esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Vibration Characteristics of Multi-Beam Structures with Stick and Slip at Joints
## 3 Result Analysis and Discussion
### Different Connection Translation Stiffness  
In the process of structural vibration, the connection force in the stick is directly related to the connection stiffness, which will impact the two segments in the connection force segment function (23) and, consequently, influence the connection characteristics of the truss structure. Figure 9 illustrates the frequency response curves of the two sections at the connection point under varying levels of connection stiffness, with an excitation force amplitude of \(F_{0}\) = 50 N, a preload force of \(F_{N}\) = 500 N, and a friction coefficient of \(\mu\) = 0.3. Under the influence of greater connection stiffness, the resonance peak is sharp, and the system can be considered as approaching a linear state. At lower stiffness levels, the resonance peaks exhibit increased width and the emergence of two distinct peaks, indicative of internal resonance characteristics in nonlinear systems. Notably, the prominence of these traits increases as stiffness decreases.  
Figure 8: The curves of equivalent damping and excitation frequency under different friction coefficients. (**a**) Fixed support boundary; (**b**) simply supported boundary.  
Figure 9: Frequency response curves with different connection stiffness conditions. (**a**) Fixed support boundary, \(L_{1}\) connection point; (**b**) fixed support boundary, \(L_{2}\) connection point; (**c**) simply supported boundary, \(L_{1}\) connection point (**d**) simply supported boundary, \(L_{2}\) connection point.  
In the case of a stiffness of \(k_{u}=8\times 10^{5}\), where the internal resonance characteristics are more pronounced, Figure 10 illustrates the frequency response as the preload force \(F_{N}\) varies. It can be seen that the internal peak gradually approaches as the preload increases. In the case of simply supported boundary conditions (Figure 10c,d), internal resonance is observed at low preload values; however, this phenomenon diminishes as the preload increases, suggesting a coupled effect between the boundary conditions and the applied load.  
The graphs in Figures 11 and 12 illustrate the variation of equivalent stiffness and damping, where the change near the resonance frequency differs greatly from that shown in Figures 4 and 7. As the connection stiffness increases, the magnitude of the equivalent stiffness changes near the resonance frequency is amplified. The resonance frequency wandering is obvious in the case of lower stiffness, thereby reflecting more obvious nonlinear characteristics.  
Figure 11: The relationship between equivalent stiffness and excitation frequency under different levels of connection stiffness. **(a)** Fixed support boundary; **(b)** simply supported boundary.  
Figure 10: Variations in the frequency response curves due to preload force. **(a)** Fixed support boundary, \(L_{1}\) connection point; **(b)** fixed support boundary, \(L_{2}\) connection point; **(c)** simply supported boundary, \(L_{1}\) connection point; **(d)** simply supported boundary, \(L_{2}\) connection point.
# Modulus Waveform Design Based on Manifold ADMM Idea in Dual-Function Radar-Communication System
## 2 System Model and Problem Formulation
### Communication Part  
Suppose that \(\mathbf{H}=\left[\mathbf{h}_{1},\mathbf{h}_{2},\ldots,\mathbf{h}_{K}\right]^{ \mathsf{T}}\in\mathbb{C}^{K\times N}\) denotes the downlink channel matrix from the transmitter to \(K\) users, and \(\mathbf{W}=\left[\mathbf{w}_{1},\mathbf{w}_{2},\ldots,\mathbf{w}_{L}\right] \in\mathbb{C}^{K\times L}\) is the noise matrix corresponding to Gaussian distribution with \(\mathbf{w}_{j}\sim\mathcal{CN}(0,N_{0}\mathbf{I}_{N})\). Defining \(\mathbf{x}_{l}=\left[x_{1,l},x_{2,l},\ldots,x_{N,l}\right]^{\mathsf{T}}\) as the \(N\)-dimensional spatial transmission data at time \(l\), \(\mathbf{X}=\left[\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{L}\right] \in\mathbb{C}^{N\times L}\) represents the transmitted signal matrix, \(L\) means the number of emitted waveforms during  
Figure 1: The DFRC system.  
one pulse, and the compact formulation of all users' signal matrix within one pulse can be written as  
\[\textbf{Y=HX+W}. \tag{1}\]  
Furthermore, define \(\textbf{S}\in\mathbb{C}^{K\times L}\) as the expected signal for \(K\) users; then the received signal matrix of (1) can be represented as  
\[\textbf{Y}=\textbf{S}+\underbrace{(\textbf{HX}-\textbf{S})}_{\text{MUI}}+ \textbf{W}, \tag{2}\]  
The second term on the right-hand side of (2) is called MUI. Additionally, the SINR for the \(i\)-th user is given by  
\[\eta_{i}=\underbrace{\frac{\text{E}\left(\left|s_{i,l}\right|^{2}\right)}{ \text{E}\left(\left|\overline{\textbf{h}_{i}^{\text{T}}}\textbf{x}_{l}-s_{i,l }\right|^{2}\right)+N_{0}}}_{\text{P, MUI}}, \tag{3}\]  
where \(\left|\overline{\textbf{h}_{i}^{T}}\textbf{x}_{l}-s_{i,l}\right|^{2}\) in the denominator part means the element of MUI. We propose a typical metric to evaluate the communication performance loss, i.e., the achievable sum-rate,  
\[R_{\text{com}}=\sum_{i=1}^{K}\log_{2}(1+\eta_{i}). \tag{4}\]  
Namely, by minimizing the MUI energy, we could obtain the expected communication performance. To avoid the nonlinear distortion of power amplifier and maximize the working efficiency of DFRC's transmitter, the CM waveform must be considered [20]. As shown in [18], the classical CM waveform can be formulated as  
\[\begin{array}{ll}\underset{\textbf{X}}{\text{min}}&\left\|\textbf{HX}- \textbf{S}\right\|_{F}^{2}\\ s.t.&\left\|\textbf{X}-\textbf{X}_{0}\right\|_{F}^{2}<\eta_{\text{min}},\\ &\left|x_{n,l}\right|=\sqrt{\frac{P_{0}}{NL}},n=1,2,\ldots,N;l=1,2,\ldots,L, \end{array} \tag{5}\]  
where \(\textbf{X}_{0}\in\mathbb{C}^{N\times L}\) is the referenced waveform with some desirable properties, and \(P_{0}\) denotes the total transmission power. To enhance the trade-off performance, the permissible square WSE norm \(\eta_{\text{min}}\) is proposed.
## 4 Results and discussion  
The retrieval model expresses the relationship between \(\sigma_{pq}\), \(mv\), and \(Hrms\) at a specific incidence angle and frequency. When the incidence angle is 25-55\({}^{\circ}\), the logarithmic relationship of Equations (10) and (11) is better. [PERSON] et al. (2006) established that CIEM was calibrated in the C-band for a radar frequency of approximately 5.4 GHz. Therefore, the application condition of Equations (13) and (14) about the frequency is \(f\) = 5.4 GHz.  
Then, the regression coefficients of the inversion model were discussed when the incidence angle changed. The database simulated by CIEM was built under the following conditions: frequency \(f\) was 5.4 GHz; \(Hrms\) was 0.5-4 cm with a step size of 0.1 cm; \(mv\) (10%, 50%) at intervals of 5%. Furthermore, the incidence angles of the empirical model were 25-55\({}^{\circ}\) with an interval of 5\({}^{\circ}\) to derive empirical coefficients at different incidence angles. Tables 3 and 4 show the values of empirical parameters \(a\), \(b\), \(c\), and \(d\) at different incidence angles. In Tables 3 and 4, when the \(Hrms\) was 0.5-4 cm and the incidence angle was 25\({}^{\circ}\), R\({}^{2}\) of the empirical coefficient was only 0.57 and 0.79 in VV and VH polarizations, respectively; other values exceeded 0.94. However, Tables 5 and 6 show that when the \(Hrms\) was 0.5-2.5 cm, R\({}^{2}\) of the empirical coefficient exceeded 0.96 in VV and VH polarizations. Therefore, the simulated values from the empirical model and CIEM compare fairly well at a specific incidence angle in VV and VH polarizations. When the maximum range of \(Hrms\) decreased from 4 to 2.5 cm and the soil surface was smooth, the empirical model had high accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\theta\) (\({}^{\circ}\)) & \(a\) (\(\theta\)) & \(b\) (\(\theta\)) & \(c\) (\(\theta\)) & \(d\) (\(  
In summary, the empirical model has good applicability at different incidence angles based on the above analysis. Therefore, the empirical model can be expressed as  
\[a(\theta)\ln(mv)+b(\theta)\ln(Hrms)+c(\theta)\ln(Hrms)\ln(mv)+d(\theta)=10^{( \sigma_{\rm p}(\rm dB)/10)} \tag{15}\]  
where \(25^{\circ}\leq\theta\leq 55^{\circ}\).
###### Abstract  
For Industrial Wireless Sensor Networks (IWSNs), sending data with timely style to the stink (or control center, CC) that is monitored by sensor nodes is a challenging issue. However, in order to save energy, wireless sensor networks based on a duty cycle are widely used in the industrial field, which can bring great delay to data transmission. We observe that if the duty cycle of a small number of nodes in the network is set to 1, the sleep delay caused by the duty cycle can be effectively reduced. Thus, in this paper, a novel Portion of Nodes with Larger Duty Cycle (PNLDC) scheme is proposed to reduce delay and optimize energy efficiency for IWSNs. In the PNLDC scheme, a portion of nodes are selected to set their duty cycle to 1, and the proportion of nodes with the duty cycle of 1 is determined according to the energy abundance of the area in which the node is located. The more the residual energy in the region, the greater the proportion of the selected nodes. Because there are a certain proportion of nodes with the duty cycle of 1 in the network, the PNLDC scheme can effectively reduce delay in IWSNs. The performance analysis and experimental results show that the proposed scheme significantly reduces the delay for forwarding data by 8.9-26.4% and delay for detection by 2.1-24.6% without reducing the network lifetime when compared with the fixed duty cycle method. Meanwhile, compared with the dynamic duty cycle strategy, the proposed scheme has certain advantages in terms of energy utilization and delay reduction.  
Keywords:industrial wireless sensor networks; duty cycle; sleep delay; energy efficient; lifetime +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Data Preprocessing  
The data preprocessing section of the study involved several steps to prepare the raw image and mask data for tumor segmentation. Initially, pictures and corresponding pixel-level masks were organized into separate folders. Data partitioning was conducted next, splitting the dataset into training (70%), validation (10%), and testing sets (20%). Subsequently, images were resized to a standard size of [224 \(\times\) 224 \(\times\) 3]. Finally, the preprocessed images and labels were combined into unified data stores for training and evaluation. These combined data stores were then utilized in subsequent steps to train and assess the efficacy of the tumor segmentation model. All models are trained using the following hyperparameters: Adam optimizer, an initial learning rate of 0.0001, a maximum of 100 epochs, and a minibatch size of 32.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 5 Applications and Use Cases of NDN
### IFA Defense in IoT Networks (NDN of Things)
#### 5.1.1 Smart Home  
In smart home NDN scenarios, an attacker might compromise an IoT device (smart light) and flood the interests, potentially blocking the home NDN hub so that other devices (door locks, sensors) cannot get data through. A solution like IfNoT would detect misbehaving light and isolate it, ensuring that the security camera feed or the data from the medical sensor in the home continue to flow [32].
# 3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities
## 4 Measuring the Deformation Trough Differential Interferometry  
### Methodology  
Section 3.2 has demonstrated the non-negligible impact of deformations in height evaluations. This impact is due to the combination of the TSX 3 cm wavelength with the small baselines obtained with TSX orbital pattern. With the chosen master image, more than 90% of the baselines have an absolute value bellow 300 m.  
This configuration enables a high accuracy on the measure of deformation using Equation (2) on an image by image basis. The phase difference is again measured using the methodology presented in Section 3.1.2. Equation (2) requires nevertheless the knowledge of the building height to measure the deformation. Thanks to our large time series, the height was estimated using only information contained in the SAR images, as presented in Section 3.3.  
Even if the measured height are in good agreement with the reference height, Table 3 shows some noticeable difference. However, with the acquisition configuration, a one meter error on this height introduces an error on the deformation of \(10^{-5}\) m when \(\bar{k}_{z}=0.01\) and 1 mm when \(\bar{k}_{z}=0.66\) leading to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Eiffel Tower** & **Cristal Tower** & **Keller Tower** & **Mirabeau Tower** \\ \hline \(h\) reference & 177.1 & 85.2 & 89.1 & 68.1 \\ \(h\) regression & 176.7 & 81.1 & 92.0 & 67.7 \\ \(R^{2}\) & 0.98 & 0.95 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reference height, height measured by linear regression over the temporal stack and coefficient of determination \(R^{2}\) of the regression.  
-centimeter precision in the worst case of the Cristal Tower. These centimeter precisions obtained with X-band images are a valuable assets for the remote monitoring of deformations.  
The next section presents the results of the daily deformation estimation. Due to the multiple deformation factors presented in Section 2.2 and the time extend of our dataset, it is not possible to justify any temporal _a priori_ on deformation, especially on a structure as complex as the Eiffel Tower. Thanks to the high accuracy obtainable with this dataset, deformations are computed for each acquisition date independently, in regard of the state of the buildings during the acquisition of the master image.  
Since \(\phi\) is the phase difference between the bottom and to top of the buildings, unwrapped globally on the whole building facades, it is implicitly assumed that the deformation is constant along the vertical axis of the building. Note that the deformation are measured in the Line of Sight direction and here are not reprojected since we do not assume any direction for the deformations.
# Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
## 4 Illustrative Example
Since leased processors fixed hardware cost \(C\!t\!t\) and fixed software cost \(C\!s\!f\) are calculated per unit time by dividing them by \(BW\), remaining opportunity cost for basic processor exists in case the batch process time \(END\) is less than batch window \(BW\). It represents the opportunity cost of the leased processors for the time units between the end of batch process \(END\) and \(BW\). From Table 2, we can see that from \(T=0\) until \(T=17\), the basic allocation cost was showing the utilization of both leased processors, which explains the zero value for opportunity cost of leased processors during the same period. However, at \(T=18\) and \(19\) one leased processor was utilized and that ended up in variable allocation cost for one processor and opportunity cost for the other. It can be also noticed that from \(T=0\) up to \(T=10\), every rented processor was utilized which ended in zero opportunity cost. After \(T=10\) some rented processors were not utilized due to unavailability of ready files such as at \(T=11\) where 3 out of 4 rented processors were utilized. Also at \(T=12\), \(13\), \(16\), \(17\), \(18\), and \(19\) none of the rented processors were utilized due to the unavailability of ready files. The above illustrative example shows the effectiveness of the developed algorithm in allocating files to processors. The algorithm managed to allocate highly prioritized and weighted files before the ones with lower priority and weight. Also, the algorithm will advise renting the necessary number of extra processors to accomplish the batch process goal while trying to minimizing cost. In the illustrated example, the program rented 4 extra processors to achieve minimum real batch process time, which is 20 time units. Although that exceeds the SLA specified time of 18 time units, it is the minimum possible execution time for this network due to network logic and predecessors\({}^{\prime}\) relations. The illustrative example shows that the batch processing is performed using a set of assumptions and constraints under which the problem makes the best decision at that time unit. Decisions are made dynamically based on the current status and previous decisions. This decision process ascertains that the best decision is taken at each iteration. We do not define the global network (of all feasible assignments of jobs to servers) to perform a direct optimization on it, we rather generate the subnetwork of feasible assignments at each iteration (Step 3 of the algorithm). This sequence of optimum decision-making ensures that the solution at the end is global. Not being optimal would mean that a better solution exists at some stage in the optimization process, which would contradict stage 3 as built.
# A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
## 2 Types of Fault in the Li-Ion Battery System
### Internal Battery Faults
#### 2.1.1 Overcharge  
Overcharge is a fault that can lead to more severe faults, such as accelerated degradation and thermal runaway. It may occur in Li-ion cells due to the capacity variation of cells in the pack, incorrect voltage and current measurement, or inaccurate SOC estimation from the BMS [13]. A normal battery pack can also get overcharged when the charger breaks down. Overcharging of Li-ion batteries leads to electrochemical reactions between battery components and the loss of active materials [14]. Additionally, in sealed batteries, the buildup of gases could cause the battery to burst [15]. Furthermore, the surface temperature of the battery increases significantly before it starts overcharging. This results in a thick SEI layer and also causes an internal short circuit inside the battery. The overcharged cathode suffers from electrolyte decomposition, metal dissolution, and phase transition, which could ultimately lead to thermal runaway and fires [16].
## 4 Extraction Module
### Rule-Based Classification
#### 4.2.1 Performance of Rule-Based Classification Models
As such, the impact of stop-word removal and lemmatization/stemming were evaluated. Various experimental scenarios examining the impact of n-gram sets, stop-word removal, and lemmatization on model performance are summarized in Table 2.  
Uni-grams had the lowest performance for both experimental scenarios (Table 2), and bi-grams demonstrated the highest performance in all three metrics in the base condition (Scenario 1). When stop-words were removed and lemmatization was applied, bi-grams had the highest recall and F1-score, with precision only differing marginally from other n-grams. Interestingly, lemmatization and stop-word removal resulted in a 2% increase in the recall of the bi-gram classifier, while the recall of the other n-gram sets decreased (Table 2). Notably, the F1-score of tri-grams (without lemmatization and with stop-words retained) was equal to the F1-score of bi-grams (with lemmatization and with stop-words removed). This result is expected as, in some cases, removing stop-words from tri-grams transforms them into bi-grams. For example, when the stop-word \"be\" is removed from the tri-gram \"shall be submitted\", the bi-gram \"shall submitted\" results. Given the importance of the recall measurement when extracting reporting requirements, and based on the findings that bi-grams resulted in the highest model accuracy (Table 1) and recall (Table 2), bi-grams are selected as the optimal classifier for rule-based text classification.
## 5 Channel Correlation Models
\[\omega_{k,\mathrm{V}}=\frac{1}{2}\Bigg{(}\arctan\!\left(\frac{d_{k}+s}{h} \right)-\arctan\!\left(\frac{d_{k}-s}{h}\right)\Bigg{)}. \tag{22}\]  
The value of \(\theta_{k,\mathrm{V}}\), which represents the angle at which the \(k\)th user's signal arrives in the vertical direction, is defined as follows [28]:  
\[\theta_{k,\mathrm{V}}=\frac{1}{2}\Bigg{(}\arctan\!\left(\frac{d_{k}+s}{h} \right)+\arctan\!\left(\frac{d_{k}-s}{h}\right)\Bigg{)}. \tag{23}\]  
The user-BS distance, BS height, and scattering ring radius are denoted as \(d_{k}\), \(h\), and \(s\), respectively, and are measured in meters. The low angular spread in both horizontal and vertical directions indicates that the channel covariance matrix (CCM) has a low-rank structure, indicating a strong spatial correlation. The expression in (24) can be used to compute the CCM \(\mathbf{R}_{k}\) of the \(k\)th user in the UPA configuration. The horizontal and vertical CCMs of \(\mathbf{R}_{k}\) are Toeplitz matrices with components computed numerically, where \(\theta_{k,\mathrm{V}}\) and \(\omega_{\mathrm{V}}\) are the vertical AoA and angular spread, respectively. \[\mathbf{R}_{k}=\mathbf{R}_{k,\mathrm{H}}\otimes\mathbf{R}_{k,\mathrm{V}}. \tag{24}\]  
The Kronecker operator is represented by the symbol \(\otimes\). The channel covariance matrices for the Laplacian model in the azimuth and elevation directions are provided in equations (25) and (26), respectively. \[\left[\mathbf{R}_{k,\mathrm{H}}\right]_{i,j}=\frac{1}{\sqrt{2}\omega_{ \mathrm{H}}}\int_{\theta_{k,\mathrm{H}}-\pi}^{\theta_{k,\mathrm{H}}+\pi}e^{- \frac{\sqrt{2}}{\omega_{\mathrm{H}}^{2}}(x-\theta_{k,\mathrm{H}})}e^{-\frac{2 \pi D}{\lambda}(i-j)\sin(x)}\mathrm{d}x, \tag{25}\]  
\[\left[\mathbf{R}_{k,\mathrm{V}}\right]_{i,j}=\frac{1}{\sqrt{2}\omega_{ \mathrm{V}}}\int_{\theta_{k,\mathrm{V}}-\pi}^{\theta_{k,\mathrm{V}}+\pi}e^{- \frac{\sqrt{2}}{\omega_{\mathrm{V}}^{2}}(x-\theta_{k,\mathrm{V}})}e^{\frac{-2 \pi D}{\lambda}(i-j)\sin(x)}\mathrm{d}x, \tag{26}\]  
where \(\theta_{k,\mathrm{H}}\) and \(\theta_{k,\mathrm{V}}\) denote the AoA in the horizontal direction and vertical direction, respectively. Parameters \(\omega_{\mathrm{H}}\) and \(\omega_{\mathrm{V}}\) represent the horizontal and vertical angular spreads in horizontal and vertical directions, respectively.
# Complex Network Modeling and Analysis of Microfracture Activity in Rock Mechanics
## 3 Construction of the Rock Microfracture Network
### Basic Parameters of Complex Networks
#### 3.2.1 Degree and Degree Distributions  
The node degree refers to the count of edges directly linked to a node, indicating the number of other nodes directly connected to it, denoted as \(k\). The degree distribution elucidates the statistical distribution of degrees across all nodes in the network, expressed as p(k). p(k) also represents the likelihood that any node's degree equals \(k\) under the random consistency principle. The node degree serves to illustrate the communication capacity and resource utilization of individual network members, and it can also reveal the scale-free characteristics of complex networks [24]. The degree \(k_{i}\) of any node \(i\) in the network can be calculated using the connection matrix according to Equation (6). The degree \(k_{i}\) quantifies the number of direct connections node i has within the network.  
\[k_{i}=\sum_{j=1}^{n}a_{ij} \tag{6}\]  
The average degree of all nodes in the network was calculated according to Equation (7) \(<\)\(k\)\(>\), the average degree provides a measure of the overall connectivity within the network.  
\[\langle k\rangle=\frac{1}{n}\sum_{j=1}^{n}k_{i} \tag{7}\]  
Figure 7: Schematic representation of a general network graph.