esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results
### SE Bounds Achieved by Type II Codebook  
In this subsection, the performance obtained with the Type II codebook for MU-MIMO scenarios is illustrated. The total SE of the network is measured as \(\sum\limits_{k=1}^{K}R^{\{k\}}\), which is known as sum-SE. The sum-SE achieved by the 5G-NR precoding matrices is compared with the theoretical block-diagonalization (BD) method described in [37]. This method is designed to remove the interference signals and maximize the SINR in (3) based on the knowledge of the channel matrix of all UEs. A perfect and imperfect CSI with \(\sigma_{e}^{2}=10^{-3}\) are assumed. In the case of 5G-NR, two beams per layer and polarization (i.e., \(L=2\)) and a QPSK alphabet (i.e., \(N_{PSK}=4\)) are considered.  
Figure 8: Average SE vs. SNR for SU-MIMO systems assuming \(N_{t}=32\), \(N_{r}=4\), and \(N_{s}=3\). The gNB is implemented with different antenna configurations. Imperfect CSI is also assumed with \(\sigma_{e}^{2}=10^{-3}\). All the curves in this figure labeled as Type I refer to the proposed 5G-NR case.  
Figure 9 illustrates the SE bounds assuming \(N_{t}=32,N_{r}=4\), and \(K=2\). The gNB is implemented following a single-panel ULA with \(N_{1}=16\), and \(N_{2}=1\). Figure 10 shows the performance for the same scenario but considering a single-panel UPA with \(N_{1}=4\), and \(N_{2}=4\). In both cases, the SE of the 5G-NR is saturated for high SNR values. This is because interference is the limiting factor that avoids performance enhancement. It should be noted that the overhead in Type II is much higher than in Type I. For this particular example, assuming \(N_{s}=1\) and the single-panel ULA configuration, 22 bits are required to report the wideband coefficients while 9 bits per subband are needed for the frequency selective parameters. Therefore, a total of 137 bits per UE is required to report the PMI. In the case of UPA, two extra bits are required for the wideband parameters which leads to a total of 139 bits per UE. Similar to the Type I codebook, the effect of the channel estimation errors with \(\sigma_{e}^{2}=10^{-3}\) hardly influences the SE results.  
Figure 10: Average SE vs. SNR for MU-MIMO systems assuming \(K=2\), \(N_{t}=32,N_{r}=4\), and different values of \(N_{s}\). The gNB is implemented assuming a single-panel UPA with \(N_{1}=4\), and \(N_{2}=4\). Imperfect CSI is also assumed with \(\sigma_{e}^{2}=10^{-3}\).  
Figure 9: Average SE vs. SNR for MU-MIMO systems assuming \(K=2\), \(N_{t}=32,N_{r}=4\), and different values of \(N_{s}\). The gNB is implemented assuming an ULA single-panel with \(N_{1}=16\), and \(N_{2}=1\). Imperfect CSI is also assumed with \(\sigma_{e}^{2}=10^{-3}\).  
Figure 11 shows the sum-SE for different numbers of multiplexed UEs with the same PRBs. The higher the value of \(K\), the higher the interference signals which limits the sum-SE performance increment. The correlation between the channel matrices of the different users influences the interference cancellation. Consequently, Type II codebook is only applicable to those users with low spatial correlation.
# Task-Based Motion Planning Using Optimal Redundancy for a Minimally Actuated Robotic Arm
## 3 Workspace Analysis
### Last Link and Any Link Workspace  
The workspace computation typically involves the targets that can be reached by the end of the last link (LL) of the arm, since this is where the end-effector is located. However, the MASR differs in that it can move the end-effector along the arm because it is attached to the mobile actuator. This means that the workspace can be calculated as the set of points that can be reached by any link of the arm (AL). The area of the AL workspace is obviously larger (or at least equal) to that of the LL workspace when given the same set of joints. In other words, the AL workspace covers a larger area with fewer joints. Therefore, the use of the AL workspace makes it possible to find a less redundant solution. Nevertheless, when computing the AL workspace, the super-position beta method remains the same with the exception of one modification that involves creating link-lines instead of points for the last link. In the example in Section 2, the LL workspace method produced a minimal assembly consisting of 3 out of 5 possible joints, in 11 iterations. Using the AL workspace for the same set of targets produced the same results (Figure 5f bottom). However, when  
Figure 5: (**a–d**) Last link (LL) workspaces scanned by joint combinations in ascending order. The numbers in the bottom right corners indicate the number of iterations. The highlighted joints were used to compute the workspace. To reach the targets, only the first, second, and third joints are needed; (**e.f**) any link (AL) workspaces, which serve to create a workspace using a single joint (**e**) and cover a larger area (**f**) for the same number of joints as used in the LL method (**d**).  
considering a simpler case such as reaching the top right target, the AL method resulted in 1 out of 5 joints (Figure 5e), compared to 2 out of 5 in the LL method (Figure 5a,b).  
The results shown in Figure 5 show that the AL method not only covered a larger area compared to the LL, but also created a workspace using a single joint (whereas the LL creates a one-dimensional line). Since the AL workspace contains its associated LL workspace, the minimal assembly found by the AL method will always be less or equally redundant compared to the LL method.
## 4 The Proposed Method  
### The Framework of Method  
To preserve the directed graph in wireless mobile networks, we propose a novel method based on differential privacy, which combines node differential privacy and graph modification to provide sufficient privacy preservation while retaining data utility. In addition, we assume that the original directed graph is a simple connected static directed graph without node attributes.  
As shown in Figure 3, the model of the developed method consists of four steps. In step 1, after the original directed graph is converted into a weight graph, the weight graph is divided into some sub-graphs according to the optimal modularity [35]. Then, the node differential privacy is utilized to generate two differential private degree sequences (an in-degree sequence and an out-degree sequence) from each sub-graph in step 2. In particular, the exponent mechanism is used to get the degree sequences of nodes in each sub-graph, while the ranking micro-aggregation is applied to add noise to them. Next, in step 3, each sub-graph is modified by adding or deleting edges according to the noised degrees. Simultaneously, the relationship between nodes is considered in edge modification. At last, all modified sub-graphs are merged into a differential private directed graph which provides privacy preservation for the original directed graph in Section 4.  
In particular, as node differential privacy provides stronger privacy preservation than graph modification and edge differential privacy, it is adopted to provide better privacy preservation for the directed graphs in mobile wireless networks. At the same  
Figure 3: The framework of method.  
time, to maintain data utility, the Louvain algorithm is used to divide the original directed graph into many sub-graphs, and graph modifications are limited in them. Then, the exponent mechanism truncates the degree sequences to add the minimum noise to the degree sequences when the privacy budget is given. Next, the ranking micro-aggregation effectively reduces the noise added to the degree sequences, which is proved by the mathematical analysis. Finally, the relationship between two nodes is utilized to modify the edges of nodes, which can retain the original graph structure. Therefore, the proposed method achieves the trade-off between privacy and data utility.  
To summarize, we develop a novel method that can achieve privacy preservation for the directed graph while maintaining the data utility.
# Frictional Behaviour of the Microstructural Surfaces Created by Cylindrical Grinding Processes
## 4 Discussion  
### Effect of the Dressing and Grinding Conditions on the Surface COF  
The features of the micro structural surface generated by grinding are determined by the control parameters of the wheel dressing and grinding operations. Based on the models expressed by Equations (1)-(5), the key influential parameters are dressing lead, dresser tip radius, grinding depth and grinding speed ratio. Dressing lead and the grinding depth are the main factors to create and control the structural texture geometry. The structural gouge depth is directly decided by the grinding depth; in the meantime, the structural gouge length, width and distribution are controlled by dressing lead and grinding speed ratio. Therefore, the friction behaviour of micro structural surface should be assessed against these process control parameters. Figure 7 presents the influence of grinding depth and dressing lead on the COF of microstructural surface.  
The structural gouge depth increases with the increase in the grinding depth and is smaller than the nominal grinding depth, due to the elastic deformation in grinding. The COF does not change linearly with the grinding depth. However, it can be seen when the grinding depth is around 1-2 um, the COF values of a microstructural surface may achieve its minimum. Thus, it is convincing that a shallow dimple gouge could give positive hydrodynamic effect on surface tribological performance.  
Dressing lead affects the shape and distribution of the structural gouges. However, its influence on the COF is not clear. When the workload is low, the smaller dressing lead may enhance the effect of the gouge depth to reduce friction.  
Another important factor is the width of the structural texture, which is determined by the grinding depth and dressing feed. The structural gouge width increases with the increase in grinding depth, as shown in Table 2. It is interesting to see that the maximum gouge widths, shown in Figure 2, are similar to the theoretical value listed in Table 2. This is because the grinding deflection has less influence in lateral directions. Based on Table 2 and Figure 6, the lower COF may be achieved with the gouge width in the range of 10-21 um. According to current observation, it is difficult to reveal the influence of micro texture shape on friction directly.
## 2 Elevation-Dependent Modeling of Satellite-Induced Code Bias
### Improved SICB Modeling
The reason for the selection of the reference elevation 45\({}^{\circ}\) is that the estimation accuracy of the ED SICBs at mid elevations is better than that at low and high elevations. The SICB correction model and SICB model are opposite in sign. We establish the SICB correction model for each frequency and for each satellite using the observations from all the selected stations and days, as the SICB does not depend on the station location, receiver type or time.
## 3 Methodology and Implementation
### Methods of Compilation of the Neural Network Module  
The integration of the described CNN algorithm with the mobile application of the system required compilation of the module into a stand-alone form, so that queries directed to the neural network could be handled. The way compilation is carried out is closely related to the algorithm design method chosen. The current dominant approach to programming neural network architectures is to use the Python language and one of the two main deep learning frameworks--TensorFlow or PyTorch. The design of the above module used PyTorch and the open-source PyTorch Metric Learning library [53] implementing the distance metric deep learning algorithm used. The remainder of this section presents selected and tested procedures for compiling the model into a stand-alone module in a form adapted to the model conditions and compares the selected solutions. Each subsection describing a specific solution is concluded with a brief summary of the most important functionalities and limitations.
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 4 Experimental Evaluation
### Results and Discussion
#### 4.3.5 Discussion  
The proposed scheme is particularly designed for search, rescue, and special operations where UAVs are responsible for sensing and tracking things on the ground. Due to 3D coverage, where UAVs are flying at different heights and high mobility, the position information of UAVs in a particular region is required to have reliable communication. Considering these practical scenarios, we have proposed the PMPHR scheme, which utilizes the PHC to share the position information of all UAVs in the network. The position information helps the UAVs to find the shortest path across the network. Moreover, UAVs have different speeds in different scenarios. Let us assume that UAVs are assigned in a forest rescue scenario, where they are responsible for controlling the fire and sharing ground information. Initially, the PHC-UAV (which has access to all UAVs' positions) collects the information of the whole network and shares to all UAVs. Based on the shared information, the UAVs easily communicate with each other and ground users. Recently, a fire accident happened in Chongqing city of China, where trees on the mountain caught fire. It was difficult for humans to perform rescue operations due to there being no roads on the mountain [52]. Therefore, UAVs were deployed to perform rescue operations, efficiently control the fire, and save valuable things. During rescue operations on mountains, it is very difficult for UAVs to maintain direct communication links and stable topology due to the high mobility and the mountain's structure. Hence, topology-based and position-based schemes suffer from having unreliable communication, whereas the proposed scheme has  
Figure 9: Normalize routing load versus number of UAV nodes at different speeds (20, 40, and 60 m/s).  
the advantage of a hybrid nature (contains the location information of all UAVs in the routing table), where the PHC can maintain an appropriate location from where it can share the location of all UAVs in the network. Furthermore, UAVs can find the shortest path based on the position information of other UAVs in the network.
## 2 Theoretical Background
### Fourier and Short-Time Fourier Transforms  
The filtering techniques developed in this paper use two transforms in order to analyze the signals' frequency content, namely the Fourier transform (_FT_) and the short time Fourier transform (_STFT_) [40]. They are defined, respectively, as follow:  
\[\mathcal{F}\{y(t)\}=Y(f)=\int_{-\infty}^{\infty}y(t)e^{-j2\pi/ft}dt, \tag{4}\]  
\[\mathcal{SF}\{y(t)\}=Y(\tau,f)=\int_{-\infty}^{\infty}y(t)w(t-\tau)e^{-j2\pi ft }dt, \tag{5}\]  
where \(w(t)\) is a window function. Here, a Blackman window function is used, which is defined by:  
\[w(t)=\begin{cases}0.42+0.5\cos\bigl{(}\frac{2\pi t}{T}\bigr{)}+0.08\cos\Bigl{(} \frac{4\pi t}{T}\Bigr{)},&-\frac{T}{2}\leq t\leq\frac{T}{2}\\ 0,&\text{otherwise}\end{cases}, \tag{6}\]  
with \(T\) being the length of the window. For the signals treated here, as presented in Section 4, the temporal length of the window, \(T\), and the overlap length of consecutive  
Figure 1: Schematic of the strain monitoring process. In the first step, an impulse-like signal, \(\delta(t)\), is sent from the transmitting transducer, Tx, and the impulse response of the signal, \(h(t)\), is received at the receiver transducer, Rx, which is the reference for the next step. In the second step, the medium is subject to strain, \(\varepsilon\), and a new impulse response is obtained. Finally, the impulse responses at both steps, \(h(0,t)\) and \(h(\varepsilon,t)\), are cross-correlated.  
windows were set to 50 \(\upmu\)s and 93% of the window length, respectively, since these values showed good compromise between time and frequency resolution.  
These transforms provide information on the frequency content of the acquired signal. In the latter, the frequency content is analyzed as a function of time, \(\tau\), since only the content inside the time window, \(w(t-\tau)\), centered at the time instant \(\tau\), is considered. The signal in the time domain can be recovered by the inverse Fourier and short-time Fourier transforms [40].
## 1 Introduction
Changing the length \(d_{\Delta}\) of the line does not change the pixel amplitude values of the image. Thus, the values calculated according to the Equations (2), (8), and (11) do not change or change slightly as a function of changes in the value of parameter \(d_{\Delta}\). The reasons for this phenomenon should be sought in the consistency of image quality as a function of changes in the line length of the image. However, appropriate calculations (in accordance with the relationships (2), (8), and (11)) of the contrast value for selected images obtained from the recorded revealing emission signals were performed. The maximum contrast value was adopted as the criterion for the correctness of determining the length \(d_{\Delta}\) of the image line. Since the contrast assessment methods do not meet the requirements for estimating the \(d_{\Delta}\) parameter, the author's proposed methods allow for effective estimation of the \(d_{\Delta}\) line length of the reconstructed image based on the analysis of the amplitude values of the pixels building the image for individual image columns. In this way, the assessment of minimizing the deviation of the graphic elements of the image from the vertical is made. In summary, our contributions to this work are three-fold:  
* the showing that the methods of contrast assessment are not effective in relation to reconstructed images in the process of electromagnetic penetration;
* the proposing three methods which allow for determining the correct line length of the reconstructed image;
* the possibility of using the proposed methods in the automatic process of line length determining. The rest of this article is organized as follows. The second section introduces the process of sampling and recording revealing emission signals and why this process has an influence on the necessity of line length determining reconstructed images. Section 3 details the proposed methods of determining and evaluating raster parameters. In Section 4 the test images and the test conditions were described. In Section 5, the experimental results and corresponding analyses are provided, and some conclusions are made in Section 6.
# Effects of Gradient Warming on Carbon and Water Fluxes in Zoige Plateau Peatland
## 3 Results  
### Effect of Gradient Warming on TS and SWC  
Warming significantly increased soil temperature (Table 1). The TS was 0.66 \({}^{\circ}\)C and 1.07 \({}^{\circ}\)C higher at a 5 cm depth in TL and TH, respectively, compared with the CK (Table 2). At depths of 10 cm and 20 cm, the TS was reduced by 0.30 \({}^{\circ}\)C and 0.47 \({}^{\circ}\)C, respectively, in the TL treatment compared to the CK. In contrast, in the TH treatment, the TS decreased by 0.19 \({}^{\circ}\)C and 0.25 \({}^{\circ}\)C, respectively, relative to the CK (Table 2). The interaction between depth and treatment was significant (\(p\) < 0.05), as the TS in the 10 cm and 20 cm layers was greater than in the 5 cm layers (Table 2).  
Warming also significantly reduced the SWC at all depths (\(p\) < 0.05) (Table 1). The SWC was 3.92% and 7.21% lower at a 5 cm depth in TL and TH, respectively, compared with the CK; at a 10 cm depth, the SWC was 6.54% and 8.63% lower in TL and TH, respectively, compared with the CK; and at a 20 cm depth, the SWC was 5.74% and 8.36% lower in TL and TH, respectively, compared with the CK (Table 2). The interaction between depth and treatment was significant (\(p\) < 0.05); the SWC was lower in TL and TH at a depth of 10 cm, but the SWC was not lower in the CK at a depth of 10 cm.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Depth**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Treatment**} & \multicolumn{2}{c}{**Time \(\times\) Treatment**} & \multicolumn{2}{c}{**Depth \(\times\) Treatment**} \\ \cline{2-10}  & _df_ & \(p\) & _df_ & \(p\) & _df_ & \(p\) & _df_ & \(p\) & _df_ & \(p\) \\ \hline TS & 2 & \textless{}0.001 & 10 & \textless{}0.001 & 2 & \textless{}0.001 & 20 & \textless{}0.001 & 4 & \textless{}0.001 \\ SWC & 2 & \textless{}0.001 & 10 & \textless{}0.001 & 2 & \textless{}0.001 & 20 & \textless{}0.001 & 4 & \textless{}0.01 \\ \hline \hline \end{tabular} Notes: TS, soil temperature; SWC, soil water content.  
\end{table}
Table 1: Results of RM-ANOVA for the effect of warming on soil temperature and soil water content.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Control (CK)** & **Low-Level Warming (TL)** & **High-Level Warming (TH)** \\ \hline
5 cm TS (\({}^{\circ}\)C) & 13.21 \(\pm\) 3.33 b  & 12.65 \(\pm\) 2.4 b  & 12.76 \(\pm\) 2.39 b  \\
20 cm TS (\({}^{\circ}\)C) & 13.12 \(\pm\) 2.15 a  & 12.66 \(\pm\) 2.05 b  & 12.87 \(\pm\) 1.86 b  \\
5 cm SWC (\({}^{\circ}\)C) & 32.39 \(\pm\) 7.67 a  & 28.47 \(\pm\) 8.64 b  & 25.18 \(\pm\) 8.22 c  \\
10 cm SWC (\({}^{\circ}\)C) & 32.39 \(\pm\) 6.53 a  & 22.58 \(\pm\) 7.78 b  & 23.75 \(\pm\) 6.72 c  \\
20 cm SWC (\({}^{\circ}\)C) & 34.46 \(\pm\) 7.54 a  & 28.71 \(\pm\) 8.72 b  & 26.10 \(\pm\) 9.07 c  \\ GPP (\(\mu\) mol m m\({}^{-2}\) s\({}^{-1}\)) & 21.19 \(\pm\) 5.13 a  & 19.96 \(\pm\) 5.50 b  \\ RE
# Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels +
## 2 System Model
### Source Activity Model  
Sources are subject to intermittent activity. Activity is also \"smooth\", i.e., the on-off patterns of sources vary slowly. A source's activity pattern is assumed to be governed by a hidden Markov model (HMM). The HMM has been used in many instances to model communication systems [37; 38; 39; 40]. An HMM imprints a memory on the system that is used to mimic the behavior of a communication system's channel coding.  
The activity pattern of a source is represented by a binary state sequence \(s(t)\) which indicates whether at time \(t\) a source is active (\(s(t)=1\)) or not (\(s(t)=0\)). As discussed previously, a physical source may be observed over multiple DOAs. The term \"source activity pattern\" is used here generically, and it refers either to a physical source or an individual DOA. It is assumed that the individual DOA activity patterns associated with a physical source are \"similar\". Two activity patterns associated with different DOAs are considered \"similar\" if they match over a prescribed fraction of the time samples at which their values are 1.  
A diagram representing the HMM of a source is shown in Figure 3. In the figure, the sequence \(s(1:T)\) represents the hidden states, while sequence \(z(1:t)\) represents the observed sequence. Like state symbols, observation symbols are binary, \(z(t)\in\{0,1\}\), where \(z(t)=1\) indicates the observed activity of the source at time \(t\), and \(z(t)=0\) indicates that source was inactive at time \(t\). In addition to states and observations, an HMM is characterized by state transition probabilities, observation symbol probabilities, and initial state probabilities [41]. These are specified below for a source.  
State transition probabilities are represented by the state transition matrix \(\mathbf{A}=\{a_{ij}\}\), \(j=0,1\) where the state transition probability distribution is given by \(a_{ij}=P(s(t)=j|s(t-1)=i)\). Observation symbols probabilities are represented by the observation matrix \(\mathbf{B}=\{b_{j}(k)\},j,k=0,1\) where \(b_{j}(k)\) denotes the observation symbol probability distribution in state \(j\), \(b_{j}(k)=P(z(t)=k|s(t)=j)\). We use notation \(b_{j}(z(t))\) to indicate the probability of observed value \(z(t)\) conditioned on \(s(t)=j\), \(b_{j}(z(t))=P(z(t)|s(t)=j)\). The initial state probability distribution is \(\pi=\{\pi_{0},\pi_{1}\}\), where \(\pi_{0}=P(s(1)=0)\) and \(\pi_{1}=P(s(1)=1)\). The parameters of an HMM are succinctly denoted by \(\Omega\), where \(\Omega=(\mathbf{A},\mathbf{B},\pi)\).