esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 A Prototype System for Pedestrian Positioning
### Tilt Estimation
\(w_{y}(k)\) and \(w_{x}(k)\) are the gyroscope errors. The measurement equation of the EKF is given by Equation (20)  
\[\mathbf{y}(\mathbf{k})=\left[ \begin{array}{c}a_{x}(k)\\ a_{y}(k)\end{array} \right]=\mathbf{g}(\mathbf{x}(k),\mathbf{v}(k))=\left[ \begin{array}{c}g\cos\alpha_{x}(k)-v_{x}(k)\\ g\cos\alpha_{y}(k)-v_{y}(k)\end{array} \right] \tag{20}\]  
The accelerometers readouts are corrupted by noise \(\mathbf{v}(k)\). Further implementation of this Extended Kalman Filter is straightforward and follows through Taylor linearisation. Details can be found in [35]. Having estimated \(\alpha_{x}\) and \(\alpha_{y}\) and complementary angles \(\beta_{x}\) and \(\beta_{y}\), the angle \(\gamma\) between the \(Z\)-axis gyroscope and the inverted gravity axis (see Figure 6) can be calculated from Equation (21). \[\gamma=2\arcsin\frac{\sqrt{\sin^{2}\beta_{x}+\sin^{2}\beta_{y}}}{2} \tag{21}\]  
The angular velocity can be corrected by Equation (22). The relative heading direction should be calculated from Equation (23). Figure 6: The relationship between the angles \(\beta_{x}\) and \(\beta_{y}\) and the angle \(\gamma\) which describes the sensor tilt and is measured between the sensor’s Z-axis and the \(-g\) axis. \[\hat{\omega_{z}}(t) = \omega_{z}(t)/\cos(\gamma) \tag{22}\] \[\Delta\phi(t) = \int\limits_{0}^{t}\hat{\omega_{z}}(t)dt \tag{23}\]  
As mentioned before, this approach is valid for limited range of pitch and roll, due to singularity in Equation (22) when \(\gamma\) approaches \(\pi/2\). The measurement of \(\gamma\) can be included from Equation (16), however with a small benefit due to considerable fluctuations of the corresponding acceleration \(a_{z}\). The average error of estimating \(\gamma\) was measured to be \(0.5^{\circ}\) with a standard deviation of \(0.45^{\circ}\). Figure 7(a,b) shows a trial with the sensor tilted by \(\approx\)15\({}^{\circ}\).
# Extracellular Vesicles and Artificial Intelligence: Unique Weapons against Breast Cancer
### Extracellular Vesicles (EVs)
#### 1.3.1 EV Biogenesis  
Exosomes are generated via the endo-lysosomal pathway by a process dependent on or independent from the endosomal sorting complex required for transport (ESCRT), through the intraluminal formation of multivesicular bodies and their fusion with the cell membrane, with consequent exocytosis, and they have a morphology typically defined as \"cup-shaped\" [19]. The chaperone Cdc37 assists protein kinase folding during exosome biogenesis and secretion, whereas autophagy-activating kinase ULK1, membrane-bound kinase SRC, and lipid kinase VPS34 intervene in exosome formation and release [20]. Microvesicles are generated by the extrusion of the cell membrane (budding/shedding), as well as apoptotic bodies when in an apoptotic cell. Apoptotic bodies can also derive from the endoplasmic reticulum, in which case they will not contain DNA. Although MVs originate as an extrusion of the plasma membrane, their arrangement in the bilayer of some lipids such as phosphatidylserine, aminophospholipids, and ethanolamine varies with respect to the structure of the mother membrane, resulting in their homogeneous (and not asymmetrical) distribution in the helix of the membrane [17].  
EVs differ from each other in size, biogenesis process, density, and the presence/abundance of specific markers. However, their characterization becomes difficult due to the overlap of the size range of MVs and exosomes. The International Society for Extracellular Vesicles (ISEV) first published the Minimal Information for Studies of Extracellular Vesicles (MISEV) in 2014, updated in 2018, underlining the importance of defining the EV subcategories. Based on the latest evidence, the ISEV defines \"extracellular vesicle (EV) as particles naturally released by cells, delimited by a lipid bilayer without the ability to replicate\" [21].
# Time Segmentation-Based Hybrid Caching in 5G-ICN Bearer Network
## 3 Path Segmentation-Based Hybrid Caching
### Caching Idea Based on Time Segmentation
#### 3.2.2 Time Series Fitting and Time Division  
Select a \(V_{i}\) from the clustered set V, and the time series set T of all base stations in the area can be obtained. We select a cycle of 24 h based on \"tidal phenomena\". Time Series Collection of Base Stations: T \(=\{X_{1},X_{2},\ldots,X_{n}\}\), and \(X_{i}=\{f_{i,1},f_{i,2},\ldots,f_{i,m}\}\). Next, we fit \(n\) time series, and the final time series: \(X_{n+1}=\sum_{i=1}^{n}\mu_{i}\times X_{i}\), \(\mu_{1}+\mu_{2}+\ldots+\mu_{m}=1\). In \(X_{n+1}\), each data satisfies \(f_{n+1,j}=\sum_{i=1}^{n}\mu_{j}\times f_{i,j}\). Based on the derivative definition, we use Formula (1) to find special points of final time series. After the segmented nodes are determined, the high and low liquidity periods are divided, and the segmentation is completed.  
\[\frac{f_{i+1,1}-f_{i,1}}{\Delta t} \tag{1}\]
# Uniform Convexity in Variable Exponent Sobolev Spaces
## 2 Inequalities
Using the above lemma, we are ready to state and prove the vector version of the fundamental inequalities of Lemma 1 in any Hilbert space. **Theorem 1**. : _Let \(\mathbf{u}\), \(\mathbf{v}\) be vectors in a Hilbert space \((\mathbb{H},\|\cdot\|)\). If \(1\leq p\leq 2\), it holds that_  
\[\left|\frac{a+b}{2}\right|^{p}+\left|\frac{a-b}{2}\right|^{p}\leq\frac{1}{2}(| a|^{p}+|b|^{p}). \tag{8}\]4  
_provided \(\|\mathbf{u}\|+\|\mathbf{v}\|\
eq 0\). In addition, if \(p\geq 2\), it holds that_  
\[\left|\frac{a+b}{2}\right|^{p}+\left|\frac{a-b}{2}\right|^{p}\leq\frac{1}{2}(| a|^{p}+|b|^{p}). \tag{8}\]5  
Proof. : If the vectors \(\mathbf{u}\), \(\mathbf{v}\) are linearly dependent, the two inequalities reduce to the scalar case. Assume that \(\mathbf{u}\) and \(\mathbf{v}\) are linearly independent. Set \(W\) as the subspace of \(\mathbb{H}\) spanned by these two vectors. Using Gram-Schmidt, there exists an orthonormal basis \(\{\mathbf{I},\mathbf{J}\}\) of \(W\). We have  
\[\left|\frac{a+b}{2}\right|^{p}+\left|\frac{a-b}{2}\right|^{p}\leq\frac{1}{2}(| a|^{p}+|b|^{p}). \tag{8}\]6  
for \((x,y)\in\mathbb{R}^{2}\) and \((a,b)\in\mathbb{R}^{2}\). Set \(z_{1}=x+iy\) and \(z_{2}=a+ib\) in \(\mathbb{C}\). Clearly, the following hold  
\[\left\{ \begin{array}{rl}\|\mathbf{u}\|^{2}&=|z_{1}|^{2}=x^{2}+y^{2},\\ \|\mathbf{v}\|^{2}&=|z_{2}|^{2}=a^{2}+b^{2},\\ \|\mathbf{u}+\mathbf{v}\|^{2}&=|z_{1}+z_{2}|^{2}=(x+a)^{2}+(y+b)^{2},\\ \|\mathbf{u}-\mathbf{v}\|^{2}&=|z_{1}-z_{2}|^{2}=(x-a)^{2}+(y-b)^{2}.\end{array} \right.\]Lemma 2 implies  
\[\left|\frac{z_{1}+z_{2}}{2}\right|^{p}+\frac{p(p-1)}{2^{p+1}}\frac{|z_{1}-z_{2} |^{2}}{(|z_{1}|^{2}+|z_{2}|^{2})^{\frac{2-p}{2}}}\leq\frac{1}{2}(|z_{1}|^{p}+|z _{2}|^{p}),\]  
for \(1<p\leq 2\), and for \(p\geq 2\), we have  
\[\left|\frac{z_{1}+z_{2}}{2}\right|^{p}+\left|\frac{z_{1}-z_{2}}{2}\right|^{p} \leq\frac{1}{2}(|z_{1}|^{p}+|z_{2}|^{p}),\]  
which obviously implies  
\[\left\|\frac{\mathbf{u}+\mathbf{v}}{2}\right\|^{p}+\frac{p(p-1)}{2^{p+1}} \frac{\|\mathbf{u}-\mathbf{v}\|^{2}}{(\|\mathbf{u}\|+\|\mathbf{v}\|)^{2-p}} \leq\frac{1}{2}(\|\mathbf{u}\|^{p}+\|\mathbf{v}\|^{p}),\]  
for \(1<p\leq 2\), provided \(\|\mathbf{u}\|+\|\mathbf{v}\|\
eq 0\). Additionally, if \(p\geq 2\), it holds that  
\[\left\|\frac{\mathbf{u}+\mathbf{v}}{2}\right\|^{p}+\left\|\frac{\mathbf{u}- \mathbf{v}}{2}\right\|^{p}\leq\frac{1}{2}(\|\mathbf{u}\|^{p}+\|\mathbf{v}\|^{ p}).\]  
The proof of Theorem 1 is complete.
# Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System
## 4 Construction of the Intelligent Mine Gas State Decision-Making System
### Core Layer
#### 4.3.5 Human Feedback-Retrieval Augmented Generation  
Although the Intelligent Mine Gas State Decision-Making System has significant advantages in improving efficiency and safety, it may also lead to an increased dependency on the system by users, thereby affecting their judgment abilities. To address this conflict, this study employs a human feedback-based RAG strategy, which is developed from the traditional RAG model. The core of this approach lies in the introduction of a key component: human feedback interruption.  
Under this mechanism, the system strategically interrupts the retrieval and generation process, presenting intermediate results to human experts. These experts assess the relevance and accuracy of the retrieval or generation results based on the context of alert events and system requirements. The system proceeds with subsequent generation steps only when the experts confirm that the results are relevant and accurate.  
By implementing this human feedback interruption mechanism, the retrieval-augmented generation technique in this study not only optimizes the efficiency of data processing and retrieval but also ensures the practicality and reliability of the output results.
# Improving Singing Voice Separation Using Curriculum Learning on Recurrent Neural Networks
###### Abstract  
Single-channel singing voice separation has been considered a difficult task, as it requires predicting two different audio sources independently from mixed vocal and instrument sounds recorded by a single microphone. We propose a new singing voice separation approach based on the curriculum learning framework, in which learning is started with only easy examples and then task difficulty is gradually increased. In this study, we regard the data providing obviously dominant characteristics of a single source as an easy case and the other data as a difficult case. To quantify the dominance property between two sources, we define a dominance factor that determines a difficulty level according to relative intensity between vocal sound and instrument sound. If a given data is determined to provide obviously dominant characteristics of a single source according to the factor, it is regarded as an easy case; otherwise, it belongs to a difficult case. Early stages in the learning focus on easy cases, thus allowing rapidly learning overall characteristics of each source. On the other hand, later stages handle difficult cases, allowing more careful and sophisticated learning. In experiments conducted on three song datasets, the proposed approach demonstrated superior performance compared to the conventional approaches.  
audio source separation; singing voice separation; weighted loss function; curriculum learning; recurrent neural network; U-Net +
Footnote †: journal: Applied  
_Article_
# Detail-Preserving Shape Unfolding
## 4 Implementation Details
### Parameters  
Through extensive theoretical and numerical analysis we finally adopt the following strategies. Each cascade is stopped when it reaches a specified iteration number or the ratio of energy between adjacent iterations below a given precision. In detail, the initial energy \(f_{s}\) can be calculated by setting \(\mathbf{P}^{\prime}=\mathbf{P}\), the initial \(\beta=5\times 10^{5}\) for most meshes, enlarging the initial \(\beta\) is a good try if the result is not satisfactory, the maximum iterations for each cascade \(k_{iter}=15\), the precision \(\varepsilon=10^{-4}\) and the maximum number of cascade \(num=4\). In addition, if the ratio \(\frac{1}{m}\sum_{i=1}^{m}\frac{l_{i}^{{}^{\prime}}}{l_{i}}\in[0.95,1.05]\), we also terminate the iteration. Here \(l_{i}^{{}^{\prime}}\) and \(l_{i}\) are the lengths of the \(i\)th edge on the intermediate and original tetrahedral mesh respectively.  
Pseudo code of our shape unfolding algorithm is shown in Algorithm 1.  
Figure 3: From left to right are the original tetrahedral mesh (**a**), the result of the first cascade (**b**), the result of the second cascade (**c**), the result of the fourth cascade (**d**), the result of the fourth cascade (**e**), and the convergence curve of the objective function (**f**). The jumps in (**f**) are caused by the incremental increasing of \(\beta\).  
```
0: Original tetrahedral mesh \(\mathcal{S}\), original edge lengths \(\{l_{i}\}\), initial energy \(f_{s}\), initial \(\beta\), the maximum cascading number \(num\), the maximum iterations \(k_{iter}\) for each cascade and the precision \(\varepsilon\).
0: Deformed tetrahedral mesh \(\mathcal{S}^{\prime}\).
1:\(i=1\), \(j=1\).
2:while true do
3: Get \(\{\mathbf{R}_{i}\}\) according to Equation (2).
4: Compute intermediate tetrahedral mesh \(\mathcal{S}^{\prime\prime}\) according to Equation (7) and intermediate edge lengths \(\{l^{\prime}_{i}\}\).
5: Compute the ratio \(\delta=\frac{1}{n}\sum_{i=1}^{m}\frac{l^{\prime}_{i}}{l_{i}}\).
6:if\(\delta\in[0.95,1.05]\)then
7: break
8:endif
9: Compute intermediate energy \(f_{\varepsilon}\) according to Equation (5).
10:if\(|f_{\varepsilon}/f_{s}-1|<\varepsilon\) or \(i>=k_{iter}\)then
11:\(\beta=2\beta\), \(j=j+1\), \(i=0\).
12:endif
13:if\(j>num\)then
14: break
15:endif
16:\(i=i+1\).
17:\(f_{s}=f_{\varepsilon}\)
18:endwhile
19: return\(\mathcal{S}^{\prime}=\mathcal{S}^{\prime\prime}\).
```  
**Algorithm 1** Shape unfolding cascading algorithm
# Unmanned Surface Vehicle Using a Leader-Follower Swarm Control Algorithm
## 5 Performance Test and Actual Sea Area Test of the Swarm USV
### Sensor Performance Test
#### 5.1.1 Communication Speed Test  
In the leader-follower swarm control, communication between the land operation console and the leader and follower USVs should proceed smoothly. The communication method adopted in this study, the decentralization method, acquires and controls each data point. Thus, communication between the land operation console and the leader and follower USVs is not smooth. Consequently, the swarm formation is difficult to maintain, and due to the delay time, the checkpoint can be incorrectly calculated at a place other than the target route. Therefore, a performance test of the communication equipment installed in each USV was conducted in the following two cases:  
1. Communication speed test between the land operation console and each USV; and
2. Communication speed test between the leader and follower USVs.  
The communication speed test between the land operation console and the USV was conducted to verify whether the real-time data coming from the sensors mounted on the USV were secured. Transmitting equipment and receiving equipment were placed at a distance of about 200 m, and the communication speed was measured using a communication speed measurement program. The communication speed measurement program used was Iperf3. The experimental results are shown in Figure 15.  
The communication speed measurement result was 94.3 Mbps between the land operation console and the leader USV, and 94.5 Mbps between the land operation console and the follower USV. The transmission bandwidth of the camera most affected by the communication speed among the sensors installed in the USV was 80 Mbps or more.  
Figure 16 shows the communication speed test result between the leader USV and the follower USV.  
Figure 15: The test result of communication speed test 1.  
In this study, the standard Ethernet communication speed of 100 Mbps was set as the target communication speed between the USVs. The obtained results confirmed that the communication speed between the leader USV and the follower USV was 107 Mbps, which satisfied the communication speed set as the target in this study.
# Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
## 2 Wireless Sensor Network Architecture  
### Sensor Node  
The architecture consists of four main units: an ESP32-S3 microcontroller unit, a GNSS module (NEO-M9N-00B), the accelerometer (ADXL355), and the power management unit. Each unit plays a distinct role in the overall functionality of the sensor node.  
* The **ESP32-S3**, in this configuration, operates in two distinct phases--data collection and transmission--with each process occurring at separate times.
* **The NEO-M9N-00B** GNSS module is used to synchronize between the different nodes. In particular, the acquisition start time is triggered at a given instant by checking it from the atomic clock of the GNSS satellites. [23; 24].
* **The ADXL355** MEMS accelerometer is chosen to ensure a reliable measurement; in fact, the main parameter that drives the selection of this accelerometer is its low noise density (20 \(\upmu\)g/\(\sqrt{\mathrm{Hz}}\)), which is important when dealing with ambient vibrations of civil infrastructures [20]. Furthermore, the selectable scale ensures that it can accurately measure the acceleration range (\(\pm\)2 g) relevant to this application without saturating or losing sensitivity. Finally, the selectable sampling frequency allows us to adjust it based on the natural frequencies of the considered structure.
* The power management unit is made up of a DC-DC converter, the **BQ24210**, which manages the energy flow from the solar panel to power the sensor node. Its input voltage regulation loop detection system is activated if the current required from the converter is too high and is causing an overload of the power source; if this happens, it reduces the current until there is no further drop in the voltage on the input. This feature allows the solar panel to work near its MPP (maximum power point) for a wide range of light conditions. In addition, it controls the charging process to prevent overcharging or discharging of the battery, thus preserving its longevity and ensuring stable operation of the sensor node. Finally, a buck-boost DC-DC converter is used to convert the battery voltage to a value of 3.3V, which is the operating voltage of the whole node.  
All components are mounted on a PCB, as shown in Figure 1. In addition to the main components previously described, each node in the network is equipped with two antennas; one is dedicated to a GNSS module and the other one, connected to the ESP32 microcontroller, is used for Wi-Fi communication. In addition, a temperature sensor is integrated into the node, providing critical environmental data for analysis.
## 2 Related Work
### Fault Diagnosis Methods  
Model-based fault diagnosis [15; 16; 17; 18] methods need to establish accurate mathematical models for monitoring objects, such as differential equations, and diagnose faults by residual errors between model inputs and outputs [19]. Traditional fault diagnosis methods can be divided into three categories: analytical model-based approaches, qualitative empirical knowledge-based approaches, and data-driven approaches [20]. [PERSON] [21] et al. combined improved composite multiscale fuzzy entropy and support vector machine (SVM) with particle swarm optimization (PSO) to diagnose rolling bearing faults. The disadvantage of this method is that it is very complicated and time-consuming to establish the model, and the accuracy of the model directly affects the performance of fault detection and diagnosis. A fault diagnosis method based on signal processing collects the sensor signals in the operation of the device, then extracts the features by observing and analyzing the hidden information in the sequence. [PERSON] [22] proposed a fault diagnosis approach to identify the faults and malfunctions existing in marine systems. [PERSON] [23] et al. proposed a quality testing approach for the quality of transformer insulating oil based on fluorescent bicolor ratio and extraction of fluorescence characteristics of dual band information. The running transformer fault diagnosis model was established, through a custom filter and visible photoelectric detector, and allowed online real-time monitoring. However, this method can only analyze the characteristics of the signal itself, and is unable to dig deeper for hidden information. Fault diagnosis methods based on artificial intelligence using machine learning and deep neural networks can extract hidden features from a large number of datasets; the trained models are used to realize fault diagnosis, which achieves excellent results. A real-time anomaly detection intelligent system [24] was proposed to address the current gaps identified within the maritime industry. [PERSON] [25] et al. proposed a new intelligent defect detection framework based on time-frequency transform that trained sparse filtering model using the speed samples of agricultural machinery. Residual samples at different speeds were used to test its validity. This method can adaptively extract the existing fault features, which is an effective intelligent method for agricultural equipment fault detection. The performance of fault detection verifies that this method not only has strong fault classification ability at different speeds, but also has higher identification accuracy compared with other methods. Approaches based on Convolutional Neural Networks [26] or graph networks [27] have also been proposed to address the problem of fault diagnosis in industrial scenarios. [PERSON] [28] et al. proposed a fault diagnosis strategy based on Long Short-Term Memory to classify and determine different fault types for time-series data sequences. The method is useful to guarantee the operational safety of equipment, and can be used as a reference for fault detection.
## 5 Case Study
### Fitting of Empirical Model  
The measured data in different \(\Delta\)SOCs are based on the same charge/discharge rate, temperature, and cut-off voltage. To determine the temperature-dependent fitting parameter \(z\), we logarithmically process and rearrange the items of Equation (8), that is:  
\[\ln(Q_{\text{loss}})=\ln(\alpha)-\left(\frac{E_{\text{a}}}{R\cdot T}\right)+z \ln(Ah) \tag{31}\]  
Draw \(\ln(Q_{\text{loss}})-\ln(\alpha)+\left(\frac{E_{\text{a}}}{KT}\right)\) as a function of \(\ln(Ah)\), and apply Equation (31) for single-step fitting optimization. The best linear regression curve is as follows.  
The power law factor \(z\) in the empirical model can be determined from the linear fitting slope shown in Figure 4, whose value is equal to 0.8121. Next, the retired battery empirical model shown in Equation (9) is used to nonlinearly fit the measured data of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Type** & **Parameter Value** \\ \hline Battery type & 18650 LFP battery \\ Rated capacity (mAh) & 1600 \\ Actual capacity (mAh) & 1280 (80\%) \\ Nominal voltage (V) & 3.70 \\ Charging cutoff voltage (V) & 4.20 \(\pm\) 0.05 \\ Shape & Cylindrical \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of LFP battery samples.  
Figure 3: The experimental data and their fourth-order polynomial fitting curves.  
battery under six conditions, and the fitting parameters and goodness of fit can be found in Table 3.  
From the value of \(R^{2}\) and \(RMSE\) in Table 3, it can be seen that the fitting accuracy of the empirical model is high. The fitting results and measured data obtained from the empirical model of Equation (9) under six conditions are shown in Figure 5.