\ WP & 0.50 & 1.25 & 0.2 & 0.3 & 0.4 & 0.6 \\ \hline \hline \end{tabular} Note. The application rate of 0.5 L/m\({}^{2}\) was not tested again, as it was already included in Phase 1.  
\end{table}
Table 2: Sample program of Phase 2, which tested the effect of the application rate on wind erosion and crust penetration resistance. The biopolymer concentrations were selected based on the results of Phase 1.  
Figure 1: Sample preparation. (**a**) Weighing of dry sample, (**b**) preparation of biopolymer solution, and (**c**) gravimetric spray-on application with splash guard.  
After the last wind tunnel cycle, each sample's cumulative soil loss (g) was determined and normalised to the total soil loss (g/m\({}^{2}\)) by dividing it by the sample surface area. In addition, the dust control effectiveness relative to the control group (C) was established for each biopolymer treatment according to Equation (1):  
\[Dust\ control\ effectiveness\ (\%)\ =\ 1-\frac{\Delta m_{BP}}{\Delta m_{C}} \tag{1}\]  
where \(\Delta m_{BP}\) denotes the total soil loss of the biopolymer-treated sample (g/m\({}^{2}\)), and \(\Delta m_{C}\) denotes the total soil loss (g/m\({}^{2}\)) of C. This allows the comparison of results with previous studies investigating the dust control effectiveness of treated soils (e.g., [20; 33; 34; 35]).
# Coordinate Descent for Variance-Component Models
## 2 Basic Coordinate Descent  
Recall that, after the REML procedure to remove the mean effect, we have \(\mathbf{Y}\sim\mathcal{N}(\mathbf{0},\mathbf{\Omega})\) and \(\mathbf{\Omega}=\sum_{i=0}^{m}\gamma_{i}\mathbf{V}_{i}\), where \(\mathbf{V}_{0}=\mathbf{I}_{n}\). We thus seek to compute:  
\[\mathbf{\hat{\gamma}} =\operatorname*{arg\,min}_{\mathbf{\gamma}}\,G(\mathbf{\Omega}),\quad \gamma_{i}\geq 0,\quad i=0,1\dots,m \tag{3}\] \[G(\mathbf{\Omega}) :=\mathbf{y}^{\top}\mathbf{\Omega}^{-1}\mathbf{y}+\ln\det\mathbf{\Omega}. \tag{4}\]  
CD can be applied to solve this problem by successively minimizing \(G\) along coordinate directions. There is significant scope for variation in the way components are selected to be updated. In the most conventional way the algorithm cycles through the parameters in the order \(\gamma_{0}\to\gamma_{1}\to\dots\to\gamma_{m}\) and updates each in turn. This version, known as cyclic coordinate descent, is shown in Algorithm 1.  
```
input:\(\mathbf{y}\) and \(\big{[}\mathbf{V}_{0},\mathbf{V}_{1},\dots,\mathbf{V}_{m}\big{]}\) where \(\mathbf{V}_{0}=\mathbf{I}_{n}\)
1 Choose \(\mathbf{\gamma}\in\{u\in\mathbb{R}^{m+1}\mid u_{i}>0\}\)
2\(\mathbf{\Omega}\leftarrow\gamma_{0}\mathbf{I}+\sum_{i=1}^{m}\gamma_{i}\mathbf{V}_ {i}\)
3 repeat
4 for\(k=0,1,\dots,m\)do
5\(\mathbf{\Omega}_{-k}\leftarrow\mathbf{\Omega}-\gamma_{k}\mathbf{V}_{k}\)
6\(\gamma_{k}\leftarrow\operatorname*{arg\,min}_{x\geq 0}G(\mathbf{\Omega}_{-k}+x \mathbf{V}_{k})\)
7\(\mathbf{\Omega}\leftarrow\mathbf{\Omega}_{-k}+\gamma_{k}\mathbf{V}_{k}\)
8 until termination test satisfied return\(\mathbf{\gamma},\mathbf{\Omega}\)
```  
**Algorithm 1** Cyclic CD for \(G(\mathbf{\Omega})\).  
If we choose to minimize along one coordinate at a time, then the update of a parameter consists of a line search along the selected coordinate direction--that is, if the selected parameter is component \(k\), then the new parameter value is updated as  
\[\gamma_{k}^{(t+1)}\leftarrow\operatorname*{arg\,min}_{x\geq 0}\,\,G\Big{(} \sum_{i=0}^{k-1}\gamma_{i}^{(t+1)}\mathbf{V}_{i}+\sum_{i=k+1}^{m}\gamma_{i}^{(t )}\mathbf{V}_{i}+x\mathbf{V}_{k}\Big{)}.\]
# Analytical Attitude Guidance Planner for Multiple Ground Targets Acquisitions
## 2 Problem Description and Proposed Solution
### Dynamical Model  
The orbital dynamic is integrated into the \(ECI\) reference frame and can be written as  
\[\begin{cases}\dot{\mathbf{r}}=\mathbf{v}\\ \dot{\mathbf{v}}=\mathbf{g}+\mathbf{a}_{p}\end{cases} \tag{1}\]  
where \(\mathbf{r}=[x,y,z]\) and \(\mathbf{v}=[v_{x},v_{y},v_{z}]\) are respectively the position and velocity vector of the satellite, \(\mathbf{g}\) is the Earth gravitational acceleration vector, and \(\mathbf{a}_{p}\) represents the acceleration due to orbit perturbations (drag, solar radiation pressure, third body, etc). The satellite attitude is described by using quaternions, with the attitude matrix expressed as a homogeneous quadratic function of their elements. The quaternion \(\mathbf{q}\) is written as \(\mathbf{q}=[q_{0};\,\mathbf{q}_{1:3}]\)[37; 38], where \(q_{0}\) is the scalar component, \(\mathbf{q}_{1:3}\in\mathbf{R}^{3}\) is the vector component, and \(||\mathbf{q}||=\sqrt{q_{0}^{2}+q_{1}^{2}+q_{2}^{2}+q_{3}^{2}}=1\). For a rigid satellite, the non-linear dynamic equations are expressed as reported in [39],  
\[\begin{cases}\dot{\mathbf{q}}=\frac{1}{2}\mathbf{\Omega}(t)\mathbf{q}(t)\\ \mathbf{I}\dot{\mathbf{\omega}}+\mathbf{\omega}\times\mathbf{I}\mathbf{\omega}=\mathbf{u}_ {c}+\mathbf{u}_{p}\end{cases} \tag{2}\]  
where \(\mathbf{\omega}\) is the satellite angular velocity expressed in the \(BF\) reference frame, \(\mathbf{I}\) is the inertia tensor, \(\mathbf{u}_{c}\) is the control torque, and \(\mathbf{u}_{p}\) is the perturbation torque, where usually \(||\mathbf{u}_{c}||>||\mathbf{u}_{p}||\). The matrix \(\Omega\) is  
\[\mathbf{\Omega}=\begin{pmatrix}0&-\mathbf{\omega}^{T}\\ \mathbf{\omega}&-[\mathbf{\omega}\times]\end{pmatrix} \tag{3}\]  
where \([\mathbf{\omega}\times]\) is the anti-symmetric matrix of the angular velocity. It is worth pointing out that, as aforementioned, the aim of this work is not to find a control providing the planned motion, but the motion required to perform a specific task. Thus, the attitude dynamics model in Equation (2) is not used to evaluate the control law, but only to derive the restrictions on the angular accelerations from the constraints on the control values (as shown in the next section).
# The Influence of Binaural Room Impulse Responses on Externalization in Virtual Reality Scenarios
## 2 Experimental Setups
### Listeners and Test Stimuli  
Ten normal-hearing subjects (2 females and 8 males, aged between 25 and 34) participated in both experiments. A 3 s long speech sentence taken from the European Broadcasting Union (EBU) Sound Quality Assessment Material (SQAM) [13] was used as the test stimulus. The obtained BRIRs (BRIR-A, BRIR-B, and BRIR-C) were convolved with the speech signal to simulate binaural recordings in the corresponding rooms (room A, B and C). In this way, three binaural signals were generated for each source direction (0\({}^{\circ}\) or 45\({}^{\circ}\)). In addition, a diotically played back (mono) speech sentence was used as an anchor signal, and subjects were informed that such stimulus should act as an \"internalized\" sound. This anchor was also included in the test stimuli to be evaluated (hidden anchor). For each source direction, four stimuli were presented in randomized order and should be evaluated in terms of externalization. The playback level of the binaurally rendered anechoic (BRIR-C) speech signal was 56 dBA (calibrated with the dummy head).  
A speech sentence was used as a test stimulus in both experiments because it resembled a real-life sound source. [PERSON] et al. [14] revealed that stimulus type had a slight but did not have a significant influence on externalization. It can therefore be assumed that our results do not change noticeably when other stimuli such as noise and music are used.
###### Abstract  
The fracture behavior of asphalt concrete is closely related to its internal structure. A deep understanding of the relationship between the internal structure and fracture behavior of asphalt concrete is very important for sustainable and durable pavement design. In this paper, a CZM-based FE model was developed to investigate the fracture behavior of asphalt concrete. An image-aided approach was used to generate the 3-D internal heterogeneous structure of asphalt concrete. A series of 2-D cross sections were extracted from the 3-D structure for finite element modeling. Then numerical simulations of SCB tests were conducted and validated with experimental results. With the validated CZM-based FE model, the effects of some critical factors, including temperature, loading rate, aggregate geometry, fracture strength, and fracture energy, on the fracture behavior of asphalt concrete were investigated. The analysis results showed that the average damage of the adhesive elements was higher than that of the cohesive elements at the peak load. At lower temperatures, asphalt concrete tends to crack earlier, and the cracking path tends to be marginally closer to the aggregates. A higher loading rate may induce more, but minor, element damage since the CZM elements in asphalt mortar cannot bear much more stress through deformation. Angular aggregates may induce a higher percentage of damaged elements, especially adhesive-damaged elements. On average, each 10% increase in fracture energy allows the specimen to bear 2.31% more load and 2.82% more displacement. Sufficient fracture energy could improve the ability of asphalt concrete to resist fracture.  
sphalt concrete fracture; cohesive zone model; finite element; semi-circular bending test; adhesive damage; cohesive damage
# WIRE: A Weighted Item Removal Method for Unsupervised Rank Aggregation
## 4 WIRE: Algorithm Design and Analysis
The right part of Figure 2 displays an exemplary preference list \(I^{(u_{6})}\). Since \(u_{6}\) has been grouped in the bucket \(b=3\), it can be derived that \(C_{b}^{(u_{6})}=0.684\). Therefore, Equation (8) indicates that \(R_{b}^{(u_{6})}=\left\lceil|I^{(u_{6})}|C_{b^{(u_{6})}}\right\rceil=\left\lceil 8 \cdot 0.684\right\rceil=6\) elements of \(I^{(u_{6})}\) should be preserved, and 2 elements should be removed. According to the figure, the three elements with the lowest preservation scores are \(\sigma_{2}\), \(\sigma_{4}\), and \(\sigma_{7}\). From these elements, \(\sigma_{2}\) is preserved because it was ranked higher than the other two; \(\sigma_{4}\) and \(\sigma_{7}\) are eventually removed from the list. **Complexity Analysis. ** In the sequel, we will prove the following lemma:  
**Lemma 1**. : _The time complexity of WIRE is upper-bounded by \(O(n|\mathcal{L}|\log|\mathcal{L}|)\), given that \(n=O(2^{|\mathcal{L}|})\), while it needs \(\Theta(n+|\mathcal{L}|)\) space._  
Proof. : The cost of sorting the ranker weights (step 1) is \(O(n\log n)\). The confidence score calculation (steps 3, 5) takes \(O(B)\) time. The cost of grouping \(n\) rankers (steps 6-11) into buckets is \(O(n)\). Calculating the preservation scores of all elements \(\mathbf{e}\) of \(\mathcal{L}\) has worst-case complexity \(O(\sum_{\mathbf{e}}|u;\mathbf{e}\in I^{(u)}|=O(n|\mathcal{L}|)\), since each element may appear in all lists. The last block of Algorithm 1 between steps 20 and 28 includes the construction of \(n\) min-heaps \(H\) (\(O(n\max_{u}|I^{(u)}|)\) worst-case cost), and \(n\max_{u}(|I^{(u)}|-R_{b}^{u})\) element removals with a worst-case cost of \(O(n(|\mathcal{L}|-|\mathcal{L}^{\prime}|))\). Hence, the total worst-case cost of the entire block is \(O(n(|\mathcal{L}|-|\mathcal{L}^{\prime}|)\log\max_{u}|I^{(u)}|)\). Consequently, the overall cost of WIRE is expressed as follows:  
\[O(n\log n+B+n+n|\mathcal{L}|+n\max_{u}|l^{(u)}|+n(|\mathcal{L}|-|\mathcal{L}^{ \prime}|)\log\max_{u}|l^{(u)}|), \tag{9}\]  
that can be upper-bounded by \(O(n|\mathcal{L}|\log|\mathcal{L}|)\), since \(B=o(n)\), \(\max_{u}|l^{(u)}|=O(|\mathcal{L}|)\), and \(n=O(2^{|\mathcal{L}|})\). Please note that this a quite pessimistic upper bound, since it assumes that every \(l^{(u)}\) will be excluded from \(\mathcal{L}^{\prime}\) in its entirety. The space complexity is linear to the number of rankers and the size of the initial aggregate list \(\mathcal{L}\), since we need to keep all \(C_{p(u_{i})}\) values, all \(P_{e}\) scores, while the maximum size of the auxiliary heap is linear to the length of the longest \(l^{(u)}\), which is \(O(|\mathcal{L}|)\).
## 5 Performance Evaluation  
To demonstrate the effectiveness of the proposed DRLSA-IA, we carry out numerous simulation experiments. We use the Python programming language to implement and simulate DRLSA-IA based on the TensorFlow framework. We assume that nodes are randomly placed. The details of the simulation parameter settings are summarized in Table 2.  
In addition, the network traffic is defined as the number of messages created every time slot. We compare DRLSA-IA with Slotted-Aloha [19] and GA [21] under the same conditions. Among them, Slotted-Aloha is a classic time-slotted MAC protocol. GA is the latest genetic algorithm-based scheduling algorithm.
## 2 Methodology
### Model Overview
#### 2.2.5 Objectives  
The goals of a ReCon are to sufficiently observe a potential region of interest (maximize temporal coverage) and to visit a selected target as often as possible (minimize revisit time) at the same time. Revisit time and temporal coverage in ROM and GOM each yield the four possible objectives related to observation performance, but only ROM revisit time and GOM coverage are considered, as shown in Table 6. Reconfiguration time should also be minimized for the agile transition between GOM and ROM, taking preferably several days rather than weeks. Constellation mass is directly related to the cost of developing, manufacturing, and launching remote sensing satellites. Although the detailed cost analysis is not the scope of this study, providing better observation performance while suppressing the satellite mass (and cost) is considered ideal.
## 2 The Camera Network  
The Highways Agency (HA) is an Executive Agency of the Department for Transport (DfT), and is responsible for operating, maintaining and improving the strategic road network in England on behalf of the UK's Secretary of State for Transport [39]. The Agency maintains a high density network of traffic cameras deployed along its motorways and major trunk roads affording a continuous monitoring of traffic, and by default the vegetation that lines these roads and are within the field of view of the cameras. An agreement was made with the HA to access and download images from these cameras: three sets of images captured at 10:00, 12:00 and 14:00 local time were downloaded from the HA website daily. Cameras might not be available due to maintenance by HA, internet connectivity, and HA censorship due to incidents.  
Access was granted to approximately 1,800 cameras in total and the size of each image varied between around 15 to 20 kB with the permitted download limit to an average bandwidth of 13 kB/s which means that it takes approximately half an hour for the images to download from all the cameras available due to the specified bandwidth limit. The digital images were downloaded and archived as heavily compressed JPEGs (352 \(\times\) 288-pixel resolution, with three channels of 8-bit RGB colour information) for subsequent processing. Filenames included a date and time stamp for easy reference. A manual survey of the images captured by each camera revealed that 900 contained areas of vegetation suitable for the study. As a preliminary investigation, the present study was based on analysis of images recorded between 28 March 2011 and 5 December 2012. This paper focuses on images downloaded from 18 cameras located over a wide geographical spread and covering a range of vegetation types (Figure 1). Table 1 provides a summary of the characteristics at each camera site.